which fintabnet data doesn't\n # Fintabnet doesn't use th tags, need to be replaced for fair comparison\n marker_table_html = fix_table_html(marker_table.html)\n gemini_table_html = fix_table_html(gemini_table)\n\n results.append({\n \"marker_table\": marker_table_html,\n \"gt_table\": gt_table_html,\n \"gemini_table\": gemini_table_html\n })\n except pdfium.PdfiumError:\n print('Broken PDF, Skipping...')\n continue\n return results, total_unaligned"
},
{
"path": "benchmarks/table/scoring.py",
"content": "\"\"\"\"\nTEDS Code Adapted from https://github.com/ibm-aur-nlp/EDD\n\"\"\"\n\nimport distance\nfrom apted import APTED, Config\nfrom apted.helpers import Tree\nfrom lxml import html\nfrom collections import deque\n\ndef wrap_table_html(table_html:str)->str:\n return f'{table_html}'\n\nclass TableTree(Tree):\n def __init__(self, tag, colspan=None, rowspan=None, content=None, *children):\n self.tag = tag\n self.colspan = colspan\n self.rowspan = rowspan\n self.content = content\n\n # Sets self.name and self.children\n super().__init__(tag, *children)\n\n def bracket(self):\n \"\"\"Show tree using brackets notation\"\"\"\n if self.tag == 'td':\n result = '\"tag\": %s, \"colspan\": %d, \"rowspan\": %d, \"text\": %s' % \\\n (self.tag, self.colspan, self.rowspan, self.content)\n else:\n result = '\"tag\": %s' % self.tag\n for child in self.children:\n result += child.bracket()\n return \"{{{}}}\".format(result)\n\nclass CustomConfig(Config):\n @staticmethod\n def maximum(*sequences):\n return max(map(len, sequences))\n\n def normalized_distance(self, *sequences):\n return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)\n\n def rename(self, node1, node2):\n if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):\n return 1.\n if node1.tag == 'td':\n if node1.content or node2.content:\n return self.normalized_distance(node1.content, node2.content)\n return 0.\n\ndef tokenize(node):\n \"\"\"\n Tokenizes table cells\n \"\"\"\n global __tokens__\n __tokens__.append('<%s>' % node.tag)\n if node.text is not None:\n __tokens__ += list(node.text)\n for n in node.getchildren():\n tokenize(n)\n if node.tag != 'unk':\n __tokens__.append('' % node.tag)\n if node.tag != 'td' and node.tail is not None:\n __tokens__ += list(node.tail)\n\ndef tree_convert_html(node, convert_cell=False, parent=None):\n \"\"\"\n Converts HTML tree to the format required by apted\n \"\"\"\n global __tokens__\n if node.tag == 'td':\n if convert_cell:\n __tokens__ = []\n tokenize(node)\n cell = __tokens__[1:-1].copy()\n else:\n cell = []\n new_node = TableTree(node.tag,\n int(node.attrib.get('colspan', '1')),\n int(node.attrib.get('rowspan', '1')),\n cell, *deque())\n else:\n new_node = TableTree(node.tag, None, None, None, *deque())\n if parent is not None:\n parent.children.append(new_node)\n if node.tag != 'td':\n for n in node.getchildren():\n tree_convert_html(n, convert_cell, new_node)\n if parent is None:\n return new_node\n\ndef similarity_eval_html(pred, true, structure_only=False):\n \"\"\"\n Computes TEDS score between the prediction and the ground truth of a given samples\n \"\"\"\n pred, true = html.fromstring(pred), html.fromstring(true)\n if pred.xpath('body/table') and true.xpath('body/table'):\n pred = pred.xpath('body/table')[0]\n true = true.xpath('body/table')[0]\n n_nodes_pred = len(pred.xpath(\".//*\"))\n n_nodes_true = len(true.xpath(\".//*\"))\n tree_pred = tree_convert_html(pred, convert_cell=not structure_only)\n tree_true = tree_convert_html(true, convert_cell=not structure_only)\n n_nodes = max(n_nodes_pred, n_nodes_true)\n distance = APTED(tree_pred, tree_true, CustomConfig()).compute_edit_distance()\n return 1.0 - (float(distance) / n_nodes)\n else:\n return 0.0\n\n"
},
{
"path": "benchmarks/table/table.py",
"content": "import os\nos.environ[\"PYTORCH_ENABLE_MPS_FALLBACK\"] = \"1\" # Transformers uses .isin for an op, which is not supported on MPS\n\nfrom pathlib import Path\nfrom itertools import repeat\nfrom typing import List\n\nimport time\nimport datasets\nfrom tqdm import tqdm\nimport click\nfrom tabulate import tabulate\nimport json\nfrom concurrent.futures import ProcessPoolExecutor\n\nfrom marker.settings import settings\nfrom benchmarks.table.inference import inference_tables\n\nfrom scoring import wrap_table_html, similarity_eval_html\n\ndef update_teds_score(result, prefix: str = \"marker\"):\n prediction, ground_truth = result[f'{prefix}_table'], result['gt_table']\n prediction, ground_truth = wrap_table_html(prediction), wrap_table_html(ground_truth)\n score = similarity_eval_html(prediction, ground_truth)\n result.update({f'{prefix}_score':score})\n return result\n\n\n@click.command(help=\"Benchmark Table to HTML Conversion\")\n@click.option(\"--result_path\", type=str, default=os.path.join(settings.OUTPUT_DIR, \"benchmark\", \"table\"), help=\"Output path for results.\")\n@click.option(\"--dataset\", type=str, default=\"datalab-to/fintabnet_bench_marker\", help=\"Dataset to use\")\n@click.option(\"--max_rows\", type=int, default=None, help=\"Maximum number of PDFs to process\")\n@click.option(\"--max_workers\", type=int, default=16, help=\"Maximum number of workers to use\")\n@click.option(\"--use_llm\", is_flag=True, help=\"Use LLM for improving table recognition.\")\n@click.option(\"--table_rec_batch_size\", type=int, default=None, help=\"Batch size for table recognition.\")\n@click.option(\"--use_gemini\", is_flag=True, help=\"Evaluate Gemini for table recognition.\")\ndef main(\n result_path: str,\n dataset: str,\n max_rows: int,\n max_workers: int,\n use_llm: bool,\n table_rec_batch_size: int | None,\n use_gemini: bool = False\n):\n start = time.time()\n\n\n dataset = datasets.load_dataset(dataset, split='train')\n dataset = dataset.shuffle(seed=0)\n\n results, total_unaligned = inference_tables(dataset, use_llm, table_rec_batch_size, max_rows, use_gemini)\n\n print(f\"Total time: {time.time() - start}.\")\n print(f\"Could not align {total_unaligned} tables from fintabnet.\")\n\n with ProcessPoolExecutor(max_workers=max_workers) as executor:\n marker_results = list(\n tqdm(\n executor.map(update_teds_score, results), desc='Computing alignment scores', total=len(results)\n )\n )\n\n avg_score = sum([r[\"marker_score\"] for r in marker_results]) / len(marker_results)\n headers = [\"Avg score\", \"Total tables\"]\n data = [f\"{avg_score:.3f}\", len(marker_results)]\n gemini_results = None\n if use_gemini:\n with ProcessPoolExecutor(max_workers=max_workers) as executor:\n gemini_results = list(\n tqdm(\n executor.map(update_teds_score, results, repeat(\"gemini\")), desc='Computing Gemini scores',\n total=len(results)\n )\n )\n avg_gemini_score = sum([r[\"gemini_score\"] for r in gemini_results]) / len(gemini_results)\n headers.append(\"Avg Gemini score\")\n data.append(f\"{avg_gemini_score:.3f}\")\n\n table = tabulate([data], headers=headers, tablefmt=\"github\")\n print(table)\n print(\"Avg score computed by comparing marker predicted HTML with original HTML\")\n\n results = {\n \"marker\": marker_results,\n \"gemini\": gemini_results\n }\n\n out_path = Path(result_path)\n out_path.mkdir(parents=True, exist_ok=True)\n with open(out_path / \"table.json\", \"w+\") as f:\n json.dump(results, f, indent=2)\n\n print(f\"Results saved to {out_path}.\")\n\nif __name__ == '__main__':\n main()"
},
{
"path": "benchmarks/throughput/__init__.py",
"content": ""
},
{
"path": "benchmarks/throughput/main.py",
"content": "import os\nimport tempfile\nimport time\nfrom multiprocessing import get_context\nfrom concurrent.futures import ProcessPoolExecutor\nimport torch\n\nimport click\nimport pypdfium2 as pdfium\nfrom tqdm import tqdm\n\nimport datasets\n\n\ndef get_next_pdf(ds: datasets.Dataset, i: int):\n while True:\n pdf = ds[i][\"pdf\"]\n filename = ds[i][\"filename\"]\n if pdf and filename.endswith(\".pdf\"):\n return pdf, filename, i + 1\n i += 1\n if i >= len(ds):\n i = 0\n\n\ndef single_batch(\n batch_size: int,\n num_threads: int,\n force_ocr: bool,\n quantize: bool,\n compile: bool,\n worker_id: int,\n chunksize: int = 100,\n):\n if quantize:\n os.environ[\"RECOGNITION_MODEL_QUANTIZE\"] = \"true\"\n if compile:\n os.environ[\"COMPILE_ALL\"] = \"true\"\n\n for item in [\n \"DETECTOR_POSTPROCESSING_CPU_WORKERS\",\n \"OPENBLAS_NUM_THREADS\",\n \"PDFTEXT_CPU_WORKERS\",\n \"OMP_NUM_THREADS\",\n ]:\n os.environ[item] = f\"{num_threads}\"\n\n torch.set_num_threads(num_threads)\n\n from marker.converters.pdf import PdfConverter\n from marker.models import create_model_dict\n from marker.output import text_from_rendered\n\n ds = datasets.load_dataset(\"datalab-to/pdfs\", split=\"train\")\n model_dict = create_model_dict()\n torch.cuda.reset_peak_memory_stats()\n\n times = []\n i = 0\n pages = 0\n chars = 0\n\n min_time = time.time()\n for _ in range(batch_size):\n pdf, fname, i = get_next_pdf(ds, i)\n print(f\"Inferencing {fname} on worker {worker_id}...\")\n\n pdf_doc = pdfium.PdfDocument(pdf)\n page_count = len(pdf_doc)\n pdf_doc.close()\n pages += page_count\n\n with tempfile.NamedTemporaryFile(suffix=\".pdf\") as f:\n f.write(pdf)\n f.flush()\n page_range_chunks = list(range(0, page_count, chunksize))\n for chunk_start in page_range_chunks:\n chunk_end = min(chunk_start + chunksize, page_count)\n page_range = list(range(chunk_start, chunk_end))\n\n block_converter = PdfConverter(\n artifact_dict=model_dict,\n config={\n \"disable_tqdm\": worker_id > 0,\n \"page_range\": page_range,\n \"force_ocr\": force_ocr,\n },\n )\n start = time.time()\n rendered = block_converter(f.name)\n markdown, _, _ = text_from_rendered(rendered)\n chars += len(markdown)\n\n total = time.time() - start\n times.append(total)\n\n max_gpu_vram = torch.cuda.max_memory_reserved() / 1024**3\n max_time = time.time()\n return sum(times), min_time, max_time, max_gpu_vram, pages, chars\n\n\n@click.command(help=\"Benchmark PDF to MD conversion throughput.\")\n@click.option(\"--workers\", default=1, help=\"Number of workers to use.\")\n@click.option(\"--batch_size\", default=1, help=\"Batch size for inference.\")\n@click.option(\"--force_ocr\", is_flag=True, help=\"Force OCR on all pages.\")\n@click.option(\"--quantize\", is_flag=True, help=\"Use quantized model.\")\n@click.option(\"--compile\", is_flag=True, help=\"Use compiled model.\")\ndef main(\n workers: int,\n batch_size: int,\n force_ocr: bool,\n quantize: bool,\n compile: bool,\n):\n total_cpus = os.cpu_count()\n start = time.time()\n current_gpu_vram = torch.cuda.memory_reserved() / 1024**3\n with ProcessPoolExecutor(\n max_workers=workers, mp_context=get_context(\"spawn\")\n ) as executor:\n cpus_per_worker = min(8, max(2, total_cpus // workers))\n futures = [\n executor.submit(\n single_batch,\n batch_size,\n cpus_per_worker,\n force_ocr,\n quantize,\n compile,\n i,\n )\n for i in range(workers)\n ]\n all_times = []\n min_time = None\n max_time = time.time()\n vrams = []\n page_count = 0\n char_count = 0\n for future in tqdm(futures, desc=\"Running marker workers...\"):\n times, min_time_worker, max_time_worker, max_vram, pages, chars = (\n future.result()\n )\n vrams.append(max_vram - current_gpu_vram)\n all_times.append(times)\n page_count += pages\n char_count += chars\n min_time = (\n min(min_time_worker, min_time)\n if min_time is not None\n else min_time_worker\n )\n max_time = max(max_time, max_time_worker)\n\n end = time.time() - start\n all_worker_time = max_time - min_time\n\n print(f\"Average time per worker: {sum(all_times) / len(all_times)}\")\n print(f\"Max time per worker: {max(all_times)}\")\n print(f\"End to end time (counting model loading), all processes: {end}\")\n print(f\"End to end time (no model loading), all processes: {all_worker_time}\")\n print(f\"Total pages: {page_count}\")\n print(f\"Total characters: {char_count}\")\n print(f\"Time per page: {all_worker_time / page_count:.2f}\")\n print(f\"Characters per second: {char_count / all_worker_time:.2f}\")\n print(f\"Max GPU VRAM: {max(vrams):.2f} GB\")\n print(f\"Average GPU VRAM: {sum(vrams) / len(vrams):.2f} GB\")\n\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "benchmarks/verify_scores.py",
"content": "import json\nimport argparse\n\n\ndef verify_scores(file_path):\n with open(file_path, 'r') as file:\n data = json.load(file)\n\n raw_scores = [data[\"scores\"][k] for k in data[\"scores\"]]\n marker_scores = [r[\"marker\"][\"heuristic\"][\"score\"] for r in raw_scores]\n marker_score = sum(marker_scores) / len(marker_scores)\n if marker_score < 90:\n raise ValueError(\"Marker score below 90\")\n\n\ndef verify_table_scores(file_path):\n with open(file_path, 'r') as file:\n data = json.load(file)\n\n avg = sum([r[\"marker_score\"] for r in data[\"marker\"]]) / len(data)\n if avg < 0.7:\n raise ValueError(\"Average score is below the required threshold of 0.7\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Verify benchmark scores\")\n parser.add_argument(\"file_path\", type=str, help=\"Path to the json file\")\n parser.add_argument(\"--type\", type=str, help=\"Type of file to verify\", default=\"marker\")\n args = parser.parse_args()\n if args.type == \"marker\":\n verify_scores(args.file_path)\n elif args.type == \"table\":\n verify_table_scores(args.file_path)\n"
},
{
"path": "chunk_convert.py",
"content": "from marker.scripts.chunk_convert import chunk_convert_cli\n\nif __name__ == \"__main__\":\n chunk_convert_cli()"
},
{
"path": "convert.py",
"content": "from marker.scripts.convert import convert_cli\n\nif __name__ == \"__main__\":\n convert_cli()\n"
},
{
"path": "convert_single.py",
"content": "from marker.scripts.convert_single import convert_single_cli\n\nif __name__ == \"__main__\":\n convert_single_cli()\n"
},
{
"path": "data/.gitignore",
"content": "latex\npdfs\nreferences"
},
{
"path": "data/examples/json/multicolcnn.json",
"content": "{\n \"children\": [\n {\n \"id\": \"/page/0/Page/277\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/0/PageHeader/14\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 18.119998931884766,\n 211.199951171875\n ],\n [\n 36.2599983215332,\n 211.199951171875\n ],\n [\n 36.2599983215332,\n 559.2799987792969\n ],\n [\n 18.119998931884766,\n 559.2799987792969\n ]\n ],\n \"bbox\": [\n 18.119998931884766,\n 211.199951171875,\n 36.2599983215332,\n 559.2799987792969\n ],\n \"children\": null,\n \"section_hierarchy\": {},\n \"images\": {}\n },\n {\n \"id\": \"/page/0/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting
\",\n \"polygon\": [\n [\n 117.5888671875,\n 105.9219970703125\n ],\n [\n 477.371826171875,\n 105.9219970703125\n ],\n [\n 477.371826171875,\n 138.201171875\n ],\n [\n 117.5888671875,\n 138.201171875\n ]\n ],\n \"bbox\": [\n 117.5888671875,\n 105.9219970703125,\n 477.371826171875,\n 138.201171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"Diptodip Deb Georgia Institute of Technology diptodipdeb@gatech.edu
\",\n \"polygon\": [\n [\n 104.81396484375,\n 163.4853515625\n ],\n [\n 259.00787353515625,\n 163.4853515625\n ],\n [\n 259.00787353515625,\n 202.3262939453125\n ],\n [\n 104.81396484375,\n 202.3262939453125\n ]\n ],\n \"bbox\": [\n 104.81396484375,\n 163.4853515625,\n 259.00787353515625,\n 202.3262939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Abstract
\",\n \"polygon\": [\n [\n 144.1845703125,\n 232.4891357421875\n ],\n [\n 190.48028564453125,\n 232.4891357421875\n ],\n [\n 190.48028564453125,\n 244.4443359375\n ],\n [\n 144.1845703125,\n 244.4443359375\n ]\n ],\n \"bbox\": [\n 144.1845703125,\n 232.4891357421875,\n 190.48028564453125,\n 244.4443359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting. Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd). Modern approaches to the counting problem involve the production of a density map via regression whose integral is equal to the number of objects in the image. However, objects in the image can occur at different scales (e.g. due to perspective effects) which can make it difficult for a learning agent to learn the proper density map. While the use of multiple columns to extract multiscale information from images has been shown before, our approach aggregates the multiscale information gathered by the multicolumn convolutional neural network to improve performance. Our experiments show that our proposed network outperforms the state-of-the-art on many benchmark datasets, and also that using our aggregation module in combination with a higher number of columns is beneficial for multiscale counting.
\",\n \"polygon\": [\n [\n 49.904296875,\n 258.9959716796875\n ],\n [\n 287.47265625,\n 258.9959716796875\n ],\n [\n 287.47265625,\n 485.33203125\n ],\n [\n 49.904296875,\n 485.33203125\n ]\n ],\n \"bbox\": [\n 49.904296875,\n 258.9959716796875,\n 287.47265625,\n 485.33203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"1. Introduction
\",\n \"polygon\": [\n [\n 50.016357421875,\n 512.06591796875\n ],\n [\n 128.49609375,\n 512.06591796875\n ],\n [\n 128.49609375,\n 524.0211181640625\n ],\n [\n 50.016357421875,\n 524.0211181640625\n ]\n ],\n \"bbox\": [\n 50.016357421875,\n 512.06591796875,\n 128.49609375,\n 524.0211181640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Learning to count the number of objects in an image is a deceptively difficult problem with many interesting applications, such as surveillance [20], traffic monitoring [14] and medical image analysis [22]. In many of these application areas, the objects to be counted vary widely in appearance, size and shape, and labeled training data is typically sparse. These factors pose a significant computer vision and machine learning challenge.
\",\n \"polygon\": [\n [\n 49.0078125,\n 533.7682189941406\n ],\n [\n 286.576171875,\n 533.7682189941406\n ],\n [\n 286.576171875,\n 627.4168395996094\n ],\n [\n 49.0078125,\n 627.4168395996094\n ]\n ],\n \"bbox\": [\n 49.0078125,\n 533.7682189941406,\n 286.576171875,\n 627.4168395996094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Lempitsky et al. [15] showed that it is possible to learn to count without learning to explicitly detect and localize individual objects. Instead, they propose learning to predict a density map whose integral over the image equals the number of objects in the image. This approach has been adopted by many later works (Cf. [18, 28]).
\",\n \"polygon\": [\n [\n 49.7548828125,\n 630.5612335205078\n ],\n [\n 287.0244140625,\n 630.5612335205078\n ],\n [\n 287.0244140625,\n 700.734375\n ],\n [\n 49.7548828125,\n 700.734375\n ]\n ],\n \"bbox\": [\n 49.7548828125,\n 630.5612335205078,\n 287.0244140625,\n 700.734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"However, in many counting problems, such as those
\",\n \"polygon\": [\n [\n 59.84033203125,\n 703.4442443847656\n ],\n [\n 287.173828125,\n 703.4442443847656\n ],\n [\n 287.173828125,\n 713.49609375\n ],\n [\n 59.84033203125,\n 713.49609375\n ]\n ],\n \"bbox\": [\n 59.84033203125,\n 703.4442443847656,\n 287.173828125,\n 713.49609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Jonathan Ventura University of Colorado Colorado Springs jventura@uccs.edu
\",\n \"polygon\": [\n [\n 291.4570007324219,\n 163.6572265625\n ],\n [\n 488.89715576171875,\n 163.6572265625\n ],\n [\n 488.89715576171875,\n 202.3262939453125\n ],\n [\n 291.4570007324219,\n 202.3262939453125\n ]\n ],\n \"bbox\": [\n 291.4570007324219,\n 163.6572265625,\n 488.89715576171875,\n 202.3262939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"counting cells in a microscope image, pedestrians in a crowd, or vehicles in a traffic jam, regressors trained on a single image scale are not reliable [18]. This is due to a variety of challenges including overlap of objects and perspective effects which cause significant variance in object shape, size and appearance.
\",\n \"polygon\": [\n [\n 308.390625,\n 234.1092529296875\n ],\n [\n 545.1151123046875,\n 234.1092529296875\n ],\n [\n 545.1151123046875,\n 303.8478698730469\n ],\n [\n 308.390625,\n 303.8478698730469\n ]\n ],\n \"bbox\": [\n 308.390625,\n 234.1092529296875,\n 545.1151123046875,\n 303.8478698730469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/0/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The most successful recent approaches address this issue by explicitly incorporating multi-scale information in the network [18,28]. These approaches either combine multiple networks which take input patches of different sizes [18] or combine multiple filtering paths (\\\"columns\\\") which have different size filters [28].
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Fully convolutional architecture diagram (not to scale). Arrows show separate columns that all take the same input. At the end of the columns, the feature maps are merged (concatenated) together and passed to another series of dilated convolutions: the aggregator, which can aggregate the multiscale information collected by the columns [25]. The input image is I with C channels. The output single channel density map is D, and integrating over this map (summing the pixels) results in the final count. Initial filter sizes are labeled with brackets or lines. Convolution operations are shown as flat rectangles, feature maps are shown as prisms. The number below each filter represents the dilation rate (1 means no dilation).
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YdqjNm3pRY0VWL6nXfB3/kqOkfSf8A9EvXo/xasDdeI7NwucWgHT/beuB+EVu0fxN0piOgm/8ARL17H43WA6tB5u3PkDGf95q8vHYh4eaqJX0PIzWcuS8HbY8Sk0vB5T9Kpyab1+X9K7u7hgJO3bWW9qjE49a2o43nV2jwKeZ1YO0mce2lk9j+VV5NIPpj8K9Ag0kS9AKnl8P7VPSiWY0Iy5ZPU7IZzV3S0PK5tNZMkH8KpMjKcYr0W90gL1ArFm0hN/TmuuFSE1eLPUw2cU6i945PB9DRg+hrpzpAx0/SmPpDZwoH5Veh2LMKL6nNYPpRW+2kPnlQfwpn9is38NFjRYyj3MOitv8AsRgfu01tIYD7hosNYui9mY1Fag0lyeFNTx6MxI+Qn8KLDliqUd2YoBPQGja390/lXTxaI+B8g6elObRXH8FLTuc7zKhe1zltpHY0ldFJpLf3R+VUZ9LkGSB+lOxvDF0p7My6Kne0kTGQefal+zMBlsge9Kxvzx7leipzGucGgW5bpRYOZEFFWxZN3pxsGFOzF7SPcpUVObVxRSsPmR6r8dohJ44sen/INTPHpJLXmcOlm4ZWReMGvV/jVF5nja1zepb501AAXwSfMkrz+1ttQ81I7e7imbuodSfyrxDyTIOl3EBwzR88g7qU2E7wl5ImBQjL+ta8tjqLF1a1ic+gwT+lWn027gsRM0AKsMNGjNuHHWgDl3jlhYbgc4oMDGIOV+UnGa3AzqVMtrNjB++u7/CnyNZTptO6JsbsGPA/Q0AYCI1vJiSPkjIz6VNcXPnReWR90cVs3scOolmAijdAAPLOAfzqJtGihLLNKZAVypiXPPvQBz6Lhee9PUY5HT1rUFnLHEwCoULYbIOVNXp/Dr29rBdNHIUkbBjxnb7/AI0Ac4UfIOODStuA24rov7JmSIhbVyG5JMZytSafoqtNukRjCAwZmIBbjHGfegDlM7MjoTUKrnORXSXuhXNrDE8kOIpGIR2xg4qCfQLyCJXNuTlsfKe3rQBgCNieAacIyHxjNaiQuIWR4yGjbgY657VNcWIWfY0bKWAOFB4BFAGegCrytTLtRQGAUHnkVca1YbY4Y5Sf4iV4q9baHJdu4mUrEiFywIJ+nFAGDKc9xikRMjORircljIp5J24yMggmmrYzGNnwFUcAE0AQhFPG4Z+tI6qhPXpTjbyLyfu+uKTY2Nx6UASbzJGBjgD0qo20n2pw3ElRTWUFSADnPWgBwkRYiuOo61T25qZgcY64phz3GMUAfTngNtnwGhb+7Y3Z/wDHpK8u/tEEfer07wZn/hn8Y6/2def+hS14grSe9d2Epxknc5MThI12nLobrXwJ61NBeqGHNc/+8PrT0aRT3rrdGLVjknl1Nxsmd7YarEuAxFbkd9aSpyVrzOGeQVdS9kUda8XFZLTqy5k7M4vZVaGkbNHa3S2kg421Q+w28j8Yrnf7Rk9ali1JwwOaVPLqtKNozOacJt8zidMmhQyLkAVWuNBRegFQ22uOigEipX1kv6VyKnmEJ73QOVLl2aZRk0bH8IqIaRz90VfOo7qjN/8ASu2NTFWszH2j6NlOTSgo+6KqPp6g/drTe8LCoDKSa6KdSt9oFWqLZlNNODH7talpoXmn7oqOKTBFbVnfCPrXNjMTiIx/d7lwqucrVJaEsHhVSuSBUF14bWMHAFbsOrrs7VSvtU3A4xXzdDGZpKtaWx6Vang40rxbuYK6DuOABT28NnGdoq7BqOH5xWj/AGipTtXo1sZjqclY46SpSWsmcw+glT90Uz+wz/dFb8t6CahN8B6V0QxuLa1REnFPSTOfl0coPuiqMmmjP3a6Wa7D8cVUOG7V30cVWt75n7ecH7sjnH00f3agbTR/drpJFUdqgKqTXbDEyZvDMKy6l74a2Ih8e6dJjGBL/wCi2ro/ipfG28R2iAkZtAf/AB9qpeAVA8ZWJHpJ/wCi2qt8aGceK7HbnH2Ff/Q3qVatWXN2PUw7eMw7U+/+Ry8mpFv4j+dRrf8APWsTdJ6Gk3yA9DXWqEErAssp2Ous9W8sjLd62I9dhdBuOeRnkV5/HLIPX86tJPMAOT+Yrir5bRqu7RzTwMqfwSOtu761ljH7vnA/iHrWbIYXkyFx/wDrrGNxLjqfzoFw+eaqlg1SVosweCm9bm6kUDDkH86fJb2+7jrn+lYi3hHGB+RqRboMRwPyNN0J3vcylhasTTFjHI+M8n2q5baGJW4x07qazYZvnBGOvpW1Z3ap1IHH9wn+tcWKlXhH3GYqTUrSegTeHgiZ+X/vk1lXGlhM8D8q3J75GTAKH/tmf8aybi43E4x+VY4Opin/ABH+A5zSf7t/iUE04FgNo/Ktey0PzCuQvaqEdwysD3+orWttWkj2856dxW+MnieX91uCqNy/et2Nm38ORfLlV6Cobrw9GM4x27U0a63ygnsO2ahk1gP39O1fO06eZ+05pSOydTB8lorUxrjTI42wcde1Uxp8Uy/dY/d9O7Yq9cXjO2QSOf7wqtDdOvOW6r/GP71fS03X5Lt6nFGclqmUptETbGRGx3BCOR3ziqF1pQRFzERkf1rpjesYoQGbhY8/N6Zqndy71X6f1rajXrc1pHZDGVYtWf4nLNp4DfcqeDTQxHyVpbAzdO/pV+0g3EAkD6111K3LG501syqKJmppOQPkFJJpWP4R+VdbHbAJyV/75NV5o4/TP4GvPjmDlKxw/Xa61bOObSxn7g/KiujkjTPANFdixDaN1mtWxvfGTQlvvEseoyRhorfTk3nAJAEj9BkH+IV5Rpeny6nrs/2Z5EjijLl+hAH4/wBa9E+OcqL4wjjad4nOlR7QDwf30nH6CvKreecEiOUjzP8AWY449K8w+iNNdEaZri6tZnlEQ3SAjkD161lw3V5BdrOkzoiOcFTnBHTirtpdzWrXUMRIEsZVsc4BrLJMOYAxdQ2Tx2oAvDXNYe5jkNzMzyvkqThTV1/EGp2N06XEMcmQRslAYDPvVGNV3LyctyFHUCoZ5zcNHG+SgOBjrQBdXxJM0eZrG2cg9fLwP0q2fEunS7PN0oLhfmZXPJ/wrHHmW6NEsbNk8A1GiyznCQEheH46UAdBBq2mzMHb7REpYgj7yqDW4nivRbBbqIyXF69ygBkK8JjpjNcThmWVkUhH4z2HNJCkluzFgGUqQeMkUAbUmvWLyMGku3z3KirLanos8JYSyqQuAkibj2/nXNyKnIUlsDLdqFk8sMWJ+Zei9hmgDXl1u2O4K9wiHAVCOF9cCprfXbR23Nd3SM3y7mwQB9KxEuZYvLlZMRlwQxX0p9zfLNM37sEk9doFAGw2oaX9qKm7nkVsYZI+B9c9arXGpaashVGu2J6swHzfT0qKWGdrENBGixIA5foRniqsY58yRRIw+Xjk0AakNzYxTMbmC9iBXcOByfanweIILVJhHaXJ81SpJYdPyrON7LcCKOZmIUeWM+lPjAhlxIHGRgMvp70AOS4s50Lsl0CBhQcHP49q3tD1Gz0+8NxLZNIrwmNUJDYOevNc+0NzbuVUK2FDE9fl7UkCP5skh3yIByVPSgDt7jxHpE1sANMwQeTsXNcVrF3b3t+8kayRR/wpsAxSg+XIrrlgw5z1+tE0huAYxCeOfqPXNAGe/kA4WRz77acjRlhiQg+64FWfPAG6OALjgjGRT1vFeSL7VGphiOMKMZoAplIskfaFB+lOjgSZtwniLAfxVYC27W8jyKMclcdvxquY7aQMd6pgYBA6mgD6N8GqW+BiqSpJsLsZXp96SvI1sRnpXr3ghVX4IRBPu/YbrH/fUleaIVzXVQm4xdjyMzrzpyiolNdPyOlKNNyelbEJQ4rQgijYjgVnVx0qe6PIWJqye5gxaQzDhTUj6S6jpXXwwRhegqRoI27CvJlnc1K1jo9jVkr8xwT6ewPQ0iWDk8A13J06Nz0FWLfSYsjIFVPiGnCN2XChiZvlTOHTT5QOhoNs6nkGvR20uJY+grA1G2iiJwBWWEz+OJnypE4nBVqC5ps5pIGNSfZHNW1ljVqtJPFjtXpVMTUWyPPWvUz47B2qZdMY1e+2RIO1Ojv4y4rlnicQ9UjRQh1ZHBorPVwaK61p2V7DgdK0RewH0r5zFZrjYztynsYfAYWcLuRhxaRJjvUN1pL4NdTHdwAdqoXt7CM9K5aGZ4udX4TorZfhYU78xy66W4arkWlSMKuR3sJftWva3MOO1duMzLFU1flOTC4GhUlZyMBtGfHeqNxpjpmuyuLuEL2rEur2EkjiowOZYupLWOheMwOGpL3ZHPLp7s2Kvx6OxTNWoruHf2rRW/hEfau3FY/FqyjE5MPhaEtZSOWu9PaM1SWzcnGK6G9vInPGKqQzxb+cV6VDF1/ZXlHU5KlOCnaL0NHwPZPF4rs5COAJP/QGqD4t23neJrNvSzUf+PvXR+FZIm1q324zhv8A0E1nfEpA3iG1z/z6j/0Jq6sBi51LzkrNOx6a/wBnwbcH1/yPMP7PGOlN/s/npXSLChAGB+VBhT0H5V3/AF1nAsbW7nPx2HHT07VZj0wsBhT+VaoVB2H5VoW3l/3VrKtjpxV0ifrNSb3OdOktj7j/AJVC+mMP4T+VdnsjYfdWohbxu33a5Y5tLqi+aqtpHG/2Y/YGpI9PYHnH513IsIvLzjmsu9jjgJxVUs39tLliiqs68Y3kzLt7HcRnb+daKWKAgHb19faqQvfKbqaf/ahZvvH8xVVY4ibutjkWurH/AGZSWAI4A/i9jTGsdwBB9O+e1RR3ZLN8/Ydx6GtO2uVEY3N6dx6VFWValqgUV1M0ae2TjP5VZi0yRux4NakN3DxmtKC5gyOnJHauDEZjiILSB0UqEKj1kc+dLk+XPoO9Kmln0H4muhe5h+TDDkD096h+1xf3h09RXIsyxMl8Js8LST+Ixm0jjt+dRpoxI4I/h/nW01/EpxuH6UJfxN0YDkdCK0WNxij8JPsKF7cxhyaSUVcsOi96o3NjsHVT9K6Ke6Xavz9cfxisa+uwQOc8f3vevQweIxE2uY56sYxfusx2g2t+NXbSHJHzAf8AAsVVknBcn+tT21wVIIOPxFetV53AmXM1qbQtv3f3h+DVnTRkNjn8qurejYMkfmPSq81whk6DpXmUfaqTuiJcttCqIWY8fyoq3BcJz2oraVaonaxKS7h8cntR4stUlh3yvp8YDE4AXzJP64/KvKIVLXqRRJy/HB6c16Z8diB4/wBPL42jTExu6A+bJXndmlleKPKuJEuFOflIUntj3pn2w+/Kx3QXasUynaxHcfhUKXcEEgkABY5yX4DCrM9rZTq4kkkSaFcnzOd5HXBFU722sVlEcFyJEVON6bTmgBbh45LhXQgMyjdg4HA7n3qxb/ZBLDCIgFDhpJif4fak1C2ltbjy7iJoXKrhccMmODxVR8xS+TFnaQDtPXP1oAtX5t4753szJJADw7LgsPWrNpdPp1k89vsIlYqW9SB0/WslAv8AqmO7aectjH0pxlWJWCOV3NnqfzoAsfay0bqwAVySRjGD60+zuIoLqNpGcW8n+sIXPFKb61EeEhkLbMMxHOfUGqMUcsoaJAwUnv2H4UAXtSjtUcS2yslvIeCeT9KzWkVQVJY479qmSOWWUQQ7GY8ZI/U+lQmOfzHRtvyHBwoIzQAG5ZY0RizxL8wTPAPrU5JdEldNqkcEDNQLBdMxUhhjGc4GKmh0+6uPLVJixckABvSgCwktuyIJI5CMYxv6n1pzXTx2kaImI43JDKMEk+p/CqbWN24kw0uY+NpqVNHuZN2HbC4zhs0ASW9zEjkywlxj8QfWkuJxNKiwiTp0PrVdtMn2lxKxXjgZzSDTbgx7xMVIHIycUAXLa+uoZvNyQehLDIFOTWLq3ysLfIzbmGzG4n1qslnKG2mR2YkYw3BzWhZ272Vxb3geNwrhgkh3Zx6igCpLK80okJcZHORgfhQrKsyqWcoOM1q6pqM2tyJK0NvAyAjECBARnqQKzBE+x2eRhtGQByTQAwSEbvLJOcg8VDKWJO8EfhUiQzOx2zMmQSuaU20mdss778Z5H5UAMgbGFYAqOOR1p9yIgURUGV6tnrTfIfODOpJ6ADJpPs0zK5EqZH8JHJ/SgD6P8FHHwNjP/Thdf+hSV5Us2O9ep+DQyfApd+NwsLvOP96SvHPO967cLHmTPOx1H2kka8dxjvV2C9Kkc1zq3HvUyXJ9a0qYZSWp5NTByWqOxh1IY5apG1NQfvVykdw3rUhlc9682WV03K7MP3sdLnUprKg/eq3DriD+KuJ3vTllcHqaxq5Jh5rVGka1eGqkdxNrymM4audv9SMzHms8SMR1NROCTVYPKqGGd4oiriKtZ2qMa07Z60C5f1NN8vNOEVetaIrU7AZ3Pc05JHz1NKsIzVyC2U9qynOEUZylHZISO7lQcMasR38xP3jSPbqtSW0KbucVxVHSceaxmm72Rcju5yvU1WuZp2zkmt21tYmUcCpZNNiI+6K8NZhQpVLOJ3/VKs4XTOTWSYN1NaVtdTAdTV99OiXsKiMccZ7V1TxdKurKJgqE6b1diG4uZivU1jTzS7jkmt5zEwxxVCaCNjxitsJUhDRxIqpt3vcyxcSDuaU3kuPvGrJtQTTlsN3avQdWju0Zr0KDTyN1JoWZweprUXTPalOmj0pfW6GxXK+xqeA7qR/F9lGScESf+gNV/wCJ0mzxDaj/AKdR/wChtUPgqz8nxdZP6CT/ANAaoPi1MY/EtmM/8uan/wAfero8lSXubHp0abngpR/vf5HLC6weSakW6BPWsP7Qc9akjuOetdTwyOWWDklc2vNz3qxDPtPJ71jrMT3qUSkd6wnh01Y5XTlFm39rGOtNS9AYcisfzjjrTfNPY1gsFGw/fOoGpHyeCOnofSsXUbl5GPI/I1WE0mzAP6n0qKXex6/qaWHwUKU+ZFucptKTKUrPu61ApbPU1cMRJ5/rSpbjd0/zivVU0kdka0IxsU42YHq3b+VXopGwOtMWAA9PT+VWoYQccVFScbGVerCXQVZpB61Ot1ICvJ4IqaO1BHIpTbqCOP1FcUqlNu1jjuMFzIxXnoBR5kpH4VPDCm4VejtI2A47VzVK9On0CMHLYw3eTNLG7571ry2KZOF4+lRLaqvarWKpuOiBwa0M6R2Kjr27Cs64kIH/ANatu4jUDp29axboDn/GuvDSUtka0Lc9mUmkOT1/KpY5G4xVc/f7fnViIZIrvaVj06kYqOxZE0nHWlMr7uc0Kh29qcEz1/lXO+U89uC6DEmIPU/mKKsJCvv+QoqJThczlOF9if49SbfHFjuUlF0xTx7ySD+leRRyDeCq7TnPy8YFer/tAeY3jezRWwv9mRlv+/steVGDy4VZs88jFcJ9iNkm/euVd8NwR7VJCTIuUhJIwOT71YitopItxOyRv9WSOD7VLGNw8mPDs+AAowQc80AJNcag2BM20bduX5IA4piWc15KoeYZddwYnoKY8ruV8zcnzYXPcVJM8WwbR93C7lPJoAjW2xI0YwzAgVpT28e4LCFJjXkNwD+P+elVLUtFvkOMlsE56ChJ4235LYVhz6rQBJCfMbaikRbfmYcnbU7yRQSs0EgPI+UcZFV7qciKC3tWVUkX5nHTk5xVONnSQRIhMmfvDuKANOJy9xEI22SMGyzHoKltmjXnYZA5y+7g5zwfp0rOGpt5y+Z97gAEdh2NWPt015LcxrGBI7KVG3BJHGB+dACXFzFJM3mJIIkPAHBrSgkaC088KkK5BVcZbdjr7VmR+XPeXAkkKCAjgnJ9605FeaOUBkZmTk46e1AFV7y4dDnjzDuXB5Y5qSS6nuoZYHbyZNzOzE9TgZH6VUs1LXD7V3+SuS7HhfTFRmUpLcSAFIowEO47ssR/XFAGtpsiyO8d0smxPlKr93mpTbxWYdJVChclM8lgemT2rMa5MpVvJeCNyNzf3vpUy3bXV7NamJwvlkx7hk8UAX7aWOSNCsqGTccI4BxzgDNZt0t2l7FK0SrHEcdeGyc0RLOk1rPa24mWL76r/Eal1DzZpShRo4eu1DkBuKAJbceayyySxoqck8YOex96ej28UDSxktK65YPgZPtVCNYJJpIFOUK7lDdCfWnX1osiwSySeUiqFyvNAFpLXMYuJS2CPnZegz2qGc+fMJ7YfIRgq3UAUtqklxb/AOkzAwEFIweOnGfrVc20yJK+VwuOc9AOpoAfJFGFaZiI2/hZW6Go45kiDlQrsODk9f8A69K3lFXvIiGjPyiMjofWkijtoGjmlwVkG0qOoNAH0N4VZj8CiWzu/s67zn6yV4gNzV7l4UTb8DynP/Hhdjn/AHpK8bSAV6GDdos5sRVULXKioxNXIYScVKsQFWIgoronPQ8uvim1oPht84q6lqMVHHIFqY3QArhqSqN6HkTnKT1GvbqKjMaike5zUDz04Rn1CMJsmZlWoGlGarS3BqsZjmuiNLudtLCN6s0fNFHnD1rNM9N+0e9X7I2WCbNhZuatRXO0dawUuPeplueOtZzw6ZhUwcuhry3ZPeo0vSrcGsp7jPeohOc9aSw0bWsOOCbWp2Vlq5XAJrT/ALYBTqK4SG5I71aF0cda8yvlFKpLmsR+/pe6mdHcavnODWbNqTMetZMlwT3quZzmuijl1KmtEJUqlTWTNf7e3rR9vY96xzNTfPNdX1WHYpYNs3FvTnrVqK/APJrmxOaeLgg1nPBwkS8LJbHVjUl29RUbakM1zouT60huD61zrLYJk+yq7Hf+Cr3zvF1knqJP/QGqh8Yc/wDCVWWP+fJf/Q3qr8O5t/jnT19RL/6LatP4sR7/ABPZn/pzX/0N63o0o0aqS7Hq4a9LCycu/wDkebiNj61LHC3oaurEoFSoiiuqVU454vTQjihY46/5FWxaGpIii+lWDPGPT864qlad9EefKpKTKRtwO1MMeO1WXmUntUJkB9KqMpvcSchnQU1n96Gaq7NxWsY3NYQuSbvemmXB61XaTFV3l68/rWyp3OunhuYuibnrVqGbkViCbkc9/WrcM3vROldDrYWyN1LnCngdKie66/jVA3HBqu9x159a5o4ZXucsMNKRrxXRD9e9advd8fe7Vysdx83Wrkd1gdazr4NTQTozpvQ3p73GcN+tZ73pz1FUJbrPeqrXHPWijgoxQRoznqzRkuSw6iqFw+aaJge9Mdwa64U1HY3pUOSRB/HVmLg9KrGTBqVJ+On+cVtJOx2VYya2L6txTwaqCYU8TCsHBnnSpSLiviiqvmjHWio9lcz9i2S/tBn/AIr2xBDH/iWR8Kf+msteZvOoVQu3Bj5xn5a9N/aDkJ8dWEWRt/s1G6c582XvXmNpaQLNm6EioRu2dCwrgPsBYQJMAOTtxt46mpJrjy7qJUJUAY3JkYP41HL9lsDM1tcS5kAKMhxt9jVZ3uRBuV9yN94E5zQAXF400Sq5LMqhQT7VdkugYo/KhjR0QZAJy9UzaXKDe8RiJXcu5cEg+lMtY5pJSseQ4ySe4oAv29q91Arxq+5sscc9KfzbRor8K4zzz9abY3dzbSojPtVkKKGbbjNEtzd3DPEm4nHlLtG7I7gGgBzN+7ggQDaxY7gen1q9YxRwWpnbaZGcqr4yAPWsoRRW6zFWLFR8p6EeoqWa6ElmsYzhU+Xtk96ALsNnHdNIMqJI/wB4HfjIHaqkN8z68AsY3yPgemfX8KZau9tdSSPNwITjLdfan3+yyWyu4nV3dMv35P8AKgBstvJ/aXkK6fvGw3bJFbGl30FkLl542ZhkeUp4PHY1BqUEMsKamsgSfdnAIYAf1qD7WJNLSxlZQzuW8zoeaAJLJJr8zNbW0oaRCx24A25qQaW9xYN5LKJGQtMjkDHOAfrxWfp2qnTrs2wJdMFMjqfQfnWoy3mmSTRSMxuLoZi8twVGOcNnHagC1ffZ9ttC1ztgjIXDY6gdTWdc3aHzN7O8mdkc65A2+maisLKC9s7u8l8x7lW4hQ7h78dcVetUiWaONvJurdoywTZtCN780ANgEUYE4n8l1cIAvK47mteeH7NpJVpV2yRgjjBzj/69YeoLJpVokEyRpG7eYFjcljnpmtq98Q6TGhhRkdnt1+bbuwccj2NAGUdJlmheWK3KuwAXJ2kAd8H1p96rx5tDKibFBZOuDWdPqLSNbtCX8zaA6gn5sVG6/a76W7RDHs2743PegDVGlXv2YB7qFY3jLFv7pPOPr/hUel2bXtlP9qulQKccj19/fFU9U1W5Fu9o6qoyCCDkgenFWrSOSy0SJ1AmST966vgbT6nPWgCYW9r5knlh4YYSAUccM1PuLYSuALdsFd2EHK47+lOCxxWcN9PcHddPtaPOPk9cVKUt2v8AzfM8+1iXDBgVyPw60Ae4eC8D4FJjdj7Bdn5+v3pK8i80CvZPDk1vN8E3ltV2w/2fdbR9DIP6V4YZveu/BxvFnJiKPtGi6Z8UC596zmlpnmnPWu3kRksEmjaW596GuPestJ/elaf3qfZoxeCV9i8Z/ekMuaz/ADvelE1VyGiwluhaZs1CzYpnnComkFUkbwpNDnkqLzfeopJKgMnNM7YUVYvLL708TVQWSpA/FApUUWzNR5tUWlxTfNpAsOjVjmqys3HWseOWrAm460nG5y1cKmy803vULT89aqNN71A0xoUR08IjQM9J53NZ4lJ71KrZp2NXh0i+stOEtUd+BSiWixk8OmXxLR5tUfNoEtLlI+rHdfDR8+P9OHtL/wCi2rf+LL7PEtn/ANeY/wDQ3rl/hfJu+IWmD2l/9FPW58ZJNnimyH/Tkv8A6G9cs43xCXkFShek4HGm4xQLr3rL80560wz4zzW/s0ciwKZsfaj6mkN2fWskTk96d5hxR7JCeAijT+1c9aeLkZrK82j7RjvR7NCeBT2Rqm4FQPMKoG6GP/r017kZx/U+lCp2KhgWnsWpJKpyS4zTZJxVOWXrWiVj0KGHsWRNz1qzHPjHNYwl5qwk3Tmjc6KmGTRrG44PNQtP161SafrUJn68+tFkYwwiRppPz3qwk5x1rFjn561bSXjrQ0mTVwqL7zHH4VXeYg1GZP5VE5yaErEUqCRaS45qQzcVnbiPX86cZeO9Fi5YZN6E0kpx1pq3BA6nr/Sq8jblNRf4/wBKZvGjG2pqC4O7rUyzH1rJVuetTo/v+lKxz1MNE0jMcdaKp7+OtFLlMVh0b/7Qxx8QdPIGSNMj49R5steZXFz5jxkvtO0D14r079oTyx8QLAvk/wDEqTgf9dZa8muGSds8pxgGvCO0mmb7TEoQhVUck96dGsk1kqxID5Zw2OrGobVHe1l2DJXk8U+wuXtHcliu4Y9qAL4vJZrnfIrOyKMAnIAFRWEiT302crvU9KgkcpJ5qsctwwHcVoeWiW0EIHzZyrDrzQBJZ6ZbXJmS6kKkKfLYN0PbNQWMcmlOklxIhUyfKmc5BBGf1q3qemNbyb7YttQAynPTNadnpEY02eW5gDAgeXvzx6kUAczcXkcd7MpVWXdkGt22EUGnR3Dxxs0gIxgEAfT1rlmg/fMYnBKZbBra8P3EV1fQw3QwqK3HZj2oAlsvDM+s30/lXUUMcab8uf0qGPS3u0ltri4/490JQKOCc4rW03R5Lhbu5tLmNmjcoLbd8zD1x6Vv6R4O1CHWtNkvULWUm4zbDx0zt/SgDjrDTp7zTpXZisUPAx3NWodMtZdNt5bgbfOcorq3IP0r2afQtBligtoIkgVSR5aj731rgvFvhqDSr+zNoxSByXVR0BFAHG6jZR+H7xWKiSRTuRqfc6umtXNkiQyeYxCykds4zj8q3bqW3TVxPdLFcJcWpEYIzg965ryZ7C3DJEVikYsjge/rQB1V1YiC8J06PyfOi2gKMbyPTHeo7vw9bW2gmWaN4r/aW5yCT9KdF4jtoV0xZYmLwBZMqeSfStCbxvY+Ib+SDU9KMLsP3bOx6DsaAOchWC90iC4vIA0vmqhOedoxUviXw2sWoJHZrGFcEgDqqjFaOl3Gk6NqF/feWtzamPEduORvyOlPttdF7P8AaWsWB2uoG7JUY9KAObvXsLE2z2n/AB8Qgbz/AHjishGmuprmZ5gu8bjnua6Xw9p66hqV1fy2ZFpHk5boD+PWor3Rze6gyWMapaSn5nHOD6+1AGTeQ2n9lW0sW8zOo3H8TmnaXexYe2v0DxhcpvzwfSoXtUt9Q+wRXAaRZdodvu9quavaW+manHZmRpCrK07cED6UAbEL2eo6UltOoEqZCN0OB0qrJLMktrYmGSEKQuXGN4NdRdW2iJp8N1DHGDwQd2K53xBrMS6hEIgs3lfMcn9BQB7x4cijt/gdLHE25F0+8wQPeTP614H5le6eEZ2uPgC0zgBn068JA7fNLXge6vRwXwsuEbljfTS1RhqaWruLUCYSY70GX3quXphkxSuV7NMsmX3pPN96qGX3pPN96Ll+yLRnx3o8/PeqLye9NEpouV7FF13zUW6oTL70wy0rlKFi0HxT/NGOtUDKaBKaLjdO5cZ803JqFXqQGgOWxMj4qTzaq7qN1O5DgmWTJTC9Qb/ejdRcagWA1TI9URJjvUqS0XJlC5aaSm+ZUJem7qCVAn8z3o8z3qsXpvme9FyvZnf/AApfd8R9LHtN/wCinre+NbY8WWIz/wAuK/8Aob1zXwkfPxJ0se03/op66L43HHi2x/68V/8AQ3rmf+8L0MJx96x51v8Aeomemb6azZFdQRhYlWUCpPPGOtUyajL470i/ZJl1rjjqKia496oyTEDrUfnZouaRoJF4znHWmtOc9aq+ZQWpXL9mi005NRM5NRFjRuouNQSJM0okxUfUUh4FA7ExlPP+NQtL15ppbnrULE80XHGKLCS89atJPgdazV61YUmhMJwTL/n89ad5me9UgxzTw5p3MHTRaMnv+tMMnv8ArVcyN60wuaLjVMub8inAA/59qpLL71cjZSP/AK1BMo8o4qBSBttKxXB/wqB2AP8A9amTFXLnmfL1oql5vHWii4vYne/tDQCTxdYsqjzP7PQZHX/WSf8A168fD5t/LYEhfXtXtXxteP8A4WVp0Uqgo2mx8np/rZa8vtPLW8vrOVlUSLgAgHOD614BgZMVzKtm0KsI4zyQOpqOWOH7CJBNmZm5THSoblDDM8fOFPGavaFpy6rfm3LsvylgQucUAS21vPcWzWaIhfaHLZHTrWnbeH9RFlb3cU8Db3IBZwCmPXNZ8AbTNVbziGGOCRwarT3Mc88x3OoLZXaeBQBa1a5vpJ/MuHQOny/J3x3960NAm8SauZ49OPnBEAcSEYAzx171TtI7G6t0+2XeGztCAYP4mtbQJ7LQiru87s0m47VygwCB+hNAHMW1uG1Jre7kaEgkPxzn0q9ZGOx1aPbG0gLBfm6kH0q1KunJOtxDmW4a43ksSBgnp1rVuLnSpb+KcqyohDOQckYoAn8S6Yui67BLYtJHHPFvUOeQe44ratvHzWdolpeqTImJFkTpgetY3jDXrPVbSwltGZmik+YlcYBrmdUldziKMlI+C4HqKAPWpbu4nEd0q3EexTIrr8wAP0rm7ldY8aXLW9jMqLZp1uG2s2aq2PxGgi0AWMltKJQoQ7TncMdc9qteEtZNw13G9quzcCiNyw96AOBvbO90nVntZZf30BxvDZA+ldzreqWKeFreyhWKS4eMFpIxxmuW8TTx3eqyNDF5bISrr6kd8U3w09suqRNeAGDn73TPb9aADRYGbVIbl3XbC4Ownk13Pii0jutNhvLbyo7jdkNIeSPY+lcTevLd6zNJZBpHY/dQZzWtqU81z4RgjlDxz27lJA2QQDQAzwxbzx+Iobm6WJYkLHnlCcdh3rZ8YXaW8kd7YOkchG1jGgC7R2x+Nc/4RtE1PxJZ2dwCYCSGUNt3cdM+9a/j3SbfS/ENna2KCC2uFz5ZfcBzgmgC/a6pDc6BFbtJGrL1C8bq6bQtPj0rT5Gik2vN8xw2RXmc1u1tFKxuBEI/u853Gulj8ZWEWjQyLIWkCAOhHOaAM6x0/S9T1a/gdMzF3laXdhgAc8duxrZ8NW+njQby9vIIpDcM75njydvbB/CuI0bSdU8QaxctphMW4ku5bACsehrsNW0LXJ5I9KmZJYY49yeWwOcYGKAOPu7bUb7SJp95jt7c5jg2kFlz94euKz9BSK61e1imCbN3zBs4b2rS1m61TQJTpjF44yOUfByDWbYXbrqUTpGhlLDaMdT6UAfUumwxW/wYuooI1jjXTrrCKcgf6yvm/NfR+midfgrci5gEEw0263Rhs44k7/SvmovXo4L4WdFFXTJt+KY0lQNJTN+TXZc6FAn31G7UmaYxzQUojC5pN9DCmYqTRD92acBTFFSdBTBjWNR5p7GmgZpAhKctLtpBxQMlHFODVHuoDUybE2eKaWxQp4pGFBIm7mnZ4pg608dKBsYTzT1YikIzQBigB5ekD0w80AUCshzNTN3NOPSmY5oGjvfhC2fiZpQ9pv8A0U9dJ8cf+Rtsef8AlxX/ANGPXM/CD/kpuk/Sb/0S9dH8dG2+LrD/AK8F/wDRj1zv/eF6HNUX7xHl7tjvQHzVeWQetMSTnrXTc1UNC4TkZqvIeKcH4qN24oCKsRvkgUwA5qQ80mKRqhV64p9NU804nigljXOKjD80SE1HzQWloTq+fSlJ+XrUS5HrS7qBWH496Y1ODU08mgEPjXp0qcKPWo4yOKm4poiTImOD1/WgNQ4pg6UBYm6imMKVTxSk5oERqeasJIQOKr4p4OFoHJXJ2mOKgaTLdaa7VCT83Wi4Rgibf70VDziikVyo9V+Omn/b/HkG2YRyRaRGyjH3v3steK3EsqzAPxIh617j8c5pdN8a2Wpx8j+zkhZexHmSH+teOan9kmjSSEnzGJLZ7e1eGeWZtzM08u9wM4xVzQ9SbSr9LlByDg/TvWaxI4NX7WNUgEgxu75oAueILm2urpZbeQuWzkeg7VZ8GWNnqOsG3vVLIUyAGxk1iyRB90hIAHfFFjObW5jnU/MpyMUAbPi2xg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UCF sample results. Left: input counting image. Middle: Ground truth density map. Right: AMDCN prediction of density map on test image. The network never saw these images during training. All density maps are one channel only (i.e. grayscale), but are colored here for clarity.
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\",\n \"polygon\": [\n [\n 308.86199951171875,\n 74.40740966796875\n ],\n [\n 464.2486267089844,\n 74.40740966796875\n ],\n [\n 464.2486267089844,\n 84.3699951171875\n ],\n [\n 308.86199951171875,\n 84.3699951171875\n ]\n ],\n \"bbox\": [\n 308.86199951171875,\n 74.40740966796875,\n 464.2486267089844,\n 84.3699951171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/2/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/2/Equation/11\",\n \"block_type\": \"Equation\",\n \"html\": \"\",\n \"polygon\": [\n [\n 364.8009948730469,\n 94.8427734375\n ],\n [\n 545.66015625,\n 94.8427734375\n ],\n [\n 545.66015625,\n 120.3662109375\n ],\n [\n 364.8009948730469,\n 120.3662109375\n ]\n ],\n \"bbox\": [\n 364.8009948730469,\n 94.8427734375,\n 545.66015625,\n 120.3662109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/2/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/2/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"A dilated convolution is essentially a generalization of the traditional 2D convolution that allows the operation to skip some inputs. This enables an increase in the size of the filter (i.e. the size of the receptive field) without losing resolution. Formally, we define from [25] the dilated convolution as
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\",\n \"polygon\": [\n [\n 308.390625,\n 260.17254638671875\n ],\n [\n 545.1151733398438,\n 260.17254638671875\n ],\n [\n 545.1151733398438,\n 365.7760925292969\n ],\n [\n 308.390625,\n 365.7760925292969\n ]\n ],\n \"bbox\": [\n 308.390625,\n 260.17254638671875,\n 545.1151733398438,\n 365.7760925292969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/2/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/2/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Looking at more scales should allow for more accurate regression of the density map. However, because not all scales will be relevant, we extend the network beyond a simple 1 × 1 convolution after the merged columns. Instead, we construct a second part of the network, the aggregator, which sets our method apart from [28], [8], and other multicolumn networks. This aggregator is another series of dilated convolutions that should appropriately consolidate the multiscale information collected by the columns. This is a capability of dilated convolutions observed by [25]. While papers such as [28] and [8] have shown that multiple columns and dilated columns are useful in extracting multiscale information, we argue in this paper that the simple aggregator module built using dilated convolutions is able to effectively make use multiscale information from multiple columns. We show compelling evidence for these claims in Section 4.5.
\",\n \"polygon\": [\n [\n 308.091796875,\n 368.2344665527344\n ],\n [\n 545.115234375,\n 368.2344665527344\n ],\n [\n 545.115234375,\n 569.4799194335938\n ],\n [\n 308.091796875,\n 569.4799194335938\n ]\n ],\n \"bbox\": [\n 308.091796875,\n 368.2344665527344,\n 545.115234375,\n 569.4799194335938\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/2/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/2/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"The network as shown in Figure 1 contains 5 columns. Note that dilations allow us to use more columns for counting than [28] or [8]. Each column looks at a larger scale than the previous (the exact dilations can also be seen in Figure 1). There are 32 feature maps for each convolution, and all inputs are zero padded prior to each convolution in order to maintain the same data shape from input to output. That is, an image input to this network will result in a density map of the same dimensions. All activations in the specified network are ReLUs. Our input pixel values are floating point 32 bit values from 0 to 1. We center our inputs at 0 by subtracting the per channel mean from each channel. When
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\",\n \"polygon\": [\n [\n 49.38134765625,\n 134.8328857421875\n ],\n [\n 286.576171875,\n 134.8328857421875\n ],\n [\n 286.576171875,\n 253.107421875\n ],\n [\n 49.38134765625,\n 253.107421875\n ]\n ],\n \"bbox\": [\n 49.38134765625,\n 134.8328857421875,\n 286.576171875,\n 253.107421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/2/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.2. Experiments
\",\n \"polygon\": [\n [\n 49.119873046875,\n 263.97784423828125\n ],\n [\n 128.95028686523438,\n 263.97784423828125\n ],\n [\n 128.95028686523438,\n 274.936767578125\n ],\n [\n 49.119873046875,\n 274.936767578125\n ]\n ],\n \"bbox\": [\n 49.119873046875,\n 263.97784423828125,\n 128.95028686523438,\n 274.936767578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/3/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"We evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data [18], UCSD crowd data [5], and WorldExpo crowd data [27]. For each of these data sets, we used labels given by the corresponding density map for each image. An example of this is shown in Figure 2. We have performed experiments on the four different splits of the UCSD data as used in [18] and the split of the UCSD data as used in [28] (which we call the original split). We also evaluated the performance of our network on the TRANCOS traffic dataset [14]. We have also experimented with higher density datasets for crowd counting, namely WorldExpo and UCF.
\",\n \"polygon\": [\n [\n 49.306640625,\n 283.5155029296875\n ],\n [\n 287.771484375,\n 283.5155029296875\n ],\n [\n 287.771484375,\n 448.89495849609375\n ],\n [\n 49.306640625,\n 448.89495849609375\n ]\n ],\n \"bbox\": [\n 49.306640625,\n 283.5155029296875,\n 287.771484375,\n 448.89495849609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/3/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"We have observed that multicolumn dilations produce density maps (and therefore counts) that often have lower loss than those of HydraCNN [18] and [28]. We measure density map regression loss via a scaled mean absolute error loss during training. We compare accuracy of the counts via mean absolute error for the crowd datasets and the GAME metric in the TRANCOS dataset as explained in Section 3.2.2. Beyond the comparison to HydraCNN, we will also compare to other recent convolutional counting methods, especially those of [21], [24], and [4] where possible.
\",\n \"polygon\": [\n [\n 49.53076171875,\n 451.6363525390625\n ],\n [\n 287.173828125,\n 451.6363525390625\n ],\n [\n 287.173828125,\n 569.25\n ],\n [\n 49.53076171875,\n 569.25\n ]\n ],\n \"bbox\": [\n 49.53076171875,\n 451.6363525390625,\n 287.173828125,\n 569.25\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/3/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"For all datasets, we generally use patched input images and ground truth density maps produced by summing a Gaussian of a fixed size (σ) for each object for training. This size varies from dataset to dataset, but remains constant within a dataset with the exception of cases in which a perspective map is used. This is explained per dataset. All experiments were performed using Keras with the Adam optimizer [10]. The learning rates used are detailed per dataset. For testing, we also use patches that can either be directly pieced together or overlapped and averaged except in the case of UCF, for which we run our network on the full image.
\",\n \"polygon\": [\n [\n 49.60546875,\n 571.9372863769531\n ],\n [\n 286.875,\n 571.9372863769531\n ],\n [\n 286.875,\n 713.49609375\n ],\n [\n 49.60546875,\n 713.49609375\n ]\n ],\n \"bbox\": [\n 49.60546875,\n 571.9372863769531,\n 286.875,\n 713.49609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/3/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Furthermore, we performed a set of experiments in which we varied the number of columns from 1 to 5 (simply by including or not including the columns as specified in Figure 1, starting with the smallest filter column and adding larger filter columns one by one). Essentially, the network is allowed to extract information at larger and larger scales in addition to the smaller scales as we include each column. We then performed the same set of experiments, varying the number of columns, but with the aggregator module removed. We perform these experiments on the original split of UCSD as specified in Section 3.2.3 and [5], the TRAN-COS dataset, and the WorldExpo dataset because these are relatively large and well defined datasets. We limit the number of epochs to 10 for all of these sets of experiments in order to control for the effect of learning time, and also compare all results using MAE for consistency. These experiments are key to determining the efficacy of the aggregator in effectively combining multiscale information and in providing evidence to support the use of multiple columns to extract multiscale information from images. We report the results of these ablation studies in Section 4.5.
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\",\n \"polygon\": [\n [\n 307.79296875,\n 339.92578125\n ],\n [\n 443.4609375,\n 339.92578125\n ],\n [\n 443.4609375,\n 350.13201904296875\n ],\n [\n 307.79296875,\n 350.13201904296875\n ]\n ],\n \"bbox\": [\n 307.79296875,\n 339.92578125,\n 443.4609375,\n 350.13201904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/3/SectionHeader/3\",\n \"4\": \"/page/3/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"UCF is a particularly challenging crowd counting dataset. There are only 50 images in the whole dataset and they are all of varying sizes and from different scenes. The number of people also varies between images from less than 100 to the thousands. The average image has on the order of 1000 people. The difficulty is due to the combination of the very low number of images in the dataset and the fact that the images are all of varying scenes, making high quality generalization crucial. Furthermore, perspective effects are particularly noticeable for many images in this dataset. Despite this, there is no perspective information available for this dataset.
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\",\n \"polygon\": [\n [\n 49.23193359375,\n 180.53985595703125\n ],\n [\n 287.173828125,\n 180.53985595703125\n ],\n [\n 287.173828125,\n 226.59893798828125\n ],\n [\n 49.23193359375,\n 226.59893798828125\n ]\n ],\n \"bbox\": [\n 49.23193359375,\n 180.53985595703125,\n 287.173828125,\n 226.59893798828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/3/SectionHeader/3\",\n \"4\": \"/page/3/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/4/TextInlineMath/3\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"For training this dataset, we take 1600 randomly sampled patches of size 80 × 80. For testing this dataset, we take 80 × 80 non-overlapping patches which we can stitch back together into the full-sized 640 × 480 images. We trained the AMDCN network with density maps produced with a Gaussian of σ = 15 as specified in [18].
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\",\n \"polygon\": [\n [\n 49.306640625,\n 314.06341552734375\n ],\n [\n 182.4345703125,\n 314.06341552734375\n ],\n [\n 182.4345703125,\n 324.0260009765625\n ],\n [\n 49.306640625,\n 324.0260009765625\n ]\n ],\n \"bbox\": [\n 49.306640625,\n 314.06341552734375,\n 182.4345703125,\n 324.0260009765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/3/SectionHeader/3\",\n \"4\": \"/page/4/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/4/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The UCSD crowd counting dataset consists of frames of video of a sidewalk. There are relatively few people in view at any given time (approximately 25 on average). Furthermore, because the dataset comes from a video, there are many nearly identical images in the dataset. For this dataset, there have been two different ways to split the data into train and test sets. Therefore, we report results using both methods of splitting the data. The first method consists of four different splits: maximal, downscale, upscale, and minimal. Minimal is particularly challenging as the train set contains only 10 images. Moreover, upscale appears to be the easiest for the majority of methods [18]. The second method of splitting this data is much more succinct, leaving 1200 images in the testing set and 800 images in the training set [28]. This split comes from the original paper, so we call it the original split [5].
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\",\n \"polygon\": [\n [\n 49.68017578125,\n 679.078125\n ],\n [\n 286.875,\n 679.078125\n ],\n [\n 286.875,\n 713.4067840576172\n ],\n [\n 49.68017578125,\n 713.4067840576172\n ]\n ],\n \"bbox\": [\n 49.68017578125,\n 679.078125,\n 286.875,\n 713.4067840576172\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/4\",\n \"2\": \"/page/3/SectionHeader/3\",\n \"4\": \"/page/4/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/4/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"\\\"minimal,\\\" and \\\"downscale.\\\" We see in Table 3 that the \\\"upscale\\\" split and \\\"downscale\\\" split give us state of the art results on counting for this dataset. For this experiment, we sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap. We also added left-right flips of each image to our training data.
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|---|
| AMDCN | 290.82 |
| Hydra2s [18] | 333.73 |
| MCNN [28] | 377.60 |
| [27] | 467.00 |
| [23] | 295.80 |
| [3] | 318.10 |
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Mean absolute error of various methods on UCF crowds
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(L=1) | GAME
(L=2) | GAME
(L=3) |
|---|
AMDCN
[18] | 9.77
10.99 | 13.16
13.75 | 15.00
16.69 | 15.87
19.32 |
[15] + SIFT
from [14] | 13.76 | 16.72 | 20.72 | 24.36 |
[13] + RGB
Norm + Filters
from [14] | 17.68 | 19.97 | 23.54 | 25.84 |
HOG-2
from [14] | 13.29 | 18.05 | 23.65 | 28.41 |
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| AMDCN (without perspective information) | 1.63 | 1.43 | 0.63 | 1.71 | 1.74 |
| AMDCN (with perspective information) | 1.60 | 1.24 | 1.37 | 1.59 | 1.72 |
| [18] (with perspective information) | 1.65 | 1.79 | 1.11 | 1.50 | - |
| [18] (without perspective information) | 2.22 | 1.93 | 1.37 | 2.38 | - |
| [15] | 1.70 | 1.28 | 1.59 | 2.02 | - |
| [13] | 1.70 | 2.16 | 1.61 | 2.20 | - |
| [19] | 1.43 | 1.30 | 1.59 | 1.62 | - |
| [2] | 1.24 | 1.31 | 1.69 | 1.49 | - |
| [27] | 1.70 | 1.26 | 1.59 | 1.52 | 1.60 |
| [28] | - | - | - | - | 1.07 |
| [1,28] | - | - | - | - | 2.16 |
| [7] | - | - | - | - | 2.25 |
| [5] | - | - | - | - | 2.24 |
| [6] | - | - | - | - | 2.07 |
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PtEf8At/8AfDf4UAS0VF9oj/2/++G/wo+0R/7f/fDf4UAS1R1DSrbU9nntOpQMoMM7xEq2Mg7SMg4H5VZ+0R/7f/fDf4UfaI/9v/vhv8KAKB8Pad58UojlVYjGyQrM4iygAQ7M7cjAxx2HoK1Ki+0R/wC3/wB8N/hR9oj/ANv/AL4b/CgCnPollcX4vJRMzh1k8vz38suuNrFM7SRgduwPYVo1ELmIjILEeoQ/4UfaI/8Ab/74b/CgCWiovtEf+3/3w3+FH2iP/b/74b/CgCWiovtEf+3/AN8N/hR9oj/2/wDvhv8ACgCWiovtEf8At/8AfDf4UG4jAyd//fB/woAloqL7RGRkb/8Avg/4UqTI7bQTnGcFSP50ASUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUhIUEngDk1H9oj/2/++D/AIUAS0VF9oj/ANv/AL4b/Cj7RH/t/wDfDf4UAS0VF9oj/wBv/vhv8KPtEf8At/8AfDf4UAS0VF9oj/2/++G/wo+0R/7f/fDf4UAS0VEbmIDJLAf7h/wo+0R/7f8A3w3+FAEtFRfaI/8Ab/74b/Cj7RH/ALf/AHw3+FAEtFRfaI/9v/vhv8KPtEf+3/3w3+FADbyzivrZrebeEJDZjcowIOQQQQRyKzm8Nac1usI+1LguWdLqRXk343bmDZbOB16YGMVp/aI/9v8A74b/AAo+0R/7f/fDf4UAOhhjggjhiQJFGoRFUcKAMACqmo6Ra6ns+0GddqsmYZ3iyrYyp2kZBwKsm5iHUsM/7B/wo+0R/wC3/wB8N/hQA6KKOCJIokCRooVVA4AHQU/FRfaI/wDb/wC+G/wo+0R/7f8A3w3+FAEtFRfaI/8Ab/74b/Cj7RH/ALf/AHw3+FAEtFRfaI/9v/vhv8KPtEf+3/3w3+FAEtFRfaYgQMtk9PkP+FH2iP8A2/8Avhv8KAJaKi+0R/7f/fDf4UfaI/8Ab/74b/CgCWmyRpLG8bjKOpVh6g0z7RH/ALf/AHw3+FH2iP8A2/8Avhv8KAM1PDenxxyKDdF5NmZmupDIAmSoDlsgDJ475Oc5NXrGxt9OtRb2yssYZmJZizMzEliSeSSSTk1J9oj/ANv/AL4b/Cj7RH/t/wDfDf4UAR3tjDfwCKUyKAwdWikaNlI6EFSDRY2Nvp1qttbIVjDM3zMWYsxLMSTySSSST61J9pizjLZ9Nh/wo+0R/wC3/wB8N/hQBLiiovtEf+3/AN8N/hR9oj/2/wDvhv8ACgCWiovtEf8At/8AfDf4UfaI/wDb/wC+G/woAloqL7RH/t/98N/hR9oj/wBv/vhv8KAJaKi+0xZIy2R/sH/Cg3MQBJLADqdh/wAKAJaKKKACiiigAooooAKKKKACiiigAooooAKKKY8qI21ic4zgKT/KgB9FRfaI/wDb/wC+G/wo+0R/7f8A3w3+FAEtFRfaI/8Ab/74b/Cj7RH/ALf/AHw3+FAEtFRfaI/9v/vhv8KPtEf+3/3w3+FAEtFRfaI/9v8A74b/AAo+0xEkZbI6/If8KAJaKi+0R/7f/fDf4UfaI/8Ab/74b/CgCWiovtEf+3/3w3+FH2iP/b/74b/CgCWs6TRLKXUReuJmkEglCGd/L3gbQ2zO3OPb3681c+0R/wC3/wB8N/hR9oj/ANv/AL4b/CgCnYaJZabOZoBM0nl+UplneTYmc7V3E4H09B6CtEgHqKi+0R/7f/fDf4UC5iPQsf8AgB/woAp2GiWWmzGaATNIYxErTTvJsQHO1dxOB/8AW9BWjUX2iP8A2/8Avhv8KPtEf+3/AN8N/hQBLRUX2iP/AG/++G/wo+0R/wC3/wB8N/hQBLRUX2iP/b/74b/Cj7RH/t/98N/hQBLRUX2iP/b/AO+G/wAKBcxEZBYj/cP+FAEtFRfaI/8Ab/74b/Cj7RH/ALf/AHw3+FAEtFRfaI/9v/vhv8KPtEf+3/3w3+FAFPUdEstVbN0JipQxuiTuiyIeqsFIDD6+p9TQNDshqK3xWYyK5lRGmcxq5XaWCZ2g4JHTufWrn2iP/b/74b/Cj7RH/t/98N/hQBLWcNEshqS3xEzSrIZUVp3MaOVKlghO0HBI6dz6mrn2iMf3/wDvg/4UC5jIyN2P9w/4UAS4oqL7RH/t/wDfDf4UfaI/9v8A74b/AAoAloqL7RH/ALf/AHw3+FH2iP8A2/8Avhv8KAJaKi+0R/7f/fDf4UfaI/8Ab/74b/CgCWiojcxAZJYD/cP+FH2iP/b/AO+G/wAKAJaKYkqSEhScjkggj+dPoAKKKKAOa8Tm6MixWN/crqEsRWztYMACTPMsh7oMrnPHbkkCkQ3b+KI0tdQuLny5ma+zgQRRlDsiA/v5KHjnGSeCAdS/0DSdTuRc32n29xMECCSRASFyTjPpkn86SHw9o9vfm+h063jui5kMqoA249Tn1OTQBcdit0MIz/J2x6+5p3mv/wA8JPzX/Gj/AJev+Af1qWgCLzX/AOeEn5r/AI0ea/8Azwk/Nf8AGpaKAIvNf/nhJ+a/40ea/wDzwk/Nf8alooAi81/+eEn5r/jR5r/88JPzX/GpaKAKyyP9ok/cSdF7r7+9Sea//PCT81/xqGO6jOqz2vIlWJJOehUkjj8RVui1hKSlsRea/wDzwk/Nf8aPNf8A54Sfmv8AjUtFAyLzX/54Sfmv+NHmv/zwk/Nf8alooAi81/8AnhJ+a/40ea//ADwk/Nf8alooArRSP5k37iT7/qv90e9Sea//ADwk/Nf8aIv9ZP8A7/8A7KKloAi81/8AnhJ+a/40ea//ADwk/Nf8alooAi81/wDnhJ+a/wCNHmv/AM8JPzX/ABqWigCLzX/54Sfmv+NHmv8A88JPzX/GpaKAK0EjiM/uJD87d1/vH3qTzX/54Sfmv+NFv/qz/vv/AOhGpaAIvNf/AJ4Sfmv+NHmv/wA8JPzX/GpaKAIvNf8A54Sfmv8AjR5r/wDPCT81/wAalooAi81/+eEn5r/jR5r/APPCT81/xqWigCtbyOLeP9xIePVf8ak81/8AnhJ+a/40W3/HtH/u1LQBF5r/APPCT81/xo81/wDnhJ+a/wCNS0UARea//PCT81/xo81/+eEn5r/jUtFAEXmv/wA8JPzX/GmySv5T/uJPunuv+NT0yX/Uv/umgCOOV/LX9xJ0Hdf8aA7NdJmNk+RupHqvoalj/wBUn+6KY3/H1H/uN/NaAJaKKKACiiigAooooAKKKKACiiigAooooAZN/qJP90/ypU/1a/QUk3+ok/3T/KlT/Vr9BQA6iiigAooooAKKKKAIrj/Un6j+YqWqupXCWmny3EgYpGAzbRk4yM1ZBDAEHIPIIot1FzK/L1FooooGFFFFABRRRQBFN/yz/wB8VLUU3/LP/fFS0AFFFFABRRRQAUUUUARP/r4vx/lUtRP/AK+L8f5VLQAUUUUAFFFFABRRRQBF/wAvQ/3D/Opai/5eh/uH+dS0AFFFFABRRRQAUUUUARJ/x8S/Rf60tx/x7S/7h/lSJ/x8S/Rf60tx/wAe0v8AuH+VAEg6CigdBRQAUUUUAFFFFABRRRQAUUUUAFFFFAHNW3il5/FLaR5MPE0kO0SEyoFQN5jLjG05AHPce+N5pEjujvdVygxuOO9ZMPhsRajHN9sdraK7kvY4NgyJZAwbL9SvzsQPfqQMVsD/AI+j/uD+ZoAPtMH/AD2j/wC+hR9pg/57R/8AfQqWigCL7TB/z2j/AO+hR9pg/wCe0f8A30KlooAi+0wf89o/++hR9pg/57R/99CpaKAIvtMH/PaP/voVHHcQ+dKfOj5I/iHpVmoo/wDXTfUfyoAPtMH/AD2j/wC+hR9pg/57R/8AfQqWigCL7TB/z2j/AO+hR9pg/wCe0f8A30KlooAi+0wf89o/++hR9pg/57R/99CpaKAIvtMH/PaP/voVHDcQjfmaP75/iFWazNG1a11eO8e0YssF1JA5P95cZx7c01FtXKUW02tkXvtMH/PaP/voUfaYP+e0f/fQqWikSRfaYP8AntH/AN9Cj7TB/wA9o/8AvoVLRQBF9pg/57R/99Cj7TB/z2j/AO+hUtFAEX2mD/ntH/30Kjt7iEQKDNGD/vCrNRW/+oX8f50AH2mD/ntH/wB9Cj7TB/z2j/76FS0UARfaYP8AntH/AN9Cj7TB/wA9o/8AvoVLRQBF9pg/57R/99Cj7TB/z2j/AO+hUtFAEL3EHlt++j6H+IUkVxAIUBmj+6P4hUr/AOrb6Gkh/wBTH/uj+VADftMH/PaP/voUfaYP+e0f/fQqWigCL7TB/wA9o/8AvoUfaYP+e0f/AH0KlooAi+0wf89o/wDvoUfaYP8AntH/AN9CpaKAK1xcQmBwJoycf3hUn2mD/ntH/wB9Ci4/493+lS0AQRyJJcuUdWGxehz3NT1EP+Pp/wDcX+ZqWgAooooAKKKKAIv+Xr/gH9alqL/l6/4B/WpaACiiigAooooAKKKKAMXVP9F1CDUxwIWWKY/9M34P5HafwNbVVZreO7W6t5RmOWMIw9iCKh0W4kn05UnObi3YwTe7Lxn8Rg/jVbx9DBe5Wa6S1+a0f4W+5mhRRRUm4UUUUAFFFFAEUX+sn/3/AP2UVLUUX+sn/wB//wBlFS0AFFFFABRRRQAUUUUARW/+rP8Avv8A+hGpait/9Wf99/8A0I1LQAUUUUAFFFFABRRRQBFbf8e0f+7UtRW3/HtH/u1LQAUUUUAFFFFABTJf9S/+6afTJf8AUv8A7poAWP8A1Sf7opjf8fUf+4381p8f+qT/AHRTG/4+o/8Acb+a0AS0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAyb/USf7p/lSp/q1+gpJv9RJ/un+VKn+rX6CgB1FFFABRRRQAUUUUAQXkay2kkbjcjgKwPcE1T0KR/7O+zSnMto5t3J77fun8V2n8avXH+pP1H8xWf/wAefiT0jvov/Iif4qf/AB2qjqmjCr7s4z+T+e342+81aKKKk3CiiigAooooAim/5Z/74qWopv8Aln/vipaACiiigAooooAKKKKAIn/18X4/yqWon/18X4/yqWgAooooAKKKKACiiigCL/l6H+4f51LUX/L0P9w/zqWgAooooAKKKKACiiigCJP+PiX6L/WluP8Aj2l/3D/KkT/j4l+i/wBaW4/49pf9w/yoAkHQUUDoKKACiiigAooooAKKKKACiiigAooooAKiH/H0f9wfzNS1EP8Aj6P+4P5mgCWiiigAooooAKKKKACoo/8AXTfUfyqWoo/9dN9R/KgCWiiigAooooAKKKKAMvxDfyadolxLBzdOBDbr6yudqfqQfwrH8MWKaFrt9pMZzE1tDMh/vMuY3P1O1D+NXbz/AImXi2ysxzDp0ZvJvTzGykY/Le34CmX/APovifR7zossk9m5/wB5d6/rH+tdMdI8ndN/qvy/E7IaQ9n/ADJv9V+X4nRUUUVzHGFFFFABRRRQAVFb/wCoX8f51LUVv/qF/H+dAEtFFFABRRRQAUUUUANf/Vt9DSQ/6mP/AHR/Klf/AFbfQ0kP+pj/AN0fyoAfRRRQAUUUUAFFFFAEVx/x7v8ASpaiuP8Aj3f6VLQBEP8Aj6f/AHF/malqIf8AH0/+4v8AM1LQAUUUUAFFFFAEX/L1/wAA/rUtRf8AL1/wD+tS0AFFFFABRRRQAUUUUARJ/wAfEv0X+tZ4/wBC8REdIr+PP/bVB/Vf/Qa0E/4+Jfov9ap63BJLpxlhGbi2YTxD1ZecfiMj8aqG9u5hiE+XnW8df8/vV0aNFR288d1bRXERzHIgdT7EZqSpNk01dBRRRQMKKKKAIov9ZP8A7/8A7KKlqKL/AFk/+/8A+yipaACiiigAooooAKKKKAIrf/Vn/ff/ANCNS1Fb/wCrP++//oRqWgAooooAKKKKACiiigCK2/49o/8AdqWorb/j2j/3aloAKKKKACiiigApkv8AqX/3TT6ZL/qX/wB00ALH/qk/3RTG/wCPqP8A3G/mtPj/ANUn+6KY3/H1H/uN/NaAJaKKKACiiigAooooAKKKKACiiigCvf3sOnWFxe3BIhgjMjkDJwBngVxFjr2r6za6lFZX0Mt5/aUSqttLEPIgaFHKqWBDYIdc4JJ3EDjjv6o3GjafdLMJbcEzTLO7KxVvMChQwYEEEKoGQRxQBW0W8N7oJd2uGljaWGX7RsLh0ZlYEoApwQcEDpitFJW2L+4k6e3+NRxWlvY6eba1iWKFFbao98kn3JJJJ7k1YT/Vr9BQAzzW/wCeEn/jv+NHmt/zwk/8d/xqWigCLzW/54Sf+O/40ea3/PCT/wAd/wAalooAi81v+eEn/jv+NHmt/wA8JP8Ax3/GpaKAK08rGE/uZByPT1+tUdbMhsBcxwSebaOLhenIX7w6913D8a0rj/Un6j+YqQgMCCMg8EGnF2dyKsOeDj3IY7nzY1kSKRkcBlI28g/jTvNb/nhJ/wCO/wCNUNCJitZbBz89lKYhnunVD/3yQPwrUokrOwqU+eCk/wCn1IvNb/nhJ/47/jR5rf8APCT/AMd/xqWikaEXmt/zwk/8d/xo81v+eEn/AI7/AI1LRQBWmlY+X+5k++PT/GpPNb/nhJ/47/jRN/yz/wB8VLQBF5rf88JP/Hf8aPNb/nhJ/wCO/wCNS0UARea3/PCT/wAd/wAaPNb/AJ4Sf+O/41LRQBF5rf8APCT/AMd/xo81v+eEn/jv+NS0UAVnlbz4v3Mnf0/xqTzW/wCeEn/jv+ND/wCvi/H+VS0ARea3/PCT/wAd/wAaPNb/AJ4Sf+O/41LRQBF5rf8APCT/AMd/xo81v+eEn/jv+NS0UARea3/PCT/x3/GjzW/54Sf+O/41LRQBW81vtIPkyfc6cev1qTzW/wCeEn/jv+NH/L0P9w/zqWgCLzW/54Sf+O/40ea3/PCT/wAd/wAalooAi81v+eEn/jv+NHmt/wA8JP8Ax3/GpaKAIvNb/nhJ/wCO/wCNHmt/zwk/8d/xqWigCskrefJ+5k6D0/xonlY28o8mQfIfT0+tSJ/x8S/Rf60tx/x7S/7h/lQBIOgooHQUUAFFFFABRRRQAUUUUAFFFFABRRRQAVEP+Po/7g/malqIf8fR/wBwfzNAEtFFFABRRRQAUUUUAFRR/wCum+o/lUtRR/66b6j+VAEtFFFABRRRQAUjMqIXYhVUZJPQClrD8UyPJpkemQsVn1KUWqkdVQ8yN+CBv0qoR5pJF04c8lETwsrXFlcavICJNTmNwueoi+7GP++AD9Sai8UAx6DNeqPmsbmO7H0RwW/8d3V0EcaQxJFGoVEUKqjoAOgqrcWqX2n3tpJ9ydXib6MMf1q1UXtefpf8DRVV7ZT6X/Dt9xcBBAIOQaKyfDF0954Z06aT/W+QqSf76/K36g1rVEo8snF9DKcXCTi+gUUUVJIUUUUAFRW/+oX8f51LUVv/AKhfx/nQBLRRRQAUUUUAFFFFADX/ANW30NJD/qY/90fypX/1bfQ0kP8AqY/90fyoAfRRRQAUUUUAFFFFAEVx/wAe7/SpaiuP+Pd/pUtAEQ/4+n/3F/malqIf8fT/AO4v8zUtABRRRQBzfid7sOsdhqFwl/LEVsrSADBkzzJISD+7GVznjr1JAqdZtQTxpHbzXSNZyWMkiQJHt2srxDcTk5PzH0AH4mrd/oGlandC5vbGKacIIxI2c7ck4+mSau/ZoftCXHlr5yIY1fHIUkEj6ZA/KgBrsVuhhGf5O2PX3NO81/8An3k/Nf8AGj/l6/4B/WpaAIvNf/n3k/Nf8aPNf/n3k/Nf8alooAi81/8An3k/Nf8AGjzX/wCfeT81/wAalooAi81/+feT81/xo81/+feT81/xqWigCssr/aJP3EnRe6+/vUnmv/z7yfmv+NCf8fEv0X+tS0AY2kSPaSXWmmF/3D74hlf9U+SO/Y7h+Fanmv8A8+8n5r/jWfqX+h6jZaiOE3fZpv8Adc/KT9Gx+ZrVqpa69zCh7qdP+X8un+XyIvNf/n3k/Nf8aPNf/n3k/Nf8aloqTci81/8An3k/Nf8AGjzX/wCfeT81/wAalooArRSv5k37iT7/AKr/AHR71J5r/wDPvJ+a/wCNEX+sn/3/AP2UVLQBF5r/APPvJ+a/40ea/wDz7yfmv+NS0UARea//AD7yfmv+NHmv/wA+8n5r/jUtFAEXmv8A8+8n5r/jR5r/APPvJ+a/41LRQBWglcRn9xIfnbuv94+9Sea//PvJ+a/40W/+rP8Avv8A+hGpaAIvNf8A595PzX/GjzX/AOfeT81/xqWigCLzX/595PzX/GjzX/595PzX/GpaKAIvNf8A595PzX/GjzX/AOfeT81/xqWigCtbyuLeP9xIeOuV/wAak81/+feT81/xotv+PaP/AHaloAi81/8An3k/Nf8AGjzX/wCfeT81/wAalooAi81/+feT81/xo81/+feT81/xqWigCLzX/wCfeT81/wAabJK/lP8AuJPunuv+NT0yX/Uv/umgCOOV/LX9xJ0Hdf8AGgOzXSZjZPkb7xHqvoalj/1Sf7opjf8AH1H/ALjfzWgCWiiigAooooAKKKKACiiigAooooAKKKKAGTf6iT/dP8qVP9Wv0FJN/qJP90/ypU/1a/QUAOooooAKKKKACiiigCK4/wBSfqP5ipaiuP8AUn6j+YqWgDKm/wBD8RQS9I72Mwt/vrll/Mbh+ArVrP1uB5tLkeEZngIni/3kOcfjgj8at286XVtFcRHMcqB1PsRmqeqTMKfu1JQ+a+e/46/MloooqTcKKKKAIpv+Wf8Avipaim/5Z/74qWgAooooAKKKKACiiigCJ/8AXxfj/Kpaif8A18X4/wAqloAKKKKACiiigAooooAi/wCXof7h/nUtRf8AL0P9w/zqWgAooooAKKKKACiiigCJP+PiX6L/AFpbj/j2l/3D/KkT/j4l+i/1pbj/AI9pf9w/yoAkHQUUDoKKACiiigAooooAKKKKACiiigAooooA5tYbuTxkY4NUvTbW8fn3MTMpQl8hIx8uQBhmPOeF9a3WYrdHEbP8g+7j19zTo7aGKeaaOMLLMQZGHVsDAz+FA/4+j/uD+ZoAPNf/AJ95PzX/ABo81/8An3k/Nf8AGpaKAIvNf/n3k/Nf8aPNf/n3k/Nf8alooAi81/8An3k/Nf8AGjzX/wCfeT81/wAalooAi81/+feT81/xqOOV/Ol/cSdR3X0+tWaij/1031H8qADzX/595PzX/GjzX/595PzX/GpaKAIvNf8A595PzX/GjzX/AOfeT81/xqWigCLzX/595PzX/GsKCRtS8Xz3HkyNDpkX2dBlf9c+Gc9ey7B/wI1tX97Fp2nXN7OcRQRtI30AzVHw3ZS2WiQ/aR/pdwWuLj/ro53EfhnH4VrD3YOXy/z/AK8zaHuwlP5L9fw0+Zo+a/8Az7yfmv8AjUcMr/P+4kPznuv+NWaih/5af75rIxMPw7I1vNq+n+TJ/o987qoK8JKBIO/qzflW55r/APPvJ+a/41jp/ovjmVeiX1gr/V4nIP6SL+Vbta1dZX7/ANP8TavrLm7pP/P8bkXmv/z7yfmv+NHmv/z7yfmv+NS0VkYkXmv/AM+8n5r/AI0ea/8Az7yfmv8AjUtFAEXmv/z7yfmv+NR28riBf3Eh/Ff8as1Fb/6hfx/nQAea/wDz7yfmv+NHmv8A8+8n5r/jUtFAEXmv/wA+8n5r/jR5r/8APvJ+a/41LRQBF5r/APPvJ+a/40ea/wDz7yfmv+NS0UAQvK/lt+4k6Huv+NJFK/kp+4k+6O6/41K/+rb6Gkh/1Mf+6P5UAN81/wDn3k/Nf8aPNf8A595PzX/GpaKAIvNf/n3k/Nf8aPNf/n3k/Nf8alooAi81/wDn3k/Nf8aPNf8A595PzX/GpaKAK1xK5gf9xIOPVf8AGpPNf/n3k/Nf8aLj/j3f6VLQBBGxa5fKMnyLwcep9DU9RD/j6f8A3F/maloAKKKKACiiigCL/l6/4B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\",\n \"polygon\": [\n [\n 308.86199951171875,\n 446.23602294921875\n ],\n [\n 398.0390625,\n 446.23602294921875\n ],\n [\n 398.0390625,\n 458.19122314453125\n ],\n [\n 308.86199951171875,\n 458.19122314453125\n ]\n ],\n \"bbox\": [\n 308.86199951171875,\n 446.23602294921875,\n 398.0390625,\n 458.19122314453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/7/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/7/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"This material is based upon work supported by the National Science Foundation under Grant No. 1359275 and 1659788. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Furthermore, we acknowledge Kyle Yee and Sridhama Prakhya for their helpful conversations and insights during the research process.
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\",\n \"polygon\": [\n [\n 90.0,\n 91.70068359375\n ],\n [\n 144.4833984375,\n 91.70068359375\n ],\n [\n 144.4833984375,\n 104.80078125\n ],\n [\n 90.0,\n 104.80078125\n ]\n ],\n \"bbox\": [\n 90.0,\n 91.70068359375,\n 144.4833984375,\n 104.80078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/1/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/1/TableOfContents/2\",\n \"block_type\": \"TableOfContents\",\n \"html\": \"| 1 | Introduction | 3 |
|---|
| 2 | Switch Transformer
2.1 Simplifying Sparse Routing
2.2 Efficient Sparse Routing
2.3 Putting It All Together: The Switch Transformer
2.4 Improved Training and Fine-Tuning Techniques | 4
5
6
8 |
|---|
| 3 | Scaling Properties
3.1 Scaling Results on a Step-Basis
3.2 Scaling Results on a Time-Basis
3.3 Scaling Versus a Larger Dense Model | 11
12
13
13 |
|---|
| 4 | Downstream Results
4.1 Fine-Tuning
4.2 Distillation
4.3 Multilingual Learning | 14
14
16
17 |
|---|
| 5 | Designing Models with Data, Model, and Expert-Parallelism
5.1 Data Parallelism
5.2 Model Parallelism
5.3 Model and Data Parallelism
5.4 Expert and Data Parallelism
5.5 Expert, Model and Data Parallelism
5.6 Towards Trillion Parameter Models | 18
20
21
22
22
22
24 |
|---|
| 6 | Related Work | 24 |
|---|
| 7 | Discussion | 25 |
|---|
| 8 | Future Work | 26 |
|---|
| 9 | Conclusion | 27 |
|---|
| A | Switch for Attention | 27 |
|---|
| B | Preventing Token Dropping with No-Token-Left-Behind | 29 |
|---|
| C | Encouraging Exploration Across Experts | 29 |
|---|
| D | Switch Transformers in Lower Compute Regimes | 29 |
|---|
| E | Relation of Upstream to Downstream Model Performance | 32 |
|---|
| F | Pseudo Code for Switch Transformers | 33 |
|---|
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2.1 Simplifying Sparse Routing
2.2 Efficient Sparse Routing
2.3 Putting It All Together: The Switch Transformer
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3.1 Scaling Results on a Step-Basis
3.2 Scaling Results on a Time-Basis
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4.1 Fine-Tuning
4.2 Distillation
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5.1 Data Parallelism
5.2 Model Parallelism
5.3 Model and Data Parallelism
5.4 Expert and Data Parallelism
5.5 Expert, Model and Data Parallelism
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Introduction
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Left Plot: Scaling properties for increasingly sparse (more experts) Switch Transformers. Right Plot: Negative log perplexity comparing Switch Transformers to T5 (Raffel et al., 2019) models using the same compute budget.
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We further show the improvements hold even with limited computational resources, using as few as two experts.\",\n \"polygon\": [\n [\n 105.3369140625,\n 175.8603515625\n ],\n [\n 524.14453125,\n 175.8603515625\n ],\n [\n 524.14453125,\n 228.99737548828125\n ],\n [\n 105.3369140625,\n 228.99737548828125\n ]\n ],\n \"bbox\": [\n 105.3369140625,\n 175.8603515625,\n 524.14453125,\n 228.99737548828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/1/SectionHeader/1\",\n \"3\": \"/page/2/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \" Successful distillation of sparse pre-trained and specialized fine-tuned models into small dense models. We reduce the model size by up to 99% while preserving 30% of the quality gains of the large sparse teacher.\",\n \"polygon\": [\n [\n 106.2333984375,\n 238.412109375\n ],\n [\n 523.546875,\n 238.412109375\n ],\n [\n 523.546875,\n 277.857421875\n ],\n [\n 106.2333984375,\n 277.857421875\n ]\n ],\n \"bbox\": [\n 106.2333984375,\n 238.412109375,\n 523.546875,\n 277.857421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/1/SectionHeader/1\",\n \"3\": \"/page/2/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/ListItem/5\",\n \"block_type\": \"ListItem\",\n \"html\": \" Improved pre-training and fine-tuning techniques: (1) selective precision training that enables training with lower bfloat16 precision (2) an initialization scheme that allows for scaling to a larger number of experts and (3) increased expert regularization that improves sparse model fine-tuning and multi-task training.\",\n \"polygon\": [\n [\n 106.2333984375,\n 285.978515625\n ],\n [\n 523.546875,\n 285.978515625\n ],\n [\n 523.546875,\n 339.513427734375\n ],\n [\n 106.2333984375,\n 339.513427734375\n ]\n ],\n \"bbox\": [\n 106.2333984375,\n 285.978515625,\n 523.546875,\n 339.513427734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/1/SectionHeader/1\",\n \"3\": \"/page/2/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/ListItem/6\",\n \"block_type\": \"ListItem\",\n \"html\": \" A measurement of the pre-training benefits on multilingual data where we find a universal improvement across all 101 languages and with 91% of languages benefiting from 4x+ speedups over the mT5 baseline (Xue et al., 2020).\",\n \"polygon\": [\n [\n 106.083984375,\n 348.8203125\n ],\n [\n 523.845703125,\n 348.8203125\n ],\n [\n 523.845703125,\n 387.9964599609375\n ],\n [\n 106.083984375,\n 387.9964599609375\n ]\n ],\n \"bbox\": [\n 106.083984375,\n 348.8203125,\n 523.845703125,\n 387.9964599609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/1/SectionHeader/1\",\n \"3\": \"/page/2/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/ListItem/7\",\n \"block_type\": \"ListItem\",\n \"html\": \" An increase in the scale of neural language models achieved by efficiently combining data, model, and expert-parallelism to create models with up to a trillion parameters. These models improve the pre-training speed of a strongly tuned T5-XXL baseline by 4x.\",\n \"polygon\": [\n [\n 105.6357421875,\n 397.546875\n ],\n [\n 522.94921875,\n 397.546875\n ],\n [\n 522.94921875,\n 450.140625\n ],\n [\n 105.6357421875,\n 450.140625\n ]\n ],\n \"bbox\": [\n 105.6357421875,\n 397.546875,\n 522.94921875,\n 450.140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/1/SectionHeader/1\",\n \"3\": \"/page/2/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/1/SectionHeader/1\",\n \"3\": \"/page/2/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/3/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"2. Switch Transformer
\",\n \"polygon\": [\n [\n 89.27490234375,\n 468.31640625\n ],\n [\n 223.224609375,\n 468.31640625\n ],\n [\n 223.224609375,\n 481.48602294921875\n ],\n [\n 89.27490234375,\n 481.48602294921875\n ]\n ],\n \"bbox\": [\n 89.27490234375,\n 468.31640625,\n 223.224609375,\n 481.48602294921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The guiding design principle for Switch Transformers is to maximize the parameter count of a Transformer model (Vaswani et al., 2017) in a simple and computationally efficient way. The benefit of scale was exhaustively studied in Kaplan et al. (2020) which uncovered powerlaw scaling with model size, data set size and computational budget. Importantly, this work advocates training large models on relatively small amounts of data as the computationally optimal approach.
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}\n },\n {\n \"id\": \"/page/4/Caption/335\",\n \"block_type\": \"Caption\",\n \"html\": \"Figure 2: Illustration of a Switch Transformer encoder block. We replace the dense feed forward network (FFN) layer present in the Transformer with a sparse Switch FFN layer (light blue). The layer operates independently on the tokens in the sequence. We diagram two tokens (x1 = \\\"More\\\" and x2 = \\\"Parameters\\\" below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).
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\",\n \"polygon\": [\n [\n 89.57373046875,\n 430.6282043457031\n ],\n [\n 262.7237243652344,\n 430.6282043457031\n ],\n [\n 262.7237243652344,\n 441.6328125\n ],\n [\n 89.57373046875,\n 441.6328125\n ]\n ],\n \"bbox\": [\n 89.57373046875,\n 430.6282043457031,\n 262.7237243652344,\n 441.6328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"4\": \"/page/4/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/4/TextInlineMath/4\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"Mixture of Expert Routing. Shazeer et al. (2017) proposed a natural language Mixtureof-Experts (MoE) layer which takes as an input a token representation x and then routes this to the best determined top-k experts, selected from a set {Ei(x)}N of N experts. The router variable Wr produces logits h(x) = Wr \\n· x which are normalized via a softmax distribution over the available N experts at that layer. The gate-value for expert i is given by,
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}\n },\n {\n \"id\": \"/page/5/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \"Figure 3: Illustration of token routing dynamics. Each expert processes a fixed batch-size of tokens modulated by the capacity factor. Each token is routed to the expert with the highest router probability, but each expert has a fixed batch size of (total tokens / num experts) × capacity factor. If the tokens are unevenly dispatched then certain experts will overflow (denoted by dotted red lines), resulting in these tokens not being processed by this layer. A larger capacity factor alleviates this overflow issue, but also increases computation and communication costs (depicted by padded white/empty slots).\",\n \"polygon\": [\n [\n 90.0,\n 428.6001892089844\n ],\n [\n 521.8234252929688,\n 428.6001892089844\n ],\n [\n 521.8234252929688,\n 534.3533325195312\n ],\n [\n 90.0,\n 534.3533325195312\n ]\n ],\n \"bbox\": [\n 90.0,\n 428.6001892089844,\n 521.8234252929688,\n 534.3533325195312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"4\": \"/page/4/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/5/SectionHeader/5\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"2.2 Efficient Sparse Routing
\",\n \"polygon\": [\n [\n 88.82666015625,\n 574.9432220458984\n ],\n [\n 245.73831176757812,\n 574.9432220458984\n ],\n [\n 245.73831176757812,\n 586.265625\n ],\n [\n 88.82666015625,\n 586.265625\n ]\n ],\n \"bbox\": [\n 88.82666015625,\n 574.9432220458984,\n 245.73831176757812,\n 586.265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/5/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"We use Mesh-Tensorflow (MTF) (Shazeer et al., 2018) which is a library, with similar semantics and API to Tensorflow (Abadi et al., 2016) that facilitates efficient distributed data and model parallel architectures. It does so by abstracting the physical set of cores to a logical mesh of processors. Tensors and computations may then be sharded per named dimensions, facilitating easy partitioning of models across dimensions. We design our model with TPUs in mind, which require statically declared sizes. Below we describe our distributed Switch Transformer implementation.
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\",\n \"polygon\": [\n [\n 89.4990234375,\n 93.29522705078125\n ],\n [\n 521.85107421875,\n 93.29522705078125\n ],\n [\n 521.85107421875,\n 172.1865234375\n ],\n [\n 89.4990234375,\n 172.1865234375\n ]\n ],\n \"bbox\": [\n 89.4990234375,\n 93.29522705078125,\n 521.85107421875,\n 172.1865234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Equation/2\",\n \"block_type\": \"Equation\",\n \"html\": \"\",\n \"polygon\": [\n [\n 165.7001953125,\n 182.8212890625\n ],\n [\n 521.9810180664062,\n 182.8212890625\n ],\n [\n 521.9810180664062,\n 209.794921875\n ],\n [\n 165.7001953125,\n 209.794921875\n ]\n ],\n \"bbox\": [\n 165.7001953125,\n 182.8212890625,\n 521.9810180664062,\n 209.794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"A capacity factor greater than 1.0 creates additional buffer to accommodate for when tokens are not perfectly balanced across experts. If too many tokens are routed to an expert (referred to later as dropped tokens), computation is skipped and the token representation is passed directly to the next layer through the residual connection. Increasing the expert capacity is not without drawbacks, however, since high values will result in wasted computation and memory. This trade-off is explained in Figure 3. Empirically we find ensuring lower rates of dropped tokens are important for the scaling of sparse expert-models. Throughout our experiments we didn't notice any dependency on the number of experts for the number of tokens dropped (typically < 1%). Using the auxiliary load balancing loss (next section) with a high enough coefficient ensured good load balancing. We study the impact that these design decisions have on model quality and speed in Table 1.
\",\n \"polygon\": [\n [\n 89.947265625,\n 220.13818359375\n ],\n [\n 521.8017578125,\n 220.13818359375\n ],\n [\n 521.8017578125,\n 366.53936767578125\n ],\n [\n 89.947265625,\n 366.53936767578125\n ]\n ],\n \"bbox\": [\n 89.947265625,\n 220.13818359375,\n 521.8017578125,\n 366.53936767578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"A Differentiable Load Balancing Loss. To encourage a balanced load across experts we add an auxiliary loss (Shazeer et al., 2017, 2018; Lepikhin et al., 2020). As in Shazeer et al. (2018); Lepikhin et al. (2020), Switch Transformers simplifies the original design in Shazeer et al. (2017) which had separate load-balancing and importance-weighting losses. For each Switch layer, this auxiliary loss is added to the total model loss during training. Given N experts indexed by i = 1 to N and a batch B with T tokens, the auxiliary loss is computed as the scaled dot-product between vectors f and P,
\",\n \"polygon\": [\n [\n 89.947265625,\n 368.9296875\n ],\n [\n 521.8467407226562,\n 368.9296875\n ],\n [\n 521.8467407226562,\n 461.3834228515625\n ],\n [\n 89.947265625,\n 461.3834228515625\n ]\n ],\n \"bbox\": [\n 89.947265625,\n 368.9296875,\n 521.8467407226562,\n 461.3834228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Equation/5\",\n \"block_type\": \"Equation\",\n \"html\": \"\",\n \"polygon\": [\n [\n 250.2685546875,\n 473.73046875\n ],\n [\n 521.9810791015625,\n 473.73046875\n ],\n [\n 521.9810791015625,\n 508.54736328125\n ],\n [\n 250.2685546875,\n 508.54736328125\n ]\n ],\n \"bbox\": [\n 250.2685546875,\n 473.73046875,\n 521.9810791015625,\n 508.54736328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"where fi is the fraction of tokens dispatched to expert i,
\",\n \"polygon\": [\n [\n 89.6484375,\n 514.246337890625\n ],\n [\n 358.892578125,\n 514.246337890625\n ],\n [\n 358.892578125,\n 526.1493530273438\n ],\n [\n 89.6484375,\n 526.1493530273438\n ]\n ],\n \"bbox\": [\n 89.6484375,\n 514.246337890625,\n 358.892578125,\n 526.1493530273438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Equation/7\",\n \"block_type\": \"Equation\",\n \"html\": \"\",\n \"polygon\": [\n [\n 231.890625,\n 535.8223266601562\n ],\n [\n 521.98095703125,\n 535.8223266601562\n ],\n [\n 521.98095703125,\n 567.31640625\n ],\n [\n 231.890625,\n 567.31640625\n ]\n ],\n \"bbox\": [\n 231.890625,\n 535.8223266601562,\n 521.98095703125,\n 567.31640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"and Pi is the fraction of the router probability allocated for expert i, 2
\",\n \"polygon\": [\n [\n 89.6484375,\n 572.7931213378906\n ],\n [\n 427.32421875,\n 572.7931213378906\n ],\n [\n 427.32421875,\n 586.3582153320312\n ],\n [\n 89.6484375,\n 586.3582153320312\n ]\n ],\n \"bbox\": [\n 89.6484375,\n 572.7931213378906,\n 427.32421875,\n 586.3582153320312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Equation/9\",\n \"block_type\": \"Equation\",\n \"html\": \"\",\n \"polygon\": [\n [\n 264.0146484375,\n 595.2151947021484\n ],\n [\n 521.9810180664062,\n 595.2151947021484\n ],\n [\n 521.9810180664062,\n 627.64453125\n ],\n [\n 264.0146484375,\n 627.64453125\n ]\n ],\n \"bbox\": [\n 264.0146484375,\n 595.2151947021484,\n 521.9810180664062,\n 627.64453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Since we seek uniform routing of the batch of tokens across the N experts, we desire both vectors to have values of 1/N. The auxiliary loss of Equation 4 encourages uniform routing since it is minimized under a uniform distribution. The objective can also be differentiated as
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\",\n \"polygon\": [\n [\n 88.6025390625,\n 93.005859375\n ],\n [\n 522.3515625,\n 93.005859375\n ],\n [\n 522.3515625,\n 185.625\n ],\n [\n 88.6025390625,\n 185.625\n ]\n ],\n \"bbox\": [\n 88.6025390625,\n 93.005859375,\n 522.3515625,\n 185.625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/7/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"2.3 Putting It All Together: The Switch Transformer
\",\n \"polygon\": [\n [\n 89.7978515625,\n 204.51019287109375\n ],\n [\n 384.890625,\n 204.51019287109375\n ],\n [\n 384.890625,\n 215.595703125\n ],\n [\n 89.7978515625,\n 215.595703125\n ]\n ],\n \"bbox\": [\n 89.7978515625,\n 204.51019287109375,\n 384.890625,\n 215.595703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\",\n \"4\": \"/page/7/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/7/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Our first test of the Switch Transformer starts with pre-training on the \\\"Colossal Clean Crawled Corpus\\\" (C4), introduced in (Raffel et al., 2019). For our pre-training objective, we use a masked language modeling task (Taylor, 1953; Fedus et al., 2018; Devlin et al., 2018) where the model is trained to predict missing tokens. In our pre-training setting, as determined in Raffel et al. (2019) to be optimal, we drop out 15% of tokens and then replace the masked sequence with a single sentinel token. To compare our models, we record the negative log perplexity.4 Throughout all tables in the paper, ↑ indicates that a higher value for that metric is better and vice-versa for ↓. A comparison of all the models studied in this work are in Table 9.
\",\n \"polygon\": [\n [\n 89.6484375,\n 225.79119873046875\n ],\n [\n 521.8588256835938,\n 225.79119873046875\n ],\n [\n 521.8588256835938,\n 345.0933837890625\n ],\n [\n 89.6484375,\n 345.0933837890625\n ]\n ],\n \"bbox\": [\n 89.6484375,\n 225.79119873046875,\n 521.8588256835938,\n 345.0933837890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\",\n \"4\": \"/page/7/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/7/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"A head-to-head comparison of the Switch Transformer and the MoE Transformer is presented in Table 1. Our Switch Transformer model is FLOP-matched to 'T5-Base' (Raffel et al., 2019) (same amount of computation per token is applied). The MoE Transformer, using top-2 routing, has two experts which each apply a separate FFN to each token and thus its FLOPS are larger. All models were trained for the same number of steps on identical hardware. Note that the MoE model going from capacity factor 2.0 to 1.25 actually slows down (840 to 790) in the above experiment setup, which is unexpected.5
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(Neg. Log Perp.) | Time to Quality
Threshold (↓)
(hours) | Speed (↑)
(examples/sec) |
|---|
| T5-Base | — | -1.731 | Not achieved† | 1600 |
| T5-Large | — | -1.550 | 131.1 | 470 |
| MoE-Base | 2.0 | -1.547 | 68.7 | 840 |
| Switch-Base | 2.0 | -1.554 | 72.8 | 860 |
| MoE-Base | 1.25 | -1.559 | 80.7 | 790 |
| Switch-Base | 1.25 | -1.553 | 65.0 | 910 |
| MoE-Base | 1.0 | -1.572 | 80.1 | 860 |
| Switch-Base | 1.0 | -1.561 | 62.8 | 1000 |
| Switch-Base+ | 1.0 | -1.534 | 67.6 | 780 |
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Head-to-head comparison measuring per step and per time benefits of the Switch Transformer over the MoE Transformer and T5 dense baselines. We measure quality by the negative log perplexity and the time to reach an arbitrary chosen quality threshold of Neg. Log Perp.=-1.50. All MoE and Switch Transformer models use 128 experts, with experts at every other feed-forward layer. For Switch-Base+, we increase the model size until it matches the speed of the MoE model by increasing the model hidden-size from 768 to 896 and the number of heads from 14 to 16. All models are trained with the same amount of computation (32 cores) and on the same hardware (TPUv3). Further note that all our models required pre-training beyond 100k steps to achieve our level threshold of -1.50. † T5-Base did not achieve this negative log perplexity in the 100k steps the models were trained.\",\n \"polygon\": [\n [\n 90.0,\n 273.73419189453125\n ],\n [\n 521.75390625,\n 273.73419189453125\n ],\n [\n 521.75390625,\n 433.6843566894531\n ],\n [\n 90.0,\n 433.6843566894531\n ]\n ],\n \"bbox\": [\n 90.0,\n 273.73419189453125,\n 521.75390625,\n 433.6843566894531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\",\n \"4\": \"/page/7/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/8/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"in the softmax computation for our router. We describe training difficulties here and the methods we use to overcome them to achieve stable and scalable training.
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| Switch-Base (float32) | -1.718 | 1160 |
| Switch-Base (bfloat16) | -3.780 [diverged] | 1390 |
| Switch-Base (Selective precision) | -1.716 | 1390 |
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We cast the local routing operations to float32 while preserving bfloat16 precision elsewhere to stabilize our model while achieving nearly equal speed to (unstable) bfloat16-precision training. We measure the quality of a 32 expert model after a fixed step count early in training its speed performance. For both Switch-Base in float32 and with Selective prevision we notice similar learning dynamics.
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(Neg. Log Perp.) | Std. Dev. of Quality
(Neg. Log Perp.) |
|---|
| Switch-Base (0.1x-init) | -2.72 | 0.01 |
| Switch-Base (1.0x-init) | -3.60 | 0.68 |
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Reducing the initialization scale results in better model quality and more stable training of Switch Transformer. Here we record the average and standard deviation of model quality, measured by the negative log perplexity, of a 32 expert model after 3.5k steps (3 random seeds each).\",\n \"polygon\": [\n [\n 90.0,\n 524.940185546875\n ],\n [\n 521.6483154296875,\n 524.940185546875\n ],\n [\n 521.6483154296875,\n 590.1328125\n ],\n [\n 90.0,\n 590.1328125\n ]\n ],\n \"bbox\": [\n 90.0,\n 524.940185546875,\n 521.6483154296875,\n 590.1328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/5/SectionHeader/5\",\n \"4\": \"/page/7/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/9/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"the average model quality, as measured by the Neg. Log Perp., is dramatically improved and there is a far reduced variance across runs. Further, this same initialization scheme is broadly effective for models spanning several orders of magnitude. We use the same approach to stably train models as small as our 223M parameter baseline to enormous models in excess of one trillion parameters.
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A sweep of dropout rates while fine-tuning Switch Transformer models pre-trained on 34B tokens of the C4 data set (higher numbers are better). We observe that using a lower standard dropout rate at all non-expert layer, with a much larger dropout rate on the expert feed-forward layers, to perform the best.
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KFYA8KGJyxA45655Mdz4bul/tO1Sxnuftk8ssU39oyRwKJCSRLGrjoWPCg7hjkZNdvRQBx2kTapZ3Guw2WmpdIb5hC/nBAjeXGMPnnb0OV3HqMcc9Ho2n/ANk6JYadv8z7Lbxw78Y3bVAz+lXFRE3bVVdx3NgYyfU06gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDNTSs6pqVzKytDewRQ7B1AXfn899Yem+EXs2sYTa6RHHZkf6ZDbKLicKMLnK4Q9CWBOccYzx11FAHO/wBk6xdW9rp+oXVtLa28sUj3C7vOuPLYMu5eiksqkkE55wBnjoqKKACiiigD/9k=\"\n }\n },\n {\n \"id\": \"/page/11/Caption/5\",\n \"block_type\": \"Caption\",\n \"html\": \"Figure 4: Scaling properties of the Switch Transformer. Left Plot: We measure the quality improvement, as measured by perplexity, as the parameters increase by scaling the number of experts. The top-left point corresponds to the T5-Base model with 223M parameters. Moving from top-left to bottom-right, we double the number of experts from 2, 4, 8 and so on until the bottom-right point of a 256 expert model with 14.7B parameters. Despite all models using an equal computational budget, we observe consistent improvements scaling the number of experts. Right Plot: Negative log perplexity per step sweeping over the number of experts. The dense baseline is shown with the purple line and we note improved sample efficiency of our Switch-Base models.
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All models trained on 32 TPUv3 cores with equal FLOPs per example. For a fixed amount of computation and training time, Switch Transformers significantly outperform the dense Transformer baseline. Our 64 expert Switch-Base model achieves the same quality in one-seventh the time of the T5-Base and continues to improve.
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Left Plot: Switch-Base is more sample efficient than both the T5-Base, and T5-Large variant, which applies 3.5x more FLOPS per token. Right Plot: As before, on a wall-clock basis, we find that Switch-Base is still faster, and yields a 2.5x speedup over T5-Large.
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\",\n \"polygon\": [\n [\n 89.349609375,\n 91.6331787109375\n ],\n [\n 522.3515625,\n 91.6331787109375\n ],\n [\n 522.3515625,\n 171.95037841796875\n ],\n [\n 89.349609375,\n 171.95037841796875\n ]\n ],\n \"bbox\": [\n 89.349609375,\n 91.6331787109375,\n 522.3515625,\n 171.95037841796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/13/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/14/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Fine-tuning tasks and data sets. We select tasks probing language capabilities including question answering, summarization and knowledge about the world. The language benchmarks GLUE (Wang et al., 2018) and SuperGLUE (Wang et al., 2019) are handled as composite mixtures with all the tasks blended in proportion to the amount of tokens present in each. These benchmarks consist of tasks requiring sentiment analysis (SST-2), word sense disambiguation (WIC), sentence similarty (MRPC, STS-B, QQP), natural language inference (MNLI, QNLI, RTE, CB), question answering (MultiRC, RECORD, BoolQ), coreference resolution (WNLI, WSC) and sentence completion (COPA) and sentence acceptability (CoLA). The CNNDM (Hermann et al., 2015) and BBC XSum (Narayan et al., 2018) data sets are used to measure the ability to summarize articles. Question answering is probed with the SQuAD data set (Rajpurkar et al., 2016) and the ARC Reasoning Challenge (Clark et al., 2018). And as in Roberts et al. (2020), we evaluate the knowledge of our models by fine-tuning on three closed-book question answering data sets: Natural Questions (Kwiatkowski et al., 2019), Web Questions (Berant et al., 2013) and Trivia QA (Joshi et al., 2017). Closed-book refers to questions posed with no supplemental reference or context material. To gauge the model's common sense reasoning we evaluate it on the Winogrande Schema Challenge (Sakaguchi et al., 2020). And finally, we test our model's natural language inference capabilities on the Adversarial NLI Benchmark (Nie et al., 2019).
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\",\n \"polygon\": [\n [\n 89.6484375,\n 426.578369140625\n ],\n [\n 521.7088623046875,\n 426.578369140625\n ],\n [\n 521.7088623046875,\n 518.9765625\n ],\n [\n 89.6484375,\n 518.9765625\n ]\n ],\n \"bbox\": [\n 89.6484375,\n 426.578369140625,\n 521.7088623046875,\n 518.9765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/13/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/14/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Fine-tuning results. We observe significant downstream improvements across many natural language tasks. Notable improvements come from SuperGLUE, where we find FLOP-matched Switch variants improve by 4.4 and 2 percentage points over the T5-Base and T5-Large baselines, respectively as well as large improvements in Winogrande, closed book Trivia QA, and XSum.8 In our fine-tuning study, the only tasks where we do not observe gains are on the AI2 Reasoning Challenge (ARC) data sets where the T5-Base outperforms Switch-Base on the challenge data set and T5-Large outperforms Switch-Large on the easy data set. Taken as a whole, we observe significant improvements spanning both reasoning and knowledge-heavy tasks. This validates our architecture, not just as one that pre-trains well, but can translate quality improvements to downstream tasks via fine-tuning.
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|---|
| T5-Base | 84.3 | 85.5 | 75.1 | 66.6 |
| Switch-Base | 86.7 | 87.2 | 79.5 | 73.3 |
| T5-Large | 87.8 | 88.1 | 82.7 | 79.1 |
| Switch-Large | 88.5 | 88.6 | 84.7 | 83.0 |
|
| Model | XSum | ANLI (R3) | ARC Easy | ARC Chal. |
|---|
| T5-Base | 18.7 | 51.8 | 56.7 | 35.5 |
| Switch-Base | 20.3 | 54.0 | 61.3 | 32.8 |
| T5-Large | 20.9 | 56.6 | 68.8 | 35.5 |
| Switch-Large | 22.3 | 58.6 | 66.0 | 35.5 |
|
| Model | CB Web QA | CB Natural QA | CB Trivia QA |
|---|
| T5-Base | 26.6 | 25.8 | 24.5 |
| Switch-Base | 27.4 | 26.8 | 30.7 |
| T5-Large | 27.7 | 27.6 | 29.5 |
| Switch-Large | 31.3 | 29.5 | 36.9 |
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\",\n \"polygon\": [\n [\n 89.4990234375,\n 513.2462158203125\n ],\n [\n 521.574951171875,\n 513.2462158203125\n ],\n [\n 521.574951171875,\n 564.99609375\n ],\n [\n 89.4990234375,\n 564.99609375\n ]\n ],\n \"bbox\": [\n 89.4990234375,\n 513.2462158203125,\n 521.574951171875,\n 564.99609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/13/SectionHeader/6\",\n \"4\": \"/page/15/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/15/TextInlineMath/5\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"Distillation techniques. In Table 6 we study a variety of distillation techniques. These techniques are built off of Sanh et al. (2019), who study distillation methods for BERT models. We find that initializing the dense model with the non-expert weights yields a modest improvement. This is possible since all models are FLOP matched, so non-expert layers will have the same dimensions. Since expert layers are usually only added at every or every other FFN layer in a Transformer, this allows for many of the weights to be initialized with trained parameters. Furthermore, we observe a distillation improvement using a mixture of 0.25 for the teacher probabilities and 0.75 for the ground truth label. By combining both techniques we preserve ≈ 30% of the quality gains from the larger sparse models with only ≈ 1/20th of the parameters. The quality gain refers to the percent of
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|---|
| T5-Base | 223M | -1.636 |
| Switch-Base | 3,800M | -1.444 |
| Distillation | 223M | (3%) -1.631 |
| + Init. non-expert weights from teacher | 223M | (20%) -1.598 |
| + 0.75 mix of hard and soft loss | 223M | (29%) -1.580 |
| Initialization Baseline (no distillation) | | |
| Init. non-expert weights from teacher | 223M | -1.639 |
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|---|
| Parameters | 223M | 1.1B | 2.0B | 3.8B | 7.4B | 14.7B |
| Pre-trained Neg. Log Perp. (↑) | -1.636 | -1.505 | -1.474 | -1.444 | -1.432 | -1.427 |
| Distilled Neg. Log Perp. (↑) | — | -1.587 | -1.585 | -1.579 | -1.582 | -1.578 |
| Percent of Teacher Performance | — | 37% | 32% | 30 % | 27 % | 28 % |
| Compression Percent | — | 82 % | 90 % | 95 % | 97 % | 99 % |
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We distill a Switch-Base model finetuned on the SuperGLUE tasks into a T5-Base model. We observe that on smaller data sets our large sparse model can be an effective teacher for distillation. 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Improvements of Switch T5 Base model over dense baseline when multi-task training on 101 languages. We observe Switch Transformers to do quite well in the multi-task training setup and yield improvements on all 101 languages.
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We histogram for each language, the step speedup of Switch Transformers over the FLOP matched T5 dense baseline to reach the same quality. Over all 101 languages, we achieve a mean step speedup over mT5-Base of 5x and, for 91% of languages, we record a 4x, or greater, speedup to reach the final perplexity of mT5-Base.
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|---|
| B | Number of tokens in the batch. |
| N | Number of total cores. |
| n | Number of ways for data-parallelism sharding. |
| m | Number of ways for model-parallelism sharding. |
| E | Number of experts in Switch layers. |
| C | Expert capacity, the batch size of each expert. |
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Each 4×4 dotted-line grid represents 16 cores and the shaded squares are the data contained on that core (either model weights or batch of tokens). We illustrate both how the model weights and the data tensors are split for each strategy. First Row: illustration of how model weights are split across the cores. Shapes of different sizes in this row represent larger weight matrices in the Feed Forward Network (FFN) layers (e.g larger df f sizes). Each color of the shaded squares identifies a unique weight matrix. The number of parameters per core is fixed, but larger weight matrices will apply more computation to each token. Second Row: illustration of how the data batch is split across cores. Each core holds the same number of tokens which maintains a fixed memory usage across all strategies. The partitioning strategies have different properties of allowing each core to either have the same tokens or different tokens across cores, which is what the different colors symbolize.\",\n \"polygon\": [\n [\n 90.0,\n 358.5008239746094\n ],\n [\n 521.9944458007812,\n 358.5008239746094\n ],\n [\n 521.9944458007812,\n 532.1414184570312\n ],\n [\n 90.0,\n 532.1414184570312\n ]\n ],\n \"bbox\": [\n 90.0,\n 358.5008239746094,\n 521.9944458007812,\n 532.1414184570312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/19/SectionHeader/4\",\n \"4\": \"/page/20/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/20/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.3 Model and Data Parallelism
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|---|
| T5-Base | 0.2B | 124B | 768 | X | 2048 | 64 | 12 |
| T5-Large | 0.7B | 425B | 1024 | X | 2816 | 64 | 16 |
| T5-XXL | 11B | 6.3T | 4096 | X | 10240 | 64 | 64 |
| Switch-Base | 7B | 124B | 768 | X | 2048 | 64 | 12 |
| Switch-Large | 26B | 425B | 1024 | X | 2816 | 64 | 16 |
| Switch-XXL | 395B | 6.3T | 4096 | X | 10240 | 64 | 64 |
| Switch-C | 1571B | 890B | 2080 | | 6144 | 64 | 32 |
| | | | | | | |
| Model | Expert Freq. | Num. Layers | Num Experts | Neg. Log Perp. @250k | Neg. Log Perp. @ 500k | | |
| T5-Base | – | 12 | – | -1.599 | -1.556 | | |
| T5-Large | – | 24 | – | -1.402 | -1.350 | | |
| T5-XXL | – | 24 | – | -1.147 | -1.095 | | |
| Switch-Base | 1/2 | 12 | 128 | -1.370 | -1.306 | | |
| Switch-Large | 1/2 | 24 | 128 | -1.248 | -1.177 | | |
| Switch-XXL | 1/2 | 24 | 64 | -1.086 | -1.008 | | |
| Switch-C | 1 | 15 | 2048 | -1.096 | -1.043 | | |
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We compare the hyperparameters and pre-training performance of the T5 models to our Switch Transformer variants. The last two columns record the pre-training model quality on the C4 data set after 250k and 500k steps, respectively. We observe that the Switch-C Transformer variant is 4x faster to a fixed perplexity (with the same compute budget) than the T5-XXL model, with the gap increasing as training progresses.\",\n \"polygon\": [\n [\n 90.0,\n 274.1431884765625\n ],\n [\n 521.6702270507812,\n 274.1431884765625\n ],\n [\n 521.6702270507812,\n 353.07421875\n ],\n [\n 90.0,\n 353.07421875\n ]\n ],\n \"bbox\": [\n 90.0,\n 274.1431884765625,\n 521.6702270507812,\n 353.07421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/19/SectionHeader/4\",\n \"4\": \"/page/21/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/22/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"depth, number of heads, and so on, are all much smaller than the T5-XXL model. In contrast, the Switch-XXL is FLOP-matched to the T5-XXL model, which allows for larger dimensions of the hyper-parameters, but at the expense of additional communication costs induced by model-parallelism (see Section 5.5 for more details).
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\",\n \"polygon\": [\n [\n 89.349609375,\n 93.29522705078125\n ],\n [\n 522.052734375,\n 93.29522705078125\n ],\n [\n 522.052734375,\n 253.24542236328125\n ],\n [\n 89.349609375,\n 253.24542236328125\n ]\n ],\n \"bbox\": [\n 89.349609375,\n 93.29522705078125,\n 522.052734375,\n 253.24542236328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/19/SectionHeader/4\",\n \"4\": \"/page/21/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/23/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Knowledge-based fine-tuning performance. Finally, we also conduct an early examination of the model's knowledge with three closed-book knowledge-based tasks: Natural Questions, WebQuestions and TriviaQA, without additional pre-training using Salient Span Masking (Guu et al., 2020). In all three cases, we observe improvements over the prior stateof-the-art T5-XXL model (without SSM). Natural Questions exact match increases to 34.4 versus the prior best of 32.8, Web Questions increases to 41.0 over 37.2, and TriviaQA increases to 47.5 versus 42.9.
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\",\n \"polygon\": [\n [\n 89.42431640625,\n 453.14385986328125\n ],\n [\n 188.08042907714844,\n 453.14385986328125\n ],\n [\n 188.08042907714844,\n 465.09906005859375\n ],\n [\n 89.42431640625,\n 465.09906005859375\n ]\n ],\n \"bbox\": [\n 89.42431640625,\n 453.14385986328125,\n 188.08042907714844,\n 465.09906005859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/23/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/23/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The importance of scale in neural networks is widely recognized and several approaches have been proposed. Recent works have scaled models to billions of parameters through using model parallelism (e.g. splitting weights and tensors across multiple cores) (Shazeer et al., 2018; Rajbhandari et al., 2019; Raffel et al., 2019; Brown et al., 2020; Shoeybi et al., 2019). Alternatively, Harlap et al. (2018); Huang et al. (2019) propose using pipeline based model parallelism, where different layers are split across devices and micro-batches are pipelined to the different layers. Finally, Product Key networks (Lample et al., 2019) were proposed to scale up the capacity of neural networks by doing a lookup for learnable embeddings based on the incoming token representations to a given layer.
\",\n \"polygon\": [\n [\n 89.349609375,\n 476.12939453125\n ],\n [\n 521.990234375,\n 476.12939453125\n ],\n [\n 521.990234375,\n 595.4325103759766\n ],\n [\n 89.349609375,\n 595.4325103759766\n ]\n ],\n \"bbox\": [\n 89.349609375,\n 476.12939453125,\n 521.990234375,\n 595.4325103759766\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/23/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/23/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Our work studies a specific model in a class of methods that do conditional computation, where computation decisions are made dynamically based on the input. Cho and Bengio (2014) proposed adaptively selecting weights based on certain bit patterns occuring in the model hidden-states. Eigen et al. (2013) built stacked expert layers with dense matrix multiplications and ReLU activations and showed promising results on jittered MNIST and monotone speech. In computer vision Puigcerver et al. (2020) manually route tokens based on semantic classes during upstream pre-training and then select the relevant experts to be used according to the downstream task.
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\",\n \"polygon\": [\n [\n 89.349609375,\n 93.29522705078125\n ],\n [\n 522.052734375,\n 93.29522705078125\n ],\n [\n 522.052734375,\n 253.30078125\n ],\n [\n 89.349609375,\n 253.30078125\n ]\n ],\n \"bbox\": [\n 89.349609375,\n 93.29522705078125,\n 522.052734375,\n 253.30078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/23/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Sparsity along the sequence length dimension (L) in the Transformer attention patterns has been a successful technique to reduce the attention complexity from O(L 2 ) (Child et al., 2019; Correia et al., 2019; Sukhbaatar et al., 2019; Kitaev et al., 2020; Zaheer et al., 2020; Beltagy et al., 2020). This has enabled learning longer sequences than previously possible. This version of the Switch Transformer does not employ attention sparsity, but these techniques are complimentary, and, as future work, these could be combined to potentially improve learning on tasks requiring long contexts.
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\",\n \"polygon\": [\n [\n 89.87255859375,\n 369.3647766113281\n ],\n [\n 167.642578125,\n 369.3647766113281\n ],\n [\n 167.642578125,\n 381.3199768066406\n ],\n [\n 89.87255859375,\n 381.3199768066406\n ]\n ],\n \"bbox\": [\n 89.87255859375,\n 369.3647766113281,\n 167.642578125,\n 381.3199768066406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/24/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"We pose and discuss questions about the Switch Transformer, and sparse expert models generally, where sparsity refers to weights, not on attention patterns.
\",\n \"polygon\": [\n [\n 89.4990234375,\n 391.8193054199219\n ],\n [\n 521.6185302734375,\n 391.8193054199219\n ],\n [\n 521.6185302734375,\n 416.49609375\n ],\n [\n 89.4990234375,\n 416.49609375\n ]\n ],\n \"bbox\": [\n 89.4990234375,\n 391.8193054199219,\n 521.6185302734375,\n 416.49609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/24/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Isn't Switch Transformer better due to sheer parameter count? Yes, and by design! Parameters, independent of the total FLOPs used, are a useful axis to scale neural language models. Large models have been exhaustively shown to perform better (Kaplan et al., 2020). But in this case, our model is more sample efficient and faster while using the same computational resources.
\",\n \"polygon\": [\n [\n 89.349609375,\n 419.8043212890625\n ],\n [\n 521.9544067382812,\n 419.8043212890625\n ],\n [\n 521.9544067382812,\n 484.9453125\n ],\n [\n 89.349609375,\n 484.9453125\n ]\n ],\n \"bbox\": [\n 89.349609375,\n 419.8043212890625,\n 521.9544067382812,\n 484.9453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/24/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"I don't have access to a supercomputer—is this still useful for me? Though this work has focused on extremely large models, we also find that models with as few as two experts improves performance while easily fitting within memory constraints of commonly available GPUs or TPUs (details in Appendix D). We therefore believe our techniques are useful in small-scale settings.
\",\n \"polygon\": [\n [\n 89.4990234375,\n 488.4373474121094\n ],\n [\n 521.9791259765625,\n 488.4373474121094\n ],\n [\n 521.9791259765625,\n 553.78125\n ],\n [\n 89.4990234375,\n 553.78125\n ]\n ],\n \"bbox\": [\n 89.4990234375,\n 488.4373474121094,\n 521.9791259765625,\n 553.78125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/24/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Do sparse models outperform dense models on the speed-accuracy Pareto curve? Yes. Across a wide variety of different models sizes, sparse models outperform dense models per step and on wall clock time. Our controlled experiments show for a fixed amount of computation and time, sparse models outperform dense models.
\",\n \"polygon\": [\n [\n 89.05078125,\n 557.0703887939453\n ],\n [\n 521.743408203125,\n 557.0703887939453\n ],\n [\n 521.743408203125,\n 608.6264801025391\n ],\n [\n 89.05078125,\n 608.6264801025391\n ]\n ],\n \"bbox\": [\n 89.05078125,\n 557.0703887939453,\n 521.743408203125,\n 608.6264801025391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/24/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"I can't deploy a trillion parameter model—can we shrink these models? We cannot fully preserve the model quality, but compression rates of 10 to 100x are achievable by distilling our sparse models into dense models while achieving ≈30% of the quality gain of the expert model.
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\",\n \"polygon\": [\n [\n 89.7978515625,\n 93.29522705078125\n ],\n [\n 522.3515625,\n 93.29522705078125\n ],\n [\n 522.3515625,\n 118.0458984375\n ],\n [\n 89.7978515625,\n 118.0458984375\n ]\n ],\n \"bbox\": [\n 89.7978515625,\n 93.29522705078125,\n 522.3515625,\n 118.0458984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/24/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Why aren't sparse models widely used already? The motivation to try sparse models has been stymied by the massive success of scaling dense models (the success of which is partially driven by co-adaptation with deep learning hardware as argued in Hooker (2020)). Further, sparse models have been subject to multiple issues including (1) model complexity, (2) training difficulties, and (3) communication costs. Switch Transformer makes strides to alleviate these issues.
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While our stability techniques were effective for our Switch-Base, Switch-Large and Switch-C models (no observed instability), they were not sufficient for Switch-XXL. We have taken early steps towards stabilizing these models, which we think may be generally useful for large models, including using regularizers for improving stability and adapted forms of gradient clipping, but this remains unsolved.\",\n \"polygon\": [\n [\n 102.19921875,\n 297.2102355957031\n ],\n [\n 521.75390625,\n 297.2102355957031\n ],\n [\n 521.75390625,\n 375.8653869628906\n ],\n [\n 102.19921875,\n 375.8653869628906\n ]\n ],\n \"bbox\": [\n 102.19921875,\n 297.2102355957031,\n 521.75390625,\n 375.8653869628906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/25/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/ListItem/6\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Generally we find that improved pre-training quality leads to better downstream results (Appendix E), though we sometimes encounter striking anomalies. For instance, despite similar perplexities modeling the C4 data set, the 1.6T parameter Switch-C achieves only an 87.7 exact match score in SQuAD, which compares unfavorably to 89.6 for the smaller Switch-XXL model. One notable difference is that the Switch-XXL model applies ≈10x the FLOPS per token than the Switch-C model, even though it has ≈4x less unique parameters (395B vs 1.6T). 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And, vice versa, this may also help in the design of future hardware.\",\n \"polygon\": [\n [\n 102.6474609375,\n 508.921875\n ],\n [\n 522.3515625,\n 508.921875\n ],\n [\n 522.3515625,\n 574.6640625\n ],\n [\n 102.6474609375,\n 574.6640625\n ]\n ],\n \"bbox\": [\n 102.6474609375,\n 508.921875,\n 522.3515625,\n 574.6640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/25/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"4. Our work falls within the family of adaptive computation algorithms. Our approach always used identical, homogeneous experts, but future designs (facilitated by more flexible infrastructure) could support heterogeneous experts. This would enable more flexible adaptation by routing to larger experts when more computation is desired perhaps for harder examples.\",\n \"polygon\": [\n [\n 102.796875,\n 588.19921875\n ],\n [\n 522.3515625,\n 588.19921875\n ],\n [\n 522.3515625,\n 653.3854064941406\n ],\n [\n 102.796875,\n 653.3854064941406\n ]\n ],\n \"bbox\": [\n 102.796875,\n 588.19921875,\n 522.3515625,\n 653.3854064941406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/25/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"5. Investigating expert layers outside the FFN layer of the Transformer. We find preliminary evidence that this similarly can improve model quality. 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However, due to training instabilities with the bfloat16 format, we instead leave this as an area for future work.
\",\n \"polygon\": [\n [\n 115.9453125,\n 92.42578125\n ],\n [\n 523.546875,\n 92.42578125\n ],\n [\n 523.546875,\n 117.94921875\n ],\n [\n 115.9453125,\n 117.94921875\n ]\n ],\n \"bbox\": [\n 115.9453125,\n 92.42578125,\n 523.546875,\n 117.94921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/25/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \"6. Examining Switch Transformer in new and across different modalities. We have thus far only considered language, but we believe that model sparsity can similarly provide advantages in new modalities, as well as multi-modal networks.\",\n \"polygon\": [\n [\n 101.7509765625,\n 130.130859375\n ],\n [\n 523.546875,\n 130.130859375\n ],\n [\n 523.546875,\n 169.014404296875\n ],\n [\n 101.7509765625,\n 169.014404296875\n ]\n ],\n \"bbox\": [\n 101.7509765625,\n 130.130859375,\n 523.546875,\n 169.014404296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/25/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"This list could easily be extended, but we hope this gives a flavor for the types of challenges that we are thinking about and what we suspect are promising future directions.
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\",\n \"polygon\": [\n [\n 89.6484375,\n 229.517578125\n ],\n [\n 171.3779296875,\n 229.517578125\n ],\n [\n 171.3779296875,\n 241.9959716796875\n ],\n [\n 89.6484375,\n 241.9959716796875\n ]\n ],\n \"bbox\": [\n 89.6484375,\n 229.517578125,\n 171.3779296875,\n 241.9959716796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Switch Transformers are scalable and effective natural language learners. We simplify Mixture of Experts to produce an architecture that is easy to understand, stable to train and vastly more sample efficient than equivalently-sized dense models. We find that these models excel across a diverse set of natural language tasks and in different training regimes, including pre-training, fine-tuning and multi-task training. These advances make it possible to train models with hundreds of billion to trillion parameters and which achieve substantial speedups relative to dense T5 baselines. We hope our work motivates sparse models as an effective architecture and that this encourages researchers and practitioners to consider these flexible models in natural language tasks, and beyond.
\",\n \"polygon\": [\n [\n 89.349609375,\n 250.59375\n ],\n [\n 522.650390625,\n 250.59375\n ],\n [\n 522.650390625,\n 372.0234375\n ],\n [\n 89.349609375,\n 372.0234375\n ]\n ],\n \"bbox\": [\n 89.349609375,\n 250.59375,\n 522.650390625,\n 372.0234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Acknowledgments
\",\n \"polygon\": [\n [\n 89.7978515625,\n 394.646484375\n ],\n [\n 196.330078125,\n 394.646484375\n ],\n [\n 196.330078125,\n 407.112060546875\n ],\n [\n 89.7978515625,\n 407.112060546875\n ]\n ],\n \"bbox\": [\n 89.7978515625,\n 394.646484375,\n 196.330078125,\n 407.112060546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/26/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The authors would like to thank Margaret Li who provided months of key insights into algorithmic improvements and suggestions for empirical studies. Hugo Larochelle for sage advising and clarifying comments on the draft, Irwan Bello for detailed comments and careful revisions, Colin Raffel and Adam Roberts for timely advice on neural language models and the T5 code-base, Yoshua Bengio for advising and encouragement on research in adaptive computation, Jascha Sohl-dickstein for interesting new directions for stabilizing new large scale models and paper revisions, and the Google Brain Team for useful discussions on the paper. Blake Hechtman who provided invaluable help in profiling and improving the training performance of our models.
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\",\n \"polygon\": [\n [\n 89.7978515625,\n 563.0625\n ],\n [\n 232.9365234375,\n 563.0625\n ],\n [\n 232.9365234375,\n 575.5070648193359\n ],\n [\n 89.7978515625,\n 575.5070648193359\n ]\n ],\n \"bbox\": [\n 89.7978515625,\n 563.0625,\n 232.9365234375,\n 575.5070648193359\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/26/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Shazeer et al. (2018); Lepikhin et al. (2020) designed MoE Transformers (Shazeer et al., 2017) by adding MoE layers into the dense feedfoward network (FFN) computations of the Transformer. Similarly, our work also replaced the FFN layer in the Transformer, but we briefly explore here an alternate design. We add Switch layers into the Transformer Self-Attention layers. To do so, we replace the trainable weight matrices that produce the queries, keys and values with Switch layers as seen in Figure 10.
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We diagram how to incorporate the Switch layer into the Self-Attention transformer block. For each token (here we show two tokens, x1 = \\\"More\\\" and x2 = \\\"Parameters\\\"), one set of weights produces the query and the other set of unique weights produces the shared keys and values. We experimented with each expert being a linear operation, as well as a FFN, as was the case throughout this work. 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| suggests a future promising direction. |
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@100k Steps (↑) | Quality
@16H (↑) | Speed
(ex/sec) (↑) |
|---|
| Experts FF | float32 | -1.548 | -1.614 | 1480 |
| Expert Attention | float32 | -1.524 | -1.606 | 1330 |
| Expert Attention | bfloat16 | [diverges] | [diverges] | - |
| Experts FF + Attention | float32 | -1.513 | -1.607 | 1240 |
| Expert FF + Attention | bfloat16 | [diverges] | [diverges] | - |
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Preventing Token Dropping with No-Token-Left-Behind
\",\n \"polygon\": [\n [\n 89.2001953125,\n 92.4776611328125\n ],\n [\n 443.4609375,\n 92.4776611328125\n ],\n [\n 443.4609375,\n 104.432861328125\n ],\n [\n 89.2001953125,\n 104.432861328125\n ]\n ],\n \"bbox\": [\n 89.2001953125,\n 92.4776611328125,\n 443.4609375,\n 104.432861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/28/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/28/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Due to software constraints on TPU accelerators, the shapes of our Tensors must be statically sized. As a result, each expert has a finite and fixed capacity to process token representations. This, however, presents an issue for our model which dynamically routes tokens at run-time that may result in an uneven distribution over experts. If the number of tokens sent to an expert is less than the expert capacity, then the computation may simply be padded – an inefficient use of the hardware, but mathematically correct. However, when the number of tokens sent to an expert is larger than its capacity (expert overflow), a protocol is needed to handle this. Lepikhin et al. (2020) adapts a Mixture-of-Expert model and addresses expert overflow by passing its representation to the next layer without processing through a residual connection which we also follow.
\",\n \"polygon\": [\n [\n 89.349609375,\n 114.04425048828125\n ],\n [\n 522.052734375,\n 114.04425048828125\n ],\n [\n 522.052734375,\n 246.89642333984375\n ],\n [\n 89.349609375,\n 246.89642333984375\n ]\n ],\n \"bbox\": [\n 89.349609375,\n 114.04425048828125,\n 522.052734375,\n 246.89642333984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/28/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/28/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"We suspected that having no computation applied to tokens could be very wasteful, especially since if there is overflow on one expert, that means another expert will have extra capacity. With this intuition we create No-Token-Left-Behind, which iteratively reroutes any tokens that are at first routed to an expert that is overflowing. Figure 11 shows a graphical description of this method, which will allow us to guarantee almost no tokens will be dropped during training and inference. We hypothesised that this could improve performance and further stabilize training, but we found no empirical benefits. We suspect that once the network learns associations between different tokens and experts, if this association is changed (e.g. sending a token to its second highest expert) then performance could be degraded.
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\",\n \"polygon\": [\n [\n 89.2001953125,\n 401.88983154296875\n ],\n [\n 350.2265625,\n 401.88983154296875\n ],\n [\n 350.2265625,\n 413.84503173828125\n ],\n [\n 89.2001953125,\n 413.84503173828125\n ]\n ],\n \"bbox\": [\n 89.2001953125,\n 401.88983154296875,\n 350.2265625,\n 413.84503173828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/28/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/28/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"At each expert-layer, the router determines to which expert to send the token. This is a discrete decision over the available experts, conditioned on information about the token's representation. Based on the incoming token representation, the router determines the best expert, however, it receives no counterfactual information about how well it would have done selecting an alternate expert. As in reinforcement learning, a classic explorationexploitation dilemma arises (Sutton and Barto, 2018). These issues have been similarly noted and addressed differently by Rosenbaum et al. (2017) which demonstrated success in multi-task learning. This particular setting most closely matches that of a contextual bandit (Robbins, 1952). Deterministically selecting the top expert always amounts to an exploitative strategy – we consider balancing exploration to seek better expert assignment.
\",\n \"polygon\": [\n [\n 89.4990234375,\n 423.4573669433594\n ],\n [\n 521.75390625,\n 423.4573669433594\n ],\n [\n 521.75390625,\n 556.3085327148438\n ],\n [\n 89.4990234375,\n 556.3085327148438\n ]\n ],\n \"bbox\": [\n 89.4990234375,\n 423.4573669433594,\n 521.75390625,\n 556.3085327148438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/28/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/28/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"To introduce exploration, we consider several approaches: 1) deterministic or argmax 2) sampling from the softmax distribution 3) input dropout on the incoming representation 4) multiplicative jitter noise on the incoming representation. The resulting impact on model quality is reported in Table 11. Throughout this work, we use input jitter to inject noise as we have found it to empirically perform the best.
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Quality of the Switch Transformer, measured by the negative log perplexity, under different randomness-strategies for selecting the expert (lower is better). There is no material speed performance difference between the variants.\",\n \"polygon\": [\n [\n 89.6484375,\n 567.31640625\n ],\n [\n 521.6160888671875,\n 567.31640625\n ],\n [\n 521.6160888671875,\n 619.1502838134766\n ],\n [\n 89.6484375,\n 619.1502838134766\n ]\n ],\n \"bbox\": [\n 89.6484375,\n 567.31640625,\n 521.6160888671875,\n 619.1502838134766\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/28/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/29/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"at the scale of 10B+ parameter models, but we show in Figure 12 as few as 2 experts produce compelling gains over a FLOP-matched counterpart. Even if a super computer is not readily available, training Switch Transformers with 2, 4, or 8 experts (as we typically recommend one expert per core) results in solid improvements over T5 dense baselines.
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We correlate the upstream performance with downstream quality on both SuperGLUE and TriviaQA (SOTA recorded without SSM), reasoning and knowledge-heavy benchmarks, respectively (validation sets). We find that, as with the baseline, the Switch model scales with improvements in the upstream pre-training task. For SuperGLUE, we find a loosely linear relation between negative log perplexity and the average SuperGLUE score. However, the dense model often performs better for a fixed perplexity, particularly in the large-scale regime. Conversely, on the knowledge-heavy task, TriviaQA, we find that the Switch Transformer may follow an improved scaling relationship – for a given upstream perplexity, it does better than a dense counterpart. Further statistics (expensive to collect and left to future work) would be necessary to confirm these observations.
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\",\n \"polygon\": [\n [\n 89.4990234375,\n 91.99072265625\n ],\n [\n 331.5787353515625,\n 91.99072265625\n ],\n [\n 331.5787353515625,\n 104.432861328125\n ],\n [\n 89.4990234375,\n 104.432861328125\n ]\n ],\n \"bbox\": [\n 89.4990234375,\n 91.99072265625,\n 331.5787353515625,\n 104.432861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/32/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Pseudocode for Switch Transformers in Mesh Tensorflow (Shazeer et al., 2018). No model parallelism is being used for the below code (see 5.4 for more details).
\",\n \"polygon\": [\n [\n 89.2001953125,\n 113.888671875\n ],\n [\n 522.94921875,\n 113.888671875\n ],\n [\n 522.94921875,\n 138.50238037109375\n ],\n [\n 89.2001953125,\n 138.50238037109375\n ]\n ],\n \"bbox\": [\n 89.2001953125,\n 113.888671875,\n 522.94921875,\n 138.50238037109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/32/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"import mesh tensorflow as mtf\\ndef load balance loss(router probs, expert mask):\\n \\\"\\\"\\\"Calculate load−balancing loss to ensure diverse expert routing.\\\"\\\"\\\"\\n # router probs is the probability assigned for each expert per token.\\n # router probs shape: [num cores, tokens per core, num experts]\\n # expert index contains the expert with the highest router probability in one−hot format.\\n # expert mask shape: [num cores, tokens per core, num experts]\\n # For each core, get the fraction of tokens routed to each expert.\\n # density 1 shape: [num cores, num experts]\\n density 1 = mtf.reduce mean(expert mask, reduced dim=tokens per core)\\n # For each core, get fraction of probability mass assigned to each expert\\n # from the router across all tokens.\\n # density 1 proxy shape: [num cores, num experts]\\n density 1 proxy = mtf.reduce mean(router probs, reduced dim=tokens per core)\\n # density l for a single core: vector of length num experts that sums to 1.\\n # density l proxy for a single core: vector of length num experts that sums to 1.\\n # Want both vectors to have uniform allocation (1/num experts) across all num expert elements.\\n # The two vectors will be pushed towards uniform allocation when the dot product is minimized.\\n loss = mtf.reduce mean(density 1 proxy ∗ density 1) ∗ (num experts ˆ 2)\\n return loss
\",\n \"polygon\": [\n [\n 90.0,\n 156.5263671875\n ],\n [\n 477.30706787109375,\n 156.5263671875\n ],\n [\n 477.30706787109375,\n 360.80859375\n ],\n [\n 90.0,\n 360.80859375\n ]\n ],\n \"bbox\": [\n 90.0,\n 156.5263671875,\n 477.30706787109375,\n 360.80859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/32/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \"Figure 14: Pseudo code for the load balance loss for Switch Transformers in Mesh Tensorflow.\",\n \"polygon\": [\n [\n 90.0,\n 382.27618408203125\n ],\n [\n 521.640380859375,\n 382.27618408203125\n ],\n [\n 521.640380859375,\n 406.73431396484375\n ],\n [\n 90.0,\n 406.73431396484375\n ]\n ],\n \"bbox\": [\n 90.0,\n 382.27618408203125,\n 521.640380859375,\n 406.73431396484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/32/PageFooter/5\",\n \"block_type\": \"PageFooter\",\n \"html\": \"\",\n \"polygon\": [\n [\n 300.1728515625,\n 724.9639129638672\n ],\n [\n 311.080078125,\n 724.9639129638672\n ],\n [\n 311.080078125,\n 735.15234375\n ],\n [\n 300.1728515625,\n 735.15234375\n ]\n ],\n \"bbox\": [\n 300.1728515625,\n 724.9639129638672,\n 311.080078125,\n 735.15234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/33/Page/873\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/33/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 239.8095703125,\n 37.992431640625\n ],\n [\n 367.8744812011719,\n 37.992431640625\n ],\n [\n 367.8744812011719,\n 49.83837890625\n ],\n [\n 239.8095703125,\n 49.83837890625\n ]\n ],\n \"bbox\": [\n 239.8095703125,\n 37.992431640625,\n 367.8744812011719,\n 49.83837890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \"import mesh tensorflow as mtf
\",\n \"polygon\": [\n [\n 90.0,\n 96.58636474609375\n ],\n [\n 205.1455078125,\n 96.58636474609375\n ],\n [\n 205.1455078125,\n 104.05841064453125\n ],\n [\n 90.0,\n 104.05841064453125\n ]\n ],\n \"bbox\": [\n 90.0,\n 96.58636474609375,\n 205.1455078125,\n 104.05841064453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"def router(inputs, capacity factor):\\n \\\"\\\"\\\"Produce the combine and dispatch tensors used for sending and\\n receiving tokens from their highest probability expert. \\\"\\\"\\\"\\n # Core layout is split across num cores for all tensors and operations.\\n # inputs shape: [num cores, tokens per core, d model]
\",\n \"polygon\": [\n [\n 90.0,\n 110.21484375\n ],\n [\n 384.9657287597656,\n 110.21484375\n ],\n [\n 384.9657287597656,\n 156.234375\n ],\n [\n 90.0,\n 156.234375\n ]\n ],\n \"bbox\": [\n 90.0,\n 110.21484375,\n 384.9657287597656,\n 156.234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"router weights = mtf.Variable(shape=[d model, num experts])
\",\n \"polygon\": [\n [\n 103.61865234375,\n 164.2587890625\n ],\n [\n 335.138671875,\n 164.2587890625\n ],\n [\n 335.138671875,\n 171.804443359375\n ],\n [\n 103.61865234375,\n 171.804443359375\n ]\n ],\n \"bbox\": [\n 103.61865234375,\n 164.2587890625,\n 335.138671875,\n 171.804443359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"# router logits shape: [num cores, tokens per core, num experts]\\nrouter logits = mtf.einsum([inputs, router weights], reduced dim=d model)
\",\n \"polygon\": [\n [\n 103.76806640625,\n 180.8876953125\n ],\n [\n 389.60577392578125,\n 180.8876953125\n ],\n [\n 389.60577392578125,\n 197.20843505859375\n ],\n [\n 103.76806640625,\n 197.20843505859375\n ]\n ],\n \"bbox\": [\n 103.76806640625,\n 180.8876953125,\n 389.60577392578125,\n 197.20843505859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/SectionHeader/5\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"if is training:
\",\n \"polygon\": [\n [\n 102.57275390625,\n 206.5078125\n ],\n [\n 162.7036895751953,\n 206.5078125\n ],\n [\n 162.7036895751953,\n 214.2421875\n ],\n [\n 102.57275390625,\n 214.2421875\n ]\n ],\n \"bbox\": [\n 102.57275390625,\n 206.5078125,\n 162.7036895751953,\n 214.2421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"# Add noise for exploration across experts. router logits += mtf.random uniform(shape=router logits.shape, minval=1−eps, maxval=1+eps)
\",\n \"polygon\": [\n [\n 111.462890625,\n 214.2421875\n ],\n [\n 472.4738464355469,\n 214.2421875\n ],\n [\n 472.4738464355469,\n 231.64453125\n ],\n [\n 111.462890625,\n 231.64453125\n ]\n ],\n \"bbox\": [\n 111.462890625,\n 214.2421875,\n 472.4738464355469,\n 231.64453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"# Convert input to softmax operation from bfloat16 to float32 for stability. router logits = mtf.to float32(router logits)
\",\n \"polygon\": [\n [\n 104.28999328613281,\n 240.54638671875\n ],\n [\n 405.8747863769531,\n 240.54638671875\n ],\n [\n 405.8747863769531,\n 256.4864501953125\n ],\n [\n 104.28999328613281,\n 256.4864501953125\n ]\n ],\n \"bbox\": [\n 104.28999328613281,\n 240.54638671875,\n 405.8747863769531,\n 256.4864501953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"# Probabilities for each token of what expert it should be sent to.\\nrouter probs = mtf.softmax(router logits, axis=−1)
\",\n \"polygon\": [\n [\n 104.28999328613281,\n 265.950439453125\n ],\n [\n 371.443359375,\n 265.950439453125\n ],\n [\n 371.443359375,\n 281.8904113769531\n ],\n [\n 104.28999328613281,\n 281.8904113769531\n ]\n ],\n \"bbox\": [\n 104.28999328613281,\n 265.950439453125,\n 371.443359375,\n 281.8904113769531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \"# Get the top−1 expert for each token. expert gate is the top−1 probability\\n# from the router for each token. expert index is what expert each token\\n# is going to be routed to.\\n# expert gate shape: [num cores, tokens per core]\\n# expert index shape: [num cores, tokens per core]\\nexpert gate, expert index = mtf.top 1(router probs, reduced dim=num experts)\\n# expert mask shape: [num cores, tokens per core, num experts]\\nexpert mask = mtf.one hot(expert index, dimension=num experts)\\n# Compute load balancing loss.\\naux loss = load balance loss(router probs, expert mask)\\n# Experts have a fixed capacity, ensure we do not exceed it. Construct\\n# the batch indices, to each expert, with position in expert\\n# make sure that not more that expert capacity examples can be routed to\\n# each expert.\\nposition in expert = mtf.cumsum(expert mask, dimension=tokens per core) ∗ expert mask\\n# Keep only tokens that fit within expert capacity.\\nexpert mask ∗= mtf.less(position in expert, expert capacity)\\nexpert mask flat = mtf.reduce sum(expert mask, reduced dim=experts dim)\\n# Mask out the experts that have overflowed the expert capacity.\\nexpert gate ∗= expert mask flat\\n# combine tensor used for combining expert outputs and scaling with router probability.\\n# combine tensor shape: [num cores, tokens per core, num experts, expert capacity]\\ncombine tensor = (\\n expert gate ∗ expert mask flat ∗\\n mtf.one hot(expert index, dimension=num experts) ∗\\n mtf.one hot(position in expert, dimension=expert capacity))\\n# Cast back outputs to bfloat16 for the rest of the layer.\\ncombine tensor = mtf.to bfloat16(combine tensor)\\n# Create binary dispatch tensor that is 1 if the token gets routed to the corresponding expert.\\n# dispatch tensor shape: [num cores, tokens per core, num experts, expert capacity]\\ndispatch tensor = mtf.cast(combine tensor, tf.bool)
\",\n \"polygon\": [\n [\n 97.5673828125,\n 290.9898986816406\n ],\n [\n 481.2746887207031,\n 290.9898986816406\n ],\n [\n 481.2746887207031,\n 627.64453125\n ],\n [\n 97.5673828125,\n 627.64453125\n ]\n ],\n \"bbox\": [\n 97.5673828125,\n 290.9898986816406,\n 481.2746887207031,\n 627.64453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"return dispatch tensor, combine tensor, aux loss
\",\n \"polygon\": [\n [\n 104.28999328613281,\n 630.0844573974609\n ],\n [\n 291.4835205078125,\n 630.0844573974609\n ],\n [\n 291.4835205078125,\n 637.5564575195312\n ],\n [\n 104.28999328613281,\n 637.5564575195312\n ]\n ],\n \"bbox\": [\n 104.28999328613281,\n 630.0844573974609,\n 291.4835205078125,\n 637.5564575195312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Figure 15: Pseudo code for the router for Switch Transformers in Mesh Tensorflow.
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\",\n \"polygon\": [\n [\n 89.7978515625,\n 96.58636474609375\n ],\n [\n 209.77734375,\n 96.58636474609375\n ],\n [\n 209.77734375,\n 104.220703125\n ],\n [\n 89.7978515625,\n 104.220703125\n ]\n ],\n \"bbox\": [\n 89.7978515625,\n 96.58636474609375,\n 209.77734375,\n 104.220703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/34/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"def switch layer(inputs, n, capacity factor, num experts): \\\"\\\"\\\"Distributed switch transformer feed−forward layer.\\\"\\\"\\\"
\",\n \"polygon\": [\n [\n 90.0,\n 113.5223388671875\n ],\n [\n 326.82220458984375,\n 113.5223388671875\n ],\n [\n 326.82220458984375,\n 129.594970703125\n ],\n [\n 90.0,\n 129.594970703125\n ]\n ],\n \"bbox\": [\n 90.0,\n 113.5223388671875,\n 326.82220458984375,\n 129.594970703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/34/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"# num cores (n) = total cores for training the model (scalar).\\n# d model = model hidden size (scalar).\\n# num experts = total number of experts.\\n# capacity factor = extra buffer for each expert.\\n# inputs shape: [batch, seq len, d model]\\nbatch, seq len, d model = inputs.get shape()\\n# Each core will route tokens per core tokens to the correct experts.\\ntokens per core = batch ∗ seq len / num cores\\n# Each expert will have shape [num cores, expert capacity, d model].\\n# Each core is responsible for sending expert capacity tokens\\n# to each expert.\\nexpert capacity = tokens per core ∗ capacity factor / num experts\\n# Reshape to setup per core expert dispatching.\\n# shape: [batch, seq len, d model] −> [num cores, tokens per core, d model]\\n# Core layout: [n, 1, 1] −> [n, 1, 1]\\ninputs = mtf.reshape(inputs, [num cores, tokens per core, d model])\\n# Core Layout: [n, 1, 1] −> [n, 1, 1, 1], [n, 1, 1, 1]\\n# dispatch tensor (boolean) shape: [num cores, tokens per core, num experts, expert capacity]\\n# dispatch tensor is used for routing tokens to the correct expert.\\n# combine tensor (float) shape: [num cores, tokens per core, num experts, expert capacity]\\n# combine tensor used for combining expert outputs and scaling with router\\n# probability.\\ndispatch tensor, combine tensor, aux loss = router(inputs, expert capacity)\\n# Matmul with large boolean tensor to assign tokens to the correct expert.\\n# Core Layout: [n, 1, 1], −> [1, n, 1, 1]\\n# expert inputs shape: [num experts, num cores, expert capacity, d model]\\nexpert inputs = mtf.einsum([inputs, dispatch tensor], reduce dims=[tokens per core])\\n# All−to−All communication. Cores split across num cores and now we want to split\\n# across num experts. This sends tokens, routed locally, to the correct expert now\\n# split across different cores.\\n# Core layout: [1, n, 1, 1] −> [n, 1, 1, 1]\\nexpert inputs = mtf.reshape(expert inputs, [num experts, num cores, expert capacity, d model])\\n# Standard feed forward computation, where each expert will have its own\\n# unique set of parameters.\\n# Total unique parameters created: num experts ∗ (d model ∗ d ff ∗ 2).\\n# expert outputs shape: [num experts, num cores, expert capacity, d model]\\nexpert outputs = feed forward(expert inputs)\\n# All−to−All communication. Cores are currently split across the experts\\n# dimension, which needs to be switched back to being split across num cores.\\n# Core Layout: [n, 1, 1, 1] −> [1, n, 1, 1]\\nexpert outputs = mtf.reshape(expert outputs, [num experts, num cores, expert capacity, d model])\\n# Convert back to input shape and multiply outputs of experts by the routing probability.\\n# expert outputs shape: [num experts, num cores, tokens per core, d model]\\n# expert outputs combined shape: [num cores, tokens per core, d model]\\n# Core Layout: [1, n, 1, 1] −> [n, 1, 1]\\nexpert outputs combined = mtf.einsum([expert outputs, combine tensor], reduce dims=[tokens per core])\\n# Remove tokens per core shapes used for local routing dispatching to match input shape.\\n# Core Layout: [n, 1, 1] −> [n, 1, 1]\\noutputs = mtf.reshape(expert outputs combined, [batch, seq len, d model])\\nreturn outputs, aux loss
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How much knowledge can you pack into the parameters of a language model? arXiv preprint arXiv:2002.08910, 2020.\",\n \"polygon\": [\n [\n 89.349609375,\n 93.29522705078125\n ],\n [\n 522.650390625,\n 93.29522705078125\n ],\n [\n 522.650390625,\n 117.75335693359375\n ],\n [\n 89.349609375,\n 117.75335693359375\n ]\n ],\n \"bbox\": [\n 89.349609375,\n 93.29522705078125,\n 522.650390625,\n 117.75335693359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/35/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/38/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \"Clemens Rosenbaum, Tim Klinger, and Matthew Riemer. Routing networks: Adaptive selection of non-linear functions for multi-task learning. arXiv preprint arXiv:1711.01239, 2017.\",\n \"polygon\": [\n [\n 89.2001953125,\n 131.19122314453125\n ],\n [\n 522.3515625,\n 131.19122314453125\n ],\n [\n 522.3515625,\n 169.1983642578125\n ],\n [\n 89.2001953125,\n 169.1983642578125\n ]\n ],\n \"bbox\": [\n 89.2001953125,\n 131.19122314453125,\n 522.3515625,\n 169.1983642578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/35/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/38/ListItem/3\",\n \"block_type\": \"ListItem\",\n \"html\": \"Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An adversarial winograd schema challenge at scale. 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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2019.\",\n \"polygon\": [\n [\n 89.2001953125,\n 233.96484375\n ],\n [\n 521.75390625,\n 233.96484375\n ],\n [\n 521.75390625,\n 258.53936767578125\n ],\n [\n 89.2001953125,\n 258.53936767578125\n ]\n ],\n \"bbox\": [\n 89.2001953125,\n 233.96484375,\n 521.75390625,\n 258.53936767578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/35/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/38/ListItem/5\",\n \"block_type\": \"ListItem\",\n \"html\": \"Noam Shazeer. 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Outrageously large neural networks: The sparsely-gated mixture-ofexperts layer. arXiv preprint arXiv:1701.06538, 2017.\",\n \"polygon\": [\n [\n 89.05078125,\n 296.32421875\n ],\n [\n 521.6185913085938,\n 296.32421875\n ],\n [\n 521.6185913085938,\n 334.51171875\n ],\n [\n 89.05078125,\n 334.51171875\n ]\n ],\n \"bbox\": [\n 89.05078125,\n 296.32421875,\n 521.6185913085938,\n 334.51171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/0/SectionHeader/2\",\n \"2\": \"/page/3/SectionHeader/8\",\n \"3\": \"/page/35/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/38/ListItem/7\",\n \"block_type\": \"ListItem\",\n \"html\": \"Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, et al. Mesh-tensorflow: Deep learning for supercomputers. 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\",\n \"polygon\": [\n [\n 86.0625,\n 154.880859375\n ],\n [\n 484.1015625,\n 154.880859375\n ],\n [\n 484.1015625,\n 187.9281005859375\n ],\n [\n 86.0625,\n 187.9281005859375\n ]\n ],\n \"bbox\": [\n 86.0625,\n 154.880859375,\n 484.1015625,\n 187.9281005859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/2/SectionHeader/0\",\n \"2\": \"/page/2/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/3/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The original form of this book is LATEX source code. Compiling this LATEX source has the effect of generating a device-independent representation of a textbook, which can be converted to other formats and printed.
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\",\n \"polygon\": [\n [\n 85.46484375,\n 370.86328125\n ],\n [\n 484.400390625,\n 370.86328125\n ],\n [\n 484.400390625,\n 394.29290771484375\n ],\n [\n 85.46484375,\n 394.29290771484375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 370.86328125,\n 484.400390625,\n 394.29290771484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/4/SectionHeader/0\",\n \"2\": \"/page/4/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/5/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Allen B. Downey Needham MA
\",\n \"polygon\": [\n [\n 85.83837890625,\n 406.0546875\n ],\n [\n 162.861328125,\n 406.0546875\n ],\n [\n 162.861328125,\n 428.6228942871094\n ],\n [\n 85.83837890625,\n 428.6228942871094\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 406.0546875,\n 162.861328125,\n 428.6228942871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/4/SectionHeader/0\",\n \"2\": \"/page/4/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/5/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Allen Downey is a Professor of Computer Science at the Franklin W. Olin College of Engineering.
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\",\n \"polygon\": [\n [\n 85.83837890625,\n 510.08203125\n ],\n [\n 211.5703125,\n 510.08203125\n ],\n [\n 211.5703125,\n 526.0249328613281\n ],\n [\n 85.83837890625,\n 526.0249328613281\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 510.08203125,\n 211.5703125,\n 526.0249328613281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/4/SectionHeader/0\",\n \"2\": \"/page/5/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/5/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Many thanks to Jeff Elkner, who translated my Java book into Python, which got this project started and introduced me to what has turned out to be my favorite language.
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\",\n \"polygon\": [\n [\n 128.12255859375,\n 84.54638671875\n ],\n [\n 236.07421875,\n 84.54638671875\n ],\n [\n 236.07421875,\n 100.29998779296875\n ],\n [\n 128.12255859375,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 84.54638671875,\n 236.07421875,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/4/SectionHeader/0\",\n \"2\": \"/page/6/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/6/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"More than 100 sharp-eyed and thoughtful readers have sent in suggestions and corrections over the past few years. Their contributions, and enthusiasm for this project, have been a huge help.
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\"block_type\": \"SectionHeader\",\n \"html\": \"Contents
\",\n \"polygon\": [\n [\n 128.197265625,\n 165.40826416015625\n ],\n [\n 231.47499084472656,\n 165.40826416015625\n ],\n [\n 231.47499084472656,\n 190.1953125\n ],\n [\n 128.197265625,\n 190.1953125\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 165.40826416015625,\n 231.47499084472656,\n 190.1953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/12/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/12/TableOfContents/1\",\n \"block_type\": \"TableOfContents\",\n \"html\": \" | Preface |
|---|
| 1 | | The way of the program | 1 |
|---|
| 1.1 | The Python programming language | 1 |
| 1.2 | What is a program? | 3 |
| 1.3 | What is debugging? | 3 |
| 1.4 | Formal and natural languages | 5 |
| 1.5 | The first program | 6 |
| 1.6 | Debugging | 7 |
| 1.7 | Glossary | 7 |
| 1.8 | Exercises | 9 |
| 2 | Variables, expressions and statements |
|---|
| 2.1 | Values and types | 11 |
| 2.2 | Variables | 12 |
| 2.3 | Variable names and keywords | 12 |
| 2.4 | Operators and operands | 13 |
| 2.5 | Expressions and statements | 14 |
| 2.6 | Interactive mode and script mode | 14 |
| 2.7 | Order of operations | 15 |
| 2.8 | String operations | 15 |
| 2.9 | Comments | 16 |
| 2.10 | Debugging | 16 |
| 2.11 | Glossary | 17 |
| 2.12 | Exercises | 18 |
| 3 | Functions | | 19 |
|---|
| 3.1 | Function calls | 19 |
| 3.2 | Type conversion functions | 19 |
| 3.3 | Math functions | 20 |
| 3.4 | Composition | 21 |
| 3.5 | Adding new functions | 21 |
| 3.6 | Definitions and uses | 22 |
| 3.7 | Flow of execution | 23 |
| 3.8 | Parameters and arguments | 23 |
| 3.9 | Variables and parameters are local | 24 |
| 3.10 | Stack diagrams | 25 |
| 3.11 | Fruitful functions and void functions | 26 |
| 3.12 | Why functions? | 26 |
| 3.13 | Importing with from | 27 |
| 3.14 | Debugging | 27 |
| 3.15 | Glossary | 28 |
| 3.16 | Exercises | 29 |
| 4 | Case study: interface design | | 31 |
|---|
| 4.1 | TurtleWorld | 31 |
| 4.2 | Simple repetition | 32 |
| 4.3 | Exercises | 33 |
| 4.4 | Encapsulation | 34 |
| 4.5 | Generalization | 34 |
| 4.6 | Interface design | 35 |
| 4.7 | Refactoring | 36 |
| 4.8 | A development plan | 37 |
| 4.9 | docstring | 37 |
| 4.10 | Debugging | 38 |
| 4.11 | Glossary | 38 |
| 4.12 | Exercises | 39 |
| 5 | | Conditionals and recursion | 41 |
|---|
| 5.1 | Modulus operator | 41 |
| 5.2 | Boolean expressions | 41 |
| 5.3 | Logical operators | 42 |
| 5.4 | Conditional execution | 42 |
| 5.5 | Alternative execution | 43 |
| 5.6 | Chained conditionals | 43 |
| 5.7 | Nested conditionals | 43 |
| 5.8 | Recursion | 44 |
| 5.9 | Stack diagrams for recursive functions | 45 |
| 5.10 | Infinite recursion | 46 |
| 5.11 | Keyboard input | 46 |
| 5.12 | Debugging | 47 |
| 5.13 | Glossary | 48 |
| 5.14 | Exercises | 49 |
| 6 | | Fruitful functions | 51 |
|---|
| 6.1 | Return values | 51 |
| 6.2 | Incremental development | 52 |
| 6.3 | Composition | 54 |
| 6.4 | Boolean functions | 54 |
| 6.5 | More recursion | 55 |
| 6.6 | Leap of faith | 57 |
| 6.7 | One more example | 57 |
| 6.8 | Checking types | 58 |
| 6.9 | Debugging | 59 |
| 6.10 | Glossary | 60 |
| 7 | Iteration | | 6 |
|---|
| 7.1 | Multiple assignment | 6 |
| 7.2 | Updating variables | 6 |
| 7.3 | The while statement | 6 |
| 7.4 | break | 6 |
| 7.5 | Square roots | 6 |
| 7.6 | Algorithms | 6 |
| 7.7 | Debugging | 6 |
| 7.8 | Glossary | 6 |
| 7.9 | Exercises | 6 |
| 8 | Strings | | 7 |
|---|
| 8.1 | A string is a sequence | 7 |
| 8.2 | len | 7 |
| 8.3 | Traversal with a for loop | 7 |
| 8.4 | String slices | 7 |
| 8.5 | Strings are immutable | 7 |
| 8.6 | Searching | 7 |
| 8.7 | Looping and counting | 7 |
| 8.8 | String methods | 7 |
| 8.9 | The in operator | 7 |
| 8.10 | String comparison | 7 |
| 8.11 | Debugging | 7 |
| 8.12 | Glossary | 7 |
| 8.13 | Exercises | 7 |
| 9 | Case study: word play | 8 |
|---|
| 9.1 | Reading word lists | 8 |
| 9.2 | Exercises | 8 |
| 9.3 | Search | 8 |
| 9.4 | Looping with indices | 8 |
| 9.5 | Debugging | 8 |
| 9.6 | Glossary | 8 |
| 9.7 | Exercises | 8 |
| 10 Lists | | 87 |
|---|
| 10.1 | A list is a sequence | 87 |
| 10.2 | Lists are mutable | 87 |
| 10.3 | Traversing a list | 89 |
| 10.4 | List operations | 89 |
| 10.5 | List slices | 89 |
| 10.6 | List methods | 90 |
| 10.7 | Map, filter and reduce | 91 |
| 10.8 | Deleting elements | 92 |
| 10.9 | Lists and strings | 93 |
| 10.10 | Objects and values | 93 |
| 10.11 | Aliasing | 94 |
| 10.12 | List arguments | 95 |
| 10.13 | Debugging | 96 |
| 10.14 | Glossary | 97 |
| 10.15 | Exercises | 98 |
| | | |
|---|
| | 11 Dictionaries | 101 |
|---|
| 11.1 | Dictionary as a set of counters | 102 |
| 11.2 | Looping and dictionaries | 103 |
| 11.3 | Reverse lookup | 104 |
| 11.4 | Dictionaries and lists | 105 |
| 11.5 | Memos | 106 |
| 11.6 | Global variables | 108 |
| 11.7 | Long integers | 109 |
| 11.8 | Debugging | 109 |
| 11.9 | Glossary | 110 |
| 11.10 | Exercises | 111 |
| 12 Tuples | | 113 |
|---|
| 12.1 | Tuples are immutable | 113 |
| 12.2 | Tuple assignment | 114 |
| 12.3 | Tuples as return values | 115 |
| 12.4 | Variable-length argument tuples | 115 |
| 12.5 | Lists and tuples | 116 |
| 12.6 | Dictionaries and tuples | 117 |
| 12.7 | Comparing tuples | 118 |
| 12.8 | Sequences of sequences | 119 |
| 12.9 | Debugging | 120 |
| 12.10 | Glossary | 121 |
| 12.11 | Exercises | 121 |
| | 13 Case study: data structure selection | 123 |
|---|
| 13.1 | Word frequency analysis | 123 |
| 13.2 | Random numbers | 124 |
| 13.3 | Word histogram | 125 |
| 13.4 | Most common words | 126 |
| 13.5 | Optional parameters | 126 |
| 13.6 | Dictionary subtraction | 127 |
| 13.7 | Random words | 127 |
| 13.8 | Markov analysis | 128 |
| 13.9 | Data structures | 129 |
| 13.10 | Debugging | 131 |
| 13.11 | Glossary | 132 |
| 13.12 | Exercises | 132 |
| | | |
| 14 Files | | 133 |
| 14.1 | Persistence | 133 |
| 14.2 | Reading and writing | 133 |
| 14.3 | Format operator | 134 |
| 14.4 | Filenames and paths | 135 |
| | | |
|---|
| 14.5 | Catching exceptions | 136 |
| 14.6 | Databases | 137 |
| 14.7 | Pickling | 137 |
| 14.8 | Pipes | 138 |
| 14.9 | Writing modules | 139 |
| 14.10 | Debugging | 140 |
| 14.11 | Glossary | 141 |
| 14.12 | Exercises | 141 |
| 15 | Classes and objects |
|---|
| 15.1 | User-defined types | 143 |
| 15.2 | Attributes | 144 |
| 15.3 | Rectangles | 145 |
| 15.4 | Instances as return values | 146 |
| 15.5 | Objects are mutable | 146 |
| 15.6 | Copying | 147 |
| 15.7 | Debugging | 148 |
| 15.8 | Glossary | 149 |
| 15.9 | Exercises | 149 |
| 16 | Classes and functions |
|---|
| 16.1 | Time | 151 |
| 16.2 | Pure functions | 151 |
| 16.3 | Modifiers | 153 |
| 16.4 | Prototyping versus planning | 154 |
| 16.5 | Debugging | 155 |
| 16.6 | Glossary | 155 |
| 16.7 | Exercises | 156 |
| | 17 Classes and methods | 157 |
|---|
| 17.1 | Object-oriented features | 157 |
| 17.2 | Printing objects | 158 |
| 17.3 | Another example | 159 |
| 17.4 | A more complicated example | 160 |
| 17.5 | The init method | 160 |
| 17.6 | The __str__ method | 161 |
| 17.7 | Operator overloading | 161 |
| 17.8 | Type-based dispatch | 162 |
| 17.9 | Polymorphism | 163 |
| 17.10 | Debugging | 164 |
| 17.11 | Interface and implementation | 164 |
| 17.12 | Glossary | 165 |
| 17.13 | Exercises | 165 |
| | 18 Inheritance | 167 |
|---|
| 18.1 | Card objects | 167 |
| 18.2 | Class attributes | 168 |
| 18.3 | Comparing cards | 169 |
| 18.4 | Decks | 170 |
| 18.5 | Printing the deck | 171 |
| 18.6 | Add, remove, shuffle and sort | 171 |
| 18.7 | Inheritance | 172 |
| 18.8 | Class diagrams | 173 |
| 18.9 | Debugging | 174 |
| 18.10 | Data encapsulation | 175 |
| 18.11 | Glossary | 176 |
| | 19 Case study: Tkinter | 179 |
|---|
| 19.1 | GUI | 179 |
| 19.2 | Buttons and callbacks | 180 |
| 19.3 | Canvas widgets | 181 |
| 19.4 | Coordinate sequences | 182 |
| 19.5 | More widgets | 182 |
| 19.6 | Packing widgets | 183 |
| 19.7 | Menus and Callables | 185 |
| 19.8 | Binding | 186 |
| 19.9 | Debugging | 188 |
| 19.10 | Glossary | 189 |
| 19.11 | Exercises | 190 |
| A | Debugging | | 193 |
|---|
| A.1 | Syntax errors | 193 |
| A.2 | Runtime errors | 195 |
| A.3 | Semantic errors | 198 |
| B | Analysis of Algorithms | | 201 |
|---|
| B.1 | Order of growth | 202 |
| B.2 | Analysis of basic Python operations | 204 |
| B.3 | Analysis of search algorithms | 205 |
| B.4 | Hashtables | 206 |
| C | Lumpy | | 211 |
|---|
| C.1 | State diagram | 211 |
| C.2 | Stack diagram | 212 |
| C.3 | Object diagrams | 213 |
| C.4 | Function and class objects | 215 |
| C.5 | Class Diagrams | 216 |
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\",\n \"polygon\": [\n [\n 127.97314453125,\n 220.236328125\n ],\n [\n 400.91961669921875,\n 220.236328125\n ],\n [\n 400.91961669921875,\n 245.6234130859375\n ],\n [\n 127.97314453125,\n 245.6234130859375\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 220.236328125,\n 400.91961669921875,\n 245.6234130859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/22/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.
