Repository: seninp/saxpy Branch: master Commit: 93da33f16594 Files: 56 Total size: 2.9 MB Directory structure: gitextract_qiq5bcio/ ├── .github/ │ └── workflows/ │ └── build.yml ├── .gitignore ├── .travis.yml ├── AUTHORS ├── LICENSE ├── MANIFEST.in ├── README.md ├── README.rst ├── conda.recipe/ │ └── meta.yaml ├── data/ │ ├── ecg0606_1.csv │ └── insect.txt ├── jupyter/ │ ├── discord.ipynb │ ├── distance.ipynb │ ├── hotsax.ipynb │ ├── insect.ipynb │ ├── paa.ipynb │ ├── sax.ipynb │ ├── str_func.ipynb │ ├── tinkah.ipynb │ ├── visit_registry.ipynb │ ├── vsm.ipynb │ └── znorm.ipynb ├── requirements.txt ├── resources/ │ └── data/ │ ├── Gun_Point/ │ │ ├── Gun_Point_TEST │ │ └── Gun_Point_TRAIN │ └── cbf/ │ ├── CBF_TEST │ └── CBF_TRAIN ├── saxpy/ │ ├── __init__.py │ ├── alphabet.py │ ├── discord.py │ ├── distance.py │ ├── hotsax.py │ ├── paa.py │ ├── sax.py │ ├── saxvsm.py │ ├── strfunc.py │ ├── util.py │ ├── visit_registry.py │ └── znorm.py ├── setup.cfg ├── setup.py ├── site/ │ └── citation.bib ├── tests/ │ ├── test_cuts.py │ ├── test_discord_bruteforce.py │ ├── test_discord_hotsax.py │ ├── test_distance.py │ ├── test_doctests.py │ ├── test_paa.py │ ├── test_registry.py │ ├── test_sax_chunking.py │ ├── test_sax_window.py │ ├── test_str.py │ ├── test_ts2string.py │ ├── test_vsm.py │ └── test_znorm.py └── tox.ini ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/build.yml ================================================ name: Python package on: - push - pull_request jobs: build: runs-on: ${{ matrix.platform }} strategy: matrix: platform: [ubuntu-latest, macOS-15, windows-latest] python-version: ['3.10', '3.11', '3.12'] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip python -m pip install tox tox-gh-actions - name: Test with tox run: tox env: PLATFORM: ${{ matrix.platform }} ================================================ FILE: .gitignore ================================================ .idea/workspace.xml build/ dist/ _build/ _generate/ .cache* .tox* .eggs* ChangeLog *.so *.py[cod] *.egg-info .coverage .pypirc jupyter/.ipynb* /.venv/ /.vscode/ .idea/ ================================================ FILE: .travis.yml ================================================ sudo: false language: python python: - "3.7" install: pip install tox-travis numpy pytest-cov codecov sklearn scipy script: tox after_success: - codecov #deploy: # provider: pypi # user: "seninp" # password: # secure: "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" notifications: email: on_success: never # default: change on_failure: always # default: always ================================================ FILE: AUTHORS ================================================ Ameya Daigavane Ameya Daigavane Pavel Senin Pavel Senin seninp ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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If this is what you want to do, use the GNU Lesser General Public License instead of this License. ================================================ FILE: MANIFEST.in ================================================ include LICENSE ================================================ FILE: README.md ================================================ Time series symbolic discretization with SAX ==== [![Latest PyPI version](https://img.shields.io/pypi/v/saxpy.svg)](https://pypi.python.org/pypi/saxpy) [![Latest Travis CI build status](https://travis-ci.org/seninp/saxpy.png)](https://travis-ci.org/seninp/saxpy) [![image](https://codecov.io/gh/seninp/saxpy/branch/master/graph/badge.svg)](https://codecov.io/gh/seninp/saxpy) [![image](http://img.shields.io/:license-gpl2-green.svg)](http://www.gnu.org/licenses/gpl-2.0.html) This code is released under [GPL v.2.0](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html) and implements in Python: * Symbolic Aggregate approXimation (SAX) (with z-normalization and PAA) [1] * EMMA -- an algorithm for time series motif discovery [2] * HOT-SAX - a time series anomaly (discord) discovery algorithm [3] * SAX-ZSCORE - an extension to SAX for multi-dimensional time-series SAX that modifies the z-normalization to multi-dimensional sequences and PAA aggregation with the average along data-dimensions. [4] * SAX-REPEAT - an extension to SAX for multi-dimensional time-series that performs standard SAX on individual dimensions, then clusters to map multi-dimensional words into strings from the required alphabet size. [4] Note that all of the library's functionality is also available in [R](https://github.com/jMotif/jmotif-R) and [Java](https://github.com/jMotif/SAX). ## References [1] Lin, J., Keogh, E., Patel, P., and Lonardi, S., [*Finding Motifs in Time Series*](http://cs.gmu.edu/~jessica/Lin_motif.pdf), The 2nd Workshop on Temporal Data Mining, the 8th ACM Int'l Conference on KDD (2002) [2] Patel, P., Keogh, E., Lin, J., Lonardi, S., [*Mining Motifs in Massive Time Series Databases*](http://www.cs.gmu.edu/~jessica/publications/motif_icdm02.pdf), In Proc. ICDM (2002) [3] Keogh, E., Lin, J., Fu, A., [*HOT SAX: Efficiently finding the most unusual time series subsequence*](http://www.cs.ucr.edu/~eamonn/HOT%20SAX%20%20long-ver.pdf), In Proc. ICDM (2005) [4] Mohammad, Y., Nishida T., [*Robust learning from demonstrations using multidimensional SAX*](https://ieeexplore.ieee.org/document/6987960), 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014) ## Citing this work If you are using this implementation for you academic work, please cite our [Grammarviz 2.0 paper](http://link.springer.com/chapter/10.1007/978-3-662-44845-8_37): [[Citation]](https://raw.githubusercontent.com/jMotif/SAX/master/citation.bib) Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S., Lerner, M., [*GrammarViz 2.0: a tool for grammar-based pattern discovery in time series*](http://csdl.ics.hawaii.edu/techreports/2014/14-06/14-06.pdf), ECML/PKDD Conference, 2014. SAX in a nutshell ------------ SAX is used to transform a sequence of rational numbers (i.e., a time series) into a sequence of letters (i.e., a string). An illustration of a time series of 128 points converted into the word of 8 letters: ![SAX in a nutshell](https://raw.githubusercontent.com/jMotif/SAX/master/src/resources/sax_transform.png) As discretization is probably the most used transformation in data mining, SAX has been widely used throughout the field. Find more information about SAX at its authors pages: [SAX overview by Jessica Lin](http://cs.gmu.edu/~jessica/sax.htm), [Eamonn Keogh's SAX page](http://www.cs.ucr.edu/~eamonn/SAX.htm), or at [sax-vsm wiki page](http://jmotif.github.io/sax-vsm_site/morea/algorithm/SAX.html). Building ------------ The code is written in Python and hosted on PyPi, so use `pip` to install it. This is what happens in my clean test environment: $ pip install saxpy Collecting saxpy Downloading saxpy-1.0.0.dev154.tar.gz (180kB) 100% |████████████████████████████████| 184kB 778kB/s Requirement already satisfied: numpy in /home/psenin/anaconda3/lib/python3.6/site-packages (from saxpy) Requirement already satisfied: pytest in /home/psenin/anaconda3/lib/python3.6/site-packages (from saxpy) ... Installing collected packages: coverage, pytest-cov, codecov, saxpy Successfully installed codecov-2.0.15 coverage-4.5.1 pytest-cov-2.5.1 saxpy-1.0.0.dev154 Simple time series to SAX conversion ------------ To convert a time series of an arbitrary length to SAX we need to define the alphabet cuts. Saxpy retrieves cuts for a normal alphabet (we use size 3 here) via `cuts_for_asize` function: from saxpy.alphabet import cuts_for_asize cuts_for_asize(3) which yields an array: array([ -inf, -0.4307273, 0.4307273]) To convert a time series to letters with SAX we use `ts_to_string` function but not forgetting to z-normalize the input time series (we use Normal alphabet): import numpy as np from saxpy.znorm import znorm from saxpy.sax import ts_to_string ts_to_string(znorm(np.array([-2, 0, 2, 0, -1])), cuts_for_asize(3)) this produces a string: 'abcba' Time series to SAX conversion with PAA aggregation (by "chunking") ------------ In order to reduce dimensionality further, the PAA (Piecewise Aggregate Approximation) is usually applied prior to SAX: import numpy as np from saxpy.znorm import znorm from saxpy.paa import paa from saxpy.sax import ts_to_string dat = np.array([-2, 0, 2, 0, -1]) dat_znorm = znorm(dat) dat_paa_3 = paa(dat_znorm, 3) ts_to_string(dat_paa_3, cuts_for_asize(3)) and a string with three letters is produced: 'acb' Time series to SAX conversion via sliding window ------------ Typically, in order to investigate the input time series structure in order to discover anomalous (i.e., discords) and recurrent (i.e., motifs) patterns we employ time series to SAX conversion via sliding window. Saxpy implements this workflow: import numpy as np from saxpy.sax import sax_via_window dat = np.array([0., 0., 0., 0., 0., -0.270340178359072, -0.367828308500142, 0.666980581124872, 1.87088147328446, 2.14548907684624, -0.480859313143032, -0.72911654245842, -0.490308602315934, -0.66152028906509, -0.221049033806403, 0.367003418871239, 0.631073992586373, 0.0487728723414486, 0.762655178750436, 0.78574757843331, 0.338239686422963, 0.784206454089066, -2.14265084073625, 2.11325193044223, 0.186018356196443, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.519132472499234, -2.604783141655, -0.244519550114012, -1.6570790528784, 3.34184602886343, 2.10361226260999, 1.9796808733979, -0.822247322003058, 1.06850578033292, -0.678811824405992, 0.804225748913681, 0.57363964388698, 0.437113583759113, 0.437208643628268, 0.989892093383503, 1.76545983424176, 0.119483882364649, -0.222311941138971, -0.74669456611669, -0.0663660879732063, 0., 0., 0., 0., 0.,]) sax_none = sax_via_window(dat, win_size=6, paa_size=3, alphabet_size=3, nr_strategy=None, znorm_threshold=0.01) sax1 the result is represented as a data structure of resulting words and their respective positions on time series: defaultdict(list, {'aac': [4, 10, 11, 30, 35], 'abc': [12, 14, 36, 44], 'acb': [5, 16, 21, 37, 43], 'acc': [13, 52, 53], 'bac': [3, 19, 34, 45, 51], 'bba': [31], 'bbb': [15, 18, 20, 22, 25, 26, 27, 28, 29, 41, 42, 46], 'bbc': [2], 'bca': [6, 17, 32, 38, 47, 48], 'caa': [8, 23, 24, 40], 'cab': [9, 50], 'cba': [7, 39, 49], 'cbb': [33], 'cca': [0, 1]}) `sax_via_window` is parameterised with a sliding window size, desired PAA aggregation, alphabet size, a numerosity reduction strategy, z-normalization threshold, and a SAX type ('unidim' for unidimensional SAX (default), 'zscore' for SAX-ZSCORE, 'repeat' for SAX-REPEAT): def sax_via_window(series, win_size, paa_size, alphabet_size=3, nr_strategy='exact', z_threshold=0.01, sax_type='unidim') Time series discord discovery with HOT-SAX ------------ Saxpy implements HOT-SAX discord discovery algorithm in `find_discords_hotsax` function which can be used as follows: import numpy as np from saxpy.hotsax import find_discords_hotsax from numpy import genfromtxt dd = genfromtxt("data/ecg0606_1.csv", delimiter=',') discords = find_discords_hotsax(dd) discords and discovers anomalies easily: [(430, 5.2790800061718386), (318, 4.1757563573086953)] The function has a similar parameterization: sliding window size, PAA and alphabet sizes, z-normalization threshold, and a parameter specifying how many discords are desired to be found: def find_discords_hotsax(series, win_size=100, num_discords=2, a_size=3, paa_size=3, z_threshold=0.01) Saxpy also provides a brute-force implementation of the discord search if you'd like to verify discords or evaluate the speed-up: find_discords_brute_force(series, win_size, num_discords=2, z_threshold=0.01) which can be called as follows: discords = find_discords_brute_force(dd[100:500], 100, 4) discords [(73, 6.198555329625453), (219, 5.5636923991016136)] Time series motif discovery with EMMA ------------ ToDo... ## Made with Aloha! ![Made with Aloha!](https://raw.githubusercontent.com/GrammarViz2/grammarviz2_src/master/src/resources/assets/aloha.jpg) ================================================ FILE: README.rst ================================================ Time series symbolic discretization with SAX ============================================= .. image:: https://img.shields.io/pypi/v/saxpy.svg :target: https://pypi.python.org/pypi/saxpy :alt: Latest PyPI version .. image:: https://travis-ci.org/seninp/saxpy.png :target: https://travis-ci.org/seninp/saxpy :alt: Latest Travis CI build status .. image:: https://codecov.io/gh/seninp/saxpy/branch/master/graph/badge.svg :target: https://codecov.io/gh/seninp/saxpy .. image:: http://img.shields.io/:license-gpl2-green.svg :target: http://www.gnu.org/licenses/gpl-2.0.html This code is released under `GPL v.2.0 `_ and implements in Python: * Symbolic Aggregate approXimation (i.e., SAX) stack [LIN2002] * a simple function for time series motif discovery [PATEL2001] * HOT-SAX - a time series anomaly (discord) discovery algorithm [KEOGH2005] .. [LIN2002] Lin, J., Keogh, E., Patel, P., and Lonardi, S., `Finding Motifs in Time Series `_, The 2nd Workshop on Temporal Data Mining, the 8th ACM Int'l Conference on KDD (2002) .. [PATEL2001] Patel, P., Keogh, E., Lin, J., Lonardi, S., `Mining Motifs in Massive Time Series Databases `__, In Proc. ICDM (2002) .. [KEOGH2005] Keogh, E., Lin, J., Fu, A., `HOT SAX: Efficiently finding the most unusual time series subsequence `__, In Proc. ICDM (2005) Note that the most of the library's functionality is also available in `R `__ and `Java `__ Citing this work: ------------------ If you are using this implementation for you academic work, please cite our `Grammarviz 2.0 paper `__: .. [SENIN2014] Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S., Lerner, M., `GrammarViz 2.0: a tool for grammar-based pattern discovery in time series `__, ECML/PKDD, 2014. In a nutshell -------------- SAX is used to transform a sequence of rational numbers (i.e., a time series) into a sequence of letters (i.e., a string) which is (typically) much shorterthan the input time series. Thus, SAX transform addresses a chief problem in time-series analysis -- the dimensionality curse. This is an illustration of a time series of 128 points converted into the word of 8 letters: .. figure:: https://raw.githubusercontent.com/jMotif/SAX/master/src/resources/sax_transform.png :alt: SAX in a nutshell SAX in a nutshell As discretization is probably the most used transformation in data mining, SAX has been widely used throughout the field. Find more information about SAX at its authors pages: `SAX overview by Jessica Lin `__, `Eamonn Keogh's SAX page `__, or at `sax-vsm wiki page `__. Installation ------------- :: $ pip install saxpy Requirements ^^^^^^^^^^^^ Compatibility ------------- Licence ------- GNU General Public License v2.0 Authors ------- `saxpy` was written by `Pavel Senin `_. ================================================ FILE: conda.recipe/meta.yaml ================================================ package: name: saxpy version: 0.1.0 build: # If the installation is complex, or different between Unix and Windows, use # separate bld.bat and build.sh files instead of this key. Add the line # "skip: True # [py<35]" (for example) to limit to Python 3.5 and newer, or # "skip: True # [not win]" to limit to Windows. script: python setup.py install --single-version-externally-managed --record=record.txt source: path: .. requirements: build: - python - setuptools - numpy run: - python test: imports: - saxpy about: summary: SAX: time series symbolic discretization stack license_file: LICENSE ================================================ FILE: data/ecg0606_1.csv ================================================ -6.095 -6.095 -6.1 -6.1 -6.095 -6.095 -6.1 -6.105 -6.105 -6.115 -6.105 -6.1 -6.095 -6.09 -6.08 -6.075 -6.065 -6.06 -6.035 -6.015 -6 -5.975 -5.955 -5.92 -5.89 -5.865 -5.83 -5.805 -5.775 -5.75 -5.725 -5.705 -5.695 -5.68 -5.67 -5.655 -5.64 -5.635 -5.635 -5.64 -5.64 -5.64 -5.65 -5.66 -5.68 -5.7 -5.71 -5.71 -5.715 -5.72 -5.73 -5.735 -5.74 -5.77 -5.78 -5.775 -5.78 -5.79 -5.785 -5.79 -5.8 -5.81 -5.815 -5.8 -5.805 -5.815 -5.82 -5.82 -5.82 -5.825 -5.82 -5.83 -5.83 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================================================ FILE: jupyter/discord.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/psenin/git/saxpy/jupyter'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os \n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.chdir(\"/home/psenin/git/saxpy\")\n", "import numpy as np\n", "from saxpy.znorm import znorm\n", "from saxpy.discord import find_discords_brute_force" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import genfromtxt\n", "dd = genfromtxt(\"data/ecg0606_1.csv\", delimiter=',') " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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NiQxXuZR1AKSV+2SiAoFWADUQtp42sC2KdGKp5NG21KULeFO20KAOAlPnn5uFZUTyTLkh\n1gCk9yGYxzWU0AqgBsLWDjrQXkAh7t/jV649cEvnTVCAOggsaSlquoUmrhMQBUToqi+mHcG0AnCB\nPWXQP4LqsUOyYFO3NTCtKIrYSbncRkFptXNCz0TIk7KIKCBTHom4Mrl+QFXsViaXlKoljD2Fe/3X\nCsANtptGMrEoJxTt4kPdWsLqqe4f9gZuvqW5HDUoKSAlTEYBEcqzz7dweYjUcoOhgMKtAbQCcAF1\noJWWAqKVS71RCqX1adfD/gQGtym8L1F2EMmibGlAT3eFlwKivG4SOgg8D2FbeAiDSxSgbgddsq+y\nvmFZnxRjC2fgljI2ocqjshatIDCtxU6u1AMq9GtYhvkarnEFCa0AXED9gFOC/oEMxtuhzhih5O0p\nQUOL0FIPlHUF1AZHUK1MKJsPagpokYM6yBfWrpYArWUMqNanf1AGvNXgI2lKqT9RQgYx9UCZVmrb\n/Iay6A004zM7nxLIkkJIWb1wr/9aAbjBVukZsjRQ+4JNOzZKzpjcOyH0AELXDI6cAhIoERDQ1K1H\nwk0BBeABhFwDaAXgAmorhTYNlPqBDMqjoFVOlLn7/pVJ5d8aATn1QGjJUhsc9PNXvhKMzfBwwqaY\ngoRWAC6wTSYSKzuYCUWeBhoyHpV6saCSRb8lpP3VL4LgsoFwUo6UsoKooA75+q8VgBuCyvKgAP1m\nJAFZZIQLI4U8yp3UyLflJI4BUFrF9DUx6nG4lkfK/awltAcwD0Gd503aXIp4O0Ly7qKUfDbh4kOr\n1IOaH0QxgKDiMCHMAuKEi7YVi6FD2JScE1oBuIA6UBVYDIA6KkfIZ9N0A6VbfCi9iaAC8dRZQDSy\nrOMwFpbRyqWnzrQHMA8RbgpIlUsclKPks4kXC0oKyO9dpaaAqGMAtFaxJYOm3w6xAWPK9Y8g2piE\nfP3XCsAN1JOekn8OirMnk2dOfEqihSJw637ckCxqCsgM2tIuF/TzgzamQLqbWsjug7luhNwF0ArA\nBeQufgXZjYDaJaf2AKyJTyGLTnHaeHu/kgKaH2TtoAmzgIKqeQCI2y4TGhxhzSgKAloBuID8phFa\n7dQWFLk8x6svWYRUC6kHEBBFSE4BEQfiaWgWSqVugdJ4CV1LlAChFYALAm257DsFMRj6gUoeTOuT\nln8m9Zx8xwDCTQFRVrSS18RUOG5YXgDxjrApkyChFYALAt10hVIWrYEXvkCa7ZgwdhJS6oEKlPcg\nqJoHgHb+Ul5Dyowi3Q56HoK+GZwi2y/9YDumCFbRuuSk1iehdqKMwwTFi1MppsCygIh7C1HGiSjj\nYZoCWuyg5nhtxz7ph5K6YPsSRSbDTR45X0wqi5AC8iVJyqBbeKREINweopBHuNCGbNGmVCZBQisA\nF1Bai0JGUFy2fwSVBRS27ClK6sE+rnAtPIB6D/zLCjILiDZ+4l8WZRttXnYQTmgF4AJ1UQxdO2jl\nmHpsYdvflnIzElqOmNgDIKeACKkMwvTZQOQRepy01Jnx6ltSsNAKwAXUtAjt1oa0LgC1RWYJIxCh\nyPBLP9tlESoTQpqFej8Amqwu5ZBAIHX3WcpzpZRlxSbCrQJ8KQDG2M2MsaOMsSeNn2sqfO5qxthu\nxthexthNfr7zZIDcxbcdU/LPxBZeyLhPys6nQSlhUq+JigIq0Xlh5Fks5N4T4Xwj9F6lkJCv/4gR\nyPgC5/x/V/ojYywK4GsArgRwBMCjjLFbOefPEXx3IKAOtIZ1c3PAseVfyPhsynPlFY4bkhUQL061\nVtBaxcR0qHpMaLVTepyknolvScHiZFBA5wPYyznfzznPAfgxgOtPwveSgJJbpAB9d0ZqjlfK9S+L\ntFUAYbor/SIWjAYgrxSnVuqEacykc5dAVlCb8lCDQgF8gDH2NGPsu4yxbpe/rwBwWPn9iPFeaBFU\npgLgf3JRpyDaFtmwUUBBeU7+RNk9RJ+yANUDoFktSoQuBfH0sHsRhAqFokYhiB5K8z4NlDF2F2Ns\nh8vP9QC+AWADgC0AjgP4vJsIl/cqXhXG2HsYY9sZY9tHRkY8ngYt6DfBUI/pJgRN33fac5UiaFoH\nq8d+rXZCZaIehyyQaZNHIYs4TZh4/Sc+V0JZmB8eQM0YAOf8Ci+CGGPfAvArlz8dAbBK+X0lgGNV\nvu+bAL4JANu2bZuTy0ffcVOR51cWeXzCOg5bJkVQHoDfu0C+iBkCqTZyCSKdESAyhirIblgepdXu\nkOkHQWwvGQT8ZgEtU369AcAOl489CmATY2wdYywB4I0AbvXzvUGDki8GaK3PEuFCBtAvZtYiFq6x\nkQaUqa1ix6tfUO7KFiQdSpp0ELZsLEJvIkj4zQL6LGNsC8R5HgTw/wAAY2w5gG9zzq/hnBcYYx8A\ncAeAKIDvcs6f9fm9gYJ8xydCIjWotE0qeZRZQIF1UfUlyfn/dBoglBQQ8RIWGAUUMt5+wVBA1cA5\nf1uF948BuEb5/TYAt/n5rpML6kWRjmcPUjmRTnzfkpyxGL+ylGNCOimMHoClUGi9MOpmcNRbmlLJ\nojRe5n0QeDGCvGGVekxIP1BM/uA8AGIKiHTR9hkDIFRMgDIeosUskJ42VPLIKSX5SkgnhSw2ESS0\nAnAB9SQlpR+Ix2aTTSGDkPu0P9Q+F23SVFxVrk9hUK1FGvCyAx+yiA2EoPrjk9JJhEZfyNd/rQDc\nQN7uN6RctlMexRMeRFYGhbygvLAw8sW0zeDUY2oKyKcscu+VcO4Sx3WCglYALqD3ABR5lLLI0/z8\ng9LyoRwbZQ8lSmWiyqArBLPL9YNg6wD8GkN0sgA675XScw0aWgG4IKhdsoQ8OlkUoN8PQFpR4YpP\nkGZiBeQhklN65As2LULnAYBm7gZJ01JDK4AaoLdkwzW57NYsrUdBKctvwDu4OAzdCYct40zIcJfd\nKCjbaKj/TxFboPKcqD3EIKEVgAvIPQBCM0p9gGgKaYitWVMunSwqeVSy6C11ydn7B32zQGJ5Ntl0\nSp3i6lHdB+oNpYKEVgAuCHMMICj+mU6efIiolROlBxCyGIApK4wKndijoHwWAlJOpBSQL0nBQysA\nFwSbBeRPVpDdQMPGZ5NetxKhLGILj9S7IZZLed0A2niYTS7hfPNvpAVzjkFAKwAXkD9ElNYnMf9M\n3/dIyvItijTgTUkW0HuInFyWkEfA2QdocPiVSBlbE/Jo7gP1uIKEVgAuoA6k2WX7/f8gH0gKeXQU\nECWfTbswBhM3ofYmwua9OoXQ3lN/soQ8Q5ZPOfMoC1QrADcEZeEBFAEm6zhsKaqqDPJOlL5zN10P\nG0JQtRjUawV9Ki6dhyhk08mijHeErVVIkNAKwAWBFltRTq6wLbJQzo+czqCUFbYgH83CAwRbKU7u\nUfimgIJ5FvzPNVVmuFWAVgAuCDSTgnJy+RMlZBB7O9b6T0ydEf6//zxv2ptA6QEEmsFG7lH4lWUd\n01YCEyomX5KCh1YALghyRzC/oG/OxZVj//KkCMqUUudxIwiqqphyY3Nyr8m/OJsUylRLEnnEHBBZ\nENgm05+soKEVgAuC3RPYn6xiiSNi7LIcymZfZhDYP0jrJ0hjANRK2JDlX1QAFrsiz7c0YgpI+X+S\nALp89asA1NRZf6ICh1YALqCnRegUSokDsUjEkOtLlCEvoMWMPHbiVxadN2GTSyCDsn8SNedMvQc1\npQdLrZyo9lGgVkxBQisAF4Q5la7EOYz1n+ZhJ56fQe0I5lciJVsQXNzEP4KkL6k9Tt+yAgsC+40B\nqL/4EhU4tAJwQVAdMgEC+qHELQ/ApywgiL5H9lc/IK0DILRkKQv7VHn0c43WowibB0C9zlI1g6O+\nB0FCKwAX2IM4xDy772AmrBgAodVDLY9k2lMqTpsAOguPctEmDSgjCCVMTOuR3gc6ZRe2XlFBQisA\nF4TaA+AcUUMDhDFv3Hx4yBVnSGX5EwWAegMX9+NGEWjvKcq4jj9RAOg8Mep7ECS0AnADMe8Jwgkh\nFAAdBURtrdDy2XTBNMrFghM/4ZRbEVI3qqM2hiirZAmdOgCUrSA0BTSvEaQH4Hd6lUpAzPAAaNot\nhJfPps0CCkiWP1EAaOtEwpy2WSaPMNhKO3f907QS2gOYhyDPAqoguxFQU0DkMQDzleKBVOX6fSjp\nFB25VWwIIbHYiTUA9bmq8CuuGFQWkE85tnH5lBU0tAJwQaCBNN+yrDRQClDz2ZRZQJQ7qdEqYVUW\nnaIj95qILXaabRfpni3qGgWqimzqcQUJrQBcYF8U6ThecexPFnUhWJHQXw3sgQSN4gyjLGoKjtoq\npt520TbfKLOACCkgyv2nw+4DaAXgAkrLU8hTxfmfXJStICgXM2rXlzZjRD0ODwVEX7gV3nvglOfX\no6DuY0UVBNYxgHkOu9tL4AGAbsEoqoVgxB6Am7z/eeoYbr71WU+ygt2Nyq+1SPck2jwdv7Ko03CJ\n70GYC8HoYwCcRJbeFH6eg7zHCOG+qpzDDAJTWD02BeBytn/5oyfwvQcOIpXJ15RFvn8soSVFu7uY\nKis83oSQZx2TewDEFJD/LCBaqsXyAAipUF+SgodWAC6g3nSlSGjJ2rKATmLl6JGJ2fpkhYyeoqbh\nqGAXdfLu59zJs47DRrVQpTBrCmiegz6XmpIz5oiYaaD+ZAFAsUrr2plcwTwenMzUlhXo4hMeSzus\nsgCHF+ZfXAA8e0DXzp8ocM7JMtjo92QIDloBuICaRy1SPDkG1EIwClRbtKdmLQUwMp31MLYAA5A+\nZdnIAkoLz7c3ocryD5t3QhIDUI5dRrh/ZBpfu3uvvf6gCii9RFo6yde/20A5rqChFYAL5D2LMPeH\n8iePHcH3H3rBs7wiKf9MXQhW+YGczlq8/6gXBUDIjZfJ8B07oVNOtQLn9cCLl/P4oQlMeYjBOMfj\ntibnCiU8uG+soQXb7cJ9+L+fwufu2I3njk95lKcO1tO/VARlsh5t2xGfgzmJ8KUAGGM3M8aOMsae\nNH6uqfC5g4yxZ4zPbPfznScDcgJEmLsG+PB/P4V/+MUOz/JoN4ThiLJggsDOoU1lFA8gVVsBFKk9\nABudQfdQUgYffXsmNSibRw6M47VffwCfu323J3m1LOyfPn4Eb/rWQ/jlU0e9ja/GIvv4oRMAgEPj\nM97GV2OOFIqlxryJEPH2QVZPU4PCA/gC53yL8XNblc9dbnxmG8F3Bgp506IRVnXhyRaKnuRRWoxF\nDsSilDGAyg/ktKIARqdzNWVR109QxhTm62Kxa1BY1k8ePlGXvGiEucqTC/WTh+qTF2HlirOgBJBe\nGPOoAGqc77VfuR8f/K8nPcmiNDhqKU7OOY5MeDvHWkkf9z8/in/5w74GRkkPTQG5QH2IVCsUsFsw\nXqxioPZE3Xl8CsdO1M6yAcREjLCgsoCcFJBQAO3JGEa9eADEwS/Sjdxtx3TcM1XVKHNZYAGY170p\n7u1RlUOLMncFMG4o8vGZ+iglYQzZMaHIOD7pbf5WSzseT+ewazCF/3nqmCdZJzMd9/sPvYBL/vlu\nPHtssqasWkkfb/3Ow7jlN7vw6MHxhsZKCQoF8AHG2NOMse8yxrorfIYD+C1j7DHG2HuqCWOMvYcx\ntp0xtn1kZIRgePVD3rMIK/cAppXMmGGPCqAa/bB/ZBqv/NJ9uOiW33uSZY8BePqXqpBGXDTCyigl\nmfu/urcFEzO1PQDq4Jedzw72Aa8HQewpHGXlCywAjKbFdffigQHW2CIRdyUsYzleFDpQ3aMYT1tj\nGkt7G1817+ngWNo8zhUclpcLat0HzrlpxNQ1Lpe/3//8KADg4f21F+1aHqLM4djpMW4SJGoqAMbY\nXYyxHS4/1wP4BoANALYAOA7g8xXEXMw53wrglQDezxi7rNL3cc6/yTnfxjnf1t/fX/8ZEcB8iFj5\nDZxUrJ7hKY8eQBXL+J7d9Sm5YgmKB+Af1R7wlEEBrexuxuRsbYuRmAEKsA7AH2pZnjuPT+GwV068\nBmUzZizYQ1MZT4rH7gGUf/6o4Wl6yepyynMqYZsC8CivWvxETTX2QrfY20qUn+sHfvgEzvzEHXh+\nKOVBVnUDQSqSoZSHdOgqXg5XDDiv60eQqKkAOOdXcM7PdPn5Jed8iHNe5JyXAHwLwPkVZBwzXocB\n/LzS58ICMwvIxe1VszFGPEwGoHoMYDZfVP5W+wEXE0hQBhQugBxbzCXeIRXAiq4WnPBAGdSKddyz\nexgv+/w92D1Y+4EEaltSd++EIq8LAAAgAElEQVQa9lSfIP6fMg7DwSpsy1kscbzyS/fhtd94wOO4\nxKtQAOUDGzMs/5lc0ZM1a3L2FVKFB6fE9fKS1eWUV04BibGt72s1x1kL9riOXaJ6L70ElWvNt18/\ncxwA8OD+sZqyuM3hKBd22FBIIx4W7Wq1Eydm8sgbaYHDHtePIOE3C2iZ8usNAMpSYxhjrYyxdnkM\n4BVunwsTqvGoqiU85FGDC97e/M32t+EpaxJ4WWRLRgyAoXxyNQL5ELmd63S2gNZEFL1tCczmi8jk\nqwe9bQFDl4foq7/fi/0jaTzikfus1lUxky/iHd97FBf+0+88ylIl+aeTohW8MLnAjqSyHi125fq7\n/F2lVrzMt2oeRanEzfkrFiLvNEvUJSdajm3jQFsdCkWR7fibal3Xm3TgPFfOORIxsbwdHPXiTVSW\nVSpxHD8hxuaF9pVxw5jLNUtl6qeQg4TfGMBnjfTOpwFcDuCvAIAxtpwxJjOClgC4nzH2FIBHAPya\nc367z+8NFHKBYMLMtv1tSlEAXjV4kVdu4KZOgsEpb+5lhDEwl/hEI+CGNctcDMZUJo+2phg6m+MA\n7OfuBvkQxSIRV4tMek9HPNIj1R7w/SP18sXuxxKPH5rA3uFpj+NCxWpsVaF7oc3kghipQAGNprLY\nvLRdyPYw30yPwmV+pDIFcA6s728FAE9We6mKvAlDAazvb8PkbN5T+mapxM1CRqeCnJrNoy0ZA+At\nwUJ+HXMxOKZmC+a8eEGJLVSWxV2PASCVLaBQ8m61270mx7gy9VPIQcKXAuCcv41zfhbn/GzO+XWc\n8+PG+8c459cYx/s55+cYP2dwzj9DMfAgUVJpkQoeQG9rwrMGL5ag9O+xYziVNS0VLwpALj5EDBCK\nXFEoLllA7U1xdLUIBXCixoKmBpTdxiatqCEP5wlUb2uwQ8nGqOehdJNVKnG89usP4E//9UFP4yqq\ni5hD2kidCl3ONbeU40y+iFS2gNOXdQDwtmDYFh8n/TArKZs2AMBY2p+8iZkc2pIx9LUlUOJiofQi\nr5LynMoUMNCRRHM86rHwsLL3qs6Jg54UgHh1K/6Uhk9TPOLNA6gyLknjre5pWRAewIKEyYtHywNf\nsj3CxoE2zxpcDfy4TdQzlosH3EtmhtwPoEKNWt0olsREdZOXyhTQlrQ8gFoUleUBlLsT+WLJXCC8\nByCVvQ/KuFTLevWajmvCIeyBfYIj9p7JolBAVTy6ox4a6JnxJpfFQgZZTzfmhxfFabfY7ZD3b4Ph\nAYx7ON9qdGg6a58fkx4pzErXLpUpoD0ZQ197wpMCkPEEtxRVeR82L233tNCqVFe5YpLXrQ0nZvKe\nqdCYy7gkBbShvxVj6aytlmIuoBWAC2TrBrcbODmbRzTCsK6vtQ4PoHL7hhMzeWzslxaZh1RLm8Xu\n6eurQm4x6eZRpDIFtDfF0NWcMMZafXxmPMFFcaaz9VUVi7GhYudTVRl5o1oqewBPH7WKomo93ICD\nAnL8TaVVvGQCWZx9uSx5jiu6mhGPMlvefW155R6d9OA2GPPNiwJQF8aye5orojUZRVeLMT9mPSgU\npZeV83ynM3m0N8XR35b0pACqxSfk/29e2o5UpuBh0RavERdPWC7aGwfEdas1f6XnGnG5B7K9yvr+\nNnDu3egICloBuMDmlrtQQB1NMSzpaPKswUUMwH3Sz+aK6GtPoike8ZRKVywKWWLBdtcAw6mMJ14c\nMJRThZiCoIBiJgVUa6G18c8Vgl9N8YhnBWAreiujM+pTANViAOr/e215YTo5ZYtiAU3xCOJRhqE6\n6QLn5EgbNSciDpPApIcFtpolKxV4PTEAyX3Ho+XGUDpbQGvS+/wAnBRQ+ULbloyhty3pLT4hKccK\n8Q4AWNfnTdnZFKfjb5ICkoZaLcOvmizLAzBkzXEcQCsAF0jXMhaJlN3A8ZkculoS6G9PetbglYKG\nuUIJhRJHSzyKPo+TvlDiiEXdKRtAtKc4/zO/wykf+41rF9LR6Sx2HLX482KJV4wppDIiKNfp8QG3\nu+TlygQQD+SExwyUaopzciaPnlZheU549JwqeWH1BvZlMZ7bPZg2aJH+tqRHzl68ugUMZSuONmOR\n9ZQlpsZhHH+T9291TwuiEebJAyhWMYbS2QJaEzF0eaQIger3VBoc3S1xT4WH1eIT0uNc09sCoLay\nq6aIp8o8gOpzxEttjVTCc50KumgUwORs3nNbZvukt//P0YlZrOxuRrd0ez3mx7sFDWUNQHMiit62\npCs3PpzKYP3f/Rr3PT9iyopGGBjc88bvem7YPN7jUgDzF99/DNd+5X7sHRZ/q7qYZUQQuD0ZQzTC\nXM9V5X2LVYLnUgGs76svA6WStTgxk8Oa3hYw5o3KKBZ5xUC8qti8LtqMuSvNmWwBLYkY+juaPGbt\nVF4s5DVrS4pF1u36c84xm7PojWq9e+Ti09EcR3dL3DUIXCpxG11iJURE4Lxy6ayggDo9JgmI8VXO\noEoZ8627JYGJmXzNNNpqXPt0tgDGgFU9zQBq1z2Y3quLpyMr4qUCqO0BGLJc4jCpTAGJaAQru5s9\nyQoai0IBpLMFnP+Zu3DNl+7z9PmSEgR2k2ULfHmY9GoMQJ0R8sFtScTQ15pwXRQff2ECJQ687TuP\nADA8gEjE6B1T/l3qov/HvaNlf5dK5gcPHbLGxhgA+wJULHGkc0W0JWNgjKGzOV7G8f7mmeM455O/\nxW1GwQ1XLTLH906bLrl3y0cNnjtxYiaP3tYEelsTGPGgTIqcI15h4ZmczZuWorc8bxmId/N0img1\nPIB6UhkjLouFqQCaYuhqSbgusD9+9DBO+/jtZoVv0aRsyr3XmVwB0QhDMhZBb6u7x/mZ23Zi8z/c\nbtJFhWoeQE5QQFYQ2BtFJYPA6sNQKom2DW1NwuPMFUrI5Kt7ieZC6xKfkJ5Yb2sSQG1PXfUAKiV+\nrO4VnlOtYLzqATgvWiqTR3uTNS4vBmSQWBQKYO/wNLKFEnYPpTz1VS9WscpmckU0J6JWaqTXSe9i\nfcodt1oSBgXkYpGplcKzuaLiAbhTQMdOzGKgPYlVPc2uHSSXdwrLQ+a8S05WPJOWRLMRXJPIy+50\nsUCfPCLkf/l3zwOo7gHI677WUABe0/wqZYxMzubR2ZxAn8eAYalU3QNY09uKaIR5XLSNuInLuGZy\nRvFca6IuGsPN20xn7RSQ2wJ7z27h8X3/QbE/RbW5m84W0ZKIgjGGntaEq+f0o0eEYXC3IbfERZ2I\nG6UkYwDJWBTN8ahnb9gtI0722Opoipneda3rV41qmTbjCULWeI2U12r1GKlMHi2JKJKxKPraEjW9\nRHsWoWNchpJrikeQiEU8Bc6DxKJQAAdGrTzgfR6KfcwgsIuFN5sXD1FdHkCFNNCZnEoBCQ/AWUwz\nOGlNttHpLAqlEmIR5po2CIjc82WdTdjY3+Za2CQfKrlolkqCLnAuZm4KoOxcjc/L99WHyLnMWjGA\nFuP7vQX5rGwbu7wTMzl0tcQ9K4Ai54hFZTFeuYXX3RI3aju8pVqyCqmzIjMmho7mmG1HNQnOOZ4+\ncsJUiPZWHHZIykby7G4eQEeTmIcPGe0OrMUnUnbNpPcKAD1t7gpgTa9Q0AeM6llJXzJWXiCVzhbR\nmogCgIhRuIxvKpO3ZYDZM7ssqPGObsO4qqUA5LlW8p7akjG0JWNIRCN1xQDcirfkcyAMNbusA6Np\nfOOefea8siVDuASn25uEV93VHC9Lnd326Tux9qZfn7T00EWhANR0PC/l9AV10jvuw0xOcLwddVFA\nqBoDkB5AocTLPJRfP2O1xh1OZVGQXLbLAynOL4MlHU3YtKQd+0fTZXGPMUd3yaK0Zh3equQ9240F\npqulXAFIOmlwSmQd2ThZF4sMsBYYPx5ArlBCOldEV3McfW0ec8ZLlekk4U3EMdCR9FzoI1JnKwRG\nk8JAmM0Xy7KxHto/juu++kecffNvAahZNuXV02mjFUckwtDVEsdMrli2B4UsNpPN0wpGDnO8CmUD\niEJGN1pEBsQPGcVTZuU5UEYRzuaLpjw3DxEAzr75tzjjE3eYv9srga3PSWUnCg+F1e5WV/DxX+7A\n3buGbf8fi5ZTLdLSZowJ46oWBVSF6pKxCcBQAI759vFf7sA/374Lf9wrlHBB8QDcnoP2pPVMOa+Z\nfC4fe2Gi6nipsCgUwDGlyZRbBP/w+Az+82Fri8eipEVg1+DFEkcmX0JzPIr2ZAwR5q4AHtw3hsde\nsPrdVEpnnMlZCkC6qupitnc4hR1HrZaxI6mMpZwqnOtIKouBjiQ2DbQhVyjZyuD3jUybFMd4OotS\niQs+2+VcVYsMgGsQUk5WzsW4reB5Of88nS0gwsTC05KIegoCc9VaVATKay49ADfaJl8s2TKNStx9\n4SkZSrezOV4Hb28oJpcWBDIILA0Ep0I/7OhyqdIFYmx2Gs5cYOWi6JhvsoHa6HQOmXyxKgU0rVjs\nPa0JTM6WZ2NJr+DIhBVTiBqVh270pTk/WuJVW4XIjpy2NFBFojQ42mwUkF3eV3//PP7jwRfwju89\nasoC3IOt06q305qomWJtZv65pbvmLEXX25Yo817lON723YfF7+pz4EKFtqm0qkIBqcbCYQ9FhBRY\nFApgcjaHdX2tiDD3PO93fO9RfPTnO/C1u/cCMPhi+YCrQVvFYo9EGDoqWD1v+tZDeN03rLYCgn4o\nd3tnjYeoOR5DX5sICqmTa5/R7+Ybb9kKwPJeopGIa+sG2f+8oymOU5aI/jF7hiwa6N3/IXbjTMQi\nKHGRtSHpqTIPQAlAAkBXSzmnrT5UY9M5e1FThRxvxpirFeUGde8D1duR+fAdzXF0tyaQyZfKCn1e\n/ZX78dLP3WP+buOe1YUnK/rjdDbH0duW9FwdK61i52ohFx5JzTgXRTn/pDIyg7YuvaKkFQvATLVU\nreIjEzN4fnja3CxmLJ2zBYGdmFEUSq8x39QU2kcPjptzXBoi5vyA/Z6ms1YCgxhfoozPnlH2znjo\nwLgpz9UDUCjHShTQL54U3vCSDjF2kwKqkAbappyr876OTWdx00+fNq+n9JzcGrjNGJ4YALNITb0W\nrcY12LxUVGxXq52Qqa4AjNoO6xqp8USvu4/5xSJRAHl0t8TR05rAbTsGy/6+tKMJAPC9Bw4CsHr3\nOJlsNWgLVODFXT4vFp9y/ln1AKQCUC1jmW2wZXUXGAOOG9ZepTqAbKGEfJGjrSlmpqypvdClG3vT\n1ZsBWFa7GdBUZJkpg8Zk7W5JIJUp2KyUsemc2VZgNJ1VFIC7ByC/382KcoNt8xvl/SklnbGjQqO6\nXYMpHD0xa15DGw2nCJM7WQ10NJm0iFN5fef+AzjrE3eY75dKVgM99ZOcc8zkRIyoozlmG6uEVACF\nkkjfLBgcoysvrixiMulApTLuem4IAPCGbavE36azjqydclpEpYCc8n7wkPCCB9qTFkVYsgwEpywA\naE1az4LTGDp2IqMcWx6FmzGkzrfOCgkWa3pE/EieVrVW5tOZ6nTXfz58CD9+9DA+9kvRmFjO3bhb\n7MS4p4CYu9lCydaaWypLSc/ZvTCXGIByT9XAvurxeDFEKLBoFEBncxxnr+zC4fGZimXhCcNqUvvt\nuKVtNicUWsSx8KgK4U7jAeUckBmldoVSnQI6OjGLRDSCJe1icRo0FqtohSCwyaMmY2hNxrCyuxl7\nlEDw2HQW152zHJuXtZvfZWUB2eVZFJB4GAcMq0vy/uPpHAanMqbVM5rKWs3gXFJUZVYGgIqB28/e\nvgtrb/q1SUsISxtlF25aOc+uGrEYmQpbSZnIB22gPYme1oQZX1DxqV89h1S2YPaVN2snHLUYuaIo\n7FNTI52KSQ0yD6cyVutgFwpItWJl5ai6l8LBsRm0JqJ4zbkrABj3s1rlbq5go4DU8weEVxONMNx4\n0VpMZwtW1plLDMCNAjoxa8/dV7eJlFtRForc9E5sdJcy35KxKFoS5VlF0iMYT4tkiaK6aDufBcXS\n7nVJsZbTSm4/qfL2zsydWSPuB8DVUJPKcsgw0IrG/JVenYT00M24mmP9UD2ek9UiYtEogK6WBN6w\nbSWyhRKePWbfik0+lINTGRSKJXuxlfIYqQs2ICxQ58KjNgCT/GylAOSskgXU3ZJAhNlplf2jaazt\na0EkImgTGcuQMQC33GfAom1OWdJuegCzuSKOnpjFhv422yS26gDcOVn5EA20i/+R7Y5//bR4cF5+\n2oCQlc4pQeBIueWTtbjPvgoewNfvERtly0XOvmirY7MChmajugoKYEyxZN1acs8YVEZrImYtihW8\nE7nxubkng0PRpU1ZUZMCUudHtlDEXTuHETcW++FU1vQAzEVR+b6UYsUu7WhCLMJsHUYPjc9gdW8r\n+hX60FzIIpGy+TGTLVb1AAYnMzhnZaciL2veA+f2qHKuyYXRLXdfZia1JWNminOxxF3pLud863Gx\n2uU9lskSavzEGdeZzlqWdo+xn4VKSTmf22Kp8tyVPY8AizqTBszO41Pm5jXpXBGpTN7sJeaMw8zm\nhUK1aFUR2JdetZyrrYmop+p2CiwOBTAjPICta8SWxWp+fKFYwqHxGcQiDMUSFzyqnPSRykFbQPDi\nztzsa75sFZvJCaymgcJVnqi07XEUNR0am8HqHkGx9LcnTYVSrXIXsKz2TQNt2D+SRqFYwr6RaXAu\nqhnNh39aWO1mHYCDf44w61yXGDSZzJLZP5pGSyKKG85dgUQ0gol0ztHauHxsJifbmjSD0BLqQ/eE\ncX8qLdqyoZa6V4FbRTIgqCnACAJHy+MJst9OS9Lywpz1GJJ+kQqgWFIqgZXPyXTHlmTMzGRRLew7\nnh1CrlDCq89eDkDQQbaiIZTPN3nNIhGGgfakaWWK8aSxuqfZ5j2WKiyK4rrZeXHAbnAMTWWwtLMJ\nfe1C3si0lXXmzIiTys5KEijP3b93j/C+zljeYSr8go0CsisUdb4NtCft3tJUBvtH0kqsLKtkPNkp\nx5l8EZxbhlBfa7nVLpVJPCq8uGrpuLO5IprjdsUpz+eT//McAOAlp/Sb17BoXCgRUHYzXKwgMGAp\nowOjwlvfsrpLU0BUKJU4UtkCOowsj1iE2YpCLvjH3yGTL+GC9T0AjIfSTH2zVwVKC0K1otSdn9SF\nRzR3k1ky7pbsTL6ARCxi/k0NjmbyReweSpkVqgPtTaYCEFy2CwUkF8ak5QHkiiXsHZnGvhExuTYM\ntKJLehsG3y3pLqf1KYO24vsND8BQAEI5tYAxhu5W0btFrcx0PkUpJaAp+8eri4XKqY5MWZ5TvApf\nXKkiW5Vl9wDKvTCphIUHIM5RffhKJW7SODKd2NqW034PpDJpTYge+U3xiG1rw+MGD/7BKzYBEIua\nuYi5LIrpbMFcEAFgSWeT6QGUShyHJ2axprcVLYmYmVllSytVzrNQLCFbKClB2zgiShsNzjkGjRRi\nc5FNZa3us2XesKU4AWBZpzAQJNfPOcfhiRnccO4KLO1sMr+nWCop9RjW+MrnW5Ot4Oot3xYZNhdv\n7BVjm845PIByOklSLW50l7Sw80WOEzOVvQnOuZE+awSB22Vlcdb8/Pq+Vrz3pRsAiLodSYU606FT\njsw6K7NLjGXv8DSWdzZhZVeLVgBUkLsgdTbHwRhDocTxu51Wvxxppb/qLMMqk4FRFyvbLNyKi8mw\nuqcF6VzRvFlyQXvF6UuwaaDd5vZGXSzZWSW4BBhWvvGAP35I5AHLplH97UnkijJgGCmr3AXUiS8m\n2MUb+wAA9z8/il8aGRRrjYrXnlbBw5vejqMEXs19BoTFGI0w0wI9ND5jKifZu6Vaf/ZpJfhlWp+2\nB1LpyDltBUorLRZAZQWg8u5mNoutAtW+yAKGB+BCi+wZTpmKTXLa6rac9gXbUCZJUW27uqfFpgCG\nU1k0x6NY1d2CWIRhOJW1UQ/O80znLIsdEB7dc8cFfSlrL1YbgdFeox7CtD4jTuVkjQ0QHkV3i0Wz\npLIFzOSKWKoogDHDq4tFyzPi1D5FgGiTAFhe0lg6hxMzeZy1otNoO2HdU7eWHM755qzJkB6oXGjH\npnNK6mbEIctuCLl5dmoMSlBxluJUkcmXwLmgaQFLmYymrHqa9f2t5vjsHkDEYbiIcXUoMQDAagfx\n/PA0Ngy04Q3nrcTHX306TgYWvAKQqWlyoQBEhshMrmBmGfz5xetwibFYqlYP4B74kh6AXABfMB5y\nmeFx/ZYV6DUqLTnnKHH3DJTZXBEtcUsBbBwQ1bulEseu44IHf8XpSwFYFjhgxQAqBYHlxF/a2YSB\n9iS2H5zA73cNY0lHEk3G90kevlKhz3Q2b1t8ohGGpR1NODIxA845jkzMYlW3OH9R0JKDupOaW3zC\nDMq5BLzVtDd5He3tG+x0QUsiiqiRigvYYwAylgBAsTwrxAAkDRePKm0DLAXwqV89Zx7LNNwiN7YL\nZdXnx9reVuwfsYLww0aNhozpDBtzTV4zFYWi4NOlxQ4Ig+PETB6HxmbMLTHlHJTdZIu2oHK5olPv\n6UBHk+mV3LN7xJDXat0fY2F0yxIzYyeGvJXdzWDMehZkxf2GgTb0tiWQzok9pStnAVnVtoBIt5yc\ntTZfOT45i1eeuVRRTpbyjDuygJwpzL0OCiiTL+KpI5NmozgRjHdSceL3GcWrA4SC6FIa6Y2ksuhr\nS5qpqydm8+Y9iDv2xXDG6Kx2MnnkCiXsPD6Fs1Z04kVrenCtQRMGjQWvAKRlKBXAmStE1sqnfvWc\n2SLixRt6Td5z1GjHEJWZMYosNcgHACuNBfCZI6K98tNGb5ylnU1G8UnOXCBMhaLIm8kX0aR4AJuX\ntmMmV8ThiRk8d3wKfW0J0+XsVxRApSwg5wQDgNOWdeD2Z0Xq61+8ZIP5fm9bAs8cmcRD+8eMqmdW\nRrOoDyQgOisenpjFzuMpzOaLptXX0yo8gEq9WYolbvDZ4h5Ia2lQ4bPfbLj4nc1xW6qkSY04LDw5\ntmjEaFSn0EnbjU3nX755wKKAKtRipHMFJGMRxKIRtCREjxZVAcjvvfHFazA0JRYK0dAMZcV4cpGV\ni8WpS9txcMzKOhtJZUxFLg2E8kIwOS67xS6OhdxP/uo5/OiRQ0jEIjh3tYhr9UqPzrA+nfPDomys\ne3rKkjYz4C57Cl26qQ/JWBTtTTEzsO8WI5rK5AVnbxgUyZjwbCTVKD2Bdb2tNs/K7tU5PU5rbEs6\nrTnCOcexExks7xJdeBkzAt5F69q5ZbBJj7OvXfyPLK7aflB41+etEbTv8FTWFjxX74Mz7gcIZTs0\nlcFUJo/R6SxW97aY3ovoOmzdA9jmrd1Dl57wyHQWxydnUeJWs8SThUWnAH7yFxcBAH70yGHcfOuz\nAIQV1ZKIoTURxYi0emS1rTKz1KwdAGZL1188eRTFEsdHfvoMAOC0Ze1mczenhccd8tSJdaqRUvmF\nO/fgJ48dwTZjggIODyDq3pvF6foCwHlrxQKxoqsZb79orfl+b6ugm/JFjqePTpYV+qhFSBIru1tw\ndGIW/3jbTgBW4KurJYEJx2LmljEi5a3oEtdNVpuq33vBuh5bDrr5QNrO00FPORqbRRjDyzYPYMNA\nG8bSIkZj8ybUhVHJjJHX5biimEans7jy9CXYuKQdhRLHsBG4jTCGiMPTUSkgQCiAYombPZmOT2Yw\n0C4Wts5mUTkrFzGTIoTD8lTGduOL1wIA7to5hF8/cxxbVnWZ97q/PWFmAcVc6MtpM2irzrd2HJvM\nYHI2j3t2j+DK05eY39dvtCeXsRNnFtCEsS9GRPFcNvS3mpb/C+NpxCIMy7uabAHnokoB2cZXTncB\nwO6hFCZn85jNF7Gss0nQly0W3eXWqC7liAG0JGI4ZaAdzxgGmtwj+H2XbwQAg4qT2Vj2RAE1UUNi\nbW8rDoymcbtRU7R5aTuiEYb2phimjOJKN6PKWV2/pD2JCBNxk8Pj4lmQRuXJwqJTAE0K5fKUYblL\nKqOvPWlLfXM+RGbGiDEZWo0NsUens6b1f905y9FipBRm8iXzpkddrM+ZXAEtcWtinWpU78qKx2vP\nWWb+zekBOF1ywOg1HovYzvH9l2/E/R+5HL//8EvMABsArDCUF2CUoDsXDMciCwgKYnAqg/v3jmJ9\nf6vZ16fbyAG3etE7g19Gip8x8ZviUQy0J82gquT8P3L1ZqzvbzMD64VSyXwgUcVabElGTQojb2Q8\nbV7abt6DmVzRsRGJPQtIVcIdzXHcs8uKEQ1OiuZ6kms/PDFTkTZzzg9ZI7FrMIXJmTxeGJsx9/ft\naIqLVEYzl93hAWTLFUAkwvCqs6w58ddXnmIey8yqnUaMwFmj4PROAOA0Y3xv/fbDGE/ncOVpSyx5\nbQlzwZYpr2p+/EQ6b9IeEhsH2rBrMIXpbAF3PjeEld3NiEUjNsqvqHgAcMwRdb6durQdjAG7jqdM\nT10aXObYuKSnHEFgJUvMPNdl7dhleDtHJmYRN4K3LYbRZ1FndoMj7Qh2y/M8MJrGY4YncclGYQjJ\nwlDVgFTHNeXorxWLRrC0owlHT8yaLUIkLXWysOgUAAD84v0Xm8eXn9pvWvSyQOm2ZwaRK5TKHvDZ\nXBFNcStrBwBeu3UlhqayppX3ISPDQ7q9cnFza2o2a7SWlmhORPGyzSKvvrslbnvY+20xgEhZBgog\nqk47HFY7Ywwru1uQjEVt7//VFafgq28+1/oc4HDx7RYZAFy0odc8fv9LN5rH3S0JFEvcDGY5U+nc\nqKmV3c2mB7BnUFy7c1Z1msHuSYNLdSveSmXtymnH0SlMZwvYPZjCkYlZFEoc6/oU6sHgxt0C8TPZ\nom1R3DjQhmyhhJJRqTuVKWCJUSUMGEVIHKIZXIXceKmc1vW1or0phicOTWC3UY9x+jJDARjdQsuC\nwIastIvFDgCfes2Z2Ly0HW+9cDUuXG/djyWdTShx4O7dIygYlcp2+rJcoZy9shMA8IyxQ9wNW1eY\nf+trS+Kh/eO4a+cwjo9ShO4AAB7pSURBVE/Oli2y4+mc2bNH4ozlQt5Pth/GnqFprDeK1+S1k1k9\nVs1DZY+zJRHDur5WPHZowqRstip0l8x4ko0RK2WJSWxe1oHjkxmMp3M4PDGDFV3NZmrtcCpj1WM4\nvES1TkRiQ38r8kWO/9p+GFedsQSJmDgfqQAsCrn2uFb3tmD3YAq7B1NIxCJmV4KThUWjALoUa2XL\nqi58/53n4+0XrcV3bjzPfL+/LYkH9onilUPjM2Vub1qpCJRY0tGEXKGEhw+MIx5ltqwMoLz3izol\nZhwUEAC8+fzVAIDXbV1ps9jVSWO6+C4UUIfDaq+ERCyCa89ejo6mGG44d4VtMeOcY3I2Z7tmgHgA\n1/W1YlVPM67fYgWplhuUjuSTEzH7DudO1xcQla07B6fAOTcXx1OWtJuKTrrl7llAedObAIDP/cnZ\nAIDnjk/iqi/eC0AEM9WAoXgoxeedXp1q3V24vge5YglDqYxpla3sbrZtPymb+5V5AFlrwxVAKK/1\n/W04OJY2z/HUpcLL62yOY3AqY2vvIa+9lAWgbL71tCZw+4cuw6dfc5btfdVYAMrbe7stPr1tSXO+\nfuiKTbYMGJVynJjJly1mEzM5dLfaFcBVZyxFNMJws5EbLzN2JAU0ZCqAcmNoyiXmdPmpA7h3zwg+\nc9tOrOxuxoCxOMrunur+2G5cu3qu568TdOptzxzHIwfGsarHSq8edqvHcFBx6nO6yfDU5TlLuHsA\n1rimjZ5CqgF52Sn9ePbYFL73wEFcurHP8o5OEhaFAnDSIgBw6aZ+3HzdGTYOc+uaLttnnMUvbgu2\n1Ng/eewIzlnZZd5AmXkgi1nctsE7YbSoUHHF6Uuw61NX46OvOs0xFmuc0Wh52ibg/hDVwtM3X4Uv\n/OkW22SdyRWRL3IzTU0iEmG4868uw93/66W2ibrNKLD72RNHAQgFUC0rAxDFLidm8jgyMYs9gyn0\ntorNXayK46wjZbAyBfQaoxht12DKrKpc29diLtoyZdAqxrMr4VZHpg0g6hzu2jlkvmfmks/kbLSI\nkzZTc9kB0b/mgX1j2D04hfZkzMyXl8FpuZmONBAkzSKDwE4vrBJ6WhO4668vM393NguUWVJOq/07\nN27DV950Lj50xSm299916Xrc9MrNtvfU6TYxkyujgJoTUXz6NWdiaUcTPnD5Rpy3Viy6rQmxYYys\nEXB6O9mCqIZtd5zrNWdZi6v0jAHhnRwYTSNXLLkma7gttGet6EQ0wvCxX+zASCqLzYYi7u8QHWCt\ngHLtIPA5KzvNFiUv32zRZjIZwZ5Eos7bfBmt+rqtK81jVZmcLCx4BTDlsshWggzCAsCX33QuAEcL\nXAddAMC2EEl+F7Dyhe97XlRDSqtQyuOcG5ua2B9IQHDk6iLiRCwierYU7eu/6wTzCrWtwQkXr8n8\n7mikzEoZ6GgyOzQCIiOkWlYGILKTAFFK/+ThE+bvVsFZxsYXV0sZjEcjWN/fin/9w34AwFsuWI2B\n9iZb/nepwqKdzhZsNJxcoAenMvjs7bsBCG+iKS7600wYmSzCinVkn2SLZQv2yu5mcC624Fw/0Gbe\nV2mRqtdVPVHJY7d6VAAAsHHAskyd53liJocIQ5mBsGlJO159TnnK4aqeFjNr7Py1PbZFlnOOiZl8\nmQcAAG86fzUe+vuX48NXnaqMhWHTkjb81/bDAMo9AGfhloSklAARW5OQXuJ/PPgCOEd5AkOmPIEh\nHo2YXtKKrmZ80FB4A+1JDE9lTK/BmY5rFgoq94Exhvs+8jLc9v9eajauA4Ri2jeSxr8/+IKrhzg5\nmy+7/ks6mvCPN5yFa89ehqvP0gqAHCdmvCuAFV3i4X/rhatx3TnLy2iWdM6+WADAmSusSdqjPBBy\n8fnZ48Iqlhy8muaXL/IyK6oa5OQUaaCAc/cwt9RNr1ArPWVKZWdz+QNeCZ949RnmsfAALFjcuBLk\nM9zo93z/MeweSpkW3oDSckKtBJanmjfy452LheqWv9bgsqUXNpLKYv9oGolYxKWpmdUjH7BSVHcY\nvHhnc9y8r7JwKpsvIRmLumyjmS9TAH9+yTrz+JyV1lz50/NWmR1bAavrqrwH0kPocTEQquHn77sI\nP3z3BWWb1UzM5NDZHLd5vF6w59OvxI/ec6GweJXMmFyhVOZNVMOpyv0xq4+Nc3WjbABhCP3gnRfg\nI1dvNvl/AHinck1Txubv6nybqmAIfemNW/DATS/DvX97ufldA+1NSOeK+KRR7+FMx5UUkPO5X9HV\nbDP4AOFl2M7RUScyOp2zxfIk3nzBanz1zVs907eUWPAKYLIOD2DjQDt++O4L8PFrxWLmfFRmlaZQ\nEj2tCfzmg5diZXcz3njeavN9J3crC9LkpJel6PU8RHJxj0VEINq521c9MQAnVA9A9tWpRzldo3DQ\nUQf9YMYAFOXktGxl1XJbMoZYhOGW3+xCtlBS8rK5TZZT0clF/Ly13eZi0ZyIIhGN4H//dg8AsXWf\n0y1PZwu23Pj2pjg6mmL41n0HAFjxBUDc6589ftTcW7o8BlA+P/raknjXJeuwtKOpzCr+6XsvMqkF\nZ4rqeDqHmJFaWA/OXd2Nizb0lRkvEzP5uuaahGxVwmApYVnxXo9yeuP51rMhq4/N+eaSqCFxyaY+\nvPelG2yKqykexY/efaH5u5MOFcVZ5WNjjGF5V7ONGnLusCbjIFKevA9Oz98N122xe1LObZuGlTqQ\nsEArAAcu2tBnRvXLuj0qTaFUnLasA/d/5GVY2mmP4L/qbGtRdHZAlBkzbjRLJUil0hyPusYA/HgA\ngGVFWRRQfQvGHR+6DJ+8/gyX7AcRRGxxxGE+//pzzGPJvQNWa17AssgsWe7W4uteJLjUz9xwlo0+\nyyk7Xh0eny0PjipdIyXUHv4XKplPsvANEJunuG2i0+aigD927el46O9fXqacO5vjePwfrsQT/3Cl\nuVRIcWPTOfS0Juq22CWcacJyD+VGoSpO2bajHnkvWtON3/2vlwAAbjDaV0sF5ZaoUQsvVu6L854O\np7KmJ1cLLz11wPa7jBVKcYNTYtGutJ2o838f+ejLzd8jSq0O5xwjqayrBzCX0AqgCpwNsER/9miV\n/7Dja2/eigP/dA2+9MYtePvFawFYE0taUW48aiVINzQeKw8C54si373xGIBFGcix1btgnLq0HX/2\n4rVlD+RUpoC2RKxsMZOLNmB3sb/1Z9vMY9leWMpz5lJLnLe2BwdveZW5E5obMvmiTTnlCiXkCqUy\nZfK+l1oV0+qi/TElMH/5qQNl88OZneQFTfEoulsTptKSi+JYOmtmzzQCp3ISCsWHPFjyZIfVnjrm\nLiAyvw780zXm4i2HV80D8Do4OTbOubkvthdsWdWFg7e8CltWiQQQef/kfRieypq0pBfIQj/Afg+m\nZgvI5Eu2v4cBWgFUgbMddCNBVsYYrt+yomwTDFMB1LHIrlGsZCcFJKkRuRNVvVCz7Yemsoiw+ugp\nu7By17e/w33x+fi1p9sWVgC48vQl+NT1goaTDdicfLGz3qESzl7ZiaZ4BB98+Sb84F12btwtNx4A\n/vbqzbjjQ5dhz6dfaXt/WWczeltFl8+vvPncskV2cibfsJUtL5kUNzqdc6UxvMuzK6exdA797X7k\nWed6yGjzoHpt9YzL9HYcSQf1Pqeff/05+NIbt9iolqnZArKFUt1Uyw/edQHu/ZvLy+6DUCb1yXrk\noy/Hox+9wmYgyOrjNb0nt9K3FhrnC+YBiiWOVKZgNgyrF2o7aM65vyCrw4O0KCDvD+Xn33AOfv3M\ncZy6pB0RRxaQs