\",\n \"polygon\": [\n [\n 128.49609375,\n 294.6796875\n ],\n [\n 525.9375,\n 294.6796875\n ],\n [\n 525.9375,\n 365.6669921875\n ],\n [\n 128.49609375,\n 365.6669921875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 294.6796875,\n 525.9375,\n 365.6669921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/22/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That's why this chapter is called, \\\"The way of the program.\\\"
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PEH/JJZv8AsGx/+grXZVxviD/kks3/AGDY/wD0Fa7KgAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5zQv+Rr8Vf8AXzb/APpPHXR1zmhf8jX4q/6+bf8A9J46AOd8D/8AJU/iN/12sv8A0U1ei1514H/5Kn8Rv+u1l/6KavRaACvOviR/yNngD/sMf+y16LXnXxI/5GzwB/2GP/ZaAPRaKKKACiiigAooooAKKKKACiiigAooooAKKKKAPOtS/wCS/aJ/2BJv/QzXoteX+JNSttK+OmiXN2ZRENGlX91C8pyXP8KAn9K67/hONC/v6h/4K7r/AON0AdESACScAd6Otc1J408PzRPFL9ueN1KsjaVckMD1BHl8iue3eDIOdLm13SG7DT7S8iQf9svLMf5rQB6NRXnP/CU39j/x667JqKD+DUdBuo3P/bSKMAf9+zUTfGXTNPuEt9c0u+s2c4EsMbyRn/vtUc/98UATfB3/AJAfiD/sP3f81rV+KlvDcfDDxAs0ayBLVpFDDOGXBBHuDWL8FbiO68N65PEWMcmuXTqWUrkHYRwea3vid/yTLxF/15PQBp+EePBehf8AYOt//Ra1s1jeEf8AkS9C/wCwdb/+i1rZoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvOtB/wCS6+Lf+wfafyFei151oP8AyXXxb/2D7T+QoA9FooooAKKKKACiiigCpqv/ACB73/r3k/8AQTXIfBz/AJJNoP8A1zk/9GvXX6r/AMge9/695P8A0E1yHwc/5JNoP/XOT/0a9AHdUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVxvxX/5Jb4h/69v/AGYV2Vcb8V/+SW+If+vb/wBmFAG/4b/5FfSP+vKH/wBAFadcx4e8S6DH4a0pH1vTVdbOEMrXSAg7Bwea0v8AhKPD/wD0HdM/8C4/8aANWiuR1K50G9uzd23jU6fcbQM2+pRtGcf9M5NyfkATVT/hIdQsP9V4o8L6tGP4bicWkh/4GrOp/wC+BQB3NedeKv8AktXgD/rlf/8Aomro+KGk2pC6rC9r6y288V3H+HlMX/NBXPaj4j0bxB8ZPAcuk6lb3axx3wkEb/MhMPAZeq59xQB6wyq6lWAZSMEEZBFeX/C7S7TRvG3j+wsIhDaxXsHlxjogKu2B7c16jXnXgL/ko3xD/wCvy2/9FtQB6LRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHnXxA/5H/4e/wDX/P8A+gCvRa86+IH/ACP/AMPf+v8An/8AQBXotABRRRQAUUUUAFcT8Xf+SU6//wBcF/8AQ1rtq4n4u/8AJKdf/wCuC/8Aoa0ASeIP+SSzf9g2P/0Fa7KuN8Qf8klm/wCwbH/6CtdlQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc5oX/I1+Kv8Ar5t//SeOujrnNC/5GvxV/wBfNv8A+k8dAHO+B/8AkqfxG/67WX/opq9FrzrwP/yVP4jf9drL/wBFNXotABXnXxI/5GzwB/2GP/Za9Frzr4kf8jZ4A/7DH/stAHotee6v4h8Sa544u/Cvhi4s9PXT4UlvL+5i81svyqomcdPX/wDX6FXnWr+EtN8T+K73VfDfia60jxDZhbe9e1G5Tx8okQ4zwPXHFAD7TWPF/hnxVpmk+I57XV9P1Nmjhvra2MLwyAZw6jIwfWpfEPi3W7zxU3hPwfBatfwxiW9vrvJitVPQYHVj6f8A18ZbeIvGvgjWtLtfFU1hq+k6hcrapfW0flTRyN93co4x9B+NT/D1lh+Ifj62uOL1r6OUA9TEQdpHtz+tADNTvfiN4MtG1i/vNO8RaZD813DFbfZ5o07smODj3q/4v8bz2vh7w3q+gzRtBqmoW8RZ03ZifOR7Guv1ya3t9B1Ca7Ki3S2kMhbpt2nNeFLHPF8G/AfnggHXImTd2QyOV/SgD6DY4Un0FeXaX461u6+D2teJZZITqVpJOsTCIBQEYAZHevUH+430NeF6H/ybl4lPYy3WP++xQB1OlW/xQ1TR7PUY/EuiKtzAkyo9geAwBwSPrWr4G8Zahqv9uad4jhtrfU9El2XMtuT5TrgkOM8jgGsHw7pPxKk8K6W1l4l0aK3aziMKPYksq7BgE9ziszwzDO3gH4gWs0bt4ljeePUbjzN32h9hwVwBhcZwMUAbenaz458erLqfh+8sdC0PzGS0kntvPmuQDjeQeApI/wD11peFfE+vQ+K7jwj4rS1fUEg+02t7aqVS4jzg5U9GH9D+Oj8M5ref4a+H2tiDGLNFOOzDhv1BrUuPEum23ie08OvI51G6haeNFQkBFzkk9uhoA8+0vVvH/inXfEUWla1pdna6bqD2qJPZ72IHTkV1vh7T/G1tqfmeINb028sthHlW1qY23djn061574R0fxPqXibxo+heK10aFNYkEkR06O58xvXLEY47V6N4c0XxVpt/JLrni9dYtjGVWAabHb7WyPm3KSTxkY96AMHUv+S/aJ/2BJv/AEM16LXnWpf8l+0T/sCTf+hmvRaAGSs6Qu0cfmOFJVM43HsM9q57PjG/6LpGkRn1L3kmPp+7UH8WH1rpKKAOb/4RJrvnV9d1e/z1jFx9mj+m2EISPZia0tM8PaNoxLabpdpaufvSRQqHb6t1P4mtKigDzr4O/wDID8Qf9h+7/mtbHxQZU+GPiIswUfY3GSccngVhfCe6gsvDHiW6upkht4ddvHkkkbCqo2kkmuQ8a3eqfEnwzrOto01l4R0yCR7NCCr6hMvAkI7ID0/xzgA9g8I/8iXoX/YOt/8A0WtbNY3hHjwVoX/YOt//AEWtc/qHxY8OWd/PZ2sepapJbnbO2m2jTJEe4LcD8s0AdzRWL4a8VaP4t09r3R7rzkRtkiMpV429GU8g1ma/8SPD/h/U/wCy5Hur7UgMtaafAZ5FHuBwPoTmgDraK5bw58QdA8T3slhaTTW+oxjc1leQmGYD1APX8M1LfeOdE07xhaeF7mWVNSulDRDy/kOc4G714NAHSUUyaaO3gkmlYLHGpdmPYAZJrmdC+IOheI9B1HWdPknaz0/d55eLaflXccDvxQB1NFedx/GjwxLCJkttZaEjIkGnuVx65Haup0vxbouteHpdc028FzYxIzSMikMu0ZIKnBBx2NAG3RXMTePtDh8H2vih3n/s25ZUjIi+fLMVGV+orpgQygjoRmgBaK5PxL8RNE8K6tFpl+l9Jdyw+cqWtsZflyRnj6VQtfizoN5dw20djrYeZ1jUvp7hQSccnsKAO7orlPE3xD0TwpqsGm36Xsl1NF5yJbW5l+XJGePpWbbfGDwpLdw29zJf2BmbYkl7ZvEhPpuIwPxoA72vOtB/5Lr4t/7B9p/IV6KCCAQcg96860H/AJLr4t/7B9p/IUAei0UUUAFFFFABRRRQBU1X/kD3v/XvJ/6Ca5D4Of8AJJtB/wCucn/o166/Vf8AkD3v/XvJ/wCgmuQ+Dn/JJtB/65yf+jXoA6PxN4k0/wAKaFPq2pOwhiwFRBlpGPRVHcmuQi8Q/E2+txqFp4S0qC1Yb0tLq8YXDL25HyqfY1H8UAsniTwJBc/8eL6uDKD90sANmfxzXpVAHLeFfG1v4n029f7JLY6np5KXlhOfnhcA9+4OODXJ+H/G/wAQ/E+jx6rpnhrRntJGZUL3jKx2kg8fhUgCw/HbWltfuS6CHugvTeCApPvjFYHww/4WL/wgdp/wj/8Awi39neZL5f2/7R52d5znZ8vXOKAPQ/BfjOXxLLqWnajpraZrOmSCO6tjIJF56MrDqDirPjzxLN4R8H3mtW9vHcS25QCOQkA7nC9vrXHfD28OkeLPE1p4p2QeJZdl3dXXmj7PJB91PLOBtAzjB5rU+MzpL8J9UkjZXRjCVZTkEeYvINAEvjDxprGjXXhyy0bTbS7vNZLBVuJCiqQoPUfU1F/a3xSz/wAixoX/AIHtWD8Q/wC0h4i+Hf8AY4tDqG+TyPte7yt3lr97bzj6V0Kt8W9674/BW3POGus4oA1/FnjBPCml2jSWb3mq3riG1sID80sp6jPZR3Nc3e+KviNodk2rar4W0uXTYh5k8NndsZ4k7nnhsD0pdZ/efHnwyl3jy00yd4B283nOPfFejTiI28gnCGEqfMDjKlcc59sUAcpf+IPEOpaXpmp+DdOsNRs7uLzWa7nMRXOMAAfjn6Vy+l+OviFq+r6pplp4b0Y3OmSLHchrxgAWGRg9+leiaBc6NdaNDJoBtTpvKxfZVCxjBOcAADrmuJ8Af8lJ+IP/AF+Q/wDoJoA7zSZNQm0q3k1W3ht79lzNFC+9FOegPeua+K//ACS3xD/17f8Aswrsq434r/8AJLfEP/Xt/wCzCgDT8O6Tpr+GdKZtPtCxs4SSYVyTsHtWl/Y+l/8AQNs/+/C/4VD4b/5FfSP+vKH/ANAFadAHNak9raXZtLPwjLfzbQd8VvFHEM+ruQPyyfaqf9ja7f8A/MO8OaRGfSA3kmPyjUH/AL6H1rsaKAOPX4d6ZcMH1W7vL9upTcttH/3zCEyPZia5rVdC0nQ/jL4Dj0rTrWzV478v5EQQuRCcFiOWPJ5Neq1514q/5LV4A/65X/8A6JoA9FrzrwF/yUb4h/8AX5bf+i2rU8c+NX8Pi30nR7cX/iXUPksrNedv/TR/RBz9cHsCRy3wesL7TPEvje01O8N5fpc25uJ+zyFHZse2SQPYdqAPWqKrahqFppNhNf386QWsK7pJX6KPU1y//C1fAv8A0M1l+bf4UAdjRWCnjTw5JoLa4mrQNpaSeW10M7Fb0PHuK24po54EmikV4nUOrqchgeQQaAH0VixeLdBn0u91OLVIHsbJilxcLkohHUZxz+FZH/C1fAv/AEM1l+bf4UAdjRWfo2uaZ4hsft2k3kd3bbynmR5xuHUfrWPqvxH8H6JfNZahr9pFcocPGCXKH0baDg/WgDqKKpWWrafqWmjUbG9gubMqWE0ThlwOvI9KXS9Vsda0+K/025S5tJc7JU6Ng4P6igC5RWdNr2lW+pvps17El4lubloTncIh1b6Vz/8AwtXwL/0Mtl+bf4UAdjRXP6N448M+Ib42WkazbXdyEL+XGTnaOp6e9Jrnjrwv4auBb6vrVrbTkZ8osWcD1KqCR+NAHQ0VmaL4i0bxHbG40fUre9iU4YwvkqfcdR+NUdY8deF/D9+bHVtatrS6ChzFITnB6HpQB0NFc5pXj3wprd4tnp2v2M9y/wB2ISYZvoDjP4V0dAHnXxA/5H/4e/8AX/P/AOgCvRa86+IH/I//AA9/6/5//QBXotABRRRQAUUUUAFcT8Xf+SU6/wD9cF/9DWu2rifi7/ySnX/+uC/+hrQBJ4g/5JLN/wBg2P8A9BWuyrjfEH/JJZv+wbH/AOgrXZUAFFFcX4n8c3Gna5F4c8PaU2r67LH5rReYI4rdP70jdvp/9bIB2lFebXfjTxp4WRb7xX4bsn0jcBNdaVOztbgnqyNyR9K2vFvjX+w9K0TUNOjhvINTvYbdXLHGyTJ3DFAHX0UjHCk+grz7RfGXijxN4PTVtE0fT5bz7bLA8M07IgReNwPrnFAHoVFeUR+OviFL4pn8OL4b0b+0IbcXLr9sbbsJwOfWtTxF4z8UeGfCFpf6ho+njVrm/W0W3Sdmj2tnad3rkUAeh0V5tfeKviPo1lLqOoeENNns7dS8y2l8fMCDqQCOcCr+s/ERLfwx4e1zS7dZ4NXvIbfbMSDGr5B6dwRigDuqKRjhSfQV59ovjLxR4m8Hpq2iaPp8t59tlgeGadkQIvG4H1zigD0KivKI/HXxCl8Uz+HF8N6N/aENuLl1+2Nt2E4HPrXoXh6fW7jSxJr9lbWl9vI8q2lMibexye9AGrXOaF/yNfir/r5t/wD0njro65zQv+Rr8Vf9fNv/AOk8dAHO+B/+Sp/Eb/rtZf8Aopq9FrzrwP8A8lT+I3/Xay/9FNXotABXnXxI/wCRs8Af9hj/ANlr0WvOviR/yNngD/sMf+y0Aei1w2t+BtSHiSfxH4U1waTqNyipdxSwCWC4xwCR2PuP8a7migDz+08C67quuWGp+MfEEWoJp8nnW1laWwiiEnZ2PViPSr/ijwK2r6xBr+i6rLo2vQp5Yuo4xIkyf3ZEPDD/ADzxXY0UAeb3PgPxZ4mCWni3xXFLpQYNJZ6da+T9owejv1x7V1HiPwfpniPwv/YMitbW8YT7O8HBgZPulfpXQUUAecP4T+I09odMn8c2v2Jl8trlNPAuGTp1zgHHfOa2pfAVjH8OZ/B2nztbW8kBi8908xsk5LkZGST7iutooApaPp/9k6LY6d5vm/ZYEh8zbt3bVAzjJx0rF0Dwi+heJPEGpi/We21iVZmtmgwYmAwfn3HcDk8YFdPRQB5wngDxF4cvLk+C/EkNjp1zIZW0+9tvNjiY9Sh6ge3861fCngefR9Zu/EGt6s+r67dIImuDGI0ij/uIo6D/AD612VFAHm0Xw68TabrGr3uheOv7Oi1K7a6kg/siObDHtudu34VuaF4f8YWGqx3Gr+OBqtmoYNa/2TFBuJHB3qcjB5rraKAPOtS/5L9on/YEm/8AQzXotedal/yX7RP+wJN/6Ga9FoAKKKKACiiigDwHwX4U1nxhcarpuoM9t4Qh1q5uZlTKtfy78BM/3F28kd/flfTviPBDa/CrXre3iSKGKwZEjRcKqgYAAHQV1scUcMYjiRUQdFUYA/CuW+J3/JMvEX/Xk9AGPr2qXGjfAVb21YpOujwIjDqpZEXP610ngjRrXQfBmlWVpGqqLdHcgcu7KCzH1JJqjbaJF4k+E1lo8zbUu9Ihj3Y+6fLXB/A4NcxoPj+78I6TDoPi7QtWS+sUEEdza2pmiuUXhWVh3xj/ADxQB1dz4fsfDd94h8W2JnW7uLNnmgDDymZFJDbcZ3cdc9zWT8HtLht/Attq7ASajqzNdXdw3LyMWOAT6D0+tV/CdrrHirxRq/ibWLO80/Sbi1+xWWn3JKs0Z+9IydAT/Ws3QNX1L4WwSeHdd0nUbzR4ZGbT9TsYDMvlsc7JAOVIyf8APNAF/wCMtnHaaBZ+KbYCLVNIu4nhnXhipYBkJ7g56VyHxCsZtT+I19f2akXun6HDqNtjqGjlDEf987hXQ6tdaj8V72x0uy0i/sPDUFwtxe3l9EYjcbTkRxqeSPf+WOdlNKum+Nl3dvYz/wBnPoYg88xHyi2/lN2MZx2oAb448Rf2l8NLQ6Y/+keIvJtLbB5Hm43fku6uX8G2MOl+C/iRYQDENtcXMSD2WLH9Kl8GeG9bj8cW2kalY3KaL4ZkuZLG4kjYRzmRv3e0kYO1SenStHQ9J1KHQ/iTHJp92j3l5dNbK0LAzgxkAoMfMCemKAM/wJ8UvD+jeA9H0+4h1R7i3tgjCGxdwTz0PQ1Y8GadfJ4c8da5cadLptpq5mntLSVdrqmxvmK9s5/Su0+HFpc2Pw60K1u7eW3uIrVVkimQo6HJ4IPINa/iCKSbw3qkUSNJI9pKqogyWJQ4AHc0AeKaj/ybRoP/AF3h/wDRzV71H/qk/wB0V4nqWi6v/wAM76Pp66TfvfwyxM9ols5mAErE5TGeldcnxR2oqnwJ44yBj/kD/wD2VAGF4t1seH/jlpl8dN1HUANHdPI06386XlzztyOK6aw+JKX+oW9oPB3i+386QR+dcaWUjTJxlm3cAdzXPeJL+90n4s6V4kXw5r1/Y/2QYmFjYtK6OzE7W6AEdxmtuP4niSVE/wCEG8bLuYDc2kYA9z83SgDn/GWvWvhv43aPqF5FcyQrpMiFbaEyPku38Iqj4/8AiBo/jDw4fDNlZXkd3qU0cUM2owfZ4YjuB3FnPXjt611eoadfSfHLSNRSyuGsk0mSN7kRMY1YscKWxgH2q38WdLudW+G+p21laSXd0PLkiiiQu5IcH5QOc4z0oA6zTrVrLTLS0aTzGghSMv8A3ioAz+lcHoP/ACXXxb/2D7T+QruNIna50aymeOWN3gQskyFHU4GQynkH61w+g/8AJdfFv/YPtP5CgD0WioLy8ttPs5ry7mSC2hQvJLIcKqjqSax/B/iq18ZaK+rWUTpam4kii3/edVONxHbPXFAG/RRRQAUUUUAVNV/5A97/ANe8n/oJrkPg5/ySbQf+ucn/AKNeuv1X/kD3v/XvJ/6Ca5D4Of8AJJtB/wCucn/o16ANvxh4VtfGGgSabcSPBIHEtvcR/ehlX7rCuYik+LNjbiw+y+Hr91GxNReZ0yOzOnr9K9GooA43wj4Km0G31O+1K+Goa/quWu7rbtUcYVFHZR/kVzXhfw18UfCWhRaPp8ng+W2iZ2VrhrkudzEnJAA7+ler0UAeZxfDTU9Xh8Q3vinU7abWNXtBaKbNGENtGDkBd3J5APPpWbc+EviBr3h2y8H6uukW+kwGNJ7+GVmkmijIwFTHBOB1r16igDgvHHhLXtU1Xw3qXhuTTEn0Z3YJqDSBGyoA+4CT0PcU3/i73p4H/O7rv6KAOM8W+D7/AMS6dpd9Bew2HiXTSJYLmIExCQgb0IPOwn8axb+D4qa9p0mj3FvoOmRTqYri/hmd22Hhti9iR6/pXptFAGX4d0K18NeH7LR7PJgtYwgZurHqWPuTk1i+GPC19ovi7xTq1zLbvBq08csCxsxZQqkHcCAAeexNddRQAVxvxX/5Jb4h/wCvb/2YV2Vcb8V/+SW+If8Ar2/9mFAG/wCG/wDkV9I/68of/QBWnWZ4b/5FfSP+vKH/ANAFadABRRRQAV5D8VdXvND+I3gu/wBOsGv75YryO3tl/jkdAi59gWBPsD0r16qsmm2cupwalJbo95BG0UUrDJRWxuA9M4FAHLeBvBcmhG41rW5xfeJtQ+a7ujyIx2ij9FHHTrj0AAzfAX/JRviH/wBflt/6LavRa868Bf8AJRviH/1+W3/otqANT4rf8ku8Qf8AXr/7MK5LQfiL8LrXw7ptveXdiLmK1iSUNpsjEOFAOT5fPNdb8Vv+SXeIP+vX/wBmFc94e+L/AIBsfDel2lzripPBaRRyL9knO1goBGQmOtAHZaXL4a8X+FZRpiQXGjXgeJlSExq3ZvlIBz74ryZtS8R6A83wmtZC93cyhNP1FnH7uzcEtnvuUAgfj6CvRLv4oeGU8HXviPTr0XlvA/kIgjeMyTEZVAGAPOR+Ga5GH4Zajq3hO41++maPxtdTDUYZ84Nuy8pCPQY4Pvj0oA3vGmg2Xhn4I6tpGnx7be3s9oPdzuGWPuTzWNoPxF+F1r4d023vLuxFzFaxJKG02RiHCgHJ8vnmpNY8Xp4v+B2v3EqeRqVtAYL62PBilDAHj0PUf/Wqx4e+L/gGx8N6XaXOuKk8FpFHIv2Sc7WCgEZCY60AXvE/jHTLf4S6jrvhOWPyZAYYJYYTEA7MEJAIByM+natvwh4M0fw/4ZtbNLG3kleJWuZpIwzzORlixPXmszWZ9I+Kvw71e18O3y3IYbI5DG8YEy4cD5gD6c+9UvDvxZ8PxaNFaeJLs6RrFmgiu7W6jYNuUYJXjkHGeKANXTfBUPhSTxLdafdBNN1GMyrp6xbUgcKclTnofTA7elVfgv8A8kq0f/tr/wCjGqv4R1fV/Gus+INXWS4i8NSw/ZdNimTaJDj5pQMZx/j7VjfDrxrong7w2vhbxNdf2TqmmySI8dyjASKXJDKcYIOaANWRVf8AaFCsAynQSCCMgjzKq/GPStOtvDWlvb2FrEzavbKTHCqkgk8cDpTvC14njD4tX3inTI5W0W108WMd06FFnk3ZO3PJA5qz8af+RX0n/sMW38zQB0viSSy8K+FdV1qysLWK4tbV3Ro4VU5xx0HTOKxfhp4RsLHwtaateW8d3rGpxi6urydQ8jM/zYyegAPQV1niDSU17w7qGkyNtW7t3h3f3SRgH86898H/ABBsPDGkweGPGUh0jVdNTyA06N5dxGvCujAYIxigBPHWmWvgzxRoHi3RoUs3nvksdQihUKk8cncqOMjHX6elUdU1bw/pHx21ObxFNaRWzaTEqG5QMpfcOnB5xmrGq6tF8VfFGj6ZoKSz6Dpl2t5fagYysbsv3Y0yOTVyCztrz9oDVkureGdBo0RCyoGAO4c80Ac/471Dwl4uh0vS/Bos7rxC17G8EllBsMKqcszOAMKB717coIQBjkgcn1ry34v6Xp2leG9P1qzsbe2urHUrdxcQxBGRd3PIHTpXqEUqTwpLE6vG6hlZTkMDyCKAPPfiB/yP/wAPf+v+f/0AV6LXnXxA/wCR/wDh7/1/z/8AoAr0WgAopiyxtI8aupdMblB5XPTI7U+gAooooAK4n4u/8kp1/wD64L/6GtdtXE/F3/klOv8A/XBf/Q1oAk8Qf8klm/7Bsf8A6CtdlXG+IP8Akks3/YNj/wDQVrsqACvNfh4Fk+IXj+afm9F9Ggz1EQU7ce1elVwniPwdrEXif/hK/CF5bW+qPGIru1uwfIu0HTOOQw9f5dwDrNbjt5tB1CO7Cm3a2kEm7pt2nNeEpJNJ8G/ARm3HbrkSoT/cEj4/Su41HSfiN4ytjpOrf2VoWlS/LdvZytLPMndVzwAf856VveJvAdrrHgq38P6fL9gNiY5LGUDPlPH90n175+tAHXP9xvpXnHwR/wCRBk/7CFx/6FQ8nxauLNtONn4dhlZfLbUlmcgDpuCYzn9M9q6rwb4Yh8IeGLXR4ZmnMeXlmYYMkjHLNjtzQBylj/ycLqv/AGBI/wD0MVH8b2lTwtpLQIrzDV7cxoxwGb5sAntzW/beFr6H4p3vihpbc2M+nJarGGbzA4YHJGMY49aPiB4WvvFel6da2MtvG9tqEN05nZgCiE5AwDzzQBg6nP8AFLWtOn0xdA0XT1ukMT3TXpk8tWGCQAOuPrWT428Px+FvAvgrRY5TKLXWLZTIRjcxLEnHbkmvYq5vxx4UHi/w8bBLk2t3FKlxa3GM+XKpyCR6UAdG/wBxvpXnHwR/5EGT/sIXH/oVDyfFq4s2042fh2GVl8ttSWZyAOm4JjOf0z2rqvBvhiHwh4YtdHhmacx5eWZhgySMcs2O3NAHKWP/ACcLqv8A2BI//QxXpNcjbeFr6H4p3vihpbc2M+nJarGGbzA4YHJGMY49a66gArnNC/5GvxV/182//pPHXR1zmhf8jX4q/wCvm3/9J46AOd8D/wDJU/iN/wBdrL/0U1ei1514H/5Kn8Rv+u1l/wCimr0WgArzr4kf8jZ4A/7DH/stei1518SP+Rs8Af8AYY/9loA9FrjPFHjW607W4PDnh7S/7V12aPzWjaTy4reP+/I39P8A62ezrzbwNiT4qePpJ/8Aj6WaBEz1EW04x7dKACXxx4r8L3tofGeh2EWl3Uqw/b9OnZlgdum9W5x710PjDxpB4Wjs7eCzl1LVr99llYwHDSnuSf4VHrWrr8mjQ6NNLr4tDpqYMv2tA0Y5GMggjriuEgaG6+P6PIVaJNBV7H0wW5K/gTQBPP4k+JWlWx1HUPCel3Nkg3y29jdsbhF79eGI9BWvf+PLVvhrdeMNGVbmOODzEjl+XDAgFWx0INdhXgcW2P4bfFG3t/8Ajxi1KUQAfdHK5A/SgDs7XxP8S7nToNRj8JaTcW8sSzLHHflXZSMjGeAcGuj8MeM7XxToFzqFvby211aF47qzn4eCVRyp/wAav+Ff+RQ0X/rxg/8AQBXCeHML8TviKlv/AMe5hhZwOnmeWc/j1oAb4f8AG3xD8T6NFq2meGtGa0mLBDJeMrfKSDx9RXaeGbvxTdfaf+El0yxstu3yfsk5k3dc5z07V5h8MD8RR4Csf7BTwudO3y+V9uNx5v3znOzjrmvU/DR8VGGf/hKV0cS7h5P9mGXbt77vM79OlAG5RRRQAUUUUAedal/yX7RP+wJN/wChmvRa861L/kv2if8AYEm/9DNei0AFFFFABRRRQAVyfxO/5Jl4i/68nrrK5P4nf8ky8Rf9eT0AafhH/kS9C/7B1v8A+i1rZrG8I/8AIl6F/wBg63/9FrWzQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeb6NLHB8b/GE00ixxR6bas7ucBQFyST2FekV4hrHhm88XfG7XtJF69rpDWtrJqPlnDzRqo2xg9gT1+n4UAa5Nz8YdZ2jzYPAthNyeVbVJVP5iMH/ADn7ul8EVVPh4FUAKt9cAAdhvNd/ZWVtp1lDZ2cCQW0CBI4oxhVUdAK4H4J/8k+/7f7n/wBDNAHotFFU9T1S30i1FzdLcGLcFJgt3mK57kICQOOuMCgC5RWZpniLRdZYrp2qWlzIPvRxyguv1XqPxFadAFTVf+QPe/8AXvJ/6Ca5D4Of8km0H/rnJ/6Neuv1X/kD3v8A17yf+gmuQ+Dn/JJtB/65yf8Ao16AOt1fVrHQtKuNT1GdYLS3TfI7dh7ep9q4UfE7V5rX+0bTwBrc2kkbxcFkWRk/vCLqRjnrTfi1/pU3hHSpv+PK91mJbhT0YDkKfzr0gAKAAAAOABQBk6H4m0rxB4ej1yyuB9hZSzNJ8pj2/eDehHeuSX4n3WqvJJ4X8IaprNjGxU3oZYI3I67N3LVm/Eqw0vw58MdattAVLdLy8QXYimLbWkcByQSduQMYGBXpWlWVvpukWdlaIqW8EKpGqjgACgDE8KeONO8VSXNokFzYapaf8fNheJslj98dx71iXHxNvG1/VdK0vwhqepnTZvJmlt5Exntwaq+MUTTvi/4K1C1AW5vDNaXG3rJHtyM+uCak+HX/ACPfxB/7CMf/AKC1AGv4d+IdlrWsnRL7Tb/RdX2eYlrfx7fNX1Rhw1QeIfiFNo3iv/hHrHw5fatd/Zhcn7M6jCk46H/PNZnxbVba48I6nCAL2DWoY42HUq+dw+hwKytf1HV9M+O7TaLoZ1i5OiqptxdJb4Xfy25uPTj3oA6iw8ca/d6hb283gHWLWKWRUeeSRNsYJ5Y47Cu4ri9M8TeNbvU7eC/+H7WNrI4WW5OrwyeUv97aBk/QV2lABRRRQAVxvxX/AOSW+If+vb/2YV2Vcb8V/wDklviH/r2/9mFAG/4b/wCRX0j/AK8of/QBWnWZ4b/5FfSP+vKH/wBAFadABRRRQAUUUUAFedeAv+SjfEP/AK/Lb/0W1ei1514C/wCSjfEP/r8tv/RbUAei0UUUAFFFFABRRRQAVWudOsbx1e6s7ed1+60sSsR9MirNFACABQAAABwAKgurCzvdv2u0guNv3fNjD4+mRViigBqIsaBEUKqjAVRgAU6iigAqC5s7W9QJdW0M6A5CyxhgPzqeigBkUUcEaxxRrHGowFQYA/Cn0UUAFFFFAHnXxA/5H/4e/wDX/P8A+gCtbxz41HhmC30/Trf7f4h1A+XYWK8kn++/og/XH1I5P4xavLo/iTwTd2lsby9iup2gtUPzSuVVVHrgsRXS+B/BlxpU9x4i8RTLeeJ9QGZ5uq26doo/QDvjrj0oAwvg0uqRzeLo9auvtWpJqpS4mzkM4UA49uwHYAV6nXnXwz/5GLx7/wBh2T+Vei0AFFVr3ULLTYVmv7y3tYmcIrzyBFLHoMk9eOlTxyJLGskbq6MMhlOQR9aAHVxPxd/5JTr/AP1wX/0Na7auJ+Lv/JKdf/64L/6GtAEniD/kks3/AGDY/wD0Fa7KuN8Qf8klm/7Bsf8A6CtdlQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc5oX/I1+Kv+vm3/APSeOujrnNC/5GvxV/182/8A6Tx0Ac74H/5Kn8Rv+u1l/wCimr0WvOvA/wDyVP4jf9drL/0U1ei0AFedfEj/AJGzwB/2GP8A2WvRa86+JH/I2eAP+wx/7LQB6LXB+JvCOtR+KE8WeEbq1h1QxCG7tbsHybtB0yRyGHr9OR37yigDy/UfDnjjx49tYeJ00zStDilWW4gs5WlluSpyFyeAv+ea3/GPgqbWptO1bQ71dN13TMi1nZcxsh6xuP7p/TmuxooA84uD8WNUtm082+gaXvGyTUIpnkIHconY+mau3Pw8S1+Fl74R0eVPPuIiDcXJI8yUkFnYgE849D2ruqKAPNbPTvizaaVb6bFc+D4YoIVhWdBcvIqqAAcEbSePTFb/AIU8Fr4Y0K+t2vGvdT1BnmvL2UYMsjDHTsB6V1dFAHlHhjw38UfCWgwaNp8ng+W2hZijXDXJc7mLHJAA7+ldp4a/4TTz5/8AhKf7A8naPJ/svzt27PO7zO2PSujooAKKKKACiiigDzrUv+S/aJ/2BJv/AEM16LXnWpf8l+0T/sCTf+hmvRaACiiigAooooAK5P4nf8ky8Rf9eT11lcn8Tv8AkmXiL/ryegDT8I/8iXoX/YOt/wD0WtbNY3hH/kS9C/7B1v8A+i1rZoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvOtB/5Lr4t/7B9p/IV6LXnWg/8AJdfFv/YPtP5CgD0WvIvhJea9B4JKado1pd2/224Pmy35hOd5yNvlt/OvXa86+Cf/ACT7/t/uf/QzQB0f9peLP+ha0/8A8Gx/+M0f2l4s/wCha0//AMGx/wDjNdFVPU7W7vLURWWovYSbgTNHEkjbe4AYEA9OSDQBzGp2eq6yoGp+B9Eu8fdM2oBiv0JgyPwrkb3UrrRZzBp10LC5HSztvEDXp+nkPBIQPZQK73/hCdMuOdVuNQ1c9xfXTNGf+2S7Y/8Ax2tuy06y02AQWFnb2sI6RwRKi/kBQB5lY+JPiTdWd0l74YtZ7EwuDcySNaOq4PJVxuJ78Itbnwc/5JNoP/XOT/0a9dfqv/IHvf8Ar3k/9BNch8HP+STaD/1zk/8ARr0AaXjzwo3i3w79ltpxbahbyrc2U56JKvTPselc9H408ewWYsrj4f3E2qquzz4rpPszt/ez2HfH616TRQB51oHw2Y+A9W0nxBMsmo63K9zeyxciORuV2+u0gfrVfTNd8d+FLGPSNU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}\n },\n {\n \"id\": \"/page/23/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Figure 1.2: A compiler translates source code into object code, which is run by a hardware executor.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 258.134765625\n ],\n [\n 482.90625,\n 258.134765625\n ],\n [\n 482.90625,\n 280.5428771972656\n ],\n [\n 85.9130859375,\n 280.5428771972656\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 258.134765625,\n 482.90625,\n 280.5428771972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/23/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Due to these advantages, almost all programs are written in high-level languages. Lowlevel languages are used only for a few specialized applications.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 298.93359375\n ],\n [\n 482.40338134765625,\n 298.93359375\n ],\n [\n 482.40338134765625,\n 321.6348876953125\n ],\n [\n 85.6142578125,\n 321.6348876953125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 298.93359375,\n 482.40338134765625,\n 321.6348876953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/23/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Two kinds of programs process high-level languages into low-level languages: interpreters and compilers. An interpreter reads a high-level program and executes it, meaning that it does what the program says. It processes the program a little at a time, alternately reading lines and performing computations. Figure 1.1 shows the structure of an interpreter.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 330.64453125\n ],\n [\n 482.90625,\n 330.64453125\n ],\n [\n 482.90625,\n 377.82421875\n ],\n [\n 85.6142578125,\n 377.82421875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 330.64453125,\n 482.90625,\n 377.82421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/23/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"A compiler reads the program and translates it completely before the program starts running. In this context, the high-level program is called the source code, and the translated program is called the object code or the executable. Once a program is compiled, you can execute it repeatedly without further translation. Figure 1.2 shows the structure of a compiler.
\",\n \"polygon\": [\n [\n 85.46484375,\n 386.912109375\n ],\n [\n 482.607421875,\n 386.912109375\n ],\n [\n 482.607421875,\n 445.85791015625\n ],\n [\n 85.46484375,\n 445.85791015625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 386.912109375,\n 482.607421875,\n 445.85791015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/23/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Python is considered an interpreted language because Python programs are executed by an interpreter. There are two ways to use the interpreter: interactive mode and script mode. In interactive mode, you type Python programs and the interpreter displays the result:
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\",\n \"polygon\": [\n [\n 85.3154296875,\n 507.2197570800781\n ],\n [\n 92.0390625,\n 507.2197570800781\n ],\n [\n 92.0390625,\n 518.9765625\n ],\n [\n 85.3154296875,\n 518.9765625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 507.2197570800781,\n 92.0390625,\n 518.9765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/23/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The chevron, >>>, is the prompt the interpreter uses to indicate that it is ready. If you type 1 + 1, the interpreter replies 2.
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 85.95379638671875\n ],\n [\n 288.8173828125,\n 85.95379638671875\n ],\n [\n 288.8173828125,\n 100.29998779296875\n ],\n [\n 128.3466796875,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 85.95379638671875,\n 288.8173828125,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/24/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"A program is a sequence of instructions that specifies how to perform a computation. The computation might be something mathematical, such as solving a system of equations or finding the roots of a polynomial, but it can also be a symbolic computation, such as searching and replacing text in a document or (strangely enough) compiling a program.
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 291.70526123046875\n ],\n [\n 452.42578125,\n 291.70526123046875\n ],\n [\n 452.42578125,\n 301.7649841308594\n ],\n [\n 128.9443359375,\n 301.7649841308594\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 291.70526123046875,\n 452.42578125,\n 301.7649841308594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/24/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Believe it or not, that's pretty much all there is to it. Every program you've ever used, no matter how complicated, is made up of instructions that look pretty much like these. So you can think of programming as the process of breaking a large, complex task into smaller and smaller subtasks until the subtasks are simple enough to be performed with one of these basic instructions.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 439.2318420410156\n ],\n [\n 292.5728454589844,\n 439.2318420410156\n ],\n [\n 292.5728454589844,\n 453.57806396484375\n ],\n [\n 128.0478515625,\n 453.57806396484375\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 439.2318420410156,\n 292.5728454589844,\n 453.57806396484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/24/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/24/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Programming is error-prone. For whimsical reasons, programming errors are called bugs and the process of tracking them down is called debugging.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 86.3349609375\n ],\n [\n 207.5361328125,\n 86.3349609375\n ],\n [\n 207.5361328125,\n 99.24493408203125\n ],\n [\n 85.6142578125,\n 99.24493408203125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 86.3349609375,\n 207.5361328125,\n 99.24493408203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/25/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The second type of error is a runtime error, so called because the error does not appear until after the program has started running. These errors are also called exceptions because they usually indicate that something exceptional (and bad) has happened.
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\",\n \"polygon\": [\n [\n 85.83837890625,\n 364.095703125\n ],\n [\n 258.78515625,\n 364.095703125\n ],\n [\n 258.78515625,\n 377.6620178222656\n ],\n [\n 85.83837890625,\n 377.6620178222656\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 364.095703125,\n 258.78515625,\n 377.6620178222656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/25/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"One of the most important skills you will acquire is debugging. Although it can be frustrating, debugging is one of the most intellectually rich, challenging, and interesting parts of programming.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 433.8984375\n ],\n [\n 483.802734375,\n 433.8984375\n ],\n [\n 483.802734375,\n 457.8319396972656\n ],\n [\n 85.763671875,\n 457.8319396972656\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 433.8984375,\n 483.802734375,\n 457.8319396972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/25/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Debugging is also like an experimental science. Once you have an idea about what is going wrong, you modify your program and try again. If your hypothesis was correct, then you can predict the result of the modification, and you take a step closer to a working program. If your hypothesis was wrong, you have to come up with a new one. As Sherlock Holmes pointed out, \\\"When you have eliminated the impossible, whatever remains, however improbable, must be the truth.\\\" (A. Conan Doyle, The Sign of Four)
\",\n \"polygon\": [\n [\n 85.763671875,\n 467.15625\n ],\n [\n 484.1015625,\n 467.15625\n ],\n [\n 484.1015625,\n 539.9229431152344\n ],\n [\n 85.763671875,\n 539.9229431152344\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 467.15625,\n 484.1015625,\n 539.9229431152344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/25/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"For some people, programming and debugging are the same thing. That is, programming is the process of gradually debugging a program until it does what you want. The idea is that you should start with a program that does something and make small modifications, debugging them as you go, so that you always have a working program.
\",\n \"polygon\": [\n [\n 85.763671875,\n 549.52734375\n ],\n [\n 483.802734375,\n 549.52734375\n ],\n [\n 483.802734375,\n 597.6249542236328\n ],\n [\n 85.763671875,\n 597.6249542236328\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 549.52734375,\n 483.802734375,\n 597.6249542236328\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/25/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/25/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"For example, Linux is an operating system that contains thousands of lines of code, but it started out as a simple program Linus Torvalds used to explore the Intel 80386 chip. According to Larry Greenfield, \\\"One of Linus's earlier projects was a program that would switch between printing AAAA and BBBB. This later evolved to Linux.\\\" (The Linux Users' Guide Beta Version 1).
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\",\n \"polygon\": [\n [\n 153.59765625,\n 224.296875\n ],\n [\n 500.8359375,\n 224.296875\n ],\n [\n 500.8359375,\n 246.73370361328125\n ],\n [\n 153.59765625,\n 246.73370361328125\n ]\n ],\n \"bbox\": [\n 153.59765625,\n 224.296875,\n 500.8359375,\n 246.73370361328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Formal languages tend to have strict rules about syntax. For example, 3 + 3 = 6 is a syntactically correct mathematical statement, but 3+ = 3$6 is not. H2O is a syntactically correct chemical formula, but 2Zz is not.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 309.955078125\n ],\n [\n 526.833984375,\n 309.955078125\n ],\n [\n 526.833984375,\n 357.69354248046875\n ],\n [\n 128.794921875,\n 357.69354248046875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 309.955078125,\n 526.833984375,\n 357.69354248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The second type of syntax rule pertains to the structure of a statement; that is, the way the tokens are arranged. The statement 3+ = 3 is illegal because even though + and = are legal tokens, you can't have one right after the other. Similarly, in a chemical formula the subscript comes after the element name, not before.
\",\n \"polygon\": [\n [\n 128.794921875,\n 368.349609375\n ],\n [\n 526.236328125,\n 368.349609375\n ],\n [\n 526.236328125,\n 414.94085693359375\n ],\n [\n 128.794921875,\n 414.94085693359375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 368.349609375,\n 526.236328125,\n 414.94085693359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 1.1. Write a well-structured English sentence with invalid tokens in it. Then write another sentence with all valid tokens but with invalid structure.
\",\n \"polygon\": [\n [\n 129.392578125,\n 416.8828125\n ],\n [\n 525.638671875,\n 416.8828125\n ],\n [\n 525.638671875,\n 439.15716552734375\n ],\n [\n 129.392578125,\n 439.15716552734375\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 416.8828125,\n 525.638671875,\n 439.15716552734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"When you read a sentence in English or a statement in a formal language, you have to figure out what the structure of the sentence is (although in a natural language you do this subconsciously). This process is called parsing.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 450.9140625\n ],\n [\n 525.9375,\n 450.9140625\n ],\n [\n 525.9375,\n 485.45086669921875\n ],\n [\n 128.9443359375,\n 485.45086669921875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 450.9140625,\n 525.9375,\n 485.45086669921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"For example, when you hear the sentence, \\\"The penny dropped,\\\" you understand that \\\"the penny\\\" is the subject and \\\"dropped\\\" is the predicate. Once you have parsed a sentence, you can figure out what it means, or the semantics of the sentence. Assuming that you know what a penny is and what it means to drop, you will understand the general implication of this sentence.
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Formal languages are designed to be nearly or completely unambiguous, which means that any statement has exactly one meaning, regardless of context.\",\n \"polygon\": [\n [\n 129.09375,\n 606.375\n ],\n [\n 525.9375,\n 606.375\n ],\n [\n 525.9375,\n 653.5618743896484\n ],\n [\n 129.09375,\n 653.5618743896484\n ]\n ],\n \"bbox\": [\n 129.09375,\n 606.375,\n 525.9375,\n 653.5618743896484\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/26/ListItem/13\",\n \"block_type\": \"ListItem\",\n \"html\": \"redundancy: In order to make up for ambiguity and reduce misunderstandings, natural languages employ lots of redundancy. As a result, they are often verbose. 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If I say, \\\"The penny dropped,\\\" there is probably no penny and nothing dropping (this idiom means that someone realized something after a period of confusion). Formal languages mean exactly what they say.\",\n \"polygon\": [\n [\n 85.3154296875,\n 88.22021484375\n ],\n [\n 483.50390625,\n 88.22021484375\n ],\n [\n 483.50390625,\n 135.38092041015625\n ],\n [\n 85.3154296875,\n 135.38092041015625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 88.22021484375,\n 483.50390625,\n 135.38092041015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/26/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/27/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"People who grow up speaking a natural language—everyone—often have a hard time adjusting to formal languages. In some ways, the difference between formal and natural language is like the difference between poetry and prose, but more so:
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 447.3996887207031\n ],\n [\n 232.6595001220703,\n 447.3996887207031\n ],\n [\n 232.6595001220703,\n 461.74591064453125\n ],\n [\n 85.9130859375,\n 461.74591064453125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 447.3996887207031,\n 232.6595001220703,\n 461.74591064453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/22/SectionHeader/5\",\n \"4\": \"/page/27/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/27/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Traditionally, the first program you write in a new language is called \\\"Hello, World!\\\" because all it does is display the words \\\"Hello, World!\\\". In Python, it looks like this:
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\",\n \"polygon\": [\n [\n 128.42138671875,\n 85.95379638671875\n ],\n [\n 236.00576782226562,\n 85.95379638671875\n ],\n [\n 236.00576782226562,\n 101.2236328125\n ],\n [\n 128.42138671875,\n 101.2236328125\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 85.95379638671875,\n 236.00576782226562,\n 101.2236328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/28/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/28/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"It is a good idea to read this book in front of a computer so you can try out the examples as you go. You can run most of the examples in interactive mode, but if you put the code in a script, it is easier to try out variations.
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 156.3310546875\n ],\n [\n 526.53515625,\n 156.3310546875\n ],\n [\n 526.53515625,\n 192.14691162109375\n ],\n [\n 128.3466796875,\n 192.14691162109375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 156.3310546875,\n 526.53515625,\n 192.14691162109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/28/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/28/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"This kind of experiment helps you remember what you read; it also helps with debugging, because you get to know what the error messages mean. It is better to make mistakes now and on purpose than later and accidentally.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 245.953125\n ],\n [\n 526.236328125,\n 245.953125\n ],\n [\n 526.236328125,\n 269.54296875\n ],\n [\n 128.794921875,\n 269.54296875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 245.953125,\n 526.236328125,\n 269.54296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/28/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/28/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"There is evidence that people naturally respond to computers as if they were people. When they work well, we think of them as teammates, and when they are obstinate or rude, we respond to them the same way we respond to rude, obstinate people (Reeves and Nass, The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places).
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\",\n \"polygon\": [\n [\n 128.49609375,\n 249.9796142578125\n ],\n [\n 525.9375,\n 249.9796142578125\n ],\n [\n 525.9375,\n 296.5483093261719\n ],\n [\n 128.49609375,\n 296.5483093261719\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 249.9796142578125,\n 525.9375,\n 296.5483093261719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/22/SectionHeader/1\",\n \"3\": \"/page/28/SectionHeader/1\",\n \"4\": \"/page/30/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/30/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"If you run a 10 kilometer race in 43 minutes 30 seconds, what is your average time per mile? What is your average speed in miles per hour? (Hint: there are 1.61 kilometers in a mile).
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\",\n \"polygon\": [\n [\n 129.09375,\n 387.10546875\n ],\n [\n 525.9375,\n 387.10546875\n ],\n [\n 525.9375,\n 422.015869140625\n ],\n [\n 129.09375,\n 422.015869140625\n ]\n ],\n \"bbox\": [\n 129.09375,\n 387.10546875,\n 525.9375,\n 422.015869140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/32/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/32/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"If you are not sure what type a value has, the interpreter can tell you.
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\",\n \"polygon\": [\n [\n 227.2587890625,\n 148.0166015625\n ],\n [\n 340.75885009765625,\n 148.0166015625\n ],\n [\n 340.75885009765625,\n 158.7529296875\n ],\n [\n 227.2587890625,\n 158.7529296875\n ]\n ],\n \"bbox\": [\n 227.2587890625,\n 148.0166015625,\n 340.75885009765625,\n 158.7529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/32/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/TextInlineMath/3\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> 1,000,000 (1, 0, 0)
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 209.21484375\n ],\n [\n 482.4034118652344,\n 209.21484375\n ],\n [\n 482.4034118652344,\n 244.24981689453125\n ],\n [\n 85.9130859375,\n 244.24981689453125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 209.21484375,\n 482.4034118652344,\n 244.24981689453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"4\": \"/page/32/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/SectionHeader/5\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"2.2 Variables
\",\n \"polygon\": [\n [\n 85.9130859375,\n 272.830078125\n ],\n [\n 180.03765869140625,\n 272.830078125\n ],\n [\n 180.03765869140625,\n 288.0208435058594\n ],\n [\n 85.9130859375,\n 288.0208435058594\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 272.830078125,\n 180.03765869140625,\n 288.0208435058594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"One of the most powerful features of a programming language is the ability to manipulate variables. A variable is a name that refers to a value.
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\",\n \"polygon\": [\n [\n 86.2119140625,\n 331.8046875\n ],\n [\n 401.0780029296875,\n 331.8046875\n ],\n [\n 401.0780029296875,\n 342.7267761230469\n ],\n [\n 86.2119140625,\n 342.7267761230469\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 331.8046875,\n 401.0780029296875,\n 342.7267761230469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/33/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> message = 'And now for something completely different'\\n>>> n = 17\\n>>> pi = 3.1415926535897932
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|---|
| as | elif | global | or | with |
| assert | else | if | pass | yield |
| break | except | import | print | |
| class | exec | in | raise | |
| continue | finally | is | return | |
| def | for | lambda | try | |
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\",\n \"polygon\": [\n [\n 128.49609375,\n 478.37109375\n ],\n [\n 525.6018676757812,\n 478.37109375\n ],\n [\n 525.6018676757812,\n 500.82080078125\n ],\n [\n 128.49609375,\n 500.82080078125\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 478.37109375,\n 525.6018676757812,\n 500.82080078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/34/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/34/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The operators +, -, *, / and ** perform addition, subtraction, multiplication, division and exponentiation, as in the following examples:
\",\n \"polygon\": [\n [\n 128.197265625,\n 510.08203125\n ],\n [\n 525.638671875,\n 510.08203125\n ],\n [\n 525.638671875,\n 532.7388000488281\n ],\n [\n 128.197265625,\n 532.7388000488281\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 510.08203125,\n 525.638671875,\n 532.7388000488281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/34/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/34/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"20+32 hour-1 hour*60+minute minute/60 5**2 (5+9)*(15-7)
\",\n \"polygon\": [\n [\n 126.703125,\n 538.3796539306641\n ],\n [\n 469.5838317871094,\n 538.3796539306641\n ],\n [\n 469.5838317871094,\n 548.3671875\n ],\n [\n 126.703125,\n 548.3671875\n ]\n ],\n \"bbox\": [\n 126.703125,\n 538.3796539306641,\n 469.5838317871094,\n 548.3671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/34/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/34/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"In some other languages, ^ is used for exponentiation, but in Python it is a bitwise operator called XOR. I won't cover bitwise operators in this book, but you can read about them at http://wiki.python.org/moin/BitwiseOperators.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 554.1326599121094\n ],\n [\n 525.6033325195312,\n 554.1326599121094\n ],\n [\n 525.6033325195312,\n 588.6338195800781\n ],\n [\n 128.0478515625,\n 588.6338195800781\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 554.1326599121094,\n 525.6033325195312,\n 588.6338195800781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/34/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/34/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"In Python 2, the division operator might not do what you expect:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 598.25390625\n ],\n [\n 415.34735107421875,\n 598.25390625\n ],\n [\n 415.34735107421875,\n 608.3578186035156\n ],\n [\n 128.3466796875,\n 608.3578186035156\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 598.25390625,\n 415.34735107421875,\n 608.3578186035156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/34/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/34/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \">>> minute = 59\\n>>> minute/60\\n0
\",\n \"polygon\": [\n [\n 128.49609375,\n 613.9986724853516\n ],\n [\n 208.05555725097656,\n 613.9986724853516\n ],\n [\n 208.05555725097656,\n 648.3492736816406\n ],\n [\n 128.49609375,\n 648.3492736816406\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 613.9986724853516,\n 208.05555725097656,\n 648.3492736816406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/34/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/34/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"The value of minute is 59, and in conventional arithmetic 59 divided by 60 is 0.98333, not 0. The reason for the discrepancy is that Python is performing floor division. When both of the operands are integers, the result is also an integer; floor division chops off the fraction part, so in this example it rounds down to zero.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 198.966796875\n ],\n [\n 298.7524108886719,\n 198.966796875\n ],\n [\n 298.7524108886719,\n 213.87689208984375\n ],\n [\n 85.6142578125,\n 213.87689208984375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 198.966796875,\n 298.7524108886719,\n 213.87689208984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/35/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/35/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"An expression is a combination of values, variables, and operators. A value all by itself is considered an expression, and so is a variable, so the following are all legal expressions (assuming that the variable x has been assigned a value):
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\",\n \"polygon\": [\n [\n 86.39997863769531,\n 265.2890625\n ],\n [\n 117.78216552734375,\n 265.2890625\n ],\n [\n 117.78216552734375,\n 300.0992736816406\n ],\n [\n 86.39997863769531,\n 300.0992736816406\n ]\n ],\n \"bbox\": [\n 86.39997863769531,\n 265.2890625,\n 117.78216552734375,\n 300.0992736816406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/35/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/35/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"A statement is a unit of code that the Python interpreter can execute. We have seen two kinds of statement: print and assignment.
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\",\n \"polygon\": [\n [\n 85.39013671875,\n 400.640625\n ],\n [\n 337.83148193359375,\n 400.640625\n ],\n [\n 337.83148193359375,\n 415.3359375\n ],\n [\n 85.39013671875,\n 415.3359375\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 400.640625,\n 337.83148193359375,\n 415.3359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/35/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/35/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"One of the benefits of working with an interpreted language is that you can test bits of code in interactive mode before you put them in a script. But there are differences between interactive mode and script mode that can be confusing.
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\",\n \"polygon\": [\n [\n 129.5419921875,\n 226.23046875\n ],\n [\n 155.7518310546875,\n 226.23046875\n ],\n [\n 155.7518310546875,\n 237.8663330078125\n ],\n [\n 129.5419921875,\n 237.8663330078125\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 226.23046875,\n 155.7518310546875,\n 237.8663330078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/35/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/36/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Now put the same statements into a script and run it. What is the output? Modify the script by transforming each expression into a print statement and then run it again.
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 243.24609375\n ],\n [\n 525.6033325195312,\n 243.24609375\n ],\n [\n 525.6033325195312,\n 266.55120849609375\n ],\n [\n 129.60000610351562,\n 266.55120849609375\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 243.24609375,\n 525.6033325195312,\n 266.55120849609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"3\": \"/page/33/SectionHeader/5\",\n \"4\": \"/page/35/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/36/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"2.7 Order of operations
\",\n \"polygon\": [\n [\n 128.42138671875,\n 295.453125\n ],\n [\n 291.357421875,\n 295.453125\n ],\n [\n 291.357421875,\n 311.4539794921875\n ],\n [\n 128.42138671875,\n 311.4539794921875\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 295.453125,\n 291.357421875,\n 311.4539794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/36/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"When more than one operator appears in an expression, the order of evaluation depends on the rules of precedence. For mathematical operators, Python follows mathematical convention. The acronym PEMDAS is a useful way to remember the rules:
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 580.8515625\n ],\n [\n 526.53515625,\n 580.8515625\n ],\n [\n 526.53515625,\n 604.6039886474609\n ],\n [\n 128.3466796875,\n 604.6039886474609\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 580.8515625,\n 526.53515625,\n 604.6039886474609\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/36/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"2.8 String operations
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\",\n \"polygon\": [\n [\n 86.28662109375,\n 115.83367919921875\n ],\n [\n 288.91265869140625,\n 115.83367919921875\n ],\n [\n 288.91265869140625,\n 165.28887939453125\n ],\n [\n 86.28662109375,\n 165.28887939453125\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 115.83367919921875,\n 288.91265869140625,\n 165.28887939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/36/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/37/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The * operator also works on strings; it performs repetition. For example, 'Spam'*3 is 'SpamSpamSpam'. If one of the operands is a string, the other has to be an integer.
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\",\n \"polygon\": [\n [\n 86.2119140625,\n 317.109375\n ],\n [\n 482.4034729003906,\n 317.109375\n ],\n [\n 482.4034729003906,\n 351.5888671875\n ],\n [\n 86.2119140625,\n 351.5888671875\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 317.109375,\n 482.4034729003906,\n 351.5888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/37/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"For this reason, it is a good idea to add notes to your programs to explain in natural language what the program is doing. These notes are called comments, and they start with the # symbol:
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\",\n \"polygon\": [\n [\n 86.0625,\n 399.09375\n ],\n [\n 364.271484375,\n 399.09375\n ],\n [\n 364.271484375,\n 421.90130615234375\n ],\n [\n 86.0625,\n 421.90130615234375\n ]\n ],\n \"bbox\": [\n 86.0625,\n 399.09375,\n 364.271484375,\n 421.90130615234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/37/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"In this case, the comment appears on a line by itself. You can also put comments at the end of a line:
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\",\n \"polygon\": [\n [\n 85.763671875,\n 468.31640625\n ],\n [\n 470.056640625,\n 468.31640625\n ],\n [\n 470.056640625,\n 479.10791015625\n ],\n [\n 85.763671875,\n 479.10791015625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 468.31640625,\n 470.056640625,\n 479.10791015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/37/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Comments are most useful when they document non-obvious features of the code. It is reasonable to assume that the reader can figure out what the code does; it is much more useful to explain why.
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\",\n \"polygon\": [\n [\n 86.40006256103516,\n 546.046875\n ],\n [\n 219.638671875,\n 546.046875\n ],\n [\n 219.638671875,\n 556.15234375\n ],\n [\n 86.40006256103516,\n 556.15234375\n ]\n ],\n \"bbox\": [\n 86.40006256103516,\n 546.046875,\n 219.638671875,\n 556.15234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/37/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"This comment contains useful information that is not in the code:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 560.35546875\n ],\n [\n 373.53515625,\n 560.35546875\n ],\n [\n 373.53515625,\n 571.2558898925781\n ],\n [\n 85.6142578125,\n 571.2558898925781\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 560.35546875,\n 373.53515625,\n 571.2558898925781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/37/Text/208\",\n \"block_type\": \"Text\",\n \"html\": \"v = 5 # velocity in meters/second.
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\",\n \"polygon\": [\n [\n 85.53955078125,\n 640.79296875\n ],\n [\n 201.41015625,\n 640.79296875\n ],\n [\n 201.41015625,\n 655.48828125\n ],\n [\n 85.53955078125,\n 655.48828125\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 640.79296875,\n 201.41015625,\n 655.48828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/37/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/37/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"At this point the syntax error you are most likely to make is an illegal variable name, like class and yield, which are keywords, or odd~job and US$, which contain illegal characters.
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\",\n \"polygon\": [\n [\n 128.49609375,\n 133.7080078125\n ],\n [\n 525.9375,\n 133.7080078125\n ],\n [\n 525.9375,\n 168.076904296875\n ],\n [\n 128.49609375,\n 168.076904296875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 133.7080078125,\n 525.9375,\n 168.076904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/37/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/38/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The runtime error you are most likely to make is a \\\"use before def;\\\" that is, trying to use a variable before you have assigned a value. This can happen if you spell a variable name wrong:
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\",\n \"polygon\": [\n [\n 128.27197265625,\n 259.75677490234375\n ],\n [\n 433.2556457519531,\n 259.75677490234375\n ],\n [\n 433.2556457519531,\n 269.86895751953125\n ],\n [\n 128.27197265625,\n 269.86895751953125\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 259.75677490234375,\n 433.2556457519531,\n 269.86895751953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/37/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/38/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"At this point the most likely cause of a semantic error is the order of operations. For example, to evaluate 1 2π , you might be tempted to write
\",\n \"polygon\": [\n [\n 128.794921875,\n 279.984375\n ],\n [\n 525.6034545898438,\n 279.984375\n ],\n [\n 525.6034545898438,\n 305.7589416503906\n ],\n [\n 128.794921875,\n 305.7589416503906\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 279.984375,\n 525.6034545898438,\n 305.7589416503906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/37/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/38/TextInlineMath/8\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> 1.0 / 2.0 * pi
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\",\n \"polygon\": [\n [\n 85.68896484375,\n 340.6116943359375\n ],\n [\n 164.83837890625,\n 340.6116943359375\n ],\n [\n 164.83837890625,\n 374.9632873535156\n ],\n [\n 85.68896484375,\n 374.9632873535156\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 340.6116943359375,\n 164.83837890625,\n 374.9632873535156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/39/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/39/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"For each of the following expressions, write the value of the expression and the type (of the value of the expression).
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\",\n \"polygon\": [\n [\n 98.015625,\n 416.109375\n ],\n [\n 179.30177307128906,\n 416.109375\n ],\n [\n 179.30177307128906,\n 508.1163024902344\n ],\n [\n 98.015625,\n 508.1163024902344\n ]\n ],\n \"bbox\": [\n 98.015625,\n 416.109375,\n 179.30177307128906,\n 508.1163024902344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/39/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/39/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Use the Python interpreter to check your answers. Exercise 2.3. Practice using the Python interpreter as a calculator:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 521.68359375\n ],\n [\n 358.59375,\n 521.68359375\n ],\n [\n 358.59375,\n 544.4997863769531\n ],\n [\n 85.6142578125,\n 544.4997863769531\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 521.68359375,\n 358.59375,\n 544.4997863769531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/39/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/39/ListGroup/160\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 97.8662109375,\n 556.5590209960938\n ],\n [\n 482.4039001464844,\n 556.5590209960938\n ],\n [\n 482.4039001464844,\n 657.6662139892578\n ],\n [\n 97.8662109375,\n 657.6662139892578\n ]\n ],\n \"bbox\": [\n 97.8662109375,\n 556.5590209960938,\n 482.4039001464844,\n 657.6662139892578\n ],\n \"children\": [\n {\n \"id\": \"/page/39/ListItem/14\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. The volume of a sphere with radius r is 4 3 πr 3 . What is the volume of a sphere with radius 5? Hint: 392.7 is wrong!\",\n \"polygon\": [\n [\n 98.61328125,\n 556.5590209960938\n ],\n [\n 482.39703369140625,\n 556.5590209960938\n ],\n [\n 482.39703369140625,\n 580.7542114257812\n ],\n [\n 98.61328125,\n 580.7542114257812\n ]\n ],\n \"bbox\": [\n 98.61328125,\n 556.5590209960938,\n 482.39703369140625,\n 580.7542114257812\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/39/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/39/ListItem/15\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Suppose the cover price of a book is $24.95, but bookstores get a 40% discount. Shipping costs $3 for the first copy and 75 cents for each additional copy. What is the total wholesale cost for 60 copies?\",\n \"polygon\": [\n [\n 98.015625,\n 590.90625\n ],\n [\n 482.4039001464844,\n 590.90625\n ],\n [\n 482.4039001464844,\n 625.3072204589844\n ],\n [\n 98.015625,\n 625.3072204589844\n ]\n ],\n \"bbox\": [\n 98.015625,\n 590.90625,\n 482.4039001464844,\n 625.3072204589844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/39/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/39/ListItem/16\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. If I leave my house at 6:52 am and run 1 mile at an easy pace (8:15 per mile), then 3 miles at tempo (7:12 per mile) and 1 mile at easy pace again, what time do I get home for breakfast?\",\n \"polygon\": [\n [\n 97.8662109375,\n 635.37890625\n ],\n [\n 482.40313720703125,\n 635.37890625\n ],\n [\n 482.40313720703125,\n 657.6662139892578\n ],\n [\n 97.8662109375,\n 657.6662139892578\n ]\n ],\n \"bbox\": [\n 97.8662109375,\n 635.37890625,\n 482.40313720703125,\n 657.6662139892578\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/39/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/39/SectionHeader/8\"\n },\n \"images\": null\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/36/SectionHeader/4\",\n \"3\": \"/page/37/SectionHeader/5\",\n \"4\": \"/page/39/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/40/Page/120\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/40/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Chapter 3
\",\n \"polygon\": [\n [\n 128.27197265625,\n 164.6455078125\n ],\n [\n 220.84561157226562,\n 164.6455078125\n ],\n [\n 220.84561157226562,\n 185.87091064453125\n ],\n [\n 128.27197265625,\n 185.87091064453125\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 164.6455078125,\n 220.84561157226562,\n 185.87091064453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/32/SectionHeader/1\",\n \"2\": \"/page/40/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/40/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Functions
\",\n \"polygon\": [\n [\n 128.49609375,\n 221.65228271484375\n ],\n [\n 242.349609375,\n 221.65228271484375\n ],\n [\n 242.349609375,\n 246.4393310546875\n ],\n [\n 128.49609375,\n 246.4393310546875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 221.65228271484375,\n 242.349609375,\n 246.4393310546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/40/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.1 Function calls
\",\n \"polygon\": [\n [\n 128.86962890625,\n 296.033203125\n ],\n [\n 253.1064453125,\n 296.033203125\n ],\n [\n 253.1064453125,\n 310.75994873046875\n ],\n [\n 128.86962890625,\n 310.75994873046875\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 296.033203125,\n 253.1064453125,\n 310.75994873046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"4\": \"/page/40/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/40/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"In the context of programming, a function is a named sequence of statements that performs a computation. When you define a function, you specify the name and the sequence of statements. Later, you can \\\"call\\\" the function by name. We have already seen one example of a function call:
\",\n \"polygon\": [\n [\n 129.5419921875,\n 324.263671875\n ],\n [\n 526.53515625,\n 324.263671875\n ],\n [\n 526.53515625,\n 371.17388916015625\n ],\n [\n 129.5419921875,\n 371.17388916015625\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 324.263671875,\n 526.53515625,\n 371.17388916015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"4\": \"/page/40/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/40/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \">>> type(32) <type 'int'>
\",\n \"polygon\": [\n [\n 128.49609375,\n 378.2109375\n ],\n [\n 193.04296875,\n 378.2109375\n ],\n [\n 193.04296875,\n 400.4713134765625\n ],\n [\n 128.49609375,\n 400.4713134765625\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 378.2109375,\n 193.04296875,\n 400.4713134765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"4\": \"/page/40/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/40/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The name of the function is type. The expression in parentheses is called the argument of the function. The result, for this function, is the type of the argument.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 524.00390625\n ],\n [\n 525.6041259765625,\n 524.00390625\n ],\n [\n 525.6041259765625,\n 546.4618835449219\n ],\n [\n 128.794921875,\n 546.4618835449219\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 524.00390625,\n 525.6041259765625,\n 546.4618835449219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/40/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/40/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> int('32')\\n32\\n>>> int('Hello')\\nValueError: invalid literal for int(): Hello\",\n \"polygon\": [\n [\n 129.60003662109375,\n 553.6027374267578\n ],\n [\n 359.7460021972656,\n 553.6027374267578\n ],\n [\n 359.7460021972656,\n 602.12109375\n ],\n [\n 129.60003662109375,\n 602.12109375\n ]\n ],\n \"bbox\": [\n 129.60003662109375,\n 553.6027374267578,\n 359.7460021972656,\n 602.12109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/40/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/40/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"int can convert floating-point values to integers, but it doesn't round off; it chops off the fraction part:
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\",\n \"polygon\": [\n [\n 85.83837890625,\n 231.064453125\n ],\n [\n 219.475341796875,\n 231.064453125\n ],\n [\n 219.475341796875,\n 246.52093505859375\n ],\n [\n 85.83837890625,\n 246.52093505859375\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 231.064453125,\n 219.475341796875,\n 246.52093505859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Python has a math module that provides most of the familiar mathematical functions. A module is a file that contains a collection of related functions.
\",\n \"polygon\": [\n [\n 86.0625,\n 257.748046875\n ],\n [\n 482.40325927734375,\n 257.748046875\n ],\n [\n 482.40325927734375,\n 280.5939025878906\n ],\n [\n 86.0625,\n 280.5939025878906\n ]\n ],\n \"bbox\": [\n 86.0625,\n 257.748046875,\n 482.40325927734375,\n 280.5939025878906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Before we can use the module, we have to import it:
\",\n \"polygon\": [\n [\n 85.763671875,\n 290.232421875\n ],\n [\n 315.5625,\n 290.232421875\n ],\n [\n 315.5625,\n 300.3009033203125\n ],\n [\n 85.763671875,\n 300.3009033203125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 290.232421875,\n 315.5625,\n 300.3009033203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> import math
\",\n \"polygon\": [\n [\n 85.9130859375,\n 305.92474365234375\n ],\n [\n 164.85548400878906,\n 305.92474365234375\n ],\n [\n 164.85548400878906,\n 315.8873291015625\n ],\n [\n 85.9130859375,\n 315.8873291015625\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 305.92474365234375,\n 164.85548400878906,\n 315.8873291015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This statement creates a module object named math. If you print the module object, you get some information about it:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 321.7131652832031\n ],\n [\n 482.4033203125,\n 321.7131652832031\n ],\n [\n 482.4033203125,\n 343.9668884277344\n ],\n [\n 85.6142578125,\n 343.9668884277344\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 321.7131652832031,\n 482.4033203125,\n 343.9668884277344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> print math\\n<module 'math' (built-in)>
\",\n \"polygon\": [\n [\n 85.3154296875,\n 349.5907287597656\n ],\n [\n 223.6728515625,\n 349.5907287597656\n ],\n [\n 223.6728515625,\n 371.747314453125\n ],\n [\n 85.3154296875,\n 371.747314453125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 349.5907287597656,\n 223.6728515625,\n 371.747314453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The module object contains the functions and variables defined in the module. To access one of the functions, you have to specify the name of the module and the name of the function, separated by a dot (also known as a period). This format is called dot notation.
\",\n \"polygon\": [\n [\n 85.166015625,\n 377.4375\n ],\n [\n 482.4034118652344,\n 377.4375\n ],\n [\n 482.4034118652344,\n 412.0208740234375\n ],\n [\n 85.166015625,\n 412.0208740234375\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 377.4375,\n 482.4034118652344,\n 412.0208740234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> ratio = signal_power / noise_power\\n>>> decibels = 10 * math.log10(ratio)\\n>>> radians = 0.7\\n>>> height = math.sin(radians)
\",\n \"polygon\": [\n [\n 85.9130859375,\n 417.64471435546875\n ],\n [\n 285.1637878417969,\n 417.64471435546875\n ],\n [\n 285.1637878417969,\n 476.3843078613281\n ],\n [\n 85.9130859375,\n 476.3843078613281\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 417.64471435546875,\n 285.1637878417969,\n 476.3843078613281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The first example uses log10 to compute a signal-to-noise ratio in decibels (assuming that signal_power and noise_power are defined). The math module also provides log, which computes logarithms base e.
\",\n \"polygon\": [\n [\n 85.763671875,\n 482.15771484375\n ],\n [\n 482.4012451171875,\n 482.15771484375\n ],\n [\n 482.4012451171875,\n 516.6588745117188\n ],\n [\n 85.763671875,\n 516.6588745117188\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 482.15771484375,\n 482.4012451171875,\n 516.6588745117188\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The second example finds the sine of radians. The name of the variable is a hint that sin and the other trigonometric functions (cos, tan, etc.) take arguments in radians. To convert from degrees to radians, divide by 360 and multiply by 2π:
\",\n \"polygon\": [\n [\n 86.0625,\n 525.9375\n ],\n [\n 482.404052734375,\n 525.9375\n ],\n [\n 482.404052734375,\n 561.5645294189453\n ],\n [\n 86.0625,\n 561.5645294189453\n ]\n ],\n \"bbox\": [\n 86.0625,\n 525.9375,\n 482.404052734375,\n 561.5645294189453\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> degrees = 45\\n>>> radians = degrees / 360.0 * 2 * math.pi\\n>>> math.sin(radians)\\n0.707106781187
\",\n \"polygon\": [\n [\n 86.0625,\n 566.3787231445312\n ],\n [\n 311.3155517578125,\n 566.3787231445312\n ],\n [\n 311.3155517578125,\n 612.9243316650391\n ],\n [\n 86.0625,\n 612.9243316650391\n ]\n ],\n \"bbox\": [\n 86.0625,\n 566.3787231445312,\n 311.3155517578125,\n 612.9243316650391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The expression math.pi gets the variable pi from the math module. The value of this variable is an approximation of π, accurate to about 15 digits.
\",\n \"polygon\": [\n [\n 85.763671875,\n 618.6967315673828\n ],\n [\n 482.39642333984375,\n 618.6967315673828\n ],\n [\n 482.39642333984375,\n 641.8135375976562\n ],\n [\n 85.763671875,\n 641.8135375976562\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 618.6967315673828,\n 482.39642333984375,\n 641.8135375976562\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"If you know your trigonometry, you can check the previous result by comparing it to the square root of two divided by two:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 650.07421875\n ],\n [\n 482.40338134765625,\n 650.07421875\n ],\n [\n 482.40338134765625,\n 672.904899597168\n ],\n [\n 85.3154296875,\n 672.904899597168\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 650.07421875,\n 482.40338134765625,\n 672.904899597168\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/41/TextInlineMath/15\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> math.sqrt(2) / 2.0 0.707106781187
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\",\n \"polygon\": [\n [\n 129.09375,\n 85.63177490234375\n ],\n [\n 247.15277099609375,\n 85.63177490234375\n ],\n [\n 247.15277099609375,\n 99.97796630859375\n ],\n [\n 129.09375,\n 99.97796630859375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 85.63177490234375,\n 247.15277099609375,\n 99.97796630859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"So far, we have looked at the elements of a program—variables, expressions, and statements—in isolation, without talking about how to combine them.
\",\n \"polygon\": [\n [\n 129.09375,\n 112.8251953125\n ],\n [\n 525.6033935546875,\n 112.8251953125\n ],\n [\n 525.6033935546875,\n 135.54193115234375\n ],\n [\n 129.09375,\n 135.54193115234375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 112.8251953125,\n 525.6033935546875,\n 135.54193115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"One of the most useful features of programming languages is their ability to take small building blocks and compose them. For example, the argument of a function can be any kind of expression, including arithmetic operators:
\",\n \"polygon\": [\n [\n 129.09375,\n 146.1796875\n ],\n [\n 525.603271484375,\n 146.1796875\n ],\n [\n 525.603271484375,\n 180.82696533203125\n ],\n [\n 129.09375,\n 180.82696533203125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 146.1796875,\n 525.603271484375,\n 180.82696533203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"x = math.sin(degrees / 360.0 * 2 * math.pi)
\",\n \"polygon\": [\n [\n 127.97314453125,\n 187.0751953125\n ],\n [\n 354.5155944824219,\n 187.0751953125\n ],\n [\n 354.5155944824219,\n 197.60235595703125\n ],\n [\n 127.97314453125,\n 197.60235595703125\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 187.0751953125,\n 354.5155944824219,\n 197.60235595703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"And even function calls:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 203.994140625\n ],\n [\n 236.52859497070312,\n 203.994140625\n ],\n [\n 236.52859497070312,\n 214.67596435546875\n ],\n [\n 129.2431640625,\n 214.67596435546875\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 203.994140625,\n 236.52859497070312,\n 214.67596435546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/TextInlineMath/6\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"x = math.exp(math.log(x + 1))
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 221.48876953125\n ],\n [\n 272.3818359375,\n 221.48876953125\n ],\n [\n 272.3818359375,\n 231.45135498046875\n ],\n [\n 129.60000610351562,\n 231.45135498046875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 221.48876953125,\n 272.3818359375,\n 231.45135498046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Almost anywhere you can put a value, you can put an arbitrary expression, with one exception: the left side of an assignment statement has to be a variable name. Any other expression on the left side is a syntax error (we will see exceptions to this rule later).
\",\n \"polygon\": [\n [\n 128.3466796875,\n 238.56231689453125\n ],\n [\n 525.6034545898438,\n 238.56231689453125\n ],\n [\n 525.6034545898438,\n 272.91400146484375\n ],\n [\n 128.3466796875,\n 272.91400146484375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 238.56231689453125,\n 525.6034545898438,\n 272.91400146484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Code/159\",\n \"block_type\": \"Code\",\n \"html\": \">>> minutes = hours * 60 # right\\n>>> hours * 60 = minutes # wrong!\\nSyntaxError: can't assign to operator
\",\n \"polygon\": [\n [\n 129.16845703125,\n 279.726806640625\n ],\n [\n 385.8679504394531,\n 279.726806640625\n ],\n [\n 385.8679504394531,\n 314.077392578125\n ],\n [\n 129.16845703125,\n 314.077392578125\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 279.726806640625,\n 385.8679504394531,\n 314.077392578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.5 Adding new functions
\",\n \"polygon\": [\n [\n 128.197265625,\n 341.8738098144531\n ],\n [\n 309.3005065917969,\n 341.8738098144531\n ],\n [\n 309.3005065917969,\n 356.22003173828125\n ],\n [\n 128.197265625,\n 356.22003173828125\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 341.8738098144531,\n 309.3005065917969,\n 356.22003173828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"So far, we have only been using the functions that come with Python, but it is also possible to add new functions. A function definition specifies the name of a new function and the sequence of statements that execute when the function is called.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 369.31640625\n ],\n [\n 525.6048583984375,\n 369.31640625\n ],\n [\n 525.6048583984375,\n 403.9789733886719\n ],\n [\n 128.3466796875,\n 403.9789733886719\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 369.31640625,\n 525.6048583984375,\n 403.9789733886719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Here is an example:
\",\n \"polygon\": [\n [\n 128.57080078125,\n 414.17578125\n ],\n [\n 217.248046875,\n 414.17578125\n ],\n [\n 217.248046875,\n 424.8749694824219\n ],\n [\n 128.57080078125,\n 424.8749694824219\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 414.17578125,\n 217.248046875,\n 424.8749694824219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"def print_lyrics():\\n print \\\"I'm a lumberjack, and I'm okay.\\\"\\n print \\\"I sleep all night and I work all day.\\\"
\",\n \"polygon\": [\n [\n 129.46728515625,\n 431.68780517578125\n ],\n [\n 385.8923034667969,\n 431.68780517578125\n ],\n [\n 385.8923034667969,\n 466.03839111328125\n ],\n [\n 129.46728515625,\n 466.03839111328125\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 431.68780517578125,\n 385.8923034667969,\n 466.03839111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"def is a keyword that indicates that this is a function definition. The name of the function is print_lyrics. The rules for function names are the same as for variable names: letters, numbers and some punctuation marks are legal, but the first character can't be a number. You can't use a keyword as the name of a function, and you should avoid having a variable and a function with the same name.
\",\n \"polygon\": [\n [\n 128.197265625,\n 472.95703125\n ],\n [\n 525.638671875,\n 472.95703125\n ],\n [\n 525.638671875,\n 531.8899536132812\n ],\n [\n 128.197265625,\n 531.8899536132812\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 472.95703125,\n 525.638671875,\n 531.8899536132812\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The empty parentheses after the name indicate that this function doesn't take any arguments.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 542.56640625\n ],\n [\n 525.6033325195312,\n 542.56640625\n ],\n [\n 525.6033325195312,\n 564.98095703125\n ],\n [\n 128.0478515625,\n 564.98095703125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 542.56640625,\n 525.6033325195312,\n 564.98095703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The first line of the function definition is called the header; the rest is called the body. The header has to end with a colon and the body has to be indented. By convention, the indentation is always four spaces (see Section 3.14). The body can contain any number of statements.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 575.4375\n ],\n [\n 525.9375,\n 575.4375\n ],\n [\n 525.9375,\n 622.4599609375\n ],\n [\n 128.6455078125,\n 622.4599609375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 575.4375,\n 525.9375,\n 622.4599609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"The strings in the print statements are enclosed in double quotes. Single quotes and double quotes do the same thing; most people use single quotes except in cases like this where a single quote (which is also an apostrophe) appears in the string.
\",\n \"polygon\": [\n [\n 128.49609375,\n 633.05859375\n ],\n [\n 525.6033935546875,\n 633.05859375\n ],\n [\n 525.6033935546875,\n 667.7449645996094\n ],\n [\n 128.49609375,\n 667.7449645996094\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 633.05859375,\n 525.6033935546875,\n 667.7449645996094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/42/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"If you type a function definition in interactive mode, the interpreter prints ellipses (...) to let you know that the definition isn't complete:
\",\n \"polygon\": [\n [\n 127.7490234375,\n 678.3046875\n ],\n [\n 525.6034545898438,\n 678.3046875\n ],\n [\n 525.6034545898438,\n 700.8349685668945\n ],\n [\n 127.7490234375,\n 700.8349685668945\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 678.3046875,\n 525.6034545898438,\n 700.8349685668945\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/43/Page/166\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/43/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.8115234375\n ],\n [\n 482.4034118652344,\n 60.8115234375\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.8115234375,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/PageHeader/16\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.39013671875,\n 60.95654296875\n ],\n [\n 96.59619140625,\n 60.95654296875\n ],\n [\n 96.59619140625,\n 70.04443359375\n ],\n [\n 85.39013671875,\n 70.04443359375\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 60.95654296875,\n 96.59619140625,\n 70.04443359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> def print_lyrics():\\n... print \\\"I'm a lumberjack, and I'm okay.\\\"\\n... print \\\"I sleep all night and I work all day.\\\"\\n...
\",\n \"polygon\": [\n [\n 86.2119140625,\n 87.205078125\n ],\n [\n 366.36328125,\n 87.205078125\n ],\n [\n 366.36328125,\n 135.2313232421875\n ],\n [\n 86.2119140625,\n 135.2313232421875\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 87.205078125,\n 366.36328125,\n 135.2313232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"To end the function, you have to enter an empty line (this is not necessary in a script).
\",\n \"polygon\": [\n [\n 86.2119140625,\n 140.185546875\n ],\n [\n 462.10955810546875,\n 140.185546875\n ],\n [\n 462.10955810546875,\n 150.81292724609375\n ],\n [\n 86.2119140625,\n 150.81292724609375\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 140.185546875,\n 462.10955810546875,\n 150.81292724609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Defining a function creates a variable with the same name.
\",\n \"polygon\": [\n [\n 85.24072265625,\n 160.0048828125\n ],\n [\n 343.7239685058594,\n 160.0048828125\n ],\n [\n 343.7239685058594,\n 170.2159423828125\n ],\n [\n 85.24072265625,\n 170.2159423828125\n ]\n ],\n \"bbox\": [\n 85.24072265625,\n 160.0048828125,\n 343.7239685058594,\n 170.2159423828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \">>> print print_lyrics\\n<function print_lyrics at 0xb7e99e9c>\\n>>> type(print_lyrics)\\n<type 'function'>\\nThe value of print_lyrics is a function object, which has type 'function'.
\",\n \"polygon\": [\n [\n 84.4189453125,\n 175.5357666015625\n ],\n [\n 423.8246765136719,\n 175.5357666015625\n ],\n [\n 423.8246765136719,\n 237.6629638671875\n ],\n [\n 84.4189453125,\n 237.6629638671875\n ]\n ],\n \"bbox\": [\n 84.4189453125,\n 175.5357666015625,\n 423.8246765136719,\n 237.6629638671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The syntax for calling the new function is the same as for built-in functions:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 240.92578125\n ],\n [\n 418.359375,\n 240.92578125\n ],\n [\n 418.359375,\n 257.06597900390625\n ],\n [\n 85.6142578125,\n 257.06597900390625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 240.92578125,\n 418.359375,\n 257.06597900390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> print_lyrics()\\nI'm a lumberjack, and I'm okay.\\nI sleep all night and I work all day.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 260.6484375\n ],\n [\n 279.9234619140625,\n 260.6484375\n ],\n [\n 279.9234619140625,\n 296.73638916015625\n ],\n [\n 85.3154296875,\n 296.73638916015625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 260.6484375,\n 279.9234619140625,\n 296.73638916015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Once you have defined a function, you can use it inside another function. For example, to repeat the previous refrain, we could write a function called repeat_lyrics:
\",\n \"polygon\": [\n [\n 86.0625,\n 302.3553771972656\n ],\n [\n 482.4034118652344,\n 302.3553771972656\n ],\n [\n 482.4034118652344,\n 324.511962890625\n ],\n [\n 86.0625,\n 324.511962890625\n ]\n ],\n \"bbox\": [\n 86.0625,\n 302.3553771972656,\n 482.4034118652344,\n 324.511962890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"def repeat_lyrics():\\n print_lyrics()\\n print_lyrics()
\",\n \"polygon\": [\n [\n 85.9130859375,\n 329.8318176269531\n ],\n [\n 192.4453125,\n 329.8318176269531\n ],\n [\n 192.4453125,\n 364.18341064453125\n ],\n [\n 85.9130859375,\n 364.18341064453125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 329.8318176269531,\n 192.4453125,\n 364.18341064453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"And then call repeat_lyrics:
\",\n \"polygon\": [\n [\n 86.361328125,\n 369.31640625\n ],\n [\n 219.638671875,\n 369.31640625\n ],\n [\n 219.638671875,\n 379.7649841308594\n ],\n [\n 86.361328125,\n 379.7649841308594\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 369.31640625,\n 219.638671875,\n 379.7649841308594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> repeat_lyrics()\\nI'm a lumberjack, and I'm okay.\\nI sleep all night and I work all day.\\nI'm a lumberjack, and I'm okay.\\nI sleep all night and I work all day.\\nBut that's not really how the song goes.
\",\n \"polygon\": [\n [\n 85.53955078125,\n 384.78515625\n ],\n [\n 281.794921875,\n 384.78515625\n ],\n [\n 281.794921875,\n 459.406005859375\n ],\n [\n 85.53955078125,\n 459.406005859375\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 384.78515625,\n 281.794921875,\n 459.406005859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/42/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.6 Definitions and uses
\",\n \"polygon\": [\n [\n 86.0625,\n 487.65234375\n ],\n [\n 253.33245849609375,\n 487.65234375\n ],\n [\n 253.33245849609375,\n 502.0660705566406\n ],\n [\n 86.0625,\n 502.0660705566406\n ]\n ],\n \"bbox\": [\n 86.0625,\n 487.65234375,\n 253.33245849609375,\n 502.0660705566406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Pulling together the code fragments from the previous section, the whole program looks like this:
\",\n \"polygon\": [\n [\n 86.361328125,\n 513.17578125\n ],\n [\n 482.4034729003906,\n 513.17578125\n ],\n [\n 482.4034729003906,\n 535.8340148925781\n ],\n [\n 86.361328125,\n 535.8340148925781\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 513.17578125,\n 482.4034729003906,\n 535.8340148925781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"def print_lyrics():\\n print \\\"I'm a lumberjack, and I'm okay.\\\"\\n print \\\"I sleep all night and I work all day.\\\"\\ndef repeat_lyrics():\\n print_lyrics()\\n print_lyrics()
\",\n \"polygon\": [\n [\n 86.0625,\n 541.1538696289062\n ],\n [\n 345.744140625,\n 541.1538696289062\n ],\n [\n 345.744140625,\n 632.671875\n ],\n [\n 86.0625,\n 632.671875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 541.1538696289062,\n 345.744140625,\n 632.671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \"repeat_lyrics()
\",\n \"polygon\": [\n [\n 85.68896484375,\n 638.7088928222656\n ],\n [\n 164.86549377441406,\n 638.7088928222656\n ],\n [\n 164.86549377441406,\n 648.9140625\n ],\n [\n 85.68896484375,\n 648.9140625\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 638.7088928222656,\n 164.86549377441406,\n 648.9140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/43/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"This program contains two function definitions: print_lyrics and repeat_lyrics. Function definitions get executed just like other statements, but the effect is to create function objects. The statements inside the function do not get executed until the function is called, and the function definition generates no output.
\",\n \"polygon\": [\n [\n 86.0625,\n 654.139892578125\n ],\n [\n 482.90625,\n 654.139892578125\n ],\n [\n 482.90625,\n 700.8350524902344\n ],\n [\n 86.0625,\n 700.8350524902344\n ]\n ],\n \"bbox\": [\n 86.0625,\n 654.139892578125,\n 482.90625,\n 700.8350524902344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/44/Page/160\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/44/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 127.8984375,\n 60.85986328125\n ],\n [\n 525.6033935546875,\n 60.85986328125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 127.8984375,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 60.85986328125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 514.283203125,\n 60.27978515625\n ],\n [\n 525.638671875,\n 60.27978515625\n ],\n [\n 525.638671875,\n 70.04443359375\n ],\n [\n 514.283203125,\n 70.04443359375\n ]\n ],\n \"bbox\": [\n 514.283203125,\n 60.27978515625,\n 525.638671875,\n 70.04443359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"As you might expect, you have to create a function before you can execute it. In other words, the function definition has to be executed before the first time it is called.
\",\n \"polygon\": [\n [\n 127.599609375,\n 88.83526611328125\n ],\n [\n 526.236328125,\n 88.83526611328125\n ],\n [\n 526.236328125,\n 111.181640625\n ],\n [\n 127.599609375,\n 111.181640625\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 88.83526611328125,\n 526.236328125,\n 111.181640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 3.1. Move the last line of this program to the top, so the function call appears before the definitions. Run the program and see what error message you get.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 113.05157470703125\n ],\n [\n 525.9375,\n 113.05157470703125\n ],\n [\n 525.9375,\n 135.20819091796875\n ],\n [\n 128.9443359375,\n 135.20819091796875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 113.05157470703125,\n 525.9375,\n 135.20819091796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 3.2. Move the function call back to the bottom and move the definition of print_lyrics after the definition of repeat_lyrics. What happens when you run this program?
\",\n \"polygon\": [\n [\n 128.3466796875,\n 137.4395751953125\n ],\n [\n 525.9375,\n 137.4395751953125\n ],\n [\n 525.9375,\n 159.62030029296875\n ],\n [\n 128.3466796875,\n 159.62030029296875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 137.4395751953125,\n 525.9375,\n 159.62030029296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/43/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.7 Flow of execution
\",\n \"polygon\": [\n [\n 127.8984375,\n 188.74676513671875\n ],\n [\n 278.2409973144531,\n 188.74676513671875\n ],\n [\n 278.2409973144531,\n 203.09295654296875\n ],\n [\n 127.8984375,\n 203.09295654296875\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 188.74676513671875,\n 278.2409973144531,\n 203.09295654296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"In order to ensure that a function is defined before its first use, you have to know the order in which statements are executed, which is called the flow of execution.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 214.62890625\n ],\n [\n 525.603515625,\n 214.62890625\n ],\n [\n 525.603515625,\n 237.33489990234375\n ],\n [\n 128.9443359375,\n 237.33489990234375\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 214.62890625,\n 525.603515625,\n 237.33489990234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Execution always begins at the first statement of the program. Statements are executed one at a time, in order from top to bottom.
\",\n \"polygon\": [\n [\n 128.794921875,\n 246.919921875\n ],\n [\n 525.638671875,\n 246.919921875\n ],\n [\n 525.638671875,\n 269.40594482421875\n ],\n [\n 128.794921875,\n 269.40594482421875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 246.919921875,\n 525.638671875,\n 269.40594482421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Function definitions do not alter the flow of execution of the program, but remember that statements inside the function are not executed until the function is called.
\",\n \"polygon\": [\n [\n 128.794921875,\n 279.017578125\n ],\n [\n 526.236328125,\n 279.017578125\n ],\n [\n 526.236328125,\n 301.4769287109375\n ],\n [\n 128.794921875,\n 301.4769287109375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 279.017578125,\n 526.236328125,\n 301.4769287109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"A function call is like a detour in the flow of execution. Instead of going to the next statement, the flow jumps to the body of the function, executes all the statements there, and then comes back to pick up where it left off.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 311.30859375\n ],\n [\n 525.9375,\n 311.30859375\n ],\n [\n 525.9375,\n 345.742919921875\n ],\n [\n 128.6455078125,\n 345.742919921875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 311.30859375,\n 525.9375,\n 345.742919921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"That sounds simple enough, until you remember that one function can call another. While in the middle of one function, the program might have to execute the statements in another function. But while executing that new function, the program might have to execute yet another function!
\",\n \"polygon\": [\n [\n 128.794921875,\n 355.39453125\n ],\n [\n 526.236328125,\n 355.39453125\n ],\n [\n 526.236328125,\n 402.2029113769531\n ],\n [\n 128.794921875,\n 402.2029113769531\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 355.39453125,\n 526.236328125,\n 402.2029113769531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Fortunately, Python is good at keeping track of where it is, so each time a function completes, the program picks up where it left off in the function that called it. When it gets to the end of the program, it terminates.
\",\n \"polygon\": [\n [\n 129.09375,\n 411.85546875\n ],\n [\n 525.9375,\n 411.85546875\n ],\n [\n 525.9375,\n 446.4679260253906\n ],\n [\n 129.09375,\n 446.4679260253906\n ]\n ],\n \"bbox\": [\n 129.09375,\n 411.85546875,\n 525.9375,\n 446.4679260253906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"What's the moral of this sordid tale? When you read a program, you don't always want to read from top to bottom. Sometimes it makes more sense if you follow the flow of execution.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 456.328125\n ],\n [\n 526.53515625,\n 456.328125\n ],\n [\n 526.53515625,\n 490.73394775390625\n ],\n [\n 129.2431640625,\n 490.73394775390625\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 456.328125,\n 526.53515625,\n 490.73394775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.8 Parameters and arguments
\",\n \"polygon\": [\n [\n 128.12255859375,\n 519.7107849121094\n ],\n [\n 335.6114196777344,\n 519.7107849121094\n ],\n [\n 335.6114196777344,\n 534.0570068359375\n ],\n [\n 128.12255859375,\n 534.0570068359375\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 519.7107849121094,\n 335.6114196777344,\n 534.0570068359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Some of the built-in functions we have seen require arguments. For example, when you call math.sin you pass a number as an argument. Some functions take more than one argument: math.pow takes two, the base and the exponent.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 546.142333984375\n ],\n [\n 525.6033935546875,\n 546.142333984375\n ],\n [\n 525.6033935546875,\n 580.4929351806641\n ],\n [\n 128.3466796875,\n 580.4929351806641\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 546.142333984375,\n 525.6033935546875,\n 580.4929351806641\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Inside the function, the arguments are assigned to variables called parameters. Here is an example of a user-defined function that takes an argument:
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\",\n \"polygon\": [\n [\n 129.5999755859375,\n 618.3577880859375\n ],\n [\n 249.90830993652344,\n 618.3577880859375\n ],\n [\n 249.90830993652344,\n 653.5546875\n ],\n [\n 129.5999755859375,\n 653.5546875\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 618.3577880859375,\n 249.90830993652344,\n 653.5546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/44/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"This function assigns the argument to a parameter named bruce. When the function is called, it prints the value of the parameter (whatever it is) twice.
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\",\n \"polygon\": [\n [\n 86.0625,\n 201.09375\n ],\n [\n 482.4033508300781,\n 201.09375\n ],\n [\n 482.4033508300781,\n 224.296875\n ],\n [\n 86.0625,\n 224.296875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 201.09375,\n 482.4033508300781,\n 224.296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/45/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> print_twice('Spam '*4)\\nSpam Spam Spam Spam\\nSpam Spam Spam Spam\\n>>> print_twice(math.cos(math.pi))\\n-1.0\\n-1.0\",\n \"polygon\": [\n [\n 86.361328125,\n 229.03778076171875\n ],\n [\n 264.2423095703125,\n 229.03778076171875\n ],\n [\n 264.2423095703125,\n 299.9713134765625\n ],\n [\n 86.361328125,\n 299.9713134765625\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 229.03778076171875,\n 264.2423095703125,\n 299.9713134765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/45/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The argument is evaluated before the function is called, so in the examples the expressions 'Spam '*4 and math.cos(math.pi) are only evaluated once.
\",\n \"polygon\": [\n [\n 86.39998626708984,\n 305.12109375\n ],\n [\n 482.40338134765625,\n 305.12109375\n ],\n [\n 482.40338134765625,\n 327.61688232421875\n ],\n [\n 86.39998626708984,\n 327.61688232421875\n ]\n ],\n \"bbox\": [\n 86.39998626708984,\n 305.12109375,\n 482.40338134765625,\n 327.61688232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/45/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"You can also use a variable as an argument:
\",\n \"polygon\": [\n [\n 86.39999389648438,\n 336.83203125\n ],\n [\n 277.213623046875,\n 336.83203125\n ],\n [\n 277.213623046875,\n 346.889892578125\n ],\n [\n 86.39999389648438,\n 346.889892578125\n ]\n ],\n \"bbox\": [\n 86.39999389648438,\n 336.83203125,\n 277.213623046875,\n 346.889892578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/45/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> michael = 'Eric, the half a bee.'\\n>>> print_twice(michael)\\nEric, the half a bee.\\nEric, the half a bee.
\",\n \"polygon\": [\n [\n 85.98779296875,\n 352.0787353515625\n ],\n [\n 279.8783874511719,\n 352.0787353515625\n ],\n [\n 279.8783874511719,\n 400.25390625\n ],\n [\n 85.98779296875,\n 400.25390625\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 352.0787353515625,\n 279.8783874511719,\n 400.25390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/44/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/45/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The name of the variable we pass as an argument (michael) has nothing to do with the name of the parameter (bruce). It doesn't matter what the value was called back home (in the caller); here in print_twice, we call everybody bruce.
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\",\n \"polygon\": [\n [\n 85.68896484375,\n 466.3828125\n ],\n [\n 342.60882568359375,\n 466.3828125\n ],\n [\n 342.60882568359375,\n 480.94195556640625\n ],\n [\n 85.68896484375,\n 480.94195556640625\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 466.3828125,\n 342.60882568359375,\n 480.94195556640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/45/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/45/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"When you create a variable inside a function, it is local, which means that it only exists inside the function. For example:
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 519.7687377929688\n ],\n [\n 232.86021423339844,\n 519.7687377929688\n ],\n [\n 232.86021423339844,\n 554.1203460693359\n ],\n [\n 85.9130859375,\n 554.1203460693359\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 519.7687377929688,\n 232.86021423339844,\n 554.1203460693359\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/45/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/45/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"This function takes two arguments, concatenates them, and prints the result twice. Here is an example that uses it:
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 244.52374267578125\n ],\n [\n 525.9375,\n 244.52374267578125\n ],\n [\n 525.9375,\n 266.82989501953125\n ],\n [\n 128.6455078125,\n 266.82989501953125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 244.52374267578125,\n 525.9375,\n 266.82989501953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/45/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/46/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.10 Stack diagrams
\",\n \"polygon\": [\n [\n 128.3466796875,\n 294.8467102050781\n ],\n [\n 269.0947265625,\n 294.8467102050781\n ],\n [\n 269.0947265625,\n 309.19293212890625\n ],\n [\n 128.3466796875,\n 309.19293212890625\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 294.8467102050781,\n 269.0947265625,\n 309.19293212890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/46/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/46/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"To keep track of which variables can be used where, it is sometimes useful to draw a stack diagram. Like state diagrams, stack diagrams show the value of each variable, but they also show the function each variable belongs to.
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\",\n \"polygon\": [\n [\n 85.166015625,\n 88.60693359375\n ],\n [\n 482.90625,\n 88.60693359375\n ],\n [\n 482.90625,\n 110.99188232421875\n ],\n [\n 85.166015625,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 88.60693359375,\n 482.90625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/41/SectionHeader/2\",\n \"4\": \"/page/46/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.11 Fruitful functions and void functions
\",\n \"polygon\": [\n [\n 85.3154296875,\n 139.025390625\n ],\n [\n 369.6944274902344,\n 139.025390625\n ],\n [\n 369.6944274902344,\n 154.137939453125\n ],\n [\n 85.3154296875,\n 154.137939453125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 139.025390625,\n 369.6944274902344,\n 154.137939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Some of the functions we are using, such as the math functions, yield results; for lack of a better name, I call them fruitful functions. Other functions, like print_twice, perform an action but don't return a value. They are called void functions.
\",\n \"polygon\": [\n [\n 84.8671875,\n 165.2255859375\n ],\n [\n 482.40447998046875,\n 165.2255859375\n ],\n [\n 482.40447998046875,\n 200.44793701171875\n ],\n [\n 84.8671875,\n 200.44793701171875\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 165.2255859375,\n 482.40447998046875,\n 200.44793701171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"When you call a fruitful function, you almost always want to do something with the result; for example, you might assign it to a variable or use it as part of an expression:
\",\n \"polygon\": [\n [\n 85.0166015625,\n 209.21484375\n ],\n [\n 482.4033203125,\n 209.21484375\n ],\n [\n 482.4033203125,\n 232.3929443359375\n ],\n [\n 85.0166015625,\n 232.3929443359375\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 209.21484375,\n 482.4033203125,\n 232.3929443359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"x = math.cos(radians)\\ngolden = (math.sqrt(5) + 1) / 2
\",\n \"polygon\": [\n [\n 84.7177734375,\n 238.05877685546875\n ],\n [\n 248.541259765625,\n 238.05877685546875\n ],\n [\n 248.541259765625,\n 260.21636962890625\n ],\n [\n 84.7177734375,\n 260.21636962890625\n ]\n ],\n \"bbox\": [\n 84.7177734375,\n 238.05877685546875,\n 248.541259765625,\n 260.21636962890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"When you call a function in interactive mode, Python displays the result:
\",\n \"polygon\": [\n [\n 86.0625,\n 264.90234375\n ],\n [\n 407.016357421875,\n 264.90234375\n ],\n [\n 407.016357421875,\n 276.14398193359375\n ],\n [\n 86.0625,\n 276.14398193359375\n ]\n ],\n \"bbox\": [\n 86.0625,\n 264.90234375,\n 407.016357421875,\n 276.14398193359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> math.sqrt(5)\\n2.2360679774997898
\",\n \"polygon\": [\n [\n 85.46484375,\n 281.810791015625\n ],\n [\n 181.986328125,\n 281.810791015625\n ],\n [\n 181.986328125,\n 303.9683837890625\n ],\n [\n 85.46484375,\n 303.9683837890625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 281.810791015625,\n 181.986328125,\n 303.9683837890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"But in a script, if you call a fruitful function all by itself, the return value is lost forever!
\",\n \"polygon\": [\n [\n 85.763671875,\n 309.76171875\n ],\n [\n 468.5625,\n 309.76171875\n ],\n [\n 468.5625,\n 319.8959655761719\n ],\n [\n 85.763671875,\n 319.8959655761719\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 309.76171875,\n 468.5625,\n 319.8959655761719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"math.sqrt(5)
\",\n \"polygon\": [\n [\n 85.46484375,\n 325.56280517578125\n ],\n [\n 149.17433166503906,\n 325.56280517578125\n ],\n [\n 149.17433166503906,\n 335.525390625\n ],\n [\n 85.46484375,\n 335.525390625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 325.56280517578125,\n 149.17433166503906,\n 335.525390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"This script computes the square root of 5, but since it doesn't store or display the result, it is not very useful.
\",\n \"polygon\": [\n [\n 85.46484375,\n 341.279296875\n ],\n [\n 482.40338134765625,\n 341.279296875\n ],\n [\n 482.40338134765625,\n 363.708984375\n ],\n [\n 85.46484375,\n 363.708984375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 341.279296875,\n 482.40338134765625,\n 363.708984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Void functions might display something on the screen or have some other effect, but they don't have a return value. If you try to assign the result to a variable, you get a special value called None.
\",\n \"polygon\": [\n [\n 85.46484375,\n 372.216796875\n ],\n [\n 482.4034423828125,\n 372.216796875\n ],\n [\n 482.4034423828125,\n 407.7869567871094\n ],\n [\n 85.46484375,\n 407.7869567871094\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 372.216796875,\n 482.4034423828125,\n 407.7869567871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> result = print_twice('Bing')\\nBing\\nBing\\n>>> print result\\nNone\",\n \"polygon\": [\n [\n 85.0166015625,\n 413.40234375\n ],\n [\n 253.73236083984375,\n 413.40234375\n ],\n [\n 253.73236083984375,\n 476.82421875\n ],\n [\n 85.0166015625,\n 476.82421875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 413.40234375,\n 253.73236083984375,\n 476.82421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The value None is not the same as the string 'None'. It is a special value that has its own type:
\",\n \"polygon\": [\n [\n 86.0625,\n 478.0097961425781\n ],\n [\n 482.399169921875,\n 478.0097961425781\n ],\n [\n 482.399169921875,\n 500.4140625\n ],\n [\n 86.0625,\n 500.4140625\n ]\n ],\n \"bbox\": [\n 86.0625,\n 478.0097961425781,\n 482.399169921875,\n 500.4140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> print type(None)\\n<type 'NoneType'>
\",\n \"polygon\": [\n [\n 85.46484375,\n 505.9827880859375\n ],\n [\n 191.0172119140625,\n 505.9827880859375\n ],\n [\n 191.0172119140625,\n 528.1393737792969\n ],\n [\n 85.46484375,\n 528.1393737792969\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 505.9827880859375,\n 191.0172119140625,\n 528.1393737792969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The functions we have written so far are all void. We will start writing fruitful functions in a few chapters.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 533.28515625\n ],\n [\n 482.4031982421875,\n 533.28515625\n ],\n [\n 482.4031982421875,\n 556.2619323730469\n ],\n [\n 85.9130859375,\n 556.2619323730469\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 533.28515625,\n 482.4031982421875,\n 556.2619323730469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/SectionHeader/16\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.12 Why functions?
\",\n \"polygon\": [\n [\n 85.3154296875,\n 584.71875\n ],\n [\n 229.04421997070312,\n 584.71875\n ],\n [\n 229.04421997070312,\n 599.4089813232422\n ],\n [\n 85.3154296875,\n 599.4089813232422\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 584.71875,\n 229.04421997070312,\n 599.4089813232422\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/47/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"It may not be clear why it is worth the trouble to divide a program into functions. There are several reasons:
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Later, if you make a change, you only have to make it in one place.\",\n \"polygon\": [\n [\n 100.28797149658203,\n 677.53125\n ],\n [\n 482.90625,\n 677.53125\n ],\n [\n 482.90625,\n 700.8349304199219\n ],\n [\n 100.28797149658203,\n 700.8349304199219\n ]\n ],\n \"bbox\": [\n 100.28797149658203,\n 677.53125,\n 482.90625,\n 700.8349304199219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": null\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/48/Page/184\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/48/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 127.8984375,\n 61.0048828125\n ],\n [\n 525.5996704101562,\n 61.0048828125\n ],\n [\n 525.5996704101562,\n 71.13372802734375\n ],\n [\n 127.8984375,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 61.0048828125,\n 525.5996704101562,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/PageHeader/20\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 514.58203125,\n 60.4248046875\n ],\n [\n 525.33984375,\n 60.4248046875\n ],\n [\n 525.33984375,\n 69.8994140625\n ],\n [\n 514.58203125,\n 69.8994140625\n ]\n ],\n \"bbox\": [\n 514.58203125,\n 60.4248046875,\n 525.33984375,\n 69.8994140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/ListGroup/182\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 142.83984375,\n 88.41357421875\n ],\n [\n 525.603515625,\n 88.41357421875\n ],\n [\n 525.603515625,\n 142.61590576171875\n ],\n [\n 142.83984375,\n 142.61590576171875\n ]\n ],\n \"bbox\": [\n 142.83984375,\n 88.41357421875,\n 525.603515625,\n 142.61590576171875\n ],\n \"children\": [\n {\n \"id\": \"/page/48/ListItem/1\",\n \"block_type\": \"ListItem\",\n \"html\": \" Dividing a long program into functions allows you to debug the parts one at a time and then assemble them into a working whole.\",\n \"polygon\": [\n [\n 143.4375,\n 88.41357421875\n ],\n [\n 525.6034545898438,\n 88.41357421875\n ],\n [\n 525.6034545898438,\n 110.99188232421875\n ],\n [\n 143.4375,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 143.4375,\n 88.41357421875,\n 525.6034545898438,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \" Well-designed functions are often useful for many programs. Once you write and debug one, you can reuse it.\",\n \"polygon\": [\n [\n 142.83984375,\n 119.9794921875\n ],\n [\n 525.603515625,\n 119.9794921875\n ],\n [\n 525.603515625,\n 142.61590576171875\n ],\n [\n 142.83984375,\n 142.61590576171875\n ]\n ],\n \"bbox\": [\n 142.83984375,\n 119.9794921875,\n 525.603515625,\n 142.61590576171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"3\": \"/page/47/SectionHeader/2\",\n \"4\": \"/page/47/SectionHeader/16\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/48/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"3.13 Importing with from
\",\n \"polygon\": [\n [\n 127.4501953125,\n 168.674072265625\n ],\n [\n 302.5994873046875,\n 168.674072265625\n ],\n [\n 302.5994873046875,\n 185.096923828125\n ],\n [\n 127.4501953125,\n 185.096923828125\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 168.674072265625,\n 302.5994873046875,\n 185.096923828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Python provides two ways to import modules; we have already seen one:
\",\n \"polygon\": [\n [\n 128.197265625,\n 196.3564453125\n ],\n [\n 452.7369689941406,\n 196.3564453125\n ],\n [\n 452.7369689941406,\n 206.54388427734375\n ],\n [\n 128.197265625,\n 206.54388427734375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 196.3564453125,\n 452.7369689941406,\n 206.54388427734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> import math\\n>>> print math\\n<module 'math' (built-in)>\\n>>> print math.pi\\n3.14159265359
\",\n \"polygon\": [\n [\n 128.49609375,\n 211.73468017578125\n ],\n [\n 265.571044921875,\n 211.73468017578125\n ],\n [\n 265.571044921875,\n 270.47528076171875\n ],\n [\n 128.49609375,\n 270.47528076171875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 211.73468017578125,\n 265.571044921875,\n 270.47528076171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"If you import math, you get a module object named math. The module object contains constants like pi and functions like sin and exp.
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 275.816650390625\n ],\n [\n 525.9375,\n 275.816650390625\n ],\n [\n 525.9375,\n 298.1228332519531\n ],\n [\n 129.5999755859375,\n 298.1228332519531\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 275.816650390625,\n 525.9375,\n 298.1228332519531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"But if you try to access pi directly, you get an error.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 307.28668212890625\n ],\n [\n 354.41015625,\n 307.28668212890625\n ],\n [\n 354.41015625,\n 317.3988342285156\n ],\n [\n 129.2431640625,\n 317.3988342285156\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 307.28668212890625,\n 354.41015625,\n 317.3988342285156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> print pi\\nTraceback (most recent call last):\\n File \\\"<stdin>\\\", line 1, in <module>\\nNameError: name 'pi' is not defined
\",\n \"polygon\": [\n [\n 129.16845703125,\n 322.5906677246094\n ],\n [\n 324.228515625,\n 322.5906677246094\n ],\n [\n 324.228515625,\n 369.1362609863281\n ],\n [\n 129.16845703125,\n 369.1362609863281\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 322.5906677246094,\n 324.228515625,\n 369.1362609863281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"As an alternative, you can import an object from a module like this:
\",\n \"polygon\": [\n [\n 127.8984375,\n 373.5703125\n ],\n [\n 426.12890625,\n 373.5703125\n ],\n [\n 426.12890625,\n 384.5898132324219\n ],\n [\n 127.8984375,\n 384.5898132324219\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 373.5703125,\n 426.12890625,\n 384.5898132324219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> from math import pi
\",\n \"polygon\": [\n [\n 129.01904296875,\n 389.7816467285156\n ],\n [\n 249.89837646484375,\n 389.7816467285156\n ],\n [\n 249.89837646484375,\n 401.02734375\n ],\n [\n 129.01904296875,\n 401.02734375\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 389.7816467285156,\n 249.89837646484375,\n 401.02734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Now you can access pi directly, without dot notation.
\",\n \"polygon\": [\n [\n 128.72021484375,\n 404.89453125\n ],\n [\n 365.32220458984375,\n 404.89453125\n ],\n [\n 365.32220458984375,\n 415.1977844238281\n ],\n [\n 128.72021484375,\n 415.1977844238281\n ]\n ],\n \"bbox\": [\n 128.72021484375,\n 404.89453125,\n 365.32220458984375,\n 415.1977844238281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> print pi\\n3.14159265359
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 420.3886413574219\n ],\n [\n 197.60472106933594,\n 420.3886413574219\n ],\n [\n 197.60472106933594,\n 442.79296875\n ],\n [\n 129.60000610351562,\n 442.79296875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 420.3886413574219,\n 197.60472106933594,\n 442.79296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Or you can use the star operator to import everything from the module:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 447.864501953125\n ],\n [\n 440.18243408203125,\n 447.864501953125\n ],\n [\n 440.18243408203125,\n 457.9997863769531\n ],\n [\n 129.2431640625,\n 457.9997863769531\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 447.864501953125,\n 440.18243408203125,\n 457.9997863769531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> from math import *\\n>>> cos(pi)\\n-1.0
\",\n \"polygon\": [\n [\n 128.6455078125,\n 463.1916198730469\n ],\n [\n 244.66799926757812,\n 463.1916198730469\n ],\n [\n 244.66799926757812,\n 497.5422058105469\n ],\n [\n 128.6455078125,\n 497.5422058105469\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 463.1916198730469,\n 244.66799926757812,\n 497.5422058105469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The advantage of importing everything from the math module is that your code can be more concise. The disadvantage is that there might be conflicts between names defined in different modules, or between a name from a module and one of your variables.
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\",\n \"polygon\": [\n [\n 128.12255859375,\n 565.5196228027344\n ],\n [\n 243.24609375,\n 565.5196228027344\n ],\n [\n 243.24609375,\n 579.8658142089844\n ],\n [\n 128.12255859375,\n 579.8658142089844\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 565.5196228027344,\n 243.24609375,\n 579.8658142089844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/48/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/48/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"If you are using a text editor to write your scripts, you might run into problems with spaces and tabs. The best way to avoid these problems is to use spaces exclusively (no tabs). Most text editors that know about Python do this by default, but some don't.
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 199.51275634765625\n ],\n [\n 228.80398559570312,\n 199.51275634765625\n ],\n [\n 228.80398559570312,\n 213.85894775390625\n ],\n [\n 128.3466796875,\n 213.85894775390625\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 199.51275634765625,\n 228.80398559570312,\n 213.85894775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/50/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 3.3. Python provides a built-in function called len that returns the length of a string, so the value of len('allen') is 5.
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\",\n \"polygon\": [\n [\n 469.49200439453125,\n 302.4707336425781\n ],\n [\n 495.6438293457031,\n 302.4707336425781\n ],\n [\n 495.6438293457031,\n 312.4333190917969\n ],\n [\n 469.49200439453125,\n 312.4333190917969\n ]\n ],\n \"bbox\": [\n 469.49200439453125,\n 302.4707336425781,\n 495.6438293457031,\n 312.4333190917969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/50/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 3.4. A function object is a value you can assign to a variable or pass as an argument. For example, do_twice is a function that takes a function object as an argument and calls it twice:
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\",\n \"polygon\": [\n [\n 127.67431640625,\n 344.0637512207031\n ],\n [\n 215.75390625,\n 344.0637512207031\n ],\n [\n 215.75390625,\n 382.46484375\n ],\n [\n 127.67431640625,\n 382.46484375\n ]\n ],\n \"bbox\": [\n 127.67431640625,\n 344.0637512207031,\n 215.75390625,\n 382.46484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/50/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Here's an example that uses do_twice to call a function named print_spam twice.
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\",\n \"polygon\": [\n [\n 126.404296875,\n 640.1709594726562\n ],\n [\n 526.236328125,\n 640.1709594726562\n ],\n [\n 526.236328125,\n 674.6032638549805\n ],\n [\n 126.404296875,\n 674.6032638549805\n ]\n ],\n \"bbox\": [\n 126.404296875,\n 640.1709594726562,\n 526.236328125,\n 674.6032638549805\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/50/ListItem/20\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Write a function that draws a grid like the following:\",\n \"polygon\": [\n [\n 141.4951171875,\n 690.6796875\n ],\n [\n 365.90264892578125,\n 690.6796875\n ],\n [\n 365.90264892578125,\n 700.6622619628906\n ],\n [\n 141.4951171875,\n 700.6622619628906\n ]\n ],\n \"bbox\": [\n 141.4951171875,\n 690.6796875,\n 365.90264892578125,\n 700.6622619628906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/51/Page/112\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/51/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 61.05322265625\n ],\n [\n 482.4034118652344,\n 61.05322265625\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 61.05322265625,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/51/PageHeader/10\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.166015625,\n 60.76318359375\n ],\n [\n 96.521484375,\n 60.76318359375\n ],\n [\n 96.521484375,\n 70.23779296875\n ],\n [\n 85.166015625,\n 70.23779296875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 60.76318359375,\n 96.521484375,\n 70.23779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/51/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \"+ - - - - + - - - - +\\n| | |\\n| | |\\n| | |\\n| | |\\n+ - - - - + - - - - +\\n| | |\\n| | |\\n| | |\\n| | |\\n+ - - - - + - - - - +
\",\n \"polygon\": [\n [\n 111.3070068359375,\n 88.68572998046875\n ],\n [\n 223.6728515625,\n 88.68572998046875\n ],\n [\n 223.6728515625,\n 220.59130859375\n ],\n [\n 111.3070068359375,\n 220.59130859375\n ]\n ],\n \"bbox\": [\n 111.3070068359375,\n 88.68572998046875,\n 223.6728515625,\n 220.59130859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/51/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Hint: to print more than one value on a line, you can print a comma-separated sequence:
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\",\n \"polygon\": [\n [\n 109.74462890625,\n 303.53466796875\n ],\n [\n 171.52734375,\n 303.53466796875\n ],\n [\n 171.52734375,\n 329.291015625\n ],\n [\n 109.74462890625,\n 329.291015625\n ]\n ],\n \"bbox\": [\n 109.74462890625,\n 303.53466796875,\n 171.52734375,\n 329.291015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/51/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The output of these statements is '+ -'.
\",\n \"polygon\": [\n [\n 111.08935546875,\n 335.865234375\n ],\n [\n 273.878662109375,\n 335.865234375\n ],\n [\n 273.878662109375,\n 345.8702697753906\n ],\n [\n 111.08935546875,\n 345.8702697753906\n ]\n ],\n \"bbox\": [\n 111.08935546875,\n 335.865234375,\n 273.878662109375,\n 345.8702697753906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/51/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"A print statement all by itself ends the current line and goes to the next line.
\",\n \"polygon\": [\n [\n 109.96875,\n 351.52734375\n ],\n [\n 422.7018737792969,\n 351.52734375\n ],\n [\n 422.7018737792969,\n 362.04925537109375\n ],\n [\n 109.96875,\n 362.04925537109375\n ]\n ],\n \"bbox\": [\n 109.96875,\n 351.52734375,\n 422.7018737792969,\n 362.04925537109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/51/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Write a function that draws a similar grid with four rows and four columns.\",\n \"polygon\": [\n [\n 98.85298156738281,\n 371.443359375\n ],\n [\n 416.8094482421875,\n 371.443359375\n ],\n [\n 416.8094482421875,\n 382.19012451171875\n ],\n [\n 98.85298156738281,\n 382.19012451171875\n ]\n ],\n \"bbox\": [\n 98.85298156738281,\n 371.443359375,\n 416.8094482421875,\n 382.19012451171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/48/SectionHeader/3\",\n \"4\": \"/page/50/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/51/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ grid. py . Credit: This exercise is based on an exercise in Oualline, Practical C Programming, Third Edition, O'Reilly Media, 1997.
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 165.43450927734375\n ],\n [\n 221.2822265625,\n 165.43450927734375\n ],\n [\n 221.2822265625,\n 186.09698486328125\n ],\n [\n 128.6455078125,\n 186.09698486328125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 165.43450927734375,\n 221.2822265625,\n 186.09698486328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/40/SectionHeader/1\",\n \"2\": \"/page/52/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Case study: interface design
\",\n \"polygon\": [\n [\n 128.6455078125,\n 222.10333251953125\n ],\n [\n 448.83984375,\n 222.10333251953125\n ],\n [\n 448.83984375,\n 246.890380859375\n ],\n [\n 128.6455078125,\n 246.890380859375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 222.10333251953125,\n 448.83984375,\n 246.890380859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Code examples from this chapter are available from http://thinkpython.com/code/ polygon.py.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 296.2265625\n ],\n [\n 526.53515625,\n 296.2265625\n ],\n [\n 526.53515625,\n 318.7899475097656\n ],\n [\n 128.3466796875,\n 318.7899475097656\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 296.2265625,\n 526.53515625,\n 318.7899475097656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"4.1 TurtleWorld
\",\n \"polygon\": [\n [\n 128.794921875,\n 351.9140625\n ],\n [\n 241.05563354492188,\n 351.9140625\n ],\n [\n 241.05563354492188,\n 366.4170227050781\n ],\n [\n 128.794921875,\n 366.4170227050781\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 351.9140625,\n 241.05563354492188,\n 366.4170227050781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"To accompany this book, I have written a package called Swampy. You can download Swampy from http://thinkpython.com/swampy; follow the instructions there to install Swampy on your system.
\",\n \"polygon\": [\n [\n 128.794921875,\n 380.337890625\n ],\n [\n 526.236328125,\n 380.337890625\n ],\n [\n 526.236328125,\n 414.95196533203125\n ],\n [\n 128.794921875,\n 414.95196533203125\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 380.337890625,\n 526.236328125,\n 414.95196533203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"A package is a collection of modules; one of the modules in Swampy is TurtleWorld, which provides a set of functions for drawing lines by steering turtles around the screen.
\",\n \"polygon\": [\n [\n 129.09375,\n 426.1640625\n ],\n [\n 525.9375,\n 426.1640625\n ],\n [\n 525.9375,\n 448.59698486328125\n ],\n [\n 129.09375,\n 448.59698486328125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 426.1640625,\n 525.9375,\n 448.59698486328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"If Swampy is installed as a package on your system, you can import TurtleWorld like this:
\",\n \"polygon\": [\n [\n 128.0478515625,\n 459.80859375\n ],\n [\n 525.9375,\n 459.80859375\n ],\n [\n 525.9375,\n 470.0469970703125\n ],\n [\n 128.0478515625,\n 470.0469970703125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 459.80859375,\n 525.9375,\n 470.0469970703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"from swampy.TurtleWorld import *
\",\n \"polygon\": [\n [\n 128.27197265625,\n 477.4138488769531\n ],\n [\n 296.9815979003906,\n 477.4138488769531\n ],\n [\n 296.9815979003906,\n 487.3764343261719\n ],\n [\n 128.27197265625,\n 487.3764343261719\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 477.4138488769531,\n 296.9815979003906,\n 487.3764343261719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"If you downloaded the Swampy modules but did not install them as a package, you can either work in the directory that contains the Swampy files, or add that directory to Python's search path. Then you can import TurtleWorld like this:
\",\n \"polygon\": [\n [\n 129.09375,\n 495.04241943359375\n ],\n [\n 525.9375,\n 495.04241943359375\n ],\n [\n 525.9375,\n 529.41796875\n ],\n [\n 129.09375,\n 529.41796875\n ]\n ],\n \"bbox\": [\n 129.09375,\n 495.04241943359375,\n 525.9375,\n 529.41796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"from TurtleWorld import *
\",\n \"polygon\": [\n [\n 127.30078125,\n 536.7598571777344\n ],\n [\n 260.578125,\n 536.7598571777344\n ],\n [\n 260.578125,\n 546.7224578857422\n ],\n [\n 127.30078125,\n 546.7224578857422\n ]\n ],\n \"bbox\": [\n 127.30078125,\n 536.7598571777344,\n 260.578125,\n 546.7224578857422\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The details of the installation process and setting Python's search path depend on your system, so rather than include those details here, I will try to maintain current information for several systems at http://thinkpython.com/swampy
\",\n \"polygon\": [\n [\n 128.49609375,\n 554.3884124755859\n ],\n [\n 526.53515625,\n 554.3884124755859\n ],\n [\n 526.53515625,\n 588.7400207519531\n ],\n [\n 128.49609375,\n 588.7400207519531\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 554.3884124755859,\n 526.53515625,\n 588.7400207519531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Create a file named mypolygon.py and type in the following code:
\",\n \"polygon\": [\n [\n 127.52490234375,\n 600.0778656005859\n ],\n [\n 419.64520263671875,\n 600.0778656005859\n ],\n [\n 419.64520263671875,\n 610.1900177001953\n ],\n [\n 127.52490234375,\n 610.1900177001953\n ]\n ],\n \"bbox\": [\n 127.52490234375,\n 600.0778656005859,\n 419.64520263671875,\n 610.1900177001953\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"from swampy.TurtleWorld import *\\nworld = TurtleWorld()\\nbob = Turtle()\\nprint bob
\",\n \"polygon\": [\n [\n 128.49609375,\n 617.5568695068359\n ],\n [\n 296.98162841796875,\n 617.5568695068359\n ],\n [\n 296.98162841796875,\n 690.29296875\n ],\n [\n 128.49609375,\n 690.29296875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 617.5568695068359,\n 296.98162841796875,\n 690.29296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/52/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"wait_for_user()
\",\n \"polygon\": [\n [\n 129.6000213623047,\n 690.7228546142578\n ],\n [\n 208.06546020507812,\n 690.7228546142578\n ],\n [\n 208.06546020507812,\n 700.6854553222656\n ],\n [\n 129.6000213623047,\n 700.6854553222656\n ]\n ],\n \"bbox\": [\n 129.6000213623047,\n 690.7228546142578,\n 208.06546020507812,\n 700.6854553222656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/53/Page/204\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/53/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.85986328125\n ],\n [\n 482.90625,\n 60.85986328125\n ],\n [\n 482.90625,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.85986328125,\n 482.90625,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"The first line imports everything from the TurtleWorld module in the swampy package.
\",\n \"polygon\": [\n [\n 85.46484375,\n 87.978515625\n ],\n [\n 468.481689453125,\n 87.978515625\n ],\n [\n 468.481689453125,\n 98.79791259765625\n ],\n [\n 85.46484375,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 87.978515625,\n 468.481689453125,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The next lines create a TurtleWorld assigned to world and a Turtle assigned to bob. Printing bob yields something like:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 108.5712890625\n ],\n [\n 482.90625,\n 108.5712890625\n ],\n [\n 482.90625,\n 131.42388916015625\n ],\n [\n 85.6142578125,\n 131.42388916015625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 108.5712890625,\n 482.90625,\n 131.42388916015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"<TurtleWorld.Turtle instance at 0xb7bfbf4c>
\",\n \"polygon\": [\n [\n 85.83837890625,\n 137.478515625\n ],\n [\n 311.37890625,\n 137.478515625\n ],\n [\n 311.37890625,\n 147.73529052734375\n ],\n [\n 85.83837890625,\n 147.73529052734375\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 137.478515625,\n 311.37890625,\n 147.73529052734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"This means that bob refers to an instance of a Turtle as defined in module TurtleWorld. In this context, \\\"instance\\\" means a member of a set; this Turtle is one of the set of possible Turtles.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 154.0107421875\n ],\n [\n 482.4034423828125,\n 154.0107421875\n ],\n [\n 482.4034423828125,\n 188.73388671875\n ],\n [\n 85.6142578125,\n 188.73388671875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 154.0107421875,\n 482.4034423828125,\n 188.73388671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"wait_for_user tells TurtleWorld to wait for the user to do something, although in this case there's not much for the user to do except close the window.
\",\n \"polygon\": [\n [\n 85.46484375,\n 198.580078125\n ],\n [\n 482.3957824707031,\n 198.580078125\n ],\n [\n 482.3957824707031,\n 221.35992431640625\n ],\n [\n 85.46484375,\n 221.35992431640625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 198.580078125,\n 482.3957824707031,\n 221.35992431640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"TurtleWorld provides several turtle-steering functions: fd and bk for forward and backward, and lt and rt for left and right turns. Also, each Turtle is holding a pen, which is either down or up; if the pen is down, the Turtle leaves a trail when it moves. The functions pu and pd stand for \\\"pen up\\\" and \\\"pen down.\\\"
\",\n \"polygon\": [\n [\n 85.763671875,\n 231.64453125\n ],\n [\n 482.4033203125,\n 231.64453125\n ],\n [\n 482.4033203125,\n 278.37493896484375\n ],\n [\n 85.763671875,\n 278.37493896484375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 231.64453125,\n 482.4033203125,\n 278.37493896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"To draw a right angle, add these lines to the program (after creating bob and before calling wait_for_user):
\",\n \"polygon\": [\n [\n 85.6142578125,\n 288.685546875\n ],\n [\n 482.90625,\n 288.685546875\n ],\n [\n 482.90625,\n 311.00091552734375\n ],\n [\n 85.6142578125,\n 311.00091552734375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 288.685546875,\n 482.90625,\n 311.00091552734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"fd(bob, 100) lt(bob) fd(bob, 100)
\",\n \"polygon\": [\n [\n 85.83837890625,\n 316.529296875\n ],\n [\n 149.16439819335938,\n 316.529296875\n ],\n [\n 149.16439819335938,\n 351.70135498046875\n ],\n [\n 85.83837890625,\n 351.70135498046875\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 316.529296875,\n 149.16439819335938,\n 351.70135498046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The first line tells bob to take 100 steps forward. The second line tells him to turn left.
\",\n \"polygon\": [\n [\n 85.763671875,\n 357.71484375\n ],\n [\n 460.2214660644531,\n 357.71484375\n ],\n [\n 460.2214660644531,\n 368.3109130859375\n ],\n [\n 85.763671875,\n 368.3109130859375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 357.71484375,\n 460.2214660644531,\n 368.3109130859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"When you run this program, you should see bob move east and then north, leaving two line segments behind.
\",\n \"polygon\": [\n [\n 85.46484375,\n 378.2109375\n ],\n [\n 482.39788818359375,\n 378.2109375\n ],\n [\n 482.39788818359375,\n 400.9369201660156\n ],\n [\n 85.46484375,\n 400.9369201660156\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 378.2109375,\n 482.39788818359375,\n 400.9369201660156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Now modify the program to draw a square. Don't go on until you've got it working!
\",\n \"polygon\": [\n [\n 85.6142578125,\n 410.6953125\n ],\n [\n 458.40234375,\n 410.6953125\n ],\n [\n 458.40234375,\n 421.3689270019531\n ],\n [\n 85.6142578125,\n 421.3689270019531\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 410.6953125,\n 458.40234375,\n 421.3689270019531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/52/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"4.2 Simple repetition
\",\n \"polygon\": [\n [\n 85.53955078125,\n 451.5947570800781\n ],\n [\n 233.82159423828125,\n 451.5947570800781\n ],\n [\n 233.82159423828125,\n 465.94097900390625\n ],\n [\n 85.53955078125,\n 465.94097900390625\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 451.5947570800781,\n 233.82159423828125,\n 465.94097900390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Chances are you wrote something like this (leaving out the code that creates TurtleWorld and waits for the user):
\",\n \"polygon\": [\n [\n 85.3154296875,\n 478.6993103027344\n ],\n [\n 482.4034423828125,\n 478.6993103027344\n ],\n [\n 482.4034423828125,\n 500.85589599609375\n ],\n [\n 85.3154296875,\n 500.85589599609375\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 478.6993103027344,\n 482.4034423828125,\n 500.85589599609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \"fd(bob, 100)\\nlt(bob)\\nfd(bob, 100)\\nlt(bob)\\nfd(bob, 100)\\nlt(bob)\\nfd(bob, 100)
\",\n \"polygon\": [\n [\n 85.763671875,\n 507.2037353515625\n ],\n [\n 157.6318359375,\n 507.2037353515625\n ],\n [\n 157.6318359375,\n 626.9153594970703\n ],\n [\n 85.763671875,\n 626.9153594970703\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 507.2037353515625,\n 157.6318359375,\n 626.9153594970703\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"We can do the same thing more concisely with a for statement. Add this example to mypolygon.py and run it again:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 633.05859375\n ],\n [\n 482.90625,\n 633.05859375\n ],\n [\n 482.90625,\n 656.26171875\n ],\n [\n 85.6142578125,\n 656.26171875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 633.05859375,\n 482.90625,\n 656.26171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \"for i in range(4):\\n print 'Hello!'
\",\n \"polygon\": [\n [\n 86.4000473022461,\n 661.67578125\n ],\n [\n 180.54661560058594,\n 661.67578125\n ],\n [\n 180.54661560058594,\n 684.2253494262695\n ],\n [\n 86.4000473022461,\n 684.2253494262695\n ]\n ],\n \"bbox\": [\n 86.4000473022461,\n 661.67578125,\n 180.54661560058594,\n 684.2253494262695\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"You should see something like this:
\",\n \"polygon\": [\n [\n 86.361328125,\n 690.872314453125\n ],\n [\n 242.349609375,\n 690.872314453125\n ],\n [\n 242.349609375,\n 700.8349151611328\n ],\n [\n 86.361328125,\n 700.8349151611328\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 690.872314453125,\n 242.349609375,\n 700.8349151611328\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/53/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"32
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\",\n \"polygon\": [\n [\n 129.01904296875,\n 87.78515625\n ],\n [\n 161.666015625,\n 87.78515625\n ],\n [\n 161.666015625,\n 124.13671875\n ],\n [\n 129.01904296875,\n 124.13671875\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 87.78515625,\n 161.666015625,\n 124.13671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Hello!
\",\n \"polygon\": [\n [\n 128.3466796875,\n 125.26873779296875\n ],\n [\n 160.98219299316406,\n 125.26873779296875\n ],\n [\n 160.98219299316406,\n 136.2216796875\n ],\n [\n 128.3466796875,\n 136.2216796875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 125.26873779296875,\n 160.98219299316406,\n 136.2216796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"This is the simplest use of the for statement; we will see more later. But that should be enough to let you rewrite your square-drawing program. Don't go on until you do.
\",\n \"polygon\": [\n [\n 128.49609375,\n 141.0556640625\n ],\n [\n 525.5963134765625,\n 141.0556640625\n ],\n [\n 525.5963134765625,\n 163.5699462890625\n ],\n [\n 128.49609375,\n 163.5699462890625\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 141.0556640625,\n 525.5963134765625,\n 163.5699462890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Here is a for statement that draws a square:
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\",\n \"polygon\": [\n [\n 129.5999755859375,\n 189.41876220703125\n ],\n [\n 223.7465362548828,\n 189.41876220703125\n ],\n [\n 223.7465362548828,\n 225.650390625\n ],\n [\n 129.5999755859375,\n 225.650390625\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 189.41876220703125,\n 223.7465362548828,\n 225.650390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The syntax of a for statement is similar to a function definition. It has a header that ends with a colon and an indented body. The body can contain any number of statements.
\",\n \"polygon\": [\n [\n 129.09375,\n 229.7109375\n ],\n [\n 525.6016845703125,\n 229.7109375\n ],\n [\n 525.6016845703125,\n 252.10784912109375\n ],\n [\n 129.09375,\n 252.10784912109375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 229.7109375,\n 525.6016845703125,\n 252.10784912109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"A for statement is sometimes called a loop because the flow of execution runs through the body and then loops back to the top. In this case, it runs the body four times.
\",\n \"polygon\": [\n [\n 129.09375,\n 261.80859375\n ],\n [\n 525.6040649414062,\n 261.80859375\n ],\n [\n 525.6040649414062,\n 284.2688293457031\n ],\n [\n 129.09375,\n 284.2688293457031\n ]\n ],\n \"bbox\": [\n 129.09375,\n 261.80859375,\n 525.6040649414062,\n 284.2688293457031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"This version is actually a little different from the previous square-drawing code because it makes another turn after drawing the last side of the square. The extra turn takes a little more time, but it simplifies the code if we do the same thing every time through the loop. This version also has the effect of leaving the turtle back in the starting position, facing in the starting direction.
\",\n \"polygon\": [\n [\n 128.794921875,\n 293.90625\n ],\n [\n 525.638671875,\n 293.90625\n ],\n [\n 525.638671875,\n 353.07421875\n ],\n [\n 128.794921875,\n 353.07421875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 293.90625,\n 525.638671875,\n 353.07421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/53/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"4.3 Exercises
\",\n \"polygon\": [\n [\n 128.0478515625,\n 382.1136779785156\n ],\n [\n 222.1787109375,\n 382.1136779785156\n ],\n [\n 222.1787109375,\n 396.45989990234375\n ],\n [\n 128.0478515625,\n 396.45989990234375\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 382.1136779785156,\n 222.1787109375,\n 396.45989990234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The following is a series of exercises using TurtleWorld. They are meant to be fun, but they have a point, too. While you are working on them, think about what the point is.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 407.98828125\n ],\n [\n 525.9375,\n 407.98828125\n ],\n [\n 525.9375,\n 430.79083251953125\n ],\n [\n 129.2431640625,\n 430.79083251953125\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 407.98828125,\n 525.9375,\n 430.79083251953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The following sections have solutions to the exercises, so don't look until you have finished (or at least tried).
\",\n \"polygon\": [\n [\n 128.794921875,\n 440.0859375\n ],\n [\n 525.638671875,\n 440.0859375\n ],\n [\n 525.638671875,\n 462.95184326171875\n ],\n [\n 128.794921875,\n 462.95184326171875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 440.0859375,\n 525.638671875,\n 462.95184326171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/ListItem/12\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Write a function called square that takes a parameter named t, which is a turtle. It should use the turtle to draw a square.\",\n \"polygon\": [\n [\n 141.345703125,\n 476.05078125\n ],\n [\n 525.638671875,\n 476.05078125\n ],\n [\n 525.638671875,\n 498.81884765625\n ],\n [\n 141.345703125,\n 498.81884765625\n ]\n ],\n \"bbox\": [\n 141.345703125,\n 476.05078125,\n 525.638671875,\n 498.81884765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Write a function call that passes bob as an argument to square, and then run the program again.
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\",\n \"polygon\": [\n [\n 152.5517578125,\n 621.84375\n ],\n [\n 525.9375,\n 621.84375\n ],\n [\n 525.9375,\n 656.4278259277344\n ],\n [\n 152.5517578125,\n 656.4278259277344\n ]\n ],\n \"bbox\": [\n 152.5517578125,\n 621.84375,\n 525.9375,\n 656.4278259277344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/54/ListItem/17\",\n \"block_type\": \"ListItem\",\n \"html\": \"4. Write a function called circle that takes a turtle, t, and radius, r, as parameters and that draws an approximate circle by invoking polygon with an appropriate length and number of sides. Test your function with a range of values of r.\",\n \"polygon\": [\n [\n 140.2998046875,\n 665.9296875\n ],\n [\n 525.6051025390625,\n 665.9296875\n ],\n [\n 525.6051025390625,\n 700.834831237793\n ],\n [\n 140.2998046875,\n 700.834831237793\n ]\n ],\n \"bbox\": [\n 140.2998046875,\n 665.9296875,\n 525.6051025390625,\n 700.834831237793\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/55/Page/222\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/55/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.76318359375\n ],\n [\n 482.90625,\n 60.76318359375\n ],\n [\n 482.90625,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.76318359375,\n 482.90625,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.166015625,\n 61.05322265625\n ],\n [\n 96.521484375,\n 61.05322265625\n ],\n [\n 96.521484375,\n 70.43115234375\n ],\n [\n 85.166015625,\n 70.43115234375\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 61.05322265625,\n 96.521484375,\n 70.43115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"Hint: figure out the circumference of the circle and make sure that length * n = circumference.
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\",\n \"polygon\": [\n [\n 109.5205078125,\n 117.043701171875\n ],\n [\n 482.4045104980469,\n 117.043701171875\n ],\n [\n 482.4045104980469,\n 151.54388427734375\n ],\n [\n 109.5205078125,\n 151.54388427734375\n ]\n ],\n \"bbox\": [\n 109.5205078125,\n 117.043701171875,\n 482.4045104980469,\n 151.54388427734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/ListItem/3\",\n \"block_type\": \"ListItem\",\n \"html\": \"5. Make a more general version of circle called arc that takes an additional parameter angle, which determines what fraction of a circle to draw. angle is in units of degrees, so when angle=360, arc should draw a complete circle.\",\n \"polygon\": [\n [\n 97.2685546875,\n 160.7783203125\n ],\n [\n 482.90625,\n 160.7783203125\n ],\n [\n 482.90625,\n 196.06585693359375\n ],\n [\n 97.2685546875,\n 196.06585693359375\n ]\n ],\n \"bbox\": [\n 97.2685546875,\n 160.7783203125,\n 482.90625,\n 196.06585693359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"4\": \"/page/54/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"4.4 Encapsulation
\",\n \"polygon\": [\n [\n 85.6142578125,\n 224.68359375\n ],\n [\n 211.9005126953125,\n 224.68359375\n ],\n [\n 211.9005126953125,\n 239.701904296875\n ],\n [\n 85.6142578125,\n 239.701904296875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 224.68359375,\n 211.9005126953125,\n 239.701904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The first exercise asks you to put your square-drawing code into a function definition and then call the function, passing the turtle as a parameter. Here is a solution:
\",\n \"polygon\": [\n [\n 85.166015625,\n 251.947265625\n ],\n [\n 482.90625,\n 251.947265625\n ],\n [\n 482.90625,\n 274.376953125\n ],\n [\n 85.166015625,\n 274.376953125\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 251.947265625,\n 482.90625,\n 274.376953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \"def square(t):
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\",\n \"polygon\": [\n [\n 96.0732421875,\n 292.378662109375\n ],\n [\n 201.4625244140625,\n 292.378662109375\n ],\n [\n 201.4625244140625,\n 327.55078125\n ],\n [\n 96.0732421875,\n 327.55078125\n ]\n ],\n \"bbox\": [\n 96.0732421875,\n 292.378662109375,\n 201.4625244140625,\n 327.55078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"square(bob)
\",\n \"polygon\": [\n [\n 85.98779296875,\n 341.1556701660156\n ],\n [\n 143.94395446777344,\n 341.1556701660156\n ],\n [\n 143.94395446777344,\n 351.52734375\n ],\n [\n 85.98779296875,\n 351.52734375\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 341.1556701660156,\n 143.94395446777344,\n 351.52734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The innermost statements, fd and lt are indented twice to show that they are inside the for loop, which is inside the function definition. The next line, square(bob), is flush with the left margin, so that is the end of both the for loop and the function definition.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 356.94140625\n ],\n [\n 482.40081787109375,\n 356.94140625\n ],\n [\n 482.40081787109375,\n 391.78582763671875\n ],\n [\n 85.6142578125,\n 391.78582763671875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 356.94140625,\n 482.40081787109375,\n 391.78582763671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Inside the function, t refers to the same turtle bob refers to, so lt(t) has the same effect as lt(bob). So why not call the parameter bob? The idea is that t can be any turtle, not just bob, so you could create a second turtle and pass it as an argument to square:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 401.773681640625\n ],\n [\n 482.4047546386719,\n 401.773681640625\n ],\n [\n 482.4047546386719,\n 436.27484130859375\n ],\n [\n 85.3154296875,\n 436.27484130859375\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 401.773681640625,\n 482.4047546386719,\n 436.27484130859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \"ray = Turtle()\\nsquare(ray)
\",\n \"polygon\": [\n [\n 85.83837890625,\n 442.01953125\n ],\n [\n 159.6250762939453,\n 442.01953125\n ],\n [\n 159.6250762939453,\n 464.44921875\n ],\n [\n 85.83837890625,\n 464.44921875\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 442.01953125,\n 159.6250762939453,\n 464.44921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Wrapping a piece of code up in a function is called encapsulation. One of the benefits of encapsulation is that it attaches a name to the code, which serves as a kind of documentation. Another advantage is that if you re-use the code, it is more concise to call a function twice than to copy and paste the body!
\",\n \"polygon\": [\n [\n 85.6142578125,\n 470.63671875\n ],\n [\n 482.90625,\n 470.63671875\n ],\n [\n 482.90625,\n 517.3098449707031\n ],\n [\n 85.6142578125,\n 517.3098449707031\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 470.63671875,\n 482.90625,\n 517.3098449707031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/SectionHeader/13\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"4.5 Generalization
\",\n \"polygon\": [\n [\n 85.763671875,\n 546.046875\n ],\n [\n 216.69219970703125,\n 546.046875\n ],\n [\n 216.69219970703125,\n 560.9458923339844\n ],\n [\n 85.763671875,\n 560.9458923339844\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 546.046875,\n 216.69219970703125,\n 560.9458923339844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/55/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The next step is to add a length parameter to square. Here is a solution:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 572.73046875\n ],\n [\n 405.1571960449219,\n 572.73046875\n ],\n [\n 405.1571960449219,\n 583.2168426513672\n ],\n [\n 85.6142578125,\n 583.2168426513672\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 572.73046875,\n 405.1571960449219,\n 583.2168426513672\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/55/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \"def square(t, length):\\n for i in range(4):\\n fd(t, length)\\n lt(t)
\",\n \"polygon\": [\n [\n 85.46484375,\n 588.97265625\n ],\n [\n 202.7548828125,\n 588.97265625\n ],\n [\n 202.7548828125,\n 637.69921875\n ],\n [\n 85.46484375,\n 637.69921875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 588.97265625,\n 202.7548828125,\n 637.69921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/55/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"square(bob, 100)
\",\n \"polygon\": [\n [\n 85.763671875,\n 650.2056884765625\n ],\n [\n 170.0957794189453,\n 650.2056884765625\n ],\n [\n 170.0957794189453,\n 661.2890625\n ],\n [\n 85.763671875,\n 661.2890625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 650.2056884765625,\n 170.0957794189453,\n 661.2890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/55/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/55/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Adding a parameter to a function is called generalization because it makes the function more general: in the previous version, the square is always the same size; in this version it can be any size.
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\",\n \"polygon\": [\n [\n 127.1513671875,\n 88.171875\n ],\n [\n 525.5977783203125,\n 88.171875\n ],\n [\n 525.5977783203125,\n 110.99188232421875\n ],\n [\n 127.1513671875,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 127.1513671875,\n 88.171875,\n 525.5977783203125,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/55/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/56/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"def polygon(t, n, length):\\n angle = 360.0 / n\\n for i in range(n):\\n fd(t, length)\\n lt(t, angle)
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 120.1947021484375\n ],\n [\n 265.5994567871094,\n 120.1947021484375\n ],\n [\n 265.5994567871094,\n 185.044921875\n ],\n [\n 129.60000610351562,\n 185.044921875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 120.1947021484375,\n 265.5994567871094,\n 185.044921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/55/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/56/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"polygon(bob, 7, 70)
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\",\n \"polygon\": [\n [\n 128.197265625,\n 252.40679931640625\n ],\n [\n 276.0601806640625,\n 252.40679931640625\n ],\n [\n 276.0601806640625,\n 262.369384765625\n ],\n [\n 128.197265625,\n 262.369384765625\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 252.40679931640625,\n 276.0601806640625,\n 262.369384765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/55/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/56/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"These are called keyword arguments because they include the parameter names as \\\"keywords\\\" (not to be confused with Python keywords like while and def).
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\",\n \"polygon\": [\n [\n 128.49609375,\n 361.9498291015625\n ],\n [\n 267.4669494628906,\n 361.9498291015625\n ],\n [\n 267.4669494628906,\n 376.2960510253906\n ],\n [\n 128.49609375,\n 376.2960510253906\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 361.9498291015625,\n 267.4669494628906,\n 376.2960510253906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/56/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/56/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The next step is to write circle, which takes a radius, r, as a parameter. Here is a simple solution that uses polygon to draw a 50-sided polygon:
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\",\n \"polygon\": [\n [\n 129.60000610351562,\n 415.2068176269531\n ],\n [\n 313.76953125,\n 415.2068176269531\n ],\n [\n 313.76953125,\n 473.9464111328125\n ],\n [\n 129.60000610351562,\n 473.9464111328125\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 415.2068176269531,\n 313.76953125,\n 473.9464111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/56/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/56/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The first line computes the circumference of a circle with radius r using the formula 2πr. Since we use math.pi, we have to import math. By convention, import statements are usually at the beginning of the script.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 479.14453125\n ],\n [\n 525.9375,\n 479.14453125\n ],\n [\n 525.9375,\n 513.8279724121094\n ],\n [\n 128.9443359375,\n 513.8279724121094\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 479.14453125,\n 525.9375,\n 513.8279724121094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/56/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/56/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"n is the number of line segments in our approximation of a circle, so length is the length of each segment. Thus, polygon draws a 50-sides polygon that approximates a circle with radius r.
\",\n \"polygon\": [\n [\n 129.392578125,\n 523.0308227539062\n ],\n [\n 525.638671875,\n 523.0308227539062\n ],\n [\n 525.638671875,\n 557.5309753417969\n ],\n [\n 129.392578125,\n 557.5309753417969\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 523.0308227539062,\n 525.638671875,\n 557.5309753417969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/56/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/56/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"One limitation of this solution is that n is a constant, which means that for very big circles, the line segments are too long, and for small circles, we waste time drawing very small segments. One solution would be to generalize the function by taking n as a parameter. This would give the user (whoever calls circle) more control, but the interface would be less clean.
\",\n \"polygon\": [\n [\n 128.794921875,\n 566.54296875\n ],\n [\n 526.236328125,\n 566.54296875\n ],\n [\n 526.236328125,\n 625.6229858398438\n ],\n [\n 128.794921875,\n 625.6229858398438\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 566.54296875,\n 526.236328125,\n 625.6229858398438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/56/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/56/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The interface of a function is a summary of how it is used: what are the parameters? What does the function do? And what is the return value? An interface is \\\"clean\\\" if it is \\\"as simple as possible, but not simpler. (Einstein)\\\"
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\",\n \"polygon\": [\n [\n 85.0166015625,\n 88.365234375\n ],\n [\n 482.3997802734375,\n 88.365234375\n ],\n [\n 482.3997802734375,\n 110.99188232421875\n ],\n [\n 85.0166015625,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 88.365234375,\n 482.3997802734375,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/56/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/57/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"def circle(t, r):\\n circumference = 2 * math.pi * r\\n n = int(circumference / 3) + 1\\n length = circumference / n\\n polygon(t, n, length)
\",\n \"polygon\": [\n [\n 86.40003204345703,\n 117.07269287109375\n ],\n [\n 269.45733642578125,\n 117.07269287109375\n ],\n [\n 269.45733642578125,\n 175.812255859375\n ],\n [\n 86.40003204345703,\n 175.812255859375\n ]\n ],\n \"bbox\": [\n 86.40003204345703,\n 117.07269287109375,\n 269.45733642578125,\n 175.812255859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/56/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/57/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Now the number of segments is (approximately) circumference/3, so the length of each segment is (approximately) 3, which is small enough that the circles look good, but big enough to be efficient, and appropriate for any size circle.
\",\n \"polygon\": [\n [\n 85.46484375,\n 181.0810546875\n ],\n [\n 482.4034729003906,\n 181.0810546875\n ],\n [\n 482.4034729003906,\n 216.54278564453125\n ],\n [\n 85.46484375,\n 216.54278564453125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 181.0810546875,\n 482.4034729003906,\n 216.54278564453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/55/SectionHeader/4\",\n \"4\": \"/page/56/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/57/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"4.7 Refactoring
\",\n \"polygon\": [\n [\n 85.6142578125,\n 244.986328125\n ],\n [\n 195.18724060058594,\n 244.986328125\n ],\n [\n 195.18724060058594,\n 260.310791015625\n ],\n [\n 85.6142578125,\n 260.310791015625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 244.986328125,\n 195.18724060058594,\n 260.310791015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/57/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/57/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"When I wrote circle, I was able to re-use polygon because a many-sided polygon is a good approximation of a circle. But arc is not as cooperative; we can't use polygon or circle to draw an arc.
\",\n \"polygon\": [\n [\n 86.0625,\n 271.86328125\n ],\n [\n 482.90625,\n 271.86328125\n ],\n [\n 482.90625,\n 307.04473876953125\n ],\n [\n 86.0625,\n 307.04473876953125\n ]\n ],\n \"bbox\": [\n 86.0625,\n 271.86328125,\n 482.90625,\n 307.04473876953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/57/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/57/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"One alternative is to start with a copy of polygon and transform it into arc. The result might look like this:
\",\n \"polygon\": [\n [\n 86.0625,\n 316.916015625\n ],\n [\n 482.405029296875,\n 316.916015625\n ],\n [\n 482.405029296875,\n 339.4027404785156\n ],\n [\n 86.0625,\n 339.4027404785156\n ]\n ],\n \"bbox\": [\n 86.0625,\n 316.916015625,\n 482.405029296875,\n 339.4027404785156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/57/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/57/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"def arc(t, r, angle):\\n arc_length = 2 * math.pi * r * angle / 360\\n n = int(arc_length / 3) + 1\\n step_length = arc_length / n\\n step_angle = float(angle) / n\\n for i in range(n):\\n fd(t, step_length)\\n lt(t, step_angle)
\",\n \"polygon\": [\n [\n 85.3154296875,\n 345.48358154296875\n ],\n [\n 326.9913635253906,\n 345.48358154296875\n ],\n [\n 326.9913635253906,\n 454.0078125\n ],\n [\n 85.3154296875,\n 454.0078125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 345.48358154296875,\n 326.9913635253906,\n 454.0078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/57/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/57/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The second half of this function looks like polygon, but we can't re-use polygon without changing the interface. We could generalize polygon to take an angle as a third argument, but then polygon would no longer be an appropriate name! Instead, let's call the more general function polyline:
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\",\n \"polygon\": [\n [\n 85.68896484375,\n 512.005615234375\n ],\n [\n 264.2424011230469,\n 512.005615234375\n ],\n [\n 264.2424011230469,\n 558.5512237548828\n ],\n [\n 85.68896484375,\n 558.5512237548828\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 512.005615234375,\n 264.2424011230469,\n 558.5512237548828\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/57/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/57/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Now we can rewrite polygon and arc to use polyline:
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\",\n \"polygon\": [\n [\n 128.86962890625,\n 263.548828125\n ],\n [\n 296.138671875,\n 263.548828125\n ],\n [\n 296.138671875,\n 278.6109619140625\n ],\n [\n 128.86962890625,\n 278.6109619140625\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 263.548828125,\n 296.138671875,\n 278.6109619140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/58/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/58/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"A development plan is a process for writing programs. The process we used in this case study is \\\"encapsulation and generalization.\\\" The steps of this process are:
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\",\n \"polygon\": [\n [\n 127.97314453125,\n 445.11328125\n ],\n [\n 228.80401611328125,\n 445.11328125\n ],\n [\n 228.80401611328125,\n 459.5309753417969\n ],\n [\n 127.97314453125,\n 459.5309753417969\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 445.11328125,\n 228.80401611328125,\n 459.5309753417969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/58/SectionHeader/5\",\n \"4\": \"/page/60/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/60/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 4.1. Download the code in this chapter from http: // thinkpython. com/ code/ polygon. py .
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 621.6186065673828\n ],\n [\n 525.5982055664062,\n 621.6186065673828\n ],\n [\n 525.5982055664062,\n 631.8984375\n ],\n [\n 128.0478515625,\n 631.8984375\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 621.6186065673828,\n 525.5982055664062,\n 631.8984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/58/SectionHeader/5\",\n \"4\": \"/page/60/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/60/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ flower. py , also requires http: // thinkpython. com/ code/ polygon. py .
\",\n \"polygon\": [\n [\n 129.09375,\n 642.7265625\n ],\n [\n 524.14453125,\n 642.7265625\n ],\n [\n 524.14453125,\n 666.703125\n ],\n [\n 129.09375,\n 666.703125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 642.7265625,\n 524.14453125,\n 666.703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/58/SectionHeader/5\",\n \"4\": \"/page/60/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/60/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 4.3. Write an appropriately general set of functions that can draw shapes as in Figure 4.2.
\",\n \"polygon\": [\n [\n 129.5998992919922,\n 667.2466201782227\n ],\n [\n 525.6047973632812,\n 667.2466201782227\n ],\n [\n 525.6047973632812,\n 677.2848052978516\n ],\n [\n 129.5998992919922,\n 677.2848052978516\n ]\n ],\n \"bbox\": [\n 129.5998992919922,\n 667.2466201782227,\n 525.6047973632812,\n 677.2848052978516\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/58/SectionHeader/5\",\n \"4\": \"/page/60/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/60/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ pie. py .
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\",\n \"polygon\": [\n [\n 84.7177734375,\n 88.51025390625\n ],\n [\n 482.4034423828125,\n 88.51025390625\n ],\n [\n 482.4034423828125,\n 123.01416015625\n ],\n [\n 84.7177734375,\n 123.01416015625\n ]\n ],\n \"bbox\": [\n 84.7177734375,\n 88.51025390625,\n 482.4034423828125,\n 123.01416015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/58/SectionHeader/5\",\n \"4\": \"/page/60/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/61/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"You should write one function for each letter, with names draw_a, draw_b, etc., and put your functions in a file named letters.py. You can download a \\\"turtle typewriter\\\" from http: // thinkpython. com/ code/ typewriter. py to help you test your code.
\",\n \"polygon\": [\n [\n 84.8671875,\n 132.64453125\n ],\n [\n 482.4041442871094,\n 132.64453125\n ],\n [\n 482.4041442871094,\n 167.5401611328125\n ],\n [\n 84.8671875,\n 167.5401611328125\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 132.64453125,\n 482.4041442871094,\n 167.5401611328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/58/SectionHeader/5\",\n \"4\": \"/page/60/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/61/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ letters. py , also requires http: // thinkpython. com/ code/ polygon. py .
\",\n \"polygon\": [\n [\n 84.8671875,\n 177.50390625\n ],\n [\n 481.11328125,\n 177.50390625\n ],\n [\n 481.11328125,\n 199.87115478515625\n ],\n [\n 84.8671875,\n 199.87115478515625\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 177.50390625,\n 481.11328125,\n 199.87115478515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/52/SectionHeader/1\",\n \"3\": \"/page/58/SectionHeader/5\",\n \"4\": \"/page/60/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/61/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 4.5. Read about spirals at http: // en. wikipedia. org/ wiki/ Spiral ; then write a program that draws an Archimedian spiral (or one of the other kinds). Solution: http: // thinkpython. com/ code/ spiral. py .
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 215.7890625\n ],\n [\n 438.6796875,\n 215.7890625\n ],\n [\n 438.6796875,\n 240.775390625\n ],\n [\n 128.3466796875,\n 240.775390625\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 215.7890625,\n 438.6796875,\n 240.775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/62/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.1 Modulus operator
\",\n \"polygon\": [\n [\n 127.8984375,\n 287.9187927246094\n ],\n [\n 280.2350769042969,\n 287.9187927246094\n ],\n [\n 280.2350769042969,\n 302.2650146484375\n ],\n [\n 127.8984375,\n 302.2650146484375\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 287.9187927246094,\n 280.2350769042969,\n 302.2650146484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/62/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/62/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The modulus operator works on integers and yields the remainder when the first operand is divided by the second. In Python, the modulus operator is a percent sign (%). The syntax is the same as for other operators:
\",\n \"polygon\": [\n [\n 128.197265625,\n 312.08203125\n ],\n [\n 526.833984375,\n 312.08203125\n ],\n [\n 526.833984375,\n 347.2169494628906\n ],\n [\n 128.197265625,\n 347.2169494628906\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 312.08203125,\n 526.833984375,\n 347.2169494628906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/62/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/62/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \">>> quotient = 7 / 3\\n>>> print quotient\\n2\\n>>> remainder = 7 % 3\\n>>> print remainder\\n1\\nSo 7 divided by 3 is 2 with 1 left over.
\",\n \"polygon\": [\n [\n 128.86962890625,\n 350.560546875\n ],\n [\n 293.7537536621094,\n 350.560546875\n ],\n [\n 293.7537536621094,\n 437.0319519042969\n ],\n [\n 128.86962890625,\n 437.0319519042969\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 350.560546875,\n 293.7537536621094,\n 437.0319519042969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/62/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/62/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The modulus operator turns out to be surprisingly useful. For example, you can check whether one number is divisible by another—if x % y is zero, then x is divisible by y.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 441.6328125\n ],\n [\n 525.6033935546875,\n 441.6328125\n ],\n [\n 525.6033935546875,\n 467.61895751953125\n ],\n [\n 128.0478515625,\n 467.61895751953125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 441.6328125,\n 525.6033935546875,\n 467.61895751953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/62/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/62/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Also, you can extract the right-most digit or digits from a number. For example, x % 10 yields the right-most digit of x (in base 10). Similarly x % 100 yields the last two digits.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 474.50390625\n ],\n [\n 525.9375,\n 474.50390625\n ],\n [\n 525.9375,\n 498.20697021484375\n ],\n [\n 128.6455078125,\n 498.20697021484375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 474.50390625,\n 525.9375,\n 498.20697021484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/62/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/62/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.2 Boolean expressions
\",\n \"polygon\": [\n [\n 128.6455078125,\n 524.77734375\n ],\n [\n 295.3703918457031,\n 524.77734375\n ],\n [\n 295.3703918457031,\n 539.4530181884766\n ],\n [\n 128.6455078125,\n 539.4530181884766\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 524.77734375,\n 295.3703918457031,\n 539.4530181884766\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/62/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/62/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"A boolean expression is an expression that is either true or false. The following examples use the operator ==, which compares two operands and produces True if they are equal and False otherwise:
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different from the mathematical symbols. A common error is to use a single equal sign (=) instead of a double equal sign (==). Remember that = is an assignment operator and == is a relational operator. There is no such thing as =< or =>.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 239.958984375\n ],\n [\n 231.890625,\n 239.958984375\n ],\n [\n 231.890625,\n 254.8179931640625\n ],\n [\n 85.763671875,\n 254.8179931640625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 239.958984375,\n 231.890625,\n 254.8179931640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/63/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/63/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"There are three logical operators: and, or, and not. The semantics (meaning) of these operators is similar to their meaning in English. For example, x > 0 and x < 10 is true only if x is greater than 0 and less than 10.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 265.095703125\n ],\n [\n 483.50390625,\n 265.095703125\n ],\n [\n 483.50390625,\n 299.93792724609375\n ],\n [\n 85.6142578125,\n 299.93792724609375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 265.095703125,\n 483.50390625,\n 299.93792724609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/63/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/63/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"n%2 == 0 or n%3 == 0 is true if either of the conditions is true, that is, if the number is divisible by 2 or 3.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 338.958984375\n ],\n [\n 482.607421875,\n 338.958984375\n ],\n [\n 482.607421875,\n 361.44793701171875\n ],\n [\n 85.763671875,\n 361.44793701171875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 338.958984375,\n 482.607421875,\n 361.44793701171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/63/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/63/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Strictly speaking, the operands of the logical operators should be boolean expressions, but Python is not very strict. Any nonzero number is interpreted as \\\"true.\\\"
\",\n \"polygon\": [\n [\n 85.46484375,\n 369.31640625\n ],\n [\n 482.90625,\n 369.31640625\n ],\n [\n 482.90625,\n 392.2039489746094\n ],\n [\n 85.46484375,\n 392.2039489746094\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 369.31640625,\n 482.90625,\n 392.2039489746094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/63/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/63/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> 17 and True
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\",\n \"polygon\": [\n [\n 86.13720703125,\n 408.8747863769531\n ],\n [\n 109.74462890625,\n 408.8747863769531\n ],\n [\n 109.74462890625,\n 419.58984375\n ],\n [\n 86.13720703125,\n 419.58984375\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 408.8747863769531,\n 109.74462890625,\n 419.58984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/63/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/63/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"This flexibility can be useful, but there are some subtleties to it that might be confusing. You might want to avoid it (unless you know what you are doing).
\",\n \"polygon\": [\n [\n 85.763671875,\n 422.68359375\n ],\n [\n 482.4033203125,\n 422.68359375\n ],\n [\n 482.4033203125,\n 445.77093505859375\n ],\n [\n 85.763671875,\n 445.77093505859375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 422.68359375,\n 482.4033203125,\n 445.77093505859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"4\": \"/page/63/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/63/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.4 Conditional execution
\",\n \"polygon\": [\n [\n 85.83837890625,\n 471.796875\n ],\n [\n 264.1640625,\n 471.796875\n ],\n [\n 264.1640625,\n 487.25201416015625\n ],\n [\n 85.83837890625,\n 487.25201416015625\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 471.796875,\n 264.1640625,\n 487.25201416015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/63/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/63/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"In order to write useful programs, we almost always need the ability to check conditions and change the behavior of the program accordingly. Conditional statements give us this ability. The simplest form is the if statement:
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\",\n \"polygon\": [\n [\n 86.40007019042969,\n 535.9921875\n ],\n [\n 222.328125,\n 535.9921875\n ],\n [\n 222.328125,\n 559.96875\n ],\n [\n 86.40007019042969,\n 559.96875\n ]\n ],\n \"bbox\": [\n 86.40007019042969,\n 535.9921875,\n 222.328125,\n 559.96875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/63/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/63/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The boolean expression after if is called the condition. If it is true, then the indented statement gets executed. If not, nothing happens.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 85.95379638671875\n ],\n [\n 302.712890625,\n 85.95379638671875\n ],\n [\n 302.712890625,\n 100.29998779296875\n ],\n [\n 128.794921875,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 85.95379638671875,\n 302.712890625,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/63/SectionHeader/12\",\n \"4\": \"/page/64/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"A second form of the if statement is alternative execution, in which there are two possibilities and the condition determines which one gets executed. The syntax looks like this:
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\",\n \"polygon\": [\n [\n 129.60000610351562,\n 141.3907470703125\n ],\n [\n 241.6025390625,\n 141.3907470703125\n ],\n [\n 241.6025390625,\n 187.93634033203125\n ],\n [\n 129.60000610351562,\n 187.93634033203125\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 141.3907470703125,\n 241.6025390625,\n 187.93634033203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/63/SectionHeader/12\",\n \"4\": \"/page/64/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"If the remainder when x is divided by 2 is 0, then we know that x is even, and the program displays a message to that effect. If the condition is false, the second set of statements is executed. Since the condition must be true or false, exactly one of the alternatives will be executed. The alternatives are called branches, because they are branches in the flow of execution.
\",\n \"polygon\": [\n [\n 128.794921875,\n 193.4560546875\n ],\n [\n 525.638671875,\n 193.4560546875\n ],\n [\n 525.638671875,\n 253.25091552734375\n ],\n [\n 128.794921875,\n 253.25091552734375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 193.4560546875,\n 525.638671875,\n 253.25091552734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/63/SectionHeader/12\",\n \"4\": \"/page/64/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/SectionHeader/5\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.6 Chained conditionals
\",\n \"polygon\": [\n [\n 128.6455078125,\n 282.69140625\n ],\n [\n 302.5634765625,\n 282.69140625\n ],\n [\n 302.5634765625,\n 297.6069641113281\n ],\n [\n 128.6455078125,\n 297.6069641113281\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 282.69140625,\n 302.5634765625,\n 297.6069641113281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Sometimes there are more than two possibilities and we need more than two branches. One way to express a computation like that is a chained conditional:
\",\n \"polygon\": [\n [\n 129.392578125,\n 309.181640625\n ],\n [\n 525.6033935546875,\n 309.181640625\n ],\n [\n 525.6033935546875,\n 332.4208984375\n ],\n [\n 129.392578125,\n 332.4208984375\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 309.181640625,\n 525.6033935546875,\n 332.4208984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"if x < y:\\n print 'x is less than y'\\nelif x > y:\\n print 'x is greater than y'\\nelse:\\n print 'x and y are equal'
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 338.6967468261719\n ],\n [\n 291.703369140625,\n 338.6967468261719\n ],\n [\n 291.703369140625,\n 409.63134765625\n ],\n [\n 129.5999755859375,\n 409.63134765625\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 338.6967468261719,\n 291.703369140625,\n 409.63134765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"elif is an abbreviation of \\\"else if.\\\" Again, exactly one branch will be executed. There is no limit on the number of elif statements. If there is an else clause, it has to be at the end, but there doesn't have to be one.
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\",\n \"polygon\": [\n [\n 129.60000610351562,\n 456.8337707519531\n ],\n [\n 234.28125,\n 456.8337707519531\n ],\n [\n 234.28125,\n 527.7683715820312\n ],\n [\n 129.60000610351562,\n 527.7683715820312\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 456.8337707519531,\n 234.28125,\n 527.7683715820312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Each condition is checked in order. If the first is false, the next is checked, and so on. If one of them is true, the corresponding branch executes, and the statement ends. Even if more than one condition is true, only the first true branch executes.
\",\n \"polygon\": [\n [\n 129.60003662109375,\n 533.671875\n ],\n [\n 525.603515625,\n 533.671875\n ],\n [\n 525.603515625,\n 568.6949462890625\n ],\n [\n 129.60003662109375,\n 568.6949462890625\n ]\n ],\n \"bbox\": [\n 129.60003662109375,\n 533.671875,\n 525.603515625,\n 568.6949462890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.7 Nested conditionals
\",\n \"polygon\": [\n [\n 128.3466796875,\n 597.8671875\n ],\n [\n 292.94586181640625,\n 597.8671875\n ],\n [\n 292.94586181640625,\n 613.0500030517578\n ],\n [\n 128.3466796875,\n 613.0500030517578\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 597.8671875,\n 292.94586181640625,\n 613.0500030517578\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"One conditional can also be nested within another. We could have written the trichotomy example like this:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 625.32421875\n ],\n [\n 525.6033935546875,\n 625.32421875\n ],\n [\n 525.6033935546875,\n 647.8639526367188\n ],\n [\n 128.3466796875,\n 647.8639526367188\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 625.32421875,\n 525.6033935546875,\n 647.8639526367188\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/64/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"if x == y:\\n print 'x and y are equal'\\nelse:\\n if x < y:
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\",\n \"polygon\": [\n [\n 107.31599426269531,\n 87.6884765625\n ],\n [\n 269.4203796386719,\n 87.6884765625\n ],\n [\n 269.4203796386719,\n 123.03729248046875\n ],\n [\n 107.31599426269531,\n 123.03729248046875\n ]\n ],\n \"bbox\": [\n 107.31599426269531,\n 87.6884765625,\n 269.4203796386719,\n 123.03729248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The outer conditional contains two branches. The first branch contains a simple statement. The second branch contains another if statement, which has two branches of its own. Those two branches are both simple statements, although they could have been conditional statements as well.
\",\n \"polygon\": [\n [\n 86.0625,\n 127.810546875\n ],\n [\n 482.4034118652344,\n 127.810546875\n ],\n [\n 482.4034118652344,\n 175.0548095703125\n ],\n [\n 86.0625,\n 175.0548095703125\n ]\n ],\n \"bbox\": [\n 86.0625,\n 127.810546875,\n 482.4034118652344,\n 175.0548095703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Although the indentation of the statements makes the structure apparent, nested conditionals become difficult to read very quickly. In general, it is a good idea to avoid them when you can.
\",\n \"polygon\": [\n [\n 85.763671875,\n 183.5947265625\n ],\n [\n 482.39678955078125,\n 183.5947265625\n ],\n [\n 482.39678955078125,\n 218.69976806640625\n ],\n [\n 85.763671875,\n 218.69976806640625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 183.5947265625,\n 482.39678955078125,\n 218.69976806640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Logical operators often provide a way to simplify nested conditional statements. For example, we can rewrite the following code using a single conditional:
\",\n \"polygon\": [\n [\n 85.46484375,\n 227.197265625\n ],\n [\n 482.4033508300781,\n 227.197265625\n ],\n [\n 482.4033508300781,\n 250.15081787109375\n ],\n [\n 85.46484375,\n 250.15081787109375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 227.197265625,\n 482.4033508300781,\n 250.15081787109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"if 0 < x:\\n if x < 10:\\n print 'x is a positive single-digit number.'
\",\n \"polygon\": [\n [\n 86.4000244140625,\n 254.84765625\n ],\n [\n 358.3153991699219,\n 254.84765625\n ],\n [\n 358.3153991699219,\n 290.0390625\n ],\n [\n 86.4000244140625,\n 290.0390625\n ]\n ],\n \"bbox\": [\n 86.4000244140625,\n 254.84765625,\n 358.3153991699219,\n 290.0390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The print statement is executed only if we make it past both conditionals, so we can get the same effect with the and operator:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 294.099609375\n ],\n [\n 482.39959716796875,\n 294.099609375\n ],\n [\n 482.39959716796875,\n 317.3047790527344\n ],\n [\n 85.9130859375,\n 317.3047790527344\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 294.099609375,\n 482.39959716796875,\n 317.3047790527344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"if 0 < x and x < 10:\\n print 'x is a positive single-digit number.'
\",\n \"polygon\": [\n [\n 85.98779296875,\n 322.4776306152344\n ],\n [\n 337.974609375,\n 322.4776306152344\n ],\n [\n 337.974609375,\n 344.6352233886719\n ],\n [\n 85.98779296875,\n 344.6352233886719\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 322.4776306152344,\n 337.974609375,\n 344.6352233886719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.8 Recursion
\",\n \"polygon\": [\n [\n 86.0625,\n 372.603515625\n ],\n [\n 184.81497192382812,\n 372.603515625\n ],\n [\n 184.81497192382812,\n 387.2398681640625\n ],\n [\n 86.0625,\n 387.2398681640625\n ]\n ],\n \"bbox\": [\n 86.0625,\n 372.603515625,\n 184.81497192382812,\n 387.2398681640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"It is legal for one function to call another; it is also legal for a function to call itself. It may not be obvious why that is a good thing, but it turns out to be one of the most magical things a program can do. For example, look at the following function:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 397.93359375\n ],\n [\n 482.4033203125,\n 397.93359375\n ],\n [\n 482.4033203125,\n 433.51171875\n ],\n [\n 85.9130859375,\n 433.51171875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 397.93359375,\n 482.4033203125,\n 433.51171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"def countdown(n):\\n if n <= 0:\\n print 'Blastoff!'\\n else:\\n print n\\n countdown(n-1)
\",\n \"polygon\": [\n [\n 86.40003967285156,\n 438.2286682128906\n ],\n [\n 218.8916015625,\n 438.2286682128906\n ],\n [\n 218.8916015625,\n 509.16326904296875\n ],\n [\n 86.40003967285156,\n 509.16326904296875\n ]\n ],\n \"bbox\": [\n 86.40003967285156,\n 438.2286682128906,\n 218.8916015625,\n 509.16326904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"If n is 0 or negative, it outputs the word, \\\"Blastoff!\\\" Otherwise, it outputs n and then calls a function named countdown—itself—passing n-1 as an argument.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 514.3359375\n ],\n [\n 482.4001159667969,\n 514.3359375\n ],\n [\n 482.4001159667969,\n 536.7918395996094\n ],\n [\n 85.9130859375,\n 536.7918395996094\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 514.3359375,\n 482.4001159667969,\n 536.7918395996094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"What happens if we call this function like this?
\",\n \"polygon\": [\n [\n 85.83837890625,\n 545.66015625\n ],\n [\n 292.16754150390625,\n 545.66015625\n ],\n [\n 292.16754150390625,\n 556.0488433837891\n ],\n [\n 85.83837890625,\n 556.0488433837891\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 545.66015625,\n 292.16754150390625,\n 556.0488433837891\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \">>> countdown(3)
\",\n \"polygon\": [\n [\n 85.46484375,\n 560.35546875\n ],\n [\n 170.0958709716797,\n 560.35546875\n ],\n [\n 170.0958709716797,\n 571.1842956542969\n ],\n [\n 85.46484375,\n 571.1842956542969\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 560.35546875,\n 170.0958709716797,\n 571.1842956542969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The execution of countdown begins with n=3, and since n is greater than 0, it outputs the value 3, and then calls itself...
\",\n \"polygon\": [\n [\n 85.6142578125,\n 575.82421875\n ],\n [\n 482.39666748046875,\n 575.82421875\n ],\n [\n 482.39666748046875,\n 598.8138427734375\n ],\n [\n 85.6142578125,\n 598.8138427734375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 575.82421875,\n 482.39666748046875,\n 598.8138427734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The execution of countdown begins with n=2, and since n is greater than 0, it outputs the value 2, and then calls itself...
\",\n \"polygon\": [\n [\n 110.865234375,\n 610.2421875\n ],\n [\n 458.701171875,\n 610.2421875\n ],\n [\n 458.701171875,\n 632.7618560791016\n ],\n [\n 110.865234375,\n 632.7618560791016\n ]\n ],\n \"bbox\": [\n 110.865234375,\n 610.2421875,\n 458.701171875,\n 632.7618560791016\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"The execution of countdown begins with n=1, and since n is greater than 0, it outputs the value 1, and then calls itself...
\",\n \"polygon\": [\n [\n 132.6796875,\n 637.69921875\n ],\n [\n 436.2890625,\n 637.69921875\n ],\n [\n 436.2890625,\n 660.3978576660156\n ],\n [\n 132.6796875,\n 660.3978576660156\n ]\n ],\n \"bbox\": [\n 132.6796875,\n 637.69921875,\n 436.2890625,\n 660.3978576660156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/65/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"The execution of countdown begins with n=0, and since n is not greater than 0, it outputs the word, \\\"Blastoff!\\\" and then returns.
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\",\n \"polygon\": [\n [\n 127.67431640625,\n 168.414794921875\n ],\n [\n 176.67333984375,\n 168.414794921875\n ],\n [\n 176.67333984375,\n 215.40234375\n ],\n [\n 127.67431640625,\n 215.40234375\n ]\n ],\n \"bbox\": [\n 127.67431640625,\n 168.414794921875,\n 176.67333984375,\n 215.40234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/65/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/66/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"A function that calls itself is recursive; the process is called recursion.
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\",\n \"polygon\": [\n [\n 128.12255859375,\n 468.1249084472656\n ],\n [\n 413.6979064941406,\n 468.1249084472656\n ],\n [\n 413.6979064941406,\n 482.47113037109375\n ],\n [\n 128.12255859375,\n 482.47113037109375\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 468.1249084472656,\n 413.6979064941406,\n 482.47113037109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/66/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/66/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"In Section 3.10, we used a stack diagram to represent the state of a program during a function call. The same kind of diagram can help interpret a recursive function.
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\",\n \"polygon\": [\n [\n 227.07000732421875,\n 220.790283203125\n ],\n [\n 341.7295837402344,\n 220.790283203125\n ],\n [\n 341.7295837402344,\n 230.7529296875\n ],\n [\n 227.07000732421875,\n 230.7529296875\n ]\n ],\n \"bbox\": [\n 227.07000732421875,\n 220.790283203125,\n 341.7295837402344,\n 230.7529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/66/SectionHeader/12\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/66/SectionHeader/12\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/67/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.10 Infinite recursion
\",\n \"polygon\": [\n [\n 85.46484375,\n 251.61279296875\n ],\n [\n 239.4022216796875,\n 251.61279296875\n ],\n [\n 239.4022216796875,\n 265.958984375\n ],\n [\n 85.46484375,\n 265.958984375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 251.61279296875,\n 239.4022216796875,\n 265.958984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/67/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"If a recursion never reaches a base case, it goes on making recursive calls forever, and the program never terminates. This is known as infinite recursion, and it is generally not a good idea. Here is a minimal program with an infinite recursion:
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\",\n \"polygon\": [\n [\n 86.4000244140625,\n 380.6177673339844\n ],\n [\n 327.01666259765625,\n 380.6177673339844\n ],\n [\n 327.01666259765625,\n 475.94036865234375\n ],\n [\n 86.4000244140625,\n 475.94036865234375\n ]\n ],\n \"bbox\": [\n 86.4000244140625,\n 380.6177673339844,\n 327.01666259765625,\n 475.94036865234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/67/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"This traceback is a little bigger than the one we saw in the previous chapter. When the error occurs, there are 1000 recurse frames on the stack!
\",\n \"polygon\": [\n [\n 85.46484375,\n 483.01171875\n ],\n [\n 482.40350341796875,\n 483.01171875\n ],\n [\n 482.40350341796875,\n 505.6289367675781\n ],\n [\n 85.46484375,\n 505.6289367675781\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 483.01171875,\n 482.40350341796875,\n 505.6289367675781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/67/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.11 Keyboard input
\",\n \"polygon\": [\n [\n 85.53955078125,\n 538.5088043212891\n ],\n [\n 229.8620147705078,\n 538.5088043212891\n ],\n [\n 229.8620147705078,\n 552.8549957275391\n ],\n [\n 85.53955078125,\n 552.8549957275391\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 538.5088043212891,\n 229.8620147705078,\n 552.8549957275391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/67/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The programs we have written so far are a bit rude in the sense that they accept no input from the user. They just do the same thing every time.
\",\n \"polygon\": [\n [\n 85.46484375,\n 566.15625\n ],\n [\n 482.4033508300781,\n 566.15625\n ],\n [\n 482.4033508300781,\n 589.0079498291016\n ],\n [\n 85.46484375,\n 589.0079498291016\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 566.15625,\n 482.4033508300781,\n 589.0079498291016\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/67/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Python 2 provides a built-in function called raw_input that gets input from the keyboard. In Python 3, it is called input. When this function is called, the program stops and waits for the user to type something. When the user presses Return or Enter, the program resumes and raw_input returns what the user typed as a string.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 600.1875\n ],\n [\n 482.4004821777344,\n 600.1875\n ],\n [\n 482.4004821777344,\n 646.9069519042969\n ],\n [\n 85.6142578125,\n 646.9069519042969\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 600.1875,\n 482.4004821777344,\n 646.9069519042969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/67/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> text = raw_input()\\nWhat are you waiting for?\\n>>> print text\\nWhat are you waiting for?
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 88.55859375\n ],\n [\n 525.9375,\n 88.55859375\n ],\n [\n 525.9375,\n 110.99188232421875\n ],\n [\n 128.3466796875,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 88.55859375,\n 525.9375,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \">>> name = raw_input('What...is your name?\\\\n')\\nWhat...is your name?\\nArthur, King of the Britons!\\n>>> print name\\nArthur, King of the Britons!\",\n \"polygon\": [\n [\n 129.5419921875,\n 117.10369873046875\n ],\n [\n 370.1404113769531,\n 117.10369873046875\n ],\n [\n 370.1404113769531,\n 175.84326171875\n ],\n [\n 129.5419921875,\n 175.84326171875\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 117.10369873046875,\n 370.1404113769531,\n 175.84326171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The sequence \\\\n at the end of the prompt represents a newline, which is a special character that causes a line break. That's why the user's input appears below the prompt.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 181.8544921875\n ],\n [\n 525.5984497070312,\n 181.8544921875\n ],\n [\n 525.5984497070312,\n 204.41082763671875\n ],\n [\n 129.2431640625,\n 204.41082763671875\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 181.8544921875,\n 525.5984497070312,\n 204.41082763671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"If you expect the user to type an integer, you can try to convert the return value to int:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 214.435546875\n ],\n [\n 510.2536926269531,\n 214.435546875\n ],\n [\n 510.2536926269531,\n 224.6068115234375\n ],\n [\n 128.6455078125,\n 224.6068115234375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 214.435546875,\n 510.2536926269531,\n 224.6068115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> prompt = 'What...is the airspeed velocity of an unladen swallow?\\\\n'\\n>>> speed = raw_input(prompt)\\nWhat...is the airspeed velocity of an unladen swallow?\\n17\\n>>> int(speed)\\n17
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 230.7176513671875\n ],\n [\n 500.8683776855469,\n 230.7176513671875\n ],\n [\n 500.8683776855469,\n 304.34765625\n ],\n [\n 129.60000610351562,\n 304.34765625\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 230.7176513671875,\n 500.8683776855469,\n 304.34765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"But if the user types something other than a string of digits, you get an error:
\",\n \"polygon\": [\n [\n 128.49609375,\n 308.06317138671875\n ],\n [\n 467.0034484863281,\n 308.06317138671875\n ],\n [\n 467.0034484863281,\n 318.0257568359375\n ],\n [\n 128.49609375,\n 318.0257568359375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 308.06317138671875,\n 467.0034484863281,\n 318.0257568359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> speed = raw_input(prompt)\\nWhat...is the airspeed velocity of an unladen swallow?\\nWhat do you mean, an African or a European swallow?\\n>>> int(speed)\\nValueError: invalid literal for int() with base 10
\",\n \"polygon\": [\n [\n 129.46728515625,\n 324.1365966796875\n ],\n [\n 412.083984375,\n 324.1365966796875\n ],\n [\n 412.083984375,\n 386.71875\n ],\n [\n 129.46728515625,\n 386.71875\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 324.1365966796875,\n 412.083984375,\n 386.71875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/67/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"We will see how to handle this kind of error later.
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\",\n \"polygon\": [\n [\n 127.82373046875,\n 428.7655944824219\n ],\n [\n 243.17886352539062,\n 428.7655944824219\n ],\n [\n 243.17886352539062,\n 443.1796875\n ],\n [\n 127.82373046875,\n 443.1796875\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 428.7655944824219,\n 243.17886352539062,\n 443.1796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The traceback Python displays when an error occurs contains a lot of information, but it can be overwhelming, especially when there are many frames on the stack. The most useful parts are usually:
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\",\n \"polygon\": [\n [\n 127.8984375,\n 548.3671875\n ],\n [\n 525.6034545898438,\n 548.3671875\n ],\n [\n 525.6034545898438,\n 570.8327789306641\n ],\n [\n 127.8984375,\n 570.8327789306641\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 548.3671875,\n 525.6034545898438,\n 570.8327789306641\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> x = 5\\n>>> y = 6\\n File \\\"<stdin>\\\", line 1\\n y = 6\\n ^
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\",\n \"polygon\": [\n [\n 128.57080078125,\n 637.9166412353516\n ],\n [\n 312.6726989746094,\n 637.9166412353516\n ],\n [\n 312.6726989746094,\n 647.8792419433594\n ],\n [\n 128.57080078125,\n 647.8792419433594\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 637.9166412353516,\n 312.6726989746094,\n 647.8792419433594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/68/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"In this example, the problem is that the second line is indented by one space. But the error message points to y, which is misleading. In general, error messages indicate where the problem was discovered, but the actual error might be earlier in the code, sometimes on a previous line.
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\",\n \"polygon\": [\n [\n 86.2119140625,\n 88.365234375\n ],\n [\n 238.6142578125,\n 88.365234375\n ],\n [\n 238.6142578125,\n 98.79791259765625\n ],\n [\n 86.2119140625,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 88.365234375,\n 238.6142578125,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/TextInlineMath/2\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"Suppose you are trying to compute a signal-to-noise ratio in decibels. The formula is SNRdb = 10 log10(Psignal/Pnoise). In Python, you might write something like this:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 108.861328125\n ],\n [\n 484.1015625,\n 108.861328125\n ],\n [\n 484.1015625,\n 134.007568359375\n ],\n [\n 85.3154296875,\n 134.007568359375\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 108.861328125,\n 484.1015625,\n 134.007568359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"import math\\nsignal_power = 9\\nnoise_power = 10\\nratio = signal_power / noise_power\\ndecibels = 10 * math.log10(ratio)\\nprint decibels
\",\n \"polygon\": [\n [\n 85.763671875,\n 137.5751953125\n ],\n [\n 265.359375,\n 137.5751953125\n ],\n [\n 265.359375,\n 209.98828125\n ],\n [\n 85.763671875,\n 209.98828125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 137.5751953125,\n 265.359375,\n 209.98828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"But when you run it in Python 2, you get an error message.
\",\n \"polygon\": [\n [\n 85.24072265625,\n 215.40234375\n ],\n [\n 346.341796875,\n 215.40234375\n ],\n [\n 346.341796875,\n 225.532958984375\n ],\n [\n 85.24072265625,\n 225.532958984375\n ]\n ],\n \"bbox\": [\n 85.24072265625,\n 215.40234375,\n 346.341796875,\n 225.532958984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"Traceback (most recent call last):\\n File \\\"snr.py\\\", line 5, in ?\\n decibels = 10 * math.log10(ratio)\\nValueError: math domain error
\",\n \"polygon\": [\n [\n 85.3154296875,\n 231.9527587890625\n ],\n [\n 279.9280090332031,\n 231.9527587890625\n ],\n [\n 279.9280090332031,\n 278.4984130859375\n ],\n [\n 85.3154296875,\n 278.4984130859375\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 231.9527587890625,\n 279.9280090332031,\n 278.4984130859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The error message indicates line 5, but there is nothing wrong with that line. To find the real error, it might be useful to print the value of ratio, which turns out to be 0. The problem is in line 4, because dividing two integers does floor division. The solution is to represent signal power and noise power with floating-point values.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 285.01171875\n ],\n [\n 482.90625,\n 285.01171875\n ],\n [\n 482.90625,\n 331.76397705078125\n ],\n [\n 85.9130859375,\n 331.76397705078125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 285.01171875,\n 482.90625,\n 331.76397705078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"In general, error messages tell you where the problem was discovered, but that is often not where it was caused.
\",\n \"polygon\": [\n [\n 85.763671875,\n 341.666015625\n ],\n [\n 482.607421875,\n 341.666015625\n ],\n [\n 482.607421875,\n 364.46197509765625\n ],\n [\n 85.763671875,\n 364.46197509765625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 341.666015625,\n 482.607421875,\n 364.46197509765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/68/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"In Python 3, this example does not cause an error; the division operator performs floatingpoint division even with integer operands.
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\"modulus operator: An operator, denoted with a percent sign (%), that works on integers and yields the remainder when one number is divided by another.\",\n \"polygon\": [\n [\n 86.4000244140625,\n 451.23980712890625\n ],\n [\n 482.3994140625,\n 451.23980712890625\n ],\n [\n 482.3994140625,\n 473.54595947265625\n ],\n [\n 86.4000244140625,\n 473.54595947265625\n ]\n ],\n \"bbox\": [\n 86.4000244140625,\n 451.23980712890625,\n 482.3994140625,\n 473.54595947265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/69/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \"boolean expression: An expression whose value is either True or False.\",\n \"polygon\": [\n [\n 85.9130859375,\n 483.78515625\n ],\n [\n 406.40625,\n 483.78515625\n ],\n [\n 406.40625,\n 494.3359680175781\n ],\n [\n 85.9130859375,\n 494.3359680175781\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 483.78515625,\n 406.40625,\n 494.3359680175781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/69/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/ListItem/12\",\n \"block_type\": \"ListItem\",\n \"html\": \"relational operator: One of the operators that compares its operands: ==, !=, >, <, >=, and <=.\",\n \"polygon\": [\n [\n 86.40005493164062,\n 504.66796875\n ],\n [\n 482.39703369140625,\n 504.66796875\n ],\n [\n 482.39703369140625,\n 527.3199768066406\n ],\n [\n 86.40005493164062,\n 527.3199768066406\n ]\n ],\n \"bbox\": [\n 86.40005493164062,\n 504.66796875,\n 482.39703369140625,\n 527.3199768066406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/69/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/ListItem/13\",\n \"block_type\": \"ListItem\",\n \"html\": \"logical operator: One of the operators that combines boolean expressions: and, or, and not.\",\n \"polygon\": [\n [\n 86.0625,\n 537.9978332519531\n ],\n [\n 482.40264892578125,\n 537.9978332519531\n ],\n [\n 482.40264892578125,\n 560.3039855957031\n ],\n [\n 86.0625,\n 560.3039855957031\n ]\n ],\n \"bbox\": [\n 86.0625,\n 537.9978332519531,\n 482.40264892578125,\n 560.3039855957031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/69/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/69/ListItem/14\",\n \"block_type\": \"ListItem\",\n \"html\": \"conditional statement: A statement that controls the flow of execution depending on some condition.\",\n \"polygon\": [\n [\n 86.361328125,\n 571.0342864990234\n ],\n [\n 482.4032287597656,\n 571.0342864990234\n ],\n [\n 482.4032287597656,\n 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The header ends with a colon (:). The body is indented relative to the header.\",\n \"polygon\": [\n [\n 86.0625,\n 637.0023040771484\n ],\n [\n 482.40289306640625,\n 637.0023040771484\n ],\n [\n 482.40289306640625,\n 659.2560119628906\n ],\n [\n 86.0625,\n 659.2560119628906\n ]\n ],\n \"bbox\": [\n 86.0625,\n 637.0023040771484,\n 482.40289306640625,\n 659.2560119628906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/69/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/69/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/69/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"branch: One of the alternative sequences of statements in a conditional statement.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 145.6341552734375\n ],\n [\n 525.603271484375,\n 145.6341552734375\n ],\n [\n 525.603271484375,\n 155.69390869140625\n ],\n [\n 128.794921875,\n 155.69390869140625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 145.6341552734375,\n 525.603271484375,\n 155.69390869140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/69/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/70/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \"infinite recursion: A recursion that doesn't have a base case, or never reaches it. Eventually, an infinite recursion causes a runtime error.\",\n \"polygon\": [\n [\n 127.7490234375,\n 179.9208984375\n ],\n [\n 525.6033935546875,\n 179.9208984375\n ],\n [\n 525.6033935546875,\n 202.43292236328125\n ],\n [\n 127.7490234375,\n 202.43292236328125\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 179.9208984375,\n 525.6033935546875,\n 202.43292236328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/69/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/70/SectionHeader/5\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"5.14 Exercises
\",\n \"polygon\": [\n [\n 129.01904296875,\n 235.6197509765625\n ],\n [\n 228.80401611328125,\n 235.6197509765625\n ],\n [\n 228.80401611328125,\n 249.9659423828125\n ],\n [\n 129.01904296875,\n 249.9659423828125\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 235.6197509765625,\n 228.80401611328125,\n 249.9659423828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/70/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 5.3. Fermat's Last Theorem says that there are no positive integers a, b, and c such that
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\",\n \"polygon\": [\n [\n 127.599609375,\n 315.755859375\n ],\n [\n 265.270751953125,\n 315.755859375\n ],\n [\n 265.270751953125,\n 325.8002014160156\n ],\n [\n 127.599609375,\n 325.8002014160156\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 315.755859375,\n 265.270751953125,\n 325.8002014160156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/70/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Write a function named check_fermat that takes four parameters—a, b, c and n—and that checks to see if Fermat's theorem holds. If n is greater than 2 and it turns out to be true that\",\n \"polygon\": [\n [\n 141.1962890625,\n 341.859375\n ],\n [\n 525.9375,\n 341.859375\n ],\n [\n 525.9375,\n 364.5061950683594\n ],\n [\n 141.1962890625,\n 364.5061950683594\n ]\n ],\n \"bbox\": [\n 141.1962890625,\n 341.859375,\n 525.9375,\n 364.5061950683594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/70/Equation/10\",\n \"block_type\": \"Equation\",\n \"html\": \"\",\n \"polygon\": [\n [\n 312.275390625,\n 381.6517333984375\n ],\n [\n 368.455078125,\n 381.6517333984375\n ],\n [\n 368.455078125,\n 393.9721984863281\n ],\n [\n 312.275390625,\n 393.9721984863281\n ]\n ],\n \"bbox\": [\n 312.275390625,\n 381.6517333984375,\n 368.455078125,\n 393.9721984863281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/70/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"the program should print, \\\"Holy smokes, Fermat was wrong!\\\" Otherwise the program should print, \\\"No, that doesn't work.\\\"
\",\n \"polygon\": [\n [\n 153.1494140625,\n 402.1875\n ],\n [\n 525.9375,\n 402.1875\n ],\n [\n 525.9375,\n 424.45819091796875\n ],\n [\n 153.1494140625,\n 424.45819091796875\n ]\n ],\n \"bbox\": [\n 153.1494140625,\n 402.1875,\n 525.9375,\n 424.45819091796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/70/ListItem/12\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Write a function that prompts the user to input values for a, b, c and n, converts them to integers, and uses check_fermat to check whether they violate Fermat's theorem.\",\n \"polygon\": [\n [\n 141.7939453125,\n 436.21875\n ],\n [\n 525.9375,\n 436.21875\n ],\n [\n 525.9375,\n 459.0263366699219\n ],\n [\n 141.7939453125,\n 459.0263366699219\n ]\n ],\n \"bbox\": [\n 141.7939453125,\n 436.21875,\n 525.9375,\n 459.0263366699219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/70/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 5.4. If you are given three sticks, you may or may not be able to arrange them in a triangle. For example, if one of the sticks is 12 inches long and the other two are one inch long, it is clear that you will not be able to get the short sticks to meet in the middle. For any three lengths, there is a simple test to see if it is possible to form a triangle:
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\",\n \"polygon\": [\n [\n 85.46484375,\n 195.29296875\n ],\n [\n 216.052734375,\n 195.29296875\n ],\n [\n 216.052734375,\n 327.64434814453125\n ],\n [\n 85.46484375,\n 327.64434814453125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 195.29296875,\n 216.052734375,\n 327.64434814453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/71/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 5.6. The Koch curve is a fractal that looks something like Figure 5.2. To draw a Koch curve with length x, all you have to do is
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Draw a Koch curve with length x/3.\",\n \"polygon\": [\n [\n 97.94091796875,\n 365.642578125\n ],\n [\n 259.3296813964844,\n 365.642578125\n ],\n [\n 259.3296813964844,\n 376.3179016113281\n ],\n [\n 97.94091796875,\n 376.3179016113281\n ]\n ],\n \"bbox\": [\n 97.94091796875,\n 365.642578125,\n 259.3296813964844,\n 376.3179016113281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/71/ListItem/6\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Turn left 60 degrees.\",\n \"polygon\": [\n [\n 98.1650390625,\n 385.751953125\n ],\n [\n 193.0593719482422,\n 385.751953125\n ],\n [\n 193.0593719482422,\n 396.3102111816406\n ],\n [\n 98.1650390625,\n 396.3102111816406\n ]\n ],\n \"bbox\": [\n 98.1650390625,\n 385.751953125,\n 193.0593719482422,\n 396.3102111816406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/71/ListItem/7\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. Draw a Koch curve with length x/3.\",\n \"polygon\": [\n [\n 98.23974609375,\n 406.0546875\n ],\n [\n 259.3296813964844,\n 406.0546875\n ],\n [\n 259.3296813964844,\n 416.64691162109375\n ],\n [\n 98.23974609375,\n 416.64691162109375\n ]\n ],\n \"bbox\": [\n 98.23974609375,\n 406.0546875,\n 259.3296813964844,\n 416.64691162109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/71/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"4. Turn right 120 degrees.\",\n \"polygon\": [\n [\n 97.716796875,\n 426.6766357421875\n ],\n [\n 205.23365783691406,\n 426.6766357421875\n ],\n [\n 205.23365783691406,\n 436.63922119140625\n ],\n [\n 97.716796875,\n 436.63922119140625\n ]\n ],\n \"bbox\": [\n 97.716796875,\n 426.6766357421875,\n 205.23365783691406,\n 436.63922119140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/71/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"5. Draw a Koch curve with length x/3.\",\n \"polygon\": [\n [\n 98.09033203125,\n 446.84063720703125\n ],\n [\n 259.3296813964844,\n 446.84063720703125\n ],\n [\n 259.3296813964844,\n 456.9759216308594\n ],\n [\n 98.09033203125,\n 456.9759216308594\n ]\n ],\n \"bbox\": [\n 98.09033203125,\n 446.84063720703125,\n 259.3296813964844,\n 456.9759216308594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/71/ListItem/10\",\n \"block_type\": \"ListItem\",\n \"html\": \"6. Turn left 60 degrees.\",\n \"polygon\": [\n [\n 97.2685546875,\n 466.76953125\n ],\n [\n 193.0593719482422,\n 466.76953125\n ],\n [\n 193.0593719482422,\n 476.96722412109375\n ],\n [\n 97.2685546875,\n 476.96722412109375\n ]\n ],\n \"bbox\": [\n 97.2685546875,\n 466.76953125,\n 193.0593719482422,\n 476.96722412109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/71/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \"7. Draw a Koch curve with length x/3.\",\n \"polygon\": [\n [\n 98.015625,\n 486.87890625\n ],\n [\n 259.3296813964844,\n 486.87890625\n ],\n [\n 259.3296813964844,\n 497.304931640625\n ],\n [\n 98.015625,\n 497.304931640625\n ]\n ],\n \"bbox\": [\n 98.015625,\n 486.87890625,\n 259.3296813964844,\n 497.304931640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/71/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The exception is if x is less than 3: in that case, you can just draw a straight line with length x.
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\",\n \"polygon\": [\n [\n 109.59521484375,\n 596.0929565429688\n ],\n [\n 348.8206787109375,\n 596.0929565429688\n ],\n [\n 348.8206787109375,\n 606.1362457275391\n ],\n [\n 109.59521484375,\n 606.1362457275391\n ]\n ],\n \"bbox\": [\n 109.59521484375,\n 596.0929565429688,\n 348.8206787109375,\n 606.1362457275391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/71/ListItem/16\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. The Koch curve can be generalized in several ways. See http: // en. wikipedia. org/ wiki/ Koch_ snowflake for examples and implement your favorite.\",\n \"polygon\": [\n [\n 97.2685546875,\n 616.2579498291016\n ],\n [\n 480.7077331542969,\n 616.2579498291016\n ],\n [\n 480.7077331542969,\n 638.4952392578125\n ],\n [\n 97.2685546875,\n 638.4952392578125\n ]\n ],\n \"bbox\": [\n 97.2685546875,\n 616.2579498291016,\n 480.7077331542969,\n 638.4952392578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/62/SectionHeader/1\",\n \"3\": \"/page/64/SectionHeader/11\",\n \"4\": \"/page/70/SectionHeader/5\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/72/Page/121\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/72/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Chapter 6
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\",\n \"polygon\": [\n [\n 128.49609375,\n 297.966796875\n ],\n [\n 253.705078125,\n 297.966796875\n ],\n [\n 253.705078125,\n 312.531982421875\n ],\n [\n 128.49609375,\n 312.531982421875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 297.966796875,\n 253.705078125,\n 312.531982421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/72/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Some of the built-in functions we have used, such as the math functions, produce results. Calling the function generates a value, which we usually assign to a variable or use as part of an expression.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 590.0927429199219\n ],\n [\n 480.515625,\n 590.0927429199219\n ],\n [\n 480.515625,\n 600.2048950195312\n ],\n [\n 128.794921875,\n 600.2048950195312\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 590.0927429199219,\n 480.515625,\n 600.2048950195312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/72/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Sometimes it is useful to have multiple return statements, one in each branch of a conditional:
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\",\n \"polygon\": [\n [\n 85.0166015625,\n 88.6552734375\n ],\n [\n 480.6423034667969,\n 88.6552734375\n ],\n [\n 480.6423034667969,\n 98.79791259765625\n ],\n [\n 85.0166015625,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 88.6552734375,\n 480.6423034667969,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"As soon as a return statement executes, the function terminates without executing any subsequent statements. Code that appears after a return statement, or any other place the flow of execution can never reach, is called dead code.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 109.0546875\n ],\n [\n 482.90625,\n 109.0546875\n ],\n [\n 482.90625,\n 144.01092529296875\n ],\n [\n 85.6142578125,\n 144.01092529296875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 109.0546875,\n 482.90625,\n 144.01092529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"In a fruitful function, it is a good idea to ensure that every possible path through the program hits a return statement. For example:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 154.30078125\n ],\n [\n 482.4032287597656,\n 154.30078125\n ],\n [\n 482.4032287597656,\n 177.02996826171875\n ],\n [\n 85.6142578125,\n 177.02996826171875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 154.30078125,\n 482.4032287597656,\n 177.02996826171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"def absolute_value(x):\\n if x < 0:\\n return -x\\n if x > 0:\\n return x
\",\n \"polygon\": [\n [\n 86.39995574951172,\n 183.77081298828125\n ],\n [\n 201.47792053222656,\n 183.77081298828125\n ],\n [\n 201.47792053222656,\n 243.6328125\n ],\n [\n 86.39995574951172,\n 243.6328125\n ]\n ],\n \"bbox\": [\n 86.39995574951172,\n 183.77081298828125,\n 201.47792053222656,\n 243.6328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"This function is incorrect because if x happens to be 0, neither condition is true, and the function ends without hitting a return statement. If the flow of execution gets to the end of a function, the return value is None, which is not the absolute value of 0.
\",\n \"polygon\": [\n [\n 85.763671875,\n 249.240234375\n ],\n [\n 482.40374755859375,\n 249.240234375\n ],\n [\n 482.40374755859375,\n 283.90203857421875\n ],\n [\n 85.763671875,\n 283.90203857421875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 249.240234375,\n 482.40374755859375,\n 283.90203857421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> print absolute_value(0)\\nNone
\",\n \"polygon\": [\n [\n 85.763671875,\n 289.458984375\n ],\n [\n 227.62973022460938,\n 289.458984375\n ],\n [\n 227.62973022460938,\n 312.80047607421875\n ],\n [\n 85.763671875,\n 312.80047607421875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 289.458984375,\n 227.62973022460938,\n 312.80047607421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"By the way, Python provides a built-in function called abs that computes absolute values. Exercise 6.1. Write a compare function that returns 1 if x > y, 0 if x == y, and -1 if x < y.
\",\n \"polygon\": [\n [\n 85.763671875,\n 319.4296875\n ],\n [\n 479.317626953125,\n 319.4296875\n ],\n [\n 479.317626953125,\n 341.8999328613281\n ],\n [\n 85.763671875,\n 341.8999328613281\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 319.4296875,\n 479.317626953125,\n 341.8999328613281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/72/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"6.2 Incremental development
\",\n \"polygon\": [\n [\n 85.39013671875,\n 373.40087890625\n ],\n [\n 288.0703125,\n 373.40087890625\n ],\n [\n 288.0703125,\n 387.7471008300781\n ],\n [\n 85.39013671875,\n 387.7471008300781\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 373.40087890625,\n 288.0703125,\n 387.7471008300781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"As you write larger functions, you might find yourself spending more time debugging.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 399.8671875\n ],\n [\n 469.0234680175781,\n 399.8671875\n ],\n [\n 469.0234680175781,\n 411.01702880859375\n ],\n [\n 85.9130859375,\n 411.01702880859375\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 399.8671875,\n 469.0234680175781,\n 411.01702880859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"To deal with increasingly complex programs, you might want to try a process called incremental development. The goal of incremental development is to avoid long debugging sessions by adding and testing only a small amount of code at a time.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 421.13671875\n ],\n [\n 482.90625,\n 421.13671875\n ],\n [\n 482.90625,\n 456.2310485839844\n ],\n [\n 85.6142578125,\n 456.2310485839844\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 421.13671875,\n 482.90625,\n 456.2310485839844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"As an example, suppose you want to find the distance between two points, given by the coordinates (x1, y1) and (x2, y2). By the Pythagorean theorem, the distance is:
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\",\n \"polygon\": [\n [\n 85.46484375,\n 533.28515625\n ],\n [\n 483.802734375,\n 533.28515625\n ],\n [\n 483.802734375,\n 555.8779296875\n ],\n [\n 85.46484375,\n 555.8779296875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 533.28515625,\n 483.802734375,\n 555.8779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"In this case, the inputs are two points, which you can represent using four numbers. The return value is the distance, which is a floating-point value.
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\",\n \"polygon\": [\n [\n 85.24072265625,\n 616.04296875\n ],\n [\n 238.09046936035156,\n 616.04296875\n ],\n [\n 238.09046936035156,\n 638.6194000244141\n ],\n [\n 85.24072265625,\n 638.6194000244141\n ]\n ],\n \"bbox\": [\n 85.24072265625,\n 616.04296875,\n 238.09046936035156,\n 638.6194000244141\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Obviously, this version doesn't compute distances; it always returns zero. But it is syntactically correct, and it runs, which means that you can test it before you make it more complicated.
\",\n \"polygon\": [\n [\n 85.46484375,\n 645.046875\n ],\n [\n 482.90625,\n 645.046875\n ],\n [\n 482.90625,\n 680.0109558105469\n ],\n [\n 85.46484375,\n 680.0109558105469\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 645.046875,\n 482.90625,\n 680.0109558105469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/73/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"To test the new function, call it with sample arguments:
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 88.02685546875\n ],\n [\n 255.12875366210938,\n 88.02685546875\n ],\n [\n 255.12875366210938,\n 110.84228515625\n ],\n [\n 128.0478515625,\n 110.84228515625\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 88.02685546875,\n 255.12875366210938,\n 110.84228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"I chose these values so that the horizontal distance is 3 and the vertical distance is 4; that way, the result is 5 (the hypotenuse of a 3-4-5 triangle). When testing a function, it is useful to know the right answer.
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\",\n \"polygon\": [\n [\n 128.197265625,\n 160.5849609375\n ],\n [\n 525.638671875,\n 160.5849609375\n ],\n [\n 525.638671875,\n 197.01251220703125\n ],\n [\n 128.197265625,\n 197.01251220703125\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 160.5849609375,\n 525.638671875,\n 197.01251220703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"def distance(x1, y1, x2, y2):\\n dx = x2 - x1\\n dy = y2 - y1\\n print 'dx is', dx\\n print 'dy is', dy\\n return 0.0
\",\n \"polygon\": [\n [\n 129.59996032714844,\n 201.763671875\n ],\n [\n 281.794921875,\n 201.763671875\n ],\n [\n 281.794921875,\n 272.6982421875\n ],\n [\n 129.59996032714844,\n 272.6982421875\n ]\n ],\n \"bbox\": [\n 129.59996032714844,\n 201.763671875,\n 281.794921875,\n 272.6982421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"If the function is working, it should display 'dx is 3' and 'dy is 4'. If so, we know that the function is getting the right arguments and performing the first computation correctly. If not, there are only a few lines to check.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 278.05078125\n ],\n [\n 525.9375,\n 278.05078125\n ],\n [\n 525.9375,\n 313.2818298339844\n ],\n [\n 128.6455078125,\n 313.2818298339844\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 278.05078125,\n 525.9375,\n 313.2818298339844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Next we compute the sum of squares of dx and dy:
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\",\n \"polygon\": [\n [\n 129.5999755859375,\n 339.15234375\n ],\n [\n 318.251953125,\n 339.15234375\n ],\n [\n 318.251953125,\n 410.165283203125\n ],\n [\n 129.5999755859375,\n 410.165283203125\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 339.15234375,\n 318.251953125,\n 410.165283203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Again, you would run the program at this stage and check the output (which should be 25). Finally, you can use math.sqrt to compute and return the result:
\",\n \"polygon\": [\n [\n 128.794921875,\n 415.3359375\n ],\n [\n 525.638671875,\n 415.3359375\n ],\n [\n 525.638671875,\n 438.5538635253906\n ],\n [\n 128.794921875,\n 438.5538635253906\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 415.3359375,\n 525.638671875,\n 438.5538635253906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \"def distance(x1, y1, x2, y2):\\n dx = x2 - x1\\n dy = y2 - y1\\n dsquared = dx**2 + dy**2\\n result = math.sqrt(dsquared)\\n return result
\",\n \"polygon\": [\n [\n 129.59996032714844,\n 444.4867248535156\n ],\n [\n 296.9761047363281,\n 444.4867248535156\n ],\n [\n 296.9761047363281,\n 515.4213256835938\n ],\n [\n 129.59996032714844,\n 515.4213256835938\n ]\n ],\n \"bbox\": [\n 129.59996032714844,\n 444.4867248535156,\n 296.9761047363281,\n 515.4213256835938\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"If that works correctly, you are done. Otherwise, you might want to print the value of result before the return statement.
\",\n \"polygon\": [\n [\n 127.8984375,\n 520.5234375\n ],\n [\n 525.9375,\n 520.5234375\n ],\n [\n 525.9375,\n 543.8099060058594\n ],\n [\n 127.8984375,\n 543.8099060058594\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 520.5234375,\n 525.9375,\n 543.8099060058594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The final version of the function doesn't display anything when it runs; it only returns a value. The print statements we wrote are useful for debugging, but once you get the function working, you should remove them. Code like that is called scaffolding because it is helpful for building the program but is not part of the final product.
\",\n \"polygon\": [\n [\n 128.197265625,\n 553.0078125\n ],\n [\n 526.236328125,\n 553.0078125\n ],\n [\n 526.236328125,\n 600.409912109375\n ],\n [\n 128.197265625,\n 600.409912109375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 553.0078125,\n 526.236328125,\n 600.409912109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"When you start out, you should add only a line or two of code at a time. As you gain more experience, you might find yourself writing and debugging bigger chunks. Either way, incremental development can save you a lot of debugging time.
\",\n \"polygon\": [\n [\n 128.197265625,\n 610.2421875\n ],\n [\n 526.236328125,\n 610.2421875\n ],\n [\n 526.236328125,\n 645.046875\n ],\n [\n 128.197265625,\n 645.046875\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 610.2421875,\n 526.236328125,\n 645.046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The key aspects of the process are:
\",\n \"polygon\": [\n [\n 127.82373046875,\n 654.71484375\n ],\n [\n 281.197265625,\n 654.71484375\n ],\n [\n 281.197265625,\n 664.831916809082\n ],\n [\n 127.82373046875,\n 664.831916809082\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 654.71484375,\n 281.197265625,\n 664.831916809082\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/74/ListItem/14\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Start with a working program and make small incremental changes. At any point, if there is an error, you should have a good idea where it is.\",\n \"polygon\": [\n [\n 142.052978515625,\n 677.53125\n ],\n [\n 525.9375,\n 677.53125\n ],\n [\n 525.9375,\n 700.8349151611328\n ],\n [\n 142.052978515625,\n 700.8349151611328\n ]\n ],\n \"bbox\": [\n 142.052978515625,\n 677.53125,\n 525.9375,\n 700.8349151611328\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/75/Page/168\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/75/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.66650390625\n ],\n [\n 482.4034729003906,\n 60.66650390625\n ],\n [\n 482.4034729003906,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.66650390625,\n 482.4034729003906,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.09130859375,\n 60.6181640625\n ],\n [\n 95.99853515625,\n 60.6181640625\n ],\n [\n 95.99853515625,\n 69.8994140625\n ],\n [\n 85.09130859375,\n 69.8994140625\n ]\n ],\n \"bbox\": [\n 85.09130859375,\n 60.6181640625,\n 95.99853515625,\n 69.8994140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/ListGroup/165\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 97.716796875,\n 88.80029296875\n ],\n [\n 482.90625,\n 88.80029296875\n ],\n [\n 482.90625,\n 157.158935546875\n ],\n [\n 97.716796875,\n 157.158935546875\n ]\n ],\n \"bbox\": [\n 97.716796875,\n 88.80029296875,\n 482.90625,\n 157.158935546875\n ],\n \"children\": [\n {\n \"id\": \"/page/75/ListItem/1\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Use temporary variables to hold intermediate values so you can display and check them.\",\n \"polygon\": [\n [\n 98.8530044555664,\n 88.80029296875\n ],\n [\n 482.90625,\n 88.80029296875\n ],\n [\n 482.90625,\n 110.99188232421875\n ],\n [\n 98.8530044555664,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 98.8530044555664,\n 88.80029296875,\n 482.90625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. Once the program is working, you might want to remove some of the scaffolding or consolidate multiple statements into compound expressions, but only if it does not make the program difficult to read.\",\n \"polygon\": [\n [\n 97.716796875,\n 122.203125\n ],\n [\n 482.40380859375,\n 122.203125\n ],\n [\n 482.40380859375,\n 157.158935546875\n ],\n [\n 97.716796875,\n 157.158935546875\n ]\n ],\n \"bbox\": [\n 97.716796875,\n 122.203125,\n 482.40380859375,\n 157.158935546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/73/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/75/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 6.2. Use incremental development to write a function called hypotenuse that returns the length of the hypotenuse of a right triangle given the lengths of the two legs as arguments. Record each stage of the development process as you go.
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\",\n \"polygon\": [\n [\n 86.0625,\n 230.725830078125\n ],\n [\n 203.9527587890625,\n 230.725830078125\n ],\n [\n 203.9527587890625,\n 245.072021484375\n ],\n [\n 86.0625,\n 245.072021484375\n ]\n ],\n \"bbox\": [\n 86.0625,\n 230.725830078125,\n 203.9527587890625,\n 245.072021484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"As you should expect by now, you can call one function from within another. This ability is called composition.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 258.71484375\n ],\n [\n 482.4032897949219,\n 258.71484375\n ],\n [\n 482.4032897949219,\n 280.8979797363281\n ],\n [\n 86.2119140625,\n 280.8979797363281\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 258.71484375,\n 482.4032897949219,\n 280.8979797363281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"As an example, we'll write a function that takes two points, the center of the circle and a point on the perimeter, and computes the area of the circle.
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\",\n \"polygon\": [\n [\n 85.39013671875,\n 366.6468505859375\n ],\n [\n 259.0119323730469,\n 366.6468505859375\n ],\n [\n 259.0119323730469,\n 377.4375\n ],\n [\n 85.39013671875,\n 377.4375\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 366.6468505859375,\n 259.0119323730469,\n 377.4375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The next step is to find the area of a circle with that radius; we just wrote that, too:
\",\n \"polygon\": [\n [\n 85.763671875,\n 383.23828125\n ],\n [\n 447.046875,\n 383.23828125\n ],\n [\n 447.046875,\n 393.8710021972656\n ],\n [\n 85.763671875,\n 393.8710021972656\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 383.23828125,\n 447.046875,\n 393.8710021972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"result = area(radius)
\",\n \"polygon\": [\n [\n 85.39013671875,\n 400.8698425292969\n ],\n [\n 197.82421875,\n 400.8698425292969\n ],\n [\n 197.82421875,\n 410.8324279785156\n ],\n [\n 85.39013671875,\n 410.8324279785156\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 400.8698425292969,\n 197.82421875,\n 410.8324279785156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Encapsulating these steps in a function, we get:
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 435.05859375\n ],\n [\n 279.9279479980469,\n 435.05859375\n ],\n [\n 279.9279479980469,\n 481.6394348144531\n ],\n [\n 85.6142578125,\n 481.6394348144531\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 435.05859375,\n 279.9279479980469,\n 481.6394348144531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The temporary variables radius and result are useful for development and debugging, but once the program is working, we can make it more concise by composing the function calls:
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\",\n \"polygon\": [\n [\n 86.28662109375,\n 530.287841796875\n ],\n [\n 300.8493957519531,\n 530.287841796875\n ],\n [\n 300.8493957519531,\n 552.4454498291016\n ],\n [\n 86.28662109375,\n 552.4454498291016\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 530.287841796875,\n 300.8493957519531,\n 552.4454498291016\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/SectionHeader/15\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"6.4 Boolean functions
\",\n \"polygon\": [\n [\n 85.0166015625,\n 584.7738647460938\n ],\n [\n 237.82412719726562,\n 584.7738647460938\n ],\n [\n 237.82412719726562,\n 599.1200561523438\n ],\n [\n 85.0166015625,\n 599.1200561523438\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 584.7738647460938,\n 237.82412719726562,\n 599.1200561523438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Functions can return booleans, which is often convenient for hiding complicated tests inside functions. For example:
\",\n \"polygon\": [\n [\n 85.0166015625,\n 612.7894134521484\n ],\n [\n 482.4032897949219,\n 612.7894134521484\n ],\n [\n 482.4032897949219,\n 634.9460144042969\n ],\n [\n 85.0166015625,\n 634.9460144042969\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 612.7894134521484,\n 482.4032897949219,\n 634.9460144042969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/75/Code/17\",\n \"block_type\": \"Code\",\n \"html\": \"def is_divisible(x, y):\\n if x % y == 0:\\n return True\\n else:\\n return False
\",\n \"polygon\": [\n [\n 86.2119140625,\n 641.9458618164062\n ],\n [\n 210.97265625,\n 641.9458618164062\n ],\n [\n 210.97265625,\n 700.6854553222656\n ],\n [\n 86.2119140625,\n 700.6854553222656\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 641.9458618164062,\n 210.97265625,\n 700.6854553222656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/76/Page/219\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/76/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.197265625,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.197265625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 61.171142578125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/PageHeader/19\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 514.880859375,\n 61.05322265625\n ],\n [\n 525.041015625,\n 61.05322265625\n ],\n [\n 525.041015625,\n 70.43115234375\n ],\n [\n 514.880859375,\n 70.43115234375\n ]\n ],\n \"bbox\": [\n 514.880859375,\n 61.05322265625,\n 525.041015625,\n 70.43115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"It is common to give boolean functions names that sound like yes/no questions; is_divisible returns either True or False to indicate whether x is divisible by y.
\",\n \"polygon\": [\n [\n 127.30078125,\n 88.51025390625\n ],\n [\n 525.9375,\n 88.51025390625\n ],\n [\n 525.9375,\n 110.99188232421875\n ],\n [\n 127.30078125,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 127.30078125,\n 88.51025390625,\n 525.9375,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Here is an example:
\",\n \"polygon\": [\n [\n 128.12255859375,\n 119.689453125\n ],\n [\n 219.041015625,\n 119.689453125\n ],\n [\n 219.041015625,\n 130.06787109375\n ],\n [\n 128.12255859375,\n 130.06787109375\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 119.689453125,\n 219.041015625,\n 130.06787109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> is_divisible(6, 4)\\nFalse\\n>>> is_divisible(6, 3)\\nTrue
\",\n \"polygon\": [\n [\n 128.3466796875,\n 135.0587158203125\n ],\n [\n 255.3486328125,\n 135.0587158203125\n ],\n [\n 255.3486328125,\n 181.604248046875\n ],\n [\n 128.3466796875,\n 181.604248046875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 135.0587158203125,\n 255.3486328125,\n 181.604248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The result of the == operator is a boolean, so we can write the function more concisely by returning it directly:
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 186.591796875\n ],\n [\n 525.638671875,\n 186.591796875\n ],\n [\n 525.638671875,\n 209.0528564453125\n ],\n [\n 129.60000610351562,\n 209.0528564453125\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 186.591796875,\n 525.638671875,\n 209.0528564453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"def is_divisible(x, y):\\n return x % y == 0
\",\n \"polygon\": [\n [\n 128.6455078125,\n 213.46875\n ],\n [\n 249.90834045410156,\n 213.46875\n ],\n [\n 249.90834045410156,\n 236.2012939453125\n ],\n [\n 128.6455078125,\n 236.2012939453125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 213.46875,\n 249.90834045410156,\n 236.2012939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Boolean functions are often used in conditional statements:
\",\n \"polygon\": [\n [\n 128.197265625,\n 241.3125\n ],\n [\n 389.1954040527344,\n 241.3125\n ],\n [\n 389.1954040527344,\n 251.45489501953125\n ],\n [\n 128.197265625,\n 251.45489501953125\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 241.3125,\n 389.1954040527344,\n 251.45489501953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/TextInlineMath/7\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"if is_divisible(x, y):
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 256.44573974609375\n ],\n [\n 266.1064453125,\n 256.44573974609375\n ],\n [\n 266.1064453125,\n 268.962890625\n ],\n [\n 129.60000610351562,\n 268.962890625\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 256.44573974609375,\n 266.1064453125,\n 268.962890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/TextInlineMath/8\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"print 'x is divisible by y'
\",\n \"polygon\": [\n [\n 147.62109375,\n 268.6407470703125\n ],\n [\n 291.955078125,\n 268.6407470703125\n ],\n [\n 291.955078125,\n 282.69140625\n ],\n [\n 147.62109375,\n 282.69140625\n ]\n ],\n \"bbox\": [\n 147.62109375,\n 268.6407470703125,\n 291.955078125,\n 282.69140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"It might be tempting to write something like:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 283.8943176269531\n ],\n [\n 327.515625,\n 283.8943176269531\n ],\n [\n 327.515625,\n 294.29296875\n ],\n [\n 128.3466796875,\n 294.29296875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 283.8943176269531,\n 327.515625,\n 294.29296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/TextInlineMath/10\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"if is_divisible(x, y) == True: print 'x is divisible by y'
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 298.8477478027344\n ],\n [\n 291.7033996582031,\n 298.8477478027344\n ],\n [\n 291.7033996582031,\n 321.0053405761719\n ],\n [\n 129.60000610351562,\n 321.0053405761719\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 298.8477478027344,\n 291.7033996582031,\n 321.0053405761719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"But the extra comparison is unnecessary. Exercise 6.3. Write a function is_between(x, y, z) that returns True if x ≤ y ≤ z or False otherwise.
\",\n \"polygon\": [\n [\n 127.8984375,\n 325.423828125\n ],\n [\n 525.6057739257812,\n 325.423828125\n ],\n [\n 525.6057739257812,\n 360.4742126464844\n ],\n [\n 127.8984375,\n 360.4742126464844\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 325.423828125,\n 525.6057739257812,\n 360.4742126464844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/75/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"6.5 More recursion
\",\n \"polygon\": [\n [\n 128.49609375,\n 388.5017395019531\n ],\n [\n 261.9006652832031,\n 388.5017395019531\n ],\n [\n 261.9006652832031,\n 402.84796142578125\n ],\n [\n 128.49609375,\n 402.84796142578125\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 388.5017395019531,\n 261.9006652832031,\n 402.84796142578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"We have only covered a small subset of Python, but you might be interested to know that this subset is a complete programming language, which means that anything that can be computed can be expressed in this language. Any program ever written could be rewritten using only the language features you have learned so far (actually, you would need a few commands to control devices like the keyboard, mouse, disks, etc., but that's all).
\",\n \"polygon\": [\n [\n 128.794921875,\n 413.7890625\n ],\n [\n 525.638671875,\n 413.7890625\n ],\n [\n 525.638671875,\n 472.8719177246094\n ],\n [\n 128.794921875,\n 472.8719177246094\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 413.7890625,\n 525.638671875,\n 472.8719177246094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Proving that claim is a nontrivial exercise first accomplished by Alan Turing, one of the first computer scientists (some would argue that he was a mathematician, but a lot of early computer scientists started as mathematicians). Accordingly, it is known as the Turing Thesis. For a more complete (and accurate) discussion of the Turing Thesis, I recommend Michael Sipser's book Introduction to the Theory of Computation.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 481.9843444824219\n ],\n [\n 525.9375,\n 481.9843444824219\n ],\n [\n 525.9375,\n 540.7239379882812\n ],\n [\n 128.6455078125,\n 540.7239379882812\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 481.9843444824219,\n 525.9375,\n 540.7239379882812\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"To give you an idea of what you can do with the tools you have learned so far, we'll evaluate a few recursively defined mathematical functions. A recursive definition is similar to a circular definition, in the sense that the definition contains a reference to the thing being defined. A truly circular definition is not very useful:
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 607.53515625\n ],\n [\n 407.70611572265625,\n 607.53515625\n ],\n [\n 407.70611572265625,\n 617.6449584960938\n ],\n [\n 128.0478515625,\n 617.6449584960938\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 607.53515625,\n 407.70611572265625,\n 617.6449584960938\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/76/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"If you saw that definition in the dictionary, you might be annoyed. On the other hand, if you looked up the definition of the factorial function, denoted with the symbol !, you might get something like this:
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 88.66259765625\n ],\n [\n 484.1015625,\n 88.66259765625\n ],\n [\n 484.1015625,\n 110.99188232421875\n ],\n [\n 85.6142578125,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 88.66259765625,\n 484.1015625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/77/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"So 3! is 3 times 2!, which is 2 times 1!, which is 1 times 0!. Putting it all together, 3! equals 3 times 2 times 1 times 1, which is 6.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 122.203125\n ],\n [\n 483.50390625,\n 122.203125\n ],\n [\n 483.50390625,\n 144.53985595703125\n ],\n [\n 85.6142578125,\n 144.53985595703125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 122.203125,\n 483.50390625,\n 144.53985595703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/77/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"If you can write a recursive definition of something, you can usually write a Python program to evaluate it. The first step is to decide what the parameters should be. In this case it should be clear that factorial takes an integer:
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\",\n \"polygon\": [\n [\n 86.40000915527344,\n 427.32421875\n ],\n [\n 424.9329528808594,\n 427.32421875\n ],\n [\n 424.9329528808594,\n 437.870849609375\n ],\n [\n 86.40000915527344,\n 437.870849609375\n ]\n ],\n \"bbox\": [\n 86.40000915527344,\n 427.32421875,\n 424.9329528808594,\n 437.870849609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/77/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Since 2 is not 0, we take the second branch and calculate the factorial of n-1...
\",\n \"polygon\": [\n [\n 111.3070068359375,\n 454.0078125\n ],\n [\n 449.8399353027344,\n 454.0078125\n ],\n [\n 449.8399353027344,\n 464.2598571777344\n ],\n [\n 111.3070068359375,\n 464.2598571777344\n ]\n ],\n \"bbox\": [\n 111.3070068359375,\n 454.0078125,\n 449.8399353027344,\n 464.2598571777344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/77/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Since 1 is not 0, we take the second branch and calculate the factorial of n-1...
\",\n \"polygon\": [\n [\n 130.2890625,\n 472.56427001953125\n ],\n [\n 435.578857421875,\n 472.56427001953125\n ],\n [\n 435.578857421875,\n 495.0\n ],\n [\n 130.2890625,\n 495.0\n ]\n ],\n \"bbox\": [\n 130.2890625,\n 472.56427001953125,\n 435.578857421875,\n 495.0\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/76/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/77/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Since 0 is 0, we take the first branch and return 1 without making any more recursive calls.
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vOWjjf4M+BrFwI7LUdRghvSvG9N7HDH04Fen6B4EtdHuL++vNRvNV1O/i8me8umG4R/wBxQBhRVPT/AIaWVv4RufC+oald6jpjkG2WQKj2uCSNjKOuTnJoAyvi5pen6b4OtNRsbWC1vtOvLc2TwoEZTvA2jHYjt7VHeaTZaz8eYP7Rto50g0RZlilXcu/zCASDwcZNatn8M1Oo2VzrfiPVdbhsHElrbXbKI1YdGbA+cj1NdIPDdmPF58S+bP8AbTZ/Y9m4eXs3bs4xnOff8KAOPsnt7L4x+JXkIitodEgZiowEQE5xj0Fc9ZtNc/D+/i8MeBraPQp4ZpFvtVu13yqQSZCgVmJ9Mn0r03/hFNObxFqOtSNNJNqFotnPEzDy/LGegxnJz61zNt8K0gsjpL+KNak0AAhdNEiqoU/wlwNxX2yKAOULFvh18LixJP8Aadryf+BV7bXE6f8ADe0tdE0nS7rVr66h0m9W7s2IRCu37qH5eV5Pv7121ABXJXf/ACUOb/sFRf8Ao2Sutrkrv/koc3/YKi/9GyUAalFFFAHL/Ef/AJJ1r3/Xo9cz4i0XTrX4FyJFaQjZYRTBtg3eYdpLZ65yTzXf61pMGvaLd6VdPIkF1GY3aIgMAfQkEfpVbUPDlnqXhV/D00k62j2625dGAk2qAAckYzx6UAW9IJbRbAkkk28ZJP8Auim6i+rIY/7Mt7KUHPmfaZ3jx6Y2o2e/pVq2gW1tYbdCSkSKilupAGOaloAwvO8V/wDQP0X/AMDpf/jVHneK/wDoH6L/AOB0v/xqt2igDC87xX/0D9F/8Dpf/jVcxr0hivzLqEehWmpOBzbavcRXDgDjiOMM3Hsa9EpiwxLK8qxoJHxucKMtjpk96APMbTUfiJ56jSbRrq2J+9qZ2oo/4FHFIfqQ1eiaU+pvZKdXgtIbv+JbSVpE/NlU/wCetXaKAKeqapZaLp02oahOILWEAySFS23Jx0AJ6muG+I/h671XRbrWYNcmNjBALg6bIP8ARp1QbsNtIbn613mo6fbarp1xYXsQltrhDHIh7g1xX/CsfMtU0258U61caKmALBpFAKjohcDJX2oA6vw7qC6r4b02/SEQLcWySCIdEyo4HtWnUcEEVrbx28EaxwxKERFHCqBgAVJQAUUUUAcF8X4xN4G8okgPe265BwRlxWZ8SdOh0zRfD2j6Vptv9judSijltlIhScAEqjtjoT3Oa7rxF4ftPE2mCwvZJo4hMk2YWAbKHI6g8U7xB4fsPEukSabqCuYmIZXjba8bjoynsRQB5z4l03xBfJpch8MaPos9ndxNb3a6kgK4P3ANi5yO2a2PCdtb6n8QfGF7fxRzXdtcR20IlUN5cO3jbnoDV6z+HwGpWl5rOv6nrK2T77WC6ZQiMOjEAfMw9TVnWvBCahrLazpmrXuj6jIgjnltSpWZR03KRgketAHI+NdK0PSPB/iKHRrkF5NQt5bq1WRStvIXXgKB8uRzitz4sMq+BYwSAWvbYD3O8Vfh+Hejp4XvtEmkurgX7+bdXcsmZpJM5D5xjII44qjJ8Mo9QtooNb8RavqSQMptw7ogiKkHOAvzHjGTngmgCpfaVZar8cIxfW6XEcGjiVY5F3Lu8wgEg8HGTRrWjapZ+PJdd0Gz0rWGW0S3l02aZY5YAOQUJ4UH3rsR4etB4qPiLzJ/tZtfsmzcPL2bt2cYznPvWTrfgddR1063pusX2kai8YimkttpWVR03KwwSPWgDhNT1eyh8BeMU0rR7nRNWEkf9oWjyZCmQgFkIOACuegFWdU0fV7rwMdK/wCEL0azs0gBiuzqaDyTjiTOwc9yc812+k+B9N07T9StrqW41KXU+L24u33PMMYA4xgDtjpWQPhgjwR6fdeJNYudEjYFdOkkXaQDkKzAZKj0oA63QRcr4e04XkiSXItoxK6PuDNtGSD3z607UX1ZDH/ZlvZSg58z7TO8ePTG1Gz39KuxxpFGscahUUBVUDgAdqdQBhed4r/6B+i/+B0v/wAao87xX/0D9F/8Dpf/AI1W7RQBhed4r/6B+i/+B0v/AMarmNekMV+ZdQj0K01JwObbV7iK4cAccRxhm49jXolMWGJZXlWNBI+NzhRlsdMnvQB5jaaj8RPPUaTaNdWxP3tTO1FH/Ao4pD9SGr0TSn1N7JTq8FpDd/xLaStIn5sqn/PWrtFAEF7dW1lYz3V46x20KF5HfoFAyTXn8jeIfiRaGK3T+xPC84wZXGbq7j/2V6Ip9+frXa+INEt/Eeh3WkXcs8UFyoV2gYB8ZB4JBHb0rlI/hbDFGscfjHxgkaAKqrqYAUDoANlAHa2Nlb6bYW9laxiO3gjWONR2UDAqxVbT7P8As/Trez+0XFz5KBPOuH3yPjuzdzVmgAooooAyvE//ACKmsf8AXlN/6Aa85tbKHQvgHJqOm26R382nB5LlV/eHeQGO7rwD+GK9Tv7OPUNPubKUssdxE0TlDggMMHHvzVOw0CxsfDcOg7WnsY7f7PiYgl0xjnAA/SgDzNtJ1a4+H66WvgrRo7FrQFbttTQFTtyJSdnXvnNW7nQtamHhPU7aXSdT1fTrDZJYXNwCJgQB5iNyM/7XStf/AIVfGbYaY/iXWX0IHjTjIu3bnOzfjdt9q1dd8D2mq3FheWN7daTf2Efk29xZkDEf9wgjBFAHN6ZqFhJqfiKG68OTaF4im01pJ4vNEkU0YBAZSvyk574q74ZdU+BsLMQFGky5J/3WrY0TwTb6ZeXeoX+oXmrajdQ+RJcXbDiP+4qgAKKyrf4XwwWb6U3iHVn0Ik7dO3qFAJztLYyVz2oA5uD/AJF/4V/9fSf+gGuj0K3hHxg8UEQxgra2rL8o4JByRW0ngnTUs/D9qJ7vZocgktjvXLkDHz/Lz+GKVvCezxo3iS11S5t2mjSO6tQqlJgoIXJIyPwoA6OsSSbxQJXEVjo5j3HaWvZQSO2R5XWtuigDC87xX/0D9F/8Dpf/AI1R53iv/oH6L/4HS/8Axqt2igDnbl/EklpMl1p2gtbsjCUS3sm0pjnOYsYxnNcU13cQuU0GaISg48vR9RubqNT6bTA8S/iBXqrosiMjqGRgQysMgj0NKqqiBEUKoGAAMAUAcLoF78RpZ1Go6Xpi2mRlrmfZMR6/uwy59iq13fbmiigDyzxRpNzq3xl060TUbmwgm0pg8ts2yRlDksqt2J4yR2q3ZwXXgz4i6Vottqt9e6XqsEpaC9mMrQugzuVjyAemK6nxL4UtfEi2srXNzZX1oxe2vLVtskZPUe4PcVW0DwVDpGrSaxe6leatqrR+UtzdsP3af3UUcLQB1FYXjJBJ4Sv0bIDKoO1iD99ehHIrdrD8YZ/4RW9x1wn/AKGtAGsvgrw9vD3GnC9YHIOoSvd4+nms2K3URI0VI1VEUYVVGAB6CuA1HxB430O9kTUoNBFgznyb/E0cIGeBKQXMZ6ckbf8Aa7VInjHxNb3cdtqWh6XaGVgsE82oSJBOT02yLE6gnsGKk9hQB3tFMhMrQRtMipKVBdEbcFbHIBwMjPfA+lPoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5K7/5KHN/2Cov/AEbJXW1xmtTy6d43N29hqE9vJpqRCS1tHmAYSOSDtBwcEUAbVFY3/CRxf9ArXP8AwVT/APxNH/CRxf8AQK1z/wAFU/8A8TQBs0Vjf8JHF/0Ctc/8FU//AMTR/wAJHF/0Ctc/8FU//wATQBs0Vjf8JHF/0Ctc/wDBVP8A/E0f8JHF/wBArXP/AAVT/wDxNAGzRWN/wkcX/QK1z/wVT/8AxNH/AAkcX/QK1z/wVT//ABNAGzRWN/wkcX/QK1z/AMFU/wD8TR/wkcX/AECtc/8ABVP/APE0AbNFY3/CRxf9ArXP/BVP/wDE0f8ACRxf9ArXP/BVP/8AE0AbNFY3/CRxf9ArXP8AwVT/APxNH/CRxf8AQK1z/wAFU/8A8TQBs0Vjf8JHF/0Ctc/8FU//AMTR/wAJHF/0Ctc/8FU//wATQBs0Vjf8JHF/0Ctc/wDBVP8A/E0f8JHF/wBArXP/AAVT/wDxNAGzRWN/wkcX/QK1z/wVT/8AxNH/AAkcX/QK1z/wVT//ABNAGzRWN/wkcX/QK1z/AMFU/wD8TR/wkcX/AECtc/8ABVP/APE0AbNFY3/CRxf9ArXP/BVP/wDE0f8ACRxf9ArXP/BVP/8AE0AbNFY3/CRxf9ArXP8AwVT/APxNH/CRxf8AQK1z/wAFU/8A8TQBs0Vjf8JHF/0Ctc/8FU//AMTR/wAJHF/0Ctc/8FU//wATQBs0Vjf8JHF/0Ctc/wDBVP8A/E0f8JHF/wBArXP/AAVT/wDxNAGzRWN/wkcX/QK1z/wVT/8AxNH/AAkcX/QK1z/wVT//ABNAGzRWN/wkcX/QK1z/AMFU/wD8TR/wkcX/AECtc/8ABVP/APE0AbNFY3/CRxf9ArXP/BVP/wDE0f8ACRxf9ArXP/BVP/8AE0AbNFY3/CRxf9ArXP8AwVT/APxNH/CRxf8AQK1z/wAFU/8A8TQBs0Vjf8JHF/0Ctc/8FU//AMTR/wAJHF/0Ctc/8FU//wATQBs0Vjf8JHF/0Ctc/wDBVP8A/E0f8JHF/wBArXP/AAVT/wDxNAGzRWN/wkcX/QK1z/wVT/8AxNH/AAkcX/QK1z/wVT//ABNAGzRWN/wkcX/QK1z/AMFU/wD8TR/wkcX/AECtc/8ABVP/APE0AbNFY3/CRxf9ArXP/BVP/wDE0f8ACRxf9ArXP/BVP/8AE0AbNFY3/CRxf9ArXP8AwVT/APxNH/CRxf8AQK1z/wAFU/8A8TQBs0Vjf8JHF/0Ctc/8FU//AMTR/wAJHF/0Ctc/8FU//wATQBs0Vjf8JHF/0Ctc/wDBVP8A/E0f8JHF/wBArXP/AAVT/wDxNAGzRWN/wkcX/QK1z/wVT/8AxNH/AAkcX/QK1z/wVT//ABNAGzRWN/wkcX/QK1z/AMFU/wD8TR/wkcX/AECtc/8ABVP/APE0AbNFY3/CRxf9ArXP/BVP/wDE0f8ACRxf9ArXP/BVP/8AE0AbNYni/wD5Fa9+if8Aoa07/hI4v+gVrn/gqn/+JrM1/VW1PRZ7K10jWmmmKKobTZlH3x1JXAFAHojKGUqwBUjBB71zw8JwWsxj0+VItMmJF1pc0QltpFPXYpP7s/T5fVc810VFAHLRNN4RvYbWWR5tBuZBFBJI5Z7KRuFjYnkxscBSeVJC8gjHU1Be2VvqNjPZXcSy286GORG6MpGDWV4Vu7iXTJLG9kaS+02ZrOeRusm0Ao592jZGPuxoA3KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5+y/0fx5q8K/cubG2uMf7YaVGP/fKp+VdBXP2P+keO9YmH3Laytrb/gZaSRv/AB1o/wA6ALviKXU4PDmoSaNB5+piBvs0eQMvjjrx7815jq3gIaL4En8RX3iDV4PEtvbfapL1r5sCbGfL2527c/LivTfEmuQeGvDl/rNypaK0iMhQHlj2H4nAryzTLjw94qa21rx3400i4YkTQaLHfxpbW3cB13Zdh3z7jmgD0/wnqN1q3hLSdQvk2XVzaxySrjHzFRk49+tFaVpcW13aRT2csU1tIoaKSJgyMvYgjgiigCaiiigAoornfDfjC08U3d8mn2N8trayGIXs0YWKZgcMEOcnHuBQB0VFFFABRTJZEhieWRtqIpZj6Adar6ZqdnrOnQahp86z2k67o5VBAYZx3+lAFuiiigAorLg1tJ/El1oos7tXt4EmNy0eIXDH7qtnkjuK1KACisbX/Elp4dfTEuoppDqF4lnF5QB2u2cFskccdq2aACuL1q0/tLxwbWe71CO3j02ORY7W/mt13GRwSRG65OAOvpXaVyV3/wAlDm/7BUX/AKNkoAh/4Rex/wCf3XP/AAeXn/x2j/hF7H/n91z/AMHl5/8AHa2qKAMX/hF7H/n91z/weXn/AMdo/wCEXsf+f3XP/B5ef/Ha2qKAMX/hF7H/AJ/dc/8AB5ef/HaP+EXsf+f3XP8AweXn/wAdraooAxf+EXsf+f3XP/B5ef8Ax2j/AIRex/5/dc/8Hl5/8draooAxf+EXsf8An91z/wAHl5/8do/4Rex/5/dc/wDB5ef/AB2tqigDF/4Rex/5/dc/8Hl5/wDHaP8AhF7H/n91z/weXn/x2tqigDF/4Rex/wCf3XP/AAeXn/x2j/hF7H/n91z/AMHl5/8AHa2JJEhieWV1SNAWZmOAAOpNcV/wtfwv5oPm3osy/li/No/2fdnH38UAbv8Awi9j/wA/uuf+Dy8/+O0f8IvY/wDP7rn/AIPLz/47WxHIksayRsHRgGVlOQQehFOoAxf+EXsf+f3XP/B5ef8Ax2j/AIRex/5/dc/8Hl5/8draooAxf+EXsf8An91z/wAHl5/8do/4Rex/5/dc/wDB5ef/AB2tqigDF/4Rex/5/dc/8Hl5/wDHaP8AhF7H/n91z/weXn/x2tqigDF/4Rex/wCf3XP/AAeXn/x2j/hF7H/n91z/AMHl5/8AHa2qKAMX/hF7H/n91z/weXn/AMdo/wCEXsf+f3XP/B5ef/Ha2qKAMX/hF7H/AJ/dc/8AB5ef/HaP+EXsf+f3XP8AweXn/wAdraooAxf+EXsf+f3XP/B5ef8Ax2j/AIRex/5/dc/8Hl5/8draooAxf+EXsf8An91z/wAHl5/8do/4Rex/5/dc/wDB5ef/AB2tqigDF/4Rex/5/dc/8Hl5/wDHaP8AhF7H/n91z/weXn/x2tqigDF/4Rex/wCf3XP/AAeXn/x2j/hF7H/n91z/AMHl5/8AHa2qKAMX/hF7H/n91z/weXn/AMdo/wCEXsf+f3XP/B5ef/Ha2qzJtesIPENrobSE31zE8yIoyAq9SfT2+lAEH/CL2P8Az+65/wCDy8/+O0f8IvY/8/uuf+Dy8/8AjtbVFAGL/wAIvY/8/uuf+Dy8/wDjtH/CL2P/AD+65/4PLz/47W1RQBi/8IvY/wDP7rn/AIPLz/47R/wi9j/z+65/4PLz/wCO1tUUAYv/AAi9j/z+65/4PLz/AOO0f8IvY/8AP7rn/g8vP/jtbVFAGL/wi9j/AM/uuf8Ag8vP/jtH/CL2P/P7rn/g8vP/AI7W1RQBi/8ACL2P/P7rn/g8vP8A47R/wi9j/wA/uuf+Dy8/+O0/WPENrot9pVpcRTO+pXH2eIxgYVsZy2T0+matw6rY3GqXOmRXCte2yq80QBygb7p9OaAKP/CL2P8Az+65/wCDy8/+O0f8IvY/8/uuf+Dy8/8AjtbVFAGL/wAIvY/8/uuf+Dy8/wDjtH/CL2P/AD+65/4PLz/47W1RQBi/8IvY/wDP7rn/AIPLz/47R/wi9j/z+65/4PLz/wCO1tUUAYv/AAi9j/z+65/4PLz/AOO0f8IvY/8AP7rn/g8vP/jtbVFAGL/wi9j/AM/uuf8Ag8vP/jtH/CL2P/P7rn/g8vP/AI7S+IfFOk+F4IpNSnZXnbZDDEheSVvRVHJqt4f8baN4ju5rK1e4gvoV3Pa3cLRShfXB6j6UAWP+EXsf+f3XP/B5ef8Ax2szxBo0WmaJcXtpqGtJPEUZGbWbtx98DkNIQfoRXW1ieL/+RWvfon/oa0AdpRRUF5eW2n2kt3eXEdvbxLueWVgqqPcmgBt/fW2mafPfXkgjt4ELyOewH8z7d6zfC1ncW+lvd30Zjv8AUZmvLlG6ozYCof8AcQIn/AapwR3Hiq+gvbmKS30S2kEtrbyoVe7kH3ZXU8qgPKqeSQGOMAV09AEF7Y2mo2j2l9awXVtJ9+GeMOjd+VPBrG/4QTwf/wBCpof/AILof/ia6CigCK2toLO2jtrWCOCCJQscUSBVQDsAOAKKlooAKKKKAK1/by3enz28N1JaySIVWeIAtGfUZBGfrXlXwrsdZtfh7ql3pupPcXbzXMdrbXW1YY5A5+ckDPPU9q9ePSvKrXwb4iX4Nat4fWL7Nqs888kcfnL86NLu27lJA3Lx1780Ac94g1u98K6RHqqfE7+0dfR0M2mrLFJDISRuQIoyoHPPt2rtPEGq6xr/AIxsfCekai+lRGy+3313CoaXaThUTPAye9cprWma1q/gOfRNC+HI0mRYk8+SUxIX2kErHt5ckjqcfnXQXlrr9jr+leN9L0K4uDJp4stQ0t3VJ0UHKsvOCQe2f/rAEx0PxhZQa3pF3rt1daO1mZbXVG8v7RE4+9Ewx8wI74rlvDuqav4X+DuhtpuoST6hrU8dpZLcIpjtCzMCVAHI4zznmu10iTxd4i1LVL7UbKXSNJaya3tNOnZGkkkOcyPj7vpjNcrp/hjxDqPww03Sl0maw1zw7dR3FsLoqI7l1ZjhSCeCDjJxz+dAGhrsPib4d21n4gbxTe6zaLcRxaja3iLtKucFo8DK4J6VY8Q3viXVfiePDWj602nWM2lLcSzLGGaP5yCUB/iPAz2Gag1xvFXxDt7TQJvC1zotkZ45dQurqZGG1DnZGActkjrXRx6FqCfFttbFtjTP7HFqJt6/6zzM7dud3TvjFAFXQ9Q1SH4k6noM2oS3drZ6TbvGJcZaQ8FycdT3rkdYuNTsNNvtR1/4nR6brsfmPFplnNEYo8Z2p5eNzZ46+veuwPh3V2+IfiLU4k8i2vdIS2trrevEoz2B3DGQc4rjvD+k63pnhV/DsHw9RNbMUkU2qzvF5TlsjzPM5djz0FAFzW9XuNf8J/DjVbvb9putVtXk2jALYbJxXr1eQad4e8QX3grwVYNotxbXOi6pEbpJnRf3aZzIvzcjntz7V6/QAVyV3/yUOb/sFRf+jZK62uSu/wDkoc3/AGCov/RslAGpRRRQBz/ji/utM8EaxfWUxhuYLZnjkUDKn15riNXi8VWfgZfGDeKbpL+K3juTZoifZthx8hXGScHkk9a7nxrp13q/gvV9PsYvNuri3ZIo9wXcx7ZJAH41neINE1G++Fk2jW1vv1BrCOEQ71HzgLkZJx2PegDp7GdrrT7a4YANLErkDsSAag1HXNJ0cxjU9UsrIy58sXNwke/HXG4jOMj86l02GS30qzhlXbJHAiMM5wQoBq1QBhf8Jr4U/wChn0X/AMD4v/iqP+E18Kf9DPov/gfF/wDFVu0UAYX/AAmvhT/oZ9F/8D4v/iqyr7x3YwXTPZa34Xu7XAxG+rJDKPXn5lb/AMdrsqyr7S76+umI1u6tbXAxDaxxqx9cuwY/ligDnI/ix4VWRYr6+js5CcD99HOp/wCBQs4A+uK63TtU0/V7UXWm3tvdwE48yCQOM+nHf2rPi8I6Isiyz2f22ZTkS38jXLA+oMhOPwxW0qqiBEUKoGAAMAUAUdb0qPXNFvNLmlliiuojE7xEBgD1xkGuI8UavpOn+G38DaLA2qam9r9kis4FD+WMY3yEcLjrzXW+LBq7eFtQTQozJqbxFIAHVCCeMgsQBgZPWuE8NN4v8L6VHZWPw0j34BmnOswb537sxxkkn8qAO/8ADWmTaN4Y0zTbiTzJra2SJ2B4JA5rVqtp811cadbzXtp9kunQNLb+YJPLbuu4cH6irNABRRRQBx/xL1jUdD8JfbdLlMd0LqFB0+YFwCp9j0rE8Qalrngrw+hu/EC3GqaxdpCtxcoqQWWQdxUdNoHrW/8AEPRr/XfDKWemwefOLuCUpvVflVwScsQOlN8f+HbvXdLsp9Oihmv9NuluoYJ8bJscFDnjketAHFz+JT4WutPvbLx/D4hhluEhvLKaWJ2Kscb49vK49K6CSXWfGfi3V9Ps9audI0rSGWEtaBfNnmIySWIOAPSoIv7a1vUbG3s/BcGhwxyh726u4oX+UdUjAByT/eqeSDXPBvi3V9QsNFm1fStWZZmW2dRLBMBg5B6g+tAFPWr/AMYeHPA2pjUL3dcW17FHZ6goTfPCzgfMozg9uRW/8RdWvtH8ILeWFw0Fx9pgTeoBOGcAjn1FYWqaL4w8TeCdbOoRrFeXVxHPY6azqfJjRgQhYcbmx3P5VB4mfxb410CDT7bwtcWAiniluDdTxguVYfLHzyO+TjpQBe8QXXiLUfiT/wAI5pertp9nLponllVAzR4cglM/xHgZ7DNZ/iLVbu28X23h3VPFd1oemxWKNHegKr3kvQlpCCFx6V1K6Nfj4qPrX2f/AIl50oW4m3r/AKzzM425z074xVLxJca9a69Mtz4cXxD4dniXy4Y442eCQdcq33gaAK663rHhPwJrGp6jqlprUdu3/Euu43BMqsQFEmBjIJ7E1y91rt3pui/25B8TLS91eNBNLpzSwmCTuY1Ucg9gRz9K0NL8AX2peH/FMD2C6HbasyPZaeZA4hZOdzY4G4gcDpUpfxJcabHpVv4BtbTV8LG+oSrC1smOsg/veuP50Aej6RqC6to1lqKLtW6gSYL6bgDj9abqOuaTo5jGp6pZWRlz5YubhI9+OuNxGcZH51ZtITbWcMBKsY0VSVUKCQOoA4H0qagDC/4TXwp/0M+i/wDgfF/8VR/wmvhT/oZ9F/8AA+L/AOKrdooAwv8AhNfCn/Qz6L/4Hxf/ABVZV947sYLpnstb8L3drgYjfVkhlHrz8yt/47XZVlX2l319dMRrd1a2uBiG1jjVj65dgx/LFAHOR/FjwqsixX19HZyE4H76OdT/AMChZwB9cV1unapp+r2outNvbe7gJx5kEgcZ9OO/tWfF4R0RZFlns/tsynIlv5GuWB9QZCcfhitpVVECIoVQMAAYAoASRBJGyNnawIODg15baeGtM8N/GjSo9NilQXGnTSSmWZ5CzZxnLEmvVK5G+0XUJvilpWsx2+dPg0+WGSbevyuTwMZyfyoA66iiigAooooAztfuJbTw7qdxA5SaK1ldGH8LBSQa8+0XVdfsPA7eOda1iS5C6fmLTlQLGTwFZiOSxPJ9M16FrttNe+HtStbdN801rJHGuQMsVIAyeOtc5a+FLi9+E8Phm+AtrprBYXBYMI5AMjkEg8gdKAOJk1y8i0L+3V+JtnJrQj+0HTvNhNueM+UE657Z65rT8ReM7q+j8LM2qy6BpGq2xnuL6JMkPgYjDEHbz3ojPiRNHj0aPwBbJrCoIf7QcQtbDHHm56njnb/+qt3XF8SaQdMWHS4dd0dbbyb6ziijRjJ/fVSMY/2RQA3QTrFnp+tSp4lg1zSVtmksrvzVeaNwpJDFRgj8ateH9Xv7v4TxatPcs9+2nSSmYgZ3hWwfTtWD4a8MXs/iHWNVg0L/AIRzTruwNqtmWXMsh/5aFF4XHSoNKPi+18DnwenheZL2O3e1F886fZ9hz8wOck4PT1oAZPfXOp6b8Mb28lM1zPeI8kjYyxKHnirXh/SbxPjH4gZtavHEMMEjqypiZWBwjcdF7Y59ami8La0uj+AIDZ4l0mdXvV81P3QCkE9fm59M1oW1tqemfFjUrttKuZrDU7eCNLuIqUiKA5385FAHc1iyeMfC8MrxS+JNHSRGKsjX0QKkdQRu4NbVFAGF/wAJr4U/6GfRf/A+L/4qj/hNfCn/AEM+i/8AgfF/8VW7RQBz83jXwyYJBB4o0ITbTsL30ZUNjjIDdM1if8LItLP/AI/LnQrpB/y007WYGP1KSFMfQFq7iZZHgkWGQRylSEcruCnHBxxn6Vif8I1Jc86nrep3mesaTfZo/piIKSPqTQBR0z4l+ENVmEEOt20c5O3y528vn0BPyn8Ca6zqMis7T9B0nSmL2Om2sEh+9IkQDt9W6n8TWjQBz2q6NpUPiG38WaleGE6fbNEvmuoiQMeW5HXt1rl9PuD41+JGn+INKtpY9H0uCSI30iFBds3G1AeSo65o8eaZ4j1LxZp0tv4b/tzRLOLzPsrXscCPOSeWDH5sDHGMc1uaHrfi251GG11LwSul2OCGuF1OKUR4HA2KMnsKAOtrC8ZFh4SvyihnCrtUnAJ3r37Vu1h+MDjwren2T/0NaANXf4vuuBDounD+8ZZbw/8AfO2IfrTrbwxCbqK91a8uNVvIm3xtckCKJvVIlwoI7MQW96qp8RfCDPKja9axPFI0cizboyjA4IIYDByKtR+N/CkpwnibRyfT7dHn8t1AG9RTY5I5oklidXjdQyupyGB6EHuKdQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVyV3/AMlDm/7BUX/o2Sutrkrv/koc3/YKi/8ARslAGpRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWJ4v8A+RWvfon/AKGtbdYni/8A5Fa9+if+hrQBuap4ctdQuRfQSS2Opqu1b22wHI7K4PEi/wCywI9MHmuVGjXNjfrCbRdMv5X/AHd9p8LSWF03pPb5+Qn1/KTtXoVFAGNoustczSaXf2yWWqWygvbq2UePoJIjgbkPTplTwR0zs1keINIk1K1ins3SLVLN/Os5mHAfHKN/sMPlYehz1Aqzo2qR6zpFtqEaNGJl+aJvvRuDhkPurAg+4oAvUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVj6p4X0vWL5b26W7W5WIReZbX09uSgJOD5brnknrWxRQBzn/AAg+jf8APXWf/B3e/wDx2j/hB9G/566z/wCDu9/+O10dFAHOf8IPo3/PXWf/AAd3v/x2j/hB9G/566z/AODu9/8AjtdHRQBzn/CD6N/z11n/AMHd7/8AHaP+EH0b/nrrP/g7vf8A47XR0UAc5/wg+jf89dZ/8Hd7/wDHaP8AhB9G/wCeus/+Du9/+O10dFAHOf8ACD6N/wA9dZ/8Hd7/APHaP+EH0b/nrrP/AIO73/47XR0UAc5/wg+jf89dZ/8AB3e//HaP+EH0b/nrrP8A4O73/wCO10dFAHOf8IPo3/PXWf8Awd3v/wAdo/4QfRv+eus/+Du9/wDjtdHRQBzn/CD6N/z11n/wd3v/AMdo/wCEH0b/AJ66z/4O73/47XR0UAc5/wAIPo3/AD11n/wd3v8A8do/4QfRv+eus/8Ag7vf/jtdHRQBzn/CD6N/z11n/wAHd7/8do/4QfRv+eus/wDg7vf/AI7XR0UAc5/wg+jf89dZ/wDB3e//AB2j/hB9G/566z/4O73/AOO10dFAHOf8IPo3/PXWf/B3e/8Ax2j/AIQfRv8AnrrP/g7vf/jtdHRQBzn/AAg+jf8APXWf/B3e/wDx2j/hB9G/566z/wCDu9/+O10dFAHOf8IPo3/PXWf/AAd3v/x2j/hB9G/566z/AODu9/8AjtdHRQBzn/CD6N/z11n/AMHd7/8AHaP+EH0b/nrrP/g7vf8A47XR0UAc5/wg+jf89dZ/8Hd7/wDHaP8AhB9G/wCeus/+Du9/+O10dFAHOf8ACD6N/wA9dZ/8Hd7/APHaP+EH0b/nrrP/AIO73/47XR0UAc5/wg+jf89dZ/8AB3e//HaP+EH0b/nrrP8A4O73/wCO10dFAHOf8IPo3/PXWf8Awd3v/wAdo/4QfRv+eus/+Du9/wDjtdHRQBzn/CD6N/z11n/wd3v/AMdo/wCEH0b/AJ66z/4O73/47XR0UAc5/wAIPo3/AD11n/wd3v8A8do/4QfRv+eus/8Ag7vf/jtdHRQBzn/CD6N/z11n/wAHd7/8do/4QfRv+eus/wDg7vf/AI7XR0UAc5/wg+jf89dZ/wDB3e//AB2j/hB9G/566z/4O73/AOO10dFAHOf8IPo3/PXWf/B3e/8Ax2j/AIQfRv8AnrrP/g7vf/jtdHRQBzn/AAg+jf8APXWf/B3e/wDx2j/hB9G/566z/wCDu9/+O10dFAHOf8IPo3/PXWf/AAd3v/x2j/hB9G/566z/AODu9/8AjtdHRQBzn/CD6N/z11n/AMHd7/8AHaP+EH0b/nrrP/g7vf8A47XR0UAc5/wg+jf89dZ/8Hd7/wDHaP8AhB9G/wCeus/+Du9/+O10dFAHOf8ACD6N/wA9dZ/8Hd7/APHaP+EH0b/nrrP/AIO73/47XR0UAc5/wg+jf89dZ/8AB3e//HaP+EH0b/nrrP8A4O73/wCO10dFAHOf8IPo3/PXWf8Awd3v/wAdpreA9CkAWX+1JUyCUl1i7dTg5GVMpB5HQ10tFABRRRQAVz2iD7F4m8QaaOI2eK/jXsBKCrY+rxO31Y10Nc/B83xDvtv8GlW+/wBsyzY/9BNAG+SACScAdSa5lfHWk3HjS18M2U0V5cSwSTSSQShlh29FOO5547V0rosiMjqGVgQQehFeXf2HpWhfHPQ4dJ061sY5NKnZ1t4ggY5xk4oA9TooooAKKKKACiik3KWK5G4dRnmgBaKKKACiiigAooooAKKTcu4rkZHbNLQAUUhZVxkgZ4GTS0AFc5q/iLUbTXhpWnaTBduLUXLyTXhhABYqAAEbP3T6V0dcld/8lDm/7BUX/o2SgCT+3fE//Qu6f/4NW/8AjNH9u+J/+hd0/wD8Grf/ABmtCigDP/t3xP8A9C7p/wD4NW/+M0f274n/AOhd0/8A8Grf/Ga0KKAM/wDt3xP/ANC7p/8A4NW/+M0f274n/wChd0//AMGrf/Ga0KKAM/8At3xP/wBC7p//AINW/wDjNH9u+J/+hd0//wAGrf8AxmtCigDP/t3xP/0Lun/+DVv/AIzR/bvif/oXdP8A/Bq3/wAZrQooAz/7d8T/APQu6f8A+DVv/jNH9u+J/wDoXdP/APBq3/xmtCigDP8A7d8T/wDQu6f/AODVv/jNH9u+J/8AoXdP/wDBq3/xmtCigDP/ALd8T/8AQu6f/wCDVv8A4zR/bvif/oXdP/8ABq3/AMZrQooAz/7d8T/9C7p//g1b/wCM0f274n/6F3T/APwat/8AGa0KKAM/+3fE/wD0Lun/APg1b/4zR/bvif8A6F3T/wDwat/8ZrQooAz/AO3fE/8A0Lun/wDg1b/4zR/bvif/AKF3T/8Awat/8ZrQooAz/wC3fE//AELun/8Ag1b/AOM0f274n/6F3T//AAat/wDGa0KKAM/+3fE//Qu6f/4NW/8AjNH9u+J/+hd0/wD8Grf/ABmtCigDP/t3xP8A9C7p/wD4NW/+M0f274n/AOhd0/8A8Grf/Ga0KKAM/wDt3xP/ANC7p/8A4NW/+M0f274n/wChd0//AMGrf/Ga0KKAM/8At3xP/wBC7p//AINW/wDjNH9u+J/+hd0//wAGrf8AxmtCigDP/t3xP/0Lun/+DVv/AIzR/bvif/oXdP8A/Bq3/wAZrQooAz/7d8T/APQu6f8A+DVv/jNH9u+J/wDoXdP/APBq3/xmtCigDP8A7d8T/wDQu6f/AODVv/jNH9u+J/8AoXdP/wDBq3/xmtCigDP/ALd8T/8AQu6f/wCDVv8A4zR/bvif/oXdP/8ABq3/AMZrQooAz/7d8T/9C7p//g1b/wCM0f274n/6F3T/APwat/8AGa0KKAM/+3fE/wD0Lun/APg1b/4zR/bvif8A6F3T/wDwat/8ZrQooAz/AO3fE/8A0Lun/wDg1b/4zR/bvif/AKF3T/8Awat/8ZrQooAz/wC3fE//AELun/8Ag1b/AOM0f274n/6F3T//AAat/wDGa0KKAM/+3fE//Qu6f/4NW/8AjNH9u+J/+hd0/wD8Grf/ABmr5YAgEgZ6Z70tAGf/AG74n/6F3T//AAat/wDGaP7d8T/9C7p//g1b/wCM1oUUAZ/9u+J/+hd0/wD8Grf/ABmj+3fE/wD0Lun/APg1b/4zWhRQBn/274n/AOhd0/8A8Grf/GaP7d8T/wDQu6f/AODVv/jNaFFAGf8A274n/wChd0//AMGrf/GaP7d8T/8AQu6f/wCDVv8A4zWhRQBn/wBu+J/+hd0//wAGrf8Axmj+3fE//Qu6f/4NW/8AjNaFFAGf/bvif/oXdP8A/Bq3/wAZqtfeLNf020a7uvDtkIEK7zHqZZgCwGQDCM9fWtmsTxf/AMite/RP/Q1oA7SiiigArnfDJ+3Xmsa2P9VeXPlW59YYRsB+hfzGHswo12/nvbn/AIR7SpWW9mUG6uI/+XOA9Wz2dgCEHXPzdFNblnaQWFlBZ2sSxW8EaxxRr0VQMAflQBNXK3vhm9ufiZpfiRJbcWdrYy2zoWPmFmOQQMYx+NdVRQAUUUUAFFFFAFa/uZbSwnuIbWS6ljQssERAaQ+gyQM/WvJPhRqdpYaBr3iXVNLubeTzJprrVJWD+coc/uwAxOV6dK9kPSvG/DWuyeG/gfquqxWsdzJDeXO2OUZTJm25Yegzn8KAN0/FWa3tYNUv/CGr2mhTMu3UHKHarHCsyA5AORXQeJvGlv4fnsrG3sbnVNVvgWtrK1A3Mo6sxPCr7mvLfiH9tX4dm51fx7HevcCJoNPs4YYonJYHA2gsygZPXsK6n7ZbaV8ZdPvNRmjitb/Q1gs55GAQyKwLKG6ZIoA0k+Ikl7p+s2h0HUrTXrGDzG09ym8o3AkRs7WA6ms/wX4+ez+FcGt+JYr5RAgUXMpV3vWZjjYAck545xW1c+I9E1LxFq+l2NrHc6hbaY7z6hEqMsanOIi45z3x0rzlpET4MeBtQciSy07UYZr0L82xA7DJHsSKAO9tfiU8eoWMGv8AhnU9Et7+QR2t1cFWRnPRXwcoT6GrfiL4h2ugeIP7CXS76/1J7ZZ7eG1UMZiWI2j0xgkk8AVhfFnWNM1bwhaaXp15b3moajeW4so4JA7E7wdwx2A7+9W44x/wvvLgM6eHxhvfzeaAL2iX+l3PxB1CRtOurPWm0uCW6aWQFFQ8hMeoPU1nt8VJri2uNS0rwjq2oaJbswe/RkUMF+8yITlgMGmR3X2L4weK7sRGXyNDik8sdW25OPxxXOLqWo658ObjXtT8cW2l2NxbyldM06CGMIeQIt7ZcsehA55oA3/HOqWut2HgTVLJi1tda1bSxlhg4IPUetem14ejq/w5+F+1g2NUtgcHOD83Fe4UAFcld/8AJQ5v+wVF/wCjZK62uSu/+Shzf9gqL/0bJQBqUUUUAZ2u6vFoOhXuqzxvJFaxGRkTG4gema5K5+J32eyj1YeG9TfQjt3aj8gAB43BM7iue/Fa3xH/AOSda9/16PWP4pVU+B06qAANLiwB9FoA72KVJoUljbcjqGU+oPIp9UtG/wCQHp//AF7R/wDoIpuo2N5eGM2urXFgFzuEMUT7/rvU9Pb1oAv0Vhf2Lq//AENV/wD+A1t/8bo/sXV/+hqv/wDwGtv/AI3QBu1nTa9pNtqH2C41G2gu8AiKWQIzZ9M9fwqn/Yur/wDQ1X//AIDW3/xusXVrbVDI+ni91zUzgblFhaCLn1eRFU/gTQB24IIyDkGivLrX4feJPtAltdabQkznZaurA/WKNI48/wDfVeiaVZ3VjZLDealNqEw6zyxohPthFAoAtyFxGxjAL4O0E4BPavMbO78UP8YNLg8QfYoozYTvBBZSOyAdCWLAZPFeoVwWp/8AJbdD/wCwVP8A+hUAd7RRRQAUUUUAY3ijxJa+FdH/ALTvY3eASpE2zGV3HGeewrJTx/bRaFd6zqGl39jZRyKlr5yfvLvd93YnXn0NU/i4ofwSqsMqb62BB7/vBVT4tQyrp+gXa3L2lraanG89wiB/IBBAfaeCAfWgDRtPiCy6laWmueHtR0ZL1xHaz3G1kdj0ViD8pPoata141+w602jaTo95rOoxRiWeO3KqsKnpuZuAT2Fcf4jsY7ldLstT8e3eqC7u4jbW1ta27MzA5DfLghR65rX8K3ltpXxB8YWWozx293c3EdzCZmC+ZDtwNpPXFAGiPiRpp8MXWstZ3cclncLbXVnIoWWKQsBg84xz1ra8SeIoPDWjDUriGSWMyxxbI8Zy7AA8/WvPvG2u6TrXgvxJLpVmFjtb63S4vERQtwwdcsGHLY6ZPrV74qa5psvg2yt4L2CaW6u7doljcMSoYEtx29/egDf1/wAe22h67/Yq6be3uoPbiaCG2UMZSWI2j0xgkk8AVHqHjqW2vbfTLHw/fX+rvbLcz2kTKot1PZ3PGc1UCK3xyZiASuhjB9P3tVNdtNM1Dx/cLp3iG78P+I4bZN8pVDFcRnkfKxw+KAOh0Xxvp2qaPqF/cxTac2mMyX0FyMNCQM9uo9Kxh8TvLgj1G88Mava6HIwC6i6qQFPR2QHIX3rltW1bW9f8EeMdGuJ7XUZtMMR+32SbVuFyGYEDjcADnFWdTMUngc3V58Rp59LuIBH9mitLYu4IA8tVABz2xxjFAHrsciTRJLGweN1DKwPBB6GnVn6Faix8P6daq0rLDbRoDKMPgKPvDsaXUbG8vDGbXVriwC53CGKJ9/13qent60AX6Kwv7F1f/oar/wD8Brb/AON0f2Lq/wD0NV//AOA1t/8AG6AN2s6bXtJttQ+wXGo20F3gERSyBGbPpnr+FU/7F1f/AKGq/wD/AAGtv/jdYurW2qGR9PF7rmpnA3KLC0EXPq8iKp/AmgDtwQRkHINFeXWvw+8SfaBLa602hJnOy1dWB+sUaRx5/wC+q9E0qzurGyWG81KbUJh1nljRCfbCKBQBbkLiNjGAXwdoJwCe1eY2d34of4waXB4g+xRRmwneCCykdkA6EsWAyeK9QrgtT/5Lbof/AGCp/wD0KgDvaKKKACiiigCtqF4mnabdXsisyW8TSsq9SFBOB+VcvofxBttdVruHTLyDSY7Yzy6jOAsSsACUH94jnJHcGtzxP/yKmsf9eU3/AKAa4U2U91+zyltZITK2lqwVBy2MM35jNAGh/wALP22w1R/DGrroJPGolVxtzjeUzu2+9a+ueNbbS30+3sLK41a+1FDLa29rj5owM7yx4A561wzPG/gBb24+I0x0t7QRtbJaWxbBXBiC4zu7Y61bu9M0NW8LaX/bOp6PrMNhmwv2VU3J3RwTgn/ZoA6vRfHEWptqlteaZd6dqWmxedPaTlSSmMgqw4IrR07xJb6j4RTxFHBKtu1s1yImxuwATj0zxXGabres/wBr654Z1DUrPWxHpjzrf20QR1OCNjheM0zQde0u0+BkbS3sG5dPkgMYcbvMIYBcdc5I4oAm8Q6tFrtx8PdVhjeOK61ESqj43KCh4OKk0PxPrFz8U9Z06bTtQFmI4lWN5EKW3B/eEZ6Njtz61iQf8i/8K/8Ar6T/ANANdLosiL8YvFCs6hms7XaCeT8p6UAd5RRWJJo+qvK7J4mvo1LEhBb25Cj05jzQBt0Vhf2Lq/8A0NV//wCA1t/8bo/sXV/+hqv/APwGtv8A43QBtTTR28Ek0rbY41LscZwAMmqmn6zpmqqTp+oWt1j7whlViv1APH41mzaVqsEEkzeKNSZY1LEJZ27sQBnhRFkn2HNcpqGhat4gYMttfXDD7lzqdvZwY9wRE0i/98igD0uiuE0DwX4l0ydZLjxne+RkE2qqswx6b5AePooru+1AGJ4ll8RR2I/4RyHT3nO7e167KEGOCAoOT9aw/hNcXF18P7Sa6laWd5pi7scknzGzXaTf6iT/AHT/ACriPhD/AMk7tP8ArtN/6MagDuqwvGLKnhS+ZmCqAhJJwAN61u1h+MAG8K3wIBBCAg/760AbE3jPw3E/lrrFrcS/88rRvtEn/fEe5v0qBr3XtcHl6fZyaPZt969vFHnkf9M4ecH3kxj+4a6GKGKBNkMaRr/dRQB+lPoAo6VpFpo1oYLVXO9zJLLIxeSZz1d2PJJ/oAMAAVeoooAKKKKACiiigAooooAKpxaTpsFhJYQ6fax2cu7fbpCqxtu+9lQMHPf1q5RQBzdr8P8AwjZRXMdv4d09EuUKSjyQdyntk9B9Ktz+E9AudDh0WfSLSXTYBiK3eMMqfTPIPvWzRQBmaP4e0fQLN7TSdNtrOBzl0ijA3n/a9fxqLTfCug6Pb3dvp+k2tvBeHNxEsY2SfUHjHJ4rYooAwdJ8E+GNCvmvdL0Oytbk5/exxDcM9cHt+Fa32Cz/ALQ+3/ZIPtpj8r7R5Y8zZnO3d1xnnFWKKAK62Fml9JfLaQLdyoI5JxGBI6jopbGSB6VjW/gTwpa6m2pQeH9PS7YkmQQDgnqQOgP0roaKAMex8J+H9NtktrTRrKOGOf7TGnkhgkv99c9G9xWxRRQAVyV3/wAlDm/7BUX/AKNkrra5K7/5KHN/2Cov/RslAGpRRRQBFc20F5bSW11BHPBINrxSoGVh6EHg0yWws57E2M1pBJaFAhgeMGMqOg2njHtViigBqIsaKiKFRQAqqMAD0FOoooAKKKKACiiigAooooAKrtY2j30d89rA13GhRJzGDIqnqA3UD2qxRQAUUUUAFFFFAEF3ZWmoQ+Te2sNzEGDbJow65HIOD3FPnt4bq3eC4iSWGRdrxyKGVh6EHrUlFAGJpXhDw7od211pmjWdtcHI8yOMbgPQHt+FTaz4a0TxAI/7W0y2vDH9xpUyy+wPXFatFAFKHRtMt9L/ALLhsLZLAqVNssQ8sg9QV6Gs6y8FeGNOSVLXQdPRZSC4MCtuwcjrnoQDW9RQBB9htPt3277LB9r2eX5/ljzNmc7d3XGe1UNZ8L6H4hMZ1bS7a7aMYR5E+ZR6A9ce1a1FAFPTtJ0/SLIWenWUFrbDny4UCgn1OOprNg8E+GLbUxqMOg2CXYbcJFhHDeoHQH3Fb1FABRRRQAUUUUAFFFFABRRRQAVXaxtHvo757WBruNCiTmMGRVPUBuoHtViigAooooAKKKKAGSxRzxPFLGskbqVdHGQwPUEdxTbe2gtLaO3toY4YI12pFGgVVHoAOAKlooAwF8EeF11P+0V0GwF3u3+Z5I+9646Z98Ve1fQdJ1+3WDVtPt7yNTlRKgO0+x6j8K0aKAM7SNA0nQYGg0rTrezjc5YRIAWPuep/GqUPgrwxBqL6hFoVgt0+SZPJHU9TjoK3qKAKY0nTRHaxjT7QJZndbL5K4gPqgx8v4VBP4d0e51qHWJtPgfUYRiO5K/OB9fxNadFABRRRQAUUUUAFFFFABRRRQAEAggjINQWdlaafbi3srWG2gUkiKGMIoJ5PA4qeigArD8YDPhW9Hsn/AKGtblYni/8A5Fa9+if+hrQBV1PQvFGlXkt1Z+I9avNNbJ8iEW7XFv7qJIyJVHplWx/eNZ9nfeJkgN4fFM+q6YrFZLq0sYfNtz3Etv5YcY74Jb/ZAr06s2fQrCbVotUEbQ30eAZoXKGVR/BJjh19mzjtigCxp1zFeabbXEF3HeRvGCLiMjbJxywxxzVquW1KE+FbuTW7IEaXK+7U7VRlUz1uEHYjq47jJ6jnqFZXQMrBlYZBByCKAFooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArlNY07W18Vf2pplnZ3UL2S27LPdmFlZXZsjCNkYb2rq6KAOSz4s/6AWmf+DRv/AIzRnxZ/0AtM/wDBo3/xmutooA5LPiz/AKAWmf8Ag0b/AOM0Z8Wf9ALTP/Bo3/xmutooA5LPiz/oBaZ/4NG/+M0Z8Wf9ALTP/Bo3/wAZrraKAOSz4s/6AWmf+DRv/jNGfFn/AEAtM/8ABo3/AMZrraKAOSz4s/6AWmf+DRv/AIzRnxZ/0AtM/wDBo3/xmutooA5LPiz/AKAWmf8Ag0b/AOM0Z8Wf9ALTP/Bo3/xmutooA5LPiz/oBaZ/4NG/+M0Z8Wf9ALTP/Bo3/wAZrraKAOSz4s/6AWmf+DRv/jNGfFn/AEAtM/8ABo3/AMZrraKAOSz4s/6AWmf+DRv/AIzRnxZ/0AtM/wDBo3/xmutooA5LPiz/AKAWmf8Ag0b/AOM0Z8Wf9ALTP/Bo3/xmutooA5LPiz/oBaZ/4NG/+M0Z8Wf9ALTP/Bo3/wAZrraKAOSz4s/6AWmf+DRv/jNGfFn/AEAtM/8ABo3/AMZrraKAOSz4s/6AWmf+DRv/AIzRnxZ/0AtM/wDBo3/xmutooA5LPiz/AKAWmf8Ag0b/AOM0Z8Wf9ALTP/Bo3/xmutooA5LPiz/oBaZ/4NG/+M0Z8Wf9ALTP/Bo3/wAZrraKAOSz4s/6AWmf+DRv/jNGfFn/AEAtM/8ABo3/AMZrraKAOSz4s/6AWmf+DRv/AIzRnxZ/0AtM/wDBo3/xmutooA5LPiz/AKAWmf8Ag0b/AOM0Z8Wf9ALTP/Bo3/xmutooA5LPiz/oBaZ/4NG/+M0Z8Wf9ALTP/Bo3/wAZrraKAOSz4s/6AWmf+DRv/jNGfFn/AEAtM/8ABo3/AMZrraKAOSz4s/6AWmf+DRv/AIzRnxZ/0AtM/wDBo3/xmutooA5LPiz/AKAWmf8Ag0b/AOM0Z8Wf9ALTP/Bo3/xmutooA5LPiz/oBaZ/4NG/+M0Z8Wf9ALTP/Bo3/wAZrraKAOSz4s/6AWmf+DRv/jNGfFn/AEAtM/8ABo3/AMZrraKAOSz4s/6AWmf+DRv/AIzRnxZ/0AtM/wDBo3/xmutooA5LPiz/AKAWmf8Ag0b/AOM0Z8Wf9ALTP/Bo3/xmutooA5LPiz/oBaZ/4NG/+M0Z8Wf9ALTP/Bo3/wAZrraKAOSz4s/6AWmf+DRv/jNGfFn/AEAtM/8ABo3/AMZrraKAOSz4s/6AWmf+DRv/AIzRnxZ/0AtM/wDBo3/xmutooA5LPiz/AKAWmf8Ag0b/AOM0Z8Wf9ALTP/Bo3/xmutooA5LPiz/oBaZ/4NG/+M1S1bT/ABXq2myWLaRpsKylQ0n9pM20BgSceSM9PWu6ooAKKKKAEZQylWAKkYIPQ1z/AIT3Wdve6E5J/sm48iEnvAyh4v8AvlWCf8ANdDXP237r4g6iq/dm0y2dh/tLJMM/kQPwFAHQVG08KTpA0sazSAlIywDMB1wO+KdIHMbiNgrkHaxGQD24rySysdesfjnpK67raam8mmTvF5VsIUiXOCAoJz060Aeu0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVO71XTtPdUvb+1tnYZVZplQkeoyauVxuo2tvc/EOTz4IpdulR48xA2P3snrQBvf8ACS6F/wBBvTf/AAKT/Gj/AISXQv8AoN6b/wCBSf41n/2Vp3/Pha/9+V/wo/srTv8Anwtf+/K/4UAaH/CS6F/0G9N/8Ck/xo/4SXQv+g3pv/gUn+NZ/wDZWnf8+Fr/AN+V/wAKP7K07/nwtf8Avyv+FAGh/wAJLoX/AEG9N/8AApP8aP8AhJdC/wCg3pv/AIFJ/jWf/ZWnf8+Fr/35X/Cj+ytO/wCfC1/78r/hQBof8JLoX/Qb03/wKT/Gj/hJdC/6Dem/+BSf41n/ANlad/z4Wv8A35X/AAo/srTv+fC1/wC/K/4UAaH/AAkuhf8AQb03/wACk/xo/wCEl0L/AKDem/8AgUn+NZ/9lad/z4Wv/flf8KP7K07/AJ8LX/vyv+FAGh/wkuhf9BvTf/ApP8aP+El0L/oN6b/4FJ/jWf8A2Vp3/Pha/wDflf8ACj+ytO/58LX/AL8r/hQBof8ACS6F/wBBvTf/AAKT/Gj/AISXQv8AoN6b/wCBSf41n/2Vp3/Pha/9+V/wo/srTv8Anwtf+/K/4UAaH/CS6F/0G9N/8Ck/xo/4SXQv+g3pv/gUn+NZ/wDZWnf8+Fr/AN+V/wAKP7K07/nwtf8Avyv+FAGh/wAJLoX/AEG9N/8AApP8aP8AhJdC/wCg3pv/AIFJ/jWf/ZWnf8+Fr/35X/Cj+ytO/wCfC1/78r/hQBof8JLoX/Qb03/wKT/Gj/hJdC/6Dem/+BSf41n/ANlad/z4Wv8A35X/AAo/srTv+fC1/wC/K/4UAaH/AAkuhf8AQb03/wACk/xo/wCEl0L/AKDem/8AgUn+NZ/9lad/z4Wv/flf8KP7K07/AJ8LX/vyv+FAGh/wkuhf9BvTf/ApP8aP+El0L/oN6b/4FJ/jWf8A2Vp3/Pha/wDflf8ACj+ytO/58LX/AL8r/hQBof8ACS6F/wBBvTf/AAKT/Gj/AISXQv8AoN6b/wCBSf41n/2Vp3/Pha/9+V/wo/srTv8Anwtf+/K/4UAaH/CS6F/0G9N/8Ck/xo/4SXQv+g3pv/gUn+NZ/wDZWnf8+Fr/AN+V/wAKP7K07/nwtf8Avyv+FAGh/wAJLoX/AEG9N/8AApP8aP8AhJdC/wCg3pv/AIFJ/jWf/ZWnf8+Fr/35X/Cj+ytO/wCfC1/78r/hQBof8JLoX/Qb03/wKT/Gj/hJdC/6Dem/+BSf41n/ANlad/z4Wv8A35X/AAo/srTv+fC1/wC/K/4UAaH/AAkuhf8AQb03/wACk/xo/wCEl0L/AKDem/8AgUn+NZ/9lad/z4Wv/flf8KP7K07/AJ8LX/vyv+FAGh/wkuhf9BvTf/ApP8aP+El0L/oN6b/4FJ/jWf8A2Vp3/Pha/wDflf8ACj+ytO/58LX/AL8r/hQBof8ACS6F/wBBvTf/AAKT/Gj/AISXQv8AoN6b/wCBSf41n/2Vp3/Pha/9+V/wo/srTv8Anwtf+/K/4UAaH/CS6F/0G9N/8Ck/xo/4SXQv+g3pv/gUn+NZ/wDZWnf8+Fr/AN+V/wAKP7K07/nwtf8Avyv+FAGh/wAJLoX/AEG9N/8AApP8aP8AhJdC/wCg3pv/AIFJ/jWf/ZWnf8+Fr/35X/Cj+ytO/wCfC1/78r/hQBof8JLoX/Qb03/wKT/Gj/hJdC/6Dem/+BSf41n/ANlad/z4Wv8A35X/AAo/srTv+fC1/wC/K/4UAaH/AAkuhf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\",\n \"polygon\": [\n [\n 128.27197265625,\n 251.32977294921875\n ],\n [\n 245.56033325195312,\n 251.32977294921875\n ],\n [\n 245.56033325195312,\n 265.67596435546875\n ],\n [\n 128.27197265625,\n 265.67596435546875\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 251.32977294921875,\n 245.56033325195312,\n 265.67596435546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/78/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/78/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Following the flow of execution is one way to read programs, but it can quickly become labyrinthine. An alternative is what I call the \\\"leap of faith.\\\" When you come to a function call, instead of following the flow of execution, you assume that the function works correctly and returns the right result.
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\",\n \"polygon\": [\n [\n 129.392578125,\n 393.486328125\n ],\n [\n 526.236328125,\n 393.486328125\n ],\n [\n 526.236328125,\n 440.89190673828125\n ],\n [\n 129.392578125,\n 440.89190673828125\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 393.486328125,\n 526.236328125,\n 440.89190673828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"4\": \"/page/78/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/78/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The same is true of recursive programs. When you get to the recursive call, instead of following the flow of execution, you should assume that the recursive call works (yields the correct result) and then ask yourself, \\\"Assuming that I can find the factorial of n − 1, can I compute the factorial of n?\\\" In this case, it is clear that you can, by multiplying by n.
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\",\n \"polygon\": [\n [\n 86.4000015258789,\n 88.22021484375\n ],\n [\n 326.9967956542969,\n 88.22021484375\n ],\n [\n 326.9967956542969,\n 171.8143310546875\n ],\n [\n 86.4000015258789,\n 171.8143310546875\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 88.22021484375,\n 326.9967956542969,\n 171.8143310546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"If you try to follow the flow of execution here, even for fairly small values of n, your head explodes. But according to the leap of faith, if you assume that the two recursive calls work correctly, then it is clear that you get the right result by adding them together.
\",\n \"polygon\": [\n [\n 85.46484375,\n 175.8603515625\n ],\n [\n 482.4042053222656,\n 175.8603515625\n ],\n [\n 482.4042053222656,\n 211.50189208984375\n ],\n [\n 85.46484375,\n 211.50189208984375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 175.8603515625,\n 482.4042053222656,\n 211.50189208984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"6.8 Checking types
\",\n \"polygon\": [\n [\n 85.39013671875,\n 239.4217529296875\n ],\n [\n 220.27874755859375,\n 239.4217529296875\n ],\n [\n 220.27874755859375,\n 253.7679443359375\n ],\n [\n 85.39013671875,\n 253.7679443359375\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 239.4217529296875,\n 220.27874755859375,\n 253.7679443359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"What happens if we call factorial and give it 1.5 as an argument?
\",\n \"polygon\": [\n [\n 85.98779296875,\n 264.708984375\n ],\n [\n 381.00238037109375,\n 264.708984375\n ],\n [\n 381.00238037109375,\n 275.0599365234375\n ],\n [\n 85.98779296875,\n 275.0599365234375\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 264.708984375,\n 381.00238037109375,\n 275.0599365234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> factorial(1.5)
\",\n \"polygon\": [\n [\n 85.53955078125,\n 279.791015625\n ],\n [\n 180.55654907226562,\n 279.791015625\n ],\n [\n 180.55654907226562,\n 290.059326171875\n ],\n [\n 85.53955078125,\n 290.059326171875\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 279.791015625,\n 180.55654907226562,\n 290.059326171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"RuntimeError: Maximum recursion depth exceeded
\",\n \"polygon\": [\n [\n 85.763671875,\n 292.291748046875\n ],\n [\n 327.515625,\n 292.291748046875\n ],\n [\n 327.515625,\n 302.25433349609375\n ],\n [\n 85.763671875,\n 302.25433349609375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 292.291748046875,\n 327.515625,\n 302.25433349609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"It looks like an infinite recursion. But how can that be? There is a base case—when n == 0. But if n is not an integer, we can miss the base case and recurse forever.
\",\n \"polygon\": [\n [\n 85.763671875,\n 307.44073486328125\n ],\n [\n 482.3996887207031,\n 307.44073486328125\n ],\n [\n 482.3996887207031,\n 329.7478942871094\n ],\n [\n 85.763671875,\n 329.7478942871094\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 307.44073486328125,\n 482.3996887207031,\n 329.7478942871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"In the first recursive call, the value of n is 0.5. In the next, it is -0.5. From there, it gets smaller (more negative), but it will never be 0.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 338.7567443847656\n ],\n [\n 482.90625,\n 338.7567443847656\n ],\n [\n 482.90625,\n 361.0628967285156\n ],\n [\n 85.6142578125,\n 361.0628967285156\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 338.7567443847656,\n 482.90625,\n 361.0628967285156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"We have two choices. We can try to generalize the factorial function to work with floating-point numbers, or we can make factorial check the type of its argument. The first option is called the gamma function and it's a little beyond the scope of this book. So we'll go for the second.
\",\n \"polygon\": [\n [\n 85.763671875,\n 369.896484375\n ],\n [\n 482.607421875,\n 369.896484375\n ],\n [\n 482.607421875,\n 416.76690673828125\n ],\n [\n 85.763671875,\n 416.76690673828125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 369.896484375,\n 482.607421875,\n 416.76690673828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"We can use the built-in function isinstance to verify the type of the argument. While we're at it, we can also make sure the argument is positive:
\",\n \"polygon\": [\n [\n 85.46484375,\n 425.390625\n ],\n [\n 482.607421875,\n 425.390625\n ],\n [\n 482.607421875,\n 448.0829162597656\n ],\n [\n 85.46484375,\n 448.0829162597656\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 425.390625,\n 482.607421875,\n 448.0829162597656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \"def factorial (n):\\n if not isinstance(n, int):\\n print 'Factorial is only defined for integers.'\\n return None\\n elif n < 0:\\n print 'Factorial is not defined for negative integers.'\\n return None\\n elif n == 0:\\n return 1\\n else:\\n return n * factorial(n-1)
\",\n \"polygon\": [\n [\n 86.39997863769531,\n 453.1207580566406\n ],\n [\n 415.8353576660156,\n 453.1207580566406\n ],\n [\n 415.8353576660156,\n 585.87890625\n ],\n [\n 86.39997863769531,\n 585.87890625\n ]\n ],\n \"bbox\": [\n 86.39997863769531,\n 453.1207580566406,\n 415.8353576660156,\n 585.87890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The first base case handles nonintegers; the second catches negative integers. In both cases, the program prints an error message and returns None to indicate that something went wrong:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 590.1328125\n ],\n [\n 482.90625,\n 590.1328125\n ],\n [\n 482.90625,\n 624.7139282226562\n ],\n [\n 85.6142578125,\n 624.7139282226562\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 590.1328125,\n 482.90625,\n 624.7139282226562\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/79/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \">>> factorial('fred')\\nFactorial is only defined for integers.\\nNone\\n>>> factorial(-2)\\nFactorial is not defined for negative integers.\\nNone\",\n \"polygon\": [\n [\n 85.68896484375,\n 627.64453125\n ],\n [\n 333.791015625,\n 627.64453125\n ],\n [\n 333.791015625,\n 700.6863708496094\n ],\n [\n 85.68896484375,\n 700.6863708496094\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 627.64453125,\n 333.791015625,\n 700.6863708496094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/80/Page/171\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/80/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.49609375,\n 60.95654296875\n ],\n [\n 525.6033935546875,\n 60.95654296875\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.49609375,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 60.95654296875,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/80/PageHeader/15\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 515.1796875,\n 60.8115234375\n ],\n [\n 525.33984375,\n 60.8115234375\n ],\n [\n 525.33984375,\n 70.189453125\n ],\n [\n 515.1796875,\n 70.189453125\n ]\n ],\n \"bbox\": [\n 515.1796875,\n 60.8115234375,\n 525.33984375,\n 70.189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/80/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"If we get past both checks, then we know that n is positive or zero, so we can prove that the recursion terminates.
\",\n \"polygon\": [\n [\n 128.49609375,\n 88.66259765625\n ],\n [\n 525.6028442382812,\n 88.66259765625\n ],\n [\n 525.6028442382812,\n 110.99188232421875\n ],\n [\n 128.49609375,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 88.66259765625,\n 525.6028442382812,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/80/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"This program demonstrates a pattern sometimes called a guardian. The first two conditionals act as guardians, protecting the code that follows from values that might cause an error. The guardians make it possible to prove the correctness of the code.
\",\n \"polygon\": [\n [\n 129.392578125,\n 122.783203125\n ],\n [\n 525.638671875,\n 122.783203125\n ],\n [\n 525.638671875,\n 157.35186767578125\n ],\n [\n 129.392578125,\n 157.35186767578125\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 122.783203125,\n 525.638671875,\n 157.35186767578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/79/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/80/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"In Section 11.3 we will see a more flexible alternative to printing an error message: raising an exception.
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\",\n \"polygon\": [\n [\n 128.27197265625,\n 226.3597412109375\n ],\n [\n 236.373046875,\n 226.3597412109375\n ],\n [\n 236.373046875,\n 240.92578125\n ],\n [\n 128.27197265625,\n 240.92578125\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 226.3597412109375,\n 236.373046875,\n 240.92578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/80/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/80/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Breaking a large program into smaller functions creates natural checkpoints for debugging. If a function is not working, there are three possibilities to consider:
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 380.53125\n ],\n [\n 525.9375,\n 380.53125\n ],\n [\n 525.9375,\n 416.109375\n ],\n [\n 128.9443359375,\n 416.109375\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 380.53125,\n 525.9375,\n 416.109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/80/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/80/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"If the parameters look good, add a print statement before each return statement that displays the return value. If possible, check the result by hand. Consider calling the function with values that make it easy to check the result (as in Section 6.2).
\",\n \"polygon\": [\n [\n 128.49609375,\n 427.32421875\n ],\n [\n 525.6033935546875,\n 427.32421875\n ],\n [\n 525.6033935546875,\n 462.3677978515625\n ],\n [\n 128.49609375,\n 462.3677978515625\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 427.32421875,\n 525.6033935546875,\n 462.3677978515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/80/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/80/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"If the function seems to be working, look at the function call to make sure the return value is being used correctly (or used at all!).
\",\n \"polygon\": [\n [\n 128.6455078125,\n 474.1171875\n ],\n [\n 525.9375,\n 474.1171875\n ],\n [\n 525.9375,\n 496.546875\n ],\n [\n 128.6455078125,\n 496.546875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 474.1171875,\n 525.9375,\n 496.546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/80/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/80/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Adding print statements at the beginning and end of a function can help make the flow of execution more visible. For example, here is a version of factorial with print statements:
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\",\n \"polygon\": [\n [\n 91.21728515625,\n 85.89990234375\n ],\n [\n 270.73828125,\n 85.89990234375\n ],\n [\n 270.73828125,\n 238.60546875\n ],\n [\n 91.21728515625,\n 238.60546875\n ]\n ],\n \"bbox\": [\n 91.21728515625,\n 85.89990234375,\n 270.73828125,\n 238.60546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/80/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/81/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"If you are confused about the flow of execution, this kind of output can be helpful. It takes some time to develop effective scaffolding, but a little bit of scaffolding can save a lot of debugging.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 238.21875\n ],\n [\n 482.4032897949219,\n 238.21875\n ],\n [\n 482.4032897949219,\n 273.41015625\n ],\n [\n 86.2119140625,\n 273.41015625\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 238.21875,\n 482.4032897949219,\n 273.41015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"3\": \"/page/78/SectionHeader/9\",\n \"4\": \"/page/80/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/81/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"6.10 Glossary
\",\n \"polygon\": [\n [\n 86.39997863769531,\n 301.25390625\n ],\n [\n 184.02586364746094,\n 301.25390625\n ],\n [\n 184.02586364746094,\n 316.90087890625\n ],\n [\n 86.39997863769531,\n 316.90087890625\n ]\n ],\n \"bbox\": [\n 86.39997863769531,\n 301.25390625,\n 184.02586364746094,\n 316.90087890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/81/ListGroup/163\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.763671875,\n 325.6171875\n ],\n [\n 482.90625,\n 325.6171875\n ],\n [\n 482.90625,\n 509.31787109375\n ],\n [\n 85.763671875,\n 509.31787109375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 325.6171875,\n 482.90625,\n 509.31787109375\n ],\n \"children\": [\n {\n \"id\": \"/page/81/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \"temporary variable: A variable used to store an intermediate value in a complex calculation.\",\n \"polygon\": [\n [\n 86.39997863769531,\n 325.6171875\n ],\n [\n 482.4025573730469,\n 325.6171875\n ],\n [\n 482.4025573730469,\n 348.0168151855469\n ],\n [\n 86.39997863769531,\n 348.0168151855469\n ]\n ],\n \"bbox\": [\n 86.39997863769531,\n 325.6171875,\n 482.4025573730469,\n 348.0168151855469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/81/ListItem/5\",\n \"block_type\": \"ListItem\",\n \"html\": \"dead code: Part of a program that can never be executed, often because it appears after a return statement.\",\n \"polygon\": [\n [\n 86.361328125,\n 357.71484375\n ],\n [\n 482.90625,\n 357.71484375\n ],\n [\n 482.90625,\n 380.2768249511719\n ],\n [\n 86.361328125,\n 380.2768249511719\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 357.71484375,\n 482.90625,\n 380.2768249511719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/81/ListItem/6\",\n \"block_type\": \"ListItem\",\n \"html\": \"None: A special value returned by functions that have no return statement or a return statement without an argument.\",\n \"polygon\": [\n [\n 86.0625,\n 389.619140625\n ],\n [\n 482.3979187011719,\n 389.619140625\n ],\n [\n 482.3979187011719,\n 412.5368347167969\n ],\n [\n 86.0625,\n 412.5368347167969\n ]\n ],\n \"bbox\": [\n 86.0625,\n 389.619140625,\n 482.3979187011719,\n 412.5368347167969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/81/ListItem/7\",\n \"block_type\": \"ListItem\",\n \"html\": \"incremental development: A program development plan intended to avoid debugging by adding and testing only a small amount of code at a time.\",\n \"polygon\": [\n [\n 85.9130859375,\n 421.5234375\n ],\n [\n 482.4027099609375,\n 421.5234375\n ],\n [\n 482.4027099609375,\n 444.7968444824219\n ],\n [\n 85.9130859375,\n 444.7968444824219\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 421.5234375,\n 482.4027099609375,\n 444.7968444824219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/81/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"scaffolding: Code that is used during program development but is not part of the final version.\",\n \"polygon\": [\n [\n 85.763671875,\n 454.0078125\n ],\n [\n 482.40289306640625,\n 454.0078125\n ],\n [\n 482.40289306640625,\n 477.0568542480469\n ],\n [\n 85.763671875,\n 477.0568542480469\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 454.0078125,\n 482.40289306640625,\n 477.0568542480469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/81/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"guardian: A programming pattern that uses a conditional statement to check for and handle circumstances that might cause an error.\",\n \"polygon\": [\n [\n 86.0625,\n 486.4921875\n ],\n [\n 482.4032287597656,\n 486.4921875\n ],\n [\n 482.4032287597656,\n 509.31787109375\n ],\n [\n 86.0625,\n 509.31787109375\n ]\n ],\n \"bbox\": [\n 86.0625,\n 486.4921875,\n 482.4032287597656,\n 509.31787109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/81/SectionHeader/10\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"6.11 Exercises
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 278.7479248046875\n ],\n [\n 525.6039428710938,\n 278.7479248046875\n ],\n [\n 525.6039428710938,\n 325.3742370605469\n ],\n [\n 128.0478515625,\n 325.3742370605469\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 278.7479248046875,\n 525.6039428710938,\n 325.3742370605469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/82/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 6.6. A palindrome is a word that is spelled the same backward and forward, like \\\"noon\\\" and \\\"redivider\\\". Recursively, a word is a palindrome if the first and last letters are the same and the middle is a palindrome.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 423.0257873535156\n ],\n [\n 228.9813232421875,\n 423.0257873535156\n ],\n [\n 228.9813232421875,\n 445.182373046875\n ],\n [\n 128.0478515625,\n 445.182373046875\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 423.0257873535156,\n 228.9813232421875,\n 445.182373046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/82/Code/362\",\n \"block_type\": \"Code\",\n \"html\": \"def middle(word):\\n return word[1:-1]
\",\n \"polygon\": [\n [\n 128.197265625,\n 459.6087951660156\n ],\n [\n 239.44204711914062,\n 459.6087951660156\n ],\n [\n 239.44204711914062,\n 481.765380859375\n ],\n [\n 128.197265625,\n 481.765380859375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 459.6087951660156,\n 239.44204711914062,\n 481.765380859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/82/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"We'll see how they work in Chapter 8.
\",\n \"polygon\": [\n [\n 129.09375,\n 486.87890625\n ],\n [\n 281.5992736816406,\n 486.87890625\n ],\n [\n 281.5992736816406,\n 496.96923828125\n ],\n [\n 129.09375,\n 496.96923828125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 486.87890625,\n 281.5992736816406,\n 496.96923828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/82/ListGroup/363\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 140.150390625,\n 508.53515625\n ],\n [\n 525.9375,\n 508.53515625\n ],\n [\n 525.9375,\n 586.7042541503906\n ],\n [\n 140.150390625,\n 586.7042541503906\n ]\n ],\n \"bbox\": [\n 140.150390625,\n 508.53515625,\n 525.9375,\n 586.7042541503906\n ],\n \"children\": [\n {\n \"id\": \"/page/82/ListItem/10\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Type these functions into a file named palindrome.py and test them out. What happens if you call middle with a string with two letters? One letter? What about the empty string, which is written '' and contains no letters?\",\n \"polygon\": [\n [\n 140.8974609375,\n 508.53515625\n ],\n [\n 525.9375,\n 508.53515625\n ],\n [\n 525.9375,\n 542.9522399902344\n ],\n [\n 140.8974609375,\n 542.9522399902344\n ]\n ],\n \"bbox\": [\n 140.8974609375,\n 508.53515625,\n 525.9375,\n 542.9522399902344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/82/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Write a function called is_palindrome that takes a string argument and returns True if it is a palindrome and False otherwise. Remember that you can use the built-in function len to check the length of a string.\",\n \"polygon\": [\n [\n 140.150390625,\n 551.84765625\n ],\n [\n 525.638671875,\n 551.84765625\n ],\n [\n 525.638671875,\n 586.7042541503906\n ],\n [\n 140.150390625,\n 586.7042541503906\n ]\n ],\n \"bbox\": [\n 140.150390625,\n 551.84765625,\n 525.638671875,\n 586.7042541503906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/82/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ palindrome_ soln. py .
\",\n \"polygon\": [\n [\n 129.5998992919922,\n 598.2549591064453\n ],\n [\n 426.3625793457031,\n 598.2549591064453\n ],\n [\n 426.3625793457031,\n 608.2982482910156\n ],\n [\n 129.5998992919922,\n 608.2982482910156\n ]\n ],\n \"bbox\": [\n 129.5998992919922,\n 598.2549591064453,\n 426.3625793457031,\n 608.2982482910156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/82/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 6.7. A number, a, is a power of b if it is divisible by b and a/b is a power of b. Write a function called is_power that takes parameters a and b and returns True if a is a power of b. Note: you will have to think about the base case.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 647.1136627197266\n ],\n [\n 525.9375,\n 647.1136627197266\n ],\n [\n 525.9375,\n 669.2702713012695\n ],\n [\n 128.0478515625,\n 669.2702713012695\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 647.1136627197266,\n 525.9375,\n 669.2702713012695\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/82/TextInlineMath/15\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"One way to find the GCD of two numbers is based on the observation that if r is the remainder when a is divided by b, then gcd(a, b) = gcd(b, r). As a base case, we can use gcd(a, 0) = a.
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\",\n \"polygon\": [\n [\n 84.8671875,\n 88.171875\n ],\n [\n 482.607421875,\n 88.171875\n ],\n [\n 482.607421875,\n 98.6483154296875\n ],\n [\n 84.8671875,\n 98.6483154296875\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 88.171875,\n 482.607421875,\n 98.6483154296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/72/SectionHeader/1\",\n \"2\": \"/page/81/SectionHeader/3\",\n \"4\": \"/page/81/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/83/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Credit: This exercise is based on an example from Abelson and Sussman's Structure and Interpretation of Computer Programs.
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\",\n \"polygon\": [\n [\n 84.8671875,\n 256.201171875\n ],\n [\n 483.50390625,\n 256.201171875\n ],\n [\n 483.50390625,\n 278.96295166015625\n ],\n [\n 84.8671875,\n 278.96295166015625\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 256.201171875,\n 483.50390625,\n 278.96295166015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"3\": \"/page/85/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> x = x+1\\nNameError: name 'x' is not defined
\",\n \"polygon\": [\n [\n 85.0166015625,\n 285.2537841796875\n ],\n [\n 264.2071533203125,\n 285.2537841796875\n ],\n [\n 264.2071533203125,\n 307.4103698730469\n ],\n [\n 85.0166015625,\n 307.4103698730469\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 285.2537841796875,\n 264.2071533203125,\n 307.4103698730469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"3\": \"/page/85/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Before you can update a variable, you have to initialize it, usually with a simple assignment:
\",\n \"polygon\": [\n [\n 85.0166015625,\n 312.46875\n ],\n [\n 482.3976135253906,\n 312.46875\n ],\n [\n 482.3976135253906,\n 336.15692138671875\n ],\n [\n 85.0166015625,\n 336.15692138671875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 312.46875,\n 482.3976135253906,\n 336.15692138671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"3\": \"/page/85/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/TextInlineMath/10\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> x = 0 >>> x = x + 1
\",\n \"polygon\": [\n [\n 85.68896484375,\n 340.3125\n ],\n [\n 143.96044921875,\n 340.3125\n ],\n [\n 143.96044921875,\n 364.67578125\n ],\n [\n 85.68896484375,\n 364.67578125\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 340.3125,\n 143.96044921875,\n 364.67578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"3\": \"/page/85/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Updating a variable by adding 1 is called an increment; subtracting 1 is called a decrement.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 369.896484375\n ],\n [\n 482.39971923828125,\n 369.896484375\n ],\n [\n 482.39971923828125,\n 381.1568908691406\n ],\n [\n 85.3154296875,\n 381.1568908691406\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 369.896484375,\n 482.39971923828125,\n 381.1568908691406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"3\": \"/page/85/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"7.3 The while statement
\",\n \"polygon\": [\n [\n 85.83837890625,\n 421.3271179199219\n ],\n [\n 251.61328125,\n 421.3271179199219\n ],\n [\n 251.61328125,\n 437.7499694824219\n ],\n [\n 85.83837890625,\n 437.7499694824219\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 421.3271179199219,\n 251.61328125,\n 437.7499694824219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Computers are often used to automate repetitive tasks. Repeating identical or similar tasks without making errors is something that computers do well and people do poorly.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 449.3671875\n ],\n [\n 482.4033508300781,\n 449.3671875\n ],\n [\n 482.4033508300781,\n 472.58392333984375\n ],\n [\n 85.9130859375,\n 472.58392333984375\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 449.3671875,\n 482.4033508300781,\n 472.58392333984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"We have seen two programs, countdown and print_n, that use recursion to perform repetition, which is also called iteration. Because iteration is so common, Python provides several language features to make it easier. One is the for statement we saw in Section 4.2. We'll get back to that later.
\",\n \"polygon\": [\n [\n 85.763671875,\n 482.84576416015625\n ],\n [\n 482.90625,\n 482.84576416015625\n ],\n [\n 482.90625,\n 529.5409240722656\n ],\n [\n 85.763671875,\n 529.5409240722656\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 482.84576416015625,\n 482.90625,\n 529.5409240722656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Another is the while statement. Here is a version of countdown that uses a while statement:
\",\n \"polygon\": [\n [\n 85.763671875,\n 539.0859375\n ],\n [\n 482.607421875,\n 539.0859375\n ],\n [\n 482.607421875,\n 549.9149169921875\n ],\n [\n 85.763671875,\n 549.9149169921875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 539.0859375,\n 482.607421875,\n 549.9149169921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \"def countdown(n):\\n while n > 0:\\n print n\\n n = n-1\\n print 'Blastoff!'
\",\n \"polygon\": [\n [\n 86.40008544921875,\n 554.5546875\n ],\n [\n 196.2124481201172,\n 554.5546875\n ],\n [\n 196.2124481201172,\n 616.4296875\n ],\n [\n 86.40008544921875,\n 616.4296875\n ]\n ],\n \"bbox\": [\n 86.40008544921875,\n 554.5546875,\n 196.2124481201172,\n 616.4296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"You can almost read the while statement as if it were English. It means, \\\"While n is greater than 0, display the value of n and then reduce the value of n by 1. When you get to 0, display the word Blastoff!\\\"
\",\n \"polygon\": [\n [\n 85.9130859375,\n 620.68359375\n ],\n [\n 482.40118408203125,\n 620.68359375\n ],\n [\n 482.40118408203125,\n 655.8859252929688\n ],\n [\n 85.9130859375,\n 655.8859252929688\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 620.68359375,\n 482.40118408203125,\n 655.8859252929688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"More formally, here is the flow of execution for a while statement:
\",\n \"polygon\": [\n [\n 85.53955078125,\n 665.9296875\n ],\n [\n 376.88201904296875,\n 665.9296875\n ],\n [\n 376.88201904296875,\n 676.2609329223633\n ],\n [\n 85.53955078125,\n 676.2609329223633\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 665.9296875,\n 376.88201904296875,\n 676.2609329223633\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/85/ListItem/19\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Evaluate the condition, yielding True or False.\",\n \"polygon\": [\n [\n 96.521484375,\n 690.29296875\n ],\n [\n 318.5557556152344,\n 690.29296875\n ],\n [\n 318.5557556152344,\n 700.8349304199219\n ],\n [\n 96.521484375,\n 700.8349304199219\n ]\n ],\n \"bbox\": [\n 96.521484375,\n 690.29296875,\n 318.5557556152344,\n 700.8349304199219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/86/Page/234\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/86/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.42138671875,\n 61.11871337890625\n ],\n [\n 525.5996704101562,\n 61.11871337890625\n ],\n [\n 525.5996704101562,\n 71.13372802734375\n ],\n [\n 128.42138671875,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 61.11871337890625,\n 525.5996704101562,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/PageHeader/16\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 514.880859375,\n 61.1982421875\n ],\n [\n 525.041015625,\n 61.1982421875\n ],\n [\n 525.041015625,\n 70.2861328125\n ],\n [\n 514.880859375,\n 70.2861328125\n ]\n ],\n \"bbox\": [\n 514.880859375,\n 61.1982421875,\n 525.041015625,\n 70.2861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/ListGroup/233\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 140.8974609375,\n 88.51025390625\n ],\n [\n 525.5966796875,\n 88.51025390625\n ],\n [\n 525.5966796875,\n 133.4578857421875\n ],\n [\n 140.8974609375,\n 133.4578857421875\n ]\n ],\n \"bbox\": [\n 140.8974609375,\n 88.51025390625,\n 525.5966796875,\n 133.4578857421875\n ],\n \"children\": [\n {\n \"id\": \"/page/86/ListItem/1\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. If the condition is false, exit the while statement and continue execution at the next statement.\",\n \"polygon\": [\n [\n 140.8974609375,\n 88.51025390625\n ],\n [\n 525.5966796875,\n 88.51025390625\n ],\n [\n 525.5966796875,\n 110.99188232421875\n ],\n [\n 140.8974609375,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 140.8974609375,\n 88.51025390625,\n 525.5966796875,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. If the condition is true, execute the body and then go back to step 1.\",\n \"polygon\": [\n [\n 141.1962890625,\n 122.4931640625\n ],\n [\n 451.2322998046875,\n 122.4931640625\n ],\n [\n 451.2322998046875,\n 133.4578857421875\n ],\n [\n 141.1962890625,\n 133.4578857421875\n ]\n ],\n \"bbox\": [\n 141.1962890625,\n 122.4931640625,\n 451.2322998046875,\n 133.4578857421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/86/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"This type of flow is called a loop because the third step loops back around to the top.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 149.3701171875\n ],\n [\n 502.62890625,\n 149.3701171875\n ],\n [\n 502.62890625,\n 160.0938720703125\n ],\n [\n 129.2431640625,\n 160.0938720703125\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 149.3701171875,\n 502.62890625,\n 160.0938720703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The body of the loop should change the value of one or more variables so that eventually the condition becomes false and the loop terminates. Otherwise the loop will repeat forever, which is called an infinite loop. An endless source of amusement for computer scientists is the observation that the directions on shampoo, \\\"Lather, rinse, repeat,\\\" are an infinite loop.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 171.4130859375\n ],\n [\n 525.6033935546875,\n 171.4130859375\n ],\n [\n 525.6033935546875,\n 230.35784912109375\n ],\n [\n 128.9443359375,\n 230.35784912109375\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 171.4130859375,\n 525.6033935546875,\n 230.35784912109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"In the case of countdown, we can prove that the loop terminates because we know that the value of n is finite, and we can see that the value of n gets smaller each time through the loop, so eventually we have to get to 0. In other cases, it is not so easy to tell:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 241.732666015625\n ],\n [\n 525.59765625,\n 241.732666015625\n ],\n [\n 525.59765625,\n 276.23284912109375\n ],\n [\n 128.3466796875,\n 276.23284912109375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 241.732666015625,\n 525.59765625,\n 276.23284912109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \"def sequence(n):
\",\n \"polygon\": [\n [\n 127.8984375,\n 283.63665771484375\n ],\n [\n 213.36328125,\n 283.63665771484375\n ],\n [\n 213.36328125,\n 293.5992431640625\n ],\n [\n 127.8984375,\n 293.5992431640625\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 283.63665771484375,\n 213.36328125,\n 293.5992431640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"while n != 1:\\n print n,\\n if n%2 == 0: # n is even\\n n = n/2\\n else: # n is odd\\n n = n*3+1
\",\n \"polygon\": [\n [\n 147.99462890625,\n 295.259765625\n ],\n [\n 335.583984375,\n 295.259765625\n ],\n [\n 335.583984375,\n 366.7652587890625\n ],\n [\n 147.99462890625,\n 366.7652587890625\n ]\n ],\n \"bbox\": [\n 147.99462890625,\n 295.259765625,\n 335.583984375,\n 366.7652587890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The condition for this loop is n != 1, so the loop will continue until n is 1, which makes the condition false.
\",\n \"polygon\": [\n [\n 129.392578125,\n 374.150390625\n ],\n [\n 525.5995483398438,\n 374.150390625\n ],\n [\n 525.5995483398438,\n 396.62384033203125\n ],\n [\n 129.392578125,\n 396.62384033203125\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 374.150390625,\n 525.5995483398438,\n 396.62384033203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Each time through the loop, the program outputs the value of n and then checks whether it is even or odd. If it is even, n is divided by 2. If it is odd, the value of n is replaced with n*3+1. For example, if the argument passed to sequence is 3, the resulting sequence is 3, 10, 5, 16, 8, 4, 2, 1.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 407.98828125\n ],\n [\n 525.6026611328125,\n 407.98828125\n ],\n [\n 525.6026611328125,\n 454.69384765625\n ],\n [\n 128.6455078125,\n 454.69384765625\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 407.98828125,\n 525.6026611328125,\n 454.69384765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Since n sometimes increases and sometimes decreases, there is no obvious proof that n will ever reach 1, or that the program terminates. For some particular values of n, we can prove termination. For example, if the starting value is a power of two, then the value of n will be even each time through the loop until it reaches 1. The previous example ends with such a sequence, starting with 16.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 465.609375\n ],\n [\n 525.9375,\n 465.609375\n ],\n [\n 525.9375,\n 524.9578552246094\n ],\n [\n 128.6455078125,\n 524.9578552246094\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 465.609375,\n 525.9375,\n 524.9578552246094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The hard question is whether we can prove that this program terminates for all positive values of n. So far, no one has been able to prove it or disprove it! (See http: //en.wikipedia.org/wiki/Collatz_conjecture.)
\",\n \"polygon\": [\n [\n 129.09375,\n 535.60546875\n ],\n [\n 525.6057739257812,\n 535.60546875\n ],\n [\n 525.6057739257812,\n 570.8328552246094\n ],\n [\n 129.09375,\n 570.8328552246094\n ]\n ],\n \"bbox\": [\n 129.09375,\n 535.60546875,\n 525.6057739257812,\n 570.8328552246094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 7.1. Rewrite the function print_n from Section 5.8 using iteration instead of recursion.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 572.73046875\n ],\n [\n 524.7421875,\n 572.73046875\n ],\n [\n 524.7421875,\n 582.9297485351562\n ],\n [\n 128.6455078125,\n 582.9297485351562\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 572.73046875,\n 524.7421875,\n 582.9297485351562\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/85/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/SectionHeader/13\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"7.4 break
\",\n \"polygon\": [\n [\n 127.7490234375,\n 614.340087890625\n ],\n [\n 198.31837463378906,\n 614.340087890625\n ],\n [\n 198.31837463378906,\n 630.7629089355469\n ],\n [\n 127.7490234375,\n 630.7629089355469\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 614.340087890625,\n 198.31837463378906,\n 630.7629089355469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Sometimes you don't know it's time to end a loop until you get half way through the body. In that case you can use the break statement to jump out of the loop.
\",\n \"polygon\": [\n [\n 128.49609375,\n 644.9972534179688\n ],\n [\n 525.6034545898438,\n 644.9972534179688\n ],\n [\n 525.6034545898438,\n 667.1538619995117\n ],\n [\n 128.49609375,\n 667.1538619995117\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 644.9972534179688,\n 525.6034545898438,\n 667.1538619995117\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/86/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"For example, suppose you want to take input from the user until they type done. You could write:
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\",\n \"polygon\": [\n [\n 86.2119140625,\n 161.26171875\n ],\n [\n 154.37936401367188,\n 161.26171875\n ],\n [\n 154.37936401367188,\n 171.81427001953125\n ],\n [\n 86.2119140625,\n 171.81427001953125\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 161.26171875,\n 154.37936401367188,\n 171.81427001953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The loop condition is True, which is always true, so the loop runs until it hits the break statement.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 179.05078125\n ],\n [\n 482.4029235839844,\n 179.05078125\n ],\n [\n 482.4029235839844,\n 201.81085205078125\n ],\n [\n 85.3154296875,\n 201.81085205078125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 179.05078125,\n 482.4029235839844,\n 201.81085205078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Each time through, it prompts the user with an angle bracket. If the user types done, the break statement exits the loop. Otherwise the program echoes whatever the user types and goes back to the top of the loop. Here's a sample run:
\",\n \"polygon\": [\n [\n 85.46484375,\n 212.501953125\n ],\n [\n 482.40045166015625,\n 212.501953125\n ],\n [\n 482.40045166015625,\n 247.8248291015625\n ],\n [\n 85.46484375,\n 247.8248291015625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 212.501953125,\n 482.40045166015625,\n 247.8248291015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"> not done\\nnot done\\n> done\\nDone!
\",\n \"polygon\": [\n [\n 85.98779296875,\n 255.36566162109375\n ],\n [\n 138.70364379882812,\n 255.36566162109375\n ],\n [\n 138.70364379882812,\n 301.9112243652344\n ],\n [\n 85.98779296875,\n 301.9112243652344\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 255.36566162109375,\n 138.70364379882812,\n 301.9112243652344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This way of writing while loops is common because you can check the condition anywhere in the loop (not just at the top) and you can express the stop condition affirmatively (\\\"stop when this happens\\\") rather than negatively (\\\"keep going until that happens.\\\").
\",\n \"polygon\": [\n [\n 86.0625,\n 309.375\n ],\n [\n 482.40447998046875,\n 309.375\n ],\n [\n 482.40447998046875,\n 344.1796875\n ],\n [\n 86.0625,\n 344.1796875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 309.375,\n 482.40447998046875,\n 344.1796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"7.5 Square roots
\",\n \"polygon\": [\n [\n 85.763671875,\n 377.9056091308594\n ],\n [\n 201.16961669921875,\n 377.9056091308594\n ],\n [\n 201.16961669921875,\n 392.2518310546875\n ],\n [\n 85.763671875,\n 392.2518310546875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 377.9056091308594,\n 201.16961669921875,\n 392.2518310546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Loops are often used in programs that compute numerical results by starting with an approximate answer and iteratively improving it.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 406.44140625\n ],\n [\n 482.40338134765625,\n 406.44140625\n ],\n [\n 482.40338134765625,\n 428.83575439453125\n ],\n [\n 86.2119140625,\n 428.83575439453125\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 406.44140625,\n 482.40338134765625,\n 428.83575439453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"For example, one way of computing square roots is Newton's method. Suppose that you want to know the square root of a. If you start with almost any estimate, x, you can compute a better estimate with the following formula:
\",\n \"polygon\": [\n [\n 86.0625,\n 440.0859375\n ],\n [\n 482.4034423828125,\n 440.0859375\n ],\n [\n 482.4034423828125,\n 474.8497619628906\n ],\n [\n 86.0625,\n 474.8497619628906\n ]\n ],\n \"bbox\": [\n 86.0625,\n 440.0859375,\n 482.4034423828125,\n 474.8497619628906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Equation/10\",\n \"block_type\": \"Equation\",\n \"html\": \"\",\n \"polygon\": [\n [\n 255.498046875,\n 493.453125\n ],\n [\n 310.86724853515625,\n 493.453125\n ],\n [\n 310.86724853515625,\n 518.306884765625\n ],\n [\n 255.498046875,\n 518.306884765625\n ]\n ],\n \"bbox\": [\n 255.498046875,\n 493.453125,\n 310.86724853515625,\n 518.306884765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"For example, if a is 4 and x is 3:
\",\n \"polygon\": [\n [\n 86.361328125,\n 522.0703125\n ],\n [\n 223.673828125,\n 522.0703125\n ],\n [\n 223.673828125,\n 532.6678771972656\n ],\n [\n 86.361328125,\n 532.6678771972656\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 522.0703125,\n 223.673828125,\n 532.6678771972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> a = 4.0\\n>>> x = 3.0\\n>>> y = (x + a/x) / 2\\n>>> print y\\n2.16666666667
\",\n \"polygon\": [\n [\n 85.46484375,\n 539.859375\n ],\n [\n 196.23760986328125,\n 539.859375\n ],\n [\n 196.23760986328125,\n 598.9493408203125\n ],\n [\n 85.46484375,\n 598.9493408203125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 539.859375,\n 196.23760986328125,\n 598.9493408203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Which is closer to the correct answer (√ 4 = 2). If we repeat the process with the new estimate, it gets even closer:
\",\n \"polygon\": [\n [\n 85.46484375,\n 597.00830078125\n ],\n [\n 482.4031677246094,\n 597.00830078125\n ],\n [\n 482.4031677246094,\n 628.9459075927734\n ],\n [\n 85.46484375,\n 628.9459075927734\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 597.00830078125,\n 482.4031677246094,\n 628.9459075927734\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> x = y\\n>>> y = (x + a/x) / 2\\n>>> print y\\n2.00641025641
\",\n \"polygon\": [\n [\n 85.166015625,\n 636.4867553710938\n ],\n [\n 197.525390625,\n 636.4867553710938\n ],\n [\n 197.525390625,\n 683.032356262207\n ],\n [\n 85.166015625,\n 683.032356262207\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 636.4867553710938,\n 197.525390625,\n 683.032356262207\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/87/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"After a few more updates, the estimate is almost exact:
\",\n \"polygon\": [\n [\n 86.13720703125,\n 690.6796875\n ],\n [\n 326.3092956542969,\n 690.6796875\n ],\n [\n 326.3092956542969,\n 700.8349227905273\n ],\n [\n 86.13720703125,\n 700.8349227905273\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 690.6796875,\n 326.3092956542969,\n 700.8349227905273\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/88/Page/204\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/88/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.0478515625,\n 60.8115234375\n ],\n [\n 525.6033935546875,\n 60.8115234375\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.0478515625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 60.8115234375,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/PageHeader/13\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 514.880859375,\n 60.76318359375\n ],\n [\n 525.638671875,\n 60.76318359375\n ],\n [\n 525.638671875,\n 70.43115234375\n ],\n [\n 514.880859375,\n 70.43115234375\n ]\n ],\n \"bbox\": [\n 514.880859375,\n 60.76318359375,\n 525.638671875,\n 70.43115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> x = y\\n>>> y = (x + a/x) / 2\\n>>> print y\\n2.00001024003\\n>>> x = y\\n>>> y = (x + a/x) / 2\\n>>> print y\\n2.00000000003
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 87.8818359375\n ],\n [\n 242.2001953125,\n 87.8818359375\n ],\n [\n 242.2001953125,\n 184.00830078125\n ],\n [\n 129.60000610351562,\n 184.00830078125\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 87.8818359375,\n 242.2001953125,\n 184.00830078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"In general we don't know ahead of time how many steps it takes to get to the right answer, but we know when we get there because the estimate stops changing:
\",\n \"polygon\": [\n [\n 128.9443359375,\n 189.9755859375\n ],\n [\n 525.6033325195312,\n 189.9755859375\n ],\n [\n 525.6033325195312,\n 212.78289794921875\n ],\n [\n 128.9443359375,\n 212.78289794921875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 189.9755859375,\n 525.6033325195312,\n 212.78289794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> x = y\\n>>> y = (x + a/x) / 2\\n>>> print y\\n2.0\\n>>> x = y\\n>>> y = (x + a/x) / 2\\n>>> print y\\n2.0
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 219.10174560546875\n ],\n [\n 242.947265625,\n 219.10174560546875\n ],\n [\n 242.947265625,\n 314.42431640625\n ],\n [\n 129.60000610351562,\n 314.42431640625\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 219.10174560546875,\n 242.947265625,\n 314.42431640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"When y == x, we can stop. Here is a loop that starts with an initial estimate, x, and improves it until it stops changing:
\",\n \"polygon\": [\n [\n 129.392578125,\n 320.8927307128906\n ],\n [\n 525.603759765625,\n 320.8927307128906\n ],\n [\n 525.603759765625,\n 343.40625\n ],\n [\n 129.392578125,\n 343.40625\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 320.8927307128906,\n 525.603759765625,\n 343.40625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"while True:\\n print x\\n y = (x + a/x) / 2\\n if y == x:\\n break\\n x = y
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 349.5177307128906\n ],\n [\n 239.4322052001953,\n 349.5177307128906\n ],\n [\n 239.4322052001953,\n 420.45233154296875\n ],\n [\n 129.60000610351562,\n 420.45233154296875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 349.5177307128906,\n 239.4322052001953,\n 420.45233154296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"For most values of a this works fine, but in general it is dangerous to test float equality. Floating-point values are only approximately right: most rational numbers, like 1/3, and irrational numbers, like √ 2, can't be represented exactly with a float.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 426.1640625\n ],\n [\n 525.603271484375,\n 426.1640625\n ],\n [\n 525.603271484375,\n 462.451904296875\n ],\n [\n 128.6455078125,\n 462.451904296875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 426.1640625,\n 525.603271484375,\n 462.451904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Rather than checking whether x and y are exactly equal, it is safer to use the built-in function abs to compute the absolute value, or magnitude, of the difference between them:
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\",\n \"polygon\": [\n [\n 147.1728515625,\n 501.36773681640625\n ],\n [\n 265.5939636230469,\n 501.36773681640625\n ],\n [\n 265.5939636230469,\n 523.5243225097656\n ],\n [\n 147.1728515625,\n 523.5243225097656\n ]\n ],\n \"bbox\": [\n 147.1728515625,\n 501.36773681640625,\n 265.5939636230469,\n 523.5243225097656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"4\": \"/page/87/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Where epsilon has a value like 0.0000001 that determines how close is close enough. Exercise 7.2. Encapsulate this loop in a function called square_root that takes a as a parameter, chooses a reasonable value of x, and returns an estimate of the square root of a.
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\",\n \"polygon\": [\n [\n 128.12255859375,\n 594.0\n ],\n [\n 236.7947998046875,\n 594.0\n ],\n [\n 236.7947998046875,\n 608.9769439697266\n ],\n [\n 128.12255859375,\n 608.9769439697266\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 594.0,\n 236.7947998046875,\n 608.9769439697266\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"3\": \"/page/88/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Newton's method is an example of an algorithm: it is a mechanical process for solving a category of problems (in this case, computing square roots).
\",\n \"polygon\": [\n [\n 128.49609375,\n 621.0703125\n ],\n [\n 525.5950927734375,\n 621.0703125\n ],\n [\n 525.5950927734375,\n 643.8498992919922\n ],\n [\n 128.49609375,\n 643.8498992919922\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 621.0703125,\n 525.5950927734375,\n 643.8498992919922\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"3\": \"/page/88/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/88/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"It is not easy to define an algorithm. It might help to start with something that is not an algorithm. When you learned to multiply single-digit numbers, you probably memorized the multiplication table. In effect, you memorized 100 specific solutions. That kind of knowledge is not algorithmic.
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\",\n \"polygon\": [\n [\n 85.3154296875,\n 87.3984375\n ],\n [\n 483.50390625,\n 87.3984375\n ],\n [\n 483.50390625,\n 135.38092041015625\n ],\n [\n 85.3154296875,\n 135.38092041015625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 87.3984375,\n 483.50390625,\n 135.38092041015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"3\": \"/page/88/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/89/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Similarly, the techniques you learned for addition with carrying, subtraction with borrowing, and long division are all algorithms. One of the characteristics of algorithms is that they do not require any intelligence to carry out. They are mechanical processes in which each step follows from the last according to a simple set of rules.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 232.611328125\n ],\n [\n 482.90625,\n 232.611328125\n ],\n [\n 482.90625,\n 256.6378173828125\n ],\n [\n 85.6142578125,\n 256.6378173828125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 232.611328125,\n 482.90625,\n 256.6378173828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/86/SectionHeader/13\",\n \"3\": \"/page/88/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/89/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Some of the things that people do naturally, without difficulty or conscious thought, are the hardest to express algorithmically. Understanding natural language is a good example. We all do it, but so far no one has been able to explain how we do it, at least not in the form of an algorithm.
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\",\n \"polygon\": [\n [\n 85.46484375,\n 340.892578125\n ],\n [\n 193.341796875,\n 340.892578125\n ],\n [\n 193.341796875,\n 357.908203125\n ],\n [\n 85.46484375,\n 357.908203125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 340.892578125,\n 193.341796875,\n 357.908203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/89/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"As you start writing bigger programs, you might find yourself spending more time debugging. More code means more chances to make an error and more place for bugs to hide.
\",\n \"polygon\": [\n [\n 85.46484375,\n 368.15625\n ],\n [\n 482.90625,\n 368.15625\n ],\n [\n 482.90625,\n 391.74609375\n ],\n [\n 85.46484375,\n 391.74609375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 368.15625,\n 482.90625,\n 391.74609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/89/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"One way to cut your debugging time is \\\"debugging by bisection.\\\" For example, if there are 100 lines in your program and you check them one at a time, it would take 100 steps.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 411.46875\n ],\n [\n 484.1015625,\n 411.46875\n ],\n [\n 484.1015625,\n 435.8877868652344\n ],\n [\n 85.6142578125,\n 435.8877868652344\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 411.46875,\n 484.1015625,\n 435.8877868652344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/89/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Instead, try to break the problem in half. Look at the middle of the program, or near it, for an intermediate value you can check. Add a print statement (or something else that has a verifiable effect) and run the program.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 489.19921875\n ],\n [\n 483.205078125,\n 489.19921875\n ],\n [\n 483.205078125,\n 512.6607971191406\n ],\n [\n 85.763671875,\n 512.6607971191406\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 489.19921875,\n 483.205078125,\n 512.6607971191406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/89/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Every time you perform a check like this, you halve the number of lines you have to search. After six steps (which is fewer than 100), you would be down to one or two lines of code, at least in theory.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 521.296875\n ],\n [\n 483.50390625,\n 521.296875\n ],\n [\n 483.50390625,\n 557.1448059082031\n ],\n [\n 85.9130859375,\n 557.1448059082031\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 521.296875,\n 483.50390625,\n 557.1448059082031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/89/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"In practice it is not always clear what the \\\"middle of the program\\\" is and not always possible to check it. It doesn't make sense to count lines and find the exact midpoint. Instead, think about places in the program where there might be errors and places where it is easy to put a check. Then choose a spot where you think the chances are about the same that the bug is before or after the check.
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\",\n \"polygon\": [\n [\n 129.2431640625,\n 88.55859375\n ],\n [\n 484.69921875,\n 88.55859375\n ],\n [\n 484.69921875,\n 98.79791259765625\n ],\n [\n 129.2431640625,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 88.55859375,\n 484.69921875,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/90/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"initialization: An assignment that gives an initial value to a variable that will be updated.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 108.16015625\n ],\n [\n 525.6035766601562,\n 108.16015625\n ],\n [\n 525.6035766601562,\n 118.21990966796875\n ],\n [\n 128.9443359375,\n 118.21990966796875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 108.16015625,\n 525.6035766601562,\n 118.21990966796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/90/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"increment: An update that increases the value of a variable (often by one).
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\",\n \"polygon\": [\n [\n 128.794921875,\n 159.134765625\n ],\n [\n 398.63671875,\n 159.134765625\n ],\n [\n 398.63671875,\n 169.25689697265625\n ],\n [\n 128.794921875,\n 169.25689697265625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 159.134765625,\n 398.63671875,\n 169.25689697265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/90/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"iteration: Repeated execution of a set of statements using either a recursive function call or a loop.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 178.3740234375\n ],\n [\n 525.6027221679688,\n 178.3740234375\n ],\n [\n 525.6027221679688,\n 200.87286376953125\n ],\n [\n 128.3466796875,\n 200.87286376953125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 178.3740234375,\n 525.6027221679688,\n 200.87286376953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/90/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"infinite loop: A loop in which the terminating condition is never satisfied.
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\",\n \"polygon\": [\n [\n 128.49609375,\n 248.41668701171875\n ],\n [\n 221.73046875,\n 248.41668701171875\n ],\n [\n 221.73046875,\n 262.76287841796875\n ],\n [\n 128.49609375,\n 262.76287841796875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 248.41668701171875,\n 221.73046875,\n 262.76287841796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/13\",\n \"4\": \"/page/90/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/90/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 7.3. To test the square root algorithm in this chapter, you could compare it with math.sqrt. Write a function named test_square_root that prints a table like this:
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\",\n \"polygon\": [\n [\n 128.72021484375,\n 301.5776672363281\n ],\n [\n 386.982421875,\n 301.5776672363281\n ],\n [\n 386.982421875,\n 409.0942687988281\n ],\n [\n 128.72021484375,\n 409.0942687988281\n ]\n ],\n \"bbox\": [\n 128.72021484375,\n 301.5776672363281,\n 386.982421875,\n 409.0942687988281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/13\",\n \"4\": \"/page/90/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/90/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The first column is a number, a; the second column is the square root of a computed with the function from Section 7.5; the third column is the square root computed by math.sqrt; the fourth column is the absolute value of the difference between the two estimates.
\",\n \"polygon\": [\n [\n 128.197265625,\n 426.55078125\n ],\n [\n 525.6033935546875,\n 426.55078125\n ],\n [\n 525.6033935546875,\n 460.9491271972656\n ],\n [\n 128.197265625,\n 460.9491271972656\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 426.55078125,\n 525.6033935546875,\n 460.9491271972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/89/SectionHeader/13\",\n \"4\": \"/page/90/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/90/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 7.4. The built-in function eval takes a string and evaluates it using the Python interpreter. For example:
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\",\n \"polygon\": [\n [\n 128.197265625,\n 164.62750244140625\n ],\n [\n 220.84561157226562,\n 164.62750244140625\n ],\n [\n 220.84561157226562,\n 185.28997802734375\n ],\n [\n 128.197265625,\n 185.28997802734375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 164.62750244140625,\n 220.84561157226562,\n 185.28997802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/84/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Strings
\",\n \"polygon\": [\n [\n 129.2431640625,\n 220.4903564453125\n ],\n [\n 211.1220703125,\n 220.4903564453125\n ],\n [\n 211.1220703125,\n 245.27740478515625\n ],\n [\n 129.2431640625,\n 245.27740478515625\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 220.4903564453125,\n 211.1220703125,\n 245.27740478515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.1 A string is a sequence
\",\n \"polygon\": [\n [\n 128.49609375,\n 294.6708068847656\n ],\n [\n 304.5088806152344,\n 294.6708068847656\n ],\n [\n 304.5088806152344,\n 309.01702880859375\n ],\n [\n 128.49609375,\n 309.01702880859375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 294.6708068847656,\n 304.5088806152344,\n 309.01702880859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"A string is a sequence of characters. You can access the characters one at a time with the bracket operator:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 321.9742431640625\n ],\n [\n 526.53515625,\n 321.9742431640625\n ],\n [\n 526.53515625,\n 344.22796630859375\n ],\n [\n 129.2431640625,\n 344.22796630859375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 321.9742431640625,\n 526.53515625,\n 344.22796630859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \">>> fruit = 'banana' >>> letter = fruit[1]
\",\n \"polygon\": [\n [\n 128.57080078125,\n 350.7878112792969\n ],\n [\n 239.43765258789062,\n 350.7878112792969\n ],\n [\n 239.43765258789062,\n 372.990234375\n ],\n [\n 128.57080078125,\n 372.990234375\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 350.7878112792969,\n 239.43765258789062,\n 372.990234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The second statement selects character number 1 from fruit and assigns it to letter.
\",\n \"polygon\": [\n [\n 129.09375,\n 379.65380859375\n ],\n [\n 506.2596740722656,\n 379.65380859375\n ],\n [\n 506.2596740722656,\n 389.7659606933594\n ],\n [\n 129.09375,\n 389.7659606933594\n ]\n ],\n \"bbox\": [\n 129.09375,\n 379.65380859375,\n 506.2596740722656,\n 389.7659606933594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The expression in brackets is called an index. The index indicates which character in the sequence you want (hence the name).
\",\n \"polygon\": [\n [\n 129.2431640625,\n 399.8671875\n ],\n [\n 526.53515625,\n 399.8671875\n ],\n [\n 526.53515625,\n 422.6039733886719\n ],\n [\n 129.2431640625,\n 422.6039733886719\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 399.8671875,\n 526.53515625,\n 422.6039733886719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"But you might not get what you expect:
\",\n \"polygon\": [\n [\n 129.46728515625,\n 433.125\n ],\n [\n 304.505859375,\n 433.125\n ],\n [\n 304.505859375,\n 443.24798583984375\n ],\n [\n 129.46728515625,\n 443.24798583984375\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 433.125,\n 304.505859375,\n 443.24798583984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \">>> print letter a
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 449.8078308105469\n ],\n [\n 213.2858123779297,\n 449.8078308105469\n ],\n [\n 213.2858123779297,\n 477.2109375\n ],\n [\n 129.5999755859375,\n 477.2109375\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 449.8078308105469,\n 213.2858123779297,\n 477.2109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"For most people, the first letter of 'banana' is b, not a. But for computer scientists, the index is an offset from the beginning of the string, and the offset of the first letter is zero.
\",\n \"polygon\": [\n [\n 129.59994506835938,\n 478.673828125\n ],\n [\n 525.9375,\n 478.673828125\n ],\n [\n 525.9375,\n 501.1875\n ],\n [\n 129.59994506835938,\n 501.1875\n ]\n ],\n \"bbox\": [\n 129.59994506835938,\n 478.673828125,\n 525.9375,\n 501.1875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> letter = fruit[0]\\n>>> print letter\\nb
\",\n \"polygon\": [\n [\n 128.42138671875,\n 507.54083251953125\n ],\n [\n 239.43759155273438,\n 507.54083251953125\n ],\n [\n 239.43759155273438,\n 541.8914337158203\n ],\n [\n 128.42138671875,\n 541.8914337158203\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 507.54083251953125,\n 239.43759155273438,\n 541.8914337158203\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"So b is the 0th letter (\\\"zero-eth\\\") of 'banana', a is the 1th letter (\\\"one-eth\\\"), and n is the 2th (\\\"two-eth\\\") letter.
\",\n \"polygon\": [\n [\n 129.09375,\n 548.3671875\n ],\n [\n 525.9375,\n 548.3671875\n ],\n [\n 525.9375,\n 570.9079895019531\n ],\n [\n 129.09375,\n 570.9079895019531\n ]\n ],\n \"bbox\": [\n 129.09375,\n 548.3671875,\n 525.9375,\n 570.9079895019531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"You can use any expression, including variables and operators, as an index, but the value of the index has to be an integer. Otherwise you get:
\",\n \"polygon\": [\n [\n 128.49609375,\n 581.23828125\n ],\n [\n 526.53515625,\n 581.23828125\n ],\n [\n 526.53515625,\n 603.7460021972656\n ],\n [\n 128.49609375,\n 603.7460021972656\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 581.23828125,\n 526.53515625,\n 603.7460021972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \">>> letter = fruit[1.5]\\nTypeError: string indices must be integers, not float
\",\n \"polygon\": [\n [\n 128.57080078125,\n 610.3058471679688\n ],\n [\n 406.8192138671875,\n 610.3058471679688\n ],\n [\n 406.8192138671875,\n 632.4624481201172\n ],\n [\n 128.57080078125,\n 632.4624481201172\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 610.3058471679688,\n 406.8192138671875,\n 632.4624481201172\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"4\": \"/page/92/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/SectionHeader/14\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.2 len
\",\n \"polygon\": [\n [\n 127.67431640625,\n 661.3962249755859\n ],\n [\n 183.7425537109375,\n 661.3962249755859\n ],\n [\n 183.7425537109375,\n 677.8190460205078\n ],\n [\n 127.67431640625,\n 677.8190460205078\n ]\n ],\n \"bbox\": [\n 127.67431640625,\n 661.3962249755859,\n 183.7425537109375,\n 677.8190460205078\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/92/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"len is a built-in function that returns the number of characters in a string:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 690.7228393554688\n ],\n [\n 452.2249755859375,\n 690.7228393554688\n ],\n [\n 452.2249755859375,\n 700.8349990844727\n ],\n [\n 128.3466796875,\n 700.8349990844727\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 690.7228393554688,\n 452.2249755859375,\n 700.8349990844727\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/93/Page/198\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/93/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.66650390625\n ],\n [\n 482.4034118652344,\n 60.66650390625\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.66650390625,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/PageHeader/16\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.68896484375,\n 60.95654296875\n ],\n [\n 96.89501953125,\n 60.95654296875\n ],\n [\n 96.89501953125,\n 70.52783203125\n ],\n [\n 85.68896484375,\n 70.52783203125\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 60.95654296875,\n 96.89501953125,\n 70.52783203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> fruit = 'banana'\\n>>> len(fruit)\\n6
\",\n \"polygon\": [\n [\n 86.2119140625,\n 88.68572998046875\n ],\n [\n 191.548828125,\n 88.68572998046875\n ],\n [\n 191.548828125,\n 123.03729248046875\n ],\n [\n 86.2119140625,\n 123.03729248046875\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 88.68572998046875,\n 191.548828125,\n 123.03729248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"To get the last letter of a string, you might be tempted to try something like this:
\",\n \"polygon\": [\n [\n 86.4000015258789,\n 130.2275390625\n ],\n [\n 438.08203125,\n 130.2275390625\n ],\n [\n 438.08203125,\n 140.8623046875\n ],\n [\n 86.4000015258789,\n 140.8623046875\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 130.2275390625,\n 438.08203125,\n 140.8623046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> length = len(fruit)\\n>>> last = fruit[length]\\nIndexError: string index out of range
\",\n \"polygon\": [\n [\n 85.46484375,\n 148.13671875\n ],\n [\n 279.93341064453125,\n 148.13671875\n ],\n [\n 279.93341064453125,\n 182.48834228515625\n ],\n [\n 85.46484375,\n 182.48834228515625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 148.13671875,\n 279.93341064453125,\n 182.48834228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The reason for the IndexError is that there is no letter in 'banana' with the index 6. Since we started counting at zero, the six letters are numbered 0 to 5. To get the last character, you have to subtract 1 from length:
\",\n \"polygon\": [\n [\n 86.361328125,\n 189.2021484375\n ],\n [\n 482.40338134765625,\n 189.2021484375\n ],\n [\n 482.40338134765625,\n 224.5579833984375\n ],\n [\n 86.361328125,\n 224.5579833984375\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 189.2021484375,\n 482.40338134765625,\n 224.5579833984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> last = fruit[length-1]\\n>>> print last\\na
\",\n \"polygon\": [\n [\n 85.9130859375,\n 231.976806640625\n ],\n [\n 222.39944458007812,\n 231.976806640625\n ],\n [\n 222.39944458007812,\n 266.32843017578125\n ],\n [\n 85.9130859375,\n 266.32843017578125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 231.976806640625,\n 222.39944458007812,\n 266.32843017578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Alternatively, you can use negative indices, which count backward from the end of the string. The expression fruit[-1] yields the last letter, fruit[-2] yields the second to last, and so on.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 273.41015625\n ],\n [\n 482.40338134765625,\n 273.41015625\n ],\n [\n 482.40338134765625,\n 308.3970031738281\n ],\n [\n 85.9130859375,\n 308.3970031738281\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 273.41015625,\n 482.40338134765625,\n 308.3970031738281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/92/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.3 Traversal with a for loop
\",\n \"polygon\": [\n [\n 85.763671875,\n 339.75921630859375\n ],\n [\n 281.5877685546875,\n 339.75921630859375\n ],\n [\n 281.5877685546875,\n 356.18206787109375\n ],\n [\n 85.763671875,\n 356.18206787109375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 339.75921630859375,\n 281.5877685546875,\n 356.18206787109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"A lot of computations involve processing a string one character at a time. Often they start at the beginning, select each character in turn, do something to it, and continue until the end. This pattern of processing is called a traversal. One way to write a traversal is with a while loop:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 369.896484375\n ],\n [\n 482.4033203125,\n 369.896484375\n ],\n [\n 482.4033203125,\n 416.9840087890625\n ],\n [\n 85.6142578125,\n 416.9840087890625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 369.896484375,\n 482.4033203125,\n 416.9840087890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \"index = 0\\nwhile index < len(fruit):\\n letter = fruit[index]\\n print letter\\n index = index + 1
\",\n \"polygon\": [\n [\n 85.763671875,\n 424.4028625488281\n ],\n [\n 219.33984375,\n 424.4028625488281\n ],\n [\n 219.33984375,\n 483.3984375\n ],\n [\n 85.763671875,\n 483.3984375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 424.4028625488281,\n 219.33984375,\n 483.3984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"This loop traverses the string and displays each letter on a line by itself. The loop condition is index < len(fruit), so when index is equal to the length of the string, the condition is false, and the body of the loop is not executed. The last character accessed is the one with the index len(fruit)-1, which is the last character in the string.
\",\n \"polygon\": [\n [\n 85.763671875,\n 490.74609375\n ],\n [\n 482.40338134765625,\n 490.74609375\n ],\n [\n 482.40338134765625,\n 537.4070434570312\n ],\n [\n 85.763671875,\n 537.4070434570312\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 490.74609375,\n 482.40338134765625,\n 537.4070434570312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/93/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.1. Write a function that takes a string as an argument and displays the letters backward, one per line.
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\",\n \"polygon\": [\n [\n 107.4287109375,\n 172.4765625\n ],\n [\n 474.7243347167969,\n 172.4765625\n ],\n [\n 474.7243347167969,\n 373.77685546875\n ],\n [\n 107.4287109375,\n 373.77685546875\n ]\n ],\n \"bbox\": [\n 107.4287109375,\n 172.4765625,\n 474.7243347167969,\n 373.77685546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/94/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.4 String slices
\",\n \"polygon\": [\n [\n 128.9443359375,\n 401.02734375\n ],\n [\n 241.945068359375,\n 401.02734375\n ],\n [\n 241.945068359375,\n 415.7750244140625\n ],\n [\n 128.9443359375,\n 415.7750244140625\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 401.02734375,\n 241.945068359375,\n 415.7750244140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/94/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"A segment of a string is called a slice. Selecting a slice is similar to selecting a character:
\",\n \"polygon\": [\n [\n 129.5419921875,\n 426.55078125\n ],\n [\n 514.7135620117188,\n 426.55078125\n ],\n [\n 514.7135620117188,\n 436.80596923828125\n ],\n [\n 129.5419921875,\n 436.80596923828125\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 426.55078125,\n 514.7135620117188,\n 436.80596923828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/94/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> s = 'Monty Python'\\n>>> print s[0:5]\\nMonty\\n>>> print s[6:12]\\nPython
\",\n \"polygon\": [\n [\n 129.2431640625,\n 441.5838317871094\n ],\n [\n 244.64134216308594,\n 441.5838317871094\n ],\n [\n 244.64134216308594,\n 501.1875\n ],\n [\n 129.2431640625,\n 501.1875\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 441.5838317871094,\n 244.64134216308594,\n 501.1875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/94/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The operator [n:m] returns the part of the string from the \\\"n-eth\\\" character to the \\\"m-eth\\\" character, including the first but excluding the last. This behavior is counterintuitive, but it might help to imagine the indices pointing between the characters, as in Figure 8.1.
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\",\n \"polygon\": [\n [\n 129.09375,\n 547.98046875\n ],\n [\n 525.603271484375,\n 547.98046875\n ],\n [\n 525.603271484375,\n 570.8070068359375\n ],\n [\n 129.09375,\n 570.8070068359375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 547.98046875,\n 525.603271484375,\n 570.8070068359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/94/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> fruit = 'banana'\\n>>> fruit[:3]\\n'ban'\\n>>> fruit[3:]\\n'ana'
\",\n \"polygon\": [\n [\n 129.392578125,\n 575.05078125\n ],\n [\n 234.1833038330078,\n 575.05078125\n ],\n [\n 234.1833038330078,\n 634.60546875\n ],\n [\n 129.392578125,\n 634.60546875\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 575.05078125,\n 234.1833038330078,\n 634.60546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/94/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"If the first index is greater than or equal to the second the result is an empty string, represented by two quotation marks:
\",\n \"polygon\": [\n [\n 129.5419921875,\n 639.24609375\n ],\n [\n 525.6005249023438,\n 639.24609375\n ],\n [\n 525.6005249023438,\n 661.5570220947266\n ],\n [\n 129.5419921875,\n 661.5570220947266\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 639.24609375,\n 525.6005249023438,\n 661.5570220947266\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/94/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \">>> fruit = 'banana'\\n>>> fruit[3:3]\\n''
\",\n \"polygon\": [\n [\n 129.59994506835938,\n 665.54296875\n ],\n [\n 234.1833038330078,\n 665.54296875\n ],\n [\n 234.1833038330078,\n 697.25390625\n ],\n [\n 129.59994506835938,\n 697.25390625\n ]\n ],\n \"bbox\": [\n 129.59994506835938,\n 665.54296875,\n 234.1833038330078,\n 697.25390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/95/Page/216\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/95/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.95654296875\n ],\n [\n 482.4034118652344,\n 60.95654296875\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.95654296875,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/PageHeader/19\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.53955078125,\n 60.8115234375\n ],\n [\n 96.59619140625,\n 60.8115234375\n ],\n [\n 96.59619140625,\n 70.8662109375\n ],\n [\n 85.53955078125,\n 70.8662109375\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 60.8115234375,\n 96.59619140625,\n 70.8662109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"An empty string contains no characters and has length 0, but other than that, it is the same as any other string.
\",\n \"polygon\": [\n [\n 85.763671875,\n 88.55859375\n ],\n [\n 482.90625,\n 88.55859375\n ],\n [\n 482.90625,\n 110.99188232421875\n ],\n [\n 85.763671875,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 88.55859375,\n 482.90625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.3. Given that fruit is a string, what does fruit[:] mean?
\",\n \"polygon\": [\n [\n 85.53955078125,\n 112.341796875\n ],\n [\n 378.8342590332031,\n 112.341796875\n ],\n [\n 378.8342590332031,\n 123.0897216796875\n ],\n [\n 85.53955078125,\n 123.0897216796875\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 112.341796875,\n 378.8342590332031,\n 123.0897216796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"4\": \"/page/94/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.5 Strings are immutable
\",\n \"polygon\": [\n [\n 85.46484375,\n 150.43359375\n ],\n [\n 264.5080871582031,\n 150.43359375\n ],\n [\n 264.5080871582031,\n 165.4459228515625\n ],\n [\n 85.46484375,\n 165.4459228515625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 150.43359375,\n 264.5080871582031,\n 165.4459228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"It is tempting to use the [] operator on the left side of an assignment, with the intention of changing a character in a string. For example:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 175.8603515625\n ],\n [\n 482.39837646484375,\n 175.8603515625\n ],\n [\n 482.39837646484375,\n 198.92694091796875\n ],\n [\n 85.3154296875,\n 198.92694091796875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 175.8603515625,\n 482.39837646484375,\n 198.92694091796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> greeting = 'Hello, world!'\\n>>> greeting[0] = 'J'\\nTypeError: 'str' object does not support item assignment
\",\n \"polygon\": [\n [\n 85.53955078125,\n 203.02734375\n ],\n [\n 379.2892150878906,\n 203.02734375\n ],\n [\n 379.2892150878906,\n 238.3114013671875\n ],\n [\n 85.53955078125,\n 238.3114013671875\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 203.02734375,\n 379.2892150878906,\n 238.3114013671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The \\\"object\\\" in this case is the string and the \\\"item\\\" is the character you tried to assign. For
\",\n \"polygon\": [\n [\n 86.4000473022461,\n 238.21875\n ],\n [\n 482.4034423828125,\n 238.21875\n ],\n [\n 482.4034423828125,\n 262.58203125\n ],\n [\n 86.4000473022461,\n 262.58203125\n ]\n ],\n \"bbox\": [\n 86.4000473022461,\n 238.21875,\n 482.4034423828125,\n 262.58203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"now, an object is the same thing as a value, but we will refine that definition later. An item is one of the values in a sequence.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 255.740234375\n ],\n [\n 482.4001770019531,\n 255.740234375\n ],\n [\n 482.4001770019531,\n 283.078125\n ],\n [\n 85.3154296875,\n 283.078125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 255.740234375,\n 482.4001770019531,\n 283.078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The reason for the error is that strings are immutable, which means you can't change an existing string. The best you can do is create a new string that is a variation on the original:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 286.9453125\n ],\n [\n 482.90625,\n 286.9453125\n ],\n [\n 482.90625,\n 309.76171875\n ],\n [\n 85.9130859375,\n 309.76171875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 286.9453125,\n 482.90625,\n 309.76171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> greeting = 'Hello, world!'\\n>>> new_greeting = 'J' + greeting[1:]\\n>>> print new_greeting\\nJello, world!
\",\n \"polygon\": [\n [\n 85.763671875,\n 314.33880615234375\n ],\n [\n 279.8952331542969,\n 314.33880615234375\n ],\n [\n 279.8952331542969,\n 362.548828125\n ],\n [\n 85.763671875,\n 362.548828125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 314.33880615234375,\n 279.8952331542969,\n 362.548828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"This example concatenates a new first letter onto a slice of greeting. It has no effect on the original string.
\",\n \"polygon\": [\n [\n 86.0625,\n 365.8359375\n ],\n [\n 482.90625,\n 365.8359375\n ],\n [\n 482.90625,\n 388.3729553222656\n ],\n [\n 86.0625,\n 388.3729553222656\n ]\n ],\n \"bbox\": [\n 86.0625,\n 365.8359375,\n 482.90625,\n 388.3729553222656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.6 Searching
\",\n \"polygon\": [\n [\n 86.13720703125,\n 416.109375\n ],\n [\n 184.02597045898438,\n 416.109375\n ],\n [\n 184.02597045898438,\n 430.8046875\n ],\n [\n 86.13720703125,\n 430.8046875\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 416.109375,\n 184.02597045898438,\n 430.8046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/95/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"What does the following function do?
\",\n \"polygon\": [\n [\n 85.83837890625,\n 441.24609375\n ],\n [\n 252.80859375,\n 441.24609375\n ],\n [\n 252.80859375,\n 452.07421875\n ],\n [\n 85.83837890625,\n 452.07421875\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 441.24609375,\n 252.80859375,\n 452.07421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/95/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"def find(word, letter):\\n index = 0\\n while index < len(word):\\n if word[index] == letter:\\n return index\\n index = index + 1\\n return -1
\",\n \"polygon\": [\n [\n 86.40008544921875,\n 456.328125\n ],\n [\n 259.98046875,\n 456.328125\n ],\n [\n 259.98046875,\n 540.0814056396484\n ],\n [\n 86.40008544921875,\n 540.0814056396484\n ]\n ],\n \"bbox\": [\n 86.40008544921875,\n 456.328125,\n 259.98046875,\n 540.0814056396484\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/95/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"In a sense, find is the opposite of the [] operator. Instead of taking an index and extracting the corresponding character, it takes a character and finds the index where that character appears. If the character is not found, the function returns -1.
\",\n \"polygon\": [\n [\n 86.0625,\n 544.5\n ],\n [\n 483.205078125,\n 544.5\n ],\n [\n 483.205078125,\n 579.7639617919922\n ],\n [\n 86.0625,\n 579.7639617919922\n ]\n ],\n \"bbox\": [\n 86.0625,\n 544.5,\n 483.205078125,\n 579.7639617919922\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/95/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"This is the first example we have seen of a return statement inside a loop. If word[index] == letter, the function breaks out of the loop and returns immediately.
\",\n \"polygon\": [\n [\n 85.763671875,\n 587.8125\n ],\n [\n 482.4141540527344,\n 587.8125\n ],\n [\n 482.4141540527344,\n 611.0749664306641\n ],\n [\n 85.763671875,\n 611.0749664306641\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 587.8125,\n 482.4141540527344,\n 611.0749664306641\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/95/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"If the character doesn't appear in the string, the program exits the loop normally and returns -1.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 618.75\n ],\n [\n 482.4034729003906,\n 618.75\n ],\n [\n 482.4034729003906,\n 642.3859710693359\n ],\n [\n 85.6142578125,\n 642.3859710693359\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 618.75,\n 482.4034729003906,\n 642.3859710693359\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/95/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"This pattern of computation—traversing a sequence and returning when we find what we are looking for—is called a search.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 650.07421875\n ],\n [\n 482.90625,\n 650.07421875\n ],\n [\n 482.90625,\n 673.6969833374023\n ],\n [\n 85.9130859375,\n 673.6969833374023\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 650.07421875,\n 482.90625,\n 673.6969833374023\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/95/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/95/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.4. Modify find so that it has a third parameter, the index in word where it should start looking.
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\",\n \"polygon\": [\n [\n 127.82373046875,\n 85.95379638671875\n ],\n [\n 307.494140625,\n 85.95379638671875\n ],\n [\n 307.494140625,\n 100.546875\n ],\n [\n 127.82373046875,\n 100.546875\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 85.95379638671875,\n 307.494140625,\n 100.546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The following program counts the number of times the letter a appears in a string:
\",\n \"polygon\": [\n [\n 128.0478515625,\n 112.6187744140625\n ],\n [\n 491.1990966796875,\n 112.6187744140625\n ],\n [\n 491.1990966796875,\n 122.73095703125\n ],\n [\n 128.0478515625,\n 122.73095703125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 112.6187744140625,\n 491.1990966796875,\n 122.73095703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"word = 'banana'\\ncount = 0\\nfor letter in word:\\n if letter == 'a':\\n count = count + 1\\nprint count
\",\n \"polygon\": [\n [\n 128.0478515625,\n 128.87176513671875\n ],\n [\n 261.9228515625,\n 128.87176513671875\n ],\n [\n 261.9228515625,\n 199.80633544921875\n ],\n [\n 128.0478515625,\n 199.80633544921875\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 128.87176513671875,\n 261.9228515625,\n 199.80633544921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"This program demonstrates another pattern of computation called a counter. The variable count is initialized to 0 and then incremented each time an a is found. When the loop exits, count contains the result—the total number of a's.
\",\n \"polygon\": [\n [\n 127.8984375,\n 206.149169921875\n ],\n [\n 525.9375,\n 206.149169921875\n ],\n [\n 525.9375,\n 240.597900390625\n ],\n [\n 127.8984375,\n 240.597900390625\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 206.149169921875,\n 525.9375,\n 240.597900390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.5. Encapsulate this code in a function named count, and generalize it so that it accepts the string and the letter as arguments.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 242.6566162109375\n ],\n [\n 525.6008911132812,\n 242.6566162109375\n ],\n [\n 525.6008911132812,\n 264.81317138671875\n ],\n [\n 128.3466796875,\n 264.81317138671875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 242.6566162109375,\n 525.6008911132812,\n 264.81317138671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.6. Rewrite this function so that instead of traversing the string, it uses the threeparameter version of find from the previous section.
\",\n \"polygon\": [\n [\n 128.49609375,\n 267.04559326171875\n ],\n [\n 525.6040649414062,\n 267.04559326171875\n ],\n [\n 525.6040649414062,\n 289.2253112792969\n ],\n [\n 128.49609375,\n 289.2253112792969\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 267.04559326171875,\n 525.6040649414062,\n 289.2253112792969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.8 String methods
\",\n \"polygon\": [\n [\n 128.3466796875,\n 318.9787292480469\n ],\n [\n 262.68975830078125,\n 318.9787292480469\n ],\n [\n 262.68975830078125,\n 333.324951171875\n ],\n [\n 128.3466796875,\n 333.324951171875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 318.9787292480469,\n 262.68975830078125,\n 333.324951171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"A method is similar to a function—it takes arguments and returns a value—but the syntax is different. For example, the method upper takes a string and returns a new string with all uppercase letters:
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\",\n \"polygon\": [\n [\n 128.49609375,\n 390.2567443847656\n ],\n [\n 505.4187316894531,\n 390.2567443847656\n ],\n [\n 505.4187316894531,\n 400.368896484375\n ],\n [\n 128.49609375,\n 400.368896484375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 390.2567443847656,\n 505.4187316894531,\n 400.368896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> word = 'banana'\\n>>> new_word = word.upper()\\n>>> print new_word\\nBANANA
\",\n \"polygon\": [\n [\n 128.86962890625,\n 406.5097351074219\n ],\n [\n 270.8299255371094,\n 406.5097351074219\n ],\n [\n 270.8299255371094,\n 453.0553283691406\n ],\n [\n 128.86962890625,\n 453.0553283691406\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 406.5097351074219,\n 270.8299255371094,\n 453.0553283691406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"This form of dot notation specifies the name of the method, upper, and the name of the string to apply the method to, word. The empty parentheses indicate that this method takes no argument.
\",\n \"polygon\": [\n [\n 129.60011291503906,\n 459.3457336425781\n ],\n [\n 525.6026000976562,\n 459.3457336425781\n ],\n [\n 525.6026000976562,\n 493.8468933105469\n ],\n [\n 129.60011291503906,\n 493.8468933105469\n ]\n ],\n \"bbox\": [\n 129.60011291503906,\n 459.3457336425781,\n 525.6026000976562,\n 493.8468933105469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"A method call is called an invocation; in this case, we would say that we are invoking upper on the word.
\",\n \"polygon\": [\n [\n 129.392578125,\n 504.0121765136719\n ],\n [\n 525.638671875,\n 504.0121765136719\n ],\n [\n 525.638671875,\n 526.2658996582031\n ],\n [\n 129.392578125,\n 526.2658996582031\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 504.0121765136719,\n 525.638671875,\n 526.2658996582031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"As it turns out, there is a string method named find that is remarkably similar to the function we wrote:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 536.3787536621094\n ],\n [\n 525.9375,\n 536.3787536621094\n ],\n [\n 525.9375,\n 558.6849060058594\n ],\n [\n 128.6455078125,\n 558.6849060058594\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 536.3787536621094,\n 525.9375,\n 558.6849060058594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> word = 'banana'\\n>>> index = word.find('a')\\n>>> print index\\n1\",\n \"polygon\": [\n [\n 129.09375,\n 564.8257598876953\n ],\n [\n 265.5585021972656,\n 564.8257598876953\n ],\n [\n 265.5585021972656,\n 611.7890625\n ],\n [\n 129.09375,\n 611.7890625\n ]\n ],\n \"bbox\": [\n 129.09375,\n 564.8257598876953,\n 265.5585021972656,\n 611.7890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"In this example, we invoke find on word and pass the letter we are looking for as a parameter.
\",\n \"polygon\": [\n [\n 129.09375,\n 617.6627655029297\n ],\n [\n 525.6044921875,\n 617.6627655029297\n ],\n [\n 525.6044921875,\n 640.40625\n ],\n [\n 129.09375,\n 640.40625\n ]\n ],\n \"bbox\": [\n 129.09375,\n 617.6627655029297,\n 525.6044921875,\n 640.40625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Actually, the find method is more general than our function; it can find substrings, not just characters:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 650.0817718505859\n ],\n [\n 525.602783203125,\n 650.0817718505859\n ],\n [\n 525.602783203125,\n 672.50390625\n ],\n [\n 128.6455078125,\n 672.50390625\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 650.0817718505859,\n 525.602783203125,\n 672.50390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/96/Code/17\",\n \"block_type\": \"Code\",\n \"html\": \">>> word.find('na')\\n2\",\n \"polygon\": [\n [\n 129.09375,\n 678.5287628173828\n ],\n [\n 228.9545135498047,\n 678.5287628173828\n ],\n [\n 228.9545135498047,\n 700.6863632202148\n ],\n [\n 129.09375,\n 700.6863632202148\n ]\n ],\n \"bbox\": [\n 129.09375,\n 678.5287628173828,\n 228.9545135498047,\n 700.6863632202148\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/97/Page/244\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/97/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.95654296875\n ],\n [\n 482.607421875,\n 60.95654296875\n ],\n [\n 482.607421875,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.95654296875,\n 482.607421875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/PageHeader/19\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.763671875,\n 60.2314453125\n ],\n [\n 97.716796875,\n 60.2314453125\n ],\n [\n 97.716796875,\n 70.4794921875\n ],\n [\n 85.763671875,\n 70.4794921875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 60.2314453125,\n 97.716796875,\n 70.4794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"It can take as a second argument the index where it should start:
\",\n \"polygon\": [\n [\n 86.0625,\n 88.0751953125\n ],\n [\n 369.1186218261719,\n 88.0751953125\n ],\n [\n 369.1186218261719,\n 98.79791259765625\n ],\n [\n 86.0625,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 86.0625,\n 88.0751953125,\n 369.1186218261719,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \">>> word.find('na', 3)\\n4\",\n \"polygon\": [\n [\n 85.83837890625,\n 103.833984375\n ],\n [\n 201.4454803466797,\n 103.833984375\n ],\n [\n 201.4454803466797,\n 126.477294921875\n ],\n [\n 85.83837890625,\n 126.477294921875\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 103.833984375,\n 201.4454803466797,\n 126.477294921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"And as a third argument the index where it should stop:
\",\n \"polygon\": [\n [\n 86.4000244140625,\n 131.484375\n ],\n [\n 333.7314453125,\n 131.484375\n ],\n [\n 333.7314453125,\n 142.26190185546875\n ],\n [\n 86.4000244140625,\n 142.26190185546875\n ]\n ],\n \"bbox\": [\n 86.4000244140625,\n 131.484375,\n 333.7314453125,\n 142.26190185546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \">>> name = 'bob'\\n>>> name.find('b', 1, 2)\\n-1\",\n \"polygon\": [\n [\n 85.46484375,\n 147.78570556640625\n ],\n [\n 211.9075927734375,\n 147.78570556640625\n ],\n [\n 211.9075927734375,\n 182.13623046875\n ],\n [\n 85.46484375,\n 182.13623046875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 147.78570556640625,\n 211.9075927734375,\n 182.13623046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"This search fails because b does not appear in the index range from 1 to 2 (not including 2). Exercise 8.7. There is a string method called count that is similar to the function in the previous exercise. Read the documentation of this method and write an invocation that counts the number of as in 'banana'.
\",\n \"polygon\": [\n [\n 85.763671875,\n 187.751953125\n ],\n [\n 482.607421875,\n 187.751953125\n ],\n [\n 482.607421875,\n 234.354248046875\n ],\n [\n 85.763671875,\n 234.354248046875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 187.751953125,\n 482.607421875,\n 234.354248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.8. Read the documentation of the string methods at http: // docs. python. org/ 2/ library/ stdtypes. html# string-methods . You might want to experiment with some of them to make sure you understand how they work. strip and replace are particularly useful.
\",\n \"polygon\": [\n [\n 86.0625,\n 235.51171875\n ],\n [\n 482.40093994140625,\n 235.51171875\n ],\n [\n 482.40093994140625,\n 270.937255859375\n ],\n [\n 86.0625,\n 270.937255859375\n ]\n ],\n \"bbox\": [\n 86.0625,\n 235.51171875,\n 482.40093994140625,\n 270.937255859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The documentation uses a syntax that might be confusing. For example, in find(sub[, start[, end]]), the brackets indicate optional arguments. So sub is required, but start is optional, and if you include start, then end is optional.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 280.177734375\n ],\n [\n 482.4034118652344,\n 280.177734375\n ],\n [\n 482.4034118652344,\n 314.9332580566406\n ],\n [\n 86.2119140625,\n 314.9332580566406\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 280.177734375,\n 482.4034118652344,\n 314.9332580566406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/93/SectionHeader/7\",\n \"3\": \"/page/95/SectionHeader/3\",\n \"4\": \"/page/96/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.9 The in operator
\",\n \"polygon\": [\n [\n 85.9130859375,\n 341.6040344238281\n ],\n [\n 220.9252471923828,\n 341.6040344238281\n ],\n [\n 220.9252471923828,\n 358.0268859863281\n ],\n [\n 85.9130859375,\n 358.0268859863281\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 341.6040344238281,\n 220.9252471923828,\n 358.0268859863281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The word in is a boolean operator that takes two strings and returns True if the first appears as a substring in the second:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 369.31640625\n ],\n [\n 482.39691162109375,\n 369.31640625\n ],\n [\n 482.39691162109375,\n 391.9988098144531\n ],\n [\n 85.6142578125,\n 391.9988098144531\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 369.31640625,\n 482.39691162109375,\n 391.9988098144531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> 'a' in 'banana'\\nTrue\\n>>> 'seed' in 'banana'
\",\n \"polygon\": [\n [\n 85.6142578125,\n 397.52166748046875\n ],\n [\n 201.44131469726562,\n 397.52166748046875\n ],\n [\n 201.44131469726562,\n 432.3515625\n ],\n [\n 85.6142578125,\n 432.3515625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 397.52166748046875,\n 201.44131469726562,\n 432.3515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"False
\",\n \"polygon\": [\n [\n 86.0625,\n 434.10467529296875\n ],\n [\n 112.55177307128906,\n 434.10467529296875\n ],\n [\n 112.55177307128906,\n 444.7265625\n ],\n [\n 86.0625,\n 444.7265625\n ]\n ],\n \"bbox\": [\n 86.0625,\n 434.10467529296875,\n 112.55177307128906,\n 444.7265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"For example, the following function prints all the letters from word1 that also appear in word2:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 448.98046875\n ],\n [\n 482.398681640625,\n 448.98046875\n ],\n [\n 482.398681640625,\n 472.0468444824219\n ],\n [\n 85.6142578125,\n 472.0468444824219\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 448.98046875,\n 482.398681640625,\n 472.0468444824219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"def in_both(word1, word2):\\n for letter in word1:\\n if letter in word2:\\n print letter
\",\n \"polygon\": [\n [\n 85.6142578125,\n 477.5697021484375\n ],\n [\n 227.60986328125,\n 477.5697021484375\n ],\n [\n 227.60986328125,\n 524.1152954101562\n ],\n [\n 85.6142578125,\n 524.1152954101562\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 477.5697021484375,\n 227.60986328125,\n 524.1152954101562\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"With well-chosen variable names, Python sometimes reads like English. You could read this loop, \\\"for (each) letter in (the first) word, if (the) letter (appears) in (the second) word, print (the) letter.\\\"
\",\n \"polygon\": [\n [\n 85.46484375,\n 528.64453125\n ],\n [\n 482.4033508300781,\n 528.64453125\n ],\n [\n 482.4033508300781,\n 564.2878570556641\n ],\n [\n 85.46484375,\n 564.2878570556641\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 528.64453125,\n 482.4033508300781,\n 564.2878570556641\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Here's what you get if you compare apples and oranges:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 573.1171875\n ],\n [\n 334.538330078125,\n 573.1171875\n ],\n [\n 334.538330078125,\n 583.9453125\n ],\n [\n 85.6142578125,\n 583.9453125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 573.1171875,\n 334.538330078125,\n 583.9453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \">>> in_both('apples', 'oranges')\\na\\ne\\ns\",\n \"polygon\": [\n [\n 85.68896484375,\n 589.4176940917969\n ],\n [\n 253.73231506347656,\n 589.4176940917969\n ],\n [\n 253.73231506347656,\n 635.9633026123047\n ],\n [\n 85.68896484375,\n 635.9633026123047\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 589.4176940917969,\n 253.73231506347656,\n 635.9633026123047\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/SectionHeader/17\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.10 String comparison
\",\n \"polygon\": [\n [\n 85.98779296875,\n 664.7117080688477\n ],\n [\n 246.83203125,\n 664.7117080688477\n ],\n [\n 246.83203125,\n 679.0579071044922\n ],\n [\n 85.98779296875,\n 679.0579071044922\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 664.7117080688477,\n 246.83203125,\n 679.0579071044922\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/97/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/97/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"The relational operators work on strings. To see if two strings are equal:
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\",\n \"polygon\": [\n [\n 129.60000610351562,\n 88.68572998046875\n ],\n [\n 291.7033996582031,\n 88.68572998046875\n ],\n [\n 291.7033996582031,\n 110.84228515625\n ],\n [\n 129.60000610351562,\n 110.84228515625\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 88.68572998046875,\n 291.7033996582031,\n 110.84228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/97/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/98/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Other relational operations are useful for putting words in alphabetical order:
\",\n \"polygon\": [\n [\n 129.09375,\n 116.982421875\n ],\n [\n 471.01824951171875,\n 116.982421875\n ],\n [\n 471.01824951171875,\n 127.19989013671875\n ],\n [\n 129.09375,\n 127.19989013671875\n ]\n ],\n \"bbox\": [\n 129.09375,\n 116.982421875,\n 471.01824951171875,\n 127.19989013671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/97/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/98/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"if word < 'banana':\\n print 'Your word,' + word + ', comes before banana.'\\nelif word > 'banana':\\n print 'Your word,' + word + ', comes after banana.'\\nelse:\\n print 'All right, bananas.'
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 133.29669189453125\n ],\n [\n 424.634765625,\n 133.29669189453125\n ],\n [\n 424.634765625,\n 204.230224609375\n ],\n [\n 129.60000610351562,\n 204.230224609375\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 133.29669189453125,\n 424.634765625,\n 204.230224609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/97/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/98/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Python does not handle uppercase and lowercase letters the same way that people do. All the uppercase letters come before all the lowercase letters, so:
\",\n \"polygon\": [\n [\n 129.60006713867188,\n 209.21484375\n ],\n [\n 525.6034545898438,\n 209.21484375\n ],\n [\n 525.6034545898438,\n 232.78277587890625\n ],\n [\n 129.60006713867188,\n 232.78277587890625\n ]\n ],\n \"bbox\": [\n 129.60006713867188,\n 209.21484375,\n 525.6034545898438,\n 232.78277587890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/97/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/98/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Your word, Pineapple, comes before banana.
\",\n \"polygon\": [\n [\n 128.12255859375,\n 238.87860107421875\n ],\n [\n 349.2853088378906,\n 238.87860107421875\n ],\n [\n 349.2853088378906,\n 248.8411865234375\n ],\n [\n 128.12255859375,\n 248.8411865234375\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 238.87860107421875,\n 349.2853088378906,\n 248.8411865234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/97/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/98/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"A common way to address this problem is to convert strings to a standard format, such as all lowercase, before performing the comparison. Keep that in mind in case you have to defend yourself against a man armed with a Pineapple.
\",\n \"polygon\": [\n [\n 128.49609375,\n 255.23614501953125\n ],\n [\n 525.603515625,\n 255.23614501953125\n ],\n [\n 525.603515625,\n 289.5877685546875\n ],\n [\n 128.49609375,\n 289.5877685546875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 255.23614501953125,\n 525.603515625,\n 289.5877685546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/97/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/98/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.11 Debugging
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\",\n \"polygon\": [\n [\n 227.5576171875,\n 145.01953125\n ],\n [\n 341.859375,\n 145.01953125\n ],\n [\n 341.859375,\n 155.55291748046875\n ],\n [\n 227.5576171875,\n 155.55291748046875\n ]\n ],\n \"bbox\": [\n 227.5576171875,\n 145.01953125,\n 341.859375,\n 155.55291748046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/99/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"File \\\"reverse.py\\\", line 15, in is_reverse\\n if word1[i] != word2[j]:\\nIndexError: string index out of range
\",\n \"polygon\": [\n [\n 86.39998626708984,\n 178.27734375\n ],\n [\n 311.3128356933594,\n 178.27734375\n ],\n [\n 311.3128356933594,\n 214.048828125\n ],\n [\n 86.39998626708984,\n 214.048828125\n ]\n ],\n \"bbox\": [\n 86.39998626708984,\n 178.27734375,\n 311.3128356933594,\n 214.048828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"For debugging this kind of error, my first move is to print the values of the indices immediately before the line where the error appears.
\",\n \"polygon\": [\n [\n 84.7177734375,\n 218.689453125\n ],\n [\n 482.40338134765625,\n 218.689453125\n ],\n [\n 482.40338134765625,\n 241.505859375\n ],\n [\n 84.7177734375,\n 241.505859375\n ]\n ],\n \"bbox\": [\n 84.7177734375,\n 218.689453125,\n 482.40338134765625,\n 241.505859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"while j > 0:\\n print i, j # print here\\n if word1[i] != word2[j]:\\n return False\\n i = i+1\\n j = j-1
\",\n \"polygon\": [\n [\n 105.56103515625,\n 247.82684326171875\n ],\n [\n 285.1339416503906,\n 247.82684326171875\n ],\n [\n 285.1339416503906,\n 334.51171875\n ],\n [\n 105.56103515625,\n 334.51171875\n ]\n ],\n \"bbox\": [\n 105.56103515625,\n 247.82684326171875,\n 285.1339416503906,\n 334.51171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Now when I run the program again, I get more information:
\",\n \"polygon\": [\n [\n 85.166015625,\n 337.21875\n ],\n [\n 350.6977233886719,\n 337.21875\n ],\n [\n 350.6977233886719,\n 347.5429992675781\n ],\n [\n 85.166015625,\n 347.5429992675781\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 337.21875,\n 350.6977233886719,\n 347.5429992675781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> is_reverse('pots', 'stop')\\n0 4\\n...\\nIndexError: string index out of range\",\n \"polygon\": [\n [\n 84.7177734375,\n 353.86785888671875\n ],\n [\n 279.9334411621094,\n 353.86785888671875\n ],\n [\n 279.9334411621094,\n 400.4134521484375\n ],\n [\n 84.7177734375,\n 400.4134521484375\n ]\n ],\n \"bbox\": [\n 84.7177734375,\n 353.86785888671875,\n 279.9334411621094,\n 400.4134521484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The first time through the loop, the value of j is 4, which is out of range for the string 'pots'. The index of the last character is 3, so the initial value for j should be len(word2)-1.
\",\n \"polygon\": [\n [\n 85.0166015625,\n 406.0546875\n ],\n [\n 482.40484619140625,\n 406.0546875\n ],\n [\n 482.40484619140625,\n 441.6328125\n ],\n [\n 85.0166015625,\n 441.6328125\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 406.0546875,\n 482.40484619140625,\n 441.6328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"If I fix that error and run the program again, I get:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 451.30078125\n ],\n [\n 304.9893493652344,\n 451.30078125\n ],\n [\n 304.9893493652344,\n 461.79803466796875\n ],\n [\n 85.3154296875,\n 461.79803466796875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 451.30078125,\n 304.9893493652344,\n 461.79803466796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> is_reverse('pots', 'stop')\\n0 3\\n1 2\\n2 1\\nTrue\",\n \"polygon\": [\n [\n 85.9130859375,\n 468.1228942871094\n ],\n [\n 250.2685546875,\n 468.1228942871094\n ],\n [\n 250.2685546875,\n 526.8624877929688\n ],\n [\n 85.9130859375,\n 526.8624877929688\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 468.1228942871094,\n 250.2685546875,\n 526.8624877929688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"This time we get the right answer, but it looks like the loop only ran three times, which is suspicious. To get a better idea of what is happening, it is useful to draw a state diagram. During the first iteration, the frame for is_reverse is shows in Figure 8.2.
\",\n \"polygon\": [\n [\n 85.46484375,\n 532.8984375\n ],\n [\n 482.607421875,\n 532.8984375\n ],\n [\n 482.607421875,\n 567.8380584716797\n ],\n [\n 85.46484375,\n 567.8380584716797\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 532.8984375,\n 482.607421875,\n 567.8380584716797\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"I took a little license by arranging the variables in the frame and adding dotted lines to show that the values of i and j indicate characters in word1 and word2.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 577.7578125\n ],\n [\n 482.4034423828125,\n 577.7578125\n ],\n [\n 482.4034423828125,\n 601.734375\n ],\n [\n 85.3154296875,\n 601.734375\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 577.7578125,\n 482.4034423828125,\n 601.734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/98/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/99/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.9. Starting with this diagram, execute the program on paper, changing the values of i and j during each iteration. Find and fix the second error in this function.
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\",\n \"polygon\": [\n [\n 129.392578125,\n 349.9181213378906\n ],\n [\n 326.82940673828125,\n 349.9181213378906\n ],\n [\n 326.82940673828125,\n 359.97784423828125\n ],\n [\n 129.392578125,\n 359.97784423828125\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 349.9181213378906,\n 326.82940673828125,\n 359.97784423828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/99/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/100/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"8.13 Exercises
\",\n \"polygon\": [\n [\n 128.9443359375,\n 389.08367919921875\n ],\n [\n 228.80398559570312,\n 389.08367919921875\n ],\n [\n 228.80398559570312,\n 403.4299011230469\n ],\n [\n 128.9443359375,\n 403.4299011230469\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 389.08367919921875,\n 228.80398559570312,\n 403.4299011230469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/100/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.10. A string slice can take a third index that specifies the \\\"step size;\\\" that is, the number of spaces between successive characters. A step size of 2 means every other character; 3 means every third, etc.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 415.4345397949219\n ],\n [\n 525.6035766601562,\n 415.4345397949219\n ],\n [\n 525.6035766601562,\n 449.7861328125\n ],\n [\n 128.6455078125,\n 449.7861328125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 415.4345397949219,\n 525.6035766601562,\n 449.7861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/100/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> fruit = 'banana'\\n>>> fruit[0:5:2]\\n'bnn'
\",\n \"polygon\": [\n [\n 127.97314453125,\n 455.84368896484375\n ],\n [\n 234.1833953857422,\n 455.84368896484375\n ],\n [\n 234.1833953857422,\n 490.74609375\n ],\n [\n 127.97314453125,\n 490.74609375\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 455.84368896484375,\n 234.1833953857422,\n 490.74609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/100/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"A step size of -1 goes through the word backwards, so the slice [::-1] generates a reversed string.
\",\n \"polygon\": [\n [\n 129.5419921875,\n 496.2065734863281\n ],\n [\n 522.5155029296875,\n 496.2065734863281\n ],\n [\n 522.5155029296875,\n 506.1922912597656\n ],\n [\n 129.5419921875,\n 506.1922912597656\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 496.2065734863281,\n 522.5155029296875,\n 506.1922912597656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/100/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Use this idiom to write a one-line version of is_palindrome from Exercise 6.6. Exercise 8.11. The following functions are all intended to check whether a string contains any
\",\n \"polygon\": [\n [\n 126.8525390625,\n 516.1765747070312\n ],\n [\n 525.5995483398438,\n 516.1765747070312\n ],\n [\n 525.5995483398438,\n 545.66015625\n ],\n [\n 126.8525390625,\n 545.66015625\n ]\n ],\n \"bbox\": [\n 126.8525390625,\n 516.1765747070312,\n 525.5995483398438,\n 545.66015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/100/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"lowercase letters, but at least some of them are wrong. For each function, describe what the function actually does (assuming that the parameter is a string).
\",\n \"polygon\": [\n [\n 129.392578125,\n 540.5645599365234\n ],\n [\n 525.6033935546875,\n 540.5645599365234\n ],\n [\n 525.6033935546875,\n 566.9296875\n ],\n [\n 129.392578125,\n 566.9296875\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 540.5645599365234,\n 525.6033935546875,\n 566.9296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/100/Code/18\",\n \"block_type\": \"Code\",\n \"html\": \"def any_lowercase1(s):\\n for c in s:\\n if c.islower():\\n return True\\n else:\\n return False\\ndef any_lowercase2(s):\\n for c in s:\\n if 'c'.islower():\\n return 'True'
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 86.431640625\n ],\n [\n 271.1865234375,\n 86.431640625\n ],\n [\n 271.1865234375,\n 318.1463623046875\n ],\n [\n 85.9130859375,\n 318.1463623046875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 86.431640625,\n 271.1865234375,\n 318.1463623046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/101/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 8.12. ROT13 is a weak form of encryption that involves \\\"rotating\\\" each letter in a word by 13 places. To rotate a letter means to shift it through the alphabet, wrapping around to the beginning if necessary, so 'A' shifted by 3 is 'D' and 'Z' shifted by 1 is 'A'.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 319.81640625\n ],\n [\n 482.40338134765625,\n 319.81640625\n ],\n [\n 482.40338134765625,\n 354.70623779296875\n ],\n [\n 85.6142578125,\n 354.70623779296875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 319.81640625,\n 482.40338134765625,\n 354.70623779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/101/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Write a function called rotate_word that takes a string and an integer as parameters, and that returns a new string that contains the letters from the original string \\\"rotated\\\" by the given amount.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 364.482421875\n ],\n [\n 482.4034423828125,\n 364.482421875\n ],\n [\n 482.4034423828125,\n 387.0372314453125\n ],\n [\n 85.6142578125,\n 387.0372314453125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 364.482421875,\n 482.4034423828125,\n 387.0372314453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/101/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"For example, \\\"cheer\\\" rotated by 7 is \\\"jolly\\\" and \\\"melon\\\" rotated by -10 is \\\"cubed\\\".
\",\n \"polygon\": [\n [\n 85.9130859375,\n 396.0\n ],\n [\n 420.75,\n 396.0\n ],\n [\n 420.75,\n 407.17523193359375\n ],\n [\n 85.9130859375,\n 407.17523193359375\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 396.0,\n 420.75,\n 407.17523193359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/101/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"You might want to use the built-in functions ord, which converts a character to a numeric code, and chr, which converts numeric codes to characters.
\",\n \"polygon\": [\n [\n 85.166015625,\n 416.49609375\n ],\n [\n 482.4031677246094,\n 416.49609375\n ],\n [\n 482.4031677246094,\n 439.5303649902344\n ],\n [\n 85.166015625,\n 439.5303649902344\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 416.49609375,\n 482.4031677246094,\n 439.5303649902344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/101/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Potentially offensive jokes on the Internet are sometimes encoded in ROT13. If you are not easily offended, find and decode some of them. Solution: http: // thinkpython. com/ code/ rotate. py .
\",\n \"polygon\": [\n [\n 85.3154296875,\n 448.59375\n ],\n [\n 482.4035339355469,\n 448.59375\n ],\n [\n 482.4035339355469,\n 484.171875\n ],\n [\n 85.3154296875,\n 484.171875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 448.59375,\n 482.4035339355469,\n 484.171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/97/SectionHeader/8\",\n \"4\": \"/page/100/SectionHeader/12\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/102/Page/154\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/102/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Chapter 9
\",\n \"polygon\": [\n [\n 129.2431640625,\n 162.73150634765625\n ],\n [\n 220.84561157226562,\n 162.73150634765625\n ],\n [\n 220.84561157226562,\n 183.39398193359375\n ],\n [\n 129.2431640625,\n 183.39398193359375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 162.73150634765625,\n 220.84561157226562,\n 183.39398193359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/92/SectionHeader/1\",\n \"2\": \"/page/102/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Case study: word play
\",\n \"polygon\": [\n [\n 128.42138671875,\n 216.69732666015625\n ],\n [\n 381.684814453125,\n 216.69732666015625\n ],\n [\n 381.684814453125,\n 241.484375\n ],\n [\n 128.42138671875,\n 241.484375\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 216.69732666015625,\n 381.684814453125,\n 241.484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"9.1 Reading word lists
\",\n \"polygon\": [\n [\n 128.27197265625,\n 288.9817810058594\n ],\n [\n 285.3853759765625,\n 288.9817810058594\n ],\n [\n 285.3853759765625,\n 303.3280029296875\n ],\n [\n 128.27197265625,\n 303.3280029296875\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 288.9817810058594,\n 285.3853759765625,\n 303.3280029296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"For the exercises in this chapter we need a list of English words. There are lots of word lists available on the Web, but the one most suitable for our purpose is one of the word lists collected and contributed to the public domain by Grady Ward as part of the Moby lexicon project (see http://wikipedia.org/wiki/Moby_Project). It is a list of 113,809 official crosswords; that is, words that are considered valid in crossword puzzles and other word games. In the Moby collection, the filename is 113809of.fic; you can download a copy, with the simpler name words.txt, from http://thinkpython.com/code/words.txt.