/y9kfHJB/LI+AyWdjSZVFjdsoxXzjkYYxiczFQscFGDpCqkBZd1eABuAeVq+Nl7\nL0KRc6sQjlnKxK1ATULm6zvx4N+9HIwJrlilWUTthB9vU8oRryOpLNb6WCxUi71U4hhP58ygeKMS\n5bkeGE2jNRFtmM5wejuye2u9z6n0IO97ftSUNWSkXXv1ACTakjGbJyiv3XAqayu48wJp5avPlKxm\nlt19w4IF7QHIFgTOfHbPUOiCbEFs9+e2WHgUBQDlNEsdY+tqSeAtF6wBY8zYdtHSAFOOHZXqHp9x\nrg/sHcXPnjhaluFQryzAOtehqWzdFY5ykZCtEuSZysXC63nGohFbFbSauGMqkzpom0QsYnpzKm2W\nzhVRKPHGeXZlV7ZcoYTjk7MNWdiWPDvFUixxMzOt0eHJ+XZwLI01va1VU5VrDA2A6u1k0ZKIltXq\n1CNPtdiB+hWAKUtxATL5IiZn8w0HblW6cf9oGhEGswAtLPCtABhjf8kY280Ye5Yx9tkKn7na+Mxe\nxthNfr/TK6SV7csqM+7g80OiXUG9HK8py7Ht4okZkRPcaOVfhNkpIN8KAEKe7Mp55elLavxH9bEB\n4tKVSgYn21nfAykLdd5hNLBTs2MANLyYqQ9lJQqoHjhTZ7vqSJ21jcsSiEPjMyhxYK2PzpBM0QB7\njCpkWbHdCGT4JpMv4p7dI1jb58M7cRgI+0fSWNvr41yZ3dgAUDdtY8oyXjm4WcU/0KAstU7kwGja\ntSXLXMMXBcQYuxzA9QDO5pxnGWMDLp+JAvgagCsBHAHwKGPsVs75c87PUsNvcEktfpGl/Gref12y\njFfVA2iYY4eoylUVgMWNN04B/WGPaKh22rIOXL9lRY3/qCLLeC1xjokZUTi1rE4FsKqnBQdveZXZ\nSlkutKPpLBKxiOcK2fKxWSmqKZf01LpkKaanOdcIYgAHRyVf7HNRNAb38AGRsaT2cqpbnkGHyg6Y\nL1rTU+M/qssCxLnmCiX8Yc+IrdCrEXnyXKUH0GiwVS0WlxvSNCpLrRM5MDp90ls9e4FfD+C9AG7h\nnGcBgHM+7PKZ8wHs5Zzv55znAPwYQmkEDt8PJSy3d+/wNOJRZiv2qEuWw+p55MB4XQFgJ6IuO3gB\nPhSAcvyz917UsDsO2M91aFJaZP6yH8wMlFQOfUrWTCNjU3l2AOYm6A3JMo4nfXubVh3Avz94EIC/\n3vAyBjA4mcH/uXMP1vW2NhwfAsS5zuaL+NB/PYmB9qTpmTU8OIhn678fE9XBW1d3VfmH2mOT9/TW\nJ4+hLRkrK9zyLksWHnJzk6JlXQ0qAIhxTWcL2HF0akEqgFMAXMoYe5gx9gfG2Hkun1kB4LDy+xHj\nvcDh3wMQD/iJmRwePjCGtb2tDVM2qtVTLAn3MuljkY1GGMbTOTx+aMIIUPujgOSqffbKzoYfHkuU\nRXfJLS4b7XLoXOf/sGcYfT5yqSOKV2daiw3TBZY3Ua19hidZxnlOZfK47/lRtDfFfBkIcr+IC//p\ndwDs7b8blSd71r/q7GUN1ydIWRJfv3sf+tqSuNGHQpHP6UP7x7B7KEVjvMDa1GZNT2MLtxzX04dF\nq/hKiQVziZqrGWPsLsbYDpef6yEopG4AFwL4GwD/l5WbZm4zhbu8J7/vPYyx7Yyx7SMjI3WcSjl8\nKwAIDb7lk3fiiUMnsKG/reb/VJSlWD1PHj6BQonj9UoefCPy9o2k8dqvP4B79oyU7TZUtzzjtYmQ\no+QceOwF0crXWSTnFZZlLDZ1ceunUp88qzhnKJVBV0u8YV7W5gFIBeAzBvC9Bw4CAL7+lq0NezmA\nrI61fv/gyzc1LAuw7kMyFsHHr/W3X608q92DKRw9MYvrtyz3da4yGP+rp8U+Gp9+zZm+x8a5oOKW\ndjQ17k0YBsIThgK4xtGxNQyo+SRxzq+o9DfG2HsB/IwLM+gRxlgJQB8AdeU+AmCV8vtKAMeqfN83\nAXwTALZt21ZRUXiBXwXgbAf9pgtWV/m0N0hLBRDVkY1CrUz87bNDaE2IZmUNeyiGuCaf1r8q687n\nhvAvfxD78/b5oFkA8UA+tH8M+SLHq/3sl6rQBcNTWSzxUZijplp+9vZdAPzNNQD44cOHAMDW+6bR\nsUn8/H0XmVtH+hXYkqjeqNCTKOP/HzskjIO3XbjG99A45zgwmsbZKztx9Zk+mqop3uvPnjha1rSv\nHkQMA2HPUAorupobL3QLEH4poF8AeBkAMMZOAZAAMOr4zKMANjHG1jHGEgDeCOBWn9/rCVMVWkF7\nBhPtoDuaYnjT+avNLRAbEqXkvj1x6ARWdjebG2Y0gqjyED55+ITvNhByAWpqMPdfhbQW//JHTwAQ\nFbReSundZQmoivNlp5XlGtQtDwCGjE3aG5Zl0EkT6RwmjBhAw/SZMrCvvXmrr8wkY3DmYaMBcxVy\nfjT7oFck5Mj2j6TRnoz5S3eF5YntOp4ydznzO7Z794hlrNGYnxgXQ6nEsXswFUr6B/CvAL4LYD1j\nbAdEcPdGzjlnjC1njN0GAJzzAoAPALgDwE4A/5dz/qzP7/UEP4U5gFjI0rkCpjIF3xV8anDpwX2j\nuGBd4xkZAGwc7M7jU7h/76ivQKuU5pf/B+wc7yvPXGrr3li/LCs4+tCBcZyxvMNXOb3aJ394KuNP\nFoTl+YsnRcdXZ0VzvbIkKKpFVXktBArApAiJ50dfe9JXPAEQz2kmX8RYOud7S0U5tt8+K7KdKhUq\nekWhxLFrMFW1RclcwtfMMLJ63ury/jEA1yi/3wbgNj/f1QhOzOQbLwKDmAyylqDeNMYyWcbr1+7Z\ni3SuiEs2+VQAhsD+drGhxdETs7jAh7sqJz6lhQfALFxrFPI8U5k8th8cx/sv31j9H2qNjVmB+OOT\n9Zf524UJWf+fsQPW61+0qvrnq47LukbUSriVUB7N/LAG58erNuUxq2+/32wz6en89rkhXLqpDyt8\n1E6o49o00Li3HyQWdCXw7qGUL2tKXbY2+3UtDWGHx2eRjEX88diwYgCXbuoze637LhwCfGVQmLKU\nC9doOwlLlhA2OJVBicPWQ78heRDehNyJyw+dzQBzE3ag8XRjKUvCS+vh2vIsic7W5I3JEyBRAMrJ\nNtqmxSZPOfad7aQcyx3NGpdlSbvyjMYLK4PEgu0FNJXJ48BoGq/b6qOgybh/EQacssTvwmNNhlef\ns9z33p8qJ/vZPzkb33vgoK9UOjm8ZJwuBgBYO6H5xXgD+ye4gTGGncensN3ITnrXJet9ydo3LCrE\nb3ntWTU+XUuWdUzpATDmXwkLOcZ8I/ZOmijmmyJwo4+4mpBlHV+80Z+XrspqtINA0AjnqHxi26fv\nwui0KPJRt5WrF6pV7D/zwTqmKAiRHkBTPIpzVnXhC3+6xbdMACSl6uq5kigUZimAelsQl8kCzMW/\nqyVeVztuN1kS1/qoZAXs18y573RD8qQsAotdlUfiISpXjjLtGKh/Dwsn1LG57f1RnyyBZCzie/0I\nCguSApKLPwBcsD58vDiFPMqsDEDsJwDQWewSJAoFwK5B0Yqj0XoCU5ZyI/75dWdX/mAdsprijbem\nMGUpMyTu0zsElLlLQP8IeYRZQDYPgNbbiUd9LrSExos1P8LV/0fFglQAuz51tXnsh/+Uk57kBhJP\netkHiMIlB0RPFoDqgbROlpJ+ABqvJ1CkAQCuPmMprjrD3ybcctH2G5cA/MUi3OUJgRTehJAnXkko\nG+WYRp5MYSbw1JVjv8aQtX6Ed5ldkBRQUzyKu/76JQ3nnkvQTno1y8O/PGqLXSoUCnmUD5Eq7w3b\nGq+cNmVRxjoMWRRB26BApgCMVxoPQKGACD0Amnuqxq98tkQhkhMkwjtzfYLEKjNeyQNfBBOC2gOQ\n7RFIFmzVjSaUR8M/C9CMS0jzU4DnlEWFCKX3CuUeEMYnABrjStp5FPdUtRl9KxRCAzIohHdkIYA5\n6Yl4bAmKh1I2qKCKAZgeAGF8AqCKATBDFp0yoYpNAP72E3DKotIDlPErwGp5QR0DoEk6kPODemw0\nmXph9gC0AqgCytz4WMS61JRBISpZsnEYtQfgOygHWCmqhMqEJjYhXilaLTBCKxZQsoCIPMRMmGNE\nxisN3aiMzWcw3sqcCu8yG96RhQCUMYCoshBSBtIiRCaj3LOUhM5QjwnGV6KMTxAutKQKQComggwg\nwDo/v3EwiXTWX7fZSiBReAHEdcQxVQxRewDzEqZlQeIB0Aa+JAVEnT3ip/mVCeJBFUx6yv90tWId\n/u9BoShk0TRbE68JIroga1jsfvYBVnF4XPTG97OpjBsovTrqmgK/oKQug8KCDQJTwEzjIphYqiVG\nxctS4qfvfTH2DE2TPOBBlbxQKM58kU6ZyD4vFDEA2RCtNUlL6flpZ2yXJwSSGAgKQpckQGi8WNlJ\n4XveJbQCqAJKCojaA5DtoKmm64vW9Pja51WFPFcq+kGCYrGQ9Q6UshrdU1iF3Pu40Z3TnHjHxWsx\n0J7Ea3zs7aziu28/D/tH0uTbGlLGAKhrFKhkaQ9gnoK2QZo1tSh41JuvOwNdLXG89NTGe+MHhWhg\nCsD/fbAUAIU3YSgAAg/gqjOW4vUvWom/ufpU37IAMWdf52PHOSfW9Lb62qS+EkjjOsQZSv5laQpo\nXoPSA1BBkZmxtLMJt/hsZRAUYkbAO0asACgUcc5YtCkszzGjPxGFAmiKR/G515/jW858A8WiHTUy\n7GjqAOjmrqQZw9oHCNBB4KqQD3ZPq9/2A3aEeUJQQD6Q1B4AVdEbQGuVrfK5o9ViBoXyNDOeCJ4r\nKYHCQJC1E3Hi54AS2gOogve+dAMuO6UPZ63oIpF386tPx1Yf+wDPF0jriaKpmQrK4DmFAvjBOy/A\nIwfHyXlxjfogU2elR+YHUodQKIAXxkRq9caQ7gYGaAVQFU3xKFlgFADefvE6MllhhrT8qYqQJCgV\ngN+2wQBwyaY+XLKpj2A0iw+v2bIcq4liCvtHxZ4MD+4bI5BmZGMR9Hda1S08w3NW0mZOUUIrAA1y\nyOCodIGpQNFET6K/nZbW06gPX3zjuWSytq3pwY8eOYwfvvsC37KyBZGNRcHS/sVLN+DyzQM4kzh1\nlhJaAWiQ4/D4LADgytNpt8GjrKjUCmDh4LVbV+DKM5agg6CGpdeI991wrv/02Xg0EurFH9AKQCMA\nvO5FK/DHvaN43+UbSOUOtPvPkf+bq07FvXtGSIKPGuEAY4xk8QcErfdvbz8Ply4Sao9xaj+dENu2\nbePbt2+f62FozDFufeoY2pJRvGxzODfW1tAIExhjj3HOt3n5rDaDNEKP63zut6uhoeEOXQegoaGh\nsUihFYCGhobGIoVWABoaGhqLFFoBaGhoaCxSaAWgoaGhsUihFYCGhobGIoVWABoaGhqLFFoBaGho\naCxShLoSmDE2AuCFBv+9D8Ao4XDmK/R1ENDXQUBfBwsL9Vqs4Zz3e/lgqBWAHzDGtnsth17I0NdB\nQF8HAX0dLOhroSkgDQ0NjUULrQA0NDQ0FikWsgL45lwPICTQ10FAXwcBfR0sLPprsWBjABoaGhoa\n1bGQPQANDQ0NjSpYcAqAMXY1Y2w3Y2wvY+ymuR5P0GCMHWSMPcMYe5Ixtt14r4cxdidj7Hnjtdt4\nnzHGvmxcm6cZY1vndvT+wBj7LmNsmDG2Q3mv7nNnjN1ofP55xtiNc3EuflDhOtzMGDtqzIsnGWPX\nKH/7O+M67GaMXaW8P6+fHcbYKsbY3YyxnYyxZxljHzTeX3RzwjM45wvmB0AUwD4A6wEkADwF4PS5\nHlfA53wQQJ/jvc8CuMk4vgnAPxvH1wD4DQAG4EIAD8/1+H2e+2UAtgLY0ei5A+gBsN947TaOu+f6\n3Aiuw80APuzy2dON5yIJYJ3xvEQXwrMDYBmArcZxO4A9xvkuujnh9WeheQDnA9jLOd/POc8B+DGA\n6+d4THOB6wH8u3H87wBeo7z/H1zgIQBdjLFlczFACnDO7wUw7ni73nO/CsCdnPNxzvkEgDsBXB38\n6OlQ4TpUwvUAfsw5z3LODwDYC/HczPtnh3N+nHP+uHGcArATwAoswjnhFQtNAawAcFj5/Yjx3kIG\nB/BbxthjjLH3GO8t4ZwfB8RDAWDAeH8xXJ96z30hX5MPGNTGdyXtgUVyHRhjawGcC+Bh6DlREQtN\nATCX9xZ6mtPFnPOtAF4J4P2MscuqfHYxXh+JSue+UK/JNwBsALAFwHEAnzfeX/DXgTHWBuCnAD7E\nOZ+q9lGX9xbUtaiFhaYAjgBYpfy+EsCxORrLSQHn/JjxOgzg5xCu/JCkdozXYePji+H61HvuC/Ka\ncM6HOOdFznkJwLcg5gWwwK8DYywOsfj/J+f8Z8bbek5UwEJTAI8C2MQYW8cYSwB4I4Bb53hMgYEx\n1soYa5fHAF4BYAfEOcvMhRsB/NI4vhXAnxnZDxcCmJSu8QJCved+B4BXMMa6DZrkFcZ78xqO2M4N\nEPMCENfhjYyxJGNsHYBNAB7BAnh2GGMMwHcA7OSc/x/lT3pOVMJcR6GpfyAi+3sgMho+OtfjCfhc\n10NkazwF4Fl5vgB6AfwOwPPGa4/xPgPwNePaPANg21yfg8/z/xEEvZGHsNre2ci5A/hziGDoXgDv\nmOvzIroO3zfO82mIhW6Z8vmPGtdhN4BXKu/P62cHwCUQVM3TAJ40fq5ZjHPC64+uBNbQ0NBYpFho\nFJCGhoaGhkdoBaChoaGxSKEVgIaGhsYihVYAGhoaGosUWgFoaGhoLFJoBaChoaGxSKEVgIaGhsYi\nhVYAGhoaGosU/z+Uwl5bJJAovwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot(dd)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(173, 5.1145707192350738),\n", " (26, 4.9594742669641372),\n", " (299, 2.3370302965046825)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "discords = find_discords_brute_force(dd[0:400], 100, 4)\n", "discords" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "173" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "discords[0][0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/distance.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/psenin/git/saxpy/jupyter'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os \n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.chdir(\"/home/psenin/git/saxpy\")\n", "import numpy as np\n", "from saxpy.distance import euclidean\n", "from saxpy.distance import early_abandoned_dist " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.13 µs ± 21.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n" ] } ], "source": [ "%timeit euclidean(np.array([1., 1.]), np.array([2., 2.]))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "early_abandoned_dist(np.array([1., 1.]), np.array([2., 2.]), 1)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.17 µs ± 242 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n" ] } ], "source": [ "a = np.array([0.5, 0.8, 0.9])\n", "b = np.array([-0.15, 0.38, 0.92])\n", "%timeit euclidean(a, b)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.77 µs ± 222 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n" ] } ], "source": [ "%timeit early_abandoned_dist(a, b, 1)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.39 µs ± 17.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n" ] } ], "source": [ "%timeit early_abandoned_dist(a, b, np.inf)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "early_abandoned_dist(a, b, 0.1)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/hotsax.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/psenin/git/saxpy/jupyter'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os \n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.chdir(\"/home/psenin/git/saxpy\")\n", "import numpy as np\n", "from saxpy.hotsax import find_discords_hotsax" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2299" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numpy import genfromtxt\n", "dd = genfromtxt(\"data/ecg0606_1.csv\", delimiter=',') \n", "len(dd)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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NiQxXuZR1AKSV+2SiAoFWADUQtp42sC2KdGKp5NG21KULeFO20KAOAlPnn5uFZUTyTLkh\n1gCk9yGYxzWU0AqgBsLWDjrQXkAh7t/jV649cEvnTVCAOggsaSlquoUmrhMQBUToqi+mHcG0AnCB\nPWXQP4LqsUOyYFO3NTCtKIrYSbncRkFptXNCz0TIk7KIKCBTHom4Mrl+QFXsViaXlKoljD2Fe/3X\nCsANtptGMrEoJxTt4kPdWsLqqe4f9gZuvqW5HDUoKSAlTEYBEcqzz7dweYjUcoOhgMKtAbQCcAF1\noJWWAqKVS71RCqX1adfD/gQGtym8L1F2EMmibGlAT3eFlwKivG4SOgg8D2FbeAiDSxSgbgddsq+y\nvmFZnxRjC2fgljI2ocqjshatIDCtxU6u1AMq9GtYhvkarnEFCa0AXED9gFOC/oEMxtuhzhih5O0p\nQUOL0FIPlHUF1AZHUK1MKJsPagpokYM6yBfWrpYArWUMqNanf1AGvNXgI2lKqT9RQgYx9UCZVmrb\n/Iay6A004zM7nxLIkkJIWb1wr/9aAbjBVukZsjRQ+4JNOzZKzpjcOyH0AELXDI6cAhIoERDQ1K1H\nwk0BBeABhFwDaAXgAmorhTYNlPqBDMqjoFVOlLn7/pVJ5d8aATn1QGjJUhsc9PNXvhKMzfBwwqaY\ngoRWAC6wTSYSKzuYCUWeBhoyHpV6saCSRb8lpP3VL4LgsoFwUo6UsoKooA75+q8VgBuCyvKgAP1m\nJAFZZIQLI4U8yp3UyLflJI4BUFrF9DUx6nG4lkfK/awltAcwD0Gd503aXIp4O0Ly7qKUfDbh4kOr\n1IOaH0QxgKDiMCHMAuKEi7YVi6FD2JScE1oBuIA6UBVYDIA6KkfIZ9N0A6VbfCi9iaAC8dRZQDSy\nrOMwFpbRyqWnzrQHMA8RbgpIlUsclKPks4kXC0oKyO9dpaaAqGMAtFaxJYOm3w6xAWPK9Y8g2piE\nfP3XCsAN1JOekn8OirMnk2dOfEqihSJw637ckCxqCsgM2tIuF/TzgzamQLqbWsjug7luhNwF0ArA\nBeQufgXZjYDaJaf2AKyJTyGLTnHaeHu/kgKaH2TtoAmzgIKqeQCI2y4TGhxhzSgKAloBuID8phFa\n7dQWFLk8x6svWYRUC6kHEBBFSE4BEQfiaWgWSqVugdJ4CV1LlAChFYALAm257DsFMRj6gUoeTOuT\nln8m9Zx8xwDCTQFRVrSS18RUOG5YXgDxjrApkyChFYALAt10hVIWrYEXvkCa7ZgwdhJS6oEKlPcg\nqJoHgHb+Ul5Dyowi3Q56HoK+GZwi2y/9YDumCFbRuuSk1iehdqKMwwTFi1MppsCygIh7C1HGiSjj\nYZoCWuyg5nhtxz7ph5K6YPsSRSbDTR45X0wqi5AC8iVJyqBbeKREINweopBHuNCGbNGmVCZBQisA\nF1Bai0JGUFy2fwSVBRS27ClK6sE+rnAtPIB6D/zLCjILiDZ+4l8WZRttXnYQTmgF4AJ1UQxdO2jl\nmHpsYdvflnIzElqOmNgDIKeACKkMwvTZQOQRepy01Jnx6ltSsNAKwAXUtAjt1oa0LgC1RWYJIxCh\nyPBLP9tlESoTQpqFej8Amqwu5ZBAIHX3WcpzpZRlxSbCrQJ8KQDG2M2MsaOMsSeNn2sqfO5qxthu\nxthexthNfr7zZIDcxbcdU/LPxBZeyLhPys6nQSlhUq+JigIq0Xlh5Fks5N4T4Xwj9F6lkJCv/4gR\nyPgC5/x/V/ojYywK4GsArgRwBMCjjLFbOefPEXx3IKAOtIZ1c3PAseVfyPhsynPlFY4bkhUQL061\nVtBaxcR0qHpMaLVTepyknolvScHiZFBA5wPYyznfzznPAfgxgOtPwveSgJJbpAB9d0ZqjlfK9S+L\ntFUAYbor/SIWjAYgrxSnVuqEacykc5dAVlCb8lCDQgF8gDH2NGPsu4yxbpe/rwBwWPn9iPFeaBFU\npgLgf3JRpyDaFtmwUUBBeU7+RNk9RJ+yANUDoFktSoQuBfH0sHsRhAqFokYhiB5K8z4NlDF2F2Ns\nh8vP9QC+AWADgC0AjgP4vJsIl/cqXhXG2HsYY9sZY9tHRkY8ngYt6DfBUI/pJgRN33fac5UiaFoH\nq8d+rXZCZaIehyyQaZNHIYs4TZh4/Sc+V0JZmB8eQM0YAOf8Ci+CGGPfAvArlz8dAbBK+X0lgGNV\nvu+bAL4JANu2bZuTy0ffcVOR51cWeXzCOg5bJkVQHoDfu0C+iBkCqTZyCSKdESAyhirIblgepdXu\nkOkHQWwvGQT8ZgEtU369AcAOl489CmATY2wdYywB4I0AbvXzvUGDki8GaK3PEuFCBtAvZtYiFq6x\nkQaUqa1ix6tfUO7KFiQdSpp0ELZsLEJvIkj4zQL6LGNsC8R5HgTw/wAAY2w5gG9zzq/hnBcYYx8A\ncAeAKIDvcs6f9fm9gYJ8xydCIjWotE0qeZRZQIF1UfUlyfn/dBoglBQQ8RIWGAUUMt5+wVBA1cA5\nf1uF948BuEb5/TYAt/n5rpML6kWRjmcPUjmRTnzfkpyxGL+ylGNCOimMHoClUGi9MOpmcNRbmlLJ\nojRe5n0QeDGCvGGVekxIP1BM/uA8AGIKiHTR9hkDIFRMgDIeosUskJ42VPLIKSX5SkgnhSw2ESS0\nAnAB9SQlpR+Ix2aTTSGDkPu0P9Q+F23SVFxVrk9hUK1FGvCyAx+yiA2EoPrjk9JJhEZfyNd/rQDc\nQN7uN6RctlMexRMeRFYGhbygvLAw8sW0zeDUY2oKyKcscu+VcO4Sx3WCglYALqD3ABR5lLLI0/z8\ng9LyoRwbZQ8lSmWiyqArBLPL9YNg6wD8GkN0sgA675XScw0aWgG4IKhdsoQ8OlkUoN8PQFpR4YpP\nkGZiBeQhklN65As2LULnAYBm7gZJ01JDK4AaoLdkwzW57NYsrUdBKctvwDu4OAzdCYct40zIcJfd\nKCjbaKj/TxFboPKcqD3EIKEVgAvIPQBCM0p9gGgKaYitWVMunSwqeVSy6C11ydn7B32zQGJ5Ntl0\nSp3i6lHdB+oNpYKEVgAuCHMMICj+mU6efIiolROlBxCyGIApK4wKndijoHwWAlJOpBSQL0nBQysA\nFwSbBeRPVpDdQMPGZ5NetxKhLGILj9S7IZZLed0A2niYTS7hfPNvpAVzjkFAKwAXkD9ElNYnMf9M\n3/dIyvItijTgTUkW0HuInFyWkEfA2QdocPiVSBlbE/Jo7gP1uIKEVgAuoA6k2WX7/f8gH0gKeXQU\nECWfTbswBhM3ofYmwua9OoXQ3lN/soQ8Q5ZPOfMoC1QrADcEZeEBFAEm6zhsKaqqDPJOlL5zN10P\nG0JQtRjUawV9Ki6dhyhk08mijHeErVVIkNAKwAWBFltRTq6wLbJQzo+czqCUFbYgH83CAwRbKU7u\nUfimgIJ5FvzPNVVmuFWAVgAuCDSTgnJy+RMlZBB7O9b6T0ydEf6//zxv2ptA6QEEmsFG7lH4lWUd\n01YCEyomX5KCh1YALghyRzC/oG/OxZVj//KkCMqUUudxIwiqqphyY3Nyr8m/OJsUylRLEnnEHBBZ\nENgm05+soKEVgAuC3RPYn6xiiSNi7LIcymZfZhDYP0jrJ0hjANRK2JDlX1QAFrsiz7c0YgpI+X+S\nALp89asA1NRZf6ICh1YALqCnRegUSokDsUjEkOtLlCEvoMWMPHbiVxadN2GTSyCDsn8SNedMvQc1\npQdLrZyo9lGgVkxBQisAF4Q5la7EOYz1n+ZhJ56fQe0I5lciJVsQXNzEP4KkL6k9Tt+yAgsC+40B\nqL/4EhU4tAJwQVAdMgEC+qHELQ/ApywgiL5H9lc/IK0DILRkKQv7VHn0c43WowibB0C9zlI1g6O+\nB0FCKwAX2IM4xDy772AmrBgAodVDLY9k2lMqTpsAOguPctEmDSgjCCVMTOuR3gc6ZRe2XlFBQisA\nF4TaA+AcUUMDhDFv3Hx4yBVnSGX5EwWAegMX9+NGEWjvKcq4jj9RAOg8Mep7ECS0AnADMe8Jwgkh\nFAAdBURtrdDy2XTBNMrFghM/4ZRbEVI3qqM2hiirZAmdOgCUrSA0BTSvEaQH4Hd6lUpAzPAAaNot\nhJfPps0CCkiWP1EAaOtEwpy2WSaPMNhKO3f907QS2gOYhyDPAqoguxFQU0DkMQDzleKBVOX6fSjp\nFB25VWwIIbHYiTUA9bmq8CuuGFQWkE85tnH5lBU0tAJwQaCBNN+yrDRQClDz2ZRZQJQ7qdEqYVUW\nnaIj95qILXaabRfpni3qGgWqimzqcQUJrQBcYF8U6ThecexPFnUhWJHQXw3sgQSN4gyjLGoKjtoq\npt520TbfKLOACCkgyv2nw+4DaAXgAkrLU8hTxfmfXJStICgXM2rXlzZjRD0ODwVEX7gV3nvglOfX\no6DuY0UVBNYxgHkOu9tL4AGAbsEoqoVgxB6Am7z/eeoYbr71WU+ygt2Nyq+1SPck2jwdv7Ko03CJ\n70GYC8HoYwCcRJbeFH6eg7zHCOG+qpzDDAJTWD02BeBytn/5oyfwvQcOIpXJ15RFvn8soSVFu7uY\nKis83oSQZx2TewDEFJD/LCBaqsXyAAipUF+SgodWAC6g3nSlSGjJ2rKATmLl6JGJ2fpkhYyeoqbh\nqGAXdfLu59zJs47DRrVQpTBrCmiegz6XmpIz5oiYaaD+ZAFAsUrr2plcwTwenMzUlhXo4hMeSzus\nsgCHF+ZfXAA8e0DXzp8ocM7JMtjo92QIDloBuICaRy1SPDkG1EIwClRbtKdmLQUwMp31MLYAA5A+\nZdnIAkoLz7c3ocryD5t3QhIDUI5dRrh/ZBpfu3uvvf6gCii9RFo6yde/20A5rqChFYAL5D2LMPeH\n8iePHcH3H3rBs7wiKf9MXQhW+YGczlq8/6gXBUDIjZfJ8B07oVNOtQLn9cCLl/P4oQlMeYjBOMfj\ntibnCiU8uG+soQXb7cJ9+L+fwufu2I3njk95lKcO1tO/VARlsh5t2xGfgzmJ8KUAGGM3M8aOMsae\nNH6uqfC5g4yxZ4zPbPfznScDcgJEmLsG+PB/P4V/+MUOz/JoN4ThiLJggsDOoU1lFA8gVVsBFKk9\nABudQfdQUgYffXsmNSibRw6M47VffwCfu323J3m1LOyfPn4Eb/rWQ/jlU0e9ja/GIvv4oRMAgEPj\nM97GV2OOFIqlxryJEPH2QVZPU4PCA/gC53yL8XNblc9dbnxmG8F3Bgp506IRVnXhyRaKnuRRWoxF\nDsSilDGAyg/ktKIARqdzNWVR109QxhTm62Kxa1BY1k8ePlGXvGiEucqTC/WTh+qTF2HlirOgBJBe\nGPOoAGqc77VfuR8f/K8nPcmiNDhqKU7OOY5MeDvHWkkf9z8/in/5w74GRkkPTQG5QH2IVCsUsFsw\nXqxioPZE3Xl8CsdO1M6yAcREjLCgsoCcFJBQAO3JGEa9eADEwS/Sjdxtx3TcM1XVKHNZYAGY170p\n7u1RlUOLMncFMG4o8vGZ+iglYQzZMaHIOD7pbf5WSzseT+ewazCF/3nqmCdZJzMd9/sPvYBL/vlu\nPHtssqasWkkfb/3Ow7jlN7vw6MHxhsZKCQoF8AHG2NOMse8yxrorfIYD+C1j7DHG2HuqCWOMvYcx\ntp0xtn1kZIRgePVD3rMIK/cAppXMmGGPCqAa/bB/ZBqv/NJ9uOiW33uSZY8BePqXqpBGXDTCyigl\nmfu/urcFEzO1PQDq4Jedzw72Aa8HQewpHGXlCywAjKbFdffigQHW2CIRdyUsYzleFDpQ3aMYT1tj\nGkt7G1817+ngWNo8zhUclpcLat0HzrlpxNQ1Lpe/3//8KADg4f21F+1aHqLM4djpMW4SJGoqAMbY\nXYyxHS4/1wP4BoANALYAOA7g8xXEXMw53wrglQDezxi7rNL3cc6/yTnfxjnf1t/fX/8ZEcB8iFj5\nDZxUrJ7hKY8eQBXL+J7d9Sm5YgmKB+Af1R7wlEEBrexuxuRsbYuRmAEKsA7AH2pZnjuPT+GwV068\nBmUzZizYQ1MZT4rH7gGUf/6o4Wl6yepyynMqYZsC8CivWvxETTX2QrfY20qUn+sHfvgEzvzEHXh+\nKOVBVnUDQSqSoZSHdOgqXg5XDDiv60eQqKkAOOdXcM7PdPn5Jed8iHNe5JyXAHwLwPkVZBwzXocB\n/LzS58ICMwvIxe1VszFGPEwGoHoMYDZfVP5W+wEXE0hQBhQugBxbzCXeIRXAiq4WnPBAGdSKddyz\nexgv+/w92D1Y+4EEaltSd++EIq8LAAAgAElEQVQa9lSfIP6fMg7DwSpsy1kscbzyS/fhtd94wOO4\nxKtQAOUDGzMs/5lc0ZM1a3L2FVKFB6fE9fKS1eWUV04BibGt72s1x1kL9riOXaJ6L70ElWvNt18/\ncxwA8OD+sZqyuM3hKBd22FBIIx4W7Wq1Eydm8sgbaYHDHtePIOE3C2iZ8usNAMpSYxhjrYyxdnkM\n4BVunwsTqvGoqiU85FGDC97e/M32t+EpaxJ4WWRLRgyAoXxyNQL5ELmd63S2gNZEFL1tCczmi8jk\nqwe9bQFDl4foq7/fi/0jaTzikfus1lUxky/iHd97FBf+0+88ylIl+aeTohW8MLnAjqSyHi125fq7\n/F2lVrzMt2oeRanEzfkrFiLvNEvUJSdajm3jQFsdCkWR7fibal3Xm3TgPFfOORIxsbwdHPXiTVSW\nVSpxHD8hxuaF9pVxw5jLNUtl6qeQg4TfGMBnjfTOpwFcDuCvAIAxtpwxJjOClgC4nzH2FIBHAPya\nc367z+8NFHKBYMLMtv1tSlEAXjV4kVdu4KZOgsEpb+5lhDEwl/hEI+CGNctcDMZUJo+2phg6m+MA\n7OfuBvkQxSIRV4tMek9HPNIj1R7w/SP18sXuxxKPH5rA3uFpj+NCxWpsVaF7oc3kghipQAGNprLY\nvLRdyPYw30yPwmV+pDIFcA6s728FAE9We6mKvAlDAazvb8PkbN5T+mapxM1CRqeCnJrNoy0ZA+At\nwUJ+HXMxOKZmC+a8eEGJLVSWxV2PASCVLaBQ8m61270mx7gy9VPIQcKXAuCcv41zfhbn/GzO+XWc\n8+PG+8c459cYx/s55+cYP2dwzj9DMfAgUVJpkQoeQG9rwrMGL5ag9O+xYziVNS0VLwpALj5EDBCK\nXFEoLllA7U1xdLUIBXCixoKmBpTdxiatqCEP5wlUb2uwQ8nGqOehdJNVKnG89usP4E//9UFP4yqq\ni5hD2kidCl3ONbeU40y+iFS2gNOXdQDwtmDYFh8n/TArKZs2AMBY2p+8iZkc2pIx9LUlUOJiofQi\nr5LynMoUMNCRRHM86rHwsLL3qs6Jg54UgHh1K/6Uhk9TPOLNA6gyLknjre5pWRAewIKEyYtHywNf\nsj3CxoE2zxpcDfy4TdQzlosH3EtmhtwPoEKNWt0olsREdZOXyhTQlrQ8gFoUleUBlLsT+WLJXCC8\nByCVvQ/KuFTLevWajmvCIeyBfYIj9p7JolBAVTy6ox4a6JnxJpfFQgZZTzfmhxfFabfY7ZD3b4Ph\nAYx7ON9qdGg6a58fkx4pzErXLpUpoD0ZQ197wpMCkPEEtxRVeR82L233tNCqVFe5YpLXrQ0nZvKe\nqdCYy7gkBbShvxVj6aytlmIuoBWAC2TrBrcbODmbRzTCsK6vtQ4PoHL7hhMzeWzslxaZh1RLm8Xu\n6eurQm4x6eZRpDIFtDfF0NWcMMZafXxmPMFFcaaz9VUVi7GhYudTVRl5o1oqewBPH7WKomo93ICD\nAnL8TaVVvGQCWZx9uSx5jiu6mhGPMlvefW155R6d9OA2GPPNiwJQF8aye5orojUZRVeLMT9mPSgU\npZeV83ynM3m0N8XR35b0pACqxSfk/29e2o5UpuBh0RavERdPWC7aGwfEdas1f6XnGnG5B7K9yvr+\nNnDu3egICloBuMDmlrtQQB1NMSzpaPKswUUMwH3Sz+aK6GtPoike8ZRKVywKWWLBdtcAw6mMJ14c\nMJRThZiCoIBiJgVUa6G18c8Vgl9N8YhnBWAreiujM+pTANViAOr/e215YTo5ZYtiAU3xCOJRhqE6\n6QLn5EgbNSciDpPApIcFtpolKxV4PTEAyX3Ho+XGUDpbQGvS+/wAnBRQ+ULbloyhty3pLT4hKccK\n8Q4AWNfnTdnZFKfjb5ICkoZaLcOvmizLAzBkzXEcQCsAF0jXMhaJlN3A8ZkculoS6G9PetbglYKG\nuUIJhRJHSzyKPo+TvlDiiEXdKRtAtKc4/zO/wykf+41rF9LR6Sx2HLX482KJV4wppDIiKNfp8QG3\nu+TlygQQD+SExwyUaopzciaPnlZheU549JwqeWH1BvZlMZ7bPZg2aJH+tqRHzl68ugUMZSuONmOR\n9ZQlpsZhHH+T9291TwuiEebJAyhWMYbS2QJaEzF0eaQIger3VBoc3S1xT4WH1eIT0uNc09sCoLay\nq6aIp8o8gOpzxEttjVTCc50KumgUwORs3nNbZvukt//P0YlZrOxuRrd0ez3mx7sFDWUNQHMiit62\npCs3PpzKYP3f/Rr3PT9iyopGGBjc88bvem7YPN7jUgDzF99/DNd+5X7sHRZ/q7qYZUQQuD0ZQzTC\nXM9V5X2LVYLnUgGs76svA6WStTgxk8Oa3hYw5o3KKBZ5xUC8qti8LtqMuSvNmWwBLYkY+juaPGbt\nVF4s5DVrS4pF1u36c84xm7PojWq9e+Ti09EcR3dL3DUIXCpxG11iJURE4Lxy6ayggDo9JgmI8VXO\noEoZ8627JYGJmXzNNNpqXPt0tgDGgFU9zQBq1z2Y3quLpyMr4qUCqO0BGLJc4jCpTAGJaAQru5s9\nyQoai0IBpLMFnP+Zu3DNl+7z9PmSEgR2k2ULfHmY9GoMQJ0R8sFtScTQ15pwXRQff2ECJQ687TuP\nADA8gEjE6B1T/l3qov/HvaNlf5dK5gcPHbLGxhgA+wJULHGkc0W0JWNgjKGzOV7G8f7mmeM455O/\nxW1GwQ1XLTLH906bLrl3y0cNnjtxYiaP3tYEelsTGPGgTIqcI15h4ZmczZuWorc8bxmId/N0img1\nPIB6UhkjLouFqQCaYuhqSbgusD9+9DBO+/jtZoVv0aRsyr3XmVwB0QhDMhZBb6u7x/mZ23Zi8z/c\nbtJFhWoeQE5QQFYQ2BtFJYPA6sNQKom2DW1NwuPMFUrI5Kt7ieZC6xKfkJ5Yb2sSQG1PXfUAKiV+\nrO4VnlOtYLzqATgvWiqTR3uTNS4vBmSQWBQKYO/wNLKFEnYPpTz1VS9WscpmckU0J6JWaqTXSe9i\nfcodt1oSBgXkYpGplcKzuaLiAbhTQMdOzGKgPYlVPc2uHSSXdwrLQ+a8S05WPJOWRLMRXJPIy+50\nsUCfPCLkf/l3zwOo7gHI677WUABe0/wqZYxMzubR2ZxAn8eAYalU3QNY09uKaIR5XLSNuInLuGZy\nRvFca6IuGsPN20xn7RSQ2wJ7z27h8X3/QbE/RbW5m84W0ZKIgjGGntaEq+f0o0eEYXC3IbfERZ2I\nG6UkYwDJWBTN8ahnb9gtI0722Opoipneda3rV41qmTbjCULWeI2U12r1GKlMHi2JKJKxKPraEjW9\nRHsWoWNchpJrikeQiEU8Bc6DxKJQAAdGrTzgfR6KfcwgsIuFN5sXD1FdHkCFNNCZnEoBCQ/AWUwz\nOGlNttHpLAqlEmIR5po2CIjc82WdTdjY3+Za2CQfKrlolkqCLnAuZm4KoOxcjc/L99WHyLnMWjGA\nFuP7vQX5rGwbu7wTMzl0tcQ9K4Ai54hFZTFeuYXX3RI3aju8pVqyCqmzIjMmho7mmG1HNQnOOZ4+\ncsJUiPZWHHZIykby7G4eQEeTmIcPGe0OrMUnUnbNpPcKAD1t7gpgTa9Q0AeM6llJXzJWXiCVzhbR\nmogCgIhRuIxvKpO3ZYDZM7ssqPGObsO4qqUA5LlW8p7akjG0JWNIRCN1xQDcirfkcyAMNbusA6Np\nfOOefea8siVDuASn25uEV93VHC9Lnd326Tux9qZfn7T00EWhANR0PC/l9AV10jvuw0xOcLwddVFA\nqBoDkB5AocTLPJRfP2O1xh1OZVGQXLbLAynOL4MlHU3YtKQd+0fTZXGPMUd3yaK0Zh3equQ9240F\npqulXAFIOmlwSmQd2ThZF4sMsBYYPx5ArlBCOldEV3McfW0ec8ZLlekk4U3EMdCR9FzoI1JnKwRG\nk8JAmM0Xy7KxHto/juu++kecffNvAahZNuXV02mjFUckwtDVEsdMrli2B4UsNpPN0wpGDnO8CmUD\niEJGN1pEBsQPGcVTZuU5UEYRzuaLpjw3DxEAzr75tzjjE3eYv9srga3PSWUnCg+F1e5WV/DxX+7A\n3buGbf8fi5ZTLdLSZowJ46oWBVSF6pKxCcBQAI759vFf7sA/374Lf9wrlHBB8QDcnoP2pPVMOa+Z\nfC4fe2Gi6nipsCgUwDGlyZRbBP/w+Az+82Fri8eipEVg1+DFEkcmX0JzPIr2ZAwR5q4AHtw3hsde\nsPrdVEpnnMlZCkC6qupitnc4hR1HrZaxI6mMpZwqnOtIKouBjiQ2DbQhVyjZyuD3jUybFMd4OotS\niQs+2+VcVYsMgGsQUk5WzsW4reB5Of88nS0gwsTC05KIegoCc9VaVATKay49ADfaJl8s2TKNStx9\n4SkZSrezOV4Hb28oJpcWBDIILA0Ep0I/7OhyqdIFYmx2Gs5cYOWi6JhvsoHa6HQOmXyxKgU0rVjs\nPa0JTM6WZ2NJr+DIhBVTiBqVh270pTk/WuJVW4XIjpy2NFBFojQ42mwUkF3eV3//PP7jwRfwju89\nasoC3IOt06q305qomWJtZv65pbvmLEXX25Yo817lON723YfF7+pz4EKFtqm0qkIBqcbCYQ9FhBRY\nFApgcjaHdX2tiDD3PO93fO9RfPTnO/C1u/cCMPhi+YCrQVvFYo9EGDoqWD1v+tZDeN03rLYCgn4o\nd3tnjYeoOR5DX5sICqmTa5/R7+Ybb9kKwPJeopGIa+sG2f+8oymOU5aI/jF7hiwa6N3/IXbjTMQi\nKHGRtSHpqTIPQAlAAkBXSzmnrT5UY9M5e1FThRxvxpirFeUGde8D1duR+fAdzXF0tyaQyZfKCn1e\n/ZX78dLP3WP+buOe1YUnK/rjdDbH0duW9FwdK61i52ohFx5JzTgXRTn/pDIyg7YuvaKkFQvATLVU\nreIjEzN4fnja3CxmLJ2zBYGdmFEUSq8x39QU2kcPjptzXBoi5vyA/Z6ms1YCgxhfoozPnlH2znjo\nwLgpz9UDUCjHShTQL54U3vCSDjF2kwKqkAbappyr876OTWdx00+fNq+n9JzcGrjNGJ4YALNITb0W\nrcY12LxUVGxXq52Qqa4AjNoO6xqp8USvu4/5xSJRAHl0t8TR05rAbTsGy/6+tKMJAPC9Bw4CsHr3\nOJlsNWgLVODFXT4vFp9y/ln1AKQCUC1jmW2wZXUXGAOOG9ZepTqAbKGEfJGjrSlmpqypvdClG3vT\n1ZsBWFa7GdBUZJkpg8Zk7W5JIJUp2KyUsemc2VZgNJ1VFIC7ByC/382KcoNt8xvl/SklnbGjQqO6\nXYMpHD0xa15DGw2nCJM7WQ10NJm0iFN5fef+AzjrE3eY75dKVgM99ZOcc8zkRIyoozlmG6uEVACF\nkkjfLBgcoysvrixiMulApTLuem4IAPCGbavE36azjqydclpEpYCc8n7wkPCCB9qTFkVYsgwEpywA\naE1az4LTGDp2IqMcWx6FmzGkzrfOCgkWa3pE/EieVrVW5tOZ6nTXfz58CD9+9DA+9kvRmFjO3bhb\n7MS4p4CYu9lCydaaWypLSc/ZvTCXGIByT9XAvurxeDFEKLBoFEBncxxnr+zC4fGZimXhCcNqUvvt\nuKVtNicUWsSx8KgK4U7jAeUckBmldoVSnQI6OjGLRDSCJe1icRo0FqtohSCwyaMmY2hNxrCyuxl7\nlEDw2HQW152zHJuXtZvfZWUB2eVZFJB4GAcMq0vy/uPpHAanMqbVM5rKWs3gXFJUZVYGgIqB28/e\nvgtrb/q1SUsISxtlF25aOc+uGrEYmQpbSZnIB22gPYme1oQZX1DxqV89h1S2YPaVN2snHLUYuaIo\n7FNTI52KSQ0yD6cyVutgFwpItWJl5ai6l8LBsRm0JqJ4zbkrABj3s1rlbq5go4DU8weEVxONMNx4\n0VpMZwtW1plLDMCNAjoxa8/dV7eJlFtRForc9E5sdJcy35KxKFoS5VlF0iMYT4tkiaK6aDufBcXS\n7nVJsZbTSm4/qfL2zsydWSPuB8DVUJPKcsgw0IrG/JVenYT00M24mmP9UD2ek9UiYtEogK6WBN6w\nbSWyhRKePWbfik0+lINTGRSKJXuxlfIYqQs2ICxQ58KjNgCT/GylAOSskgXU3ZJAhNlplf2jaazt\na0EkImgTGcuQMQC33GfAom1OWdJuegCzuSKOnpjFhv422yS26gDcOVn5EA20i/+R7Y5//bR4cF5+\n2oCQlc4pQeBIueWTtbjPvgoewNfvERtly0XOvmirY7MChmajugoKYEyxZN1acs8YVEZrImYtihW8\nE7nxubkng0PRpU1ZUZMCUudHtlDEXTuHETcW++FU1vQAzEVR+b6UYsUu7WhCLMJsHUYPjc9gdW8r\n+hX60FzIIpGy+TGTLVb1AAYnMzhnZaciL2veA+f2qHKuyYXRLXdfZia1JWNminOxxF3pLud863Gx\n2uU9lskSavzEGdeZzlqWdo+xn4VKSTmf22Kp8tyVPY8AizqTBszO41Pm5jXpXBGpTN7sJeaMw8zm\nhUK1aFUR2JdetZyrrYmop+p2CiwOBTAjPICta8SWxWp+fKFYwqHxGcQiDMUSFzyqnPSRykFbQPDi\nztzsa75sFZvJCaymgcJVnqi07XEUNR0am8HqHkGx9LcnTYVSrXIXsKz2TQNt2D+SRqFYwr6RaXAu\nqhnNh39aWO1mHYCDf44w61yXGDSZzJLZP5pGSyKKG85dgUQ0gol0ztHauHxsJifbmjSD0BLqQ/eE\ncX8qLdqyoZa6V4FbRTIgqCnACAJHy+MJst9OS9Lywpz1GJJ+kQqgWFIqgZXPyXTHlmTMzGRRLew7\nnh1CrlDCq89eDkDQQbaiIZTPN3nNIhGGgfakaWWK8aSxuqfZ5j2WKiyK4rrZeXHAbnAMTWWwtLMJ\nfe1C3si0lXXmzIiTys5KEijP3b93j/C+zljeYSr8go0CsisUdb4NtCft3tJUBvtH0kqsLKtkPNkp\nx5l8EZxbhlBfa7nVLpVJPCq8uGrpuLO5IprjdsUpz+eT//McAOAlp/Sb17BoXCgRUHYzXKwgMGAp\nowOjwlvfsrpLU0BUKJU4UtkCOowsj1iE2YpCLvjH3yGTL+GC9T0AjIfSTH2zVwVKC0K1otSdn9SF\nRzR3k1ky7pbsTL6ARCxi/k0NjmbyReweSpkVqgPtTaYCEFy2CwUkF8ak5QHkiiXsHZnGvhExuTYM\ntKJLehsG3y3pLqf1KYO24vsND8BQAEI5tYAxhu5W0btFrcx0PkUpJaAp+8eri4XKqY5MWZ5TvApf\nXKkiW5Vl9wDKvTCphIUHIM5RffhKJW7SODKd2NqW034PpDJpTYge+U3xiG1rw+MGD/7BKzYBEIua\nuYi5LIrpbMFcEAFgSWeT6QGUShyHJ2axprcVLYmYmVllSytVzrNQLCFbKClB2zgiShsNzjkGjRRi\nc5FNZa3us2XesKU4AWBZpzAQJNfPOcfhiRnccO4KLO1sMr+nWCop9RjW+MrnW5Ot4Oot3xYZNhdv\n7BVjm845PIByOklSLW50l7Sw80WOEzOVvQnOuZE+awSB22Vlcdb8/Pq+Vrz3pRsAiLodSYU606FT\njsw6K7NLjGXv8DSWdzZhZVeLVgBUkLsgdTbHwRhDocTxu51Wvxxppb/qLMMqk4FRFyvbLNyKi8mw\nuqcF6VzRvFlyQXvF6UuwaaDd5vZGXSzZWSW4BBhWvvGAP35I5AHLplH97UnkijJgGCmr3AXUiS8m\n2MUb+wAA9z8/il8aGRRrjYrXnlbBw5vejqMEXs19BoTFGI0w0wI9ND5jKifZu6Vaf/ZpJfhlWp+2\nB1LpyDltBUorLRZAZQWg8u5mNoutAtW+yAKGB+BCi+wZTpmKTXLa6rac9gXbUCZJUW27uqfFpgCG\nU1k0x6NY1d2CWIRhOJW1UQ/O80znLIsdEB7dc8cFfSlrL1YbgdFeox7CtD4jTuVkjQ0QHkV3i0Wz\npLIFzOSKWKoogDHDq4tFyzPi1D5FgGiTAFhe0lg6hxMzeZy1otNoO2HdU7eWHM755qzJkB6oXGjH\npnNK6mbEIctuCLl5dmoMSlBxluJUkcmXwLmgaQFLmYymrHqa9f2t5vjsHkDEYbiIcXUoMQDAagfx\n/PA0Ngy04Q3nrcTHX306TgYWvAKQqWlyoQBEhshMrmBmGfz5xetwibFYqlYP4B74kh6AXABfMB5y\nmeFx/ZYV6DUqLTnnKHH3DJTZXBEtcUsBbBwQ1bulEseu44IHf8XpSwFYFjhgxQAqBYHlxF/a2YSB\n9iS2H5zA73cNY0lHEk3G90kevlKhz3Q2b1t8ohGGpR1NODIxA845jkzMYlW3OH9R0JKDupOaW3zC\nDMq5BLzVtDd5He3tG+x0QUsiiqiRigvYYwAylgBAsTwrxAAkDRePKm0DLAXwqV89Zx7LNNwiN7YL\nZdXnx9reVuwfsYLww0aNhozpDBtzTV4zFYWi4NOlxQ4Ig+PETB6HxmbMLTHlHJTdZIu2oHK5olPv\n6UBHk+mV3LN7xJDXat0fY2F0yxIzYyeGvJXdzWDMehZkxf2GgTb0tiWQzok9pStnAVnVtoBIt5yc\ntTZfOT45i1eeuVRRTpbyjDuygJwpzL0OCiiTL+KpI5NmozgRjHdSceL3GcWrA4SC6FIa6Y2ksuhr\nS5qpqydm8+Y9iDv2xXDG6Kx2MnnkCiXsPD6Fs1Z04kVrenCtQRMGjQWvAKRlKBXAmStE1sqnfvWc\n2SLixRt6Td5z1GjHEJWZMYosNcgHACuNBfCZI6K98tNGb5ylnU1G8UnOXCBMhaLIm8kX0aR4AJuX\ntmMmV8ThiRk8d3wKfW0J0+XsVxRApSwg5wQDgNOWdeD2Z0Xq61+8ZIP5fm9bAs8cmcRD+8eMqmdW\nRrOoDyQgOisenpjFzuMpzOaLptXX0yo8gEq9WYolbvDZ4h5Ia2lQ4bPfbLj4nc1xW6qkSY04LDw5\ntmjEaFSn0EnbjU3nX755wKKAKtRipHMFJGMRxKIRtCREjxZVAcjvvfHFazA0JRYK0dAMZcV4cpGV\ni8WpS9txcMzKOhtJZUxFLg2E8kIwOS67xS6OhdxP/uo5/OiRQ0jEIjh3tYhr9UqPzrA+nfPDomys\ne3rKkjYz4C57Cl26qQ/JWBTtTTEzsO8WI5rK5AVnbxgUyZjwbCTVKD2Bdb2tNs/K7tU5PU5rbEs6\nrTnCOcexExks7xJdeBkzAt5F69q5ZbBJj7OvXfyPLK7aflB41+etEbTv8FTWFjxX74Mz7gcIZTs0\nlcFUJo/R6SxW97aY3ovoOmzdA9jmrd1Dl57wyHQWxydnUeJWs8SThUWnAH7yFxcBAH70yGHcfOuz\nAIQV1ZKIoTURxYi0emS1rTKz1KwdAGZL1188eRTFEsdHfvoMAOC0Ze1mczenhccd8tSJdaqRUvmF\nO/fgJ48dwTZjggIODyDq3pvF6foCwHlrxQKxoqsZb79orfl+b6ugm/JFjqePTpYV+qhFSBIru1tw\ndGIW/3jbTgBW4KurJYEJx2LmljEi5a3oEtdNVpuq33vBuh5bDrr5QNrO00FPORqbRRjDyzYPYMNA\nG8bSIkZj8ybUhVHJjJHX5biimEans7jy9CXYuKQdhRLHsBG4jTCGiMPTUSkgQCiAYombPZmOT2Yw\n0C4Wts5mUTkrFzGTIoTD8lTGduOL1wIA7to5hF8/cxxbVnWZ97q/PWFmAcVc6MtpM2irzrd2HJvM\nYHI2j3t2j+DK05eY39dvtCeXsRNnFtCEsS9GRPFcNvS3mpb/C+NpxCIMy7uabAHnokoB2cZXTncB\nwO6hFCZn85jNF7Gss0nQly0W3eXWqC7liAG0JGI4ZaAdzxgGmtwj+H2XbwQAg4qT2Vj2RAE1UUNi\nbW8rDoymcbtRU7R5aTuiEYb2phimjOJKN6PKWV2/pD2JCBNxk8Pj4lmQRuXJwqJTAE0K5fKUYblL\nKqOvPWlLfXM+RGbGiDEZWo0NsUens6b1f905y9FipBRm8iXzpkddrM+ZXAEtcWtinWpU78qKx2vP\nWWb+zekBOF1ywOg1HovYzvH9l2/E/R+5HL//8EvMABsArDCUF2CUoDsXDMciCwgKYnAqg/v3jmJ9\nf6vZ16fbyAG3etE7g19Gip8x8ZviUQy0J82gquT8P3L1ZqzvbzMD64VSyXwgUcVabElGTQojb2Q8\nbV7abt6DmVzRsRGJPQtIVcIdzXHcs8uKEQ1OiuZ6kms/PDFTkTZzzg9ZI7FrMIXJmTxeGJsx9/ft\naIqLVEYzl93hAWTLFUAkwvCqs6w58ddXnmIey8yqnUaMwFmj4PROAOA0Y3xv/fbDGE/ncOVpSyx5\nbQlzwZYpr2p+/EQ6b9IeEhsH2rBrMIXpbAF3PjeEld3NiEUjNsqvqHgAcMwRdb6durQdjAG7jqdM\nT10aXObYuKSnHEFgJUvMPNdl7dhleDtHJmYRN4K3LYbRZ1FndoMj7Qh2y/M8MJrGY4YncclGYQjJ\nwlDVgFTHNeXorxWLRrC0owlHT8yaLUIkLXWysOgUAAD84v0Xm8eXn9pvWvSyQOm2ZwaRK5TKHvDZ\nXBFNcStrBwBeu3UlhqayppX3ISPDQ7q9cnFza2o2a7SWlmhORPGyzSKvvrslbnvY+20xgEhZBgog\nqk47HFY7Ywwru1uQjEVt7//VFafgq28+1/oc4HDx7RYZAFy0odc8fv9LN5rH3S0JFEvcDGY5U+nc\nqKmV3c2mB7BnUFy7c1Z1msHuSYNLdSveSmXtymnH0SlMZwvYPZjCkYlZFEoc6/oU6sHgxt0C8TPZ\nom1R3DjQhmyhhJJRqTuVKWCJUSUMGEVIHKIZXIXceKmc1vW1or0phicOTWC3UY9x+jJDARjdQsuC\nwIastIvFDgCfes2Z2Ly0HW+9cDUuXG/djyWdTShx4O7dIygYlcp2+rJcoZy9shMA8IyxQ9wNW1eY\nf+trS+Kh/eO4a+cwjo9ShO4AAB7pSURBVE/Oli2y4+mc2bNH4ozlQt5Pth/GnqFprDeK1+S1k1k9\nVs1DZY+zJRHDur5WPHZowqRstip0l8x4ko0RK2WJSWxe1oHjkxmMp3M4PDGDFV3NZmrtcCpj1WM4\nvES1TkRiQ38r8kWO/9p+GFedsQSJmDgfqQAsCrn2uFb3tmD3YAq7B1NIxCJmV4KThUWjALoUa2XL\nqi58/53n4+0XrcV3bjzPfL+/LYkH9onilUPjM2Vub1qpCJRY0tGEXKGEhw+MIx5ltqwMoLz3izol\nZhwUEAC8+fzVAIDXbV1ps9jVSWO6+C4UUIfDaq+ERCyCa89ejo6mGG44d4VtMeOcY3I2Z7tmgHgA\n1/W1YlVPM67fYgWplhuUjuSTEzH7DudO1xcQla07B6fAOTcXx1OWtJuKTrrl7llAedObAIDP/cnZ\nAIDnjk/iqi/eC0AEM9WAoXgoxeedXp1q3V24vge5YglDqYxpla3sbrZtPymb+5V5AFlrwxVAKK/1\n/W04OJY2z/HUpcLL62yOY3AqY2vvIa+9lAWgbL71tCZw+4cuw6dfc5btfdVYAMrbe7stPr1tSXO+\nfuiKTbYMGJVynJjJly1mEzM5dLfaFcBVZyxFNMJws5EbLzN2JAU0ZCqAcmNoyiXmdPmpA7h3zwg+\nc9tOrOxuxoCxOMrunur+2G5cu3qu568TdOptzxzHIwfGsarHSq8edqvHcFBx6nO6yfDU5TlLuHsA\n1rimjZ5CqgF52Sn9ePbYFL73wEFcurHP8o5OEhaFAnDSIgBw6aZ+3HzdGTYOc+uaLttnnMUvbgu2\n1Ng/eewIzlnZZd5AmXkgi1nctsE7YbSoUHHF6Uuw61NX46OvOs0xFmuc0Wh52ibg/hDVwtM3X4Uv\n/OkW22SdyRWRL3IzTU0iEmG4868uw93/66W2ibrNKLD72RNHAQgFUC0rAxDFLidm8jgyMYs9gyn0\ntorNXayK46wjZbAyBfQaoxht12DKrKpc29diLtoyZdAqxrMr4VZHpg0g6hzu2jlkvmfmks/kbLSI\nkzZTc9kB0b/mgX1j2D04hfZkzMyXl8FpuZmONBAkzSKDwE4vrBJ6WhO4668vM393NguUWVJOq/07\nN27DV950Lj50xSm299916Xrc9MrNtvfU6TYxkyujgJoTUXz6NWdiaUcTPnD5Rpy3Viy6rQmxYYys\nEXB6O9mCqIZtd5zrNWdZi6v0jAHhnRwYTSNXLLkma7gttGet6EQ0wvCxX+zASCqLzYYi7u8QHWCt\ngHLtIPA5KzvNFiUv32zRZjIZwZ5Eos7bfBmt+rqtK81jVZmcLCx4BTDlsshWggzCAsCX33QuAEcL\nXAddAMC2EEl+F7Dyhe97XlRDSqtQyuOcG5ua2B9IQHDk6iLiRCwierYU7eu/6wTzCrWtwQkXr8n8\n7mikzEoZ6GgyOzQCIiOkWlYGILKTAFFK/+ThE+bvVsFZxsYXV0sZjEcjWN/fin/9w34AwFsuWI2B\n9iZb/nepwqKdzhZsNJxcoAenMvjs7bsBCG+iKS7600wYmSzCinVkn2SLZQv2yu5mcC624Fw/0Gbe\nV2mRqtdVPVHJY7d6VAAAsHHAskyd53liJocIQ5mBsGlJO159TnnK4aqeFjNr7Py1PbZFlnOOiZl8\nmQcAAG86fzUe+vuX48NXnaqMhWHTkjb81/bDAMo9AGfhloSklAARW5OQXuJ/PPgCOEd5AkOmPIEh\nHo2YXtKKrmZ80FB4A+1JDE9lTK/BmY5rFgoq94Exhvs+8jLc9v9eajauA4Ri2jeSxr8/+IKrhzg5\nmy+7/ks6mvCPN5yFa89ehqvP0gqAHCdmvCuAFV3i4X/rhatx3TnLy2iWdM6+WADAmSusSdqjPBBy\n8fnZ48Iqlhy8muaXL/IyK6oa5OQUaaCAc/cwt9RNr1ArPWVKZWdz+QNeCZ949RnmsfAALFjcuBLk\nM9zo93z/MeweSpkW3oDSckKtBJanmjfy452LheqWv9bgsqUXNpLKYv9oGolYxKWpmdUjH7BSVHcY\nvHhnc9y8r7JwKpsvIRmLumyjmS9TAH9+yTrz+JyV1lz50/NWmR1bAavrqrwH0kPocTEQquHn77sI\nP3z3BWWb1UzM5NDZHLd5vF6w59OvxI/ec6GweJXMmFyhVOZNVMOpyv0xq4+Nc3WjbABhCP3gnRfg\nI1dvNvl/AHinck1Txubv6nybqmAIfemNW/DATS/DvX97ufldA+1NSOeK+KRR7+FMx5UUkPO5X9HV\nbDP4AOFl2M7RUScyOp2zxfIk3nzBanz1zVs907eUWPAKYLIOD2DjQDt++O4L8PFrxWLmfFRmlaZQ\nEj2tCfzmg5diZXcz3njeavN9J3crC9LkpJel6PU8RHJxj0VEINq521c9MQAnVA9A9tWpRzldo3DQ\nUQf9YMYAFOXktGxl1XJbMoZYhOGW3+xCtlBS8rK5TZZT0clF/Ly13eZi0ZyIIhGN4H//dg8AsXWf\n0y1PZwu23Pj2pjg6mmL41n0HAFjxBUDc6589ftTcW7o8BlA+P/raknjXJeuwtKOpzCr+6XsvMqkF\nZ4rqeDqHmJFaWA/OXd2Nizb0lRkvEzP5uuaahGxVwmApYVnxXo9yeuP51rMhq4/N+eaSqCFxyaY+\nvPelG2yKqykexY/efaH5u5MOFcVZ5WNjjGF5V7ONGnLusCbjIFKevA9Oz98N122xe1LObZuGlTqQ\nsEArAAcu2tBnRvXLuj0qTaFUnLasA/d/5GVY2mmP4L/qbGtRdHZAlBkzbjRLJUil0hyPusYA/HgA\ngGVFWRRQfQvGHR+6DJ+8/gyX7AcRRGxxxGE+//pzzGPJvQNWa17AssgsWe7W4uteJLjUz9xwlo0+\nyyk7Xh0eny0PjipdIyXUHv4XKplPsvANEJunuG2i0+aigD927el46O9fXqacO5vjePwfrsQT/3Cl\nuVRIcWPTOfS0Juq22CWcacJyD+VGoSpO2bajHnkvWtON3/2vlwAAbjDaV0sF5ZaoUQsvVu6L854O\np7KmJ1cLLz11wPa7jBVKcYNTYtGutJ2o838f+ejLzd8jSq0O5xwjqayrBzCX0AqgCpwNsER/9miV\n/7Dja2/eigP/dA2+9MYtePvFawFYE0taUW48aiVINzQeKw8C54si373xGIBFGcix1btgnLq0HX/2\n4rVlD+RUpoC2RKxsMZOLNmB3sb/1Z9vMY9leWMpz5lJLnLe2BwdveZW5E5obMvmiTTnlCiXkCqUy\nZfK+l1oV0+qi/TElMH/5qQNl88OZneQFTfEoulsTptKSi+JYOmtmzzQCp3ISCsWHPFjyZIfVnjrm\nLiAyvw780zXm4i2HV80D8Do4OTbOubkvthdsWdWFg7e8CltWiQQQef/kfRieypq0pBfIQj/Afg+m\nZgvI5Eu2v4cBWgFUgbMddCNBVsYYrt+yomwTDFMB1LHIrlGsZCcFJKkRuRNVvVCz7Yemsoiw+ugp\nu7By17e/w33x+fi1p9sWVgC48vQl+NT1goaTDdicfLGz3qESzl7ZiaZ4BB98+Sb84F12btwtNx4A\n/vbqzbjjQ5dhz6dfaXt/WWczeltFl8+vvPncskV2cibfsJUtL5kUNzqdc6UxvMuzK6exdA797X7k\nWed6yGjzoHpt9YzL9HYcSQf1Pqeff/05+NIbt9iolqnZArKFUt1Uyw/edQHu/ZvLy+6DUCb1yXrk\noy/Hox+9wmYgyOrjNb0nt9K3FhrnC+YBiiWOVKZgNgyrF2o7aM65vyCrw4O0KCDvD+Xn33AOfv3M\ncZy6pB0RRxaQs/y9kfHJB/LI+AyWdjSZVFjdsoxXzjkYYxiczFQscFGDpCqkBZd1eABuAeVq+Nl7\nL0KRc6sQjlnKxK1ATULm6zvx4N+9HIwJrlilWUTthB9vU8oRryOpLNb6WCxUi71U4hhP58ygeKMS\n5bkeGE2jNRFtmM5wejuye2u9z6n0IO97ftSUNWSkXXv1ACTakjGbJyiv3XAqayu48wJp5avPlKxm\nlt19w4IF7QHIFgTOfHbPUOiCbEFs9+e2WHgUBQDlNEsdY+tqSeAtF6wBY8zYdtHSAFOOHZXqHp9x\nrg/sHcXPnjhaluFQryzAOtehqWzdFY5ykZCtEuSZysXC63nGohFbFbSauGMqkzpom0QsYnpzKm2W\nzhVRKPHGeXZlV7ZcoYTjk7MNWdiWPDvFUixxMzOt0eHJ+XZwLI01va1VU5VrDA2A6u1k0ZKIltXq\n1CNPtdiB+hWAKUtxATL5IiZn8w0HblW6cf9oGhEGswAtLPCtABhjf8kY280Ye5Yx9tkKn7na+Mxe\nxthNfr/TK6SV7csqM+7g80OiXUG9HK8py7Ht4okZkRPcaOVfhNkpIN8KAEKe7Mp55elLavxH9bEB\n4tKVSgYn21nfAykLdd5hNLBTs2MANLyYqQ9lJQqoHjhTZ7vqSJ21jcsSiEPjMyhxYK2PzpBM0QB7\njCpkWbHdCGT4JpMv4p7dI1jb58M7cRgI+0fSWNvr41yZ3dgAUDdtY8oyXjm4WcU/0KAstU7kwGja\ntSXLXMMXBcQYuxzA9QDO5pxnGWMDLp+JAvgagCsBHAHwKGPsVs75c87PUsNvcEktfpGl/Gref12y\njFfVA2iYY4eoylUVgMWNN04B/WGPaKh22rIOXL9lRY3/qCLLeC1xjokZUTi1rE4FsKqnBQdveZXZ\nSlkutKPpLBKxiOcK2fKxWSmqKZf01LpkKaanOdcIYgAHRyVf7HNRNAb38AGRsaT2cqpbnkGHyg6Y\nL1rTU+M/qssCxLnmCiX8Yc+IrdCrEXnyXKUH0GiwVS0WlxvSNCpLrRM5MDp90ls9e4FfD+C9AG7h\nnGcBgHM+7PKZ8wHs5Zzv55znAPwYQmkEDt8PJSy3d+/wNOJRZiv2qEuWw+p55MB4XQFgJ6IuO3gB\nPhSAcvyz917UsDsO2M91aFJaZP6yH8wMlFQOfUrWTCNjU3l2AOYm6A3JMo4nfXubVh3Avz94EIC/\n3vAyBjA4mcH/uXMP1vW2NhwfAsS5zuaL+NB/PYmB9qTpmTU8OIhn678fE9XBW1d3VfmH2mOT9/TW\nJ4+hLRkrK9zyLksWHnJzk6JlXQ0qAIhxTWcL2HF0akEqgFMAXMoYe5gx9gfG2Hkun1kB4LDy+xHj\nvcDh3wMQD/iJmRwePjCGtb2tDVM2qtVTLAn3MuljkY1GGMbTOTx+aMIIUPujgOSqffbKzoYfHkuU\nRXfJLS4b7XLoXOf/sGcYfT5yqSOKV2daiw3TBZY3Ua19hidZxnlOZfK47/lRtDfFfBkIcr+IC//p\ndwDs7b8blSd71r/q7GUN1ydIWRJfv3sf+tqSuNGHQpHP6UP7x7B7KEVjvMDa1GZNT2MLtxzX04dF\nq/hKiQVziZqrGWPsLsbYDpef6yEopG4AFwL4GwD/l5WbZm4zhbu8J7/vPYyx7Yyx7SMjI3WcSjl8\nKwAIDb7lk3fiiUMnsKG/reb/VJSlWD1PHj6BQonj9UoefCPy9o2k8dqvP4B79oyU7TZUtzzjtYmQ\no+QceOwF0crXWSTnFZZlLDZ1ceunUp88qzhnKJVBV0u8YV7W5gFIBeAzBvC9Bw4CAL7+lq0NezmA\nrI61fv/gyzc1LAuw7kMyFsHHr/W3X608q92DKRw9MYvrtyz3da4yGP+rp8U+Gp9+zZm+x8a5oOKW\ndjQ17k0YBsIThgK4xtGxNQyo+SRxzq+o9DfG2HsB/IwLM+gRxlgJQB8AdeU+AmCV8vtKAMeqfN83\nAXwTALZt21ZRUXiBXwXgbAf9pgtWV/m0N0hLBRDVkY1CrUz87bNDaE2IZmUNeyiGuCaf1r8q687n\nhvAvfxD78/b5oFkA8UA+tH8M+SLHq/3sl6rQBcNTWSzxUZijplp+9vZdAPzNNQD44cOHAMDW+6bR\nsUn8/H0XmVtH+hXYkqjeqNCTKOP/HzskjIO3XbjG99A45zgwmsbZKztx9Zk+mqop3uvPnjha1rSv\nHkQMA2HPUAorupobL3QLEH4poF8AeBkAMMZOAZAAMOr4zKMANjHG1jHGEgDeCOBWn9/rCVMVWkF7\nBhPtoDuaYnjT+avNLRAbEqXkvj1x6ARWdjebG2Y0gqjyED55+ITvNhByAWpqMPdfhbQW//JHTwAQ\nFbReSundZQmoivNlp5XlGtQtDwCGjE3aG5Zl0EkT6RwmjBhAw/SZMrCvvXmrr8wkY3DmYaMBcxVy\nfjT7oFck5Mj2j6TRnoz5S3eF5YntOp4ydznzO7Z794hlrNGYnxgXQ6nEsXswFUr6B/CvAL4LYD1j\nbAdEcPdGzjlnjC1njN0GAJzzAoAPALgDwE4A/5dz/qzP7/UEP4U5gFjI0rkCpjIF3xV8anDpwX2j\nuGBd4xkZAGwc7M7jU7h/76ivQKuU5pf/B+wc7yvPXGrr3li/LCs4+tCBcZyxvMNXOb3aJ394KuNP\nFoTl+YsnRcdXZ0VzvbIkKKpFVXktBArApAiJ50dfe9JXPAEQz2kmX8RYOud7S0U5tt8+K7KdKhUq\nekWhxLFrMFW1RclcwtfMMLJ63ury/jEA1yi/3wbgNj/f1QhOzOQbLwKDmAyylqDeNMYyWcbr1+7Z\ni3SuiEs2+VQAhsD+drGhxdETs7jAh7sqJz6lhQfALFxrFPI8U5k8th8cx/sv31j9H2qNjVmB+OOT\n9Zf524UJWf+fsQPW61+0qvrnq47LukbUSriVUB7N/LAG58erNuUxq2+/32wz6en89rkhXLqpDyt8\n1E6o49o00Li3HyQWdCXw7qGUL2tKXbY2+3UtDWGHx2eRjEX88diwYgCXbuoze637LhwCfGVQmLKU\nC9doOwlLlhA2OJVBicPWQ78heRDehNyJyw+dzQBzE3ag8XRjKUvCS+vh2vIsic7W5I3JEyBRAMrJ\nNtqmxSZPOfad7aQcyx3NGpdlSbvyjMYLK4PEgu0FNJXJ48BoGq/b6qOgybh/EQacssTvwmNNhlef\ns9z33p8qJ/vZPzkb33vgoK9UOjm8ZJwuBgBYO6H5xXgD+ye4gTGGncensN3ITnrXJet9ydo3LCrE\nb3ntWTU+XUuWdUzpATDmXwkLOcZ8I/ZOmijmmyJwo4+4mpBlHV+80Z+XrspqtINA0AjnqHxi26fv\nwui0KPJRt5WrF6pV7D/zwTqmKAiRHkBTPIpzVnXhC3+6xbdMACSl6uq5kigUZimAelsQl8kCzMW/\nqyVeVztuN1kS1/qoZAXs18y573RD8qQsAotdlUfiISpXjjLtGKh/Dwsn1LG57f1RnyyBZCzie/0I\nCguSApKLPwBcsD58vDiFPMqsDEDsJwDQWewSJAoFwK5B0Yqj0XoCU5ZyI/75dWdX/mAdsprijbem\nMGUpMyTu0zsElLlLQP8IeYRZQDYPgNbbiUd9LrSExos1P8LV/0fFglQAuz51tXnsh/+Uk57kBhJP\netkHiMIlB0RPFoDqgbROlpJ+ABqvJ1CkAQCuPmMprjrD3ybcctH2G5cA/MUi3OUJgRTehJAnXkko\nG+WYRp5MYSbw1JVjv8aQtX6Ed5ldkBRQUzyKu/76JQ3nnkvQTno1y8O/PGqLXSoUCnmUD5Eq7w3b\nGq+cNmVRxjoMWRRB26BApgCMVxoPQKGACD0Amnuqxq98tkQhkhMkwjtzfYLEKjNeyQNfBBOC2gOQ\n7RFIFmzVjSaUR8M/C9CMS0jzU4DnlEWFCKX3CuUeEMYnABrjStp5FPdUtRl9KxRCAzIohHdkIYA5\n6Yl4bAmKh1I2qKCKAZgeAGF8AqCKATBDFp0yoYpNAP72E3DKotIDlPErwGp5QR0DoEk6kPODemw0\nmXph9gC0AqgCytz4WMS61JRBISpZsnEYtQfgOygHWCmqhMqEJjYhXilaLTBCKxZQsoCIPMRMmGNE\nxisN3aiMzWcw3sqcCu8yG96RhQCUMYCoshBSBtIiRCaj3LOUhM5QjwnGV6KMTxAutKQKQComggwg\nwDo/v3EwiXTWX7fZSiBReAHEdcQxVQxRewDzEqZlQeIB0Aa+JAVEnT3ip/mVCeJBFUx6yv90tWId\n/u9BoShk0TRbE68JIroga1jsfvYBVnF4XPTG97OpjBsovTrqmgK/oKQug8KCDQJTwEzjIphYqiVG\nxctS4qfvfTH2DE2TPOBBlbxQKM58kU6ZyD4vFDEA2RCtNUlL6flpZ2yXJwSSGAgKQpckQGi8WNlJ\n4XveJbQCqAJKCojaA5DtoKmm64vW9Pja51WFPFcq+kGCYrGQ9Q6UshrdU1iF3Pu40Z3TnHjHxWsx\n0J7Ea3zs7aziu28/D/tH0uTbGlLGAKhrFKhkaQ9gnoK2QZo1tSh41JuvOwNdLXG89NTGe+MHhWhg\nCsD/fbAUAIU3YSgAAg/gqjOW4vUvWom/ufpU37IAMWdf52PHOSfW9Lb62qS+EkjjOsQZSv5laQpo\nXoPSA1BBkZmxtLMJt/hsZRAUYkbAO0asACgUcc5YtCkszzGjPxGFAmiKR/G515/jW858A8WiHTUy\n7GjqAOjmrqQZw9oHCNBB4KqQD3ZPq9/2A3aEeUJQQD6Q1B4AVdEbQGuVrfK5o9ViBoXyNDOeCJ4r\nKYHCQJC1E3Hi54AS2gOogve+dAMuO6UPZ63oIpF386tPx1Yf+wDPF0jriaKpmQrK4DmFAvjBOy/A\nIwfHyXlxjfogU2elR+YHUodQKIAXxkRq9caQ7gYGaAVQFU3xKFlgFADefvE6MllhhrT8qYqQJCgV\ngN+2wQBwyaY+XLKpj2A0iw+v2bIcq4liCvtHxZ4MD+4bI5BmZGMR9Hda1S08w3NW0mZOUUIrAA1y\nyOCodIGpQNFET6K/nZbW06gPX3zjuWSytq3pwY8eOYwfvvsC37KyBZGNRcHS/sVLN+DyzQM4kzh1\nlhJaAWiQ4/D4LADgytNpt8GjrKjUCmDh4LVbV+DKM5agg6CGpdeI991wrv/02Xg0EurFH9AKQCMA\nvO5FK/DHvaN43+UbSOUOtPvPkf+bq07FvXtGSIKPGuEAY4xk8QcErfdvbz8Ply4Sao9xaj+dENu2\nbePbt2+f62FozDFufeoY2pJRvGxzODfW1tAIExhjj3HOt3n5rDaDNEKP63zut6uhoeEOXQegoaGh\nsUihFYCGhobGIoVWABoaGhqLFFoBaGhoaCxSaAWgoaGhsUihFYCGhobGIoVWABoaGhqLFFoBaGho\naCxShLoSmDE2AuCFBv+9D8Ao4XDmK/R1ENDXQUBfBwsL9Vqs4Zz3e/lgqBWAHzDGtnsth17I0NdB\nQF8HAX0dLOhroSkgDQ0NjUULrQA0NDQ0FikWsgL45lwPICTQ10FAXwcBfR0sLPprsWBjABoaGhoa\n1bGQPQANDQ0NjSpYcAqAMXY1Y2w3Y2wvY+ymuR5P0GCMHWSMPcMYe5Ixtt14r4cxdidj7Hnjtdt4\nnzHGvmxcm6cZY1vndvT+wBj7LmNsmDG2Q3mv7nNnjN1ofP55xtiNc3EuflDhOtzMGDtqzIsnGWPX\nKH/7O+M67GaMXaW8P6+fHcbYKsbY3YyxnYyxZxljHzTeX3RzwjM45wvmB0AUwD4A6wEkADwF4PS5\nHlfA53wQQJ/jvc8CuMk4vgnAPxvH1wD4DQAG4EIAD8/1+H2e+2UAtgLY0ei5A+gBsN947TaOu+f6\n3Aiuw80APuzy2dON5yIJYJ3xvEQXwrMDYBmArcZxO4A9xvkuujnh9WeheQDnA9jLOd/POc8B+DGA\n6+d4THOB6wH8u3H87wBeo7z/H1zgIQBdjLFlczFACnDO7wUw7ni73nO/CsCdnPNxzvkEgDsBXB38\n6OlQ4TpUwvUAfsw5z3LODwDYC/HczPtnh3N+nHP+uHGcArATwAoswjnhFQtNAawAcFj5/Yjx3kIG\nB/BbxthjjLH3GO8t4ZwfB8RDAWDAeH8xXJ96z30hX5MPGNTGdyXtgUVyHRhjawGcC+Bh6DlREQtN\nATCX9xZ6mtPFnPOtAF4J4P2MscuqfHYxXh+JSue+UK/JNwBsALAFwHEAnzfeX/DXgTHWBuCnAD7E\nOZ+q9lGX9xbUtaiFhaYAjgBYpfy+EsCxORrLSQHn/JjxOgzg5xCu/JCkdozXYePji+H61HvuC/Ka\ncM6HOOdFznkJwLcg5gWwwK8DYywOsfj/J+f8Z8bbek5UwEJTAI8C2MQYW8cYSwB4I4Bb53hMgYEx\n1soYa5fHAF4BYAfEOcvMhRsB/NI4vhXAnxnZDxcCmJSu8QJCved+B4BXMMa6DZrkFcZ78xqO2M4N\nEPMCENfhjYyxJGNsHYBNAB7BAnh2GGMMwHcA7OSc/x/lT3pOVMJcR6GpfyAi+3sgMho+OtfjCfhc\n10NkazwF4Fl5vgB6AfwOwPPGa4/xPgPwNePaPANg21yfg8/z/xEEvZGHsNre2ci5A/hziGDoXgDv\nmOvzIroO3zfO82mIhW6Z8vmPGtdhN4BXKu/P62cHwCUQVM3TAJ40fq5ZjHPC64+uBNbQ0NBYpFho\nFJCGhoaGhkdoBaChoaGxSKEVgIaGhsYihVYAGhoaGosUWgFoaGhoLFJoBaChoaGxSKEVgIaGhsYi\nhVYAGhoaGosU/z+Uwl5bJJAovwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot(dd)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "discords = find_discords_hotsax(dd)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(430, 5.2790800061718386), (318, 4.1757563573086953)]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "discords" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(173, 5.1145707192350738),\n", " (26, 4.9594742669641381),\n", " (299, 2.3370302965046825)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "discords = find_discords_hotsax(dd[0:400], 100, 4)\n", "discords" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/insect.