\",\n \"polygon\": [\n [\n 127.8984375,\n 314.2833557128906\n ],\n [\n 525.9375,\n 314.2833557128906\n ],\n [\n 525.9375,\n 397.4119567871094\n ],\n [\n 127.8984375,\n 397.4119567871094\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 314.2833557128906,\n 525.9375,\n 397.4119567871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"This file is in plain text, so you can open it with a text editor, but you can also read it from Python. The built-in function open takes the name of the file as a parameter and returns a file object you can use to read the file.
\",\n \"polygon\": [\n [\n 127.8984375,\n 405.28125\n ],\n [\n 525.9375,\n 405.28125\n ],\n [\n 525.9375,\n 440.5479431152344\n ],\n [\n 127.8984375,\n 440.5479431152344\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 405.28125,\n 525.9375,\n 440.5479431152344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> fin = open('words.txt')\\n>>> print fin\\n<open file 'words.txt', mode 'r' at 0xb7f4b380>\",\n \"polygon\": [\n [\n 128.86962890625,\n 443.56640625\n ],\n [\n 375.3861999511719,\n 443.56640625\n ],\n [\n 375.3861999511719,\n 482.23828125\n ],\n [\n 128.86962890625,\n 482.23828125\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 443.56640625,\n 375.3861999511719,\n 482.23828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"fin is a common name for a file object used for input. Mode 'r' indicates that this file is open for reading (as opposed to 'w' for writing).
\",\n \"polygon\": [\n [\n 129.5419921875,\n 484.37579345703125\n ],\n [\n 525.9375,\n 484.37579345703125\n ],\n [\n 525.9375,\n 506.98828125\n ],\n [\n 129.5419921875,\n 506.98828125\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 484.37579345703125,\n 525.9375,\n 506.98828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The file object provides several methods for reading, including readline, which reads characters from the file until it gets to a newline and returns the result as a string:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 514.72265625\n ],\n [\n 525.9375,\n 514.72265625\n ],\n [\n 525.9375,\n 537.6239624023438\n ],\n [\n 128.6455078125,\n 537.6239624023438\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 514.72265625,\n 525.9375,\n 537.6239624023438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> fin.readline()\\n'aa\\\\r\\\\n'
\",\n \"polygon\": [\n [\n 128.72021484375,\n 542.1796875\n ],\n [\n 223.75668334960938,\n 542.1796875\n ],\n [\n 223.75668334960938,\n 564.4454193115234\n ],\n [\n 128.72021484375,\n 564.4454193115234\n ]\n ],\n \"bbox\": [\n 128.72021484375,\n 542.1796875,\n 223.75668334960938,\n 564.4454193115234\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The first word in this particular list is \\\"aa,\\\" which is a kind of lava. The sequence \\\\r\\\\n represents two whitespace characters, a carriage return and a newline, that separate this word from the next.
\",\n \"polygon\": [\n [\n 128.49609375,\n 568.86328125\n ],\n [\n 525.9375,\n 568.86328125\n ],\n [\n 525.9375,\n 603.7589721679688\n ],\n [\n 128.49609375,\n 603.7589721679688\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 568.86328125,\n 525.9375,\n 603.7589721679688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The file object keeps track of where it is in the file, so if you call readline again, you get the next word:
\",\n \"polygon\": [\n [\n 128.49609375,\n 612.17578125\n ],\n [\n 525.9375,\n 612.17578125\n ],\n [\n 525.9375,\n 634.7009887695312\n ],\n [\n 128.49609375,\n 634.7009887695312\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 612.17578125,\n 525.9375,\n 634.7009887695312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \">>> fin.readline()\\n'aah\\\\r\\\\n'
\",\n \"polygon\": [\n [\n 128.27197265625,\n 638.47265625\n ],\n [\n 225.31640625,\n 638.47265625\n ],\n [\n 225.31640625,\n 661.5214385986328\n ],\n [\n 128.27197265625,\n 661.5214385986328\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 638.47265625,\n 225.31640625,\n 661.5214385986328\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/102/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The next word is \\\"aah,\\\" which is a perfectly legitimate word, so stop looking at me like that. Or, if it's the whitespace that's bothering you, we can get rid of it with the string method strip:
\",\n \"polygon\": [\n [\n 129.392578125,\n 665.54296875\n ],\n [\n 526.236328125,\n 665.54296875\n ],\n [\n 526.236328125,\n 700.8349990844727\n ],\n [\n 129.392578125,\n 700.8349990844727\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 665.54296875,\n 526.236328125,\n 700.8349990844727\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/103/Page/226\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/103/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 59.69970703125\n ],\n [\n 482.40350341796875,\n 59.69970703125\n ],\n [\n 482.40350341796875,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 59.69970703125,\n 482.40350341796875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> line = fin.readline()\\n>>> word = line.strip()\\n>>> print word
\",\n \"polygon\": [\n [\n 85.83837890625,\n 87.35009765625\n ],\n [\n 217.16905212402344,\n 87.35009765625\n ],\n [\n 217.16905212402344,\n 126.650390625\n ],\n [\n 85.83837890625,\n 126.650390625\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 87.35009765625,\n 217.16905212402344,\n 126.650390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"aahed
\",\n \"polygon\": [\n [\n 85.83837890625,\n 125.26873779296875\n ],\n [\n 113.03173828125,\n 125.26873779296875\n ],\n [\n 113.03173828125,\n 136.2216796875\n ],\n [\n 85.83837890625,\n 136.2216796875\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 125.26873779296875,\n 113.03173828125,\n 136.2216796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"You can also use a file object as part of a for loop. This program reads words.txt and prints each word, one per line:
\",\n \"polygon\": [\n [\n 85.46484375,\n 138.1552734375\n ],\n [\n 482.607421875,\n 138.1552734375\n ],\n [\n 482.607421875,\n 162.33392333984375\n ],\n [\n 85.46484375,\n 162.33392333984375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 138.1552734375,\n 482.607421875,\n 162.33392333984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"fin = open('words.txt')\\nfor line in fin:\\n word = line.strip()\\n print word\",\n \"polygon\": [\n [\n 85.166015625,\n 166.095703125\n ],\n [\n 211.1220703125,\n 166.095703125\n ],\n [\n 211.1220703125,\n 216.17578125\n ],\n [\n 85.166015625,\n 216.17578125\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 166.095703125,\n 211.1220703125,\n 216.17578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 9.1. Write a program that reads words.txt and prints only the words with more than 20 characters (not counting whitespace).
\",\n \"polygon\": [\n [\n 85.6142578125,\n 215.733642578125\n ],\n [\n 482.4020690917969,\n 215.733642578125\n ],\n [\n 482.4020690917969,\n 237.8912353515625\n ],\n [\n 85.6142578125,\n 237.8912353515625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 215.733642578125,\n 482.4020690917969,\n 237.8912353515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"4\": \"/page/102/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"9.2 Exercises
\",\n \"polygon\": [\n [\n 85.6142578125,\n 264.12890625\n ],\n [\n 178.430908203125,\n 264.12890625\n ],\n [\n 178.430908203125,\n 279.781005859375\n ],\n [\n 85.6142578125,\n 279.781005859375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 264.12890625,\n 178.430908203125,\n 279.781005859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"There are solutions to these exercises in the next section. You should at least attempt each one before you read the solutions.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 290.0390625\n ],\n [\n 482.90625,\n 290.0390625\n ],\n [\n 482.90625,\n 312.8759460449219\n ],\n [\n 85.9130859375,\n 312.8759460449219\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 290.0390625,\n 482.90625,\n 312.8759460449219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 9.2. In 1939 Ernest Vincent Wright published a 50,000 word novel called Gadsby that does not contain the letter \\\"e.\\\" Since \\\"e\\\" is the most common letter in English, that's not easy to do.
\",\n \"polygon\": [\n [\n 85.763671875,\n 314.40234375\n ],\n [\n 482.90625,\n 314.40234375\n ],\n [\n 482.90625,\n 349.2862548828125\n ],\n [\n 85.763671875,\n 349.2862548828125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 314.40234375,\n 482.90625,\n 349.2862548828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"In fact, it is difficult to construct a solitary thought without using that most common symbol. It is slow going at first, but with caution and hours of training you can gradually gain facility.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 357.134765625\n ],\n [\n 482.90625,\n 357.134765625\n ],\n [\n 482.90625,\n 380.21026611328125\n ],\n [\n 85.6142578125,\n 380.21026611328125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 357.134765625,\n 482.90625,\n 380.21026611328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"All right, I'll stop now.
\",\n \"polygon\": [\n [\n 85.763671875,\n 387.4921875\n ],\n [\n 178.8330535888672,\n 387.4921875\n ],\n [\n 178.8330535888672,\n 398.9402770996094\n ],\n [\n 85.763671875,\n 398.9402770996094\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 387.4921875,\n 178.8330535888672,\n 398.9402770996094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Write a function called has_no_e that returns True if the given word doesn't have the letter \\\"e\\\" in it.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 406.44140625\n ],\n [\n 482.3984069824219,\n 406.44140625\n ],\n [\n 482.3984069824219,\n 429.8642883300781\n ],\n [\n 85.6142578125,\n 429.8642883300781\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 406.44140625,\n 482.3984069824219,\n 429.8642883300781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Modify your program from the previous section to print only the words that have no \\\"e\\\" and compute the percentage of the words in the list have no \\\"e.\\\"
\",\n \"polygon\": [\n [\n 85.46484375,\n 437.765625\n ],\n [\n 482.4034729003906,\n 437.765625\n ],\n [\n 482.4034729003906,\n 460.7882995605469\n ],\n [\n 85.46484375,\n 460.7882995605469\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 437.765625,\n 482.4034729003906,\n 460.7882995605469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 9.3. Write a function named avoids that takes a word and a string of forbidden letters, and that returns True if the word doesn't use any of the forbidden letters.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 462.515625\n ],\n [\n 482.4031066894531,\n 462.515625\n ],\n [\n 482.4031066894531,\n 485.200439453125\n ],\n [\n 85.3154296875,\n 485.200439453125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 462.515625,\n 482.4031066894531,\n 485.200439453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Modify your program to prompt the user to enter a string of forbidden letters and then print the number of words that don't contain any of them. Can you find a combination of 5 forbidden letters that excludes the smallest number of words?
\",\n \"polygon\": [\n [\n 85.46484375,\n 493.06640625\n ],\n [\n 482.607421875,\n 493.06640625\n ],\n [\n 482.607421875,\n 528.2953186035156\n ],\n [\n 85.46484375,\n 528.2953186035156\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 493.06640625,\n 482.607421875,\n 528.2953186035156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 9.4. Write a function named uses_only that takes a word and a string of letters, and that returns True if the word contains only letters in the list. Can you make a sentence using only the letters acefhlo? Other than \\\"Hoe alfalfa?\\\"
\",\n \"polygon\": [\n [\n 85.46484375,\n 529.8046875\n ],\n [\n 482.607421875,\n 529.8046875\n ],\n [\n 482.607421875,\n 564.9014739990234\n ],\n [\n 85.46484375,\n 564.9014739990234\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 529.8046875,\n 482.607421875,\n 564.9014739990234\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 9.5. Write a function named uses_all that takes a word and a string of required letters, and that returns True if the word uses all the required letters at least once. How many words are there that use all the vowels aeiou? How about aeiouy?
\",\n \"polygon\": [\n [\n 85.3154296875,\n 566.15625\n ],\n [\n 482.90625,\n 566.15625\n ],\n [\n 482.90625,\n 601.4844818115234\n ],\n [\n 85.3154296875,\n 601.4844818115234\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 566.15625,\n 482.90625,\n 601.4844818115234\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 9.6. Write a function called is_abecedarian that returns True if the letters in a word appear in alphabetical order (double letters are ok). How many abecedarian words are there?
\",\n \"polygon\": [\n [\n 85.166015625,\n 602.89453125\n ],\n [\n 482.607421875,\n 602.89453125\n ],\n [\n 482.607421875,\n 625.850341796875\n ],\n [\n 85.166015625,\n 625.850341796875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 602.89453125,\n 482.607421875,\n 625.850341796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/SectionHeader/18\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"9.3 Search
\",\n \"polygon\": [\n [\n 86.13720703125,\n 652.78125\n ],\n [\n 162.50668334960938,\n 652.78125\n ],\n [\n 162.50668334960938,\n 667.7410888671875\n ],\n [\n 86.13720703125,\n 667.7410888671875\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 652.78125,\n 162.50668334960938,\n 667.7410888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/103/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"All of the exercises in the previous section have something in common; they can be solved with the search pattern we saw in Section 8.6. The simplest example is:
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\",\n \"polygon\": [\n [\n 129.60000610351562,\n 87.8818359375\n ],\n [\n 260.578125,\n 87.8818359375\n ],\n [\n 260.578125,\n 147.42529296875\n ],\n [\n 129.60000610351562,\n 147.42529296875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 87.8818359375,\n 260.578125,\n 147.42529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The for loop traverses the characters in word. If we find the letter \\\"e\\\", we can immediately return False; otherwise we have to go to the next letter. If we exit the loop normally, that means we didn't find an \\\"e\\\", so we return True.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 152.3671875\n ],\n [\n 525.9375,\n 152.3671875\n ],\n [\n 525.9375,\n 187.671875\n ],\n [\n 128.3466796875,\n 187.671875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 152.3671875,\n 525.9375,\n 187.671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"avoids is a more general version of has_no_e but it has the same structure:
\",\n \"polygon\": [\n [\n 128.0478515625,\n 197.033203125\n ],\n [\n 458.746337890625,\n 197.033203125\n ],\n [\n 458.746337890625,\n 207.35089111328125\n ],\n [\n 128.0478515625,\n 207.35089111328125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 197.033203125,\n 458.746337890625,\n 207.35089111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Code/200\",\n \"block_type\": \"Code\",\n \"html\": \"def avoids(word, forbidden):\\n for letter in word:\\n if letter in forbidden:\\n return False\\n return True
\",\n \"polygon\": [\n [\n 129.60006713867188,\n 212.94671630859375\n ],\n [\n 292.25390625,\n 212.94671630859375\n ],\n [\n 292.25390625,\n 271.686279296875\n ],\n [\n 129.60006713867188,\n 271.686279296875\n ]\n ],\n \"bbox\": [\n 129.60006713867188,\n 212.94671630859375,\n 292.25390625,\n 271.686279296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"We can return False as soon as we find a forbidden letter; if we get to the end of the loop, we return True.
\",\n \"polygon\": [\n [\n 128.794921875,\n 277.27734375\n ],\n [\n 526.236328125,\n 277.27734375\n ],\n [\n 526.236328125,\n 299.73785400390625\n ],\n [\n 128.794921875,\n 299.73785400390625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 277.27734375,\n 526.236328125,\n 299.73785400390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"uses_only is similar except that the sense of the condition is reversed:
\",\n \"polygon\": [\n [\n 127.8984375,\n 308.794921875\n ],\n [\n 438.08203125,\n 308.794921875\n ],\n [\n 438.08203125,\n 319.4178466796875\n ],\n [\n 127.8984375,\n 319.4178466796875\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 308.794921875,\n 438.08203125,\n 319.4178466796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"def uses_only(word, available):\\n for letter in word:\\n if letter not in available:\\n return False\\n return True
\",\n \"polygon\": [\n [\n 129.60008239746094,\n 324.45703125\n ],\n [\n 312.6628723144531,\n 324.45703125\n ],\n [\n 312.6628723144531,\n 383.7532958984375\n ],\n [\n 129.60008239746094,\n 383.7532958984375\n ]\n ],\n \"bbox\": [\n 129.60008239746094,\n 324.45703125,\n 312.6628723144531,\n 383.7532958984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Instead of a list of forbidden letters, we have a list of available letters. If we find a letter in word that is not in available, we can return False.
\",\n \"polygon\": [\n [\n 129.60008239746094,\n 388.458984375\n ],\n [\n 525.9375,\n 388.458984375\n ],\n [\n 525.9375,\n 411.80487060546875\n ],\n [\n 129.60008239746094,\n 411.80487060546875\n ]\n ],\n \"bbox\": [\n 129.60008239746094,\n 388.458984375,\n 525.9375,\n 411.80487060546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"uses_all is similar except that we reverse the role of the word and the string of letters:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 421.13671875\n ],\n [\n 511.12823486328125,\n 421.13671875\n ],\n [\n 511.12823486328125,\n 431.48486328125\n ],\n [\n 128.3466796875,\n 431.48486328125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 421.13671875,\n 511.12823486328125,\n 431.48486328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"def uses_all(word, required):\\n for letter in required:\\n if letter not in word:\\n return False\\n return True
\",\n \"polygon\": [\n [\n 129.60008239746094,\n 437.0797119140625\n ],\n [\n 291.955078125,\n 437.0797119140625\n ],\n [\n 291.955078125,\n 499.25390625\n ],\n [\n 129.60008239746094,\n 499.25390625\n ]\n ],\n \"bbox\": [\n 129.60008239746094,\n 437.0797119140625,\n 291.955078125,\n 499.25390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Instead of traversing the letters in word, the loop traverses the required letters. If any of the required letters do not appear in the word, we can return False.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 501.1875\n ],\n [\n 525.9375,\n 501.1875\n ],\n [\n 525.9375,\n 523.8718872070312\n ],\n [\n 128.6455078125,\n 523.8718872070312\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 501.1875,\n 525.9375,\n 523.8718872070312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"If you were really thinking like a computer scientist, you would have recognized that uses_all was an instance of a previously-solved problem, and you would have written:
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\",\n \"polygon\": [\n [\n 129.60015869140625,\n 561.3407440185547\n ],\n [\n 317.89776611328125,\n 561.3407440185547\n ],\n [\n 317.89776611328125,\n 583.4983520507812\n ],\n [\n 129.60015869140625,\n 583.4983520507812\n ]\n ],\n \"bbox\": [\n 129.60015869140625,\n 561.3407440185547,\n 317.89776611328125,\n 583.4983520507812\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"This is an example of a program development method called problem recognition, which means that you recognize the problem you are working on as an instance of a previouslysolved problem, and apply a previously-developed solution.
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\",\n \"polygon\": [\n [\n 127.37548828125,\n 652.4447631835938\n ],\n [\n 301.3241271972656,\n 652.4447631835938\n ],\n [\n 301.3241271972656,\n 666.7909545898438\n ],\n [\n 127.37548828125,\n 666.7909545898438\n ]\n ],\n \"bbox\": [\n 127.37548828125,\n 652.4447631835938,\n 301.3241271972656,\n 666.7909545898438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/104/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/104/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"I wrote the functions in the previous section with for loops because I only needed the characters in the strings; I didn't have to do anything with the indices.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 88.68572998046875\n ],\n [\n 482.40411376953125,\n 88.68572998046875\n ],\n [\n 482.40411376953125,\n 110.99188232421875\n ],\n [\n 85.763671875,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 88.68572998046875,\n 482.40411376953125,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/104/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/105/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"def is_abecedarian(word):\\n previous = word[0]\\n for c in word:\\n if c < previous:\\n return False\\n previous = c\\n return True
\",\n \"polygon\": [\n [\n 85.68896484375,\n 115.99267578125\n ],\n [\n 218.7421875,\n 115.99267578125\n ],\n [\n 218.7421875,\n 199.121337890625\n ],\n [\n 85.68896484375,\n 199.121337890625\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 115.99267578125,\n 218.7421875,\n 199.121337890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/104/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/105/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"An alternative is to use recursion:
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 441.6328125\n ],\n [\n 482.4034729003906,\n 441.6328125\n ],\n [\n 482.4034729003906,\n 464.638916015625\n ],\n [\n 85.9130859375,\n 464.638916015625\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 441.6328125,\n 482.4034729003906,\n 464.638916015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/104/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/105/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"If we get to the end of the loop without finding a fault, then the word passes the test. To convince yourself that the loop ends correctly, consider an example like 'flossy'. The length of the word is 6, so the last time the loop runs is when i is 4, which is the index of the second-to-last character. On the last iteration, it compares the second-to-last character to the last, which is what we want.
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\",\n \"polygon\": [\n [\n 129.31787109375,\n 119.08172607421875\n ],\n [\n 302.2065124511719,\n 119.08172607421875\n ],\n [\n 302.2065124511719,\n 142.3125\n ],\n [\n 129.31787109375,\n 142.3125\n ]\n ],\n \"bbox\": [\n 129.31787109375,\n 119.08172607421875,\n 302.2065124511719,\n 142.3125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/104/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Assuming you did Exercise 8.9.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 149.080078125\n ],\n [\n 268.1982421875,\n 149.080078125\n ],\n [\n 268.1982421875,\n 159.58990478515625\n ],\n [\n 129.2431640625,\n 159.58990478515625\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 149.080078125,\n 268.1982421875,\n 159.58990478515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/104/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"9.5 Debugging
\",\n \"polygon\": [\n [\n 128.0478515625,\n 194.8095703125\n ],\n [\n 236.00576782226562,\n 194.8095703125\n ],\n [\n 236.00576782226562,\n 209.6015625\n ],\n [\n 128.0478515625,\n 209.6015625\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 194.8095703125,\n 236.00576782226562,\n 209.6015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Testing programs is hard. The functions in this chapter are relatively easy to test because you can check the results by hand. Even so, it is somewhere between difficult and impossible to choose a set of words that test for all possible errors.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 223.91015625\n ],\n [\n 525.9375,\n 223.91015625\n ],\n [\n 525.9375,\n 258.93182373046875\n ],\n [\n 128.9443359375,\n 258.93182373046875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 223.91015625,\n 525.9375,\n 258.93182373046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Taking has_no_e as an example, there are two obvious cases to check: words that have an 'e' should return False; words that don't should return True. You should have no trouble coming up with one of each.
\",\n \"polygon\": [\n [\n 128.197265625,\n 270.703125\n ],\n [\n 526.236328125,\n 270.703125\n ],\n [\n 526.236328125,\n 305.4937744140625\n ],\n [\n 128.197265625,\n 305.4937744140625\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 270.703125,\n 526.236328125,\n 305.4937744140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Within each case, there are some less obvious subcases. Among the words that have an \\\"e,\\\" you should test words with an \\\"e\\\" at the beginning, the end, and somewhere in the middle. You should test long words, short words, and very short words, like the empty string. The empty string is an example of a special case, which is one of the non-obvious cases where errors often lurk.
\",\n \"polygon\": [\n [\n 127.8984375,\n 316.916015625\n ],\n [\n 526.53515625,\n 316.916015625\n ],\n [\n 526.53515625,\n 376.4447937011719\n ],\n [\n 127.8984375,\n 376.4447937011719\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 316.916015625,\n 526.53515625,\n 376.4447937011719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"In addition to the test cases you generate, you can also test your program with a word list like words.txt. By scanning the output, you might be able to catch errors, but be careful: you might catch one kind of error (words that should not be included, but are) and not another (words that should be included, but aren't).
\",\n \"polygon\": [\n [\n 128.197265625,\n 388.458984375\n ],\n [\n 526.236328125,\n 388.458984375\n ],\n [\n 526.236328125,\n 435.2008056640625\n ],\n [\n 128.197265625,\n 435.2008056640625\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 388.458984375,\n 526.236328125,\n 435.2008056640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"In general, testing can help you find bugs, but it is not easy to generate a good set of test cases, and even if you do, you can't be sure your program is correct.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 446.66015625\n ],\n [\n 525.9375,\n 446.66015625\n ],\n [\n 525.9375,\n 469.56781005859375\n ],\n [\n 128.3466796875,\n 469.56781005859375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 446.66015625,\n 525.9375,\n 469.56781005859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"According to a legendary computer scientist:
\",\n \"polygon\": [\n [\n 129.5419921875,\n 481.078125\n ],\n [\n 327.515625,\n 481.078125\n ],\n [\n 327.515625,\n 491.90625\n ],\n [\n 129.5419921875,\n 491.90625\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 481.078125,\n 327.515625,\n 491.90625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Program testing can be used to show the presence of bugs, but never to show their absence!
\",\n \"polygon\": [\n [\n 154.5070343017578,\n 508.53515625\n ],\n [\n 500.8359375,\n 508.53515625\n ],\n [\n 500.8359375,\n 531.8438110351562\n ],\n [\n 154.5070343017578,\n 531.8438110351562\n ]\n ],\n \"bbox\": [\n 154.5070343017578,\n 508.53515625,\n 500.8359375,\n 531.8438110351562\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"— Edsger W. Dijkstra
\",\n \"polygon\": [\n [\n 153.896484375,\n 539.0859375\n ],\n [\n 250.716796875,\n 539.0859375\n ],\n [\n 250.716796875,\n 549.7598114013672\n ],\n [\n 153.896484375,\n 549.7598114013672\n ]\n ],\n \"bbox\": [\n 153.896484375,\n 539.0859375,\n 250.716796875,\n 549.7598114013672\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/SectionHeader/13\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"9.6 Glossary
\",\n \"polygon\": [\n [\n 128.794921875,\n 590.9436798095703\n ],\n [\n 220.05284118652344,\n 590.9436798095703\n ],\n [\n 220.05284118652344,\n 605.2898712158203\n ],\n [\n 128.794921875,\n 605.2898712158203\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 590.9436798095703,\n 220.05284118652344,\n 605.2898712158203\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/106/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/106/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"file object: A value that represents an open file.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 85.271484375\n ],\n [\n 179.4462890625,\n 85.271484375\n ],\n [\n 179.4462890625,\n 99.97796630859375\n ],\n [\n 85.763671875,\n 99.97796630859375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 85.271484375,\n 179.4462890625,\n 99.97796630859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/107/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/107/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 9.7. This question is based on a Puzzler that was broadcast on the radio program Car Talk (http: // www. cartalk. com/ content/ puzzlers ):
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\",\n \"polygon\": [\n [\n 109.5205078125,\n 148.11328125\n ],\n [\n 458.40234375,\n 148.11328125\n ],\n [\n 458.40234375,\n 219.4151611328125\n ],\n [\n 109.5205078125,\n 219.4151611328125\n ]\n ],\n \"bbox\": [\n 109.5205078125,\n 148.11328125,\n 458.40234375,\n 219.4151611328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/107/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/107/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Write a program to find it. Solution: http: // thinkpython. com/ code/ cartalk1. py . Exercise 9.8. Here's another Car Talk Puzzler (http: // www. cartalk. com/ content/ puzzlers ):
\",\n \"polygon\": [\n [\n 85.3154296875,\n 233.19140625\n ],\n [\n 481.11328125,\n 233.19140625\n ],\n [\n 481.11328125,\n 267.93914794921875\n ],\n [\n 85.3154296875,\n 267.93914794921875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 233.19140625,\n 481.11328125,\n 267.93914794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/107/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/107/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"\\\"I was driving on the highway the other day and I happened to notice my odometer. Like most odometers, it shows six digits, in whole miles only. So, if my car had 300,000 miles, for example, I'd see 3-0-0-0-0-0.
\",\n \"polygon\": [\n [\n 110.4169921875,\n 281.91796875\n ],\n [\n 457.8046875,\n 281.91796875\n ],\n [\n 457.8046875,\n 316.4641418457031\n ],\n [\n 110.4169921875,\n 316.4641418457031\n ]\n ],\n \"bbox\": [\n 110.4169921875,\n 281.91796875,\n 457.8046875,\n 316.4641418457031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/107/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/107/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"\\\"Now, what I saw that day was very interesting. I noticed that the last 4 digits were palindromic; that is, they read the same forward as backward. For example, 5-4-4-5 is a palindrome, so my odometer could have read 3-1-5-4-4-5.
\",\n \"polygon\": [\n [\n 109.8193359375,\n 322.330078125\n ],\n [\n 458.40234375,\n 322.330078125\n ],\n [\n 458.40234375,\n 357.0321350097656\n ],\n [\n 109.8193359375,\n 357.0321350097656\n ]\n ],\n \"bbox\": [\n 109.8193359375,\n 322.330078125,\n 458.40234375,\n 357.0321350097656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/107/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/107/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"\\\"One mile later, the last 5 numbers were palindromic. For example, it could have read 3-6-5-4-5-6. One mile after that, the middle 4 out of 6 numbers were palindromic. And you ready for this? One mile later, all 6 were palindromic!
\",\n \"polygon\": [\n [\n 110.7158203125,\n 362.7421875\n ],\n [\n 457.8046875,\n 362.7421875\n ],\n [\n 457.8046875,\n 398.70703125\n ],\n [\n 110.7158203125,\n 398.70703125\n ]\n ],\n \"bbox\": [\n 110.7158203125,\n 362.7421875,\n 457.8046875,\n 398.70703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/103/SectionHeader/6\",\n \"3\": \"/page/103/SectionHeader/18\",\n \"4\": \"/page/107/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/107/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"\\\"The question is, what was on the odometer when I first looked?\\\"
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\",\n \"polygon\": [\n [\n 128.42138671875,\n 163.1953125\n ],\n [\n 232.787109375,\n 163.1953125\n ],\n [\n 232.787109375,\n 184.333984375\n ],\n [\n 128.42138671875,\n 184.333984375\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 163.1953125,\n 232.787109375,\n 184.333984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/102/SectionHeader/1\",\n \"2\": \"/page/108/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/108/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Lists
\",\n \"polygon\": [\n [\n 127.7490234375,\n 218.57733154296875\n ],\n [\n 184.2275390625,\n 218.57733154296875\n ],\n [\n 184.2275390625,\n 243.3643798828125\n ],\n [\n 127.7490234375,\n 243.3643798828125\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 218.57733154296875,\n 184.2275390625,\n 243.3643798828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/108/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.1 A list is a sequence
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}\n },\n {\n \"id\": \"/page/109/Caption/2\",\n \"block_type\": \"Caption\",\n \"html\": \"Figure 10.1: State diagram.
\",\n \"polygon\": [\n [\n 225.55101013183594,\n 263.548828125\n ],\n [\n 343.353515625,\n 263.548828125\n ],\n [\n 343.353515625,\n 273.95294189453125\n ],\n [\n 225.55101013183594,\n 273.95294189453125\n ]\n ],\n \"bbox\": [\n 225.55101013183594,\n 263.548828125,\n 343.353515625,\n 273.95294189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/109/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Unlike strings, lists are mutable. When the bracket operator appears on the left side of an assignment, it identifies the element of the list that will be assigned.
\",\n \"polygon\": [\n [\n 85.46484375,\n 298.16015625\n ],\n [\n 482.607421875,\n 298.16015625\n ],\n [\n 482.607421875,\n 321.1739196777344\n ],\n [\n 85.46484375,\n 321.1739196777344\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 298.16015625,\n 482.607421875,\n 321.1739196777344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \">>> numbers = [17, 123]\\n>>> numbers[1] = 5\\n>>> print numbers\\n[17, 5]
\",\n \"polygon\": [\n [\n 85.46484375,\n 329.957763671875\n ],\n [\n 206.6983642578125,\n 329.957763671875\n ],\n [\n 206.6983642578125,\n 376.50335693359375\n ],\n [\n 85.46484375,\n 376.50335693359375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 329.957763671875,\n 206.6983642578125,\n 376.50335693359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The one-eth element of numbers, which used to be 123, is now 5.
\",\n \"polygon\": [\n [\n 85.83837890625,\n 385.4377746582031\n ],\n [\n 367.88360595703125,\n 385.4377746582031\n ],\n [\n 367.88360595703125,\n 395.5499267578125\n ],\n [\n 85.83837890625,\n 395.5499267578125\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 385.4377746582031,\n 367.88360595703125,\n 395.5499267578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"You can think of a list as a relationship between indices and elements. This relationship is called a mapping; each index \\\"maps to\\\" one of the elements. Figure 10.1 shows the state diagram for cheeses, numbers and empty:
\",\n \"polygon\": [\n [\n 85.46484375,\n 407.21484375\n ],\n [\n 482.4034729003906,\n 407.21484375\n ],\n [\n 482.4034729003906,\n 442.80694580078125\n ],\n [\n 85.46484375,\n 442.80694580078125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 407.21484375,\n 482.4034729003906,\n 442.80694580078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Lists are represented by boxes with the word \\\"list\\\" outside and the elements of the list inside. cheeses refers to a list with three elements indexed 0, 1 and 2. numbers contains two elements; the diagram shows that the value of the second element has been reassigned from 123 to 5. empty refers to a list with no elements.
\",\n \"polygon\": [\n [\n 85.46484375,\n 454.78125\n ],\n [\n 482.90625,\n 454.78125\n ],\n [\n 482.90625,\n 502.2579650878906\n ],\n [\n 85.46484375,\n 502.2579650878906\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 454.78125,\n 482.90625,\n 502.2579650878906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"List indices work the same way as string indices:
\",\n \"polygon\": [\n [\n 85.53955078125,\n 514.3359375\n ],\n [\n 300.97442626953125,\n 514.3359375\n ],\n [\n 300.97442626953125,\n 525.1259765625\n ],\n [\n 85.53955078125,\n 525.1259765625\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 514.3359375,\n 300.97442626953125,\n 525.1259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/ListGroup/190\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 100.28799438476562,\n 543.33984375\n ],\n [\n 465.3626708984375,\n 543.33984375\n ],\n [\n 465.3626708984375,\n 603.9659881591797\n ],\n [\n 100.28799438476562,\n 603.9659881591797\n ]\n ],\n \"bbox\": [\n 100.28799438476562,\n 543.33984375,\n 465.3626708984375,\n 603.9659881591797\n ],\n \"children\": [\n {\n \"id\": \"/page/109/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \" Any integer expression can be used as an index.\",\n \"polygon\": [\n [\n 100.28799438476562,\n 543.33984375\n ],\n [\n 322.0454406738281,\n 543.33984375\n ],\n [\n 322.0454406738281,\n 554.3209838867188\n ],\n [\n 100.28799438476562,\n 554.3209838867188\n ]\n ],\n \"bbox\": [\n 100.28799438476562,\n 543.33984375,\n 322.0454406738281,\n 554.3209838867188\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/ListItem/10\",\n \"block_type\": \"ListItem\",\n \"html\": \" If you try to read or write an element that does not exist, you get an IndexError.\",\n \"polygon\": [\n [\n 100.28799438476562,\n 568.08984375\n ],\n [\n 465.3626708984375,\n 568.08984375\n ],\n [\n 465.3626708984375,\n 579.1439819335938\n ],\n [\n 100.28799438476562,\n 579.1439819335938\n ]\n ],\n \"bbox\": [\n 100.28799438476562,\n 568.08984375,\n 465.3626708984375,\n 579.1439819335938\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \" If an index has a negative value, it counts backward from the end of the list.\",\n \"polygon\": [\n [\n 100.28802490234375,\n 593.61328125\n ],\n [\n 444.2169494628906,\n 593.61328125\n ],\n [\n 444.2169494628906,\n 603.9659881591797\n ],\n [\n 100.28802490234375,\n 603.9659881591797\n ]\n ],\n \"bbox\": [\n 100.28802490234375,\n 593.61328125,\n 444.2169494628906,\n 603.9659881591797\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/109/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The in operator also works on lists.
\",\n \"polygon\": [\n [\n 85.83837890625,\n 622.6171875\n ],\n [\n 243.84375,\n 622.6171875\n ],\n [\n 243.84375,\n 633.1619873046875\n ],\n [\n 85.83837890625,\n 633.1619873046875\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 622.6171875,\n 243.84375,\n 633.1619873046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/109/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \">>> cheeses = ['Cheddar', 'Edam', 'Gouda']\\n>>> 'Edam' in cheeses\\nTrue\\n>>> 'Brie' in cheeses\\nFalse
\",\n \"polygon\": [\n [\n 86.2119140625,\n 641.9458312988281\n ],\n [\n 306.0244445800781,\n 641.9458312988281\n ],\n [\n 306.0244445800781,\n 700.6854248046875\n ],\n [\n 86.2119140625,\n 700.6854248046875\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 641.9458312988281,\n 306.0244445800781,\n 700.6854248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/110/Page/198\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/110/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.72021484375,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.72021484375,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.72021484375,\n 61.171142578125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/PageHeader/19\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 514.58203125,\n 60.71484375\n ],\n [\n 525.9375,\n 60.71484375\n ],\n [\n 525.9375,\n 69.609375\n ],\n [\n 514.58203125,\n 69.609375\n ]\n ],\n \"bbox\": [\n 514.58203125,\n 60.71484375,\n 525.9375,\n 69.609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/108/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.3 Traversing a list
\",\n \"polygon\": [\n [\n 128.6455078125,\n 85.95379638671875\n ],\n [\n 275.6689453125,\n 85.95379638671875\n ],\n [\n 275.6689453125,\n 100.29998779296875\n ],\n [\n 128.6455078125,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 85.95379638671875,\n 275.6689453125,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The most common way to traverse the elements of a list is with a for loop. The syntax is the same as for strings:
\",\n \"polygon\": [\n [\n 128.0478515625,\n 113.9853515625\n ],\n [\n 525.600341796875,\n 113.9853515625\n ],\n [\n 525.600341796875,\n 136.61700439453125\n ],\n [\n 128.0478515625,\n 136.61700439453125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 113.9853515625,\n 525.600341796875,\n 136.61700439453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"for cheese in cheeses: print cheese
\",\n \"polygon\": [\n [\n 128.49609375,\n 143.966796875\n ],\n [\n 244.677978515625,\n 143.966796875\n ],\n [\n 244.677978515625,\n 166.1243896484375\n ],\n [\n 128.49609375,\n 166.1243896484375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 143.966796875,\n 244.677978515625,\n 166.1243896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"This works well if you only need to read the elements of the list. But if you want to write or update the elements, you need the indices. A common way to do that is to combine the functions range and len:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 173.443359375\n ],\n [\n 525.6033935546875,\n 173.443359375\n ],\n [\n 525.6033935546875,\n 208.12396240234375\n ],\n [\n 129.2431640625,\n 208.12396240234375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 173.443359375,\n 525.6033935546875,\n 208.12396240234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"for i in range(len(numbers)):\\n numbers[i] = numbers[i] * 2
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 215.40234375\n ],\n [\n 291.7458190917969,\n 215.40234375\n ],\n [\n 291.7458190917969,\n 237.63134765625\n ],\n [\n 129.60000610351562,\n 237.63134765625\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 215.40234375,\n 291.7458190917969,\n 237.63134765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This loop traverses the list and updates each element. len returns the number of elements in the list. range returns a list of indices from 0 to n − 1, where n is the length of the list. Each time through the loop i gets the index of the next element. The assignment statement in the body uses i to read the old value of the element and to assign the new value.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 245.1307373046875\n ],\n [\n 525.9375,\n 245.1307373046875\n ],\n [\n 525.9375,\n 291.8258972167969\n ],\n [\n 129.2431640625,\n 291.8258972167969\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 245.1307373046875,\n 525.9375,\n 291.8258972167969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"A for loop over an empty list never executes the body:
\",\n \"polygon\": [\n [\n 129.16845703125,\n 302.607421875\n ],\n [\n 371.443359375,\n 302.607421875\n ],\n [\n 371.443359375,\n 313.2589111328125\n ],\n [\n 129.16845703125,\n 313.2589111328125\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 302.607421875,\n 371.443359375,\n 313.2589111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"for x in []:\\n print 'This never happens.'
\",\n \"polygon\": [\n [\n 129.59999084472656,\n 320.396484375\n ],\n [\n 291.703369140625,\n 320.396484375\n ],\n [\n 291.703369140625,\n 342.766357421875\n ],\n [\n 129.59999084472656,\n 342.766357421875\n ]\n ],\n \"bbox\": [\n 129.59999084472656,\n 320.396484375,\n 291.703369140625,\n 342.766357421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Although a list can contain another list, the nested list still counts as a single element. The length of this list is four:
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 349.98046875\n ],\n [\n 525.6033325195312,\n 349.98046875\n ],\n [\n 525.6033325195312,\n 372.5719299316406\n ],\n [\n 129.5999755859375,\n 372.5719299316406\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 349.98046875,\n 525.6033325195312,\n 372.5719299316406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"['spam', 1, ['Brie', 'Roquefort', 'Pol le Veq'], [1, 2, 3]]
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 379.9217834472656\n ],\n [\n 438.978515625,\n 379.9217834472656\n ],\n [\n 438.978515625,\n 389.8843688964844\n ],\n [\n 129.5999755859375,\n 389.8843688964844\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 379.9217834472656,\n 438.978515625,\n 389.8843688964844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.4 List operations
\",\n \"polygon\": [\n [\n 128.86962890625,\n 423.0703125\n ],\n [\n 266.66363525390625,\n 423.0703125\n ],\n [\n 266.66363525390625,\n 437.6109924316406\n ],\n [\n 128.86962890625,\n 437.6109924316406\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 423.0703125,\n 266.66363525390625,\n 437.6109924316406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The + operator concatenates lists:
\",\n \"polygon\": [\n [\n 128.49609375,\n 451.62078857421875\n ],\n [\n 275.3020324707031,\n 451.62078857421875\n ],\n [\n 275.3020324707031,\n 461.7329406738281\n ],\n [\n 128.49609375,\n 461.7329406738281\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 451.62078857421875,\n 275.3020324707031,\n 461.7329406738281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Code/191\",\n \"block_type\": \"Code\",\n \"html\": \">>> a = [1, 2, 3]\\n>>> b = [4, 5, 6]\\n>>> c = a + b\\n>>> print c\\n[1, 2, 3, 4, 5, 6]
\",\n \"polygon\": [\n [\n 129.2431640625,\n 469.0827941894531\n ],\n [\n 226.6611328125,\n 469.0827941894531\n ],\n [\n 226.6611328125,\n 527.87109375\n ],\n [\n 129.2431640625,\n 527.87109375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 469.0827941894531,\n 226.6611328125,\n 527.87109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Similarly, the * operator repeats a list a given number of times:
\",\n \"polygon\": [\n [\n 127.82373046875,\n 534.83203125\n ],\n [\n 404.8648681640625,\n 534.83203125\n ],\n [\n 404.8648681640625,\n 545.4349670410156\n ],\n [\n 127.82373046875,\n 545.4349670410156\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 534.83203125,\n 404.8648681640625,\n 545.4349670410156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/TextInlineMath/15\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> [0] * 4 [0, 0, 0, 0] >>> [1, 2, 3] * 3 [1, 2, 3, 1, 2, 3, 1, 2, 3]
\",\n \"polygon\": [\n [\n 128.3466796875,\n 552.7848205566406\n ],\n [\n 270.8198547363281,\n 552.7848205566406\n ],\n [\n 270.8198547363281,\n 600.9609375\n ],\n [\n 128.3466796875,\n 600.9609375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 552.7848205566406,\n 270.8198547363281,\n 600.9609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"The first example repeats [0] four times. The second example repeats the list [1, 2, 3] three times.
\",\n \"polygon\": [\n [\n 128.794921875,\n 606.76171875\n ],\n [\n 525.611328125,\n 606.76171875\n ],\n [\n 525.611328125,\n 629.19140625\n ],\n [\n 128.794921875,\n 629.19140625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 606.76171875,\n 525.611328125,\n 629.19140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/SectionHeader/17\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.5 List slices
\",\n \"polygon\": [\n [\n 127.1513671875,\n 662.3668518066406\n ],\n [\n 233.2353515625,\n 662.3668518066406\n ],\n [\n 233.2353515625,\n 676.7130432128906\n ],\n [\n 127.1513671875,\n 676.7130432128906\n ]\n ],\n \"bbox\": [\n 127.1513671875,\n 662.3668518066406,\n 233.2353515625,\n 676.7130432128906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/110/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"The slice operator also works on lists:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 690.8723983764648\n ],\n [\n 294.39141845703125,\n 690.8723983764648\n ],\n [\n 294.39141845703125,\n 700.8349990844727\n ],\n [\n 128.3466796875,\n 700.8349990844727\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 690.8723983764648,\n 294.39141845703125,\n 700.8349990844727\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/111/Page/248\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/111/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.521484375\n ],\n [\n 482.4033508300781,\n 60.521484375\n ],\n [\n 482.4033508300781,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.521484375,\n 482.4033508300781,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/PageHeader/16\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.24072265625,\n 60.4248046875\n ],\n [\n 96.89501953125,\n 60.4248046875\n ],\n [\n 96.89501953125,\n 70.0927734375\n ],\n [\n 85.24072265625,\n 70.0927734375\n ]\n ],\n \"bbox\": [\n 85.24072265625,\n 60.4248046875,\n 96.89501953125,\n 70.0927734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['a', 'b', 'c', 'd', 'e', 'f']\\n>>> t[1:3]\\n['b', 'c']\\n>>> t[:4]\\n['a', 'b', 'c', 'd']\\n>>> t[3:]\\n['d', 'e', 'f']
\",\n \"polygon\": [\n [\n 86.0625,\n 85.60986328125\n ],\n [\n 285.107421875,\n 85.60986328125\n ],\n [\n 285.107421875,\n 171.8143310546875\n ],\n [\n 86.0625,\n 171.8143310546875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 85.60986328125,\n 285.107421875,\n 171.8143310546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"If you omit the first index, the slice starts at the beginning. If you omit the second, the slice goes to the end. So if you omit both, the slice is a copy of the whole list.
\",\n \"polygon\": [\n [\n 85.166015625,\n 179.244140625\n ],\n [\n 482.4033508300781,\n 179.244140625\n ],\n [\n 482.4033508300781,\n 202.85089111328125\n ],\n [\n 85.166015625,\n 202.85089111328125\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 179.244140625,\n 482.4033508300781,\n 202.85089111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> t[:]\\n['a', 'b', 'c', 'd', 'e', 'f']
\",\n \"polygon\": [\n [\n 85.3154296875,\n 210.375\n ],\n [\n 243.6943359375,\n 210.375\n ],\n [\n 243.6943359375,\n 233.5882568359375\n ],\n [\n 85.3154296875,\n 233.5882568359375\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 210.375,\n 243.6943359375,\n 233.5882568359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Since lists are mutable, it is often useful to make a copy before performing operations that fold, spindle or mutilate lists.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 241.505859375\n ],\n [\n 482.90625,\n 241.505859375\n ],\n [\n 482.90625,\n 264.6258544921875\n ],\n [\n 85.3154296875,\n 264.6258544921875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 241.505859375,\n 482.90625,\n 264.6258544921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"A slice operator on the left side of an assignment can update multiple elements:
\",\n \"polygon\": [\n [\n 85.46484375,\n 276.310546875\n ],\n [\n 436.14703369140625,\n 276.310546875\n ],\n [\n 436.14703369140625,\n 287.2898254394531\n ],\n [\n 85.46484375,\n 287.2898254394531\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 276.310546875,\n 436.14703369140625,\n 287.2898254394531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['a', 'b', 'c', 'd', 'e', 'f']\\n>>> t[1:3] = ['x', 'y']\\n>>> print t\\n['a', 'x', 'y', 'd', 'e', 'f']
\",\n \"polygon\": [\n [\n 85.24072265625,\n 295.87066650390625\n ],\n [\n 285.1074523925781,\n 295.87066650390625\n ],\n [\n 285.1074523925781,\n 342.416259765625\n ],\n [\n 85.24072265625,\n 342.416259765625\n ]\n ],\n \"bbox\": [\n 85.24072265625,\n 295.87066650390625,\n 285.1074523925781,\n 342.416259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/110/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.6 List methods
\",\n \"polygon\": [\n [\n 85.46484375,\n 379.4886779785156\n ],\n [\n 210.70993041992188,\n 379.4886779785156\n ],\n [\n 210.70993041992188,\n 393.83489990234375\n ],\n [\n 85.46484375,\n 393.83489990234375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 379.4886779785156,\n 210.70993041992188,\n 393.83489990234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Python provides methods that operate on lists. For example, append adds a new element to the end of a list:
\",\n \"polygon\": [\n [\n 85.46484375,\n 409.1484375\n ],\n [\n 483.50390625,\n 409.1484375\n ],\n [\n 483.50390625,\n 431.8758544921875\n ],\n [\n 85.46484375,\n 431.8758544921875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 409.1484375,\n 483.50390625,\n 431.8758544921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['a', 'b', 'c']\\n>>> t.append('d')\\n>>> print t\\n['a', 'b', 'c', 'd']\",\n \"polygon\": [\n [\n 85.3154296875,\n 440.4566955566406\n ],\n [\n 206.67153930664062,\n 440.4566955566406\n ],\n [\n 206.67153930664062,\n 487.265625\n ],\n [\n 85.3154296875,\n 487.265625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 440.4566955566406,\n 206.67153930664062,\n 487.265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"extend takes a list as an argument and appends all of the elements:
\",\n \"polygon\": [\n [\n 85.763671875,\n 495.38671875\n ],\n [\n 381.39581298828125,\n 495.38671875\n ],\n [\n 381.39581298828125,\n 505.8438415527344\n ],\n [\n 85.763671875,\n 505.8438415527344\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 495.38671875,\n 381.39581298828125,\n 505.8438415527344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \">>> t1 = ['a', 'b', 'c']\\n>>> t2 = ['d', 'e']\\n>>> t1.extend(t2)\\n>>> print t1\\n['a', 'b', 'c', 'd', 'e']
\",\n \"polygon\": [\n [\n 85.39013671875,\n 514.4246826171875\n ],\n [\n 217.12962341308594,\n 514.4246826171875\n ],\n [\n 217.12962341308594,\n 573.1652984619141\n ],\n [\n 85.39013671875,\n 573.1652984619141\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 514.4246826171875,\n 217.12962341308594,\n 573.1652984619141\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"This example leaves t2 unmodified.
\",\n \"polygon\": [\n [\n 85.83837890625,\n 581.8956909179688\n ],\n [\n 244.88302612304688,\n 581.8956909179688\n ],\n [\n 244.88302612304688,\n 592.06640625\n ],\n [\n 85.83837890625,\n 592.06640625\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 581.8956909179688,\n 244.88302612304688,\n 592.06640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"sort arranges the elements of the list from low to high:
\",\n \"polygon\": [\n [\n 85.68896484375,\n 604.0546875\n ],\n [\n 328.5072326660156,\n 604.0546875\n ],\n [\n 328.5072326660156,\n 614.6718444824219\n ],\n [\n 85.68896484375,\n 614.6718444824219\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 604.0546875,\n 328.5072326660156,\n 614.6718444824219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['d', 'c', 'e', 'b', 'a']\\n>>> t.sort()\\n>>> print t\\n['a', 'b', 'c', 'd', 'e']
\",\n \"polygon\": [\n [\n 85.39013671875,\n 623.00390625\n ],\n [\n 258.9626770019531,\n 623.00390625\n ],\n [\n 258.9626770019531,\n 669.7982864379883\n ],\n [\n 85.39013671875,\n 669.7982864379883\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 623.00390625,\n 258.9626770019531,\n 669.7982864379883\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/111/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"List methods are all void; they modify the list and return None. If you accidentally write t = t.sort(), you will be disappointed with the result.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 677.14453125\n ],\n [\n 482.4026794433594,\n 677.14453125\n ],\n [\n 482.4026794433594,\n 700.8348541259766\n ],\n [\n 85.3154296875,\n 700.8348541259766\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 677.14453125,\n 482.4026794433594,\n 700.8348541259766\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/112/Page/211\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/112/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.794921875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.794921875,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 61.171142578125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/PageHeader/20\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 515.1796875,\n 61.43994140625\n ],\n [\n 525.9375,\n 61.43994140625\n ],\n [\n 525.9375,\n 70.72119140625\n ],\n [\n 515.1796875,\n 70.72119140625\n ]\n ],\n \"bbox\": [\n 515.1796875,\n 61.43994140625,\n 525.9375,\n 70.72119140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/111/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.7 Map, filter and reduce
\",\n \"polygon\": [\n [\n 128.12255859375,\n 85.95379638671875\n ],\n [\n 313.76953125,\n 85.95379638671875\n ],\n [\n 313.76953125,\n 100.29998779296875\n ],\n [\n 128.12255859375,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 85.95379638671875,\n 313.76953125,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"To add up all the numbers in a list, you can use a loop like this:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 112.6318359375\n ],\n [\n 406.5302734375,\n 112.6318359375\n ],\n [\n 406.5302734375,\n 122.846923828125\n ],\n [\n 128.3466796875,\n 122.846923828125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 112.6318359375,\n 406.5302734375,\n 122.846923828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"def add_all(t):\\n total = 0\\n for x in t:\\n total += x\\n return total
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 129.07171630859375\n ],\n [\n 223.73666381835938,\n 129.07171630859375\n ],\n [\n 223.73666381835938,\n 187.811279296875\n ],\n [\n 129.60000610351562,\n 187.811279296875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 129.07171630859375,\n 223.73666381835938,\n 187.811279296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"total is initialized to 0. Each time through the loop, x gets one element from the list. The += operator provides a short way to update a variable. This augmented assignment statement:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 193.8427734375\n ],\n [\n 525.9375,\n 193.8427734375\n ],\n [\n 525.9375,\n 228.6868896484375\n ],\n [\n 128.6455078125,\n 228.6868896484375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 193.8427734375,\n 525.9375,\n 228.6868896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/TextInlineMath/5\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"total += x
\",\n \"polygon\": [\n [\n 147.99462890625,\n 234.544921875\n ],\n [\n 203.203125,\n 234.544921875\n ],\n [\n 203.203125,\n 244.873291015625\n ],\n [\n 147.99462890625,\n 244.873291015625\n ]\n ],\n \"bbox\": [\n 147.99462890625,\n 234.544921875,\n 203.203125,\n 244.873291015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"is equivalent to:
\",\n \"polygon\": [\n [\n 128.49609375,\n 251.397216796875\n ],\n [\n 199.6171875,\n 251.397216796875\n ],\n [\n 199.6171875,\n 261.35986328125\n ],\n [\n 128.49609375,\n 261.35986328125\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 251.397216796875,\n 199.6171875,\n 261.35986328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/TextInlineMath/7\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"total = total + x
\",\n \"polygon\": [\n [\n 144.333984375,\n 267.58465576171875\n ],\n [\n 239.4322509765625,\n 267.58465576171875\n ],\n [\n 239.4322509765625,\n 277.5472412109375\n ],\n [\n 144.333984375,\n 277.5472412109375\n ]\n ],\n \"bbox\": [\n 144.333984375,\n 267.58465576171875,\n 239.4322509765625,\n 277.5472412109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"As the loop executes, total accumulates the sum of the elements; a variable used this way is sometimes called an accumulator.
\",\n \"polygon\": [\n [\n 129.6000518798828,\n 283.920654296875\n ],\n [\n 525.6009521484375,\n 283.920654296875\n ],\n [\n 525.6009521484375,\n 306.226806640625\n ],\n [\n 129.6000518798828,\n 306.226806640625\n ]\n ],\n \"bbox\": [\n 129.6000518798828,\n 283.920654296875,\n 525.6009521484375,\n 306.226806640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Adding up the elements of a list is such a common operation that Python provides it as a built-in function, sum:
\",\n \"polygon\": [\n [\n 129.60009765625,\n 316.5732116699219\n ],\n [\n 525.6033935546875,\n 316.5732116699219\n ],\n [\n 525.6033935546875,\n 338.72979736328125\n ],\n [\n 129.60009765625,\n 338.72979736328125\n ]\n ],\n \"bbox\": [\n 129.60009765625,\n 316.5732116699219,\n 525.6033935546875,\n 338.72979736328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = [1, 2, 3]\\n>>> sum(t)\\n6
\",\n \"polygon\": [\n [\n 128.12255859375,\n 344.95465087890625\n ],\n [\n 218.51629638671875,\n 344.95465087890625\n ],\n [\n 218.51629638671875,\n 379.30523681640625\n ],\n [\n 128.12255859375,\n 379.30523681640625\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 344.95465087890625,\n 218.51629638671875,\n 379.30523681640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"An operation like this that combines a sequence of elements into a single value is sometimes called reduce.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 385.8292236328125\n ],\n [\n 525.6034545898438,\n 385.8292236328125\n ],\n [\n 525.6034545898438,\n 407.9858093261719\n ],\n [\n 128.3466796875,\n 407.9858093261719\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 385.8292236328125,\n 525.6034545898438,\n 407.9858093261719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 10.1. Write a function called nested_sum that takes a nested list of integers and add up the elements from all of the nested lists.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 409.921875\n ],\n [\n 525.604736328125,\n 409.921875\n ],\n [\n 525.604736328125,\n 432.2021179199219\n ],\n [\n 128.9443359375,\n 432.2021179199219\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 409.921875,\n 525.604736328125,\n 432.2021179199219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Sometimes you want to traverse one list while building another. For example, the following function takes a list of strings and returns a new list that contains capitalized strings:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 442.40625\n ],\n [\n 525.6034545898438,\n 442.40625\n ],\n [\n 525.6034545898438,\n 464.8768310546875\n ],\n [\n 128.3466796875,\n 464.8768310546875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 442.40625,\n 525.6034545898438,\n 464.8768310546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \"def capitalize_all(t):\\n res = []\\n for s in t:\\n res.append(s.capitalize())\\n return res
\",\n \"polygon\": [\n [\n 129.60011291503906,\n 471.1016845703125\n ],\n [\n 307.4325256347656,\n 471.1016845703125\n ],\n [\n 307.4325256347656,\n 529.8412780761719\n ],\n [\n 129.60011291503906,\n 529.8412780761719\n ]\n ],\n \"bbox\": [\n 129.60011291503906,\n 471.1016845703125,\n 307.4325256347656,\n 529.8412780761719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"res is initialized with an empty list; each time through the loop, we append the next element. So res is another kind of accumulator.
\",\n \"polygon\": [\n [\n 127.8984375,\n 536.2156982421875\n ],\n [\n 525.9375,\n 536.2156982421875\n ],\n [\n 525.9375,\n 558.5218505859375\n ],\n [\n 127.8984375,\n 558.5218505859375\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 536.2156982421875,\n 525.9375,\n 558.5218505859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"An operation like capitalize_all is sometimes called a map because it \\\"maps\\\" a function (in this case the method capitalize) onto each of the elements in a sequence.
\",\n \"polygon\": [\n [\n 127.8984375,\n 568.7176971435547\n ],\n [\n 525.9375,\n 568.7176971435547\n ],\n [\n 525.9375,\n 591.0248565673828\n ],\n [\n 127.8984375,\n 591.0248565673828\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 568.7176971435547,\n 525.9375,\n 591.0248565673828\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 10.2. Use capitalize_all to write a function named capitalize_nested that takes a nested list of strings and returns a new nested list with all strings capitalized.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 593.0835571289062\n ],\n [\n 525.5971069335938,\n 593.0835571289062\n ],\n [\n 525.5971069335938,\n 615.26953125\n ],\n [\n 128.3466796875,\n 615.26953125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 593.0835571289062,\n 525.5971069335938,\n 615.26953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"Another common operation is to select some of the elements from a list and return a sublist. For example, the following function takes a list of strings and returns a list that contains only the uppercase strings:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 625.7109375\n ],\n [\n 525.9375,\n 625.7109375\n ],\n [\n 525.9375,\n 660.10986328125\n ],\n [\n 128.6455078125,\n 660.10986328125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 625.7109375,\n 525.9375,\n 660.10986328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/112/Code/19\",\n \"block_type\": \"Code\",\n \"html\": \"def only_upper(t):\\n res = []\\n for s in t:
\",\n \"polygon\": [\n [\n 129.60012817382812,\n 666.3347015380859\n ],\n [\n 223.75665283203125,\n 666.3347015380859\n ],\n [\n 223.75665283203125,\n 700.685302734375\n ],\n [\n 129.60012817382812,\n 700.685302734375\n ]\n ],\n \"bbox\": [\n 129.60012817382812,\n 666.3347015380859,\n 223.75665283203125,\n 700.685302734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/113/Page/268\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/113/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.27978515625\n ],\n [\n 482.4033508300781,\n 60.27978515625\n ],\n [\n 482.4033508300781,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.27978515625,\n 482.4033508300781,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/PageHeader/19\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.46484375,\n 60.8115234375\n ],\n [\n 96.521484375,\n 60.8115234375\n ],\n [\n 96.521484375,\n 70.3828125\n ],\n [\n 85.46484375,\n 70.3828125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 60.8115234375,\n 96.521484375,\n 70.3828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \"if s.isupper():\\n res.append(s)\\nreturn res
\",\n \"polygon\": [\n [\n 107.1298828125,\n 88.41357421875\n ],\n [\n 217.15371704101562,\n 88.41357421875\n ],\n [\n 217.15371704101562,\n 123.03729248046875\n ],\n [\n 107.1298828125,\n 123.03729248046875\n ]\n ],\n \"bbox\": [\n 107.1298828125,\n 88.41357421875,\n 217.15371704101562,\n 123.03729248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"isupper is a string method that returns True if the string contains only upper case letters.
\",\n \"polygon\": [\n [\n 86.40000915527344,\n 126.45703125\n ],\n [\n 479.3203125,\n 126.45703125\n ],\n [\n 479.3203125,\n 137.2169189453125\n ],\n [\n 86.40000915527344,\n 137.2169189453125\n ]\n ],\n \"bbox\": [\n 86.40000915527344,\n 126.45703125,\n 479.3203125,\n 137.2169189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"An operation like only_upper is called a filter because it selects some of the elements and filters out the others.
\",\n \"polygon\": [\n [\n 85.46484375,\n 143.859375\n ],\n [\n 482.40386962890625,\n 143.859375\n ],\n [\n 482.40386962890625,\n 167.4139404296875\n ],\n [\n 85.46484375,\n 167.4139404296875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 143.859375,\n 482.40386962890625,\n 167.4139404296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Most common list operations can be expressed as a combination of map, filter and reduce. Because these operations are so common, Python provides language features to support them, including the built-in function map and an operator called a \\\"list comprehension.\\\" Exercise 10.3. Write a function that takes a list of numbers and returns the cumulative sum; that
\",\n \"polygon\": [\n [\n 85.46484375,\n 174.5068359375\n ],\n [\n 482.4034423828125,\n 174.5068359375\n ],\n [\n 482.4034423828125,\n 221.90179443359375\n ],\n [\n 85.46484375,\n 221.90179443359375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 174.5068359375,\n 482.4034423828125,\n 221.90179443359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"is, a new list where the ith element is the sum of the first i + 1 elements from the original list. For example, the cumulative sum of [1, 2, 3] is [1, 3, 6].
\",\n \"polygon\": [\n [\n 85.6142578125,\n 219.65625\n ],\n [\n 482.3965759277344,\n 219.65625\n ],\n [\n 482.3965759277344,\n 246.7265625\n ],\n [\n 85.6142578125,\n 246.7265625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 219.65625,\n 482.3965759277344,\n 246.7265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/112/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.8 Deleting elements
\",\n \"polygon\": [\n [\n 85.6142578125,\n 272.443359375\n ],\n [\n 245.77203369140625,\n 272.443359375\n ],\n [\n 245.77203369140625,\n 287.138671875\n ],\n [\n 85.6142578125,\n 287.138671875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 272.443359375,\n 245.77203369140625,\n 287.138671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"There are several ways to delete elements from a list. If you know the index of the element you want, you can use pop:
\",\n \"polygon\": [\n [\n 85.46484375,\n 296.806640625\n ],\n [\n 482.40350341796875,\n 296.806640625\n ],\n [\n 482.40350341796875,\n 319.45391845703125\n ],\n [\n 85.46484375,\n 319.45391845703125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 296.806640625,\n 482.40350341796875,\n 319.45391845703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['a', 'b', 'c']\\n>>> x = t.pop(1)\\n>>> print t\\n['a', 'c']\\n>>> print x\\nb
\",\n \"polygon\": [\n [\n 85.6142578125,\n 323.37176513671875\n ],\n [\n 206.6704864501953,\n 323.37176513671875\n ],\n [\n 206.6704864501953,\n 394.3063659667969\n ],\n [\n 85.6142578125,\n 394.3063659667969\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 323.37176513671875,\n 206.6704864501953,\n 394.3063659667969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"pop modifies the list and returns the element that was removed. If you don't provide an index, it deletes and returns the last element.
\",\n \"polygon\": [\n [\n 85.46484375,\n 397.93359375\n ],\n [\n 482.3990478515625,\n 397.93359375\n ],\n [\n 482.3990478515625,\n 420.6809387207031\n ],\n [\n 85.46484375,\n 420.6809387207031\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 397.93359375,\n 482.3990478515625,\n 420.6809387207031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"If you don't need the removed value, you can use the del operator:
\",\n \"polygon\": [\n [\n 85.39013671875,\n 428.09765625\n ],\n [\n 381.49713134765625,\n 428.09765625\n ],\n [\n 381.49713134765625,\n 438.6829528808594\n ],\n [\n 85.39013671875,\n 438.6829528808594\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 428.09765625,\n 381.49713134765625,\n 438.6829528808594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/TextInlineMath/11\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> t = ['a', 'b', 'c'] >>> del t[1] >>> print t ['a', 'c']
\",\n \"polygon\": [\n [\n 85.3154296875,\n 442.40625\n ],\n [\n 206.67051696777344,\n 442.40625\n ],\n [\n 206.67051696777344,\n 496.546875\n ],\n [\n 85.3154296875,\n 496.546875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 442.40625,\n 206.67051696777344,\n 496.546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"If you know the element you want to remove (but not the index), you can use remove:
\",\n \"polygon\": [\n [\n 85.763671875,\n 493.06640625\n ],\n [\n 462.9958190917969,\n 493.06640625\n ],\n [\n 462.9958190917969,\n 503.3279724121094\n ],\n [\n 85.763671875,\n 503.3279724121094\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 493.06640625,\n 462.9958190917969,\n 503.3279724121094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Code/262\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['a', 'b', 'c']\\n>>> t.remove('b')\\n>>> print t\\n['a', 'c']\",\n \"polygon\": [\n [\n 85.6142578125,\n 506.98828125\n ],\n [\n 206.67054748535156,\n 506.98828125\n ],\n [\n 206.67054748535156,\n 561.12890625\n ],\n [\n 85.6142578125,\n 561.12890625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 506.98828125,\n 206.67054748535156,\n 561.12890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The return value from remove is None.
\",\n \"polygon\": [\n [\n 86.40019226074219,\n 557.8598327636719\n ],\n [\n 254.302734375,\n 557.8598327636719\n ],\n [\n 254.302734375,\n 568.86328125\n ],\n [\n 86.40019226074219,\n 568.86328125\n ]\n ],\n \"bbox\": [\n 86.40019226074219,\n 557.8598327636719,\n 254.302734375,\n 568.86328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"To remove more than one element, you can use del with a slice index:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 575.8618316650391\n ],\n [\n 393.24322509765625,\n 575.8618316650391\n ],\n [\n 393.24322509765625,\n 586.265625\n ],\n [\n 86.2119140625,\n 586.265625\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 575.8618316650391,\n 393.24322509765625,\n 586.265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['a', 'b', 'c', 'd', 'e', 'f']\\n>>> del t[1:5]\\n>>> print t\\n['a', 'f']
\",\n \"polygon\": [\n [\n 85.68896484375,\n 589.74609375\n ],\n [\n 286.27734375,\n 589.74609375\n ],\n [\n 286.27734375,\n 638.47265625\n ],\n [\n 85.68896484375,\n 638.47265625\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 589.74609375,\n 286.27734375,\n 638.47265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"As usual, the slice selects all the elements up to, but not including, the second index. Exercise 10.4. Write a function called middle that takes a list and returns a new list that contains all but the first and last elements. So middle([1,2,3,4]) should return [2,3].
\",\n \"polygon\": [\n [\n 85.763671875,\n 639.6328125\n ],\n [\n 482.40374755859375,\n 639.6328125\n ],\n [\n 482.40374755859375,\n 674.8574447631836\n ],\n [\n 85.763671875,\n 674.8574447631836\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 639.6328125,\n 482.40374755859375,\n 674.8574447631836\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/113/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 10.5. Write a function called chop that takes a list, modifies it by removing the first and last elements, and returns None.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 674.4375\n ],\n [\n 482.396240234375,\n 674.4375\n ],\n [\n 482.396240234375,\n 699.2464447021484\n ],\n [\n 85.6142578125,\n 699.2464447021484\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 674.4375,\n 482.396240234375,\n 699.2464447021484\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/114/Page/239\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/114/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 129.16845703125,\n 60.95654296875\n ],\n [\n 525.6033935546875,\n 60.95654296875\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 129.16845703125,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 60.95654296875,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 514.283203125,\n 60.908203125\n ],\n [\n 525.638671875,\n 60.908203125\n ],\n [\n 525.638671875,\n 70.0927734375\n ],\n [\n 514.283203125,\n 70.0927734375\n ]\n ],\n \"bbox\": [\n 514.283203125,\n 60.908203125,\n 525.638671875,\n 70.0927734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/113/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.9 Lists and strings
\",\n \"polygon\": [\n [\n 128.6455078125,\n 85.95379638671875\n ],\n [\n 277.4619140625,\n 85.95379638671875\n ],\n [\n 277.4619140625,\n 100.29998779296875\n ],\n [\n 128.6455078125,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 85.95379638671875,\n 277.4619140625,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"A string is a sequence of characters and a list is a sequence of values, but a list of characters is not the same as a string. To convert from a string to a list of characters, you can use list:
\",\n \"polygon\": [\n [\n 128.794921875,\n 112.8251953125\n ],\n [\n 525.638671875,\n 112.8251953125\n ],\n [\n 525.638671875,\n 135.52398681640625\n ],\n [\n 128.794921875,\n 135.52398681640625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 112.8251953125,\n 525.638671875,\n 135.52398681640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> s = 'spam'\\n>>> t = list(s)\\n>>> print t\\n['s', 'p', 'a', 'm']
\",\n \"polygon\": [\n [\n 128.49609375,\n 153.6240234375\n ],\n [\n 234.1833953857422,\n 153.6240234375\n ],\n [\n 234.1833953857422,\n 200.8333740234375\n ],\n [\n 128.49609375,\n 200.8333740234375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 153.6240234375,\n 234.1833953857422,\n 200.8333740234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Because list is the name of a built-in function, you should avoid using it as a variable name. I also avoid l because it looks too much like 1. So that's why I use t.
\",\n \"polygon\": [\n [\n 129.392578125,\n 206.89453125\n ],\n [\n 525.638671875,\n 206.89453125\n ],\n [\n 525.638671875,\n 229.8590087890625\n ],\n [\n 129.392578125,\n 229.8590087890625\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 206.89453125,\n 525.638671875,\n 229.8590087890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The list function breaks a string into individual letters. If you want to break a string into words, you can use the split method:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 239.765625\n ],\n [\n 525.6027221679688,\n 239.765625\n ],\n [\n 525.6027221679688,\n 262.70599365234375\n ],\n [\n 129.2431640625,\n 262.70599365234375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 239.765625,\n 525.6027221679688,\n 262.70599365234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> s = 'pining for the fjords'\\n>>> t = s.split()\\n>>> print t\\n['pining', 'for', 'the', 'fjords']
\",\n \"polygon\": [\n [\n 129.46728515625,\n 269.27484130859375\n ],\n [\n 307.3914489746094,\n 269.27484130859375\n ],\n [\n 307.3914489746094,\n 315.8204345703125\n ],\n [\n 129.46728515625,\n 315.8204345703125\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 269.27484130859375,\n 307.3914489746094,\n 315.8204345703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"An optional argument called a delimiter specifies which characters to use as word boundaries. The following example uses a hyphen as a delimiter:
\",\n \"polygon\": [\n [\n 129.60006713867188,\n 321.943359375\n ],\n [\n 525.6026611328125,\n 321.943359375\n ],\n [\n 525.6026611328125,\n 344.84600830078125\n ],\n [\n 129.60006713867188,\n 344.84600830078125\n ]\n ],\n \"bbox\": [\n 129.60006713867188,\n 321.943359375,\n 525.6026611328125,\n 344.84600830078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> s = 'spam-spam-spam'\\n>>> delimiter = '-'\\n>>> s.split(delimiter)\\n['spam', 'spam', 'spam']
\",\n \"polygon\": [\n [\n 127.52490234375,\n 351.41485595703125\n ],\n [\n 259.98046875,\n 351.41485595703125\n ],\n [\n 259.98046875,\n 397.96044921875\n ],\n [\n 127.52490234375,\n 397.96044921875\n ]\n ],\n \"bbox\": [\n 127.52490234375,\n 351.41485595703125,\n 259.98046875,\n 397.96044921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"join is the inverse of split. It takes a list of strings and concatenates the elements. join is a string method, so you have to invoke it on the delimiter and pass the list as a parameter:
\",\n \"polygon\": [\n [\n 129.5419921875,\n 404.12109375\n ],\n [\n 525.6035766601562,\n 404.12109375\n ],\n [\n 525.6035766601562,\n 426.98602294921875\n ],\n [\n 129.5419921875,\n 426.98602294921875\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 404.12109375,\n 525.6035766601562,\n 426.98602294921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['pining', 'for', 'the', 'fjords']\\n>>> delimiter = ' '\\n>>> delimiter.join(t)\\n'pining for the fjords'
\",\n \"polygon\": [\n [\n 127.37548828125,\n 444.33984375\n ],\n [\n 349.22454833984375,\n 444.33984375\n ],\n [\n 349.22454833984375,\n 493.83984375\n ],\n [\n 127.37548828125,\n 493.83984375\n ]\n ],\n \"bbox\": [\n 127.37548828125,\n 444.33984375,\n 349.22454833984375,\n 493.83984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"In this case the delimiter is a space character, so join puts a space between words. To concatenate strings without spaces, you can use the empty string, '', as a delimiter.
\",\n \"polygon\": [\n [\n 128.794921875,\n 499.0138854980469\n ],\n [\n 525.602783203125,\n 499.0138854980469\n ],\n [\n 525.602783203125,\n 521.3200378417969\n ],\n [\n 128.794921875,\n 521.3200378417969\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 499.0138854980469,\n 525.602783203125,\n 521.3200378417969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.10 Objects and values
\",\n \"polygon\": [\n [\n 127.8984375,\n 551.84765625\n ],\n [\n 300.5208435058594,\n 551.84765625\n ],\n [\n 300.5208435058594,\n 566.5540924072266\n ],\n [\n 127.8984375,\n 566.5540924072266\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 551.84765625,\n 300.5208435058594,\n 566.5540924072266\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"If we execute these assignment statements:
\",\n \"polygon\": [\n [\n 127.8984375,\n 578.91796875\n ],\n [\n 317.843505859375,\n 578.91796875\n ],\n [\n 317.843505859375,\n 589.5840454101562\n ],\n [\n 127.8984375,\n 589.5840454101562\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 578.91796875,\n 317.843505859375,\n 589.5840454101562\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \"a = 'banana'\\nb = 'banana'
\",\n \"polygon\": [\n [\n 127.4501953125,\n 596.1528930664062\n ],\n [\n 192.35057067871094,\n 596.1528930664062\n ],\n [\n 192.35057067871094,\n 618.3105010986328\n ],\n [\n 127.4501953125,\n 618.3105010986328\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 596.1528930664062,\n 192.35057067871094,\n 618.3105010986328\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"We know that a and b both refer to a string, but we don't know whether they refer to the same string. There are two possible states, shown in Figure 10.2.
\",\n \"polygon\": [\n [\n 129.5419921875,\n 624.55078125\n ],\n [\n 525.5989379882812,\n 624.55078125\n ],\n [\n 525.5989379882812,\n 647.3350524902344\n ],\n [\n 129.5419921875,\n 647.3350524902344\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 624.55078125,\n 525.5989379882812,\n 647.3350524902344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/114/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"In one case, a and b refer to two different objects that have the same value. In the second case, they refer to the same object.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 690.6796875\n ],\n [\n 501.010986328125,\n 690.6796875\n ],\n [\n 501.010986328125,\n 700.8350601196289\n ],\n [\n 128.0478515625,\n 700.8350601196289\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 690.6796875,\n 501.010986328125,\n 700.8350601196289\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/12\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/114/SectionHeader/12\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/115/Page/201\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/115/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 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\",\n \"polygon\": [\n [\n 129.59999084472656,\n 262.58203125\n ],\n [\n 198.5712890625,\n 262.58203125\n ],\n [\n 198.5712890625,\n 297.7734375\n ],\n [\n 129.59999084472656,\n 297.7734375\n ]\n ],\n \"bbox\": [\n 129.59999084472656,\n 262.58203125,\n 198.5712890625,\n 297.7734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/115/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Although this behavior can be useful, it is error-prone. In general, it is safer to avoid aliasing when you are working with mutable objects.
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\",\n \"polygon\": [\n [\n 129.2431640625,\n 352.270751953125\n ],\n [\n 192.350341796875,\n 352.270751953125\n ],\n [\n 192.350341796875,\n 374.4273376464844\n ],\n [\n 129.2431640625,\n 374.4273376464844\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 352.270751953125,\n 192.350341796875,\n 374.4273376464844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/115/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"It almost never makes a difference whether a and b refer to the same string or not.
\",\n \"polygon\": [\n [\n 129.5419921875,\n 380.337890625\n ],\n [\n 489.36553955078125,\n 380.337890625\n ],\n [\n 489.36553955078125,\n 390.8279113769531\n ],\n [\n 129.5419921875,\n 390.8279113769531\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 380.337890625,\n 489.36553955078125,\n 390.8279113769531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/115/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/SectionHeader/10\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.12 List arguments
\",\n \"polygon\": [\n [\n 128.197265625,\n 420.4227600097656\n ],\n [\n 273.836669921875,\n 420.4227600097656\n ],\n [\n 273.836669921875,\n 434.76898193359375\n ],\n [\n 128.197265625,\n 434.76898193359375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 420.4227600097656,\n 273.836669921875,\n 434.76898193359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"When you pass a list to a function, the function gets a reference to the list. If the function modifies a list parameter, the caller sees the change. For example, delete_head removes the first element from a list:
\",\n \"polygon\": [\n [\n 128.197265625,\n 447.23333740234375\n ],\n [\n 525.638671875,\n 447.23333740234375\n ],\n [\n 525.638671875,\n 481.5849304199219\n ],\n [\n 128.197265625,\n 481.5849304199219\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 447.23333740234375,\n 525.638671875,\n 481.5849304199219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"def delete_head(t):\\n del t[0]
\",\n \"polygon\": [\n [\n 127.8984375,\n 487.7227783203125\n ],\n [\n 228.98683166503906,\n 487.7227783203125\n ],\n [\n 228.98683166503906,\n 509.8793640136719\n ],\n [\n 127.8984375,\n 509.8793640136719\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 487.7227783203125,\n 228.98683166503906,\n 509.8793640136719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Here's how it is used:
\",\n \"polygon\": [\n [\n 127.8984375,\n 515.8828125\n ],\n [\n 224.54351806640625,\n 515.8828125\n ],\n [\n 224.54351806640625,\n 526.2799377441406\n ],\n [\n 127.8984375,\n 526.2799377441406\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 515.8828125,\n 224.54351806640625,\n 526.2799377441406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> letters = ['a', 'b', 'c']\\n>>> delete_head(letters)\\n>>> print letters\\n['b', 'c']
\",\n \"polygon\": [\n [\n 127.82373046875,\n 532.4177856445312\n ],\n [\n 281.245361328125,\n 532.4177856445312\n ],\n [\n 281.245361328125,\n 578.9633941650391\n ],\n [\n 127.82373046875,\n 578.9633941650391\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 532.4177856445312,\n 281.245361328125,\n 578.9633941650391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The parameter t and the variable letters are aliases for the same object. The stack diagram looks like Figure 10.5.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 584.33203125\n ],\n [\n 525.6002807617188,\n 584.33203125\n ],\n [\n 525.6002807617188,\n 607.5579528808594\n ],\n [\n 128.6455078125,\n 607.5579528808594\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 584.33203125,\n 525.6002807617188,\n 607.5579528808594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Since the list is shared by two frames, I drew it between them.
\",\n \"polygon\": [\n [\n 127.7490234375,\n 617.58984375\n ],\n [\n 401.7183837890625,\n 617.58984375\n ],\n [\n 401.7183837890625,\n 627.7799530029297\n ],\n [\n 127.7490234375,\n 627.7799530029297\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 617.58984375,\n 401.7183837890625,\n 627.7799530029297\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"It is important to distinguish between operations that modify lists and operations that create new lists. For example, the append method modifies a list, but the + operator creates a new list:
\",\n \"polygon\": [\n [\n 128.197265625,\n 637.3125\n ],\n [\n 525.6033325195312,\n 637.3125\n ],\n [\n 525.6033325195312,\n 672.3909606933594\n ],\n [\n 128.197265625,\n 672.3909606933594\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 637.3125,\n 525.6033325195312,\n 672.3909606933594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/116/TextInlineMath/18\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> t1 = [1, 2] >>> t2 = t1.append(3)
\",\n \"polygon\": [\n [\n 128.3466796875,\n 678.5287933349609\n ],\n [\n 239.4376220703125,\n 678.5287933349609\n ],\n [\n 239.4376220703125,\n 700.6853942871094\n ],\n [\n 128.3466796875,\n 700.6853942871094\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 678.5287933349609,\n 239.4376220703125,\n 700.6853942871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/117/Page/176\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/117/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.4248046875\n ],\n [\n 482.4033508300781,\n 60.4248046875\n ],\n [\n 482.4033508300781,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.4248046875,\n 482.4033508300781,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.83837890625,\n 61.05322265625\n ],\n [\n 96.74560546875,\n 61.05322265625\n ],\n [\n 96.74560546875,\n 70.14111328125\n ],\n [\n 85.83837890625,\n 70.14111328125\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 61.05322265625,\n 96.74560546875,\n 70.14111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> print t1\\n[1, 2, 3]\\n>>> print t2\\nNone\\n>>> t3 = t1 + [4]\\n>>> print t3\\n[1, 2, 3, 4]
\",\n \"polygon\": [\n [\n 86.2119140625,\n 87.8818359375\n ],\n [\n 175.3162078857422,\n 87.8818359375\n ],\n [\n 175.3162078857422,\n 184.00830078125\n ],\n [\n 86.2119140625,\n 184.00830078125\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 87.8818359375,\n 175.3162078857422,\n 184.00830078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"This difference is important when you write functions that are supposed to modify lists. For example, this function does not delete the head of a list:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 190.3623046875\n ],\n [\n 482.4034118652344,\n 190.3623046875\n ],\n [\n 482.4034118652344,\n 212.57489013671875\n ],\n [\n 86.2119140625,\n 212.57489013671875\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 190.3623046875,\n 482.4034118652344,\n 212.57489013671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/TextInlineMath/3\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"def bad_delete_head(t): t = t[1:] # WRONG!
\",\n \"polygon\": [\n [\n 86.40000915527344,\n 217.529296875\n ],\n [\n 269.4473571777344,\n 217.529296875\n ],\n [\n 269.4473571777344,\n 240.84228515625\n ],\n [\n 86.40000915527344,\n 240.84228515625\n ]\n ],\n \"bbox\": [\n 86.40000915527344,\n 217.529296875,\n 269.4473571777344,\n 240.84228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The slice operator creates a new list and the assignment makes t refer to it, but none of that has any effect on the list that was passed as an argument.
\",\n \"polygon\": [\n [\n 85.763671875,\n 246.33984375\n ],\n [\n 482.3985595703125,\n 246.33984375\n ],\n [\n 482.3985595703125,\n 269.40887451171875\n ],\n [\n 85.763671875,\n 269.40887451171875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 246.33984375,\n 482.3985595703125,\n 269.40887451171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"An alternative is to write a function that creates and returns a new list. For example, tail returns all but the first element of a list:
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\",\n \"polygon\": [\n [\n 86.4000244140625,\n 306.28125\n ],\n [\n 170.0803985595703,\n 306.28125\n ],\n [\n 170.0803985595703,\n 330.2578125\n ],\n [\n 86.4000244140625,\n 330.2578125\n ]\n ],\n \"bbox\": [\n 86.4000244140625,\n 306.28125,\n 170.0803985595703,\n 330.2578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"This function leaves the original list unmodified. Here's how it is used:
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\",\n \"polygon\": [\n [\n 85.763671875,\n 352.5467224121094\n ],\n [\n 238.04542541503906,\n 352.5467224121094\n ],\n [\n 238.04542541503906,\n 399.48046875\n ],\n [\n 85.763671875,\n 399.48046875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 352.5467224121094,\n 238.04542541503906,\n 399.48046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/116/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"10.13 Debugging
\",\n \"polygon\": [\n [\n 85.53955078125,\n 428.7527160644531\n ],\n [\n 207.15200805664062,\n 428.7527160644531\n ],\n [\n 207.15200805664062,\n 443.56640625\n ],\n [\n 85.53955078125,\n 443.56640625\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 428.7527160644531,\n 207.15200805664062,\n 443.56640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Careless use of lists (and other mutable objects) can lead to long hours of debugging. Here are some common pitfalls and ways to avoid them:
\",\n \"polygon\": [\n [\n 86.0625,\n 455.5242919921875\n ],\n [\n 482.40350341796875,\n 455.5242919921875\n ],\n [\n 482.40350341796875,\n 477.6808776855469\n ],\n [\n 86.0625,\n 477.6808776855469\n ]\n ],\n \"bbox\": [\n 86.0625,\n 455.5242919921875,\n 482.40350341796875,\n 477.6808776855469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Don't forget that most list methods modify the argument and return None. This is the opposite of the string methods, which return a new string and leave the original alone.\",\n \"polygon\": [\n [\n 98.314453125,\n 491.1328125\n ],\n [\n 482.607421875,\n 491.1328125\n ],\n [\n 482.607421875,\n 526.3108825683594\n ],\n [\n 98.314453125,\n 526.3108825683594\n ]\n ],\n \"bbox\": [\n 98.314453125,\n 491.1328125,\n 482.607421875,\n 526.3108825683594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"If you are used to writing string code like this:
\",\n \"polygon\": [\n [\n 109.74462890625,\n 532.125\n ],\n [\n 315.263671875,\n 532.125\n ],\n [\n 315.263671875,\n 542.5378875732422\n ],\n [\n 109.74462890625,\n 542.5378875732422\n ]\n ],\n \"bbox\": [\n 109.74462890625,\n 532.125,\n 315.263671875,\n 542.5378875732422\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/TextInlineMath/13\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"word = word.strip()
\",\n \"polygon\": [\n [\n 110.19287109375,\n 552.62109375\n ],\n [\n 210.6938934326172,\n 552.62109375\n ],\n [\n 210.6938934326172,\n 562.663330078125\n ],\n [\n 110.19287109375,\n 562.663330078125\n ]\n ],\n \"bbox\": [\n 110.19287109375,\n 552.62109375,\n 210.6938934326172,\n 562.663330078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"It is tempting to write list code like this:
\",\n \"polygon\": [\n [\n 109.44580078125,\n 572.73046875\n ],\n [\n 286.27734375,\n 572.73046875\n ],\n [\n 286.27734375,\n 583.0868835449219\n ],\n [\n 109.44580078125,\n 583.0868835449219\n ]\n ],\n \"bbox\": [\n 109.44580078125,\n 572.73046875,\n 286.27734375,\n 583.0868835449219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/TextInlineMath/15\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"t = t.sort() # WRONG!
\",\n \"polygon\": [\n [\n 111.3070068359375,\n 593.2497253417969\n ],\n [\n 273.4383544921875,\n 593.2497253417969\n ],\n [\n 273.4383544921875,\n 603.28125\n ],\n [\n 111.3070068359375,\n 603.28125\n ]\n ],\n \"bbox\": [\n 111.3070068359375,\n 593.2497253417969,\n 273.4383544921875,\n 603.28125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Because sort returns None, the next operation you perform with t is likely to fail.
\",\n \"polygon\": [\n [\n 110.267578125,\n 613.3359375\n ],\n [\n 468.3831787109375,\n 613.3359375\n ],\n [\n 468.3831787109375,\n 623.6358795166016\n ],\n [\n 110.267578125,\n 623.6358795166016\n ]\n ],\n \"bbox\": [\n 110.267578125,\n 613.3359375,\n 468.3831787109375,\n 623.6358795166016\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/117/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Before using list methods and operators, you should read the documentation carefully and then test them in interactive mode. The methods and operators that lists share with other sequences (like strings) are documented at http://docs.python. org/2/library/stdtypes.html#typesseq. The methods and operators that only apply to mutable sequences are documented at http://docs.python.org/2/library/ stdtypes.html#typesseq-mutable.
\",\n \"polygon\": [\n [\n 110.56640625,\n 629.19140625\n ],\n [\n 482.4609069824219,\n 629.19140625\n ],\n [\n 482.4609069824219,\n 700.8348846435547\n ],\n [\n 110.56640625,\n 700.8348846435547\n ]\n ],\n \"bbox\": [\n 110.56640625,\n 629.19140625,\n 482.4609069824219,\n 700.8348846435547\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/118/Page/215\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/118/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 129.60000610351562,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 129.60000610351562,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 61.171142578125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/PageHeader/22\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 514.58203125,\n 61.1982421875\n ],\n [\n 525.33984375,\n 61.1982421875\n ],\n [\n 525.33984375,\n 70.4794921875\n ],\n [\n 514.58203125,\n 70.4794921875\n ]\n ],\n \"bbox\": [\n 514.58203125,\n 61.1982421875,\n 525.33984375,\n 70.4794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/ListItem/1\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Pick an idiom and stick with it.\",\n \"polygon\": [\n [\n 141.943359375,\n 88.4619140625\n ],\n [\n 292.25390625,\n 88.4619140625\n ],\n [\n 292.25390625,\n 98.79791259765625\n ],\n [\n 141.943359375,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 141.943359375,\n 88.4619140625,\n 292.25390625,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Part of the problem with lists is that there are too many ways to do things. For example, to remove an element from a list, you can use pop, remove, del, or even a slice assignment.
\",\n \"polygon\": [\n [\n 154.1953125,\n 104.3173828125\n ],\n [\n 525.6039428710938,\n 104.3173828125\n ],\n [\n 525.6039428710938,\n 139.24383544921875\n ],\n [\n 154.1953125,\n 139.24383544921875\n ]\n ],\n \"bbox\": [\n 154.1953125,\n 104.3173828125,\n 525.6039428710938,\n 139.24383544921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"To add an element, you can use the append method or the + operator. Assuming that t is a list and x is a list element, these are right:
\",\n \"polygon\": [\n [\n 153.59765625,\n 144.7294921875\n ],\n [\n 525.6051025390625,\n 144.7294921875\n ],\n [\n 525.6051025390625,\n 167.496826171875\n ],\n [\n 153.59765625,\n 167.496826171875\n ]\n ],\n \"bbox\": [\n 153.59765625,\n 144.7294921875,\n 525.6051025390625,\n 167.496826171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/Code/187\",\n \"block_type\": \"Code\",\n \"html\": \"t.append(x)\\nt = t + [x]
\",\n \"polygon\": [\n [\n 154.1953125,\n 176.95465087890625\n ],\n [\n 212.16796875,\n 176.95465087890625\n ],\n [\n 212.16796875,\n 199.1112060546875\n ],\n [\n 154.1953125,\n 199.1112060546875\n ]\n ],\n \"bbox\": [\n 154.1953125,\n 176.95465087890625,\n 212.16796875,\n 199.1112060546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"And these are wrong:
\",\n \"polygon\": [\n [\n 153.298828125,\n 208.8681640625\n ],\n [\n 249.2713165283203,\n 208.8681640625\n ],\n [\n 249.2713165283203,\n 218.8828125\n ],\n [\n 153.298828125,\n 218.8828125\n ]\n ],\n \"bbox\": [\n 153.298828125,\n 208.8681640625,\n 249.2713165283203,\n 218.8828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/Table/6\",\n \"block_type\": \"Table\",\n \"html\": \"| t.append([x]) | | # WRONG! |
| t = t.append(x) | | # WRONG! |
| t + [x] | | # WRONG! |
| t = t + x | | # WRONG! |
\",\n \"polygon\": [\n [\n 151.58056640625,\n 227.197265625\n ],\n [\n 318.849609375,\n 227.197265625\n ],\n [\n 318.849609375,\n 274.834228515625\n ],\n [\n 151.58056640625,\n 274.834228515625\n ]\n ],\n \"bbox\": [\n 151.58056640625,\n 227.197265625,\n 318.849609375,\n 274.834228515625\n ],\n \"children\": [\n {\n \"id\": \"/page/118/TableCell/201\",\n \"block_type\": \"TableCell\",\n \"html\": \"t.append([x]) | \",\n \"polygon\": [\n [\n 151.58056640625,\n 227.197265625\n ],\n [\n 152.58056640625,\n 227.197265625\n ],\n [\n 152.58056640625,\n 228.197265625\n ],\n [\n 151.58056640625,\n 228.197265625\n ]\n ],\n \"bbox\": [\n 151.58056640625,\n 227.197265625,\n 152.58056640625,\n 228.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/202\",\n \"block_type\": \"TableCell\",\n \"html\": \" | \",\n \"polygon\": [\n [\n 152.58056640625,\n 227.197265625\n ],\n [\n 153.58056640625,\n 227.197265625\n ],\n [\n 153.58056640625,\n 228.197265625\n ],\n [\n 152.58056640625,\n 228.197265625\n ]\n ],\n \"bbox\": [\n 152.58056640625,\n 227.197265625,\n 153.58056640625,\n 228.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/203\",\n \"block_type\": \"TableCell\",\n \"html\": \"# WRONG! | \",\n \"polygon\": [\n [\n 153.58056640625,\n 227.197265625\n ],\n [\n 154.58056640625,\n 227.197265625\n ],\n [\n 154.58056640625,\n 228.197265625\n ],\n [\n 153.58056640625,\n 228.197265625\n ]\n ],\n \"bbox\": [\n 153.58056640625,\n 227.197265625,\n 154.58056640625,\n 228.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/204\",\n \"block_type\": \"TableCell\",\n \"html\": \"t = t.append(x) | \",\n \"polygon\": [\n [\n 151.58056640625,\n 228.197265625\n ],\n [\n 152.58056640625,\n 228.197265625\n ],\n [\n 152.58056640625,\n 229.197265625\n ],\n [\n 151.58056640625,\n 229.197265625\n ]\n ],\n \"bbox\": [\n 151.58056640625,\n 228.197265625,\n 152.58056640625,\n 229.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/205\",\n \"block_type\": \"TableCell\",\n \"html\": \" | \",\n \"polygon\": [\n [\n 152.58056640625,\n 228.197265625\n ],\n [\n 153.58056640625,\n 228.197265625\n ],\n [\n 153.58056640625,\n 229.197265625\n ],\n [\n 152.58056640625,\n 229.197265625\n ]\n ],\n \"bbox\": [\n 152.58056640625,\n 228.197265625,\n 153.58056640625,\n 229.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/206\",\n \"block_type\": \"TableCell\",\n \"html\": \"# WRONG! | \",\n \"polygon\": [\n [\n 153.58056640625,\n 228.197265625\n ],\n [\n 154.58056640625,\n 228.197265625\n ],\n [\n 154.58056640625,\n 229.197265625\n ],\n [\n 153.58056640625,\n 229.197265625\n ]\n ],\n \"bbox\": [\n 153.58056640625,\n 228.197265625,\n 154.58056640625,\n 229.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/207\",\n \"block_type\": \"TableCell\",\n \"html\": \"t + [x] | \",\n \"polygon\": [\n [\n 151.58056640625,\n 229.197265625\n ],\n [\n 152.58056640625,\n 229.197265625\n ],\n [\n 152.58056640625,\n 230.197265625\n ],\n [\n 151.58056640625,\n 230.197265625\n ]\n ],\n \"bbox\": [\n 151.58056640625,\n 229.197265625,\n 152.58056640625,\n 230.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/208\",\n \"block_type\": \"TableCell\",\n \"html\": \" | \",\n \"polygon\": [\n [\n 152.58056640625,\n 229.197265625\n ],\n [\n 153.58056640625,\n 229.197265625\n ],\n [\n 153.58056640625,\n 230.197265625\n ],\n [\n 152.58056640625,\n 230.197265625\n ]\n ],\n \"bbox\": [\n 152.58056640625,\n 229.197265625,\n 153.58056640625,\n 230.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/209\",\n \"block_type\": \"TableCell\",\n \"html\": \"# WRONG! | \",\n \"polygon\": [\n [\n 153.58056640625,\n 229.197265625\n ],\n [\n 154.58056640625,\n 229.197265625\n ],\n [\n 154.58056640625,\n 230.197265625\n ],\n [\n 153.58056640625,\n 230.197265625\n ]\n ],\n \"bbox\": [\n 153.58056640625,\n 229.197265625,\n 154.58056640625,\n 230.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/210\",\n \"block_type\": \"TableCell\",\n \"html\": \"t = t + x | \",\n \"polygon\": [\n [\n 151.58056640625,\n 230.197265625\n ],\n [\n 152.58056640625,\n 230.197265625\n ],\n [\n 152.58056640625,\n 231.197265625\n ],\n [\n 151.58056640625,\n 231.197265625\n ]\n ],\n \"bbox\": [\n 151.58056640625,\n 230.197265625,\n 152.58056640625,\n 231.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/211\",\n \"block_type\": \"TableCell\",\n \"html\": \" | \",\n \"polygon\": [\n [\n 152.58056640625,\n 230.197265625\n ],\n [\n 153.58056640625,\n 230.197265625\n ],\n [\n 153.58056640625,\n 231.197265625\n ],\n [\n 152.58056640625,\n 231.197265625\n ]\n ],\n \"bbox\": [\n 152.58056640625,\n 230.197265625,\n 153.58056640625,\n 231.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/TableCell/212\",\n \"block_type\": \"TableCell\",\n \"html\": \"# WRONG! | \",\n \"polygon\": [\n [\n 153.58056640625,\n 230.197265625\n ],\n [\n 154.58056640625,\n 230.197265625\n ],\n [\n 154.58056640625,\n 231.197265625\n ],\n [\n 153.58056640625,\n 231.197265625\n ]\n ],\n \"bbox\": [\n 153.58056640625,\n 230.197265625,\n 154.58056640625,\n 231.197265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/118/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Try out each of these examples in interactive mode to make sure you understand what they do. Notice that only the last one causes a runtime error; the other three are legal, but they do the wrong thing.
\",\n \"polygon\": [\n [\n 153.7470703125,\n 284.23828125\n ],\n [\n 525.9375,\n 284.23828125\n ],\n [\n 525.9375,\n 318.9427795410156\n ],\n [\n 153.7470703125,\n 318.9427795410156\n ]\n ],\n \"bbox\": [\n 153.7470703125,\n 284.23828125,\n 525.9375,\n 318.9427795410156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. Make copies to avoid aliasing.\",\n \"polygon\": [\n [\n 140.748046875,\n 328.9021911621094\n ],\n [\n 287.3675537109375,\n 328.9021911621094\n ],\n [\n 287.3675537109375,\n 338.8647766113281\n ],\n [\n 140.748046875,\n 338.8647766113281\n ]\n ],\n \"bbox\": [\n 140.748046875,\n 328.9021911621094,\n 287.3675537109375,\n 338.8647766113281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"If you want to use a method like sort that modifies the argument, but you need to keep the original list as well, you can make a copy.
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\",\n \"polygon\": [\n [\n 153.1494140625,\n 376.57464599609375\n ],\n [\n 212.04107666015625,\n 376.57464599609375\n ],\n [\n 212.04107666015625,\n 398.7312316894531\n ],\n [\n 153.1494140625,\n 398.7312316894531\n ]\n ],\n \"bbox\": [\n 153.1494140625,\n 376.57464599609375,\n 212.04107666015625,\n 398.7312316894531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/117/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"In this example you could also use the built-in function sorted, which returns a new, sorted list and leaves the original alone. But in that case you should avoid using sorted as a variable name!
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\",\n \"polygon\": [\n [\n 129.2431640625,\n 471.78265380859375\n ],\n [\n 234.4306640625,\n 471.78265380859375\n ],\n [\n 234.4306640625,\n 486.1288757324219\n ],\n [\n 129.2431640625,\n 486.1288757324219\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 471.78265380859375,\n 234.4306640625,\n 486.1288757324219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/118/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/118/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"list: A sequence of values.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 363.322265625\n ],\n [\n 482.90625,\n 363.322265625\n ],\n [\n 482.90625,\n 385.7174072265625\n ],\n [\n 85.6142578125,\n 385.7174072265625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 363.322265625,\n 482.90625,\n 385.7174072265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/119/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/119/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 10.7. Two words are anagrams if you can rearrange the letters from one to spell the other. Write a function called is_anagram that takes two strings and returns True if they are anagrams. Exercise 10.8. The (so-called) Birthday Paradox:
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 132.64453125\n ],\n [\n 525.9375,\n 132.64453125\n ],\n [\n 525.9375,\n 155.34515380859375\n ],\n [\n 128.0478515625,\n 155.34515380859375\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 132.64453125,\n 525.9375,\n 155.34515380859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/119/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/120/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Write a function called bisect that takes a sorted list and a target value and returns the index of the value in the list, if it's there, or None if it's not.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 165.0322265625\n ],\n [\n 525.9375,\n 165.0322265625\n ],\n [\n 525.9375,\n 187.7003173828125\n ],\n [\n 128.0478515625,\n 187.7003173828125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 165.0322265625,\n 525.9375,\n 187.7003173828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/119/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/120/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Or you could read the documentation of the bisect module and use that! Solution: http: // thinkpython. com/ code/ inlist. py .
\",\n \"polygon\": [\n [\n 128.9443359375,\n 197.7099609375\n ],\n [\n 524.7421875,\n 197.7099609375\n ],\n [\n 524.7421875,\n 220.00921630859375\n ],\n [\n 128.9443359375,\n 220.00921630859375\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 197.7099609375,\n 524.7421875,\n 220.00921630859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/119/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/120/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 10.12. Two words are a \\\"reverse pair\\\" if each is the reverse of the other. Write a program that finds all the reverse pairs in the word list. Solution: http: // thinkpython. com/ code/ reverse_ pair. py .
\",\n \"polygon\": [\n [\n 128.6455078125,\n 221.58984375\n ],\n [\n 525.6035766601562,\n 221.58984375\n ],\n [\n 525.6035766601562,\n 256.5921630859375\n ],\n [\n 128.6455078125,\n 256.5921630859375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 221.58984375,\n 525.6035766601562,\n 256.5921630859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"4\": \"/page/119/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/120/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 10.13. Two words \\\"interlock\\\" if taking alternating letters from each forms a new word. For example, \\\"shoe\\\" and \\\"cold\\\" interlock to form \\\"schooled.\\\" Solution: http: // thinkpython. com/ code/ interlock. py . Credit: This exercise is inspired by an example at http: // puzzlers. org .
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\",\n \"polygon\": [\n [\n 128.794921875,\n 162.1318359375\n ],\n [\n 232.189453125,\n 162.1318359375\n ],\n [\n 232.189453125,\n 183.33795166015625\n ],\n [\n 128.794921875,\n 183.33795166015625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 162.1318359375,\n 232.189453125,\n 183.33795166015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/108/SectionHeader/1\",\n \"2\": \"/page/122/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Dictionaries
\",\n \"polygon\": [\n [\n 129.31787109375,\n 216.5625\n ],\n [\n 268.646484375,\n 216.5625\n ],\n [\n 268.646484375,\n 241.371337890625\n ],\n [\n 129.31787109375,\n 241.371337890625\n ]\n ],\n \"bbox\": [\n 129.31787109375,\n 216.5625,\n 268.646484375,\n 241.371337890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"A dictionary is like a list, but more general. In a list, the indices have to be integers; in a dictionary they can be (almost) any type.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 285.591796875\n ],\n [\n 525.9375,\n 285.591796875\n ],\n [\n 525.9375,\n 308.1558837890625\n ],\n [\n 128.9443359375,\n 308.1558837890625\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 285.591796875,\n 525.9375,\n 308.1558837890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"You can think of a dictionary as a mapping between a set of indices (which are called keys) and a set of values. Each key maps to a value. The association of a key and a value is called a key-value pair or sometimes an item.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 316.72265625\n ],\n [\n 525.9375,\n 316.72265625\n ],\n [\n 525.9375,\n 351.23590087890625\n ],\n [\n 128.9443359375,\n 351.23590087890625\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 316.72265625,\n 525.9375,\n 351.23590087890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"As an example, we'll build a dictionary that maps from English to Spanish words, so the keys and the values are all strings.
\",\n \"polygon\": [\n [\n 129.09375,\n 359.455078125\n ],\n [\n 525.9375,\n 359.455078125\n ],\n [\n 525.9375,\n 382.12091064453125\n ],\n [\n 129.09375,\n 382.12091064453125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 359.455078125,\n 525.9375,\n 382.12091064453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The function dict creates a new dictionary with no items. Because dict is the name of a built-in function, you should avoid using it as a variable name.
\",\n \"polygon\": [\n [\n 129.5419921875,\n 390.69976806640625\n ],\n [\n 525.9375,\n 390.69976806640625\n ],\n [\n 525.9375,\n 413.0069274902344\n ],\n [\n 129.5419921875,\n 413.0069274902344\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 390.69976806640625,\n 525.9375,\n 413.0069274902344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> eng2sp = dict()\\n>>> print eng2sp\\n{}\",\n \"polygon\": [\n [\n 129.5419921875,\n 417.61376953125\n ],\n [\n 228.97686767578125,\n 417.61376953125\n ],\n [\n 228.97686767578125,\n 451.9653625488281\n ],\n [\n 129.5419921875,\n 451.9653625488281\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 417.61376953125,\n 228.97686767578125,\n 451.9653625488281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The squiggly-brackets, {}, represent an empty dictionary. To add items to the dictionary, you can use square brackets:
\",\n \"polygon\": [\n [\n 129.59994506835938,\n 456.7217712402344\n ],\n [\n 525.60009765625,\n 456.7217712402344\n ],\n [\n 525.60009765625,\n 479.0279235839844\n ],\n [\n 129.59994506835938,\n 479.0279235839844\n ]\n ],\n \"bbox\": [\n 129.59994506835938,\n 456.7217712402344,\n 525.60009765625,\n 479.0279235839844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> eng2sp['one'] = 'uno'
\",\n \"polygon\": [\n [\n 129.2431640625,\n 483.6357727050781\n ],\n [\n 260.329345703125,\n 483.6357727050781\n ],\n [\n 260.329345703125,\n 493.5983581542969\n ],\n [\n 129.2431640625,\n 493.5983581542969\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 483.6357727050781,\n 260.329345703125,\n 493.5983581542969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"This line creates an item that maps from the key 'one' to the value 'uno'. If we print the dictionary again, we see a key-value pair with a colon between the key and value:
\",\n \"polygon\": [\n [\n 129.392578125,\n 498.09375\n ],\n [\n 525.638671875,\n 498.09375\n ],\n [\n 525.638671875,\n 520.6619262695312\n ],\n [\n 129.392578125,\n 520.6619262695312\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 498.09375,\n 525.638671875,\n 520.6619262695312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> print eng2sp\\n{'one': 'uno'}\",\n \"polygon\": [\n [\n 128.9443359375,\n 525.2687683105469\n ],\n [\n 213.2858428955078,\n 525.2687683105469\n ],\n [\n 213.2858428955078,\n 547.4253692626953\n ],\n [\n 128.9443359375,\n 547.4253692626953\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 525.2687683105469,\n 213.2858428955078,\n 547.4253692626953\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"This output format is also an input format. For example, you can create a new dictionary with three items:
\",\n \"polygon\": [\n [\n 128.0478515625,\n 552.3323211669922\n ],\n [\n 525.6033325195312,\n 552.3323211669922\n ],\n [\n 525.6033325195312,\n 574.4889221191406\n ],\n [\n 128.0478515625,\n 574.4889221191406\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 552.3323211669922,\n 525.6033325195312,\n 574.4889221191406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'}\",\n \"polygon\": [\n [\n 129.09375,\n 579.0967712402344\n ],\n [\n 434.49609375,\n 579.0967712402344\n ],\n [\n 434.49609375,\n 595.93359375\n ],\n [\n 129.09375,\n 595.93359375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 579.0967712402344,\n 434.49609375,\n 595.93359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"But if you print eng2sp, you might be surprised:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 593.8157653808594\n ],\n [\n 341.9150390625,\n 593.8157653808594\n ],\n [\n 341.9150390625,\n 605.21484375\n ],\n [\n 129.2431640625,\n 605.21484375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 593.8157653808594,\n 341.9150390625,\n 605.21484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> print eng2sp\\n{'one': 'uno', 'three': 'tres', 'two': 'dos'}\",\n \"polygon\": [\n [\n 129.16845703125,\n 608.5357666015625\n ],\n [\n 364.9114685058594,\n 608.5357666015625\n ],\n [\n 364.9114685058594,\n 630.6923675537109\n ],\n [\n 129.16845703125,\n 630.6923675537109\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 608.5357666015625,\n 364.9114685058594,\n 630.6923675537109\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The order of the key-value pairs is not the same. In fact, if you type the same example on your computer, you might get a different result. In general, the order of items in a dictionary is unpredictable.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 634.21875\n ],\n [\n 525.9375,\n 634.21875\n ],\n [\n 525.9375,\n 669.9499282836914\n ],\n [\n 129.2431640625,\n 669.9499282836914\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 634.21875,\n 525.9375,\n 669.9499282836914\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/122/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"But that's not a problem because the elements of a dictionary are never indexed with integer indices. Instead, you use the keys to look up the corresponding values:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 677.91796875\n ],\n [\n 525.603515625,\n 677.91796875\n ],\n [\n 525.603515625,\n 700.8349304199219\n ],\n [\n 128.6455078125,\n 700.8349304199219\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 677.91796875,\n 525.603515625,\n 700.8349304199219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/123/Page/215\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/123/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.76318359375\n ],\n [\n 482.40338134765625,\n 60.76318359375\n ],\n [\n 482.40338134765625,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.76318359375,\n 482.40338134765625,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.9130859375,\n 61.0048828125\n ],\n [\n 101.900390625,\n 61.0048828125\n ],\n [\n 101.900390625,\n 70.4794921875\n ],\n [\n 85.9130859375,\n 70.4794921875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 61.0048828125,\n 101.900390625,\n 70.4794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> print eng2sp['two']\\n'dos'
\",\n \"polygon\": [\n [\n 85.763671875,\n 88.41357421875\n ],\n [\n 206.6703643798828,\n 88.41357421875\n ],\n [\n 206.6703643798828,\n 110.84228515625\n ],\n [\n 85.763671875,\n 110.84228515625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 88.41357421875,\n 206.6703643798828,\n 110.84228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The key 'two' always maps to the value 'dos' so the order of the items doesn't matter.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 117.7476806640625\n ],\n [\n 469.84698486328125,\n 117.7476806640625\n ],\n [\n 469.84698486328125,\n 127.85986328125\n ],\n [\n 85.3154296875,\n 127.85986328125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 117.7476806640625,\n 469.84698486328125,\n 127.85986328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"If the key isn't in the dictionary, you get an exception:
\",\n \"polygon\": [\n [\n 85.46484375,\n 138.638671875\n ],\n [\n 322.03533935546875,\n 138.638671875\n ],\n [\n 322.03533935546875,\n 148.69989013671875\n ],\n [\n 85.46484375,\n 148.69989013671875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 138.638671875,\n 322.03533935546875,\n 148.69989013671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \">>> print eng2sp['four']\\nKeyError: 'four'
\",\n \"polygon\": [\n [\n 85.166015625,\n 155.4547119140625\n ],\n [\n 211.90037536621094,\n 155.4547119140625\n ],\n [\n 211.90037536621094,\n 177.6123046875\n ],\n [\n 85.166015625,\n 177.6123046875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 155.4547119140625,\n 211.90037536621094,\n 177.6123046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The len function works on dictionaries; it returns the number of key-value pairs:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 184.5177001953125\n ],\n [\n 441.6226501464844,\n 184.5177001953125\n ],\n [\n 441.6226501464844,\n 194.6298828125\n ],\n [\n 85.9130859375,\n 194.6298828125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 184.5177001953125,\n 441.6226501464844,\n 194.6298828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> len(eng2sp)\\n3
\",\n \"polygon\": [\n [\n 84.8671875,\n 200.513671875\n ],\n [\n 166.1484375,\n 200.513671875\n ],\n [\n 166.1484375,\n 223.542236328125\n ],\n [\n 84.8671875,\n 223.542236328125\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 200.513671875,\n 166.1484375,\n 223.542236328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The in operator works on dictionaries; it tells you whether something appears as a key in the dictionary (appearing as a value is not good enough).
\",\n \"polygon\": [\n [\n 85.763671875,\n 230.42449951171875\n ],\n [\n 482.40032958984375,\n 230.42449951171875\n ],\n [\n 482.40032958984375,\n 252.75384521484375\n ],\n [\n 85.763671875,\n 252.75384521484375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 230.42449951171875,\n 482.40032958984375,\n 252.75384521484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> 'one' in eng2sp\\nTrue\\n>>> 'uno' in eng2sp\\nFalse
\",\n \"polygon\": [\n [\n 85.46484375,\n 259.5096435546875\n ],\n [\n 188.4111328125,\n 259.5096435546875\n ],\n [\n 188.4111328125,\n 306.0552673339844\n ],\n [\n 85.46484375,\n 306.0552673339844\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 259.5096435546875,\n 188.4111328125,\n 306.0552673339844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"To see whether something appears as a value in a dictionary, you can use the method values, which returns the values as a list, and then use the in operator:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 313.048828125\n ],\n [\n 482.4034423828125,\n 313.048828125\n ],\n [\n 482.4034423828125,\n 335.266845703125\n ],\n [\n 85.9130859375,\n 335.266845703125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 313.048828125,\n 482.4034423828125,\n 335.266845703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> vals = eng2sp.values()\\n>>> 'uno' in vals\\nTrue
\",\n \"polygon\": [\n [\n 85.3154296875,\n 342.022705078125\n ],\n [\n 222.3994140625,\n 342.022705078125\n ],\n [\n 222.3994140625,\n 376.3742980957031\n ],\n [\n 85.3154296875,\n 376.3742980957031\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 342.022705078125,\n 222.3994140625,\n 376.3742980957031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The in operator uses different algorithms for lists and dictionaries. For lists, it uses a search algorithm, as in Section 8.6. As the list gets longer, the search time gets longer in direct proportion. For dictionaries, Python uses an algorithm called a hashtable that has a remarkable property: the in operator takes about the same amount of time no matter how many items there are in a dictionary. I won't explain how that's possible, but you can read more about it at http://en.wikipedia.org/wiki/Hash_table.
\",\n \"polygon\": [\n [\n 85.46484375,\n 383.27972412109375\n ],\n [\n 482.607421875,\n 383.27972412109375\n ],\n [\n 482.607421875,\n 454.39453125\n ],\n [\n 85.46484375,\n 454.39453125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 383.27972412109375,\n 482.607421875,\n 454.39453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.1. Write a function that reads the words in words.txt and stores them as keys in a dictionary. It doesn't matter what the values are. Then you can use the in operator as a fast way to check whether a string is in the dictionary.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 456.422607421875\n ],\n [\n 482.40447998046875,\n 456.422607421875\n ],\n [\n 482.40447998046875,\n 490.773193359375\n ],\n [\n 85.6142578125,\n 490.773193359375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 456.422607421875,\n 482.40447998046875,\n 490.773193359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"If you did Exercise 10.11, you can compare the speed of this implementation with the list in operator and the bisection search.
\",\n \"polygon\": [\n [\n 85.763671875,\n 501.6506042480469\n ],\n [\n 482.400390625,\n 501.6506042480469\n ],\n [\n 482.400390625,\n 523.8071899414062\n ],\n [\n 85.763671875,\n 523.8071899414062\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 501.6506042480469,\n 482.400390625,\n 523.8071899414062\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/SectionHeader/14\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"11.1 Dictionary as a set of counters
\",\n \"polygon\": [\n [\n 85.46484375,\n 555.328125\n ],\n [\n 322.28021240234375,\n 555.328125\n ],\n [\n 322.28021240234375,\n 569.7739410400391\n ],\n [\n 85.46484375,\n 569.7739410400391\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 555.328125,\n 322.28021240234375,\n 569.7739410400391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Suppose you are given a string and you want to count how many times each letter appears. There are several ways you could do it:
\",\n \"polygon\": [\n [\n 85.166015625,\n 583.1022796630859\n ],\n [\n 482.40338134765625,\n 583.1022796630859\n ],\n [\n 482.40338134765625,\n 605.2588806152344\n ],\n [\n 85.166015625,\n 605.2588806152344\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 583.1022796630859,\n 482.40338134765625,\n 605.2588806152344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/ListGroup/213\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 98.314453125,\n 620.296875\n ],\n [\n 482.40386962890625,\n 620.296875\n ],\n [\n 482.40386962890625,\n 700.8348922729492\n ],\n [\n 98.314453125,\n 700.8348922729492\n ]\n ],\n \"bbox\": [\n 98.314453125,\n 620.296875,\n 482.40386962890625,\n 700.8348922729492\n ],\n \"children\": [\n {\n \"id\": \"/page/123/ListItem/16\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. You could create 26 variables, one for each letter of the alphabet. Then you could traverse the string and, for each character, increment the corresponding counter, probably using a chained conditional.\",\n \"polygon\": [\n [\n 98.61328125,\n 620.296875\n ],\n [\n 482.40386962890625,\n 620.296875\n ],\n [\n 482.40386962890625,\n 655.0848846435547\n ],\n [\n 98.61328125,\n 655.0848846435547\n ]\n ],\n \"bbox\": [\n 98.61328125,\n 620.296875,\n 482.40386962890625,\n 655.0848846435547\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/123/ListItem/17\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. You could create a list with 26 elements. Then you could convert each character to a number (using the built-in function ord), use the number as an index into the list, and increment the appropriate counter.\",\n \"polygon\": [\n [\n 98.314453125,\n 666.31640625\n ],\n [\n 482.4033203125,\n 666.31640625\n ],\n [\n 482.4033203125,\n 700.8348922729492\n ],\n [\n 98.314453125,\n 700.8348922729492\n ]\n ],\n \"bbox\": [\n 98.314453125,\n 666.31640625,\n 482.4033203125,\n 700.8348922729492\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": null\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/124/Page/231\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/124/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.197265625,\n 60.71484375\n ],\n [\n 525.6033935546875,\n 60.71484375\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.197265625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 60.71484375,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/PageHeader/16\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 509.80078125,\n 60.85986328125\n ],\n [\n 525.9375,\n 60.85986328125\n ],\n [\n 525.9375,\n 70.33447265625\n ],\n [\n 509.80078125,\n 70.33447265625\n ]\n ],\n \"bbox\": [\n 509.80078125,\n 60.85986328125,\n 525.9375,\n 70.33447265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/ListItem/1\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. You could create a dictionary with characters as keys and counters as the corresponding values. The first time you see a character, you would add an item to the dictionary. After that you would increment the value of an existing item.\",\n \"polygon\": [\n [\n 141.1962890625,\n 87.93017578125\n ],\n [\n 525.6039428710938,\n 87.93017578125\n ],\n [\n 525.6039428710938,\n 123.1868896484375\n ],\n [\n 141.1962890625,\n 123.1868896484375\n ]\n ],\n \"bbox\": [\n 141.1962890625,\n 87.93017578125,\n 525.6039428710938,\n 123.1868896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Each of these options performs the same computation, but each of them implements that computation in a different way.
\",\n \"polygon\": [\n [\n 127.599609375,\n 130.6142578125\n ],\n [\n 525.638671875,\n 130.6142578125\n ],\n [\n 525.638671875,\n 153.87994384765625\n ],\n [\n 127.599609375,\n 153.87994384765625\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 130.6142578125,\n 525.638671875,\n 153.87994384765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"An implementation is a way of performing a computation; some implementations are better than others. For example, an advantage of the dictionary implementation is that we don't have to know ahead of time which letters appear in the string and we only have to make room for the letters that do appear.
\",\n \"polygon\": [\n [\n 127.8984375,\n 161.1650390625\n ],\n [\n 525.9375,\n 161.1650390625\n ],\n [\n 525.9375,\n 208.51702880859375\n ],\n [\n 127.8984375,\n 208.51702880859375\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 161.1650390625,\n 525.9375,\n 208.51702880859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Here is what the code might look like:
\",\n \"polygon\": [\n [\n 128.197265625,\n 215.7890625\n ],\n [\n 296.82220458984375,\n 215.7890625\n ],\n [\n 296.82220458984375,\n 226.571044921875\n ],\n [\n 128.197265625,\n 226.571044921875\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 215.7890625,\n 296.82220458984375,\n 226.571044921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"def histogram(s):\\n d = dict()\\n for c in s:\\n if c not in d:\\n d[c] = 1\\n else:\\n d[c] += 1\\n return d
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 230.5418701171875\n ],\n [\n 249.8203125,\n 230.5418701171875\n ],\n [\n 249.8203125,\n 325.8645324707031\n ],\n [\n 129.60000610351562,\n 325.8645324707031\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 230.5418701171875,\n 249.8203125,\n 325.8645324707031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The name of the function is histogram, which is a statistical term for a set of counters (or frequencies).
\",\n \"polygon\": [\n [\n 129.6000213623047,\n 330.0373840332031\n ],\n [\n 525.9375,\n 330.0373840332031\n ],\n [\n 525.9375,\n 352.2911071777344\n ],\n [\n 129.6000213623047,\n 352.2911071777344\n ]\n ],\n \"bbox\": [\n 129.6000213623047,\n 330.0373840332031,\n 525.9375,\n 352.2911071777344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The first line of the function creates an empty dictionary. The for loop traverses the string. Each time through the loop, if the character c is not in the dictionary, we create a new item with key c and the initial value 1 (since we have seen this letter once). If c is already in the dictionary we increment d[c].
\",\n \"polygon\": [\n [\n 128.3466796875,\n 359.841796875\n ],\n [\n 525.6044921875,\n 359.841796875\n ],\n [\n 525.6044921875,\n 406.9281005859375\n ],\n [\n 128.3466796875,\n 406.9281005859375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 359.841796875,\n 525.6044921875,\n 406.9281005859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Here's how it works:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 414.94921875\n ],\n [\n 223.3740234375,\n 414.94921875\n ],\n [\n 223.3740234375,\n 424.9820861816406\n ],\n [\n 128.6455078125,\n 424.9820861816406\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 414.94921875,\n 223.3740234375,\n 424.9820861816406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> h = histogram('brontosaurus')\\n>>> print h\\n{'a': 1, 'b': 1, 'o': 2, 'n': 1, 's': 2, 'r': 2, 'u': 2, 't': 1}\",\n \"polygon\": [\n [\n 129.59996032714844,\n 427.7109375\n ],\n [\n 476.9296875,\n 427.7109375\n ],\n [\n 476.9296875,\n 468.703125\n ],\n [\n 129.59996032714844,\n 468.703125\n ]\n ],\n \"bbox\": [\n 129.59996032714844,\n 427.7109375,\n 476.9296875,\n 468.703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The histogram indicates that the letters 'a' and 'b' appear once; 'o' appears twice, and so on.
\",\n \"polygon\": [\n [\n 128.794921875,\n 467.4239501953125\n ],\n [\n 525.6051635742188,\n 467.4239501953125\n ],\n [\n 525.6051635742188,\n 489.97265625\n ],\n [\n 128.794921875,\n 489.97265625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 467.4239501953125,\n 525.6051635742188,\n 489.97265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.2. Dictionaries have a method called get that takes a key and a default value. If the key appears in the dictionary, get returns the corresponding value; otherwise it returns the default value. For example:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 491.51953125\n ],\n [\n 525.6024169921875,\n 491.51953125\n ],\n [\n 525.6024169921875,\n 526.1414184570312\n ],\n [\n 129.2431640625,\n 526.1414184570312\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 491.51953125,\n 525.6024169921875,\n 526.1414184570312\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> h = histogram('a')\\n>>> print h\\n{'a': 1}\\n>>> h.get('a', 0)\\n1\\n>>> h.get('b', 0)\\n0\",\n \"polygon\": [\n [\n 129.60009765625,\n 530.2839660644531\n ],\n [\n 244.64146423339844,\n 530.2839660644531\n ],\n [\n 244.64146423339844,\n 613.4125823974609\n ],\n [\n 129.60009765625,\n 613.4125823974609\n ]\n ],\n \"bbox\": [\n 129.60009765625,\n 530.2839660644531,\n 244.64146423339844,\n 613.4125823974609\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Use get to write histogram more concisely. You should be able to eliminate the if statement.
\",\n \"polygon\": [\n [\n 128.794921875,\n 616.4296875\n ],\n [\n 508.27825927734375,\n 616.4296875\n ],\n [\n 508.27825927734375,\n 627.4955902099609\n ],\n [\n 128.794921875,\n 627.4955902099609\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 616.4296875,\n 508.27825927734375,\n 627.4955902099609\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/123/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/SectionHeader/14\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"11.2 Looping and dictionaries
\",\n \"polygon\": [\n [\n 127.4501953125,\n 653.94140625\n ],\n [\n 333.2156982421875,\n 653.94140625\n ],\n [\n 333.2156982421875,\n 668.4161911010742\n ],\n [\n 127.4501953125,\n 668.4161911010742\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 653.94140625,\n 333.2156982421875,\n 668.4161911010742\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/124/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"If you use a dictionary in a for statement, it traverses the keys of the dictionary. For example, print_hist prints each key and the corresponding value:
\",\n \"polygon\": [\n [\n 128.197265625,\n 678.3046875\n ],\n [\n 525.5980834960938,\n 678.3046875\n ],\n [\n 525.5980834960938,\n 700.8351440429688\n ],\n [\n 128.197265625,\n 700.8351440429688\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 678.3046875,\n 525.5980834960938,\n 700.8351440429688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/125/Page/192\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/125/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.521484375\n ],\n [\n 482.40338134765625,\n 60.521484375\n ],\n [\n 482.40338134765625,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.521484375,\n 482.40338134765625,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/TextInlineMath/1\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"def print_hist(h): for c in h: print c, h[c]
\",\n \"polygon\": [\n [\n 86.4000015258789,\n 88.68572998046875\n ],\n [\n 196.22775268554688,\n 88.68572998046875\n ],\n [\n 196.22775268554688,\n 123.03729248046875\n ],\n [\n 86.4000015258789,\n 123.03729248046875\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 88.68572998046875,\n 196.22775268554688,\n 123.03729248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Here's what the output looks like:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 129.744140625\n ],\n [\n 237.1201171875,\n 129.744140625\n ],\n [\n 237.1201171875,\n 140.40887451171875\n ],\n [\n 85.9130859375,\n 140.40887451171875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 129.744140625,\n 237.1201171875,\n 140.40887451171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> h = histogram('parrot')\\n>>> print_hist(h)\\na 1\\np 1\\nr 2\\nt 1\\no 1\",\n \"polygon\": [\n [\n 85.763671875,\n 147.51971435546875\n ],\n [\n 227.58737182617188,\n 147.51971435546875\n ],\n [\n 227.58737182617188,\n 231.064453125\n ],\n [\n 85.763671875,\n 231.064453125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 147.51971435546875,\n 227.58737182617188,\n 231.064453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Again, the keys are in no particular order.
\",\n \"polygon\": [\n [\n 85.68896484375,\n 237.05859375\n ],\n [\n 269.6719970703125,\n 237.05859375\n ],\n [\n 269.6719970703125,\n 248.02093505859375\n ],\n [\n 85.68896484375,\n 248.02093505859375\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 237.05859375,\n 269.6719970703125,\n 248.02093505859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.3. Dictionaries have a method called keys that returns the keys of the dictionary, in no particular order, as a list.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 249.046875\n ],\n [\n 482.39501953125,\n 249.046875\n ],\n [\n 482.39501953125,\n 272.2371826171875\n ],\n [\n 85.9130859375,\n 272.2371826171875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 249.046875,\n 482.39501953125,\n 272.2371826171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Modify print_hist to print the keys and their values in alphabetical order.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 283.078125\n ],\n [\n 390.69482421875,\n 283.078125\n ],\n [\n 390.69482421875,\n 293.4543151855469\n ],\n [\n 85.6142578125,\n 293.4543151855469\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 283.078125,\n 390.69482421875,\n 293.4543151855469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/124/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"11.3 Reverse lookup
\",\n \"polygon\": [\n [\n 85.39013671875,\n 326.1167297363281\n ],\n [\n 228.26959228515625,\n 326.1167297363281\n ],\n [\n 228.26959228515625,\n 340.46295166015625\n ],\n [\n 85.39013671875,\n 340.46295166015625\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 326.1167297363281,\n 228.26959228515625,\n 340.46295166015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Given a dictionary d and a key k, it is easy to find the corresponding value v = d[k]. This operation is called a lookup.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 354.041015625\n ],\n [\n 482.90625,\n 354.041015625\n ],\n [\n 482.90625,\n 376.4458923339844\n ],\n [\n 85.6142578125,\n 376.4458923339844\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 354.041015625,\n 482.90625,\n 376.4458923339844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"But what if you have v and you want to find k? You have two problems: first, there might be more than one key that maps to the value v. Depending on the application, you might be able to pick one, or you might have to make a list that contains all of them. Second, there is no simple syntax to do a reverse lookup; you have to search.
\",\n \"polygon\": [\n [\n 85.46484375,\n 387.10546875\n ],\n [\n 482.607421875,\n 387.10546875\n ],\n [\n 482.607421875,\n 434.222900390625\n ],\n [\n 85.46484375,\n 434.222900390625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 387.10546875,\n 482.607421875,\n 434.222900390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Here is a function that takes a value and returns the first key that maps to that value:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 444.7265625\n ],\n [\n 458.3935852050781,\n 444.7265625\n ],\n [\n 458.3935852050781,\n 455.4169006347656\n ],\n [\n 85.3154296875,\n 455.4169006347656\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 444.7265625,\n 458.3935852050781,\n 455.4169006347656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \"def reverse_lookup(d, v):\\n for k in d:\\n if d[k] == v:\\n return k\\n raise ValueError
\",\n \"polygon\": [\n [\n 84.4189453125,\n 462.5277404785156\n ],\n [\n 218.7421875,\n 462.5277404785156\n ],\n [\n 218.7421875,\n 521.2683410644531\n ],\n [\n 84.4189453125,\n 521.2683410644531\n ]\n ],\n \"bbox\": [\n 84.4189453125,\n 462.5277404785156,\n 218.7421875,\n 521.2683410644531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"This function is yet another example of the search pattern, but it uses a feature we haven't seen before, raise. The raise statement causes an exception; in this case it causes a ValueError, which generally indicates that there is something wrong with the value of a parameter.
\",\n \"polygon\": [\n [\n 85.46484375,\n 528.2578125\n ],\n [\n 483.205078125,\n 528.2578125\n ],\n [\n 483.205078125,\n 575.2229309082031\n ],\n [\n 85.46484375,\n 575.2229309082031\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 528.2578125,\n 483.205078125,\n 575.2229309082031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"If we get to the end of the loop, that means v doesn't appear in the dictionary as a value, so we raise an exception.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 586.265625\n ],\n [\n 482.90625,\n 586.265625\n ],\n [\n 482.90625,\n 608.6119384765625\n ],\n [\n 85.3154296875,\n 608.6119384765625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 586.265625,\n 482.90625,\n 608.6119384765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Here is an example of a successful reverse lookup:
\",\n \"polygon\": [\n [\n 85.39013671875,\n 618.75\n ],\n [\n 307.494140625,\n 618.75\n ],\n [\n 307.494140625,\n 629.8069458007812\n ],\n [\n 85.39013671875,\n 629.8069458007812\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 618.75,\n 307.494140625,\n 629.8069458007812\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \">>> h = histogram('parrot')\\n>>> k = reverse_lookup(h, 2)\\n>>> print k\\nr\",\n \"polygon\": [\n [\n 85.6142578125,\n 636.9167938232422\n ],\n [\n 232.86021423339844,\n 636.9167938232422\n ],\n [\n 232.86021423339844,\n 683.4623947143555\n ],\n [\n 85.6142578125,\n 683.4623947143555\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 636.9167938232422,\n 232.86021423339844,\n 683.4623947143555\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"And an unsuccessful one:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 690.6796875\n ],\n [\n 199.318359375,\n 690.6796875\n ],\n [\n 199.318359375,\n 700.8349609375\n ],\n [\n 85.9130859375,\n 700.8349609375\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 690.6796875,\n 199.318359375,\n 700.8349609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/125/Text/188\",\n \"block_type\": \"Text\",\n \"html\": \"104
\\n\",\n \"polygon\": [\n [\n 84.8671875,\n 60.134765625\n ],\n [\n 100.5556640625,\n 60.134765625\n ],\n [\n 100.5556640625,\n 69.029296875\n ],\n [\n 84.8671875,\n 69.029296875\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 60.134765625,\n 100.5556640625,\n 69.029296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/126/Page/195\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/126/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.197265625,\n 60.908203125\n ],\n [\n 525.6033935546875,\n 60.908203125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.197265625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 60.908203125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/PageHeader/14\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 510.697265625,\n 60.66650390625\n ],\n [\n 526.236328125,\n 60.66650390625\n ],\n [\n 526.236328125,\n 69.65771484375\n ],\n [\n 510.697265625,\n 69.65771484375\n ]\n ],\n \"bbox\": [\n 510.697265625,\n 60.66650390625,\n 526.236328125,\n 69.65771484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> k = reverse_lookup(h, 3)\\nTraceback (most recent call last):\\n File \\\"<stdin>\\\", line 1, in ?\\n File \\\"<stdin>\\\", line 5, in reverse_lookup\\nValueError
\",\n \"polygon\": [\n [\n 128.57080078125,\n 88.0751953125\n ],\n [\n 354.5129089355469,\n 88.0751953125\n ],\n [\n 354.5129089355469,\n 147.42529296875\n ],\n [\n 128.57080078125,\n 147.42529296875\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 88.0751953125,\n 354.5129089355469,\n 147.42529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The result when you raise an exception is the same as when Python raises one: it prints a traceback and an error message.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 154.30078125\n ],\n [\n 525.6033935546875,\n 154.30078125\n ],\n [\n 525.6033935546875,\n 176.89093017578125\n ],\n [\n 129.2431640625,\n 176.89093017578125\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 154.30078125,\n 525.6033935546875,\n 176.89093017578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The raise statement takes a detailed error message as an optional argument. For example:
\",\n \"polygon\": [\n [\n 129.09375,\n 187.6552734375\n ],\n [\n 525.595458984375,\n 187.6552734375\n ],\n [\n 525.595458984375,\n 197.98394775390625\n ],\n [\n 129.09375,\n 197.98394775390625\n ]\n ],\n \"bbox\": [\n 129.09375,\n 187.6552734375,\n 525.595458984375,\n 197.98394775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \">>> raise ValueError('value does not appear in the dictionary') Traceback (most recent call last): File \\\"<stdin>\\\", line 1, in ? ValueError: value does not appear in the dictionary
\",\n \"polygon\": [\n [\n 128.0478515625,\n 204.99273681640625\n ],\n [\n 460.1953125,\n 204.99273681640625\n ],\n [\n 460.1953125,\n 251.538330078125\n ],\n [\n 128.0478515625,\n 251.538330078125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 204.99273681640625,\n 460.1953125,\n 251.538330078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"A reverse lookup is much slower than a forward lookup; if you have to do it often, or if the dictionary gets big, the performance of your program will suffer.
\",\n \"polygon\": [\n [\n 129.09375,\n 258.328125\n ],\n [\n 525.9375,\n 258.328125\n ],\n [\n 525.9375,\n 281.0039367675781\n ],\n [\n 129.09375,\n 281.0039367675781\n ]\n ],\n \"bbox\": [\n 129.09375,\n 258.328125,\n 525.9375,\n 281.0039367675781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.4. Modify reverse_lookup so that it builds and returns a list of all keys that map to v, or an empty list if there are none.
\",\n \"polygon\": [\n [\n 128.794921875,\n 283.0626525878906\n ],\n [\n 525.638671875,\n 283.0626525878906\n ],\n [\n 525.638671875,\n 305.2433776855469\n ],\n [\n 128.794921875,\n 305.2433776855469\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 283.0626525878906,\n 525.638671875,\n 305.2433776855469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/125/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"11.4 Dictionaries and lists
\",\n \"polygon\": [\n [\n 127.4501953125,\n 337.6007995605469\n ],\n [\n 308.4684143066406,\n 337.6007995605469\n ],\n [\n 308.4684143066406,\n 351.947021484375\n ],\n [\n 127.4501953125,\n 351.947021484375\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 337.6007995605469,\n 308.4684143066406,\n 351.947021484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Lists can appear as values in a dictionary. For example, if you were given a dictionary that maps from letters to frequencies, you might want to invert it; that is, create a dictionary that maps from frequencies to letters. Since there might be several letters with the same frequency, each value in the inverted dictionary should be a list of letters.
\",\n \"polygon\": [\n [\n 128.197265625,\n 365.255859375\n ],\n [\n 525.6033935546875,\n 365.255859375\n ],\n [\n 525.6033935546875,\n 412.17596435546875\n ],\n [\n 128.197265625,\n 412.17596435546875\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 365.255859375,\n 525.6033935546875,\n 412.17596435546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Here is a function that inverts a dictionary:
\",\n \"polygon\": [\n [\n 128.0478515625,\n 423.0703125\n ],\n [\n 318.16204833984375,\n 423.0703125\n ],\n [\n 318.16204833984375,\n 433.2689514160156\n ],\n [\n 128.0478515625,\n 433.2689514160156\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 423.0703125,\n 318.16204833984375,\n 433.2689514160156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"def invert_dict(d):\\n inverse = dict()\\n for key in d:\\n val = d[key]\\n if val not in inverse:\\n inverse[val] = [key]\\n else:\\n inverse[val].append(key)\\n return inverse
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 440.2778015136719\n ],\n [\n 317.8876953125,\n 440.2778015136719\n ],\n [\n 317.8876953125,\n 547.7954254150391\n ],\n [\n 129.5999755859375,\n 547.7954254150391\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 440.2778015136719,\n 317.8876953125,\n 547.7954254150391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/126/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Each time through the loop, key gets a key from d and val gets the corresponding value. If val is not in inverse, that means we haven't seen it before, so we create a new item and initialize it with a singleton (a list that contains a single element). Otherwise we have seen this value before, so we append the corresponding key to the list.
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\",\n \"polygon\": [\n [\n 225.55101013183594,\n 208.828125\n ],\n [\n 343.353515625,\n 208.828125\n ],\n [\n 343.353515625,\n 219.55291748046875\n ],\n [\n 225.55101013183594,\n 219.55291748046875\n ]\n ],\n \"bbox\": [\n 225.55101013183594,\n 208.828125,\n 343.353515625,\n 219.55291748046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/127/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Figure 11.1 is a state diagram showing hist and inverse. A dictionary is represented as a box with the type dict above it and the key-value pairs inside. If the values are integers, floats or strings, I usually draw them inside the box, but I usually draw lists outside the box, just to keep the diagram simple.
\",\n \"polygon\": [\n [\n 85.166015625,\n 238.9921875\n ],\n [\n 483.205078125,\n 238.9921875\n ],\n [\n 483.205078125,\n 286.171875\n ],\n [\n 85.166015625,\n 286.171875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 238.9921875,\n 483.205078125,\n 286.171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/127/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Lists can be values in a dictionary, as this example shows, but they cannot be keys. Here's what happens if you try:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 293.326171875\n ],\n [\n 482.90625,\n 293.326171875\n ],\n [\n 482.90625,\n 316.60791015625\n ],\n [\n 85.6142578125,\n 316.60791015625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 293.326171875,\n 482.90625,\n 316.60791015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/127/Code/241\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = [1, 2, 3]\\n>>> d = dict()\\n>>> d[t] = 'oops'\\nTraceback (most recent call last):\\n File \\\"<stdin>\\\", line 1, in ?\\nTypeError: list objects are unhashable
\",\n \"polygon\": [\n [\n 85.763671875,\n 319.81640625\n ],\n [\n 285.163818359375,\n 319.81640625\n ],\n [\n 285.163818359375,\n 391.9113464355469\n ],\n [\n 85.763671875,\n 391.9113464355469\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 319.81640625,\n 285.163818359375,\n 391.9113464355469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/126/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/127/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"I mentioned earlier that a dictionary is implemented using a hashtable and that means that the keys have to be hashable.
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\",\n \"polygon\": [\n [\n 275.51953125,\n 252.52734375\n ],\n [\n 379.14202880859375,\n 252.52734375\n ],\n [\n 379.14202880859375,\n 263.3519287109375\n ],\n [\n 275.51953125,\n 263.3519287109375\n ]\n ],\n \"bbox\": [\n 275.51953125,\n 252.52734375,\n 379.14202880859375,\n 263.3519287109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/128/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"the run time increases very quickly.
\",\n \"polygon\": [\n [\n 128.57080078125,\n 285.591796875\n ],\n [\n 285.2457275390625,\n 285.591796875\n ],\n [\n 285.2457275390625,\n 296.1878967285156\n ],\n [\n 128.57080078125,\n 296.1878967285156\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 285.591796875,\n 285.2457275390625,\n 296.1878967285156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"To understand why, consider Figure 11.2, which shows the call graph for fibonacci with n=4:
\",\n \"polygon\": [\n [\n 127.7490234375,\n 306.087890625\n ],\n [\n 525.5999145507812,\n 306.087890625\n ],\n [\n 525.5999145507812,\n 328.6828918457031\n ],\n [\n 127.7490234375,\n 328.6828918457031\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 306.087890625,\n 525.5999145507812,\n 328.6828918457031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"A call graph shows a set of function frames, with lines connecting each frame to the frames of the functions it calls. At the top of the graph, fibonacci with n=4 calls fibonacci with n=3 and n=2. In turn, fibonacci with n=3 calls fibonacci with n=2 and n=1. And so on.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 338.958984375\n ],\n [\n 525.9375,\n 338.958984375\n ],\n [\n 525.9375,\n 373.37188720703125\n ],\n [\n 128.6455078125,\n 373.37188720703125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 338.958984375,\n 525.9375,\n 373.37188720703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Count how many times fibonacci(0) and fibonacci(1) are called. This is an inefficient solution to the problem, and it gets worse as the argument gets bigger.
\",\n \"polygon\": [\n [\n 128.197265625,\n 383.5597229003906\n ],\n [\n 526.236328125,\n 383.5597229003906\n ],\n [\n 526.236328125,\n 405.8658752441406\n ],\n [\n 128.197265625,\n 405.8658752441406\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 383.5597229003906,\n 526.236328125,\n 405.8658752441406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"One solution is to keep track of values that have already been computed by storing them in a dictionary. A previously computed value that is stored for later use is called a memo. Here is a \\\"memoized\\\" version of fibonacci:
\",\n \"polygon\": [\n [\n 128.49609375,\n 415.72265625\n ],\n [\n 525.9375,\n 415.72265625\n ],\n [\n 525.9375,\n 450.55487060546875\n ],\n [\n 128.49609375,\n 450.55487060546875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 415.72265625,\n 525.9375,\n 450.55487060546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"known = {0:0, 1:1}\\ndef fibonacci(n):\\n if n in known:\\n return known[n]\\n res = fibonacci(n-1) + fibonacci(n-2)\\n known[n] = res\\n return res\",\n \"polygon\": [\n [\n 127.52490234375,\n 456.771728515625\n ],\n [\n 344.548828125,\n 456.771728515625\n ],\n [\n 344.548828125,\n 564.2883453369141\n ],\n [\n 127.52490234375,\n 564.2883453369141\n ]\n ],\n \"bbox\": [\n 127.52490234375,\n 456.771728515625,\n 344.548828125,\n 564.2883453369141\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"known is a dictionary that keeps track of the Fibonacci numbers we already know. It starts with two items: 0 maps to 0 and 1 maps to 1.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 570.6547393798828\n ],\n [\n 525.9375,\n 570.6547393798828\n ],\n [\n 525.9375,\n 592.9608917236328\n ],\n [\n 128.0478515625,\n 592.9608917236328\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 570.6547393798828,\n 525.9375,\n 592.9608917236328\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Whenever fibonacci is called, it checks known. If the result is already there, it can return immediately. Otherwise it has to compute the new value, add it to the dictionary, and return it.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 603.1497344970703\n ],\n [\n 525.6033935546875,\n 603.1497344970703\n ],\n [\n 525.6033935546875,\n 637.6498870849609\n ],\n [\n 128.3466796875,\n 637.6498870849609\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 603.1497344970703,\n 525.6033935546875,\n 637.6498870849609\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.6. Run this version of fibonacci and the original with a range of parameters and compare their run times.
\",\n \"polygon\": [\n [\n 128.49609375,\n 639.6328125\n ],\n [\n 525.6038208007812,\n 639.6328125\n ],\n [\n 525.6038208007812,\n 661.8661956787109\n ],\n [\n 128.49609375,\n 661.8661956787109\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 639.6328125,\n 525.6038208007812,\n 661.8661956787109\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/128/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.7. Memoize the Ackermann function from Exercise 6.5 and see if memoization makes it possible to evaluate the function with bigger arguments. Hint: no. Solution: http: // thinkpython. com/ code/ ackermann_ memo. py .
\",\n \"polygon\": [\n [\n 128.794921875,\n 663.99609375\n ],\n [\n 525.6021728515625,\n 663.99609375\n ],\n [\n 525.6021728515625,\n 698.4492034912109\n ],\n [\n 128.794921875,\n 698.4492034912109\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 663.99609375,\n 525.6021728515625,\n 698.4492034912109\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/129/Page/197\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/129/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.134765625\n ],\n [\n 482.90625,\n 60.134765625\n ],\n [\n 482.90625,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.134765625,\n 482.90625,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.763671875,\n 60.0380859375\n ],\n [\n 101.900390625,\n 60.0380859375\n ],\n [\n 101.900390625,\n 69.99609375\n ],\n [\n 85.763671875,\n 69.99609375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 60.0380859375,\n 101.900390625,\n 69.99609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/127/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"11.6 Global variables
\",\n \"polygon\": [\n [\n 85.46484375,\n 85.60986328125\n ],\n [\n 234.62493896484375,\n 85.60986328125\n ],\n [\n 234.62493896484375,\n 100.29998779296875\n ],\n [\n 85.46484375,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 85.60986328125,\n 234.62493896484375,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"In the previous example, known is created outside the function, so it belongs to the special frame called __main__. Variables in __main__ are sometimes called global because they can be accessed from any function. Unlike local variables, which disappear when their function ends, global variables persist from one function call to the next.
\",\n \"polygon\": [\n [\n 85.166015625,\n 111.375\n ],\n [\n 483.50390625,\n 111.375\n ],\n [\n 483.50390625,\n 158.8529052734375\n ],\n [\n 85.166015625,\n 158.8529052734375\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 111.375,\n 483.50390625,\n 158.8529052734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"It is common to use global variables for flags; that is, boolean variables that indicate (\\\"flag\\\") whether a condition is true. For example, some programs use a flag named verbose to control the level of detail in the output:
\",\n \"polygon\": [\n [\n 85.46484375,\n 166.7724609375\n ],\n [\n 483.802734375,\n 166.7724609375\n ],\n [\n 483.802734375,\n 203.0399169921875\n ],\n [\n 85.46484375,\n 203.0399169921875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 166.7724609375,\n 483.802734375,\n 203.0399169921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"verbose = True\\ndef example1():\\n if verbose:\\n print 'Running example1'
\",\n \"polygon\": [\n [\n 85.83837890625,\n 208.7547607421875\n ],\n [\n 254.302734375,\n 208.7547607421875\n ],\n [\n 254.302734375,\n 267.494384765625\n ],\n [\n 85.83837890625,\n 267.494384765625\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 208.7547607421875,\n 254.302734375,\n 267.494384765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"If you try to reassign a global variable, you might be surprised. The following example is supposed to keep track of whether the function has been called:
\",\n \"polygon\": [\n [\n 85.763671875,\n 271.86328125\n ],\n [\n 482.90625,\n 271.86328125\n ],\n [\n 482.90625,\n 295.6649475097656\n ],\n [\n 85.763671875,\n 295.6649475097656\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 271.86328125,\n 482.90625,\n 295.6649475097656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \"been_called = False\\ndef example2():\\n been_called = True # WRONG
\",\n \"polygon\": [\n [\n 85.53955078125,\n 301.3797912597656\n ],\n [\n 285.1484680175781,\n 301.3797912597656\n ],\n [\n 285.1484680175781,\n 347.9253845214844\n ],\n [\n 85.53955078125,\n 347.9253845214844\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 301.3797912597656,\n 285.1484680175781,\n 347.9253845214844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"But if you run it you will see that the value of been_called doesn't change. The problem is that example2 creates a new local variable named been_called. The local variable goes away when the function ends, and has no effect on the global variable.
\",\n \"polygon\": [\n [\n 85.763671875,\n 352.107421875\n ],\n [\n 483.50390625,\n 352.107421875\n ],\n [\n 483.50390625,\n 388.29095458984375\n ],\n [\n 85.763671875,\n 388.29095458984375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 352.107421875,\n 483.50390625,\n 388.29095458984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"To reassign a global variable inside a function you have to declare the global variable before you use it:
\",\n \"polygon\": [\n [\n 86.0625,\n 396.38671875\n ],\n [\n 482.607421875,\n 396.38671875\n ],\n [\n 482.607421875,\n 420.2839660644531\n ],\n [\n 86.0625,\n 420.2839660644531\n ]\n ],\n \"bbox\": [\n 86.0625,\n 396.38671875,\n 482.607421875,\n 420.2839660644531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \"been_called = False\\ndef example2():\\n global been_called\\n been_called = True
\",\n \"polygon\": [\n [\n 86.40008544921875,\n 425.9988098144531\n ],\n [\n 223.822265625,\n 425.9988098144531\n ],\n [\n 223.822265625,\n 484.7384033203125\n ],\n [\n 86.40008544921875,\n 484.7384033203125\n ]\n ],\n \"bbox\": [\n 86.40008544921875,\n 425.9988098144531,\n 223.822265625,\n 484.7384033203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The global statement tells the interpreter something like, \\\"In this function, when I say been_called, I mean the global variable; don't create a local one.\\\"
\",\n \"polygon\": [\n [\n 86.40008544921875,\n 489.5859375\n ],\n [\n 483.205078125,\n 489.5859375\n ],\n [\n 483.205078125,\n 512.9089660644531\n ],\n [\n 86.40008544921875,\n 512.9089660644531\n ]\n ],\n \"bbox\": [\n 86.40008544921875,\n 489.5859375,\n 483.205078125,\n 512.9089660644531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Here's an example that tries to update a global variable:
\",\n \"polygon\": [\n [\n 86.13720703125,\n 521.68359375\n ],\n [\n 332.296875,\n 521.68359375\n ],\n [\n 332.296875,\n 532.7079772949219\n ],\n [\n 86.13720703125,\n 532.7079772949219\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 521.68359375,\n 332.296875,\n 532.7079772949219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"count = 0\\ndef example3():\\n count = count + 1 # WRONG
\",\n \"polygon\": [\n [\n 86.361328125,\n 536.765625\n ],\n [\n 290.162109375,\n 536.765625\n ],\n [\n 290.162109375,\n 587.8125\n ],\n [\n 86.361328125,\n 587.8125\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 536.765625,\n 290.162109375,\n 587.8125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"If you run it you get:
\",\n \"polygon\": [\n [\n 85.763671875,\n 590.9823760986328\n ],\n [\n 177.35862731933594,\n 590.9823760986328\n ],\n [\n 177.35862731933594,\n 601.734375\n ],\n [\n 85.763671875,\n 601.734375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 590.9823760986328,\n 177.35862731933594,\n 601.734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"UnboundLocalError: local variable 'count' referenced before assignment
\",\n \"polygon\": [\n [\n 85.166015625,\n 606.375\n ],\n [\n 452.4832458496094,\n 606.375\n ],\n [\n 452.4832458496094,\n 617.203125\n ],\n [\n 85.166015625,\n 617.203125\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 606.375,\n 452.4832458496094,\n 617.203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Python assumes that count is local, which means that you are reading it before writing it. The solution, again, is to declare count global.
\",\n \"polygon\": [\n [\n 85.0166015625,\n 621.0703125\n ],\n [\n 482.90625,\n 621.0703125\n ],\n [\n 482.90625,\n 644.79296875\n ],\n [\n 85.0166015625,\n 644.79296875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 621.0703125,\n 482.90625,\n 644.79296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \"def example3():\\n global count\\n count += 1
\",\n \"polygon\": [\n [\n 86.40007019042969,\n 650.5078125\n ],\n [\n 173.1708984375,\n 650.5078125\n ],\n [\n 173.1708984375,\n 685.65234375\n ],\n [\n 86.40007019042969,\n 685.65234375\n ]\n ],\n \"bbox\": [\n 86.40007019042969,\n 650.5078125,\n 173.1708984375,\n 685.65234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/129/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"If the global value is mutable, you can modify it without declaring it:
\",\n \"polygon\": [\n [\n 86.13720703125,\n 689.90625\n ],\n [\n 389.671875,\n 689.90625\n ],\n [\n 389.671875,\n 700.8349761962891\n ],\n [\n 86.13720703125,\n 700.8349761962891\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 689.90625,\n 389.671875,\n 700.8349761962891\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/130/Page/195\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/130/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.27197265625,\n 60.8115234375\n ],\n [\n 525.6033935546875,\n 60.8115234375\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.27197265625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 60.8115234375,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 510.99609375,\n 60.8115234375\n ],\n [\n 525.9375,\n 60.8115234375\n ],\n [\n 525.9375,\n 70.189453125\n ],\n [\n 510.99609375,\n 70.189453125\n ]\n ],\n \"bbox\": [\n 510.99609375,\n 60.8115234375,\n 525.9375,\n 70.189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \"known = {0:0, 1:1}\\ndef example4():\\n known[2] = 1\",\n \"polygon\": [\n [\n 127.8984375,\n 88.68572998046875\n ],\n [\n 223.74656677246094,\n 88.68572998046875\n ],\n [\n 223.74656677246094,\n 135.2313232421875\n ],\n [\n 127.8984375,\n 135.2313232421875\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 88.68572998046875,\n 223.74656677246094,\n 135.2313232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"So you can add, remove and replace elements of a global list or dictionary, but if you want to reassign the variable, you have to declare it:
\",\n \"polygon\": [\n [\n 128.794921875,\n 140.958984375\n ],\n [\n 525.638671875,\n 140.958984375\n ],\n [\n 525.638671875,\n 163.93792724609375\n ],\n [\n 128.794921875,\n 163.93792724609375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 140.958984375,\n 525.638671875,\n 163.93792724609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"def example5():\\n global known\\n known = dict()
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 170.18975830078125\n ],\n [\n 223.74111938476562,\n 170.18975830078125\n ],\n [\n 223.74111938476562,\n 204.540283203125\n ],\n [\n 129.60000610351562,\n 204.540283203125\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 170.18975830078125,\n 223.74111938476562,\n 204.540283203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/129/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"11.7 Long integers
\",\n \"polygon\": [\n [\n 127.7490234375,\n 234.62371826171875\n ],\n [\n 259.2333984375,\n 234.62371826171875\n ],\n [\n 259.2333984375,\n 249.43359375\n ],\n [\n 127.7490234375,\n 249.43359375\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 234.62371826171875,\n 259.2333984375,\n 249.43359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"If you compute fibonacci(50), you get:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 261.228515625\n ],\n [\n 307.8939514160156,\n 261.228515625\n ],\n [\n 307.8939514160156,\n 271.55487060546875\n ],\n [\n 128.3466796875,\n 271.55487060546875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 261.228515625,\n 307.8939514160156,\n 271.55487060546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> fibonacci(50)\\n12586269025L
\",\n \"polygon\": [\n [\n 129.09375,\n 277.8056640625\n ],\n [\n 218.52618408203125,\n 277.8056640625\n ],\n [\n 218.52618408203125,\n 299.9622802734375\n ],\n [\n 129.09375,\n 299.9622802734375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 277.8056640625,\n 218.52618408203125,\n 299.9622802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The L at the end indicates that the result is a long integer, or type long. In Python 3, long is gone; all integers, even really big ones, are type int.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 306.36370849609375\n ],\n [\n 525.9375,\n 306.36370849609375\n ],\n [\n 525.9375,\n 328.66986083984375\n ],\n [\n 128.6455078125,\n 328.66986083984375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 306.36370849609375,\n 525.9375,\n 328.66986083984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Values with type int have a limited range; long integers can be arbitrarily big, but as they get bigger they consume more space and time.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 338.8927001953125\n ],\n [\n 525.6017456054688,\n 338.8927001953125\n ],\n [\n 525.6017456054688,\n 361.1988525390625\n ],\n [\n 128.6455078125,\n 361.1988525390625\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 338.8927001953125,\n 525.6017456054688,\n 361.1988525390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The mathematical operators work on long integers, and the functions in the math module, too, so in general any code that works with int will also work with long.
\",\n \"polygon\": [\n [\n 128.794921875,\n 370.86328125\n ],\n [\n 525.638671875,\n 370.86328125\n ],\n [\n 525.638671875,\n 393.72784423828125\n ],\n [\n 128.794921875,\n 393.72784423828125\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 370.86328125,\n 525.638671875,\n 393.72784423828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Any time the result of a computation is too big to be represented with an integer, Python converts the result as a long integer:
\",\n \"polygon\": [\n [\n 129.09375,\n 403.34765625\n ],\n [\n 525.6033935546875,\n 403.34765625\n ],\n [\n 525.6033935546875,\n 426.2568359375\n ],\n [\n 129.09375,\n 426.2568359375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 403.34765625,\n 525.6033935546875,\n 426.2568359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \">>> 1000 * 1000\\n1000000\\n>>> 100000 * 100000\\n10000000000L
\",\n \"polygon\": [\n [\n 127.8984375,\n 432.5086669921875\n ],\n [\n 228.97698974609375,\n 432.5086669921875\n ],\n [\n 228.97698974609375,\n 479.05426025390625\n ],\n [\n 127.8984375,\n 479.05426025390625\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 432.5086669921875,\n 228.97698974609375,\n 479.05426025390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"In the first case the result has type int; in the second case it is long.
\",\n \"polygon\": [\n [\n 128.27197265625,\n 484.55859375\n ],\n [\n 425.5577392578125,\n 484.55859375\n ],\n [\n 425.5577392578125,\n 495.56683349609375\n ],\n [\n 128.27197265625,\n 495.56683349609375\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 484.55859375,\n 425.5577392578125,\n 495.56683349609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.8. Exponentiation of large integers is the basis of common algorithms for public-key encryption. Read the Wikipedia page on the RSA algorithm (http: // en. wikipedia. org/ wiki/ RSA_ ( algorithm) ) and write functions to encode and decode messages.
\",\n \"polygon\": [\n [\n 129.392578125,\n 496.16015625\n ],\n [\n 525.6006469726562,\n 496.16015625\n ],\n [\n 525.6006469726562,\n 532.51171875\n ],\n [\n 129.392578125,\n 532.51171875\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 496.16015625,\n 525.6006469726562,\n 532.51171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/SectionHeader/14\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"11.8 Debugging
\",\n \"polygon\": [\n [\n 127.52490234375,\n 561.90234375\n ],\n [\n 243.17897033691406,\n 561.90234375\n ],\n [\n 243.17897033691406,\n 576.4298858642578\n ],\n [\n 127.52490234375,\n 576.4298858642578\n ]\n ],\n \"bbox\": [\n 127.52490234375,\n 561.90234375,\n 243.17897033691406,\n 576.4298858642578\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/130/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"As you work with bigger datasets it can become unwieldy to debug by printing and checking data by hand. Here are some suggestions for debugging large datasets:
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\",\n \"polygon\": [\n [\n 109.669921875,\n 129.357421875\n ],\n [\n 482.4039611816406,\n 129.357421875\n ],\n [\n 482.4039611816406,\n 151.60089111328125\n ],\n [\n 109.669921875,\n 151.60089111328125\n ]\n ],\n \"bbox\": [\n 109.669921875,\n 129.357421875,\n 482.4039611816406,\n 151.60089111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/131/ListItem/3\",\n \"block_type\": \"ListItem\",\n \"html\": \"Write self-checks: Sometimes you can write code to check for errors automatically. For example, if you are computing the average of a list of numbers, you could check that the result is not greater than the largest element in the list or less than the smallest. This is called a \\\"sanity check\\\" because it detects results that are \\\"insane.\\\"\",\n \"polygon\": [\n [\n 85.3154296875,\n 161.455078125\n ],\n [\n 482.4039001464844,\n 161.455078125\n ],\n [\n 482.4039001464844,\n 208.4298095703125\n ],\n [\n 85.3154296875,\n 208.4298095703125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 161.455078125,\n 482.4039001464844,\n 208.4298095703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/130/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/131/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Another kind of check compares the results of two different computations to see if they are consistent. This is called a \\\"consistency check.\\\"
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\",\n \"polygon\": [\n [\n 128.42138671875,\n 180.6627197265625\n ],\n [\n 236.373046875,\n 180.6627197265625\n ],\n [\n 236.373046875,\n 195.0089111328125\n ],\n [\n 128.42138671875,\n 195.0089111328125\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 180.6627197265625,\n 236.373046875,\n 195.0089111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/132/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/132/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.9. If you did Exercise 10.8, you already have a function named has_duplicates that takes a list as a parameter and returns True if there is any object that appears more than once in the list.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 251.3671875\n ],\n [\n 525.638671875,\n 251.3671875\n ],\n [\n 525.638671875,\n 273.86517333984375\n ],\n [\n 128.794921875,\n 273.86517333984375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 251.3671875,\n 525.638671875,\n 273.86517333984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/132/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/132/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 11.10. Two words are \\\"rotate pairs\\\" if you can rotate one of them and get the other (see rotate_word in Exercise 8.12).
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\",\n \"polygon\": [\n [\n 129.60003662109375,\n 571.5703125\n ],\n [\n 490.1864013671875,\n 571.5703125\n ],\n [\n 490.1864013671875,\n 581.9361724853516\n ],\n [\n 129.60003662109375,\n 581.9361724853516\n ]\n ],\n \"bbox\": [\n 129.60003662109375,\n 571.5703125,\n 490.1864013671875,\n 581.9361724853516\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/132/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/132/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"To check whether two words are homophones, you can use the CMU Pronouncing Dictionary. You can download it from http: // www. speech. cs. cmu. edu/ cgi-bin/ cmudict or from http: // thinkpython. com/ code/ c06d and you can also download http: // thinkpython. com/ code/ pronounce. py , which provides a function named read_dictionary that reads the pronouncing dictionary and returns a Python dictionary that maps from each word to a string that describes its primary pronunciation.
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\",\n \"polygon\": [\n [\n 128.49609375,\n 673.139892578125\n ],\n [\n 523.9028930664062,\n 673.139892578125\n ],\n [\n 523.9028930664062,\n 695.3771896362305\n ],\n [\n 128.49609375,\n 695.3771896362305\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 673.139892578125,\n 523.9028930664062,\n 695.3771896362305\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/132/SectionHeader/4\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/132/SectionHeader/4\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/133/Page/3\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/133/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.08642578125\n ],\n [\n 482.40338134765625,\n 60.08642578125\n ],\n [\n 482.40338134765625,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.08642578125,\n 482.40338134765625,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/132/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/133/PageHeader/1\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 84.8671875,\n 60.521484375\n ],\n [\n 101.00390625,\n 60.521484375\n ],\n [\n 101.00390625,\n 70.6728515625\n ],\n [\n 84.8671875,\n 70.6728515625\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 60.521484375,\n 101.00390625,\n 70.6728515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/132/SectionHeader/4\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"4\": \"/page/132/SectionHeader/4\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/134/Page/151\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/134/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Chapter 12
\",\n \"polygon\": [\n [\n 128.9443359375,\n 162.615234375\n ],\n [\n 232.6376953125,\n 162.615234375\n ],\n [\n 232.6376953125,\n 183.574951171875\n ],\n [\n 128.9443359375,\n 183.574951171875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 162.615234375,\n 232.6376953125,\n 183.574951171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/122/SectionHeader/1\",\n \"2\": \"/page/134/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Tuples
\",\n \"polygon\": [\n [\n 128.6455078125,\n 217.059326171875\n ],\n [\n 204.87843322753906,\n 217.059326171875\n ],\n [\n 204.87843322753906,\n 241.84637451171875\n ],\n [\n 128.6455078125,\n 241.84637451171875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 217.059326171875,\n 204.87843322753906,\n 241.84637451171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"12.1 Tuples are immutable
\",\n \"polygon\": [\n [\n 128.27197265625,\n 289.458984375\n ],\n [\n 311.4381103515625,\n 289.458984375\n ],\n [\n 311.4381103515625,\n 303.8710021972656\n ],\n [\n 128.27197265625,\n 303.8710021972656\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 289.458984375,\n 311.4381103515625,\n 303.8710021972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"A tuple is a sequence of values. The values can be any type, and they are indexed by integers, so in that respect tuples are a lot like lists. The important difference is that tuples are immutable.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 315.0073547363281\n ],\n [\n 527.1328125,\n 315.0073547363281\n ],\n [\n 527.1328125,\n 349.35894775390625\n ],\n [\n 128.6455078125,\n 349.35894775390625\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 315.0073547363281,\n 527.1328125,\n 349.35894775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Syntactically, a tuple is a comma-separated list of values:
\",\n \"polygon\": [\n [\n 128.42138671875,\n 357.71484375\n ],\n [\n 380.109375,\n 357.71484375\n ],\n [\n 380.109375,\n 368.2869567871094\n ],\n [\n 128.42138671875,\n 368.2869567871094\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 357.71484375,\n 380.109375,\n 368.2869567871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \">>> t = 'a', 'b', 'c', 'd', 'e'
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 373.1318054199219\n ],\n [\n 291.7033996582031,\n 373.1318054199219\n ],\n [\n 291.7033996582031,\n 383.0943908691406\n ],\n [\n 129.60000610351562,\n 383.0943908691406\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 373.1318054199219,\n 291.7033996582031,\n 383.0943908691406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Although it is not necessary, it is common to enclose tuples in parentheses:
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 387.4921875\n ],\n [\n 458.103515625,\n 387.4921875\n ],\n [\n 458.103515625,\n 398.2009582519531\n ],\n [\n 129.60000610351562,\n 398.2009582519531\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 387.4921875,\n 458.103515625,\n 398.2009582519531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ('a', 'b', 'c', 'd', 'e')\",\n \"polygon\": [\n [\n 128.86962890625,\n 402.9609375\n ],\n [\n 302.1624450683594,\n 402.9609375\n ],\n [\n 302.1624450683594,\n 413.0083923339844\n ],\n [\n 128.86962890625,\n 413.0083923339844\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 402.9609375,\n 302.1624450683594,\n 413.0083923339844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"To create a tuple with a single element, you have to include a final comma:
\",\n \"polygon\": [\n [\n 128.49609375,\n 417.26953125\n ],\n [\n 457.2200622558594,\n 417.26953125\n ],\n [\n 457.2200622558594,\n 428.1149597167969\n ],\n [\n 128.49609375,\n 428.1149597167969\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 417.26953125,\n 457.2200622558594,\n 428.1149597167969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> t1 = 'a',\\n>>> type(t1)\\n<type 'tuple'>\\nA value in parentheses is not a tuple:\\n>>> t2 = ('a')\",\n \"polygon\": [\n [\n 129.60006713867188,\n 432.9598083496094\n ],\n [\n 292.10003662109375,\n 432.9598083496094\n ],\n [\n 292.10003662109375,\n 497.2243957519531\n ],\n [\n 129.60006713867188,\n 497.2243957519531\n ]\n ],\n \"bbox\": [\n 129.60006713867188,\n 432.9598083496094,\n 292.10003662109375,\n 497.2243957519531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> type(t2)\\n<type 'str'>
\",\n \"polygon\": [\n [\n 127.599609375,\n 496.546875\n ],\n [\n 193.640625,\n 496.546875\n ],\n [\n 193.640625,\n 521.6123962402344\n ],\n [\n 127.599609375,\n 521.6123962402344\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 496.546875,\n 193.640625,\n 521.6123962402344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Another way to create a tuple is the built-in function tuple. With no argument, it creates an empty tuple:
\",\n \"polygon\": [\n [\n 128.9443359375,\n 526.6068115234375\n ],\n [\n 525.9375,\n 526.6068115234375\n ],\n [\n 525.9375,\n 549.140625\n ],\n [\n 128.9443359375,\n 549.140625\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 526.6068115234375,\n 525.9375,\n 549.140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = tuple()\\n>>> print t\\n()
\",\n \"polygon\": [\n [\n 128.49609375,\n 553.7578125\n ],\n [\n 208.05557250976562,\n 553.7578125\n ],\n [\n 208.05557250976562,\n 588.1094207763672\n ],\n [\n 128.49609375,\n 588.1094207763672\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 553.7578125,\n 208.05557250976562,\n 588.1094207763672\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"If the argument is a sequence (string, list or tuple), the result is a tuple with the elements of the sequence:
\",\n \"polygon\": [\n [\n 128.197265625,\n 592.453125\n ],\n [\n 526.236328125,\n 592.453125\n ],\n [\n 526.236328125,\n 615.65625\n ],\n [\n 128.197265625,\n 615.65625\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 592.453125,\n 526.236328125,\n 615.65625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = tuple('lupins')\\n>>> print t\\n('l', 'u', 'p', 'i', 'n', 's')\",\n \"polygon\": [\n [\n 128.3466796875,\n 620.2548217773438\n ],\n [\n 286.4745178222656,\n 620.2548217773438\n ],\n [\n 286.4745178222656,\n 654.6064300537109\n ],\n [\n 128.3466796875,\n 654.6064300537109\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 620.2548217773438,\n 286.4745178222656,\n 654.6064300537109\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Because tuple is the name of a built-in function, you should avoid using it as a variable name.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 658.1953125\n ],\n [\n 526.53515625,\n 658.1953125\n ],\n [\n 526.53515625,\n 681.9069900512695\n ],\n [\n 128.9443359375,\n 681.9069900512695\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 658.1953125,\n 526.53515625,\n 681.9069900512695\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/134/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Most list operators also work on tuples. The bracket operator indexes an element:
\",\n \"polygon\": [\n [\n 128.197265625,\n 690.29296875\n ],\n [\n 488.583984375,\n 690.29296875\n ],\n [\n 488.583984375,\n 700.8349914550781\n ],\n [\n 128.197265625,\n 700.8349914550781\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 690.29296875,\n 488.583984375,\n 700.8349914550781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/135/Page/181\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/135/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 61.1015625\n ],\n [\n 482.4034118652344,\n 61.1015625\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 61.1015625,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/PageHeader/20\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.6142578125,\n 60.76318359375\n ],\n [\n 101.6015625,\n 60.76318359375\n ],\n [\n 101.6015625,\n 70.23779296875\n ],\n [\n 85.6142578125,\n 70.23779296875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 60.76318359375,\n 101.6015625,\n 70.23779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ('a', 'b', 'c', 'd', 'e')\\n>>> print t[0]\\n'a'\",\n \"polygon\": [\n [\n 85.98779296875,\n 86.3349609375\n ],\n [\n 260.578125,\n 86.3349609375\n ],\n [\n 260.578125,\n 123.03729248046875\n ],\n [\n 85.98779296875,\n 123.03729248046875\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 86.3349609375,\n 260.578125,\n 123.03729248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"And the slice operator selects a range of elements.
\",\n \"polygon\": [\n [\n 86.40003204345703,\n 130.4208984375\n ],\n [\n 306.0,\n 130.4208984375\n ],\n [\n 306.0,\n 141.28887939453125\n ],\n [\n 86.40003204345703,\n 141.28887939453125\n ]\n ],\n \"bbox\": [\n 86.40003204345703,\n 130.4208984375,\n 306.0,\n 141.28887939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/TextInlineMath/3\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> print t[1:3] ('b', 'c')
\",\n \"polygon\": [\n [\n 85.9130859375,\n 148.9833984375\n ],\n [\n 170.0858612060547,\n 148.9833984375\n ],\n [\n 170.0858612060547,\n 171.436279296875\n ],\n [\n 85.9130859375,\n 171.436279296875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 148.9833984375,\n 170.0858612060547,\n 171.436279296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"But if you try to modify one of the elements of the tuple, you get an error:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 179.05078125\n ],\n [\n 409.7860107421875,\n 179.05078125\n ],\n [\n 409.7860107421875,\n 189.68890380859375\n ],\n [\n 85.3154296875,\n 189.68890380859375\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 179.05078125,\n 409.7860107421875,\n 189.68890380859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \">>> t[0] = 'A' TypeError: object doesn't support item assignment
\",\n \"polygon\": [\n [\n 85.166015625,\n 197.6787109375\n ],\n [\n 343.65234375,\n 197.6787109375\n ],\n [\n 343.65234375,\n 219.8363037109375\n ],\n [\n 85.166015625,\n 219.8363037109375\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 197.6787109375,\n 343.65234375,\n 219.8363037109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"You can't modify the elements of a tuple, but you can replace one tuple with another:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 227.00390625\n ],\n [\n 460.66497802734375,\n 227.00390625\n ],\n [\n 460.66497802734375,\n 238.087890625\n ],\n [\n 85.3154296875,\n 238.087890625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 227.00390625,\n 460.66497802734375,\n 238.087890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ('A',) + t[1:]\\n>>> print t\\n('A', 'b', 'c', 'd', 'e')\",\n \"polygon\": [\n [\n 85.46484375,\n 245.759765625\n ],\n [\n 217.12942504882812,\n 245.759765625\n ],\n [\n 217.12942504882812,\n 280.43035888671875\n ],\n [\n 85.46484375,\n 280.43035888671875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 245.759765625,\n 217.12942504882812,\n 280.43035888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/134/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"12.2 Tuple assignment
\",\n \"polygon\": [\n [\n 85.46484375,\n 315.5625\n ],\n [\n 241.53985595703125,\n 315.5625\n ],\n [\n 241.53985595703125,\n 330.0780029296875\n ],\n [\n 85.46484375,\n 330.0780029296875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 315.5625,\n 241.53985595703125,\n 330.0780029296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"It is often useful to swap the values of two variables. With conventional assignments, you have to use a temporary variable. For example, to swap a and b:
\",\n \"polygon\": [\n [\n 85.166015625,\n 345.1353454589844\n ],\n [\n 482.4034118652344,\n 345.1353454589844\n ],\n [\n 482.4034118652344,\n 367.29193115234375\n ],\n [\n 85.166015625,\n 367.29193115234375\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 345.1353454589844,\n 482.4034118652344,\n 367.29193115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Code/177\",\n \"block_type\": \"Code\",\n \"html\": \">>> temp = a\\n>>> a = b\\n>>> b = temp
\",\n \"polygon\": [\n [\n 85.68896484375,\n 375.28277587890625\n ],\n [\n 153.4482421875,\n 375.28277587890625\n ],\n [\n 153.4482421875,\n 409.63336181640625\n ],\n [\n 85.68896484375,\n 409.63336181640625\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 375.28277587890625,\n 153.4482421875,\n 409.63336181640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"This solution is cumbersome; tuple assignment is more elegant:
\",\n \"polygon\": [\n [\n 85.68896484375,\n 417.8262023925781\n ],\n [\n 367.7926940917969,\n 417.8262023925781\n ],\n [\n 367.7926940917969,\n 427.88592529296875\n ],\n [\n 85.68896484375,\n 427.88592529296875\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 417.8262023925781,\n 367.7926940917969,\n 427.88592529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/TextInlineMath/12\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> a, b = b, a
\",\n \"polygon\": [\n [\n 85.166015625,\n 435.87677001953125\n ],\n [\n 164.85556030273438,\n 435.87677001953125\n ],\n [\n 164.85556030273438,\n 445.88671875\n ],\n [\n 85.166015625,\n 445.88671875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 435.87677001953125,\n 164.85556030273438,\n 445.88671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The left side is a tuple of variables; the right side is a tuple of expressions. Each value is assigned to its respective variable. All the expressions on the right side are evaluated before any of the assignments.
\",\n \"polygon\": [\n [\n 85.46484375,\n 453.62109375\n ],\n [\n 482.4035339355469,\n 453.62109375\n ],\n [\n 482.4035339355469,\n 488.47991943359375\n ],\n [\n 85.46484375,\n 488.47991943359375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 453.62109375,\n 482.4035339355469,\n 488.47991943359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The number of variables on the left and the number of values on the right have to be the same:
\",\n \"polygon\": [\n [\n 85.46484375,\n 500.02734375\n ],\n [\n 482.607421875,\n 500.02734375\n ],\n [\n 482.607421875,\n 522.7479248046875\n ],\n [\n 85.46484375,\n 522.7479248046875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 500.02734375,\n 482.607421875,\n 522.7479248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \">>> a, b = 1, 2, 3\\nValueError: too many values to unpack
\",\n \"polygon\": [\n [\n 85.39013671875,\n 530.73876953125\n ],\n [\n 280.30078125,\n 530.73876953125\n ],\n [\n 280.30078125,\n 552.8953704833984\n ],\n [\n 85.39013671875,\n 552.8953704833984\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 530.73876953125,\n 280.30078125,\n 552.8953704833984\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"More generally, the right side can be any kind of sequence (string, list or tuple). For example, to split an email address into a user name and a domain, you could write:
\",\n \"polygon\": [\n [\n 85.46484375,\n 561.12890625\n ],\n [\n 482.4034729003906,\n 561.12890625\n ],\n [\n 482.4034729003906,\n 583.3419189453125\n ],\n [\n 85.46484375,\n 583.3419189453125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 561.12890625,\n 482.4034729003906,\n 583.3419189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Code/17\",\n \"block_type\": \"Code\",\n \"html\": \">>> addr = 'monty@python.org'\\n>>> uname, domain = addr.split('@')\",\n \"polygon\": [\n [\n 85.46484375,\n 591.29296875\n ],\n [\n 269.42047119140625,\n 591.29296875\n ],\n [\n 269.42047119140625,\n 613.4893646240234\n ],\n [\n 85.46484375,\n 613.4893646240234\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 591.29296875,\n 269.42047119140625,\n 613.4893646240234\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"The return value from split is a list with two elements; the first element is assigned to uname, the second to domain.
\",\n \"polygon\": [\n [\n 85.46484375,\n 621.6297607421875\n ],\n [\n 482.40069580078125,\n 621.6297607421875\n ],\n [\n 482.40069580078125,\n 643.9359130859375\n ],\n [\n 85.46484375,\n 643.9359130859375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 621.6297607421875,\n 482.40069580078125,\n 643.9359130859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/135/Code/19\",\n \"block_type\": \"Code\",\n \"html\": \">>> print uname\\nmonty\\n>>> print domain\\npython.org
\",\n \"polygon\": [\n [\n 85.53955078125,\n 651.9267578125\n ],\n [\n 173.3203125,\n 651.9267578125\n ],\n [\n 173.3203125,\n 698.80078125\n ],\n [\n 85.53955078125,\n 698.80078125\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 651.9267578125,\n 173.3203125,\n 698.80078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/136/Page/203\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/136/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.3466796875,\n 61.14990234375\n ],\n [\n 525.6033935546875,\n 61.14990234375\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.3466796875,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 61.14990234375,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/PageHeader/20\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 509.501953125,\n 60.37646484375\n ],\n [\n 526.833984375,\n 60.37646484375\n ],\n [\n 526.833984375,\n 70.33447265625\n ],\n [\n 509.501953125,\n 70.33447265625\n ]\n ],\n \"bbox\": [\n 509.501953125,\n 60.37646484375,\n 526.833984375,\n 70.33447265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/135/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"12.3 Tuples as return values
\",\n \"polygon\": [\n [\n 128.86962890625,\n 85.95379638671875\n ],\n [\n 319.80194091796875,\n 85.95379638671875\n ],\n [\n 319.80194091796875,\n 100.29998779296875\n ],\n [\n 128.86962890625,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 85.95379638671875,\n 319.80194091796875,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Strictly speaking, a function can only return one value, but if the value is a tuple, the effect is the same as returning multiple values. For example, if you want to divide two integers and compute the quotient and remainder, it is inefficient to compute x/y and then x%y. It is better to compute them both at the same time.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 110.98828125\n ],\n [\n 526.53515625,\n 110.98828125\n ],\n [\n 526.53515625,\n 158.57501220703125\n ],\n [\n 128.3466796875,\n 158.57501220703125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 110.98828125,\n 526.53515625,\n 158.57501220703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The built-in function divmod takes two arguments and returns a tuple of two values, the quotient and remainder. You can store the result as a tuple:
\",\n \"polygon\": [\n [\n 128.197265625,\n 167.255859375\n ],\n [\n 526.236328125,\n 167.255859375\n ],\n [\n 526.236328125,\n 190.28997802734375\n ],\n [\n 128.197265625,\n 190.28997802734375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 167.255859375,\n 526.236328125,\n 190.28997802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = divmod(7, 3)\\n>>> print t\\n(2, 1)\\nOr use tuple assignment to store the elements separately:\\n>>> quot, rem = divmod(7, 3)\\n>>> print quot\\n2\\n>>> print rem\\n1
\",\n \"polygon\": [\n [\n 126.703125,\n 195.0029296875\n ],\n [\n 380.54779052734375,\n 195.0029296875\n ],\n [\n 380.54779052734375,\n 309.954345703125\n ],\n [\n 126.703125,\n 309.954345703125\n ]\n ],\n \"bbox\": [\n 126.703125,\n 195.0029296875,\n 380.54779052734375,\n 309.954345703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Here is an example of a function that returns a tuple:
\",\n \"polygon\": [\n [\n 128.794921875,\n 314.40234375\n ],\n [\n 361.880859375,\n 314.40234375\n ],\n [\n 361.880859375,\n 325.6539001464844\n ],\n [\n 128.794921875,\n 325.6539001464844\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 314.40234375,\n 361.880859375,\n 325.6539001464844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \"def min_max(t):\\n return min(t), max(t)
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 330.451171875\n ],\n [\n 260.3636169433594,\n 330.451171875\n ],\n [\n 260.3636169433594,\n 353.2473449707031\n ],\n [\n 129.5999755859375,\n 353.2473449707031\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 330.451171875,\n 260.3636169433594,\n 353.2473449707031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"max and min are built-in functions that find the largest and smallest elements of a sequence. min_max computes both and returns a tuple of two values.
\",\n \"polygon\": [\n [\n 129.59996032714844,\n 357.71484375\n ],\n [\n 525.595458984375,\n 357.71484375\n ],\n [\n 525.595458984375,\n 381.1408996582031\n ],\n [\n 129.59996032714844,\n 381.1408996582031\n ]\n ],\n \"bbox\": [\n 129.59996032714844,\n 357.71484375,\n 525.595458984375,\n 381.1408996582031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"12.4 Variable-length argument tuples
\",\n \"polygon\": [\n [\n 128.6455078125,\n 409.53515625\n ],\n [\n 381.8205261230469,\n 409.53515625\n ],\n [\n 381.8205261230469,\n 423.9659729003906\n ],\n [\n 128.6455078125,\n 423.9659729003906\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 409.53515625,\n 381.8205261230469,\n 423.9659729003906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Functions can take a variable number of arguments. A parameter name that begins with * gathers arguments into a tuple. For example, printall takes any number of arguments and prints them:
\",\n \"polygon\": [\n [\n 128.794921875,\n 435.05859375\n ],\n [\n 525.603271484375,\n 435.05859375\n ],\n [\n 525.603271484375,\n 470.0458984375\n ],\n [\n 128.794921875,\n 470.0458984375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 435.05859375,\n 525.603271484375,\n 470.0458984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"def printall(*args):\\n print args
\",\n \"polygon\": [\n [\n 128.27197265625,\n 475.4837341308594\n ],\n [\n 234.28125,\n 475.4837341308594\n ],\n [\n 234.28125,\n 497.64031982421875\n ],\n [\n 128.27197265625,\n 497.64031982421875\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 475.4837341308594,\n 234.28125,\n 497.64031982421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The gather parameter can have any name you like, but args is conventional. Here's how the function works:
\",\n \"polygon\": [\n [\n 128.9443359375,\n 503.12109375\n ],\n [\n 525.6024169921875,\n 503.12109375\n ],\n [\n 525.6024169921875,\n 525.5339050292969\n ],\n [\n 128.9443359375,\n 525.5339050292969\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 503.12109375,\n 525.6024169921875,\n 525.5339050292969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> printall(1, 2.0, '3')\\n(1, 2.0, '3')
\",\n \"polygon\": [\n [\n 127.7490234375,\n 530.578125\n ],\n [\n 260.329345703125,\n 530.578125\n ],\n [\n 260.329345703125,\n 553.1283569335938\n ],\n [\n 127.7490234375,\n 553.1283569335938\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 530.578125,\n 260.329345703125,\n 553.1283569335938\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The complement of gather is scatter. If you have a sequence of values and you want to pass it to a function as multiple arguments, you can use the * operator. For example, divmod takes exactly two arguments; it doesn't work with a tuple:
\",\n \"polygon\": [\n [\n 128.794921875,\n 558.421875\n ],\n [\n 525.638671875,\n 558.421875\n ],\n [\n 525.638671875,\n 593.2159118652344\n ],\n [\n 128.794921875,\n 593.2159118652344\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 558.421875,\n 525.638671875,\n 593.2159118652344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = (7, 3)\\n>>> divmod(t)\\nTypeError: divmod expected 2 arguments, got 1
\",\n \"polygon\": [\n [\n 129.46728515625,\n 598.25390625\n ],\n [\n 364.9762878417969,\n 598.25390625\n ],\n [\n 364.9762878417969,\n 633.0043640136719\n ],\n [\n 129.46728515625,\n 633.0043640136719\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 598.25390625,\n 364.9762878417969,\n 633.0043640136719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"TypeError: init() expected 2 arguments, got 3
\\n\",\n \"polygon\": [\n [\n 128.27197265625,\n 628.03125\n ],\n [\n 343.353515625,\n 628.03125\n ],\n [\n 343.353515625,\n 638.0859375\n ],\n [\n 128.27197265625,\n 638.0859375\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 628.03125,\n 343.353515625,\n 638.0859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"But if you scatter the tuple, it works:
\",\n \"polygon\": [\n [\n 128.12255859375,\n 638.7403106689453\n ],\n [\n 289.7188720703125,\n 638.7403106689453\n ],\n [\n 289.7188720703125,\n 649.6875\n ],\n [\n 128.12255859375,\n 649.6875\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 638.7403106689453,\n 289.7188720703125,\n 649.6875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/TextInlineMath/17\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> divmod(*t)
\",\n \"polygon\": [\n [\n 129.01904296875,\n 654.1397552490234\n ],\n [\n 202.82508850097656,\n 654.1397552490234\n ],\n [\n 202.82508850097656,\n 664.76953125\n ],\n [\n 129.01904296875,\n 664.76953125\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 654.1397552490234,\n 202.82508850097656,\n 664.76953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"(2, 1)
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 666.3347473144531\n ],\n [\n 160.98216247558594,\n 666.3347473144531\n ],\n [\n 160.98216247558594,\n 676.7578125\n ],\n [\n 129.5999755859375,\n 676.7578125\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 666.3347473144531,\n 160.98216247558594,\n 676.7578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/136/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 12.1. Many of the built-in functions use variable-length argument tuples. For example, max and min can take any number of arguments:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 678.3046875\n ],\n [\n 525.5968017578125,\n 678.3046875\n ],\n [\n 525.5968017578125,\n 700.734375\n ],\n [\n 128.3466796875,\n 700.734375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 678.3046875,\n 525.5968017578125,\n 700.734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/137/Page/210\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/137/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.521484375\n ],\n [\n 482.4034118652344,\n 60.521484375\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.521484375,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/PageHeader/17\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.763671875,\n 60.2314453125\n ],\n [\n 102.6474609375,\n 60.2314453125\n ],\n [\n 102.6474609375,\n 70.4794921875\n ],\n [\n 85.763671875,\n 70.4794921875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 60.2314453125,\n 102.6474609375,\n 70.4794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> max(1,2,3)\\n3\\nBut sum does not.\\n>>> sum(1,2,3)\\nTypeError: sum expected at most 2 arguments, got 3\\nWrite a function called sumall that takes any number of arguments and returns their sum.
\",\n \"polygon\": [\n [\n 86.0625,\n 84.64306640625\n ],\n [\n 452.357177734375,\n 84.64306640625\n ],\n [\n 452.357177734375,\n 172.08984375\n ],\n [\n 86.0625,\n 172.08984375\n ]\n ],\n \"bbox\": [\n 86.0625,\n 84.64306640625,\n 452.357177734375,\n 172.08984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"4\": \"/page/136/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"12.5 Lists and tuples
\",\n \"polygon\": [\n [\n 85.763671875,\n 199.16015625\n ],\n [\n 229.5,\n 199.16015625\n ],\n [\n 229.5,\n 215.7159423828125\n ],\n [\n 85.763671875,\n 215.7159423828125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 199.16015625,\n 229.5,\n 215.7159423828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"zip is a built-in function that takes two or more sequences and \\\"zips\\\" them into a list of tuples where each tuple contains one element from each sequence. In Python 3, zip returns an iterator of tuples, but for most purposes, an iterator behaves like a list.
\",\n \"polygon\": [\n [\n 85.763671875,\n 226.810546875\n ],\n [\n 483.50390625,\n 226.810546875\n ],\n [\n 483.50390625,\n 262.49285888671875\n ],\n [\n 85.763671875,\n 262.49285888671875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 226.810546875,\n 483.50390625,\n 262.49285888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"This example zips a string and a list:
\",\n \"polygon\": [\n [\n 86.13720703125,\n 270.896484375\n ],\n [\n 247.130859375,\n 270.896484375\n ],\n [\n 247.130859375,\n 282.6878356933594\n ],\n [\n 86.13720703125,\n 282.6878356933594\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 270.896484375,\n 247.130859375,\n 282.6878356933594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> s = 'abc'\\n>>> t = [0, 1, 2]\\n>>> zip(s, t)\\n[('a', 0), ('b', 1), ('c', 2)]\",\n \"polygon\": [\n [\n 86.13720703125,\n 288.7986755371094\n ],\n [\n 243.27981567382812,\n 288.7986755371094\n ],\n [\n 243.27981567382812,\n 336.638671875\n ],\n [\n 86.13720703125,\n 336.638671875\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 288.7986755371094,\n 243.27981567382812,\n 336.638671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The result is a list of tuples where each tuple contains a character from the string and the corresponding element from the list.