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "znorm_tinkah.ipynb\n" ] } ], "source": [ "import os\n", "files = [f for f in os.listdir('.') if os.path.isfile(f)]\n", "for f in files:\n", " print(f)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from numpy import genfromtxt\n", "dd = genfromtxt(\"/home/psenin/git/saxpy/data/insect.txt\", delimiter=',') " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "18667" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dd)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(dd[1])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/psenin/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " 'Matplotlib is building the font cache using fc-list. '\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot(dd)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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f+tpZYTY0b5hl+punGBH8HNyup0VrVkcADJ+s2o0vN9qHExW53k7iPShlslBN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\nVw3p4ul4iUQiSWU8KQDG2G7+twzARwCGAihVp3b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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "def znorm(series, znorm_threshold=0.01):\n", " \"\"\"Fallback Python implementation.\"\"\"\n", " sd = np.std(series)\n", " if (sd < znorm_threshold):\n", " return series\n", " mean = np.mean(series)\n", " res = (series - mean)/sd\n", " return res" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "dd_norm = znorm(dd, 0.001)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(dd_norm)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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1KMTKnYdReLAcS74rSW5rkas9TmMks150ixZ6pSxVDECrXZHKBFplqkyMj3+m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\nTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(dd_norm)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "84.3 µs ± 3.98 µs per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n" ] } ], "source": [ "%timeit -r 10 a = znorm(dd, 0.001)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "setup = '''\n", "from numpy import genfromtxt\n", "dd = genfromtxt(\"/home/psenin/git/saxpy/data/insect.txt\", delimiter=',')\n", "from saxpy import znorm\n", "'''\n", "import timeit\n", "timeit.Timer('a=znorm.znorm(dd, 0.001)', setup=setup).repeat(7, 1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "setup = '''\n", "from numpy import genfromtxt\n", "dd = genfromtxt(\"/home/psenin/git/saxpy/data/insect.txt\", delimiter=',')\n", "from saxpy import znorm\n", "'''\n", "import timeit\n", "timeit.Timer('a=znorm.znorm(dd, 0.001)', setup=setup).repeat(7, 1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import std\n", "from saxpy import znorm as zn" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"Test the znorm implementation.\"\"\"\n", "ts = [-1., -2., -1., 0., 2., 1., 1., 0.]\n", "z_thrsh = 0.001\n", "\n", "x_scaled = [x / 100.0 for x in ts]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "zn.znorm(x_scaled, z_thrsh)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'builtin_function_or_method' object has no attribute 'znorm'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msaxpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mznorm\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mzn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetsourcelines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mznorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'builtin_function_or_method' object has no attribute 'znorm'" ] } ], "source": [ "import inspect\n", "from saxpy import znorm as zn\n", "lines = inspect.getsourcelines(zn.znorm)\n", "print(\"\".join(lines[0]))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "def znorm(series, znorm_threshold=0.01):\n", " \"\"\"Fallback Python implementation.\"\"\"\n", " sd = np.std(series)\n", " if (sd < znorm_threshold):\n", " return series\n", " mean = np.mean(series)\n", " res = np.zeros(len(series))\n", " for i in range(0, len(series)):\n", " res[i] = (series[i] - mean) / sd\n", " return res.ravel()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mean = np.mean(zz)\n", "mean" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "std = np.std(zz, axis=0, ddof=1)\n", "std" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/paa.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.append(\"..\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'saxpy'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msaxpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpaa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'saxpy'" ] } ], "source": [ "from saxpy import paa" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "def paa(series, paa_segments):\n", " \"\"\"PAA implementation.\"\"\"\n", " series_len = len(series)\n", "\n", " # check for the trivial case\n", " if (series_len == paa_segments):\n", " return np.copy(series)\n", " else:\n", " res = np.zeros(paa_segments)\n", " if (series_len % paa_segments == 0):\n", " inc = series_len // paa_segments\n", " for i in range(0, series_len):\n", " idx = i // inc\n", " res[idx] = res[idx] + series[i]\n", " return res / inc\n", " else:\n", " for i in range(0, paa_segments * series_len):\n", " idx = i // series_len\n", " pos = i // paa_segments\n", " res[idx] = res[idx] + series[pos]\n", " return res / series_len" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ts = np.array([1, 1, 2, 2, 3, 3])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 2, 2, 3, 3])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "paa(ts, 6)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1., 2., 3.])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "paa(ts, 3)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 1.33333333, 1.66666667, 2.33333333, 2.66666667, 3. ])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "paa(paa(ts, 4), 6)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.94054407, -0.69562387, 1.1463276 , 1.638693 , 0.90645757,\n", " 0.48984043, -0.16033437, -0.16033437, -1.00114287, -1.22333867])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dat = np.array([-0.9796808, -0.8622706, -0.6123005, 0.8496459, 1.739691,\n", " 1.588194, 1.095829, 0.5277147, 0.4709033, -0.2865819,\n", " 0.0921607, -0.2865819, -0.9039323, -1.195564, -1.237226])\n", "\n", "paa(dat, 10)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ -3.33333332e-08, -3.33333333e-08, 4.00000000e-07,\n", " -4.44089210e-16, -2.66666667e-07, -3.33333331e-08,\n", " -3.33333333e-08, -3.33333333e-08, -1.33333333e-07,\n", " -3.33333333e-07])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dat_paa = np.array([-0.9405441, -0.6956239, 1.146328, 1.638693, 0.9064573,\n", " 0.4898404, -0.1603344, -0.1603344, -1.001143,\n", " -1.223339])\n", "dat_paa - paa(dat, 10)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "def cuts_for_asize(a_size):\n", " \"\"\"Generate a set of alphabet cuts for its size.\"\"\"\n", " options = {\n", " 2: np.array([-np.inf, 0.00]),\n", " 3: np.array([-np.inf, -0.4307273, 0.4307273]),\n", " 4: np.array([-np.inf, -0.6744898, 0, 0.6744898]),\n", " 5: np.array([-np.inf, -0.841621233572914, -0.2533471031358,\n", " 0.2533471031358, 0.841621233572914]),\n", " 6: np.array([-np.inf, -0.967421566101701, -0.430727299295457, 0,\n", " 0.430727299295457, 0.967421566101701]),\n", " 7: np.array([-np.inf, -1.06757052387814, -0.565948821932863,\n", " -0.180012369792705, 0.180012369792705, 0.565948821932863,\n", " 1.06757052387814]),\n", " 8: np.array([-np.inf, -1.15034938037601, -0.674489750196082,\n", " -0.318639363964375, 0, 0.318639363964375,\n", " 0.674489750196082, 1.15034938037601]),\n", " 9: np.array([-np.inf, -1.22064034884735, -0.764709673786387,\n", " -0.430727299295457, -0.139710298881862, 0.139710298881862,\n", " 0.430727299295457, 0.764709673786387, 1.22064034884735]),\n", " 10: np.array([-np.inf, -1.2815515655446, -0.841621233572914,\n", " -0.524400512708041, -0.2533471031358, 0, 0.2533471031358,\n", " 0.524400512708041, 0.841621233572914, 1.2815515655446]),\n", " 11: np.array([-np.inf, -1.33517773611894, -0.908457868537385,\n", " -0.604585346583237, -0.348755695517045,\n", " -0.114185294321428, 0.114185294321428, 0.348755695517045,\n", " 0.604585346583237, 0.908457868537385, 1.33517773611894]),\n", " 12: np.array([-np.inf, -1.38299412710064, -0.967421566101701,\n", " -0.674489750196082, -0.430727299295457,\n", " -0.210428394247925, 0, 0.210428394247925,\n", " 0.430727299295457, 0.674489750196082, 0.967421566101701,\n", " 1.38299412710064]),\n", " 13: np.array([-np.inf, -1.42607687227285, -1.0200762327862,\n", " -0.736315917376129, -0.502402223373355,\n", " -0.293381232121193, -0.0965586152896391,\n", " 0.0965586152896394, 0.293381232121194, 0.502402223373355,\n", " 0.73631591737613, 1.0200762327862, 1.42607687227285]),\n", " 14: np.array([-np.inf, -1.46523379268552, -1.06757052387814,\n", " -0.791638607743375, -0.565948821932863, -0.36610635680057,\n", " -0.180012369792705, 0, 0.180012369792705,\n", " 0.36610635680057, 0.565948821932863, 0.791638607743375,\n", " 1.06757052387814, 1.46523379268552]),\n", " 15: np.array([-np.inf, -1.50108594604402, -1.11077161663679,\n", " -0.841621233572914, -0.622925723210088,\n", " -0.430727299295457, -0.2533471031358, -0.0836517339071291,\n", " 0.0836517339071291, 0.2533471031358, 0.430727299295457,\n", " 0.622925723210088, 0.841621233572914, 1.11077161663679,\n", " 1.50108594604402]),\n", " 16: np.array([-np.inf, -1.53412054435255, -1.15034938037601,\n", " -0.887146559018876, -0.674489750196082,\n", " -0.488776411114669, -0.318639363964375,\n", " -0.157310684610171, 0, 0.157310684610171,\n", " 0.318639363964375, 0.488776411114669, 0.674489750196082,\n", " 0.887146559018876, 1.15034938037601, 1.53412054435255]),\n", " 17: np.array([-np.inf, -1.5647264713618, -1.18683143275582,\n", " -0.928899491647271, -0.721522283982343,\n", " -0.541395085129088, -0.377391943828554,\n", " -0.223007830940367, -0.0737912738082727,\n", " 0.0737912738082727, 0.223007830940367, 0.377391943828554,\n", " 0.541395085129088, 0.721522283982343, 0.928899491647271,\n", " 1.18683143275582, 1.5647264713618]),\n", " 18: np.array([-np.inf, -1.59321881802305, -1.22064034884735,\n", " -0.967421566101701, -0.764709673786387,\n", " -0.589455797849779, -0.430727299295457,\n", " -0.282216147062508, -0.139710298881862, 0,\n", " 0.139710298881862, 0.282216147062508, 0.430727299295457,\n", " 0.589455797849779, 0.764709673786387, 0.967421566101701,\n", " 1.22064034884735, 1.59321881802305]),\n", " 19: np.array([-np.inf, -1.61985625863827, -1.25211952026522,\n", " -1.00314796766253, -0.8045963803603, -0.633640000779701,\n", " -0.47950565333095, -0.336038140371823, -0.199201324789267,\n", " -0.0660118123758407, 0.0660118123758406,\n", " 0.199201324789267, 0.336038140371823, 0.47950565333095,\n", " 0.633640000779701, 0.8045963803603, 1.00314796766253,\n", " 1.25211952026522, 1.61985625863827]),\n", " 20: np.array([-np.inf, -1.64485362695147, -1.2815515655446,\n", " -1.03643338949379, -0.841621233572914, -0.674489750196082,\n", " -0.524400512708041, -0.385320466407568, -0.2533471031358,\n", " -0.125661346855074, 0, 0.125661346855074, 0.2533471031358,\n", " 0.385320466407568, 0.524400512708041, 0.674489750196082,\n", " 0.841621233572914, 1.03643338949379, 1.2815515655446,\n", " 1.64485362695147]),\n", " }\n", " \n", " return options[a_size]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ -inf, -0.4307273, 0.4307273])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cuts_for_asize(3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/psenin/git/saxpy/jupyter'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['',\n", " '/home/psenin/anaconda3/lib/python36.zip',\n", " '/home/psenin/anaconda3/lib/python3.6',\n", " '/home/psenin/anaconda3/lib/python3.6/lib-dynload',\n", " '/home/psenin/anaconda3/lib/python3.6/site-packages',\n", " '/home/psenin/anaconda3/lib/python3.6/site-packages/IPython/extensions',\n", " '/home/psenin/.ipython',\n", " '..']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.path" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/sax.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/psenin/git/saxpy/jupyter'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os \n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.chdir(\"/home/psenin/git/saxpy\")\n", "import numpy as np\n", "from saxpy.strfunc import idx2letter \n", "from saxpy.znorm import znorm\n", "from saxpy.paa import paa\n", "from saxpy.alphabet import cuts_for_asize\n", "from saxpy.sax import sax_via_window" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "defaultdict(list,\n", " {'aac': [4, 10, 11, 30, 35],\n", " 'abc': [12, 14, 36, 44],\n", " 'acb': [5, 16, 21, 37, 43],\n", " 'acc': [13, 52, 53],\n", " 'bac': [3, 19, 34, 45, 51],\n", " 'bba': [31],\n", " 'bbb': [15, 18, 20, 22, 25, 26, 27, 28, 29, 41, 42, 46],\n", " 'bbc': [2],\n", " 'bca': [6, 17, 32, 38, 47, 48],\n", " 'caa': [8, 23, 24, 40],\n", " 'cab': [9, 50],\n", " 'cba': [7, 39, 49],\n", " 'cbb': [33],\n", " 'cca': [0, 1]})" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dat = np.array([0., 0., 0., 0., 0., -0.270340178359072, -0.367828308500142,\n", " 0.666980581124872, 1.87088147328446, 2.14548907684624,\n", " -0.480859313143032, -0.72911654245842, -0.490308602315934,\n", " -0.66152028906509, -0.221049033806403, 0.367003418871239,\n", " 0.631073992586373, 0.0487728723414486, 0.762655178750436,\n", " 0.78574757843331, 0.338239686422963, 0.784206454089066,\n", " -2.14265084073625, 2.11325193044223, 0.186018356196443,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.519132472499234,\n", " -2.604783141655, -0.244519550114012, -1.6570790528784,\n", " 3.34184602886343, 2.10361226260999, 1.9796808733979,\n", " -0.822247322003058, 1.06850578033292, -0.678811824405992,\n", " 0.804225748913681, 0.57363964388698, 0.437113583759113,\n", " 0.437208643628268, 0.989892093383503, 1.76545983424176,\n", " 0.119483882364649, -0.222311941138971, -0.74669456611669,\n", " -0.0663660879732063, 0., 0., 0., 0., 0.,])\n", "\n", "sax1 = sax_via_window(dat, 6, 3, 3, \"none\", 0.01)\n", "\n", "sax1" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ -inf, -0.4307273, 0.4307273])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from saxpy.znorm import znorm\n", "from saxpy.alphabet import cuts_for_asize\n", "cuts_for_asize(3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'acb'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from saxpy.znorm import znorm\n", "from saxpy.paa import paa\n", "from saxpy.sax import ts_to_string\n", "ts_to_string(paa(znorm(np.array([-2, 0, 2, 0, -1])), 3), cuts_for_asize(3))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'acb'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from saxpy.alphabet import cuts_for_asize\n", "from saxpy.znorm import znorm\n", "from saxpy.paa import paa\n", "from saxpy.sax import ts_to_string\n", "\n", "dat = np.array([-2, 0, 2, 0, -1])\n", "dat_znorm = znorm(dat)\n", "dat_paa_3 = paa(dat_znorm, 3)\n", "\n", "ts_to_string(dat_paa_3, cuts_for_asize(3))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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UU6AE+TdPM/8OtbZjnj1ufLK//SrrQLBXrJi82JXu+E3g58AWpdR5pdSv23HcZsDMYV/d\nWWrELsKeybyZ7uhJX4rdErGXTaVZMcWGbWSmnQ5P13bf45ljw6zvaWdDr9/ycZ/bybI2twi7BS47\nDqK1/ogdx2lGBsbm6e/wZlkJhQj63JwemV3kVTUP4ehCK6bb72Hf2Yl6LakpiVQZsecLNjI3sWv5\nKSoci/PCyVE+vGtNwef1SS67JWLFVMnAePF2vZlIT/ZsUgVKrmxhH5+LkEg0bjVkoxGOxfFUIOw+\nd+EpSpkRe7EZo1pr22o09p4ZZz4az2vDmEi/GGtE2KvEyGEvR9iNzdNGLuG2IhpPcGp4hieODPI3\nz57i0QMXSvqeR/YOECtQQDIfjeN0KNzOdLvZrnYP8YQWy6pE4glNNK4rsmLAbCtgLcimsCsFI0Xy\nxb+17zxv/OJPOXp5qqJ1ZPLM68N4nA5u2thT8HnSL8YaW6yYpUokluDSVKjkjBgwInazhLtU+6ae\nfOOFM/z9C2c4NzZHPCOCdijYvak3lZlgxXcPXODT3zrM6q42brnSOrMhFE3gczmy+oj3BMzq0zDL\n2gu3aRBIdcKsJCsGCg/bGJ2J0O5x4ve6im6eHh+aIRRN8Kl/PcSjD+zGXcb81VyeOTbMjVd04fcW\nlijpF2ONROxVcHFiHq0pOYcdoKOtudoKfP350wB84vYr+d8f3sF3PnkL3/7EzSQ0/ODlSwW/9/FD\nFwEKDhzOnJ5k0u033iykw2NpVDoWz6RQ696x2TDdfg+9AW/RiP3yZAiP08GrF6f4ylMnK1qLeZxj\ng9Pctrl4WnRf0MtcJM5sg7cerjUSsVdBqqtjGR57R0YjsOXBRVmWbYzNRjg7Osdn3rmV37r9yqzH\ntq4IsufQRT52ywbL7x2eDvP8iRGgcB+SeSthb09G7FIqXhJmA69qrJhCm6c9AS8dvuIR++WpEDvX\ndrKy08efP3mcO7ctt2zeVYxnX0+mOW4pQdgzUh6LRfdLCYnYqyBVnFRCnxiTZurweHBgHIDr1i6s\n/HvPjlXsOzvO+eSbWy4/ePkSpnNTKJoKR9PTk0y6A9LhsRzCUVPYK3s5F6qGHpuN0JOK2AsL++BU\niOUdXr5w7xvo8nv4r48cqmhgyjOvD9Pf4WVLf/HIR2afWiPCXgVnx2ZxOxUrLPpY5CPdm6O+VswL\nJ0aKVhIePDeB06G4Zs3CqOveHasAePyQtR2z59BFNibzjwtF7JZWTDJil6HWpZGyYqrx2AvksRtW\njIeRmXDeTX+tNYNTIVZ0+Ohs9/C/3n8NRy9P8+dPHi9rLbF4gp8dH+b2q/pKmt+6vEOKlKwQYa+C\n08OzrO/x4ypjk6gRIvYXTo7wH/72RR56trAPemBggi39Qdo9Cz/iru1u57p1nexJ+uiZDIzNse/s\nOB+8YQ1upypqxbTlCHubx0mb2ymzT0vEDivG6m+ktTasmGTEHoommEv29sllaj5GKJpgxTIjyLlr\nez8fvH4NX3n6JIfPl16TcOj8BFOhGLdftbyk55tWzNCU9IvJRIS9Ck6NzHJFnqq4fKR6ste474ZJ\nLJ7gC3teA+C54yN5n5dIaA6em+C6ddYNmADec+0qjlya4sTQdNb9jx82xP7eHasKZlyAdcQOyaHW\nYsWURPWbp27Lv9FsJE4klqDb76EnKaD57JjLSWHtz/j0+vn3bKcv4OVT/3qIuUhpgcwzx4ZxKLh1\nk3UWVS5d7R6cDiVWTA4i7BUST2jOjs6ysa9cYa9vxP6PvzjLscFpdq7t5PCFSSbnrN9gTo3MMB2O\nsdPCXze559qVOBTsybFj9hy8yHXrOlnb3Y7f6yrosYcsPHZICrtYMSVRrcce9LmIxBOpNwgT8xOT\nacVAcWE3I3aAZW1uvvShazk5PMNHHvpFSXbJM68Pc926rpLTXB0ORW/AI1ZMDiLsFXJhfJ5oXKd8\n5FIJeFwoVR+PfXQmzJd/8jpv3tzL5969Da3h56eso/b954yPz9et68p7vOUdPm7a2MPjhy6mvNfj\ng9McvTyd8uADXhfTZXrssDQidq01/+cnr3N8cLr4kwuQsmIqrIvIN2zDbCHQE/BkNNyy/psMmhF7\nMHu/6far+vjqR3dxbHCaD/zV85wansm7jrHZCIcvTBatNs1FctkXIsJeISdHjAv0it5AWd/nSA7b\nqEdbgT/9t2PMReL8wXu2s3NtJ36Pk5/lsWMOnJugw+cq+sZ1745VnB6Z5ZULRrXhnkMXcSh497Ur\ngcLFL7C0hf3Vi1P83yeO8/0i9QDFqNaKyfcp0vz9d/u96WlFefrFDE4awm5uZmbytu39PHz/zcyF\n43zwr15g31nrYWs/Oz6M1vm7OeajLyDVp7mIsFfI6WGjkVe5VgxAh89dcyvm8PkJHv7lAB+/ZQOb\nlgdxJ8u1zVzzXA4OTLBjbScOR+HMhLuvXoHbqdhz6AJaa/YcusgtV/ayPBm5BXwuZgv4q6FYASum\nxYX9yaPGuLl8G5Klkt48rTwrBhZmL5kNwHr8HrqTIwtH8kTsl6dCdLW781ZT71zbyXc+eQud7R7+\nw9+8yI9eWfhm9szrw3T7PZZj8AohEftCJKO/Qk6NzBD0uVLDl8vBaN1bOysmkdD84Z5X6fF7+S93\nbU7dv3tTL08cHWIgp9/NbDjGsctTvO2OzVaHy6Kz3cNtm/v43uFLvPvaVZwdneOBt2xKPR7wujg3\nap3rDsYEpdysGDCEfS4SzxvRtwJPHBkECqeDlkLKY6/UiikasXtwOx10trvzeuyDU6GsjVMr1vf4\n+fYnbuG+b/yS3/rH/bR7stc7H43znmtXFQ0mclke9DEyYzSNK/d7WxUR9go5PTLLxr5ASbm2udS6\nJ/t3D1xg/7kJ/uRD19LhS29K3ZqcTPPCyRF+pXtd6v7D5ydJaApmxGRy785VPHF0iC88/ioep4N3\nXL0i9VihHGmtNaFYfisGDHFZVWKv+2ZiaDrEofPGoOZqy+GrtmK8xjWR+3cam43gdTlSAtwb8Oa1\nYi6XIOxg/F3/+Tdu4mvPnWYiZ3PcoVTRNr1W9AW9xBOa8blIKntnqSPCXiGnh2d5U5HOc/kI+typ\nzabFZjYc449/dJSdazv54PXZL5rNywMsD3r52fERfuXGtLAfHDA2TneuKU3Y79rWj8/t4MC5Cd62\nvZ9lbek3j0LCHokn0JolKexPJW2Ydo/TBmGv0opJDdvI/hQ5OmPksJvBS4/fk9eKGZwK84aVpVko\nPreTB966qfgTS8T0/4emwyLsScRjr4C5SIyLk6Gyc9hNahmxP/P6MMPTYT5995YFH1OVUty6qZcX\nTo5m9T4/cG6cK3r9dJVoM/m9Lu7c1g+kK1IzH5uLxLM6Q5qEIsmxeBbCblpcoy3qs//0yBCrlvl4\nw6oOZsN2eez2ZsWMzYZT7R0AeoPWbQWi8QQjM2H6l5VegW0nMvt0ISLsFXBmxPCMK9k4BXPztDYe\n+/MnRgh4XbxxQ7fl47s39TI2G+FIsoe21poDAxOW/WEKcd+tV3DH1uXclRR4EzPjwmoDNT3IeuFl\naL6pjLegsIeicZ47PsId25Ybef4lFu/kIxyNoxRZPe3Lwfwb5WZqGe0E0hFwvuyT4ekwWlNWaw07\n6ZPZpwsQYa+AU6lUx+oi9loM23j+xAg3bezO2/Zgd7LCz8yOuTgZYng6zM4S/XWT69Z18fWP30hb\nzoaYP080CNbTk0xaOWL/+alR5qNx7tzWj7+AVVUq4XgCj9NR0X4PGBaOVesHs52ASW/Aw3QotqCQ\nKV2cVB8bRBqBLUSEvQLMVMfKhd1NLKEJRcvvfFcO58fnODM6l3fIBRiVgpuWB3juxChg2DAA163N\nX5hUDubHfCsf2RxknftmAManGqdDtWTE/uSRIdrcTm7e2IPf42SuWismWtm8UxOllGW9gdkAzMT0\nr3PbKady2IP1idj9XhftHqdE7BmIsFfA6ZFZVi7zWTbHKoV0Qcji2jEvJMXazH7Jx62bennp9Cih\naJwD5ybwuhxsXWlPs/hUKp2FsJtvbFZWjMOh6Gp3t1zErrXmiSOD3Lq5F5/bWbTlQimEY4mKUx1N\nchuBhaJx5iLxLGHvzdMvZtCinUCtWS657FmIsFfAyQqaf2WSz9O0m+dOjNAX9LJ5eeHq2Fs39RKK\nJth/bpyDAxNcu2ZZVWPNMikUsReyYsDIjCnWA7zZOHp5mouTIe7aZnQvDCQ99mpsuXAsXlXEbqwj\nu2guszjJJF+/mMtTYdxOlWq3XA+kSCkbEfYy0Vpzenim4o1TIJVLvpgRu9aaF06OsPvKnqLe65s2\nduN0KJ4+NszLFyYLNv4ql3wZF5C2YnwWVgzAjjWdPHl0iJ8dH7ZtPfXGLEp66xZD2P1eFwmd/l1U\nQjhWnRUDC4vmMhuAmaQj9hwrZirE8qCvrsVBMtQ6GxH2MhmdjTAVipXdIyaTWkTsxwanGZmJcEsJ\n7U+DPjc713byzZfOEYklCjb+Kpd85epgZHNA/oj9D+59A5v6AjzwT/s5WaB5VDPxxNEhdqxZxvJk\nBok/+aZWTcqj4bFXZ8UEczZxMxuAmeSzYi5Phui36BFTS/oCXunJnoEIe5mcHqm8R4xJLaYoPZ/0\n13eX2Nf61k29qY/ipVaclkIhYS/ksZvf+7cf24Xb6eC+b+xdUKnYbIzMhDk4MMEdW9Mpof4CVlWp\nhGPxiqcnmeR67JkNwEzaPE78HueCIqXB6VBd/XUwBr9MhWKcGGqNAKBabBF2pdTdSqljSqkTSqnP\n2HHMRiXV/KsKj72jbfF7sj9/YoQrev2sLrFq09xg7e/wsnKZfZWehdIdC2XFmKztbuevP3oD58fn\neOCf9xONL24m0WL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ixWTT7sk/0HpyPkpHMvNp5TIfFzM3T2NxW3LYwfhbGKmMrSvsPQEv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\nEXZBEIQWQ4RdEAShxfj/VKgZ5TMqqO4AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot(dat)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "defaultdict(list,\n", " {'aac': [4, 