\",\n \"polygon\": [\n [\n 86.0625,\n 340.119140625\n ],\n [\n 482.4034118652344,\n 340.119140625\n ],\n [\n 482.4034118652344,\n 363.91082763671875\n ],\n [\n 86.0625,\n 363.91082763671875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 340.119140625,\n 482.4034118652344,\n 363.91082763671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"If the sequences are not the same length, the result has the length of the shorter one.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 372.0234375\n ],\n [\n 454.4782409667969,\n 372.0234375\n ],\n [\n 454.4782409667969,\n 384.1058349609375\n ],\n [\n 85.6142578125,\n 384.1058349609375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 372.0234375,\n 454.4782409667969,\n 384.1058349609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/TextInlineMath/8\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> zip('Anne', 'Elk') [('A', 'E'), ('n', 'l'), ('n', 'k')]
\",\n \"polygon\": [\n [\n 85.68896484375,\n 388.458984375\n ],\n [\n 274.6507568359375,\n 388.458984375\n ],\n [\n 274.6507568359375,\n 412.374267578125\n ],\n [\n 85.68896484375,\n 412.374267578125\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 388.458984375,\n 274.6507568359375,\n 412.374267578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"You can use tuple assignment in a for loop to traverse a list of tuples:
\",\n \"polygon\": [\n [\n 85.68896484375,\n 416.8828125\n ],\n [\n 391.7991943359375,\n 416.8828125\n ],\n [\n 391.7991943359375,\n 428.746826171875\n ],\n [\n 85.68896484375,\n 428.746826171875\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 416.8828125,\n 391.7991943359375,\n 428.746826171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"t = [('a', 0), ('b', 1), ('c', 2)]\\nfor letter, number in t:\\n print number, letter\",\n \"polygon\": [\n [\n 85.39013671875,\n 434.857666015625\n ],\n [\n 264.19586181640625,\n 434.857666015625\n ],\n [\n 264.19586181640625,\n 469.86328125\n ],\n [\n 85.39013671875,\n 469.86328125\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 434.857666015625,\n 264.19586181640625,\n 469.86328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Each time through the loop, Python selects the next tuple in the list and assigns the elements to letter and number. The output of this loop is:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 474.890625\n ],\n [\n 482.4034729003906,\n 474.890625\n ],\n [\n 482.4034729003906,\n 497.77581787109375\n ],\n [\n 85.6142578125,\n 497.77581787109375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 474.890625,\n 482.4034729003906,\n 497.77581787109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/ListItem/207\",\n \"block_type\": \"ListItem\",\n \"html\": \"0 a 1 b 2 c\",\n \"polygon\": [\n [\n 85.166015625,\n 503.88665771484375\n ],\n [\n 102.9462890625,\n 503.88665771484375\n ],\n [\n 102.9462890625,\n 538.2382659912109\n ],\n [\n 85.166015625,\n 538.2382659912109\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 503.88665771484375,\n 102.9462890625,\n 538.2382659912109\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"If you combine zip, for and tuple assignment, you get a useful idiom for traversing two (or more) sequences at the same time. For example, has_match takes two sequences, t1 and t2, and returns True if there is an index i such that t1[i] == t2[i]:
\",\n \"polygon\": [\n [\n 85.0166015625,\n 543.33984375\n ],\n [\n 482.90625,\n 543.33984375\n ],\n [\n 482.90625,\n 578.9998321533203\n ],\n [\n 85.0166015625,\n 578.9998321533203\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 543.33984375,\n 482.90625,\n 578.9998321533203\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \"def has_match(t1, t2):\\n for x, y in zip(t1, t2):\\n if x == y:\\n return True\\n return False
\",\n \"polygon\": [\n [\n 86.28662109375,\n 584.71875\n ],\n [\n 232.84571838378906,\n 584.71875\n ],\n [\n 232.84571838378906,\n 643.8502960205078\n ],\n [\n 86.28662109375,\n 643.8502960205078\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 584.71875,\n 232.84571838378906,\n 643.8502960205078\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"If you need to traverse the elements of a sequence and their indices, you can use the built-in function enumerate:
\",\n \"polygon\": [\n [\n 85.166015625,\n 649.30078125\n ],\n [\n 482.607421875,\n 649.30078125\n ],\n [\n 482.607421875,\n 672.50390625\n ],\n [\n 85.166015625,\n 672.50390625\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 649.30078125,\n 482.607421875,\n 672.50390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/137/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \"for index, element in enumerate('abc'):\\n print index, element\",\n \"polygon\": [\n [\n 85.9130859375,\n 677.91796875\n ],\n [\n 290.3377685546875,\n 677.91796875\n ],\n [\n 290.3377685546875,\n 700.6852874755859\n ],\n [\n 85.9130859375,\n 700.6852874755859\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 677.91796875,\n 290.3377685546875,\n 700.6852874755859\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/138/Page/189\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/138/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.197265625,\n 61.1015625\n ],\n [\n 525.6033935546875,\n 61.1015625\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.197265625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 61.1015625,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/PageHeader/20\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 510.3984375,\n 60.521484375\n ],\n [\n 525.9375,\n 60.521484375\n ],\n [\n 525.9375,\n 70.189453125\n ],\n [\n 510.3984375,\n 70.189453125\n ]\n ],\n \"bbox\": [\n 510.3984375,\n 60.521484375,\n 525.9375,\n 70.189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"The output of this loop is:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 88.171875\n ],\n [\n 242.97439575195312,\n 88.171875\n ],\n [\n 242.97439575195312,\n 98.79791259765625\n ],\n [\n 128.3466796875,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 88.171875,\n 242.97439575195312,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"0 a 1 b 2 c Again.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 104.80078125\n ],\n [\n 165.7001953125,\n 104.80078125\n ],\n [\n 165.7001953125,\n 156.02496337890625\n ],\n [\n 128.6455078125,\n 156.02496337890625\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 104.80078125,\n 165.7001953125,\n 156.02496337890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"12.6 Dictionaries and tuples
\",\n \"polygon\": [\n [\n 128.57080078125,\n 186.1083984375\n ],\n [\n 322.0256042480469,\n 186.1083984375\n ],\n [\n 322.0256042480469,\n 200.4730224609375\n ],\n [\n 128.57080078125,\n 200.4730224609375\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 186.1083984375,\n 322.0256042480469,\n 200.4730224609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Dictionaries have a method called items that returns a list of tuples, where each tuple is a key-value pair.
\",\n \"polygon\": [\n [\n 129.09375,\n 212.6953125\n ],\n [\n 525.5999755859375,\n 212.6953125\n ],\n [\n 525.5999755859375,\n 235.33001708984375\n ],\n [\n 129.09375,\n 235.33001708984375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 212.6953125,\n 525.5999755859375,\n 235.33001708984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> d = {'a':0, 'b':1, 'c':2}\\n>>> t = d.items()\\n>>> print t\\n[('a', 0), ('c', 2), ('b', 1)]\",\n \"polygon\": [\n [\n 129.16845703125,\n 241.6368408203125\n ],\n [\n 286.4798583984375,\n 241.6368408203125\n ],\n [\n 286.4798583984375,\n 288.1824645996094\n ],\n [\n 129.16845703125,\n 288.1824645996094\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 241.6368408203125,\n 286.4798583984375,\n 288.1824645996094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"As you should expect from a dictionary, the items are in no particular order. In Python 3, items returns an iterator, but for many purposes, iterators behave like lists.
\",\n \"polygon\": [\n [\n 129.60003662109375,\n 294.099609375\n ],\n [\n 525.6033935546875,\n 294.099609375\n ],\n [\n 525.6033935546875,\n 316.9450378417969\n ],\n [\n 129.60003662109375,\n 316.9450378417969\n ]\n ],\n \"bbox\": [\n 129.60003662109375,\n 294.099609375,\n 525.6033935546875,\n 316.9450378417969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Going in the other direction, you can use a list of tuples to initialize a new dictionary:
\",\n \"polygon\": [\n [\n 128.794921875,\n 327.1640625\n ],\n [\n 504.15386962890625,\n 327.1640625\n ],\n [\n 504.15386962890625,\n 337.3360290527344\n ],\n [\n 128.794921875,\n 337.3360290527344\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 327.1640625,\n 504.15386962890625,\n 337.3360290527344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = [('a', 0), ('c', 2), ('b', 1)]\\n>>> d = dict(t)\\n>>> print d\\n{'a': 0, 'c': 2, 'b': 1}\",\n \"polygon\": [\n [\n 129.5419921875,\n 343.40625\n ],\n [\n 328.3128967285156,\n 343.40625\n ],\n [\n 328.3128967285156,\n 393.6796875\n ],\n [\n 129.5419921875,\n 393.6796875\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 343.40625,\n 328.3128967285156,\n 393.6796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Combining dict with zip yields a concise way to create a dictionary:
\",\n \"polygon\": [\n [\n 127.4501953125,\n 396.6448974609375\n ],\n [\n 434.49609375,\n 396.6448974609375\n ],\n [\n 434.49609375,\n 406.7570495605469\n ],\n [\n 127.4501953125,\n 406.7570495605469\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 396.6448974609375,\n 434.49609375,\n 406.7570495605469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> d = dict(zip('abc', range(3)))\\n>>> print d\\n{'a': 0, 'c': 2, 'b': 1}\",\n \"polygon\": [\n [\n 128.57080078125,\n 413.0649108886719\n ],\n [\n 307.40447998046875,\n 413.0649108886719\n ],\n [\n 307.40447998046875,\n 447.4154968261719\n ],\n [\n 128.57080078125,\n 447.4154968261719\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 413.0649108886719,\n 307.40447998046875,\n 447.4154968261719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The dictionary method update also takes a list of tuples and adds them, as key-value pairs, to an existing dictionary.
\",\n \"polygon\": [\n [\n 129.60011291503906,\n 453.8719177246094\n ],\n [\n 525.9375,\n 453.8719177246094\n ],\n [\n 525.9375,\n 476.1790771484375\n ],\n [\n 129.60011291503906,\n 476.1790771484375\n ]\n ],\n \"bbox\": [\n 129.60011291503906,\n 453.8719177246094,\n 525.9375,\n 476.1790771484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Combining items, tuple assignment and for, you get the idiom for traversing the keys and values of a dictionary:
\",\n \"polygon\": [\n [\n 129.09375,\n 486.4569091796875\n ],\n [\n 525.5986328125,\n 486.4569091796875\n ],\n [\n 525.5986328125,\n 508.7640686035156\n ],\n [\n 129.09375,\n 508.7640686035156\n ]\n ],\n \"bbox\": [\n 129.09375,\n 486.4569091796875,\n 525.5986328125,\n 508.7640686035156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"for key, val in d.items():\\n print val, key
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\",\n \"polygon\": [\n [\n 127.8984375,\n 560.1039276123047\n ],\n [\n 149.2646484375,\n 560.1039276123047\n ],\n [\n 149.2646484375,\n 596.70703125\n ],\n [\n 127.8984375,\n 596.70703125\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 560.1039276123047,\n 149.2646484375,\n 596.70703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Again.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 601.0604858398438\n ],\n [\n 160.0224609375,\n 601.0604858398438\n ],\n [\n 160.0224609375,\n 611.40234375\n ],\n [\n 128.6455078125,\n 611.40234375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 601.0604858398438,\n 160.0224609375,\n 611.40234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"It is common to use tuples as keys in dictionaries (primarily because you can't use lists). For example, a telephone directory might map from last-name, first-name pairs to telephone numbers. Assuming that we have defined last, first and number, we could write:
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\",\n \"polygon\": [\n [\n 127.7490234375,\n 662.1099395751953\n ],\n [\n 286.5209655761719,\n 662.1099395751953\n ],\n [\n 286.5209655761719,\n 672.0725402832031\n ],\n [\n 127.7490234375,\n 672.0725402832031\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 662.1099395751953,\n 286.5209655761719,\n 672.0725402832031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/138/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"The expression in brackets is a tuple. We could use tuple assignment to traverse this dictionary.
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|---|
| ('Cleese', 'John') → '08700 100 222' |
|---|
| ('Chapman', 'Graham') → '08700 100 222' |
|---|
| ('Idle', 'Eric') → '08700 100 222' |
|---|
| ('Gilliam', 'Terry') → '08700 100 222' |
|---|
| ('Jones', 'Terry') → '08700 100 222' |
|---|
| ('Palin', 'Michael') → '08700 100 222' |
|---|
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\",\n \"polygon\": [\n [\n 85.46484375,\n 344.1796875\n ],\n [\n 482.4014892578125,\n 344.1796875\n ],\n [\n 482.4014892578125,\n 367.201904296875\n ],\n [\n 85.46484375,\n 367.201904296875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 344.1796875,\n 482.4014892578125,\n 367.201904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"4\": \"/page/138/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/139/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"There are two ways to represent tuples in a state diagram. The more detailed version shows the indices and elements just as they appear in a list. For example, the tuple ('Cleese', 'John') would appear as in Figure 12.1.
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\",\n \"polygon\": [\n [\n 129.6000213623047,\n 188.84771728515625\n ],\n [\n 307.4338073730469,\n 188.84771728515625\n ],\n [\n 307.4338073730469,\n 324.0703125\n ],\n [\n 129.6000213623047,\n 324.0703125\n ]\n ],\n \"bbox\": [\n 129.6000213623047,\n 188.84771728515625,\n 307.4338073730469,\n 324.0703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/139/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/140/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The first loop builds a list of tuples, where each tuple is a word preceded by its length.
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 345.2997131347656\n ],\n [\n 525.59912109375,\n 345.2997131347656\n ],\n [\n 525.59912109375,\n 367.6058654785156\n ],\n [\n 128.9443359375,\n 367.6058654785156\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 345.2997131347656,\n 525.59912109375,\n 367.6058654785156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/139/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/140/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The second loop traverses the list of tuples and builds a list of words in descending order of length.
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\",\n \"polygon\": [\n [\n 127.97314453125,\n 488.0967102050781\n ],\n [\n 326.0281982421875,\n 488.0967102050781\n ],\n [\n 326.0281982421875,\n 502.44293212890625\n ],\n [\n 127.97314453125,\n 502.44293212890625\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 488.0967102050781,\n 326.0281982421875,\n 502.44293212890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/139/SectionHeader/12\",\n \"4\": \"/page/140/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/140/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"I have focused on lists of tuples, but almost all of the examples in this chapter also work with lists of lists, tuples of tuples, and tuples of lists. To avoid enumerating the possible combinations, it is sometimes easier to talk about sequences of sequences.
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But Python provides the built-in functions sorted and reversed, which take any sequence as a parameter and return a new list with the same elements in a different order.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 222.5216064453125\n ],\n [\n 201.2607421875,\n 222.5216064453125\n ],\n [\n 201.2607421875,\n 237.4453125\n ],\n [\n 85.6142578125,\n 237.4453125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 222.5216064453125,\n 201.2607421875,\n 237.4453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/141/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Lists, dictionaries and tuples are known generically as data structures; in this chapter we are starting to see compound data structures, like lists of tuples, and dictionaries that contain tuples as keys and lists as values. Compound data structures are useful, but they are prone to what I call shape errors; that is, errors caused when a data structure has the wrong type, size or composition. For example, if you are expecting a list with one integer and I give you a plain old integer (not in a list), it won't work.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 246.533203125\n ],\n [\n 482.90625,\n 246.533203125\n ],\n [\n 482.90625,\n 317.9517517089844\n ],\n [\n 85.6142578125,\n 317.9517517089844\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 246.533203125,\n 482.90625,\n 317.9517517089844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/141/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"To help debug these kinds of errors, I have written a module called structshape that provides a function, also called structshape, that takes any kind of data structure as an argument and returns a string that summarizes its shape. You can download it from http://thinkpython.com/code/structshape.py
\",\n \"polygon\": [\n [\n 85.6142578125,\n 325.037109375\n ],\n [\n 482.4033508300781,\n 325.037109375\n ],\n [\n 482.4033508300781,\n 372.603515625\n ],\n [\n 85.6142578125,\n 372.603515625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 325.037109375,\n 482.4033508300781,\n 372.603515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/141/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Here's the result for a simple list:
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\",\n \"polygon\": [\n [\n 129.46728515625,\n 432.2087707519531\n ],\n [\n 261.474609375,\n 432.2087707519531\n ],\n [\n 261.474609375,\n 442.40625\n ],\n [\n 129.46728515625,\n 442.40625\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 432.2087707519531,\n 261.474609375,\n 442.40625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/142/ListItem/12\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Write a program that reads a word list from a file (see Section 9.1) and prints all the sets of words that are anagrams.\",\n \"polygon\": [\n [\n 141.943359375,\n 457.875\n ],\n [\n 526.833984375,\n 457.875\n ],\n [\n 526.833984375,\n 480.3046875\n ],\n [\n 141.943359375,\n 480.3046875\n ]\n ],\n \"bbox\": [\n 141.943359375,\n 457.875,\n 526.833984375,\n 480.3046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/142/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Here is an example of what the output might look like:
\",\n \"polygon\": [\n [\n 153.298828125,\n 486.4921875\n ],\n [\n 372.33984375,\n 486.4921875\n ],\n [\n 372.33984375,\n 497.1173400878906\n ],\n [\n 153.298828125,\n 497.1173400878906\n ]\n ],\n \"bbox\": [\n 153.298828125,\n 486.4921875,\n 372.33984375,\n 497.1173400878906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/142/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"['deltas', 'desalt', 'lasted', 'salted', 'slated', 'staled'] ['retainers', 'ternaries'] ['generating', 'greatening'] ['resmelts', 'smelters', 'termless']
\",\n \"polygon\": [\n [\n 154.5070037841797,\n 508.89288330078125\n ],\n [\n 468.25445556640625,\n 508.89288330078125\n ],\n [\n 468.25445556640625,\n 555.4384918212891\n ],\n [\n 154.5070037841797,\n 555.4384918212891\n ]\n ],\n \"bbox\": [\n 154.5070037841797,\n 508.89288330078125,\n 468.25445556640625,\n 555.4384918212891\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/142/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Hint: you might want to build a dictionary that maps from a set of letters to a list of words that can be spelled with those letters. The question is, how can you represent the set of letters in a way that can be used as a key?
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\",\n \"polygon\": [\n [\n 85.46484375,\n 88.0751953125\n ],\n [\n 482.90625,\n 88.0751953125\n ],\n [\n 482.90625,\n 147.40216064453125\n ],\n [\n 85.46484375,\n 147.40216064453125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 88.0751953125,\n 482.90625,\n 147.40216064453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/143/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 12.6. Here's another Car Talk Puzzler (http: // www. cartalk. com/ content/ puzzlers ):
\",\n \"polygon\": [\n [\n 85.46484375,\n 149.2734375\n ],\n [\n 480.720703125,\n 149.2734375\n ],\n [\n 480.720703125,\n 171.79119873046875\n ],\n [\n 85.46484375,\n 171.79119873046875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 149.2734375,\n 480.720703125,\n 171.79119873046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/143/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"What is the longest English word, that remains a valid English word, as you remove its letters one at a time?
\",\n \"polygon\": [\n [\n 110.7158203125,\n 185.9150390625\n ],\n [\n 457.4974060058594,\n 185.9150390625\n ],\n [\n 457.4974060058594,\n 208.12115478515625\n ],\n [\n 110.7158203125,\n 208.12115478515625\n ]\n ],\n \"bbox\": [\n 110.7158203125,\n 185.9150390625,\n 457.4974060058594,\n 208.12115478515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/143/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Now, letters can be removed from either end, or the middle, but you can't rearrange any of the letters. Every time you drop a letter, you wind up with another English word. If you do that, you're eventually going to wind up with one letter and that too is going to be an English word—one that's found in the dictionary. I want to know what's the longest word and how many letters does it have?
\",\n \"polygon\": [\n [\n 110.1181640625,\n 214.33856201171875\n ],\n [\n 459.0,\n 214.33856201171875\n ],\n [\n 459.0,\n 273.07818603515625\n ],\n [\n 110.1181640625,\n 273.07818603515625\n ]\n ],\n \"bbox\": [\n 110.1181640625,\n 214.33856201171875,\n 459.0,\n 273.07818603515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/143/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"I'm going to give you a little modest example: Sprite. Ok? You start off with sprite, you take a letter off, one from the interior of the word, take the r away, and we're left with the word spite, then we take the e off the end, we're left with spit, we take the s off, we're left with pit, it, and I.
\",\n \"polygon\": [\n [\n 110.1181640625,\n 278.82421875\n ],\n [\n 459.0,\n 278.82421875\n ],\n [\n 459.0,\n 325.8402099609375\n ],\n [\n 110.1181640625,\n 325.8402099609375\n ]\n ],\n \"bbox\": [\n 110.1181640625,\n 278.82421875,\n 459.0,\n 325.8402099609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/143/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Write a program to find all words that can be reduced in this way, and then find the longest one.
\",\n \"polygon\": [\n [\n 85.46484375,\n 339.5390625\n ],\n [\n 470.3984680175781,\n 339.5390625\n ],\n [\n 470.3984680175781,\n 349.9761962890625\n ],\n [\n 85.46484375,\n 349.9761962890625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 339.5390625,\n 470.3984680175781,\n 349.9761962890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/137/SectionHeader/2\",\n \"3\": \"/page/141/SectionHeader/4\",\n \"4\": \"/page/142/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/143/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"This exercise is a little more challenging than most, so here are some suggestions:
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\",\n \"polygon\": [\n [\n 129.01904296875,\n 166.9874267578125\n ],\n [\n 231.591796875,\n 166.9874267578125\n ],\n [\n 231.591796875,\n 187.64990234375\n ],\n [\n 129.01904296875,\n 187.64990234375\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 166.9874267578125,\n 231.591796875,\n 187.64990234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/134/SectionHeader/1\",\n \"2\": \"/page/144/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Case study: data structure selection
\",\n \"polygon\": [\n [\n 128.57080078125,\n 225.209228515625\n ],\n [\n 423.439453125,\n 225.209228515625\n ],\n [\n 423.439453125,\n 280.4812927246094\n ],\n [\n 128.57080078125,\n 280.4812927246094\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 225.209228515625,\n 423.439453125,\n 280.4812927246094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"13.1 Word frequency analysis
\",\n \"polygon\": [\n [\n 128.86962890625,\n 331.611328125\n ],\n [\n 331.3649597167969,\n 331.611328125\n ],\n [\n 331.3649597167969,\n 346.58087158203125\n ],\n [\n 128.86962890625,\n 346.58087158203125\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 331.611328125,\n 331.3649597167969,\n 346.58087158203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"As usual, you should at least attempt the following exercises before you read my solutions. Exercise 13.1. Write a program that reads a file, breaks each line into words, strips whitespace and punctuation from the words, and converts them to lowercase.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 362.162109375\n ],\n [\n 526.53515625,\n 362.162109375\n ],\n [\n 526.53515625,\n 397.11712646484375\n ],\n [\n 128.6455078125,\n 397.11712646484375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 362.162109375,\n 526.53515625,\n 397.11712646484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Hint: The string module provides strings named whitespace, which contains space, tab, newline, etc., and punctuation which contains the punctuation characters. Let's see if we can make Python swear:
\",\n \"polygon\": [\n [\n 128.794921875,\n 409.921875\n ],\n [\n 526.236328125,\n 409.921875\n ],\n [\n 526.236328125,\n 444.5091247558594\n ],\n [\n 128.794921875,\n 444.5091247558594\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 409.921875,\n 526.236328125,\n 444.5091247558594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \">>> import string >>> print string.punctuation !\\\"#$%&'()*+,-./:;<=>?@[\\\\]^_`{|}~
\",\n \"polygon\": [\n [\n 128.794921875,\n 453.6006774902344\n ],\n [\n 296.9364929199219,\n 453.6006774902344\n ],\n [\n 296.9364929199219,\n 488.0390625\n ],\n [\n 128.794921875,\n 488.0390625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 453.6006774902344,\n 296.9364929199219,\n 488.0390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Also, you might consider using the string methods strip, replace and translate. Exercise 13.2. Go to Project Gutenberg (http: // gutenberg. org ) and download your favorite out-of-copyright book in plain text format.
\",\n \"polygon\": [\n [\n 129.392578125,\n 496.99755859375\n ],\n [\n 525.638671875,\n 496.99755859375\n ],\n [\n 525.638671875,\n 531.3515625\n ],\n [\n 129.392578125,\n 531.3515625\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 496.99755859375,\n 525.638671875,\n 531.3515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Modify your program from the previous exercise to read the book you downloaded, skip over the header information at the beginning of the file, and process the rest of the words as before.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 544.3895568847656\n ],\n [\n 525.9375,\n 544.3895568847656\n ],\n [\n 525.9375,\n 566.5461578369141\n ],\n [\n 128.9443359375,\n 566.5461578369141\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 544.3895568847656,\n 525.9375,\n 566.5461578369141\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Then modify the program to count the total number of words in the book, and the number of times each word is used.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 579.5865631103516\n ],\n [\n 526.53515625,\n 579.5865631103516\n ],\n [\n 526.53515625,\n 601.7441711425781\n ],\n [\n 128.6455078125,\n 601.7441711425781\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 579.5865631103516,\n 526.53515625,\n 601.7441711425781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Print the number of different words used in the book. Compare different books by different authors, written in different eras. Which author uses the most extensive vocabulary?
\",\n \"polygon\": [\n [\n 129.392578125,\n 614.109375\n ],\n [\n 526.236328125,\n 614.109375\n ],\n [\n 526.236328125,\n 639.6328125\n ],\n [\n 129.392578125,\n 639.6328125\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 614.109375,\n 526.236328125,\n 639.6328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 13.3. Modify the program from the previous exercise to print the 20 most frequently-used words in the book.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 639.1725769042969\n ],\n [\n 526.53515625,\n 639.1725769042969\n ],\n [\n 526.53515625,\n 661.3301849365234\n ],\n [\n 129.2431640625,\n 661.3301849365234\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 639.1725769042969,\n 526.53515625,\n 661.3301849365234\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/144/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 13.4. Modify the previous program to read a word list (see Section 9.1) and then print all the words in the book that are not in the word list. How many of them are typos? How many of them are common words that should be in the word list, and how many of them are really obscure?
\",\n \"polygon\": [\n [\n 128.6455078125,\n 663.5615844726562\n ],\n [\n 526.53515625,\n 663.5615844726562\n ],\n [\n 526.53515625,\n 698.0858917236328\n ],\n [\n 128.6455078125,\n 698.0858917236328\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 663.5615844726562,\n 526.53515625,\n 698.0858917236328\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/145/Page/212\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/145/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.27978515625\n ],\n [\n 484.400390625,\n 60.27978515625\n ],\n [\n 484.400390625,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.27978515625,\n 484.400390625,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/PageHeader/16\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 84.8671875,\n 60.47314453125\n ],\n [\n 100.705078125,\n 60.47314453125\n ],\n [\n 100.705078125,\n 70.23779296875\n ],\n [\n 84.8671875,\n 70.23779296875\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 60.47314453125,\n 100.705078125,\n 70.23779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"13.2 Random numbers
\",\n \"polygon\": [\n [\n 85.39013671875,\n 85.95379638671875\n ],\n [\n 243.544921875,\n 85.95379638671875\n ],\n [\n 243.544921875,\n 100.546875\n ],\n [\n 85.39013671875,\n 100.546875\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 85.95379638671875,\n 243.544921875,\n 100.546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Given the same inputs, most computer programs generate the same outputs every time, so they are said to be deterministic. Determinism is usually a good thing, since we expect the same calculation to yield the same result. For some applications, though, we want the computer to be unpredictable. Games are an obvious example, but there are more.
\",\n \"polygon\": [\n [\n 85.46484375,\n 115.7255859375\n ],\n [\n 483.802734375,\n 115.7255859375\n ],\n [\n 483.802734375,\n 162.81597900390625\n ],\n [\n 85.46484375,\n 162.81597900390625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 115.7255859375,\n 483.802734375,\n 162.81597900390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Making a program truly nondeterministic turns out to be not so easy, but there are ways to make it at least seem nondeterministic. One of them is to use algorithms that generate pseudorandom numbers. Pseudorandom numbers are not truly random because they are generated by a deterministic computation, but just by looking at the numbers it is all but impossible to distinguish them from random.
\",\n \"polygon\": [\n [\n 85.763671875,\n 174.990234375\n ],\n [\n 483.205078125,\n 174.990234375\n ],\n [\n 483.205078125,\n 234.3199462890625\n ],\n [\n 85.763671875,\n 234.3199462890625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 174.990234375,\n 483.205078125,\n 234.3199462890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The random module provides functions that generate pseudorandom numbers (which I will simply call \\\"random\\\" from here on).
\",\n \"polygon\": [\n [\n 85.9130859375,\n 245.56640625\n ],\n [\n 483.50390625,\n 245.56640625\n ],\n [\n 483.50390625,\n 269.23992919921875\n ],\n [\n 85.9130859375,\n 269.23992919921875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 245.56640625,\n 483.50390625,\n 269.23992919921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The function random returns a random float between 0.0 and 1.0 (including 0.0 but not 1.0). Each time you call random, you get the next number in a long series. To see a sample, run this loop:
\",\n \"polygon\": [\n [\n 86.0625,\n 281.337890625\n ],\n [\n 482.607421875,\n 281.337890625\n ],\n [\n 482.607421875,\n 316.3548889160156\n ],\n [\n 86.0625,\n 316.3548889160156\n ]\n ],\n \"bbox\": [\n 86.0625,\n 281.337890625,\n 482.607421875,\n 316.3548889160156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \"import random
\",\n \"polygon\": [\n [\n 85.98779296875,\n 324.45703125\n ],\n [\n 156.884765625,\n 324.45703125\n ],\n [\n 156.884765625,\n 334.9603271484375\n ],\n [\n 85.98779296875,\n 334.9603271484375\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 324.45703125,\n 156.884765625,\n 334.9603271484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"for i in range(10):\\n x = random.random()\\n print x
\",\n \"polygon\": [\n [\n 86.0625,\n 349.3867492675781\n ],\n [\n 206.70286560058594,\n 349.3867492675781\n ],\n [\n 206.70286560058594,\n 383.7373352050781\n ],\n [\n 86.0625,\n 383.7373352050781\n ]\n ],\n \"bbox\": [\n 86.0625,\n 349.3867492675781,\n 206.70286560058594,\n 383.7373352050781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The function randint takes parameters low and high and returns an integer between low and high (including both).
\",\n \"polygon\": [\n [\n 85.9130859375,\n 391.166015625\n ],\n [\n 482.4040832519531,\n 391.166015625\n ],\n [\n 482.4040832519531,\n 414.8359069824219\n ],\n [\n 85.9130859375,\n 414.8359069824219\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 391.166015625,\n 482.4040832519531,\n 414.8359069824219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> random.randint(5, 10)\\n5\\n>>> random.randint(5, 10)\\n9
\",\n \"polygon\": [\n [\n 85.6142578125,\n 421.91015625\n ],\n [\n 218.2939453125,\n 421.91015625\n ],\n [\n 218.2939453125,\n 470.02435302734375\n ],\n [\n 85.6142578125,\n 470.02435302734375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 421.91015625,\n 218.2939453125,\n 470.02435302734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"To choose an element from a sequence at random, you can use choice:
\",\n \"polygon\": [\n [\n 86.361328125,\n 477.59765625\n ],\n [\n 396.30767822265625,\n 477.59765625\n ],\n [\n 396.30767822265625,\n 488.9289245605469\n ],\n [\n 86.361328125,\n 488.9289245605469\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 477.59765625,\n 396.30767822265625,\n 488.9289245605469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = [1, 2, 3]\\n>>> random.choice(t)\\n2\\n>>> random.choice(t)\\n3
\",\n \"polygon\": [\n [\n 85.763671875,\n 497.57177734375\n ],\n [\n 192.8935546875,\n 497.57177734375\n ],\n [\n 192.8935546875,\n 556.3113861083984\n ],\n [\n 85.763671875,\n 556.3113861083984\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 497.57177734375,\n 192.8935546875,\n 556.3113861083984\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The random module also provides functions to generate random values from continuous distributions including Gaussian, exponential, gamma, and a few more.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 563.8359375\n ],\n [\n 482.399169921875,\n 563.8359375\n ],\n [\n 482.399169921875,\n 587.4099426269531\n ],\n [\n 86.2119140625,\n 587.4099426269531\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 563.8359375,\n 482.399169921875,\n 587.4099426269531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 13.5. Write a function named choose_from_hist that takes a histogram as defined in Section 11.1 and returns a random value from the histogram, chosen with probability in proportion to frequency. For example, for this histogram:
\",\n \"polygon\": [\n [\n 85.763671875,\n 588.19921875\n ],\n [\n 483.205078125,\n 588.19921875\n ],\n [\n 483.205078125,\n 623.8202514648438\n ],\n [\n 85.763671875,\n 623.8202514648438\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 588.19921875,\n 483.205078125,\n 623.8202514648438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['a', 'a', 'b']\\n>>> hist = histogram(t)\\n>>> print hist\\n{'a': 2, 'b': 1}\",\n \"polygon\": [\n [\n 85.763671875,\n 631.8984375\n ],\n [\n 206.70835876464844,\n 631.8984375\n ],\n [\n 206.70835876464844,\n 679.181396484375\n ],\n [\n 85.763671875,\n 679.181396484375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 631.8984375,\n 206.70835876464844,\n 679.181396484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/145/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"your function should return 'a' with probability 2/3 and 'b' with probability 1/3.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 686.8125\n ],\n [\n 426.0146789550781,\n 686.8125\n ],\n [\n 426.0146789550781,\n 698.0859603881836\n ],\n [\n 86.2119140625,\n 698.0859603881836\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 686.8125,\n 426.0146789550781,\n 698.0859603881836\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/146/Page/185\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/146/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.6455078125,\n 61.05322265625\n ],\n [\n 525.6033935546875,\n 61.05322265625\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.6455078125,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 61.05322265625,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/PageHeader/17\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 509.80078125,\n 60.8115234375\n ],\n [\n 525.9375,\n 60.8115234375\n ],\n [\n 525.9375,\n 69.8994140625\n ],\n [\n 509.80078125,\n 69.8994140625\n ]\n ],\n \"bbox\": [\n 509.80078125,\n 60.8115234375,\n 525.9375,\n 69.8994140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/145/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"13.3 Word histogram
\",\n \"polygon\": [\n [\n 127.97314453125,\n 85.95379638671875\n ],\n [\n 275.220703125,\n 85.95379638671875\n ],\n [\n 275.220703125,\n 100.29998779296875\n ],\n [\n 127.97314453125,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 85.95379638671875,\n 275.220703125,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"You should attempt the previous exercises before you go on. You can download my solution from http://thinkpython.com/code/analyze_book.py. You will also need http: //thinkpython.com/code/emma.txt.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 112.4384765625\n ],\n [\n 525.6058349609375,\n 112.4384765625\n ],\n [\n 525.6058349609375,\n 146.88494873046875\n ],\n [\n 128.6455078125,\n 146.88494873046875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 112.4384765625,\n 525.6058349609375,\n 146.88494873046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Here is a program that reads a file and builds a histogram of the words in the file:
\",\n \"polygon\": [\n [\n 128.794921875,\n 156.427734375\n ],\n [\n 487.986328125,\n 156.427734375\n ],\n [\n 487.986328125,\n 166.91094970703125\n ],\n [\n 128.794921875,\n 166.91094970703125\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 156.427734375,\n 487.986328125,\n 166.91094970703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"import string\\ndef process_file(filename):\\n hist = dict()\\n fp = open(filename)\\n for line in fp:\\n process_line(line, hist)\\n return hist\\ndef process_line(line, hist):\\n line = line.replace('-', ' ')\\n for word in line.split():\\n word = word.strip(string.punctuation + string.whitespace)\\n word = word.lower()\\n hist[word] = hist.get(word, 0) + 1\",\n \"polygon\": [\n [\n 128.3466796875,\n 172.853759765625\n ],\n [\n 473.94140625,\n 172.853759765625\n ],\n [\n 473.94140625,\n 382.8515625\n ],\n [\n 128.3466796875,\n 382.8515625\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 172.853759765625,\n 473.94140625,\n 382.8515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"hist = process_file('emma.txt')
\",\n \"polygon\": [\n [\n 129.46728515625,\n 392.35076904296875\n ],\n [\n 291.70343017578125,\n 392.35076904296875\n ],\n [\n 291.70343017578125,\n 402.3133544921875\n ],\n [\n 129.46728515625,\n 402.3133544921875\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 392.35076904296875,\n 291.70343017578125,\n 402.3133544921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This program reads emma.txt, which contains the text of Emma by Jane Austen.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 408.375\n ],\n [\n 478.33929443359375,\n 408.375\n ],\n [\n 478.33929443359375,\n 418.5179138183594\n ],\n [\n 129.2431640625,\n 418.5179138183594\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 408.375,\n 478.33929443359375,\n 418.5179138183594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"process_file loops through the lines of the file, passing them one at a time to process_line. The histogram hist is being used as an accumulator.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 428.4307556152344\n ],\n [\n 525.6028442382812,\n 428.4307556152344\n ],\n [\n 525.6028442382812,\n 450.7379150390625\n ],\n [\n 128.0478515625,\n 450.7379150390625\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 428.4307556152344,\n 525.6028442382812,\n 450.7379150390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"process_line uses the string method replace to replace hyphens with spaces before using split to break the line into a list of strings. It traverses the list of words and uses strip and lower to remove punctuation and convert to lower case. (It is a shorthand to say that strings are \\\"converted;\\\" remember that string are immutable, so methods like strip and lower return new strings.)
\",\n \"polygon\": [\n [\n 129.09375,\n 460.6517639160156\n ],\n [\n 525.6058349609375,\n 460.6517639160156\n ],\n [\n 525.6058349609375,\n 519.5409240722656\n ],\n [\n 129.09375,\n 519.5409240722656\n ]\n ],\n \"bbox\": [\n 129.09375,\n 460.6517639160156,\n 525.6058349609375,\n 519.5409240722656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Finally, process_line updates the histogram by creating a new item or incrementing an existing one.
\",\n \"polygon\": [\n [\n 128.197265625,\n 529.4547729492188\n ],\n [\n 525.599609375,\n 529.4547729492188\n ],\n [\n 525.599609375,\n 551.7609252929688\n ],\n [\n 128.197265625,\n 551.7609252929688\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 529.4547729492188,\n 525.599609375,\n 551.7609252929688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"To count the total number of words in the file, we can add up the frequencies in the histogram:
\",\n \"polygon\": [\n [\n 128.197265625,\n 561.8243255615234\n ],\n [\n 525.6035766601562,\n 561.8243255615234\n ],\n [\n 525.6035766601562,\n 584.33203125\n ],\n [\n 128.197265625,\n 584.33203125\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 561.8243255615234,\n 525.6035766601562,\n 584.33203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \"def total_words(hist):\\n return sum(hist.values())
\",\n \"polygon\": [\n [\n 128.6455078125,\n 589.359375\n ],\n [\n 281.2851867675781,\n 589.359375\n ],\n [\n 281.2851867675781,\n 612.0803833007812\n ],\n [\n 128.6455078125,\n 612.0803833007812\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 589.359375,\n 281.2851867675781,\n 612.0803833007812\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The number of different words is just the number of items in the dictionary:
\",\n \"polygon\": [\n [\n 128.794921875,\n 616.81640625\n ],\n [\n 463.78125,\n 616.81640625\n ],\n [\n 463.78125,\n 628.283935546875\n ],\n [\n 128.794921875,\n 628.283935546875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 616.81640625,\n 463.78125,\n 628.283935546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"def different_words(hist):
\",\n \"polygon\": [\n [\n 129.01904296875,\n 633.83203125\n ],\n [\n 265.5995178222656,\n 633.83203125\n ],\n [\n 265.5995178222656,\n 646.20703125\n ],\n [\n 129.01904296875,\n 646.20703125\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 633.83203125,\n 265.5995178222656,\n 646.20703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"return len(hist)
\",\n \"polygon\": [\n [\n 145.3798828125,\n 646.4197845458984\n ],\n [\n 234.21189880371094,\n 646.4197845458984\n ],\n [\n 234.21189880371094,\n 658.96875\n ],\n [\n 145.3798828125,\n 658.96875\n ]\n ],\n \"bbox\": [\n 145.3798828125,\n 646.4197845458984,\n 234.21189880371094,\n 658.96875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Here is some code to print the results:
\",\n \"polygon\": [\n [\n 128.197265625,\n 662.6243438720703\n ],\n [\n 295.696533203125,\n 662.6243438720703\n ],\n [\n 295.696533203125,\n 672.890625\n ],\n [\n 128.197265625,\n 672.890625\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 662.6243438720703,\n 295.696533203125,\n 672.890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/146/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \"print 'Total number of words:', total_words(hist)\\nprint 'Number of different words:', different_words(hist)
\",\n \"polygon\": [\n [\n 128.3466796875,\n 678.5287780761719\n ],\n [\n 427.6984558105469,\n 678.5287780761719\n ],\n [\n 427.6984558105469,\n 700.6853790283203\n ],\n [\n 128.3466796875,\n 700.6853790283203\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 678.5287780761719,\n 427.6984558105469,\n 700.6853790283203\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/147/Page/162\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/147/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.8115234375\n ],\n [\n 483.802734375,\n 60.8115234375\n ],\n [\n 483.802734375,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.8115234375,\n 483.802734375,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/PageHeader/15\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.46484375,\n 60.6181640625\n ],\n [\n 102.0498046875,\n 60.6181640625\n ],\n [\n 102.0498046875,\n 70.0927734375\n ],\n [\n 85.46484375,\n 70.0927734375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 60.6181640625,\n 102.0498046875,\n 70.0927734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"And the results:
\",\n \"polygon\": [\n [\n 86.0625,\n 88.41357421875\n ],\n [\n 156.45700073242188,\n 88.41357421875\n ],\n [\n 156.45700073242188,\n 98.79791259765625\n ],\n [\n 86.0625,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 86.0625,\n 88.41357421875,\n 156.45700073242188,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Total number of words: 161080 Number of different words: 7214
\",\n \"polygon\": [\n [\n 85.6142578125,\n 104.2237548828125\n ],\n [\n 249.6708984375,\n 104.2237548828125\n ],\n [\n 249.6708984375,\n 126.38037109375\n ],\n [\n 85.6142578125,\n 126.38037109375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 104.2237548828125,\n 249.6708984375,\n 126.38037109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/146/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"13.4 Most common words
\",\n \"polygon\": [\n [\n 85.46484375,\n 154.7841796875\n ],\n [\n 263.7333679199219,\n 154.7841796875\n ],\n [\n 263.7333679199219,\n 169.3389892578125\n ],\n [\n 85.46484375,\n 169.3389892578125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 154.7841796875,\n 263.7333679199219,\n 169.3389892578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"To find the most common words, we can apply the DSU pattern; most_common takes a histogram and returns a list of word-frequency tuples, sorted in reverse order by frequency:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 180.2109375\n ],\n [\n 482.40350341796875,\n 180.2109375\n ],\n [\n 482.40350341796875,\n 203.4140625\n ],\n [\n 85.3154296875,\n 203.4140625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 180.2109375,\n 482.40350341796875,\n 203.4140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"def most_common(hist):\\n t = []\\n for key, value in hist.items():\\n t.append((value, key))\\n t.sort(reverse=True)\\n return t
\",\n \"polygon\": [\n [\n 86.39999389648438,\n 208.6387939453125\n ],\n [\n 269.4672546386719,\n 208.6387939453125\n ],\n [\n 269.4672546386719,\n 291.7673034667969\n ],\n [\n 86.39999389648438,\n 291.7673034667969\n ]\n ],\n \"bbox\": [\n 86.39999389648438,\n 208.6387939453125,\n 269.4672546386719,\n 291.7673034667969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Here is a loop that prints the ten most common words:
\",\n \"polygon\": [\n [\n 85.98779296875,\n 296.2265625\n ],\n [\n 326.4787292480469,\n 296.2265625\n ],\n [\n 326.4787292480469,\n 307.4548645019531\n ],\n [\n 85.98779296875,\n 307.4548645019531\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 296.2265625,\n 326.4787292480469,\n 307.4548645019531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"t = most_common(hist)\\nprint 'The most common words are:'\\nfor freq, word in t[0:10]:\\n print word, '\\\\t', freq
\",\n \"polygon\": [\n [\n 84.7177734375,\n 312.8807067871094\n ],\n [\n 264.1913757324219,\n 312.8807067871094\n ],\n [\n 264.1913757324219,\n 359.4263000488281\n ],\n [\n 84.7177734375,\n 359.4263000488281\n ]\n ],\n \"bbox\": [\n 84.7177734375,\n 312.8807067871094,\n 264.1913757324219,\n 359.4263000488281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"And here are the results from Emma:
\",\n \"polygon\": [\n [\n 86.39999389648438,\n 363.708984375\n ],\n [\n 248.625,\n 363.708984375\n ],\n [\n 248.625,\n 375.1138610839844\n ],\n [\n 86.39999389648438,\n 375.1138610839844\n ]\n ],\n \"bbox\": [\n 86.39999389648438,\n 363.708984375,\n 248.625,\n 375.1138610839844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The most common words are: to 5242 the 5205 and 4897 of 4295 i 3191 a 3130 it 2529 her 2483 was 2400 she 2364
\",\n \"polygon\": [\n [\n 85.53955078125,\n 379.7578125\n ],\n [\n 230.6953125,\n 379.7578125\n ],\n [\n 230.6953125,\n 512.4453125\n ],\n [\n 85.53955078125,\n 512.4453125\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 379.7578125,\n 230.6953125,\n 512.4453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/SectionHeader/10\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"13.5 Optional parameters
\",\n \"polygon\": [\n [\n 85.3154296875,\n 540.6328125\n ],\n [\n 260.9214782714844,\n 540.6328125\n ],\n [\n 260.9214782714844,\n 555.4039306640625\n ],\n [\n 85.3154296875,\n 555.4039306640625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 540.6328125,\n 260.9214782714844,\n 555.4039306640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"We have seen built-in functions and methods that take a variable number of arguments. It is possible to write user-defined functions with optional arguments, too. For example, here is a function that prints the most common words in a histogram
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\",\n \"polygon\": [\n [\n 86.28662109375,\n 606.8987274169922\n ],\n [\n 285.10736083984375,\n 606.8987274169922\n ],\n [\n 285.10736083984375,\n 665.9296875\n ],\n [\n 86.28662109375,\n 665.9296875\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 606.8987274169922,\n 285.10736083984375,\n 665.9296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/147/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The first parameter is required; the second is optional. The default value of num is 10.
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\",\n \"polygon\": [\n [\n 127.30078125,\n 102.7767333984375\n ],\n [\n 382.8355712890625,\n 102.7767333984375\n ],\n [\n 382.8355712890625,\n 113.115234375\n ],\n [\n 127.30078125,\n 113.115234375\n ]\n ],\n \"bbox\": [\n 127.30078125,\n 102.7767333984375,\n 382.8355712890625,\n 113.115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"print_most_common(hist, 20)
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 160.681640625\n ],\n [\n 525.9375,\n 160.681640625\n ],\n [\n 525.9375,\n 183.52386474609375\n ],\n [\n 128.6455078125,\n 183.52386474609375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 160.681640625,\n 525.9375,\n 183.52386474609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/147/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"13.6 Dictionary subtraction
\",\n \"polygon\": [\n [\n 127.82373046875,\n 209.6015625\n ],\n [\n 316.8609924316406,\n 209.6015625\n ],\n [\n 316.8609924316406,\n 224.30792236328125\n ],\n [\n 127.82373046875,\n 224.30792236328125\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 209.6015625,\n 316.8609924316406,\n 224.30792236328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Finding the words from the book that are not in the word list from words.txt is a problem you might recognize as set subtraction; that is, we want to find all the words from one set (the words in the book) that are not in another set (the words in the list).
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\",\n \"polygon\": [\n [\n 129.09375,\n 276.697265625\n ],\n [\n 525.598876953125,\n 276.697265625\n ],\n [\n 525.598876953125,\n 311.501953125\n ],\n [\n 129.09375,\n 311.501953125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 276.697265625,\n 525.598876953125,\n 311.501953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \"def subtract(d1, d2):\\n res = dict()\\n for key in d1:\\n if key not in d2:\\n res[key] = None\\n return res
\",\n \"polygon\": [\n [\n 129.60003662109375,\n 315.3616943359375\n ],\n [\n 273.7265625,\n 315.3616943359375\n ],\n [\n 273.7265625,\n 386.2952880859375\n ],\n [\n 129.60003662109375,\n 386.2952880859375\n ]\n ],\n \"bbox\": [\n 129.60003662109375,\n 315.3616943359375,\n 273.7265625,\n 386.2952880859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"To find the words in the book that are not in words.txt, we can use process_file to build a histogram for words.txt, and then subtract:
\",\n \"polygon\": [\n [\n 128.49609375,\n 389.42578125\n ],\n [\n 525.9375,\n 389.42578125\n ],\n [\n 525.9375,\n 412.7308654785156\n ],\n [\n 128.49609375,\n 412.7308654785156\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 389.42578125,\n 525.9375,\n 412.7308654785156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \"words = process_file('words.txt')\\ndiff = subtract(hist, words)\",\n \"polygon\": [\n [\n 127.599609375,\n 415.72265625\n ],\n [\n 325.72265625,\n 415.72265625\n ],\n [\n 325.72265625,\n 443.56640625\n ],\n [\n 127.599609375,\n 443.56640625\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 415.72265625,\n 325.72265625,\n 443.56640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"print \\\"The words in the book that aren't in the word list are:\\\"\\nfor word in diff.keys():
\",\n \"polygon\": [\n [\n 128.6455078125,\n 452.84765625\n ],\n [\n 459.59765625,\n 452.84765625\n ],\n [\n 459.59765625,\n 475.4503173828125\n ],\n [\n 128.6455078125,\n 475.4503173828125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 452.84765625,\n 459.59765625,\n 475.4503173828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"print word,
\",\n \"polygon\": [\n [\n 148.3681640625,\n 477.59765625\n ],\n [\n 208.05010986328125,\n 477.59765625\n ],\n [\n 208.05010986328125,\n 487.6443176269531\n ],\n [\n 148.3681640625,\n 487.6443176269531\n ]\n ],\n \"bbox\": [\n 148.3681640625,\n 477.59765625,\n 208.05010986328125,\n 487.6443176269531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Here are some of the results from Emma:
\",\n \"polygon\": [\n [\n 127.67431640625,\n 491.1328125\n ],\n [\n 308.091796875,\n 491.1328125\n ],\n [\n 308.091796875,\n 501.8858947753906\n ],\n [\n 127.67431640625,\n 501.8858947753906\n ]\n ],\n \"bbox\": [\n 127.67431640625,\n 491.1328125,\n 308.091796875,\n 501.8858947753906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \"The words in the book that aren't in the word list are:\\n rencontre jane's blanche woodhouses disingenuousness\\nfriend's venice apartment ...
\",\n \"polygon\": [\n [\n 129.46728515625,\n 505.86474609375\n ],\n [\n 417.2294616699219,\n 505.86474609375\n ],\n [\n 417.2294616699219,\n 540.2163543701172\n ],\n [\n 129.46728515625,\n 540.2163543701172\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 505.86474609375,\n 417.2294616699219,\n 540.2163543701172\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Some of these words are names and possessives. Others, like \\\"rencontre,\\\" are no longer in common use. But a few are common words that should really be in the list!
\",\n \"polygon\": [\n [\n 128.794921875,\n 544.4943084716797\n ],\n [\n 526.236328125,\n 544.4943084716797\n ],\n [\n 526.236328125,\n 566.6519165039062\n ],\n [\n 128.794921875,\n 566.6519165039062\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 544.4943084716797,\n 526.236328125,\n 566.6519165039062\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Code/17\",\n \"block_type\": \"Code\",\n \"html\": \"Exercise 13.6. Python provides a data structure called set that provides many common set opera-\\ntions. Read the documentation at http: // docs. python. org/ 2/ library/ stdtypes. html#\\ntypes-set and write a program that uses set subtraction to find words in the book that are not in\\nthe word list. Solution: http: // thinkpython. com/ code/ analyze_ book2. py .
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\",\n \"polygon\": [\n [\n 127.7490234375,\n 641.8667907714844\n ],\n [\n 269.3935546875,\n 641.8667907714844\n ],\n [\n 269.3935546875,\n 656.2129821777344\n ],\n [\n 127.7490234375,\n 656.2129821777344\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 641.8667907714844,\n 269.3935546875,\n 656.2129821777344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/148/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"To choose a random word from the histogram, the simplest algorithm is to build a list with multiple copies of each word, according to the observed frequency, and then choose from the list:
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\",\n \"polygon\": [\n [\n 86.4000015258789,\n 88.68572998046875\n ],\n [\n 253.77613830566406,\n 88.68572998046875\n ],\n [\n 253.77613830566406,\n 147.2431640625\n ],\n [\n 86.4000015258789,\n 147.2431640625\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 88.68572998046875,\n 253.77613830566406,\n 147.2431640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/149/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"return random.choice(t)
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\",\n \"polygon\": [\n [\n 85.3154296875,\n 366.22265625\n ],\n [\n 480.6768493652344,\n 366.22265625\n ],\n [\n 480.6768493652344,\n 388.65234375\n ],\n [\n 85.3154296875,\n 388.65234375\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 366.22265625,\n 480.6768493652344,\n 388.65234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/148/SectionHeader/18\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/149/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"13.8 Markov analysis
\",\n \"polygon\": [\n [\n 85.46484375,\n 417.85174560546875\n ],\n [\n 234.6392822265625,\n 417.85174560546875\n ],\n [\n 234.6392822265625,\n 432.1979675292969\n ],\n [\n 85.46484375,\n 432.1979675292969\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 417.85174560546875,\n 234.6392822265625,\n 432.1979675292969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/149/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"If you choose words from the book at random, you can get a sense of the vocabulary, you probably won't get a sentence:
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\",\n \"polygon\": [\n [\n 85.166015625,\n 472.4567565917969\n ],\n [\n 410.888671875,\n 472.4567565917969\n ],\n [\n 410.888671875,\n 482.625\n ],\n [\n 85.166015625,\n 482.625\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 472.4567565917969,\n 410.888671875,\n 482.625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/149/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"A series of random words seldom makes sense because there is no relationship between successive words. For example, in a real sentence you would expect an article like \\\"the\\\" to be followed by an adjective or a noun, and probably not a verb or adverb.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 488.0390625\n ],\n [\n 482.90625,\n 488.0390625\n ],\n [\n 482.90625,\n 522.9749145507812\n ],\n [\n 85.3154296875,\n 522.9749145507812\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 488.0390625,\n 482.90625,\n 522.9749145507812\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/149/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"One way to measure these kinds of relationships is Markov analysis, which characterizes, for a given sequence of words, the probability of the word that comes next. For example, the song Eric, the Half a Bee begins:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 532.51171875\n ],\n [\n 482.4034423828125,\n 532.51171875\n ],\n [\n 482.4034423828125,\n 567.3519134521484\n ],\n [\n 85.3154296875,\n 567.3519134521484\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 532.51171875,\n 482.4034423828125,\n 567.3519134521484\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/149/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Half a bee, philosophically, Must, ipso facto, half not be. But half the bee has got to be Vis a vis, its entity. D'you see?
\",\n \"polygon\": [\n [\n 110.56640625,\n 580.078125\n ],\n [\n 243.7598114013672,\n 580.078125\n ],\n [\n 243.7598114013672,\n 627.6689147949219\n ],\n [\n 110.56640625,\n 627.6689147949219\n ]\n ],\n \"bbox\": [\n 110.56640625,\n 580.078125,\n 243.7598114013672,\n 627.6689147949219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/149/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"But can a bee be said to be Or not to be an entire bee When half the bee is not a bee Due to some ancient injury?
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\",\n \"polygon\": [\n [\n 127.7490234375,\n 88.171875\n ],\n [\n 525.6033935546875,\n 88.171875\n ],\n [\n 525.6033935546875,\n 110.99188232421875\n ],\n [\n 127.7490234375,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 88.171875,\n 525.6033935546875,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The result of Markov analysis is a mapping from each prefix (like \\\"half the\\\" and \\\"the bee\\\") to all possible suffixes (like \\\"has\\\" and \\\"is\\\").
\",\n \"polygon\": [\n [\n 128.49609375,\n 120.7529296875\n ],\n [\n 526.53515625,\n 120.7529296875\n ],\n [\n 526.53515625,\n 142.99188232421875\n ],\n [\n 128.49609375,\n 142.99188232421875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 120.7529296875,\n 526.53515625,\n 142.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Given this mapping, you can generate a random text by starting with any prefix and choosing at random from the possible suffixes. Next, you can combine the end of the prefix and the new suffix to form the next prefix, and repeat.
\",\n \"polygon\": [\n [\n 127.8984375,\n 152.6572265625\n ],\n [\n 525.9375,\n 152.6572265625\n ],\n [\n 525.9375,\n 187.18695068359375\n ],\n [\n 127.8984375,\n 187.18695068359375\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 152.6572265625,\n 525.9375,\n 187.18695068359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"For example, if you start with the prefix \\\"Half a,\\\" then the next word has to be \\\"bee,\\\" because the prefix only appears once in the text. The next prefix is \\\"a bee,\\\" so the next suffix might be \\\"philosophically,\\\" \\\"be\\\" or \\\"due.\\\"
\",\n \"polygon\": [\n [\n 128.49609375,\n 196.3564453125\n ],\n [\n 525.9375,\n 196.3564453125\n ],\n [\n 525.9375,\n 231.38092041015625\n ],\n [\n 128.49609375,\n 231.38092041015625\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 196.3564453125,\n 525.9375,\n 231.38092041015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"In this example the length of the prefix is always two, but you can do Markov analysis with any prefix length. The length of the prefix is called the \\\"order\\\" of the analysis. Exercise 13.8. Markov analysis:
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\",\n \"polygon\": [\n [\n 174.9638671875,\n 385.44268798828125\n ],\n [\n 505.01953125,\n 385.44268798828125\n ],\n [\n 505.01953125,\n 419.7942810058594\n ],\n [\n 174.9638671875,\n 419.7942810058594\n ]\n ],\n \"bbox\": [\n 174.9638671875,\n 385.44268798828125,\n 505.01953125,\n 419.7942810058594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"For this example, I left the punctuation attached to the words. The result is almost syntactically correct, but not quite. Semantically, it almost makes sense, but not quite.
\",\n \"polygon\": [\n [\n 153.7470703125,\n 425.74169921875\n ],\n [\n 525.9375,\n 425.74169921875\n ],\n [\n 525.9375,\n 447.8982849121094\n ],\n [\n 153.7470703125,\n 447.8982849121094\n ]\n ],\n \"bbox\": [\n 153.7470703125,\n 425.74169921875,\n 525.9375,\n 447.8982849121094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"What happens if you increase the prefix length? Does the random text make more sense?
\",\n \"polygon\": [\n [\n 152.8505859375,\n 453.97369384765625\n ],\n [\n 507.5916748046875,\n 453.97369384765625\n ],\n [\n 507.5916748046875,\n 463.936279296875\n ],\n [\n 152.8505859375,\n 463.936279296875\n ]\n ],\n \"bbox\": [\n 152.8505859375,\n 453.97369384765625,\n 507.5916748046875,\n 463.936279296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. Once your program is working, you might want to try a mash-up: if you analyze text from two or more books, the random text you generate will blend the vocabulary and phrases from the sources in interesting ways.\",\n \"polygon\": [\n [\n 141.345703125,\n 473.8556823730469\n ],\n [\n 525.638671875,\n 473.8556823730469\n ],\n [\n 525.638671875,\n 508.207275390625\n ],\n [\n 141.345703125,\n 508.207275390625\n ]\n ],\n \"bbox\": [\n 141.345703125,\n 473.8556823730469,\n 525.638671875,\n 508.207275390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Credit: This case study is based on an example from Kernighan and Pike, The Practice of Programming, Addison-Wesley, 1999.
\",\n \"polygon\": [\n [\n 129.5419921875,\n 521.296875\n ],\n [\n 525.9375,\n 521.296875\n ],\n [\n 525.9375,\n 543.81298828125\n ],\n [\n 129.5419921875,\n 543.81298828125\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 521.296875,\n 525.9375,\n 543.81298828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/149/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"You should attempt this exercise before you go on; then you can can download my solution from http://thinkpython.com/code/markov.py. You will also need http:// thinkpython.com/code/emma.txt.
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 616.81640625\n ],\n [\n 268.24176025390625,\n 616.81640625\n ],\n [\n 268.24176025390625,\n 631.2310333251953\n ],\n [\n 128.3466796875,\n 631.2310333251953\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 616.81640625,\n 268.24176025390625,\n 631.2310333251953\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/150/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/150/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Using Markov analysis to generate random text is fun, but there is also a point to this exercise: data structure selection. In your solution to the previous exercises, you had to choose:
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 127.23046875\n ],\n [\n 482.90625,\n 127.23046875\n ],\n [\n 482.90625,\n 149.99798583984375\n ],\n [\n 85.6142578125,\n 149.99798583984375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 127.23046875,\n 482.90625,\n 149.99798583984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/150/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/151/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"For the prefixes, the most obvious options are string, list of strings, or tuple of strings. For the suffixes, one option is a list; another is a histogram (dictionary).
\",\n \"polygon\": [\n [\n 85.6142578125,\n 157.587890625\n ],\n [\n 482.4033508300781,\n 157.587890625\n ],\n [\n 482.4033508300781,\n 180.84197998046875\n ],\n [\n 85.6142578125,\n 180.84197998046875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 157.587890625,\n 482.4033508300781,\n 180.84197998046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/150/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/151/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"How should you choose? The first step is to think about the operations you will need to implement for each data structure. For the prefixes, we need to be able to remove words from the beginning and add to the end. For example, if the current prefix is \\\"Half a,\\\" and the next word is \\\"bee,\\\" you need to be able to form the next prefix, \\\"a bee.\\\"
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\",\n \"polygon\": [\n [\n 86.4000015258789,\n 283.6787414550781\n ],\n [\n 248.54579162597656,\n 283.6787414550781\n ],\n [\n 248.54579162597656,\n 305.8353271484375\n ],\n [\n 86.4000015258789,\n 305.8353271484375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 283.6787414550781,\n 248.54579162597656,\n 305.8353271484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/150/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/151/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"shift takes a tuple of words, prefix, and a string, word, and forms a new tuple that has all the words in prefix except the first, and word added to the end.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 341.0859375\n ],\n [\n 482.4032287597656,\n 341.0859375\n ],\n [\n 482.4032287597656,\n 363.7008972167969\n ],\n [\n 85.763671875,\n 363.7008972167969\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 341.0859375,\n 482.4032287597656,\n 363.7008972167969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/150/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/151/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Adding a new suffix is equally easy for the list implementation or the histogram. Choosing a random element from a list is easy; choosing from a histogram is harder to do efficiently (see Exercise 13.7).
\",\n \"polygon\": [\n [\n 85.763671875,\n 371.63671875\n ],\n [\n 482.90625,\n 371.63671875\n ],\n [\n 482.90625,\n 406.7388916015625\n ],\n [\n 85.763671875,\n 406.7388916015625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 371.63671875,\n 482.90625,\n 406.7388916015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/150/SectionHeader/14\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/151/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"So far we have been talking mostly about ease of implementation, but there are other factors to consider in choosing data structures. One is run time. Sometimes there is a theoretical reason to expect one data structure to be faster than other; for example, I mentioned that the in operator is faster for dictionaries than for lists, at least when the number of elements is large.
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\",\n \"polygon\": [\n [\n 129.60000610351562,\n 85.95379638671875\n ],\n [\n 250.35195922851562,\n 85.95379638671875\n ],\n [\n 250.35195922851562,\n 100.29998779296875\n ],\n [\n 129.60000610351562,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 85.95379638671875,\n 250.35195922851562,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/152/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/152/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"When you are debugging a program, and especially if you are working on a hard bug, there are four things to try:
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 330.7403869628906\n ],\n [\n 525.9375,\n 330.7403869628906\n ],\n [\n 525.9375,\n 352.89697265625\n ],\n [\n 128.6455078125,\n 352.89697265625\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 330.7403869628906,\n 525.9375,\n 352.89697265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/152/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/152/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"For example, reading your code might help if the problem is a typographical error, but not if the problem is a conceptual misunderstanding. If you don't understand what your program does, you can read it 100 times and never see the error, because the error is in your head.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 362.935546875\n ],\n [\n 526.53515625,\n 362.935546875\n ],\n [\n 526.53515625,\n 409.57098388671875\n ],\n [\n 128.6455078125,\n 409.57098388671875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 362.935546875,\n 526.53515625,\n 409.57098388671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/152/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/152/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Running experiments can help, especially if you run small, simple tests. But if you run experiments without thinking or reading your code, you might fall into a pattern I call \\\"random walk programming,\\\" which is the process of making random changes until the program does the right thing. Needless to say, random walk programming can take a long time.
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\",\n \"polygon\": [\n [\n 85.68896484375,\n 194.1328125\n ],\n [\n 328.72967529296875,\n 194.1328125\n ],\n [\n 328.72967529296875,\n 204.21990966796875\n ],\n [\n 85.68896484375,\n 204.21990966796875\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 194.1328125,\n 328.72967529296875,\n 204.21990966796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/153/ListItem/6\",\n \"block_type\": \"ListItem\",\n \"html\": \"benchmarking: The process of choosing between data structures by implementing alternatives and testing them on a sample of the possible inputs.\",\n \"polygon\": [\n [\n 85.6142578125,\n 213.46875\n ],\n [\n 482.4027404785156,\n 213.46875\n ],\n [\n 482.4027404785156,\n 236.57794189453125\n ],\n [\n 85.6142578125,\n 236.57794189453125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 213.46875,\n 482.4027404785156,\n 236.57794189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/153/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"13.12 Exercises
\",\n \"polygon\": [\n [\n 85.53955078125,\n 265.482421875\n ],\n [\n 192.77706909179688,\n 265.482421875\n ],\n [\n 192.77706909179688,\n 280.2669677734375\n ],\n [\n 85.53955078125,\n 280.2669677734375\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 265.482421875,\n 192.77706909179688,\n 280.2669677734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/153/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 13.9. The \\\"rank\\\" of a word is its position in a list of words sorted by frequency: the most common word has rank 1, the second most common has rank 2, etc.
\",\n \"polygon\": [\n [\n 85.46484375,\n 292.166015625\n ],\n [\n 483.205078125,\n 292.166015625\n ],\n [\n 483.205078125,\n 314.59619140625\n ],\n [\n 85.46484375,\n 314.59619140625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 292.166015625,\n 483.205078125,\n 314.59619140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/153/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Zipf's law describes a relationship between the ranks and frequencies of words in natural languages (http: // en. wikipedia. org/ wiki/ Zipf's_ law ). Specifically, it predicts that the frequency, f , of the word with rank r is:
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\",\n \"polygon\": [\n [\n 84.8671875,\n 399.48046875\n ],\n [\n 483.205078125,\n 399.48046875\n ],\n [\n 483.205078125,\n 421.9402160644531\n ],\n [\n 84.8671875,\n 421.9402160644531\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 399.48046875,\n 483.205078125,\n 421.9402160644531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/153/Equation/12\",\n \"block_type\": \"Equation\",\n \"html\": \"\",\n \"polygon\": [\n [\n 237.7177734375,\n 443.3782958984375\n ],\n [\n 331.1015625,\n 443.3782958984375\n ],\n [\n 331.1015625,\n 454.44390869140625\n ],\n [\n 237.7177734375,\n 454.44390869140625\n ]\n ],\n \"bbox\": [\n 237.7177734375,\n 443.3782958984375,\n 331.1015625,\n 454.44390869140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/153/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"So if you plot log f versus log r, you should get a straight line with slope −s and intercept log c.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 461.6702880859375\n ],\n [\n 471.8552551269531,\n 461.6702880859375\n ],\n [\n 471.8552551269531,\n 472.5703125\n ],\n [\n 85.9130859375,\n 472.5703125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 461.6702880859375,\n 471.8552551269531,\n 472.5703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/153/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Write a program that reads a text from a file, counts word frequencies, and prints one line for each word, in descending order of frequency, with log f and log r. Use the graphing program of your choice to plot the results and check whether they form a straight line. Can you estimate the value of s?
\",\n \"polygon\": [\n [\n 85.6142578125,\n 481.8515625\n ],\n [\n 482.90625,\n 481.8515625\n ],\n [\n 482.90625,\n 529.283203125\n ],\n [\n 85.6142578125,\n 529.283203125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 481.8515625,\n 482.90625,\n 529.283203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/153/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ zipf. py . To make the plots, you might have to install matplotlib (see http: // matplotlib. sourceforge. net/ ).
\",\n \"polygon\": [\n [\n 85.46484375,\n 539.3779144287109\n ],\n [\n 482.4043273925781,\n 539.3779144287109\n ],\n [\n 482.4043273925781,\n 561.6152038574219\n ],\n [\n 85.46484375,\n 561.6152038574219\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 539.3779144287109,\n 482.4043273925781,\n 561.6152038574219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"3\": \"/page/144/SectionHeader/2\",\n \"4\": \"/page/153/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/154/Page/119\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/154/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Chapter 14
\",\n \"polygon\": [\n [\n 128.794921875,\n 162.615234375\n ],\n [\n 232.787109375,\n 162.615234375\n ],\n [\n 232.787109375,\n 184.32196044921875\n ],\n [\n 128.794921875,\n 184.32196044921875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 162.615234375,\n 232.787109375,\n 184.32196044921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/144/SectionHeader/1\",\n \"2\": \"/page/154/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Files
\",\n \"polygon\": [\n [\n 127.8984375,\n 217.916015625\n ],\n [\n 184.974609375,\n 217.916015625\n ],\n [\n 184.974609375,\n 243.34136962890625\n ],\n [\n 127.8984375,\n 243.34136962890625\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 217.916015625,\n 184.974609375,\n 243.34136962890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.1 Persistence
\",\n \"polygon\": [\n [\n 128.6455078125,\n 291.7667541503906\n ],\n [\n 242.4990234375,\n 291.7667541503906\n ],\n [\n 242.4990234375,\n 306.11297607421875\n ],\n [\n 128.6455078125,\n 306.11297607421875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 291.7667541503906,\n 242.4990234375,\n 306.11297607421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Most of the programs we have seen so far are transient in the sense that they run for a short time and produce some output, but when they end, their data disappears. If you run the program again, it starts with a clean slate.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 317.996337890625\n ],\n [\n 526.53515625,\n 317.996337890625\n ],\n [\n 526.53515625,\n 352.3479309082031\n ],\n [\n 128.3466796875,\n 352.3479309082031\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 317.996337890625,\n 526.53515625,\n 352.3479309082031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Other programs are persistent: they run for a long time (or all the time); they keep at least some of their data in permanent storage (a hard drive, for example); and if they shut down and restart, they pick up where they left off.
\",\n \"polygon\": [\n [\n 128.49609375,\n 361.58203125\n ],\n [\n 525.9375,\n 361.58203125\n ],\n [\n 525.9375,\n 396.41192626953125\n ],\n [\n 128.49609375,\n 396.41192626953125\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 361.58203125,\n 525.9375,\n 396.41192626953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Examples of persistent programs are operating systems, which run pretty much whenever a computer is on, and web servers, which run all the time, waiting for requests to come in on the network.
\",\n \"polygon\": [\n [\n 129.09375,\n 405.28125\n ],\n [\n 525.9375,\n 405.28125\n ],\n [\n 525.9375,\n 440.4769287109375\n ],\n [\n 129.09375,\n 440.4769287109375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 405.28125,\n 525.9375,\n 440.4769287109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"One of the simplest ways for programs to maintain their data is by reading and writing text files. We have already seen programs that read text files; in this chapter we will see programs that write them.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 449.75390625\n ],\n [\n 526.53515625,\n 449.75390625\n ],\n [\n 526.53515625,\n 484.5409240722656\n ],\n [\n 128.3466796875,\n 484.5409240722656\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 449.75390625,\n 526.53515625,\n 484.5409240722656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"An alternative is to store the state of the program in a database. In this chapter I will present a simple database and a module, pickle, that makes it easy to store program data.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 493.83984375\n ],\n [\n 525.9375,\n 493.83984375\n ],\n [\n 525.9375,\n 516.4109191894531\n ],\n [\n 128.3466796875,\n 516.4109191894531\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 493.83984375,\n 525.9375,\n 516.4109191894531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.2 Reading and writing
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\",\n \"polygon\": [\n [\n 129.2431640625,\n 658.96875\n ],\n [\n 526.53515625,\n 658.96875\n ],\n [\n 526.53515625,\n 681.1599349975586\n ],\n [\n 129.2431640625,\n 681.1599349975586\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 658.96875,\n 526.53515625,\n 681.1599349975586\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/154/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The write method puts data into the file.
\",\n \"polygon\": [\n [\n 128.49609375,\n 690.29296875\n ],\n [\n 310.78125,\n 690.29296875\n ],\n [\n 310.78125,\n 700.8349380493164\n ],\n [\n 128.49609375,\n 700.8349380493164\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 690.29296875,\n 310.78125,\n 700.8349380493164\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/155/Page/224\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/155/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.71484375\n ],\n [\n 482.4034118652344,\n 60.71484375\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.71484375,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/PageHeader/21\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.46484375,\n 61.1015625\n ],\n [\n 101.6015625,\n 61.1015625\n ],\n [\n 101.6015625,\n 70.3828125\n ],\n [\n 85.46484375,\n 70.3828125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 61.1015625,\n 101.6015625,\n 70.3828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/TextInlineMath/1\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> line1 = \\\"This here's the wattle,\\\\n\\\" >>> fout.write(line1)
\",\n \"polygon\": [\n [\n 86.4000015258789,\n 88.0751953125\n ],\n [\n 290.3558654785156,\n 88.0751953125\n ],\n [\n 290.3558654785156,\n 110.84228515625\n ],\n [\n 86.4000015258789,\n 110.84228515625\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 88.0751953125,\n 290.3558654785156,\n 110.84228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Again, the file object keeps track of where it is, so if you call write again, it adds the new data to the end.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 116.4990234375\n ],\n [\n 482.4018859863281,\n 116.4990234375\n ],\n [\n 482.4018859863281,\n 139.60589599609375\n ],\n [\n 85.6142578125,\n 139.60589599609375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 116.4990234375,\n 482.4018859863281,\n 139.60589599609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \">>> line2 = \\\"the emblem of our land.\\\\n\\\" >>> fout.write(line2)
\",\n \"polygon\": [\n [\n 85.53955078125,\n 145.9127197265625\n ],\n [\n 290.4609375,\n 145.9127197265625\n ],\n [\n 290.4609375,\n 168.0693359375\n ],\n [\n 85.53955078125,\n 168.0693359375\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 145.9127197265625,\n 290.4609375,\n 168.0693359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"When you are done writing, you have to close the file.
\",\n \"polygon\": [\n [\n 85.98779296875,\n 174.1201171875\n ],\n [\n 323.96807861328125,\n 174.1201171875\n ],\n [\n 323.96807861328125,\n 184.637939453125\n ],\n [\n 85.98779296875,\n 184.637939453125\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 174.1201171875,\n 323.96807861328125,\n 184.637939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \">>> fout.close()
\",\n \"polygon\": [\n [\n 85.68896484375,\n 190.94573974609375\n ],\n [\n 172.423828125,\n 190.94573974609375\n ],\n [\n 172.423828125,\n 200.9083251953125\n ],\n [\n 85.68896484375,\n 200.9083251953125\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 190.94573974609375,\n 172.423828125,\n 200.9083251953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/154/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.3 Format operator
\",\n \"polygon\": [\n [\n 85.83837890625,\n 227.187744140625\n ],\n [\n 232.189453125,\n 227.187744140625\n ],\n [\n 232.189453125,\n 241.533935546875\n ],\n [\n 85.83837890625,\n 241.533935546875\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 227.187744140625,\n 232.189453125,\n 241.533935546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The argument of write has to be a string, so if we want to put other values in a file, we have to convert them to strings. The easiest way to do that is with str:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 254.07421875\n ],\n [\n 482.40325927734375,\n 254.07421875\n ],\n [\n 482.40325927734375,\n 276.39093017578125\n ],\n [\n 85.6142578125,\n 276.39093017578125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 254.07421875,\n 482.40325927734375,\n 276.39093017578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> x = 52\\n>>> fout.write(str(x))
\",\n \"polygon\": [\n [\n 85.9130859375,\n 282.69775390625\n ],\n [\n 201.47799682617188,\n 282.69775390625\n ],\n [\n 201.47799682617188,\n 304.8543395996094\n ],\n [\n 85.9130859375,\n 304.8543395996094\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 282.69775390625,\n 201.47799682617188,\n 304.8543395996094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"An alternative is to use the format operator, %. When applied to integers, % is the modulus operator. But when the first operand is a string, % is the format operator.
\",\n \"polygon\": [\n [\n 86.0625,\n 311.311767578125\n ],\n [\n 482.39886474609375,\n 311.311767578125\n ],\n [\n 482.39886474609375,\n 333.617919921875\n ],\n [\n 86.0625,\n 333.617919921875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 311.311767578125,\n 482.39886474609375,\n 333.617919921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The first operand is the format string, which contains one or more format sequences, which specify how the second operand is formatted. The result is a string.
\",\n \"polygon\": [\n [\n 85.763671875,\n 343.79296875\n ],\n [\n 482.3996887207031,\n 343.79296875\n ],\n [\n 482.3996887207031,\n 366.2029113769531\n ],\n [\n 85.763671875,\n 366.2029113769531\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 343.79296875,\n 482.3996887207031,\n 366.2029113769531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"For example, the format sequence '%d' means that the second operand should be formatted as an integer (d stands for \\\"decimal\\\"):
\",\n \"polygon\": [\n [\n 85.6142578125,\n 376.470703125\n ],\n [\n 482.40252685546875,\n 376.470703125\n ],\n [\n 482.40252685546875,\n 398.78790283203125\n ],\n [\n 85.6142578125,\n 398.78790283203125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 376.470703125,\n 482.40252685546875,\n 398.78790283203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> camels = 42\\n>>> '%d' % camels\\n'42'
\",\n \"polygon\": [\n [\n 85.09130859375,\n 404.89453125\n ],\n [\n 176.90625,\n 404.89453125\n ],\n [\n 176.90625,\n 439.44635009765625\n ],\n [\n 85.09130859375,\n 439.44635009765625\n ]\n ],\n \"bbox\": [\n 85.09130859375,\n 404.89453125,\n 176.90625,\n 439.44635009765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The result is the string '42', which is not to be confused with the integer value 42.
\",\n \"polygon\": [\n [\n 85.46484375,\n 445.5\n ],\n [\n 447.9716796875,\n 445.5\n ],\n [\n 447.9716796875,\n 456.0149230957031\n ],\n [\n 85.46484375,\n 456.0149230957031\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 445.5,\n 447.9716796875,\n 456.0149230957031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"A format sequence can appear anywhere in the string, so you can embed a value in a sentence:
\",\n \"polygon\": [\n [\n 85.46484375,\n 465.99609375\n ],\n [\n 482.4034118652344,\n 465.99609375\n ],\n [\n 482.4034118652344,\n 488.59991455078125\n ],\n [\n 85.46484375,\n 488.59991455078125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 465.99609375,\n 482.4034118652344,\n 488.59991455078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \">>> camels = 42\\n>>> 'I have spotted %d camels.' % camels\\n'I have spotted 42 camels.'
\",\n \"polygon\": [\n [\n 85.46484375,\n 494.9067687988281\n ],\n [\n 295.573974609375,\n 494.9067687988281\n ],\n [\n 295.573974609375,\n 529.2583618164062\n ],\n [\n 85.46484375,\n 529.2583618164062\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 494.9067687988281,\n 295.573974609375,\n 529.2583618164062\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"If there is more than one format sequence in the string, the second argument has to be a tuple. Each format sequence is matched with an element of the tuple, in order.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 535.60546875\n ],\n [\n 482.90625,\n 535.60546875\n ],\n [\n 482.90625,\n 558.0209350585938\n ],\n [\n 85.6142578125,\n 558.0209350585938\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 535.60546875,\n 482.90625,\n 558.0209350585938\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"The following example uses '%d' to format an integer, '%g' to format a floating-point number (don't ask why), and '%s' to format a string:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 567.703125\n ],\n [\n 482.90625,\n 567.703125\n ],\n [\n 482.90625,\n 590.6059265136719\n ],\n [\n 85.6142578125,\n 590.6059265136719\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 567.703125,\n 482.90625,\n 590.6059265136719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Code/18\",\n \"block_type\": \"Code\",\n \"html\": \">>> 'In %d years I have spotted %g %s.' % (3, 0.1, 'camels')\\n'In 3 years I have spotted 0.1 camels.'
\",\n \"polygon\": [\n [\n 85.3154296875,\n 596.9127807617188\n ],\n [\n 400.1474304199219,\n 596.9127807617188\n ],\n [\n 400.1474304199219,\n 619.0703887939453\n ],\n [\n 85.3154296875,\n 619.0703887939453\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 596.9127807617188,\n 400.1474304199219,\n 619.0703887939453\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"The number of elements in the tuple has to match the number of format sequences in the string. Also, the types of the elements have to match the format sequences:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 625.6763458251953\n ],\n [\n 482.4034118652344,\n 625.6763458251953\n ],\n [\n 482.4034118652344,\n 647.8329467773438\n ],\n [\n 85.3154296875,\n 647.8329467773438\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 625.6763458251953,\n 482.4034118652344,\n 647.8329467773438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/155/Code/20\",\n \"block_type\": \"Code\",\n \"html\": \">>> '%d %d %d' % (1, 2)\\nTypeError: not enough arguments for format string\\n>>> '%d' % 'dollars'\\nTypeError: illegal argument type for built-in operation
\",\n \"polygon\": [\n [\n 85.3154296875,\n 653.5546875\n ],\n [\n 376.5234375,\n 653.5546875\n ],\n [\n 376.5234375,\n 700.6853942871094\n ],\n [\n 85.3154296875,\n 700.6853942871094\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 653.5546875,\n 376.5234375,\n 700.6853942871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/156/Page/198\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/156/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.0478515625,\n 60.8115234375\n ],\n [\n 525.6033935546875,\n 60.8115234375\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.0478515625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 60.8115234375,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/PageHeader/21\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 509.203125,\n 60.56982421875\n ],\n [\n 525.9375,\n 60.56982421875\n ],\n [\n 525.9375,\n 69.94775390625\n ],\n [\n 509.203125,\n 69.94775390625\n ]\n ],\n \"bbox\": [\n 509.203125,\n 60.56982421875,\n 525.9375,\n 69.94775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"In the first example, there aren't enough elements; in the second, the element is the wrong type.
\",\n \"polygon\": [\n [\n 127.1513671875,\n 88.70361328125\n ],\n [\n 525.6033935546875,\n 88.70361328125\n ],\n [\n 525.6033935546875,\n 110.99188232421875\n ],\n [\n 127.1513671875,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 127.1513671875,\n 88.70361328125,\n 525.6033935546875,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The format operator is powerful, but it can be difficult to use. You can read more about it at http://docs.python.org/2/library/stdtypes.html#string-formatting.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 120.65625\n ],\n [\n 526.53515625,\n 120.65625\n ],\n [\n 526.53515625,\n 143.39483642578125\n ],\n [\n 128.3466796875,\n 143.39483642578125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 120.65625,\n 526.53515625,\n 143.39483642578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/155/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.4 Filenames and paths
\",\n \"polygon\": [\n [\n 127.82373046875,\n 172.94970703125\n ],\n [\n 303.7198791503906,\n 172.94970703125\n ],\n [\n 303.7198791503906,\n 187.2958984375\n ],\n [\n 127.82373046875,\n 187.2958984375\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 172.94970703125,\n 303.7198791503906,\n 187.2958984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Files are organized into directories (also called \\\"folders\\\"). Every running program has a \\\"current directory,\\\" which is the default directory for most operations. For example, when you open a file for reading, Python looks for it in the current directory.
\",\n \"polygon\": [\n [\n 129.392578125,\n 199.353515625\n ],\n [\n 525.6033935546875,\n 199.353515625\n ],\n [\n 525.6033935546875,\n 234.09185791015625\n ],\n [\n 129.392578125,\n 234.09185791015625\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 199.353515625,\n 525.6033935546875,\n 234.09185791015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The os module provides functions for working with files and directories (\\\"os\\\" stands for \\\"operating system\\\"). os.getcwd returns the name of the current directory:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 243.6328125\n ],\n [\n 525.6028442382812,\n 243.6328125\n ],\n [\n 525.6028442382812,\n 266.49481201171875\n ],\n [\n 129.2431640625,\n 266.49481201171875\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 243.6328125,\n 525.6028442382812,\n 266.49481201171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> import os\\n>>> cwd = os.getcwd()\\n>>> print cwd\\n/home/dinsdale
\",\n \"polygon\": [\n [\n 128.72021484375,\n 272.61962890625\n ],\n [\n 239.43768310546875,\n 272.61962890625\n ],\n [\n 239.43768310546875,\n 320.009765625\n ],\n [\n 128.72021484375,\n 320.009765625\n ]\n ],\n \"bbox\": [\n 128.72021484375,\n 272.61962890625,\n 239.43768310546875,\n 320.009765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"cwd stands for \\\"current working directory.\\\" The result in this example is /home/dinsdale, which is the home directory of a user named dinsdale.
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 325.43963623046875\n ],\n [\n 525.5996704101562,\n 325.43963623046875\n ],\n [\n 525.5996704101562,\n 347.74578857421875\n ],\n [\n 129.60000610351562,\n 347.74578857421875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 325.43963623046875,\n 525.5996704101562,\n 347.74578857421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"A string like cwd that identifies a file is called a path. A relative path starts from the current directory; an absolute path starts from the topmost directory in the file system.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 357.71484375\n ],\n [\n 525.6044311523438,\n 357.71484375\n ],\n [\n 525.6044311523438,\n 380.1488037109375\n ],\n [\n 128.6455078125,\n 380.1488037109375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 357.71484375,\n 525.6044311523438,\n 380.1488037109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The paths we have seen so far are simple filenames, so they are relative to the current directory. To find the absolute path to a file, you can use os.path.abspath:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 389.619140625\n ],\n [\n 525.603515625,\n 389.619140625\n ],\n [\n 525.603515625,\n 412.55181884765625\n ],\n [\n 128.3466796875,\n 412.55181884765625\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 389.619140625,\n 525.603515625,\n 412.55181884765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> os.path.abspath('memo.txt')\\n'/home/dinsdale/memo.txt'\",\n \"polygon\": [\n [\n 129.2431640625,\n 418.6766662597656\n ],\n [\n 291.7033996582031,\n 418.6766662597656\n ],\n [\n 291.7033996582031,\n 440.833251953125\n ],\n [\n 129.2431640625,\n 440.833251953125\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 418.6766662597656,\n 291.7033996582031,\n 440.833251953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"os.path.exists checks whether a file or directory exists:
\",\n \"polygon\": [\n [\n 128.794921875,\n 447.046875\n ],\n [\n 381.2076110839844,\n 447.046875\n ],\n [\n 381.2076110839844,\n 457.2198181152344\n ],\n [\n 128.794921875,\n 457.2198181152344\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 447.046875,\n 381.2076110839844,\n 457.2198181152344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> os.path.exists('memo.txt')\",\n \"polygon\": [\n [\n 128.794921875,\n 463.2890625\n ],\n [\n 286.875,\n 463.2890625\n ],\n [\n 286.875,\n 477.984375\n ],\n [\n 128.794921875,\n 477.984375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 463.2890625,\n 286.875,\n 477.984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"True
\",\n \"polygon\": [\n [\n 128.6455078125,\n 475.5386657714844\n ],\n [\n 152.103515625,\n 475.5386657714844\n ],\n [\n 152.103515625,\n 486.4921875\n ],\n [\n 128.6455078125,\n 486.4921875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 475.5386657714844,\n 152.103515625,\n 486.4921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"If it exists, os.path.isdir checks whether it's a directory:
\",\n \"polygon\": [\n [\n 127.30078125,\n 491.51953125\n ],\n [\n 384.890625,\n 491.51953125\n ],\n [\n 384.890625,\n 501.8878173828125\n ],\n [\n 127.30078125,\n 501.8878173828125\n ]\n ],\n \"bbox\": [\n 127.30078125,\n 491.51953125,\n 384.890625,\n 501.8878173828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \">>> os.path.isdir('memo.txt')\\nFalse\\n>>> os.path.isdir('music')\\nTrue\",\n \"polygon\": [\n [\n 128.197265625,\n 507.76171875\n ],\n [\n 281.24542236328125,\n 507.76171875\n ],\n [\n 281.24542236328125,\n 556.48828125\n ],\n [\n 128.197265625,\n 556.48828125\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 507.76171875,\n 281.24542236328125,\n 556.48828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Similarly, os.path.isfile checks whether it's a file.
\",\n \"polygon\": [\n [\n 129.16845703125,\n 560.8326721191406\n ],\n [\n 359.490234375,\n 560.8326721191406\n ],\n [\n 359.490234375,\n 570.94482421875\n ],\n [\n 129.16845703125,\n 570.94482421875\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 560.8326721191406,\n 359.490234375,\n 570.94482421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"os.listdir returns a list of the files (and other directories) in the given directory:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 580.46484375\n ],\n [\n 487.653564453125,\n 580.46484375\n ],\n [\n 487.653564453125,\n 591.15283203125\n ],\n [\n 128.3466796875,\n 591.15283203125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 580.46484375,\n 487.653564453125,\n 591.15283203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Code/18\",\n \"block_type\": \"Code\",\n \"html\": \">>> os.listdir(cwd)\\n['music', 'photos', 'memo.txt']
\",\n \"polygon\": [\n [\n 127.8984375,\n 597.2776794433594\n ],\n [\n 291.7034606933594,\n 597.2776794433594\n ],\n [\n 291.7034606933594,\n 619.4342803955078\n ],\n [\n 127.8984375,\n 619.4342803955078\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 597.2776794433594,\n 291.7034606933594,\n 619.4342803955078\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"To demonstrate these functions, the following example \\\"walks\\\" through a directory, prints the names of all the files, and calls itself recursively on all the directories.
\",\n \"polygon\": [\n [\n 128.794921875,\n 624.9375\n ],\n [\n 525.638671875,\n 624.9375\n ],\n [\n 525.638671875,\n 648.0158386230469\n ],\n [\n 128.794921875,\n 648.0158386230469\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 624.9375,\n 525.638671875,\n 648.0158386230469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/156/Code/20\",\n \"block_type\": \"Code\",\n \"html\": \"def walk(dirname):\\n for name in os.listdir(dirname):\\n path = os.path.join(dirname, name)
\",\n \"polygon\": [\n [\n 127.97314453125,\n 653.94140625\n ],\n [\n 349.2754211425781,\n 653.94140625\n ],\n [\n 349.2754211425781,\n 688.4912872314453\n ],\n [\n 127.97314453125,\n 688.4912872314453\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 653.94140625,\n 349.2754211425781,\n 688.4912872314453\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/157/Page/235\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/157/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.2314453125\n ],\n [\n 482.4034118652344,\n 60.2314453125\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.2314453125,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/PageHeader/16\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.166015625,\n 60.18310546875\n ],\n [\n 102.6474609375,\n 60.18310546875\n ],\n [\n 102.6474609375,\n 70.52783203125\n ],\n [\n 85.166015625,\n 70.52783203125\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 60.18310546875,\n 102.6474609375,\n 70.52783203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \"if os.path.isfile(path):\\n print path\\nelse:\\n walk(path)
\",\n \"polygon\": [\n [\n 123.416015625,\n 87.8818359375\n ],\n [\n 253.77169799804688,\n 87.8818359375\n ],\n [\n 253.77169799804688,\n 135.2313232421875\n ],\n [\n 123.416015625,\n 135.2313232421875\n ]\n ],\n \"bbox\": [\n 123.416015625,\n 87.8818359375,\n 253.77169799804688,\n 135.2313232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"os.path.join takes a directory and a file name and joins them into a complete path. Exercise 14.1. The os module provides a function called walk that is similar to this one but more versatile. Read the documentation and use it to print the names of the files in a given directory and its subdirectories.
\",\n \"polygon\": [\n [\n 85.763671875,\n 139.60546875\n ],\n [\n 483.50390625,\n 139.60546875\n ],\n [\n 483.50390625,\n 187.172119140625\n ],\n [\n 85.763671875,\n 187.172119140625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 139.60546875,\n 483.50390625,\n 187.172119140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ walk. py .
\",\n \"polygon\": [\n [\n 85.46484375,\n 195.6796875\n ],\n [\n 323.9296875,\n 195.6796875\n ],\n [\n 323.9296875,\n 206.52410888671875\n ],\n [\n 85.46484375,\n 206.52410888671875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 195.6796875,\n 323.9296875,\n 206.52410888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"4\": \"/page/156/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.5 Catching exceptions
\",\n \"polygon\": [\n [\n 85.39013671875,\n 233.384765625\n ],\n [\n 258.1875,\n 233.384765625\n ],\n [\n 258.1875,\n 249.285888671875\n ],\n [\n 85.39013671875,\n 249.285888671875\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 233.384765625,\n 258.1875,\n 249.285888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"A lot of things can go wrong when you try to read and write files. If you try to open a file that doesn't exist, you get an IOError:
\",\n \"polygon\": [\n [\n 85.763671875,\n 258.328125\n ],\n [\n 483.205078125,\n 258.328125\n ],\n [\n 483.205078125,\n 283.0028076171875\n ],\n [\n 85.763671875,\n 283.0028076171875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 258.328125,\n 483.205078125,\n 283.0028076171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> fin = open('bad_file')\\nIOError: [Errno 2] No such file or directory: 'bad_file'\",\n \"polygon\": [\n [\n 85.763671875,\n 286.171875\n ],\n [\n 379.2314147949219,\n 286.171875\n ],\n [\n 379.2314147949219,\n 310.4282531738281\n ],\n [\n 85.763671875,\n 310.4282531738281\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 286.171875,\n 379.2314147949219,\n 310.4282531738281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"If you don't have permission to access a file:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 313.435546875\n ],\n [\n 284.3349609375,\n 313.435546875\n ],\n [\n 284.3349609375,\n 325.9588317871094\n ],\n [\n 86.2119140625,\n 325.9588317871094\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 313.435546875,\n 284.3349609375,\n 325.9588317871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \">>> fout = open('/etc/passwd', 'w') IOError: [Errno 13] Permission denied: '/etc/passwd' And if you try to open a directory for reading, you get
\",\n \"polygon\": [\n [\n 86.0625,\n 328.130859375\n ],\n [\n 358.3153991699219,\n 328.130859375\n ],\n [\n 358.3153991699219,\n 368.91485595703125\n ],\n [\n 86.0625,\n 368.91485595703125\n ]\n ],\n \"bbox\": [\n 86.0625,\n 328.130859375,\n 358.3153991699219,\n 368.91485595703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \">>> fin = open('/home') IOError: [Errno 21] Is a directory
\",\n \"polygon\": [\n [\n 85.46484375,\n 370.08984375\n ],\n [\n 264.2424011230469,\n 370.08984375\n ],\n [\n 264.2424011230469,\n 396.3403015136719\n ],\n [\n 85.46484375,\n 396.3403015136719\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 370.08984375,\n 264.2424011230469,\n 396.3403015136719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"To avoid these errors, you could use functions like os.path.exists and os.path.isfile, but it would take a lot of time and code to check all the possibilities (if \\\"Errno 21\\\" is any indication, there are at least 21 things that can go wrong).
\",\n \"polygon\": [\n [\n 85.763671875,\n 398.70703125\n ],\n [\n 482.607421875,\n 398.70703125\n ],\n [\n 482.607421875,\n 436.2598876953125\n ],\n [\n 85.763671875,\n 436.2598876953125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 398.70703125,\n 482.607421875,\n 436.2598876953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"It is better to go ahead and try—and deal with problems if they happen—which is exactly what the try statement does. The syntax is similar to an if statement:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 443.953125\n ],\n [\n 482.40338134765625,\n 443.953125\n ],\n [\n 482.40338134765625,\n 467.9296875\n ],\n [\n 85.6142578125,\n 467.9296875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 443.953125,\n 482.40338134765625,\n 467.9296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"try:\\n fin = open('bad_file')\\n for line in fin:\\n print line\\n fin.close()\\nexcept:\\n print 'Something went wrong.'\",\n \"polygon\": [\n [\n 86.39998626708984,\n 473.0747375488281\n ],\n [\n 258.96136474609375,\n 473.0747375488281\n ],\n [\n 258.96136474609375,\n 556.2033538818359\n ],\n [\n 86.39998626708984,\n 556.2033538818359\n ]\n ],\n \"bbox\": [\n 86.39998626708984,\n 473.0747375488281,\n 258.96136474609375,\n 556.2033538818359\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Python starts by executing the try clause. If all goes well, it skips the except clause and proceeds. If an exception occurs, it jumps out of the try clause and executes the except clause.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 559.96875\n ],\n [\n 482.90625,\n 559.96875\n ],\n [\n 482.90625,\n 596.3203125\n ],\n [\n 85.9130859375,\n 596.3203125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 559.96875,\n 482.90625,\n 596.3203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Handling an exception with a try statement is called catching an exception. In this example, the except clause prints an error message that is not very helpful. In general, catching an exception gives you a chance to fix the problem, or try again, or at least end the program gracefully.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 603.66796875\n ],\n [\n 482.90625,\n 603.66796875\n ],\n [\n 482.90625,\n 652.0579223632812\n ],\n [\n 85.9130859375,\n 652.0579223632812\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 603.66796875,\n 482.90625,\n 652.0579223632812\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/157/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 14.2. Write a function called sed that takes as arguments a pattern string, a replacement string, and two filenames; it should read the first file and write the contents into the second file (creating it if necessary). If the pattern string appears anywhere in the file, it should be replaced with the replacement string.
\",\n \"polygon\": [\n [\n 85.763671875,\n 652.39453125\n ],\n [\n 482.607421875,\n 652.39453125\n ],\n [\n 482.607421875,\n 700.6622314453125\n ],\n [\n 85.763671875,\n 700.6622314453125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 652.39453125,\n 482.607421875,\n 700.6622314453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/158/Page/203\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/158/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.12255859375,\n 61.05322265625\n ],\n [\n 525.6033935546875,\n 61.05322265625\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.12255859375,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 61.05322265625,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/PageHeader/21\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 509.80078125,\n 60.76318359375\n ],\n [\n 525.33984375,\n 60.76318359375\n ],\n [\n 525.33984375,\n 70.43115234375\n ],\n [\n 509.80078125,\n 70.43115234375\n ]\n ],\n \"bbox\": [\n 509.80078125,\n 60.76318359375,\n 525.33984375,\n 70.43115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"If an error occurs while opening, reading, writing or closing files, your program should catch the exception, print an error message, and exit. Solution: http: // thinkpython. com/ code/ sed. py .
\",\n \"polygon\": [\n [\n 127.7490234375,\n 88.51025390625\n ],\n [\n 525.6034545898438,\n 88.51025390625\n ],\n [\n 525.6034545898438,\n 123.01416015625\n ],\n [\n 127.7490234375,\n 123.01416015625\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 88.51025390625,\n 525.6034545898438,\n 123.01416015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.6 Databases
\",\n \"polygon\": [\n [\n 128.27197265625,\n 155.07421875\n ],\n [\n 235.97708129882812,\n 155.07421875\n ],\n [\n 235.97708129882812,\n 169.46295166015625\n ],\n [\n 128.27197265625,\n 169.46295166015625\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 155.07421875,\n 235.97708129882812,\n 169.46295166015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"A database is a file that is organized for storing data. Most databases are organized like a dictionary in the sense that they map from keys to values. The biggest difference is that the database is on disk (or other permanent storage), so it persists after the program ends.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 182.6279296875\n ],\n [\n 525.9375,\n 182.6279296875\n ],\n [\n 525.9375,\n 217.36688232421875\n ],\n [\n 128.6455078125,\n 217.36688232421875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 182.6279296875,\n 525.9375,\n 217.36688232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The module anydbm provides an interface for creating and updating database files. As an example, I'll create a database that contains captions for image files.
\",\n \"polygon\": [\n [\n 128.794921875,\n 227.77734375\n ],\n [\n 525.604736328125,\n 227.77734375\n ],\n [\n 525.604736328125,\n 250.56182861328125\n ],\n [\n 128.794921875,\n 250.56182861328125\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 227.77734375,\n 525.604736328125,\n 250.56182861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Opening a database is similar to opening other files:
\",\n \"polygon\": [\n [\n 127.8984375,\n 260.6484375\n ],\n [\n 358.2017822265625,\n 260.6484375\n ],\n [\n 358.2017822265625,\n 271.56182861328125\n ],\n [\n 127.8984375,\n 271.56182861328125\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 260.6484375,\n 358.2017822265625,\n 271.56182861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> import anydbm\\n>>> db = anydbm.open('captions.db', 'c')\",\n \"polygon\": [\n [\n 129.5419921875,\n 278.4786376953125\n ],\n [\n 339.46875,\n 278.4786376953125\n ],\n [\n 339.46875,\n 300.6352233886719\n ],\n [\n 129.5419921875,\n 300.6352233886719\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 278.4786376953125,\n 339.46875,\n 300.6352233886719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The mode 'c' means that the database should be created if it doesn't already exist. The result is a database object that can be used (for most operations) like a dictionary. If you create a new item, anydbm updates the database file.
\",\n \"polygon\": [\n [\n 129.392578125,\n 307.0546875\n ],\n [\n 525.638671875,\n 307.0546875\n ],\n [\n 525.638671875,\n 342.2017822265625\n ],\n [\n 129.392578125,\n 342.2017822265625\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 307.0546875,\n 525.638671875,\n 342.2017822265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/TextInlineMath/8\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \">>> db['cleese.png'] = 'Photo of John Cleese.'
\",\n \"polygon\": [\n [\n 128.86962890625,\n 348.8203125\n ],\n [\n 370.14044189453125,\n 348.8203125\n ],\n [\n 370.14044189453125,\n 359.0812072753906\n ],\n [\n 128.86962890625,\n 359.0812072753906\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 348.8203125,\n 370.14044189453125,\n 359.0812072753906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"When you access one of the items, anydbm reads the file:
\",\n \"polygon\": [\n [\n 128.27197265625,\n 366.029296875\n ],\n [\n 376.224609375,\n 366.029296875\n ],\n [\n 376.224609375,\n 376.259765625\n ],\n [\n 128.27197265625,\n 376.259765625\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 366.029296875,\n 376.224609375,\n 376.259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> print db['cleese.png']\\nPhoto of John Cleese.
\",\n \"polygon\": [\n [\n 127.599609375,\n 383.17559814453125\n ],\n [\n 265.5584411621094,\n 383.17559814453125\n ],\n [\n 265.5584411621094,\n 405.3321838378906\n ],\n [\n 127.599609375,\n 405.3321838378906\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 383.17559814453125,\n 265.5584411621094,\n 405.3321838378906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"If you make another assignment to an existing key, anydbm replaces the old value:
\",\n \"polygon\": [\n [\n 128.49609375,\n 412.3985900878906\n ],\n [\n 487.41802978515625,\n 412.3985900878906\n ],\n [\n 487.41802978515625,\n 422.5107421875\n ],\n [\n 128.49609375,\n 422.5107421875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 412.3985900878906,\n 487.41802978515625,\n 422.5107421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> db['cleese.png'] = 'Photo of John Cleese doing a silly walk.'\\n>>> print db['cleese.png']\\nPhoto of John Cleese doing a silly walk.
\",\n \"polygon\": [\n [\n 129.60009765625,\n 428.484375\n ],\n [\n 469.4934997558594,\n 428.484375\n ],\n [\n 469.4934997558594,\n 464.0625\n ],\n [\n 129.60009765625,\n 464.0625\n ]\n ],\n \"bbox\": [\n 129.60009765625,\n 428.484375,\n 469.4934997558594,\n 464.0625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Many dictionary methods, like keys and items, also work with database objects. So does iteration with a for statement.
\",\n \"polygon\": [\n [\n 128.49609375,\n 470.8445739746094\n ],\n [\n 525.599853515625,\n 470.8445739746094\n ],\n [\n 525.599853515625,\n 493.1507263183594\n ],\n [\n 128.49609375,\n 493.1507263183594\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 470.8445739746094,\n 525.599853515625,\n 493.1507263183594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"for key in db: print key
\",\n \"polygon\": [\n [\n 127.30078125,\n 500.06756591796875\n ],\n [\n 202.82525634765625,\n 500.06756591796875\n ],\n [\n 202.82525634765625,\n 522.2241516113281\n ],\n [\n 127.30078125,\n 522.2241516113281\n ]\n ],\n \"bbox\": [\n 127.30078125,\n 500.06756591796875,\n 202.82525634765625,\n 522.2241516113281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"As with other files, you should close the database when you are done:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 529.03125\n ],\n [\n 437.484375,\n 529.03125\n ],\n [\n 437.484375,\n 539.4027252197266\n ],\n [\n 129.2431640625,\n 539.4027252197266\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 529.03125,\n 437.484375,\n 539.4027252197266\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Code/200\",\n \"block_type\": \"Code\",\n \"html\": \">>> db.close()
\",\n \"polygon\": [\n [\n 129.6001434326172,\n 546.3185729980469\n ],\n [\n 202.82525634765625,\n 546.3185729980469\n ],\n [\n 202.82525634765625,\n 556.2811737060547\n ],\n [\n 129.6001434326172,\n 556.2811737060547\n ]\n ],\n \"bbox\": [\n 129.6001434326172,\n 546.3185729980469,\n 202.82525634765625,\n 556.2811737060547\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/SectionHeader/17\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.7 Pickling
\",\n \"polygon\": [\n [\n 127.8984375,\n 584.33203125\n ],\n [\n 224.02682495117188,\n 584.33203125\n ],\n [\n 224.02682495117188,\n 598.7357788085938\n ],\n [\n 127.8984375,\n 598.7357788085938\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 584.33203125,\n 224.02682495117188,\n 598.7357788085938\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"A limitation of anydbm is that the keys and values have to be strings. If you try to use any other type, you get an error.
\",\n \"polygon\": [\n [\n 128.197265625,\n 612.1395721435547\n ],\n [\n 525.638671875,\n 612.1395721435547\n ],\n [\n 525.638671875,\n 634.4457244873047\n ],\n [\n 128.197265625,\n 634.4457244873047\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 612.1395721435547,\n 525.638671875,\n 634.4457244873047\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"The pickle module can help. It translates almost any type of object into a string suitable for storage in a database, and then translates strings back into objects.
\",\n \"polygon\": [\n [\n 128.49609375,\n 645.3345642089844\n ],\n [\n 525.9375,\n 645.3345642089844\n ],\n [\n 525.9375,\n 667.6407241821289\n ],\n [\n 128.49609375,\n 667.6407241821289\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 645.3345642089844,\n 525.9375,\n 667.6407241821289\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/158/Text/20\",\n \"block_type\": \"Text\",\n \"html\": \"pickle.dumps takes an object as a parameter and returns a string representation (dumps is short for \\\"dump string\\\"):
\",\n \"polygon\": [\n [\n 128.0478515625,\n 678.528564453125\n ],\n [\n 525.600341796875,\n 678.528564453125\n ],\n [\n 525.600341796875,\n 700.8347244262695\n ],\n [\n 128.0478515625,\n 700.8347244262695\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 678.528564453125,\n 525.600341796875,\n 700.8347244262695\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/159/Page/266\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/159/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.18310546875\n ],\n [\n 482.4034118652344,\n 60.18310546875\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.18310546875,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/PageHeader/17\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.763671875,\n 60.08642578125\n ],\n [\n 102.19921875,\n 60.08642578125\n ],\n [\n 102.19921875,\n 69.94775390625\n ],\n [\n 85.763671875,\n 69.94775390625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 60.08642578125,\n 102.19921875,\n 69.94775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> import pickle\\n>>> t = [1, 2, 3]\\n>>> pickle.dumps(t)\\n'(lp0\\\\nI1\\\\naI2\\\\naI3\\\\na.'
\",\n \"polygon\": [\n [\n 86.13720703125,\n 88.68572998046875\n ],\n [\n 211.90036010742188,\n 88.68572998046875\n ],\n [\n 211.90036010742188,\n 135.2313232421875\n ],\n [\n 86.13720703125,\n 135.2313232421875\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 88.68572998046875,\n 211.90036010742188,\n 135.2313232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The format isn't obvious to human readers; it is meant to be easy for pickle to interpret. pickle.loads (\\\"load string\\\") reconstitutes the object:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 139.025390625\n ],\n [\n 482.3997497558594,\n 139.025390625\n ],\n [\n 482.3997497558594,\n 162.866943359375\n ],\n [\n 85.6142578125,\n 162.866943359375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 139.025390625,\n 482.3997497558594,\n 162.866943359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> t1 = [1, 2, 3]\\n>>> s = pickle.dumps(t1)\\n>>> t2 = pickle.loads(s)\\n>>> print t2\\n[1, 2, 3]
\",\n \"polygon\": [\n [\n 85.3154296875,\n 167.7392578125\n ],\n [\n 211.93870544433594,\n 167.7392578125\n ],\n [\n 211.93870544433594,\n 227.390625\n ],\n [\n 85.3154296875,\n 227.390625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 167.7392578125,\n 211.93870544433594,\n 227.390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Although the new object has the same value as the old, it is not (in general) the same object:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 231.451171875\n ],\n [\n 482.4033203125,\n 231.451171875\n ],\n [\n 482.4033203125,\n 242.2269287109375\n ],\n [\n 86.2119140625,\n 242.2269287109375\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 231.451171875,\n 482.4033203125,\n 242.2269287109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> t1 == t2\\nTrue\\n>>> t1 is t2\\nFalse
\",\n \"polygon\": [\n [\n 84.94189453125,\n 247.306640625\n ],\n [\n 152.77587890625,\n 247.306640625\n ],\n [\n 152.77587890625,\n 293.95233154296875\n ],\n [\n 84.94189453125,\n 293.95233154296875\n ]\n ],\n \"bbox\": [\n 84.94189453125,\n 247.306640625,\n 152.77587890625,\n 293.95233154296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"In other words, pickling and then unpickling has the same effect as copying the object.
\",\n \"polygon\": [\n [\n 86.0625,\n 298.546875\n ],\n [\n 466.3138122558594,\n 298.546875\n ],\n [\n 466.3138122558594,\n 309.3929138183594\n ],\n [\n 86.0625,\n 309.3929138183594\n ]\n ],\n \"bbox\": [\n 86.0625,\n 298.546875,\n 466.3138122558594,\n 309.3929138183594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"You can use pickle to store non-strings in a database. In fact, this combination is so common that it has been encapsulated in a module called shelve.
\",\n \"polygon\": [\n [\n 85.46484375,\n 317.49609375\n ],\n [\n 482.4024658203125,\n 317.49609375\n ],\n [\n 482.4024658203125,\n 340.84991455078125\n ],\n [\n 85.46484375,\n 340.84991455078125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 317.49609375,\n 482.4024658203125,\n 340.84991455078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 14.3. If you download my solution to Exercise 12.4 from http: // thinkpython. com/ code/ anagram_ sets. py , you'll see that it creates a dictionary that maps from a sorted string of letters to the list of words that can be spelled with those letters. For example, 'opst' maps to the list ['opts', 'post', 'pots', 'spot', 'stop', 'tops'].
\",\n \"polygon\": [\n [\n 85.9130859375,\n 342.82891845703125\n ],\n [\n 482.90625,\n 342.82891845703125\n ],\n [\n 482.90625,\n 389.4783630371094\n ],\n [\n 85.9130859375,\n 389.4783630371094\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 342.82891845703125,\n 482.90625,\n 389.4783630371094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Write a module that imports anagram_sets and provides two new functions: store_anagrams should store the anagram dictionary in a \\\"shelf;\\\" read_anagrams should look up a word and return a list of its anagrams. Solution: http: // thinkpython. com/ code/ anagram_ db. py
\",\n \"polygon\": [\n [\n 85.6142578125,\n 398.3203125\n ],\n [\n 482.4270324707031,\n 398.3203125\n ],\n [\n 482.4270324707031,\n 433.125\n ],\n [\n 85.6142578125,\n 433.125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 398.3203125,\n 482.4270324707031,\n 433.125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"4\": \"/page/158/SectionHeader/17\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/SectionHeader/10\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.8 Pipes
\",\n \"polygon\": [\n [\n 85.68896484375,\n 460.1953125\n ],\n [\n 162.5625,\n 460.1953125\n ],\n [\n 162.5625,\n 475.7430114746094\n ],\n [\n 85.68896484375,\n 475.7430114746094\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 460.1953125,\n 162.5625,\n 475.7430114746094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Most operating systems provide a command-line interface, also known as a shell. Shells usually provide commands to navigate the file system and launch applications. For example, in Unix you can change directories with cd, display the contents of a directory with ls, and launch a web browser by typing (for example) firefox.
\",\n \"polygon\": [\n [\n 85.763671875,\n 486.4921875\n ],\n [\n 482.4032287597656,\n 486.4921875\n ],\n [\n 482.4032287597656,\n 533.7599487304688\n ],\n [\n 85.763671875,\n 533.7599487304688\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 486.4921875,\n 482.4032287597656,\n 533.7599487304688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Any program that you can launch from the shell can also be launched from Python using a pipe. A pipe is an object that represents a running program.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 542.1796875\n ],\n [\n 482.4032897949219,\n 542.1796875\n ],\n [\n 482.4032897949219,\n 565.3828125\n ],\n [\n 85.6142578125,\n 565.3828125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 542.1796875,\n 482.4032897949219,\n 565.3828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"For example, the Unix command ls -l normally displays the contents of the current directory (in long format). You can launch ls with os.popen1 :
\",\n \"polygon\": [\n [\n 85.763671875,\n 573.50390625\n ],\n [\n 482.90625,\n 573.50390625\n ],\n [\n 482.90625,\n 596.6749572753906\n ],\n [\n 85.763671875,\n 596.6749572753906\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 573.50390625,\n 482.90625,\n 596.6749572753906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> cmd = 'ls -l'\\n>>> fp = os.popen(cmd)
\",\n \"polygon\": [\n [\n 86.13720703125,\n 600.9609375\n ],\n [\n 201.4779510498047,\n 600.9609375\n ],\n [\n 201.4779510498047,\n 624.0114135742188\n ],\n [\n 86.13720703125,\n 624.0114135742188\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 600.9609375,\n 201.4779510498047,\n 624.0114135742188\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The argument is a string that contains a shell command. The return value is an object that behaves just like an open file. You can read the output from the ls process one line at a time with readline or get the whole thing at once with read:
\",\n \"polygon\": [\n [\n 86.0625,\n 628.41796875\n ],\n [\n 482.90625,\n 628.41796875\n ],\n [\n 482.90625,\n 663.8409729003906\n ],\n [\n 86.0625,\n 663.8409729003906\n ]\n ],\n \"bbox\": [\n 86.0625,\n 628.41796875,\n 482.90625,\n 663.8409729003906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/159/Footnote/16\",\n \"block_type\": \"Footnote\",\n \"html\": \"1popen is deprecated now, which means we are supposed to stop using it and start using the subprocess module. But for simple cases, I find subprocess more complicated than necessary. So I am going to keep using popen until they take it away.
\",\n \"polygon\": [\n [\n 86.361328125,\n 671.9081573486328\n ],\n [\n 482.607421875,\n 671.9081573486328\n ],\n [\n 482.607421875,\n 700.734375\n ],\n [\n 86.361328125,\n 700.734375\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 671.9081573486328,\n 482.607421875,\n 700.734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/160/Page/210\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/160/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 129.01904296875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 129.01904296875,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 61.171142578125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/PageHeader/17\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 509.80078125,\n 61.1015625\n ],\n [\n 525.9375,\n 61.1015625\n ],\n [\n 525.9375,\n 70.3828125\n ],\n [\n 509.80078125,\n 70.3828125\n ]\n ],\n \"bbox\": [\n 509.80078125,\n 61.1015625,\n 525.9375,\n 70.3828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \">>> res = fp.read()
\",\n \"polygon\": [\n [\n 127.7490234375,\n 88.68572998046875\n ],\n [\n 249.9697265625,\n 88.68572998046875\n ],\n [\n 249.9697265625,\n 100.2568359375\n ],\n [\n 127.7490234375,\n 100.2568359375\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 88.68572998046875,\n 249.9697265625,\n 100.2568359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"When you are done, you close the pipe like a file:
\",\n \"polygon\": [\n [\n 127.7490234375,\n 103.833984375\n ],\n [\n 345.4097900390625,\n 103.833984375\n ],\n [\n 345.4097900390625,\n 114.04888916015625\n ],\n [\n 127.7490234375,\n 114.04888916015625\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 103.833984375,\n 345.4097900390625,\n 114.04888916015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> stat = fp.close()\\n>>> print stat\\nNone
\",\n \"polygon\": [\n [\n 128.197265625,\n 119.188720703125\n ],\n [\n 239.43765258789062,\n 119.188720703125\n ],\n [\n 239.43765258789062,\n 156.041015625\n ],\n [\n 128.197265625,\n 156.041015625\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 119.188720703125,\n 239.43765258789062,\n 156.041015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The return value is the final status of the ls process; None means that it ended normally (with no errors).
\",\n \"polygon\": [\n [\n 129.09375,\n 158.8287353515625\n ],\n [\n 525.595947265625,\n 158.8287353515625\n ],\n [\n 525.595947265625,\n 181.13494873046875\n ],\n [\n 129.09375,\n 181.13494873046875\n ]\n ],\n \"bbox\": [\n 129.09375,\n 158.8287353515625,\n 525.595947265625,\n 181.13494873046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"For example, most Unix systems provide a command called md5sum that reads the contents of a file and computes a \\\"checksum.\\\" You can read about MD5 at http://en.wikipedia. org/wiki/Md5. This command provides an efficient way to check whether two files have the same contents. The probability that different contents yield the same checksum is very small (that is, unlikely to happen before the universe collapses).
\",\n \"polygon\": [\n [\n 128.794921875,\n 189.9755859375\n ],\n [\n 525.638671875,\n 189.9755859375\n ],\n [\n 525.638671875,\n 249.13494873046875\n ],\n [\n 128.794921875,\n 249.13494873046875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 189.9755859375,\n 525.638671875,\n 249.13494873046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"You can use a pipe to run md5sum from Python and get the result:
\",\n \"polygon\": [\n [\n 128.794921875,\n 258.24578857421875\n ],\n [\n 414.5018005371094,\n 258.24578857421875\n ],\n [\n 414.5018005371094,\n 268.35797119140625\n ],\n [\n 128.794921875,\n 268.35797119140625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 258.24578857421875,\n 414.5018005371094,\n 268.35797119140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> filename = 'book.tex'\\n>>> cmd = 'md5sum ' + filename\\n>>> fp = os.popen(cmd)\\n>>> res = fp.read()\\n>>> stat = fp.close()\\n>>> print res\\n1e0033f0ed0656636de0d75144ba32e0 book.tex\\n>>> print stat\\nNone
\",\n \"polygon\": [\n [\n 128.72021484375,\n 273.497802734375\n ],\n [\n 350.82421875,\n 273.497802734375\n ],\n [\n 350.82421875,\n 382.658203125\n ],\n [\n 128.72021484375,\n 382.658203125\n ]\n ],\n \"bbox\": [\n 128.72021484375,\n 273.497802734375,\n 350.82421875,\n 382.658203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 14.4. In a large collection of MP3 files, there may be more than one copy of the same song, stored in different directories or with different file names. The goal of this exercise is to search for duplicates.
\",\n \"polygon\": [\n [\n 128.49609375,\n 383.22369384765625\n ],\n [\n 525.6034545898438,\n 383.22369384765625\n ],\n [\n 525.6034545898438,\n 417.57427978515625\n ],\n [\n 128.49609375,\n 417.57427978515625\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 383.22369384765625,\n 525.6034545898438,\n 417.57427978515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/ListGroup/208\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 140.0009765625,\n 428.87109375\n ],\n [\n 525.603271484375,\n 428.87109375\n ],\n [\n 525.603271484375,\n 514.6114196777344\n ],\n [\n 140.0009765625,\n 514.6114196777344\n ]\n ],\n \"bbox\": [\n 140.0009765625,\n 428.87109375,\n 525.603271484375,\n 514.6114196777344\n ],\n \"children\": [\n {\n \"id\": \"/page/160/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Write a program that searches a directory and all of its subdirectories, recursively, and returns a list of complete paths for all files with a given suffix (like .mp3). Hint: os.path provides several useful functions for manipulating file and path names.\",\n \"polygon\": [\n [\n 140.0009765625,\n 428.87109375\n ],\n [\n 525.603271484375,\n 428.87109375\n ],\n [\n 525.603271484375,\n 463.624267578125\n ],\n [\n 140.0009765625,\n 463.624267578125\n ]\n ],\n \"bbox\": [\n 140.0009765625,\n 428.87109375,\n 525.603271484375,\n 463.624267578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/ListItem/10\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. To recognize duplicates, you can use md5sum to compute a \\\"checksum\\\" for each files. If two files have the same checksum, they probably have the same contents.\",\n \"polygon\": [\n [\n 140.748046875,\n 472.18359375\n ],\n [\n 525.5972290039062,\n 472.18359375\n ],\n [\n 525.5972290039062,\n 495.2032775878906\n ],\n [\n 140.748046875,\n 495.2032775878906\n ]\n ],\n \"bbox\": [\n 140.748046875,\n 472.18359375,\n 525.5972290039062,\n 495.2032775878906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. To double-check, you can use the Unix command diff.\",\n \"polygon\": [\n [\n 140.5986328125,\n 504.28125\n ],\n [\n 375.03265380859375,\n 504.28125\n ],\n [\n 375.03265380859375,\n 514.6114196777344\n ],\n [\n 140.5986328125,\n 514.6114196777344\n ]\n ],\n \"bbox\": [\n 140.5986328125,\n 504.28125,\n 375.03265380859375,\n 514.6114196777344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/160/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ find_ duplicates. py .
\",\n \"polygon\": [\n [\n 127.4501953125,\n 526.2059936523438\n ],\n [\n 426.3626708984375,\n 526.2059936523438\n ],\n [\n 426.3626708984375,\n 536.2492980957031\n ],\n [\n 127.4501953125,\n 536.2492980957031\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 526.2059936523438,\n 426.3626708984375,\n 536.2492980957031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/SectionHeader/13\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"14.9 Writing modules
\",\n \"polygon\": [\n [\n 127.1513671875,\n 564.4838562011719\n ],\n [\n 279.5537109375,\n 564.4838562011719\n ],\n [\n 279.5537109375,\n 578.8300476074219\n ],\n [\n 127.1513671875,\n 578.8300476074219\n ]\n ],\n \"bbox\": [\n 127.1513671875,\n 564.4838562011719,\n 279.5537109375,\n 578.8300476074219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Any file that contains Python code can be imported as a module. For example, suppose you have a file named wc.py with the following code:
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\",\n \"polygon\": [\n [\n 129.5999298095703,\n 617.5568542480469\n ],\n [\n 291.7457275390625,\n 617.5568542480469\n ],\n [\n 291.7457275390625,\n 687.19921875\n ],\n [\n 129.5999298095703,\n 687.19921875\n ]\n ],\n \"bbox\": [\n 129.5999298095703,\n 617.5568542480469,\n 291.7457275390625,\n 687.19921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/160/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"print linecount('wc.py')
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\",\n \"polygon\": [\n [\n 85.763671875,\n 87.93017578125\n ],\n [\n 482.90625,\n 87.93017578125\n ],\n [\n 482.90625,\n 110.99188232421875\n ],\n [\n 85.763671875,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 87.93017578125,\n 482.90625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \">>> import wc\\n7
\",\n \"polygon\": [\n [\n 85.46484375,\n 115.62890625\n ],\n [\n 187.8134765625,\n 115.62890625\n ],\n [\n 187.8134765625,\n 143.279296875\n ],\n [\n 85.46484375,\n 143.279296875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 115.62890625,\n 187.8134765625,\n 143.279296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Now you have a module object wc:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 147.6317138671875\n ],\n [\n 239.8095703125,\n 147.6317138671875\n ],\n [\n 239.8095703125,\n 158.4580078125\n ],\n [\n 85.6142578125,\n 158.4580078125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 147.6317138671875,\n 239.8095703125,\n 158.4580078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \">>> print wc\\n<module 'wc' from 'wc.py'>
\",\n \"polygon\": [\n [\n 85.39013671875,\n 164.90972900390625\n ],\n [\n 227.70703125,\n 164.90972900390625\n ],\n [\n 227.70703125,\n 187.06732177734375\n ],\n [\n 85.39013671875,\n 187.06732177734375\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 164.90972900390625,\n 227.70703125,\n 187.06732177734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"That provides a function called linecount:
\",\n \"polygon\": [\n [\n 85.98779296875,\n 193.8427734375\n ],\n [\n 277.3125,\n 193.8427734375\n ],\n [\n 277.3125,\n 204.49493408203125\n ],\n [\n 85.98779296875,\n 204.49493408203125\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 193.8427734375,\n 277.3125,\n 204.49493408203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> wc.linecount('wc.py')\\n7\",\n \"polygon\": [\n [\n 85.166015625,\n 211.53515625\n ],\n [\n 217.12937927246094,\n 211.53515625\n ],\n [\n 217.12937927246094,\n 233.818359375\n ],\n [\n 85.166015625,\n 233.818359375\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 211.53515625,\n 217.12937927246094,\n 233.818359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"So that's how you write modules in Python.
\",\n \"polygon\": [\n [\n 86.0625,\n 240.15234375\n ],\n [\n 280.30078125,\n 240.15234375\n ],\n [\n 280.30078125,\n 251.2469482421875\n ],\n [\n 86.0625,\n 251.2469482421875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 240.15234375,\n 280.30078125,\n 251.2469482421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The only problem with this example is that when you import the module it executes the test code at the bottom. Normally when you import a module, it defines new functions but it doesn't execute them.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 261.615234375\n ],\n [\n 482.90625,\n 261.615234375\n ],\n [\n 482.90625,\n 296.8849182128906\n ],\n [\n 85.9130859375,\n 296.8849182128906\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 261.615234375,\n 482.90625,\n 296.8849182128906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Programs that will be imported as modules often use the following idiom:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 307.0546875\n ],\n [\n 412.3828125,\n 307.0546875\n ],\n [\n 412.3828125,\n 318.1349182128906\n ],\n [\n 85.9130859375,\n 318.1349182128906\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 307.0546875,\n 412.3828125,\n 318.1349182128906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"if __name__ == '__main__':\\n print linecount('wc.py')\",\n \"polygon\": [\n [\n 85.9130859375,\n 325.23046875\n ],\n [\n 232.9365234375,\n 325.23046875\n ],\n [\n 232.9365234375,\n 347.66015625\n ],\n [\n 85.9130859375,\n 347.66015625\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 325.23046875,\n 232.9365234375,\n 347.66015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"__name__ is a built-in variable that is set when the program starts. If the program is running as a script, __name__ has the value __main__; in that case, the test code is executed. Otherwise, if the module is being imported, the test code is skipped.
\",\n \"polygon\": [\n [\n 85.763671875,\n 354.62109375\n ],\n [\n 482.39801025390625,\n 354.62109375\n ],\n [\n 482.39801025390625,\n 389.2759094238281\n ],\n [\n 85.763671875,\n 389.2759094238281\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 354.62109375,\n 482.39801025390625,\n 389.2759094238281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 14.5. Type this example into a file named wc.py and run it as a script. Then run the Python interpreter and import wc. What is the value of __name__ when the module is being imported?
\",\n \"polygon\": [\n [\n 85.6142578125,\n 390.97265625\n ],\n [\n 482.3984069824219,\n 390.97265625\n ],\n [\n 482.3984069824219,\n 425.68621826171875\n ],\n [\n 85.6142578125,\n 425.68621826171875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 390.97265625,\n 482.3984069824219,\n 425.68621826171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Warning: If you import a module that has already been imported, Python does nothing. It does not re-read the file, even if it has changed.
\",\n \"polygon\": [\n [\n 85.763671875,\n 435.4453125\n ],\n [\n 482.607421875,\n 435.4453125\n ],\n [\n 482.607421875,\n 459.1302185058594\n ],\n [\n 85.763671875,\n 459.1302185058594\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 435.4453125,\n 482.607421875,\n 459.1302185058594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/159/SectionHeader/10\",\n \"4\": \"/page/160/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"If you want to reload a module, you can use the built-in function reload, but it can be tricky, so the safest thing to do is restart the interpreter and then import the module again.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 524.77734375\n ],\n [\n 207.1519775390625,\n 524.77734375\n ],\n [\n 207.1519775390625,\n 539.7729644775391\n ],\n [\n 85.6142578125,\n 539.7729644775391\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 524.77734375,\n 207.1519775390625,\n 539.7729644775391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/161/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"When you are reading and writing files, you might run into problems with whitespace. These errors can be hard to debug because spaces, tabs and newlines are normally invisible:
\",\n \"polygon\": [\n [\n 86.0625,\n 553.0078125\n ],\n [\n 482.607421875,\n 553.0078125\n ],\n [\n 482.607421875,\n 575.8339233398438\n ],\n [\n 86.0625,\n 575.8339233398438\n ]\n ],\n \"bbox\": [\n 86.0625,\n 553.0078125,\n 482.607421875,\n 575.8339233398438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/161/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Code/17\",\n \"block_type\": \"Code\",\n \"html\": \">>> s = '1 2\\\\t 3\\\\n 4'\\n>>> print s\\n1 2 3\\n 4
\",\n \"polygon\": [\n [\n 85.9130859375,\n 582.78515625\n ],\n [\n 196.21238708496094,\n 582.78515625\n ],\n [\n 196.21238708496094,\n 629.5453796386719\n ],\n [\n 85.9130859375,\n 629.5453796386719\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 582.78515625,\n 196.21238708496094,\n 629.5453796386719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/161/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"The built-in function repr can help. It takes any object as an argument and returns a string representation of the object. For strings, it represents whitespace characters with backslash sequences:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 636.15234375\n ],\n [\n 482.90625,\n 636.15234375\n ],\n [\n 482.90625,\n 671.361946105957\n ],\n [\n 86.2119140625,\n 671.361946105957\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 636.15234375,\n 482.90625,\n 671.361946105957\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/161/SectionHeader/15\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/161/Code/19\",\n \"block_type\": \"Code\",\n \"html\": \">>> print repr(s)\\n'1 2\\\\t 3\\\\n 4'
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 548.1956634521484\n ],\n [\n 235.97705078125,\n 548.1956634521484\n ],\n [\n 235.97705078125,\n 562.5418548583984\n ],\n [\n 128.3466796875,\n 562.5418548583984\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 548.1956634521484,\n 235.97705078125,\n 562.5418548583984\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/157/SectionHeader/4\",\n \"3\": \"/page/161/SectionHeader/15\",\n \"4\": \"/page/162/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/162/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 14.6. The urllib module provides methods for manipulating URLs and downloading information from the web. The following example downloads and prints a secret message from thinkpython.com:
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 163.001953125\n ],\n [\n 232.6376953125,\n 163.001953125\n ],\n [\n 232.6376953125,\n 184.200927734375\n ],\n [\n 128.6455078125,\n 184.200927734375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 163.001953125,\n 232.6376953125,\n 184.200927734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/154/SectionHeader/1\",\n \"2\": \"/page/164/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/164/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Classes and objects
\",\n \"polygon\": [\n [\n 128.49609375,\n 218.302734375\n ],\n [\n 348.4700927734375,\n 218.302734375\n ],\n [\n 348.4700927734375,\n 243.09832763671875\n ],\n [\n 128.49609375,\n 243.09832763671875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 218.302734375,\n 348.4700927734375,\n 243.09832763671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/164/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Code examples from this chapter are available from http://thinkpython.com/code/ Point1.py; solutions to the exercises are available from http://thinkpython.com/code/ Point1_soln.py.
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\",\n \"polygon\": [\n [\n 129.09375,\n 597.8671875\n ],\n [\n 349.2797546386719,\n 597.8671875\n ],\n [\n 349.2797546386719,\n 621.6053314208984\n ],\n [\n 129.09375,\n 621.6053314208984\n ]\n ],\n \"bbox\": [\n 129.09375,\n 597.8671875,\n 349.2797546386719,\n 621.6053314208984\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/164/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"This header indicates that the new class is a Point, which is a kind of object, which is a built-in type.
\",\n \"polygon\": [\n [\n 128.794921875,\n 627.2257385253906\n ],\n [\n 525.638671875,\n 627.2257385253906\n ],\n [\n 525.638671875,\n 649.5318908691406\n ],\n [\n 128.794921875,\n 649.5318908691406\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 627.2257385253906,\n 525.638671875,\n 649.5318908691406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/164/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The body is a docstring that explains what the class is for. You can define variables and functions inside a class definition, but we will get back to that later.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 658.1953125\n ],\n [\n 525.9375,\n 658.1953125\n ],\n [\n 525.9375,\n 681.2808990478516\n ],\n [\n 128.3466796875,\n 681.2808990478516\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 658.1953125,\n 525.9375,\n 681.2808990478516\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/164/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Defining a class named Point creates a class object.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 690.6796875\n ],\n [\n 355.60546875,\n 690.6796875\n ],\n [\n 355.60546875,\n 700.8348999023438\n ],\n [\n 128.9443359375,\n 700.8348999023438\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 690.6796875,\n 355.60546875,\n 700.8348999023438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/165/Page/214\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/165/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.27978515625\n ],\n [\n 482.4034118652344,\n 60.27978515625\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.27978515625,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/PageHeader/20\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 84.49365234375,\n 59.94140625\n ],\n [\n 101.52685546875,\n 59.94140625\n ],\n [\n 101.52685546875,\n 70.0927734375\n ],\n [\n 84.49365234375,\n 70.0927734375\n ]\n ],\n \"bbox\": [\n 84.49365234375,\n 59.94140625,\n 101.52685546875,\n 70.0927734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \"x\\n y\\n 3.0\\n 4.0\\nblank\\n Point
\",\n \"polygon\": [\n [\n 236.64968872070312,\n 85.31982421875\n ],\n [\n 336.48046875,\n 85.31982421875\n ],\n [\n 336.48046875,\n 129.55078125\n ],\n [\n 236.64968872070312,\n 129.55078125\n ]\n ],\n \"bbox\": [\n 236.64968872070312,\n 85.31982421875,\n 336.48046875,\n 129.55078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Caption/2\",\n \"block_type\": \"Caption\",\n \"html\": \"Figure 15.1: Object diagram.
\",\n \"polygon\": [\n [\n 222.1787109375,\n 143.9560546875\n ],\n [\n 346.5672607421875,\n 143.9560546875\n ],\n [\n 346.5672607421875,\n 154.7529296875\n ],\n [\n 222.1787109375,\n 154.7529296875\n ]\n ],\n \"bbox\": [\n 222.1787109375,\n 143.9560546875,\n 346.5672607421875,\n 154.7529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> print Point\\n<class '__main__.Point'>
\",\n \"polygon\": [\n [\n 85.46484375,\n 174.603515625\n ],\n [\n 215.9033203125,\n 174.603515625\n ],\n [\n 215.9033203125,\n 198.0\n ],\n [\n 85.46484375,\n 198.0\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 174.603515625,\n 215.9033203125,\n 198.0\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Because Point is defined at the top level, its \\\"full name\\\" is __main__.Point.
\",\n \"polygon\": [\n [\n 85.46484375,\n 201.09375\n ],\n [\n 421.17767333984375,\n 201.09375\n ],\n [\n 421.17767333984375,\n 211.889892578125\n ],\n [\n 85.46484375,\n 211.889892578125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 201.09375,\n 421.17767333984375,\n 211.889892578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The class object is like a factory for creating objects. To create a Point, you call Point as if it were a function.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 219.462890625\n ],\n [\n 482.40020751953125,\n 219.462890625\n ],\n [\n 482.40020751953125,\n 242.77288818359375\n ],\n [\n 85.6142578125,\n 242.77288818359375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 219.462890625,\n 482.40020751953125,\n 242.77288818359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> blank = Point()\\n>>> print blank\\n<__main__.Point instance at 0xb7e9d3ac>
\",\n \"polygon\": [\n [\n 84.8671875,\n 247.377685546875\n ],\n [\n 290.3941345214844,\n 247.377685546875\n ],\n [\n 290.3941345214844,\n 281.91796875\n ],\n [\n 84.8671875,\n 281.91796875\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 247.377685546875,\n 290.3941345214844,\n 281.91796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The return value is a reference to a Point object, which we assign to blank. Creating a new object is called instantiation, and the object is an instance of the class.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 286.171875\n ],\n [\n 482.4039611816406,\n 286.171875\n ],\n [\n 482.4039611816406,\n 308.7898254394531\n ],\n [\n 85.9130859375,\n 308.7898254394531\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 286.171875,\n 482.4039611816406,\n 308.7898254394531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"When you print an instance, Python tells you what class it belongs to and where it is stored in memory (the prefix 0x means that the following number is in hexadecimal).
\",\n \"polygon\": [\n [\n 85.46484375,\n 316.72265625\n ],\n [\n 482.607421875,\n 316.72265625\n ],\n [\n 482.607421875,\n 339.6728210449219\n ],\n [\n 85.46484375,\n 339.6728210449219\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 316.72265625,\n 482.607421875,\n 339.6728210449219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"4\": \"/page/164/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"15.2 Attributes
\",\n \"polygon\": [\n [\n 85.68896484375,\n 366.22265625\n ],\n [\n 192.77703857421875,\n 366.22265625\n ],\n [\n 192.77703857421875,\n 381.3328857421875\n ],\n [\n 85.68896484375,\n 381.3328857421875\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 366.22265625,\n 192.77703857421875,\n 381.3328857421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"You can assign values to an instance using dot notation:
\",\n \"polygon\": [\n [\n 85.763671875,\n 391.166015625\n ],\n [\n 331.69921875,\n 391.166015625\n ],\n [\n 331.69921875,\n 402.1918029785156\n ],\n [\n 85.763671875,\n 402.1918029785156\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 391.166015625,\n 331.69921875,\n 402.1918029785156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \">>> blank.x = 3.0\\n>>> blank.y = 4.0
\",\n \"polygon\": [\n [\n 85.3154296875,\n 406.0546875\n ],\n [\n 175.8603515625,\n 406.0546875\n ],\n [\n 175.8603515625,\n 429.2578125\n ],\n [\n 85.3154296875,\n 429.2578125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 406.0546875,\n 175.8603515625,\n 429.2578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"This syntax is similar to the syntax for selecting a variable from a module, such as math.pi or string.whitespace. In this case, though, we are assigning values to named elements of an object. These elements are called attributes.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 433.51171875\n ],\n [\n 482.4085388183594,\n 433.51171875\n ],\n [\n 482.4085388183594,\n 468.2088317871094\n ],\n [\n 85.6142578125,\n 468.2088317871094\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 433.51171875,\n 482.4085388183594,\n 468.2088317871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"As a noun, \\\"AT-trib-ute\\\" is pronounced with emphasis on the first syllable, as opposed to \\\"a-TRIB-ute,\\\" which is a verb.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 475.27734375\n ],\n [\n 482.4033203125,\n 475.27734375\n ],\n [\n 482.4033203125,\n 499.0918273925781\n ],\n [\n 85.6142578125,\n 499.0918273925781\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 475.27734375,\n 482.4033203125,\n 499.0918273925781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The following diagram shows the result of these assignments. A state diagram that shows an object and its attributes is called an object diagram; see Figure 15.1.
\",\n \"polygon\": [\n [\n 85.46484375,\n 506.6015625\n ],\n [\n 482.40325927734375,\n 506.6015625\n ],\n [\n 482.40325927734375,\n 529.9748229980469\n ],\n [\n 85.46484375,\n 529.9748229980469\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 506.6015625,\n 482.40325927734375,\n 529.9748229980469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The variable blank refers to a Point object, which contains two attributes. Each attribute refers to a floating-point number.
\",\n \"polygon\": [\n [\n 85.46484375,\n 537.5390625\n ],\n [\n 482.90625,\n 537.5390625\n ],\n [\n 482.90625,\n 560.8578186035156\n ],\n [\n 85.46484375,\n 560.8578186035156\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 537.5390625,\n 482.90625,\n 560.8578186035156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"You can read the value of an attribute using the same syntax:
\",\n \"polygon\": [\n [\n 85.53955078125,\n 569.25\n ],\n [\n 353.74627685546875,\n 569.25\n ],\n [\n 353.74627685546875,\n 579.5468139648438\n ],\n [\n 85.53955078125,\n 579.5468139648438\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 569.25,\n 353.74627685546875,\n 579.5468139648438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Code/17\",\n \"block_type\": \"Code\",\n \"html\": \">>> print blank.y\\n4.0\\n>>> x = blank.x\\n>>> print x\\n3.0
\",\n \"polygon\": [\n [\n 84.8671875,\n 584.1516571044922\n ],\n [\n 175.31614685058594,\n 584.1516571044922\n ],\n [\n 175.31614685058594,\n 642.8912658691406\n ],\n [\n 84.8671875,\n 642.8912658691406\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 584.1516571044922,\n 175.31614685058594,\n 642.8912658691406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"The expression blank.x means, \\\"Go to the object blank refers to and get the value of x.\\\" In this case, we assign that value to a variable named x. There is no conflict between the variable x and the attribute x.
\",\n \"polygon\": [\n [\n 86.0625,\n 646.98046875\n ],\n [\n 482.4050598144531,\n 646.98046875\n ],\n [\n 482.4050598144531,\n 682.1468200683594\n ],\n [\n 86.0625,\n 682.1468200683594\n ]\n ],\n \"bbox\": [\n 86.0625,\n 646.98046875,\n 482.4050598144531,\n 682.1468200683594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/165/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"You can use dot notation as part of any expression. For example:
\",\n \"polygon\": [\n [\n 86.39995574951172,\n 690.29296875\n ],\n [\n 370.248046875,\n 690.29296875\n ],\n [\n 370.248046875,\n 700.8348159790039\n ],\n [\n 86.39995574951172,\n 700.8348159790039\n ]\n ],\n \"bbox\": [\n 86.39995574951172,\n 690.29296875,\n 370.248046875,\n 700.8348159790039\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/166/Page/185\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/166/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.6455078125,\n 61.1015625\n ],\n [\n 525.6033935546875,\n 61.1015625\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.6455078125,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 61.1015625,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/166/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 510.3984375,\n 60.908203125\n ],\n [\n 525.9375,\n 60.908203125\n ],\n [\n 525.9375,\n 70.2861328125\n ],\n [\n 510.3984375,\n 70.2861328125\n ]\n ],\n \"bbox\": [\n 510.3984375,\n 60.908203125,\n 525.9375,\n 70.2861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/166/Code/1\",\n \"block_type\": \"Code\",\n \"html\": \">>> print '(%g, %g)' % (blank.x, blank.y)\\n(3.0, 4.0)\\n>>> distance = math.sqrt(blank.x**2 + blank.y**2)\\n>>> print distance\\n5.0
\",\n \"polygon\": [\n [\n 128.6455078125,\n 88.31689453125\n ],\n [\n 385.9077453613281,\n 88.31689453125\n ],\n [\n 385.9077453613281,\n 147.42529296875\n ],\n [\n 128.6455078125,\n 147.42529296875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 88.31689453125,\n 385.9077453613281,\n 147.42529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/166/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"You can pass an instance as an argument in the usual way. For example:
\",\n \"polygon\": [\n [\n 128.794921875,\n 152.8505859375\n ],\n [\n 445.8515625,\n 152.8505859375\n ],\n [\n 445.8515625,\n 163.60888671875\n ],\n [\n 128.794921875,\n 163.60888671875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 152.8505859375,\n 445.8515625,\n 163.60888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/166/TextInlineMath/3\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"def print_point(p): print '(%g, %g)' % (p.x, p.y)
\",\n \"polygon\": [\n [\n 128.57080078125,\n 169.52972412109375\n ],\n [\n 302.1754150390625,\n 169.52972412109375\n ],\n [\n 302.1754150390625,\n 191.686279296875\n ],\n [\n 128.57080078125,\n 191.686279296875\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 169.52972412109375,\n 302.1754150390625,\n 191.686279296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/166/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"print_point takes a point as an argument and displays it in mathematical notation. To invoke it, you can pass blank as an argument:
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\",\n \"polygon\": [\n [\n 127.82373046875,\n 317.8707580566406\n ],\n [\n 239.96533203125,\n 317.8707580566406\n ],\n [\n 239.96533203125,\n 332.21697998046875\n ],\n [\n 127.82373046875,\n 332.21697998046875\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 317.8707580566406,\n 239.96533203125,\n 332.21697998046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/166/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/166/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Sometimes it is obvious what the attributes of an object should be, but other times you have to make decisions. For example, imagine you are designing a class to represent rectangles. What attributes would you use to specify the location and size of a rectangle? You can ignore angle; to keep things simple, assume that the rectangle is either vertical or horizontal.
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\",\n \"polygon\": [\n [\n 86.2119140625,\n 192.59674072265625\n ],\n [\n 191.01724243164062,\n 192.59674072265625\n ],\n [\n 191.01724243164062,\n 226.947265625\n ],\n [\n 86.2119140625,\n 226.947265625\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 192.59674072265625,\n 191.01724243164062,\n 226.947265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/166/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The expression box.corner.x means, \\\"Go to the object box refers to and select the attribute named corner; then go to that object and select the attribute named x.\\\"
\",\n \"polygon\": [\n [\n 86.2119140625,\n 233.21868896484375\n ],\n [\n 482.4002990722656,\n 233.21868896484375\n ],\n [\n 482.4002990722656,\n 255.52484130859375\n ],\n [\n 86.2119140625,\n 255.52484130859375\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 233.21868896484375,\n 482.4002990722656,\n 255.52484130859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/166/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Figure 15.2 shows the state of this object. An object that is an attribute of another object is embedded.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 265.482421875\n ],\n [\n 482.4033508300781,\n 265.482421875\n ],\n [\n 482.4033508300781,\n 287.9248046875\n ],\n [\n 86.2119140625,\n 287.9248046875\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 265.482421875,\n 482.4033508300781,\n 287.9248046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/166/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"15.4 Instances as return values
\",\n \"polygon\": [\n [\n 85.98779296875,\n 317.4716491699219\n ],\n [\n 294.3768615722656,\n 317.4716491699219\n ],\n [\n 294.3768615722656,\n 331.81787109375\n ],\n [\n 85.98779296875,\n 331.81787109375\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 317.4716491699219,\n 294.3768615722656,\n 331.81787109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Functions can return instances. For example, find_center takes a Rectangle as an argument and returns a Point that contains the coordinates of the center of the Rectangle:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 344.108642578125\n ],\n [\n 482.4006042480469,\n 344.108642578125\n ],\n [\n 482.4006042480469,\n 366.416015625\n ],\n [\n 85.6142578125,\n 366.416015625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 344.108642578125,\n 482.4006042480469,\n 366.416015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"def find_center(rect):\\n p = Point()\\n p.x = rect.corner.x + rect.width/2.0\\n p.y = rect.corner.y + rect.height/2.0\\n return p
\",\n \"polygon\": [\n [\n 86.40005493164062,\n 372.5366516113281\n ],\n [\n 300.8494873046875,\n 372.5366516113281\n ],\n [\n 300.8494873046875,\n 431.2772521972656\n ],\n [\n 86.40005493164062,\n 431.2772521972656\n ]\n ],\n \"bbox\": [\n 86.40005493164062,\n 372.5366516113281,\n 300.8494873046875,\n 431.2772521972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Here is an example that passes box as an argument and assigns the resulting Point to center:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 436.9921875\n ],\n [\n 482.3963317871094,\n 436.9921875\n ],\n [\n 482.3963317871094,\n 459.8548278808594\n ],\n [\n 86.2119140625,\n 459.8548278808594\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 436.9921875,\n 482.3963317871094,\n 459.8548278808594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> center = find_center(box)\\n>>> print_point(center)\\n(50.0, 100.0)
\",\n \"polygon\": [\n [\n 86.2119140625,\n 465.9766845703125\n ],\n [\n 238.09054565429688,\n 465.9766845703125\n ],\n [\n 238.09054565429688,\n 500.4140625\n ],\n [\n 86.2119140625,\n 500.4140625\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 465.9766845703125,\n 238.09054565429688,\n 500.4140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"15.5 Objects are mutable
\",\n \"polygon\": [\n [\n 85.98779296875,\n 530.023681640625\n ],\n [\n 257.888671875,\n 530.023681640625\n ],\n [\n 257.888671875,\n 544.3698883056641\n ],\n [\n 85.98779296875,\n 544.3698883056641\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 530.023681640625,\n 257.888671875,\n 544.3698883056641\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"You can change the state of an object by making an assignment to one of its attributes. For example, to change the size of a rectangle without changing its position, you can modify the values of width and height:
\",\n \"polygon\": [\n [\n 86.0625,\n 556.48828125\n ],\n [\n 482.607421875,\n 556.48828125\n ],\n [\n 482.607421875,\n 591.1618499755859\n ],\n [\n 86.0625,\n 591.1618499755859\n ]\n ],\n \"bbox\": [\n 86.0625,\n 556.48828125,\n 482.607421875,\n 591.1618499755859\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"box.width = box.width + 50\\nbox.height = box.width + 100
\",\n \"polygon\": [\n [\n 86.361328125,\n 597.28369140625\n ],\n [\n 232.8601837158203,\n 597.28369140625\n ],\n [\n 232.8601837158203,\n 619.4402923583984\n ],\n [\n 86.361328125,\n 619.4402923583984\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 597.28369140625,\n 232.8601837158203,\n 619.4402923583984\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"You can also write functions that modify objects. For example, grow_rectangle takes a Rectangle object and two numbers, dwidth and dheight, and adds the numbers to the width and height of the rectangle:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 625.7109375\n ],\n [\n 482.90625,\n 625.7109375\n ],\n [\n 482.90625,\n 660.515625\n ],\n [\n 85.6142578125,\n 660.515625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 625.7109375,\n 482.90625,\n 660.515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/167/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \"def grow_rectangle(rect, dwidth, dheight):\\n rect.width += dwidth\\n rect.height += dheight
\",\n \"polygon\": [\n [\n 86.28662109375,\n 666.3347015380859\n ],\n [\n 306.09521484375,\n 666.3347015380859\n ],\n [\n 306.09521484375,\n 700.685302734375\n ],\n [\n 86.28662109375,\n 700.685302734375\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 666.3347015380859,\n 306.09521484375,\n 700.685302734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/168/Page/230\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/168/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.86962890625,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.86962890625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 61.171142578125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/PageHeader/12\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 510.3984375,\n 61.1015625\n ],\n [\n 525.33984375,\n 61.1015625\n ],\n [\n 525.33984375,\n 70.3828125\n ],\n [\n 510.3984375,\n 70.3828125\n ]\n ],\n \"bbox\": [\n 510.3984375,\n 61.1015625,\n 525.33984375,\n 70.3828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"Here is an example that demonstrates the effect:
\",\n \"polygon\": [\n [\n 127.52490234375,\n 88.365234375\n ],\n [\n 340.962890625,\n 88.365234375\n ],\n [\n 340.962890625,\n 98.79791259765625\n ],\n [\n 127.52490234375,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 127.52490234375,\n 88.365234375,\n 340.962890625,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \">>> print box.width\\n100.0\\n>>> print box.height\\n200.0\\n>>> grow_rectangle(box, 50, 100)\\n>>> print box.width\\n150.0\\n>>> print box.height\\n300.0
\",\n \"polygon\": [\n [\n 128.6455078125,\n 106.52471923828125\n ],\n [\n 296.9815979003906,\n 106.52471923828125\n ],\n [\n 296.9815979003906,\n 214.04229736328125\n ],\n [\n 128.6455078125,\n 214.04229736328125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 106.52471923828125,\n 296.9815979003906,\n 214.04229736328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Inside the function, rect is an alias for box, so if the function modifies rect, box changes. Exercise 15.2. Write a function named move_rectangle that takes a Rectangle and two numbers named dx and dy. It should change the location of the rectangle by adding dx to the x coordinate of corner and adding dy to the y coordinate of corner.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 221.918701171875\n ],\n [\n 525.9375,\n 221.918701171875\n ],\n [\n 525.9375,\n 268.46429443359375\n ],\n [\n 128.9443359375,\n 268.46429443359375\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 221.918701171875,\n 525.9375,\n 268.46429443359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/167/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"15.6 Copying
\",\n \"polygon\": [\n [\n 127.7490234375,\n 302.9747009277344\n ],\n [\n 225.7646484375,\n 302.9747009277344\n ],\n [\n 225.7646484375,\n 317.3209228515625\n ],\n [\n 127.7490234375,\n 317.3209228515625\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 302.9747009277344,\n 225.7646484375,\n 317.3209228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Aliasing can make a program difficult to read because changes in one place might have unexpected effects in another place. It is hard to keep track of all the variables that might refer to a given object.
\",\n \"polygon\": [\n [\n 129.09375,\n 331.41796875\n ],\n [\n 525.6033935546875,\n 331.41796875\n ],\n [\n 525.6033935546875,\n 366.35986328125\n ],\n [\n 129.09375,\n 366.35986328125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 331.41796875,\n 525.6033935546875,\n 366.35986328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Copying an object is often an alternative to aliasing. The copy module contains a function called copy that can duplicate any object:
\",\n \"polygon\": [\n [\n 128.794921875,\n 377.82421875\n ],\n [\n 525.638671875,\n 377.82421875\n ],\n [\n 525.638671875,\n 400.3648681640625\n ],\n [\n 128.794921875,\n 400.3648681640625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 377.82421875,\n 525.638671875,\n 400.3648681640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> p1 = Point()\\n>>> p1.x = 3.0\\n>>> p1.y = 4.0\\n>>> import copy\\n>>> p2 = copy.copy(p1)
\",\n \"polygon\": [\n [\n 129.6000518798828,\n 408.0917053222656\n ],\n [\n 244.6780242919922,\n 408.0917053222656\n ],\n [\n 244.6780242919922,\n 483.3984375\n ],\n [\n 129.6000518798828,\n 483.3984375\n ]\n ],\n \"bbox\": [\n 129.6000518798828,\n 408.0917053222656,\n 244.6780242919922,\n 483.3984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"p1 and p2 contain the same data, but they are not the same Point.
\",\n \"polygon\": [\n [\n 128.57080078125,\n 486.9027099609375\n ],\n [\n 416.267578125,\n 486.9027099609375\n ],\n [\n 416.267578125,\n 497.0148620605469\n ],\n [\n 128.57080078125,\n 497.0148620605469\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 486.9027099609375,\n 416.267578125,\n 497.0148620605469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> print_point(p1)\\n(3.0, 4.0)\\n>>> print_point(p2)\\n(3.0, 4.0)\\n>>> p1 is p2\\nFalse\\n>>> p1 == p2\\nFalse
\",\n \"polygon\": [\n [\n 129.6000518798828,\n 504.74169921875\n ],\n [\n 231.4423828125,\n 504.74169921875\n ],\n [\n 231.4423828125,\n 600.1875\n ],\n [\n 129.6000518798828,\n 600.1875\n ]\n ],\n \"bbox\": [\n 129.6000518798828,\n 504.74169921875,\n 231.4423828125,\n 600.1875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/168/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The is operator indicates that p1 and p2 are not the same object, which is what we expected. But you might have expected == to yield True because these points contain the same data. In that case, you will be disappointed to learn that for instances, the default behavior of the == operator is the same as the is operator; it checks object identity, not object equivalence. This behavior can be changed—we'll see how later.
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\"\n }\n },\n {\n \"id\": \"/page/169/Caption/2\",\n \"block_type\": \"Caption\",\n \"html\": \"Figure 15.3: Object diagram.
\",\n \"polygon\": [\n [\n 222.23399353027344,\n 148.5966796875\n ],\n [\n 346.5672607421875,\n 148.5966796875\n ],\n [\n 346.5672607421875,\n 158.7529296875\n ],\n [\n 222.23399353027344,\n 158.7529296875\n ]\n ],\n \"bbox\": [\n 222.23399353027344,\n 148.5966796875,\n 346.5672607421875,\n 158.7529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/169/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \">>> box2 = copy.copy(box)\\n>>> box2 is box\\nFalse\\n>>> box2.corner is box.corner\\nTrue
\",\n \"polygon\": [\n [\n 85.68896484375,\n 179.05078125\n ],\n [\n 239.0625,\n 179.05078125\n ],\n [\n 239.0625,\n 241.3125\n ],\n [\n 85.68896484375,\n 241.3125\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 179.05078125,\n 239.0625,\n 241.3125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Figure 15.3 shows what the object diagram looks like. This operation is called a shallow copy because it copies the object and any references it contains, but not the embedded objects.
\",\n \"polygon\": [\n [\n 85.763671875,\n 242.713134765625\n ],\n [\n 482.4046325683594,\n 242.713134765625\n ],\n [\n 482.4046325683594,\n 277.16082763671875\n ],\n [\n 85.763671875,\n 277.16082763671875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 242.713134765625,\n 482.4046325683594,\n 277.16082763671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"For most applications, this is not what you want. In this example, invoking grow_rectangle on one of the Rectangles would not affect the other, but invoking move_rectangle on either would affect both! This behavior is confusing and error-prone.
\",\n \"polygon\": [\n [\n 85.763671875,\n 285.205078125\n ],\n [\n 482.40338134765625,\n 285.205078125\n ],\n [\n 482.40338134765625,\n 320.34881591796875\n ],\n [\n 85.763671875,\n 320.34881591796875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 285.205078125,\n 482.40338134765625,\n 320.34881591796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Fortunately, the copy module contains a method named deepcopy that copies not only the object but also the objects it refers to, and the objects they refer to, and so on. You will not be surprised to learn that this operation is called a deep copy.
\",\n \"polygon\": [\n [\n 85.46484375,\n 328.7109375\n ],\n [\n 482.607421875,\n 328.7109375\n ],\n [\n 482.607421875,\n 363.5368347167969\n ],\n [\n 85.46484375,\n 363.5368347167969\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 328.7109375,\n 482.607421875,\n 363.5368347167969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> box3 = copy.deepcopy(box)\\n>>> box3 is box\\nFalse\\n>>> box3.corner is box.corner\\nFalse
\",\n \"polygon\": [\n [\n 85.39013671875,\n 367.76953125\n ],\n [\n 238.09048461914062,\n 367.76953125\n ],\n [\n 238.09048461914062,\n 426.9922790527344\n ],\n [\n 85.39013671875,\n 426.9922790527344\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 367.76953125,\n 238.09048461914062,\n 426.9922790527344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"box3 and box are completely separate objects.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 431.19140625\n ],\n [\n 286.43975830078125,\n 431.19140625\n ],\n [\n 286.43975830078125,\n 441.9698486328125\n ],\n [\n 85.9130859375,\n 441.9698486328125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 431.19140625,\n 286.43975830078125,\n 441.9698486328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 15.3. Write a version of move_rectangle that creates and returns a new Rectangle instead of modifying the old one.
\",\n \"polygon\": [\n [\n 85.46484375,\n 443.953125\n ],\n [\n 482.607421875,\n 443.953125\n ],\n [\n 482.607421875,\n 466.1851501464844\n ],\n [\n 85.46484375,\n 466.1851501464844\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 443.953125,\n 482.607421875,\n 466.1851501464844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/168/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/SectionHeader/10\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"15.7 Debugging
\",\n \"polygon\": [\n [\n 85.763671875,\n 493.453125\n ],\n [\n 199.9788055419922,\n 493.453125\n ],\n [\n 199.9788055419922,\n 508.17291259765625\n ],\n [\n 85.763671875,\n 508.17291259765625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 493.453125,\n 199.9788055419922,\n 508.17291259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"When you start working with objects, you are likely to encounter some new exceptions. If you try to access an attribute that doesn't exist, you get an AttributeError:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 518.58984375\n ],\n [\n 483.50390625,\n 518.58984375\n ],\n [\n 483.50390625,\n 541.3368530273438\n ],\n [\n 85.9130859375,\n 541.3368530273438\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 518.58984375,\n 483.50390625,\n 541.3368530273438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> p = Point()\\n>>> print p.z\\nAttributeError: Point instance has no attribute 'z'
\",\n \"polygon\": [\n [\n 86.28662109375,\n 546.0527038574219\n ],\n [\n 354.41015625,\n 546.0527038574219\n ],\n [\n 354.41015625,\n 582.01171875\n ],\n [\n 86.28662109375,\n 582.01171875\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 546.0527038574219,\n 354.41015625,\n 582.01171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"If you are not sure what type an object is, you can ask:
\",\n \"polygon\": [\n [\n 85.763671875,\n 585.10546875\n ],\n [\n 325.72265625,\n 585.10546875\n ],\n [\n 325.72265625,\n 595.380859375\n ],\n [\n 85.763671875,\n 595.380859375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 585.10546875,\n 325.72265625,\n 595.380859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> type(p)\\n<type '__main__.Point'>
\",\n \"polygon\": [\n [\n 86.0625,\n 600.0967102050781\n ],\n [\n 206.6703643798828,\n 600.0967102050781\n ],\n [\n 206.6703643798828,\n 622.2533111572266\n ],\n [\n 86.0625,\n 622.2533111572266\n ]\n ],\n \"bbox\": [\n 86.0625,\n 600.0967102050781,\n 206.6703643798828,\n 622.2533111572266\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"If you are not sure whether an object has a particular attribute, you can use the built-in function hasattr:
\",\n \"polygon\": [\n [\n 85.763671875,\n 626.87109375\n ],\n [\n 482.40338134765625,\n 626.87109375\n ],\n [\n 482.40338134765625,\n 649.4248657226562\n ],\n [\n 85.763671875,\n 649.4248657226562\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 626.87109375,\n 482.40338134765625,\n 649.4248657226562\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/169/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \">>> hasattr(p, 'x')\\nTrue\\n>>> hasattr(p, 'z')\\nFalse
\",\n \"polygon\": [\n [\n 85.9130859375,\n 654.1407165527344\n ],\n [\n 186.9169921875,\n 654.1407165527344\n ],\n [\n 186.9169921875,\n 700.6863098144531\n ],\n [\n 85.9130859375,\n 700.6863098144531\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 654.1407165527344,\n 186.9169921875,\n 700.6863098144531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/170/Page/197\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/170/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.42138671875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.42138671875,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 61.171142578125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/PageHeader/23\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 510.3984375,\n 60.908203125\n ],\n [\n 525.33984375,\n 60.908203125\n ],\n [\n 525.33984375,\n 69.802734375\n ],\n [\n 510.3984375,\n 69.802734375\n ]\n ],\n \"bbox\": [\n 510.3984375,\n 60.908203125,\n 525.33984375,\n 69.802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"The first argument can be any object; the second argument is a string that contains the name of the attribute.
\",\n \"polygon\": [\n [\n 127.599609375,\n 88.365234375\n ],\n [\n 526.236328125,\n 88.365234375\n ],\n [\n 526.236328125,\n 110.99188232421875\n ],\n [\n 127.599609375,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 88.365234375,\n 526.236328125,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/169/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"15.8 Glossary
\",\n \"polygon\": [\n [\n 127.4501953125,\n 138.96173095703125\n ],\n [\n 227.2587890625,\n 138.96173095703125\n ],\n [\n 227.2587890625,\n 153.30792236328125\n ],\n [\n 127.4501953125,\n 153.30792236328125\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 138.96173095703125,\n 227.2587890625,\n 153.30792236328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"class: A user-defined type. A class definition creates a new class object.
\",\n \"polygon\": [\n [\n 128.49609375,\n 161.30615234375\n ],\n [\n 444.357421875,\n 161.30615234375\n ],\n [\n 444.357421875,\n 171.36590576171875\n ],\n [\n 128.49609375,\n 171.36590576171875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 161.30615234375,\n 444.357421875,\n 171.36590576171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \"class object: An object that contains information about a user-defined type. The class object can be used to create instances of the type.\",\n \"polygon\": [\n [\n 128.197265625,\n 180.59765625\n ],\n [\n 525.638671875,\n 180.59765625\n ],\n [\n 525.638671875,\n 202.88885498046875\n ],\n [\n 128.197265625,\n 202.88885498046875\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 180.59765625,\n 525.638671875,\n 202.88885498046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"instance: An object that belongs to a class.
\",\n \"polygon\": [\n [\n 128.49609375,\n 212.1580810546875\n ],\n [\n 317.27569580078125,\n 212.1580810546875\n ],\n [\n 317.27569580078125,\n 222.21783447265625\n ],\n [\n 128.49609375,\n 222.21783447265625\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 212.1580810546875,\n 317.27569580078125,\n 222.21783447265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"attribute: One of the named values associated with an object.
\",\n \"polygon\": [\n [\n 129.16845703125,\n 231.2578125\n ],\n [\n 400.73162841796875,\n 231.2578125\n ],\n [\n 400.73162841796875,\n 241.54681396484375\n ],\n [\n 129.16845703125,\n 241.54681396484375\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 231.2578125,\n 400.73162841796875,\n 241.54681396484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/193\",\n \"block_type\": \"Text\",\n \"html\": \"embedded (object): An object that is stored as an attribute of another object.
\",\n \"polygon\": [\n [\n 129.09375,\n 250.787109375\n ],\n [\n 467.068359375,\n 250.787109375\n ],\n [\n 467.068359375,\n 260.87481689453125\n ],\n [\n 129.09375,\n 260.87481689453125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 250.787109375,\n 467.068359375,\n 260.87481689453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/ListGroup/194\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.6455078125,\n 270.14404296875\n ],\n [\n 525.9375,\n 270.14404296875\n ],\n [\n 525.9375,\n 367.6387939453125\n ],\n [\n 128.6455078125,\n 367.6387939453125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 270.14404296875,\n 525.9375,\n 367.6387939453125\n ],\n \"children\": [\n {\n \"id\": \"/page/170/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"shallow copy: To copy the contents of an object, including any references to embedded objects; implemented by the copy function in the copy module.\",\n \"polygon\": [\n [\n 128.6455078125,\n 270.14404296875\n ],\n [\n 525.9375,\n 270.14404296875\n ],\n [\n 525.9375,\n 292.39776611328125\n ],\n [\n 128.6455078125,\n 292.39776611328125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 270.14404296875,\n 525.9375,\n 292.39776611328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"deep copy: To copy the contents of an object as well as any embedded objects, and any objects embedded in them, and so on; implemented by the deepcopy function in the copy module.\",\n \"polygon\": [\n [\n 128.9443359375,\n 301.66705322265625\n ],\n [\n 525.6045532226562,\n 301.66705322265625\n ],\n [\n 525.6045532226562,\n 336.11578369140625\n ],\n [\n 128.9443359375,\n 336.11578369140625\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 301.66705322265625,\n 525.6045532226562,\n 336.11578369140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/ListItem/10\",\n \"block_type\": \"ListItem\",\n \"html\": \"object diagram: A diagram that shows objects, their attributes, and the values of the attributes.\",\n \"polygon\": [\n [\n 129.09375,\n 345.33984375\n ],\n [\n 525.6028442382812,\n 345.33984375\n ],\n [\n 525.6028442382812,\n 367.6387939453125\n ],\n [\n 129.09375,\n 367.6387939453125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 345.33984375,\n 525.6028442382812,\n 367.6387939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/2\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/170/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"15.9 Exercises
\",\n \"polygon\": [\n [\n 128.57080078125,\n 395.608642578125\n ],\n [\n 228.80392456054688,\n 395.608642578125\n ],\n [\n 228.80392456054688,\n 409.9548645019531\n ],\n [\n 128.57080078125,\n 409.9548645019531\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 395.608642578125,\n 228.80392456054688,\n 409.9548645019531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 15.4. Swampy (see Chapter 4) provides a module named World, which defines a userdefined type also called World. You can import it like this:
\",\n \"polygon\": [\n [\n 129.59994506835938,\n 421.13671875\n ],\n [\n 525.6023559570312,\n 421.13671875\n ],\n [\n 525.6023559570312,\n 443.3272399902344\n ],\n [\n 129.59994506835938,\n 443.3272399902344\n ]\n ],\n \"bbox\": [\n 129.59994506835938,\n 421.13671875,\n 525.6023559570312,\n 443.3272399902344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"from swampy.World import World
\",\n \"polygon\": [\n [\n 129.2431640625,\n 448.5516662597656\n ],\n [\n 286.5208435058594,\n 448.5516662597656\n ],\n [\n 286.5208435058594,\n 458.5142517089844\n ],\n [\n 129.2431640625,\n 458.5142517089844\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 448.5516662597656,\n 286.5208435058594,\n 458.5142517089844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Or, depending on how you installed Swampy, like this:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 463.71453857421875\n ],\n [\n 349.62890625,\n 463.71453857421875\n ],\n [\n 349.62890625,\n 473.6771240234375\n ],\n [\n 129.2431640625,\n 473.6771240234375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 463.71453857421875,\n 349.62890625,\n 473.6771240234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"from World import World
\",\n \"polygon\": [\n [\n 128.86962890625,\n 478.9236755371094\n ],\n [\n 251.015625,\n 478.9236755371094\n ],\n [\n 251.015625,\n 488.8862609863281\n ],\n [\n 128.86962890625,\n 488.8862609863281\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 478.9236755371094,\n 251.015625,\n 488.8862609863281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"The following code creates a World object and calls the mainloop method, which waits for the user.
\",\n \"polygon\": [\n [\n 129.59994506835938,\n 493.83984375\n ],\n [\n 525.6014404296875,\n 493.83984375\n ],\n [\n 525.6014404296875,\n 509.30859375\n ],\n [\n 129.59994506835938,\n 509.30859375\n ]\n ],\n \"bbox\": [\n 129.59994506835938,\n 493.83984375,\n 525.6014404296875,\n 509.30859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Code/192\",\n \"block_type\": \"Code\",\n \"html\": \"world = World()\\nworld.mainloop()
\",\n \"polygon\": [\n [\n 128.86962890625,\n 509.2956848144531\n ],\n [\n 213.2957763671875,\n 509.2956848144531\n ],\n [\n 213.2957763671875,\n 531.4522705078125\n ],\n [\n 128.86962890625,\n 531.4522705078125\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 509.2956848144531,\n 213.2957763671875,\n 531.4522705078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"A window should appear with a title bar and an empty square. We will use this window to draw Points, Rectangles and other shapes. Add the following lines before calling mainloop and run the program again.
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\",\n \"polygon\": [\n [\n 129.60000610351562,\n 576.2109375\n ],\n [\n 459.035400390625,\n 576.2109375\n ],\n [\n 459.035400390625,\n 610.6013031005859\n ],\n [\n 129.60000610351562,\n 610.6013031005859\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 576.2109375,\n 459.035400390625,\n 610.6013031005859\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/170/Text/20\",\n \"block_type\": \"Text\",\n \"html\": \"You should see a green rectangle with a black outline. The first line creates a Canvas, which appears in the window as a white square. The Canvas object provides methods like rectangle for drawing various shapes.
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\",\n \"polygon\": [\n [\n 111.30699920654297,\n 335.32073974609375\n ],\n [\n 357.3984375,\n 335.32073974609375\n ],\n [\n 357.3984375,\n 357.47833251953125\n ],\n [\n 111.30699920654297,\n 357.47833251953125\n ]\n ],\n \"bbox\": [\n 111.30699920654297,\n 335.32073974609375,\n 357.3984375,\n 357.47833251953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/164/SectionHeader/1\",\n \"3\": \"/page/165/SectionHeader/9\",\n \"4\": \"/page/170/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/171/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"I have written a small program that lists the available colors; you can download it from http: // thinkpython. com/ code/ color_ list. py .
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\",\n \"polygon\": [\n [\n 128.86962890625,\n 216.830322265625\n ],\n [\n 376.008544921875,\n 216.830322265625\n ],\n [\n 376.008544921875,\n 241.61737060546875\n ],\n [\n 128.86962890625,\n 241.61737060546875\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 216.830322265625,\n 376.008544921875,\n 241.61737060546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/172/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Code examples from this chapter are available from http://thinkpython.com/code/ Time1.py.
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\",\n \"polygon\": [\n [\n 128.86962890625,\n 336.0127868652344\n ],\n [\n 202.53607177734375,\n 336.0127868652344\n ],\n [\n 202.53607177734375,\n 350.3590087890625\n ],\n [\n 128.86962890625,\n 350.3590087890625\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 336.0127868652344,\n 202.53607177734375,\n 350.3590087890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/172/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"As another example of a user-defined type, we'll define a class called Time that records the time of day. The class definition looks like this:
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\",\n \"polygon\": [\n [\n 150.51597595214844,\n 437.0458068847656\n ],\n [\n 166.20706176757812,\n 437.0458068847656\n ],\n [\n 166.20706176757812,\n 447.0083923339844\n ],\n [\n 150.51597595214844,\n 447.0083923339844\n ]\n ],\n \"bbox\": [\n 150.51597595214844,\n 437.0458068847656,\n 166.20706176757812,\n 447.0083923339844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/172/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The state diagram for the Time object looks like Figure 16.1.
\",\n \"polygon\": [\n [\n 128.86962890625,\n 518.1548461914062\n ],\n [\n 391.166015625,\n 518.1548461914062\n ],\n [\n 391.166015625,\n 528.2669982910156\n ],\n [\n 128.86962890625,\n 528.2669982910156\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 518.1548461914062,\n 391.166015625,\n 528.2669982910156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/172/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 16.1. Write a function called print_time that takes a Time object and prints it in the form hour:minute:second. Hint: the format sequence '%.2d' prints an integer using at least two digits, including a leading zero if necessary.
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\",\n \"polygon\": [\n [\n 85.46484375,\n 286.3658447265625\n ],\n [\n 482.607421875,\n 286.3658447265625\n ],\n [\n 482.607421875,\n 333.0610046386719\n ],\n [\n 85.46484375,\n 333.0610046386719\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 286.3658447265625,\n 482.607421875,\n 333.0610046386719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/173/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"To test this function, I'll create two Time objects: start contains the start time of a movie, like Monty Python and the Holy Grail, and duration contains the run time of the movie, which is one hour 35 minutes.
\",\n \"polygon\": [\n [\n 85.46484375,\n 343.54986572265625\n ],\n [\n 482.40179443359375,\n 343.54986572265625\n ],\n [\n 482.40179443359375,\n 378.051025390625\n ],\n [\n 85.46484375,\n 378.051025390625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 343.54986572265625,\n 482.40179443359375,\n 378.051025390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/173/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"add_time figures out when the movie will be done.
\",\n \"polygon\": [\n [\n 85.166015625,\n 388.5398864746094\n ],\n [\n 311.0869140625,\n 388.5398864746094\n ],\n [\n 311.0869140625,\n 398.65203857421875\n ],\n [\n 85.166015625,\n 398.65203857421875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 388.5398864746094,\n 311.0869140625,\n 398.65203857421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/173/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> start = Time()\\n>>> start.hour = 9\\n>>> start.minute = 45\\n>>> start.second = 0\\n>>> duration = Time()\\n>>> duration.hour = 1\\n>>> duration.minute = 35\\n>>> duration.second = 0\\n>>> done = add_time(start, duration)\\n>>> print_time(done)\\n10:80:00
\",\n \"polygon\": [\n [\n 86.28662109375,\n 405.1698913574219\n ],\n [\n 274.7030944824219,\n 405.1698913574219\n ],\n [\n 274.7030944824219,\n 561.4645233154297\n ],\n [\n 86.28662109375,\n 561.4645233154297\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 405.1698913574219,\n 274.7030944824219,\n 561.4645233154297\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/173/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The result, 10:80:00 might not be what you were hoping for. The problem is that this function does not deal with cases where the number of seconds or minutes adds up to more than sixty. When that happens, we have to \\\"carry\\\" the extra seconds into the minute column or the extra minutes into the hour column.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 567.703125\n ],\n [\n 483.50390625,\n 567.703125\n ],\n [\n 483.50390625,\n 614.8270874023438\n ],\n [\n 85.6142578125,\n 614.8270874023438\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 567.703125,\n 483.50390625,\n 614.8270874023438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/173/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Here's an improved version:
\",\n \"polygon\": [\n [\n 85.39013671875,\n 625.32421875\n ],\n [\n 212.466796875,\n 625.32421875\n ],\n [\n 212.466796875,\n 635.4280853271484\n ],\n [\n 85.39013671875,\n 635.4280853271484\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 625.32421875,\n 212.466796875,\n 635.4280853271484\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/173/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \"def add_time(t1, t2):\\n sum = Time()\\n sum.hour = t1.hour + t2.hour\\n sum.minute = t1.minute + t2.minute\\n sum.second = t1.second + t2.second
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\",\n \"polygon\": [\n [\n 149.115234375,\n 100.8797607421875\n ],\n [\n 258.78515625,\n 100.8797607421875\n ],\n [\n 258.78515625,\n 186.205078125\n ],\n [\n 149.115234375,\n 186.205078125\n ]\n ],\n \"bbox\": [\n 149.115234375,\n 100.8797607421875,\n 258.78515625,\n 186.205078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"return sum
\",\n \"polygon\": [\n [\n 148.74169921875,\n 198.4346923828125\n ],\n [\n 202.81964111328125,\n 198.4346923828125\n ],\n [\n 202.81964111328125,\n 208.39727783203125\n ],\n [\n 148.74169921875,\n 208.39727783203125\n ]\n ],\n \"bbox\": [\n 148.74169921875,\n 198.4346923828125,\n 202.81964111328125,\n 208.39727783203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Although this function is correct, it is starting to get big. We will see a shorter alternative later.
\",\n \"polygon\": [\n [\n 128.197265625,\n 212.990234375\n ],\n [\n 525.6033935546875,\n 212.990234375\n ],\n [\n 525.6033935546875,\n 235.51171875\n ],\n [\n 128.197265625,\n 235.51171875\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 212.990234375,\n 525.6033935546875,\n 235.51171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/172/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/SectionHeader/4\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"16.3 Modifiers
\",\n \"polygon\": [\n [\n 128.3466796875,\n 262.001953125\n ],\n [\n 232.80654907226562,\n 262.001953125\n ],\n [\n 232.80654907226562,\n 276.3709716796875\n ],\n [\n 128.3466796875,\n 276.3709716796875\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 262.001953125,\n 232.80654907226562,\n 276.3709716796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Sometimes it is useful for a function to modify the objects it gets as parameters. In that case, the changes are visible to the caller. Functions that work this way are called modifiers.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 286.751953125\n ],\n [\n 525.6033325195312,\n 286.751953125\n ],\n [\n 525.6033325195312,\n 309.1129150390625\n ],\n [\n 129.2431640625,\n 309.1129150390625\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 286.751953125,\n 525.6033325195312,\n 309.1129150390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"increment, which adds a given number of seconds to a Time object, can be written naturally as a modifier. Here is a rough draft:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 317.302734375\n ],\n [\n 525.6021118164062,\n 317.302734375\n ],\n [\n 525.6021118164062,\n 339.6849060058594\n ],\n [\n 129.2431640625,\n 339.6849060058594\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 317.302734375,\n 525.6021118164062,\n 339.6849060058594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"def increment(time, seconds):\\n time.second += seconds\\n if time.second >= 60:\\n time.second -= 60\\n time.minute += 1\\n if time.minute >= 60:\\n time.minute -= 60\\n time.hour += 1
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 343.9777526855469\n ],\n [\n 281.2904968261719,\n 343.9777526855469\n ],\n [\n 281.2904968261719,\n 467.15625\n ],\n [\n 129.5999755859375,\n 467.15625\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 343.9777526855469,\n 281.2904968261719,\n 467.15625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The first line performs the basic operation; the remainder deals with the special cases we saw before.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 468.2823486328125\n ],\n [\n 525.6033325195312,\n 468.2823486328125\n ],\n [\n 525.6033325195312,\n 490.4389343261719\n ],\n [\n 129.2431640625,\n 490.4389343261719\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 468.2823486328125,\n 525.6033325195312,\n 490.4389343261719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Is this function correct? What happens if the parameter seconds is much greater than sixty?
\",\n \"polygon\": [\n [\n 128.49609375,\n 498.70379638671875\n ],\n [\n 525.6008911132812,\n 498.70379638671875\n ],\n [\n 525.6008911132812,\n 508.8159484863281\n ],\n [\n 128.49609375,\n 508.8159484863281\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 498.70379638671875,\n 525.6008911132812,\n 508.8159484863281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"In that case, it is not enough to carry once; we have to keep doing it until time.second is less than sixty. One solution is to replace the if statements with while statements. That would make the function correct, but not very efficient.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 517.04296875\n ],\n [\n 525.6044311523438,\n 517.04296875\n ],\n [\n 525.6044311523438,\n 551.5819396972656\n ],\n [\n 128.3466796875,\n 551.5819396972656\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 517.04296875,\n 525.6044311523438,\n 551.5819396972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 16.3. Write a correct version of increment that doesn't contain any loops.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 553.6416473388672\n ],\n [\n 470.35546875,\n 553.6416473388672\n ],\n [\n 470.35546875,\n 563.6798400878906\n ],\n [\n 128.6455078125,\n 563.6798400878906\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 553.6416473388672,\n 470.35546875,\n 563.6798400878906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Anything that can be done with modifiers can also be done with pure functions. In fact, some programming languages only allow pure functions. There is some evidence that programs that use pure functions are faster to develop and less error-prone than programs that use modifiers. But modifiers are convenient at times, and functional programs tend to be less efficient.
\",\n \"polygon\": [\n [\n 127.599609375,\n 571.95703125\n ],\n [\n 525.638671875,\n 571.95703125\n ],\n [\n 525.638671875,\n 630.9309539794922\n ],\n [\n 127.599609375,\n 630.9309539794922\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 571.95703125,\n 525.638671875,\n 630.9309539794922\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"In general, I recommend that you write pure functions whenever it is reasonable and resort to modifiers only if there is a compelling advantage. This approach might be called a functional programming style.
\",\n \"polygon\": [\n [\n 128.197265625,\n 639.3463592529297\n ],\n [\n 525.638671875,\n 639.3463592529297\n ],\n [\n 525.638671875,\n 673.6969680786133\n ],\n [\n 128.197265625,\n 673.6969680786133\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 639.3463592529297,\n 525.638671875,\n 673.6969680786133\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"4\": \"/page/174/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/174/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 16.4. Write a \\\"pure\\\" version of increment that creates and returns a new Time object rather than modifying the parameter.
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\",\n \"polygon\": [\n [\n 84.94189453125,\n 85.271484375\n ],\n [\n 314.666015625,\n 85.271484375\n ],\n [\n 314.666015625,\n 100.353515625\n ],\n [\n 84.94189453125,\n 100.353515625\n ]\n ],\n \"bbox\": [\n 84.94189453125,\n 85.271484375,\n 314.666015625,\n 100.353515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"The development plan I am demonstrating is called \\\"prototype and patch.\\\" For each function, I wrote a prototype that performed the basic calculation and then tested it, patching errors along the way.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 109.44140625\n ],\n [\n 483.50390625,\n 109.44140625\n ],\n [\n 483.50390625,\n 145.576904296875\n ],\n [\n 85.6142578125,\n 145.576904296875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 109.44140625,\n 483.50390625,\n 145.576904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"This approach can be effective, especially if you don't yet have a deep understanding of the problem. But incremental corrections can generate code that is unnecessarily complicated—since it deals with many special cases—and unreliable—since it is hard to know if you have found all the errors.
\",\n \"polygon\": [\n [\n 85.763671875,\n 152.6572265625\n ],\n [\n 483.50390625,\n 152.6572265625\n ],\n [\n 483.50390625,\n 200.87591552734375\n ],\n [\n 85.763671875,\n 200.87591552734375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 152.6572265625,\n 483.50390625,\n 200.87591552734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"An alternative is planned development, in which high-level insight into the problem can make the programming much easier. In this case, the insight is that a Time object is really a three-digit number in base 60 (see http://en.wikipedia.org/wiki/Sexagesimal.)! The second attribute is the \\\"ones column,\\\" the minute attribute is the \\\"sixties column,\\\" and the hour attribute is the \\\"thirty-six hundreds column.\\\"
\",\n \"polygon\": [\n [\n 85.763671875,\n 207.66796875\n ],\n [\n 483.50390625,\n 207.66796875\n ],\n [\n 483.50390625,\n 268.36993408203125\n ],\n [\n 85.763671875,\n 268.36993408203125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 207.66796875,\n 483.50390625,\n 268.36993408203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"When we wrote add_time and increment, we were effectively doing addition in base 60, which is why we had to carry from one column to the next.
\",\n \"polygon\": [\n [\n 85.763671875,\n 274.95703125\n ],\n [\n 483.205078125,\n 274.95703125\n ],\n [\n 483.205078125,\n 299.28192138671875\n ],\n [\n 85.763671875,\n 299.28192138671875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 274.95703125,\n 483.205078125,\n 299.28192138671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This observation suggests another approach to the whole problem—we can convert Time objects to integers and take advantage of the fact that the computer knows how to do integer arithmetic.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 305.89453125\n ],\n [\n 484.1015625,\n 305.89453125\n ],\n [\n 484.1015625,\n 342.3869323730469\n ],\n [\n 85.6142578125,\n 342.3869323730469\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 305.89453125,\n 484.1015625,\n 342.3869323730469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Here is a function that converts Times to integers:
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\",\n \"polygon\": [\n [\n 85.83837890625,\n 365.7367858886719\n ],\n [\n 306.0797424316406,\n 365.7367858886719\n ],\n [\n 306.0797424316406,\n 412.2823791503906\n ],\n [\n 85.83837890625,\n 412.2823791503906\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 365.7367858886719,\n 306.0797424316406,\n 412.2823791503906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"And here is the function that converts integers to Times (recall that divmod divides the first argument by the second and returns the quotient and remainder as a tuple).
\",\n \"polygon\": [\n [\n 85.0166015625,\n 415.3359375\n ],\n [\n 482.90625,\n 415.3359375\n ],\n [\n 482.90625,\n 439.3719482421875\n ],\n [\n 85.0166015625,\n 439.3719482421875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 415.3359375,\n 482.90625,\n 439.3719482421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"def int_to_time(seconds):\\n time = Time()\\n minutes, time.second = divmod(seconds, 60)\\n time.hour, time.minute = divmod(minutes, 60)\\n return time
\",\n \"polygon\": [\n [\n 86.39996337890625,\n 442.79296875\n ],\n [\n 337.4718933105469,\n 442.79296875\n ],\n [\n 337.4718933105469,\n 502.744384765625\n ],\n [\n 86.39996337890625,\n 502.744384765625\n ]\n ],\n \"bbox\": [\n 86.39996337890625,\n 442.79296875,\n 337.4718933105469,\n 502.744384765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"You might have to think a bit, and run some tests, to convince yourself that these functions are correct. One way to test them is to check that time_to_int(int_to_time(x)) == x for many values of x. This is an example of a consistency check.
\",\n \"polygon\": [\n [\n 85.763671875,\n 505.828125\n ],\n [\n 482.607421875,\n 505.828125\n ],\n [\n 482.607421875,\n 542.0279541015625\n ],\n [\n 85.763671875,\n 542.0279541015625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 505.828125,\n 482.607421875,\n 542.0279541015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Once you are convinced they are correct, you can use them to rewrite add_time:
\",\n \"polygon\": [\n [\n 85.46484375,\n 549.52734375\n ],\n [\n 437.783203125,\n 549.52734375\n ],\n [\n 437.783203125,\n 560.7449493408203\n ],\n [\n 85.46484375,\n 560.7449493408203\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 549.52734375,\n 437.783203125,\n 560.7449493408203\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"def add_time(t1, t2):\\n seconds = time_to_int(t1) + time_to_int(t2)\\n return int_to_time(seconds)
\",\n \"polygon\": [\n [\n 86.39999389648438,\n 564.22265625\n ],\n [\n 336.48046875,\n 564.22265625\n ],\n [\n 336.48046875,\n 600.57421875\n ],\n [\n 86.39999389648438,\n 600.57421875\n ]\n ],\n \"bbox\": [\n 86.39999389648438,\n 564.22265625,\n 336.48046875,\n 600.57421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"This version is shorter than the original, and easier to verify. Exercise 16.5. Rewrite increment using time_to_int and int_to_time.
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 633.83203125\n ],\n [\n 482.90625,\n 633.83203125\n ],\n [\n 482.90625,\n 657.7299652099609\n ],\n [\n 85.9130859375,\n 657.7299652099609\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 633.83203125,\n 482.90625,\n 657.7299652099609\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/175/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"But if we have the insight to treat times as base 60 numbers and make the investment of writing the conversion functions (time_to_int and int_to_time), we get a program that is shorter, easier to read and debug, and more reliable.
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\",\n \"polygon\": [\n [\n 127.7490234375,\n 183.3677978515625\n ],\n [\n 243.3955078125,\n 183.3677978515625\n ],\n [\n 243.3955078125,\n 197.7139892578125\n ],\n [\n 127.7490234375,\n 197.7139892578125\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 183.3677978515625,\n 243.3955078125,\n 197.7139892578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\",\n \"4\": \"/page/176/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/176/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"A Time object is well-formed if the values of minute and second are between 0 and 60 (including 0 but not 60) and if hour is positive. hour and minute should be integral values, but we might allow second to have a fraction part.
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 252.9140625\n ],\n [\n 525.6036376953125,\n 252.9140625\n ],\n [\n 525.6036376953125,\n 275.49786376953125\n ],\n [\n 128.9443359375,\n 275.49786376953125\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 252.9140625,\n 525.6036376953125,\n 275.49786376953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\",\n \"4\": \"/page/176/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/176/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Writing code to check your invariants can help you detect errors and find their causes. For example, you might have a function like valid_time that takes a Time object and returns False if it violates an invariant:
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\",\n \"polygon\": [\n [\n 129.60003662109375,\n 324.45703125\n ],\n [\n 438.6796875,\n 324.45703125\n ],\n [\n 438.6796875,\n 395.768310546875\n ],\n [\n 129.60003662109375,\n 395.768310546875\n ]\n ],\n \"bbox\": [\n 129.60003662109375,\n 324.45703125,\n 438.6796875,\n 395.768310546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\",\n \"4\": \"/page/176/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/176/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Then at the beginning of each function you could check the arguments to make sure they are valid:
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\",\n \"polygon\": [\n [\n 127.97314453125,\n 624.05078125\n ],\n [\n 227.22596740722656,\n 624.05078125\n ],\n [\n 227.22596740722656,\n 638.39697265625\n ],\n [\n 127.97314453125,\n 638.39697265625\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 624.05078125,\n 227.22596740722656,\n 638.39697265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\",\n \"4\": \"/page/176/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/176/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"prototype and patch: A development plan that involves writing a rough draft of a program, testing, and correcting errors as they are found.
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Most pure functions are fruitful.\",\n \"polygon\": [\n [\n 85.0166015625,\n 88.7381591796875\n ],\n [\n 482.90625,\n 88.7381591796875\n ],\n [\n 482.90625,\n 110.99188232421875\n ],\n [\n 85.0166015625,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 88.7381591796875,\n 482.90625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/172/SectionHeader/1\",\n \"3\": \"/page/175/SectionHeader/1\",\n \"4\": \"/page/176/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/177/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \"modifier: A function that changes one or more of the objects it receives as arguments. 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\",\n \"polygon\": [\n [\n 129.16845703125,\n 215.208984375\n ],\n [\n 366.662109375,\n 215.208984375\n ],\n [\n 366.662109375,\n 240.700439453125\n ],\n [\n 129.16845703125,\n 240.700439453125\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 215.208984375,\n 366.662109375,\n 240.700439453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/178/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Code examples from this chapter are available from http://thinkpython.com/code/ Time2.py.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 284.23828125\n ],\n [\n 526.53515625,\n 284.23828125\n ],\n [\n 526.53515625,\n 307.1499938964844\n ],\n [\n 128.9443359375,\n 307.1499938964844\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 284.23828125,\n 526.53515625,\n 307.1499938964844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/178/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"17.1 Object-oriented features
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\",\n \"polygon\": [\n [\n 129.2431640625,\n 358.1015625\n ],\n [\n 525.9375,\n 358.1015625\n ],\n [\n 525.9375,\n 381.0639953613281\n ],\n [\n 129.2431640625,\n 381.0639953613281\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 358.1015625,\n 525.9375,\n 381.0639953613281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"4\": \"/page/178/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/178/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"It is not easy to define object-oriented programming, but we have already seen some of its characteristics:
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 305.314453125\n ],\n [\n 435.69140625,\n 305.314453125\n ],\n [\n 435.69140625,\n 329.0272521972656\n ],\n [\n 85.9130859375,\n 329.0272521972656\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 305.314453125,\n 435.69140625,\n 329.0272521972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"To call this function, you have to pass a Time object as an argument:
\",\n \"polygon\": [\n [\n 85.763671875,\n 334.3926696777344\n ],\n [\n 383.4679870605469,\n 334.3926696777344\n ],\n [\n 383.4679870605469,\n 344.50482177734375\n ],\n [\n 85.763671875,\n 344.50482177734375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 334.3926696777344,\n 383.4679870605469,\n 344.50482177734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> start = Time()\\n>>> start.hour = 9\\n>>> start.minute = 45\\n>>> start.second = 00\\n>>> print_time(start)\\n09:45:00
\",\n \"polygon\": [\n [\n 86.0625,\n 349.7206726074219\n ],\n [\n 197.3759765625,\n 349.7206726074219\n ],\n [\n 197.3759765625,\n 420.6552734375\n ],\n [\n 86.0625,\n 420.6552734375\n ]\n ],\n \"bbox\": [\n 86.0625,\n 349.7206726074219,\n 197.3759765625,\n 420.6552734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"To make print_time a method, all we have to do is move the function definition inside the class definition. Notice the change in indentation.
\",\n \"polygon\": [\n [\n 86.40001678466797,\n 425.77734375\n ],\n [\n 482.4044189453125,\n 425.77734375\n ],\n [\n 482.4044189453125,\n 448.32684326171875\n ],\n [\n 86.40001678466797,\n 448.32684326171875\n ]\n ],\n \"bbox\": [\n 86.40001678466797,\n 425.77734375,\n 482.4044189453125,\n 448.32684326171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \"class Time(object):\\n def print_time(time):\\n print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)
\",\n \"polygon\": [\n [\n 86.40001678466797,\n 453.54168701171875\n ],\n [\n 459.59765625,\n 453.54168701171875\n ],\n [\n 459.59765625,\n 489.19921875\n ],\n [\n 86.40001678466797,\n 489.19921875\n ]\n ],\n \"bbox\": [\n 86.40001678466797,\n 453.54168701171875,\n 459.59765625,\n 489.19921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Now there are two ways to call print_time. The first (and less common) way is to use function syntax:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 492.29296875\n ],\n [\n 482.39813232421875,\n 492.29296875\n ],\n [\n 482.39813232421875,\n 515.5648498535156\n ],\n [\n 86.2119140625,\n 515.5648498535156\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 492.29296875,\n 482.39813232421875,\n 515.5648498535156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \">>> Time.print_time(start)\\n09:45:00
\",\n \"polygon\": [\n [\n 85.98779296875,\n 519.75\n ],\n [\n 226.51171875,\n 519.75\n ],\n [\n 226.51171875,\n 542.9373016357422\n ],\n [\n 85.98779296875,\n 542.9373016357422\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 519.75,\n 226.51171875,\n 542.9373016357422\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"In this use of dot notation, Time is the name of the class, and print_time is the name of the method. start is passed as a parameter.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 548.3027038574219\n ],\n [\n 482.4007263183594,\n 548.3027038574219\n ],\n [\n 482.4007263183594,\n 570.6088562011719\n ],\n [\n 85.9130859375,\n 570.6088562011719\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 548.3027038574219,\n 482.4007263183594,\n 570.6088562011719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The second (and more concise) way is to use method syntax:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 579.69140625\n ],\n [\n 352.6171875,\n 579.69140625\n ],\n [\n 352.6171875,\n 589.9088592529297\n ],\n [\n 85.9130859375,\n 589.9088592529297\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 579.69140625,\n 352.6171875,\n 589.9088592529297\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Code/16\",\n \"block_type\": \"Code\",\n \"html\": \">>> start.print_time()\\n09:45:00
\",\n \"polygon\": [\n [\n 85.763671875,\n 594.38671875\n ],\n [\n 201.4779510498047,\n 594.38671875\n ],\n [\n 201.4779510498047,\n 617.2813110351562\n ],\n [\n 85.763671875,\n 617.2813110351562\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 594.38671875,\n 201.4779510498047,\n 617.2813110351562\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"In this use of dot notation, print_time is the name of the method (again), and start is the object the method is invoked on, which is called the subject. Just as the subject of a sentence is what the sentence is about, the subject of a method invocation is what the method is about.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 622.6171875\n ],\n [\n 482.90625,\n 622.6171875\n ],\n [\n 482.90625,\n 669.3418731689453\n ],\n [\n 85.6142578125,\n 669.3418731689453\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 622.6171875,\n 482.90625,\n 669.3418731689453\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/179/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"Inside the method, the subject is assigned to the first parameter, so in this case start is assigned to time.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 88.22021484375\n ],\n [\n 525.6015014648438,\n 88.22021484375\n ],\n [\n 525.6015014648438,\n 110.99188232421875\n ],\n [\n 128.0478515625,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 88.22021484375,\n 525.6015014648438,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"class Time(object):\\n def print_time(self):\\n print '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)
\",\n \"polygon\": [\n [\n 129.60006713867188,\n 117.315673828125\n ],\n [\n 495.69622802734375,\n 117.315673828125\n ],\n [\n 495.69622802734375,\n 151.787109375\n ],\n [\n 129.60006713867188,\n 151.787109375\n ]\n ],\n \"bbox\": [\n 129.60006713867188,\n 117.315673828125,\n 495.69622802734375,\n 151.787109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The reason for this convention is an implicit metaphor:
\",\n \"polygon\": [\n [\n 128.6455078125,\n 157.6845703125\n ],\n [\n 371.14453125,\n 157.6845703125\n ],\n [\n 371.14453125,\n 168.2518310546875\n ],\n [\n 128.6455078125,\n 168.2518310546875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 157.6845703125,\n 371.14453125,\n 168.2518310546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/ListGroup/203\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 142.541015625,\n 181.7578125\n ],\n [\n 525.638671875,\n 181.7578125\n ],\n [\n 525.638671875,\n 250.09393310546875\n ],\n [\n 142.541015625,\n 250.09393310546875\n ]\n ],\n \"bbox\": [\n 142.541015625,\n 181.7578125,\n 525.638671875,\n 250.09393310546875\n ],\n \"children\": [\n {\n \"id\": \"/page/180/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \" The syntax for a function call, print_time(start), suggests that the function is the active agent. It says something like, \\\"Hey print_time! Here's an object for you to print.\\\"\",\n \"polygon\": [\n [\n 142.541015625,\n 181.7578125\n ],\n [\n 525.638671875,\n 181.7578125\n ],\n [\n 525.638671875,\n 217.3359375\n ],\n [\n 142.541015625,\n 217.3359375\n ]\n ],\n \"bbox\": [\n 142.541015625,\n 181.7578125,\n 525.638671875,\n 217.3359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/ListItem/5\",\n \"block_type\": \"ListItem\",\n \"html\": \" In object-oriented programming, the objects are the active agents. A method invocation like start.print_time() says \\\"Hey start! Please print yourself.\\\"\",\n \"polygon\": [\n [\n 142.541015625,\n 226.6171875\n ],\n [\n 525.6036987304688,\n 226.6171875\n ],\n [\n 525.6036987304688,\n 250.09393310546875\n ],\n [\n 142.541015625,\n 250.09393310546875\n ]\n ],\n \"bbox\": [\n 142.541015625,\n 226.6171875,\n 525.6036987304688,\n 250.09393310546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/180/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This change in perspective might be more polite, but it is not obvious that it is useful. In the examples we have seen so far, it may not be. But sometimes shifting responsibility from the functions onto the objects makes it possible to write more versatile functions, and makes it easier to maintain and reuse code.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 264.708984375\n ],\n [\n 525.603515625,\n 264.708984375\n ],\n [\n 525.603515625,\n 311.3119201660156\n ],\n [\n 128.6455078125,\n 311.3119201660156\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 264.708984375,\n 525.603515625,\n 311.3119201660156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 17.1. Rewrite time_to_int (from Section 16.4) as a method. It is probably not appropriate to rewrite int_to_time as a method; what object you would invoke it on?
\",\n \"polygon\": [\n [\n 128.6455078125,\n 313.37164306640625\n ],\n [\n 525.6038818359375,\n 313.37164306640625\n ],\n [\n 525.6038818359375,\n 335.5513610839844\n ],\n [\n 128.6455078125,\n 335.5513610839844\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 313.37164306640625,\n 525.6038818359375,\n 335.5513610839844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"17.3 Another example
\",\n \"polygon\": [\n [\n 127.52490234375,\n 365.8359375\n ],\n [\n 282.09375,\n 365.8359375\n ],\n [\n 282.09375,\n 380.197998046875\n ],\n [\n 127.52490234375,\n 380.197998046875\n ]\n ],\n \"bbox\": [\n 127.52490234375,\n 365.8359375,\n 282.09375,\n 380.197998046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Here's a version of increment (from Section 16.3) rewritten as a method:
\",\n \"polygon\": [\n [\n 127.4501953125,\n 391.939453125\n ],\n [\n 449.4375,\n 391.939453125\n ],\n [\n 449.4375,\n 402.8829345703125\n ],\n [\n 127.4501953125,\n 402.8829345703125\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 391.939453125,\n 449.4375,\n 402.8829345703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"# inside class Time:
\",\n \"polygon\": [\n [\n 129.2431640625,\n 409.2057800292969\n ],\n [\n 234.2073974609375,\n 409.2057800292969\n ],\n [\n 234.2073974609375,\n 419.1683654785156\n ],\n [\n 129.2431640625,\n 419.1683654785156\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 409.2057800292969,\n 234.2073974609375,\n 419.1683654785156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Code/11\",\n \"block_type\": \"Code\",\n \"html\": \"def increment(self, seconds):\\n seconds += self.time_to_int()\\n return int_to_time(seconds)
\",\n \"polygon\": [\n [\n 148.0693359375,\n 433.51171875\n ],\n [\n 323.9296875,\n 433.51171875\n ],\n [\n 323.9296875,\n 467.9463806152344\n ],\n [\n 148.0693359375,\n 467.9463806152344\n ]\n ],\n \"bbox\": [\n 148.0693359375,\n 433.51171875,\n 323.9296875,\n 467.9463806152344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"This version assumes that time_to_int is written as a method, as in Exercise 17.1. Also, note that it is a pure function, not a modifier.
\",\n \"polygon\": [\n [\n 128.794921875,\n 474.4187927246094\n ],\n [\n 525.638671875,\n 474.4187927246094\n ],\n [\n 525.638671875,\n 496.7249450683594\n ],\n [\n 128.794921875,\n 496.7249450683594\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 474.4187927246094,\n 525.638671875,\n 496.7249450683594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Here's how you would invoke increment:
\",\n \"polygon\": [\n [\n 127.599609375,\n 507.0198059082031\n ],\n [\n 315.539794921875,\n 507.0198059082031\n ],\n [\n 315.539794921875,\n 517.1319580078125\n ],\n [\n 127.599609375,\n 517.1319580078125\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 507.0198059082031,\n 315.539794921875,\n 517.1319580078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> start.print_time()\\n09:45:00\\n>>> end = start.increment(1337)\\n>>> end.print_time()\\n10:07:17
\",\n \"polygon\": [\n [\n 128.42138671875,\n 523.455810546875\n ],\n [\n 291.7513732910156,\n 523.455810546875\n ],\n [\n 291.7513732910156,\n 582.1954193115234\n ],\n [\n 128.42138671875,\n 582.1954193115234\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 523.455810546875,\n 291.7513732910156,\n 582.1954193115234\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The subject, start, gets assigned to the first parameter, self. The argument, 1337, gets assigned to the second parameter, seconds.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 588.6678161621094\n ],\n [\n 525.6004638671875,\n 588.6678161621094\n ],\n [\n 525.6004638671875,\n 611.015625\n ],\n [\n 128.3466796875,\n 611.015625\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 588.6678161621094,\n 525.6004638671875,\n 611.015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"This mechanism can be confusing, especially if you make an error. For example, if you invoke increment with two arguments, you get:
\",\n \"polygon\": [\n [\n 127.30078125,\n 620.68359375\n ],\n [\n 525.6035766601562,\n 620.68359375\n ],\n [\n 525.6035766601562,\n 643.5759735107422\n ],\n [\n 127.30078125,\n 643.5759735107422\n ]\n ],\n \"bbox\": [\n 127.30078125,\n 620.68359375,\n 525.6035766601562,\n 643.5759735107422\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Code/17\",\n \"block_type\": \"Code\",\n \"html\": \">>> end = start.increment(1337, 460)\\nTypeError: increment() takes exactly 2 arguments (3 given)
\",\n \"polygon\": [\n [\n 127.97314453125,\n 649.8988189697266\n ],\n [\n 432.98126220703125,\n 649.8988189697266\n ],\n [\n 432.98126220703125,\n 672.0564117431641\n ],\n [\n 127.97314453125,\n 672.0564117431641\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 649.8988189697266,\n 432.98126220703125,\n 672.0564117431641\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/180/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"The error message is initially confusing, because there are only two arguments in parentheses. But the subject is also considered an argument, so all together that's three.
\",\n \"polygon\": [\n [\n 127.8984375,\n 677.91796875\n ],\n [\n 525.6036376953125,\n 677.91796875\n ],\n [\n 525.6036376953125,\n 700.8349761962891\n ],\n [\n 127.8984375,\n 700.8349761962891\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 677.91796875,\n 525.6036376953125,\n 700.8349761962891\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/181/Page/208\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/181/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.18310546875\n ],\n [\n 482.4034423828125,\n 60.18310546875\n ],\n [\n 482.4034423828125,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.18310546875,\n 482.4034423828125,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/PageHeader/22\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.39013671875,\n 60.134765625\n ],\n [\n 101.97509765625,\n 60.134765625\n ],\n [\n 101.97509765625,\n 70.576171875\n ],\n [\n 85.39013671875,\n 70.576171875\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 60.134765625,\n 101.97509765625,\n 70.576171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/180/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"17.4 A more complicated example
\",\n \"polygon\": [\n [\n 85.3154296875,\n 85.70654296875\n ],\n [\n 317.35546875,\n 85.70654296875\n ],\n [\n 317.35546875,\n 100.29998779296875\n ],\n [\n 85.3154296875,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 85.70654296875,\n 317.35546875,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/181/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"is_after (from Exercise 16.2) is slightly more complicated because it takes two Time objects as parameters. In this case it is conventional to name the first parameter self and the second parameter other:
\",\n \"polygon\": [\n [\n 84.8671875,\n 110.021484375\n ],\n [\n 483.205078125,\n 110.021484375\n ],\n [\n 483.205078125,\n 145.38592529296875\n ],\n [\n 84.8671875,\n 145.38592529296875\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 110.021484375,\n 483.205078125,\n 145.38592529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/181/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"def is_after(self, other):\\n return self.time_to_int() > other.time_to_int()
\",\n \"polygon\": [\n [\n 100.705078125,\n 161.1650390625\n ],\n [\n 375.328125,\n 161.1650390625\n ],\n [\n 375.328125,\n 196.374267578125\n ],\n [\n 100.705078125,\n 196.374267578125\n ]\n ],\n \"bbox\": [\n 100.705078125,\n 161.1650390625,\n 375.328125,\n 196.374267578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/181/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"To use this method, you have to invoke it on one object and pass the other as an argument: >>> end.is_after(start)
\",\n \"polygon\": [\n [\n 85.9130859375,\n 199.546875\n ],\n [\n 482.4034423828125,\n 199.546875\n ],\n [\n 482.4034423828125,\n 226.6171875\n ],\n [\n 85.9130859375,\n 226.6171875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 199.546875,\n 482.4034423828125,\n 226.6171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/181/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"True
\",\n \"polygon\": [\n [\n 85.763671875,\n 226.6171875\n ],\n [\n 107.32149505615234,\n 226.6171875\n ],\n [\n 107.32149505615234,\n 237.67822265625\n ],\n [\n 85.763671875,\n 237.67822265625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 226.6171875,\n 107.32149505615234,\n 237.67822265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/181/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"One nice thing about this syntax is that it almost reads like English: \\\"end is after start?\\\"
\",\n \"polygon\": [\n [\n 86.2119140625,\n 241.505859375\n ],\n [\n 470.5677795410156,\n 241.505859375\n ],\n [\n 470.5677795410156,\n 252.3828125\n ],\n [\n 86.2119140625,\n 252.3828125\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 241.505859375,\n 470.5677795410156,\n 252.3828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/179/SectionHeader/4\",\n \"4\": \"/page/181/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"17.5 The init method
\",\n \"polygon\": [\n [\n 85.763671875,\n 278.4375\n ],\n [\n 231.84181213378906,\n 278.4375\n ],\n [\n 231.84181213378906,\n 293.90625\n ],\n [\n 85.763671875,\n 293.90625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 278.4375,\n 231.84181213378906,\n 293.90625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/23\",\n \"block_type\": \"Text\",\n \"html\": \"# inside class Time:
\",\n \"polygon\": [\n [\n 86.4000244140625,\n 149.8287353515625\n ],\n [\n 191.00730895996094,\n 149.8287353515625\n ],\n [\n 191.00730895996094,\n 159.79132080078125\n ],\n [\n 86.4000244140625,\n 159.79132080078125\n ]\n ],\n \"bbox\": [\n 86.4000244140625,\n 149.8287353515625,\n 191.00730895996094,\n 159.79132080078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The init method (short for \\\"initialization\\\") is a special method that gets invoked when an object is instantiated. Its full name is __init__ (two underscore characters, followed by init, and then two more underscores). An init method for the Time class might look like this:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 303.57421875\n ],\n [\n 482.4036865234375,\n 303.57421875\n ],\n [\n 482.4036865234375,\n 351.0957946777344\n ],\n [\n 86.2119140625,\n 351.0957946777344\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 303.57421875,\n 482.4036865234375,\n 351.0957946777344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \"# inside class Time:
\",\n \"polygon\": [\n [\n 86.4000244140625,\n 354.234375\n ],\n [\n 200.8125,\n 354.234375\n ],\n [\n 200.8125,\n 365.501220703125\n ],\n [\n 86.4000244140625,\n 365.501220703125\n ]\n ],\n \"bbox\": [\n 86.4000244140625,\n 354.234375,\n 200.8125,\n 365.501220703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"def __init__(self, hour=0, minute=0, second=0):\\n self.hour = hour\\n self.minute = minute\\n self.second = second
\",\n \"polygon\": [\n [\n 104.51513671875,\n 375.890625\n ],\n [\n 353.8125,\n 375.890625\n ],\n [\n 353.8125,\n 426.47222900390625\n ],\n [\n 104.51513671875,\n 426.47222900390625\n ]\n ],\n \"bbox\": [\n 104.51513671875,\n 375.890625,\n 353.8125,\n 426.47222900390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"It is common for the parameters of __init__ to have the same names as the attributes. The statement
\",\n \"polygon\": [\n [\n 85.9130859375,\n 430.03125\n ],\n [\n 482.404052734375,\n 430.03125\n ],\n [\n 482.404052734375,\n 453.37078857421875\n ],\n [\n 85.9130859375,\n 453.37078857421875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 430.03125,\n 482.404052734375,\n 453.37078857421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"self.hour = hour
\",\n \"polygon\": [\n [\n 127.4501953125,\n 457.48828125\n ],\n [\n 212.6162109375,\n 457.48828125\n ],\n [\n 212.6162109375,\n 467.7762145996094\n ],\n [\n 127.4501953125,\n 467.7762145996094\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 457.48828125,\n 212.6162109375,\n 467.7762145996094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"stores the value of the parameter hour as an attribute of self.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 472.3686218261719\n ],\n [\n 356.46368408203125,\n 472.3686218261719\n ],\n [\n 356.46368408203125,\n 482.625\n ],\n [\n 85.9130859375,\n 482.625\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 472.3686218261719,\n 356.46368408203125,\n 482.625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The parameters are optional, so if you call Time with no arguments, you get the default values.
\",\n \"polygon\": [\n [\n 86.4000244140625,\n 489.19921875\n ],\n [\n 482.90625,\n 489.19921875\n ],\n [\n 482.90625,\n 513.2017822265625\n ],\n [\n 86.4000244140625,\n 513.2017822265625\n ]\n ],\n \"bbox\": [\n 86.4000244140625,\n 489.19921875,\n 482.90625,\n 513.2017822265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \">>> time = Time()\\n>>> time.print_time()\\n00:00:00
\",\n \"polygon\": [\n [\n 86.13720703125,\n 515.49609375\n ],\n [\n 196.2476348876953,\n 515.49609375\n ],\n [\n 196.2476348876953,\n 551.9952239990234\n ],\n [\n 86.13720703125,\n 551.9952239990234\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 515.49609375,\n 196.2476348876953,\n 551.9952239990234\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"If you provide one argument, it overrides hour:
\",\n \"polygon\": [\n [\n 85.68896484375,\n 554.94140625\n ],\n [\n 295.541015625,\n 554.94140625\n ],\n [\n 295.541015625,\n 566.6997680664062\n ],\n [\n 85.68896484375,\n 566.6997680664062\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 554.94140625,\n 295.541015625,\n 566.6997680664062\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Code/17\",\n \"block_type\": \"Code\",\n \"html\": \">>> time = Time (9)\\n>>> time.print_time()\\n09:00:00
\",\n \"polygon\": [\n [\n 86.0625,\n 567.703125\n ],\n [\n 196.2476043701172,\n 567.703125\n ],\n [\n 196.2476043701172,\n 607.921875\n ],\n [\n 86.0625,\n 607.921875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 567.703125,\n 196.2476043701172,\n 607.921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"If you provide two arguments, they override hour and minute.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 609.08203125\n ],\n [\n 362.13067626953125,\n 609.08203125\n ],\n [\n 362.13067626953125,\n 620.1987609863281\n ],\n [\n 86.2119140625,\n 620.1987609863281\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 609.08203125,\n 362.13067626953125,\n 620.1987609863281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Code/19\",\n \"block_type\": \"Code\",\n \"html\": \">>> time = Time(9, 45)\\n>>> time.print_time()\\n09:45:00
\",\n \"polygon\": [\n [\n 85.3154296875,\n 624.55078125\n ],\n [\n 201.46800231933594,\n 624.55078125\n ],\n [\n 201.46800231933594,\n 658.9922027587891\n ],\n [\n 85.3154296875,\n 658.9922027587891\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 624.55078125,\n 201.46800231933594,\n 658.9922027587891\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/20\",\n \"block_type\": \"Text\",\n \"html\": \"And if you provide three arguments, they override all three default values.
\",\n \"polygon\": [\n [\n 86.39999389648438,\n 662.0625\n ],\n [\n 416.56640625,\n 662.0625\n ],\n [\n 416.56640625,\n 673.6967620849609\n ],\n [\n 86.39999389648438,\n 673.6967620849609\n ]\n ],\n \"bbox\": [\n 86.39999389648438,\n 662.0625,\n 416.56640625,\n 673.6967620849609\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"3\": \"/page/181/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/181/Text/21\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 17.2. Write an init method for the Point class that takes x and y as optional parameters and assigns them to the corresponding attributes.
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 83.87713623046875\n ],\n [\n 302.9117126464844,\n 83.87713623046875\n ],\n [\n 302.9117126464844,\n 100.29998779296875\n ],\n [\n 128.6455078125,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 83.87713623046875,\n 302.9117126464844,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"__str__ is a special method, like __init__, that is supposed to return a string representation of an object.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 116.208984375\n ],\n [\n 525.595703125,\n 116.208984375\n ],\n [\n 525.595703125,\n 139.09197998046875\n ],\n [\n 128.9443359375,\n 139.09197998046875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 116.208984375,\n 525.595703125,\n 139.09197998046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"For example, here is a str method for Time objects:
\",\n \"polygon\": [\n [\n 128.72021484375,\n 152.0771484375\n ],\n [\n 356.203125,\n 152.0771484375\n ],\n [\n 356.203125,\n 162.29296875\n ],\n [\n 128.72021484375,\n 162.29296875\n ]\n ],\n \"bbox\": [\n 128.72021484375,\n 152.0771484375,\n 356.203125,\n 162.29296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"# inside class Time:\\n def __str__(self):\\n return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)
\",\n \"polygon\": [\n [\n 129.60003662109375,\n 171.4097900390625\n ],\n [\n 501.134765625,\n 171.4097900390625\n ],\n [\n 501.134765625,\n 217.955322265625\n ],\n [\n 129.60003662109375,\n 217.955322265625\n ]\n ],\n \"bbox\": [\n 129.60003662109375,\n 171.4097900390625,\n 501.134765625,\n 217.955322265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"When you print an object, Python invokes the str method:
\",\n \"polygon\": [\n [\n 128.12255859375,\n 227.22174072265625\n ],\n [\n 392.958984375,\n 227.22174072265625\n ],\n [\n 392.958984375,\n 237.33392333984375\n ],\n [\n 128.12255859375,\n 237.33392333984375\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 227.22174072265625,\n 392.958984375,\n 237.33392333984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> time = Time(9, 45)\\n>>> print time\\n09:45:00
\",\n \"polygon\": [\n [\n 129.2431640625,\n 246.146484375\n ],\n [\n 244.6680908203125,\n 246.146484375\n ],\n [\n 244.6680908203125,\n 280.80224609375\n ],\n [\n 129.2431640625,\n 280.80224609375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 246.146484375,\n 244.6680908203125,\n 280.80224609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"When I write a new class, I almost always start by writing __init__, which makes it easier to instantiate objects, and __str__, which is useful for debugging.
\",\n \"polygon\": [\n [\n 129.09375,\n 290.0696716308594\n ],\n [\n 525.6041259765625,\n 290.0696716308594\n ],\n [\n 525.6041259765625,\n 318.076171875\n ],\n [\n 129.09375,\n 318.076171875\n ]\n ],\n \"bbox\": [\n 129.09375,\n 290.0696716308594,\n 525.6041259765625,\n 318.076171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 17.3. Write a str method for the Point class. Create a Point object and print it.
\",\n \"polygon\": [\n [\n 129.60008239746094,\n 314.4345397949219\n ],\n [\n 492.3472900390625,\n 314.4345397949219\n ],\n [\n 492.3472900390625,\n 324.47271728515625\n ],\n [\n 129.60008239746094,\n 324.47271728515625\n ]\n ],\n \"bbox\": [\n 129.60008239746094,\n 314.4345397949219,\n 492.3472900390625,\n 324.47271728515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"17.7 Operator overloading
\",\n \"polygon\": [\n [\n 127.8984375,\n 363.1026611328125\n ],\n [\n 311.080078125,\n 363.1026611328125\n ],\n [\n 311.080078125,\n 377.4488830566406\n ],\n [\n 127.8984375,\n 377.4488830566406\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 363.1026611328125,\n 311.080078125,\n 377.4488830566406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"By defining other special methods, you can specify the behavior of operators on userdefined types. For example, if you define a method named __add__ for the Time class, you can use the + operator on Time objects.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 393.873046875\n ],\n [\n 525.603515625,\n 393.873046875\n ],\n [\n 525.603515625,\n 428.434814453125\n ],\n [\n 128.3466796875,\n 428.434814453125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 393.873046875,\n 525.603515625,\n 428.434814453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Here is what the definition might look like:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 441.24609375\n ],\n [\n 319.0688171386719,\n 441.24609375\n ],\n [\n 319.0688171386719,\n 451.63482666015625\n ],\n [\n 128.3466796875,\n 451.63482666015625\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 441.24609375,\n 319.0688171386719,\n 451.63482666015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"# inside class Time:\\n def __add__(self, other):\\n seconds = self.time_to_int() + other.time_to_int()\\n return int_to_time(seconds)
\",\n \"polygon\": [\n [\n 129.60012817382812,\n 460.752685546875\n ],\n [\n 432.9712219238281,\n 460.752685546875\n ],\n [\n 432.9712219238281,\n 519.4922790527344\n ],\n [\n 129.60012817382812,\n 519.4922790527344\n ]\n ],\n \"bbox\": [\n 129.60012817382812,\n 460.752685546875,\n 432.9712219238281,\n 519.4922790527344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"And here is how you could use it:
\",\n \"polygon\": [\n [\n 129.09375,\n 528.64453125\n ],\n [\n 278.5078125,\n 528.64453125\n ],\n [\n 278.5078125,\n 538.870849609375\n ],\n [\n 129.09375,\n 538.870849609375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 528.64453125,\n 278.5078125,\n 538.870849609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \">>> start = Time(9, 45)\\n>>> duration = Time(1, 35)\\n>>> print start + duration\\n11:20:00
\",\n \"polygon\": [\n [\n 128.0478515625,\n 547.9886932373047\n ],\n [\n 265.8076171875,\n 547.9886932373047\n ],\n [\n 265.8076171875,\n 594.5343017578125\n ],\n [\n 128.0478515625,\n 594.5343017578125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 547.9886932373047,\n 265.8076171875,\n 594.5343017578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"When you apply the + operator to Time objects, Python invokes __add__. When you print the result, Python invokes __str__. So there is quite a lot happening behind the scenes!
\",\n \"polygon\": [\n [\n 128.3466796875,\n 603.8007049560547\n ],\n [\n 526.53515625,\n 603.8007049560547\n ],\n [\n 526.53515625,\n 626.1078643798828\n ],\n [\n 128.3466796875,\n 626.1078643798828\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 603.8007049560547,\n 526.53515625,\n 626.1078643798828\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Changing the behavior of an operator so that it works with user-defined types is called operator overloading. For every operator in Python there is a corresponding special method, like __add__. For more details, see http://docs.python.org/2/reference/datamodel. html#specialnames.
\",\n \"polygon\": [\n [\n 127.7490234375,\n 639.2481536865234\n ],\n [\n 525.6763916015625,\n 639.2481536865234\n ],\n [\n 525.6763916015625,\n 686.0390625\n ],\n [\n 127.7490234375,\n 686.0390625\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 639.2481536865234,\n 525.6763916015625,\n 686.0390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/182/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 17.4. Write an add method for the Point class.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 687.9505767822266\n ],\n [\n 356.3946228027344,\n 687.9505767822266\n ],\n [\n 356.3946228027344,\n 697.9887619018555\n ],\n [\n 128.6455078125,\n 697.9887619018555\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 687.9505767822266,\n 356.3946228027344,\n 697.9887619018555\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/183/Page/197\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/183/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.4248046875\n ],\n [\n 482.4034423828125,\n 60.4248046875\n ],\n [\n 482.4034423828125,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.4248046875,\n 482.4034423828125,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/PageHeader/16\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 84.8671875,\n 60.71484375\n ],\n [\n 101.00390625,\n 60.71484375\n ],\n [\n 101.00390625,\n 70.2861328125\n ],\n [\n 84.8671875,\n 70.2861328125\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 60.71484375,\n 101.00390625,\n 70.2861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/182/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"17.8 Type-based dispatch
\",\n \"polygon\": [\n [\n 85.6142578125,\n 85.95379638671875\n ],\n [\n 261.3251953125,\n 85.95379638671875\n ],\n [\n 261.3251953125,\n 100.29998779296875\n ],\n [\n 85.6142578125,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 85.95379638671875,\n 261.3251953125,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"In the previous section we added two Time objects, but you also might want to add an integer to a Time object. The following is a version of __add__ that checks the type of other and invokes either add_time or increment:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 111.4716796875\n ],\n [\n 483.50390625,\n 111.4716796875\n ],\n [\n 483.50390625,\n 146.45294189453125\n ],\n [\n 85.3154296875,\n 146.45294189453125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 111.4716796875,\n 483.50390625,\n 146.45294189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"# inside class Time:
\",\n \"polygon\": [\n [\n 85.53955078125,\n 151.013671875\n ],\n [\n 191.00726318359375,\n 151.013671875\n ],\n [\n 191.00726318359375,\n 161.92535400390625\n ],\n [\n 85.53955078125,\n 161.92535400390625\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 151.013671875,\n 191.00726318359375,\n 161.92535400390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"def __add__(self, other):\\n if isinstance(other, Time):\\n return self.add_time(other)\\n else:\\n return self.increment(other)\\ndef add_time(self, other):\\n seconds = self.time_to_int() + other.time_to_int()\\n return int_to_time(seconds)\\ndef increment(self, seconds):\\n seconds += self.time_to_int()\\n return int_to_time(seconds)
\",\n \"polygon\": [\n [\n 105.26220703125,\n 174.41015625\n ],\n [\n 395.6484375,\n 174.41015625\n ],\n [\n 395.6484375,\n 332.96484375\n ],\n [\n 105.26220703125,\n 332.96484375\n ]\n ],\n \"bbox\": [\n 105.26220703125,\n 174.41015625,\n 395.6484375,\n 332.96484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The built-in function isinstance takes a value and a class object, and returns True if the value is an instance of the class.
\",\n \"polygon\": [\n [\n 86.0625,\n 337.9921875\n ],\n [\n 482.3995056152344,\n 337.9921875\n ],\n [\n 482.3995056152344,\n 360.6119079589844\n ],\n [\n 86.0625,\n 360.6119079589844\n ]\n ],\n \"bbox\": [\n 86.0625,\n 337.9921875,\n 482.3995056152344,\n 360.6119079589844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"If other is a Time object, __add__ invokes add_time. Otherwise it assumes that the parameter is a number and invokes increment. This operation is called a type-based dispatch because it dispatches the computation to different methods based on the type of the arguments.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 369.703125\n ],\n [\n 482.4048767089844,\n 369.703125\n ],\n [\n 482.4048767089844,\n 416.7889099121094\n ],\n [\n 85.6142578125,\n 416.7889099121094\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 369.703125,\n 482.4048767089844,\n 416.7889099121094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Here are examples that use the + operator with different types:
\",\n \"polygon\": [\n [\n 85.53955078125,\n 425.390625\n ],\n [\n 361.880859375,\n 425.390625\n ],\n [\n 361.880859375,\n 436.38189697265625\n ],\n [\n 85.53955078125,\n 436.38189697265625\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 425.390625,\n 361.880859375,\n 436.38189697265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \">>> start = Time(9, 45)\\n>>> duration = Time(1, 35)\\n>>> print start + duration\\n11:20:00\\n>>> print start + 1337\\n10:07:17
\",\n \"polygon\": [\n [\n 85.53955078125,\n 441.89276123046875\n ],\n [\n 222.3993377685547,\n 441.89276123046875\n ],\n [\n 222.3993377685547,\n 512.8263549804688\n ],\n [\n 85.53955078125,\n 512.8263549804688\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 441.89276123046875,\n 222.3993377685547,\n 512.8263549804688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Unfortunately, this implementation of addition is not commutative. If the integer is the first operand, you get
\",\n \"polygon\": [\n [\n 85.9130859375,\n 518.58984375\n ],\n [\n 482.40325927734375,\n 518.58984375\n ],\n [\n 482.40325927734375,\n 540.7929382324219\n ],\n [\n 85.9130859375,\n 540.7929382324219\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 518.58984375,\n 482.40325927734375,\n 540.7929382324219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \">>> print 1337 + start
\",\n \"polygon\": [\n [\n 85.3154296875,\n 546.3027801513672\n ],\n [\n 201.46791076660156,\n 546.3027801513672\n ],\n [\n 201.46791076660156,\n 556.48828125\n ],\n [\n 85.3154296875,\n 556.48828125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 546.3027801513672,\n 201.46791076660156,\n 556.48828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"TypeError: unsupported operand type(s) for +: 'int' and 'instance'
\",\n \"polygon\": [\n [\n 86.0625,\n 558.4967803955078\n ],\n [\n 431.5223083496094,\n 558.4967803955078\n ],\n [\n 431.5223083496094,\n 568.4765625\n ],\n [\n 86.0625,\n 568.4765625\n ]\n ],\n \"bbox\": [\n 86.0625,\n 558.4967803955078,\n 431.5223083496094,\n 568.4765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The problem is, instead of asking the Time object to add an integer, Python is asking an integer to add a Time object, and it doesn't know how to do that. But there is a clever solution for this problem: the special method __radd__, which stands for \\\"right-side add.\\\" This method is invoked when a Time object appears on the right side of the + operator. Here's the definition:
\",\n \"polygon\": [\n [\n 85.46484375,\n 573.890625\n ],\n [\n 482.607421875,\n 573.890625\n ],\n [\n 482.607421875,\n 633.4453125\n ],\n [\n 85.46484375,\n 633.4453125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 573.890625,\n 482.607421875,\n 633.4453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"# inside class Time:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 638.5177764892578\n ],\n [\n 191.00721740722656,\n 638.5177764892578\n ],\n [\n 191.00721740722656,\n 648.4803771972656\n ],\n [\n 85.3154296875,\n 648.4803771972656\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 638.5177764892578,\n 191.00721740722656,\n 648.4803771972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Code/14\",\n \"block_type\": \"Code\",\n \"html\": \"def __radd__(self, other):\\n return self.__add__(other)
\",\n \"polygon\": [\n [\n 90.6943359375,\n 662.9067687988281\n ],\n [\n 264.2323913574219,\n 662.9067687988281\n ],\n [\n 264.2323913574219,\n 689.51953125\n ],\n [\n 90.6943359375,\n 689.51953125\n ]\n ],\n \"bbox\": [\n 90.6943359375,\n 662.9067687988281,\n 264.2323913574219,\n 689.51953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/183/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"And here's how it's used:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 690.8723373413086\n ],\n [\n 198.35963439941406,\n 690.8723373413086\n ],\n [\n 198.35963439941406,\n 701.5078125\n ],\n [\n 85.3154296875,\n 701.5078125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 690.8723373413086,\n 198.35963439941406,\n 701.5078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/184/Page/184\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/184/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 129.2431640625,\n 61.0048828125\n ],\n [\n 525.6033935546875,\n 61.0048828125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 129.2431640625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 61.0048828125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/PageHeader/15\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 510.099609375,\n 60.908203125\n ],\n [\n 525.638671875,\n 60.908203125\n ],\n [\n 525.638671875,\n 70.2861328125\n ],\n [\n 510.099609375,\n 70.2861328125\n ]\n ],\n \"bbox\": [\n 510.099609375,\n 60.908203125,\n 525.638671875,\n 70.2861328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/183/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Code/182\",\n \"block_type\": \"Code\",\n \"html\": \">>> print 1337 + start\\n10:07:17\\nExercise 17.5. Write an add method for Points that works with either a Point object or a tuple:
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\",\n \"polygon\": [\n [\n 128.42138671875,\n 223.13671875\n ],\n [\n 266.29058837890625,\n 223.13671875\n ],\n [\n 266.29058837890625,\n 237.53094482421875\n ],\n [\n 128.42138671875,\n 237.53094482421875\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 223.13671875,\n 266.29058837890625,\n 237.53094482421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Type-based dispatch is useful when it is necessary, but (fortunately) it is not always necessary. Often you can avoid it by writing functions that work correctly for arguments with different types.
\",\n \"polygon\": [\n [\n 128.49609375,\n 250.013671875\n ],\n [\n 525.6033935546875,\n 250.013671875\n ],\n [\n 525.6033935546875,\n 284.6479187011719\n ],\n [\n 128.49609375,\n 284.6479187011719\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 250.013671875,\n 525.6033935546875,\n 284.6479187011719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Many of the functions we wrote for strings will actually work for any kind of sequence. For example, in Section 11.1 we used histogram to count the number of times each letter appears in a word.
\",\n \"polygon\": [\n [\n 129.09375,\n 294.486328125\n ],\n [\n 525.6034545898438,\n 294.486328125\n ],\n [\n 525.6034545898438,\n 329.677734375\n ],\n [\n 129.09375,\n 329.677734375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 294.486328125,\n 525.6034545898438,\n 329.677734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"def histogram(s):\\n d = dict()\\n for c in s:\\n if c not in d:\\n d[c] = 1\\n else:\\n d[c] = d[c]+1\\n return d
\",\n \"polygon\": [\n [\n 129.5999755859375,\n 335.8267822265625\n ],\n [\n 260.34375,\n 335.8267822265625\n ],\n [\n 260.34375,\n 431.150390625\n ],\n [\n 129.5999755859375,\n 431.150390625\n ]\n ],\n \"bbox\": [\n 129.5999755859375,\n 335.8267822265625,\n 260.34375,\n 431.150390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"This function also works for lists, tuples, and even dictionaries, as long as the elements of s are hashable, so they can be used as keys in d.
\",\n \"polygon\": [\n [\n 129.59999084472656,\n 436.60546875\n ],\n [\n 526.53515625,\n 436.60546875\n ],\n [\n 526.53515625,\n 459.9599609375\n ],\n [\n 129.59999084472656,\n 459.9599609375\n ]\n ],\n \"bbox\": [\n 129.59999084472656,\n 436.60546875,\n 526.53515625,\n 459.9599609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Code/9\",\n \"block_type\": \"Code\",\n \"html\": \">>> t = ['spam', 'egg', 'spam', 'spam', 'bacon', 'spam']\\n>>> histogram(t)\\n{'bacon': 1, 'egg': 1, 'spam': 4}\",\n \"polygon\": [\n [\n 128.86962890625,\n 466.3138122558594\n ],\n [\n 422.4314270019531,\n 466.3138122558594\n ],\n [\n 422.4314270019531,\n 501.1875\n ],\n [\n 128.86962890625,\n 501.1875\n ]\n ],\n \"bbox\": [\n 128.86962890625,\n 466.3138122558594,\n 422.4314270019531,\n 501.1875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Functions that can work with several types are called polymorphic. Polymorphism can facilitate code reuse. For example, the built-in function sum, which adds the elements of a sequence, works as long as the elements of the sequence support addition.
\",\n \"polygon\": [\n [\n 129.5419921875,\n 506.98828125\n ],\n [\n 525.9375,\n 506.98828125\n ],\n [\n 525.9375,\n 541.6689758300781\n ],\n [\n 129.5419921875,\n 541.6689758300781\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 506.98828125,\n 525.9375,\n 541.6689758300781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Since Time objects provide an add method, they work with sum:
\",\n \"polygon\": [\n [\n 128.57080078125,\n 551.84765625\n ],\n [\n 409.095703125,\n 551.84765625\n ],\n [\n 409.095703125,\n 562.1059722900391\n ],\n [\n 128.57080078125,\n 562.1059722900391\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 551.84765625,\n 409.095703125,\n 562.1059722900391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \">>> t1 = Time(7, 43)\\n>>> t2 = Time(7, 41)\\n>>> t3 = Time(7, 37)\\n>>> total = sum([t1, t2, t3])\\n>>> print total\\n23:01:00
\",\n \"polygon\": [\n [\n 129.09375,\n 568.4598236083984\n ],\n [\n 281.29058837890625,\n 568.4598236083984\n ],\n [\n 281.29058837890625,\n 639.6328125\n ],\n [\n 129.09375,\n 639.6328125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 568.4598236083984,\n 281.29058837890625,\n 639.6328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"In general, if all of the operations inside a function work with a given type, then the function works with that type.
\",\n \"polygon\": [\n [\n 128.197265625,\n 646.04638671875\n ],\n [\n 525.6034545898438,\n 646.04638671875\n ],\n [\n 525.6034545898438,\n 668.2039947509766\n ],\n [\n 128.197265625,\n 668.2039947509766\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 646.04638671875,\n 525.6034545898438,\n 668.2039947509766\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/184/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The best kind of polymorphism is the unintentional kind, where you discover that a function you already wrote can be applied to a type you never planned for.
\",\n \"polygon\": [\n [\n 127.8984375,\n 678.3046875\n ],\n [\n 525.6034545898438,\n 678.3046875\n ],\n [\n 525.6034545898438,\n 700.8349990844727\n ],\n [\n 127.8984375,\n 700.8349990844727\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 678.3046875,\n 525.6034545898438,\n 700.8349990844727\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/185/Page/179\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/185/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 59.60302734375\n ],\n [\n 483.205078125,\n 59.60302734375\n ],\n [\n 483.205078125,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 59.60302734375,\n 483.205078125,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.83837890625,\n 59.5546875\n ],\n [\n 102.57275390625,\n 59.5546875\n ],\n [\n 102.57275390625,\n 70.4794921875\n ],\n [\n 85.83837890625,\n 70.4794921875\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 59.5546875,\n 102.57275390625,\n 70.4794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/182/SectionHeader/1\",\n \"4\": \"/page/184/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"17.10 Debugging
\",\n \"polygon\": [\n [\n 85.39013671875,\n 84.83642578125\n ],\n [\n 207.984375,\n 84.83642578125\n ],\n [\n 207.984375,\n 101.3203125\n ],\n [\n 85.39013671875,\n 101.3203125\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 84.83642578125,\n 207.984375,\n 101.3203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"It is legal to add attributes to objects at any point in the execution of a program, but if you are a stickler for type theory, it is a dubious practice to have objects of the same type with different attribute sets. It is usually a good idea to initialize all of an object's attributes in the init method.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 110.1181640625\n ],\n [\n 484.1015625,\n 110.1181640625\n ],\n [\n 484.1015625,\n 158.32891845703125\n ],\n [\n 85.6142578125,\n 158.32891845703125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 110.1181640625,\n 484.1015625,\n 158.32891845703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"If you are not sure whether an object has a particular attribute, you can use the built-in function hasattr (see Section 15.7).
\",\n \"polygon\": [\n [\n 85.763671875,\n 165.2255859375\n ],\n [\n 483.205078125,\n 165.2255859375\n ],\n [\n 483.205078125,\n 189.79888916015625\n ],\n [\n 85.763671875,\n 189.79888916015625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 165.2255859375,\n 483.205078125,\n 189.79888916015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Another way to access the attributes of an object is through the special attribute __dict__, which is a dictionary that maps attribute names (as strings) and values:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 196.9365234375\n ],\n [\n 483.50390625,\n 196.9365234375\n ],\n [\n 483.50390625,\n 221.2689208984375\n ],\n [\n 85.6142578125,\n 221.2689208984375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 196.9365234375,\n 483.50390625,\n 221.2689208984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> p = Point(3, 4)\\n>>> print p.__dict__\\n{'y': 4, 'x': 3}\",\n \"polygon\": [\n [\n 85.166015625,\n 226.459716796875\n ],\n [\n 191.3994140625,\n 226.459716796875\n ],\n [\n 191.3994140625,\n 260.811279296875\n ],\n [\n 85.166015625,\n 260.811279296875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 226.459716796875,\n 191.3994140625,\n 260.811279296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"For purposes of debugging, you might find it useful to keep this function handy:
\",\n \"polygon\": [\n [\n 85.763671875,\n 264.515625\n ],\n [\n 442.564453125,\n 264.515625\n ],\n [\n 442.564453125,\n 276.264892578125\n ],\n [\n 85.763671875,\n 276.264892578125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 264.515625,\n 442.564453125,\n 276.264892578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"def print_attributes(obj):\\n for attr in obj.__dict__:\\n print attr, getattr(obj, attr)
\",\n \"polygon\": [\n [\n 86.4000473022461,\n 281.4557189941406\n ],\n [\n 285.1539306640625,\n 281.4557189941406\n ],\n [\n 285.1539306640625,\n 315.80731201171875\n ],\n [\n 86.4000473022461,\n 315.80731201171875\n ]\n ],\n \"bbox\": [\n 86.4000473022461,\n 281.4557189941406,\n 285.1539306640625,\n 315.80731201171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"print_attributes traverses the items in the object's dictionary and prints each attribute name and its corresponding value.
\",\n \"polygon\": [\n [\n 85.46484375,\n 319.4296875\n ],\n [\n 482.3988952636719,\n 319.4296875\n ],\n [\n 482.3988952636719,\n 343.4548645019531\n ],\n [\n 85.46484375,\n 343.4548645019531\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 319.4296875,\n 482.3988952636719,\n 343.4548645019531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The built-in function getattr takes an object and an attribute name (as a string) and returns the attribute's value.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 351.140625\n ],\n [\n 483.50390625,\n 351.140625\n ],\n [\n 483.50390625,\n 374.92486572265625\n ],\n [\n 85.6142578125,\n 374.92486572265625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 351.140625,\n 483.50390625,\n 374.92486572265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/SectionHeader/10\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"17.11 Interface and implementation
\",\n \"polygon\": [\n [\n 85.3154296875,\n 400.640625\n ],\n [\n 328.26263427734375,\n 400.640625\n ],\n [\n 328.26263427734375,\n 417.40594482421875\n ],\n [\n 85.3154296875,\n 417.40594482421875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 400.640625,\n 328.26263427734375,\n 417.40594482421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"One of the goals of object-oriented design is to make software more maintainable, which means that you can keep the program working when other parts of the system change, and modify the program to meet new requirements.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 426.55078125\n ],\n [\n 482.4035339355469,\n 426.55078125\n ],\n [\n 482.4035339355469,\n 463.2398681640625\n ],\n [\n 85.9130859375,\n 463.2398681640625\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 426.55078125,\n 482.4035339355469,\n 463.2398681640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"A design principle that helps achieve that goal is to keep interfaces separate from implementations. For objects, that means that the methods a class provides should not depend on how the attributes are represented.
\",\n \"polygon\": [\n [\n 85.763671875,\n 471.0234375\n ],\n [\n 482.4034118652344,\n 471.0234375\n ],\n [\n 482.4034118652344,\n 506.90386962890625\n ],\n [\n 85.763671875,\n 506.90386962890625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 471.0234375,\n 482.4034118652344,\n 506.90386962890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"For example, in this chapter we developed a class that represents a time of day. Methods provided by this class include time_to_int, is_after, and add_time.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 514.3359375\n ],\n [\n 482.40338134765625,\n 514.3359375\n ],\n [\n 482.40338134765625,\n 538.3738708496094\n ],\n [\n 85.9130859375,\n 538.3738708496094\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 514.3359375,\n 482.40338134765625,\n 538.3738708496094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"We could implement those methods in several ways. The details of the implementation depend on how we represent time. In this chapter, the attributes of a Time object are hour, minute, and second.
\",\n \"polygon\": [\n [\n 85.763671875,\n 546.046875\n ],\n [\n 482.90625,\n 546.046875\n ],\n [\n 482.90625,\n 582.0378723144531\n ],\n [\n 85.763671875,\n 582.0378723144531\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 546.046875,\n 482.90625,\n 582.0378723144531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"As an alternative, we could replace these attributes with a single integer representing the number of seconds since midnight. This implementation would make some methods, like is_after, easier to write, but it makes some methods harder.
\",\n \"polygon\": [\n [\n 85.763671875,\n 589.74609375\n ],\n [\n 483.50390625,\n 589.74609375\n ],\n [\n 483.50390625,\n 625.7018737792969\n ],\n [\n 85.763671875,\n 625.7018737792969\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 589.74609375,\n 483.50390625,\n 625.7018737792969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"After you deploy a new class, you might discover a better implementation. If other parts of the program are using your class, it might be time-consuming and error-prone to change the interface.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 633.05859375\n ],\n [\n 483.50390625,\n 633.05859375\n ],\n [\n 483.50390625,\n 669.41015625\n ],\n [\n 85.9130859375,\n 669.41015625\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 633.05859375,\n 483.50390625,\n 669.41015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/185/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"But if you designed the interface carefully, you can change the implementation without changing the interface, which means that other parts of the program don't have to change.
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\",\n \"polygon\": [\n [\n 129.60000610351562,\n 88.171875\n ],\n [\n 525.9375,\n 88.171875\n ],\n [\n 525.9375,\n 147.57489013671875\n ],\n [\n 129.60000610351562,\n 147.57489013671875\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 88.171875,\n 525.9375,\n 147.57489013671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/186/TextInlineMath/2\",\n \"block_type\": \"TextInlineMath\",\n \"html\": \"Exercise 17.6. Download the code from this chapter (http://thinkpython.com/code/ Time2.py). Change the attributes of Time to be a single integer representing seconds since mid night. Then modify the methods (and the function int_to_time) to work with the new implemen tation. You should not have to modify the test code in main. When you are done, the output should be the same as before. Solution: http://thinkpython.com/code/Time2_soln.py
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 523.1208190917969\n ],\n [\n 236.2236328125,\n 523.1208190917969\n ],\n [\n 236.2236328125,\n 537.4670257568359\n ],\n [\n 128.6455078125,\n 537.4670257568359\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 523.1208190917969,\n 236.2236328125,\n 537.4670257568359\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\",\n \"4\": \"/page/186/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/186/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 17.7. This exercise is a cautionary tale about one of the most common, and difficult to find, errors in Python. Write a definition for a class named Kangaroo with the following methods:
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\",\n \"polygon\": [\n [\n 85.46484375,\n 339.93267822265625\n ],\n [\n 482.607421875,\n 339.93267822265625\n ],\n [\n 482.607421875,\n 374.2842712402344\n ],\n [\n 85.46484375,\n 374.2842712402344\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 339.93267822265625,\n 482.607421875,\n 374.2842712402344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/185/SectionHeader/10\",\n \"4\": \"/page/186/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/187/Caption/10\",\n \"block_type\": \"Caption\",\n \"html\": \"The following loop creates a cube of spheres:
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\",\n \"polygon\": [\n [\n 129.09375,\n 163.1953125\n ],\n [\n 233.68359375,\n 163.1953125\n ],\n [\n 233.68359375,\n 184.29296875\n ],\n [\n 129.09375,\n 184.29296875\n ]\n ],\n \"bbox\": [\n 129.09375,\n 163.1953125,\n 233.68359375,\n 184.29296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/178/SectionHeader/1\",\n \"2\": \"/page/188/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/188/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Inheritance
\",\n \"polygon\": [\n [\n 128.9443359375,\n 218.302734375\n ],\n [\n 259.8310546875,\n 218.302734375\n ],\n [\n 259.8310546875,\n 243.2823486328125\n ],\n [\n 128.9443359375,\n 243.2823486328125\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 218.302734375,\n 259.8310546875,\n 243.2823486328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/188/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"In this chapter I present classes to represent playing cards, decks of cards, and poker hands. If you don't play poker, you can read about it at http://en.wikipedia.org/wiki/Poker, but you don't have to; I'll tell you what you need to know for the exercises. Code examples from this chapter are available from http://thinkpython.com/code/Card.py.
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 395.9047546386719\n ],\n [\n 250.8662109375,\n 395.9047546386719\n ],\n [\n 250.8662109375,\n 410.30859375\n ],\n [\n 128.6455078125,\n 410.30859375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 395.9047546386719,\n 250.8662109375,\n 410.30859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/188/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/188/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"There are fifty-two cards in a deck, each of which belongs to one of four suits and one of thirteen ranks. The suits are Spades, Hearts, Diamonds, and Clubs (in descending order in bridge). The ranks are Ace, 2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, and King. Depending on the game that you are playing, an Ace may be higher than King or lower than 2.
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|---|
| Hearts | → | 2 |
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| Diamonds | → | 1 |
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| Clubs | → | 0 |
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 88.80029296875\n ],\n [\n 483.50390625,\n 88.80029296875\n ],\n [\n 483.50390625,\n 110.99188232421875\n ],\n [\n 85.9130859375,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 88.80029296875,\n 483.50390625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/188/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/189/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Jack 7→ 11 Queen 7→ 12 King 7→ 13
\",\n \"polygon\": [\n [\n 89.2001953125,\n 119.1822509765625\n ],\n [\n 171.52734375,\n 119.1822509765625\n ],\n [\n 171.52734375,\n 154.63690185546875\n ],\n [\n 89.2001953125,\n 154.63690185546875\n ]\n ],\n \"bbox\": [\n 89.2001953125,\n 119.1822509765625,\n 171.52734375,\n 154.63690185546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/188/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/189/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"I am using the 7→ symbol to make it clear that these mappings are not part of the Python program. They are part of the program design, but they don't appear explicitly in the code.
\",\n \"polygon\": [\n [\n 85.46484375,\n 162.3482666015625\n ],\n [\n 483.50390625,\n 162.3482666015625\n ],\n [\n 483.50390625,\n 185.818359375\n ],\n [\n 85.46484375,\n 185.818359375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 162.3482666015625,\n 483.50390625,\n 185.818359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/188/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/189/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The class definition for Card looks like this:
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\",\n \"polygon\": [\n [\n 86.39998626708984,\n 221.9366455078125\n ],\n [\n 321.837890625,\n 221.9366455078125\n ],\n [\n 321.837890625,\n 294.486328125\n ],\n [\n 86.39998626708984,\n 294.486328125\n ]\n ],\n \"bbox\": [\n 86.39998626708984,\n 221.9366455078125,\n 321.837890625,\n 294.486328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/188/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/189/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"As usual, the init method takes an optional parameter for each attribute. The default card is the 2 of Clubs.
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 328.7109375\n ],\n [\n 417.1640625,\n 328.7109375\n ],\n [\n 417.1640625,\n 339.4608459472656\n ],\n [\n 85.9130859375,\n 339.4608459472656\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 328.7109375,\n 417.1640625,\n 339.4608459472656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/188/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/189/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"queen_of_diamonds = Card(1, 12)
\",\n \"polygon\": [\n [\n 85.763671875,\n 344.4856872558594\n ],\n [\n 248.5512237548828,\n 344.4856872558594\n ],\n [\n 248.5512237548828,\n 354.4482727050781\n ],\n [\n 85.763671875,\n 354.4482727050781\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 344.4856872558594,\n 248.5512237548828,\n 354.4482727050781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/188/SectionHeader/4\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/189/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"18.2 Class attributes
\",\n \"polygon\": [\n [\n 85.763671875,\n 382.49969482421875\n ],\n [\n 227.42312622070312,\n 382.49969482421875\n ],\n [\n 227.42312622070312,\n 396.8459167480469\n ],\n [\n 85.763671875,\n 396.8459167480469\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 382.49969482421875,\n 227.42312622070312,\n 396.8459167480469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/189/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/189/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"In order to print Card objects in a way that people can easily read, we need a mapping from the integer codes to the corresponding ranks and suits. A natural way to do that is with lists of strings. We assign these lists to class attributes:
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\",\n \"polygon\": [\n [\n 262.8193359375,\n 224.296875\n ],\n [\n 389.7672424316406,\n 224.296875\n ],\n [\n 389.7672424316406,\n 234.7529296875\n ],\n [\n 262.8193359375,\n 234.7529296875\n ]\n ],\n \"bbox\": [\n 262.8193359375,\n 224.296875,\n 389.7672424316406,\n 234.7529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/189/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/189/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/190/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Putting it all together, the expression Card.rank_names[self.rank] means \\\"use the attribute rank from the object self as an index into the list rank_names from the class Card, and select the appropriate string.\\\"
\",\n \"polygon\": [\n [\n 128.9443359375,\n 256.974609375\n ],\n [\n 525.9375,\n 256.974609375\n ],\n [\n 525.9375,\n 292.284912109375\n ],\n [\n 128.9443359375,\n 292.284912109375\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 256.974609375,\n 525.9375,\n 292.284912109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/189/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The first element of rank_names is None because there is no card with rank zero. By including None as a place-keeper, we get a mapping with the nice property that the index 2 maps to the string '2', and so on. To avoid this tweak, we could have used a dictionary instead of a list.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 302.607421875\n ],\n [\n 525.9375,\n 302.607421875\n ],\n [\n 525.9375,\n 349.5279235839844\n ],\n [\n 128.3466796875,\n 349.5279235839844\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 302.607421875,\n 525.9375,\n 349.5279235839844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/189/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"With the methods we have so far, we can create and print cards:
\",\n \"polygon\": [\n [\n 127.7490234375,\n 359.6484375\n ],\n [\n 409.8777770996094,\n 359.6484375\n ],\n [\n 409.8777770996094,\n 370.1889343261719\n ],\n [\n 127.7490234375,\n 370.1889343261719\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 359.6484375,\n 409.8777770996094,\n 370.1889343261719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/189/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/Code/6\",\n \"block_type\": \"Code\",\n \"html\": \">>> card1 = Card(2, 11)\\n>>> print card1\\nJack of Hearts
\",\n \"polygon\": [\n [\n 129.16845703125,\n 376.6640625\n ],\n [\n 249.89834594726562,\n 376.6640625\n ],\n [\n 249.89834594726562,\n 411.1163635253906\n ],\n [\n 129.16845703125,\n 411.1163635253906\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 376.6640625,\n 249.89834594726562,\n 411.1163635253906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/189/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Figure 18.1 is a diagram of the Card class object and one Card instance. Card is a class object, so it has type type. card1 has type Card. (To save space, I didn't draw the contents of suit_names and rank_names).
\",\n \"polygon\": [\n [\n 129.5419921875,\n 417.65625\n ],\n [\n 525.9375,\n 417.65625\n ],\n [\n 525.9375,\n 452.3429260253906\n ],\n [\n 129.5419921875,\n 452.3429260253906\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 417.65625,\n 525.9375,\n 452.3429260253906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/189/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"18.3 Comparing cards
\",\n \"polygon\": [\n [\n 128.3466796875,\n 483.01171875\n ],\n [\n 281.0240783691406,\n 483.01171875\n ],\n [\n 281.0240783691406,\n 497.5999755859375\n ],\n [\n 128.3466796875,\n 497.5999755859375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 483.01171875,\n 281.0240783691406,\n 497.5999755859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"For built-in types, there are relational operators (<, >, ==, etc.) that compare values and determine when one is greater than, less than, or equal to another. For user-defined types, we can override the behavior of the built-in operators by providing a method named __cmp__.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 554.94140625\n ],\n [\n 525.638671875,\n 554.94140625\n ],\n [\n 525.638671875,\n 590.0769195556641\n ],\n [\n 128.794921875,\n 590.0769195556641\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 554.94140625,\n 525.638671875,\n 590.0769195556641\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The correct ordering for cards is not obvious. For example, which is better, the 3 of Clubs or the 2 of Diamonds? One has a higher rank, but the other has a higher suit. In order to compare cards, you have to decide whether rank or suit is more important.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 600.1875\n ],\n [\n 525.9375,\n 600.1875\n ],\n [\n 525.9375,\n 635.1259155273438\n ],\n [\n 128.0478515625,\n 635.1259155273438\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 600.1875,\n 525.9375,\n 635.1259155273438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The answer might depend on what game you are playing, but to keep things simple, we'll make the arbitrary choice that suit is more important, so all of the Spades outrank all of the Diamonds, and so on.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 645.43359375\n ],\n [\n 525.9375,\n 645.43359375\n ],\n [\n 525.9375,\n 680.1749267578125\n ],\n [\n 128.0478515625,\n 680.1749267578125\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 645.43359375,\n 525.9375,\n 680.1749267578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/190/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"With that decided, we can write __cmp__:
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\",\n \"polygon\": [\n [\n 86.4000015258789,\n 88.68572998046875\n ],\n [\n 321.240234375,\n 88.68572998046875\n ],\n [\n 321.240234375,\n 247.88671875\n ],\n [\n 86.4000015258789,\n 247.88671875\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 88.68572998046875,\n 321.240234375,\n 247.88671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"You can write this more concisely using tuple comparison:
\",\n \"polygon\": [\n [\n 86.28662109375,\n 251.173828125\n ],\n [\n 343.65234375,\n 251.173828125\n ],\n [\n 343.65234375,\n 261.324951171875\n ],\n [\n 86.28662109375,\n 261.324951171875\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 251.173828125,\n 343.65234375,\n 261.324951171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"# inside class Card:
\",\n \"polygon\": [\n [\n 85.83837890625,\n 267.40777587890625\n ],\n [\n 191.00729370117188,\n 267.40777587890625\n ],\n [\n 191.00729370117188,\n 277.370361328125\n ],\n [\n 85.83837890625,\n 277.370361328125\n ]\n ],\n \"bbox\": [\n 85.83837890625,\n 267.40777587890625,\n 191.00729370117188,\n 277.370361328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"def __cmp__(self, other):\\n t1 = self.suit, self.rank\\n t2 = other.suit, other.rank\\n return cmp(t1, t2)
\",\n \"polygon\": [\n [\n 107.31600952148438,\n 291.779296875\n ],\n [\n 270.140625,\n 291.779296875\n ],\n [\n 270.140625,\n 338.34136962890625\n ],\n [\n 107.31600952148438,\n 338.34136962890625\n ]\n ],\n \"bbox\": [\n 107.31600952148438,\n 291.779296875,\n 270.140625,\n 338.34136962890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The built-in function cmp has the same interface as the method __cmp__: it takes two values and returns a positive number if the first is larger, a negative number if the second is larger, and 0 if they are equal.
\",\n \"polygon\": [\n [\n 85.763671875,\n 343.79296875\n ],\n [\n 482.607421875,\n 343.79296875\n ],\n [\n 482.607421875,\n 379.074951171875\n ],\n [\n 85.763671875,\n 379.074951171875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 343.79296875,\n 482.607421875,\n 379.074951171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"In Python 3, cmp no longer exists, and the __cmp__ method is not supported. Instead you should provide __lt__, which returns True if self is less than other. You can implement __lt__ using tuples and the < operator.
\",\n \"polygon\": [\n [\n 85.763671875,\n 388.458984375\n ],\n [\n 482.4022521972656,\n 388.458984375\n ],\n [\n 482.4022521972656,\n 423.62994384765625\n ],\n [\n 85.763671875,\n 423.62994384765625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 388.458984375,\n 482.4022521972656,\n 423.62994384765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 18.1. Write a __cmp__ method for Time objects. Hint: you can use tuple comparison, but you also might consider using integer subtraction.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 424.6171875\n ],\n [\n 482.4032287597656,\n 424.6171875\n ],\n [\n 482.4032287597656,\n 447.8452453613281\n ],\n [\n 85.6142578125,\n 447.8452453613281\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 424.6171875,\n 482.4032287597656,\n 447.8452453613281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/190/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"18.4 Decks
\",\n \"polygon\": [\n [\n 85.763671875,\n 477.44677734375\n ],\n [\n 166.48049926757812,\n 477.44677734375\n ],\n [\n 166.48049926757812,\n 491.7929992675781\n ],\n [\n 85.763671875,\n 491.7929992675781\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 477.44677734375,\n 166.48049926757812,\n 491.7929992675781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/191/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Now that we have Cards, the next step is to define Decks. Since a deck is made up of cards, it is natural for each Deck to contain a list of cards as an attribute.
\",\n \"polygon\": [\n [\n 85.763671875,\n 504.1793518066406\n ],\n [\n 482.40338134765625,\n 504.1793518066406\n ],\n [\n 482.40338134765625,\n 526.3359375\n ],\n [\n 85.763671875,\n 526.3359375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 504.1793518066406,\n 482.40338134765625,\n 526.3359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/191/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The following is a class definition for Deck. The init method creates the attribute cards and generates the standard set of fifty-two cards:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 535.9921875\n ],\n [\n 482.3996887207031,\n 535.9921875\n ],\n [\n 482.3996887207031,\n 558.6969299316406\n ],\n [\n 85.9130859375,\n 558.6969299316406\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 535.9921875,\n 482.3996887207031,\n 558.6969299316406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/191/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Code/185\",\n \"block_type\": \"Code\",\n \"html\": \"class Deck(object):
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\",\n \"polygon\": [\n [\n 105.71044921875,\n 587.8125\n ],\n [\n 291.05859375,\n 587.8125\n ],\n [\n 291.05859375,\n 660.1024017333984\n ],\n [\n 105.71044921875,\n 660.1024017333984\n ]\n ],\n \"bbox\": [\n 105.71044921875,\n 587.8125,\n 291.05859375,\n 660.1024017333984\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/191/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/191/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The easiest way to populate the deck is with a nested loop. The outer loop enumerates the suits from 0 to 3. The inner loop enumerates the ranks from 1 to 13. Each iteration creates a new Card with the current suit and rank, and appends it to self.cards.
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\",\n \"polygon\": [\n [\n 127.82373046875,\n 85.95379638671875\n ],\n [\n 283.587890625,\n 85.95379638671875\n ],\n [\n 283.587890625,\n 100.29998779296875\n ],\n [\n 127.82373046875,\n 100.29998779296875\n ]\n ],\n \"bbox\": [\n 127.82373046875,\n 85.95379638671875,\n 283.587890625,\n 100.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Here is a __str__ method for Deck: #inside class Deck: def __str__(self): res = [] for card in self.cards: res.append(str(card)) return '\\\\n'.join(res)
\",\n \"polygon\": [\n [\n 129.16845703125,\n 111.955078125\n ],\n [\n 302.1966247558594,\n 111.955078125\n ],\n [\n 302.1966247558594,\n 211.97137451171875\n ],\n [\n 129.16845703125,\n 211.97137451171875\n ]\n ],\n \"bbox\": [\n 129.16845703125,\n 111.955078125,\n 302.1966247558594,\n 211.97137451171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"This method demonstrates an efficient way to accumulate a large string: building a list of strings and then using join. The built-in function str invokes the __str__ method on each card and returns the string representation.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 218.109375\n ],\n [\n 525.9375,\n 218.109375\n ],\n [\n 525.9375,\n 252.74993896484375\n ],\n [\n 128.9443359375,\n 252.74993896484375\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 218.109375,\n 525.9375,\n 252.74993896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Since we invoke join on a newline character, the cards are separated by newlines. Here's what the result looks like:
\",\n \"polygon\": [\n [\n 128.197265625,\n 262.1953125\n ],\n [\n 525.5950927734375,\n 262.1953125\n ],\n [\n 525.5950927734375,\n 285.15692138671875\n ],\n [\n 128.197265625,\n 285.15692138671875\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 262.1953125,\n 525.5950927734375,\n 285.15692138671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> deck = Deck()\\n>>> print deck\\nAce of Clubs\\n2 of Clubs\\n3 of Clubs\\n...\\n10 of Spades\\nJack of Spades\\nQueen of Spades\\nKing of Spades
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 291.2857666015625\n ],\n [\n 218.51620483398438,\n 291.2857666015625\n ],\n [\n 218.51620483398438,\n 410.99737548828125\n ],\n [\n 129.60000610351562,\n 410.99737548828125\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 291.2857666015625,\n 218.51620483398438,\n 410.99737548828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Even though the result appears on 52 lines, it is one long string that contains newlines.
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 416.49609375\n ],\n [\n 509.60345458984375,\n 416.49609375\n ],\n [\n 509.60345458984375,\n 427.387939453125\n ],\n [\n 129.60000610351562,\n 427.387939453125\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 416.49609375,\n 509.60345458984375,\n 427.387939453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/SectionHeader/7\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"18.6 Add, remove, shuffle and sort
\",\n \"polygon\": [\n [\n 128.49609375,\n 456.71484375\n ],\n [\n 362.4675598144531,\n 456.71484375\n ],\n [\n 362.4675598144531,\n 471.302001953125\n ],\n [\n 128.49609375,\n 471.302001953125\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 456.71484375,\n 362.4675598144531,\n 471.302001953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"To deal cards, we would like a method that removes a card from the deck and returns it. The list method pop provides a convenient way to do that:
\",\n \"polygon\": [\n [\n 129.5419921875,\n 483.3984375\n ],\n [\n 525.6033935546875,\n 483.3984375\n ],\n [\n 525.6033935546875,\n 505.9099426269531\n ],\n [\n 129.5419921875,\n 505.9099426269531\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 483.3984375,\n 525.6033935546875,\n 505.9099426269531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"#inside class Deck:
\",\n \"polygon\": [\n [\n 128.197265625,\n 512.015625\n ],\n [\n 228.9868927001953,\n 512.015625\n ],\n [\n 228.9868927001953,\n 522.0013732910156\n ],\n [\n 128.197265625,\n 522.0013732910156\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 512.015625,\n 228.9868927001953,\n 522.0013732910156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"def pop_card(self):\\n return self.cards.pop()
\",\n \"polygon\": [\n [\n 150.51600646972656,\n 536.4267883300781\n ],\n [\n 291.7413635253906,\n 536.4267883300781\n ],\n [\n 291.7413635253906,\n 558.5843963623047\n ],\n [\n 150.51600646972656,\n 558.5843963623047\n ]\n ],\n \"bbox\": [\n 150.51600646972656,\n 536.4267883300781,\n 291.7413635253906,\n 558.5843963623047\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Since pop removes the last card in the list, we are dealing from the bottom of the deck. In real life \\\"bottom dealing\\\" is frowned upon, but in this context it's ok.
\",\n \"polygon\": [\n [\n 128.197265625,\n 564.609375\n ],\n [\n 525.638671875,\n 564.609375\n ],\n [\n 525.638671875,\n 587.1689453125\n ],\n [\n 128.197265625,\n 587.1689453125\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 564.609375,\n 525.638671875,\n 587.1689453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"To add a card, we can use the list method append:
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\",\n \"polygon\": [\n [\n 129.60003662109375,\n 613.5107879638672\n ],\n [\n 291.74139404296875,\n 613.5107879638672\n ],\n [\n 291.74139404296875,\n 660.12890625\n ],\n [\n 129.60003662109375,\n 660.12890625\n ]\n ],\n \"bbox\": [\n 129.60003662109375,\n 613.5107879638672,\n 291.74139404296875,\n 660.12890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/192/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"A method like this that uses another function without doing much real work is sometimes called a veneer. The metaphor comes from woodworking, where it is common to glue a thin layer of good quality wood to the surface of a cheaper piece of wood.
\",\n \"polygon\": [\n [\n 129.2431640625,\n 665.54296875\n ],\n [\n 525.9375,\n 665.54296875\n ],\n [\n 525.9375,\n 700.8349533081055\n ],\n [\n 129.2431640625,\n 700.8349533081055\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 665.54296875,\n 525.9375,\n 700.8349533081055\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/193/Page/213\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/193/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 86.4000015258789,\n 60.8115234375\n ],\n [\n 482.40338134765625,\n 60.8115234375\n ],\n [\n 482.40338134765625,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 60.8115234375,\n 482.40338134765625,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/PageHeader/20\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 85.763671875,\n 60.47314453125\n ],\n [\n 102.19921875,\n 60.47314453125\n ],\n [\n 102.19921875,\n 70.52783203125\n ],\n [\n 85.763671875,\n 70.52783203125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 60.47314453125,\n 102.19921875,\n 70.52783203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"In this case we are defining a \\\"thin\\\" method that expresses a list operation in terms that are appropriate for decks.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 88.12353515625\n ],\n [\n 483.50390625,\n 88.12353515625\n ],\n [\n 483.50390625,\n 110.99188232421875\n ],\n [\n 85.3154296875,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 88.12353515625,\n 483.50390625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"As another example, we can write a Deck method named shuffle using the function shuffle from the random module:
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\",\n \"polygon\": [\n [\n 94.8779296875,\n 162.615234375\n ],\n [\n 264.2324523925781,\n 162.615234375\n ],\n [\n 264.2324523925781,\n 196.08935546875\n ],\n [\n 94.8779296875,\n 196.08935546875\n ]\n ],\n \"bbox\": [\n 94.8779296875,\n 162.615234375,\n 264.2324523925781,\n 196.08935546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Don't forget to import random.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 201.287109375\n ],\n [\n 221.2822265625,\n 201.287109375\n ],\n [\n 221.2822265625,\n 212.48797607421875\n ],\n [\n 85.9130859375,\n 212.48797607421875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 201.287109375,\n 221.2822265625,\n 212.48797607421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/21\",\n \"block_type\": \"Text\",\n \"html\": \"# inside class Deck:
\",\n \"polygon\": [\n [\n 86.39999389648438,\n 149.54376220703125\n ],\n [\n 191.0072784423828,\n 149.54376220703125\n ],\n [\n 191.0072784423828,\n 159.50634765625\n ],\n [\n 86.39999389648438,\n 159.50634765625\n ]\n ],\n \"bbox\": [\n 86.39999389648438,\n 149.54376220703125,\n 191.0072784423828,\n 159.50634765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 18.2. Write a Deck method named sort that uses the list method sort to sort the cards in a Deck. sort uses the __cmp__ method we defined to determine sort order.
\",\n \"polygon\": [\n [\n 85.763671875,\n 213.85546875\n ],\n [\n 482.40118408203125,\n 213.85546875\n ],\n [\n 482.40118408203125,\n 236.7274169921875\n ],\n [\n 85.763671875,\n 236.7274169921875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 213.85546875,\n 482.40118408203125,\n 236.7274169921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"4\": \"/page/192/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"18.7 Inheritance
\",\n \"polygon\": [\n [\n 85.9130859375,\n 265.482421875\n ],\n [\n 200.8125,\n 265.482421875\n ],\n [\n 200.8125,\n 280.8150634765625\n ],\n [\n 85.9130859375,\n 280.8150634765625\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 265.482421875,\n 200.8125,\n 280.8150634765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The language feature most often associated with object-oriented programming is inheritance. Inheritance is the ability to define a new class that is a modified version of an existing class.
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\",\n \"polygon\": [\n [\n 86.0625,\n 337.21875\n ],\n [\n 482.4034118652344,\n 337.21875\n ],\n [\n 482.4034118652344,\n 372.2380065917969\n ],\n [\n 86.0625,\n 372.2380065917969\n ]\n ],\n \"bbox\": [\n 86.0625,\n 337.21875,\n 482.4034118652344,\n 372.2380065917969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"As an example, let's say we want a class to represent a \\\"hand,\\\" that is, the set of cards held by one player. A hand is similar to a deck: both are made up of a set of cards, and both require operations like adding and removing cards.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 382.271484375\n ],\n [\n 482.90625,\n 382.271484375\n ],\n [\n 482.90625,\n 416.8828125\n ],\n [\n 85.9130859375,\n 416.8828125\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 382.271484375,\n 482.90625,\n 416.8828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"A hand is also different from a deck; there are operations we want for hands that don't make sense for a deck. For example, in poker we might compare two hands to see which one wins. In bridge, we might compute a score for a hand in order to make a bid.
\",\n \"polygon\": [\n [\n 85.763671875,\n 426.1640625\n ],\n [\n 483.802734375,\n 426.1640625\n ],\n [\n 483.802734375,\n 461.4560241699219\n ],\n [\n 85.763671875,\n 461.4560241699219\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 426.1640625,\n 483.802734375,\n 461.4560241699219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"This relationship between classes—similar, but different—lends itself to inheritance.
\",\n \"polygon\": [\n [\n 86.0625,\n 470.25\n ],\n [\n 455.49444580078125,\n 470.25\n ],\n [\n 455.49444580078125,\n 481.6770324707031\n ],\n [\n 86.0625,\n 481.6770324707031\n ]\n ],\n \"bbox\": [\n 86.0625,\n 470.25,\n 455.49444580078125,\n 481.6770324707031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The definition of a child class is like other class definitions, but the name of the parent class appears in parentheses:
\",\n \"polygon\": [\n [\n 85.763671875,\n 490.74609375\n ],\n [\n 483.205078125,\n 490.74609375\n ],\n [\n 483.205078125,\n 514.092041015625\n ],\n [\n 85.763671875,\n 514.092041015625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 490.74609375,\n 483.205078125,\n 514.092041015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"class Hand(Deck):
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\",\n \"polygon\": [\n [\n 105.71044921875,\n 532.125\n ],\n [\n 322.435546875,\n 532.125\n ],\n [\n 322.435546875,\n 542.3854827880859\n ],\n [\n 105.71044921875,\n 542.3854827880859\n ]\n ],\n \"bbox\": [\n 105.71044921875,\n 532.125,\n 322.435546875,\n 542.3854827880859\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"This definition indicates that Hand inherits from Deck; that means we can use methods like pop_card and add_card for Hands as well as Decks.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 547.98046875\n ],\n [\n 484.1015625,\n 547.98046875\n ],\n [\n 484.1015625,\n 570.9790344238281\n ],\n [\n 85.3154296875,\n 570.9790344238281\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 547.98046875,\n 484.1015625,\n 570.9790344238281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Hand also inherits __init__ from Deck, but it doesn't really do what we want: instead of populating the hand with 52 new cards, the init method for Hands should initialize cards with an empty list.
\",\n \"polygon\": [\n [\n 85.166015625,\n 580.8515625\n ],\n [\n 483.205078125,\n 580.8515625\n ],\n [\n 483.205078125,\n 615.5880432128906\n ],\n [\n 85.166015625,\n 615.5880432128906\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 580.8515625,\n 483.205078125,\n 615.5880432128906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/193/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"If we provide an init method in the Hand class, it overrides the one in the Deck class:
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\",\n \"polygon\": [\n [\n 129.2431640625,\n 88.171875\n ],\n [\n 400.4296875,\n 88.171875\n ],\n [\n 400.4296875,\n 98.79791259765625\n ],\n [\n 129.2431640625,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 88.171875,\n 400.4296875,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/194/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \">>> hand = Hand('new hand')\\n>>> print hand.cards\\n[]\\n>>> print hand.label\\nnew hand\",\n \"polygon\": [\n [\n 128.3466796875,\n 104.220703125\n ],\n [\n 270.7873840332031,\n 104.220703125\n ],\n [\n 270.7873840332031,\n 163.00030517578125\n ],\n [\n 128.3466796875,\n 163.00030517578125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 104.220703125,\n 270.7873840332031,\n 163.00030517578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/194/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"But the other methods are inherited from Deck, so we can use pop_card and add_card to deal a card:
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\",\n \"polygon\": [\n [\n 149.63818359375,\n 300.69580078125\n ],\n [\n 349.2699279785156,\n 300.69580078125\n ],\n [\n 349.2699279785156,\n 335.04638671875\n ],\n [\n 149.63818359375,\n 335.04638671875\n ]\n ],\n \"bbox\": [\n 149.63818359375,\n 300.69580078125,\n 349.2699279785156,\n 335.04638671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/194/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"move_cards takes two arguments, a Hand object and the number of cards to deal. It modifies both self and hand, and returns None.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 372.216796875\n ],\n [\n 525.9375,\n 372.216796875\n ],\n [\n 525.9375,\n 406.89996337890625\n ],\n [\n 128.0478515625,\n 406.89996337890625\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 372.216796875,\n 525.9375,\n 406.89996337890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/194/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 18.3. Write a Deck method called deal_hands that takes two parameters, the number of hands and the number of cards per hand, and that creates new Hand objects, deals the appropriate number of cards per hand, and returns a list of Hand objects.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 408.375\n ],\n [\n 525.9375,\n 408.375\n ],\n [\n 525.9375,\n 443.3102722167969\n ],\n [\n 128.6455078125,\n 443.3102722167969\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 408.375,\n 525.9375,\n 443.3102722167969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/194/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Inheritance is a useful feature. Some programs that would be repetitive without inheritance can be written more elegantly with it. Inheritance can facilitate code reuse, since you can customize the behavior of parent classes without having to modify them. In some cases, the inheritance structure reflects the natural structure of the problem, which makes the program easier to understand.
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\",\n \"polygon\": [\n [\n 128.3466796875,\n 520.91015625\n ],\n [\n 525.9375,\n 520.91015625\n ],\n [\n 525.9375,\n 567.9349670410156\n ],\n [\n 128.3466796875,\n 567.9349670410156\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 520.91015625,\n 525.9375,\n 567.9349670410156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/194/SectionHeader/13\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"18.8 Class diagrams
\",\n \"polygon\": [\n [\n 127.4501953125,\n 596.4488372802734\n ],\n [\n 268.25604248046875,\n 596.4488372802734\n ],\n [\n 268.25604248046875,\n 610.7950286865234\n ],\n [\n 127.4501953125,\n 610.7950286865234\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 596.4488372802734,\n 268.25604248046875,\n 610.7950286865234\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\",\n \"4\": \"/page/194/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/194/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"So far we have seen stack diagrams, which show the state of a program, and object diagrams, which show the attributes of an object and their values. These diagrams represent a snapshot in the execution of a program, so they change as the program runs.
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\",\n \"polygon\": [\n [\n 86.0625,\n 347.2734375\n ],\n [\n 482.607421875,\n 347.2734375\n ],\n [\n 482.607421875,\n 371.16192626953125\n ],\n [\n 86.0625,\n 371.16192626953125\n ]\n ],\n \"bbox\": [\n 86.0625,\n 347.2734375,\n 482.607421875,\n 371.16192626953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\",\n \"4\": \"/page/194/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/195/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The arrow with a hollow triangle head represents an IS-A relationship; in this case it indicates that Hand inherits from Deck.
\",\n \"polygon\": [\n [\n 85.763671875,\n 378.984375\n ],\n [\n 483.50390625,\n 378.984375\n ],\n [\n 483.50390625,\n 403.0799255371094\n ],\n [\n 85.763671875,\n 403.0799255371094\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 378.984375,\n 483.50390625,\n 403.0799255371094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\",\n \"4\": \"/page/194/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/195/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The standard arrow head represents a HAS-A relationship; in this case a Deck has references to Card objects.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 411.08203125\n ],\n [\n 484.1015625,\n 411.08203125\n ],\n [\n 484.1015625,\n 434.9969177246094\n ],\n [\n 85.9130859375,\n 434.9969177246094\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 411.08203125,\n 484.1015625,\n 434.9969177246094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\",\n \"4\": \"/page/194/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/195/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The star (*) near the arrow head is a multiplicity; it indicates how many Cards a Deck has. A multiplicity can be a simple number, like 52, a range, like 5..7 or a star, which indicates that a Deck can have any number of Cards.
\",\n \"polygon\": [\n [\n 86.0625,\n 443.1796875\n ],\n [\n 484.400390625,\n 443.1796875\n ],\n [\n 484.400390625,\n 479.1089172363281\n ],\n [\n 86.0625,\n 479.1089172363281\n ]\n ],\n \"bbox\": [\n 86.0625,\n 443.1796875,\n 484.400390625,\n 479.1089172363281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\",\n \"4\": \"/page/194/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/195/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"A more detailed diagram might show that a Deck actually contains a list of Cards, but built-in types like list and dict are usually not included in class diagrams.
\",\n \"polygon\": [\n [\n 85.763671875,\n 486.87890625\n ],\n [\n 483.50390625,\n 486.87890625\n ],\n [\n 483.50390625,\n 511.02691650390625\n ],\n [\n 85.763671875,\n 511.02691650390625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 486.87890625,\n 483.50390625,\n 511.02691650390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/193/SectionHeader/6\",\n \"4\": \"/page/194/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/195/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 18.4. Read TurtleWorld.py, World.py and Gui.py and draw a class diagram that shows the relationships among the classes defined there.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 563.0625\n ],\n [\n 202.0078125,\n 563.0625\n ],\n [\n 202.0078125,\n 578.53125\n ],\n [\n 85.763671875,\n 578.53125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 563.0625,\n 202.0078125,\n 578.53125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/195/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Inheritance can make debugging a challenge because when you invoke a method on an object, you might not know which method will be invoked.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 620.68359375\n ],\n [\n 484.69921875,\n 620.68359375\n ],\n [\n 484.69921875,\n 668.9179382324219\n ],\n [\n 85.6142578125,\n 668.9179382324219\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 620.68359375,\n 484.69921875,\n 668.9179382324219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/195/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"Any time you are unsure about the flow of execution through your program, the simplest solution is to add print statements at the beginning of the relevant methods. If
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\",\n \"polygon\": [\n [\n 128.49609375,\n 382.8515625\n ],\n [\n 461.091796875,\n 382.8515625\n ],\n [\n 461.091796875,\n 392.83489990234375\n ],\n [\n 128.49609375,\n 392.83489990234375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 382.8515625,\n 461.091796875,\n 392.83489990234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/196/SectionHeader/10\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"18.10 Data encapsulation
\",\n \"polygon\": [\n [\n 129.01904296875,\n 423.11175537109375\n ],\n [\n 302.5148010253906,\n 423.11175537109375\n ],\n [\n 302.5148010253906,\n 437.4579772949219\n ],\n [\n 129.01904296875,\n 437.4579772949219\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 423.11175537109375,\n 302.5148010253906,\n 437.4579772949219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/196/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Chapter 16 demonstrates a development plan we might call \\\"object-oriented design.\\\" We identified objects we needed—Time, Point and Rectangle—and defined classes to represent them. In each case there is an obvious correspondence between the object and some entity in the real world (or at least a mathematical world).
\",\n \"polygon\": [\n [\n 128.6455078125,\n 449.75390625\n ],\n [\n 525.9375,\n 449.75390625\n ],\n [\n 525.9375,\n 496.784912109375\n ],\n [\n 128.6455078125,\n 496.784912109375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 449.75390625,\n 525.9375,\n 496.784912109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/196/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"But sometimes it is less obvious what objects you need and how they should interact. In that case you need a different development plan. In the same way that we discovered function interfaces by encapsulation and generalization, we can discover class interfaces by data encapsulation.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 506.6015625\n ],\n [\n 525.9375,\n 506.6015625\n ],\n [\n 525.9375,\n 553.8169250488281\n ],\n [\n 128.9443359375,\n 553.8169250488281\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 506.6015625,\n 525.9375,\n 553.8169250488281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/196/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Markov analysis, from Section 13.8, provides a good example. If you download my code from http://thinkpython.com/code/markov.py, you'll see that it uses two global variables—suffix_map and prefix—that are read and written from several functions.
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\",\n \"polygon\": [\n [\n 128.57080078125,\n 605.019775390625\n ],\n [\n 208.05560302734375,\n 605.019775390625\n ],\n [\n 208.05560302734375,\n 627.1763763427734\n ],\n [\n 128.57080078125,\n 627.1763763427734\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 605.019775390625,\n 208.05560302734375,\n 627.1763763427734\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/196/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"Because these variables are global we can only run one analysis at a time. If we read two texts, their prefixes and suffixes would be added to the same data structures (which makes for some interesting generated text).
\",\n \"polygon\": [\n [\n 128.6455078125,\n 633.4453125\n ],\n [\n 525.9375,\n 633.4453125\n ],\n [\n 525.9375,\n 668.1919479370117\n ],\n [\n 128.6455078125,\n 668.1919479370117\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 633.4453125,\n 525.9375,\n 668.1919479370117\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/196/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"To run multiple analyses, and keep them separate, we can encapsulate the state of each analysis in an object. Here's what that looks like:
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\",\n \"polygon\": [\n [\n 86.4000015258789,\n 88.22021484375\n ],\n [\n 197.525390625,\n 88.22021484375\n ],\n [\n 197.525390625,\n 98.6483154296875\n ],\n [\n 86.4000015258789,\n 98.6483154296875\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 88.22021484375,\n 197.525390625,\n 98.6483154296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"def __init__(self):\\n self.suffix_map = {}\\n self.prefix = ()\",\n \"polygon\": [\n [\n 107.31600189208984,\n 110.98828125\n ],\n [\n 232.85025024414062,\n 110.98828125\n ],\n [\n 232.85025024414062,\n 147.42529296875\n ],\n [\n 107.31600189208984,\n 147.42529296875\n ]\n ],\n \"bbox\": [\n 107.31600189208984,\n 110.98828125,\n 232.85025024414062,\n 147.42529296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Next, we transform the functions into methods. For example, here's process_word:
\",\n \"polygon\": [\n [\n 84.568359375,\n 152.2705078125\n ],\n [\n 453.0234375,\n 152.2705078125\n ],\n [\n 453.0234375,\n 163.534912109375\n ],\n [\n 84.568359375,\n 163.534912109375\n ]\n ],\n \"bbox\": [\n 84.568359375,\n 152.2705078125,\n 453.0234375,\n 163.534912109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"def process_word(self, word, order=2):\\n if len(self.prefix) < order:\\n self.prefix += (word,)\\n return\\n try:\\n self.suffix_map[self.prefix].append(word)\\n except KeyError:\\n # if there is no entry for this prefix, make one\\n self.suffix_map[self.prefix] = [word]\\n self.prefix = shift(self.prefix, word)
\",\n \"polygon\": [\n [\n 107.3160400390625,\n 169.3817138671875\n ],\n [\n 410.291015625,\n 169.3817138671875\n ],\n [\n 410.291015625,\n 313.4813232421875\n ],\n [\n 107.3160400390625,\n 313.4813232421875\n ]\n ],\n \"bbox\": [\n 107.3160400390625,\n 169.3817138671875,\n 410.291015625,\n 313.4813232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Transforming a program like this—changing the design without changing the function—is another example of refactoring (see Section 4.7).
\",\n \"polygon\": [\n [\n 84.568359375,\n 318.65625\n ],\n [\n 482.4033508300781,\n 318.65625\n ],\n [\n 482.4033508300781,\n 341.7848815917969\n ],\n [\n 84.568359375,\n 341.7848815917969\n ]\n ],\n \"bbox\": [\n 84.568359375,\n 318.65625,\n 482.4033508300781,\n 341.7848815917969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This example suggests a development plan for designing objects and methods:
\",\n \"polygon\": [\n [\n 85.9130859375,\n 351.52734375\n ],\n [\n 432.703125,\n 351.52734375\n ],\n [\n 432.703125,\n 361.71588134765625\n ],\n [\n 85.9130859375,\n 361.71588134765625\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 351.52734375,\n 432.703125,\n 361.71588134765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/ListGroup/181\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 97.64208984375,\n 374.537109375\n ],\n [\n 482.90625,\n 374.537109375\n ],\n [\n 482.90625,\n 457.451904296875\n ],\n [\n 97.64208984375,\n 457.451904296875\n ]\n ],\n \"bbox\": [\n 97.64208984375,\n 374.537109375,\n 482.90625,\n 457.451904296875\n ],\n \"children\": [\n {\n \"id\": \"/page/197/ListItem/7\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Start by writing functions that read and write global variables (when necessary).\",\n \"polygon\": [\n [\n 98.85304260253906,\n 374.537109375\n ],\n [\n 464.677734375,\n 374.537109375\n ],\n [\n 464.677734375,\n 385.2928771972656\n ],\n [\n 98.85304260253906,\n 385.2928771972656\n ]\n ],\n \"bbox\": [\n 98.85304260253906,\n 374.537109375,\n 464.677734375,\n 385.2928771972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Once you get the program working, look for associations between global variables and the functions that use them.\",\n \"polygon\": [\n [\n 98.4638671875,\n 395.31829833984375\n ],\n [\n 482.90625,\n 395.31829833984375\n ],\n [\n 482.90625,\n 417.4748840332031\n ],\n [\n 98.4638671875,\n 417.4748840332031\n ]\n ],\n \"bbox\": [\n 98.4638671875,\n 395.31829833984375,\n 482.90625,\n 417.4748840332031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. Encapsulate related variables as attributes of an object.\",\n \"polygon\": [\n [\n 97.64208984375,\n 427.5013122558594\n ],\n [\n 351.5244140625,\n 427.5013122558594\n ],\n [\n 351.5244140625,\n 437.4638977050781\n ],\n [\n 97.64208984375,\n 437.4638977050781\n ]\n ],\n \"bbox\": [\n 97.64208984375,\n 427.5013122558594,\n 351.5244140625,\n 437.4638977050781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/197/ListItem/10\",\n \"block_type\": \"ListItem\",\n \"html\": \"4. Transform the associated functions into methods of the new class.\",\n \"polygon\": [\n [\n 98.38916015625,\n 447.43359375\n ],\n [\n 400.062255859375,\n 447.43359375\n ],\n [\n 400.062255859375,\n 457.451904296875\n ],\n [\n 98.38916015625,\n 457.451904296875\n ]\n ],\n \"bbox\": [\n 98.38916015625,\n 447.43359375,\n 400.062255859375,\n 457.451904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/196/SectionHeader/10\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/197/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 18.5. Download my code from Section 13.8 (http: // thinkpython. com/ code/ markov. py ), and follow the steps described above to encapsulate the global variables as attributes of a new class called Markov. Solution: http: // thinkpython. com/ code/ Markov. py (note the capital M).
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\",\n \"polygon\": [\n [\n 151.9541015625,\n 528.7936706542969\n ],\n [\n 483.9549255371094,\n 528.7936706542969\n ],\n [\n 483.9549255371094,\n 538.7794189453125\n ],\n [\n 151.9541015625,\n 538.7794189453125\n ]\n ],\n \"bbox\": [\n 151.9541015625,\n 528.7936706542969,\n 483.9549255371094,\n 538.7794189453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/198/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/198/ListGroup/223\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 139.4033203125,\n 544.5\n ],\n [\n 526.236328125,\n 544.5\n ],\n [\n 526.236328125,\n 700.6622772216797\n ],\n [\n 139.4033203125,\n 700.6622772216797\n ]\n ],\n \"bbox\": [\n 139.4033203125,\n 544.5,\n 526.236328125,\n 700.6622772216797\n ],\n \"children\": [\n {\n \"id\": \"/page/198/ListItem/20\",\n \"block_type\": \"ListItem\",\n \"html\": \"PokerHand.py : An incomplete implementation of a class that represents a poker hand, and some code that tests it.\",\n \"polygon\": [\n [\n 151.8046875,\n 544.5\n ],\n [\n 525.598388671875,\n 544.5\n ],\n [\n 525.598388671875,\n 567.0982666015625\n ],\n [\n 151.8046875,\n 567.0982666015625\n ]\n ],\n \"bbox\": [\n 151.8046875,\n 544.5,\n 525.598388671875,\n 567.0982666015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/198/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/198/ListItem/21\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. If you run PokerHand.py, it deals seven 7-card poker hands and checks to see if any of them contains a flush. Read this code carefully before you go on.\",\n \"polygon\": [\n [\n 139.4033203125,\n 576.984375\n ],\n [\n 525.9375,\n 576.984375\n ],\n [\n 525.9375,\n 599.4252624511719\n ],\n [\n 139.4033203125,\n 599.4252624511719\n ]\n ],\n \"bbox\": [\n 139.4033203125,\n 576.984375,\n 525.9375,\n 599.4252624511719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/198/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/198/ListItem/22\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. Add methods to PokerHand.py named has_pair, has_twopair, etc. that return True or False according to whether or not the hand meets the relevant criteria. 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Also, steer is allowed to change the heading of the Turtle but not the position.\",\n \"polygon\": [\n [\n 97.41796875,\n 537.5390625\n ],\n [\n 483.50390625,\n 537.5390625\n ],\n [\n 483.50390625,\n 572.5252990722656\n ],\n [\n 97.41796875,\n 572.5252990722656\n ]\n ],\n \"bbox\": [\n 97.41796875,\n 537.5390625,\n 483.50390625,\n 572.5252990722656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/198/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/198/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/199/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Adjust the rules and your steer method for good quality play; for example, it should be possible for the slow Turtle to tag the faster Turtles eventually.
\",\n \"polygon\": [\n [\n 110.4169921875,\n 578.14453125\n ],\n [\n 484.1015625,\n 578.14453125\n ],\n [\n 484.1015625,\n 600.8761596679688\n ],\n [\n 110.4169921875,\n 600.8761596679688\n ]\n ],\n \"bbox\": [\n 110.4169921875,\n 578.14453125,\n 484.1015625,\n 600.8761596679688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/198/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/199/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Solution: http: // thinkpython. com/ code/ Tagger. py .
\",\n \"polygon\": [\n [\n 86.40000915527344,\n 614.9688720703125\n ],\n [\n 334.6875,\n 614.9688720703125\n ],\n [\n 334.6875,\n 625.32421875\n ],\n [\n 86.40000915527344,\n 625.32421875\n ]\n ],\n \"bbox\": [\n 86.40000915527344,\n 614.9688720703125,\n 334.6875,\n 625.32421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/198/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"3\": \"/page/195/SectionHeader/13\",\n \"4\": \"/page/198/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/200/Page/120\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/200/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Chapter 19
\",\n \"polygon\": [\n [\n 128.9443359375,\n 163.58203125\n ],\n [\n 232.0400390625,\n 163.58203125\n ],\n [\n 232.0400390625,\n 185.2369384765625\n ],\n [\n 128.9443359375,\n 185.2369384765625\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 163.58203125,\n 232.0400390625,\n 185.2369384765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/188/SectionHeader/1\",\n \"2\": \"/page/200/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Case study: Tkinter
\",\n \"polygon\": [\n [\n 128.27197265625,\n 220.236328125\n ],\n [\n 352.318359375,\n 220.236328125\n ],\n [\n 352.318359375,\n 245.17132568359375\n ],\n [\n 128.27197265625,\n 245.17132568359375\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 220.236328125,\n 352.318359375,\n 245.17132568359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/SectionHeader/2\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"19.1 GUI
\",\n \"polygon\": [\n [\n 128.794921875,\n 294.5127258300781\n ],\n [\n 198.421875,\n 294.5127258300781\n ],\n [\n 198.421875,\n 308.85894775390625\n ],\n [\n 128.794921875,\n 308.85894775390625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 294.5127258300781,\n 198.421875,\n 308.85894775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Most of the programs we have seen so far are text-based, but many programs use graphical user interfaces, also known as GUIs.
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\",\n \"polygon\": [\n [\n 128.794921875,\n 354.041015625\n ],\n [\n 526.833984375,\n 354.041015625\n ],\n [\n 526.833984375,\n 388.97589111328125\n ],\n [\n 128.794921875,\n 388.97589111328125\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 354.041015625,\n 526.833984375,\n 388.97589111328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The one I will present in this chapter is Tkinter because I think it is the easiest to get started with. Most of the concepts in this chapter apply to the other GUI modules, too.
\",\n \"polygon\": [\n [\n 128.49609375,\n 399.09375\n ],\n [\n 526.53515625,\n 399.09375\n ],\n [\n 526.53515625,\n 421.7608947753906\n ],\n [\n 128.49609375,\n 421.7608947753906\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 399.09375,\n 526.53515625,\n 421.7608947753906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"There are several books and web pages about Tkinter. One of the best online resources is An Introduction to Tkinter by Fredrik Lundh.
\",\n \"polygon\": [\n [\n 127.8984375,\n 431.578125\n ],\n [\n 526.53515625,\n 431.578125\n ],\n [\n 526.53515625,\n 454.5458984375\n ],\n [\n 127.8984375,\n 454.5458984375\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 431.578125,\n 526.53515625,\n 454.5458984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"I have written a module called Gui.py that comes with Swampy. It provides a simplified interface to the functions and classes in Tkinter. The examples in this chapter are based on this module.
\",\n \"polygon\": [\n [\n 128.49609375,\n 464.8359375\n ],\n [\n 526.53515625,\n 464.8359375\n ],\n [\n 526.53515625,\n 499.52490234375\n ],\n [\n 128.49609375,\n 499.52490234375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 464.8359375,\n 526.53515625,\n 499.52490234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Here is a simple example that creates and displays a Gui:
\",\n \"polygon\": [\n [\n 128.197265625,\n 510.08203125\n ],\n [\n 381.603515625,\n 510.08203125\n ],\n [\n 381.603515625,\n 520.13671875\n ],\n [\n 128.197265625,\n 520.13671875\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 510.08203125,\n 381.603515625,\n 520.13671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"To create a GUI, you have to import Gui from Swampy:
\",\n \"polygon\": [\n [\n 128.27197265625,\n 530.19140625\n ],\n [\n 374.1328125,\n 530.19140625\n ],\n [\n 374.1328125,\n 540.7069091796875\n ],\n [\n 128.27197265625,\n 540.7069091796875\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 530.19140625,\n 374.1328125,\n 540.7069091796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"from swampy.Gui import *
\",\n \"polygon\": [\n [\n 128.27197265625,\n 551.1857604980469\n ],\n [\n 255.19921875,\n 551.1857604980469\n ],\n [\n 255.19921875,\n 561.1483612060547\n ],\n [\n 128.27197265625,\n 561.1483612060547\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 551.1857604980469,\n 255.19921875,\n 561.1483612060547\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Or, depending on how you installed Swampy, like this:
\",\n \"polygon\": [\n [\n 128.12255859375,\n 571.18359375\n ],\n [\n 371.7421875,\n 571.18359375\n ],\n [\n 371.7421875,\n 581.888916015625\n ],\n [\n 128.12255859375,\n 581.888916015625\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 571.18359375,\n 371.7421875,\n 581.888916015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"from Gui import *
\",\n \"polygon\": [\n [\n 128.42138671875,\n 592.3677673339844\n ],\n [\n 218.51617431640625,\n 592.3677673339844\n ],\n [\n 218.51617431640625,\n 602.3303680419922\n ],\n [\n 128.42138671875,\n 602.3303680419922\n ]\n ],\n \"bbox\": [\n 128.42138671875,\n 592.3677673339844,\n 218.51617431640625,\n 602.3303680419922\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/200/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Then instantiate a Gui object:
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\",\n \"polygon\": [\n [\n 85.0166015625,\n 87.8818359375\n ],\n [\n 483.50390625,\n 87.8818359375\n ],\n [\n 483.50390625,\n 110.99188232421875\n ],\n [\n 85.0166015625,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 87.8818359375,\n 483.50390625,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"This Gui doesn't do much because it doesn't have any widgets. Widgets are the elements that make up a GUI; they include:
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\",\n \"polygon\": [\n [\n 85.09130859375,\n 198.193359375\n ],\n [\n 273.89569091796875,\n 198.193359375\n ],\n [\n 273.89569091796875,\n 209.27191162109375\n ],\n [\n 85.09130859375,\n 209.27191162109375\n ]\n ],\n \"bbox\": [\n 85.09130859375,\n 198.193359375,\n 273.89569091796875,\n 209.27191162109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Scrollbar: A widget that controls the visible part of another widget.
\",\n \"polygon\": [\n [\n 84.64306640625,\n 219.26953125\n ],\n [\n 385.787109375,\n 219.26953125\n ],\n [\n 385.787109375,\n 229.84088134765625\n ],\n [\n 84.64306640625,\n 229.84088134765625\n ]\n ],\n \"bbox\": [\n 84.64306640625,\n 219.26953125,\n 385.787109375,\n 229.84088134765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Frame: A container, often invisible, that contains other widgets.
\",\n \"polygon\": [\n [\n 85.166015625,\n 239.572265625\n ],\n [\n 367.7836608886719,\n 239.572265625\n ],\n [\n 367.7836608886719,\n 250.40887451171875\n ],\n [\n 85.166015625,\n 250.40887451171875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 239.572265625,\n 367.7836608886719,\n 250.40887451171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"The empty gray square you see when you create a Gui is a Frame. When you create a new widget, it is added to this Frame.
\",\n \"polygon\": [\n [\n 85.46484375,\n 264.12890625\n ],\n [\n 482.90625,\n 264.12890625\n ],\n [\n 482.90625,\n 287.1778564453125\n ],\n [\n 85.46484375,\n 287.1778564453125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 264.12890625,\n 482.90625,\n 287.1778564453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/200/SectionHeader/2\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"19.2 Buttons and callbacks
\",\n \"polygon\": [\n [\n 85.68896484375,\n 316.916015625\n ],\n [\n 269.2710266113281,\n 316.916015625\n ],\n [\n 269.2710266113281,\n 331.5769348144531\n ],\n [\n 85.68896484375,\n 331.5769348144531\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 316.916015625,\n 269.2710266113281,\n 331.5769348144531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The method bu creates a Button widget:
\",\n \"polygon\": [\n [\n 85.98779296875,\n 343.986328125\n ],\n [\n 260.95184326171875,\n 343.986328125\n ],\n [\n 260.95184326171875,\n 354.21588134765625\n ],\n [\n 85.98779296875,\n 354.21588134765625\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 343.986328125,\n 260.95184326171875,\n 354.21588134765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"button = g.bu(text='Press me.')
\",\n \"polygon\": [\n [\n 85.98779296875,\n 360.228515625\n ],\n [\n 248.50340270996094,\n 360.228515625\n ],\n [\n 248.50340270996094,\n 370.46929931640625\n ],\n [\n 85.98779296875,\n 370.46929931640625\n ]\n ],\n \"bbox\": [\n 85.98779296875,\n 360.228515625,\n 248.50340270996094,\n 370.46929931640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"The return value from bu is a Button object. The button that appears in the Frame is a graphical representation of this object; you can control the button by invoking methods on it.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 376.6640625\n ],\n [\n 483.50390625,\n 376.6640625\n ],\n [\n 483.50390625,\n 411.46875\n ],\n [\n 86.2119140625,\n 411.46875\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 376.6640625,\n 483.50390625,\n 411.46875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"bu takes up to 32 parameters that control the appearance and function of the button. These parameters are called options. Instead of providing values for all 32 options, you can use keyword arguments, like text='Press me.', to specify only the options you need and use the default values for the rest.
\",\n \"polygon\": [\n [\n 86.0625,\n 420.75\n ],\n [\n 483.802734375,\n 420.75\n ],\n [\n 483.802734375,\n 468.36688232421875\n ],\n [\n 86.0625,\n 468.36688232421875\n ]\n ],\n \"bbox\": [\n 86.0625,\n 420.75,\n 483.802734375,\n 468.36688232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"When you add a widget to the Frame, it gets \\\"shrink-wrapped;\\\" that is, the Frame shrinks to the size of the Button. If you add more widgets, the Frame grows to accommodate them.
\",\n \"polygon\": [\n [\n 86.2119140625,\n 478.37109375\n ],\n [\n 482.90625,\n 478.37109375\n ],\n [\n 482.90625,\n 500.9358825683594\n ],\n [\n 86.2119140625,\n 500.9358825683594\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 478.37109375,\n 482.90625,\n 500.9358825683594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The method la creates a Label widget:
\",\n \"polygon\": [\n [\n 86.28662109375,\n 522.84375\n ],\n [\n 256.693359375,\n 522.84375\n ],\n [\n 256.693359375,\n 533.5048828125\n ],\n [\n 86.28662109375,\n 533.5048828125\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 522.84375,\n 256.693359375,\n 533.5048828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"label = g.la(text='Press the button.')
\",\n \"polygon\": [\n [\n 86.28662109375,\n 539.47265625\n ],\n [\n 285.380859375,\n 539.47265625\n ],\n [\n 285.380859375,\n 549.7573394775391\n ],\n [\n 86.28662109375,\n 549.7573394775391\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 539.47265625,\n 285.380859375,\n 549.7573394775391\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"By default, Tkinter stacks the widgets top-to-bottom and centers them. We'll see how to override that behavior soon.
\",\n \"polygon\": [\n [\n 86.0625,\n 555.71484375\n ],\n [\n 484.1015625,\n 555.71484375\n ],\n [\n 484.1015625,\n 578.5038909912109\n ],\n [\n 86.0625,\n 578.5038909912109\n ]\n ],\n \"bbox\": [\n 86.0625,\n 555.71484375,\n 484.1015625,\n 578.5038909912109\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"If you press the button, you will see that it doesn't do much. That's because you haven't \\\"wired it up;\\\" that is, you haven't told it what to do!
\",\n \"polygon\": [\n [\n 85.6142578125,\n 588.5859375\n ],\n [\n 483.50390625,\n 588.5859375\n ],\n [\n 483.50390625,\n 611.0728912353516\n ],\n [\n 85.6142578125,\n 611.0728912353516\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 588.5859375,\n 483.50390625,\n 611.0728912353516\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/201/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"The option that controls the behavior of a button is command. The value of command is a function that gets executed when the button is pressed. For example, here is a function that creates a new Label:
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\",\n \"polygon\": [\n [\n 128.27197265625,\n 88.68572998046875\n ],\n [\n 428.818359375,\n 88.68572998046875\n ],\n [\n 428.818359375,\n 98.9033203125\n ],\n [\n 128.27197265625,\n 98.9033203125\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 88.68572998046875,\n 428.818359375,\n 98.9033203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"When you press this button, it should execute make_label and a new label should appear.
\",\n \"polygon\": [\n [\n 128.197265625,\n 103.060546875\n ],\n [\n 523.248046875,\n 103.060546875\n ],\n [\n 523.248046875,\n 113.2689208984375\n ],\n [\n 128.197265625,\n 113.2689208984375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 103.060546875,\n 523.248046875,\n 113.2689208984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The value of the command option is a function object, which is known as a callback because after you call bu to create the button, the flow of execution \\\"calls back\\\" when the user presses the button.
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 164.4521484375\n ],\n [\n 525.602783203125,\n 164.4521484375\n ],\n [\n 525.602783203125,\n 198.93084716796875\n ],\n [\n 128.9443359375,\n 198.93084716796875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 164.4521484375,\n 525.602783203125,\n 198.93084716796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The challenge of event-driven programming is to construct a set of widgets and callbacks that work correctly (or at least generate appropriate error messages) for any sequence of user actions.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 207.28125\n ],\n [\n 525.9375,\n 207.28125\n ],\n [\n 525.9375,\n 241.7628173828125\n ],\n [\n 128.6455078125,\n 241.7628173828125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 207.28125,\n 525.9375,\n 241.7628173828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 19.1. Write a program that creates a GUI with a single button. When the button is pressed it should create a second button. When that button is pressed, it should create a label that says, \\\"Nice job!\\\".
\",\n \"polygon\": [\n [\n 129.2431640625,\n 243.821533203125\n ],\n [\n 525.6007690429688,\n 243.821533203125\n ],\n [\n 525.6007690429688,\n 278.173095703125\n ],\n [\n 129.2431640625,\n 278.173095703125\n ]\n ],\n \"bbox\": [\n 129.2431640625,\n 243.821533203125,\n 525.6007690429688,\n 278.173095703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/201/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"What happens if you press the buttons more than once? Solution: http: // thinkpython. com/ code/ button_ demo. py
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 335.95166015625\n ],\n [\n 272.3818359375,\n 335.95166015625\n ],\n [\n 272.3818359375,\n 350.2978820800781\n ],\n [\n 128.6455078125,\n 350.2978820800781\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 335.95166015625,\n 272.3818359375,\n 350.2978820800781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"One of the most versatile widgets is the Canvas, which creates a region for drawing lines, circles and other shapes. If you did Exercise 15.4 you are already familiar with canvases.
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\",\n \"polygon\": [\n [\n 128.6455078125,\n 465.22265625\n ],\n [\n 260.3293762207031,\n 465.22265625\n ],\n [\n 260.3293762207031,\n 475.4482727050781\n ],\n [\n 128.6455078125,\n 475.4482727050781\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 465.22265625,\n 260.3293762207031,\n 475.4482727050781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The value of bg is a string that names a color. The set of legal color names is different for different implementations of Python, but all implementations provide at least:
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 506.6217041015625\n ],\n [\n 255.1287384033203,\n 506.6217041015625\n ],\n [\n 255.1287384033203,\n 540.9733123779297\n ],\n [\n 128.9443359375,\n 540.9733123779297\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 506.6217041015625,\n 255.1287384033203,\n 540.9733123779297\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Shapes on a Canvas are called items. For example, the Canvas method circle draws (you guessed it) a circle:
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\",\n \"polygon\": [\n [\n 128.27197265625,\n 572.1466979980469\n ],\n [\n 359.682373046875,\n 572.1466979980469\n ],\n [\n 359.682373046875,\n 582.1092987060547\n ],\n [\n 128.27197265625,\n 582.1092987060547\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 572.1466979980469,\n 359.682373046875,\n 582.1092987060547\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"The first argument is a coordinate pair that specifies the center of the circle; the second is the radius.
\",\n \"polygon\": [\n [\n 128.9443359375,\n 586.65234375\n ],\n [\n 525.6033325195312,\n 586.65234375\n ],\n [\n 525.6033325195312,\n 608.9248504638672\n ],\n [\n 128.9443359375,\n 608.9248504638672\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 586.65234375,\n 525.6033325195312,\n 608.9248504638672\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/20\",\n \"block_type\": \"Text\",\n \"html\": \"Gui.py provides a standard Cartesian coordinate system with the origin at the center of the Canvas and the positive y axis pointing up. This is different from some other graphics systems where the origin is in the upper left corner, with the y axis pointing down.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 617.2546997070312\n ],\n [\n 525.9375,\n 617.2546997070312\n ],\n [\n 525.9375,\n 651.755859375\n ],\n [\n 128.6455078125,\n 651.755859375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 617.2546997070312,\n 525.9375,\n 651.755859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/21\",\n \"block_type\": \"Text\",\n \"html\": \"The fill option specifies that the circle should be filled in with red.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 660.0867004394531\n ],\n [\n 426.9684143066406,\n 660.0867004394531\n ],\n [\n 426.9684143066406,\n 670.1988677978516\n ],\n [\n 128.3466796875,\n 670.1988677978516\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 660.0867004394531,\n 426.9684143066406,\n 670.1988677978516\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/202/Text/22\",\n \"block_type\": \"Text\",\n \"html\": \"The return value from circle is an Item object that provides methods for modifying the item on the canvas. For example, you can use config to change any of the circle's options:
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\",\n \"polygon\": [\n [\n 84.49365234375,\n 88.55859375\n ],\n [\n 368.7860412597656,\n 88.55859375\n ],\n [\n 368.7860412597656,\n 98.6483154296875\n ],\n [\n 84.49365234375,\n 98.6483154296875\n ]\n ],\n \"bbox\": [\n 84.49365234375,\n 88.55859375,\n 368.7860412597656,\n 98.6483154296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"width is the thickness of the outline in pixels; outline is the color. Exercise 19.2. Write a program that creates a Canvas and a Button. When the user presses the Button, it should draw a circle on the canvas.
\",\n \"polygon\": [\n [\n 85.46484375,\n 105.62774658203125\n ],\n [\n 484.998046875,\n 105.62774658203125\n ],\n [\n 484.998046875,\n 139.95623779296875\n ],\n [\n 85.46484375,\n 139.95623779296875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 105.62774658203125,\n 484.998046875,\n 139.95623779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/202/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"19.4 Coordinate sequences
\",\n \"polygon\": [\n [\n 85.53955078125,\n 171.6064453125\n ],\n [\n 269.6870422363281,\n 171.6064453125\n ],\n [\n 269.6870422363281,\n 186.14697265625\n ],\n [\n 85.53955078125,\n 186.14697265625\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 171.6064453125,\n 269.6870422363281,\n 186.14697265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The rectangle method takes a sequence of coordinates that specify opposite corners of the rectangle. This example draws a blue rectangle with the lower left corner at the origin and the upper right corner at (200, 100):
\",\n \"polygon\": [\n [\n 85.3154296875,\n 198.966796875\n ],\n [\n 482.4034118652344,\n 198.966796875\n ],\n [\n 482.4034118652344,\n 233.93096923828125\n ],\n [\n 85.3154296875,\n 233.93096923828125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 198.966796875,\n 482.4034118652344,\n 233.93096923828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"canvas.rectangle([[0, 0], [200, 100]],\\n fill='blue', outline='orange', width=10)
\",\n \"polygon\": [\n [\n 85.24072265625,\n 240.345703125\n ],\n [\n 384.4730529785156,\n 240.345703125\n ],\n [\n 384.4730529785156,\n 262.91839599609375\n ],\n [\n 85.24072265625,\n 262.91839599609375\n ]\n ],\n \"bbox\": [\n 85.24072265625,\n 240.345703125,\n 384.4730529785156,\n 262.91839599609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This way of specifying corners is called a bounding box because the two points bound the rectangle.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 269.9296875\n ],\n [\n 483.50390625,\n 269.9296875\n ],\n [\n 483.50390625,\n 292.2039794921875\n ],\n [\n 85.6142578125,\n 292.2039794921875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 269.9296875,\n 483.50390625,\n 292.2039794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"oval takes a bounding box and draws an oval within the specified rectangle:
\",\n \"polygon\": [\n [\n 85.166015625,\n 302.80078125\n ],\n [\n 423.439453125,\n 302.80078125\n ],\n [\n 423.439453125,\n 313.11798095703125\n ],\n [\n 85.166015625,\n 313.11798095703125\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 302.80078125,\n 423.439453125,\n 313.11798095703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"canvas.oval([[0, 0], [200, 100]], outline='orange', width=10)
\",\n \"polygon\": [\n [\n 85.3154296875,\n 319.236328125\n ],\n [\n 405.80859375,\n 319.236328125\n ],\n [\n 405.80859375,\n 329.9114074707031\n ],\n [\n 85.3154296875,\n 329.9114074707031\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 319.236328125,\n 405.80859375,\n 329.9114074707031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"line takes a sequence of coordinates and draws a line that connects the points. This example draws two legs of a triangle:
\",\n \"polygon\": [\n [\n 85.46484375,\n 336.638671875\n ],\n [\n 482.3957824707031,\n 336.638671875\n ],\n [\n 482.3957824707031,\n 359.1979675292969\n ],\n [\n 85.46484375,\n 359.1979675292969\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 336.638671875,\n 482.3957824707031,\n 359.1979675292969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"canvas.line([[0, 100], [100, 200], [200, 100]], width=10)
\",\n \"polygon\": [\n [\n 85.68896484375,\n 366.02783203125\n ],\n [\n 384.5508117675781,\n 366.02783203125\n ],\n [\n 384.5508117675781,\n 375.99041748046875\n ],\n [\n 85.68896484375,\n 375.99041748046875\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 366.02783203125,\n 384.5508117675781,\n 375.99041748046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"polygon takes the same arguments, but it draws the last leg of the polygon (if necessary) and fills it in:
\",\n \"polygon\": [\n [\n 85.3154296875,\n 382.658203125\n ],\n [\n 482.3980407714844,\n 382.658203125\n ],\n [\n 482.3980407714844,\n 405.2769775390625\n ],\n [\n 85.3154296875,\n 405.2769775390625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 382.658203125,\n 482.3980407714844,\n 405.2769775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"canvas.polygon([[0, 100], [100, 200], [200, 100]], fill='red', outline='orange', width=10)
\",\n \"polygon\": [\n [\n 85.763671875,\n 411.85546875\n ],\n [\n 369.94921875,\n 411.85546875\n ],\n [\n 369.94921875,\n 434.264404296875\n ],\n [\n 85.763671875,\n 434.264404296875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 411.85546875,\n 369.94921875,\n 434.264404296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/SectionHeader/13\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"19.5 More widgets
\",\n \"polygon\": [\n [\n 85.6142578125,\n 465.609375\n ],\n [\n 216.30496215820312,\n 465.609375\n ],\n [\n 216.30496215820312,\n 480.4320373535156\n ],\n [\n 85.6142578125,\n 480.4320373535156\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 465.609375,\n 216.30496215820312,\n 480.4320373535156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"Tkinter provides two widgets that let users type text: an Entry, which is a single line, and a Text widget, which has multiple lines.
\",\n \"polygon\": [\n [\n 85.763671875,\n 493.06640625\n ],\n [\n 482.40350341796875,\n 493.06640625\n ],\n [\n 482.40350341796875,\n 516.0209655761719\n ],\n [\n 85.763671875,\n 516.0209655761719\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 493.06640625,\n 482.40350341796875,\n 516.0209655761719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"en creates a new Entry:
\",\n \"polygon\": [\n [\n 86.0625,\n 526.7109375\n ],\n [\n 190.5029296875,\n 526.7109375\n ],\n [\n 190.5029296875,\n 536.9349670410156\n ],\n [\n 86.0625,\n 536.9349670410156\n ]\n ],\n \"bbox\": [\n 86.0625,\n 526.7109375,\n 190.5029296875,\n 536.9349670410156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"entry = g.en(text='Default text.')
\",\n \"polygon\": [\n [\n 85.39013671875,\n 543.7658081054688\n ],\n [\n 264.1914978027344,\n 543.7658081054688\n ],\n [\n 264.1914978027344,\n 553.78125\n ],\n [\n 85.39013671875,\n 553.78125\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 543.7658081054688,\n 264.1914978027344,\n 553.78125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"The text option allows you to put text into the entry when it is created. The get method returns the contents of the Entry (which may have been changed by the user):
\",\n \"polygon\": [\n [\n 85.6142578125,\n 560.35546875\n ],\n [\n 482.90625,\n 560.35546875\n ],\n [\n 482.90625,\n 583.0149688720703\n ],\n [\n 85.6142578125,\n 583.0149688720703\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 560.35546875,\n 482.90625,\n 583.0149688720703\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Code/18\",\n \"block_type\": \"Code\",\n \"html\": \">>> entry.get()\\n'Default text.'
\",\n \"polygon\": [\n [\n 85.9130859375,\n 589.359375\n ],\n [\n 168.6884765625,\n 589.359375\n ],\n [\n 168.6884765625,\n 612.0014190673828\n ],\n [\n 85.9130859375,\n 612.0014190673828\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 589.359375,\n 168.6884765625,\n 612.0014190673828\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/19\",\n \"block_type\": \"Text\",\n \"html\": \"te creates a Text widget:
\",\n \"polygon\": [\n [\n 85.68896484375,\n 618.36328125\n ],\n [\n 195.43359375,\n 618.36328125\n ],\n [\n 195.43359375,\n 629.0939636230469\n ],\n [\n 85.68896484375,\n 629.0939636230469\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 618.36328125,\n 195.43359375,\n 629.0939636230469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/20\",\n \"block_type\": \"Text\",\n \"html\": \"text = g.te(width=100, height=5)
\",\n \"polygon\": [\n [\n 85.763671875,\n 635.9238128662109\n ],\n [\n 253.78172302246094,\n 635.9238128662109\n ],\n [\n 253.78172302246094,\n 645.8864135742188\n ],\n [\n 85.763671875,\n 645.8864135742188\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 635.9238128662109,\n 253.78172302246094,\n 645.8864135742188\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/21\",\n \"block_type\": \"Text\",\n \"html\": \"width and height are the dimensions of the widget in characters and lines.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 652.39453125\n ],\n [\n 415.96875,\n 652.39453125\n ],\n [\n 415.96875,\n 662.9789733886719\n ],\n [\n 85.9130859375,\n 662.9789733886719\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 652.39453125,\n 415.96875,\n 662.9789733886719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Text/22\",\n \"block_type\": \"Text\",\n \"html\": \"insert puts text into the Text widget:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 673.27734375\n ],\n [\n 253.5556640625,\n 673.27734375\n ],\n [\n 253.5556640625,\n 683.8929824829102\n ],\n [\n 86.2119140625,\n 683.8929824829102\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 673.27734375,\n 253.5556640625,\n 683.8929824829102\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/203/Code/23\",\n \"block_type\": \"Code\",\n \"html\": \"text.insert(END, 'A line of text.')
\",\n \"polygon\": [\n [\n 86.361328125,\n 690.6796875\n ],\n [\n 269.4205017089844,\n 690.6796875\n ],\n [\n 269.4205017089844,\n 700.734375\n ],\n [\n 86.361328125,\n 700.734375\n ]\n ],\n \"bbox\": [\n 86.361328125,\n 690.6796875,\n 269.4205017089844,\n 700.734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/204/Page/202\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/204/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.794921875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 61.171142578125\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.794921875,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 61.171142578125,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/PageHeader/19\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 509.80078125,\n 60.76318359375\n ],\n [\n 525.9375,\n 60.76318359375\n ],\n [\n 525.9375,\n 69.94775390625\n ],\n [\n 509.80078125,\n 69.94775390625\n ]\n ],\n \"bbox\": [\n 509.80078125,\n 60.76318359375,\n 525.9375,\n 69.94775390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/1\",\n \"block_type\": \"Text\",\n \"html\": \"END is a special index that indicates the last character in the Text widget.
\",\n \"polygon\": [\n [\n 127.599609375,\n 88.68572998046875\n ],\n [\n 445.552734375,\n 88.68572998046875\n ],\n [\n 445.552734375,\n 98.79791259765625\n ],\n [\n 127.599609375,\n 98.79791259765625\n ]\n ],\n \"bbox\": [\n 127.599609375,\n 88.68572998046875,\n 445.552734375,\n 98.79791259765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"You can also specify a character using a dotted index, like 1.1, which has the line number before the dot and the column number after. The following example adds the letters 'nother' after the first character of the first line.
\",\n \"polygon\": [\n [\n 127.8984375,\n 107.314453125\n ],\n [\n 525.6033935546875,\n 107.314453125\n ],\n [\n 525.6033935546875,\n 141.92486572265625\n ],\n [\n 127.8984375,\n 141.92486572265625\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 107.314453125,\n 525.6033935546875,\n 141.92486572265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \">>> text.insert(1.1, 'nother')
\",\n \"polygon\": [\n [\n 127.8984375,\n 146.1796875\n ],\n [\n 286.4743957519531,\n 146.1796875\n ],\n [\n 286.4743957519531,\n 156.54229736328125\n ],\n [\n 127.8984375,\n 156.54229736328125\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 146.1796875,\n 286.4743957519531,\n 156.54229736328125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The get method reads the text in the widget; it takes a start and end index as arguments. The following example returns all the text in the widget, including the newline character:
\",\n \"polygon\": [\n [\n 127.7490234375,\n 160.48828125\n ],\n [\n 525.5955200195312,\n 160.48828125\n ],\n [\n 525.5955200195312,\n 183.6539306640625\n ],\n [\n 127.7490234375,\n 183.6539306640625\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 160.48828125,\n 525.5955200195312,\n 183.6539306640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \">>> text.get(0.0, END)\\n'Another line of text.\\\\n'
\",\n \"polygon\": [\n [\n 128.794921875,\n 188.30877685546875\n ],\n [\n 260.32940673828125,\n 188.30877685546875\n ],\n [\n 260.32940673828125,\n 210.46533203125\n ],\n [\n 128.794921875,\n 210.46533203125\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 188.30877685546875,\n 260.32940673828125,\n 210.46533203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The delete method removes text from the widget; the following example deletes all but the first two characters:
\",\n \"polygon\": [\n [\n 128.3466796875,\n 214.62890625\n ],\n [\n 525.603515625,\n 214.62890625\n ],\n [\n 525.603515625,\n 237.57696533203125\n ],\n [\n 128.3466796875,\n 237.57696533203125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 214.62890625,\n 525.603515625,\n 237.57696533203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \">>> text.delete(1.2, END)\\n>>> text.get(0.0, END)\\n'An\\\\n'
\",\n \"polygon\": [\n [\n 128.49609375,\n 242.2318115234375\n ],\n [\n 260.36907958984375,\n 242.2318115234375\n ],\n [\n 260.36907958984375,\n 276.5823974609375\n ],\n [\n 128.49609375,\n 276.5823974609375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 242.2318115234375,\n 260.36907958984375,\n 276.5823974609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise 19.3. Modify your solution to Exercise 19.2 by adding an Entry widget and a second button. When the user presses the second button, it should read a color name from the Entry and use it to change the fill color of the circle. Use config to modify the existing circle; don't create a new one.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 278.79168701171875\n ],\n [\n 525.6036376953125,\n 278.79168701171875\n ],\n [\n 525.6036376953125,\n 325.3372802734375\n ],\n [\n 128.6455078125,\n 325.3372802734375\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 278.79168701171875,\n 525.6036376953125,\n 325.3372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Your program should handle the case where the user tries to change the color of a circle that hasn't been created, and the case where the color name is invalid.
\",\n \"polygon\": [\n [\n 128.794921875,\n 333.73828125\n ],\n [\n 525.6034545898438,\n 333.73828125\n ],\n [\n 525.6034545898438,\n 356.2702941894531\n ],\n [\n 128.794921875,\n 356.2702941894531\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 333.73828125,\n 525.6034545898438,\n 356.2702941894531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"You can see my solution at http: // thinkpython. com/ code/ circle_ demo. py .
\",\n \"polygon\": [\n [\n 127.8984375,\n 364.9649963378906\n ],\n [\n 474.76568603515625,\n 364.9649963378906\n ],\n [\n 474.76568603515625,\n 375.00830078125\n ],\n [\n 127.8984375,\n 375.00830078125\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 364.9649963378906,\n 474.76568603515625,\n 375.00830078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/203/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/SectionHeader/11\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"19.6 Packing widgets
\",\n \"polygon\": [\n [\n 128.3466796875,\n 402.5648498535156\n ],\n [\n 277.02154541015625,\n 402.5648498535156\n ],\n [\n 277.02154541015625,\n 416.91107177734375\n ],\n [\n 128.3466796875,\n 416.91107177734375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 402.5648498535156,\n 277.02154541015625,\n 416.91107177734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"So far we have been stacking widgets in a single column, but in most GUIs the layout is more complicated. For example, Figure 19.1 shows a simplified version of TurtleWorld (see Chapter 4).
\",\n \"polygon\": [\n [\n 128.9443359375,\n 427.32421875\n ],\n [\n 525.6033935546875,\n 427.32421875\n ],\n [\n 525.6033935546875,\n 462.2090148925781\n ],\n [\n 128.9443359375,\n 462.2090148925781\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 427.32421875,\n 525.6033935546875,\n 462.2090148925781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"This section presents the code that creates this GUI, broken into a series of steps. You can download the complete example from http://thinkpython.com/code/ SimpleTurtleWorld.py.
\",\n \"polygon\": [\n [\n 129.09375,\n 470.9854431152344\n ],\n [\n 526.53515625,\n 470.9854431152344\n ],\n [\n 526.53515625,\n 505.3360290527344\n ],\n [\n 129.09375,\n 505.3360290527344\n ]\n ],\n \"bbox\": [\n 129.09375,\n 470.9854431152344,\n 526.53515625,\n 505.3360290527344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"At the top level, this GUI contains two widgets—a Canvas and a Frame—arranged in a row. So the first step is to create the row.
\",\n \"polygon\": [\n [\n 128.794921875,\n 513.94921875\n ],\n [\n 525.638671875,\n 513.94921875\n ],\n [\n 525.638671875,\n 536.26904296875\n ],\n [\n 128.794921875,\n 536.26904296875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 513.94921875,\n 525.638671875,\n 536.26904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \"class SimpleTurtleWorld(TurtleWorld):\\n \\\"\\\"\\\"This class is identical to TurtleWorld, but the code that\\n lays out the GUI is simplified for explanatory purposes.\\\"\\\"\\\"\\n def setup(self):\\n self.row()\\n ...
\",\n \"polygon\": [\n [\n 129.59999084472656,\n 540.9238891601562\n ],\n [\n 467.96484375,\n 540.9238891601562\n ],\n [\n 467.96484375,\n 624.0525054931641\n ],\n [\n 129.59999084472656,\n 624.0525054931641\n ]\n ],\n \"bbox\": [\n 129.59999084472656,\n 540.9238891601562,\n 467.96484375,\n 624.0525054931641\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/16\",\n \"block_type\": \"Text\",\n \"html\": \"setup is the function that creates and arranges the widgets. Arranging widgets in a GUI is called packing.
\",\n \"polygon\": [\n [\n 127.7490234375,\n 628.8569030761719\n ],\n [\n 525.6046752929688,\n 628.8569030761719\n ],\n [\n 525.6046752929688,\n 651.62109375\n ],\n [\n 127.7490234375,\n 651.62109375\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 628.8569030761719,\n 525.6046752929688,\n 651.62109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"row creates a row Frame and makes it the \\\"current Frame.\\\" Until this Frame is closed or another Frame is created, all subsequent widgets are packed in a row.
\",\n \"polygon\": [\n [\n 128.197265625,\n 659.7421875\n ],\n [\n 525.638671875,\n 659.7421875\n ],\n [\n 525.638671875,\n 682.171875\n ],\n [\n 128.197265625,\n 682.171875\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 659.7421875,\n 525.638671875,\n 682.171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/204/Text/18\",\n \"block_type\": \"Text\",\n \"html\": \"Here is the code that creates the Canvas and the column Frame that hold the other widgets:
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\",\n \"polygon\": [\n [\n 126.8525390625,\n 88.41357421875\n ],\n [\n 525.59814453125,\n 88.41357421875\n ],\n [\n 525.59814453125,\n 110.99188232421875\n ],\n [\n 126.8525390625,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 126.8525390625,\n 88.41357421875,\n 525.59814453125,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/ListGroup/195\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 141.4951171875,\n 125.7802734375\n ],\n [\n 525.9375,\n 125.7802734375\n ],\n [\n 525.9375,\n 181.44287109375\n ],\n [\n 141.4951171875,\n 181.44287109375\n ]\n ],\n \"bbox\": [\n 141.4951171875,\n 125.7802734375,\n 525.9375,\n 181.44287109375\n ],\n \"children\": [\n {\n \"id\": \"/page/206/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \" When you use row, col or gr to create a Frame, it goes on top of the stack and becomes the current Frame.\",\n \"polygon\": [\n [\n 142.83984375,\n 125.7802734375\n ],\n [\n 525.9375,\n 125.7802734375\n ],\n [\n 525.9375,\n 148.2398681640625\n ],\n [\n 142.83984375,\n 148.2398681640625\n ]\n ],\n \"bbox\": [\n 142.83984375,\n 125.7802734375,\n 525.9375,\n 148.2398681640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/ListItem/3\",\n \"block_type\": \"ListItem\",\n \"html\": \" When you use endrow, endcol or endgr to close a Frame, it gets popped off the stack and the previous Frame on the stack becomes the current Frame.\",\n \"polygon\": [\n [\n 141.4951171875,\n 158.94140625\n ],\n [\n 525.598388671875,\n 158.94140625\n ],\n [\n 525.598388671875,\n 181.44287109375\n ],\n [\n 141.4951171875,\n 181.44287109375\n ]\n ],\n \"bbox\": [\n 141.4951171875,\n 158.94140625,\n 525.598388671875,\n 181.44287109375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/206/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The method run_file reads the contents of the Entry, uses it as a filename, reads the contents and passes it to run_code. self.inter is an Interpreter object that knows how to take a string and execute it as Python code.
\",\n \"polygon\": [\n [\n 128.0478515625,\n 196.259765625\n ],\n [\n 525.9375,\n 196.259765625\n ],\n [\n 525.9375,\n 230.88385009765625\n ],\n [\n 128.0478515625,\n 230.88385009765625\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 196.259765625,\n 525.9375,\n 230.88385009765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"def run_file(self):\\n filename = self.en_file.get()\\n fp = open(filename)\\n source = fp.read()\\n self.inter.run_code(source, filename)
\",\n \"polygon\": [\n [\n 150.5159912109375,\n 237.43267822265625\n ],\n [\n 364.9763488769531,\n 237.43267822265625\n ],\n [\n 364.9763488769531,\n 296.2265625\n ],\n [\n 150.5159912109375,\n 296.2265625\n ]\n ],\n \"bbox\": [\n 150.5159912109375,\n 237.43267822265625,\n 364.9763488769531,\n 296.2265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The last two widgets are a Text widget and a Button:
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\",\n \"polygon\": [\n [\n 170.33203125,\n 319.53173828125\n ],\n [\n 428.51953125,\n 319.53173828125\n ],\n [\n 428.51953125,\n 362.7421875\n ],\n [\n 170.33203125,\n 362.7421875\n ]\n ],\n \"bbox\": [\n 170.33203125,\n 319.53173828125,\n 428.51953125,\n 362.7421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"self.bu(text='Run code', command=self.run_text)
\",\n \"polygon\": [\n [\n 169.734375,\n 368.3087463378906\n ],\n [\n 417.2407531738281,\n 368.3087463378906\n ],\n [\n 417.2407531738281,\n 378.2713317871094\n ],\n [\n 169.734375,\n 378.2713317871094\n ]\n ],\n \"bbox\": [\n 169.734375,\n 368.3087463378906,\n 417.2407531738281,\n 378.2713317871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"run_text is similar to run_file except that it takes the code from the Text widget instead of from a file:
\",\n \"polygon\": [\n [\n 128.197265625,\n 384.9697570800781\n ],\n [\n 525.595458984375,\n 384.9697570800781\n ],\n [\n 525.595458984375,\n 407.27691650390625\n ],\n [\n 128.197265625,\n 407.27691650390625\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 384.9697570800781,\n 525.595458984375,\n 407.27691650390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"def run_text(self):\\n source = self.te_code.get(1.0, END)\\n self.inter.run_code(source, '<user-provided code>')
\",\n \"polygon\": [\n [\n 150.5159912109375,\n 413.7890625\n ],\n [\n 438.11834716796875,\n 413.7890625\n ],\n [\n 438.11834716796875,\n 448.1763610839844\n ],\n [\n 150.5159912109375,\n 448.1763610839844\n ]\n ],\n \"bbox\": [\n 150.5159912109375,\n 413.7890625,\n 438.11834716796875,\n 448.1763610839844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Unfortunately, the details of widget layout are different in other languages, and in different Python modules. Tkinter alone provides three different mechanisms for arranging widgets. These mechanisms are called geometry managers. The one I demonstrated in this section is the \\\"grid\\\" geometry manager; the others are called \\\"pack\\\" and \\\"place\\\".
\",\n \"polygon\": [\n [\n 128.794921875,\n 454.39453125\n ],\n [\n 525.6044311523438,\n 454.39453125\n ],\n [\n 525.6044311523438,\n 501.5699462890625\n ],\n [\n 128.794921875,\n 501.5699462890625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 454.39453125,\n 525.6044311523438,\n 501.5699462890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"Fortunately, most of the concepts in this section apply to other GUI modules and other languages.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 512.2393493652344\n ],\n [\n 525.6034545898438,\n 512.2393493652344\n ],\n [\n 525.6034545898438,\n 534.83203125\n ],\n [\n 128.3466796875,\n 534.83203125\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 512.2393493652344,\n 525.6034545898438,\n 534.83203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/204/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/SectionHeader/13\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"19.7 Menus and Callables
\",\n \"polygon\": [\n [\n 128.27197265625,\n 565.2227935791016\n ],\n [\n 308.091796875,\n 565.2227935791016\n ],\n [\n 308.091796875,\n 579.5689849853516\n ],\n [\n 128.27197265625,\n 579.5689849853516\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 565.2227935791016,\n 308.091796875,\n 579.5689849853516\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/206/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"A Menubutton is a widget that looks like a button, but when pressed it pops up a menu. After the user selects an item, the menu disappears.
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\",\n \"polygon\": [\n [\n 85.166015625,\n 88.6552734375\n ],\n [\n 482.40081787109375,\n 88.6552734375\n ],\n [\n 482.40081787109375,\n 110.99188232421875\n ],\n [\n 85.166015625,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 88.6552734375,\n 482.40081787109375,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"for color in colors:\\n g.mi(mb, text=color, command=Callable(set_color, color))
\",\n \"polygon\": [\n [\n 85.3154296875,\n 117.585693359375\n ],\n [\n 400.2363586425781,\n 117.585693359375\n ],\n [\n 400.2363586425781,\n 141.92578125\n ],\n [\n 85.3154296875,\n 141.92578125\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 117.585693359375,\n 400.2363586425781,\n 141.92578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The first argument of mi is the Menubutton these items are associated with.
\",\n \"polygon\": [\n [\n 85.0166015625,\n 145.986328125\n ],\n [\n 416.99591064453125,\n 145.986328125\n ],\n [\n 416.99591064453125,\n 156.597900390625\n ],\n [\n 85.0166015625,\n 156.597900390625\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 145.986328125,\n 416.99591064453125,\n 156.597900390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The command option is a Callable object, which is something new. So far we have seen functions and bound methods used as callbacks, which works fine if you don't have to pass any arguments to the function. Otherwise you have to construct a Callable object that contains a function, like set_color, and its arguments, like color.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 166.869140625\n ],\n [\n 482.4034118652344,\n 166.869140625\n ],\n [\n 482.4034118652344,\n 213.85797119140625\n ],\n [\n 85.3154296875,\n 213.85797119140625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 166.869140625,\n 482.4034118652344,\n 213.85797119140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"The Callable object stores a reference to the function and the arguments as attributes. Later, when the user clicks on a menu item, the callback calls the function and passes the stored arguments.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 224.490234375\n ],\n [\n 482.90625,\n 224.490234375\n ],\n [\n 482.90625,\n 258.9239501953125\n ],\n [\n 85.6142578125,\n 258.9239501953125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 224.490234375,\n 482.90625,\n 258.9239501953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Here is what set_color might look like:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 268.76953125\n ],\n [\n 263.6532897949219,\n 268.76953125\n ],\n [\n 263.6532897949219,\n 279.6009521484375\n ],\n [\n 85.6142578125,\n 279.6009521484375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 268.76953125,\n 263.6532897949219,\n 279.6009521484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"def set_color(color):\\n mb.config(text=color)\\n print color
\",\n \"polygon\": [\n [\n 85.763671875,\n 286.19476318359375\n ],\n [\n 217.16360473632812,\n 286.19476318359375\n ],\n [\n 217.16360473632812,\n 321.169921875\n ],\n [\n 85.763671875,\n 321.169921875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 286.19476318359375,\n 217.16360473632812,\n 321.169921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"When the user selects a menu item and set_color is called, it configures the Menubutton to display the newly-selected color. It also print the color; if you try this example, you can confirm that set_color is called when you select an item (and not called when you create the Callable object).
\",\n \"polygon\": [\n [\n 85.3154296875,\n 326.77734375\n ],\n [\n 482.4033203125,\n 326.77734375\n ],\n [\n 482.4033203125,\n 373.9839172363281\n ],\n [\n 85.3154296875,\n 373.9839172363281\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 326.77734375,\n 482.4033203125,\n 373.9839172363281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/206/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/SectionHeader/9\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"19.8 Binding
\",\n \"polygon\": [\n [\n 85.763671875,\n 404.94476318359375\n ],\n [\n 179.89453125,\n 404.94476318359375\n ],\n [\n 179.89453125,\n 419.2909851074219\n ],\n [\n 85.763671875,\n 419.2909851074219\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 404.94476318359375,\n 179.89453125,\n 419.2909851074219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"A binding is an association between a widget, an event and a callback: when an event (like a button press) happens on a widget, the callback is invoked.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 431.96484375\n ],\n [\n 482.4049072265625,\n 431.96484375\n ],\n [\n 482.4049072265625,\n 454.5489196777344\n ],\n [\n 85.6142578125,\n 454.5489196777344\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 431.96484375,\n 482.4049072265625,\n 454.5489196777344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Many widgets have default bindings. For example, when you press a button, the default binding changes the relief of the button to make it look depressed. When you release the button, the binding restores the appearance of the button and invokes the callback specified with the command option.
\",\n \"polygon\": [\n [\n 85.46484375,\n 464.8359375\n ],\n [\n 482.4034423828125,\n 464.8359375\n ],\n [\n 482.4034423828125,\n 511.8089294433594\n ],\n [\n 85.46484375,\n 511.8089294433594\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 464.8359375,\n 482.4034423828125,\n 511.8089294433594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"You can use the bind method to override these default bindings or to add new ones. For example, this code creates a binding for a canvas (you can download the code in this section from http://thinkpython.com/code/draggable_demo.py):
\",\n \"polygon\": [\n [\n 85.763671875,\n 521.68359375\n ],\n [\n 482.40423583984375,\n 521.68359375\n ],\n [\n 482.40423583984375,\n 556.8749389648438\n ],\n [\n 85.763671875,\n 556.8749389648438\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 521.68359375,\n 482.40423583984375,\n 556.8749389648438\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Code/13\",\n \"block_type\": \"Code\",\n \"html\": \"ca.bind('<ButtonPress-1>', make_circle)\",\n \"polygon\": [\n [\n 86.28662109375,\n 563.44921875\n ],\n [\n 290.35308837890625,\n 563.44921875\n ],\n [\n 290.35308837890625,\n 573.4313812255859\n ],\n [\n 86.28662109375,\n 573.4313812255859\n ]\n ],\n \"bbox\": [\n 86.28662109375,\n 563.44921875,\n 290.35308837890625,\n 573.4313812255859\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The first argument is an event string; this event is triggered when the user presses the left mouse button. Other mouse events include ButtonMotion, ButtonRelease and Double-Button.
\",\n \"polygon\": [\n [\n 85.763671875,\n 580.078125\n ],\n [\n 482.607421875,\n 580.078125\n ],\n [\n 482.607421875,\n 614.8828125\n ],\n [\n 85.763671875,\n 614.8828125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 580.078125,\n 482.607421875,\n 614.8828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/207/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"The second argument is an event handler. An event handler is a function or bound method, like a callback, but an important difference is that an event handler takes an Event object as a parameter. Here is an example:
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\",\n \"polygon\": [\n [\n 129.392578125,\n 88.0751953125\n ],\n [\n 525.638671875,\n 88.0751953125\n ],\n [\n 525.638671875,\n 147.57489013671875\n ],\n [\n 129.392578125,\n 147.57489013671875\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 88.0751953125,\n 525.638671875,\n 147.57489013671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"For Entry widgets, it is common to bind the <Return> event, which is triggered when the user presses the Return or Enter key. For example, the following code creates a Button and an Entry.
\",\n \"polygon\": [\n [\n 129.09375,\n 157.201171875\n ],\n [\n 525.6021118164062,\n 157.201171875\n ],\n [\n 525.6021118164062,\n 191.94488525390625\n ],\n [\n 129.09375,\n 191.94488525390625\n ]\n ],\n \"bbox\": [\n 129.09375,\n 157.201171875,\n 525.6021118164062,\n 191.94488525390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"bu = g.bu('Make text item:', make_text)\\nen = g.en()\\nen.bind('<Return>', make_text)\",\n \"polygon\": [\n [\n 129.59999084472656,\n 197.806640625\n ],\n [\n 333.5504150390625,\n 197.806640625\n ],\n [\n 333.5504150390625,\n 232.19427490234375\n ],\n [\n 129.59999084472656,\n 232.19427490234375\n ]\n ],\n \"bbox\": [\n 129.59999084472656,\n 197.806640625,\n 333.5504150390625,\n 232.19427490234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"make_text is called when the Button is pressed or when the user hits Return while typing in the Entry. To make this work, we need a function that can be called as a command (with no arguments) or as an event handler (with an Event as an argument):
\",\n \"polygon\": [\n [\n 128.9443359375,\n 237.4453125\n ],\n [\n 525.6033935546875,\n 237.4453125\n ],\n [\n 525.6033935546875,\n 272.74188232421875\n ],\n [\n 128.9443359375,\n 272.74188232421875\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 237.4453125,\n 525.6033935546875,\n 272.74188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"def make_text(event=None):\\n text = en.get()\\n item = ca.text([0,0], text)
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 318.462890625\n ],\n [\n 502.04083251953125,\n 318.462890625\n ],\n [\n 502.04083251953125,\n 329.1498718261719\n ],\n [\n 128.9443359375,\n 329.1498718261719\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 318.462890625,\n 502.04083251953125,\n 329.1498718261719\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"It is also possible to create bindings for Canvas items. The following is a class definition for Draggable, which is a child class of Item that provides bindings that implement dragand-drop capability.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 338.765625\n ],\n [\n 525.6033325195312,\n 338.765625\n ],\n [\n 525.6033325195312,\n 373.5198669433594\n ],\n [\n 128.6455078125,\n 373.5198669433594\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 338.765625,\n 525.6033325195312,\n 373.5198669433594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"class Draggable(Item):
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\",\n \"polygon\": [\n [\n 128.794921875,\n 480.3046875\n ],\n [\n 525.603271484375,\n 480.3046875\n ],\n [\n 525.603271484375,\n 503.0939025878906\n ],\n [\n 128.794921875,\n 503.0939025878906\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 480.3046875,\n 525.603271484375,\n 503.0939025878906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"The event handler select stores the coordinates of the current event and the original color of the item, then changes the color to yellow:
\",\n \"polygon\": [\n [\n 128.197265625,\n 512.7890625\n ],\n [\n 525.5986938476562,\n 512.7890625\n ],\n [\n 525.5986938476562,\n 535.2698974609375\n ],\n [\n 128.197265625,\n 535.2698974609375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 512.7890625,\n 525.5986938476562,\n 535.2698974609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Code/12\",\n \"block_type\": \"Code\",\n \"html\": \"def select(self, event):\\n self.dragx = event.x\\n self.dragy = event.y\\n self.fill = self.cget('fill')\\n self.config(fill='yellow')\",\n \"polygon\": [\n [\n 150.5160369873047,\n 541.1677398681641\n ],\n [\n 323.07843017578125,\n 541.1677398681641\n ],\n [\n 323.07843017578125,\n 612.5625\n ],\n [\n 150.5160369873047,\n 612.5625\n ]\n ],\n \"bbox\": [\n 150.5160369873047,\n 541.1677398681641,\n 323.07843017578125,\n 612.5625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"cget stands for \\\"get configuration;\\\" it takes the name of an option as a string and returns the current value of that option.
\",\n \"polygon\": [\n [\n 128.49609375,\n 617.9765625\n ],\n [\n 525.5963134765625,\n 617.9765625\n ],\n [\n 525.5963134765625,\n 640.4559020996094\n ],\n [\n 128.49609375,\n 640.4559020996094\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 617.9765625,\n 525.5963134765625,\n 640.4559020996094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"drag computes how far the object has moved relative to the starting place, updates the stored coordinates, and then moves the item.
\",\n \"polygon\": [\n [\n 127.4501953125,\n 650.07421875\n ],\n [\n 525.6035766601562,\n 650.07421875\n ],\n [\n 525.6035766601562,\n 672.6309127807617\n ],\n [\n 127.4501953125,\n 672.6309127807617\n ]\n ],\n \"bbox\": [\n 127.4501953125,\n 650.07421875,\n 525.6035766601562,\n 672.6309127807617\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/208/Code/15\",\n \"block_type\": \"Code\",\n \"html\": \"def drag(self, event):\\n dx = event.x - self.dragx
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\",\n \"polygon\": [\n [\n 125.58251953125,\n 86.57666015625\n ],\n [\n 258.9920959472656,\n 86.57666015625\n ],\n [\n 258.9920959472656,\n 159.62030029296875\n ],\n [\n 125.58251953125,\n 159.62030029296875\n ]\n ],\n \"bbox\": [\n 125.58251953125,\n 86.57666015625,\n 258.9920959472656,\n 159.62030029296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"This computation is done in pixel coordinates; there is no need to convert to Canvas coordinates.
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\",\n \"polygon\": [\n [\n 85.53955078125,\n 197.419921875\n ],\n [\n 308.5013427734375,\n 197.419921875\n ],\n [\n 308.5013427734375,\n 208.3829345703125\n ],\n [\n 85.53955078125,\n 208.3829345703125\n ]\n ],\n \"bbox\": [\n 85.53955078125,\n 197.419921875,\n 308.5013427734375,\n 208.3829345703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"def drop(self, event):\\n self.config(fill=self.fill)
\",\n \"polygon\": [\n [\n 107.31600952148438,\n 214.4937744140625\n ],\n [\n 269.4627990722656,\n 214.4937744140625\n ],\n [\n 269.4627990722656,\n 236.6513671875\n ],\n [\n 107.31600952148438,\n 236.6513671875\n ]\n ],\n \"bbox\": [\n 107.31600952148438,\n 214.4937744140625,\n 269.4627990722656,\n 236.6513671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"You can use the Draggable class to add drag-and-drop capability to an existing item. For example, here is a modified version of make_circle that uses circle to create an Item and Draggable to make it draggable:
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\",\n \"polygon\": [\n [\n 86.39999389648438,\n 283.52484130859375\n ],\n [\n 327.814453125,\n 283.52484130859375\n ],\n [\n 327.814453125,\n 330.0704345703125\n ],\n [\n 86.39999389648438,\n 330.0704345703125\n ]\n ],\n \"bbox\": [\n 86.39999389648438,\n 283.52484130859375,\n 327.814453125,\n 330.0704345703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"This example demonstrates one of the benefits of inheritance: you can modify the capabilities of a parent class without modifying its definition. This is particularly useful if you want to change behavior defined in a module you did not write.
\",\n \"polygon\": [\n [\n 85.46484375,\n 335.671875\n ],\n [\n 482.4033508300781,\n 335.671875\n ],\n [\n 482.4033508300781,\n 370.8320007324219\n ],\n [\n 85.46484375,\n 370.8320007324219\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 335.671875,\n 482.4033508300781,\n 370.8320007324219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/207/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"19.9 Debugging
\",\n \"polygon\": [\n [\n 85.6142578125,\n 400.25390625\n ],\n [\n 200.6630859375,\n 400.25390625\n ],\n [\n 200.6630859375,\n 414.94921875\n ],\n [\n 85.6142578125,\n 414.94921875\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 400.25390625,\n 200.6630859375,\n 414.94921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/209/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"One of the challenges of GUI programming is keeping track of which things happen while the GUI is being built and which things happen later in response to user events.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 426.55078125\n ],\n [\n 482.40325927734375,\n 426.55078125\n ],\n [\n 482.40325927734375,\n 449.2760009765625\n ],\n [\n 85.6142578125,\n 449.2760009765625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 426.55078125,\n 482.40325927734375,\n 449.2760009765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/209/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"For example, when you are setting up a callback, it is a common error to call the function rather than passing a reference to it:
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\",\n \"polygon\": [\n [\n 85.166015625,\n 487.7778625488281\n ],\n [\n 254.6015625,\n 487.7778625488281\n ],\n [\n 254.6015625,\n 512.015625\n ],\n [\n 85.166015625,\n 512.015625\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 487.7778625488281,\n 254.6015625,\n 512.015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/209/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"g.bu(text='This is wrong!', command=the_callback())
\",\n \"polygon\": [\n [\n 85.46484375,\n 524.3608703613281\n ],\n [\n 353.1261291503906,\n 524.3608703613281\n ],\n [\n 353.1261291503906,\n 534.3234558105469\n ],\n [\n 85.46484375,\n 534.3234558105469\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 524.3608703613281,\n 353.1261291503906,\n 534.3234558105469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"4\": \"/page/209/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/209/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"If you run this code, you will see that it calls the_callback immediately, and then creates the button. When you press the button, it does nothing because the return value from the_callback is None. Usually you do not want to invoke a callback while you are setting up the GUI; it should only be invoked later in response to a user event.
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\",\n \"polygon\": [\n [\n 85.166015625,\n 196.1630859375\n ],\n [\n 482.607421875,\n 196.1630859375\n ],\n [\n 482.607421875,\n 232.03125\n ],\n [\n 85.166015625,\n 232.03125\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 196.1630859375,\n 482.607421875,\n 232.03125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"g.la(image=photo)\\ng.bu(image=photo)
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\",\n \"polygon\": [\n [\n 85.763671875,\n 267.99609375\n ],\n [\n 482.90625,\n 267.99609375\n ],\n [\n 482.90625,\n 291.3233642578125\n ],\n [\n 85.763671875,\n 291.3233642578125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 267.99609375,\n 482.90625,\n 291.3233642578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The name of the PIL module is Image, but Tkinter defines an object with the same name. To avoid the conflict, you can use import...as like this:
\",\n \"polygon\": [\n [\n 85.46484375,\n 301.25390625\n ],\n [\n 483.802734375,\n 301.25390625\n ],\n [\n 483.802734375,\n 324.9725036621094\n ],\n [\n 85.46484375,\n 324.9725036621094\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 301.25390625,\n 483.802734375,\n 324.9725036621094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"import Image as PIL import ImageTk
\",\n \"polygon\": [\n [\n 85.763671875,\n 332.4709167480469\n ],\n [\n 186.46875,\n 332.4709167480469\n ],\n [\n 186.46875,\n 354.62750244140625\n ],\n [\n 85.763671875,\n 354.62750244140625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 332.4709167480469,\n 186.46875,\n 354.62750244140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The first line imports Image and gives it the local name PIL. The second line imports ImageTk, which can translate a PIL image into a Tkinter PhotoImage. Here's an example:
\",\n \"polygon\": [\n [\n 86.2119140625,\n 361.775390625\n ],\n [\n 482.39971923828125,\n 361.775390625\n ],\n [\n 482.39971923828125,\n 384.2593688964844\n ],\n [\n 86.2119140625,\n 384.2593688964844\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 361.775390625,\n 482.39971923828125,\n 384.2593688964844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/Code/10\",\n \"block_type\": \"Code\",\n \"html\": \"image = PIL.open('allen.png')\\nphoto2 = ImageTk.PhotoImage(image)\\ng.la(image=photo2)\",\n \"polygon\": [\n [\n 85.46484375,\n 391.74609375\n ],\n [\n 264.2424011230469,\n 391.74609375\n ],\n [\n 264.2424011230469,\n 426.13250732421875\n ],\n [\n 85.46484375,\n 426.13250732421875\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 391.74609375,\n 264.2424011230469,\n 426.13250732421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/ListGroup/231\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 97.2685546875,\n 442.01953125\n ],\n [\n 482.90625,\n 442.01953125\n ],\n [\n 482.90625,\n 523.7943725585938\n ],\n [\n 97.2685546875,\n 523.7943725585938\n ]\n ],\n \"bbox\": [\n 97.2685546875,\n 442.01953125,\n 482.90625,\n 523.7943725585938\n ],\n \"children\": [\n {\n \"id\": \"/page/211/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \"1. Download image_demo.py, danger.gif and allen.png from http: // thinkpython. com/ code . Run image_demo.py. You might have to install PIL and ImageTk. They are probably in your software repository, but if not you can get them from http: // pythonware. com/ products/ pil .\",\n \"polygon\": [\n [\n 97.8662109375,\n 442.01953125\n ],\n [\n 482.90625,\n 442.01953125\n ],\n [\n 482.90625,\n 489.2273864746094\n ],\n [\n 97.8662109375,\n 489.2273864746094\n ]\n ],\n \"bbox\": [\n 97.8662109375,\n 442.01953125,\n 482.90625,\n 489.2273864746094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/ListItem/12\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. In image_demo.py change the name of the second PhotoImage from photo2 to photo and run the program again. You should see the second PhotoImage but not the first.\",\n \"polygon\": [\n [\n 97.2685546875,\n 500.4140625\n ],\n [\n 482.90625,\n 500.4140625\n ],\n [\n 482.90625,\n 523.7943725585938\n ],\n [\n 97.2685546875,\n 523.7943725585938\n ]\n ],\n \"bbox\": [\n 97.2685546875,\n 500.4140625,\n 482.90625,\n 523.7943725585938\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/211/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"The problem is that when you reassign photo it overwrites the reference to the first PhotoImage, which then disappears. The same thing happens if you assign a PhotoImage to a local variable; it disappears when the function ends.
\",\n \"polygon\": [\n [\n 111.30706787109375,\n 530.19140625\n ],\n [\n 482.90625,\n 530.19140625\n ],\n [\n 482.90625,\n 565.4663696289062\n ],\n [\n 111.30706787109375,\n 565.4663696289062\n ]\n ],\n \"bbox\": [\n 111.30706787109375,\n 530.19140625,\n 482.90625,\n 565.4663696289062\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"To avoid this problem, you have to store a reference to each PhotoImage you want to keep. You can use a global variable, or store PhotoImages in a data structure or as an attribute of an object.
\",\n \"polygon\": [\n [\n 111.30706787109375,\n 571.95703125\n ],\n [\n 483.50390625,\n 571.95703125\n ],\n [\n 483.50390625,\n 607.1393737792969\n ],\n [\n 111.30706787109375,\n 607.1393737792969\n ]\n ],\n \"bbox\": [\n 111.30706787109375,\n 571.95703125,\n 483.50390625,\n 607.1393737792969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"This behavior can be frustrating, which is why I am warning you (and why the example image says \\\"Danger!\\\").
\",\n \"polygon\": [\n [\n 111.30706787109375,\n 613.3359375\n ],\n [\n 482.90625,\n 613.3359375\n ],\n [\n 482.90625,\n 636.6173706054688\n ],\n [\n 111.30706787109375,\n 636.6173706054688\n ]\n ],\n \"bbox\": [\n 111.30706787109375,\n 613.3359375,\n 482.90625,\n 636.6173706054688\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/ListItem/16\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. Starting with this example, write a program that takes the name of a directory and loops through all the files, displaying any files that PIL recognizes as images. You can use a try statement to catch the files PIL doesn't recognize.\",\n \"polygon\": [\n [\n 98.1650390625,\n 647.3671875\n ],\n [\n 483.50390625,\n 647.3671875\n ],\n [\n 483.50390625,\n 683.3793792724609\n ],\n [\n 98.1650390625,\n 683.3793792724609\n ]\n ],\n \"bbox\": [\n 98.1650390625,\n 647.3671875,\n 483.50390625,\n 683.3793792724609\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/211/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"When the user clicks on the image, the program should display the next one.
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\",\n \"polygon\": [\n [\n 128.197265625,\n 137.10687255859375\n ],\n [\n 525.638671875,\n 137.10687255859375\n ],\n [\n 525.638671875,\n 171.53814697265625\n ],\n [\n 128.197265625,\n 171.53814697265625\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 137.10687255859375,\n 525.638671875,\n 171.53814697265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/212/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Write a simple vector graphics editor using Tkinter. At a minimum, it should allow users to draw lines, circles and rectangles, and it should use Canvas.dump to generate a Postscript description of the contents of the Canvas.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 270.703125\n ],\n [\n 525.9375,\n 270.703125\n ],\n [\n 525.9375,\n 292.9221496582031\n ],\n [\n 128.0478515625,\n 292.9221496582031\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 270.703125,\n 525.9375,\n 292.9221496582031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/212/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"At a minimum your browser should handle plain text and hyperlinks. As a challenge you could handle background colors, text formatting tags and images.
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\",\n \"polygon\": [\n [\n 86.4000015258789,\n 59.5546875\n ],\n [\n 482.4034118652344,\n 59.5546875\n ],\n [\n 482.4034118652344,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 59.5546875,\n 482.4034118652344,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"3\": \"/page/211/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/214/Page/102\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/214/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Appendix A
\",\n \"polygon\": [\n [\n 128.49609375,\n 163.58203125\n ],\n [\n 246.533203125,\n 163.58203125\n ],\n [\n 246.533203125,\n 185.823974609375\n ],\n [\n 128.49609375,\n 185.823974609375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 163.58203125,\n 246.533203125,\n 185.823974609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/200/SectionHeader/1\",\n \"2\": \"/page/214/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/214/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Debugging
\",\n \"polygon\": [\n [\n 128.794921875,\n 220.04296875\n ],\n [\n 259.083984375,\n 220.04296875\n ],\n [\n 259.083984375,\n 246.34539794921875\n ],\n [\n 128.794921875,\n 246.34539794921875\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 220.04296875,\n 259.083984375,\n 246.34539794921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/214/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Different kinds of errors can occur in a program, and it is useful to distinguish among them in order to track them down more quickly:
\",\n \"polygon\": [\n [\n 128.794921875,\n 294.6796875\n ],\n [\n 527.431640625,\n 294.6796875\n ],\n [\n 527.431640625,\n 317.970947265625\n ],\n [\n 128.794921875,\n 317.970947265625\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 294.6796875,\n 527.431640625,\n 317.970947265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/214/ListGroup/100\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 142.0927734375,\n 332.96484375\n ],\n [\n 527.1328125,\n 332.96484375\n ],\n [\n 527.1328125,\n 485.4669494628906\n ],\n [\n 142.0927734375,\n 485.4669494628906\n ]\n ],\n \"bbox\": [\n 142.0927734375,\n 332.96484375,\n 527.1328125,\n 485.4669494628906\n ],\n \"children\": [\n {\n \"id\": \"/page/214/ListItem/3\",\n \"block_type\": \"ListItem\",\n \"html\": \" Syntax errors are produced by Python when it is translating the source code into byte code. They usually indicate that there is something wrong with the syntax of the program. Example: Omitting the colon at the end of a def statement yields the somewhat redundant message SyntaxError: invalid syntax.\",\n \"polygon\": [\n [\n 142.9892578125,\n 332.96484375\n ],\n [\n 526.53515625,\n 332.96484375\n ],\n [\n 526.53515625,\n 380.6179504394531\n ],\n [\n 142.9892578125,\n 380.6179504394531\n ]\n ],\n \"bbox\": [\n 142.9892578125,\n 332.96484375,\n 526.53515625,\n 380.6179504394531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/214/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \" Runtime errors are produced by the interpreter if something goes wrong while the program is running. Most runtime error messages include information about where the error occurred and what functions were executing. Example: An infinite recursion eventually causes the runtime error \\\"maximum recursion depth exceeded.\\\"\",\n \"polygon\": [\n [\n 143.48800659179688,\n 391.166015625\n ],\n [\n 527.1328125,\n 391.166015625\n ],\n [\n 527.1328125,\n 439.1389465332031\n ],\n [\n 143.48800659179688,\n 439.1389465332031\n ]\n ],\n \"bbox\": [\n 143.48800659179688,\n 391.166015625,\n 527.1328125,\n 439.1389465332031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/214/ListItem/5\",\n \"block_type\": \"ListItem\",\n \"html\": \" Semantic errors are problems with a program that runs without producing error messages but doesn't do the right thing. Example: An expression may not be evaluated in the order you expect, yielding an incorrect result.\",\n \"polygon\": [\n [\n 142.0927734375,\n 449.75390625\n ],\n [\n 527.1328125,\n 449.75390625\n ],\n [\n 527.1328125,\n 485.4669494628906\n ],\n [\n 142.0927734375,\n 485.4669494628906\n ]\n ],\n \"bbox\": [\n 142.0927734375,\n 449.75390625,\n 527.1328125,\n 485.4669494628906\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/214/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The first step in debugging is to figure out which kind of error you are dealing with. Although the following sections are organized by error type, some techniques are applicable in more than one situation.
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\",\n \"polygon\": [\n [\n 129.46728515625,\n 568.3808135986328\n ],\n [\n 252.509765625,\n 568.3808135986328\n ],\n [\n 252.509765625,\n 582.7270050048828\n ],\n [\n 129.46728515625,\n 582.7270050048828\n ]\n ],\n \"bbox\": [\n 129.46728515625,\n 568.3808135986328,\n 252.509765625,\n 582.7270050048828\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/214/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"Syntax errors are usually easy to fix once you figure out what they are. Unfortunately, the error messages are often not helpful. The most common messages are SyntaxError: invalid syntax and SyntaxError: invalid token, neither of which is very informative.
\",\n \"polygon\": [\n [\n 129.09375,\n 596.3203125\n ],\n [\n 526.53515625,\n 596.3203125\n ],\n [\n 526.53515625,\n 643.074951171875\n ],\n [\n 129.09375,\n 643.074951171875\n ]\n ],\n \"bbox\": [\n 129.09375,\n 596.3203125,\n 526.53515625,\n 643.074951171875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/214/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"On the other hand, the message does tell you where in the program the problem occurred. Actually, it tells you where Python noticed a problem, which is not necessarily where the error is. Sometimes the error is prior to the location of the error message, often on the preceding line.
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\",\n \"polygon\": [\n [\n 85.0166015625,\n 87.3984375\n ],\n [\n 484.69921875,\n 87.3984375\n ],\n [\n 484.69921875,\n 110.99188232421875\n ],\n [\n 85.0166015625,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 87.3984375,\n 484.69921875,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"If you are copying code from a book, start by comparing your code to the book's code very carefully. Check every character. At the same time, remember that the book might be wrong, so if you see something that looks like a syntax error, it might be.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 120.5595703125\n ],\n [\n 484.1015625,\n 120.5595703125\n ],\n [\n 484.1015625,\n 155.85791015625\n ],\n [\n 85.3154296875,\n 155.85791015625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 120.5595703125,\n 484.1015625,\n 155.85791015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Here are some ways to avoid the most common syntax errors:
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Make sure you are not using a Python keyword for a variable name.\",\n \"polygon\": [\n [\n 98.8530044555664,\n 189.3955078125\n ],\n [\n 410.888671875,\n 189.3955078125\n ],\n [\n 410.888671875,\n 201.09991455078125\n ],\n [\n 98.8530044555664,\n 201.09991455078125\n ]\n ],\n \"bbox\": [\n 98.8530044555664,\n 189.3955078125,\n 410.888671875,\n 201.09991455078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/ListItem/5\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. Check that you have a colon at the end of the header of every compound statement, including for, while, if, and def statements.\",\n \"polygon\": [\n [\n 98.015625,\n 210.568359375\n ],\n [\n 483.802734375,\n 210.568359375\n ],\n [\n 483.802734375,\n 234.03790283203125\n ],\n [\n 98.015625,\n 234.03790283203125\n ]\n ],\n \"bbox\": [\n 98.015625,\n 210.568359375,\n 483.802734375,\n 234.03790283203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/ListItem/6\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. Make sure that any strings in the code have matching quotation marks.\",\n \"polygon\": [\n [\n 97.8662109375,\n 244.01953125\n ],\n [\n 424.93359375,\n 244.01953125\n ],\n [\n 424.93359375,\n 254.7808837890625\n ],\n [\n 97.8662109375,\n 254.7808837890625\n ]\n ],\n \"bbox\": [\n 97.8662109375,\n 244.01953125,\n 424.93359375,\n 254.7808837890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/ListItem/7\",\n \"block_type\": \"ListItem\",\n \"html\": \"4. If you have multiline strings with triple quotes (single or double), make sure you have terminated the string properly. An unterminated string may cause an invalid token error at the end of your program, or it may treat the following part of the program as a string until it comes to the next string. In the second case, it might not produce an error message at all!\",\n \"polygon\": [\n [\n 98.1650390625,\n 265.095703125\n ],\n [\n 483.50390625,\n 265.095703125\n ],\n [\n 483.50390625,\n 324.3018798828125\n ],\n [\n 98.1650390625,\n 324.3018798828125\n ]\n ],\n \"bbox\": [\n 98.1650390625,\n 265.095703125,\n 483.50390625,\n 324.3018798828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"5. An unclosed opening operator—(, {, or [—makes Python continue with the next line as part of the current statement. Generally, an error occurs almost immediately in the next line.\",\n \"polygon\": [\n [\n 97.41796875,\n 333.931640625\n ],\n [\n 484.1015625,\n 333.931640625\n ],\n [\n 484.1015625,\n 369.4338684082031\n ],\n [\n 97.41796875,\n 369.4338684082031\n ]\n ],\n \"bbox\": [\n 97.41796875,\n 333.931640625,\n 484.1015625,\n 369.4338684082031\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"6. 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The best way to avoid this problem is to use a text editor that knows about Python and generates consistent indentation.\",\n \"polygon\": [\n [\n 97.716796875,\n 399.8671875\n ],\n [\n 484.1015625,\n 399.8671875\n ],\n [\n 484.1015625,\n 447.5038757324219\n ],\n [\n 97.716796875,\n 447.5038757324219\n ]\n ],\n \"bbox\": [\n 97.716796875,\n 399.8671875,\n 484.1015625,\n 447.5038757324219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/215/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"If nothing works, move on to the next section...
\",\n \"polygon\": [\n [\n 86.0625,\n 460.96875\n ],\n [\n 294.046875,\n 460.96875\n ],\n [\n 294.046875,\n 472.2688903808594\n ],\n [\n 86.0625,\n 472.2688903808594\n ]\n ],\n \"bbox\": [\n 86.0625,\n 460.96875,\n 294.046875,\n 472.2688903808594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/214/SectionHeader/7\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"A.1.1 I keep making changes and it makes no difference.
\",\n \"polygon\": [\n [\n 85.763671875,\n 498.48046875\n ],\n [\n 403.41796875,\n 498.48046875\n ],\n [\n 403.41796875,\n 511.0899658203125\n ],\n [\n 85.763671875,\n 511.0899658203125\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 498.48046875,\n 403.41796875,\n 511.0899658203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/215/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/215/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"If the interpreter says there is an error and you don't see it, that might be because you and the interpreter are not looking at the same code. Check your programming environment to make sure that the program you are editing is the one Python is trying to run.
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\",\n \"polygon\": [\n [\n 86.40000915527344,\n 169.576171875\n ],\n [\n 306.298828125,\n 169.576171875\n ],\n [\n 306.298828125,\n 252.9383544921875\n ],\n [\n 86.40000915527344,\n 252.9383544921875\n ]\n ],\n \"bbox\": [\n 86.40000915527344,\n 169.576171875,\n 306.298828125,\n 252.9383544921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/217/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/217/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Now when you run the program, you will see three lines of output for each time through the loop. The last time through the loop, the condition should be false. If the loop keeps going, you will be able to see the values of x and y, and you might figure out why they are not being updated correctly.
\",\n \"polygon\": [\n [\n 85.46484375,\n 256.587890625\n ],\n [\n 483.50390625,\n 256.587890625\n ],\n [\n 483.50390625,\n 305.4678955078125\n ],\n [\n 85.46484375,\n 305.4678955078125\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 256.587890625,\n 483.50390625,\n 305.4678955078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/217/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/217/SectionHeader/6\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Infinite Recursion
\",\n \"polygon\": [\n [\n 85.763671875,\n 328.904296875\n ],\n [\n 169.2861328125,\n 328.904296875\n ],\n [\n 169.2861328125,\n 340.1767883300781\n ],\n [\n 85.763671875,\n 340.1767883300781\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 328.904296875,\n 169.2861328125,\n 340.1767883300781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/217/SectionHeader/6\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/217/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Most of the time, an infinite recursion will cause the program to run for a while and then produce a Maximum recursion depth exceeded error.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 546.046875\n ],\n [\n 525.9375,\n 546.046875\n ],\n [\n 525.9375,\n 581.0047912597656\n ],\n [\n 128.0478515625,\n 581.0047912597656\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 546.046875,\n 525.9375,\n 581.0047912597656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/217/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/218/SectionHeader/13\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"A.2.4 I added so many print statements I get inundated with output.
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\",\n \"polygon\": [\n [\n 85.0166015625,\n 87.25341796875\n ],\n [\n 484.1015625,\n 87.25341796875\n ],\n [\n 484.1015625,\n 123.1868896484375\n ],\n [\n 85.0166015625,\n 123.1868896484375\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 87.25341796875,\n 484.1015625,\n 123.1868896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/218/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Second, clean up the program. Remove dead code and reorganize the program to make it as easy to read as possible. For example, if you suspect that the problem is in a deeply nested part of the program, try rewriting that part with simpler structure. If you suspect a large function, try splitting it into smaller functions and testing them separately.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 133.3212890625\n ],\n [\n 482.90625,\n 133.3212890625\n ],\n [\n 482.90625,\n 180.84283447265625\n ],\n [\n 85.3154296875,\n 180.84283447265625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 133.3212890625,\n 482.90625,\n 180.84283447265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/218/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Often the process of finding the minimal test case leads you to the bug. If you find that a program works in one situation but not in another, that gives you a clue about what is going on.
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\",\n \"polygon\": [\n [\n 85.3154296875,\n 236.28515625\n ],\n [\n 482.90625,\n 236.28515625\n ],\n [\n 482.90625,\n 259.57379150390625\n ],\n [\n 85.3154296875,\n 259.57379150390625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 236.28515625,\n 482.90625,\n 259.57379150390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"4\": \"/page/218/SectionHeader/13\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/SectionHeader/5\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"A.3 Semantic errors
\",\n \"polygon\": [\n [\n 86.2119140625,\n 290.8125\n ],\n [\n 225.7646484375,\n 290.8125\n ],\n [\n 225.7646484375,\n 306.06982421875\n ],\n [\n 86.2119140625,\n 306.06982421875\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 290.8125,\n 225.7646484375,\n 306.06982421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"In some ways, semantic errors are the hardest to debug, because the interpreter provides no information about what is wrong. Only you know what the program is supposed to do.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 319.236328125\n ],\n [\n 483.50390625,\n 319.236328125\n ],\n [\n 483.50390625,\n 342.052734375\n ],\n [\n 85.9130859375,\n 342.052734375\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 319.236328125,\n 483.50390625,\n 342.052734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The first step is to make a connection between the program text and the behavior you are seeing. You need a hypothesis about what the program is actually doing. One of the things that makes that hard is that computers run so fast.
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\",\n \"polygon\": [\n [\n 85.68896484375,\n 472.18359375\n ],\n [\n 270.8448791503906,\n 472.18359375\n ],\n [\n 270.8448791503906,\n 485.61285400390625\n ],\n [\n 85.68896484375,\n 485.61285400390625\n ]\n ],\n \"bbox\": [\n 85.68896484375,\n 472.18359375,\n 270.8448791503906,\n 485.61285400390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/219/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"You should ask yourself these questions:
\",\n \"polygon\": [\n [\n 85.763671875,\n 497.3203125\n ],\n [\n 264.86004638671875,\n 497.3203125\n ],\n [\n 264.86004638671875,\n 508.1484375\n ],\n [\n 85.763671875,\n 508.1484375\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 497.3203125,\n 264.86004638671875,\n 508.1484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/219/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/ListGroup/134\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 100.28802490234375,\n 522.45703125\n ],\n [\n 484.400390625,\n 522.45703125\n ],\n [\n 484.400390625,\n 650.5758056640625\n ],\n [\n 100.28802490234375,\n 650.5758056640625\n ]\n ],\n \"bbox\": [\n 100.28802490234375,\n 522.45703125,\n 484.400390625,\n 650.5758056640625\n ],\n \"children\": [\n {\n \"id\": \"/page/219/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \" Is there something the program was supposed to do but which doesn't seem to be happening? Find the section of the code that performs that function and make sure it is executing when you think it should.\",\n \"polygon\": [\n [\n 100.28802490234375,\n 522.45703125\n ],\n [\n 484.400390625,\n 522.45703125\n ],\n [\n 484.400390625,\n 558.2757873535156\n ],\n [\n 100.28802490234375,\n 558.2757873535156\n ]\n ],\n \"bbox\": [\n 100.28802490234375,\n 522.45703125,\n 484.400390625,\n 558.2757873535156\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/219/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/ListItem/12\",\n \"block_type\": \"ListItem\",\n \"html\": \" Is something happening that shouldn't? Find code in your program that performs that function and see if it is executing when it shouldn't.\",\n \"polygon\": [\n [\n 100.28802490234375,\n 569.25\n ],\n [\n 483.802734375,\n 569.25\n ],\n [\n 483.802734375,\n 592.453125\n ],\n [\n 100.28802490234375,\n 592.453125\n ]\n ],\n \"bbox\": [\n 100.28802490234375,\n 569.25,\n 483.802734375,\n 592.453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/219/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/219/ListItem/13\",\n \"block_type\": \"ListItem\",\n \"html\": \" Is a section of code producing an effect that is not what you expected? Make sure that you understand the code in question, especially if it involves invocations to functions or methods in other Python modules. Read the documentation for the functions you invoke. Try them out by writing simple test cases and checking the results.\",\n \"polygon\": [\n [\n 100.28802490234375,\n 602.5078125\n ],\n [\n 483.802734375,\n 602.5078125\n ],\n [\n 483.802734375,\n 650.5758056640625\n ],\n [\n 100.28802490234375,\n 650.5758056640625\n ]\n ],\n \"bbox\": [\n 100.28802490234375,\n 602.5078125,\n 483.802734375,\n 650.5758056640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/219/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/219/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/219/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"In order to program, you need to have a mental model of how programs work. If you write a program that doesn't do what you expect, very often the problem is not in the program; it's in your mental model.
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\",\n \"polygon\": [\n [\n 128.0478515625,\n 87.83349609375\n ],\n [\n 525.9375,\n 87.83349609375\n ],\n [\n 525.9375,\n 123.1868896484375\n ],\n [\n 128.0478515625,\n 123.1868896484375\n ]\n ],\n \"bbox\": [\n 128.0478515625,\n 87.83349609375,\n 525.9375,\n 123.1868896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/219/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Of course, you should be building and testing components as you develop the program. If you encounter a problem, there should be only a small amount of new code that is not known to be correct.
\",\n \"polygon\": [\n [\n 129.09375,\n 131.9677734375\n ],\n [\n 525.9375,\n 131.9677734375\n ],\n [\n 525.9375,\n 166.6558837890625\n ],\n [\n 129.09375,\n 166.6558837890625\n ]\n ],\n \"bbox\": [\n 129.09375,\n 131.9677734375,\n 525.9375,\n 166.6558837890625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/219/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/SectionHeader/3\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"A.3.2 I've got a big hairy expression and it doesn't do what I expect.
\",\n \"polygon\": [\n [\n 129.392578125,\n 190.3623046875\n ],\n [\n 504.9574279785156,\n 190.3623046875\n ],\n [\n 504.9574279785156,\n 202.9779052734375\n ],\n [\n 129.392578125,\n 202.9779052734375\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 190.3623046875,\n 504.9574279785156,\n 202.9779052734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/220/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Writing complex expressions is fine as long as they are readable, but they can be hard to debug. It is often a good idea to break a complex expression into a series of assignments to temporary variables.
\",\n \"polygon\": [\n [\n 129.392578125,\n 212.30859375\n ],\n [\n 525.6033935546875,\n 212.30859375\n ],\n [\n 525.6033935546875,\n 247.40191650390625\n ],\n [\n 129.392578125,\n 247.40191650390625\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 212.30859375,\n 525.6033935546875,\n 247.40191650390625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/220/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"For example:
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 271.4765625\n ],\n [\n 469.60369873046875,\n 271.4765625\n ],\n [\n 469.60369873046875,\n 281.44232177734375\n ],\n [\n 128.9443359375,\n 281.44232177734375\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 271.4765625,\n 469.60369873046875,\n 281.44232177734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/220/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"This can be rewritten as:
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\",\n \"polygon\": [\n [\n 129.09375,\n 301.69775390625\n ],\n [\n 354.5255432128906,\n 301.69775390625\n ],\n [\n 354.5255432128906,\n 336.04833984375\n ],\n [\n 129.09375,\n 336.04833984375\n ]\n ],\n \"bbox\": [\n 129.09375,\n 301.69775390625,\n 354.5255432128906,\n 336.04833984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/220/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The explicit version is easier to read because the variable names provide additional documentation, and it is easier to debug because you can check the types of the intermediate variables and display their values.
\",\n \"polygon\": [\n [\n 129.60000610351562,\n 341.0859375\n ],\n [\n 525.6034545898438,\n 341.0859375\n ],\n [\n 525.6034545898438,\n 375.6959228515625\n ],\n [\n 129.60000610351562,\n 375.6959228515625\n ]\n ],\n \"bbox\": [\n 129.60000610351562,\n 341.0859375,\n 525.6034545898438,\n 375.6959228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/220/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"Another problem that can occur with big expressions is that the order of evaluation may not be what you expect. For example, if you are translating the expression x 2π into Python, you might write:
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 424.1617431640625\n ],\n [\n 228.9769287109375,\n 424.1617431640625\n ],\n [\n 228.9769287109375,\n 434.28515625\n ],\n [\n 128.9443359375,\n 434.28515625\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 424.1617431640625,\n 228.9769287109375,\n 434.28515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/220/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"That is not correct because multiplication and division have the same precedence and are evaluated from left to right. So this expression computes xπ/2.
\",\n \"polygon\": [\n [\n 129.392578125,\n 438.5390625\n ],\n [\n 525.638671875,\n 438.5390625\n ],\n [\n 525.638671875,\n 462.38653564453125\n ],\n [\n 129.392578125,\n 462.38653564453125\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 438.5390625,\n 525.638671875,\n 462.38653564453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/220/SectionHeader/3\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/220/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"A good way to debug expressions is to add parentheses to make the order of evaluation explicit:
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\",\n \"polygon\": [\n [\n 84.79248046875,\n 85.80322265625\n ],\n [\n 339.767578125,\n 85.80322265625\n ],\n [\n 339.767578125,\n 99.24493408203125\n ],\n [\n 84.79248046875,\n 99.24493408203125\n ]\n ],\n \"bbox\": [\n 84.79248046875,\n 85.80322265625,\n 339.767578125,\n 99.24493408203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/221/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/221/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"First, try getting away from the computer for a few minutes. Computers emit waves that affect the brain, causing these symptoms:
\",\n \"polygon\": [\n [\n 84.8671875,\n 108.66796875\n ],\n [\n 484.69921875,\n 108.66796875\n ],\n [\n 484.69921875,\n 132.53094482421875\n ],\n [\n 84.8671875,\n 132.53094482421875\n ]\n ],\n \"bbox\": [\n 84.8671875,\n 108.66796875,\n 484.69921875,\n 132.53094482421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/221/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/221/ListGroup/119\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 99.9580078125,\n 144.1494140625\n ],\n [\n 484.1015625,\n 144.1494140625\n ],\n [\n 484.1015625,\n 221.38494873046875\n ],\n [\n 99.9580078125,\n 221.38494873046875\n ]\n ],\n \"bbox\": [\n 99.9580078125,\n 144.1494140625,\n 484.1015625,\n 221.38494873046875\n ],\n \"children\": [\n {\n \"id\": \"/page/221/ListItem/3\",\n \"block_type\": \"ListItem\",\n \"html\": \" Frustration and rage.\",\n \"polygon\": [\n [\n 100.03271484375,\n 144.1494140625\n ],\n [\n 204.697265625,\n 144.1494140625\n ],\n [\n 204.697265625,\n 156.66693115234375\n ],\n [\n 100.03271484375,\n 156.66693115234375\n ]\n ],\n \"bbox\": [\n 100.03271484375,\n 144.1494140625,\n 204.697265625,\n 156.66693115234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/221/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/221/ListItem/4\",\n \"block_type\": \"ListItem\",\n \"html\": \" Superstitious beliefs (\\\"the computer hates me\\\") and magical thinking (\\\"the program only works when I wear my hat backward\\\").\",\n \"polygon\": [\n [\n 99.9580078125,\n 164.935546875\n ],\n [\n 483.50390625,\n 164.935546875\n ],\n [\n 483.50390625,\n 189.02593994140625\n ],\n [\n 99.9580078125,\n 189.02593994140625\n ]\n ],\n \"bbox\": [\n 99.9580078125,\n 164.935546875,\n 483.50390625,\n 189.02593994140625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/221/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/221/ListItem/5\",\n \"block_type\": \"ListItem\",\n \"html\": \" Random walk programming (the attempt to program by writing every possible program and choosing the one that does the right thing).\",\n \"polygon\": [\n [\n 100.28800201416016,\n 196.83984375\n ],\n [\n 484.1015625,\n 196.83984375\n ],\n [\n 484.1015625,\n 221.38494873046875\n ],\n [\n 100.28800201416016,\n 221.38494873046875\n ]\n ],\n \"bbox\": [\n 100.28800201416016,\n 196.83984375,\n 484.1015625,\n 221.38494873046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/221/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/221/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/221/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"If you find yourself suffering from any of these symptoms, get up and go for a walk. When you are calm, think about the program. What is it doing? What are some possible causes of that behavior? When was the last time you had a working program, and what did you do next?