10, 11, 30, 35],\n", " 'abc': [12, 14, 36, 44],\n", " 'acb': [5, 16, 21, 37, 43],\n", " 'acc': [13, 52, 53],\n", " 'bac': [3, 19, 34, 45, 51],\n", " 'bba': [31],\n", " 'bbb': [15, 18, 20, 22, 25, 26, 27, 28, 29, 41, 42, 46],\n", " 'bbc': [2],\n", " 'bca': [6, 17, 32, 38, 47, 48],\n", " 'caa': [8, 23, 24, 40],\n", " 'cab': [9, 50],\n", " 'cba': [7, 39, 49],\n", " 'cbb': [33],\n", " 'cca': [0, 1]})" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "saxpy.ts_to_string()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sax1['cca']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "54" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "elements_num = 0\n", "for key in sax1:\n", " elements_num += len(sax1[key])\n", "elements_num " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "defaultdict(list,\n", " {'aac': [4, 10, 30, 35],\n", " 'abc': [12, 14, 36, 44],\n", " 'acb': [5, 16, 21, 37, 43],\n", " 'acc': [13, 52],\n", " 'bac': [3, 19, 34, 45, 51],\n", " 'bba': [31],\n", " 'bbb': [15, 18, 20, 22, 25, 41, 46],\n", " 'bbc': [2],\n", " 'bca': [6, 17, 32, 38, 47],\n", " 'caa': [8, 23, 40],\n", " 'cab': [9, 50],\n", " 'cba': [7, 39, 49],\n", " 'cbb': [33],\n", " 'cca': [0]})" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sax_exact = sax_via_window(dat, 6, 3, 3, \"exact\", 0.01)\n", "sax_exact" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "defaultdict(list,\n", " {'aac': [10, 30],\n", " 'acb': [21, 37, 43],\n", " 'acc': [13, 52],\n", " 'bac': [19, 34, 45],\n", " 'bba': [31],\n", " 'bbc': [2],\n", " 'bca': [6, 17, 47],\n", " 'caa': [8, 23],\n", " 'cab': [50],\n", " 'cba': [39],\n", " 'cca': [0]})" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sax_mindist = sax_via_window(dat, 6, 3, 3, \"mindist\", 0.01)\n", "sax_mindist" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/str_func.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/psenin/git/saxpy/jupyter'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os \n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "os.chdir(\"/home/psenin/git/saxpy\")\n", "import numpy as np\n", "from saxpy.strfunc import idx2letter \n", "from saxpy.znorm import znorm\n", "from saxpy.paa import paa\n", "from saxpy.alphabet import cuts_for_asize" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from saxpy.sax import sax_by_chunking\n", "dat1 = np.array([2.02, 2.33, 2.99, 6.85, 9.2, 8.8, 7.5, 6, 5.85,\n", " 3.85, 4.85, 3.85, 2.22, 1.45, 1.34])\n", "dats1_9_7 = \"bcggfddba\"\n", "dats1_10_11 = \"bcjkiheebb\"\n", "dats1_14_10 = \"bcdijjhgfeecbb\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'bcggfddba'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sax_by_chunking(dat1, 9, 7)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dats1_9_7 <- \"bcggfddba\"\n", "dats1_10_11 <- \"bcjkiheebb\"\n", "dats1_14_10 <- \"bcdijjhgfeecbb\"\n", "\n", "test_that(\"points to letters, i.e. SAX, CPP #2\", {\n", " str1_10_11 <- paste( matrix(unlist(sax_by_chunking(dat1, 10, 11, 0.01)),\n", " nrow = 10, byrow = T)[,1], collapse = \"\")\n", " str1_14_10 <- paste( matrix(unlist(sax_by_chunking(dat1, 14, 10, 0.01)),\n", " nrow = 14, byrow = T)[,1], collapse = \"\")\n", " str1_9_7 <- paste( matrix(unlist(sax_by_chunking(dat1, 9, 7, 0.01)),\n", " nrow = 9, byrow = T)[,1], collapse = \"\")\n", " expect_equal(str1_10_11, dats1_10_11)\n", " expect_equal(str1_14_10, dats1_14_10)\n", " expect_equal(str1_9_7, dats1_9_7)\n", "})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/tinkah.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def idx2letter(idx):\n", " \"\"\"Convert a numerical index to a char.\"\"\"\n", " if 0 <= idx < 20:\n", " return chr(97 + idx)\n", " else:\n", " raise ValueError('A wrong idx value supplied.')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'a'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx2letter(0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'h'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx2letter(7)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'t'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx2letter(19)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "A wrong idx value supplied.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0midx2letter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m\u001b[0m in \u001b[0;36midx2letter\u001b[0;34m(idx)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mchr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m97\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'A wrong idx value supplied.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: A wrong idx value supplied." ] } ], "source": [ "idx2letter(-1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "A wrong idx value supplied.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0midx2letter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m\u001b[0m in \u001b[0;36midx2letter\u001b[0;34m(idx)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mchr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m97\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'A wrong idx value supplied.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: A wrong idx value supplied." ] } ], "source": [ "idx2letter(20)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/visit_registry.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os \n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.chdir(\"/home/psenin/git/saxpy\")\n", "import numpy as np\n", "from saxpy.strfunc import idx2letter \n", "from saxpy.znorm import znorm\n", "from saxpy.paa import paa\n", "from saxpy.alphabet import cuts_for_asize\n", "from saxpy.sax import sax_via_window\n", "from saxpy.visit_registry import VisitRegistry" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%timeit\n", "arr = VisitRegistry(10000)\n", "while 0 < arr.get_unvisited_count():\n", " num = arr.get_next_unvisited()\n", " arr.mark_visited(num)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%timeit\n", "arr = VisitRegistry(10000)\n", "while 0 < arr.get_unvisited_count():\n", " num = arr.get_next_unvisited2()\n", " arr.mark_visited(num)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/vsm.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\ng6e164\\\\PycharmProjects\\\\saxpy'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os \n", "#os.chdir(\"/home/psenin/git/saxpy\")\n", "os.chdir('C:/Users/ng6e164/PycharmProjects/saxpy')\n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from saxpy.sax import sax_via_window\n", "from saxpy.saxvsm import series_to_wordbag\n", "from saxpy.saxvsm import manyseries_to_wordbag\n", "from saxpy.saxvsm import bags_to_tfidf\n", "from saxpy.saxvsm import cosine_similarity\n", "from saxpy.saxvsm import class_for_bag\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"{'bag1': {'this': 10, 'is': 1, 'a': 2, 'sample': 1}, 'bag2': {'this': 10, 'is': 1, 'another': 2, 'example': 3}}\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bag1 = { \"this\": 10, \"is\": 1, \"a\": 2, \"sample\": 1}\n", "bag2 = { \"this\": 10, \"is\": 1, \"another\": 2, \"example\": 3}\n", "bags = {\"bag1\":bag1, \"bag2\":bag2}\n", "str(bags)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['bag1', 'bag2']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[*bags.copy()]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'classes': ['bag1', 'bag2'],\n", " 'vectors': {'a': [0.76150001041880899, 0.0],\n", " 'another': [0.0, 0.76150001041880899],\n", " 'example': [0.0, 0.96090602783640278],\n", " 'sample': [0.48045301391820139, 0.0]}}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = bags_to_tfidf(bags)\n", "res" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'a': [0.76150001041880899, 0.0],\n", " 'another': [0.0, 0.76150001041880899],\n", " 'example': [0.0, 0.96090602783640278],\n", " 'sample': [0.48045301391820139, 0.0]}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res[\"vectors\"]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from saxpy.util import read_ucr_data\n", "dd = read_ucr_data('resources/data/cbf/CBF_TRAIN')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "3\n", "2\n" ] } ], "source": [ "win = 30\n", "paa = 6\n", "alp = 6\n", "na_strategy = \"exact\"\n", "ztresh = 0.01\n", "\n", "bags = {}\n", "\n", "for key, arr in dd.items():\n", " print(key)\n", " bags[key] = manyseries_to_wordbag(dd[key], win, paa, alp, na_strategy, ztresh)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['1', '3', '2']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[*bags.copy()]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['1', '3', '2']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectors = bags_to_tfidf(bags)\n", "vectors['classes']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dt = read_ucr_data('resources/data/cbf/CBF_TEST')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SMVjcWQ18yhr2cvWw0wCYm+VyRkW+aDRtbVALycMQUiScmFnB0xfNNsQzy6Jd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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import datetime\n", "import numpy as np\n", "\n", "series = dt['1'][6]\n", "x = np.arange(0, len(series))\n", "y = np.asarray(series)\n", "\n", "plt.plot(x,y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_bag = series_to_wordbag(series, 30, 6, 6, \"exact\", 0.01)\n", "#test_bag" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'1': 0.8096998752732335, '2': 0.91433733786773486, '3': 0.95867368251031904}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = cosine_similarity(vectors, test_bag)\n", "res" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_for_bag(res)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "1\n", " misclassified 72 as 2 {'1': 0.92853215910979048, '3': 0.94644982977028103, '2': 0.90361918327181945}\n", " misclassified 91 as 2 {'1': 0.89773989722580827, '3': 0.95640189691324418, '2': 0.89753754493831839}\n", " misclassified 256 as 2 {'1': 0.93156990542330831, '3': 0.94594144806166813, '2': 0.90273991600777048}\n", "3\n", " misclassified 223 as 1 {'1': 0.86883795152559462, '3': 0.89417006609748306, '2': 0.98599573407109053}\n" ] } ], "source": [ "for cls in [*dt.copy()]:\n", " print(cls)\n", " i = 0\n", " for s in dt[cls]:\n", " sim = cosine_similarity(vectors, \n", " series_to_wordbag(s, 30, 6, 6, \"none\", 0.01))\n", " res = class_for_bag(sim)\n", " if res != cls:\n", " print(\" misclassified\", i, \"as\", res, sim)\n", " i = i + 1\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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x1SfKq1XrUdkHyHpuSS0PQMp/rcgEktdxiTFTobIWQAb3L1iqdGdjadOrqtwU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\nQBAEQZfyDx731mgaDqKaAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "series = dt['1'][72]\n", "x = np.arange(0, len(series))\n", "y = np.asarray(series)\n", "plt.plot(x,y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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qD8bWM3ZdxAeFYx8MmcKczCnoCXoqvk+487BPwrSR4ZdEMRZh/9C/PIfxbj/e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\nn12nsFt7sq9kCoj4qrc+uHY4gkOjUXz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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "series = dt['3'][223]\n", "x = np.arange(0, len(series))\n", "y = np.asarray(series)\n", "plt.plot(x,y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "train = read_ucr_data('resources/data/cbf/CBF_TRAIN')\n", "test = read_ucr_data('resources/data/cbf/CBF_TEST')\n", "\n", "win = 30\n", "paa = 6\n", "alp = 6\n", "na_strategy = \"exact\"\n", "zthresh = 0.01\n", "\n", "def test_accuracy(dd_train, dd_test, sax_win, sax_paa, sax_alp, sax_strategy, z_threshold):\n", " \n", " train_bags = {}\n", " for key, arr in dd_train.items():\n", " train_bags[key] = manyseries_to_wordbag(dd[key], sax_win, sax_paa,\n", " sax_alp, sax_strategy, z_threshold)\n", " \n", " tfidf_vectors = bags_to_tfidf(train_bags)\n", "\n", " correct = 0\n", " count = 0\n", "\n", " for cls in [*dd_test.copy()]:\n", " for s in dd_test[cls]:\n", " sim = cosine_similarity(tfidf_vectors, \n", " series_to_wordbag(s, sax_win, sax_paa,\n", " sax_alp, sax_strategy, z_threshold))\n", " res = class_for_bag(sim)\n", " if res == cls:\n", " correct = correct + 1\n", " count = count + 1\n", " \n", " return correct / count" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9944444444444445" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_accuracy(train, test, win, paa, alp, na_strategy, zthresh)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: jupyter/znorm.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.append(\"..\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.81649658, -1.63299316, -0.81649658, 0. , 1.63299316,\n", " 0.81649658, 0.81649658, 0. ])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from saxpy import znorm\n", "ts = [-1., -2., -1., 0., 2., 1., 1., 0.]\n", "z_thrsh = 0.001\n", "x_scaled = [x / 100.0 for x in ts]\n", "znorm.znorm(x_scaled, z_thrsh)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ts = [-0.1, -0.2, 0.2, 0.1]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.63245553, -1.26491106, 1.26491106, 0.63245553])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "znorm.znorm(ts, z_thrsh)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.158113883008419" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numpy import std\n", "std(znorm.znorm(ts, 0.5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/psenin/git/saxpy/jupyter'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['',\n", " '/home/psenin/anaconda3/lib/python36.zip',\n", " '/home/psenin/anaconda3/lib/python3.6',\n", " '/home/psenin/anaconda3/lib/python3.6/lib-dynload',\n", " '/home/psenin/anaconda3/lib/python3.6/site-packages',\n", " '/home/psenin/anaconda3/lib/python3.6/site-packages/IPython/extensions',\n", " '/home/psenin/.ipython',\n", " '..']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.path" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", 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1.1672337e+000 9.1549682e-001 5.2948368e-001 8.2609106e-001 1.0162801e+000 8.9997316e-001 9.2021327e-001 6.2249335e-001 3.4791477e-001 8.3031441e-001 1.1259322e+000 8.9458992e-001 1.0507245e+000 8.7218722e-001 1.0804476e+000 1.2956721e+000 5.3001033e-001 5.1094571e-001 5.4032580e-001 1.2226683e+000 1.1324517e+000 6.5389235e-001 1.0042315e+000 4.8009269e-001 8.4074081e-001 1.5652768e+000 8.2850945e-001 1.0977412e+000 1.0064765e+000 8.1694658e-001 5.4726510e-001 2.8903930e-001 7.7089542e-001 6.3850346e-001 5.1423648e-001 1.1087905e+000 6.4572451e-001 9.5856423e-001 1.0223428e+000 7.9619170e-001 1.0128395e+000 1.4103904e+000 4.2841327e-001 6.9654772e-001 7.1962016e-001 4.1732416e-001 8.8260555e-001 1.1400573e+000 1.5878430e+000 1.3297604e+000 1.2515660e+000 5.5293607e-001 1.1342261e+000 1.2679424e+000 8.3754173e-001 1.1141439e+000 4.0572680e-001 1.1670415e+000 3.2157638e-001 3.8995827e-001 6.8733026e-001 8.5897314e-001 1.5531322e+000 -7.4530347e-001 -1.0565726e+000 -7.1709464e-001 -9.7293723e-001 -1.2045143e+000 -1.1297703e+000 -1.0605064e+000 -8.3042165e-001 -9.1764300e-001 -8.4730283e-001 -7.9779742e-001 -1.6820651e+000 -6.8949721e-001 -1.0339823e+000 -8.9081320e-001 -6.4021358e-001 -8.4529122e-001 -1.0201387e+000 -4.0586891e-001 -7.3054385e-001 -1.1521869e+000 -8.5923243e-001 -1.0586327e+000 -1.1725252e-001 -1.2548128e+000 -4.8167128e-001 -1.2181962e+000 -1.2254463e+000 -1.1708605e+000 -1.3251094e+000 -8.9458237e-001 ================================================ FILE: saxpy/__init__.py ================================================ """SAX stack implementation.""" __version__ = '0.1.0' __author__ = 'Pavel Senin ' __all__ = [] ================================================ FILE: saxpy/alphabet.py ================================================ """Implements Alphabet cuts.""" import numpy as np def cuts_for_asize(a_size): """Generate a set of alphabet cuts for its size.""" """ Typically, we generate cuts in R as follows: get_cuts_for_num <- function(num) { cuts = c(-Inf) for (i in 1:(num-1)) { cuts = c(cuts, qnorm(i * 1/num)) } cuts } get_cuts_for_num(3) """ options = { 2: np.array([-np.inf, 0.00]), 3: np.array([-np.inf, -0.4307273, 0.4307273]), 4: np.array([-np.inf, -0.6744898, 0, 0.6744898]), 5: np.array([-np.inf, -0.841621233572914, -0.2533471031358, 0.2533471031358, 0.841621233572914]), 6: np.array([-np.inf, -0.967421566101701, -0.430727299295457, 0, 0.430727299295457, 0.967421566101701]), 7: np.array([-np.inf, -1.06757052387814, -0.565948821932863, -0.180012369792705, 0.180012369792705, 0.565948821932863, 1.06757052387814]), 8: np.array([-np.inf, -1.15034938037601, -0.674489750196082, -0.318639363964375, 0, 0.318639363964375, 0.674489750196082, 1.15034938037601]), 9: np.array([-np.inf, -1.22064034884735, -0.764709673786387, -0.430727299295457, -0.139710298881862, 0.139710298881862, 0.430727299295457, 0.764709673786387, 1.22064034884735]), 10: np.array([-np.inf, -1.2815515655446, -0.841621233572914, -0.524400512708041, -0.2533471031358, 0, 0.2533471031358, 0.524400512708041, 0.841621233572914, 1.2815515655446]), 11: np.array([-np.inf, -1.33517773611894, -0.908457868537385, -0.604585346583237, -0.348755695517045, -0.114185294321428, 0.114185294321428, 0.348755695517045, 0.604585346583237, 0.908457868537385, 1.33517773611894]), 12: np.array([-np.inf, -1.38299412710064, -0.967421566101701, -0.674489750196082, -0.430727299295457, -0.210428394247925, 0, 0.210428394247925, 0.430727299295457, 0.674489750196082, 0.967421566101701, 1.38299412710064]), 13: np.array([-np.inf, -1.42607687227285, -1.0200762327862, -0.736315917376129, -0.502402223373355, -0.293381232121193, -0.0965586152896391, 0.0965586152896394, 0.293381232121194, 0.502402223373355, 0.73631591737613, 1.0200762327862, 1.42607687227285]), 14: np.array([-np.inf, -1.46523379268552, -1.06757052387814, -0.791638607743375, -0.565948821932863, -0.36610635680057, -0.180012369792705, 0, 0.180012369792705, 0.36610635680057, 0.565948821932863, 0.791638607743375, 1.06757052387814, 1.46523379268552]), 15: np.array([-np.inf, -1.50108594604402, -1.11077161663679, -0.841621233572914, -0.622925723210088, -0.430727299295457, -0.2533471031358, -0.0836517339071291, 0.0836517339071291, 0.2533471031358, 0.430727299295457, 0.622925723210088, 0.841621233572914, 1.11077161663679, 1.50108594604402]), 16: np.array([-np.inf, -1.53412054435255, -1.15034938037601, -0.887146559018876, -0.674489750196082, -0.488776411114669, -0.318639363964375, -0.157310684610171, 0, 0.157310684610171, 0.318639363964375, 0.488776411114669, 0.674489750196082, 0.887146559018876, 1.15034938037601, 1.53412054435255]), 17: np.array([-np.inf, -1.5647264713618, -1.18683143275582, -0.928899491647271, -0.721522283982343, -0.541395085129088, -0.377391943828554, -0.223007830940367, -0.0737912738082727, 0.0737912738082727, 0.223007830940367, 0.377391943828554, 0.541395085129088, 0.721522283982343, 0.928899491647271, 1.18683143275582, 1.5647264713618]), 18: np.array([-np.inf, -1.59321881802305, -1.22064034884735, -0.967421566101701, -0.764709673786387, -0.589455797849779, -0.430727299295457, -0.282216147062508, -0.139710298881862, 0, 0.139710298881862, 0.282216147062508, 0.430727299295457, 0.589455797849779, 0.764709673786387, 0.967421566101701, 1.22064034884735, 1.59321881802305]), 19: np.array([-np.inf, -1.61985625863827, -1.25211952026522, -1.00314796766253, -0.8045963803603, -0.633640000779701, -0.47950565333095, -0.336038140371823, -0.199201324789267, -0.0660118123758407, 0.0660118123758406, 0.199201324789267, 0.336038140371823, 0.47950565333095, 0.633640000779701, 0.8045963803603, 1.00314796766253, 1.25211952026522, 1.61985625863827]), 20: np.array([-np.inf, -1.64485362695147, -1.2815515655446, -1.03643338949379, -0.841621233572914, -0.674489750196082, -0.524400512708041, -0.385320466407568, -0.2533471031358, -0.125661346855074, 0, 0.125661346855074, 0.2533471031358, 0.385320466407568, 0.524400512708041, 0.674489750196082, 0.841621233572914, 1.03643338949379, 1.2815515655446, 1.64485362695147]), } return options[a_size] ================================================ FILE: saxpy/discord.py ================================================ """Discord discovery routines.""" import numpy as np from saxpy.visit_registry import VisitRegistry from saxpy.distance import early_abandoned_euclidean from saxpy.znorm import znorm def find_discords_brute_force(series, win_size, num_discords=2, znorm_threshold=0.01): """Early-abandoned distance-based discord discovery.""" discords = list() globalRegistry = VisitRegistry(len(series) - win_size + 1) znorms = np.array([znorm(series[pos: pos + win_size], znorm_threshold) for pos in range(len(series) - win_size + 1)]) while len(discords) < num_discords: bestDiscord = find_best_discord_brute_force(series, win_size, globalRegistry, znorms) if -1 == bestDiscord[0]: break discords.append(bestDiscord) mark_start = max(0, bestDiscord[0] - win_size + 1) mark_end = bestDiscord[0] + win_size globalRegistry.mark_visited_range(mark_start, mark_end) return discords def find_best_discord_brute_force(series, win_size, global_registry, znorms): """Early-abandoned distance-based discord discovery.""" best_so_far_distance = -1.0 best_so_far_index = -1 outer_registry = global_registry.clone() outer_idx = outer_registry.get_next_unvisited() while ~np.isnan(outer_idx): outer_registry.mark_visited(outer_idx) candidate_seq = znorms[outer_idx] nn_distance = np.inf inner_registry = VisitRegistry(len(series) - win_size + 1) inner_idx = inner_registry.get_next_unvisited() while ~np.isnan(inner_idx): inner_registry.mark_visited(inner_idx) if abs(inner_idx - outer_idx) >= win_size: curr_seq = znorms[inner_idx] dist = early_abandoned_euclidean(candidate_seq, curr_seq, nn_distance) if (~np.isnan(dist)) and (dist < nn_distance): nn_distance = dist inner_idx = inner_registry.get_next_unvisited() if ~(np.inf == nn_distance) and (nn_distance > best_so_far_distance): best_so_far_distance = nn_distance best_so_far_index = outer_idx outer_idx = outer_registry.get_next_unvisited() return best_so_far_index, best_so_far_distance ================================================ FILE: saxpy/distance.py ================================================ """Distance computation.""" import numpy as np def euclidean(a, b): """Compute a Euclidean distance value.""" return np.sqrt(np.sum((a-b)**2)) def early_abandoned_euclidean(a, b, upper_limit): """Compute a Euclidean distance value in early abandoning fashion.""" lim = upper_limit * upper_limit res = 0. for i in range(0, len(a)): res += np.dot((a[i]-b[i]), (a[i]-b[i])) if res > lim: return np.nan return np.sqrt(res) ================================================ FILE: saxpy/hotsax.py ================================================ """Implements HOT-SAX.""" import numpy as np from saxpy.znorm import znorm from saxpy.sax import sax_via_window from saxpy.distance import euclidean def find_discords_hotsax(series, win_size=100, num_discords=2, alphabet_size=3, paa_size=3, znorm_threshold=0.01, sax_type='unidim'): """HOT-SAX-driven discords discovery.""" discords = list() global_registry = set() # Z-normalized versions for every subsequence. znorms = np.array([znorm(series[pos: pos + win_size], znorm_threshold) for pos in range(len(series) - win_size + 1)]) # SAX words for every subsequence. sax_data = sax_via_window(series, win_size=win_size, paa_size=paa_size, alphabet_size=alphabet_size, nr_strategy=None, znorm_threshold=0.01, sax_type=sax_type) """[2.0] build the 'magic' array""" magic_array = list() for k, v in sax_data.items(): magic_array.append((k, len(v))) """[2.1] sort it ascending by the number of occurrences""" magic_array = sorted(magic_array, key=lambda tup: tup[1]) while len(discords) < num_discords: best_discord = find_best_discord_hotsax(series, win_size, global_registry, sax_data, magic_array, znorms) if -1 == best_discord[0]: break discords.append(best_discord) mark_start = max(0, best_discord[0] - win_size + 1) mark_end = best_discord[0] + win_size for i in range(mark_start, mark_end): global_registry.add(i) return discords def find_best_discord_hotsax(series, win_size, global_registry, sax_data, magic_array, znorms): """Find the best discord with hotsax.""" """[3.0] define the key vars""" best_so_far_position = -1 best_so_far_distance = 0. distance_calls = 0 visit_array = np.zeros(len(series), dtype=int) """[4.0] and we are off iterating over the magic array entries""" for entry in magic_array: """[5.0] current SAX words and the number of other sequences mapping to the same SAX word.""" curr_word = entry[0] occurrences = sax_data[curr_word] """[6.0] jumping around by the same word occurrences makes it easier to nail down the possibly small distance value -- so we can be efficient and all that...""" for curr_pos in occurrences: if curr_pos in global_registry: continue """[7.0] we don't want an overlapping subsequence""" mark_start = curr_pos - win_size + 1 mark_end = curr_pos + win_size visit_set = set(range(mark_start, mark_end)) """[8.0] here is our subsequence in question""" cur_seq = znorms[curr_pos] """[9.0] let's see what is NN distance""" nn_dist = np.inf do_random_search = True """[10.0] ordered by occurrences search first""" for next_pos in occurrences: """[11.0] skip bad pos""" if next_pos in visit_set: continue else: visit_set.add(next_pos) """[12.0] distance we compute""" dist = euclidean(cur_seq, znorms[next_pos]) distance_calls += 1 """[13.0] keep the books up-to-date""" if dist < nn_dist: nn_dist = dist if dist < best_so_far_distance: do_random_search = False break """[13.0] if not broken above, we shall proceed with random search""" if do_random_search: """[14.0] build that random visit order array""" curr_idx = 0 for i in range(0, (len(series) - win_size + 1)): if not(i in visit_set): visit_array[curr_idx] = i curr_idx += 1 it_order = np.random.permutation(visit_array[0:curr_idx]) curr_idx -= 1 """[15.0] and go random""" while curr_idx >= 0: rand_pos = it_order[curr_idx] curr_idx -= 1 dist = euclidean(cur_seq, znorms[rand_pos]) distance_calls += 1 """[16.0] keep the books up-to-date again""" if dist < nn_dist: nn_dist = dist if dist < best_so_far_distance: nn_dist = dist break """[17.0] and BIGGER books""" if (nn_dist > best_so_far_distance) and (nn_dist < np.inf): best_so_far_distance = nn_dist best_so_far_position = curr_pos return best_so_far_position, best_so_far_distance ================================================ FILE: saxpy/paa.py ================================================ """Implements PAA.""" from __future__ import division import numpy as np def paa(series, paa_segment_size, sax_type='unidim'): """PAA implementation. >>> paa([1, 2, 3], 3, 'unidim') array([1., 2., 3.]) >>> paa([1, 2, 3], 1, 'unidim') array([2.]) >>> paa([4, 3, 8, 5], 1, 'unidim') array([5.]) >>> paa([[1, 2, 3], [6, 5, 4]], 1, 'repeat') array([[3.5, 3.5, 3.5]]) >>> paa([[1, 2, 3], [6, 5, 4]], 2, 'repeat') array([[1., 2., 3.], [6., 5., 4.]]) """ series = np.array(series) series_len = series.shape[0] if sax_type in ['repeat', 'energy']: num_dims = series.shape[1] else: num_dims = 1 is_multidimensional = (len(series.shape) > 1) and (series.shape[1] > 1) if not is_multidimensional: series = series.reshape(series.shape[0], 1) res = np.zeros((num_dims, paa_segment_size)) for dim in range(num_dims): # Check if we can evenly divide the series. if series_len % paa_segment_size == 0: inc = series_len // paa_segment_size for i in range(0, series_len): idx = i // inc np.add.at(res[dim], idx, np.mean(series[i][dim])) res[dim] /= inc # Process otherwise. else: for i in range(0, paa_segment_size * series_len): idx = i // series_len pos = i // paa_segment_size np.add.at(res[dim], idx, np.mean(series[pos][dim])) res[dim] /= series_len if sax_type in ['repeat', 'energy']: return res.T else: return res.flatten() ================================================ FILE: saxpy/sax.py ================================================ """Converts a normlized timeseries to SAX symbols.""" from collections import defaultdict from saxpy.strfunc import idx2letter from saxpy.znorm import znorm from saxpy.paa import paa from saxpy.alphabet import cuts_for_asize import numpy as np from sklearn.cluster import KMeans # For SAX-REPEAT. def get_sax_list(series, cuts): """ >>> get_sax_list([[1, 2, -3], [4, 9, -2], [5, 7, -8], [0, 3, -1], [-1, -2, -10]], cuts_for_asize(3)) [[2, 2, 0], [2, 2, 0], [2, 2, 0], [1, 2, 0], [0, 0, 0]] """ series = np.array(series) a_size = len(cuts) multidim_sax_list = [] for i in range(series.shape[0]): multidim_sax = [] for j in range(series.shape[1]): num = series[i][j] # If the number is below 0, start from the bottom, otherwise from the top if num >= 0: j = a_size - 1 while j > 0 and cuts[j] >= num: j = j - 1 multidim_sax.append(j) else: j = 1 while j < a_size and cuts[j] <= num: j = j + 1 multidim_sax.append(j - 1) multidim_sax_list.append(multidim_sax) return multidim_sax_list def ts_to_string(series, cuts): """A straightforward num-to-string conversion. >>> ts_to_string([-1, 0, 1], cuts_for_asize(3)) 'abc' >>> ts_to_string([1, -1, 1], cuts_for_asize(3)) 'cac' """ series = np.array(series) a_size = len(cuts) sax = list() for i in range(series.shape[0]): num = series[i] # If the number is below 0, start from the bottom, otherwise from the top if num >= 0: j = a_size - 1 while j > 0 and cuts[j] >= num: j = j - 1 sax.append(idx2letter(j)) else: j = 1 while j < a_size and cuts[j] <= num: j = j + 1 sax.append(idx2letter(j-1)) return ''.join(sax) def is_mindist_zero(a, b): """Check mindist.""" if len(a) != len(b): return 0 else: for i in range(0, len(b)): if abs(ord(a[i]) - ord(b[i])) > 1: return 0 return 1 def sax_by_chunking(series, paa_size, alphabet_size=3, znorm_threshold=0.01): """Simple chunking conversion implementation.""" paa_rep = paa(znorm(series, znorm_threshold), paa_size) cuts = cuts_for_asize(alphabet_size) return ts_to_string(paa_rep, cuts) def sax_via_window(series, win_size, paa_size, alphabet_size=3, nr_strategy='exact', znorm_threshold=0.01, sax_type='unidim'): """Simple via window conversion implementation. # SAX-ENERGY >>> sax_via_window([[1, 2, 3], [4, 5, 6]], win_size=1, paa_size=3, sax_type='energy', nr_strategy=None)['abc'] [0, 1] >>> sax_via_window([[1, 2, 3, 4], [4, 5, 6, 7]], win_size=1, paa_size=4, sax_type='energy', nr_strategy=None)['aacc'] [0, 1] >>> sax_via_window([[1, 2, 3, 4], [4, 5, 6, 7]], win_size=2, paa_size=4, sax_type='energy', nr_strategy=None)['aaccaacc'] [0] # SAX-REPEAT >>> sax_via_window([[1, 2, 3], [4, 5, 6], [7, 8, 9]], win_size=2, paa_size=2, sax_type='repeat', nr_strategy=None)['ac'] [0, 1] >>> sax_via_window([[1, 2, 3], [4, 5, 6], [7, 8, 9]], win_size=1, paa_size=1, sax_type='repeat', nr_strategy=None)['a'] [0, 1, 2] # SAX-INDEPENDENT >>> sax_via_window([[1, 2, 3, 4], [4, 5, 6, 7]], win_size=2, paa_size=2, sax_type='independent', nr_strategy=None)['acacacac'] [0] >>> sax_via_window([[1, 2], [4, 5], [7, 8]], win_size=2, paa_size=2, sax_type='independent', nr_strategy=None)['acac'] [0, 1] >>> sax_via_window([[1, 2], [4, 8], [7, 5]], win_size=2, paa_size=2, sax_type='independent', nr_strategy=None)['acac'] [0] >>> sax_via_window([[1, 2], [4, 8], [7, 5]], win_size=2, paa_size=2, sax_type='independent', nr_strategy=None)['acca'] [1] """ # Convert to numpy array. series = np.array(series) # Check on dimensions. if len(series.shape) > 2: raise ValueError('Please reshape time-series to stack dimensions along the 2nd dimension, so that the array shape is a 2-tuple.') # PAA size is the length of the PAA sequence. if sax_type != 'energy' and paa_size > win_size: raise ValueError('PAA size cannot be greater than the window size.') if sax_type == 'energy' and len(series.shape) == 1: raise ValueError('Must pass a multidimensional time-series to SAX-ENERGY.') # Breakpoints. cuts = cuts_for_asize(alphabet_size) # Dictionary mapping SAX words to indices. sax = defaultdict(list) if sax_type == 'repeat': # Maps indices to multi-dimensional SAX words. multidim_sax_dict = [] # List of all the multi-dimensional SAX words. multidim_sax_list = [] # Sliding window across time dimension. for i in range(series.shape[0] - win_size + 1): # Subsection starting at this index. sub_section = series[i: i + win_size] # Z-normalized subsection. if win_size == 1: zn = sub_section else: zn = znorm(sub_section, znorm_threshold) # PAA representation of subsection. paa_rep = paa(zn, paa_size, 'repeat') # SAX representation of subsection, but in terms of multi-dimensional vectors. multidim_sax = get_sax_list(paa_rep, cuts) # Update data-structures. multidim_sax_dict.append(multidim_sax) multidim_sax_list.extend(multidim_sax) # Cluster with k-means++. kmeans = KMeans(n_clusters=alphabet_size, random_state=0).fit(multidim_sax_list) # Cluster indices in sorted order. order = np.lexsort(np.rot90(kmeans.cluster_centers_)) # Sliding window across time dimension. prev_word = '' for i in range(series.shape[0] - win_size + 1): # Map cluster indices to new SAX letters. curr_word_list = map(lambda cluster_index: idx2letter(order[cluster_index]), kmeans.predict(multidim_sax_dict[i])) curr_word = ''.join(curr_word_list) if '' != prev_word: if 'exact' == nr_strategy and prev_word == curr_word: continue elif 'mindist' == nr_strategy and is_mindist_zero(prev_word, curr_word): continue prev_word = curr_word sax[curr_word].append(i) else: # Sliding window across time dimension. prev_word = '' for i in range(series.shape[0] - win_size + 1): # Subsection starting at this index. sub_section = series[i: i + win_size] if sax_type == 'energy': curr_word = '' for energy_dist in sub_section: # Normalize energy distribution. energy_zn = znorm(energy_dist, znorm_threshold) # PAA representation of energy distribution. paa_rep = paa(energy_zn, paa_size, 'unidim') # paa_rep = energy_zn # SAX representation of the energy distribution. energy_word = ts_to_string(paa_rep, cuts) # Add to current word. curr_word += energy_word elif sax_type == 'independent': curr_word = '' for dim in range(sub_section.shape[1]): # Obtain the subsequence restricted to one dimension. one_dimension_sub_section = sub_section[:, dim] # Z-normalized subsection. zn = znorm(one_dimension_sub_section, znorm_threshold) # PAA representation of subsection. paa_rep = paa(zn, paa_size, 'unidim') # Get the SAX word - just a unidimensional SAX. one_dim_word = ts_to_string(paa_rep, cuts) # Add this dimensions' representation to the overall SAX word. curr_word += one_dim_word else: # Z-normalized subsection. zn = znorm(sub_section, znorm_threshold) # PAA representation of subsection. paa_rep = paa(zn, paa_size, sax_type) # SAX representation of subsection. curr_word = ts_to_string(paa_rep, cuts) if '' != prev_word: if 'exact' == nr_strategy and prev_word == curr_word: continue elif 'mindist' == nr_strategy and is_mindist_zero(prev_word, curr_word): continue prev_word = curr_word sax[curr_word].append(i) return sax ================================================ FILE: saxpy/saxvsm.py ================================================ """Implements VSM.""" import math import numpy as np from saxpy.sax import sax_via_window def series_to_wordbag(series, win_size, paa_size, alphabet_size=3, nr_strategy='exact', z_threshold=0.01): """VSM implementation.""" sax = sax_via_window(series, win_size, paa_size, alphabet_size, nr_strategy, z_threshold) # convert the dict to a wordbag frequencies = {} for k, v in sax.items(): frequencies[k] = len(v) return frequencies def manyseries_to_wordbag(series_npmatrix, win_size, paa_size, alphabet_size=3, nr_strategy='exact', z_threshold=0.01): """VSM implementation.""" frequencies = {} for row in series_npmatrix: tmp_freq = series_to_wordbag(np.squeeze(np.asarray(row)), win_size, paa_size, alphabet_size, nr_strategy, z_threshold) for k, v in tmp_freq.items(): if k in frequencies: frequencies[k] += v else: frequencies[k] = v return frequencies def bags_to_tfidf(bags_dict): """VSM implementation.""" # classes count_size = len(bags_dict) classes = [*bags_dict.copy()] # word occurrence frequency counts counts = {} # compute frequencies idx = 0 for name in classes: for word, count in bags_dict[name].items(): if word in counts: counts[word][idx] = count else: counts[word] = [0] * count_size counts[word][idx] = count idx = idx + 1 # compute tf*idf tfidf = {} # the resulting vectors dictionary idx = 0 for word, freqs in counts.items(): # document frequency df_counter = 0 for i in freqs: if i > 0: df_counter = df_counter + 1 # if the word is everywhere, dismiss it if df_counter == len(freqs): continue # tf*idf vector tf_idf = [0.0] * len(freqs) i_idx = 0 for i in freqs: if i != 0: tf = np.log(1 + i) idf = np.log(len(freqs) / df_counter) tf_idf[i_idx] = tf * idf i_idx = i_idx + 1 tfidf[word] = tf_idf idx = idx + 1 return {"vectors": tfidf, "classes": classes} def tfidf_to_vector(tfidf, vector_label): """VSM implementation.""" if vector_label in tfidf['classes']: idx = tfidf['classes'].index(vector_label) weight_vec = {} for word, weights in tfidf['vectors'].items(): weight_vec[word] = weights[idx] return weight_vec else: return [] def cosine_measure(weight_vec, test_bag): """VSM implementation.""" sumxx, sumxy, sumyy = 0, 0, 0 for word in set([*weight_vec.copy()]).union([*test_bag.copy()]): x, y = 0, 0 if word in weight_vec.keys(): x = weight_vec[word] if word in test_bag.keys(): y = test_bag[word] sumxx += x * x sumyy += y * y sumxy += x * y return sumxy / math.sqrt(sumxx * sumyy) def cosine_similarity(tfidf, test_bag): """VSM implementation.""" res = {} for cls in tfidf['classes']: res[cls] = 1. - cosine_measure(tfidf_to_vector(tfidf, cls), test_bag) return res def class_for_bag(similarity_dict): # do i need to take care about equal values? return min(similarity_dict, key=lambda x: similarity_dict[x]) ================================================ FILE: saxpy/strfunc.py ================================================ """Convert a normlized timeseries to SAX symbols.""" def idx2letter(idx): """Convert a numerical index to a char.""" if 0 <= idx < 20: return chr(97 + idx) else: raise ValueError('A wrong idx value supplied.') ================================================ FILE: saxpy/util.py ================================================ """Some helpers.""" import re def read_ucr_data(fname): data = [] with open(fname, 'r') as fp: read_lines = fp.readlines() for line in read_lines: tokens = re.split("\\s+", line.strip()) t0 = tokens.pop(0) if re.match("^\d+?