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\",\n \"polygon\": [\n [\n 85.9130859375,\n 290.0390625\n ],\n [\n 484.69921875,\n 290.0390625\n ],\n [\n 484.69921875,\n 326.6299133300781\n ],\n [\n 85.9130859375,\n 326.6299133300781\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 290.0390625,\n 484.69921875,\n 326.6299133300781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/221/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/221/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"A.3.5 No, I really need help.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 351.333984375\n ],\n [\n 249.0732421875,\n 351.333984375\n ],\n [\n 249.0732421875,\n 364.482421875\n ],\n [\n 85.9130859375,\n 364.482421875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 351.333984375,\n 249.0732421875,\n 364.482421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"3\": \"/page/219/SectionHeader/5\",\n \"4\": \"/page/221/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/221/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"It happens. Even the best programmers occasionally get stuck. Sometimes you work on a program so long that you can't see the error. A fresh pair of eyes is just the thing.
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\",\n \"polygon\": [\n [\n 128.12255859375,\n 163.388671875\n ],\n [\n 243.544921875,\n 163.388671875\n ],\n [\n 243.544921875,\n 184.8819580078125\n ],\n [\n 128.12255859375,\n 184.8819580078125\n ]\n ],\n \"bbox\": [\n 128.12255859375,\n 163.388671875,\n 243.544921875,\n 184.8819580078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/214/SectionHeader/1\",\n \"2\": \"/page/222/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Analysis of Algorithms
\",\n \"polygon\": [\n [\n 127.97314453125,\n 218.689453125\n ],\n [\n 393.9792175292969,\n 218.689453125\n ],\n [\n 393.9792175292969,\n 244.46136474609375\n ],\n [\n 127.97314453125,\n 244.46136474609375\n ]\n ],\n \"bbox\": [\n 127.97314453125,\n 218.689453125,\n 393.9792175292969,\n 244.46136474609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"This appendix is an edited excerpt from Think Complexity, by Allen B. Downey, also published by O'Reilly Media (2011). When you are done with this book, you might want to move on to that one.
\",\n \"polygon\": [\n [\n 153.7470703125,\n 288.10546875\n ],\n [\n 503.82421875,\n 288.10546875\n ],\n [\n 503.82421875,\n 323.3669128417969\n ],\n [\n 153.7470703125,\n 323.3669128417969\n ]\n ],\n \"bbox\": [\n 153.7470703125,\n 288.10546875,\n 503.82421875,\n 323.3669128417969\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Analysis of algorithms is a branch of computer science that studies the performance of algorithms, especially their run time and space requirements. See http://en.wikipedia. org/wiki/Analysis_of_algorithms.
\",\n \"polygon\": [\n [\n 129.392578125,\n 337.21875\n ],\n [\n 526.833984375,\n 337.21875\n ],\n [\n 526.833984375,\n 372.07391357421875\n ],\n [\n 129.392578125,\n 372.07391357421875\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 337.21875,\n 526.833984375,\n 372.07391357421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"The practical goal of algorithm analysis is to predict the performance of different algorithms in order to guide design decisions.
\",\n \"polygon\": [\n [\n 129.392578125,\n 381.498046875\n ],\n [\n 526.833984375,\n 381.498046875\n ],\n [\n 526.833984375,\n 404.5078125\n ],\n [\n 129.392578125,\n 404.5078125\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 381.498046875,\n 526.833984375,\n 404.5078125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"During the 2008 United States Presidential Campaign, candidate Barack Obama was asked to perform an impromptu analysis when he visited Google. Chief executive Eric Schmidt jokingly asked him for \\\"the most efficient way to sort a million 32-bit integers.\\\" Obama had apparently been tipped off, because he quickly replied, \\\"I think the bubble sort would be the wrong way to go.\\\" See http://www.youtube.com/watch?v=k4RRi_ntQc8.
\",\n \"polygon\": [\n [\n 129.09375,\n 414.5625\n ],\n [\n 527.1328125,\n 414.5625\n ],\n [\n 527.1328125,\n 473.51690673828125\n ],\n [\n 129.09375,\n 473.51690673828125\n ]\n ],\n \"bbox\": [\n 129.09375,\n 414.5625,\n 527.1328125,\n 473.51690673828125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"This is true: bubble sort is conceptually simple but slow for large datasets. The answer Schmidt was probably looking for is \\\"radix sort\\\" (http://en.wikipedia.org/wiki/ Radix_sort) 1 .
\",\n \"polygon\": [\n [\n 128.49609375,\n 483.3984375\n ],\n [\n 527.1328125,\n 483.3984375\n ],\n [\n 527.1328125,\n 518.203125\n ],\n [\n 128.49609375,\n 518.203125\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 483.3984375,\n 527.1328125,\n 518.203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The goal of algorithm analysis is to make meaningful comparisons between algorithms, but there are some problems:
\",\n \"polygon\": [\n [\n 128.794921875,\n 527.484375\n ],\n [\n 526.833984375,\n 527.484375\n ],\n [\n 526.833984375,\n 550.5708923339844\n ],\n [\n 128.794921875,\n 550.5708923339844\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 527.484375,\n 526.833984375,\n 550.5708923339844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/ListGroup/126\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 141.345703125,\n 563.44921875\n ],\n [\n 526.833984375,\n 563.44921875\n ],\n [\n 526.833984375,\n 644.2734375\n ],\n [\n 141.345703125,\n 644.2734375\n ]\n ],\n \"bbox\": [\n 141.345703125,\n 563.44921875,\n 526.833984375,\n 644.2734375\n ],\n \"children\": [\n {\n \"id\": \"/page/222/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \" The relative performance of the algorithms might depend on characteristics of the hardware, so one algorithm might be faster on Machine A, another on Machine B. The general solution to this problem is to specify a machine model and analyze the number of steps, or operations, an algorithm requires under a given model.\",\n \"polygon\": [\n [\n 143.138671875,\n 563.44921875\n ],\n [\n 526.833984375,\n 563.44921875\n ],\n [\n 526.833984375,\n 611.4718933105469\n ],\n [\n 143.138671875,\n 611.4718933105469\n ]\n ],\n \"bbox\": [\n 143.138671875,\n 563.44921875,\n 526.833984375,\n 611.4718933105469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/222/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \" Relative performance might depend on the details of the dataset. For example, some sorting algorithms run faster if the data are already partially sorted; other algorithms\",\n \"polygon\": [\n [\n 141.345703125,\n 621.0703125\n ],\n [\n 526.833984375,\n 621.0703125\n ],\n [\n 526.833984375,\n 644.2734375\n ],\n [\n 141.345703125,\n 644.2734375\n ]\n ],\n \"bbox\": [\n 141.345703125,\n 621.0703125,\n 526.833984375,\n 644.2734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/222/Footnote/10\",\n \"block_type\": \"Footnote\",\n \"html\": \"1 But if you get a question like this in an interview, I think a better answer is, \\\"The fastest way to sort a million integers is to use whatever sort function is provided by the language I'm using. Its performance is good enough for the vast majority of applications, but if it turned out that my application was too slow, I would use a profiler to see where the time was being spent. If it looked like a faster sort algorithm would have a significant effect on performance, then I would look around for a good implementation of radix sort.\\\"
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\",\n \"polygon\": [\n [\n 111.0146484375,\n 88.41357421875\n ],\n [\n 482.90625,\n 88.41357421875\n ],\n [\n 482.90625,\n 123.1868896484375\n ],\n [\n 111.0146484375,\n 123.1868896484375\n ]\n ],\n \"bbox\": [\n 111.0146484375,\n 88.41357421875,\n 482.90625,\n 123.1868896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/223/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \" Relative performance also depends on the size of the problem. A sorting algorithm that is fast for small lists might be slow for long lists. The usual solution to this problem is to express run time (or number of operations) as a function of problem size, and to compare the functions asymptotically as the problem size increases.\",\n \"polygon\": [\n [\n 100.28800964355469,\n 131.09765625\n ],\n [\n 482.4037780761719,\n 131.09765625\n ],\n [\n 482.4037780761719,\n 178.78692626953125\n ],\n [\n 100.28800964355469,\n 178.78692626953125\n ]\n ],\n \"bbox\": [\n 100.28800964355469,\n 131.09765625,\n 482.4037780761719,\n 178.78692626953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/223/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The good thing about this kind of comparison that it lends itself to simple classification of algorithms. For example, if I know that the run time of Algorithm A tends to be proportional to the size of the input, n, and Algorithm B tends to be proportional to n 2 , then I expect A to be faster than B for large values of n.
\",\n \"polygon\": [\n [\n 85.166015625,\n 188.4287109375\n ],\n [\n 483.205078125,\n 188.4287109375\n ],\n [\n 483.205078125,\n 235.866943359375\n ],\n [\n 85.166015625,\n 235.866943359375\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 188.4287109375,\n 483.205078125,\n 235.866943359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/223/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"This kind of analysis comes with some caveats, but we'll get to that later.
\",\n \"polygon\": [\n [\n 85.3154296875,\n 243.826171875\n ],\n [\n 405.8507080078125,\n 243.826171875\n ],\n [\n 405.8507080078125,\n 254.658935546875\n ],\n [\n 85.3154296875,\n 254.658935546875\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 243.826171875,\n 405.8507080078125,\n 254.658935546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/223/SectionHeader/5\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"B.1 Order of growth
\",\n \"polygon\": [\n [\n 85.09130859375,\n 281.337890625\n ],\n [\n 228.603515625,\n 281.337890625\n ],\n [\n 228.603515625,\n 296.4639892578125\n ],\n [\n 85.09130859375,\n 296.4639892578125\n ]\n ],\n \"bbox\": [\n 85.09130859375,\n 281.337890625,\n 228.603515625,\n 296.4639892578125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"3\": \"/page/223/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/223/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Suppose you have analyzed two algorithms and expressed their run times in terms of the size of the input: Algorithm A takes 100n + 1 steps to solve a problem with size n; Algorithm B takes n 2 + n + 1 steps.
\",\n \"polygon\": [\n [\n 85.0166015625,\n 306.474609375\n ],\n [\n 482.90625,\n 306.474609375\n ],\n [\n 482.90625,\n 341.81591796875\n ],\n [\n 85.0166015625,\n 341.81591796875\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 306.474609375,\n 482.90625,\n 341.81591796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"3\": \"/page/223/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/223/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"The following table shows the run time of these algorithms for different problem sizes:
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size | Run time of
Algorithm A | Run time of
Algorithm B |
|---|
| 10 | 1 001 | 111 |
| 100 | 10 001 | 10 101 |
| 1 000 | 100 001 | 1 001 001 |
| 10 000 | 1 000 001 | > 1010 |
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But for n = 100 they are about the same, and for larger values A is much better.
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\",\n \"polygon\": [\n [\n 129.392578125,\n 144.1939697265625\n ],\n [\n 525.638671875,\n 144.1939697265625\n ],\n [\n 525.638671875,\n 168.25189208984375\n ],\n [\n 129.392578125,\n 168.25189208984375\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 144.1939697265625,\n 525.638671875,\n 168.25189208984375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"3\": \"/page/223/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/224/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The following table shows some of the orders of growth that appear most commonly in algorithmic analysis, in increasing order of badness.
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growth | Name |
|---|
| O(1) | constant |
| O(logbn) | logarithmic (for any b) |
| O(n) | linear |
| O(nlogbn) | \\\"en log en\\\" |
| O(n2) | quadratic |
| O(n3) | cubic |
| O(cn) | exponential (for any c) |
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the base of the logarithm doesn't matter; changing bases is the equivalent of multiplying by a constant, which doesn't change the order of growth. Similarly, all exponential functions belong to the same order of growth regardless of the base of the exponent. Exponential functions grow very quickly, so exponential algorithms are only useful for small problems.
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What is the order of growth of n3 + n 2? What about 1000000n 3 + n 2? What about n3 + 1000000n 2?\",\n \"polygon\": [\n [\n 141.4951171875,\n 427.9840087890625\n ],\n [\n 525.47216796875,\n 427.9840087890625\n ],\n [\n 525.47216796875,\n 452.04290771484375\n ],\n [\n 141.4951171875,\n 452.04290771484375\n ]\n ],\n \"bbox\": [\n 141.4951171875,\n 427.9840087890625,\n 525.47216796875,\n 452.04290771484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"3\": \"/page/223/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/224/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. What is the order of growth of (n 2 + n) · (n + 1)? Before you start multiplying, remember that you only need the leading term.\",\n \"polygon\": [\n [\n 141.943359375,\n 461.2640075683594\n ],\n [\n 525.6023559570312,\n 461.2640075683594\n ],\n [\n 525.6023559570312,\n 485.1492004394531\n ],\n [\n 141.943359375,\n 485.1492004394531\n ]\n ],\n \"bbox\": [\n 141.943359375,\n 461.2640075683594,\n 525.6023559570312,\n 485.1492004394531\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"3\": \"/page/223/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/224/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. 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If f1 is in O(g) and f2 is in O(h), what can we say about f1 + f2?\",\n \"polygon\": [\n [\n 142.01806640625,\n 537.6458129882812\n ],\n [\n 421.9453125,\n 537.6458129882812\n ],\n [\n 421.9453125,\n 549.7585906982422\n ],\n [\n 142.01806640625,\n 549.7585906982422\n ]\n ],\n \"bbox\": [\n 142.01806640625,\n 537.6458129882812,\n 421.9453125,\n 549.7585906982422\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"3\": \"/page/223/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/224/ListItem/12\",\n \"block_type\": \"ListItem\",\n \"html\": \"6. If f1 is in O(g) and f2 is O(h), what can we say about f1 · f2?\",\n \"polygon\": [\n [\n 142.05307006835938,\n 558.5962677001953\n ],\n [\n 406.40625,\n 558.5962677001953\n ],\n [\n 406.40625,\n 570.8435821533203\n ],\n [\n 142.05307006835938,\n 570.8435821533203\n ]\n ],\n \"bbox\": [\n 142.05307006835938,\n 558.5962677001953,\n 406.40625,\n 570.8435821533203\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"3\": \"/page/223/SectionHeader/5\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"3\": \"/page/223/SectionHeader/5\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/224/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Programmers who care about performance often find this kind of analysis hard to swallow. They have a point: sometimes the coefficients and the non-leading terms make a real difference. Sometimes the details of the hardware, the programming language, and the characteristics of the input make a big difference. And for small problems asymptotic behavior is irrelevant.
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\",\n \"polygon\": [\n [\n 84.568359375,\n 84.54638671875\n ],\n [\n 357.99609375,\n 84.54638671875\n ],\n [\n 357.99609375,\n 100.353515625\n ],\n [\n 84.568359375,\n 100.353515625\n ]\n ],\n \"bbox\": [\n 84.568359375,\n 84.54638671875,\n 357.99609375,\n 100.353515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Most arithmetic operations are constant time; multiplication usually takes longer than addition and subtraction, and division takes even longer, but these run times don't depend on the magnitude of the operands. Very large integers are an exception; in that case the run time increases with the number of digits.
\",\n \"polygon\": [\n [\n 85.46484375,\n 109.634765625\n ],\n [\n 484.998046875,\n 109.634765625\n ],\n [\n 484.998046875,\n 159.00103759765625\n ],\n [\n 85.46484375,\n 159.00103759765625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 109.634765625,\n 484.998046875,\n 159.00103759765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Indexing operations—reading or writing elements in a sequence or dictionary—are also constant time, regardless of the size of the data structure.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 355.78125\n ],\n [\n 482.39666748046875,\n 355.78125\n ],\n [\n 482.39666748046875,\n 368.2929992675781\n ],\n [\n 85.763671875,\n 368.2929992675781\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 355.78125,\n 482.39666748046875,\n 368.2929992675781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"As a rule of thumb, if the body of a loop is in O(n a ) then the whole loop is in O(n a+1 ). The exception is if you can show that the loop exits after a constant number of iterations. If a loop runs k times regardless of n, then the loop is in O(n a ), even for large k.
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\",\n \"polygon\": [\n [\n 85.763671875,\n 432.3515625\n ],\n [\n 484.400390625,\n 432.3515625\n ],\n [\n 484.400390625,\n 457.1015625\n ],\n [\n 85.763671875,\n 457.1015625\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 432.3515625,\n 484.400390625,\n 457.1015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Most string and tuple operations are linear, except indexing and len, which are constant time. The built-in functions min and max are linear. The run-time of a slice operation is proportional to the length of the output, but independent of the size of the input.
\",\n \"polygon\": [\n [\n 85.9130859375,\n 464.8359375\n ],\n [\n 483.50390625,\n 464.8359375\n ],\n [\n 483.50390625,\n 501.29998779296875\n ],\n [\n 85.9130859375,\n 501.29998779296875\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 464.8359375,\n 483.50390625,\n 501.29998779296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/Text/12\",\n \"block_type\": \"Text\",\n \"html\": \"All string methods are linear, but if the lengths of the strings are bounded by a constant for example, operations on single characters—they are considered constant time.
\",\n \"polygon\": [\n [\n 85.763671875,\n 508.1484375\n ],\n [\n 482.607421875,\n 508.1484375\n ],\n [\n 482.607421875,\n 533.4409790039062\n ],\n [\n 85.763671875,\n 533.4409790039062\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 508.1484375,\n 482.607421875,\n 533.4409790039062\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"Most list methods are linear, but there are some exceptions:
\",\n \"polygon\": [\n [\n 85.6142578125,\n 541.79296875\n ],\n [\n 347.23828125,\n 541.79296875\n ],\n [\n 347.23828125,\n 553.39453125\n ],\n [\n 85.6142578125,\n 553.39453125\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 541.79296875,\n 347.23828125,\n 553.39453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/ListGroup/270\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 99.9580078125,\n 565.3828125\n ],\n [\n 484.69921875,\n 565.3828125\n ],\n [\n 484.69921875,\n 641.4009857177734\n ],\n [\n 99.9580078125,\n 641.4009857177734\n ]\n ],\n \"bbox\": [\n 99.9580078125,\n 565.3828125,\n 484.69921875,\n 641.4009857177734\n ],\n \"children\": [\n {\n \"id\": \"/page/225/ListItem/14\",\n \"block_type\": \"ListItem\",\n \"html\": \" Adding an element to the end of a list is constant time on average; when it runs out of room it occasionally gets copied to a bigger location, but the total time for n operations is O(n), so we say that the \\\"amortized\\\" time for one operation is O(1).\",\n \"polygon\": [\n [\n 100.28785705566406,\n 565.3828125\n ],\n [\n 484.69921875,\n 565.3828125\n ],\n [\n 484.69921875,\n 601.734375\n ],\n [\n 100.28785705566406,\n 601.734375\n ]\n ],\n \"bbox\": [\n 100.28785705566406,\n 565.3828125,\n 484.69921875,\n 601.734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/ListItem/15\",\n \"block_type\": \"ListItem\",\n \"html\": \" Removing an element from the end of a list is constant time.\",\n \"polygon\": [\n [\n 99.9580078125,\n 609.08203125\n ],\n [\n 375.92578125,\n 609.08203125\n ],\n [\n 375.92578125,\n 621.45703125\n ],\n [\n 99.9580078125,\n 621.45703125\n ]\n ],\n \"bbox\": [\n 99.9580078125,\n 609.08203125,\n 375.92578125,\n 621.45703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/ListItem/16\",\n \"block_type\": \"ListItem\",\n \"html\": \" Sorting is O(n log n).\",\n \"polygon\": [\n [\n 100.287841796875,\n 628.8046875\n ],\n [\n 203.501953125,\n 628.8046875\n ],\n [\n 203.501953125,\n 641.4009857177734\n ],\n [\n 100.287841796875,\n 641.4009857177734\n ]\n ],\n \"bbox\": [\n 100.287841796875,\n 628.8046875,\n 203.501953125,\n 641.4009857177734\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/225/Text/17\",\n \"block_type\": \"Text\",\n \"html\": \"Most dictionary operations and methods are constant time, but there are some exceptions:
\",\n \"polygon\": [\n [\n 84.7177734375,\n 653.16796875\n ],\n [\n 481.7109375,\n 653.16796875\n ],\n [\n 481.7109375,\n 665.0209884643555\n ],\n [\n 84.7177734375,\n 665.0209884643555\n ]\n ],\n \"bbox\": [\n 84.7177734375,\n 653.16796875,\n 481.7109375,\n 665.0209884643555\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/225/ListItem/18\",\n \"block_type\": \"ListItem\",\n \"html\": \" The run time of copy is proportional to the number of elements, but not the size of the elements (it copies references, not the elements themselves).\",\n \"polygon\": [\n [\n 100.287841796875,\n 676.7578125\n ],\n [\n 484.69921875,\n 676.7578125\n ],\n [\n 484.69921875,\n 700.8349914550781\n ],\n [\n 100.287841796875,\n 700.8349914550781\n ]\n ],\n \"bbox\": [\n 100.287841796875,\n 676.7578125,\n 484.69921875,\n 700.8349914550781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/226/Page/209\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/226/PageHeader/0\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 128.27197265625,\n 60.76318359375\n ],\n [\n 525.6033935546875,\n 60.76318359375\n ],\n [\n 525.6033935546875,\n 71.13372802734375\n ],\n [\n 128.27197265625,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 128.27197265625,\n 60.76318359375,\n 525.6033935546875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/PageHeader/18\",\n \"block_type\": \"PageHeader\",\n \"html\": \"\",\n \"polygon\": [\n [\n 510.697265625,\n 60.521484375\n ],\n [\n 525.638671875,\n 60.521484375\n ],\n [\n 525.638671875,\n 69.802734375\n ],\n [\n 510.697265625,\n 69.802734375\n ]\n ],\n \"bbox\": [\n 510.697265625,\n 60.521484375,\n 525.638671875,\n 69.802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/ListGroup/207\",\n \"block_type\": \"ListGroup\",\n \"html\": \"\",\n \"polygon\": [\n [\n 142.3916015625,\n 88.31689453125\n ],\n [\n 526.53515625,\n 88.31689453125\n ],\n [\n 526.53515625,\n 179.24285888671875\n ],\n [\n 142.3916015625,\n 179.24285888671875\n ]\n ],\n \"bbox\": [\n 142.3916015625,\n 88.31689453125,\n 526.53515625,\n 179.24285888671875\n ],\n \"children\": [\n {\n \"id\": \"/page/226/ListItem/1\",\n \"block_type\": \"ListItem\",\n \"html\": \" The run time of update is proportional to the size of the dictionary passed as a parameter, not the dictionary being updated.\",\n \"polygon\": [\n [\n 143.4375,\n 88.31689453125\n ],\n [\n 525.6045532226562,\n 88.31689453125\n ],\n [\n 525.6045532226562,\n 110.99188232421875\n ],\n [\n 143.4375,\n 110.99188232421875\n ]\n ],\n \"bbox\": [\n 143.4375,\n 88.31689453125,\n 525.6045532226562,\n 110.99188232421875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/ListItem/2\",\n \"block_type\": \"ListItem\",\n \"html\": \" keys, values and items are linear because they return new lists; iterkeys, itervalues and iteritems are constant time because they return iterators. But if you loop through the iterators, the loop will be linear. Using the \\\"iter\\\" functions saves some overhead, but it doesn't change the order of growth unless the number of items you access is bounded.\",\n \"polygon\": [\n [\n 142.3916015625,\n 120.076171875\n ],\n [\n 526.53515625,\n 120.076171875\n ],\n [\n 526.53515625,\n 179.24285888671875\n ],\n [\n 142.3916015625,\n 179.24285888671875\n ]\n ],\n \"bbox\": [\n 142.3916015625,\n 120.076171875,\n 526.53515625,\n 179.24285888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/226/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"The performance of dictionaries is one of the minor miracles of computer science. We will see how they work in Section B.4.
\",\n \"polygon\": [\n [\n 129.392578125,\n 190.458984375\n ],\n [\n 525.638671875,\n 190.458984375\n ],\n [\n 525.638671875,\n 213.38189697265625\n ],\n [\n 129.392578125,\n 213.38189697265625\n ]\n ],\n \"bbox\": [\n 129.392578125,\n 190.458984375,\n 525.638671875,\n 213.38189697265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Exercise B.2. Read the Wikipedia page on sorting algorithms at http: // en. wikipedia. org/ wiki/ Sorting_ algorithm and answer the following questions:
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What is a \\\"comparison sort?\\\" What is the best worst-case order of growth for a comparison sort? What is the best worst-case order of growth for any sort algorithm?\",\n \"polygon\": [\n [\n 141.4951171875,\n 249.58062744140625\n ],\n [\n 525.9375,\n 249.58062744140625\n ],\n [\n 525.9375,\n 271.73724365234375\n ],\n [\n 141.4951171875,\n 271.73724365234375\n ]\n ],\n \"bbox\": [\n 141.4951171875,\n 249.58062744140625,\n 525.9375,\n 271.73724365234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/ListItem/6\",\n \"block_type\": \"ListItem\",\n \"html\": \"2. What is the order of growth of bubble sort, and why does Barack Obama think it is \\\"the wrong way to go?\\\"\",\n \"polygon\": [\n [\n 141.345703125,\n 280.951171875\n ],\n [\n 525.638671875,\n 280.951171875\n ],\n [\n 525.638671875,\n 303.4052429199219\n ],\n [\n 141.345703125,\n 303.4052429199219\n ]\n ],\n \"bbox\": [\n 141.345703125,\n 280.951171875,\n 525.638671875,\n 303.4052429199219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/ListItem/7\",\n \"block_type\": \"ListItem\",\n \"html\": \"3. What is the order of growth of radix sort? What preconditions do we need to use it?\",\n \"polygon\": [\n [\n 140.8974609375,\n 312.46875\n ],\n [\n 488.6320495605469,\n 312.46875\n ],\n [\n 488.6320495605469,\n 322.8792419433594\n ],\n [\n 140.8974609375,\n 322.8792419433594\n ]\n ],\n \"bbox\": [\n 140.8974609375,\n 312.46875,\n 488.6320495605469,\n 322.8792419433594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/ListItem/8\",\n \"block_type\": \"ListItem\",\n \"html\": \"4. What is a stable sort and why might it matter in practice?\",\n \"polygon\": [\n [\n 142.05300903320312,\n 332.384765625\n ],\n [\n 386.6547546386719,\n 332.384765625\n ],\n [\n 386.6547546386719,\n 342.35223388671875\n ],\n [\n 142.05300903320312,\n 342.35223388671875\n ]\n ],\n \"bbox\": [\n 142.05300903320312,\n 332.384765625,\n 386.6547546386719,\n 342.35223388671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/ListItem/9\",\n \"block_type\": \"ListItem\",\n \"html\": \"5. What is the worst sorting algorithm (that has a name)?\",\n \"polygon\": [\n [\n 141.42041015625,\n 351.8636474609375\n ],\n [\n 375.626953125,\n 351.8636474609375\n ],\n [\n 375.626953125,\n 361.82623291015625\n ],\n [\n 141.42041015625,\n 361.82623291015625\n ]\n ],\n \"bbox\": [\n 141.42041015625,\n 351.8636474609375,\n 375.626953125,\n 361.82623291015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/ListItem/10\",\n \"block_type\": \"ListItem\",\n \"html\": \"6. What sort algorithm does the C library use? What sort algorithm does Python use? Are these algorithms stable? You might have to Google around to find these answers.\",\n \"polygon\": [\n [\n 141.64453125,\n 371.056640625\n ],\n [\n 525.6032104492188,\n 371.056640625\n ],\n [\n 525.6032104492188,\n 393.4942321777344\n ],\n [\n 141.64453125,\n 393.4942321777344\n ]\n ],\n \"bbox\": [\n 141.64453125,\n 371.056640625,\n 525.6032104492188,\n 393.4942321777344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/ListItem/11\",\n \"block_type\": \"ListItem\",\n \"html\": \"7. Many of the non-comparison sorts are linear, so why does does Python use an O(n log n) comparison sort?\",\n \"polygon\": [\n [\n 140.5986328125,\n 402.2098693847656\n ],\n [\n 525.9375,\n 402.2098693847656\n ],\n [\n 525.9375,\n 425.1622314453125\n ],\n [\n 140.5986328125,\n 425.1622314453125\n ]\n ],\n \"bbox\": [\n 140.5986328125,\n 402.2098693847656,\n 525.9375,\n 425.1622314453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/226/SectionHeader/12\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"B.3 Analysis of search algorithms
\",\n \"polygon\": [\n [\n 129.01904296875,\n 453.54278564453125\n ],\n [\n 358.3215026855469,\n 453.54278564453125\n ],\n [\n 358.3215026855469,\n 467.8890075683594\n ],\n [\n 129.01904296875,\n 467.8890075683594\n ]\n ],\n \"bbox\": [\n 129.01904296875,\n 453.54278564453125,\n 358.3215026855469,\n 467.8890075683594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/226/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"A search is an algorithm that takes a collection and a target item and determines whether the target is in the collection, often returning the index of the target.
\",\n \"polygon\": [\n [\n 129.5419921875,\n 479.3282165527344\n ],\n [\n 525.9375,\n 479.3282165527344\n ],\n [\n 525.9375,\n 501.5819396972656\n ],\n [\n 129.5419921875,\n 501.5819396972656\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 479.3282165527344,\n 525.9375,\n 501.5819396972656\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/226/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/226/Text/14\",\n \"block_type\": \"Text\",\n \"html\": \"The simplest search algorithm is a \\\"linear search,\\\" which traverses the items of the collection in order, stopping if it finds the target. In the worst case it has to traverse the entire collection, so the run time is linear.
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\",\n \"polygon\": [\n [\n 85.0166015625,\n 88.4619140625\n ],\n [\n 482.90625,\n 88.4619140625\n ],\n [\n 482.90625,\n 110.84228515625\n ],\n [\n 85.0166015625,\n 110.84228515625\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 88.4619140625,\n 482.90625,\n 110.84228515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/226/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/227/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Or you could read the documentation of the bisect module and use that!
\",\n \"polygon\": [\n [\n 85.39013671875,\n 120.1728515625\n ],\n [\n 381.4031066894531,\n 120.1728515625\n ],\n [\n 381.4031066894531,\n 130.82330322265625\n ],\n [\n 85.39013671875,\n 130.82330322265625\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 120.1728515625,\n 381.4031066894531,\n 130.82330322265625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/226/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/227/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Bisection search can be much faster than linear search, but it requires the sequence to be in order, which might require extra work.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 172.4765625\n ],\n [\n 482.40386962890625,\n 172.4765625\n ],\n [\n 482.40386962890625,\n 219.71295166015625\n ],\n [\n 85.6142578125,\n 219.71295166015625\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 172.4765625,\n 482.40386962890625,\n 219.71295166015625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/226/SectionHeader/12\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/227/SectionHeader/5\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"B.4 Hashtables
\",\n \"polygon\": [\n [\n 85.6142578125,\n 248.2734375\n ],\n [\n 194.537109375,\n 248.2734375\n ],\n [\n 194.537109375,\n 263.1820068359375\n ],\n [\n 85.6142578125,\n 263.1820068359375\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 248.2734375,\n 194.537109375,\n 263.1820068359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/227/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"To explain how hashtables work and why their performance is so good, I start with a simple implementation of a map and gradually improve it until it's a hashtable.
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\",\n \"polygon\": [\n [\n 85.0166015625,\n 435.83203125\n ],\n [\n 482.40325927734375,\n 435.83203125\n ],\n [\n 482.40325927734375,\n 459.03515625\n ],\n [\n 85.0166015625,\n 459.03515625\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 435.83203125,\n 482.40325927734375,\n 459.03515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/227/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"class LinearMap(object):
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\",\n \"polygon\": [\n [\n 106.00927734375,\n 485.33203125\n ],\n [\n 271.3359375,\n 485.33203125\n ],\n [\n 271.3359375,\n 621.45703125\n ],\n [\n 106.00927734375,\n 621.45703125\n ]\n ],\n \"bbox\": [\n 106.00927734375,\n 485.33203125,\n 271.3359375,\n 621.45703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/227/Text/13\",\n \"block_type\": \"Text\",\n \"html\": \"add appends a key-value tuple to the list of items, which takes constant time.
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 646.3528900146484\n ],\n [\n 482.4022216796875,\n 646.3528900146484\n ],\n [\n 482.4022216796875,\n 668.6600494384766\n ],\n [\n 85.6142578125,\n 668.6600494384766\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 646.3528900146484,\n 482.4022216796875,\n 668.6600494384766\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/227/Text/15\",\n \"block_type\": \"Text\",\n \"html\": \"An alternative is to keep the list sorted by key. Then get could use a bisection search, which is O(log n). But inserting a new item in the middle of a list is linear, so this might
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\",\n \"polygon\": [\n [\n 128.197265625,\n 87.8818359375\n ],\n [\n 525.6397094726562,\n 87.8818359375\n ],\n [\n 525.6397094726562,\n 123.1868896484375\n ],\n [\n 128.197265625,\n 123.1868896484375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 87.8818359375,\n 525.6397094726562,\n 123.1868896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/228/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"One way to improve LinearMap is to break the list of key-value pairs into smaller lists. Here's an implementation called BetterMap, which is a list of 100 LinearMaps. As we'll see in a second, the order of growth for get is still linear, but BetterMap is a step on the path toward hashtables:
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\",\n \"polygon\": [\n [\n 129.6000518798828,\n 204.9609375\n ],\n [\n 346.04296875,\n 204.9609375\n ],\n [\n 346.04296875,\n 414.0838317871094\n ],\n [\n 129.6000518798828,\n 414.0838317871094\n ]\n ],\n \"bbox\": [\n 129.6000518798828,\n 204.9609375,\n 346.04296875,\n 414.0838317871094\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/228/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"find_map is used by add and get to figure out which map to put the new item in, or which map to search.
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\",\n \"polygon\": [\n [\n 128.9443359375,\n 452.8016662597656\n ],\n [\n 525.6033325195312,\n 452.8016662597656\n ],\n [\n 525.6033325195312,\n 487.3028259277344\n ],\n [\n 128.9443359375,\n 487.3028259277344\n ]\n ],\n \"bbox\": [\n 128.9443359375,\n 452.8016662597656,\n 525.6033325195312,\n 487.3028259277344\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/228/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Hashable objects that are considered equal return the same hash value, but the converse is not necessarily true: two different objects can return the same hash value.
\",\n \"polygon\": [\n [\n 128.3466796875,\n 495.0\n ],\n [\n 525.9375,\n 495.0\n ],\n [\n 525.9375,\n 517.8148193359375\n ],\n [\n 128.3466796875,\n 517.8148193359375\n ]\n ],\n \"bbox\": [\n 128.3466796875,\n 495.0,\n 525.9375,\n 517.8148193359375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/228/Text/8\",\n \"block_type\": \"Text\",\n \"html\": \"find_map uses the modulus operator to wrap the hash values into the range from 0 to len(self.maps), so the result is a legal index into the list. Of course, this means that many different hash values will wrap onto the same index. But if the hash function spreads things out pretty evenly (which is what hash functions are designed to do), then we expect n/100 items per LinearMap.
\",\n \"polygon\": [\n [\n 128.794921875,\n 525.55078125\n ],\n [\n 525.638671875,\n 525.55078125\n ],\n [\n 525.638671875,\n 584.9098205566406\n ],\n [\n 128.794921875,\n 584.9098205566406\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 525.55078125,\n 525.638671875,\n 584.9098205566406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/228/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Since the run time of LinearMap.get is proportional to the number of items, we expect BetterMap to be about 100 times faster than LinearMap. The order of growth is still linear, but the leading coefficient is smaller. That's nice, but still not as good as a hashtable.
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\",\n \"polygon\": [\n [\n 127.7490234375,\n 635.765625\n ],\n [\n 525.6033935546875,\n 635.765625\n ],\n [\n 525.6033935546875,\n 682.5168380737305\n ],\n [\n 127.7490234375,\n 682.5168380737305\n ]\n ],\n \"bbox\": [\n 127.7490234375,\n 635.765625,\n 525.6033935546875,\n 682.5168380737305\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/228/Text/11\",\n \"block_type\": \"Text\",\n \"html\": \"Here is an implementation of a hashtable:
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\",\n \"polygon\": [\n [\n 85.3154296875,\n 384.978515625\n ],\n [\n 482.4005432128906,\n 384.978515625\n ],\n [\n 482.4005432128906,\n 407.8648681640625\n ],\n [\n 85.3154296875,\n 407.8648681640625\n ]\n ],\n \"bbox\": [\n 85.3154296875,\n 384.978515625,\n 482.4005432128906,\n 407.8648681640625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/229/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"get just dispatches to BetterMap. The real work happens in add, which checks the number of items and the size of the BetterMap: if they are equal, the average number of items per LinearMap is 1, so it calls resize.
\",\n \"polygon\": [\n [\n 85.166015625,\n 417.26953125\n ],\n [\n 482.4026794433594,\n 417.26953125\n ],\n [\n 482.4026794433594,\n 452.6288757324219\n ],\n [\n 85.166015625,\n 452.6288757324219\n ]\n ],\n \"bbox\": [\n 85.166015625,\n 417.26953125,\n 482.4026794433594,\n 452.6288757324219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/229/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"resize make a new BetterMap, twice as big as the previous one, and then \\\"rehashes\\\" the items from the old map to the new.
\",\n \"polygon\": [\n [\n 85.46484375,\n 462.515625\n ],\n [\n 482.4041748046875,\n 462.515625\n ],\n [\n 482.4041748046875,\n 485.1978759765625\n ],\n [\n 85.46484375,\n 485.1978759765625\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 462.515625,\n 482.4041748046875,\n 485.1978759765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/229/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Rehashing is necessary because changing the number of LinearMaps changes the denominator of the modulus operator in find_map. That means that some objects that used to wrap into the same LinearMap will get split up (which is what we wanted, right?).
\",\n \"polygon\": [\n [\n 85.46484375,\n 495.0\n ],\n [\n 482.4034118652344,\n 495.0\n ],\n [\n 482.4034118652344,\n 529.9618835449219\n ],\n [\n 85.46484375,\n 529.9618835449219\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 495.0,\n 482.4034118652344,\n 529.9618835449219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/229/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"Rehashing is linear, so resize is linear, which might seem bad, since I promised that add would be constant time. But remember that we don't have to resize every time, so add is usually constant time and only occasionally linear. The total amount of work to run add n times is proportional to n, so the average time of each add is constant time!
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\",\n \"polygon\": [\n [\n 129.59999084472656,\n 391.939453125\n ],\n [\n 526.53515625,\n 391.939453125\n ],\n [\n 526.53515625,\n 427.08489990234375\n ],\n [\n 129.59999084472656,\n 427.08489990234375\n ]\n ],\n \"bbox\": [\n 129.59999084472656,\n 391.939453125,\n 526.53515625,\n 427.08489990234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/230/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"The extra work of rehashing appears as a sequence of increasingly tall towers with increasing space between them. Now if you knock over the towers, amortizing the cost of resizing over all adds, you can see graphically that the total cost after n adds is 2n − 2.
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\",\n \"polygon\": [\n [\n 86.4000015258789,\n 59.5546875\n ],\n [\n 482.607421875,\n 59.5546875\n ],\n [\n 482.607421875,\n 71.13372802734375\n ],\n [\n 86.4000015258789,\n 71.13372802734375\n ]\n ],\n \"bbox\": [\n 86.4000015258789,\n 59.5546875,\n 482.607421875,\n 71.13372802734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/225/SectionHeader/1\",\n \"4\": \"/page/227/SectionHeader/5\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/232/Page/129\",\n \"block_type\": \"Page\",\n \"html\": \"\",\n \"polygon\": [\n [\n 0.0,\n 0.0\n ],\n [\n 612.0,\n 0.0\n ],\n [\n 612.0,\n 792.0\n ],\n [\n 0.0,\n 792.0\n ]\n ],\n \"bbox\": [\n 0.0,\n 0.0,\n 612.0,\n 792.0\n ],\n \"children\": [\n {\n \"id\": \"/page/232/SectionHeader/0\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Appendix C
\",\n \"polygon\": [\n [\n 128.49609375,\n 161.068359375\n ],\n [\n 246.533203125,\n 161.068359375\n ],\n [\n 246.533203125,\n 182.990966796875\n ],\n [\n 128.49609375,\n 182.990966796875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 161.068359375,\n 246.533203125,\n 182.990966796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/222/SectionHeader/1\",\n \"2\": \"/page/232/SectionHeader/0\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/232/SectionHeader/1\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"Lumpy
\",\n \"polygon\": [\n [\n 129.09375,\n 215.89129638671875\n ],\n [\n 212.765625,\n 215.89129638671875\n ],\n [\n 212.765625,\n 241.69921875\n ],\n [\n 129.09375,\n 241.69921875\n ]\n ],\n \"bbox\": [\n 129.09375,\n 215.89129638671875,\n 212.765625,\n 241.69921875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/232/Text/2\",\n \"block_type\": \"Text\",\n \"html\": \"Throughout the book, I have used diagrams to represent the state of running programs.
\",\n \"polygon\": [\n [\n 129.09375,\n 286.9453125\n ],\n [\n 514.58203125,\n 286.9453125\n ],\n [\n 514.58203125,\n 297.2768859863281\n ],\n [\n 129.09375,\n 297.2768859863281\n ]\n ],\n \"bbox\": [\n 129.09375,\n 286.9453125,\n 514.58203125,\n 297.2768859863281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/232/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"In Section 2.2, we used a state diagram to show the names and values of variables. In Section 3.10 I introduced a stack diagram, which shows one frame for each function call. Each frame shows the parameters and local variables for the function or method. Stack diagrams for recursive functions appear in Section 5.9 and Section 6.5.
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\",\n \"polygon\": [\n [\n 128.197265625,\n 360.421875\n ],\n [\n 526.236328125,\n 360.421875\n ],\n [\n 526.236328125,\n 382.743896484375\n ],\n [\n 128.197265625,\n 382.743896484375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 360.421875,\n 526.236328125,\n 382.743896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/232/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Section 15.2 introduces object diagrams, which show the state of an object's attributes, and their attributes, and so on. Section 15.3 has object diagrams for Rectangles and their embedded Points. Section 16.1 shows the state of a Time object. Section 18.2 has a diagram that includes a class object and an instance, each with their own attributes.
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\",\n \"polygon\": [\n [\n 226.0290069580078,\n 220.623046875\n ],\n [\n 342.770751953125,\n 220.623046875\n ],\n [\n 342.770751953125,\n 232.0838623046875\n ],\n [\n 226.0290069580078,\n 232.0838623046875\n ]\n ],\n \"bbox\": [\n 226.0290069580078,\n 220.623046875,\n 342.770751953125,\n 232.0838623046875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"4\": \"/page/232/SectionHeader/11\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"4\": \"/page/232/SectionHeader/11\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/233/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"from swampy.Lumpy import Lumpy
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\",\n \"polygon\": [\n [\n 85.6142578125,\n 279.2109375\n ],\n [\n 210.375,\n 279.2109375\n ],\n [\n 210.375,\n 301.8622741699219\n ],\n [\n 85.6142578125,\n 301.8622741699219\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 279.2109375,\n 210.375,\n 301.8622741699219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"4\": \"/page/232/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/233/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"message = 'And now for something completely different'\\nn = 17\\npi = 3.1415926535897932
\",\n \"polygon\": [\n [\n 86.13720703125,\n 313.62890625\n ],\n [\n 368.7734069824219,\n 313.62890625\n ],\n [\n 368.7734069824219,\n 350.6402893066406\n ],\n [\n 86.13720703125,\n 350.6402893066406\n ]\n ],\n \"bbox\": [\n 86.13720703125,\n 313.62890625,\n 368.7734069824219,\n 350.6402893066406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"4\": \"/page/232/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/233/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"lumpy.object_diagram()
\",\n \"polygon\": [\n [\n 85.9130859375,\n 364.095703125\n ],\n [\n 201.47799682617188,\n 364.095703125\n ],\n [\n 201.47799682617188,\n 375.0282897949219\n ],\n [\n 85.9130859375,\n 375.0282897949219\n ]\n ],\n \"bbox\": [\n 85.9130859375,\n 364.095703125,\n 201.47799682617188,\n 375.0282897949219\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"4\": \"/page/232/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/233/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"The first line imports the Lumpy class from swampy.Lumpy. If you don't have Swampy installed as a package, make sure the Swampy files are in Python's search path and use this import statement instead:
\",\n \"polygon\": [\n [\n 85.763671875,\n 380.53125\n ],\n [\n 482.607421875,\n 380.53125\n ],\n [\n 482.607421875,\n 416.49285888671875\n ],\n [\n 85.763671875,\n 416.49285888671875\n ]\n ],\n \"bbox\": [\n 85.763671875,\n 380.53125,\n 482.607421875,\n 416.49285888671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"4\": \"/page/232/SectionHeader/11\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/233/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"from Lumpy import Lumpy
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\",\n \"polygon\": [\n [\n 264.1640625,\n 230.87109375\n ],\n [\n 388.4765625,\n 230.87109375\n ],\n [\n 388.4765625,\n 240.95196533203125\n ],\n [\n 264.1640625,\n 240.95196533203125\n ]\n ],\n \"bbox\": [\n 264.1640625,\n 230.87109375,\n 388.4765625,\n 240.95196533203125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/234/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"from swampy.Lumpy import Lumpy\\ndef countdown(n):\\n if n <= 0:\\n print 'Blastoff!'\\n lumpy.object_diagram()\\n else:\\n print n\\n countdown(n-1)\\nlumpy = Lumpy()\\nlumpy.make_reference()
\",\n \"polygon\": [\n [\n 129.59996032714844,\n 262.67877197265625\n ],\n [\n 286.5208435058594,\n 262.67877197265625\n ],\n [\n 286.5208435058594,\n 406.77838134765625\n ],\n [\n 129.59996032714844,\n 406.77838134765625\n ]\n ],\n \"bbox\": [\n 129.59996032714844,\n 262.67877197265625,\n 286.5208435058594,\n 406.77838134765625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/234/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"countdown(3)
\",\n \"polygon\": [\n [\n 129.5419921875,\n 405.66796875\n ],\n [\n 205.7431640625,\n 405.66796875\n ],\n [\n 205.7431640625,\n 418.973388671875\n ],\n [\n 129.5419921875,\n 418.973388671875\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 405.66796875,\n 205.7431640625,\n 418.973388671875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/234/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Figure C.2 shows the result. Each frame is represented with a box that has the function's name outside and variables inside. Since this function is recursive, there is one frame for each level of recursion.
\",\n \"polygon\": [\n [\n 128.6455078125,\n 423.84375\n ],\n [\n 525.9375,\n 423.84375\n ],\n [\n 525.9375,\n 459.1739501953125\n ],\n [\n 128.6455078125,\n 459.1739501953125\n ]\n ],\n \"bbox\": [\n 128.6455078125,\n 423.84375,\n 525.9375,\n 459.1739501953125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/234/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"Remember that a stack diagram shows the state of the program at a particular point in its execution. To get the diagram you want, sometimes you have to think about where to invoke object_diagram.
\",\n \"polygon\": [\n [\n 128.49609375,\n 468.703125\n ],\n [\n 525.9375,\n 468.703125\n ],\n [\n 525.9375,\n 503.1959533691406\n ],\n [\n 128.49609375,\n 503.1959533691406\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 468.703125,\n 525.9375,\n 503.1959533691406\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/234/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"In this case I invoke object_diagram after executing the base case of the recursion; that way the stack diagram shows each level of the recursion. You can call object_diagram more than once to get a series of snapshots of the program's execution.
\",\n \"polygon\": [\n [\n 128.794921875,\n 512.7178039550781\n ],\n [\n 526.236328125,\n 512.7178039550781\n ],\n [\n 526.236328125,\n 547.2189636230469\n ],\n [\n 128.794921875,\n 547.2189636230469\n ]\n ],\n \"bbox\": [\n 128.794921875,\n 512.7178039550781,\n 526.236328125,\n 547.2189636230469\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/234/SectionHeader/8\",\n \"block_type\": \"SectionHeader\",\n \"html\": \"C.3 Object diagrams
\",\n \"polygon\": [\n [\n 127.8984375,\n 575.4375\n ],\n [\n 273.12890625,\n 575.4375\n ],\n [\n 273.12890625,\n 590.2020111083984\n ],\n [\n 127.8984375,\n 590.2020111083984\n ]\n ],\n \"bbox\": [\n 127.8984375,\n 575.4375,\n 273.12890625,\n 590.2020111083984\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/234/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/234/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"This example generates an object diagram showing the lists from Section 10.1. You can download it from http://thinkpython.com/code/lumpy_demo3.py.
\",\n \"polygon\": [\n [\n 128.197265625,\n 601.734375\n ],\n [\n 525.638671875,\n 601.734375\n ],\n [\n 525.638671875,\n 624.9375\n ],\n [\n 128.197265625,\n 624.9375\n ]\n ],\n \"bbox\": [\n 128.197265625,\n 601.734375,\n 525.638671875,\n 624.9375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/234/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/234/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"from swampy.Lumpy import Lumpy
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\",\n \"polygon\": [\n [\n 223.224609375,\n 314.98931884765625\n ],\n [\n 346.04296875,\n 314.98931884765625\n ],\n [\n 346.04296875,\n 324.951904296875\n ],\n [\n 223.224609375,\n 324.951904296875\n ]\n ],\n \"bbox\": [\n 223.224609375,\n 314.98931884765625,\n 346.04296875,\n 324.951904296875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/234/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/234/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/235/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"numbers = [17, 123]\\nempty = []
\",\n \"polygon\": [\n [\n 85.46484375,\n 346.28875732421875\n ],\n [\n 185.7769317626953,\n 346.28875732421875\n ],\n [\n 185.7769317626953,\n 368.4453430175781\n ],\n [\n 85.46484375,\n 368.4453430175781\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 346.28875732421875,\n 185.7769317626953,\n 368.4453430175781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/234/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/235/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"lumpy.object_diagram()
\",\n \"polygon\": [\n [\n 85.39013671875,\n 382.8515625\n ],\n [\n 201.4779815673828,\n 382.8515625\n ],\n [\n 201.4779815673828,\n 392.8343505859375\n ],\n [\n 85.39013671875,\n 392.8343505859375\n ]\n ],\n \"bbox\": [\n 85.39013671875,\n 382.8515625,\n 201.4779815673828,\n 392.8343505859375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/234/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/235/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"Figure C.3 shows the result. Lists are represented by a box that shows the indices mapping to the elements. This representation is slightly misleading, since indices are not actually part of the list, but I think they make the diagram easier to read. The empty list is represented by an empty box.
\",\n \"polygon\": [\n [\n 85.46484375,\n 397.93359375\n ],\n [\n 482.90625,\n 397.93359375\n ],\n [\n 482.90625,\n 445.00091552734375\n ],\n [\n 85.46484375,\n 445.00091552734375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 397.93359375,\n 482.90625,\n 445.00091552734375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/234/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/235/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"And here's an example showing the dictionaries from Section 11.4. You can download it from http://thinkpython.com/code/lumpy_demo4.py.
\",\n \"polygon\": [\n [\n 85.6142578125,\n 454.0078125\n ],\n [\n 482.40325927734375,\n 454.0078125\n ],\n [\n 482.40325927734375,\n 476.6009216308594\n ],\n [\n 85.6142578125,\n 476.6009216308594\n ]\n ],\n \"bbox\": [\n 85.6142578125,\n 454.0078125,\n 482.40325927734375,\n 476.6009216308594\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/234/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/235/Text/7\",\n \"block_type\": \"Text\",\n \"html\": \"from swampy.Lumpy import Lumpy
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\",\n \"polygon\": [\n [\n 225.615234375,\n 197.9033203125\n ],\n [\n 343.65234375,\n 197.9033203125\n ],\n [\n 343.65234375,\n 208.0546875\n ],\n [\n 225.615234375,\n 208.0546875\n ]\n ],\n \"bbox\": [\n 225.615234375,\n 197.9033203125,\n 343.65234375,\n 208.0546875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/236/SectionHeader/8\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/236/SectionHeader/8\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/237/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"But if you are passing functions and classes as parameters, you might want them to appear. This example shows what that looks like; you can download it from http://thinkpython. com/code/lumpy_demo6.py.
\",\n \"polygon\": [\n [\n 85.0166015625,\n 230.677734375\n ],\n [\n 482.43389892578125,\n 230.677734375\n ],\n [\n 482.43389892578125,\n 265.5469970703125\n ],\n [\n 85.0166015625,\n 265.5469970703125\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 230.677734375,\n 482.43389892578125,\n 265.5469970703125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/236/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/237/Code/4\",\n \"block_type\": \"Code\",\n \"html\": \"import copy\\nfrom swampy.Lumpy import Lumpy\\nlumpy = Lumpy()\\nlumpy.make_reference()\\nclass Point(object):\\n \\\"\\\"\\\"Represents a point in 2-D space.\\\"\\\"\\\"\\nclass Rectangle(object):\\n \\\"\\\"\\\"Represents a rectangle.\\\"\\\"\\\"\\ndef instantiate(constructor):\\n \\\"\\\"\\\"Instantiates a new object.\\\"\\\"\\\"\\n obj = constructor()\\n lumpy.object_diagram()\\n return obj
\",\n \"polygon\": [\n [\n 85.0166015625,\n 272.07879638671875\n ],\n [\n 306.07977294921875,\n 272.07879638671875\n ],\n [\n 306.07977294921875,\n 484.9453125\n ],\n [\n 85.0166015625,\n 484.9453125\n ]\n ],\n \"bbox\": [\n 85.0166015625,\n 272.07879638671875,\n 306.07977294921875,\n 484.9453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/233/SectionHeader/16\",\n \"4\": \"/page/236/SectionHeader/8\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/237/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"point = instantiate(Point)
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}\n },\n {\n \"id\": \"/page/238/Caption/2\",\n \"block_type\": \"Caption\",\n \"html\": \"Figure C.8: Class diagram.
\",\n \"polygon\": [\n [\n 266.853515625,\n 322.330078125\n ],\n [\n 386.384765625,\n 322.330078125\n ],\n [\n 386.384765625,\n 332.6518859863281\n ],\n [\n 266.853515625,\n 332.6518859863281\n ]\n ],\n \"bbox\": [\n 266.853515625,\n 322.330078125,\n 386.384765625,\n 332.6518859863281\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n }\n ],\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": null\n },\n {\n \"id\": \"/page/238/Text/3\",\n \"block_type\": \"Text\",\n \"html\": \"Class diagrams are different. They show the classes that make up a program and the relationships between them. They are timeless in the sense that they describe the program as a whole, not any particular point in time. For example, if an instance of Class A generally contains a reference to an instance of Class B, we say there is a \\\"HAS-A relationship\\\" between those classes.
\",\n \"polygon\": [\n [\n 128.49609375,\n 353.267578125\n ],\n [\n 525.6034545898438,\n 353.267578125\n ],\n [\n 525.6034545898438,\n 412.51287841796875\n ],\n [\n 128.49609375,\n 412.51287841796875\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 353.267578125,\n 525.6034545898438,\n 412.51287841796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/238/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Here's an example that shows a HAS-A relationship. You can download it from http: //thinkpython.com/code/lumpy_demo7.py.
\",\n \"polygon\": [\n [\n 128.49609375,\n 421.13671875\n ],\n [\n 525.6057739257812,\n 421.13671875\n ],\n [\n 525.6057739257812,\n 443.89886474609375\n ],\n [\n 128.49609375,\n 443.89886474609375\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 421.13671875,\n 525.6057739257812,\n 443.89886474609375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/238/Code/5\",\n \"block_type\": \"Code\",\n \"html\": \"from swampy.Lumpy import Lumpy
\",\n \"polygon\": [\n [\n 128.49609375,\n 449.0077209472656\n ],\n [\n 286.875,\n 449.0077209472656\n ],\n [\n 286.875,\n 459.03515625\n ],\n [\n 128.49609375,\n 459.03515625\n ]\n ],\n \"bbox\": [\n 128.49609375,\n 449.0077209472656,\n 286.875,\n 459.03515625\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/238/Text/6\",\n \"block_type\": \"Text\",\n \"html\": \"lumpy = Lumpy() lumpy.make_reference()
\",\n \"polygon\": [\n [\n 128.72021484375,\n 473.3957214355469\n ],\n [\n 244.67799377441406,\n 473.3957214355469\n ],\n [\n 244.67799377441406,\n 495.5533142089844\n ],\n [\n 128.72021484375,\n 495.5533142089844\n ]\n ],\n \"bbox\": [\n 128.72021484375,\n 473.3957214355469,\n 244.67799377441406,\n 495.5533142089844\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/238/Code/7\",\n \"block_type\": \"Code\",\n \"html\": \"box = Rectangle()\\nbox.width = 100.0\\nbox.height = 200.0\\nbox.corner = Point()\\nbox.corner.x = 0.0\\nbox.corner.y = 0.0
\",\n \"polygon\": [\n [\n 128.57080078125,\n 509.9787292480469\n ],\n [\n 234.21726989746094,\n 509.9787292480469\n ],\n [\n 234.21726989746094,\n 580.9133453369141\n ],\n [\n 128.57080078125,\n 580.9133453369141\n ]\n ],\n \"bbox\": [\n 128.57080078125,\n 509.9787292480469,\n 234.21726989746094,\n 580.9133453369141\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/238/Code/8\",\n \"block_type\": \"Code\",\n \"html\": \"lumpy.class_diagram()
\",\n \"polygon\": [\n [\n 129.09375,\n 595.3397521972656\n ],\n [\n 239.4476318359375,\n 595.3397521972656\n ],\n [\n 239.4476318359375,\n 605.3023529052734\n ],\n [\n 129.09375,\n 605.3023529052734\n ]\n ],\n \"bbox\": [\n 129.09375,\n 595.3397521972656,\n 239.4476318359375,\n 605.3023529052734\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/238/Text/9\",\n \"block_type\": \"Text\",\n \"html\": \"Figure C.7 shows the result. Each class is represented with a box that contains the name of the class, any methods the class provides, any class variables, and any instance variables. In this example, Rectangle and Point have instance variables, but no methods or class variables.
\",\n \"polygon\": [\n [\n 129.5419921875,\n 610.2421875\n ],\n [\n 525.9375,\n 610.2421875\n ],\n [\n 525.9375,\n 657.2549133300781\n ],\n [\n 129.5419921875,\n 657.2549133300781\n ]\n ],\n \"bbox\": [\n 129.5419921875,\n 610.2421875,\n 525.9375,\n 657.2549133300781\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/238/Text/10\",\n \"block_type\": \"Text\",\n \"html\": \"The arrow from Rectangle to Point shows that Rectangles contain an embedded Point. In addition, Rectangle and Point both inherit from object, which is represented in the diagram with a triangle-headed arrow.
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\",\n \"polygon\": [\n [\n 85.46484375,\n 88.365234375\n ],\n [\n 482.40582275390625,\n 88.365234375\n ],\n [\n 482.40582275390625,\n 123.1868896484375\n ],\n [\n 85.46484375,\n 123.1868896484375\n ]\n ],\n \"bbox\": [\n 85.46484375,\n 88.365234375,\n 482.40582275390625,\n 123.1868896484375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/239/Code/2\",\n \"block_type\": \"Code\",\n \"html\": \"from swampy.Lumpy import Lumpy\\nfrom PokerHand import *\\nlumpy = Lumpy()\\nlumpy.make_reference()\\ndeck = Deck()\\nhand = PokerHand()\\ndeck.move_cards(hand, 7)
\",\n \"polygon\": [\n [\n 86.2119140625,\n 129.23968505859375\n ],\n [\n 243.3955078125,\n 129.23968505859375\n ],\n [\n 243.3955078125,\n 261.615234375\n ],\n [\n 86.2119140625,\n 261.615234375\n ]\n ],\n \"bbox\": [\n 86.2119140625,\n 129.23968505859375,\n 243.3955078125,\n 261.615234375\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/239/Code/3\",\n \"block_type\": \"Code\",\n \"html\": \"lumpy.class_diagram()
\",\n \"polygon\": [\n [\n 86.40000915527344,\n 263.3775634765625\n ],\n [\n 196.330078125,\n 263.3775634765625\n ],\n [\n 196.330078125,\n 273.796875\n ],\n [\n 86.40000915527344,\n 273.796875\n ]\n ],\n \"bbox\": [\n 86.40000915527344,\n 263.3775634765625,\n 196.330078125,\n 273.796875\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/239/Text/4\",\n \"block_type\": \"Text\",\n \"html\": \"Figure C.8 shows the result. PokerHand inherits from Hand, which inherits from Deck. Both Deck and PokerHand have Cards.
\",\n \"polygon\": [\n [\n 86.0625,\n 278.4375\n ],\n [\n 482.90625,\n 278.4375\n ],\n [\n 482.90625,\n 301.8497314453125\n ],\n [\n 86.0625,\n 301.8497314453125\n ]\n ],\n \"bbox\": [\n 86.0625,\n 278.4375,\n 482.90625,\n 301.8497314453125\n ],\n \"children\": null,\n \"section_hierarchy\": {\n \"1\": \"/page/232/SectionHeader/1\",\n \"2\": \"/page/237/SectionHeader/9\"\n },\n \"images\": {}\n },\n {\n \"id\": \"/page/239/Text/5\",\n \"block_type\": \"Text\",\n \"html\": \"This diagram does not show that Hand also has cards, because in the program there are no instances of Hand. This example demonstrates a limitation of Lumpy; it only knows about the attributes and HAS-A relationships of objects that are instantiated.
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"path": "data/examples/markdown/multicolcnn/multicolcnn.md",
"content": "# An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting\n\nDiptodip Deb Georgia Institute of Technology diptodipdeb@gatech.edu\n\n# Abstract\n\n*We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting. Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd). Modern approaches to the counting problem involve the production of a density map via regression whose integral is equal to the number of objects in the image. However, objects in the image can occur at different scales (e.g. due to perspective effects) which can make it difficult for a learning agent to learn the proper density map. While the use of multiple columns to extract multiscale information from images has been shown before, our approach aggregates the multiscale information gathered by the multicolumn convolutional neural network to improve performance. Our experiments show that our proposed network outperforms the state-of-the-art on many benchmark datasets, and also that using our aggregation module in combination with a higher number of columns is beneficial for multiscale counting.*\n\n# 1. Introduction\n\nLearning to count the number of objects in an image is a deceptively difficult problem with many interesting applications, such as surveillance [\\[20\\]](#page-8-0), traffic monitoring [\\[14\\]](#page-8-1) and medical image analysis [\\[22\\]](#page-8-2). In many of these application areas, the objects to be counted vary widely in appearance, size and shape, and labeled training data is typically sparse. These factors pose a significant computer vision and machine learning challenge.\n\nLempitsky et al. [\\[15\\]](#page-8-3) showed that it is possible to learn to count without learning to explicitly detect and localize individual objects. Instead, they propose learning to predict a density map whose integral over the image equals the number of objects in the image. This approach has been adopted by many later works (Cf. [\\[18,](#page-8-4) [28\\]](#page-9-0)).\n\nHowever, in many counting problems, such as those\n\nJonathan Ventura University of Colorado Colorado Springs jventura@uccs.edu\n\ncounting cells in a microscope image, pedestrians in a crowd, or vehicles in a traffic jam, regressors trained on a single image scale are not reliable [\\[18\\]](#page-8-4). This is due to a variety of challenges including overlap of objects and perspective effects which cause significant variance in object shape, size and appearance.\n\nThe most successful recent approaches address this issue by explicitly incorporating multi-scale information in the network [\\[18,](#page-8-4)[28\\]](#page-9-0). These approaches either combine multiple networks which take input patches of different sizes [\\[18\\]](#page-8-4) or combine multiple filtering paths (\"columns\") which have different size filters [\\[28\\]](#page-9-0).\n\nFollowing on the intuition that multiscale integration is key to achieving good counting performance, we propose to incorporate dilated filters [\\[25\\]](#page-8-5) into a multicolumn convolutional neural network design [\\[28\\]](#page-9-0). Dilated filters exponentially increase the network's receptive field without an exponential increase in parameters, allowing for efficient use of multiscale information. Convolutional neural networks with dilated filters have proven to provide competitive performance in image segmentation where multiscale analysis is also critical [\\[25,](#page-8-5) [26\\]](#page-8-6). By incorporating dilated filters into the multicolumn network design, we greatly increase the ability of the network to selectively aggregate multiscale information, without the need for explicit perspective maps during training and testing. We propose the \"aggregated multicolumn dilated convolution network\" or AMDCN which uses dilations to aggregate multiscale information. Our extensive experimental evaluation shows that this proposed network outperforms previous methods on many benchmark datasets.\n\n# 2. Related Work\n\nCounting using a supervised regressor to formulate a density map was first shown by [\\[15\\]](#page-8-3). In this paper, Lempitsky et al. show that the minimal annotation of a single dot blurred by a Gaussian kernel produces a sufficient density map to train a network to count. All of the counting methods that we examine as well as the method we use in\n\n\n\nFigure 1. Fully convolutional architecture diagram (not to scale). Arrows show separate columns that all take the same input. At the end of the columns, the feature maps are merged (concatenated) together and passed to another series of dilated convolutions: the aggregator, which can aggregate the multiscale information collected by the columns [\\[25\\]](#page-8-5). The input image is I with C channels. The output single channel density map is D, and integrating over this map (summing the pixels) results in the final count. Initial filter sizes are labeled with brackets or lines. Convolution operations are shown as flat rectangles, feature maps are shown as prisms. The number below each filter represents the dilation rate (1 means no dilation).\n\nour paper follow this method of producing a density map via regression. This is particularly advantageous because a sufficiently accurate regressor can also locate the objects in the image via this method. However, the Lempitsky paper ignores the issue of perspective scaling and other scaling issues. The work of [\\[27\\]](#page-8-7) introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches.\n\nThese issues are addressed by the work of [\\[18\\]](#page-8-4). Rubio et al. show that a fully convolutional neural network can be used to produce a supervised regressor that produces density maps as in [\\[15\\]](#page-8-3). They further demonstrate a method dubbed HydraCNN which essentially combines multiple convolutional networks that take in differently scaled image patches in order to incorporate multiscale, global information from the image. The premise of this method is that a single regressor will fail to accurately represent the difference in values of the features of an image caused by perspective shifts (scaling effects) [\\[18\\]](#page-8-4).\n\nHowever, the architectures of both [\\[18\\]](#page-8-4) and [\\[27\\]](#page-8-7) are not fully convolutional due to requiring multiple image patches and, as discussed in [\\[25\\]](#page-8-5), the experiments of [\\[11,](#page-8-8) [17\\]](#page-8-9) and [\\[9,](#page-8-10) [12,](#page-8-11) [16\\]](#page-8-12) leave it unclear as to whether rescaling patches of the image is truly necessary in order to solve dense prediction problems via convolutional neural networks. Moreover, these approaches seem to saturate in performance at three columns, which means the network is extracting information from fewer scales. The work of [\\[25\\]](#page-8-5) proposes the use of dilated convolutions as a simpler alternative that does not require sampling of rescaled image patches to provide global, scale-aware information to the network. A fully convolutional approach to multiscale counting has been proposed by [\\[28\\]](#page-9-0), in which a multicolumn convolutional network gathers features of different scales by using convolutions of increasing kernel sizes from column to column instead of scaling image patches. Further, DeepLab has used dilated convolutions in multiple columns to extract scale information for segmentation [\\[8\\]](#page-8-13). We build on these approaches with our aggregator module as described in Section [3.1,](#page-2-0) which should allow for extracting information from more scales.\n\nIt should be noted that other methods of counting exist, including training a network to recognize deep object features via only providing the counts of the objects of interest in an image [\\[21\\]](#page-8-14) and using CNNs (convolutional neural networks) along with boosting in order to improve the results\n\n\n\nFigure 2. UCF sample results. Left: input counting image. Middle: Ground truth density map. Right: AMDCN prediction of density map on test image. The network never saw these images during training. All density maps are one channel only (i.e. grayscale), but are colored here for clarity.\n\nof regression for production of density maps [\\[24\\]](#page-8-15). In the same spirit, [\\[4\\]](#page-8-16) combines deep and shallow convolutions within the same network, providing accurate counting of dense objects (e.g. the UCF50 crowd dataset).\n\nIn this paper, however, we aim to apply the dilated convolution method of [\\[25\\]](#page-8-5), which has shown to be able to incorporate multiscale perspective information without using multiple inputs or a complicated network architecture, as well as the multicolumn approach of [\\[8,](#page-8-13) [28\\]](#page-9-0) to aggregate multiscale information for the counting problem.\n\n# 3. Method\n\n### 3.1. Dilated Convolutions for Multicolumn Networks\n\nWe propose the use of dilated convolutions as an attractive alternative to the architecture of the HydraCNN [\\[18\\]](#page-8-4), which seems to saturate in performance at 3 or more columns. We refer to our proposed network as the aggregated multicolumn dilated convolution network1, henceforth shortened as the AMDCN. The architecture of the AMDCN is inspired by the multicolumn counting network of [\\[28\\]](#page-9-0). Extracting features from multiple scales is a good idea when attempting to perform perspective-free counting and increasing the convolution kernel size across columns is an efficient method of doing so. However, the number of parameters increases exponentially as larger kernels are used in these columns to extract features at larger scales. Therefore, we propose using dilated convolutions rather than larger kernels.\n\nDilated convolutions, as discussed in [\\[25\\]](#page-8-5), allow for the exponential increase of the receptive field with a linear increase in the number of parameters with respect to each hidden layer.\n\nIn a traditional 2D convolution, we define a real valued function $F : \\mathbb{Z}^2 \\rightarrow \\mathbb{R}$, an input $\\Omega_r = [-r, r]^2 \\in \\mathbb{Z}^2$, and a filter function $k : \\Omega_r \\rightarrow \\mathbb{R}$. In this case, a convolution operation as defined in [\\[25\\]](#page-8-5) is given by\n\n$$(F*k)(\\mathbf{p}) = \\sum_{\\mathbf{s}+\\mathbf{t}=\\mathbf{p}} F(\\mathbf{s})k(\\mathbf{t}).\\tag{1}$$\n\nA dilated convolution is essentially a generalization of the traditional 2D convolution that allows the operation to skip some inputs. This enables an increase in the size of the filter (i.e. the size of the receptive field) without losing resolution. Formally, we define from [\\[25\\]](#page-8-5) the dilated convolution as\n\n$$(F \\ast_l k)(\\mathbf{p}) = \\sum_{\\mathbf{s} + l\\mathbf{t} = \\mathbf{p}} F(\\mathbf{s}) k(\\mathbf{t}) \\tag{2}$$\n\nwhere l is the index of the current layer of the convolution.\n\nUsing dilations to construct the aggregator in combination with the multicolumn idea will allow for the construction of a network with more than just 3 or 4 columns as in [\\[28\\]](#page-9-0) and [\\[8\\]](#page-8-13), because the aggregator should prevent the saturation of performance with increasing numbers of columns. Therefore the network will be able to extract useful features from more scales. We take advantage of dilations within the columns as well to provide large receptive fields with fewer parameters.\n\nLooking at more scales should allow for more accurate regression of the density map. However, because not all scales will be relevant, we extend the network beyond a simple 1 × 1 convolution after the merged columns. Instead, we construct a second part of the network, the aggregator, which sets our method apart from [\\[28\\]](#page-9-0), [\\[8\\]](#page-8-13), and other multicolumn networks. This aggregator is another series of dilated convolutions that should appropriately consolidate the multiscale information collected by the columns. This is a capability of dilated convolutions observed by [\\[25\\]](#page-8-5). While papers such as [\\[28\\]](#page-9-0) and [\\[8\\]](#page-8-13) have shown that multiple columns and dilated columns are useful in extracting multiscale information, we argue in this paper that the simple aggregator module built using dilated convolutions is able to effectively make use multiscale information from multiple columns. We show compelling evidence for these claims in Section 4.5.\n\nThe network as shown in Figure [1](#page-1-0) contains 5 columns. Note that dilations allow us to use more columns for counting than [\\[28\\]](#page-9-0) or [\\[8\\]](#page-8-13). Each column looks at a larger scale than the previous (the exact dilations can also be seen in Figure [1](#page-1-0)). There are 32 feature maps for each convolution, and all inputs are zero padded prior to each convolution in order to maintain the same data shape from input to output. That is, an image input to this network will result in a density map of the same dimensions. All activations in the specified network are ReLUs. Our input pixel values are floating point 32 bit values from 0 to 1. We center our inputs at 0 by subtracting the per channel mean from each channel. When\n\n1 Implementation available on [https://github.com/](https://github.com/diptodip/counting) [diptodip/counting](https://github.com/diptodip/counting).\n\ntraining, we use a scaled mean absolute error for our loss function:\n\n$$L = \\frac{1}{n} \\sum_{i=1}^{n} |\\hat{y}_i - \\gamma y_i| \\tag{3}$$\n\nwhere $\\n \\gamma\\n $ is the scale factor, $\\n \\hat{y}_i\\n $ is the prediction, $\\n y_i\\n $ is the true value, and $n$ is the number of pixels. We use a scaled mean absolute error because the target values are so small that it is numerically unstable to regress to these values. At testing time, when retrieving the output density map from the network, we scale the pixel values by $\\gamma^{-1}$ to obtain the correct value. This approach is more numerically stable and avoids having the network learn to output only zeros by weighting the nonzero values highly. For all our datasets, we set $\\gamma = 255$.\n\n#### 3.2. Experiments\n\nWe evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data [\\[18\\]](#page-8-4), UCSD crowd data [\\[5\\]](#page-8-17), and WorldExpo crowd data [\\[27\\]](#page-8-7). For each of these data sets, we used labels given by the corresponding density map for each image. An example of this is shown in Figure [2.](#page-2-2) We have performed experiments on the four different splits of the UCSD data as used in [\\[18\\]](#page-8-4) and the split of the UCSD data as used in [\\[28\\]](#page-9-0) (which we call the original split). We also evaluated the performance of our network on the TRANCOS traffic dataset [\\[14\\]](#page-8-1). We have also experimented with higher density datasets for crowd counting, namely WorldExpo and UCF.\n\nWe have observed that multicolumn dilations produce density maps (and therefore counts) that often have lower loss than those of HydraCNN [\\[18\\]](#page-8-4) and [\\[28\\]](#page-9-0). We measure density map regression loss via a scaled mean absolute error loss during training. We compare accuracy of the counts via mean absolute error for the crowd datasets and the GAME metric in the TRANCOS dataset as explained in Section [3.2.2.](#page-3-0) Beyond the comparison to HydraCNN, we will also compare to other recent convolutional counting methods, especially those of [\\[21\\]](#page-8-14), [\\[24\\]](#page-8-15), and [\\[4\\]](#page-8-16) where possible.\n\nFor all datasets, we generally use patched input images and ground truth density maps produced by summing a Gaussian of a fixed size ($σ$) for each object for training. This size varies from dataset to dataset, but remains constant within a dataset with the exception of cases in which a perspective map is used. This is explained per dataset. All experiments were performed using Keras with the Adam optimizer [\\[10\\]](#page-8-18). The learning rates used are detailed per dataset. For testing, we also use patches that can either be directly pieced together or overlapped and averaged except in the case of UCF, for which we run our network on the full image.\n\nFurthermore, we performed a set of experiments in which we varied the number of columns from 1 to 5 (simply by including or not including the columns as specified in Figure [1,](#page-1-0) starting with the smallest filter column and adding larger filter columns one by one). Essentially, the network is allowed to extract information at larger and larger scales in addition to the smaller scales as we include each column. We then performed the same set of experiments, varying the number of columns, but with the aggregator module removed. We perform these experiments on the original split of UCSD as specified in Section [3.2.3](#page-4-0) and [\\[5\\]](#page-8-17), the TRAN-COS dataset, and the WorldExpo dataset because these are relatively large and well defined datasets. We limit the number of epochs to 10 for all of these sets of experiments in order to control for the effect of learning time, and also compare all results using MAE for consistency. These experiments are key to determining the efficacy of the aggregator in effectively combining multiscale information and in providing evidence to support the use of multiple columns to extract multiscale information from images. We report the results of these ablation studies in Section [4.5.](#page-5-0)\n\n#### 3.2.1 UCF50 Crowd Counting\n\nUCF is a particularly challenging crowd counting dataset. There are only 50 images in the whole dataset and they are all of varying sizes and from different scenes. The number of people also varies between images from less than 100 to the thousands. The average image has on the order of 1000 people. The difficulty is due to the combination of the very low number of images in the dataset and the fact that the images are all of varying scenes, making high quality generalization crucial. Furthermore, perspective effects are particularly noticeable for many images in this dataset. Despite this, there is no perspective information available for this dataset.\n\nWe take 1600 random patches of size $150 \\,\\times\\, 150$ for the training. For testing, we do not densely scan the image as in [\\[18\\]](#page-8-4) but instead test on the whole image. In order to standardize the image sizes, we pad each image out with zeros until all images are $1024 \\times 1024$. We then suppress output in the regions where we added padding when testing. This provides a cleaner resulting density map for these large crowds. The ground truth density maps are produced by annotating each object with a Gaussian of $\\sigma = 15$.\n\n#### 3.2.2 TRANCOS Traffic Counting\n\nTRANCOS is a traffic counting dataset that comes with its own metric [\\[14\\]](#page-8-1). This metric is known as $GAME$, which stands for Grid Average Mean absolute Error. $GAME $splits a given density map into $4^{L}$ grids, or subarrays, and obtains a mean absolute error within each grid separately. The value of $L$ is a parameter chosen by the user. These individual errors are summed to obtain the final error for a particular image. The intuition behind this metric is that it is desirable to penalize a density map whose overall count might match the ground truth, but whose shape does not match the ground truth [\\[14\\]](#page-8-1). More formally, we define\n\n$$GAME(L) = \\frac{1}{N} \\cdot \\sum_{n=1}^{N} \\left( \\sum_{l=1}^{4^L} |e_n^l - t_n^l| \\right) \\qquad (4)$$\n\nwhere $N$ refers to the number of images, $L$ is the level parameter for $GAME$, $e_{n}^{l}$ is the predicted or estimated count in region $l$ of image $n$ and $t_{n}^{l}$ is the ground truth count in region $l$ of image $n$ [\\[14\\]](#page-8-1).\n\nFor training this dataset, we take 1600 randomly sampled patches of size 80 × 80. For testing this dataset, we take 80 × 80 non-overlapping patches which we can stitch back together into the full-sized 640 × 480 images. We trained the AMDCN network with density maps produced with a Gaussian of $σ$ = 15 as specified in [\\[18\\]](#page-8-4).\n\n#### 3.2.3 UCSD Crowd Counting\n\nThe UCSD crowd counting dataset consists of frames of video of a sidewalk. There are relatively few people in view at any given time (approximately 25 on average). Furthermore, because the dataset comes from a video, there are many nearly identical images in the dataset. For this dataset, there have been two different ways to split the data into train and test sets. Therefore, we report results using both methods of splitting the data. The first method consists of four different splits: maximal, downscale, upscale, and minimal. Minimal is particularly challenging as the train set contains only 10 images. Moreover, upscale appears to be the easiest for the majority of methods [\\[18\\]](#page-8-4). The second method of splitting this data is much more succinct, leaving 1200 images in the testing set and 800 images in the training set [\\[28\\]](#page-9-0). This split comes from the original paper, so we call it the original split [\\[5\\]](#page-8-17).\n\nFor this dataset, each object is annotated with a 2D Gaussian of covariance $\\Sigma = 8 \\cdot 1_{2\\times2}$. The ground truth map is produced by summing these. When we make use of the perspective maps provided, we divide $\\Sigma$ by the perspective map value at that pixel x, represented by $M(x)$. The provided perspective map for UCSD contains both a horizontal and vertical direction so we take the square root of the provided combined value. For training, we take 1600 random 79 × 119 pixel patches and for testing, we split each test image up into quadrants (which have dimension 79 × 119). There are two different ways to split the dataset into training and testing sets. We have experimented on the split that gave [\\[18\\]](#page-8-4) the best results as well as the split used in [\\[28\\]](#page-9-0).\n\nFirst, we split the dataset into four separate groups of training and testing sets as used in [\\[18\\]](#page-8-4) and originally defined by [\\[20\\]](#page-8-0). These groups are \"upscale,\" \"maximal,\" \"minimal,\" and \"downscale.\" We see in Table [3](#page-6-0) that the \"upscale\" split and \"downscale\" split give us state of the art results on counting for this dataset. For this experiment, we sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap. We also added left-right flips of each image to our training data.\n\nWe then evaluate the original split. For this experiment, we similarly sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap.\n\n#### 3.2.4 WorldExpo '10 Crowd Counting\n\nThe WorldExpo dataset [\\[27\\]](#page-8-7) contains a larger number of people (approximately 50 on average, which is double that of UCSD) and contains images from multiple locations. Perspective effects are also much more noticeable in this dataset as compared to UCSD. These qualities of the dataset serve to increase the difficulty of counting. Like UCSD, the WorldExpo dataset was constructed from frames of video recordings of crowds. This means that, unlike UCF, this dataset contains a relatively large number of training and testing images. We experiment on this dataset with and without perspective information.\n\nWithout perspective maps, we generate label density maps for this dataset in the same manner as previously described: a 2D Gaussian with $\\sigma = 15$. We take 16000 150 × 150 randomly sampled patches for training. For testing, we densely scan the image, producing 150 × 150 patches at a stride of 100.\n\nWhen perspective maps are used, however, we follow the procedure as described in [\\[27\\]](#page-8-7), which involves estimating a “crowd density distribution kernel” as the sum of two 2D Gaussians: a symmetric Gaussian for the head and an ellipsoid Gaussian for the body. These are scaled by the perspective map $M$ provided, where $M(x)$ gives the number of pixels that represents a meter at pixel $x$ [\\[27\\]](#page-8-7). Note that the meaning of this perspective map is distinct from the meaning of the perspective map provided for the UCSD dataset. Using this information, the density contribution from a person with head pixel $x$ is given by the following sum of normalized Gaussians:\n\n$$D_{\\mathbf{x}} = \\frac{1}{||Z||} (\\mathcal{N}_h(\\mathbf{x}, \\sigma_h) + \\mathcal{N}_b(\\mathbf{x}_b, \\Sigma_b)) \\qquad (5)$$\n\nwhere $x_b$ is the center of the body, which is 0.875 meters down from the head on average, and can be determined from the perspective map $M$ and the head center $x$ [\\[27\\]](#page-8-7). We sum these Gaussians for each person to pro-\n\n| Method | MAE |\n|--------------|--------|\n| AMDCN | 290.82 |\n| Hydra2s [18] | 333.73 |\n| MCNN [28] | 377.60 |\n| [27] | 467.00 |\n| [23] | 295.80 |\n| [3] | 318.10 |\n\nTable 1. Mean absolute error of various methods on UCF crowds\n\nduce the final density map. We set $\\sigma = 0.2M(\\mathbf{x})$ for $N_h $and $\\sigma_x = 0.2M(\\mathbf{x}), \\sigma_y = 0.5M(\\mathbf{x})$ for $\\Sigma_b$ in $N_b$.\n\n# 4. Results\n\n#### 4.1. UCF Crowd Counting\n\nThe UCF dataset is particularly challenging due to the large number of people in the images, the variety of the scenes, as well as the low number of training images. We see in Figure [2](#page-2-2) that because the UCF dataset has over 1000 people on average in each image, the shapes output by the network in the density map are not as well defined or separated as in the UCSD dataset.\n\nWe report a state of the art result on this dataset in Table [1,](#page-5-1) following the standard protocol of 5-fold cross validation. Our MAE on the dataset is 290.82, which is approximately 5 lower than the previous state of the art, HydraCNN [\\[18\\]](#page-8-4). This is particularly indicative of the power of an aggregated multicolumn dilation network. Despite not making use of perspective information, the AMDCN is still able to produce highly accurate density maps for UCF.\n\n#### 4.2. TRANCOS Traffic Counting\n\nOur network performs very well on the TRANCOS dataset. Indeed, as confirmed by the GAME score, AMDCN produces the most accurate count and shape combined as compared to other methods. Table 2 shows that we achieve state of the art results as measured by the GAME metric [\\[14\\]](#page-8-1) across all levels.\n\n#### 4.3. UCSD Crowd Counting\n\nResults are shown in Table [3](#page-6-0) and Figure [3.](#page-6-1) We see that the \"original\" split as defined by the creators of the dataset in [\\[5\\]](#page-8-17) and used in [\\[28\\]](#page-9-0) gives us somewhat worse results for counting on this dataset. Results were consistent over multiple trainings. Again, including the perspective map does not seem to increase performance on this dataset. Despite this, we see in Table [3](#page-6-0) and Figure [3](#page-6-1) that the results are comparable to the state of the art. In fact, for two of the splits, our proposed network beats the state of the art. For the upscale split, the AMDCN is the state of the art by a large relative margin. This is compelling because it shows that accurate perspective-free counting can be achieved without\n\n| Method | GAME
(L=0) | GAME
(L=1) | GAME
(L=2) | GAME
(L=3) |\n|-------------------------------------------|---------------|---------------|---------------|---------------|\n| AMDCN | 9.77 | 13.16 | 15.00 | 15.87 |\n| [18] | 10.99 | 13.75 | 16.69 | 19.32 |\n| [15] + SIFT
from [14] | 13.76 | 16.72 | 20.72 | 24.36 |\n| [13] + RGB
Norm + Filters
from [14] | 17.68 | 19.97 | 23.54 | 25.84 |\n| HOG-2
from [14] | 13.29 | 18.05 | 23.65 | 28.41 |\n\nTable 2. Mean absolute error of various methods on TRANCOS traffic\n\ncreating image pyramids or requiring perspective maps as labels using the techniques presented by the AMDCN.\n\n#### 4.4. WorldExpo '10 Crowd Counting\n\nOur network performs reasonably well on the more challenging WorldExpo dataset. While it does not beat the state of the art, our results are comparable. What is more, we do not need to use the perspective maps to obtain these results. As seen in Table [4,](#page-7-1) the AMDCN is capable of incorporating the perspective effects without scaling the Gaussians with perspective information. This shows that it is possible to achieve counting results that approach the state of the art with much simpler labels for the counting training data.\n\n#### 4.5. Ablation Studies\n\nWe report the results of the ablation studies in Figure [4.](#page-7-2) We note from these plots that while there is variation in performance, a few trends stand out. Most importantly, the lowest errors are consistently with a combination of a larger number of columns and including the aggregator module. Notably for the TRANCOS dataset, including the aggregator consistently improves performance. Generally, the aggregator tends to decrease the variance in performance of the network. Some of the variance that we see in the plots can be explained by: (1) for lower numbers of columns, including an aggregator is not as likely to help as there is not much separation of multiscale information across columns and (2) for the UCSD dataset, there is less of a perspective effect than TRANCOS and WorldExpo so a simpler network is more likely to perform comparably to a larger network. These results verify the notion that using more columns increases accuracy, and also support our justification for the use of the aggregator module.\n\n\n\n(a) UCSD upscale split. (b) UCSD original split.\n\nFigure 3. UCSD crowd counting dataset. Both plots show comparisons of predicted and ground truth counts over time. While AMDCN does not beat the state of the art on the original split, the predictions still follow the true counts reasonably. The jump in the original split is due to that testing set including multiple scenes of highly varying counts.\n\n| Method | maximal | downscale | upscale | minimal | original |\n|-----------------------------------------|---------|-----------|---------|---------|----------|\n| AMDCN (without perspective information) | 1.63 | 1.43 | 0.63 | 1.71 | 1.74 |\n| AMDCN (with perspective information) | 1.60 | 1.24 | 1.37 | 1.59 | 1.72 |\n| [18] (with perspective information) | 1.65 | 1.79 | 1.11 | 1.50 | - |\n| [18] (without perspective information) | 2.22 | 1.93 | 1.37 | 2.38 | - |\n| [15] | 1.70 | 1.28 | 1.59 | 2.02 | - |\n| [13] | 1.70 | 2.16 | 1.61 | 2.20 | - |\n| [19] | 1.43 | 1.30 | 1.59 | 1.62 | - |\n| [2] | 1.24 | 1.31 | 1.69 | 1.49 | - |\n| [27] | 1.70 | 1.26 | 1.59 | 1.52 | 1.60 |\n| [28] | - | - | - | - | 1.07 |\n| [1, 28] | - | - | - | - | 2.16 |\n| [7] | - | - | - | - | 2.25 |\n| [5] | - | - | - | - | 2.24 |\n| [6] | - | - | - | - | 2.07 |\n\nTable 3. Mean absolute error of various methods on UCSD crowds\n\n# 5. Conclusion\n\n#### 5.1. Summary\n\nWe have proposed the use of aggregated multicolumn dilated convolutions, the AMDCN, as an alternative to the HydraCNN [\\[18\\]](#page-8-4) or multicolumn CNN [\\[28\\]](#page-9-0) for the vision task of counting objects in images. Inspired by the multicolumn approach to multiscale problems, we also employ dilations to increase the receptive field of our columns. We then aggregate this multiscale information using another series of dilated convolutions to enable a wide network and detect features at more scales. This method takes advantage of the ability of dilated convolutions to provide exponentially increasing receptive fields. We have performed experiments on the challenging UCF crowd counting dataset, the TRANCOS traffic dataset, multiple splits of the UCSD crowd counting dataset, and the WorldExpo crowd counting dataset.\n\n\n\nFigure 4. Ablation studies on various datasets in which the number of columns is varied and the aggregator is included or not included. The results generally support the use of more columns and an aggregator module.\n\n| Method | MAE |\n|--------------------------------------------|-------------|\n| AMDCN (without perspective information) | 16.6 |\n| AMDCN (with perspective information) | 14.9 |\n| LBP+RR [28] (with perspective information) | 31.0 |\n| MCNN [28] (with perspective information) | 11.6 |\n| [27] (with perspective information) | 12.9 |\n\nTable 4. Mean absolute error of various methods on WorldExpo crowds\n\nWe obtain superior or comparable results in most of these datasets. The AMDCN is capable of outperforming these approaches completely especially when perspective information is not provided, as in UCF and TRANCOS. These results show that the AMDCN performs surprisingly well and is also robust to scale effects. Further, our ablation study of removing the aggregator network shows that using more columns and an aggregator provides the best accuracy for counting — especially so when there is no perspective information.\n\n#### 5.2. Future Work\n\nIn addition to an analysis of performance on counting, a density regressor can also be used to locate objects in the image. As mentioned previously, if the regressor is accurate and precise enough, the resulting density map can be used to locate the objects in the image. We expect that in order to do this, one must regress each object to a single point rather than a region specified by a Gaussian. Perhaps this might be accomplished by applying non-maxima suppression to the final layer activations.\n\nIndeed, the method of applying dilated filters to a multicolumn convolutional network in order to enable extracting features of a large number of scales can be applied to various other dense prediction tasks, such as object segmentation at multiple scales or single image depth map prediction. Though we have only conducted experiments on counting and used 5 columns, the architecture presented can be extended and adapted to a variety of tasks that require information at multiple scales.\n\n# Acknowledgment\n\nThis material is based upon work supported by the National Science Foundation under Grant No. 1359275 and 1659788. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Furthermore, we acknowledge Kyle Yee and Sridhama Prakhya for their helpful conversations and insights during the research process.\n\n# References\n\n- [1] S. An, W. Liu, and S. Venkatesh. Face recognition using kernel ridge regression. In *Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on*, pages 1–7. IEEE, 2007.\n- [2] C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman. Interactive object counting. In *European Conference on Computer Vision*, pages 504–518. Springer, 2014.\n- [3] D. Babu Sam, S. Surya, and R. Venkatesh Babu. Switching convolutional neural network for crowd\n\ncounting. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 5744–5752, 2017.\n\n- [4] L. Boominathan, S. S. Kruthiventi, and R. V. Babu. Crowdnet: A deep convolutional network for dense crowd counting. In *Proceedings of the 2016 ACM on Multimedia Conference*, pages 640–644. ACM, 2016.\n- [5] A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos. 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In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 1861–1870, 2017.\n- [24] E. Walach and L. Wolf. Learning to count with cnn boosting. In *European Conference on Computer Vision*, pages 660–676. Springer, 2016.\n- [25] F. Yu and V. Koltun. Multi-scale context aggregation by dilated convolutions. *arXiv preprint arXiv:1511.07122*, 2015.\n- [26] F. Yu, V. Koltun, and T. Funkhouser. Dilated residual networks. *arXiv preprint arXiv:1705.09914*, 2017.\n- [27] C. Zhang, H. Li, X. Wang, and X. Yang. Crossscene crowd counting via deep convolutional neural networks. In *Proceedings of the IEEE Conference on*\n\n*Computer Vision and Pattern Recognition*, pages 833– 841, 2015.\n\n- [\\[28\\]](#page-8-8) Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma. Single-image crowd counting via multi-column convolutional neural network. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni tion*, pages 589–597, 2016."
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"content": "# Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity\n\n### William Fedus∗\n\nliamfedus@google.com\n\n### Barret Zoph∗\n\nbarretzoph@google.com\n\n#### Noam Shazeer\n\nnoam@google.com Google, Mountain View, CA 94043, USA\n\nEditor: Alexander Clark\n\n### Abstract\n\nIn deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) models defy this and instead select different parameters for each incoming example. The result is a sparsely-activated model—with an outrageous number of parameters—but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs, and training instability. We address these with the introduction of the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques mitigate the instabilities, and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large (Raffel et al., 2019) to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the “Colossal Clean Crawled Corpus”, and achieve a 4x speedup over the T5-XXL model.[1](#page-0-0)[2](#page-0-1)\n\nKeywords: mixture-of-experts, natural language processing, sparsity, large-scale machine learning, distributed computing\n\n©2022 William Fedus, Barret Zoph and Noam Shazeer.\n\n∗. Equal contribution.\n\n1. JAX code for Switch Transformer and all model checkpoints are available at [https://github.com/](https://github.com/google-research/t5x) [google-research/t5x](https://github.com/google-research/t5x)\n\n2. Tensorflow code for Switch Transformer is available at [https://github.com/tensorflow/mesh/blob/](https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/moe.py) [master/mesh_tensorflow/transformer/moe.py](https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/moe.py)\n\n## Contents\n\n| 1 | Introduction | 3 | |\n|---|-----------------------------------------------------------|----|----|\n| 2 | Switch Transformer | 4 | |\n| | 2.1 Simplifying Sparse Routing | 5 | |\n| | 2.2 Efficient Sparse Routing | 6 | |\n| | 2.3 Putting It All Together: The Switch Transformer | 8 | |\n| | 2.4 Improved Training and Fine-Tuning Techniques | 8 | |\n| 3 | Scaling Properties | 11 | |\n| | 3.1 Scaling Results on a Step-Basis | 12 | |\n| | 3.2 Scaling Results on a Time-Basis | 13 | |\n| | 3.3 Scaling Versus a Larger Dense Model | 13 | |\n| 4 | Downstream Results | 14 | |\n| | 4.1 Fine-Tuning | 14 | |\n| | 4.2 Distillation | 16 | |\n| | 4.3 Multilingual Learning | 17 | |\n| 5 | Designing Models with Data, Model, and Expert-Parallelism | 18 | |\n| | 5.1 Data Parallelism | 20 | |\n| | 5.2 Model Parallelism | 20 | |\n| | 5.3 Model and Data Parallelism | 21 | |\n| | 5.4 Expert and Data Parallelism | 22 | |\n| | 5.5 Expert, Model and Data Parallelism | 22 | |\n| | 5.6 Towards Trillion Parameter Models | 22 | |\n| 6 | Related Work | 24 | |\n| 7 | Discussion | 25 | |\n| 8 | Future Work | 26 | |\n| 9 | Conclusion | 27 | |\n| A | Switch for Attention | 27 | |\n| B | Preventing Token Dropping with No-Token-Left-Behind | 29 | |\n| C | Encouraging Exploration Across Experts | 29 | |\n| D | Switch Transformers in Lower Compute Regimes | | 29 |\n| E | Relation of Upstream to Downstream Model Performance | | 32 |\n| F | Pseudo Code for Switch Transformers | 33 | |\n\n### 1. Introduction\n\nLarge scale training has been an effective path towards flexible and powerful neural language models [(Radford et al.,](#page-37-1) [2018;](#page-37-1) [Kaplan et al.,](#page-36-0) [2020;](#page-36-0) [Brown et al.,](#page-35-0) [2020)](#page-35-0). Simple architectures backed by a generous computational budget, data set size and parameter count—surpass more complicated algorithms [(Sutton,](#page-38-0) [2019)](#page-38-0). An approach followed in [Radford et al.](#page-37-1) [(2018)](#page-37-1); [Raffel et al.](#page-37-0) [(2019)](#page-37-0); [Brown et al.](#page-35-0) [(2020)](#page-35-0) expands the model size of a densely-activated Transformer [(Vaswani et al.,](#page-39-1) [2017)](#page-39-1). While effective, it is also extremely computationally intensive [(Strubell et al.,](#page-38-1) [2019)](#page-38-1). Inspired by the success of model scale, but seeking greater computational efficiency, we instead propose a sparsely-activated expert model: the Switch Transformer. In our case the sparsity comes from activating a subset of the neural network weights for each incoming example.\n\n\n\nFigure 1: Scaling and sample efficiency of Switch Transformers. Left Plot: Scaling properties for increasingly sparse (more experts) Switch Transformers. Right Plot: Negative log perplexity comparing Switch Transformers to T5 [(Raffel et al.,](#page-37-0) [2019)](#page-37-0) models using the same compute budget.\n\nSparse training is an active area of research and engineering [(Gray et al.,](#page-35-1) [2017;](#page-35-1) [Gale](#page-35-2) [et al.,](#page-35-2) [2020)](#page-35-2), but as of today, machine learning libraries and hardware accelerators still cater to dense matrix multiplications. To have an efficient sparse algorithm, we start with the Mixture-of-Expert (MoE) paradigm [(Jacobs et al.,](#page-36-1) [1991;](#page-36-1) [Jordan and Jacobs,](#page-36-2) [1994;](#page-36-2) [Shazeer](#page-38-2) [et al.,](#page-38-2) [2017)](#page-38-2), and simplify it to yield training stability and computational benefits. MoE models have had notable successes in machine translation [(Shazeer et al.,](#page-38-2) [2017,](#page-38-2) [2018;](#page-38-3) [Lep](#page-37-2)[ikhin et al.,](#page-37-2) [2020)](#page-37-2), however, widespread adoption is hindered by complexity, communication costs, and training instabilities.\n\nWe address these issues, and then go beyond translation, to find that these class of algorithms are broadly valuable in natural language. We measure superior scaling on a diverse set of natural language tasks and across three regimes in NLP: pre-training, finetuning and multi-task training. While this work focuses on scale, we also show that the Switch Transformer architecture not only excels in the domain of supercomputers, but is beneficial even with only a few computational cores. Further, our large sparse models can be distilled [(Hinton et al.,](#page-36-3) [2015)](#page-36-3) into small dense versions while preserving 30% of the sparse model quality gain. Our contributions are the following:\n\n- The Switch Transformer architecture, which simplifies and improves over Mixture of Experts.\n- Scaling properties and a benchmark against the strongly tuned T5 model [(Raffel et al.,](#page-37-0) [2019)](#page-37-0) where we measure 7x+ pre-training speedups while still using the same FLOPS per token. We further show the improvements hold even with limited computational resources, using as few as two experts.\n- Successful distillation of sparse pre-trained and specialized fine-tuned models into small dense models. We reduce the model size by up to 99% while preserving 30% of the quality gains of the large sparse teacher.\n- Improved pre-training and fine-tuning techniques: (1) selective precision training that enables training with lower bfloat16 precision (2) an initialization scheme that allows for scaling to a larger number of experts and (3) increased expert regularization that improves sparse model fine-tuning and multi-task training.\n- A measurement of the pre-training benefits on multilingual data where we find a universal improvement across all 101 languages and with 91% of languages benefiting from 4x+ speedups over the mT5 baseline [(Xue et al.,](#page-39-2) [2020)](#page-39-2).\n- An increase in the scale of neural language models achieved by efficiently combining data, model, and expert-parallelism to create models with up to a trillion parameters. These models improve the pre-training speed of a strongly tuned T5-XXL baseline by 4x.\n\n## 2. Switch Transformer\n\nThe guiding design principle for Switch Transformers is to maximize the parameter count of a Transformer model [(Vaswani et al.,](#page-39-1) [2017)](#page-39-1) in a simple and computationally efficient way. The benefit of scale was exhaustively studied in [Kaplan et al.](#page-36-0) [(2020)](#page-36-0) which uncovered powerlaw scaling with model size, data set size and computational budget. Importantly, this work advocates training large models on relatively small amounts of data as the computationally optimal approach.\n\nHeeding these results, we investigate a fourth axis: increase the parameter count while keeping the floating point operations (FLOPs) per example constant. Our hypothesis is that the parameter count, independent of total computation performed, is a separately important axis on which to scale. We achieve this by designing a sparsely activated model that efficiently uses hardware designed for dense matrix multiplications such as GPUs and TPUs. Our work here focuses on TPU architectures, but these class of models may be similarly trained on GPU clusters. In our distributed training setup, our sparsely activated layers split unique weights on different devices. Therefore, the weights of the model increase with the number of devices, all while maintaining a manageable memory and computational footprint on each device.\n\n\n\nFigure 2: Illustration of a Switch Transformer encoder block. We replace the dense feed forward network (FFN) layer present in the Transformer with a sparse Switch FFN layer (light blue). The layer operates independently on the tokens in the sequence. We diagram two tokens ($x_1$ = “More” and $x_2$ = “Parameters” below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).\n\n### 2.1 Simplifying Sparse Routing\n\nMixture of Expert Routing. [Shazeer et al.](#page-38-2) [(2017)](#page-38-2) proposed a natural language Mixtureof-Experts (MoE) layer which takes as an input a token representation $x$ and then routes this to the best determined top-$k$ experts, selected from a set ${E_i(x)\\}_{i=1}^N$ of $N$ experts. The router variable $W_r$ produces logits $h(x) = W_r \\cdot x$ which are normalized via a softmax distribution over the available $N$ experts at that layer. The gate-value for expert $i$ is given by,\n\n$$p_i(x) = \\frac{e^{h(x)_i}}{\\sum_j^N e^{h(x)_j}}.\\tag{1}$$\n\nThe top-$k$ gate values are selected for routing the token $x$. If $\\mathcal{T}$ is the set of selected top-$k$ indices then the output computation of the layer is the linearly weighted combination of each expert's computation on the token by the gate value,\n\n\n$$y = \\sum_{i \\in \\mathcal{T}} p_i(x) E_i(x). \\tag{2}$$\n\n**Switch Routing:** Rethinking Mixture-of-Experts. [Shazeer et al.](#page-38-2) (2017) conjectured that routing to $k > 1$ experts was necessary in order to have non-trivial gradients to the routing functions. The authors intuited that learning to route would not work without the ability to compare at least two experts. [Ramachandran and Le](#page-37-3) (2018) went further to study the top-$k$ decision and found that higher $k$-values in lower layers in the model were important for models with many routing layers. Contrary to these ideas, we instead use a simplified strategy where we route to only a single expert. We show this simplification preserves model quality, reduces routing computation and performs better. This $k = 1 $routing strategy is later referred to as a Switch layer. Note that for both MoE and Switch Routing, the gate value $p_i(x)$ in Equation [2](#page-4-1) permits differentiability of the router.\n\nThe benefits for the Switch layer are three-fold: (1) The router computation is reduced as we are only routing a token to a single expert. (2) The batch size (expert capacity) of each expert can be at least halved since each token is only being routed to a single expert.[3](#page-5-1) (3) The routing implementation is simplified and communication costs are reduced. Figure [3](#page-5-2) shows an example of routing with different expert capacity factors.\n\n\n\n- Figure 3: Illustration of token routing dynamics. Each expert processes a fixed batch-size of tokens modulated by the capacity factor. Each token is routed to the expert with the highest router probability, but each expert has a fixed batch size of (total_tokens / num_experts) × capacity_factor. If the tokens are unevenly dispatched then certain experts will overflow (denoted by dotted red lines), resulting in these tokens not being processed by this layer. A larger capacity factor alleviates this overflow issue, but also increases computation and communication costs (depicted by padded white/empty slots).\n### 2.2 Efficient Sparse Routing\n\nWe use Mesh-Tensorflow (MTF) [(Shazeer et al.,](#page-38-3) [2018)](#page-38-3) which is a library, with similar semantics and API to Tensorflow [(Abadi et al.,](#page-35-3) [2016)](#page-35-3) that facilitates efficient distributed data and model parallel architectures. It does so by abstracting the physical set of cores to a logical mesh of processors. Tensors and computations may then be sharded per named dimensions, facilitating easy partitioning of models across dimensions. We design our model with TPUs in mind, which require statically declared sizes. Below we describe our distributed Switch Transformer implementation.\n\n3. See Section [2.2](#page-5-0) for a technical description.\n\nDistributed Switch Implementation. All of our tensor shapes are statically determined at compilation time, but our computation is dynamic due to the routing decisions at training and inference. Because of this, one important technical consideration is how to set the expert capacity. The expert capacity—the number of tokens each expert computes—is set by evenly dividing the number of tokens in the batch across the number of experts, and then further expanding by a capacity factor,\n\n$$\\text{expected capacity} = \\left(\\frac{\\text{tokens per batch}}{\\text{number of experts}}\\right) \\times \\text{capacity factor}.\\tag{3}$$\n\nA capacity factor greater than 1.0 creates additional buffer to accommodate for when tokens are not perfectly balanced across experts. If too many tokens are routed to an expert (referred to later as dropped tokens), computation is skipped and the token representation is passed directly to the next layer through the residual connection. Increasing the expert capacity is not without drawbacks, however, since high values will result in wasted computation and memory. This trade-off is explained in Figure [3.](#page-5-2) Empirically we find ensuring lower rates of dropped tokens are important for the scaling of sparse expert-models. Throughout our experiments we didn't notice any dependency on the number of experts for the number of tokens dropped (typically $< 1%$). Using the auxiliary load balancing loss (next section) with a high enough coefficient ensured good load balancing. We study the impact that these design decisions have on model quality and speed in Table [1.](#page-8-0) \n\nA Differentiable Load Balancing Loss. To encourage a balanced load across experts we add an auxiliary loss (Shazeer et al., 2017, 2018; Lepikhin et al., 2020). As in Shazeer et al. (2018); Lepikhin et al. (2020), Switch Transformers simplifies the original design in Shazeer et al. (2017) which had separate load-balancing and importance-weighting losses. For each Switch layer, this auxiliary loss is added to the total model loss during training. Given $N$ experts indexed by $i = 1$ to $N$ and a batch $\\mathcal{B}$ with $T$ tokens, the auxiliary loss is computed as the scaled dot-product between vectors $f$ and $P$,\n\n$$\\text{loss} = \\alpha \\cdot N \\cdot \\sum_{i=1}^{N} f_i \\cdot P_i \\tag{4}$$\n\nwhere $f_i$ is the fraction of tokens dispatched to expert $i$,\n\n$$f_i = \\frac{1}{T} \\sum_{x \\in \\mathcal{B}} \\mathbb{1} \\{ \\operatorname*{argmax} \\, p(x) = i \\} \\tag{5}$$\n\nand $P_i$ is the fraction of the router probability allocated for expert $i$,2\n\n$$P_i = \\frac{1}{T} \\sum_{x \\in \\mathcal{B}} p_i(x). \\tag{6}$$\n\nSince we seek uniform routing of the batch of tokens across the $N$ experts, we desire both vectors to have values of $1/N$. The auxiliary loss of Equation [4](#page-6-1) encourages uniform routing since it is minimized under a uniform distribution. The objective can also be differentiated as\n\nA potential source of confusion: $p_i(x)$ is the probability of routing token $x$ to expert $i$. $P_i$ is the probability fraction to expert $i$ across all tokens in the batch $B$.\n\nthe $P$-vector is differentiable, but the $f$-vector is not. The final loss is multiplied by expert count $N$ to keep the loss constant as the number of experts varies since under uniform routing $\\sum_{i=1}^{N}(f_i$ · Pi) = PN i=1( 1 N · 1 N ) = 1 N . Finally, a hyper-parameter α is a multiplicative coefficient for these auxiliary losses; throughout this work we use an α = 10−2 which was sufficiently large to ensure load balancing while small enough to not to overwhelm the primary cross-entropy objective. We swept hyper-parameter ranges of $\\alpha$ from $10^{-1}$ to $10^{-5}$ in powers of 10 and found $10^{-2}$ balanced load quickly without interfering with training loss.\n\n#### 2.3 Putting It All Together: The Switch Transformer\n\nOur first test of the Switch Transformer starts with pre-training on the \"Colossal Clean Crawled Corpus\" (C4), introduced in [(Raffel et al.,](#page-37-0) [2019)](#page-37-0). For our pre-training objective, we use a masked language modeling task [(Taylor,](#page-38-4) [1953;](#page-38-4) [Fedus et al.,](#page-35-4) [2018;](#page-35-4) [Devlin et al.,](#page-35-5) [2018)](#page-35-5) where the model is trained to predict missing tokens. In our pre-training setting, as determined in [Raffel et al.](#page-37-0) [(2019)](#page-37-0) to be optimal, we drop out 15% of tokens and then replace the masked sequence with a single sentinel token. To compare our models, we record the negative log perplexity.[4](#page-7-2) Throughout all tables in the paper, ↑ indicates that a higher value for that metric is better and vice-versa for ↓. A comparison of all the models studied in this work are in Table [9.](#page-22-0)\n\nA head-to-head comparison of the Switch Transformer and the MoE Transformer is presented in Table [1.](#page-8-0) Our Switch Transformer model is FLOP-matched to 'T5-Base' [(Raffel](#page-37-0) [et al.,](#page-37-0) [2019)](#page-37-0) (same amount of computation per token is applied). The MoE Transformer, using top-2 routing, has two experts which each apply a separate FFN to each token and thus its FLOPS are larger. All models were trained for the same number of steps on identical hardware. Note that the MoE model going from capacity factor 2.0 to 1.25 actually slows down (840 to 790) in the above experiment setup, which is unexpected.[5](#page-7-3)\n\nWe highlight three key findings from Table [1:](#page-8-0) (1) Switch Transformers outperform both carefully tuned dense models and MoE Transformers on a speed-quality basis. For a fixed amount of computation and wall-clock time, Switch Transformers achieve the best result. (2) The Switch Transformer has a smaller computational footprint than the MoE counterpart. If we increase its size to match the training speed of the MoE Transformer, we find this outperforms all MoE and Dense models on a per step basis as well. (3) Switch Transformers perform better at lower capacity factors (1.0, 1.25). Smaller expert capacities are indicative of the scenario in the large model regime where model memory is very scarce and the capacity factor will want to be made as small as possible.\n\n#### 2.4 Improved Training and Fine-Tuning Techniques\n\nSparse expert models may introduce training difficulties over a vanilla Transformer. Instability can result because of the hard-switching (routing) decisions at each of these layers. Further, low precision formats like bfloat16 [(Wang and Kanwar,](#page-39-3) [2019)](#page-39-3) can exacerbate issues\n\n4. We use log base-e for this metric so the units are nats.\n\n5. Note that speed measurements are both a function of the algorithm and the implementation details. Switch Transformer reduces the necessary computation relative to MoE (algorithm), but the final speed differences are impacted by low-level optimizations (implementation).\n\n| Model | Capacity
Factor | Quality after
100k steps (↑)
(Neg. Log Perp.) | Time to Quality
Threshold (↓)
(hours) | Speed (↑)
(examples/sec) |\n|--------------|--------------------|-----------------------------------------------------|---------------------------------------------|-----------------------------|\n| T5-Base | — | -1.731 | Not achieved† | 1600 |\n| T5-Large | — | -1.550 | 131.1 | 470 |\n| MoE-Base | 2.0 | -1.547 | 68.7 | 840 |\n| Switch-Base | 2.0 | -1.554 | 72.8 | 860 |\n| MoE-Base | 1.25 | -1.559 | 80.7 | 790 |\n| Switch-Base | 1.25 | -1.553 | 65.0 | 910 |\n| MoE-Base | 1.0 | -1.572 | 80.1 | 860 |\n| Switch-Base | 1.0 | -1.561 | 62.8 | 1000 |\n| Switch-Base+ | 1.0 | -1.534 | 67.6 | 780 |\n\n- Table 1: Benchmarking Switch versus MoE. Head-to-head comparison measuring per step and per time benefits of the Switch Transformer over the MoE Transformer and T5 dense baselines. We measure quality by the negative log perplexity and the time to reach an arbitrary chosen quality threshold of Neg. Log Perp.=-1.50. All MoE and Switch Transformer models use 128 experts, with experts at every other feed-forward layer. For Switch-Base+, we increase the model size until it matches the speed of the MoE model by increasing the model hidden-size from 768 to 896 and the number of heads from 14 to 16. All models are trained with the same amount of computation (32 cores) and on the same hardware (TPUv3). Further note that all our models required pre-training beyond 100k steps to achieve our level threshold of -1.50. † T5-Base did not achieve this negative log perplexity in the 100k steps the models were trained.\nin the softmax computation for our router. We describe training difficulties here and the methods we use to overcome them to achieve stable and scalable training.\n\nSelective precision with large sparse models. Model instability hinders the ability to train using efficient bfloat16 precision, and as a result, [Lepikhin et al.](#page-37-2) [(2020)](#page-37-2) trains with float32 precision throughout their MoE Transformer. However, we show that by instead selectively casting to float32 precision within a localized part of the model, stability may be achieved, without incurring expensive communication cost of float32 tensors. This technique is inline with modern mixed precision training strategies where certain parts of the model and gradient updates are done in higher precision [Micikevicius et al.](#page-37-4) [(2017)](#page-37-4). Table [2](#page-9-0) shows that our approach permits nearly equal speed to bfloat16 training while conferring the training stability of float32.\n\nTo achieve this, we cast the router input to float32 precision. The router function takes the tokens as input and produces the dispatch and combine tensors used for the selection and recombination of expert computation (refer to Code Block [15](#page-33-0) in the Appendix for details). Importantly, the float32 precision is only used within the body of the router function—on computations local to that device. Because the resulting dispatch and combine tensors are recast to bfloat16 precision at the end of the function, no expensive float32 tensors\n\n| Model
(precision) | Quality
(Neg. Log Perp.) (↑) | Speed
(Examples/sec) (↑) |\n|-----------------------------------|---------------------------------|-----------------------------|\n| Switch-Base (float32) | -1.718 | 1160 |\n| Switch-Base (bfloat16) | -3.780 [diverged] | 1390 |\n| Switch-Base (Selective precision) | -1.716 | 1390 |\n\nTable 2: Selective precision. We cast the local routing operations to float32 while preserving bfloat16 precision elsewhere to stabilize our model while achieving nearly equal speed to (unstable) bfloat16-precision training. We measure the quality of a 32 expert model after a fixed step count early in training its speed performance. For both Switch-Base in float32 and with Selective prevision we notice similar learning dynamics.\n\nare broadcast through all-to-all communication operations, but we still benefit from the increased stability of float32.\n\nSmaller parameter initialization for stability. Appropriate initialization is critical to successful training in deep learning and we especially observe this to be true for Switch Transformer. We initialize our weight matrices by drawing elements from a truncated normal distribution with mean $\\mu = 0$ and standard deviation $\\sigma = \\sqrt{s/n}$ where $s$ is a scale hyper-parameter and $n$ is the number of input units in the weight tensor (e.g. fan-in).[6](#page-9-1)\n\nAs an additional remedy to the instability, we recommend reducing the default Transformer initialization scale $s = 1.0$ by a factor of 10. This both improves quality and reduces the likelihood of destabilized training in our experiments. Table [3](#page-9-2) measures the improvement of the model quality and reduction of the variance early in training. We find that\n\n| Model (Initialization scale) | Average Quality
(Neg. Log Perp.) | Std. Dev. of Quality
(Neg. Log Perp.) |\n|------------------------------|-------------------------------------|------------------------------------------|\n| Switch-Base (0.1x-init) | -2.72 | 0.01 |\n| Switch-Base (1.0x-init) | -3.60 | 0.68 |\n\n- Table 3: Reduced initialization scale improves stability. Reducing the initialization scale results in better model quality and more stable training of Switch Transformer. Here we record the average and standard deviation of model quality, measured by the negative log perplexity, of a 32 expert model after 3.5k steps (3 random seeds each).\nthe average model quality, as measured by the Neg. Log Perp., is dramatically improved and there is a far reduced variance across runs. Further, this same initialization scheme is broadly effective for models spanning several orders of magnitude. We use the same approach to stably train models as small as our 223M parameter baseline to enormous models in excess of one trillion parameters.\n\n6. Values greater than two standard deviations from the mean are resampled.\n\nRegularizing large sparse models. Our paper considers the common NLP approach of pre-training on a large corpus followed by fine-tuning on smaller downstream tasks such as summarization or question answering. One issue that naturally arises is overfitting since many fine-tuning tasks have very few examples. During fine-tuning of standard Transformers, [Raffel et al.](#page-37-0) [(2019)](#page-37-0) use dropout [(Srivastava et al.,](#page-38-5) [2014)](#page-38-5) at each layer to prevent overfitting. Our Switch Transformers have significantly more parameters than the FLOP matched dense baseline, which can lead to more severe overfitting on these smaller downstream tasks.\n\n| Model (dropout) | GLUE | CNNDM | SQuAD | SuperGLUE |\n|-----------------------------|-------------|-------------|-------------|-------------|\n| T5-Base (d=0.1) | 82.9 | 19.6 | 83.5 | 72.4 |\n| Switch-Base (d=0.1) | 84.7 | 19.1 | 83.7 | 73.0 |\n| Switch-Base (d=0.2) | 84.4 | 19.2 | 83.9 | 73.2 |\n| Switch-Base (d=0.3) | 83.9 | 19.6 | 83.4 | 70.7 |\n| Switch-Base (d=0.1, ed=0.4) | 85.2 | 19.6 | 83.7 | 73.0 |\n\nTable 4: Fine-tuning regularization results. A sweep of dropout rates while fine-tuning Switch Transformer models pre-trained on 34B tokens of the C4 data set (higher numbers are better). We observe that using a lower standard dropout rate at all non-expert layer, with a much larger dropout rate on the expert feed-forward layers, to perform the best.\n\nWe thus propose a simple way to alleviate this issue during fine-tuning: increase the dropout inside the experts, which we name as expert dropout. During fine-tuning we simply increase the dropout rate by a significant amount only at the interim feed-forward computation at each expert layer. Table [4](#page-10-1) has the results for our expert dropout protocol. We observe that simply increasing the dropout across all layers leads to worse performance. However, setting a smaller dropout rate (0.1) at non-expert layers and a much larger dropout rate (0.4) at expert layers leads to performance improvements on four smaller downstream tasks.\n\n### 3. Scaling Properties\n\nWe present a study of the scaling properties of the Switch Transformer architecture during pre-training. Per [Kaplan et al.](#page-36-0) [(2020)](#page-36-0), we consider a regime where the model is not bottlenecked by either the computational budget or amount of data. To avoid the data bottleneck, we use the large C4 corpus with over 180B target tokens [(Raffel et al.,](#page-37-0) [2019)](#page-37-0) and we train until diminishing returns are observed.\n\nThe number of experts is the most efficient dimension for scaling our model. Increasing the experts keeps the computational cost approximately fixed since the model only selects one expert per token, regardless of the number of experts to choose from. The router must compute a probability distribution over more experts, however, this is a lightweight computation of cost $O(d_{model} \\times \\text{num experts})$ where $d_{model}$ is the embedding dimension of tokens passed between the layers. In this section, we consider the scaling properties on a step-basis and a time-basis with a fixed computational budget.\n\n#### 3.1 Scaling Results on a Step-Basis\n\nFigure [4](#page-11-1) demonstrates consistent scaling benefits with the number of experts when training all models for a fixed number of steps. We observe a clear trend: when keeping the FLOPS per token fixed, having more parameters (experts) speeds up training. The left Figure demonstrates consistent scaling properties (with fixed FLOPS per token) between sparse model parameters and test loss. This reveals the advantage of scaling along this additional axis of sparse model parameters. Our right Figure measures sample efficiency of a dense model variant and four FLOP-matched sparse variants. We find that increasing the number of experts leads to more sample efficient models. Our Switch-Base 64 expert model achieves the same performance of the T5-Base model at step 60k at step 450k, which is a 7.5x speedup in terms of step time. In addition, consistent with the findings of [Kaplan et al.](#page-36-0) [(2020)](#page-36-0), we find that larger models are also more sample efficient—learning more quickly for a fixed number of observed tokens.\n\n\n\n- Figure 4: Scaling properties of the Switch Transformer. Left Plot: We measure the quality improvement, as measured by perplexity, as the parameters increase by scaling the number of experts. The top-left point corresponds to the T5-Base model with 223M parameters. Moving from top-left to bottom-right, we double the number of experts from 2, 4, 8 and so on until the bottom-right point of a 256 expert model with 14.7B parameters. Despite all models using an equal computational budget, we observe consistent improvements scaling the number of experts. Right Plot: Negative log perplexity per step sweeping over the number of experts. The dense baseline is shown with the purple line and we note improved sample efficiency of our Switch-Base models.\n#### 3.2 Scaling Results on a Time-Basis\n\nFigure [4](#page-11-1) demonstrates that on a step basis, as we increase the number of experts, the performance consistently improves. While our models have roughly the same amount of FLOPS per token as the baseline, our Switch Transformers incurs additional communication costs across devices as well as the extra computation of the routing mechanism. Therefore, the increased sample efficiency observed on a step-basis doesn't necessarily translate to a better model quality as measured by wall-clock. This raises the question:\n\nFor a fixed training duration and computational budget, should one train a dense or a sparse model?\n\n\n\nFigure 5: Speed advantage of Switch Transformer. All models trained on 32 TPUv3 cores with equal FLOPs per example. For a fixed amount of computation and training time, Switch Transformers significantly outperform the dense Transformer baseline. Our 64 expert Switch-Base model achieves the same quality in one-seventh the time of the T5-Base and continues to improve.\n\nFigures [5](#page-12-2) and [6](#page-13-2) address this question. Figure [5](#page-12-2) measures the pre-training model quality as a function of time. For a fixed training duration and computational budget, Switch Transformers yield a substantial speed-up. In this setting, our Switch-Base 64 expert model trains in one-seventh the time that it would take the T5-Base to get similar perplexity.\n\n#### 3.3 Scaling Versus a Larger Dense Model\n\nThe above analysis shows that a computationally-matched dense model is outpaced by its Switch counterpart. Figure [6](#page-13-2) considers a different scenario: what if we instead had allocated our resources to a larger dense model? We do so now, measuring Switch-Base against the next strong baseline, T5-Large. But despite T5-Large applying 3.5x more FLOPs per token, Switch-Base is still more sample efficient and yields a 2.5x speedup. Furthermore, more gains can be had simply by designing a new, larger sparse version, Switch-Large, which is FLOP-matched to T5-Large. We do this and demonstrate superior scaling and fine-tuning in the following section.\n\n\n\nFigure 6: Scaling Transformer models with Switch layers or with standard dense model scaling. Left Plot: Switch-Base is more sample efficient than both the T5-Base, and T5-Large variant, which applies 3.5x more FLOPS per token. Right Plot: As before, on a wall-clock basis, we find that Switch-Base is still faster, and yields a 2.5x speedup over T5-Large.\n\n### 4. Downstream Results\n\nSection [3](#page-10-0) demonstrated the superior scaling properties while pre-training, but we now validate that these gains translate to improved language learning abilities on downstream tasks. We begin by fine-tuning on a diverse set of NLP tasks. Next we study reducing the memory footprint of our sparse models by over 90% by distilling into small—and easily deployed—dense baselines. Finally, we conclude this section measuring the improvements in a multi-task, multilingual setting, where we show that Switch Transformers are strong multi-task learners, improving over the multilingual T5-base model across all 101 languages.\n\n### 4.1 Fine-Tuning\n\nBaseline and Switch models used for fine-tuning. Our baselines are the highly-tuned 223M parameter T5-Base model and the 739M parameter T5-Large model [(Raffel et al.,](#page-37-0) [2019)](#page-37-0). For both versions, we design a FLOP-matched Switch Transformer, with many more parameters, which is summarized in Table 9.[7](#page-13-3) Our baselines differ slightly from those in [Raffel et al.](#page-37-0) [(2019)](#page-37-0) because we pre-train on an improved C4 corpus which removes intraexample text duplication and thus increases the efficacy as a pre-training task [Lee et al.](#page-37-5)\n\n7. FLOPS are calculated for the forward pass as done in [Kaplan et al.](#page-36-0) [(2020)](#page-36-0).\n\n[(2021)](#page-37-5). In our protocol we pre-train with $2^{20}$ (1,048,576) tokens per batch for 550k steps amounting to 576B total tokens. We then fine-tune across a diverse set of tasks using a dropout rate of 0.1 for all layers except the Switch layers, which use a dropout rate of 0.4 (see Table [4](#page-10-1)). We fine-tune using a batch-size of 1M for 16k steps and for each task, we evaluate model quality every 200-steps and report the peak performance as computed on the validation set.\n\nFine-tuning tasks and data sets. We select tasks probing language capabilities including question answering, summarization and knowledge about the world. The language benchmarks GLUE [(Wang et al.,](#page-39-4) [2018)](#page-39-4) and SuperGLUE [(Wang et al.,](#page-39-5) [2019)](#page-39-5) are handled as composite mixtures with all the tasks blended in proportion to the amount of tokens present in each. These benchmarks consist of tasks requiring sentiment analysis (SST-2), word sense disambiguation (WIC), sentence similarty (MRPC, STS-B, QQP), natural language inference (MNLI, QNLI, RTE, CB), question answering (MultiRC, RECORD, BoolQ), coreference resolution (WNLI, WSC) and sentence completion (COPA) and sentence acceptability (CoLA). The CNNDM [(Hermann et al.,](#page-36-4) [2015)](#page-36-4) and BBC XSum [(Narayan](#page-37-6) [et al.,](#page-37-6) [2018)](#page-37-6) data sets are used to measure the ability to summarize articles. Question answering is probed with the SQuAD data set [(Rajpurkar et al.,](#page-37-7) [2016)](#page-37-7) and the ARC Reasoning Challenge [(Clark et al.,](#page-35-6) [2018)](#page-35-6). And as in [Roberts et al.](#page-38-6) [(2020)](#page-38-6), we evaluate the knowledge of our models by fine-tuning on three closed-book question answering data sets: Natural Questions [(Kwiatkowski et al.,](#page-36-5) [2019)](#page-36-5), Web Questions [(Berant et al.,](#page-35-7) [2013)](#page-35-7) and Trivia QA [(Joshi et al.,](#page-36-6) [2017)](#page-36-6). Closed-book refers to questions posed with no supplemental reference or context material. To gauge the model's common sense reasoning we evaluate it on the Winogrande Schema Challenge [(Sakaguchi et al.,](#page-38-7) [2020)](#page-38-7). And finally, we test our model's natural language inference capabilities on the Adversarial NLI Benchmark [(Nie et al.,](#page-37-8) [2019)](#page-37-8).\n\nFine-tuning metrics. The following evaluation metrics are used throughout the paper: We report the average scores across all subtasks for GLUE and SuperGLUE. The Rouge-2 metric is used both the CNNDM and XSum. In SQuAD and the closed book tasks (Web, Natural, and Trivia Questions) we report the percentage of answers exactly matching the target (refer to [Roberts et al.](#page-38-6) [(2020)](#page-38-6) for further details and deficiency of this measure). Finally, in ARC Easy, ARC Challenge, ANLI, and Winogrande we report the accuracy of the generated responses.\n\nFine-tuning results. We observe significant downstream improvements across many natural language tasks. Notable improvements come from SuperGLUE, where we find FLOP-matched Switch variants improve by 4.4 and 2 percentage points over the T5-Base and T5-Large baselines, respectively as well as large improvements in Winogrande, closed book Trivia QA, and XSum.[8](#page-14-0) In our fine-tuning study, the only tasks where we do not observe gains are on the AI2 Reasoning Challenge (ARC) data sets where the T5-Base outperforms Switch-Base on the challenge data set and T5-Large outperforms Switch-Large on the easy data set. Taken as a whole, we observe significant improvements spanning both reasoning and knowledge-heavy tasks. This validates our architecture, not just as one that pre-trains well, but can translate quality improvements to downstream tasks via fine-tuning.\n\n8. Our T5 and Switch models were pre-trained with $2^{20}$ tokens per batch for 550k steps on a revised C4 data set for fair comparisons.\n\n| Model | GLUE | SQuAD | SuperGLUE | Winogrande (XL) |\n|--------------|-------------|---------------|--------------|-----------------|\n| T5-Base | 84.3 | 85.5 | 75.1 | 66.6 |\n| Switch-Base | 86.7 | 87.2 | 79.5 | 73.3 |\n| T5-Large | 87.8 | 88.1 | 82.7 | 79.1 |\n| Switch-Large | 88.5 | 88.6 | 84.7 | 83.0 |\n| Model | XSum | ANLI (R3) | ARC Easy | ARC Chal. |\n| T5-Base | 18.7 | 51.8 | 56.7 | 35.5 |\n| Switch-Base | 20.3 | 54.0 | 61.3 | 32.8 |\n| T5-Large | 20.9 | 56.6 | 68.8 | 35.5 |\n| Switch-Large | 22.3 | 58.6 | 66.0 | 35.5 |\n| Model | CB Web QA | CB Natural QA | CB Trivia QA | |\n| T5-Base | 26.6 | 25.8 | 24.5 | |\n| Switch-Base | 27.4 | 26.8 | 30.7 | |\n| T5-Large | 27.7 | 27.6 | 29.5 | |\n| Switch-Large | 31.3 | 29.5 | 36.9 | |\n\nTable 5: Fine-tuning results. Fine-tuning results of T5 baselines and Switch models across a diverse set of natural language tests (validation sets; higher numbers are better). We compare FLOP-matched Switch models to the T5-Base and T5-Large baselines. For most tasks considered, we find significant improvements of the Switchvariants. We observe gains across both model sizes and across both reasoning and knowledge-heavy language tasks.\n\n#### 4.2 Distillation\n\nDeploying massive neural networks with billions, or trillions, of parameters is inconvenient. To alleviate this, we study distilling [(Hinton et al.,](#page-36-3) [2015)](#page-36-3) large sparse models into small dense models. Future work could additionally study distilling large models into smaller sparse models.\n\nDistillation techniques. In Table [6](#page-16-1) we study a variety of distillation techniques. These techniques are built off of [Sanh et al.](#page-38-8) [(2019)](#page-38-8), who study distillation methods for BERT models. We find that initializing the dense model with the non-expert weights yields a modest improvement. This is possible since all models are FLOP matched, so non-expert layers will have the same dimensions. Since expert layers are usually only added at every or every other FFN layer in a Transformer, this allows for many of the weights to be initialized with trained parameters. Furthermore, we observe a distillation improvement using a mixture of 0.25 for the teacher probabilities and 0.75 for the ground truth label. By combining both techniques we preserve $\\approx$ 30% of the quality gains from the larger sparse models with only $\\approx$ 1/20th of the parameters. The quality gain refers to the percent of\n\n| Technique | Parameters | Quality (↑) |\n|-------------------------------------------|------------|--------------|\n| T5-Base | 223M | -1.636 |\n| Switch-Base | 3,800M | -1.444 |\n| Distillation | 223M | (3%) -1.631 |\n| + Init. non-expert weights from teacher | 223M | (20%) -1.598 |\n| + 0.75 mix of hard and soft loss | 223M | (29%) -1.580 |\n| Initialization Baseline (no distillation) | | |\n| Init. non-expert weights from teacher | 223M | -1.639 |\n\nthe quality difference between Switch-Base (Teacher) and T5-Base (Student). Therefore, a quality gain of 100% implies the Student equals the performance of the Teacher.\n\n- Table 6: Distilling Switch Transformers for Language Modeling. Initializing T5-Base with the non-expert weights from Switch-Base and using a loss from a mixture of teacher and ground-truth labels obtains the best performance. We can distill 30% of the performance improvement of a large sparse model with 100x more parameters back into a small dense model. For a final baseline, we find no improvement of T5-Base initialized with the expert weights, but trained normally without distillation.\nAchievable compression rates. Using our best distillation technique described in Table [6,](#page-16-1) we distill a wide variety of sparse models into dense models. We distill Switch-Base versions, sweeping over an increasing number of experts, which corresponds to varying between 1.1B to 14.7B parameters. Through distillation, we can preserve 37% of the quality gain of the 1.1B parameter model while compressing 82%. At the extreme, where we compress the model 99%, we are still able to maintain 28% of the teacher's model quality improvement.\n\nDistilling a fine-tuned model. We conclude this with a study of distilling a finetuned sparse model into a dense model. Table [8](#page-17-1) shows results of distilling a 7.4B parameter Switch-Base model, fine-tuned on the SuperGLUE task, into the 223M T5-Base. Similar to our pre-training results, we find we are able to preserve 30% of the gains of the sparse model when distilling into a FLOP matched dense variant. One potential future avenue, not considered here, may examine the specific experts being used for fine-tuning tasks and extracting them to achieve better model compression.\n\n#### 4.3 Multilingual Learning\n\nIn our final set of downstream experiments, we measure the model quality and speed tradeoffs while pre-training on a mixture of 101 different languages. We build and benchmark off the recent work of mT5 [(Xue et al.,](#page-39-2) [2020)](#page-39-2), a multilingual extension to T5. We pre-train on the multilingual variant of the Common Crawl data set (mC4) spanning 101 languages introduced in mT5, but due to script variants within certain languages, the mixture contains 107 tasks.\n\nIn Figure [7](#page-18-0) we plot the quality improvement in negative log perplexity for all languages of a FLOP-matched Switch model, mSwitch-Base to the T5 base variant, mT5-Base. After\n\n| | Dense | Sparse | | | | |\n|--------------------------------|--------|--------|--------|--------|--------|--------|\n| Parameters | 223M | 1.1B | 2.0B | 3.8B | 7.4B | 14.7B |\n| Pre-trained Neg. Log Perp. (↑) | -1.636 | -1.505 | -1.474 | -1.444 | -1.432 | -1.427 |\n| Distilled Neg. Log Perp. (↑) | — | -1.587 | -1.585 | -1.579 | -1.582 | -1.578 |\n| Percent of Teacher Performance | — | 37% | 32% | 30 % | 27 % | 28 % |\n| Compression Percent | — | 82 % | 90 % | 95 % | 97 % | 99 % |\n\n- Table 7: Distillation compression rates. We measure the quality when distilling large sparse models into a dense baseline. Our baseline, T5-Base, has a -1.636 Neg. Log Perp. quality. In the right columns, we then distill increasingly large sparse models into this same architecture. Through a combination of weight-initialization and a mixture of hard and soft losses, we can shrink our sparse teachers by 95%+ while preserving 30% of the quality gain. However, for significantly better and larger pre-trained teachers, we expect larger student models would be necessary to achieve these compression rates.\n\n| Model | Parameters | FLOPS | SuperGLUE (↑) |\n|-------------------|------------|-------|---------------|\n| T5-Base | 223M | 124B | 74.6 |\n| Switch-Base | 7410M | 124B | 81.3 |\n| Distilled T5-Base | 223M | 124B | (30%) 76.6 |\n\n- Table 8: Distilling a fine-tuned SuperGLUE model. We distill a Switch-Base model finetuned on the SuperGLUE tasks into a T5-Base model. We observe that on smaller data sets our large sparse model can be an effective teacher for distillation. We find that we again achieve 30% of the teacher's performance on a 97% compressed model.\npre-training both versions for 1M steps, we find that on all 101 languages considered, Switch Transformer increases the final negative log perplexity over the baseline. In Figure 8, we present a different view and now histogram the per step speed-up of using Switch Transformer over the mT5-Base.[9](#page-17-2) We find a mean speed-up over mT5-Base of 5x and that 91% of languages achieve at least a 4x speedup. This presents evidence that Switch Transformers are effective multi-task and multi-lingual learners.\n\n### 5. Designing Models with Data, Model, and Expert-Parallelism\n\nArbitrarily increasing the number of experts is subject to diminishing returns (Figure [4)](#page-11-1). Here we describe complementary scaling strategies. The common way to scale a Transformer is to increase dimensions in tandem, like $d_{model}$ or $d_{ff}$. This increases both the parameters\n\n9. The speedup on a step basis is computed as the ratio of the number of steps for the baseline divided by the number of steps required by our model to reach that same quality.\n\n\n\nFigure 7: Multilingual pre-training on 101 languages. Improvements of Switch T5 Base model over dense baseline when multi-task training on 101 languages. We observe Switch Transformers to do quite well in the multi-task training setup and yield improvements on all 101 languages.\n\n\n\nFigure 8: Multilingual pre-training on 101 languages. We histogram for each language, the step speedup of Switch Transformers over the FLOP matched T5 dense baseline to reach the same quality. Over all 101 languages, we achieve a mean step speedup over mT5-Base of 5x and, for 91% of languages, we record a 4x, or greater, speedup to reach the final perplexity of mT5-Base.\n\nand computation performed and is ultimately limited by the memory per accelerator. Once it exceeds the size of the accelerator's memory, single program multiple data (SPMD) modelparallelism can be employed. This section studies the trade-offs of combining data, model, and expert-parallelism.\n\nReviewing the Feed-Forward Network (FFN) Layer. We use the FFN layer as an example of how data, model and expert-parallelism works in Mesh TensorFlow [(Shazeer](#page-38-3) [et al.,](#page-38-3) [2018)](#page-38-3) and review it briefly here. We assume $B$ tokens in the batch, each of dimension $d_{model}$. Both the input $(x)$ and output $(y)$ of the FFN are of size $[B, d_{model}]$ and the intermediate $(h)$ is of size $[B, d_{ff}]$ where $d_{ff}$ is typically several times larger than $d_{model}$. In the FFN, the intermediate is $h = xW_{in}$ and then the output of the layer is $y = ReLU(h)W_{out}$. Thus $W_{in}$ and $W_{out}$ are applied independently to each token and have sizes $[d_{model}, d_{ff}] $and $[d_{ff}, d_{model}]$.\n\nWe describe two aspects of partitioning: how the weights and batches of data divide over cores, depicted in Figure [9.](#page-20-1) We denote all cores available as $N$ which Mesh Tensorflow may then remap into a logical multidimensional mesh of processors. Here we create a two-dimensional logical mesh, with one dimension representing the number of ways for data-parallel sharding $(n)$ and the other, the model-parallel sharding $(m)$. The total cores must equal the ways to shard across both data and model-parallelism, e.g. $N = n \\times m$. To shard the layer across cores, the tensors containing that batch of $B$ tokens are sharded across $n$ data-parallel cores, so each core contains $B/n$ tokens. Tensors and variables with $d_{ff}$ are then sharded across $m$ model-parallel cores. For the variants with experts-layers, we consider $E$ experts, each of which can process up to $C$ tokens.\n\n| Term | Description |\n|------|-------------------------------------------------|\n| B | Number of tokens in the batch. |\n| N | Number of total cores. |\n| n | Number of ways for data-parallelism sharding. |\n| m | Number of ways for model-parallelism sharding. |\n| E | Number of experts in Switch layers. |\n| C | Expert capacity, the batch size of each expert. |\n\n#### 5.1 Data Parallelism\n\nWhen training data parallel models, which is the standard for distributed training, then all cores are allocated to the data-parallel dimension or $n = N, m = 1$. This has the advantage that no communication is needed until the entire forward and backward pass is finished and the gradients need to be then aggregated across all cores. This corresponds to the left-most column of Figure [9.](#page-20-1)\n\n#### 5.2 Model Parallelism\n\nWe now consider a scenario where all cores are allocated exclusively to the model-parallel dimension and so $n = 1, m = N$. Now all cores must keep the full $B$ tokens and each core will contain a unique slice of the weights. For each forward and backward pass, a communication cost is now incurred. Each core sends a tensor of $[B, d_{model}]$ to compute the second matrix multiplication $ReLU(h)W_{out}$ because the $d_{ff}$ dimension is partitioned and must be summed over. As a general rule, whenever a dimension that is partitioned across cores must be summed, then an all-reduce operation is added for both the forward and backward pass. This contrasts with pure data parallelism where an all-reduce only occurs at the end of the entire forward and backward pass.\n\n\n\n#### **How the** *model weights* **are split over cores**\n\n#### **How the** *data* **is split over cores**\n\n\n\n- Figure 9: Data and weight partitioning strategies. Each 4×4 dotted-line grid represents 16 cores and the shaded squares are the data contained on that core (either model weights or batch of tokens). We illustrate both how the model weights and the data tensors are split for each strategy. First Row: illustration of how model weights are split across the cores. Shapes of different sizes in this row represent larger weight matrices in the Feed Forward Network (FFN) layers (e.g larger $d_{ff}$ sizes). Each color of the shaded squares identifies a unique weight matrix. The number of parameters per core is fixed, but larger weight matrices will apply more computation to each token. Second Row: illustration of how the data batch is split across cores. Each core holds the same number of tokens which maintains a fixed memory usage across all strategies. The partitioning strategies have different properties of allowing each core to either have the same tokens or different tokens across cores, which is what the different colors symbolize.\n#### 5.3 Model and Data Parallelism\n\nIt is common to mix both model and data parallelism for large scale models, which was done in the largest T5 models [(Raffel et al.,](#page-37-0) [2019;](#page-37-0) [Xue et al.,](#page-39-2) [2020)](#page-39-2) and in GPT-3 [(Brown et al.,](#page-35-0) [2020)](#page-35-0). With a total of $N = n \\times m$ cores, now each core will be responsible for $B/n$ tokens and $d_{ff}/m$ of both the weights and intermediate activation. In the forward and backward pass each core communicates a tensor of size $[B/n, d_{model}]$ in an all-reduce operation.\n\n#### 5.4 Expert and Data Parallelism\n\nNext we describe the partitioning strategy for expert and data parallelism. Switch Transformers will allocate all of their cores to the data partitioning dimension $n$, which will also correspond to the number of experts in the model. For each token per core a router locally computes assignments to the experts. The output is a binary matrix of size $[n, B/n, E$, $C]$ which is partitioned across the first dimension and determines expert assignment. This binary matrix is then used to do a gather via matrix multiplication with the input tensor of $[n, B/n, d_{model}]$.\n\n$$\\text{einsum}([n, B/n, d_{model}], [n, B/n, E, C], \\text{dimension} = [B/n]) \\tag{7}$$\n\nresulting in the final tensor of shape $[n, E, C, d_{model}]$, which is sharded across the first dimension. Because each core has its own expert, we do an all-to-all communication of size $[E, C, d_{model}]$ to now shard the $E$ dimension instead of the $n$-dimension. There are additional communication costs of bfloat16 tensors of size $E \\times C \\times d_{model}$ in the forward pass to analogously receive the tokens from each expert located on different cores. See Appendix [F](#page-32-0) for a detailed analysis of the expert partitioning code.\n\n#### 5.5 Expert, Model and Data Parallelism\n\nIn the design of our best model, we seek to balance the FLOPS per token and the parameter count. When we scale the number of experts, we increase the number of parameters, but do not change the FLOPS per token. In order to increase FLOPS, we must also increase the $d_{ff}$ dimension (which also increases parameters, but at a slower rate). This presents a trade-off: as we increase $d_{ff}$ we will run out of memory per core, which then necessitates increasing $m$. But since we have a fixed number of cores $N$, and $N = n \\times m$, we must decrease $n$, which forces use of a smaller batch-size (in order to hold tokens per core constant).\n\nWhen combining both model and expert-parallelism, we will have all-to-all communication costs from routing the tokens to the correct experts along with the internal all-reduce communications from the model parallelism. Balancing the FLOPS, communication costs and memory per core becomes quite complex when combining all three methods where the best mapping is empirically determined. See our further analysis in section [5.6](#page-21-2) for how the number of experts effects the downstream performance as well.\n\n#### 5.6 Towards Trillion Parameter Models\n\nCombining expert, model and data parallelism, we design two large Switch Transformer models, one with 395 billion and 1.6 trillion parameters, respectively. We study how these models perform on both up-stream pre-training as language models and their downstream fine-tuning performance. The parameters, FLOPs per sequence and hyper-parameters of the two different models are listed below in Table 9. Standard hyper-parameters of the Transformer, including $d_{model}$, $d_{ff}$, $d_{kv}$, number of heads and number of layers are described, as well as a less common feature, $FFN_{GEGLU}$, which refers to a variation of the FFN layer where the expansion matrix is substituted with two sets of weights which are non-linearly combined (Shazeer, 2020).\n\nThe Switch-C model is designed using only expert-parallelism, and no model-parallelism, as described earlier in Section [5.4.](#page-21-0) As a result, the hyper-parameters controlling the width,\n\n| Model | Parameters | FLOPs/seq | dmodel | F F NGEGLU | df f | dkv | Num. Heads |\n|--------------|--------------|-------------|-------------|----------------------|-----------------------|-----|------------|\n| T5-Base | 0.2B | 124B | 768 | X | 2048 | 64 | 12 |\n| T5-Large | 0.7B | 425B | 1024 | X | 2816 | 64 | 16 |\n| T5-XXL | 11B | 6.3T | 4096 | X | 10240 | 64 | 64 |\n| Switch-Base | 7B | 124B | 768 | X | 2048 | 64 | 12 |\n| Switch-Large | 26B | 425B | 1024 | X | 2816 | 64 | 16 |\n| Switch-XXL | 395B | 6.3T | 4096 | X | 10240 | 64 | 64 |\n| Switch-C | 1571B | 890B | 2080 | | 6144 | 64 | 32 |\n| | | | | | | | |\n| Model | Expert Freq. | Num. Layers | Num Experts | Neg. Log Perp. @250k | Neg. Log Perp. @ 500k | | |\n| T5-Base | – | 12 | – | -1.599 | -1.556 | | |\n| T5-Large | – | 24 | – | -1.402 | -1.350 | | |\n| T5-XXL | – | 24 | – | -1.147 | -1.095 | | |\n| Switch-Base | 1/2 | 12 | 128 | -1.370 | -1.306 | | |\n| Switch-Large | 1/2 | 24 | 128 | -1.248 | -1.177 | | |\n| Switch-XXL | 1/2 | 24 | 64 | -1.086 | -1.008 | | |\n| Switch-C | 1 | 15 | 2048 | -1.096 | -1.043 | | |\n\n- Table 9: Switch model design and pre-training performance. We compare the hyperparameters and pre-training performance of the T5 models to our Switch Transformer variants. The last two columns record the pre-training model quality on the C4 data set after 250k and 500k steps, respectively. We observe that the Switch-C Transformer variant is 4x faster to a fixed perplexity (with the same compute budget) than the T5-XXL model, with the gap increasing as training progresses.\ndepth, number of heads, and so on, are all much smaller than the T5-XXL model. In contrast, the Switch-XXL is FLOP-matched to the T5-XXL model, which allows for larger dimensions of the hyper-parameters, but at the expense of additional communication costs induced by model-parallelism (see Section [5.5](#page-21-1) for more details).\n\nSample efficiency versus T5-XXL. In the final two columns of Table [9](#page-22-0) we record the negative log perplexity on the C4 corpus after 250k and 500k steps, respectively. After 250k steps, we find both Switch Transformer variants to improve over the T5-XXL version's negative log perplexity by over 0.061.[10](#page-22-1) To contextualize the significance of a gap of 0.061, we note that the T5-XXL model had to train for an additional 250k steps to increase 0.052. The gap continues to increase with additional training, with the Switch-XXL model out-performing the T5-XXL by 0.087 by 500k steps.\n\nTraining instability. However, as described in the introduction, large sparse models can be unstable, and as we increase the scale, we encounter some sporadic issues. We find that the larger Switch-C model, with 1.6T parameters and 2048 experts, exhibits no training instability at all. Instead, the Switch XXL version, with nearly 10x larger FLOPs per sequence, is sometimes unstable. As a result, though this is our better model on a step-basis, we do not pre-train for a full 1M steps, in-line with the final reported results of T5 [(Raffel et al.,](#page-37-0) [2019)](#page-37-0).\n\n10. This reported quality difference is a lower bound, and may actually be larger. The T5-XXL was pretrained on an easier C4 data set which included duplicated, and thus easily copied, snippets within examples.\n\nReasoning fine-tuning performance. As a preliminary assessment of the model quality, we use a Switch-XXL model partially pre-trained on 503B tokens, or approximately half the text used by the T5-XXL model. Using this checkpoint, we conduct multi-task training for efficiency, where all tasks are learned jointly, rather than individually fine-tuned. We find that SQuAD accuracy on the validation set increases to 89.7 versus state-of-the-art of 91.3. Next, the average SuperGLUE test score is recorded at 87.5 versus the T5 version obtaining a score of 89.3 compared to the state-of-the-art of 90.0 [(Wang et al.,](#page-39-5) [2019)](#page-39-5). On ANLI [(Nie et al.,](#page-37-8) [2019)](#page-37-8), Switch XXL improves over the prior state-of-the-art to get a 65.7 accuracy versus the prior best of 49.4 [(Yang et al.,](#page-39-6) [2020)](#page-39-6). We note that while the Switch-XXL has state-of-the-art Neg. Log Perp. on the upstream pre-training task, its gains have not yet fully translated to SOTA downstream performance. We study this issue more in Appendix [E.](#page-31-0)\n\nKnowledge-based fine-tuning performance. Finally, we also conduct an early examination of the model's knowledge with three closed-book knowledge-based tasks: Natural Questions, WebQuestions and TriviaQA, without additional pre-training using Salient Span Masking [(Guu et al.,](#page-36-7) [2020)](#page-36-7). In all three cases, we observe improvements over the prior stateof-the-art T5-XXL model (without SSM). Natural Questions exact match increases to 34.4 versus the prior best of 32.8, Web Questions increases to 41.0 over 37.2, and TriviaQA increases to 47.5 versus 42.9.\n\nSumming up, despite training on less than half the data of other models, we already find comparable, and sometimes state-of-the-art, model quality. Currently, the Switch Transformer translates substantial upstream gains better to knowledge-based tasks, than reasoning-tasks (see Appendix [E)](#page-31-0). Extracting stronger fine-tuning performance from large expert models is an active research question, and the pre-training perplexity indicates future improvements should be possible.\n\n### 6. Related Work\n\nThe importance of scale in neural networks is widely recognized and several approaches have been proposed. Recent works have scaled models to billions of parameters through using model parallelism (e.g. splitting weights and tensors across multiple cores) [(Shazeer et al.,](#page-38-3) [2018;](#page-38-3) [Rajbhandari et al.,](#page-37-9) [2019;](#page-37-9) [Raffel et al.,](#page-37-0) [2019;](#page-37-0) [Brown et al.,](#page-35-0) [2020;](#page-35-0) [Shoeybi et al.,](#page-38-10) [2019)](#page-38-10). Alternatively, [Harlap et al.](#page-36-8) [(2018)](#page-36-8); [Huang et al.](#page-36-9) [(2019)](#page-36-9) propose using pipeline based model parallelism, where different layers are split across devices and micro-batches are pipelined to the different layers. Finally, Product Key networks [(Lample et al.,](#page-37-10) [2019)](#page-37-10) were proposed to scale up the capacity of neural networks by doing a lookup for learnable embeddings based on the incoming token representations to a given layer.\n\nOur work studies a specific model in a class of methods that do conditional computation, where computation decisions are made dynamically based on the input. [Cho and Bengio](#page-35-8) [(2014)](#page-35-8) proposed adaptively selecting weights based on certain bit patterns occuring in the model hidden-states. [Eigen et al.](#page-35-9) [(2013)](#page-35-9) built stacked expert layers with dense matrix multiplications and ReLU activations and showed promising results on jittered MNIST and monotone speech. In computer vision [Puigcerver et al.](#page-37-11) [(2020)](#page-37-11) manually route tokens based on semantic classes during upstream pre-training and then select the relevant experts to be used according to the downstream task.\n\nMixture of Experts (MoE), in the context of modern deep learning architectures, was proven effective in [Shazeer et al.](#page-38-2) [(2017)](#page-38-2). That work added an MoE layer which was stacked between LSTM [(Hochreiter and Schmidhuber,](#page-36-10) [1997)](#page-36-10) layers, and tokens were separately routed to combinations of experts. This resulted in state-of-the-art results in language modeling and machine translation benchmarks. The MoE layer was reintroduced into the Transformer architecture by the Mesh Tensorflow library [(Shazeer et al.,](#page-38-3) [2018)](#page-38-3) where MoE layers were introduced as a substitute of the FFN layers, however, there were no accompanying NLP results. More recently, through advances in machine learning infrastructure, GShard [(Lepikhin et al.,](#page-37-2) [2020)](#page-37-2), which extended the XLA compiler, used the MoE Transformer to dramatically improve machine translation across 100 languages. Finally [Fan et al.](#page-35-10) [(2021)](#page-35-10) chooses a different deterministic MoE strategy to split the model parameters into non-overlapping groups of languages.\n\nSparsity along the sequence length dimension $(L)$ in the Transformer attention patterns has been a successful technique to reduce the attention complexity from $O(L^2)$ (Child et al., 2019; Correia et al., 2019; Sukhbaatar et al., 2019; Kitaev et al., 2020; Zaheer et al., 2020; Beltagy et al., 2020). This has enabled learning longer sequences than previously possible. This version of the Switch Transformer does not employ attention sparsity, but these techniques are complimentary, and, as future work, these could be combined to potentially improve learning on tasks requiring long contexts.\n\n### 7. Discussion\n\nWe pose and discuss questions about the Switch Transformer, and sparse expert models generally, where sparsity refers to weights, not on attention patterns.\n\nIsn't Switch Transformer better due to sheer parameter count? Yes, and by design! Parameters, independent of the total FLOPs used, are a useful axis to scale neural language models. Large models have been exhaustively shown to perform better [(Kaplan](#page-36-0) [et al.,](#page-36-0) [2020)](#page-36-0). But in this case, our model is more sample efficient and faster while using the same computational resources.\n\nI don't have access to a supercomputer—is this still useful for me? Though this work has focused on extremely large models, we also find that models with as few as two experts improves performance while easily fitting within memory constraints of commonly available GPUs or TPUs (details in Appendix [D)](#page-28-2). We therefore believe our techniques are useful in small-scale settings.\n\nDo sparse models outperform dense models on the speed-accuracy Pareto curve? Yes. Across a wide variety of different models sizes, sparse models outperform dense models per step and on wall clock time. Our controlled experiments show for a fixed amount of computation and time, sparse models outperform dense models.\n\nI can't deploy a trillion parameter model—can we shrink these models? We cannot fully preserve the model quality, but compression rates of 10 to 100x are achievable by distilling our sparse models into dense models while achieving $\\approx$30% of the quality gain of the expert model.\n\nWhy use Switch Transformer instead of a model-parallel dense model? On a time basis, Switch Transformers can be far more efficient than dense-models with sharded parameters (Figure [6)](#page-13-2). Also, we point out that this decision is *not* mutually exclusive—we can, and do, use model-parallelism in Switch Transformers, increasing the FLOPs per token, but incurring the slowdown of conventional model-parallelism.\n\nWhy aren't sparse models widely used already? The motivation to try sparse models has been stymied by the massive success of scaling dense models (the success of which is partially driven by co-adaptation with deep learning hardware as argued in [Hooker](#page-36-12) [(2020)](#page-36-12)). Further, sparse models have been subject to multiple issues including (1) model complexity, (2) training difficulties, and (3) communication costs. Switch Transformer makes strides to alleviate these issues.\n\n### 8. Future Work\n\nThis paper lays out a simplified architecture, improved training procedures, and a study of how sparse models scale. However, there remain many open future directions which we briefly describe here:\n\n- 1. A significant challenge is further improving training stability for the largest models. While our stability techniques were effective for our Switch-Base, Switch-Large and Switch-C models (no observed instability), they were not sufficient for Switch-XXL. We have taken early steps towards stabilizing these models, which we think may be generally useful for large models, including using regularizers for improving stability and adapted forms of gradient clipping, but this remains unsolved.\n- 2. Generally we find that improved pre-training quality leads to better downstream results (Appendix [E)](#page-31-0), though we sometimes encounter striking anomalies. For instance, despite similar perplexities modeling the C4 data set, the 1.6T parameter Switch-C achieves only an 87.7 exact match score in SQuAD, which compares unfavorably to 89.6 for the smaller Switch-XXL model. One notable difference is that the Switch-XXL model applies ≈10x the FLOPS per token than the Switch-C model, even though it has ≈4x less unique parameters (395B vs 1.6T). This suggests a poorly understood dependence between fine-tuning quality, FLOPS per token and number of parameters.\n- 3. Perform a comprehensive study of scaling relationships to guide the design of architectures blending data, model and expert-parallelism. Ideally, given the specs of a hardware configuration (computation, memory, communication) one could more rapidly design an optimal model. And, vice versa, this may also help in the design of future hardware.\n- 4. Our work falls within the family of adaptive computation algorithms. Our approach always used identical, homogeneous experts, but future designs (facilitated by more flexible infrastructure) could support heterogeneous experts. This would enable more flexible adaptation by routing to larger experts when more computation is desired perhaps for harder examples.\n- 5. Investigating expert layers outside the FFN layer of the Transformer. We find preliminary evidence that this similarly can improve model quality. In Appendix [A,](#page-26-1) we report quality improvement adding these inside Self-Attention layers, where our\n\nlayer replaces the weight matrices which produce Q, K, V. However, due to training instabilities with the bfloat16 format, we instead leave this as an area for future work.\n\n- 6. Examining Switch Transformer in new and across different modalities. We have thus far only considered language, but we believe that model sparsity can similarly provide advantages in new modalities, as well as multi-modal networks.\nThis list could easily be extended, but we hope this gives a flavor for the types of challenges that we are thinking about and what we suspect are promising future directions.\n\n### 9. Conclusion\n\nSwitch Transformers are scalable and effective natural language learners. We simplify Mixture of Experts to produce an architecture that is easy to understand, stable to train and vastly more sample efficient than equivalently-sized dense models. We find that these models excel across a diverse set of natural language tasks and in different training regimes, including pre-training, fine-tuning and multi-task training. These advances make it possible to train models with hundreds of billion to trillion parameters and which achieve substantial speedups relative to dense T5 baselines. We hope our work motivates sparse models as an effective architecture and that this encourages researchers and practitioners to consider these flexible models in natural language tasks, and beyond.\n\n### Acknowledgments\n\nThe authors would like to thank Margaret Li who provided months of key insights into algorithmic improvements and suggestions for empirical studies. Hugo Larochelle for sage advising and clarifying comments on the draft, Irwan Bello for detailed comments and careful revisions, Colin Raffel and Adam Roberts for timely advice on neural language models and the T5 code-base, Yoshua Bengio for advising and encouragement on research in adaptive computation, Jascha Sohl-dickstein for interesting new directions for stabilizing new large scale models and paper revisions, and the Google Brain Team for useful discussions on the paper. Blake Hechtman who provided invaluable help in profiling and improving the training performance of our models.\n\n### A. Switch for Attention\n\n[Shazeer et al.](#page-38-3) [(2018)](#page-38-3); [Lepikhin et al.](#page-37-2) [(2020)](#page-37-2) designed MoE Transformers [(Shazeer et al.,](#page-38-2) [2017)](#page-38-2) by adding MoE layers into the dense feedfoward network (FFN) computations of the Transformer. Similarly, our work also replaced the FFN layer in the Transformer, but we briefly explore here an alternate design. We add Switch layers into the Transformer Self-Attention layers. To do so, we replace the trainable weight matrices that produce the queries, keys and values with Switch layers as seen in Figure [10.](#page-27-0)\n\nTable [10](#page-27-1) records the quality after a fixed number of steps as well as training time for several variants. Though we find improvements, we also found these layers to be more unstable when using bfloat16 precision and thus we did not include them in the final variant.\n\n\n\n- Figure 10: Switch layers in attention. We diagram how to incorporate the Switch layer into the Self-Attention transformer block. For each token (here we show two tokens, $x_1$ = “More” and $x_2$ = “Parameters”), one set of weights produces the query and the other set of unique weights produces the shared keys and values. We experimented with each expert being a linear operation, as well as a FFN, as was the case throughout this work. While we found quality improvements using this, we found this to be more unstable when used with low precision number formats, and thus leave it for future work.\n\n| | | | | | However, when these layers do train stably, we believe the preliminary positive results | |\n|----------------------------------------|--|--|--|--|-----------------------------------------------------------------------------------------|--|\n| suggests a future promising direction. | | | | | | |\n\n| Model | Precision | Quality
@100k Steps (↑) | Quality
@16H (↑) | Speed
(ex/sec) (↑) |\n|------------------------|-----------|----------------------------|---------------------|-----------------------|\n| Experts FF | float32 | -1.548 | -1.614 | 1480 |\n| Expert Attention | float32 | -1.524 | -1.606 | 1330 |\n| Expert Attention | bfloat16 | [diverges] | [diverges] | – |\n| Experts FF + Attention | float32 | -1.513 | -1.607 | 1240 |\n| Expert FF + Attention | bfloat16 | [diverges] | [diverges] | – |\n\n- Table 10: Switch attention layer results. All models have 32 experts and train with 524k tokens per batch. Experts FF is when experts replace the FFN in the Transformer, which is our standard setup throughout the paper. Experts FF + Attention is when experts are used to replace both the FFN and the Self-Attention layers. When training with bfloat16 precision the models that have experts attention diverge.\n### B. Preventing Token Dropping with No-Token-Left-Behind\n\nDue to software constraints on TPU accelerators, the shapes of our Tensors must be statically sized. As a result, each expert has a finite and fixed capacity to process token representations. This, however, presents an issue for our model which dynamically routes tokens at run-time that may result in an uneven distribution over experts. If the number of tokens sent to an expert is less than the expert capacity, then the computation may simply be padded – an inefficient use of the hardware, but mathematically correct. However, when the number of tokens sent to an expert is larger than its capacity (expert overflow), a protocol is needed to handle this. [Lepikhin et al.](#page-37-2) [(2020)](#page-37-2) adapts a Mixture-of-Expert model and addresses expert overflow by passing its representation to the next layer without processing through a residual connection which we also follow.\n\nWe suspected that having no computation applied to tokens could be very wasteful, especially since if there is overflow on one expert, that means another expert will have extra capacity. With this intuition we create *No-Token-Left-Behind*, which iteratively reroutes any tokens that are at first routed to an expert that is overflowing. Figure [11](#page-29-0) shows a graphical description of this method, which will allow us to guarantee almost no tokens will be dropped during training and inference. We hypothesised that this could improve performance and further stabilize training, but we found no empirical benefits. We suspect that once the network learns associations between different tokens and experts, if this association is changed (e.g. sending a token to its second highest expert) then performance could be degraded.\n\n### C. Encouraging Exploration Across Experts\n\nAt each expert-layer, the router determines to which expert to send the token. This is a discrete decision over the available experts, conditioned on information about the token's representation. Based on the incoming token representation, the router determines the best expert, however, it receives no counterfactual information about how well it would have done selecting an alternate expert. As in reinforcement learning, a classic explorationexploitation dilemma arises [(Sutton and Barto,](#page-38-12) [2018)](#page-38-12). These issues have been similarly noted and addressed differently by [Rosenbaum et al.](#page-38-13) [(2017)](#page-38-13) which demonstrated success in multi-task learning. This particular setting most closely matches that of a contextual bandit [(Robbins,](#page-37-12) [1952)](#page-37-12). Deterministically selecting the top expert always amounts to an exploitative strategy – we consider balancing exploration to seek better expert assignment.\n\nTo introduce exploration, we consider several approaches: 1) deterministic or argmax 2) sampling from the softmax distribution 3) input dropout on the incoming representation 4) multiplicative jitter noise on the incoming representation. The resulting impact on model quality is reported in Table [11.](#page-29-1) Throughout this work, we use input jitter to inject noise as we have found it to empirically perform the best.\n\n### D. Switch Transformers in Lower Compute Regimes\n\nSwitch Transformer is also an effective architecture at small scales as well as in regimes with thousands of cores and trillions of parameters. Many of our prior experiments were\n\n\n\n- Figure 11: Diagram of the No-Token-Left-Behind Routing. Stage 1 is equivalent to Switch routing where tokens are routed to the expert with the highest probability from the router. In Stage 2 we look at all tokens that have overflowed and route them to the expert with which has the second highest probability. Tokens can still be overflowed if their second highest expert has too many tokens, but this allows most of the tokens to be routed. This process can be iterated to guarantee virtually no tokens are dropped at all.\n\n| Model | Quality (Neg. Log Perp.) (↑) |\n|----------------|------------------------------|\n| Argmax | -1.471 |\n| Sample softmax | -1.570 |\n| Input dropout | -1.480 |\n| Input jitter | -1.468 |\n\n- Table 11: Router Exploration Strategies. Quality of the Switch Transformer, measured by the negative log perplexity, under different randomness-strategies for selecting the expert (lower is better). There is no material speed performance difference between the variants.\nat the scale of 10B+ parameter models, but we show in Figure [12](#page-30-0) as few as 2 experts produce compelling gains over a FLOP-matched counterpart. Even if a super computer is not readily available, training Switch Transformers with 2, 4, or 8 experts (as we typically recommend one expert per core) results in solid improvements over T5 dense baselines.\n\n\n\nFigure 12: Switch Transformer with few experts. Switch Transformer improves over the baseline even with very few experts. Here we show scaling properties at very small scales, where we improve over the T5-Base model using 2, 4, and 8 experts.\n\n### E. Relation of Upstream to Downstream Model Performance\n\nThere is no guarantee that a model's quality on a pre-training objective will translate to downstream task results. Figure [13](#page-31-1) presents the correlation of the upstream model quality, for both dense and Switch models, on the C4 pre-training task with two downstream task measures: average SuperGLUE performance and TriviaQA score. We choose these two tasks as one probes the model's reasoning and the other factual knowledge.\n\n\n\nFigure 13: Upstream pre-trained quality to downstream model quality. We correlate the upstream performance with downstream quality on both SuperGLUE and TriviaQA (SOTA recorded without SSM), reasoning and knowledge-heavy benchmarks, respectively (validation sets). We find that, as with the baseline, the Switch model scales with improvements in the upstream pre-training task. For SuperGLUE, we find a loosely linear relation between negative log perplexity and the average SuperGLUE score. However, the dense model often performs better for a fixed perplexity, particularly in the large-scale regime. Conversely, on the knowledge-heavy task, TriviaQA, we find that the Switch Transformer may follow an improved scaling relationship – for a given upstream perplexity, it does better than a dense counterpart. Further statistics (expensive to collect and left to future work) would be necessary to confirm these observations.\n\nWe find a consistent correlation, indicating that for both baseline and Switch models, improved pre-training leads to better downstream results. Additionally, for a fixed upstream perplexity we find that both Switch and dense models perform similarly in the small to medium model size regime. However, in the largest model regime (T5-11B/T5-XXL) our largest Switch models, as mentioned in Section [5.6,](#page-21-2) do not always translate their upstream perplexity well to downstream fine-tuning on the SuperGLUE task. This warrants future investigation and study to fully realize the potential of sparse models. Understanding the fine-tuning dynamics with expert-models is very complicated and is dependent on regularization, load-balancing, and fine-tuning hyper-parameters.\n\n### F. Pseudo Code for Switch Transformers\n\nPseudocode for Switch Transformers in Mesh Tensorflow [(Shazeer et al.,](#page-38-3) [2018)](#page-38-3). No model parallelism is being used for the below code (see [5.4](#page-21-0) for more details).\n\n```\nimport mesh tensorflow as mtf\ndef load balance loss(router probs, expert mask):\n \"\"\"Calculate load−balancing loss to ensure diverse expert routing.\"\"\"\n # router probs is the probability assigned for each expert per token.\n # router probs shape: [num cores, tokens per core, num experts]\n # expert index contains the expert with the highest router probability in one−hot format.\n # expert mask shape: [num cores, tokens per core, num experts]\n # For each core, get the fraction of tokens routed to each expert.\n # density 1 shape: [num cores, num experts]\n density 1 = mtf.reduce mean(expert mask, reduced dim=tokens per core)\n # For each core, get fraction of probability mass assigned to each expert\n # from the router across all tokens.\n # density 1 proxy shape: [num cores, num experts]\n density 1 proxy = mtf.reduce mean(router probs, reduced dim=tokens per core)\n # density l for a single core: vector of length num experts that sums to 1.\n # density l proxy for a single core: vector of length num experts that sums to 1.\n # Want both vectors to have uniform allocation (1/num experts) across all num expert elements.\n # The two vectors will be pushed towards uniform allocation when the dot product is minimized.\n loss = mtf.reduce mean(density 1 proxy ∗ density 1) ∗ (num experts ˆ 2)\n return loss\n```\n- Figure 14: Pseudo code for the load balance loss for Switch Transformers in Mesh Tensorflow.\n\n```\nimport mesh tensorflow as mtf\n```\n\n```\ndef router(inputs, capacity factor):\n \"\"\"Produce the combine and dispatch tensors used for sending and\n receiving tokens from their highest probability expert. \"\"\"\n # Core layout is split across num cores for all tensors and operations.\n # inputs shape: [num cores, tokens per core, d model]\n```\n#### router weights = mtf.Variable(shape=[d model, num experts])\n\n```\n# router logits shape: [num cores, tokens per core, num experts]\nrouter logits = mtf.einsum([inputs, router weights], reduced dim=d model)\n```\n#### if is training:\n\n# Add noise for exploration across experts. router logits += mtf.random uniform(shape=router logits.shape, minval=1−eps, maxval=1+eps)\n\n```\n# Convert input to softmax operation from bfloat16 to float32 for stability.\nrouter logits = mtf.to float32(router logits)\n```\n\n```\n# Probabilities for each token of what expert it should be sent to.\nrouter probs = mtf.softmax(router logits, axis=−1)\n```\n\n```\n# Get the top−1 expert for each token. expert gate is the top−1 probability\n# from the router for each token. expert index is what expert each token\n# is going to be routed to.\n# expert gate shape: [num cores, tokens per core]\n# expert index shape: [num cores, tokens per core]\nexpert gate, expert index = mtf.top 1(router probs, reduced dim=num experts)\n# expert mask shape: [num cores, tokens per core, num experts]\nexpert mask = mtf.one hot(expert index, dimension=num experts)\n# Compute load balancing loss.\naux loss = load balance loss(router probs, expert mask)\n# Experts have a fixed capacity, ensure we do not exceed it. Construct\n# the batch indices, to each expert, with position in expert\n# make sure that not more that expert capacity examples can be routed to\n# each expert.\nposition in expert = mtf.cumsum(expert mask, dimension=tokens per core) ∗ expert mask\n# Keep only tokens that fit within expert capacity.\nexpert mask ∗= mtf.less(position in expert, expert capacity)\nexpert mask flat = mtf.reduce sum(expert mask, reduced dim=experts dim)\n# Mask out the experts that have overflowed the expert capacity.\nexpert gate ∗= expert mask flat\n# combine tensor used for combining expert outputs and scaling with router probability.\n# combine tensor shape: [num cores, tokens per core, num experts, expert capacity]\ncombine tensor = (\n expert gate ∗ expert mask flat ∗\n mtf.one hot(expert index, dimension=num experts) ∗\n mtf.one hot(position in expert, dimension=expert capacity))\n# Cast back outputs to bfloat16 for the rest of the layer.\ncombine tensor = mtf.to bfloat16(combine tensor)\n# Create binary dispatch tensor that is 1 if the token gets routed to the corresponding expert.\n# dispatch tensor shape: [num cores, tokens per core, num experts, expert capacity]\ndispatch tensor = mtf.cast(combine tensor, tf.bool)\n```\n\n```\nreturn dispatch tensor, combine tensor, aux loss\n```\nFigure 15: Pseudo code for the router for Switch Transformers in Mesh Tensorflow.\n\n```\nimport mesh tensorflow as mtf\n```\ndef switch layer(inputs, n, capacity factor, num experts): \"\"\"Distributed switch transformer feed−forward layer.\"\"\"\n\n```\n# num cores (n) = total cores for training the model (scalar).\n# d model = model hidden size (scalar).\n# num experts = total number of experts.\n# capacity factor = extra buffer for each expert.\n# inputs shape: [batch, seq len, d model]\nbatch, seq len, d model = inputs.get shape()\n# Each core will route tokens per core tokens to the correct experts.\ntokens per core = batch ∗ seq len / num cores\n# Each expert will have shape [num cores, expert capacity, d model].\n# Each core is responsible for sending expert capacity tokens\n# to each expert.\nexpert capacity = tokens per core ∗ capacity factor / num experts\n# Reshape to setup per core expert dispatching.\n# shape: [batch, seq len, d model] −> [num cores, tokens per core, d model]\n# Core layout: [n, 1, 1] −> [n, 1, 1]\ninputs = mtf.reshape(inputs, [num cores, tokens per core, d model])\n# Core Layout: [n, 1, 1] −> [n, 1, 1, 1], [n, 1, 1, 1]\n# dispatch tensor (boolean) shape: [num cores, tokens per core, num experts, expert capacity]\n# dispatch tensor is used for routing tokens to the correct expert.\n# combine tensor (float) shape: [num cores, tokens per core, num experts, expert capacity]\n# combine tensor used for combining expert outputs and scaling with router\n# probability.\ndispatch tensor, combine tensor, aux loss = router(inputs, expert capacity)\n# Matmul with large boolean tensor to assign tokens to the correct expert.\n# Core Layout: [n, 1, 1], −> [1, n, 1, 1]\n# expert inputs shape: [num experts, num cores, expert capacity, d model]\nexpert inputs = mtf.einsum([inputs, dispatch tensor], reduce dims=[tokens per core])\n# All−to−All communication. Cores split across num cores and now we want to split\n# across num experts. This sends tokens, routed locally, to the correct expert now\n# split across different cores.\n# Core layout: [1, n, 1, 1] −> [n, 1, 1, 1]\nexpert inputs = mtf.reshape(expert inputs, [num experts, num cores, expert capacity, d model])\n# Standard feed forward computation, where each expert will have its own\n# unique set of parameters.\n# Total unique parameters created: num experts ∗ (d model ∗ d ff ∗ 2).\n# expert outputs shape: [num experts, num cores, expert capacity, d model]\nexpert outputs = feed forward(expert inputs)\n# All−to−All communication. Cores are currently split across the experts\n# dimension, which needs to be switched back to being split across num cores.\n# Core Layout: [n, 1, 1, 1] −> [1, n, 1, 1]\nexpert outputs = mtf.reshape(expert outputs, [num experts, num cores, expert capacity, d model])\n# Convert back to input shape and multiply outputs of experts by the routing probability.\n# expert outputs shape: [num experts, num cores, tokens per core, d model]\n# expert outputs combined shape: [num cores, tokens per core, d model]\n# Core Layout: [1, n, 1, 1] −> [n, 1, 1]\nexpert outputs combined = mtf.einsum([expert outputs, combine tensor], reduce dims=[tokens per core])\n# Remove tokens per core shapes used for local routing dispatching to match input shape.\n# Core Layout: [n, 1, 1] −> [n, 1, 1]\noutputs = mtf.reshape(expert outputs combined, [batch, seq len, d model])\nreturn outputs, aux loss\n```\nFigure 16: Pseudo code of the Switch Transformer layer in Mesh Tensorflow.\n\n### References\n\n- Mart´ın Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 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Energy and policy considerations for deep learning in nlp. arXiv preprint arXiv:1906.02243, 2019.\n- Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, and Armand Joulin. Adaptive attention span in transformers. arXiv preprint arXiv:1905.07799, 2019.\n- Rich Sutton. The Bitter Lesson. http://www.incompleteideas.net/IncIdeas/BitterLesson.html, 2019.\n- Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. Stanford University, 2018.\n- Wilson L Taylor. \"cloze procedure\": A new tool for measuring readability. Journalism quarterly, 30(4):415–433, 1953.\n- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998–6008, 2017.\n- Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461, 2018.\n- Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. Superglue: A stickier benchmark for generalpurpose language understanding systems. In Advances in Neural Information Processing Systems, pages 3266–3280, 2019.\n- Shibo Wang and Pankaj Kanwar. Bfloat16: The secret to high performance on cloud tpus. Google Cloud Blog, 2019.\n- Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. mt5: A massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934, 2020.\n- Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. Xlnet: Generalized autoregressive pretraining for language understanding, 2020.\n- Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. Big bird: Transformers for longer sequences. arXiv preprint arXiv:2007.14062, 2020."
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"content": "{\n \"table_of_contents\": [\n {\n \"title\": \"Switch Transformers: Scaling to Trillion Parameter Models\\nwith Simple and Efficient Sparsity\",\n \"heading_level\": null,\n \"page_id\": 0,\n \"polygon\": [\n [\n 93.0849609375,\n 101.5679931640625\n ],\n [\n 515.77734375,\n 101.5679931640625\n ],\n [\n 515.77734375,\n 133.84625244140625\n ],\n [\n 93.0849609375,\n 133.84625244140625\n ]\n ]\n },\n {\n \"title\": \"William Fedus\\u2217\",\n \"heading_level\": null,\n \"page_id\": 0,\n \"polygon\": [\n [\n 89.2001953125,\n 151.53192138671875\n ],\n [\n 174.6650390625,\n 151.53192138671875\n ],\n [\n 174.6650390625,\n 164.20635986328125\n ],\n [\n 89.2001953125,\n 164.20635986328125\n ]\n ]\n },\n {\n \"title\": \"Barret Zoph\\u2217\",\n \"heading_level\": null,\n \"page_id\": 0,\n \"polygon\": [\n [\n 90.00000762939453,\n 181.7108154296875\n ],\n [\n 165.849609375,\n 181.7108154296875\n ],\n [\n 165.849609375,\n 193.87738037109375\n ],\n [\n 90.00000762939453,\n 193.87738037109375\n ]\n ]\n },\n {\n \"title\": \"Noam Shazeer\",\n \"heading_level\": null,\n \"page_id\": 0,\n \"polygon\": [\n [\n 90.00001525878906,\n 214.2322998046875\n ],\n [\n 169.0691680908203,\n 214.2322998046875\n ],\n [\n 169.0691680908203,\n 225.14141845703125\n ],\n [\n 90.00001525878906,\n 225.14141845703125\n ]\n ]\n },\n {\n \"title\": \"Abstract\",\n \"heading_level\": null,\n \"page_id\": 0,\n \"polygon\": [\n [\n 280.29803466796875,\n 310.811767578125\n ],\n [\n 331.6575927734375,\n 310.811767578125\n ],\n [\n 331.6575927734375,\n 322.7669677734375\n ],\n [\n 280.29803466796875,\n 322.7669677734375\n ]\n ]\n },\n {\n \"title\": \"Contents\",\n \"heading_level\": null,\n \"page_id\": 1,\n \"polygon\": [\n [\n 90.0,\n 91.70068359375\n ],\n [\n 144.55810546875,\n 91.70068359375\n ],\n [\n 144.55810546875,\n 104.80078125\n ],\n [\n 90.0,\n 104.80078125\n ]\n ]\n },\n {\n \"title\": \"1. Introduction\",\n \"heading_level\": null,\n \"page_id\": 2,\n \"polygon\": [\n [\n 89.349609375,\n 92.4776611328125\n ],\n [\n 180.24981689453125,\n 92.4776611328125\n ],\n [\n 180.24981689453125,\n 104.432861328125\n ],\n [\n 89.349609375,\n 104.432861328125\n ]\n ]\n },\n {\n \"title\": \"2. Switch Transformer\",\n \"heading_level\": null,\n \"page_id\": 3,\n \"polygon\": [\n [\n 89.349609375,\n 468.31640625\n ],\n [\n 223.5234375,\n 468.31640625\n ],\n [\n 223.5234375,\n 481.48602294921875\n ],\n [\n 89.349609375,\n 481.48602294921875\n ]\n ]\n },\n {\n \"title\": \"2.1 Simplifying Sparse Routing\",\n \"heading_level\": null,\n \"page_id\": 4,\n \"polygon\": [\n [\n 89.4990234375,\n 430.6282043457031\n ],\n [\n 262.7237243652344,\n 430.6282043457031\n ],\n [\n 262.7237243652344,\n 442.01953125\n ],\n [\n 89.4990234375,\n 442.01953125\n ]\n ]\n },\n {\n \"title\": \"2.2 Efficient Sparse Routing\",\n \"heading_level\": null,\n \"page_id\": 5,\n \"polygon\": [\n [\n 88.67724609375,\n 574.9432220458984\n ],\n [\n 245.73831176757812,\n 574.9432220458984\n ],\n [\n 245.73831176757812,\n 586.265625\n ],\n [\n 88.67724609375,\n 586.265625\n ]\n ]\n },\n {\n \"title\": \"2.3 Putting It All Together: The Switch Transformer\",\n \"heading_level\": null,\n \"page_id\": 7,\n \"polygon\": [\n [\n 89.7978515625,\n 204.51019287109375\n ],\n [\n 384.890625,\n 204.51019287109375\n ],\n [\n 384.890625,\n 215.4193115234375\n ],\n [\n 89.7978515625,\n 215.4193115234375\n ]\n ]\n },\n {\n \"title\": \"2.4 Improved Training and Fine-Tuning Techniques\",\n \"heading_level\": null,\n \"page_id\": 7,\n \"polygon\": [\n [\n 89.6484375,\n 584.8343353271484\n ],\n [\n 374.24749755859375,\n 584.8343353271484\n ],\n [\n 374.24749755859375,\n 595.93359375\n ],\n [\n 89.6484375,\n 595.93359375\n ]\n ]\n },\n {\n \"title\": \"3. Scaling Properties\",\n \"heading_level\": null,\n \"page_id\": 10,\n \"polygon\": [\n [\n 89.349609375,\n 547.7267608642578\n ],\n [\n 214.4091796875,\n 547.7267608642578\n ],\n [\n 214.4091796875,\n 559.6819610595703\n ],\n [\n 89.349609375,\n 559.6819610595703\n ]\n ]\n },\n {\n \"title\": \"3.1 Scaling Results on a Step-Basis\",\n \"heading_level\": null,\n \"page_id\": 11,\n \"polygon\": [\n [\n 89.4990234375,\n 142.49920654296875\n ],\n [\n 284.6510925292969,\n 142.49920654296875\n ],\n [\n 284.6510925292969,\n 153.4083251953125\n ],\n [\n 89.4990234375,\n 153.4083251953125\n ]\n ]\n },\n {\n \"title\": \"3.2 Scaling Results on a Time-Basis\",\n \"heading_level\": null,\n \"page_id\": 12,\n \"polygon\": [\n [\n 88.9013671875,\n 93.29522705078125\n ],\n [\n 288.8173828125,\n 93.29522705078125\n ],\n [\n 288.8173828125,\n 104.204345703125\n ],\n [\n 88.9013671875,\n 104.204345703125\n ]\n ]\n },\n {\n \"title\": \"3.3 Scaling Versus a Larger Dense Model\",\n \"heading_level\": null,\n \"page_id\": 12,\n \"polygon\": [\n [\n 88.67724609375,\n 634.0722198486328\n ],\n [\n 318.0220031738281,\n 634.0722198486328\n ],\n [\n 318.0220031738281,\n 645.046875\n ],\n [\n 88.67724609375,\n 645.046875\n ]\n ]\n },\n {\n \"title\": \"4. Downstream Results\",\n \"heading_level\": null,\n \"page_id\": 13,\n \"polygon\": [\n [\n 89.6484375,\n 453.3246765136719\n ],\n [\n 228.3046875,\n 453.3246765136719\n ],\n [\n 228.3046875,\n 465.2798767089844\n ],\n [\n 89.6484375,\n 465.2798767089844\n ]\n ]\n },\n {\n \"title\": \"4.1 Fine-Tuning\",\n \"heading_level\": null,\n \"page_id\": 13,\n \"polygon\": [\n [\n 89.6484375,\n 585.1042327880859\n ],\n [\n 180.193359375,\n 585.1042327880859\n ],\n [\n 180.193359375,\n 596.3203125\n ],\n [\n 89.6484375,\n 596.3203125\n ]\n ]\n },\n {\n \"title\": \"4.2 Distillation\",\n \"heading_level\": null,\n \"page_id\": 15,\n \"polygon\": [\n [\n 89.2001953125,\n 488.9872131347656\n ],\n [\n 173.33460998535156,\n 488.9872131347656\n ],\n [\n 173.33460998535156,\n 499.8963317871094\n ],\n [\n 89.2001953125,\n 499.8963317871094\n ]\n ]\n },\n {\n \"title\": \"4.3 Multilingual Learning\",\n \"heading_level\": null,\n \"page_id\": 16,\n \"polygon\": [\n [\n 89.349609375,\n 579.69140625\n ],\n [\n 232.48828125,\n 579.69140625\n ],\n [\n 232.48828125,\n 590.7133483886719\n ],\n [\n 89.349609375,\n 590.7133483886719\n ]\n ]\n },\n {\n \"title\": \"5. 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Related Work\",\n \"heading_level\": null,\n \"page_id\": 23,\n \"polygon\": [\n [\n 89.42431640625,\n 453.14385986328125\n ],\n [\n 188.08042907714844,\n 453.14385986328125\n ],\n [\n 188.08042907714844,\n 465.09906005859375\n ],\n [\n 89.42431640625,\n 465.09906005859375\n ]\n ]\n },\n {\n \"title\": \"7. Discussion\",\n \"heading_level\": null,\n \"page_id\": 24,\n \"polygon\": [\n [\n 89.72314453125,\n 369.3647766113281\n ],\n [\n 167.57725524902344,\n 369.3647766113281\n ],\n [\n 167.57725524902344,\n 381.3199768066406\n ],\n [\n 89.72314453125,\n 381.3199768066406\n ]\n ]\n },\n {\n \"title\": \"8. Future Work\",\n \"heading_level\": null,\n \"page_id\": 25,\n \"polygon\": [\n [\n 89.947265625,\n 219.65625\n ],\n [\n 181.986328125,\n 219.65625\n ],\n [\n 181.986328125,\n 231.62786865234375\n ],\n [\n 89.947265625,\n 231.62786865234375\n ]\n ]\n },\n {\n \"title\": \"9. 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Preventing Token Dropping with No-Token-Left-Behind\",\n \"heading_level\": null,\n \"page_id\": 28,\n \"polygon\": [\n [\n 89.2001953125,\n 92.4776611328125\n ],\n [\n 443.4609375,\n 92.4776611328125\n ],\n [\n 443.4609375,\n 104.432861328125\n ],\n [\n 89.2001953125,\n 104.432861328125\n ]\n ]\n },\n {\n \"title\": \"C. Encouraging Exploration Across Experts\",\n \"heading_level\": null,\n \"page_id\": 28,\n \"polygon\": [\n [\n 89.349609375,\n 401.88983154296875\n ],\n [\n 350.525390625,\n 401.88983154296875\n ],\n [\n 350.525390625,\n 413.84503173828125\n ],\n [\n 89.349609375,\n 413.84503173828125\n ]\n ]\n },\n {\n \"title\": \"D. Switch Transformers in Lower Compute Regimes\",\n \"heading_level\": null,\n \"page_id\": 28,\n \"polygon\": [\n [\n 89.2001953125,\n 659.2198486328125\n ],\n [\n 399.234375,\n 659.2198486328125\n ],\n [\n 399.234375,\n 671.175048828125\n ],\n [\n 89.2001953125,\n 671.175048828125\n ]\n ]\n },\n {\n \"title\": \"E. 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"content": "# Think Python\n\n## How to Think Like a Computer Scientist\n\nVersion 2.0.17\n\n# Think Python\n\n## How to Think Like a Computer Scientist\n\nVersion 2.0.17\n\nAllen Downey\n\nGreen Tea Press\n\nNeedham, Massachusetts\n\nCopyright © 2012 Allen Downey.\n\nGreen Tea Press 9 Washburn Ave Needham MA 02492\n\nPermission is granted to copy, distribute, and/or modify this document under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported License, which is available at [http:](http://creativecommons.org/licenses/by-nc/3.0/) [//creativecommons.org/licenses/by-nc/3.0/](http://creativecommons.org/licenses/by-nc/3.0/).\n\nThe original form of this book is LATEX source code. Compiling this LATEX source has the effect of generating a device-independent representation of a textbook, which can be converted to other formats and printed.\n\nThe $\\LaTeX$ source for this book is available from \n\n# **Preface**\n\n## **The strange history of this book**\n\nIn January 1999 I was preparing to teach an introductory programming class in Java. I had taught it three times and I was getting frustrated. The failure rate in the class was too high and, even for students who succeeded, the overall level of achievement was too low.\n\nOne of the problems I saw was the books. They were too big, with too much unnecessary detail about Java, and not enough high-level guidance about how to program. And they all suffered from the trap door effect: they would start out easy, proceed gradually, and then somewhere around Chapter 5 the bottom would fall out. The students would get too much new material, too fast, and I would spend the rest of the semester picking up the pieces.\n\nTwo weeks before the first day of classes, I decided to write my own book. My goals were:\n\n- Keep it short. It is better for students to read 10 pages than not read 50 pages.\n- Be careful with vocabulary. I tried to minimize the jargon and define each term at first use.\n- Build gradually. To avoid trap doors, I took the most difficult topics and split them into a series of small steps.\n- Focus on programming, not the programming language. I included the minimum useful subset of Java and left out the rest.\n\nI needed a title, so on a whim I chose *How to Think Like a Computer Scientist*.\n\nMy first version was rough, but it worked. Students did the reading, and they understood enough that I could spend class time on the hard topics, the interesting topics and (most important) letting the students practice.\n\nI released the book under the GNU Free Documentation License, which allows users to copy, modify, and distribute the book.\n\nWhat happened next is the cool part. Jeff Elkner, a high school teacher in Virginia, adopted my book and translated it into Python. He sent me a copy of his translation, and I had the unusual experience of learning Python by reading my own book. As Green Tea Press, I published the first Python version in 2001.\n\nIn 2003 I started teaching at Olin College and I got to teach Python for the first time. The contrast with Java was striking. Students struggled less, learned more, worked on more interesting projects, and generally had a lot more fun.\n\nOver the last nine years I continued to develop the book, correcting errors, improving some of the examples and adding material, especially exercises.\n\nThe result is this book, now with the less grandiose title *Think Python*. Some of the changes are:\n\n- I added a section about debugging at the end of each chapter. These sections present general techniques for finding and avoiding bugs, and warnings about Python pitfalls.\n- I added more exercises, ranging from short tests of understanding to a few substantial projects. And I wrote solutions for most of them.\n- I added a series of case studies—longer examples with exercises, solutions, and discussion. Some are based on Swampy, a suite of Python programs I wrote for use in my classes. Swampy, code examples, and some solutions are available from .\n- I expanded the discussion of program development plans and basic design patterns.\n- I added appendices about debugging, analysis of algorithms, and UML diagrams with Lumpy.\n\nI hope you enjoy working with this book, and that it helps you learn to program and think, at least a little bit, like a computer scientist.\n\nAllen B. Downey Needham MA\n\nAllen Downey is a Professor of Computer Science at the Franklin W. Olin College of Engineering.\n\n## **Acknowledgments**\n\nMany thanks to Jeff Elkner, who translated my Java book into Python, which got this project started and introduced me to what has turned out to be my favorite language.\n\nThanks also to Chris Meyers, who contributed several sections to *How to Think Like a Computer Scientist*.\n\nThanks to the Free Software Foundation for developing the GNU Free Documentation License, which helped make my collaboration with Jeff and Chris possible, and Creative Commons for the license I am using now.\n\nThanks to the editors at Lulu who worked on *How to Think Like a Computer Scientist*.\n\nThanks to all the students who worked with earlier versions of this book and all the contributors (listed below) who sent in corrections and suggestions.\n\n## **Contributor List**\n\nMore than 100 sharp-eyed and thoughtful readers have sent in suggestions and corrections over the past few years. Their contributions, and enthusiasm for this project, have been a huge help.\n\nIf you have a suggestion or correction, please send email to feedback@thinkpython.com. If I make a change based on your feedback, I will add you to the contributor list (unless you ask to be omitted).\n\nIf you include at least part of the sentence the error appears in, that makes it easy for me to search. Page and section numbers are fine, too, but not quite as easy to work with. Thanks!\n\n- Lloyd Hugh Allen sent in a correction to Section 8.4.\n- Yvon Boulianne sent in a correction of a semantic error in Chapter 5.\n- Fred Bremmer submitted a correction in Section 2.1.\n- Jonah Cohen wrote the Perl scripts to convert the LaTeX source for this book into beautiful HTML.\n- Michael Conlon sent in a grammar correction in Chapter 2 and an improvement in style in Chapter 1, and he initiated discussion on the technical aspects of interpreters.\n- Benoit Girard sent in a correction to a humorous mistake in Section 5.6.\n- Courtney Gleason and Katherine Smith wrote horsebet.py, which was used as a case study in an earlier version of the book. Their program can now be found on the website.\n- Lee Harr submitted more corrections than we have room to list here, and indeed he should be listed as one of the principal editors of the text.\n- James Kaylin is a student using the text. He has submitted numerous corrections.\n- David Kershaw fixed the broken catTwice function in Section 3.10.\n- Eddie Lam has sent in numerous corrections to Chapters 1, 2, and 3. He also fixed the Makefile so that it creates an index the first time it is run and helped us set up a versioning scheme.\n- Man-Yong Lee sent in a correction to the example code in Section 2.4.\n- David Mayo pointed out that the word \"unconsciously\" in Chapter 1 needed to be changed to \"subconsciously\".\n- Chris McAloon sent in several corrections to Sections 3.9 and 3.10.\n- Matthew J. Moelter has been a long-time contributor who sent in numerous corrections and suggestions to the book.\n- Simon Dicon Montford reported a missing function definition and several typos in Chapter 3. He also found errors in the increment function in Chapter 13.\n- John Ouzts corrected the definition of \"return value\" in Chapter 3.\n- Kevin Parks sent in valuable comments and suggestions as to how to improve the distribution of the book.\n- David Pool sent in a typo in the glossary of Chapter 1, as well as kind words of encouragement.\n- Michael Schmitt sent in a correction to the chapter on files and exceptions.\n- Robin Shaw pointed out an error in Section 13.1, where the printTime function was used in an example without being defined.\n- Paul Sleigh found an error in Chapter 7 and a bug in Jonah Cohen's Perl script that generates HTML from LaTeX.\n- Craig T. Snydal is testing the text in a course at Drew University. He has contributed several valuable suggestions and corrections.\n- Ian Thomas and his students are using the text in a programming course. They are the first ones to test the chapters in the latter half of the book, and they have made numerous corrections and suggestions.\n- Keith Verheyden sent in a correction in Chapter 3.\n- Peter Winstanley let us know about a longstanding error in our Latin in Chapter 3.\n- Chris Wrobel made corrections to the code in the chapter on file I/O and exceptions.\n- Moshe Zadka has made invaluable contributions to this project. In addition to writing the first draft of the chapter on Dictionaries, he provided continual guidance in the early stages of the book.\n- Christoph Zwerschke sent several corrections and pedagogic suggestions, and explained the difference between *gleich* and *selbe*.\n- James Mayer sent us a whole slew of spelling and typographical errors, including two in the contributor list.\n- Hayden McAfee caught a potentially confusing inconsistency between two examples.\n- Angel Arnal is part of an international team of translators working on the Spanish version of the text. He has also found several errors in the English version.\n- Tauhidul Hoque and Lex Berezhny created the illustrations in Chapter 1 and improved many of the other illustrations.\n- Dr. Michele Alzetta caught an error in Chapter 8 and sent some interesting pedagogic comments and suggestions about Fibonacci and Old Maid.\n- Andy Mitchell caught a typo in Chapter 1 and a broken example in Chapter 2.\n- Kalin Harvey suggested a clarification in Chapter 7 and caught some typos.\n- Christopher P. Smith caught several typos and helped us update the book for Python 2.2.\n- David Hutchins caught a typo in the Foreword.\n- Gregor Lingl is teaching Python at a high school in Vienna, Austria. He is working on a German translation of the book, and he caught a couple of bad errors in Chapter 5.\n- Julie Peters caught a typo in the Preface.\n- Florin Oprina sent in an improvement in makeTime, a correction in printTime, and a nice typo.\n- D. J. Webre suggested a clarification in Chapter 3.\n- Ken found a fistful of errors in Chapters 8, 9 and 11.\n- Ivo Wever caught a typo in Chapter 5 and suggested a clarification in Chapter 3.\n- Curtis Yanko suggested a clarification in Chapter 2.\n- Ben Logan sent in a number of typos and problems with translating the book into HTML.\n- Jason Armstrong saw the missing word in Chapter 2.\n- Louis Cordier noticed a spot in Chapter 16 where the code didn't match the text.\n- Brian Cain suggested several clarifications in Chapters 2 and 3.\n- Rob Black sent in a passel of corrections, including some changes for Python 2.2.\n- Jean-Philippe Rey at Ecole Centrale Paris sent a number of patches, including some updates for Python 2.2 and other thoughtful improvements.\n- Jason Mader at George Washington University made a number of useful suggestions and corrections.\n- Jan Gundtofte-Bruun reminded us that \"a error\" is an error.\n- Abel David and Alexis Dinno reminded us that the plural of \"matrix\" is \"matrices\", not \"matrixes\". This error was in the book for years, but two readers with the same initials reported it on the same day. Weird.\n- Charles Thayer encouraged us to get rid of the semi-colons we had put at the ends of some statements and to clean up our use of \"argument\" and \"parameter\".\n- Roger Sperberg pointed out a twisted piece of logic in Chapter 3.\n- Sam Bull pointed out a confusing paragraph in Chapter 2.\n- Andrew Cheung pointed out two instances of \"use before def.\"\n- C. Corey Capel spotted the missing word in the Third Theorem of Debugging and a typo in Chapter 4.\n- Alessandra helped clear up some Turtle confusion.\n- Wim Champagne found a brain-o in a dictionary example.\n- Douglas Wright pointed out a problem with floor division in arc.\n- Jared Spindor found some jetsam at the end of a sentence.\n- Lin Peiheng sent a number of very helpful suggestions.\n- Ray Hagtvedt sent in two errors and a not-quite-error.\n- Torsten Hübsch pointed out an inconsistency in Swampy.\n- Inga Petuhhov corrected an example in Chapter 14.\n- Arne Babenhauserheide sent several helpful corrections.\n- Mark E. Casida is is good at spotting repeated words.\n- Scott Tyler filled in a that was missing. And then sent in a heap of corrections.\n- Gordon Shephard sent in several corrections, all in separate emails.\n- Andrew Turner spotted an error in Chapter 8.\n- Adam Hobart fixed a problem with floor division in arc.\n- Daryl Hammond and Sarah Zimmerman pointed out that I served up math.pi too early. And Zim spotted a typo.\n- George Sass found a bug in a Debugging section.\n- Brian Bingham suggested Exercise [11.10.](#page-132-0)\n- Leah Engelbert-Fenton pointed out that I used tuple as a variable name, contrary to my own advice. And then found a bunch of typos and a \"use before def.\"\n- Joe Funke spotted a typo.\n- Chao-chao Chen found an inconsistency in the Fibonacci example.\n- Jeff Paine knows the difference between space and spam.\n- Lubos Pintes sent in a typo.\n- Gregg Lind and Abigail Heithoff suggested Exercise [14.4.](#page-160-0)\n- Max Hailperin has sent in a number of corrections and suggestions. Max is one of the authors of the extraordinary *Concrete Abstractions*, which you might want to read when you are done with this book.\n- Chotipat Pornavalai found an error in an error message.\n- Stanislaw Antol sent a list of very helpful suggestions.\n- Eric Pashman sent a number of corrections for Chapters 4–11.\n- Miguel Azevedo found some typos.\n- Jianhua Liu sent in a long list of corrections.\n- Nick King found a missing word.\n- Martin Zuther sent a long list of suggestions.\n- Adam Zimmerman found an inconsistency in my instance of an \"instance\" and several other errors.\n- Ratnakar Tiwari suggested a footnote explaining degenerate triangles.\n- Anurag Goel suggested another solution for is_abecedarian and sent some additional corrections. And he knows how to spell Jane Austen.\n- Kelli Kratzer spotted one of the typos.\n- Mark Griffiths pointed out a confusing example in Chapter 3.\n- Roydan Ongie found an error in my Newton's method.\n- Patryk Wolowiec helped me with a problem in the HTML version.\n- Mark Chonofsky told me about a new keyword in Python 3.\n- Russell Coleman helped me with my geometry.\n- Wei Huang spotted several typographical errors.\n- Karen Barber spotted the the oldest typo in the book.\n- Nam Nguyen found a typo and pointed out that I used the Decorator pattern but didn't mention it by name.\n- Stéphane Morin sent in several corrections and suggestions.\n- Paul Stoop corrected a typo in uses_only.\n- Eric Bronner pointed out a confusion in the discussion of the order of operations.\n- Alexandros Gezerlis set a new standard for the number and quality of suggestions he submitted. We are deeply grateful!\n- Gray Thomas knows his right from his left.\n- Giovanni Escobar Sosa sent a long list of corrections and suggestions.\n- Alix Etienne fixed one of the URLs.\n- Kuang He found a typo.\n- Daniel Neilson corrected an error about the order of operations.\n- Will McGinnis pointed out that polyline was defined differently in two places.\n- Swarup Sahoo spotted a missing semi-colon.\n- Frank Hecker pointed out an exercise that was under-specified, and some broken links.\n- Animesh B helped me clean up a confusing example.\n- Martin Caspersen found two round-off errors.\n- Gregor Ulm sent several corrections and suggestions.\n- Dimitrios Tsirigkas suggested I clarify an exercise.\n- Carlos Tafur sent a page of corrections and suggestions.\n- Martin Nordsletten found a bug in an exercise solution.\n- Lars O.D. Christensen found a broken reference.\n- Victor Simeone found a typo.\n- Sven Hoexter pointed out that a variable named input shadows a built-in function.\n- Viet Le found a typo.\n- Stephen Gregory pointed out the problem with cmp in Python 3.\n- Matthew Shultz let me know about a broken link.\n- Lokesh Kumar Makani let me know about some broken links and some changes in error messages.\n- Ishwar Bhat corrected my statement of Fermat's last theorem.\n- Brian McGhie suggested a clarification.\n- Andrea Zanella translated the book into Italian, and sent a number of corrections along the way.\n\n# **Contents**\n\n| Preface | | v | |\n|-----------|-----------------------------------------|------------------------------------------|-----|\n| 1 | The way of the program | 1 | |\n| | 1.1 The Python programming language | 1 | |\n| | 1.2 What is a program? | 3 | |\n| | 1.3 What is debugging? | 3 | |\n| | 1.4 Formal and natural languages | 5 | |\n| | 1.5 The first program | 6 | |\n| | 1.6 Debugging | 7 | |\n| | 1.7 Glossary | 7 | |\n| | 1.8 Exercises | 9 | |\n| 2 | Variables, expressions and statements | 11 | |\n| | 2.1 Values and types | 11 | |\n| | 2.2 Variables | 12 | |\n| | 2.3 Variable names and keywords | 12 | |\n| | 2.4 Operators and operands | 13 | |\n| | 2.5 Expressions and statements | 14 | |\n| | 2.6 Interactive mode and script mode | 14 | |\n| | 2.7 Order of operations | 15 | |\n| | 2.8 String operations | 15 | |\n| | 2.9 Comments | 16 | |\n| | 2.10 Debugging | 16 | |\n| | 2.11 Glossary | 17 | |\n| | 2.12 Exercises | 18 | |\n| 3 | Functions | | 19 |\n| | 3.1 | Function calls
. | 19 |\n| | 3.2 | Type conversion functions | 19 |\n| | 3.3 | Math functions | 20 |\n| | 3.4 | Composition | 21 |\n| | 3.5 | Adding new functions
. | 21 |\n| | 3.6 | Definitions and uses
. | 22 |\n| | 3.7 | Flow of execution
. | 23 |\n| | 3.8 | Parameters and arguments | 23 |\n| | 3.9 | Variables and parameters are local
. | 24 |\n| | 3.10 | Stack diagrams
. | 25 |\n| | 3.11 | Fruitful functions and void functions | 26 |\n| | 3.12 | Why functions?