\.\d+?e[\+\-]?\d+?$", t0) is None: class_label = t0 else: class_label = str(int(float(t0))) data_line = [] for token in tokens: data_line.append(float(token)) data.append((class_label, data_line)) res = {} for key, arr in data: if key in res.keys(): res[key].append(arr) else: dat = [arr] res[key] = dat return res ================================================ FILE: saxpy/visit_registry.py ================================================ """Keeps visited indexes in check.""" import numpy as np import random class VisitRegistry: """A straightforward visit array implementation.""" def __init__(self, capacity=0): """Constructor.""" self.remaining = set() for num in range(capacity): self.remaining.add(num) def get_unvisited_count(self): """An unvisited count getter.""" return len(self.remaining) def mark_visited(self, index): """Set a single index as visited.""" self.remaining.discard(index) def mark_visited_range(self, start, stop): """Set a range as visited.""" for i in range(start, stop): self.mark_visited(i) def get_next_unvisited(self): """Next unvisited entry.""" if self.get_unvisited_count() == 0: return np.nan return random.choice(tuple(self.remaining)) def clone(self): """Make the array's copy.""" clone = VisitRegistry() clone.remaining = self.remaining.copy() return clone ================================================ FILE: saxpy/znorm.py ================================================ """Implements znorm.""" from __future__ import division import numpy as np from scipy.linalg import sqrtm from scipy.linalg import inv def l2norm(array): """ :param array: numpy array :return: non-negative real indicating the L2-norm of the array. >>> '%0.2f' % l2norm(np.array([1, 2, 3])) '3.74' >>> '%0.2f' % l2norm(np.array([1, 2, 3])[1]) '2.00' >>> '%0.2f' % l2norm(np.array([1, 2, 3])[1:]) '3.61' """ return np.sqrt(np.sum(np.square(array))) def znorm(series, znorm_threshold=0.01): """Znorm implementation. >>> print(['{:0.2f}'.format(x) for x in znorm([1, 2, 3])]) ['-1.22', '0.00', '1.22'] >>> print(['{:0.2f}'.format(x) for x in znorm([3, 2, 1])]) ['1.22', '0.00', '-1.22'] >>> print(['{:0.2f}'.format(x) for x in znorm([1, 2])]) ['-1.00', '1.00'] >>> print(['{:0.2f}'.format(x) for x in np.sum(znorm([[1, 2, 3], [6, 5, 4]]), axis=0)]) ['0.00', '0.00', '0.00'] >>> znorm([[1, 2, 3], [6, 5, 4]]) array([[-1., -1., -1.], [ 1., 1., 1.]]) """ series = np.array(series) original_series_shape = series.shape is_multidimensional = (len(series.shape) > 1) and (series.shape[1] > 1) mu = np.average(series, axis=0) C = np.cov(series, bias=True, rowvar=not is_multidimensional) # Only update those subsequences with variance over the threshold. if is_multidimensional: series = series - mu C = np.diagonal(C) indexes = (C >= np.square(znorm_threshold)) series[:, indexes] = (series[:, indexes] / np.sqrt(C[indexes])) else: series = series - mu if C >= np.square(znorm_threshold): series /= np.sqrt(C) # Check on shape returned. assert(series.shape == original_series_shape) return series ================================================ FILE: setup.cfg ================================================ [metadata] name = saxpy version=1.0.1 author = Pavel Senin author-email = seninp@gmail.org summary = SAX, HOTSAX, EMMA implementations for Python license = GPL2 description-file = README.rst home-page = https://github.com/seninp/saxpy.git requires-python = >=3.7 classifier = Development Status :: 4 - Beta Environment :: Console Intended Audience :: Developers Intended Audience :: Information Technology Operating System :: OS Independent Programming Language :: Python [files] packages = saxpy ================================================ FILE: setup.py ================================================ # -*- coding: utf-8 -*- """building saxpy.""" from setuptools import setup setup( setup_requires=['pbr>=5.8.0', 'setuptools>=60.5.0'], pbr=True, ) ================================================ FILE: site/citation.bib ================================================ @article{senin2018grammarviz, author = {Senin, Pavel and Lin, Jessica and Wang, Xing and Oates, Tim and Gandhi, Sunil and Boedihardjo, Arnold P. and Chen, Crystal and Frankenstein, Susan}, title = {GrammarViz 3.0: Interactive Discovery of Variable-Length Time Series Patterns}, journal = {ACM Trans. Knowl. Discov. Data}, issue_date = {February 2018}, volume = {12}, number = {1}, month = feb, year = {2018}, issn = {1556-4681}, pages = {10:1--10:28}, articleno = {10}, numpages = {28}, url = {http://doi.acm.org/10.1145/3051126}, doi = {10.1145/3051126}, acmid = {3051126}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Interactive data mining}, } ================================================ FILE: tests/test_cuts.py ================================================ """Testing PAA implementation.""" from saxpy import alphabet def test_sizing(): """Test alphabet sizes.""" for s in range(2, 20): assert len(alphabet.cuts_for_asize(s)) == s ================================================ FILE: tests/test_discord_bruteforce.py ================================================ """Testing brute force discord search implementation.""" import numpy as np from saxpy.discord import find_discords_brute_force def test_brute_force(): """Test brute force discord discovery.""" dd = np.genfromtxt("data/ecg0606_1.csv", delimiter=',') discords = find_discords_brute_force(dd[0:400], 100, 4) assert 3 == len(discords) assert 173 == discords[0][0] ================================================ FILE: tests/test_discord_hotsax.py ================================================ """Testing HOTSAX discord search implementation.""" import numpy as np from saxpy.hotsax import find_discords_hotsax def test_brute_force(): """Test HOT-SAX discord discovery.""" dd = np.genfromtxt("data/ecg0606_1.csv", delimiter=',') discords = find_discords_hotsax(dd) assert 430 == discords[0][0] assert 318 == discords[1][0] discords = find_discords_hotsax(dd[0:400], 100, 4) assert 3 == len(discords) assert 173 == discords[0][0] ================================================ FILE: tests/test_distance.py ================================================ """Testing PAA implementation.""" import pytest import numpy as np from saxpy import distance def test_euclidean(): """Test euclidean distance.""" assert pytest.approx(np.sqrt(2), 0.0000001) == distance.euclidean( np.array([1., 1.]), np.array([2., 2.])) a = np.array([0.5, 0.8, 0.9]) b = np.array([-0.15, 0.38, 0.92]) assert pytest.approx(0.7741447, 0.0000001) == distance.euclidean(a, b) def test_early_abandoned(): """Test euclidean distance.""" assert pytest.approx(np.sqrt(2), 0.0000001) ==\ distance.early_abandoned_euclidean(np.array([1., 1.]), np.array([2., 2.]), np.inf) a = np.array([0.5, 0.8, 0.9]) b = np.array([-0.15, 0.38, 0.92]) assert pytest.approx(0.7741447, 0.0000001) ==\ distance.early_abandoned_euclidean(a, b, np.inf) assert 1 == np.isnan(distance.early_abandoned_euclidean(a, b, 0.1)) ================================================ FILE: tests/test_doctests.py ================================================ import doctest import warnings from sklearn.exceptions import ConvergenceWarning from saxpy import paa, sax, znorm def run_doctests(module): """Helper to run doctests on a module and fail if any fail.""" with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message=r"Number of distinct clusters", category=ConvergenceWarning, module=r"^sklearn(\.|$)", ) failures, _ = doctest.testmod( module, optionflags=doctest.NORMALIZE_WHITESPACE ) assert failures == 0, f"Doctests failed in {module.__name__}" def test_paa_doctests(): """Test PAA module doctests.""" run_doctests(paa) def test_sax_doctests(): """Test SAX module doctests.""" run_doctests(sax) def test_znorm_doctests(): """Test Z-norm module doctests.""" run_doctests(znorm) ================================================ FILE: tests/test_paa.py ================================================ """Testing PAA implementation.""" import numpy as np from saxpy import paa def test_paa1(): """Test points to points via PAA.""" y = np.array([-1, -2, -1, 0, 2, 1, 1, 0]) v = np.array([-1.375, 0.75, 0.625]) np.testing.assert_array_equal(paa.paa(y, 3), v) np.testing.assert_array_equal(paa.paa(y, 8), y) np.testing.assert_array_equal(paa.paa(y, 2), np.array([-1, 1])) def test_paa2(): """An irregular points to points via PAA (15 -> 10).""" dat = np.array([-0.9796808, -0.8622706, -0.6123005, 0.8496459, 1.739691, 1.588194, 1.095829, 0.5277147, 0.4709033, -0.2865819, 0.0921607, -0.2865819, -0.9039323, -1.195564, -1.237226]) dat_paa = np.array([-0.9405441, -0.6956239, 1.146328, 1.638693, 0.9064573, 0.4898404, -0.1603344, -0.1603344, -1.001143, -1.223339]) np.testing.assert_array_almost_equal(paa.paa(dat, 10), dat_paa, decimal=6) def test_paa3(): """An irregular points to points via PAA.""" dat = np.array([-1.289433, -0.9992189, -0.5253246, -0.06612478, -0.2791935, 0.08816637, -0.06612478, 0.595123, 0.8926845, 0.8228861, 1.741286, 1.770675, -0.2791935, -1.197593, -1.208614]) dat_paa = np.array([-1.192695, -0.6832894, -0.1371477, -0.03428692, 0.1542912, 0.7934974, 1.129019, 1.760878, -0.5853268, -1.20494]) np.testing.assert_array_almost_equal(paa.paa(dat, 10), dat_paa, decimal=6) ================================================ FILE: tests/test_registry.py ================================================ """Testing PAA implementation.""" import numpy as np from saxpy.visit_registry import VisitRegistry def test_sizing(): """Test the registry.""" reg = VisitRegistry(77) assert 77 == reg.get_unvisited_count() reg.mark_visited(0) assert 76 == reg.get_unvisited_count() reg.mark_visited_range(70, 77) assert 69 == reg.get_unvisited_count() reg.mark_visited(0) assert 69 == reg.get_unvisited_count() reg.mark_visited(1) assert 68 == reg.get_unvisited_count() reg.mark_visited(reg.get_next_unvisited()) assert 67 == reg.get_unvisited_count() reg.mark_visited_range(0, 77) assert np.isnan(reg.get_next_unvisited()) ================================================ FILE: tests/test_sax_chunking.py ================================================ """Testing SAX implementation.""" import numpy as np from saxpy.sax import sax_by_chunking def test_chunking(): """Test SAX by chunking.""" dat1 = np.array([2.02, 2.33, 2.99, 6.85, 9.2, 8.8, 7.5, 6, 5.85, 3.85, 4.85, 3.85, 2.22, 1.45, 1.34]) dats1_9_7 = "bcggfddba" dats1_10_11 = "bcjkjheebb" dats1_14_10 = "bbdijjigfeecbb" assert dats1_9_7 == sax_by_chunking(dat1, 9, 7) assert dats1_10_11 == sax_by_chunking(dat1, 10, 11) assert dats1_14_10 == sax_by_chunking(dat1, 14, 10) dat2 = np.array([0.5, 1.29, 2.58, 3.83, 3.25, 4.25, 3.83, 5.63, 6.44, 6.25, 8.75, 8.83, 3.25, 0.75, 0.72]) dats2_9_7 = "accdefgda" dats2_10_11 = "bcefgijkcb" dats2_14_10 = "abdeeffhijjfbb" assert dats2_9_7 == sax_by_chunking(dat2, 9, 7) assert dats2_10_11 == sax_by_chunking(dat2, 10, 11) assert dats2_14_10 == sax_by_chunking(dat2, 14, 10) ================================================ FILE: tests/test_sax_window.py ================================================ """Testing SAX implementation.""" import numpy as np from saxpy.sax import sax_via_window def test_via_window(): """Test SAX via window.""" dat = np.array([0., 0., 0., 0., 0., -0.270340178359072, -0.367828308500142, 0.666980581124872, 1.87088147328446, 2.14548907684624, -0.480859313143032, -0.72911654245842, -0.490308602315934, -0.66152028906509, -0.221049033806403, 0.367003418871239, 0.631073992586373, 0.0487728723414486, 0.762655178750436, 0.78574757843331, 0.338239686422963, 0.784206454089066, -2.14265084073625, 2.11325193044223, 0.186018356196443, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.519132472499234, -2.604783141655, -0.244519550114012, -1.6570790528784, 3.34184602886343, 2.10361226260999, 1.9796808733979, -0.822247322003058, 1.06850578033292, -0.678811824405992, 0.804225748913681, 0.57363964388698, 0.437113583759113, 0.437208643628268, 0.989892093383503, 1.76545983424176, 0.119483882364649, -0.222311941138971, -0.74669456611669, -0.0663660879732063, 0., 0., 0., 0., 0.]) sax_none = sax_via_window(dat, win_size=6, paa_size=3, alphabet_size=3, nr_strategy=None, znorm_threshold=0.01) elements_num = 0 for key in sax_none: elements_num += len(sax_none[key]) assert len(dat) - 6 + 1 == elements_num cca = sax_none['cca'] assert np.array_equal(np.array(cca), np.array([0, 1])) sax_exact = sax_via_window(dat, 6, 3, 3, "exact", 0.01) cca = sax_exact['cca'] assert np.array_equal(np.array(cca), np.array([0])) sax_mindist = sax_via_window(dat, 6, 3, 3, "mindist", 0.01) cca = sax_mindist['cca'] bbc = sax_mindist['bbc'] assert np.array_equal(np.array(cca), np.array([0])) assert np.array_equal(np.array(bbc), np.array([2])) ================================================ FILE: tests/test_str.py ================================================ """Testing STR functions.""" import pytest from saxpy.strfunc import idx2letter def test_sizing(): """Test idx to char.""" assert 'a' == idx2letter(0) assert 'h' == idx2letter(7) assert 't' == idx2letter(19) with pytest.raises(ValueError, match=r'.* idx'): idx2letter(-1) with pytest.raises(ValueError, match=r'.* idx .*'): idx2letter(20) ================================================ FILE: tests/test_ts2string.py ================================================ """Testing PAA implementation.""" import numpy as np from saxpy import alphabet from saxpy import sax def test_stringing(): """Test string conversion.""" # 11: np.array([-np.inf, -1.33517773611894, -0.908457868537385, # -0.604585346583237, -0.348755695517045, # -0.114185294321428, 0.114185294321428, 0.348755695517045, # 0.604585346583237, 0.908457868537385, 1.33517773611894]), ab = sax.ts_to_string(np.array([-1.33517773611895, -1.33517773611894]), alphabet.cuts_for_asize(11)) assert 'ab' == ab kj = sax.ts_to_string(np.array([1.33517773611895, 1.33517773611894]), alphabet.cuts_for_asize(11)) assert 'kj' == kj def test_mindist(): """Test MINDIST.""" assert sax.is_mindist_zero('ab', 'ab') assert sax.is_mindist_zero('ab', 'bc') assert sax.is_mindist_zero('abcd', 'bccc') assert 0 == sax.is_mindist_zero('ab', 'bd') assert 0 == sax.is_mindist_zero('ab', 'abc') ================================================ FILE: tests/test_vsm.py ================================================ """Testing SAX implementation.""" import numpy as np from saxpy.sax import sax_via_window from saxpy.saxvsm import series_to_wordbag from saxpy.saxvsm import manyseries_to_wordbag from saxpy.saxvsm import bags_to_tfidf def test_series_to_wordbag(): """Test TS to vector.""" dat = np.array([0., 0., 0., 0., 0., -0.270340178359072, -0.367828308500142, 0.666980581124872, 1.87088147328446, 2.14548907684624, -0.480859313143032, -0.72911654245842, -0.490308602315934, -0.66152028906509, -0.221049033806403, 0.367003418871239, 0.631073992586373, 0.0487728723414486, 0.762655178750436, 0.78574757843331, 0.338239686422963, 0.784206454089066, -2.14265084073625, 2.11325193044223, 0.186018356196443, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.519132472499234, -2.604783141655, -0.244519550114012, -1.6570790528784, 3.34184602886343, 2.10361226260999, 1.9796808733979, -0.822247322003058, 1.06850578033292, -0.678811824405992, 0.804225748913681, 0.57363964388698, 0.437113583759113, 0.437208643628268, 0.989892093383503, 1.76545983424176, 0.119483882364649, -0.222311941138971, -0.74669456611669, -0.0663660879732063, 0., 0., 0., 0., 0.]) sax_none = sax_via_window(dat, 6, 3, 3, "none", 0.01) wordbag = series_to_wordbag(dat, 6, 3, 3, "none", 0.01) wordbag2 = manyseries_to_wordbag(np.array([dat, dat]), 6, 3, 3, "none", 0.01) frequencies = {} for k, v in sax_none.items(): frequencies[k] = len(v) for k, v in wordbag.items(): assert v == frequencies[k] for k, v in wordbag2.items(): assert v == frequencies[k] * 2 def test_vsm(): """Test TF*IDF""" bag1 = {"this": 10, "is": 1, "a": 2, "sample": 1} bag2 = {"this": 10, "is": 1, "another": 2, "example": 3} bags = {"bag1": bag1, "bag2": bag2} res = bags_to_tfidf(bags) example = np.log(1. + 3.) * np.log(2. / 1.) bag2_idx = res["classes"].index("bag2") assert res["vectors"]["example"][bag2_idx] == example a = np.log(1. + 2.) * np.log(2. / 1.) bag1_idx = res["classes"].index("bag1") assert res["vectors"]["a"][bag1_idx] == a #def test_cosine(): ================================================ FILE: tests/test_znorm.py ================================================ """Testing znorm implementation.""" import pytest from numpy import array, std, mean from saxpy import znorm def test_znorm(): """Test the znorm implementation.""" # test std is 1 and mean is 0 ts = array([-1., -2., -1., 0., 2., 1., 1., 0.]) z_thrsh = 0.001 x_scaled = [x / 100.0 for x in ts] assert pytest.approx(1.0, 0.000001) == std(znorm.znorm(x_scaled, z_thrsh)) assert pytest.approx(0.0, 0.000001) == mean(znorm.znorm(x_scaled, z_thrsh)) # test std and mean wouldnt change on hi threshold ts = array([-0.1, -0.2, 0.2, 0.1]) z_thrsh = 0.5 ts_mean = mean(ts) ts_sd = std(ts) assert ts_mean == mean(znorm.znorm(ts, z_thrsh)) assert ts_sd == std(znorm.znorm(ts, z_thrsh)) ================================================ FILE: tox.ini ================================================ [tox] envlist = py{310,311,312}-{linux,macos,windows} [gh-actions] python = 3.10: py310 3.11: py311, mypy 3.12: py312 [gh-actions:env] PLATFORM = ubuntu-latest: linux macos-latest: macos windows-latest: windows [testenv] commands=py.test --cov saxpy tests deps = -rrequirements.txt setenv = PYTHONPATH = .