. | 26 |\n| | 3.13 | Importing with from
. | 27 |\n| | 3.14 | Debugging | 27 |\n| | 3.15 | Glossary
. | 28 |\n| | 3.16 | Exercises
. | 29 |\n| 4 | | Case study: interface design | 31 |\n| | 4.1 | TurtleWorld
. | 31 |\n| | 4.2 | Simple repetition
. | 32 |\n| | 4.3 | Exercises
. | 33 |\n| | 4.4 | Encapsulation
. | 34 |\n| | 4.5 | Generalization | 34 |\n| | 4.6 | Interface design
. | 35 |\n| | 4.7 | Refactoring | 36 |\n| | 4.8 | A development plan
. | 37 |\n| | 4.9 | docstring | 37 |\n| | 4.10 | Debugging | 38 |\n| | 4.11 | Glossary
. | 38 |\n| | 4.12 | Exercises
. | 39 |\n| | | | |\n| 5 | | Conditionals and recursion | 41 |\n| | 5.1 | Modulus operator | 41 |\n| | 5.2 | Boolean expressions | 41 |\n| | 5.3 | Logical operators
. | 42 |\n| | 5.4 | Conditional execution
. | 42 |\n| | 5.5 | Alternative execution | 43 |\n| | 5.6 | Chained conditionals | 43 |\n| | 5.7 | Nested conditionals | 43 |\n| | 5.8 | Recursion | 44 |\n| | 5.9 | Stack diagrams for recursive functions | 45 |\n| | 5.10 | Infinite recursion
. | 46 |\n| | 5.11 | Keyboard input
. | 46 |\n| | 5.12 | Debugging | 47 |\n| | 5.13 | Glossary
. | 48 |\n| | 5.14 | Exercises
. | 49 |\n| | | | |\n| 6 | | Fruitful functions | 51 |\n| | 6.1 | Return values
. | 51 |\n| | 6.2 | Incremental development
. | 52 |\n| | 6.3 | Composition | 54 |\n| | 6.4 | Boolean functions | 54 |\n| | 6.5 | More recursion | 55 |\n| | 6.6 | Leap of faith
. | 57 |\n| | 6.7 | One more example
. | 57 |\n| | 6.8 | Checking types
. | 58 |\n| | 6.9 | Debugging | 59 |\n| | 6.10 | Glossary
. | 60 |\n| | 6.11 | Exercises
. | 60 |\n| 7 | Iteration | | 6 |\n| | 7.1 Multiple assignment | | 6 |\n| | 7.2 Updating variables | | 6 |\n| | 7.3 The while statement | | 6 |\n| | 7.4 break | | 6 |\n| | 7.5 Square roots | | 6 |\n| | 7.6 Algorithms | | 6 |\n| | 7.7 Debugging | | 6 |\n| | 7.8 Glossary | | 6 |\n| | 7.9 Exercises | | 6 |\n| 8 | Strings | | 7 |\n| | 8.1 A string is a sequence | | 7 |\n| | 8.2 len | | 7 |\n| | 8.3 Traversal with a for loop | | 7 |\n| | 8.4 String slices | | 7 |\n| | 8.5 Strings are immutable | | 7 |\n| | 8.6 Searching | | 7 |\n| | 8.7 Looping and counting | | 7 |\n| | 8.8 String methods | | 7 |\n| | 8.9 The in operator | | 7 |\n| | 8.10 String comparison | | 7 |\n| | 8.11 Debugging | | 7 |\n| | 8.12 Glossary | | 7 |\n| | 8.13 Exercises | | 7 |\n| 9 | Case study: word play | | 8 |\n| | 9.1 Reading word lists | | 8 |\n| | 9.2 Exercises | | 8 |\n| | 9.3 Search | | 8 |\n| | 9.4 Looping with indices | | 8 |\n| | 9.5 Debugging | | 8 |\n| | 9.6 Glossary | | 8 |\n| | 9.7 Exercises | | 8 |\n| 10 Lists | | 87 | |\n| 10.1 | A list is a sequence
. | 87 | |\n| 10.2 | Lists are mutable
. | 87 | |\n| 10.3 | Traversing a list
. | 89 | |\n| 10.4 | List operations | 89 | |\n| 10.5 | List slices | 89 | |\n| 10.6 | List methods | 90 | |\n| 10.7 | Map, filter and reduce
. | 91 | |\n| 10.8 | Deleting elements | 92 | |\n| 10.9 | Lists and strings | 93 | |\n| 10.10 | Objects and values
. | 93 | |\n| 10.11 | Aliasing | 94 | |\n| 10.12 | List arguments | 95 | |\n| 10.13 | Debugging | 96 | |\n| 10.14 | Glossary
. | 97 | |\n| 10.15 | Exercises
. | 98 | |\n| | | | |\n| | 11 Dictionaries | 101 | |\n| 11.1 | Dictionary as a set of counters | 102 | |\n| 11.2 | Looping and dictionaries | 103 | |\n| 11.3 | Reverse lookup
. | 104 | |\n| 11.4 | Dictionaries and lists
. | 105 | |\n| 11.5 | Memos
. | 106 | |\n| 11.6 | Global variables | 108 | |\n| 11.7 | Long integers
. | 109 | |\n| 11.8 | Debugging | 109 | |\n| 11.9 | Glossary
. | 110 | |\n| 11.10 | Exercises
. | 111 | |\n| 12 Tuples | | 113 | |\n| 12.1 | Tuples are immutable | 113 | |\n| 12.2 | Tuple assignment
. | 114 | |\n| 12.3 | Tuples as return values | 115 | |\n| 12.4 | Variable-length argument tuples
. | 115 | |\n| 12.5 | Lists and tuples
. | 116 | |\n| 12.6 | Dictionaries and tuples | 117 | |\n| 12.7 | Comparing tuples | 118 | |\n| 12.8 | Sequences of sequences | 119 | |\n| 12.9 | Debugging | 120 | |\n| 12.10 | Glossary
. | 121 | |\n| 12.11 | Exercises
. | 121 | |\n| | 13 Case study: data structure selection | 123 | |\n| 13.1 | Word frequency analysis | 123 | |\n| 13.2 | Random numbers | 124 | |\n| 13.3 | Word histogram | 125 | |\n| 13.4 | Most common words | 126 | |\n| 13.5 | Optional parameters
. | 126 | |\n| 13.6 | Dictionary subtraction
. | 127 | |\n| 13.7 | Random words
. | 127 | |\n| 13.8 | Markov analysis | 128 | |\n| 13.9 | Data structures | 129 | |\n| 13.10 | Debugging | 131 | |\n| 13.11 | Glossary
. | 132 | |\n| 13.12 | Exercises
. | 132 | |\n| | | | |\n| 14 Files | | 133 | |\n| 14.1 | Persistence | 133 | |\n| 14.2 | Reading and writing
. | 133 | |\n| 14.3 | Format operator | 134 | |\n| 14.4 | Filenames and paths
. | 135 | |\n| 14.5 | Catching exceptions | 136 | |\n| 14.6 | Databases | 137 | |\n| 14.7 | Pickling | 137 | |\n| 14.8 | Pipes | 138 | |\n| 14.9 | Writing modules | 139 | |\n| 14.10 | Debugging | 140 | |\n| 14.11 | Glossary | 141 | |\n| 14.12 | Exercises | 141 | |\n| 15 | Classes and objects | 143 | |\n| 15.1 | User-defined types | 143 | |\n| 15.2 | Attributes | 144 | |\n| 15.3 | Rectangles | 145 | |\n| 15.4 | Instances as return values | 146 | |\n| 15.5 | Objects are mutable | 146 | |\n| 15.6 | Copying | 147 | |\n| 15.7 | Debugging | 148 | |\n| 15.8 | Glossary | 149 | |\n| 15.9 | Exercises | 149 | |\n| 16 | Classes and functions | 151 | |\n| 16.1 | Time | 151 | |\n| 16.2 | Pure functions | 151 | |\n| 16.3 | Modifiers | 153 | |\n| 16.4 | Prototyping versus planning | 154 | |\n| 16.5 | Debugging | 155 | |\n| 16.6 | Glossary | 155 | |\n| 16.7 | Exercises | 156 | |\n| 17 | Classes and methods | 157 | |\n| 17.1 | Object-oriented features | 157 | |\n| 17.2 | Printing objects | 158 | |\n| 17.3 | Another example | 159 | |\n| 17.4 | A more complicated example | 160 | |\n| 17.5 | The init method | 160 | |\n| 17.6 | The __str__ method | 161 | |\n| 17.7 | Operator overloading | 161 | |\n| 17.8 | Type-based dispatch | 162 | |\n| 17.9 | Polymorphism | 163 | |\n| 17.10 | Debugging | 164 | |\n| 17.11 | Interface and implementation | 164 | |\n| 17.12 | Glossary | 165 | |\n| 17.13 | Exercises | 165 | |\n| 18 | Inheritance | 167 | |\n| 18.1 | Card objects | 167 | |\n| 18.2 | Class attributes | 168 | |\n| 18.3 | Comparing cards | 169 | |\n| 18.4 | Decks | 170 | |\n| 18.5 | Printing the deck | 171 | |\n| 18.6 | Add, remove, shuffle and sort | 171 | |\n| 18.7 | Inheritance | 172 | |\n| 18.8 | Class diagrams | 173 | |\n| 18.9 | Debugging | 174 | |\n| 18.10 | Data encapsulation | 175 | |\n| | | 19 Case study: Tkinter | 179 |\n| | 19.1 | GUI
. | 179 |\n| | 19.2 | Buttons and callbacks | 180 |\n| | 19.3 | Canvas widgets
. | 181 |\n| | 19.4 | Coordinate sequences | 182 |\n| | 19.5 | More widgets
. | 182 |\n| | 19.6 | Packing widgets | 183 |\n| | 19.7 | Menus and Callables
. | 185 |\n| | 19.8 | Binding | 186 |\n| | 19.9 | Debugging | 188 |\n| | 19.10 | Glossary
. | 189 |\n| | 19.11 | Exercises
. | 190 |\n| A | Debugging | | 193 |\n| | A.1 | Syntax errors | 193 |\n| | A.2 | Runtime errors | 195 |\n| | A.3 | Semantic errors
. | 198 |\n| B | | Analysis of Algorithms | 201 |\n| | B.1 | Order of growth | 202 |\n| | B.2 | Analysis of basic Python operations
. | 204 |\n| | B.3 | Analysis of search algorithms
. | 205 |\n| | B.4 | Hashtables | 206 |\n| C | Lumpy | | 211 |\n| | C.1 | State diagram
. | 211 |\n| | C.2 | Stack diagram
. | 212 |\n| | C.3 | Object diagrams | 213 |\n| | C.4 | Function and class objects
. | 215 |\n| | C.5 | Class Diagrams
. | 216 |\n| | | | |\n\n# **Chapter 1**\n\n# **The way of the program**\n\nThe goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.\n\nThe single most important skill for a computer scientist is **problem solving**. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That's why this chapter is called, \"The way of the program.\"\n\nOn one level, you will be learning to program, a useful skill by itself. On another level, you will use programming as a means to an end. As we go along, that end will become clearer.\n\n### **1.1 The Python programming language**\n\nThe programming language you will learn is Python. Python is an example of a **high-level language**; other high-level languages you might have heard of are C, C++, Perl, and Java.\n\nThere are also **low-level languages**, sometimes referred to as \"machine languages\" or \"assembly languages.\" Loosely speaking, computers can only run programs written in lowlevel languages. So programs written in a high-level language have to be processed before they can run. This extra processing takes some time, which is a small disadvantage of high-level languages.\n\nThe advantages are enormous. First, it is much easier to program in a high-level language. Programs written in a high-level language take less time to write, they are shorter and easier to read, and they are more likely to be correct. Second, high-level languages are **portable**, meaning that they can run on different kinds of computers with few or no modifications. Low-level programs can run on only one kind of computer and have to be rewritten to run on another.\n\n\n\nFigure 1.1: An interpreter processes the program a little at a time, alternately reading lines and performing computations.\n\n\n\nFigure 1.2: A compiler translates source code into object code, which is run by a hardware executor.\n\nDue to these advantages, almost all programs are written in high-level languages. Lowlevel languages are used only for a few specialized applications.\n\nTwo kinds of programs process high-level languages into low-level languages: **interpreters** and **compilers**. An interpreter reads a high-level program and executes it, meaning that it does what the program says. It processes the program a little at a time, alternately reading lines and performing computations. Figure [1.1](#page-23-0) shows the structure of an interpreter.\n\nA compiler reads the program and translates it completely before the program starts running. In this context, the high-level program is called the **source code**, and the translated program is called the **object code** or the **executable**. Once a program is compiled, you can execute it repeatedly without further translation. Figure [1.2](#page-23-1) shows the structure of a compiler.\n\nPython is considered an interpreted language because Python programs are executed by an interpreter. There are two ways to use the interpreter: **interactive mode** and **script mode**. In interactive mode, you type Python programs and the interpreter displays the result:\n\n>>> 1 + 1\n\n2\n\nThe chevron, >>>, is the **prompt** the interpreter uses to indicate that it is ready. If you type 1 + 1, the interpreter replies 2.\n\nAlternatively, you can store code in a file and use the interpreter to execute the contents of the file, which is called a **script**. By convention, Python scripts have names that end with .py.\n\nTo execute the script, you have to tell the interpreter the name of the file. If you have a script named dinsdale.py and you are working in a UNIX command window, you type python dinsdale.py. In other development environments, the details of executing scripts are different. You can find instructions for your environment at the Python website [http:](http://python.org) [//python.org](http://python.org).\n\nWorking in interactive mode is convenient for testing small pieces of code because you can type and execute them immediately. But for anything more than a few lines, you should save your code as a script so you can modify and execute it in the future.\n\n#### **1.2 What is a program?**\n\nA **program** is a sequence of instructions that specifies how to perform a computation. The computation might be something mathematical, such as solving a system of equations or finding the roots of a polynomial, but it can also be a symbolic computation, such as searching and replacing text in a document or (strangely enough) compiling a program.\n\nThe details look different in different languages, but a few basic instructions appear in just about every language:\n\n**input:** Get data from the keyboard, a file, or some other device.\n\n**output:** Display data on the screen or send data to a file or other device.\n\n**math:** Perform basic mathematical operations like addition and multiplication.\n\n**conditional execution:** Check for certain conditions and execute the appropriate code.\n\n**repetition:** Perform some action repeatedly, usually with some variation.\n\nBelieve it or not, that's pretty much all there is to it. Every program you've ever used, no matter how complicated, is made up of instructions that look pretty much like these. So you can think of programming as the process of breaking a large, complex task into smaller and smaller subtasks until the subtasks are simple enough to be performed with one of these basic instructions.\n\nThat may be a little vague, but we will come back to this topic when we talk about **algorithms**.\n\n#### **1.3 What is debugging?**\n\nProgramming is error-prone. For whimsical reasons, programming errors are called **bugs** and the process of tracking them down is called **debugging**.\n\nThree kinds of errors can occur in a program: syntax errors, runtime errors, and semantic errors. It is useful to distinguish between them in order to track them down more quickly.\n\n#### **1.3.1 Syntax errors**\n\nPython can only execute a program if the syntax is correct; otherwise, the interpreter displays an error message. **Syntax** refers to the structure of a program and the rules about that structure. For example, parentheses have to come in matching pairs, so (1 + 2) is legal, but 8) is a **syntax error**.\n\nIn English, readers can tolerate most syntax errors, which is why we can read the poetry of e. e. cummings without spewing error messages. Python is not so forgiving. If there is a single syntax error anywhere in your program, Python will display an error message and quit, and you will not be able to run your program. During the first few weeks of your programming career, you will probably spend a lot of time tracking down syntax errors. As you gain experience, you will make fewer errors and find them faster.\n\n#### **1.3.2 Runtime errors**\n\nThe second type of error is a runtime error, so called because the error does not appear until after the program has started running. These errors are also called **exceptions** because they usually indicate that something exceptional (and bad) has happened.\n\nRuntime errors are rare in the simple programs you will see in the first few chapters, so it might be a while before you encounter one.\n\n#### **1.3.3 Semantic errors**\n\nThe third type of error is the **semantic error**. If there is a semantic error in your program, it will run successfully in the sense that the computer will not generate any error messages, but it will not do the right thing. It will do something else. Specifically, it will do what you told it to do.\n\nThe problem is that the program you wrote is not the program you wanted to write. The meaning of the program (its semantics) is wrong. Identifying semantic errors can be tricky because it requires you to work backward by looking at the output of the program and trying to figure out what it is doing.\n\n#### **1.3.4 Experimental debugging**\n\nOne of the most important skills you will acquire is debugging. Although it can be frustrating, debugging is one of the most intellectually rich, challenging, and interesting parts of programming.\n\nIn some ways, debugging is like detective work. You are confronted with clues, and you have to infer the processes and events that led to the results you see.\n\nDebugging is also like an experimental science. Once you have an idea about what is going wrong, you modify your program and try again. If your hypothesis was correct, then you can predict the result of the modification, and you take a step closer to a working program. If your hypothesis was wrong, you have to come up with a new one. As Sherlock Holmes pointed out, \"When you have eliminated the impossible, whatever remains, however improbable, must be the truth.\" (A. Conan Doyle, *The Sign of Four*)\n\nFor some people, programming and debugging are the same thing. That is, programming is the process of gradually debugging a program until it does what you want. The idea is that you should start with a program that does *something* and make small modifications, debugging them as you go, so that you always have a working program.\n\nFor example, Linux is an operating system that contains thousands of lines of code, but it started out as a simple program Linus Torvalds used to explore the Intel 80386 chip. According to Larry Greenfield, \"One of Linus's earlier projects was a program that would switch between printing AAAA and BBBB. This later evolved to Linux.\" (*The Linux Users' Guide* Beta Version 1).\n\nLater chapters will make more suggestions about debugging and other programming practices.\n\n#### **1.4 Formal and natural languages**\n\n**Natural languages** are the languages people speak, such as English, Spanish, and French. They were not designed by people (although people try to impose some order on them); they evolved naturally.\n\n**Formal languages** are languages that are designed by people for specific applications. For example, the notation that mathematicians use is a formal language that is particularly good at denoting relationships among numbers and symbols. Chemists use a formal language to represent the chemical structure of molecules. And most importantly:\n\n#### **Programming languages are formal languages that have been designed to express computations.**\n\nFormal languages tend to have strict rules about syntax. For example, $3 + 3 = 6$ is a syntactically correct mathematical statement, but $3+ = 3\\$6$ is not. $H_2O$ is a syntactically correct chemical formula, but 2$Zz$ is not.\n\nSyntax rules come in two flavors, pertaining to **tokens** and structure. Tokens are the basic elements of the language, such as words, numbers, and chemical elements. One of the problems with 3+ = 3\\$6 is that \\$ is not a legal token in mathematics (at least as far as I know). Similarly, 2$Zz$ is not legal because there is no element with the abbreviation $Zz$.\n\nThe second type of syntax rule pertains to the structure of a statement; that is, the way the tokens are arranged. The statement 3+ = 3 is illegal because even though + and = are legal tokens, you can't have one right after the other. Similarly, in a chemical formula the subscript comes after the element name, not before.\n\n**Exercise 1.1.** *Write a well-structured English sentence with invalid tokens in it. Then write another sentence with all valid tokens but with invalid structure.*\n\nWhen you read a sentence in English or a statement in a formal language, you have to figure out what the structure of the sentence is (although in a natural language you do this subconsciously). This process is called **parsing**.\n\nFor example, when you hear the sentence, \"The penny dropped,\" you understand that \"the penny\" is the subject and \"dropped\" is the predicate. Once you have parsed a sentence, you can figure out what it means, or the semantics of the sentence. Assuming that you know what a penny is and what it means to drop, you will understand the general implication of this sentence.\n\nAlthough formal and natural languages have many features in common—tokens, structure, syntax, and semantics—there are some differences:\n\n- **ambiguity:** Natural languages are full of ambiguity, which people deal with by using contextual clues and other information. Formal languages are designed to be nearly or completely unambiguous, which means that any statement has exactly one meaning, regardless of context.\n- **redundancy:** In order to make up for ambiguity and reduce misunderstandings, natural languages employ lots of redundancy. As a result, they are often verbose. Formal languages are less redundant and more concise.\n\n- **literalness:** Natural languages are full of idiom and metaphor. If I say, \"The penny dropped,\" there is probably no penny and nothing dropping (this idiom means that someone realized something after a period of confusion). Formal languages mean exactly what they say.\nPeople who grow up speaking a natural language—everyone—often have a hard time adjusting to formal languages. In some ways, the difference between formal and natural language is like the difference between poetry and prose, but more so:\n\n- **Poetry:** Words are used for their sounds as well as for their meaning, and the whole poem together creates an effect or emotional response. Ambiguity is not only common but often deliberate.\n- **Prose:** The literal meaning of words is more important, and the structure contributes more meaning. Prose is more amenable to analysis than poetry but still often ambiguous.\n- **Programs:** The meaning of a computer program is unambiguous and literal, and can be understood entirely by analysis of the tokens and structure.\n\nHere are some suggestions for reading programs (and other formal languages). First, remember that formal languages are much more dense than natural languages, so it takes longer to read them. Also, the structure is very important, so it is usually not a good idea to read from top to bottom, left to right. Instead, learn to parse the program in your head, identifying the tokens and interpreting the structure. Finally, the details matter. Small errors in spelling and punctuation, which you can get away with in natural languages, can make a big difference in a formal language.\n\n#### **1.5 The first program**\n\nTraditionally, the first program you write in a new language is called \"Hello, World!\" because all it does is display the words \"Hello, World!\". In Python, it looks like this:\n\n```\nprint 'Hello, World!'\n```\nThis is an example of a **print statement**, which doesn't actually print anything on paper. It displays a value on the screen. In this case, the result is the words\n\nHello, World!\n\nThe quotation marks in the program mark the beginning and end of the text to be displayed; they don't appear in the result.\n\nIn Python 3, the syntax for printing is slightly different:\n\n```\nprint('Hello, World!')\n```\nThe parentheses indicate that print is a function. We'll get to functions in Chapter [3.](#page-40-0)\n\nFor the rest of this book, I'll use the print statement. If you are using Python 3, you will have to translate. But other than that, there are very few differences we have to worry about.\n\n### **1.6 Debugging**\n\nIt is a good idea to read this book in front of a computer so you can try out the examples as you go. You can run most of the examples in interactive mode, but if you put the code in a script, it is easier to try out variations.\n\nWhenever you are experimenting with a new feature, you should try to make mistakes. For example, in the \"Hello, world!\" program, what happens if you leave out one of the quotation marks? What if you leave out both? What if you spell print wrong?\n\nThis kind of experiment helps you remember what you read; it also helps with debugging, because you get to know what the error messages mean. It is better to make mistakes now and on purpose than later and accidentally.\n\nProgramming, and especially debugging, sometimes brings out strong emotions. If you are struggling with a difficult bug, you might feel angry, despondent or embarrassed.\n\nThere is evidence that people naturally respond to computers as if they were people. When they work well, we think of them as teammates, and when they are obstinate or rude, we respond to them the same way we respond to rude, obstinate people (Reeves and Nass, *The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places*).\n\nPreparing for these reactions might help you deal with them. One approach is to think of the computer as an employee with certain strengths, like speed and precision, and particular weaknesses, like lack of empathy and inability to grasp the big picture.\n\nYour job is to be a good manager: find ways to take advantage of the strengths and mitigate the weaknesses. And find ways to use your emotions to engage with the problem, without letting your reactions interfere with your ability to work effectively.\n\nLearning to debug can be frustrating, but it is a valuable skill that is useful for many activities beyond programming. At the end of each chapter there is a debugging section, like this one, with my thoughts about debugging. I hope they help!\n\n#### **1.7 Glossary**\n\n**problem solving:** The process of formulating a problem, finding a solution, and expressing the solution.\n\n- **high-level language:** A programming language like Python that is designed to be easy for humans to read and write.\n- **low-level language:** A programming language that is designed to be easy for a computer to execute; also called \"machine language\" or \"assembly language.\"\n- **portability:** A property of a program that can run on more than one kind of computer.\n- **interpret:** To execute a program in a high-level language by translating it one line at a time.\n- **compile:** To translate a program written in a high-level language into a low-level language all at once, in preparation for later execution.\n\n**source code:** A program in a high-level language before being compiled.\n\n- **object code:** The output of the compiler after it translates the program.\n- **executable:** Another name for object code that is ready to be executed.\n- **prompt:** Characters displayed by the interpreter to indicate that it is ready to take input from the user.\n- **script:** A program stored in a file (usually one that will be interpreted).\n- **interactive mode:** A way of using the Python interpreter by typing commands and expres sions at the prompt.\n- **script mode:** A way of using the Python interpreter to read and execute statements in a script.\n- **program:** A set of instructions that specifies a computation.\n- **algorithm:** A general process for solving a category of problems.\n\n**bug:** An error in a program.\n\n- **debugging:** The process of finding and removing any of the three kinds of programming errors.\n- **syntax:** The structure of a program.\n- **syntax error:** An error in a program that makes it impossible to parse (and therefore impossible to interpret).\n- **exception:** An error that is detected while the program is running.\n- **semantics:** The meaning of a program.\n- **semantic error:** An error in a program that makes it do something other than what the programmer intended.\n- **natural language:** Any one of the languages that people speak that evolved naturally.\n- **formal language:** Any one of the languages that people have designed for specific purposes, such as representing mathematical ideas or computer programs; all programming languages are formal languages.\n- **token:** One of the basic elements of the syntactic structure of a program, analogous to a word in a natural language.\n- **parse:** To examine a program and analyze the syntactic structure.\n- **print statement:** An instruction that causes the Python interpreter to display a value on the screen.\n\n#### **1.8 Exercises**\n\n**Exercise 1.2.** *Use a web browser to go to the Python website* [http: // python. org](http://python.org) *. This page contains information about Python and links to Python-related pages, and it gives you the ability to search the Python documentation.*\n\n*For example, if you enter* print *in the search window, the first link that appears is the documentation of the* print *statement. At this point, not all of it will make sense to you, but it is good to know where it is.*\n\n**Exercise 1.3.** *Start the Python interpreter and type* help() *to start the online help utility. Or you can type* help('print') *to get information about the* print *statement.*\n\n*If this example doesn't work, you may need to install additional Python documentation or set an environment variable; the details depend on your operating system and version of Python.*\n\n**Exercise 1.4.** *Start the Python interpreter and use it as a calculator. Python's syntax for math operations is almost the same as standard mathematical notation. For example, the symbols* +*,* - *and* / *denote addition, subtraction and division, as you would expect. The symbol for multiplication is* **.*\n\n*If you run a 10 kilometer race in 43 minutes 30 seconds, what is your average time per mile? What is your average speed in miles per hour? (Hint: there are 1.61 kilometers in a mile).*\n\n**10 Chapter 1. The way of the program**\n\n# **Chapter 2**\n\n# **Variables, expressions and statements**\n\n#### **2.1 Values and types**\n\nA **value** is one of the basic things a program works with, like a letter or a number. The values we have seen so far are 1, 2, and 'Hello, World!'.\n\nThese values belong to different **types**: 2 is an integer, and 'Hello, World!' is a **string**, so-called because it contains a \"string\" of letters. You (and the interpreter) can identify strings because they are enclosed in quotation marks.\n\nIf you are not sure what type a value has, the interpreter can tell you.\n\n```\n>>> type('Hello, World!')\n\n>>> type(17)\n\n```\nNot surprisingly, strings belong to the type str and integers belong to the type int. Less obviously, numbers with a decimal point belong to a type called float, because these numbers are represented in a format called **floating-point**.\n\n```\n>>> type(3.2)\n\n```\nWhat about values like '17' and '3.2'? They look like numbers, but they are in quotation marks like strings.\n\n```\n>>> type('17')\n\n>>> type('3.2')\n\n```\nThey're strings.\n\nWhen you type a large integer, you might be tempted to use commas between groups of three digits, as in 1,000,000. This is not a legal integer in Python, but it is legal:\n\nmessage n pi $\\longrightarrow$ 3.1415926535897932 17 'And now for something completely different'\n\nFigure 2.1: State diagram.\n\n>>> 1,000,000 (1, 0, 0)\n\nWell, that's not what we expected at all! Python interprets 1,000,000 as a commaseparated sequence of integers. This is the first example we have seen of a semantic error: the code runs without producing an error message, but it doesn't do the \"right\" thing.\n\n### **2.2 Variables**\n\nOne of the most powerful features of a programming language is the ability to manipulate **variables**. A variable is a name that refers to a value.\n\nAn **assignment statement** creates new variables and gives them values:\n\n```\n>>> message = 'And now for something completely different'\n>>> n = 17\n>>> pi = 3.1415926535897932\n```\nThis example makes three assignments. The first assigns a string to a new variable named message; the second gives the integer 17 to n; the third assigns the (approximate) value of *π* to pi.\n\nA common way to represent variables on paper is to write the name with an arrow pointing to the variable's value. This kind of figure is called a **state diagram** because it shows what state each of the variables is in (think of it as the variable's state of mind). Figure [2.1](#page-33-2) shows the result of the previous example.\n\nThe type of a variable is the type of the value it refers to.\n\n```\n>>> type(message)\n\n>>> type(n)\n\n>>> type(pi)\n\n```\n#### **2.3 Variable names and keywords**\n\nProgrammers generally choose names for their variables that are meaningful—they document what the variable is used for.\n\nVariable names can be arbitrarily long. They can contain both letters and numbers, but they have to begin with a letter. It is legal to use uppercase letters, but it is a good idea to begin variable names with a lowercase letter (you'll see why later).\n\nThe underscore character, _, can appear in a name. It is often used in names with multiple words, such as my_name or airspeed_of_unladen_swallow.\n\nIf you give a variable an illegal name, you get a syntax error:\n\n```\n>>> 76trombones = 'big parade'\nSyntaxError: invalid syntax\n>>> more@ = 1000000\nSyntaxError: invalid syntax\n>>> class = 'Advanced Theoretical Zymurgy'\nSyntaxError: invalid syntax\n```\n76trombones is illegal because it does not begin with a letter. more@ is illegal because it contains an illegal character, @. But what's wrong with class?\n\nIt turns out that class is one of Python's **keywords**. The interpreter uses keywords to recognize the structure of the program, and they cannot be used as variable names.\n\nPython 2 has 31 keywords:\n\n| and | del | from | not | while |\n|----------|---------|--------|--------|-------|\n| as | elif | global | or | with |\n| assert | else | if | pass | yield |\n| break | except | import | print | |\n| class | exec | in | raise | |\n| continue | finally | is | return | |\n| def | for | lambda | try | |\n\nIn Python 3, exec is no longer a keyword, but nonlocal is.\n\nYou might want to keep this list handy. If the interpreter complains about one of your variable names and you don't know why, see if it is on this list.\n\n#### **2.4 Operators and operands**\n\n**Operators** are special symbols that represent computations like addition and multiplication. The values the operator is applied to are called **operands**.\n\nThe operators +, -, *, / and ** perform addition, subtraction, multiplication, division and exponentiation, as in the following examples:\n\n20+32 hour-1 hour*60+minute minute/60 5**2 (5+9)*(15-7)\n\nIn some other languages, ^ is used for exponentiation, but in Python it is a bitwise operator called XOR. I won't cover bitwise operators in this book, but you can read about them at .\n\nIn Python 2, the division operator might not do what you expect:\n\n```\n>>> minute = 59\n>>> minute/60\n0\n```\nThe value of minute is 59, and in conventional arithmetic 59 divided by 60 is 0.98333, not 0. The reason for the discrepancy is that Python is performing **floor division**. When both of the operands are integers, the result is also an integer; floor division chops off the fraction part, so in this example it rounds down to zero.\n\nIn Python 3, the result of this division is a float. The new operator // performs floor division.\n\nIf either of the operands is a floating-point number, Python performs floating-point division, and the result is a float:\n\n>>> minute/60.0 0.98333333333333328\n\n### **2.5 Expressions and statements**\n\nAn **expression** is a combination of values, variables, and operators. A value all by itself is considered an expression, and so is a variable, so the following are all legal expressions (assuming that the variable x has been assigned a value):\n\n17 x x + 17\n\nA **statement** is a unit of code that the Python interpreter can execute. We have seen two kinds of statement: print and assignment.\n\nTechnically an expression is also a statement, but it is probably simpler to think of them as different things. The important difference is that an expression has a value; a statement does not.\n\n#### **2.6 Interactive mode and script mode**\n\nOne of the benefits of working with an interpreted language is that you can test bits of code in interactive mode before you put them in a script. But there are differences between interactive mode and script mode that can be confusing.\n\nFor example, if you are using Python as a calculator, you might type\n\n```\n>>> miles = 26.2\n>>> miles * 1.61\n42.182\n```\nThe first line assigns a value to miles, but it has no visible effect. The second line is an expression, so the interpreter evaluates it and displays the result. So we learn that a marathon is about 42 kilometers.\n\nBut if you type the same code into a script and run it, you get no output at all. In script mode an expression, all by itself, has no visible effect. Python actually evaluates the expression, but it doesn't display the value unless you tell it to:\n\nmiles = 26.2 print miles * 1.61\n\nThis behavior can be confusing at first.\n\nA script usually contains a sequence of statements. If there is more than one statement, the results appear one at a time as the statements execute.\n\nFor example, the script\n\nprint 1 x = 2 print x produces the output 1 2 The assignment statement produces no output. **Exercise 2.1.** *Type the following statements in the Python interpreter to see what they do:* 5 x = 5\n\nx + 1\n\n*Now put the same statements into a script and run it. What is the output? Modify the script by transforming each expression into a print statement and then run it again.*\n\n## **2.7 Order of operations**\n\nWhen more than one operator appears in an expression, the order of evaluation depends on the **rules of precedence**. For mathematical operators, Python follows mathematical convention. The acronym **PEMDAS** is a useful way to remember the rules:\n\n- **P**arentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3-1) is 4, and (1+1)**(5-2) is 8. You can also use parentheses to make an expression easier to read, as in (minute * 100) / 60, even if it doesn't change the result.\n- **E**xponentiation has the next highest precedence, so 2**1+1 is 3, not 4, and 3*1**3 is 3, not 27.\n- **M**ultiplication and **D**ivision have the same precedence, which is higher than **A**ddition and **S**ubtraction, which also have the same precedence. So 2*3-1 is 5, not 4, and 6+4/2 is 8, not 5.\n- Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression $degrees / 2 * pi$, the division happens first and the result is multiplied by pi. To divide by 2*π*, you can use parentheses or write degrees / 2 / pi.\n\nI don't work very hard to remember rules of precedence for other operators. If I can't tell by looking at the expression, I use parentheses to make it obvious.\n\n### **2.8 String operations**\n\nIn general, you can't perform mathematical operations on strings, even if the strings look like numbers, so the following are illegal:\n\n$'2'-'1'$ $'eggs'/'easy'$ $'third'*'a charm' $\n\nThe + operator works with strings, but it might not do what you expect: it performs **concatenation**, which means joining the strings by linking them end-to-end. For example:\n\n```\nfirst = 'throat'\nsecond = 'warbler'\nprint first + second\nThe output of this program is throatwarbler.\n```\nThe * operator also works on strings; it performs repetition. For example, 'Spam'*3 is 'SpamSpamSpam'. If one of the operands is a string, the other has to be an integer.\n\nThis use of + and * makes sense by analogy with addition and multiplication. Just as 4*3 is equivalent to 4+4+4, we expect $'Spam'*3$ to be the same as $'Spam'+'Spam'+'Spam'$, and it is. On the other hand, there is a significant way in which string concatenation and repetition are different from integer addition and multiplication. Can you think of a property that addition has that string concatenation does not?\n\n### **2.9 Comments**\n\nAs programs get bigger and more complicated, they get more difficult to read. Formal languages are dense, and it is often difficult to look at a piece of code and figure out what it is doing, or why.\n\nFor this reason, it is a good idea to add notes to your programs to explain in natural language what the program is doing. These notes are called **comments**, and they start with the # symbol:\n\n```\n# compute the percentage of the hour that has elapsed\npercentage = (minute * 100) / 60\n```\nIn this case, the comment appears on a line by itself. You can also put comments at the end of a line:\n\n```\npercentage = (minute * 100) / 60 # percentage of an hour\n```\nEverything from the # to the end of the line is ignored—it has no effect on the program.\n\nComments are most useful when they document non-obvious features of the code. It is reasonable to assume that the reader can figure out *what* the code does; it is much more useful to explain *why*.\n\nThis comment is redundant with the code and useless:\n\nv = 5 # assign 5 to v\n\nThis comment contains useful information that is not in the code:\n\nGood variable names can reduce the need for comments, but long names can make complex expressions hard to read, so there is a tradeoff.\n\n#### **2.10 Debugging**\n\nAt this point the syntax error you are most likely to make is an illegal variable name, like class and yield, which are keywords, or odd~job and US\\$, which contain illegal characters.\n\nIf you put a space in a variable name, Python thinks it is two operands without an operator:\n\n>>> bad name = 5 SyntaxError: invalid syntax\n\nFor syntax errors, the error messages don't help much. The most common messages are SyntaxError: invalid syntax and SyntaxError: invalid token, neither of which is very informative.\n\nThe runtime error you are most likely to make is a \"use before def;\" that is, trying to use a variable before you have assigned a value. This can happen if you spell a variable name wrong:\n\n```\n>>> principal = 327.68\n>>> interest = principle * rate\nNameError: name 'principle' is not defined\n```\nVariables names are case sensitive, so LaTeX is not the same as latex.\n\nAt this point the most likely cause of a semantic error is the order of operations. For example, to evaluate 1 2*π* , you might be tempted to write\n\n>>> 1.0 / 2.0 * pi\n\nBut the division happens first, so you would get $\\pi/2$, which is not the same thing! There is no way for Python to know what you meant to write, so in this case you don't get an error message; you just get the wrong answer.\n\n#### **2.11 Glossary**\n\n**value:** One of the basic units of data, like a number or string, that a program manipulates.\n\n- **type:** A category of values. The types we have seen so far are integers (type int), floatingpoint numbers (type float), and strings (type str).\n- **integer:** A type that represents whole numbers.\n\n**floating-point:** A type that represents numbers with fractional parts.\n\n**string:** A type that represents sequences of characters.\n\n**variable:** A name that refers to a value.\n\n- **statement:** A section of code that represents a command or action. So far, the statements we have seen are assignments and print statements.\n**assignment:** A statement that assigns a value to a variable.\n\n**state diagram:** A graphical representation of a set of variables and the values they refer to.\n\n- **keyword:** A reserved word that is used by the compiler to parse a program; you cannot use keywords like if, def, and while as variable names.\n- **operator:** A special symbol that represents a simple computation like addition, multiplication, or string concatenation.\n\n**operand:** One of the values on which an operator operates.\n\n**floor division:** The operation that divides two numbers and chops off the fraction part.\n\n- **expression:** A combination of variables, operators, and values that represents a single result value.\n- **evaluate:** To simplify an expression by performing the operations in order to yield a single value.\n- **rules of precedence:** The set of rules governing the order in which expressions involving multiple operators and operands are evaluated.\n\n**concatenate:** To join two operands end-to-end.\n\n- **comment:** Information in a program that is meant for other programmers (or anyone reading the source code) and has no effect on the execution of the program.\n#### **2.12 Exercises**\n\n**Exercise 2.2.** *Assume that we execute the following assignment statements:*\n\n```\nwidth = 17\nheight = 12.0\ndelimiter = '.'\n```\n*For each of the following expressions, write the value of the expression and the type (of the value of the expression).*\n\n```\n1. width/2\n2. width/2.0\n3. height/3\n4. 1 + 2 * 5\n5. delimiter * 5\n```\n*Use the Python interpreter to check your answers.* **Exercise 2.3.** *Practice using the Python interpreter as a calculator:*\n\n- *1. The volume of a sphere with radius r is* $\\frac{4}{3}\\pi r^3$*. What is the volume of a sphere with radius 5? Hint: 392.7 is wrong!*\n- *2. Suppose the cover price of a book is \\$24.95, but bookstores get a 40% discount. Shipping costs \\$3 for the first copy and 75 cents for each additional copy. What is the total wholesale cost for 60 copies?*\n- *3. If I leave my house at 6:52 am and run 1 mile at an easy pace (8:15 per mile), then 3 miles at tempo (7:12 per mile) and 1 mile at easy pace again, what time do I get home for breakfast?*\n\n# **Chapter 3**\n\n# **Functions**\n\n#### **3.1 Function calls**\n\nIn the context of programming, a **function** is a named sequence of statements that performs a computation. When you define a function, you specify the name and the sequence of statements. Later, you can \"call\" the function by name. We have already seen one example of a **function call**:\n\n>>> type(32) \n\nThe name of the function is type. The expression in parentheses is called the **argument** of the function. The result, for this function, is the type of the argument.\n\nIt is common to say that a function \"takes\" an argument and \"returns\" a result. The result is called the **return value**.\n\n### **3.2 Type conversion functions**\n\nPython provides built-in functions that convert values from one type to another. The int function takes any value and converts it to an integer, if it can, or complains otherwise:\n\n```\n>>> int('32')\n32\n>>> int('Hello')\nValueError: invalid literal for int(): Hello\n```\nint can convert floating-point values to integers, but it doesn't round off; it chops off the fraction part:\n\n```\n>>> int(3.99999)\n3\n>>> int(-2.3)\n-2\n```\nfloat converts integers and strings to floating-point numbers:\n\n```\n>>> float(32)\n32.0\n>>> float('3.14159')\n3.14159\nFinally, str converts its argument to a string:\n>>> str(32)\n'32'\n>>> str(3.14159)\n'3.14159'\n```\n### **3.3 Math functions**\n\nPython has a math module that provides most of the familiar mathematical functions. A **module** is a file that contains a collection of related functions.\n\nBefore we can use the module, we have to import it:\n\n```\n>>> import math\n```\nThis statement creates a **module object** named math. If you print the module object, you get some information about it:\n\n```\n>>> print math\n\n```\nThe module object contains the functions and variables defined in the module. To access one of the functions, you have to specify the name of the module and the name of the function, separated by a dot (also known as a period). This format is called **dot notation**.\n\n```\n>>> ratio = signal_power / noise_power\n>>> decibels = 10 * math.log10(ratio)\n>>> radians = 0.7\n>>> height = math.sin(radians)\n```\nThe first example uses log10 to compute a signal-to-noise ratio in decibels (assuming that signal_power and noise_power are defined). The math module also provides log, which computes logarithms base e.\n\nThe second example finds the sine of radians. The name of the variable is a hint that sin and the other trigonometric functions (cos, tan, etc.) take arguments in radians. To convert from degrees to radians, divide by 360 and multiply by 2*π*:\n\n```\n>>> degrees = 45\n>>> radians = degrees / 360.0 * 2 * math.pi\n>>> math.sin(radians)\n0.707106781187\n```\nThe expression math.pi gets the variable pi from the math module. The value of this variable is an approximation of *π*, accurate to about 15 digits.\n\nIf you know your trigonometry, you can check the previous result by comparing it to the square root of two divided by two:\n\n>>> math.sqrt(2) / 2.0 0.707106781187\n\n#### **3.4 Composition**\n\nSo far, we have looked at the elements of a program—variables, expressions, and statements—in isolation, without talking about how to combine them.\n\nOne of the most useful features of programming languages is their ability to take small building blocks and **compose** them. For example, the argument of a function can be any kind of expression, including arithmetic operators:\n\n```\nx = math.sin(degrees / 360.0 * 2 * math.pi)\n```\nAnd even function calls:\n\nx = math.exp(math.log(x+1))\n\nAlmost anywhere you can put a value, you can put an arbitrary expression, with one exception: the left side of an assignment statement has to be a variable name. Any other expression on the left side is a syntax error (we will see exceptions to this rule later).\n\n#### **3.5 Adding new functions**\n\nSo far, we have only been using the functions that come with Python, but it is also possible to add new functions. A **function definition** specifies the name of a new function and the sequence of statements that execute when the function is called.\n\nHere is an example:\n\n```\ndef print_lyrics():\n print \"I'm a lumberjack, and I'm okay.\"\n print \"I sleep all night and I work all day.\"\n```\ndef is a keyword that indicates that this is a function definition. The name of the function is print_lyrics. The rules for function names are the same as for variable names: letters, numbers and some punctuation marks are legal, but the first character can't be a number. You can't use a keyword as the name of a function, and you should avoid having a variable and a function with the same name.\n\nThe empty parentheses after the name indicate that this function doesn't take any arguments.\n\nThe first line of the function definition is called the **header**; the rest is called the **body**. The header has to end with a colon and the body has to be indented. By convention, the indentation is always four spaces (see Section [3.14)](#page-48-1). The body can contain any number of statements.\n\nThe strings in the print statements are enclosed in double quotes. Single quotes and double quotes do the same thing; most people use single quotes except in cases like this where a single quote (which is also an apostrophe) appears in the string.\n\nIf you type a function definition in interactive mode, the interpreter prints ellipses (*...*) to let you know that the definition isn't complete:\n\n```\n>>> def print_lyrics():\n... print \"I'm a lumberjack, and I'm okay.\"\n... print \"I sleep all night and I work all day.\"\n...\n```\nTo end the function, you have to enter an empty line (this is not necessary in a script).\n\nDefining a function creates a variable with the same name.\n\n```\n>>> print print_lyrics\n\n>>> type(print_lyrics)\n\nThe value of print_lyrics is a function object, which has type 'function'.\n```\nThe syntax for calling the new function is the same as for built-in functions:\n\n```\n>>> print_lyrics()\nI'm a lumberjack, and I'm okay.\nI sleep all night and I work all day.\n```\nOnce you have defined a function, you can use it inside another function. For example, to repeat the previous refrain, we could write a function called repeat_lyrics:\n\n```\ndef repeat_lyrics():\n print_lyrics()\n print_lyrics()\n```\nAnd then call repeat_lyrics:\n\n```\n>>> repeat_lyrics()\nI'm a lumberjack, and I'm okay.\nI sleep all night and I work all day.\nI'm a lumberjack, and I'm okay.\nI sleep all night and I work all day.\nBut that's not really how the song goes.\n```\n#### **3.6 Definitions and uses**\n\nPulling together the code fragments from the previous section, the whole program looks like this:\n\n```\ndef print_lyrics():\n print \"I'm a lumberjack, and I'm okay.\"\n print \"I sleep all night and I work all day.\"\ndef repeat_lyrics():\n print_lyrics()\n print_lyrics()\n```\n\n```\nrepeat_lyrics()\n```\nThis program contains two function definitions: print_lyrics and repeat_lyrics. Function definitions get executed just like other statements, but the effect is to create function objects. The statements inside the function do not get executed until the function is called, and the function definition generates no output.\n\nAs you might expect, you have to create a function before you can execute it. In other words, the function definition has to be executed before the first time it is called.\n\n**Exercise 3.1.** *Move the last line of this program to the top, so the function call appears before the definitions. Run the program and see what error message you get.*\n\n**Exercise 3.2.** *Move the function call back to the bottom and move the definition of* print_lyrics *after the definition of* repeat_lyrics*. What happens when you run this program?*\n\n#### **3.7 Flow of execution**\n\nIn order to ensure that a function is defined before its first use, you have to know the order in which statements are executed, which is called the **flow of execution**.\n\nExecution always begins at the first statement of the program. Statements are executed one at a time, in order from top to bottom.\n\nFunction definitions do not alter the flow of execution of the program, but remember that statements inside the function are not executed until the function is called.\n\nA function call is like a detour in the flow of execution. Instead of going to the next statement, the flow jumps to the body of the function, executes all the statements there, and then comes back to pick up where it left off.\n\nThat sounds simple enough, until you remember that one function can call another. While in the middle of one function, the program might have to execute the statements in another function. But while executing that new function, the program might have to execute yet another function!\n\nFortunately, Python is good at keeping track of where it is, so each time a function completes, the program picks up where it left off in the function that called it. When it gets to the end of the program, it terminates.\n\nWhat's the moral of this sordid tale? When you read a program, you don't always want to read from top to bottom. Sometimes it makes more sense if you follow the flow of execution.\n\n#### **3.8 Parameters and arguments**\n\nSome of the built-in functions we have seen require arguments. For example, when you call math.sin you pass a number as an argument. Some functions take more than one argument: math.pow takes two, the base and the exponent.\n\nInside the function, the arguments are assigned to variables called **parameters**. Here is an example of a user-defined function that takes an argument:\n\n```\ndef print_twice(bruce):\n print bruce\n print bruce\n```\nThis function assigns the argument to a parameter named bruce. When the function is called, it prints the value of the parameter (whatever it is) twice.\n\nThis function works with any value that can be printed.\n\n```\n>>> print_twice('Spam')\nSpam\nSpam\n>>> print_twice(17)\n17\n17\n>>> print_twice(math.pi)\n3.14159265359\n3.14159265359\n```\nThe same rules of composition that apply to built-in functions also apply to user-defined functions, so we can use any kind of expression as an argument for print_twice:\n\n```\n>>> print_twice('Spam '*4)\nSpam Spam Spam Spam\nSpam Spam Spam Spam\n>>> print_twice(math.cos(math.pi))\n-1.0\n-1.0\n```\nThe argument is evaluated before the function is called, so in the examples the expressions 'Spam '*4 and math.cos(math.pi) are only evaluated once.\n\nYou can also use a variable as an argument:\n\n```\n>>> michael = 'Eric, the half a bee.'\n>>> print_twice(michael)\nEric, the half a bee.\nEric, the half a bee.\n```\nThe name of the variable we pass as an argument (michael) has nothing to do with the name of the parameter (bruce). It doesn't matter what the value was called back home (in the caller); here in print_twice, we call everybody bruce.\n\n#### **3.9 Variables and parameters are local**\n\nWhen you create a variable inside a function, it is **local**, which means that it only exists inside the function. For example:\n\n```\ndef cat_twice(part1, part2):\n cat = part1 + part2\n print_twice(cat)\n```\nThis function takes two arguments, concatenates them, and prints the result twice. Here is an example that uses it:\n\n```\n>>> line1 = 'Bing tiddle '\n>>> line2 = 'tiddle bang.'\n>>> cat_twice(line1, line2)\nBing tiddle tiddle bang.\nBing tiddle tiddle bang.\n```\nWhen cat_twice terminates, the variable cat is destroyed. If we try to print it, we get an exception:\n\n```\n>>> print cat\nNameError: name 'cat' is not defined\n```\n\n\nFigure 3.1: Stack diagram.\n\nParameters are also local. For example, outside print_twice, there is no such thing as bruce.\n\n#### **3.10 Stack diagrams**\n\nTo keep track of which variables can be used where, it is sometimes useful to draw a **stack diagram**. Like state diagrams, stack diagrams show the value of each variable, but they also show the function each variable belongs to.\n\nEach function is represented by a **frame**. A frame is a box with the name of a function beside it and the parameters and variables of the function inside it. The stack diagram for the previous example is shown in Figure [3.1.](#page-46-1)\n\nThe frames are arranged in a stack that indicates which function called which, and so on. In this example, print_twice was called by cat_twice, and cat_twice was called by __main__, which is a special name for the topmost frame. When you create a variable outside of any function, it belongs to __main__.\n\nEach parameter refers to the same value as its corresponding argument. So, part1 has the same value as line1, part2 has the same value as line2, and bruce has the same value as cat.\n\nIf an error occurs during a function call, Python prints the name of the function, and the name of the function that called it, and the name of the function that called *that*, all the way back to __main__.\n\nFor example, if you try to access cat from within print_twice, you get a NameError:\n\n```\nTraceback (innermost last):\n File \"test.py\", line 13, in __main__\n cat_twice(line1, line2)\n File \"test.py\", line 5, in cat_twice\n print_twice(cat)\n File \"test.py\", line 9, in print_twice\n print cat\nNameError: name 'cat' is not defined\n```\nThis list of functions is called a **traceback**. It tells you what program file the error occurred in, and what line, and what functions were executing at the time. It also shows the line of code that caused the error.\n\nThe order of the functions in the traceback is the same as the order of the frames in the stack diagram. The function that is currently running is at the bottom.\n\n### **3.11 Fruitful functions and void functions**\n\nSome of the functions we are using, such as the math functions, yield results; for lack of a better name, I call them **fruitful functions**. Other functions, like print_twice, perform an action but don't return a value. They are called **void functions**.\n\nWhen you call a fruitful function, you almost always want to do something with the result; for example, you might assign it to a variable or use it as part of an expression:\n\n```\nx = math.cos(radians)\ngolden = (math.sqrt(5) + 1) / 2\n```\nWhen you call a function in interactive mode, Python displays the result:\n\n```\n>>> math.sqrt(5)\n2.2360679774997898\n```\nBut in a script, if you call a fruitful function all by itself, the return value is lost forever!\n\nmath.sqrt(5)\n\nThis script computes the square root of 5, but since it doesn't store or display the result, it is not very useful.\n\nVoid functions might display something on the screen or have some other effect, but they don't have a return value. If you try to assign the result to a variable, you get a special value called None.\n\n```\n>>> result = print_twice('Bing')\nBing\nBing\n>>> print result\nNone\n```\nThe value None is not the same as the string 'None'. It is a special value that has its own type:\n\n```\n>>> print type(None)\n\n```\nThe functions we have written so far are all void. We will start writing fruitful functions in a few chapters.\n\n#### **3.12 Why functions?**\n\nIt may not be clear why it is worth the trouble to divide a program into functions. There are several reasons:\n\n- Creating a new function gives you an opportunity to name a group of statements, which makes your program easier to read and debug.\n- Functions can make a program smaller by eliminating repetitive code. Later, if you make a change, you only have to make it in one place.\n- Dividing a long program into functions allows you to debug the parts one at a time and then assemble them into a working whole.\n- Well-designed functions are often useful for many programs. Once you write and debug one, you can reuse it.\n\n## **3.13 Importing with** from\n\nPython provides two ways to import modules; we have already seen one:\n\n```\n>>> import math\n>>> print math\n\n>>> print math.pi\n3.14159265359\n```\nIf you import math, you get a module object named math. The module object contains constants like pi and functions like sin and exp.\n\nBut if you try to access pi directly, you get an error.\n\n```\n>>> print pi\nTraceback (most recent call last):\n File \"\", line 1, in \nNameError: name 'pi' is not defined\n```\nAs an alternative, you can import an object from a module like this:\n\n```\n>>> from math import pi\n```\nNow you can access pi directly, without dot notation.\n\n```\n>>> print pi\n3.14159265359\n```\nOr you can use the star operator to import *everything* from the module:\n\n```\n>>> from math import *\n>>> cos(pi)\n-1.0\n```\nThe advantage of importing everything from the math module is that your code can be more concise. The disadvantage is that there might be conflicts between names defined in different modules, or between a name from a module and one of your variables.\n\n#### **3.14 Debugging**\n\nIf you are using a text editor to write your scripts, you might run into problems with spaces and tabs. The best way to avoid these problems is to use spaces exclusively (no tabs). Most text editors that know about Python do this by default, but some don't.\n\nTabs and spaces are usually invisible, which makes them hard to debug, so try to find an editor that manages indentation for you.\n\nAlso, don't forget to save your program before you run it. Some development environments do this automatically, but some don't. In that case the program you are looking at in the text editor is not the same as the program you are running.\n\nDebugging can take a long time if you keep running the same, incorrect, program over and over!\n\nMake sure that the code you are looking at is the code you are running. If you're not sure, put something like print 'hello' at the beginning of the program and run it again. If you don't see hello, you're not running the right program!\n\n#### **3.15 Glossary**\n\n- **function:** A named sequence of statements that performs some useful operation. Functions may or may not take arguments and may or may not produce a result.\n- **function definition:** A statement that creates a new function, specifying its name, parameters, and the statements it executes.\n- **function object:** A value created by a function definition. The name of the function is a variable that refers to a function object.\n- **header:** The first line of a function definition.\n- **body:** The sequence of statements inside a function definition.\n- **parameter:** A name used inside a function to refer to the value passed as an argument.\n- **function call:** A statement that executes a function. It consists of the function name followed by an argument list.\n- **argument:** A value provided to a function when the function is called. This value is assigned to the corresponding parameter in the function.\n- **local variable:** A variable defined inside a function. A local variable can only be used inside its function.\n- **return value:** The result of a function. If a function call is used as an expression, the return value is the value of the expression.\n- **fruitful function:** A function that returns a value.\n- **void function:** A function that doesn't return a value.\n- **module:** A file that contains a collection of related functions and other definitions.\n- **import statement:** A statement that reads a module file and creates a module object.\n- **module object:** A value created by an import statement that provides access to the values defined in a module.\n- **dot notation:** The syntax for calling a function in another module by specifying the module name followed by a dot (period) and the function name.\n- **composition:** Using an expression as part of a larger expression, or a statement as part of a larger statement.\n\n**flow of execution:** The order in which statements are executed during a program run.\n\n- **stack diagram:** A graphical representation of a stack of functions, their variables, and the values they refer to.\n- **frame:** A box in a stack diagram that represents a function call. It contains the local variables and parameters of the function.\n\n**traceback:** A list of the functions that are executing, printed when an exception occurs.\n\n#### **3.16 Exercises**\n\n**Exercise 3.3.** *Python provides a built-in function called* len *that returns the length of a string, so the value of* len('allen') *is 5.*\n\n*Write a function named* right_justify *that takes a string named* s *as a parameter and prints the string with enough leading spaces so that the last letter of the string is in column 70 of the display.*\n\n```\n>>> right_justify('allen')\n```\nallen\n\n**Exercise 3.4.** *A function object is a value you can assign to a variable or pass as an argument. For example,* do_twice *is a function that takes a function object as an argument and calls it twice:*\n\n```\ndef do_twice(f):\n f()\n f()\n```\n*Here's an example that uses* do_twice *to call a function named* print_spam *twice.*\n\n```\ndef print_spam():\n print 'spam'\n```\n\n```\ndo_twice(print_spam)\n```\n- *1. Type this example into a script and test it.*\n- *2. Modify* do_twice *so that it takes two arguments, a function object and a value, and calls the function twice, passing the value as an argument.*\n- *3. Write a more general version of* print_spam*, called* print_twice*, that takes a string as a parameter and prints it twice.*\n- *4. Use the modified version of* do_twice *to call* print_twice *twice, passing* 'spam' *as an argument.*\n- *5. Define a new function called* do_four *that takes a function object and a value and calls the function four times, passing the value as a parameter. There should be only two statements in the body of this function, not four.*\n\n*Solution:* *.* **Exercise 3.5.** *This exercise can be done using only the statements and other features we have learned so far.*\n\n- *1. Write a function that draws a grid like the following:*\n\n```\n+ - - - - + - - - - +\n| | |\n| | |\n| | |\n| | |\n+ - - - - + - - - - +\n| | |\n| | |\n| | |\n| | |\n+ - - - - + - - - - +\n```\n*Hint: to print more than one value on a line, you can print a comma-separated sequence:*\n\n```\nprint '+', '-'\n```\n*If the sequence ends with a comma, Python leaves the line unfinished, so the value printed next appears on the same line.*\n\n```\nprint '+',\nprint '-'\n```\n*The output of these statements is* '+ -'*.*\n\n*A* print *statement all by itself ends the current line and goes to the next line.*\n\n- *2. Write a function that draws a similar grid with four rows and four columns.*\n*Solution:* *Credit: This exercise is based on an exercise in Oualline,* Practical C Programming, Third Edition*, O'Reilly Media, 1997.*\n\n# **Chapter 4**\n\n# **Case study: interface design**\n\nCode examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/polygon.py) [polygon.py](http://thinkpython.com/code/polygon.py).\n\n#### **4.1 TurtleWorld**\n\nTo accompany this book, I have written a package called Swampy. You can download Swampy from ; follow the instructions there to install Swampy on your system.\n\nA **package** is a collection of modules; one of the modules in Swampy is TurtleWorld, which provides a set of functions for drawing lines by steering turtles around the screen.\n\nIf Swampy is installed as a package on your system, you can import TurtleWorld like this:\n\n```\nfrom swampy.TurtleWorld import *\n```\nIf you downloaded the Swampy modules but did not install them as a package, you can either work in the directory that contains the Swampy files, or add that directory to Python's search path. Then you can import TurtleWorld like this:\n\nfrom TurtleWorld import *\n\nThe details of the installation process and setting Python's search path depend on your system, so rather than include those details here, I will try to maintain current information for several systems at \n\nCreate a file named mypolygon.py and type in the following code:\n\n```\nfrom swampy.TurtleWorld import *\nworld = TurtleWorld()\nbob = Turtle()\nprint bob\n```\nwait_for_user()\n\nThe first line imports everything from the TurtleWorld module in the swampy package.\n\nThe next lines create a TurtleWorld assigned to world and a Turtle assigned to bob. Printing bob yields something like:\n\n\n\nThis means that bob refers to an **instance** of a Turtle as defined in module TurtleWorld. In this context, \"instance\" means a member of a set; this Turtle is one of the set of possible Turtles.\n\nwait_for_user tells TurtleWorld to wait for the user to do something, although in this case there's not much for the user to do except close the window.\n\nTurtleWorld provides several turtle-steering functions: fd and bk for forward and backward, and lt and rt for left and right turns. Also, each Turtle is holding a pen, which is either down or up; if the pen is down, the Turtle leaves a trail when it moves. The functions pu and pd stand for \"pen up\" and \"pen down.\"\n\nTo draw a right angle, add these lines to the program (after creating bob and before calling wait_for_user):\n\nfd(bob, 100) lt(bob) fd(bob, 100)\n\nThe first line tells bob to take 100 steps forward. The second line tells him to turn left.\n\nWhen you run this program, you should see bob move east and then north, leaving two line segments behind.\n\nNow modify the program to draw a square. Don't go on until you've got it working!\n\n#### **4.2 Simple repetition**\n\nChances are you wrote something like this (leaving out the code that creates TurtleWorld and waits for the user):\n\n```\nfd(bob, 100)\nlt(bob)\nfd(bob, 100)\nlt(bob)\nfd(bob, 100)\nlt(bob)\nfd(bob, 100)\n```\nWe can do the same thing more concisely with a for statement. Add this example to mypolygon.py and run it again:\n\n```\nfor i in range(4):\n print 'Hello!'\n```\nYou should see something like this:\n\nHello! Hello! Hello!\n\nHello!\n\nThis is the simplest use of the for statement; we will see more later. But that should be enough to let you rewrite your square-drawing program. Don't go on until you do.\n\nHere is a for statement that draws a square:\n\nfor i in range(4): fd(bob, 100) lt(bob)\n\nThe syntax of a for statement is similar to a function definition. It has a header that ends with a colon and an indented body. The body can contain any number of statements.\n\nA for statement is sometimes called a **loop** because the flow of execution runs through the body and then loops back to the top. In this case, it runs the body four times.\n\nThis version is actually a little different from the previous square-drawing code because it makes another turn after drawing the last side of the square. The extra turn takes a little more time, but it simplifies the code if we do the same thing every time through the loop. This version also has the effect of leaving the turtle back in the starting position, facing in the starting direction.\n\n#### **4.3 Exercises**\n\nThe following is a series of exercises using TurtleWorld. They are meant to be fun, but they have a point, too. While you are working on them, think about what the point is.\n\nThe following sections have solutions to the exercises, so don't look until you have finished (or at least tried).\n\n- 1. Write a function called square that takes a parameter named t, which is a turtle. It should use the turtle to draw a square.\nWrite a function call that passes bob as an argument to square, and then run the program again.\n\n- 2. Add another parameter, named length, to square. Modify the body so length of the sides is length, and then modify the function call to provide a second argument. Run the program again. Test your program with a range of values for length.\n- 3. The functions lt and rt make 90-degree turns by default, but you can provide a second argument that specifies the number of degrees. For example, lt(bob, 45) turns bob 45 degrees to the left.\n\nMake a copy of square and change the name to polygon. Add another parameter named n and modify the body so it draws an n-sided regular polygon. Hint: The exterior angles of an n-sided regular polygon are 360/*n* degrees.\n\n- 4. Write a function called circle that takes a turtle, t, and radius, r, as parameters and that draws an approximate circle by invoking polygon with an appropriate length and number of sides. Test your function with a range of values of r.\nHint: figure out the circumference of the circle and make sure that length * n = circumference.\n\nAnother hint: if bob is too slow for you, you can speed him up by changing bob.delay, which is the time between moves, in seconds. bob.delay = 0.01 ought to get him moving.\n\n- 5. Make a more general version of circle called arc that takes an additional parameter angle, which determines what fraction of a circle to draw. angle is in units of degrees, so when angle=360, arc should draw a complete circle.\n#### **4.4 Encapsulation**\n\nThe first exercise asks you to put your square-drawing code into a function definition and then call the function, passing the turtle as a parameter. Here is a solution:\n\n```\ndef square(t):\n```\n\n```\nfor i in range(4):\n fd(t, 100)\n lt(t)\n```\nsquare(bob)\n\nThe innermost statements, fd and lt are indented twice to show that they are inside the for loop, which is inside the function definition. The next line, square(bob), is flush with the left margin, so that is the end of both the for loop and the function definition.\n\nInside the function, t refers to the same turtle bob refers to, so lt(t) has the same effect as lt(bob). So why not call the parameter bob? The idea is that t can be any turtle, not just bob, so you could create a second turtle and pass it as an argument to square:\n\n```\nray = Turtle()\nsquare(ray)\n```\nWrapping a piece of code up in a function is called **encapsulation**. One of the benefits of encapsulation is that it attaches a name to the code, which serves as a kind of documentation. Another advantage is that if you re-use the code, it is more concise to call a function twice than to copy and paste the body!\n\n#### **4.5 Generalization**\n\nThe next step is to add a length parameter to square. Here is a solution:\n\n```\ndef square(t, length):\n for i in range(4):\n fd(t, length)\n lt(t)\n```\nsquare(bob, 100)\n\nAdding a parameter to a function is called **generalization** because it makes the function more general: in the previous version, the square is always the same size; in this version it can be any size.\n\nThe next step is also a generalization. Instead of drawing squares, polygon draws regular polygons with any number of sides. Here is a solution :rule\n\n```\ndef polygon(t, n, length):\n angle = 360.0 / n\n for i in range(n):\n fd(t, length)\n lt(t, angle)\n```\n\n```\npolygon(bob, 7, 70)\n```\nThis draws a 7-sided polygon with side length 70. If you have more than a few numeric arguments, it is easy to forget what they are, or what order they should be in. It is legal, and sometimes helpful, to include the names of the parameters in the argument list:\n\n```\npolygon(bob, n=7, length=70)\n```\nThese are called **keyword arguments** because they include the parameter names as \"keywords\" (not to be confused with Python keywords like while and def).\n\nThis syntax makes the program more readable. It is also a reminder about how arguments and parameters work: when you call a function, the arguments are assigned to the parameters.\n\n#### **4.6 Interface design**\n\nThe next step is to write circle, which takes a radius, r, as a parameter. Here is a simple solution that uses polygon to draw a 50-sided polygon:\n\n```\ndef circle(t, r):\n circumference = 2 * math.pi * r\n n = 50\n length = circumference / n\n polygon(t, n, length)\n```\nThe first line computes the circumference of a circle with radius r using the formula 2*πr*. Since we use math.pi, we have to import math. By convention, import statements are usually at the beginning of the script.\n\nn is the number of line segments in our approximation of a circle, so length is the length of each segment. Thus, polygon draws a 50-sides polygon that approximates a circle with radius r.\n\nOne limitation of this solution is that n is a constant, which means that for very big circles, the line segments are too long, and for small circles, we waste time drawing very small segments. One solution would be to generalize the function by taking n as a parameter. This would give the user (whoever calls circle) more control, but the interface would be less clean.\n\nThe **interface** of a function is a summary of how it is used: what are the parameters? What does the function do? And what is the return value? An interface is \"clean\" if it is \"as simple as possible, but not simpler. (Einstein)\"\n\nIn this example, r belongs in the interface because it specifies the circle to be drawn. n is less appropriate because it pertains to the details of *how* the circle should be rendered.\n\nRather than clutter up the interface, it is better to choose an appropriate value of n depending on circumference:\n\n```\ndef circle(t, r):\n circumference = 2 * math.pi * r\n n = int(circumference / 3) + 1\n length = circumference / n\n polygon(t, n, length)\n```\nNow the number of segments is (approximately) circumference/3, so the length of each segment is (approximately) 3, which is small enough that the circles look good, but big enough to be efficient, and appropriate for any size circle.\n\n### **4.7 Refactoring**\n\nWhen I wrote circle, I was able to re-use polygon because a many-sided polygon is a good approximation of a circle. But arc is not as cooperative; we can't use polygon or circle to draw an arc.\n\nOne alternative is to start with a copy of polygon and transform it into arc. The result might look like this:\n\n```\ndef arc(t, r, angle):\n arc_length = 2 * math.pi * r * angle / 360\n n = int(arc_length / 3) + 1\n step_length = arc_length / n\n step_angle = float(angle) / n\n for i in range(n):\n fd(t, step_length)\n lt(t, step_angle)\n```\nThe second half of this function looks like polygon, but we can't re-use polygon without changing the interface. We could generalize polygon to take an angle as a third argument, but then polygon would no longer be an appropriate name! Instead, let's call the more general function polyline:\n\n```\ndef polyline(t, n, length, angle):\n for i in range(n):\n fd(t, length)\n lt(t, angle)\n```\nNow we can rewrite polygon and arc to use polyline:\n\n```\ndef polygon(t, n, length):\n angle = 360.0 / n\n polyline(t, n, length, angle)\n```\n\n```\ndef arc(t, r, angle):\n arc_length = 2 * math.pi * r * angle / 360\n n = int(arc_length / 3) + 1\n step_length = arc_length / n\n step_angle = float(angle) / n\n polyline(t, n, step_length, step_angle)\n```\nFinally, we can rewrite circle to use arc:\n\nThis process—rearranging a program to improve function interfaces and facilitate code reuse—is called **refactoring**. In this case, we noticed that there was similar code in arc and polygon, so we \"factored it out\" into polyline.\n\nIf we had planned ahead, we might have written polyline first and avoided refactoring, but often you don't know enough at the beginning of a project to design all the interfaces. Once you start coding, you understand the problem better. Sometimes refactoring is a sign that you have learned something.\n\n### **4.8 A development plan**\n\nA **development plan** is a process for writing programs. The process we used in this case study is \"encapsulation and generalization.\" The steps of this process are:\n\n- 1. Start by writing a small program with no function definitions.\n- 2. Once you get the program working, encapsulate it in a function and give it a name.\n- 3. Generalize the function by adding appropriate parameters.\n- 4. Repeat steps 1–3 until you have a set of working functions. Copy and paste working code to avoid retyping (and re-debugging).\n- 5. Look for opportunities to improve the program by refactoring. For example, if you have similar code in several places, consider factoring it into an appropriately general function.\n\nThis process has some drawbacks—we will see alternatives later—but it can be useful if you don't know ahead of time how to divide the program into functions. This approach lets you design as you go along.\n\n#### **4.9 docstring**\n\nA **docstring** is a string at the beginning of a function that explains the interface (\"doc\" is short for \"documentation\"). Here is an example:\n\n```\ndef polyline(t, n, length, angle):\n \"\"\"Draws n line segments with the given length and\n angle (in degrees) between them. t is a turtle.\n \"\"\"\n for i in range(n):\n fd(t, length)\n lt(t, angle)\n```\nThis docstring is a triple-quoted string, also known as a multiline string because the triple quotes allow the string to span more than one line.\n\nIt is terse, but it contains the essential information someone would need to use this function. It explains concisely what the function does (without getting into the details of how it does it). It explains what effect each parameter has on the behavior of the function and what type each parameter should be (if it is not obvious).\n\nWriting this kind of documentation is an important part of interface design. A welldesigned interface should be simple to explain; if you are having a hard time explaining one of your functions, that might be a sign that the interface could be improved.\n\n#### **4.10 Debugging**\n\nAn interface is like a contract between a function and a caller. The caller agrees to provide certain parameters and the function agrees to do certain work.\n\nFor example, polyline requires four arguments: t has to be a Turtle; n is the number of line segments, so it has to be an integer; length should be a positive number; and angle has to be a number, which is understood to be in degrees.\n\nThese requirements are called **preconditions** because they are supposed to be true before the function starts executing. Conversely, conditions at the end of the function are **postconditions**. Postconditions include the intended effect of the function (like drawing line segments) and any side effects (like moving the Turtle or making other changes in the World).\n\nPreconditions are the responsibility of the caller. If the caller violates a (properly documented!) precondition and the function doesn't work correctly, the bug is in the caller, not the function.\n\n#### **4.11 Glossary**\n\n- **instance:** A member of a set. The TurtleWorld in this chapter is a member of the set of TurtleWorlds.\n- **loop:** A part of a program that can execute repeatedly.\n- **encapsulation:** The process of transforming a sequence of statements into a function definition.\n- **generalization:** The process of replacing something unnecessarily specific (like a number) with something appropriately general (like a variable or parameter).\n- **keyword argument:** An argument that includes the name of the parameter as a \"keyword.\"\n- **interface:** A description of how to use a function, including the name and descriptions of the arguments and return value.\n- **refactoring:** The process of modifying a working program to improve function interfaces and other qualities of the code.\n\n\n\nFigure 4.1: Turtle flowers.\n\n\n\nFigure 4.2: Turtle pies.\n\n**development plan:** A process for writing programs.\n\n- **docstring:** A string that appears in a function definition to document the function's interface.\n**precondition:** A requirement that should be satisfied by the caller before a function starts.\n\n**postcondition:** A requirement that should be satisfied by the function before it ends.\n\n#### **4.12 Exercises**\n\n**Exercise 4.1.** *Download the code in this chapter from* [http://thinkpython.com/code/](http://thinkpython.com/code/polygon.py) [polygon. py](http://thinkpython.com/code/polygon.py) *.*\n\n- *1. Write appropriate docstrings for* polygon*,* arc *and* circle*.*\n- *2. Draw a stack diagram that shows the state of the program while executing* circle(bob, radius)*. You can do the arithmetic by hand or add* print *statements to the code.*\n- *3. The version of* arc *in Section [4.7](#page-57-0) is not very accurate because the linear approximation of the circle is always outside the true circle. As a result, the turtle ends up a few units away from the correct destination. My solution shows a way to reduce the effect of this error. Read the code and see if it makes sense to you. If you draw a diagram, you might see how it works.*\n\n**Exercise 4.2.** *Write an appropriately general set of functions that can draw flowers as in Figure [4.1.](#page-60-1)*\n\n*Solution:* [http: // thinkpython. com/ code/ flower. py](http://thinkpython.com/code/flower.py) *, also requires* [http:](http://thinkpython.com/code/polygon.py) [// thinkpython. com/ code/ polygon. py](http://thinkpython.com/code/polygon.py) *.*\n\n**Exercise 4.3.** *Write an appropriately general set of functions that can draw shapes as in Figure [4.2.](#page-60-2)*\n\n*Solution:* *.* \n\n**Exercise 4.4.** *The letters of the alphabet can be constructed from a moderate number of basic elements, like vertical and horizontal lines and a few curves. Design a font that can be drawn with a minimal number of basic elements and then write functions that draw letters of the alphabet.*\n\n*You should write one function for each letter, with names* draw_a*,* draw_b*, etc., and put your functions in a file named* letters.py*. You can download a \"turtle typewriter\" from* [http: //](http://thinkpython.com/code/typewriter.py) [thinkpython. com/ code/ typewriter. py](http://thinkpython.com/code/typewriter.py) *to help you test your code.*\n\n*Solution:* [http: // thinkpython. com/ code/ letters. py](http://thinkpython.com/code/letters.py) *, also requires* [http:](http://thinkpython.com/code/polygon.py) [// thinkpython. com/ code/ polygon. py](http://thinkpython.com/code/polygon.py) *.*\n\n**Exercise 4.5.** *Read about spirals at* *; then write a program that draws an Archimedian spiral (or one of the other kinds). Solution:* [http:](http://thinkpython.com/code/spiral.py) [// thinkpython. com/ code/ spiral. py](http://thinkpython.com/code/spiral.py) *.*\n\n# **Chapter 5**\n\n# **Conditionals and recursion**\n\n#### **5.1 Modulus operator**\n\nThe **modulus operator** works on integers and yields the remainder when the first operand is divided by the second. In Python, the modulus operator is a percent sign (%). The syntax is the same as for other operators:\n\n```\n>>> quotient = 7 / 3\n>>> print quotient\n2\n>>> remainder = 7 % 3\n>>> print remainder\n1\nSo 7 divided by 3 is 2 with 1 left over.\n```\nThe modulus operator turns out to be surprisingly useful. For example, you can check whether one number is divisible by another—if x % y is zero, then x is divisible by y.\n\nAlso, you can extract the right-most digit or digits from a number. For example, $x % 10 $yields the right-most digit of x (in base 10). Similarly x % 100 yields the last two digits.\n\n#### **5.2 Boolean expressions**\n\nA **boolean expression** is an expression that is either true or false. The following examples use the operator ==, which compares two operands and produces True if they are equal and False otherwise:\n\n```\n>>> 5 == 5\nTrue\n>>> 5 == 6\nFalse\n```\nTrue and False are special values that belong to the type bool; they are not strings:\n\n```\n>>> type(True)\n\n>>> type(False)\n\n```\nThe == operator is one of the **relational operators**; the others are:\n\n| x != y | # x is not equal to y |\n|--------|-----------------------------------|\n| x > y | # x is greater than y |\n| x < y | # x is less than y |\n| x >= y | # x is greater than or equal to y |\n| x <= y | # x is less than or equal to y |\n\nAlthough these operations are probably familiar to you, the Python symbols are different from the mathematical symbols. A common error is to use a single equal sign (=) instead of a double equal sign (==). Remember that = is an assignment operator and == is a relational operator. There is no such thing as =< or =>.\n\n#### **5.3 Logical operators**\n\nThere are three **logical operators**: and, or, and not. The semantics (meaning) of these operators is similar to their meaning in English. For example, $x > 0$ and $x < 10$ is true only if x is greater than 0 *and* less than 10.\n\n$n%2 == 0$ or $n%3 == 0$ is true if *either* of the conditions is true, that is, if the number is divisible by 2 *or* 3.\n\nFinally, the not operator negates a boolean expression, so not (x > y) is true if x > y is false, that is, if x is less than or equal to y.\n\nStrictly speaking, the operands of the logical operators should be boolean expressions, but Python is not very strict. Any nonzero number is interpreted as \"true.\"\n\n```\n>>> 17 and True\n```\nTrue\n\nThis flexibility can be useful, but there are some subtleties to it that might be confusing. You might want to avoid it (unless you know what you are doing).\n\n### **5.4 Conditional execution**\n\nIn order to write useful programs, we almost always need the ability to check conditions and change the behavior of the program accordingly. **Conditional statements** give us this ability. The simplest form is the if statement:\n\n```\nif x > 0:\n print 'x is positive'\n```\nThe boolean expression after if is called the **condition**. If it is true, then the indented statement gets executed. If not, nothing happens.\n\nif statements have the same structure as function definitions: a header followed by an indented body. Statements like this are called **compound statements**.\n\nThere is no limit on the number of statements that can appear in the body, but there has to be at least one. Occasionally, it is useful to have a body with no statements (usually as a place keeper for code you haven't written yet). In that case, you can use the pass statement, which does nothing.\n\nif x < 0: pass # need to handle negative values!\n\n#### **5.5 Alternative execution**\n\nA second form of the if statement is **alternative execution**, in which there are two possibilities and the condition determines which one gets executed. The syntax looks like this:\n\nif x%2 == 0: print 'x is even' else: print 'x is odd'\n\nIf the remainder when x is divided by 2 is 0, then we know that x is even, and the program displays a message to that effect. If the condition is false, the second set of statements is executed. Since the condition must be true or false, exactly one of the alternatives will be executed. The alternatives are called **branches**, because they are branches in the flow of execution.\n\n### **5.6 Chained conditionals**\n\nSometimes there are more than two possibilities and we need more than two branches. One way to express a computation like that is a **chained conditional**:\n\n```\nif x < y:\n print 'x is less than y'\nelif x > y:\n print 'x is greater than y'\nelse:\n print 'x and y are equal'\n```\nelif is an abbreviation of \"else if.\" Again, exactly one branch will be executed. There is no limit on the number of elif statements. If there is an else clause, it has to be at the end, but there doesn't have to be one.\n\n```\nif choice == 'a':\n draw_a()\nelif choice == 'b':\n draw_b()\nelif choice == 'c':\n draw_c()\n```\nEach condition is checked in order. If the first is false, the next is checked, and so on. If one of them is true, the corresponding branch executes, and the statement ends. Even if more than one condition is true, only the first true branch executes.\n\n### **5.7 Nested conditionals**\n\nOne conditional can also be nested within another. We could have written the trichotomy example like this:\n\n```\nif x == y:\n print 'x and y are equal'\nelse:\n if x < y:\n```\nprint 'x is less than y' else: print 'x is greater than y'\n\nThe outer conditional contains two branches. The first branch contains a simple statement. The second branch contains another if statement, which has two branches of its own. Those two branches are both simple statements, although they could have been conditional statements as well.\n\nAlthough the indentation of the statements makes the structure apparent, **nested conditionals** become difficult to read very quickly. In general, it is a good idea to avoid them when you can.\n\nLogical operators often provide a way to simplify nested conditional statements. For example, we can rewrite the following code using a single conditional:\n\n```\nif 0 < x:\n if x < 10:\n print 'x is a positive single-digit number.'\n```\nThe print statement is executed only if we make it past both conditionals, so we can get the same effect with the and operator:\n\n```\nif 0 < x and x < 10:\n print 'x is a positive single-digit number.'\n```\n#### **5.8 Recursion**\n\nIt is legal for one function to call another; it is also legal for a function to call itself. It may not be obvious why that is a good thing, but it turns out to be one of the most magical things a program can do. For example, look at the following function:\n\n```\ndef countdown(n):\n if n <= 0:\n print 'Blastoff!'\n else:\n print n\n countdown(n-1)\n```\nIf n is 0 or negative, it outputs the word, \"Blastoff!\" Otherwise, it outputs n and then calls a function named countdown—itself—passing n-1 as an argument.\n\nWhat happens if we call this function like this?\n\n```\n>>> countdown(3)\n```\nThe execution of countdown begins with n=3, and since n is greater than 0, it outputs the value 3, and then calls itself...\n\nThe execution of countdown begins with n=2, and since n is greater than 0, it outputs the value 2, and then calls itself...\n\nThe execution of countdown begins with n=1, and since n is greater than 0, it outputs the value 1, and then calls itself...\n\nThe execution of countdown begins with n=0, and since n is not greater than 0, it outputs the word, \"Blastoff!\" and then returns.\n\nThe countdown that got n=1 returns.\n\nThe countdown that got n=2 returns.\n\nThe countdown that got n=3 returns.\n\nAnd then you're back in __main__. So, the total output looks like this:\n\n```\n3\n2\n1\nBlastoff!\n```\nA function that calls itself is **recursive**; the process is called **recursion**.\n\nAs another example, we can write a function that prints a string n times.\n\n```\ndef print_n(s, n):\n if n <= 0:\n return\n print s\n print_n(s, n-1)\n```\nIf n <= 0 the return statement exits the function. The flow of execution immediately returns to the caller, and the remaining lines of the function are not executed.\n\nThe rest of the function is similar to countdown: if n is greater than 0, it displays s and then calls itself to display s n − 1 additional times. So the number of lines of output is 1 + (n -1), which adds up to n.\n\nFor simple examples like this, it is probably easier to use a for loop. But we will see examples later that are hard to write with a for loop and easy to write with recursion, so it is good to start early.\n\n#### **5.9 Stack diagrams for recursive functions**\n\nIn Section [3.10,](#page-46-0) we used a stack diagram to represent the state of a program during a function call. The same kind of diagram can help interpret a recursive function.\n\nEvery time a function gets called, Python creates a new function frame, which contains the function's local variables and parameters. For a recursive function, there might be more than one frame on the stack at the same time.\n\nFigure [5.1](#page-67-2) shows a stack diagram for countdown called with n = 3.\n\nAs usual, the top of the stack is the frame for __main__. It is empty because we did not create any variables in __main__ or pass any arguments to it.\n\nThe four countdown frames have different values for the parameter n. The bottom of the stack, where n=0, is called the **base case**. It does not make a recursive call, so there are no more frames.\n\n**Exercise 5.1.** *Draw a stack diagram for* print_n *called with* s = 'Hello' *and* n=2*.* **Exercise 5.2.** *Write a function called* do_n *that takes a function object and a number,* n*, as arguments, and that calls the given function* n *times.*\n\n\n\nFigure 5.1: Stack diagram.\n\n#### **5.10 Infinite recursion**\n\nIf a recursion never reaches a base case, it goes on making recursive calls forever, and the program never terminates. This is known as **infinite recursion**, and it is generally not a good idea. Here is a minimal program with an infinite recursion:\n\nIn most programming environments, a program with infinite recursion does not really run forever. Python reports an error message when the maximum recursion depth is reached:\n\n```\nFile \"\", line 2, in recurse\n File \"\", line 2, in recurse\n File \"\", line 2, in recurse\n .\n .\n .\n File \"\", line 2, in recurse\nRuntimeError: Maximum recursion depth exceeded\n```\nThis traceback is a little bigger than the one we saw in the previous chapter. When the error occurs, there are 1000 recurse frames on the stack!\n\n#### **5.11 Keyboard input**\n\nThe programs we have written so far are a bit rude in the sense that they accept no input from the user. They just do the same thing every time.\n\nPython 2 provides a built-in function called raw_input that gets input from the keyboard. In Python 3, it is called input. When this function is called, the program stops and waits for the user to type something. When the user presses Return or Enter, the program resumes and raw_input returns what the user typed as a string.\n\n```\n>>> text = raw_input()\nWhat are you waiting for?\n>>> print text\nWhat are you waiting for?\n```\nBefore getting input from the user, it is a good idea to print a prompt telling the user what to input. raw_input can take a prompt as an argument:\n\n```\n>>> name = raw_input('What...is your name?\\n')\nWhat...is your name?\nArthur, King of the Britons!\n>>> print name\nArthur, King of the Britons!\n```\nThe sequence \\n at the end of the prompt represents a **newline**, which is a special character that causes a line break. That's why the user's input appears below the prompt.\n\nIf you expect the user to type an integer, you can try to convert the return value to int:\n\n```\n>>> prompt = 'What...is the airspeed velocity of an unladen swallow?\\n'\n>>> speed = raw_input(prompt)\nWhat...is the airspeed velocity of an unladen swallow?\n17\n>>> int(speed)\n17\n```\nBut if the user types something other than a string of digits, you get an error:\n\n```\n>>> speed = raw_input(prompt)\nWhat...is the airspeed velocity of an unladen swallow?\nWhat do you mean, an African or a European swallow?\n>>> int(speed)\nValueError: invalid literal for int() with base 10\n```\nWe will see how to handle this kind of error later.\n\n#### **5.12 Debugging**\n\nThe traceback Python displays when an error occurs contains a lot of information, but it can be overwhelming, especially when there are many frames on the stack. The most useful parts are usually:\n\n- What kind of error it was, and\n- Where it occurred.\n\nSyntax errors are usually easy to find, but there are a few gotchas. Whitespace errors can be tricky because spaces and tabs are invisible and we are used to ignoring them.\n\n```\n>>> x = 5\n>>> y = 6\n File \"\", line 1\n y = 6\n ^\n```\nIndentationError: unexpected indent\n\nIn this example, the problem is that the second line is indented by one space. But the error message points to y, which is misleading. In general, error messages indicate where the problem was discovered, but the actual error might be earlier in the code, sometimes on a previous line.\n\nThe same is true of runtime errors.\n\nSuppose you are trying to compute a signal-to-noise ratio in decibels. The formula is $SNR_{db} = 10 log_{10}(P_{signal} / P_{noise})$. In Python, you might write something like this:\n\n```\nimport math\nsignal_power = 9\nnoise_power = 10\nratio = signal_power / noise_power\ndecibels = 10 * math.log10(ratio)\nprint decibels\n```\nBut when you run it in Python 2, you get an error message.\n\n```\nTraceback (most recent call last):\n File \"snr.py\", line 5, in ?\n decibels = 10 * math.log10(ratio)\nValueError: math domain error\n```\nThe error message indicates line 5, but there is nothing wrong with that line. To find the real error, it might be useful to print the value of ratio, which turns out to be 0. The problem is in line 4, because dividing two integers does floor division. The solution is to represent signal power and noise power with floating-point values.\n\nIn general, error messages tell you where the problem was discovered, but that is often not where it was caused.\n\nIn Python 3, this example does not cause an error; the division operator performs floatingpoint division even with integer operands.\n\n#### **5.13 Glossary**\n\n- **modulus operator:** An operator, denoted with a percent sign (%), that works on integers and yields the remainder when one number is divided by another.\n- **boolean expression:** An expression whose value is either True or False.\n- **relational operator:** One of the operators that compares its operands: ==, !=, >, <, >=, and <=.\n- **logical operator:** One of the operators that combines boolean expressions: and, or, and not.\n- **conditional statement:** A statement that controls the flow of execution depending on some condition.\n- **condition:** The boolean expression in a conditional statement that determines which branch is executed.\n- **compound statement:** A statement that consists of a header and a body. The header ends with a colon (:). The body is indented relative to the header.\n\n**branch:** One of the alternative sequences of statements in a conditional statement.\n\n**chained conditional:** A conditional statement with a series of alternative branches.\n\n- **nested conditional:** A conditional statement that appears in one of the branches of another conditional statement.\n**recursion:** The process of calling the function that is currently executing.\n\n- **base case:** A conditional branch in a recursive function that does not make a recursive call.\n- **infinite recursion:** A recursion that doesn't have a base case, or never reaches it. Eventually, an infinite recursion causes a runtime error.\n\n#### **5.14 Exercises**\n\n**Exercise 5.3.** *Fermat's Last Theorem says that there are no positive integers a, b, and c such that*\n\n$$a^n + b^n = c^n$$\n\n*for any values of n greater than 2.*\n\n- *1. Write a function named* check_fermat *that takes four parameters—*a*,* b*,* c *and* n*—and that checks to see if Fermat's theorem holds. If n is greater than 2 and it turns out to be true that*\n\n$$a^n + b^n = c^n$$\n\n*the program should print, \"Holy smokes, Fermat was wrong!\" Otherwise the program should print, \"No, that doesn't work.\"*\n\n- *2. Write a function that prompts the user to input values for* a*,* b*,* c *and* n*, converts them to integers, and uses* check_fermat *to check whether they violate Fermat's theorem.*\n**Exercise 5.4.** *If you are given three sticks, you may or may not be able to arrange them in a triangle. For example, if one of the sticks is 12 inches long and the other two are one inch long, it is clear that you will not be able to get the short sticks to meet in the middle. For any three lengths, there is a simple test to see if it is possible to form a triangle:*\n\n*If any of the three lengths is greater than the sum of the other two, then you cannot form a triangle. Otherwise, you can. (If the sum of two lengths equals the third, they form what is called a \"degenerate\" triangle.)*\n\n- *1. Write a function named* is_triangle *that takes three integers as arguments, and that prints either \"Yes\" or \"No,\" depending on whether you can or cannot form a triangle from sticks with the given lengths.*\n- *2. Write a function that prompts the user to input three stick lengths, converts them to integers, and uses* is_triangle *to check whether sticks with the given lengths can form a triangle.*\n\nThe following exercises use TurtleWorld from Chapter [4:](#page-52-0)\n\n**Exercise 5.5.** *Read the following function and see if you can figure out what it does. Then run it (see the examples in Chapter [4)](#page-52-0).*\n\n\n\nFigure 5.2: A Koch curve.\n\n```\ndef draw(t, length, n):\n if n == 0:\n return\n angle = 50\n fd(t, length*n)\n lt(t, angle)\n draw(t, length, n-1)\n rt(t, 2*angle)\n draw(t, length, n-1)\n lt(t, angle)\n bk(t, length*n)\n```\n**Exercise 5.6.** *The Koch curve is a fractal that looks something like Figure [5.2.](#page-71-0) To draw a Koch curve with length x, all you have to do is*\n\n- 1. Draw a Koch curve with length $x/3$.\n- *2. Turn left 60 degrees.*\n- 3. Draw a Koch curve with length $x/3$.\n- *4. Turn right 120 degrees.*\n- *5. Draw a Koch curve with length x*/3*.*\n- *6. Turn left 60 degrees.*\n- *7. Draw a Koch curve with length x*/3*.*\n\n*The exception is if x is less than 3: in that case, you can just draw a straight line with length x.*\n\n- *1. Write a function called* koch *that takes a turtle and a length as parameters, and that uses the turtle to draw a Koch curve with the given length.*\n- *2. Write a function called* snowflake *that draws three Koch curves to make the outline of a snowflake.*\n\nSolution: \n\n- 3. The Koch curve can be generalized in several ways. See [http://en.wikipedia.org/](http://en.wikipedia.org/wiki/Koch_snowflake) [wiki/ Koch_ snowflake](http://en.wikipedia.org/wiki/Koch_snowflake) *for examples and implement your favorite.*\n# **Chapter 6**\n\n# **Fruitful functions**\n\n#### **6.1 Return values**\n\nSome of the built-in functions we have used, such as the math functions, produce results. Calling the function generates a value, which we usually assign to a variable or use as part of an expression.\n\ne = math.exp(1.0) height = radius * math.sin(radians)\n\nAll of the functions we have written so far are void; they print something or move turtles around, but their return value is None.\n\nIn this chapter, we are (finally) going to write fruitful functions. The first example is area, which returns the area of a circle with the given radius:\n\n```\ndef area(radius):\n temp = math.pi * radius**2\n return temp\n```\nWe have seen the return statement before, but in a fruitful function the return statement includes an expression. This statement means: \"Return immediately from this function and use the following expression as a return value.\" The expression can be arbitrarily complicated, so we could have written this function more concisely:\n\n```\ndef area(radius):\n return math.pi * radius**2\n```\nOn the other hand, **temporary variables** like temp often make debugging easier.\n\nSometimes it is useful to have multiple return statements, one in each branch of a conditional:\n\n```\ndef absolute_value(x):\n if x < 0:\n return -x\n else:\n return x\n```\nSince these return statements are in an alternative conditional, only one will be executed.\n\nAs soon as a return statement executes, the function terminates without executing any subsequent statements. Code that appears after a return statement, or any other place the flow of execution can never reach, is called **dead code**.\n\nIn a fruitful function, it is a good idea to ensure that every possible path through the program hits a return statement. For example:\n\n```\ndef absolute_value(x):\n if x < 0:\n return -x\n if x > 0:\n return x\n```\nThis function is incorrect because if x happens to be 0, neither condition is true, and the function ends without hitting a return statement. If the flow of execution gets to the end of a function, the return value is None, which is not the absolute value of 0.\n\n```\n>>> print absolute_value(0)\nNone\n```\nBy the way, Python provides a built-in function called abs that computes absolute values. **Exercise 6.1.** *Write a* compare *function that returns* 1 *if* $x > y$*,* 0 *if* $x == y$*, and* -1 *if* $x < y$*.* \n\n#### **6.2 Incremental development**\n\nAs you write larger functions, you might find yourself spending more time debugging.\n\nTo deal with increasingly complex programs, you might want to try a process called **incremental development**. The goal of incremental development is to avoid long debugging sessions by adding and testing only a small amount of code at a time.\n\nAs an example, suppose you want to find the distance between two points, given by the coordinates ($x_1$, $y_1$) and ($x_2$, $y_2$). By the Pythagorean theorem, the distance is:\n\n$$\\text{distance} = \\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$$\n\nThe first step is to consider what a distance function should look like in Python. In other words, what are the inputs (parameters) and what is the output (return value)?\n\nIn this case, the inputs are two points, which you can represent using four numbers. The return value is the distance, which is a floating-point value.\n\nAlready you can write an outline of the function:\n\n```\ndef distance(x1, y1, x2, y2):\n return 0.0\n```\nObviously, this version doesn't compute distances; it always returns zero. But it is syntactically correct, and it runs, which means that you can test it before you make it more complicated.\n\nTo test the new function, call it with sample arguments:\n\n>>> distance(1, 2, 4, 6) 0.0\n\nI chose these values so that the horizontal distance is 3 and the vertical distance is 4; that way, the result is 5 (the hypotenuse of a 3-4-5 triangle). When testing a function, it is useful to know the right answer.\n\nAt this point we have confirmed that the function is syntactically correct, and we can start adding code to the body. A reasonable next step is to find the differences $x_2 - x_1$ and $y_2 - y_1$. The next version stores those values in temporary variables and prints them.\n\n```\ndef distance(x1, y1, x2, y2):\n dx = x2 - x1\n dy = y2 - y1\n print 'dx is', dx\n print 'dy is', dy\n return 0.0\n```\nIf the function is working, it should display 'dx is 3' and 'dy is 4'. If so, we know that the function is getting the right arguments and performing the first computation correctly. If not, there are only a few lines to check.\n\nNext we compute the sum of squares of dx and dy:\n\n```\ndef distance(x1, y1, x2, y2):\n dx = x2 - x1\n dy = y2 - y1\n dsquared = dx**2 + dy**2\n print 'dsquared is: ', dsquared\n return 0.0\n```\nAgain, you would run the program at this stage and check the output (which should be 25). Finally, you can use math.sqrt to compute and return the result:\n\n```\ndef distance(x1, y1, x2, y2):\n dx = x2 - x1\n dy = y2 - y1\n dsquared = dx**2 + dy**2\n result = math.sqrt(dsquared)\n return result\n```\nIf that works correctly, you are done. Otherwise, you might want to print the value of result before the return statement.\n\nThe final version of the function doesn't display anything when it runs; it only returns a value. The print statements we wrote are useful for debugging, but once you get the function working, you should remove them. Code like that is called **scaffolding** because it is helpful for building the program but is not part of the final product.\n\nWhen you start out, you should add only a line or two of code at a time. As you gain more experience, you might find yourself writing and debugging bigger chunks. Either way, incremental development can save you a lot of debugging time.\n\nThe key aspects of the process are:\n\n- 1. Start with a working program and make small incremental changes. At any point, if there is an error, you should have a good idea where it is.\n- 2. Use temporary variables to hold intermediate values so you can display and check them.\n- 3. Once the program is working, you might want to remove some of the scaffolding or consolidate multiple statements into compound expressions, but only if it does not make the program difficult to read.\n\n**Exercise 6.2.** *Use incremental development to write a function called* hypotenuse *that returns the length of the hypotenuse of a right triangle given the lengths of the two legs as arguments. Record each stage of the development process as you go.*\n\n#### **6.3 Composition**\n\nAs you should expect by now, you can call one function from within another. This ability is called **composition**.\n\nAs an example, we'll write a function that takes two points, the center of the circle and a point on the perimeter, and computes the area of the circle.\n\nAssume that the center point is stored in the variables xc and yc, and the perimeter point is in xp and yp. The first step is to find the radius of the circle, which is the distance between the two points. We just wrote a function, distance, that does that:\n\nradius = distance(xc, yc, xp, yp)\n\nThe next step is to find the area of a circle with that radius; we just wrote that, too:\n\n```\nresult = area(radius)\n```\nEncapsulating these steps in a function, we get:\n\n```\ndef circle_area(xc, yc, xp, yp):\n radius = distance(xc, yc, xp, yp)\n result = area(radius)\n return result\n```\nThe temporary variables radius and result are useful for development and debugging, but once the program is working, we can make it more concise by composing the function calls:\n\n```\ndef circle_area(xc, yc, xp, yp):\n return area(distance(xc, yc, xp, yp))\n```\n#### **6.4 Boolean functions**\n\nFunctions can return booleans, which is often convenient for hiding complicated tests inside functions. For example:\n\n```\ndef is_divisible(x, y):\n if x % y == 0:\n return True\n else:\n return False\n```\nIt is common to give boolean functions names that sound like yes/no questions; is_divisible returns either True or False to indicate whether x is divisible by y.\n\nHere is an example:\n\n```\n>>> is_divisible(6, 4)\nFalse\n>>> is_divisible(6, 3)\nTrue\n```\nThe result of the == operator is a boolean, so we can write the function more concisely by returning it directly:\n\n```\ndef is_divisible(x, y):\n return x % y == 0\n```\nBoolean functions are often used in conditional statements:\n\nif is_divisible(x, y):\n\nprint 'x is divisible by y'\n\nIt might be tempting to write something like:\n\nif is_divisible(x, y) == True: print 'x is divisible by y'\n\nBut the extra comparison is unnecessary. **Exercise 6.3.** *Write a function* is_between(x, y, z) *that returns* True *if x* ≤ *y* ≤ *z or* False *otherwise.*\n\n#### **6.5 More recursion**\n\nWe have only covered a small subset of Python, but you might be interested to know that this subset is a *complete* programming language, which means that anything that can be computed can be expressed in this language. Any program ever written could be rewritten using only the language features you have learned so far (actually, you would need a few commands to control devices like the keyboard, mouse, disks, etc., but that's all).\n\nProving that claim is a nontrivial exercise first accomplished by Alan Turing, one of the first computer scientists (some would argue that he was a mathematician, but a lot of early computer scientists started as mathematicians). Accordingly, it is known as the Turing Thesis. For a more complete (and accurate) discussion of the Turing Thesis, I recommend Michael Sipser's book *Introduction to the Theory of Computation*.\n\nTo give you an idea of what you can do with the tools you have learned so far, we'll evaluate a few recursively defined mathematical functions. A recursive definition is similar to a circular definition, in the sense that the definition contains a reference to the thing being defined. A truly circular definition is not very useful:\n\n**vorpal:** An adjective used to describe something that is vorpal.\n\nIf you saw that definition in the dictionary, you might be annoyed. On the other hand, if you looked up the definition of the factorial function, denoted with the symbol !, you might get something like this:\n\n$$\\begin{aligned} 0! &= 1\\\\ n! &= n(n-1)! \\end{aligned}$$\n\nThis definition says that the factorial of 0 is 1, and the factorial of any other value, *n*, is *n* multiplied by the factorial of *n* − 1.\n\nSo 3! is 3 times 2!, which is 2 times 1!, which is 1 times 0!. Putting it all together, 3! equals 3 times 2 times 1 times 1, which is 6.\n\nIf you can write a recursive definition of something, you can usually write a Python program to evaluate it. The first step is to decide what the parameters should be. In this case it should be clear that factorial takes an integer:\n\n```\ndef factorial(n):\n```\nIf the argument happens to be 0, all we have to do is return 1:\n\n```\ndef factorial(n):\n if n == 0:\n return 1\n```\nOtherwise, and this is the interesting part, we have to make a recursive call to find the factorial of $n - 1$ and then multiply it by $n$:\n\n```\ndef factorial(n):\n if n == 0:\n return 1\n else:\n recurse = factorial(n-1)\n result = n * recurse\n return result\n```\nThe flow of execution for this program is similar to the flow of countdown in Section [5.8.](#page-65-0) If we call factorial with the value 3:\n\nSince 3 is not 0, we take the second branch and calculate the factorial of n-1...\n\nSince 2 is not 0, we take the second branch and calculate the factorial of n-1...\n\nSince 1 is not 0, we take the second branch and calculate the factorial of n-1...\n\nSince 0 *is* 0, we take the first branch and return 1 without making any more recursive calls.\n\nThe return value (1) is multiplied by *n*, which is 1, and the result is returned.\n\nThe return value (1) is multiplied by *n*, which is 2, and the result is returned.\n\nThe return value (2) is multiplied by *n*, which is 3, and the result, 6, becomes the return value of the function call that started the whole process.\n\nFigure [6.1](#page-78-2) shows what the stack diagram looks like for this sequence of function calls.\n\nThe return values are shown being passed back up the stack. In each frame, the return value is the value of result, which is the product of n and recurse.\n\nIn the last frame, the local variables recurse and result do not exist, because the branch that creates them does not execute.\n\n\n\nFigure 6.1: Stack diagram.\n\n#### **6.6 Leap of faith**\n\nFollowing the flow of execution is one way to read programs, but it can quickly become labyrinthine. An alternative is what I call the \"leap of faith.\" When you come to a function call, instead of following the flow of execution, you *assume* that the function works correctly and returns the right result.\n\nIn fact, you are already practicing this leap of faith when you use built-in functions. When you call math.cos or math.exp, you don't examine the bodies of those functions. You just assume that they work because the people who wrote the built-in functions were good programmers.\n\nThe same is true when you call one of your own functions. For example, in Section [6.4,](#page-75-1) we wrote a function called is_divisible that determines whether one number is divisible by another. Once we have convinced ourselves that this function is correct—by examining the code and testing—we can use the function without looking at the body again.\n\nThe same is true of recursive programs. When you get to the recursive call, instead of following the flow of execution, you should assume that the recursive call works (yields the correct result) and then ask yourself, “Assuming that I can find the factorial of $n - 1$, can I compute the factorial of $n$?” In this case, it is clear that you can, by multiplying by $n$.\n\nOf course, it's a bit strange to assume that the function works correctly when you haven't finished writing it, but that's why it's called a leap of faith!\n\n### **6.7 One more example**\n\nAfter factorial, the most common example of a recursively defined mathematical function is fibonacci, which has the following definition (see [http://en.wikipedia.org/](http://en.wikipedia.org/wiki/Fibonacci_number) [wiki/Fibonacci_number](http://en.wikipedia.org/wiki/Fibonacci_number)):\n\n```\nfibonacci(0) = 0\nfibonacci(1) = 1\nfibonacci(n) = fibonacci(n − 1) + fibonacci(n − 2)\n```\nTranslated into Python, it looks like this:\n\n```\ndef fibonacci (n):\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return fibonacci(n-1) + fibonacci(n-2)\n```\nIf you try to follow the flow of execution here, even for fairly small values of *n*, your head explodes. But according to the leap of faith, if you assume that the two recursive calls work correctly, then it is clear that you get the right result by adding them together.\n\n#### **6.8 Checking types**\n\nWhat happens if we call factorial and give it 1.5 as an argument?\n\n```\n>>> factorial(1.5)\n```\nRuntimeError: Maximum recursion depth exceeded\n\nIt looks like an infinite recursion. But how can that be? There is a base case—when n == 0. But if n is not an integer, we can *miss* the base case and recurse forever.\n\nIn the first recursive call, the value of n is 0.5. In the next, it is -0.5. From there, it gets smaller (more negative), but it will never be 0.\n\nWe have two choices. We can try to generalize the factorial function to work with floating-point numbers, or we can make factorial check the type of its argument. The first option is called the gamma function and it's a little beyond the scope of this book. So we'll go for the second.\n\nWe can use the built-in function isinstance to verify the type of the argument. While we're at it, we can also make sure the argument is positive:\n\n```\ndef factorial (n):\n if not isinstance(n, int):\n print 'Factorial is only defined for integers.'\n return None\n elif n < 0:\n print 'Factorial is not defined for negative integers.'\n return None\n elif n == 0:\n return 1\n else:\n return n * factorial(n-1)\n```\nThe first base case handles nonintegers; the second catches negative integers. In both cases, the program prints an error message and returns None to indicate that something went wrong:\n\n```\n>>> factorial('fred')\nFactorial is only defined for integers.\nNone\n>>> factorial(-2)\nFactorial is not defined for negative integers.\nNone\n```\nIf we get past both checks, then we know that *n* is positive or zero, so we can prove that the recursion terminates.\n\nThis program demonstrates a pattern sometimes called a **guardian**. The first two conditionals act as guardians, protecting the code that follows from values that might cause an error. The guardians make it possible to prove the correctness of the code.\n\nIn Section [11.3](#page-125-0) we will see a more flexible alternative to printing an error message: raising an exception.\n\n#### **6.9 Debugging**\n\nBreaking a large program into smaller functions creates natural checkpoints for debugging. If a function is not working, there are three possibilities to consider:\n\n- There is something wrong with the arguments the function is getting; a precondition is violated.\n- There is something wrong with the function; a postcondition is violated.\n- There is something wrong with the return value or the way it is being used.\n\nTo rule out the first possibility, you can add a print statement at the beginning of the function and display the values of the parameters (and maybe their types). Or you can write code that checks the preconditions explicitly.\n\nIf the parameters look good, add a print statement before each return statement that displays the return value. If possible, check the result by hand. Consider calling the function with values that make it easy to check the result (as in Section [6.2)](#page-73-0).\n\nIf the function seems to be working, look at the function call to make sure the return value is being used correctly (or used at all!).\n\nAdding print statements at the beginning and end of a function can help make the flow of execution more visible. For example, here is a version of factorial with print statements:\n\n```\ndef factorial(n):\n space = ' ' * (4 * n)\n print space, 'factorial', n\n if n == 0:\n print space, 'returning 1'\n return 1\n else:\n recurse = factorial(n-1)\n result = n * recurse\n print space, 'returning', result\n return result\n```\nspace is a string of space characters that controls the indentation of the output. Here is the result of factorial(5) :\n\n```\nfactorial 5\n factorial 4\n factorial 3\n factorial 2\n factorial 1\nfactorial 0\nreturning 1\n returning 1\n returning 2\n returning 6\n returning 24\n returning 120\n```\nIf you are confused about the flow of execution, this kind of output can be helpful. It takes some time to develop effective scaffolding, but a little bit of scaffolding can save a lot of debugging.\n\n#### **6.10 Glossary**\n\n- **temporary variable:** A variable used to store an intermediate value in a complex calculation.\n- **dead code:** Part of a program that can never be executed, often because it appears after a return statement.\n- None**:** A special value returned by functions that have no return statement or a return statement without an argument.\n- **incremental development:** A program development plan intended to avoid debugging by adding and testing only a small amount of code at a time.\n- **scaffolding:** Code that is used during program development but is not part of the final version.\n- **guardian:** A programming pattern that uses a conditional statement to check for and handle circumstances that might cause an error.\n\n#### **6.11 Exercises**\n\n**Exercise 6.4.** *Draw a stack diagram for the following program. What does the program print? Solution:* [http: // thinkpython. com/ code/ stack_ diagram. py](http://thinkpython.com/code/stack_diagram.py) *.*\n\n```\ndef b(z):\n prod = a(z, z)\n print z, prod\n return prod\ndef a(x, y):\n```\n\n```\nx = x + 1\nreturn x * y\n```\n\n```\ndef c(x, y, z):\n total = x + y + z\n square = b(total)**2\n return square\nx = 1\ny = x + 1\nprint c(x, y+3, x+y)\nExercise 6.5. The Ackermann function, A(m, n), is defined:\n```\n\n$$A(m,n) = \\begin{cases} n+1 & \\text{if } m=0\\\\ A(m-1,1) & \\text{if } m>0 \\text{ and } n=0\\\\ A(m-1,A(m,n-1)) & \\text{if } m>0 \\text{ and } n>0. \\end{cases}$$\n\n*See* . *Write a function named* ack that evaluates Ackermann's function. Use your function to evaluate $ack(3, 4)$, which should be 125. What happens for larger values of $m$ and $n$? Solution: [http://thinkpython.com/code/](http://thinkpython.com/code/ackermann.py) [ackermann. py](http://thinkpython.com/code/ackermann.py) *.*\n\n**Exercise 6.6.** *A palindrome is a word that is spelled the same backward and forward, like \"noon\" and \"redivider\". Recursively, a word is a palindrome if the first and last letters are the same and the middle is a palindrome.*\n\n*The following are functions that take a string argument and return the first, last, and middle letters:*\n\n```\ndef first(word):\n return word[0]\n```\n\n```\ndef last(word):\n return word[-1]\n```\n*We'll see how they work in Chapter [8.](#page-92-0)*\n\n- *1. Type these functions into a file named* palindrome.py *and test them out. What happens if you call* middle *with a string with two letters? One letter? What about the empty string, which is written* '' *and contains no letters?*\n- *2. Write a function called* is_palindrome *that takes a string argument and returns* True *if it is a palindrome and* False *otherwise. Remember that you can use the built-in function* len *to check the length of a string.*\n\n*Solution:* [http: // thinkpython. com/ code/ palindrome_ soln. py](http://thinkpython.com/code/palindrome_soln.py) *.*\n\n**Exercise 6.7.** *A number, a, is a power of b if it is divisible by b and a*/*b is a power of b. Write a function called* is_power *that takes parameters* a *and* b *and returns* True *if* a *is a power of* b*. Note: you will have to think about the base case.*\n\n**Exercise 6.8.** *The greatest common divisor (GCD) of a and b is the largest number that divides both of them with no remainder.*\n\n*One way to find the GCD of two numbers is based on the observation that if r is the remainder when* a is divided by b, then $gcd(a, b) = gcd(b, r)$. As a base case, we can use $gcd(a, 0) = a$.\n\n*Write a function called* gcd *that takes parameters* a *and* b *and returns their greatest common divisor.*\n\nCredit: This exercise is based on an example from Abelson and Sussman's Structure and Interpretation of Computer Programs*.*\n\n# **Chapter 7**\n\n# **Iteration**\n\n#### **7.1 Multiple assignment**\n\nAs you may have discovered, it is legal to make more than one assignment to the same variable. A new assignment makes an existing variable refer to a new value (and stop referring to the old value).\n\nbruce = 5 print bruce, bruce = 7 print bruce\n\nThe output of this program is 5 7, because the first time bruce is printed, its value is 5, and the second time, its value is 7. The comma at the end of the first print statement suppresses the newline, which is why both outputs appear on the same line.\n\nFigure [7.1](#page-85-2) shows what **multiple assignment** looks like in a state diagram.\n\nWith multiple assignment it is especially important to distinguish between an assignment operation and a statement of equality. Because Python uses the equal sign (=) for assignment, it is tempting to interpret a statement like $a = b$ as a statement of equality. It is not!\n\nFirst, equality is a symmetric relation and assignment is not. For example, in mathematics, if $a = 7$ then $7 = a$. But in Python, the statement $a = 7$ is legal and $7 = a$ is not.\n\nFurthermore, in mathematics, a statement of equality is either true or false, for all time. If $a = b$ now, then $a$ will always equal $b$. In Python, an assignment statement can make two variables equal, but they don't have to stay that way:\n\na = 5 b = a # a and b are now equal a = 3 # a and b are no longer equal\n\nThe third line changes the value of a but does not change the value of b, so they are no longer equal.\n\nAlthough multiple assignment is frequently helpful, you should use it with caution. If the values of variables change frequently, it can make the code difficult to read and debug.\n\n\n\nFigure 7.1: State diagram.\n\n### **7.2 Updating variables**\n\nOne of the most common forms of multiple assignment is an **update**, where the new value of the variable depends on the old.\n\nx = x+1\n\nThis means \"get the current value of x, add one, and then update x with the new value.\"\n\nIf you try to update a variable that doesn't exist, you get an error, because Python evaluates the right side before it assigns a value to x:\n\n```\n>>> x = x+1\nNameError: name 'x' is not defined\n```\nBefore you can update a variable, you have to **initialize** it, usually with a simple assignment:\n\n>>> x = 0 >>> x = x+1\n\nUpdating a variable by adding 1 is called an **increment**; subtracting 1 is called a **decrement**.\n\n## **7.3 The** while **statement**\n\nComputers are often used to automate repetitive tasks. Repeating identical or similar tasks without making errors is something that computers do well and people do poorly.\n\nWe have seen two programs, countdown and print_n, that use recursion to perform repetition, which is also called **iteration**. Because iteration is so common, Python provides several language features to make it easier. One is the for statement we saw in Section [4.2.](#page-53-0) We'll get back to that later.\n\nAnother is the while statement. Here is a version of countdown that uses a while statement:\n\n```\ndef countdown(n):\n while n > 0:\n print n\n n = n-1\n print 'Blastoff!'\n```\nYou can almost read the while statement as if it were English. It means, \"While n is greater than 0, display the value of n and then reduce the value of n by 1. When you get to 0, display the word Blastoff!\"\n\nMore formally, here is the flow of execution for a while statement:\n\n- 1. Evaluate the condition, yielding True or False.\n- 2. If the condition is false, exit the while statement and continue execution at the next statement.\n- 3. If the condition is true, execute the body and then go back to step 1.\n\nThis type of flow is called a **loop** because the third step loops back around to the top.\n\nThe body of the loop should change the value of one or more variables so that eventually the condition becomes false and the loop terminates. Otherwise the loop will repeat forever, which is called an **infinite loop**. An endless source of amusement for computer scientists is the observation that the directions on shampoo, \"Lather, rinse, repeat,\" are an infinite loop.\n\nIn the case of countdown, we can prove that the loop terminates because we know that the value of n is finite, and we can see that the value of n gets smaller each time through the loop, so eventually we have to get to 0. In other cases, it is not so easy to tell:\n\n```\ndef sequence(n):\n```\n\n```\nwhile n != 1:\n print n,\n if n%2 == 0: # n is even\n n = n/2\n else: # n is odd\n n = n*3+1\n```\nThe condition for this loop is n != 1, so the loop will continue until n is 1, which makes the condition false.\n\nEach time through the loop, the program outputs the value of n and then checks whether it is even or odd. If it is even, n is divided by 2. If it is odd, the value of n is replaced with n*3+1. For example, if the argument passed to sequence is 3, the resulting sequence is 3, 10, 5, 16, 8, 4, 2, 1.\n\nSince n sometimes increases and sometimes decreases, there is no obvious proof that n will ever reach 1, or that the program terminates. For some particular values of n, we can prove termination. For example, if the starting value is a power of two, then the value of n will be even each time through the loop until it reaches 1. The previous example ends with such a sequence, starting with 16.\n\nThe hard question is whether we can prove that this program terminates for *all positive values* of n. So far, no one has been able to prove it *or* disprove it! (See [http:](http://en.wikipedia.org/wiki/Collatz_conjecture) [//en.wikipedia.org/wiki/Collatz_conjecture](http://en.wikipedia.org/wiki/Collatz_conjecture).)\n\n**Exercise 7.1.** *Rewrite the function* print_n *from Section [5.8](#page-65-0) using iteration instead of recursion.*\n\n## **7.4** break\n\nSometimes you don't know it's time to end a loop until you get half way through the body. In that case you can use the break statement to jump out of the loop.\n\nFor example, suppose you want to take input from the user until they type done. You could write:\n\n```\nwhile True:\n line = raw_input('> ')\n if line == 'done':\n break\n print line\n```\nprint 'Done!'\n\nThe loop condition is True, which is always true, so the loop runs until it hits the break statement.\n\nEach time through, it prompts the user with an angle bracket. If the user types done, the break statement exits the loop. Otherwise the program echoes whatever the user types and goes back to the top of the loop. Here's a sample run:\n\n```\n> not done\nnot done\n> done\nDone!\n```\nThis way of writing while loops is common because you can check the condition anywhere in the loop (not just at the top) and you can express the stop condition affirmatively (\"stop when this happens\") rather than negatively (\"keep going until that happens.\").\n\n#### **7.5 Square roots**\n\nLoops are often used in programs that compute numerical results by starting with an approximate answer and iteratively improving it.\n\nFor example, one way of computing square roots is Newton's method. Suppose that you want to know the square root of *a*. If you start with almost any estimate, *x*, you can compute a better estimate with the following formula:\n\n$$y = \\frac{x + a/x}{2}$$\n\nFor example, if *a* is 4 and *x* is 3:\n\n```\n>>> a = 4.0\n>>> x = 3.0\n>>> y = (x + a/x) / 2\n>>> print y\n2.16666666667\n```\n√/4 = 2). If we repeat the process with the new estimate, it gets even closer:\n\n```\n>>> x = y\n>>> y = (x + a/x) / 2\n>>> print y\n2.00641025641\n```\nAfter a few more updates, the estimate is almost exact:\n\n```\n>>> x = y\n>>> y = (x + a/x) / 2\n>>> print y\n2.00001024003\n>>> x = y\n>>> y = (x + a/x) / 2\n>>> print y\n2.00000000003\n```\nIn general we don't know ahead of time how many steps it takes to get to the right answer, but we know when we get there because the estimate stops changing:\n\n```\n>>> x = y\n>>> y = (x + a/x) / 2\n>>> print y\n2.0\n>>> x = y\n>>> y = (x + a/x) / 2\n>>> print y\n2.0\n```\nWhen y == x, we can stop. Here is a loop that starts with an initial estimate, x, and improves it until it stops changing:\n\n```\nwhile True:\n print x\n y = (x + a/x) / 2\n if y == x:\n break\n x = y\n```\nFor most values of a this works fine, but in general it is dangerous to test float equality. Floating-point values are only approximately right: most rational numbers, like 1/3, and irrational numbers, like √ 2, can't be represented exactly with a float.\n\nRather than checking whether x and y are exactly equal, it is safer to use the built-in function abs to compute the absolute value, or magnitude, of the difference between them:\n\n```\nif abs(y-x) < epsilon:\n break\n```\nWhere epsilon has a value like 0.0000001 that determines how close is close enough. **Exercise 7.2.** *Encapsulate this loop in a function called* square_root *that takes* a *as a parameter, chooses a reasonable value of* x*, and returns an estimate of the square root of* a*.*\n\n### **7.6 Algorithms**\n\nNewton's method is an example of an **algorithm**: it is a mechanical process for solving a category of problems (in this case, computing square roots).\n\nIt is not easy to define an algorithm. It might help to start with something that is not an algorithm. When you learned to multiply single-digit numbers, you probably memorized the multiplication table. In effect, you memorized 100 specific solutions. That kind of knowledge is not algorithmic.\n\nBut if you were “lazy,” you probably cheated by learning a few tricks. For example, to find the product of $n$ and 9, you can write $n$ – 1 as the first digit and 10 – $n$ as the second digit. This trick is a general solution for multiplying any single-digit number by 9. That's an algorithm!\n\nSimilarly, the techniques you learned for addition with carrying, subtraction with borrowing, and long division are all algorithms. One of the characteristics of algorithms is that they do not require any intelligence to carry out. They are mechanical processes in which each step follows from the last according to a simple set of rules.\n\nIn my opinion, it is embarrassing that humans spend so much time in school learning to execute algorithms that, quite literally, require no intelligence.\n\nOn the other hand, the process of designing algorithms is interesting, intellectually challenging, and a central part of what we call programming.\n\nSome of the things that people do naturally, without difficulty or conscious thought, are the hardest to express algorithmically. Understanding natural language is a good example. We all do it, but so far no one has been able to explain *how* we do it, at least not in the form of an algorithm.\n\n## **7.7 Debugging**\n\nAs you start writing bigger programs, you might find yourself spending more time debugging. More code means more chances to make an error and more place for bugs to hide.\n\nOne way to cut your debugging time is \"debugging by bisection.\" For example, if there are 100 lines in your program and you check them one at a time, it would take 100 steps.\n\nInstead, try to break the problem in half. Look at the middle of the program, or near it, for an intermediate value you can check. Add a print statement (or something else that has a verifiable effect) and run the program.\n\nIf the mid-point check is incorrect, there must be a problem in the first half of the program. If it is correct, the problem is in the second half.\n\nEvery time you perform a check like this, you halve the number of lines you have to search. After six steps (which is fewer than 100), you would be down to one or two lines of code, at least in theory.\n\nIn practice it is not always clear what the \"middle of the program\" is and not always possible to check it. It doesn't make sense to count lines and find the exact midpoint. Instead, think about places in the program where there might be errors and places where it is easy to put a check. Then choose a spot where you think the chances are about the same that the bug is before or after the check.\n\n## **7.8 Glossary**\n\n**multiple assignment:** Making more than one assignment to the same variable during the execution of a program.\n\n**update:** An assignment where the new value of the variable depends on the old.\n\n**initialization:** An assignment that gives an initial value to a variable that will be updated.\n\n**increment:** An update that increases the value of a variable (often by one).\n\n**decrement:** An update that decreases the value of a variable.\n\n**iteration:** Repeated execution of a set of statements using either a recursive function call or a loop.\n\n**infinite loop:** A loop in which the terminating condition is never satisfied.\n\n#### **7.9 Exercises**\n\n**Exercise 7.3.** *To test the square root algorithm in this chapter, you could compare it with* math.sqrt*. Write a function named* test_square_root *that prints a table like this:*\n\n```\n1.0 1.0 1.0 0.0\n2.0 1.41421356237 1.41421356237 2.22044604925e-16\n3.0 1.73205080757 1.73205080757 0.0\n4.0 2.0 2.0 0.0\n5.0 2.2360679775 2.2360679775 0.0\n6.0 2.44948974278 2.44948974278 0.0\n7.0 2.64575131106 2.64575131106 0.0\n8.0 2.82842712475 2.82842712475 4.4408920985e-16\n9.0 3.0 3.0 0.0\n```\n*The first column is a number, a; the second column is the square root of a computed with the function from Section [7.5;](#page-87-0) the third column is the square root computed by* math.sqrt*; the fourth column is the absolute value of the difference between the two estimates.*\n\n**Exercise 7.4.** *The built-in function* eval *takes a string and evaluates it using the Python interpreter. For example:*\n\n```\n>>> eval('1 + 2 * 3')\n7\n>>> import math\n>>> eval('math.sqrt(5)')\n2.2360679774997898\n>>> eval('type(math.pi)')\n\n```\n*Write a function called* eval_loop *that iteratively prompts the user, takes the resulting input and evaluates it using* eval*, and prints the result.*\n\n*It should continue until the user enters* 'done'*, and then return the value of the last expression it evaluated.*\n\n**Exercise 7.5.** *The mathematician Srinivasa Ramanujan found an infinite series that can be used to generate a numerical approximation of* 1/*π:*\n\n$$\\frac{1}{\\pi} = \\frac{2\\sqrt{2}}{9801} \\sum_{k=0}^{\\infty} \\frac{(4k)!(1103 + 26390k)}{(k!)^4 396^{4k}}$$\n\n*Write a function called* estimate_pi *that uses this formula to compute and return an estimate of π. It should use a* while *loop to compute terms of the summation until the last term is smaller than* 1e-15 (which is Python notation for $10^{-15}$). You can check the result by comparing it to math.pi.\n\nSolution: .\n\n# **Chapter 8**\n\n# **Strings**\n\n#### **8.1 A string is a sequence**\n\nA string is a **sequence** of characters. You can access the characters one at a time with the bracket operator:\n\n>>> fruit = 'banana' >>> letter = fruit[1]\n\nThe second statement selects character number 1 from fruit and assigns it to letter.\n\nThe expression in brackets is called an **index**. The index indicates which character in the sequence you want (hence the name).\n\nBut you might not get what you expect:\n\n>>> print letter a\n\nFor most people, the first letter of 'banana' is b, not a. But for computer scientists, the index is an offset from the beginning of the string, and the offset of the first letter is zero.\n\n```\n>>> letter = fruit[0]\n>>> print letter\nb\n```\nSo b is the 0th letter (\"zero-eth\") of 'banana', a is the 1th letter (\"one-eth\"), and n is the 2th (\"two-eth\") letter.\n\nYou can use any expression, including variables and operators, as an index, but the value of the index has to be an integer. Otherwise you get:\n\n```\n>>> letter = fruit[1.5]\nTypeError: string indices must be integers, not float\n```\n## **8.2** len\n\nlen is a built-in function that returns the number of characters in a string:\n\n```\n>>> fruit = 'banana'\n>>> len(fruit)\n6\n```\nTo get the last letter of a string, you might be tempted to try something like this:\n\n```\n>>> length = len(fruit)\n>>> last = fruit[length]\nIndexError: string index out of range\n```\nThe reason for the IndexError is that there is no letter in 'banana' with the index 6. Since we started counting at zero, the six letters are numbered 0 to 5. To get the last character, you have to subtract 1 from length:\n\n```\n>>> last = fruit[length-1]\n>>> print last\na\n```\nAlternatively, you can use negative indices, which count backward from the end of the string. The expression fruit[-1] yields the last letter, fruit[-2] yields the second to last, and so on.\n\n## **8.3 Traversal with a** for **loop**\n\nA lot of computations involve processing a string one character at a time. Often they start at the beginning, select each character in turn, do something to it, and continue until the end. This pattern of processing is called a **traversal**. One way to write a traversal is with a while loop:\n\n```\nindex = 0\nwhile index < len(fruit):\n letter = fruit[index]\n print letter\n index = index + 1\n```\nThis loop traverses the string and displays each letter on a line by itself. The loop condition is index < len(fruit), so when index is equal to the length of the string, the condition is false, and the body of the loop is not executed. The last character accessed is the one with the index len(fruit)-1, which is the last character in the string.\n\n**Exercise 8.1.** *Write a function that takes a string as an argument and displays the letters backward, one per line.*\n\nAnother way to write a traversal is with a for loop:\n\n```\nfor char in fruit:\n print char\n```\nEach time through the loop, the next character in the string is assigned to the variable char. The loop continues until no characters are left.\n\nThe following example shows how to use concatenation (string addition) and a for loop to generate an abecedarian series (that is, in alphabetical order). In Robert McCloskey's book *Make Way for Ducklings*, the names of the ducklings are Jack, Kack, Lack, Mack, Nack, Ouack, Pack, and Quack. This loop outputs these names in order:\n\n\n\nFigure 8.1: Slice indices.\n\n```\nprefixes = 'JKLMNOPQ'\nsuffix = 'ack'\nfor letter in prefixes:\n print letter + suffix\nThe output is:\nJack\nKack\nLack\nMack\nNack\nOack\nPack\nQack\nOf course, that's not quite right because \"Ouack\" and \"Quack\" are misspelled.\nExercise 8.2. Modify the program to fix this error.\n```\n#### **8.4 String slices**\n\nA segment of a string is called a **slice**. Selecting a slice is similar to selecting a character:\n\n```\n>>> s = 'Monty Python'\n>>> print s[0:5]\nMonty\n>>> print s[6:12]\nPython\n```\nThe operator [n:m] returns the part of the string from the \"n-eth\" character to the \"m-eth\" character, including the first but excluding the last. This behavior is counterintuitive, but it might help to imagine the indices pointing *between* the characters, as in Figure [8.1.](#page-94-1)\n\nIf you omit the first index (before the colon), the slice starts at the beginning of the string. If you omit the second index, the slice goes to the end of the string:\n\n```\n>>> fruit = 'banana'\n>>> fruit[:3]\n'ban'\n>>> fruit[3:]\n'ana'\n```\nIf the first index is greater than or equal to the second the result is an **empty string**, represented by two quotation marks:\n\n```\n>>> fruit = 'banana'\n>>> fruit[3:3]\n''\n```\nAn empty string contains no characters and has length 0, but other than that, it is the same as any other string.\n\n**Exercise 8.3.** *Given that* fruit *is a string, what does* fruit[:] *mean?*\n\n### **8.5 Strings are immutable**\n\nIt is tempting to use the [] operator on the left side of an assignment, with the intention of changing a character in a string. For example:\n\n```\n>>> greeting = 'Hello, world!'\n>>> greeting[0] = 'J'\nTypeError: 'str' object does not support item assignment\n```\nThe \"object\" in this case is the string and the \"item\" is the character you tried to assign. For now, an **object** is the same thing as a value, but we will refine that definition later. An **item** is one of the values in a sequence.\n\nThe reason for the error is that strings are **immutable**, which means you can't change an existing string. The best you can do is create a new string that is a variation on the original:\n\n```\n>>> greeting = 'Hello, world!'\n>>> new_greeting = 'J' + greeting[1:]\n>>> print new_greeting\nJello, world!\n```\nThis example concatenates a new first letter onto a slice of greeting. It has no effect on the original string.\n\n#### **8.6 Searching**\n\nWhat does the following function do?\n\n```\ndef find(word, letter):\n index = 0\n while index < len(word):\n if word[index] == letter:\n return index\n index = index + 1\n return -1\n```\nIn a sense, find is the opposite of the [] operator. Instead of taking an index and extracting the corresponding character, it takes a character and finds the index where that character appears. If the character is not found, the function returns -1.\n\nThis is the first example we have seen of a return statement inside a loop. If word[index] == letter, the function breaks out of the loop and returns immediately.\n\nIf the character doesn't appear in the string, the program exits the loop normally and returns -1.\n\nThis pattern of computation—traversing a sequence and returning when we find what we are looking for—is called a **search**.\n\n**Exercise 8.4.** *Modify* find *so that it has a third parameter, the index in* word *where it should start looking.*\n\n#### **8.7 Looping and counting**\n\nThe following program counts the number of times the letter a appears in a string:\n\n```\nword = 'banana'\ncount = 0\nfor letter in word:\n if letter == 'a':\n count = count + 1\nprint count\n```\nThis program demonstrates another pattern of computation called a **counter**. The variable count is initialized to 0 and then incremented each time an a is found. When the loop exits, count contains the result—the total number of a's.\n\n**Exercise 8.5.** *Encapsulate this code in a function named* count*, and generalize it so that it accepts the string and the letter as arguments.*\n\n**Exercise 8.6.** *Rewrite this function so that instead of traversing the string, it uses the threeparameter version of* find *from the previous section.*\n\n#### **8.8 String methods**\n\nA **method** is similar to a function—it takes arguments and returns a value—but the syntax is different. For example, the method upper takes a string and returns a new string with all uppercase letters:\n\nInstead of the function syntax upper(word), it uses the method syntax word.upper().\n\n```\n>>> word = 'banana'\n>>> new_word = word.upper()\n>>> print new_word\nBANANA\n```\nThis form of dot notation specifies the name of the method, upper, and the name of the string to apply the method to, word. The empty parentheses indicate that this method takes no argument.\n\nA method call is called an **invocation**; in this case, we would say that we are invoking upper on the word.\n\nAs it turns out, there is a string method named find that is remarkably similar to the function we wrote:\n\n```\n>>> word = 'banana'\n>>> index = word.find('a')\n>>> print index\n1\n```\nIn this example, we invoke find on word and pass the letter we are looking for as a parameter.\n\nActually, the find method is more general than our function; it can find substrings, not just characters:\n\n```\n>>> word.find('na')\n2\n```\nIt can take as a second argument the index where it should start:\n\n```\n>>> word.find('na', 3)\n4\n```\nAnd as a third argument the index where it should stop:\n\n```\n>>> name = 'bob'\n>>> name.find('b', 1, 2)\n-1\n```\nThis search fails because b does not appear in the index range from 1 to 2 (not including 2). **Exercise 8.7.** *There is a string method called* count *that is similar to the function in the previous exercise. Read the documentation of this method and write an invocation that counts the number of* a*s in* 'banana'*.*\n\n**Exercise 8.8.** *Read the documentation of the string methods at* [http: // docs. python. org/ 2/](http://docs.python.org/2/library/stdtypes.html#string-methods) [library/ stdtypes. html# string-methods](http://docs.python.org/2/library/stdtypes.html#string-methods) *. You might want to experiment with some of them to make sure you understand how they work.* strip *and* replace *are particularly useful.*\n\n*The documentation uses a syntax that might be confusing. For example, in* find(sub[, start[, end]])*, the brackets indicate optional arguments. So* sub *is required, but* start *is optional, and if you include* start*, then* end *is optional.*\n\n## **8.9 The** in **operator**\n\nThe word in is a boolean operator that takes two strings and returns True if the first appears as a substring in the second:\n\n```\n>>> 'a' in 'banana'\nTrue\n>>> 'seed' in 'banana'\n```\nFalse\n\nFor example, the following function prints all the letters from word1 that also appear in word2:\n\n```\ndef in_both(word1, word2):\n for letter in word1:\n if letter in word2:\n print letter\n```\nWith well-chosen variable names, Python sometimes reads like English. You could read this loop, \"for (each) letter in (the first) word, if (the) letter (appears) in (the second) word, print (the) letter.\"\n\nHere's what you get if you compare apples and oranges:\n\n```\n>>> in_both('apples', 'oranges')\na\ne\ns\n```\n#### **8.10 String comparison**\n\nThe relational operators work on strings. To see if two strings are equal:\n\nif word == 'banana': print 'All right, bananas.'\n\nOther relational operations are useful for putting words in alphabetical order:\n\n```\nif word < 'banana':\n print 'Your word,' + word + ', comes before banana.'\nelif word > 'banana':\n print 'Your word,' + word + ', comes after banana.'\nelse:\n print 'All right, bananas.'\n```\nPython does not handle uppercase and lowercase letters the same way that people do. All the uppercase letters come before all the lowercase letters, so:\n\nYour word, Pineapple, comes before banana.\n\nA common way to address this problem is to convert strings to a standard format, such as all lowercase, before performing the comparison. Keep that in mind in case you have to defend yourself against a man armed with a Pineapple.\n\n#### **8.11 Debugging**\n\nWhen you use indices to traverse the values in a sequence, it is tricky to get the beginning and end of the traversal right. Here is a function that is supposed to compare two words and return True if one of the words is the reverse of the other, but it contains two errors:\n\n```\ndef is_reverse(word1, word2):\n if len(word1) != len(word2):\n return False\n i = 0\n j = len(word2)\n while j > 0:\n if word1[i] != word2[j]:\n return False\n i = i+1\n j = j-1\n return True\n```\nThe first if statement checks whether the words are the same length. If not, we can return False immediately and then, for the rest of the function, we can assume that the words are the same length. This is an example of the guardian pattern in Section [6.8.](#page-79-0)\n\ni and j are indices: i traverses word1 forward while j traverses word2 backward. If we find two letters that don't match, we can return False immediately. If we get through the whole loop and all the letters match, we return True.\n\nIf we test this function with the words \"pots\" and \"stop\", we expect the return value True, but we get an IndexError:\n\n>>> is_reverse('pots', 'stop') ...\n\n\n\nFigure 8.2: State diagram.\n\n```\nFile \"reverse.py\", line 15, in is_reverse\n if word1[i] != word2[j]:\nIndexError: string index out of range\n```\nFor debugging this kind of error, my first move is to print the values of the indices immediately before the line where the error appears.\n\n```\nwhile j > 0:\n print i, j # print here\n if word1[i] != word2[j]:\n return False\n i = i+1\n j = j-1\n```\nNow when I run the program again, I get more information:\n\n```\n>>> is_reverse('pots', 'stop')\n0 4\n...\nIndexError: string index out of range\n```\nThe first time through the loop, the value of j is 4, which is out of range for the string 'pots'. The index of the last character is 3, so the initial value for j should be len(word2)-1.\n\nIf I fix that error and run the program again, I get:\n\n```\n>>> is_reverse('pots', 'stop')\n0 3\n1 2\n2 1\nTrue\n```\nThis time we get the right answer, but it looks like the loop only ran three times, which is suspicious. To get a better idea of what is happening, it is useful to draw a state diagram. During the first iteration, the frame for is_reverse is shows in Figure [8.2.](#page-99-1)\n\nI took a little license by arranging the variables in the frame and adding dotted lines to show that the values of i and j indicate characters in word1 and word2.\n\n**Exercise 8.9.** *Starting with this diagram, execute the program on paper, changing the values of* i *and* j *during each iteration. Find and fix the second error in this function.*\n\n#### **8.12 Glossary**\n\n**object:** Something a variable can refer to. For now, you can use \"object\" and \"value\" interchangeably.\n\n- **sequence:** An ordered set; that is, a set of values where each value is identified by an integer index.\n**item:** One of the values in a sequence.\n\n**index:** An integer value used to select an item in a sequence, such as a character in a string.\n\n**slice:** A part of a string specified by a range of indices.\n\n- **empty string:** A string with no characters and length 0, represented by two quotation marks.\n**immutable:** The property of a sequence whose items cannot be assigned.\n\n- **traverse:** To iterate through the items in a sequence, performing a similar operation on each.\n**search:** A pattern of traversal that stops when it finds what it is looking for.\n\n- **counter:** A variable used to count something, usually initialized to zero and then incremented.\n**method:** A function that is associated with an object and called using dot notation.\n\n**invocation:** A statement that calls a method.\n\n#### **8.13 Exercises**\n\n**Exercise 8.10.** *A string slice can take a third index that specifies the \"step size;\" that is, the number of spaces between successive characters. A step size of 2 means every other character; 3 means every third, etc.*\n\n```\n>>> fruit = 'banana'\n>>> fruit[0:5:2]\n'bnn'\n```\n*A step size of -1 goes through the word backwards, so the slice* [::-1] *generates a reversed string.*\n\n*Use this idiom to write a one-line version of* is_palindrome *from Exercise [6.6.](#page-82-0)* **Exercise 8.11.** *The following functions are all* intended *to check whether a string contains any*\n\n*lowercase letters, but at least some of them are wrong. For each function, describe what the function actually does (assuming that the parameter is a string).*\n\n```\ndef any_lowercase1(s):\n for c in s:\n if c.islower():\n return True\n else:\n return False\ndef any_lowercase2(s):\n for c in s:\n if 'c'.islower():\n return 'True'\n```\n\n```\nelse:\n return 'False'\ndef any_lowercase3(s):\n for c in s:\n flag = c.islower()\n return flag\ndef any_lowercase4(s):\n flag = False\n for c in s:\n flag = flag or c.islower()\n return flag\ndef any_lowercase5(s):\n for c in s:\n if not c.islower():\n return False\n return True\n```\n**Exercise 8.12.** *ROT13 is a weak form of encryption that involves \"rotating\" each letter in a word by 13 places. To rotate a letter means to shift it through the alphabet, wrapping around to the beginning if necessary, so 'A' shifted by 3 is 'D' and 'Z' shifted by 1 is 'A'.*\n\n*Write a function called* rotate_word *that takes a string and an integer as parameters, and that returns a new string that contains the letters from the original string \"rotated\" by the given amount.*\n\n*For example, \"cheer\" rotated by 7 is \"jolly\" and \"melon\" rotated by -10 is \"cubed\".*\n\n*You might want to use the built-in functions* ord*, which converts a character to a numeric code, and* chr*, which converts numeric codes to characters.*\n\n*Potentially offensive jokes on the Internet are sometimes encoded in ROT13. If you are not easily offended, find and decode some of them. Solution:* [http: // thinkpython. com/ code/ rotate.](http://thinkpython.com/code/rotate.py) [py](http://thinkpython.com/code/rotate.py) *.*\n\n# **Chapter 9**\n\n# **Case study: word play**\n\n#### **9.1 Reading word lists**\n\nFor the exercises in this chapter we need a list of English words. There are lots of word lists available on the Web, but the one most suitable for our purpose is one of the word lists collected and contributed to the public domain by Grady Ward as part of the Moby lexicon project (see ). It is a list of 113,809 official crosswords; that is, words that are considered valid in crossword puzzles and other word games. In the Moby collection, the filename is 113809of.fic; you can download a copy, with the simpler name words.txt, from .\n\nThis file is in plain text, so you can open it with a text editor, but you can also read it from Python. The built-in function open takes the name of the file as a parameter and returns a **file object** you can use to read the file.\n\n```\n>>> fin = open('words.txt')\n>>> print fin\n\n```\nfin is a common name for a file object used for input. Mode 'r' indicates that this file is open for reading (as opposed to 'w' for writing).\n\nThe file object provides several methods for reading, including readline, which reads characters from the file until it gets to a newline and returns the result as a string:\n\n```\n>>> fin.readline()\n'aa\\r\\n'\n```\nThe first word in this particular list is \"aa,\" which is a kind of lava. The sequence \\r\\n represents two whitespace characters, a carriage return and a newline, that separate this word from the next.\n\nThe file object keeps track of where it is in the file, so if you call readline again, you get the next word:\n\n```\n>>> fin.readline()\n'aah\\r\\n'\n```\nThe next word is \"aah,\" which is a perfectly legitimate word, so stop looking at me like that. Or, if it's the whitespace that's bothering you, we can get rid of it with the string method strip:\n\n```\n>>> line = fin.readline()\n>>> word = line.strip()\n>>> print word\n```\naahed\n\nYou can also use a file object as part of a for loop. This program reads words.txt and prints each word, one per line:\n\n```\nfin = open('words.txt')\nfor line in fin:\n word = line.strip()\n print word\n```\n**Exercise 9.1.** *Write a program that reads* words.txt *and prints only the words with more than 20 characters (not counting whitespace).*\n\n## **9.2 Exercises**\n\nThere are solutions to these exercises in the next section. You should at least attempt each one before you read the solutions.\n\n**Exercise 9.2.** *In 1939 Ernest Vincent Wright published a 50,000 word novel called* Gadsby *that does not contain the letter \"e.\" Since \"e\" is the most common letter in English, that's not easy to do.*\n\n*In fact, it is difficult to construct a solitary thought without using that most common symbol. It is slow going at first, but with caution and hours of training you can gradually gain facility.*\n\n*All right, I'll stop now.*\n\n*Write a function called* has_no_e *that returns* True *if the given word doesn't have the letter \"e\" in it.*\n\n*Modify your program from the previous section to print only the words that have no \"e\" and compute the percentage of the words in the list have no \"e.\"*\n\n**Exercise 9.3.** *Write a function named* avoids *that takes a word and a string of forbidden letters, and that returns* True *if the word doesn't use any of the forbidden letters.*\n\n*Modify your program to prompt the user to enter a string of forbidden letters and then print the number of words that don't contain any of them. Can you find a combination of 5 forbidden letters that excludes the smallest number of words?*\n\n**Exercise 9.4.** *Write a function named* uses_only *that takes a word and a string of letters, and that returns* True *if the word contains only letters in the list. Can you make a sentence using only the letters* acefhlo*? Other than \"Hoe alfalfa?\"*\n\n**Exercise 9.5.** *Write a function named* uses_all *that takes a word and a string of required letters, and that returns* True *if the word uses all the required letters at least once. How many words are there that use all the vowels* aeiou*? How about* aeiouy*?*\n\n**Exercise 9.6.** *Write a function called* is_abecedarian *that returns* True *if the letters in a word appear in alphabetical order (double letters are ok). How many abecedarian words are there?*\n\n### **9.3 Search**\n\nAll of the exercises in the previous section have something in common; they can be solved with the search pattern we saw in Section [8.6.](#page-95-1) The simplest example is:\n\n```\ndef has_no_e(word):\n for letter in word:\n if letter == 'e':\n return False\n return True\n```\nThe for loop traverses the characters in word. If we find the letter \"e\", we can immediately return False; otherwise we have to go to the next letter. If we exit the loop normally, that means we didn't find an \"e\", so we return True.\n\navoids is a more general version of has_no_e but it has the same structure:\n\nWe can return False as soon as we find a forbidden letter; if we get to the end of the loop, we return True.\n\nuses_only is similar except that the sense of the condition is reversed:\n\n```\ndef uses_only(word, available):\n for letter in word:\n if letter not in available:\n return False\n return True\n```\nInstead of a list of forbidden letters, we have a list of available letters. If we find a letter in word that is not in available, we can return False.\n\nuses_all is similar except that we reverse the role of the word and the string of letters:\n\n```\ndef uses_all(word, required):\n for letter in required:\n if letter not in word:\n return False\n return True\n```\nInstead of traversing the letters in word, the loop traverses the required letters. If any of the required letters do not appear in the word, we can return False.\n\nIf you were really thinking like a computer scientist, you would have recognized that uses_all was an instance of a previously-solved problem, and you would have written:\n\n```\ndef uses_all(word, required):\n return uses_only(required, word)\n```\nThis is an example of a program development method called **problem recognition**, which means that you recognize the problem you are working on as an instance of a previouslysolved problem, and apply a previously-developed solution.\n\n#### **9.4 Looping with indices**\n\nI wrote the functions in the previous section with for loops because I only needed the characters in the strings; I didn't have to do anything with the indices.\n\nFor is_abecedarian we have to compare adjacent letters, which is a little tricky with a for loop:\n\n```\ndef is_abecedarian(word):\n previous = word[0]\n for c in word:\n if c < previous:\n return False\n previous = c\n return True\n```\nAn alternative is to use recursion:\n\n```\ndef is_abecedarian(word):\n if len(word) <= 1:\n return True\n if word[0] > word[1]:\n return False\n return is_abecedarian(word[1:])\n```\nAnother option is to use a while loop:\n\n```\ndef is_abecedarian(word):\n i = 0\n while i < len(word)-1:\n if word[i+1] < word[i]:\n return False\n i = i+1\n return True\n```\nThe loop starts at i=0 and ends when i=len(word)-1. Each time through the loop, it compares the *i*th character (which you can think of as the current character) to the *i* + 1th character (which you can think of as the next).\n\nIf the next character is less than (alphabetically before) the current one, then we have discovered a break in the abecedarian trend, and we return False.\n\nIf we get to the end of the loop without finding a fault, then the word passes the test. To convince yourself that the loop ends correctly, consider an example like 'flossy'. The length of the word is 6, so the last time the loop runs is when i is 4, which is the index of the second-to-last character. On the last iteration, it compares the second-to-last character to the last, which is what we want.\n\nHere is a version of is_palindrome (see Exercise [6.6)](#page-82-0) that uses two indices; one starts at the beginning and goes up; the other starts at the end and goes down.\n\n```\ndef is_palindrome(word):\n i = 0\n j = len(word)-1\n while i**9.5 Debugging**\n\nTesting programs is hard. The functions in this chapter are relatively easy to test because you can check the results by hand. Even so, it is somewhere between difficult and impossible to choose a set of words that test for all possible errors.\n\nTaking has_no_e as an example, there are two obvious cases to check: words that have an 'e' should return False; words that don't should return True. You should have no trouble coming up with one of each.\n\nWithin each case, there are some less obvious subcases. Among the words that have an “e,” you should test words with an “e” at the beginning, the end, and somewhere in the middle. You should test long words, short words, and very short words, like the empty string. The empty string is an example of a **special case**, which is one of the non-obvious cases where errors often lurk.\n\nIn addition to the test cases you generate, you can also test your program with a word list like words.txt. By scanning the output, you might be able to catch errors, but be careful: you might catch one kind of error (words that should not be included, but are) and not another (words that should be included, but aren't).\n\nIn general, testing can help you find bugs, but it is not easy to generate a good set of test cases, and even if you do, you can't be sure your program is correct.\n\nAccording to a legendary computer scientist:\n\nProgram testing can be used to show the presence of bugs, but never to show their absence!\n\n— Edsger W. Dijkstra\n\n#### **9.6 Glossary**\n\n**file object:** A value that represents an open file.\n\n- **problem recognition:** A way of solving a problem by expressing it as an instance of a previously-solved problem.\n- **special case:** A test case that is atypical or non-obvious (and less likely to be handled correctly).\n\n#### **9.7 Exercises**\n\n**Exercise 9.7.** *This question is based on a Puzzler that was broadcast on the radio program* Car Talk *(*[http: // www. cartalk. com/ content/ puzzlers](http://www.cartalk.com/content/puzzlers) *):*\n\n*Give me a word with three consecutive double letters. I'll give you a couple of words that almost qualify, but don't. For example, the word committee, c-o-m-m-i-t-t-e-e. It would be great except for the 'i' that sneaks in there. Or Mississippi: M-i-s-s-i-s-s-ip-p-i. If you could take out those i's it would work. But there is a word that has three consecutive pairs of letters and to the best of my knowledge this may be the only word. Of course there are probably 500 more but I can only think of one. What is the word?*\n\n*Write a program to find it. Solution:* [http: // thinkpython. com/ code/ cartalk1. py](http://thinkpython.com/code/cartalk1.py) *.* **Exercise 9.8.** *Here's another* Car Talk *Puzzler (*[http: // www. cartalk. com/ content/](http://www.cartalk.com/content/puzzlers) [puzzlers](http://www.cartalk.com/content/puzzlers) *):*\n\n*\"I was driving on the highway the other day and I happened to notice my odometer. Like most odometers, it shows six digits, in whole miles only. So, if my car had 300,000 miles, for example, I'd see 3-0-0-0-0-0.*\n\n*\"Now, what I saw that day was very interesting. I noticed that the last 4 digits were palindromic; that is, they read the same forward as backward. For example, 5-4-4-5 is a palindrome, so my odometer could have read 3-1-5-4-4-5.*\n\n*\"One mile later, the last 5 numbers were palindromic. For example, it could have read 3-6-5-4-5-6. One mile after that, the middle 4 out of 6 numbers were palindromic. And you ready for this? One mile later, all 6 were palindromic!*\n\n*\"The question is, what was on the odometer when I first looked?\"*\n\n*Write a Python program that tests all the six-digit numbers and prints any numbers that satisfy these requirements. Solution:* [http: // thinkpython. com/ code/ cartalk2. py](http://thinkpython.com/code/cartalk2.py) *.* **Exercise 9.9.** *Here's another* Car Talk *Puzzler you can solve with a search (*[http: // www.](http://www.cartalk.com/content/puzzlers) [cartalk. com/ content/ puzzlers](http://www.cartalk.com/content/puzzlers) *):*\n\n*\"Recently I had a visit with my mom and we realized that the two digits that make up my age when reversed resulted in her age. For example, if she's 73, I'm 37. We wondered how often this has happened over the years but we got sidetracked with other topics and we never came up with an answer.*\n\n*\"When I got home I figured out that the digits of our ages have been reversible six times so far. I also figured out that if we're lucky it would happen again in a few years, and if we're really lucky it would happen one more time after that. In other words, it would have happened 8 times over all. So the question is, how old am I now?\"*\n\n*Write a Python program that searches for solutions to this Puzzler. Hint: you might find the string method* zfill *useful.*\n\n*Solution:* [http: // thinkpython. com/ code/ cartalk3. py](http://thinkpython.com/code/cartalk3.py) *.*\n\n# **Chapter 10**\n\n# **Lists**\n\n#### **10.1 A list is a sequence**\n\nLike a string, a **list** is a sequence of values. In a string, the values are characters; in a list, they can be any type. The values in a list are called **elements** or sometimes **items**.\n\nThere are several ways to create a new list; the simplest is to enclose the elements in square brackets ([ and ]):\n\n[10, 20, 30, 40]\n\n['crunchy frog', 'ram bladder', 'lark vomit']\n\nThe first example is a list of four integers. The second is a list of three strings. The elements of a list don't have to be the same type. The following list contains a string, a float, an integer, and (lo!) another list:\n\n['spam', 2.0, 5, [10, 20]]\n\nA list within another list is **nested**.\n\nA list that contains no elements is called an empty list; you can create one with empty brackets, [].\n\nAs you might expect, you can assign list values to variables:\n\n```\n>>> cheeses = ['Cheddar', 'Edam', 'Gouda']\n>>> numbers = [17, 123]\n>>> empty = []\n>>> print cheeses, numbers, empty\n['Cheddar', 'Edam', 'Gouda'] [17, 123] []\n```\n#### **10.2 Lists are mutable**\n\nThe syntax for accessing the elements of a list is the same as for accessing the characters of a string—the bracket operator. The expression inside the brackets specifies the index. Remember that the indices start at 0:\n\n```\n>>> print cheeses[0]\nCheddar\n```\n\n\nFigure 10.1: State diagram.\n\nUnlike strings, lists are mutable. When the bracket operator appears on the left side of an assignment, it identifies the element of the list that will be assigned.\n\n```\n>>> numbers = [17, 123]\n>>> numbers[1] = 5\n>>> print numbers\n[17, 5]\n```\nThe one-eth element of numbers, which used to be 123, is now 5.\n\nYou can think of a list as a relationship between indices and elements. This relationship is called a **mapping**; each index \"maps to\" one of the elements. Figure [10.1](#page-109-0) shows the state diagram for cheeses, numbers and empty:\n\nLists are represented by boxes with the word \"list\" outside and the elements of the list inside. cheeses refers to a list with three elements indexed 0, 1 and 2. numbers contains two elements; the diagram shows that the value of the second element has been reassigned from 123 to 5. empty refers to a list with no elements.\n\nList indices work the same way as string indices:\n\n- Any integer expression can be used as an index.\n- If you try to read or write an element that does not exist, you get an IndexError.\n- If an index has a negative value, it counts backward from the end of the list.\n\nThe in operator also works on lists.\n\n```\n>>> cheeses = ['Cheddar', 'Edam', 'Gouda']\n>>> 'Edam' in cheeses\nTrue\n>>> 'Brie' in cheeses\nFalse\n```\n#### **10.3 Traversing a list**\n\nThe most common way to traverse the elements of a list is with a for loop. The syntax is the same as for strings:\n\nfor cheese in cheeses: print cheese\n\nThis works well if you only need to read the elements of the list. But if you want to write or update the elements, you need the indices. A common way to do that is to combine the functions range and len:\n\n```\nfor i in range(len(numbers)):\n numbers[i] = numbers[i] * 2\n```\nThis loop traverses the list and updates each element. len returns the number of elements in the list. range returns a list of indices from 0 to *n* − 1, where *n* is the length of the list. Each time through the loop i gets the index of the next element. The assignment statement in the body uses i to read the old value of the element and to assign the new value.\n\nA for loop over an empty list never executes the body:\n\n```\nfor x in []:\n print 'This never happens.'\n```\nAlthough a list can contain another list, the nested list still counts as a single element. The length of this list is four:\n\n```\n['spam', 1, ['Brie', 'Roquefort', 'Pol le Veq'], [1, 2, 3]]\n```\n#### **10.4 List operations**\n\nThe + operator concatenates lists:\n\nSimilarly, the * operator repeats a list a given number of times:\n\n>>> [0] * 4 [0, 0, 0, 0] >>> [1, 2, 3] * 3 [1, 2, 3, 1, 2, 3, 1, 2, 3]\n\nThe first example repeats [0] four times. The second example repeats the list [1, 2, 3] three times.\n\n#### **10.5 List slices**\n\nThe slice operator also works on lists:\n\n```\n>>> t = ['a', 'b', 'c', 'd', 'e', 'f']\n>>> t[1:3]\n['b', 'c']\n>>> t[:4]\n['a', 'b', 'c', 'd']\n>>> t[3:]\n['d', 'e', 'f']\n```\nIf you omit the first index, the slice starts at the beginning. If you omit the second, the slice goes to the end. So if you omit both, the slice is a copy of the whole list.\n\n```\n>>> t[:]\n['a', 'b', 'c', 'd', 'e', 'f']\n```\nSince lists are mutable, it is often useful to make a copy before performing operations that fold, spindle or mutilate lists.\n\nA slice operator on the left side of an assignment can update multiple elements:\n\n```\n>>> t = ['a', 'b', 'c', 'd', 'e', 'f']\n>>> t[1:3] = ['x', 'y']\n>>> print t\n['a', 'x', 'y', 'd', 'e', 'f']\n```\n#### **10.6 List methods**\n\nPython provides methods that operate on lists. For example, append adds a new element to the end of a list:\n\n```\n>>> t = ['a', 'b', 'c']\n>>> t.append('d')\n>>> print t\n['a', 'b', 'c', 'd']\n```\nextend takes a list as an argument and appends all of the elements:\n\n```\n>>> t1 = ['a', 'b', 'c']\n>>> t2 = ['d', 'e']\n>>> t1.extend(t2)\n>>> print t1\n['a', 'b', 'c', 'd', 'e']\n```\nThis example leaves t2 unmodified.\n\nsort arranges the elements of the list from low to high:\n\n```\n>>> t = ['d', 'c', 'e', 'b', 'a']\n>>> t.sort()\n>>> print t\n['a', 'b', 'c', 'd', 'e']\n```\nList methods are all void; they modify the list and return None. If you accidentally write t = t.sort(), you will be disappointed with the result.\n\n#### **10.7 Map, filter and reduce**\n\nTo add up all the numbers in a list, you can use a loop like this:\n\n```\ndef add_all(t):\n total = 0\n for x in t:\n total += x\n return total\n```\ntotal is initialized to 0. Each time through the loop, x gets one element from the list. The += operator provides a short way to update a variable. This **augmented assignment statement**:\n\ntotal += x\n\nis equivalent to:\n\ntotal = total + x\n\nAs the loop executes, total accumulates the sum of the elements; a variable used this way is sometimes called an **accumulator**.\n\nAdding up the elements of a list is such a common operation that Python provides it as a built-in function, sum:\n\n```\n>>> t = [1, 2, 3]\n>>> sum(t)\n6\n```\nAn operation like this that combines a sequence of elements into a single value is sometimes called **reduce**.\n\n**Exercise 10.1.** *Write a function called* nested_sum *that takes a nested list of integers and add up the elements from all of the nested lists.*\n\nSometimes you want to traverse one list while building another. For example, the following function takes a list of strings and returns a new list that contains capitalized strings:\n\n```\ndef capitalize_all(t):\n res = []\n for s in t:\n res.append(s.capitalize())\n return res\n```\nres is initialized with an empty list; each time through the loop, we append the next element. So res is another kind of accumulator.\n\nAn operation like capitalize_all is sometimes called a **map** because it \"maps\" a function (in this case the method capitalize) onto each of the elements in a sequence.\n\n**Exercise 10.2.** *Use* capitalize_all *to write a function named* capitalize_nested *that takes a nested list of strings and returns a new nested list with all strings capitalized.*\n\nAnother common operation is to select some of the elements from a list and return a sublist. For example, the following function takes a list of strings and returns a list that contains only the uppercase strings:\n\n```\ndef only_upper(t):\n res = []\n for s in t:\n```\n\n```\nif s.isupper():\n res.append(s)\nreturn res\n```\nisupper is a string method that returns True if the string contains only upper case letters.\n\nAn operation like only_upper is called a **filter** because it selects some of the elements and filters out the others.\n\nMost common list operations can be expressed as a combination of map, filter and reduce. Because these operations are so common, Python provides language features to support them, including the built-in function map and an operator called a \"list comprehension.\" **Exercise 10.3.** *Write a function that takes a list of numbers and returns the cumulative sum; that*\n\n*is, a new list where the ith element is the sum of the first i* + 1 *elements from the original list. For example, the cumulative sum of* [1, 2, 3] *is* [1, 3, 6]*.*\n\n#### **10.8 Deleting elements**\n\nThere are several ways to delete elements from a list. If you know the index of the element you want, you can use pop:\n\n```\n>>> t = ['a', 'b', 'c']\n>>> x = t.pop(1)\n>>> print t\n['a', 'c']\n>>> print x\nb\n```\npop modifies the list and returns the element that was removed. If you don't provide an index, it deletes and returns the last element.\n\nIf you don't need the removed value, you can use the del operator:\n\n>>> t = ['a', 'b', 'c'] >>> del t[1] >>> print t ['a', 'c']\n\nIf you know the element you want to remove (but not the index), you can use remove:\n\n```\n>>> t = ['a', 'b', 'c']\n>>> t.remove('b')\n>>> print t\n['a', 'c']\n```\nThe return value from remove is None.\n\nTo remove more than one element, you can use del with a slice index:\n\n```\n>>> t = ['a', 'b', 'c', 'd', 'e', 'f']\n>>> del t[1:5]\n>>> print t\n['a', 'f']\n```\nAs usual, the slice selects all the elements up to, but not including, the second index. **Exercise 10.4.** *Write a function called* middle *that takes a list and returns a new list that contains* all but the first and last elements. So middle([1,2,3,4]) should return [2,3].\n\n**Exercise 10.5.** *Write a function called* chop *that takes a list, modifies it by removing the first and last elements, and returns* None*.*\n\n#### **10.9 Lists and strings**\n\nA string is a sequence of characters and a list is a sequence of values, but a list of characters is not the same as a string. To convert from a string to a list of characters, you can use list:\n\n```\n>>> s = 'spam'\n>>> t = list(s)\n>>> print t\n['s', 'p', 'a', 'm']\n```\nBecause list is the name of a built-in function, you should avoid using it as a variable name. I also avoid l because it looks too much like 1. So that's why I use t.\n\nThe list function breaks a string into individual letters. If you want to break a string into words, you can use the split method:\n\n```\n>>> s = 'pining for the fjords'\n>>> t = s.split()\n>>> print t\n['pining', 'for', 'the', 'fjords']\n```\nAn optional argument called a **delimiter** specifies which characters to use as word boundaries. The following example uses a hyphen as a delimiter:\n\n```\n>>> s = 'spam-spam-spam'\n>>> delimiter = '-'\n>>> s.split(delimiter)\n['spam', 'spam', 'spam']\n```\njoin is the inverse of split. It takes a list of strings and concatenates the elements. join is a string method, so you have to invoke it on the delimiter and pass the list as a parameter:\n\n```\n>>> t = ['pining', 'for', 'the', 'fjords']\n>>> delimiter = ' '\n>>> delimiter.join(t)\n'pining for the fjords'\n```\nIn this case the delimiter is a space character, so join puts a space between words. To concatenate strings without spaces, you can use the empty string, '', as a delimiter.\n\n#### **10.10 Objects and values**\n\nIf we execute these assignment statements:\n\n```\na = 'banana'\nb = 'banana'\n```\nWe know that a and b both refer to a string, but we don't know whether they refer to the *same* string. There are two possible states, shown in Figure [10.2.](#page-115-1)\n\nIn one case, a and b refer to two different objects that have the same value. In the second case, they refer to the same object.\n\nTo check whether two variables refer to the same object, you can use the is operator.\n\n\n\nFigure 10.2: State diagram.\n\n\n\n| a → | [ 1, 2, 3 ] |\n|-----|-------------|\n| b → | [ 1, 2, 3 ] |\n\nFigure 10.3: State diagram.\n\n```\n>>> a = 'banana'\n>>> b = 'banana'\n>>> a is b\nTrue\n```\nIn this example, Python only created one string object, and both a and b refer to it.\n\nBut when you create two lists, you get two objects:\n\n>>> a = [1, 2, 3] >>> b = [1, 2, 3] >>> a is b False\n\nSo the state diagram looks like Figure [10.3.](#page-115-2)\n\nIn this case we would say that the two lists are **equivalent**, because they have the same elements, but not **identical**, because they are not the same object. If two objects are identical, they are also equivalent, but if they are equivalent, they are not necessarily identical.\n\nUntil now, we have been using \"object\" and \"value\" interchangeably, but it is more precise to say that an object has a value. If you execute [1,2,3], you get a list object whose value is a sequence of integers. If another list has the same elements, we say it has the same value, but it is not the same object.\n\n#### **10.11 Aliasing**\n\nIf a refers to an object and you assign b = a, then both variables refer to the same object:\n\n```\n>>> a = [1, 2, 3]\n>>> b = a\n>>> b is a\nTrue\n```\nThe state diagram looks like Figure [10.4.](#page-116-1)\n\nThe association of a variable with an object is called a **reference**. In this example, there are two references to the same object.\n\nAn object with more than one reference has more than one name, so we say that the object is **aliased**.\n\nIf the aliased object is mutable, changes made with one alias affect the other:\n\n\n$$\\sum_{\\mathfrak{a} \\preccurlyeq \\mathfrak{b}} [1, 2, 3]$$\n\nFigure 10.4: State diagram.\n\n\n\nFigure 10.5: Stack diagram.\n\n>>> b[0] = 17 >>> print a [17, 2, 3]\n\nAlthough this behavior can be useful, it is error-prone. In general, it is safer to avoid aliasing when you are working with mutable objects.\n\nFor immutable objects like strings, aliasing is not as much of a problem. In this example:\n\na = 'banana' b = 'banana'\n\nIt almost never makes a difference whether a and b refer to the same string or not.\n\n#### **10.12 List arguments**\n\nWhen you pass a list to a function, the function gets a reference to the list. If the function modifies a list parameter, the caller sees the change. For example, delete_head removes the first element from a list:\n\n```\ndef delete_head(t):\n del t[0]\n```\nHere's how it is used:\n\n```\n>>> letters = ['a', 'b', 'c']\n>>> delete_head(letters)\n>>> print letters\n['b', 'c']\n```\nThe parameter t and the variable letters are aliases for the same object. The stack diagram looks like Figure [10.5.](#page-116-2)\n\nSince the list is shared by two frames, I drew it between them.\n\nIt is important to distinguish between operations that modify lists and operations that create new lists. For example, the append method modifies a list, but the + operator creates a new list:\n\n>>> t1 = [1, 2] >>> t2 = t1.append(3)\n\n```\n>>> print t1\n[1, 2, 3]\n>>> print t2\nNone\n>>> t3 = t1 + [4]\n>>> print t3\n[1, 2, 3, 4]\n```\nThis difference is important when you write functions that are supposed to modify lists. For example, this function *does not* delete the head of a list:\n\ndef bad_delete_head(t): t = t[1:] # WRONG!\n\nThe slice operator creates a new list and the assignment makes t refer to it, but none of that has any effect on the list that was passed as an argument.\n\nAn alternative is to write a function that creates and returns a new list. For example, tail returns all but the first element of a list:\n\n```\ndef tail(t):\n return t[1:]\n```\nThis function leaves the original list unmodified. Here's how it is used:\n\n```\n>>> letters = ['a', 'b', 'c']\n>>> rest = tail(letters)\n>>> print rest\n['b', 'c']\n```\n#### **10.13 Debugging**\n\nCareless use of lists (and other mutable objects) can lead to long hours of debugging. Here are some common pitfalls and ways to avoid them:\n\n- 1. Don't forget that most list methods modify the argument and return None. This is the opposite of the string methods, which return a new string and leave the original alone.\nIf you are used to writing string code like this:\n\nword = word.strip()\n\nIt is tempting to write list code like this:\n\nt = t.sort() # WRONG!\n\nBecause sort returns None, the next operation you perform with t is likely to fail.\n\nBefore using list methods and operators, you should read the documentation carefully and then test them in interactive mode. The methods and operators that lists share with other sequences (like strings) are documented at [http://docs.python.](http://docs.python.org/2/library/stdtypes.html#typesseq) [org/2/library/stdtypes.html#typesseq](http://docs.python.org/2/library/stdtypes.html#typesseq). The methods and operators that only apply to mutable sequences are documented at [http://docs.python.org/2/library/](http://docs.python.org/2/library/stdtypes.html#typesseq-mutable) [stdtypes.html#typesseq-mutable](http://docs.python.org/2/library/stdtypes.html#typesseq-mutable).\n\n- 2. Pick an idiom and stick with it.\nPart of the problem with lists is that there are too many ways to do things. For example, to remove an element from a list, you can use pop, remove, del, or even a slice assignment.\n\nTo add an element, you can use the append method or the + operator. Assuming that t is a list and x is a list element, these are right:\n\nt.append(x) t = t + [x]\n\nAnd these are wrong:\n\n| t.append([x]) | # WRONG! |\n|-----------------|----------|\n| t = t.append(x) | # WRONG! |\n| t + [x] | # WRONG! |\n| t = t + x | # WRONG! |\n\nTry out each of these examples in interactive mode to make sure you understand what they do. Notice that only the last one causes a runtime error; the other three are legal, but they do the wrong thing.\n\n- 3. Make copies to avoid aliasing.\nIf you want to use a method like sort that modifies the argument, but you need to keep the original list as well, you can make a copy.\n\norig = t[:] t.sort()\n\nIn this example you could also use the built-in function sorted, which returns a new, sorted list and leaves the original alone. But in that case you should avoid using sorted as a variable name!\n\n#### **10.14 Glossary**\n\n**list:** A sequence of values.\n\n**element:** One of the values in a list (or other sequence), also called items.\n\n**index:** An integer value that indicates an element in a list.\n\n**nested list:** A list that is an element of another list.\n\n**list traversal:** The sequential accessing of each element in a list.\n\n- **mapping:** A relationship in which each element of one set corresponds to an element of another set. For example, a list is a mapping from indices to elements.\n- **accumulator:** A variable used in a loop to add up or accumulate a result.\n- **augmented assignment:** A statement that updates the value of a variable using an operator like +=.\n- **reduce:** A processing pattern that traverses a sequence and accumulates the elements into a single result.\n- **map:** A processing pattern that traverses a sequence and performs an operation on each element.\n- **filter:** A processing pattern that traverses a list and selects the elements that satisfy some criterion.\n- **object:** Something a variable can refer to. An object has a type and a value.\n- **equivalent:** Having the same value.\n\n**identical:** Being the same object (which implies equivalence).\n\n**reference:** The association between a variable and its value.\n\n**aliasing:** A circumstance where two or more variables refer to the same object.\n\n**delimiter:** A character or string used to indicate where a string should be split.\n\n#### **10.15 Exercises**\n\n**Exercise 10.6.** *Write a function called* is_sorted *that takes a list as a parameter and returns* True *if the list is sorted in ascending order and* False *otherwise. You can assume (as a precondition) that the elements of the list can be compared with the relational operators* <*,* >*, etc.*\n\n*For example,* is_sorted([1,2,2]) *should return* True *and* is_sorted(['b','a']) *should return* False*.*\n\n**Exercise 10.7.** *Two words are anagrams if you can rearrange the letters from one to spell the other. Write a function called* is_anagram *that takes two strings and returns* True *if they are anagrams.* **Exercise 10.8.** *The (so-called) Birthday Paradox:*\n\n- *1. Write a function called* has_duplicates *that takes a list and returns* True *if there is any element that appears more than once. It should not modify the original list.*\n- *2. If there are 23 students in your class, what are the chances that two of you have the same birthday? You can estimate this probability by generating random samples of 23 birthdays and checking for matches. Hint: you can generate random birthdays with the* randint *function in the* random *module.*\n\n*You can read about this problem at* [http: // en. wikipedia. org/ wiki/ Birthday_ paradox](http://en.wikipedia.org/wiki/Birthday_paradox) *, and you can download my solution from* [http: // thinkpython. com/ code/ birthday. py](http://thinkpython.com/code/birthday.py) *.* **Exercise 10.9.** *Write a function called* remove_duplicates *that takes a list and returns a new list with only the unique elements from the original. Hint: they don't have to be in the same order.* **Exercise 10.10.** *Write a function that reads the file* words.txt *and builds a list with one element per word. Write two versions of this function, one using the* append *method and the other using the idiom* t = t + [x]*. Which one takes longer to run? Why?*\n\n*Hint: use the* time *module to measure elapsed time. Solution:* [http: // thinkpython. com/](http://thinkpython.com/code/wordlist.py) [code/ wordlist. py](http://thinkpython.com/code/wordlist.py) *.*\n\n**Exercise 10.11.** *To check whether a word is in the word list, you could use the* in *operator, but it would be slow because it searches through the words in order.*\n\n*Because the words are in alphabetical order, we can speed things up with a bisection search (also known as binary search), which is similar to what you do when you look a word up in the dictionary.* *You start in the middle and check to see whether the word you are looking for comes before the word in the middle of the list. If so, then you search the first half of the list the same way. Otherwise you search the second half.*\n\n*Either way, you cut the remaining search space in half. If the word list has 113,809 words, it will take about 17 steps to find the word or conclude that it's not there.*\n\n*Write a function called* bisect *that takes a sorted list and a target value and returns the index of the value in the list, if it's there, or* None *if it's not.*\n\n*Or you could read the documentation of the* bisect *module and use that! Solution:* [http: //](http://thinkpython.com/code/inlist.py) [thinkpython. com/ code/ inlist. py](http://thinkpython.com/code/inlist.py) *.*\n\n**Exercise 10.12.** *Two words are a \"reverse pair\" if each is the reverse of the other. Write a program that finds all the reverse pairs in the word list. Solution:* [http: // thinkpython. com/ code/](http://thinkpython.com/code/reverse_pair.py) [reverse_ pair. py](http://thinkpython.com/code/reverse_pair.py) *.*\n\n**Exercise 10.13.** *Two words \"interlock\" if taking alternating letters from each forms a new word. For example, \"shoe\" and \"cold\" interlock to form \"schooled.\" Solution:* [http: //](http://thinkpython.com/code/interlock.py) [thinkpython. com/ code/ interlock. py](http://thinkpython.com/code/interlock.py) *. Credit: This exercise is inspired by an example at* [http: // puzzlers. org](http://puzzlers.org) *.*\n\n- *1. Write a program that finds all pairs of words that interlock. Hint: don't enumerate all pairs!*\n- *2. Can you find any words that are three-way interlocked; that is, every third letter forms a word, starting from the first, second or third?*\n\n# **Chapter 11**\n\n# **Dictionaries**\n\nA **dictionary** is like a list, but more general. In a list, the indices have to be integers; in a dictionary they can be (almost) any type.\n\nYou can think of a dictionary as a mapping between a set of indices (which are called **keys**) and a set of values. Each key maps to a value. The association of a key and a value is called a **key-value pair** or sometimes an **item**.\n\nAs an example, we'll build a dictionary that maps from English to Spanish words, so the keys and the values are all strings.\n\nThe function dict creates a new dictionary with no items. Because dict is the name of a built-in function, you should avoid using it as a variable name.\n\n```\n>>> eng2sp = dict()\n>>> print eng2sp\n{}\n```\nThe squiggly-brackets, {}, represent an empty dictionary. To add items to the dictionary, you can use square brackets:\n\n```\n>>> eng2sp['one'] = 'uno'\n```\nThis line creates an item that maps from the key 'one' to the value 'uno'. If we print the dictionary again, we see a key-value pair with a colon between the key and value:\n\n```\n>>> print eng2sp\n{'one': 'uno'}\n```\nThis output format is also an input format. For example, you can create a new dictionary with three items:\n\n```\n>>> eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'}\n```\nBut if you print eng2sp, you might be surprised:\n\n```\n>>> print eng2sp\n{'one': 'uno', 'three': 'tres', 'two': 'dos'}\n```\nThe order of the key-value pairs is not the same. In fact, if you type the same example on your computer, you might get a different result. In general, the order of items in a dictionary is unpredictable.\n\nBut that's not a problem because the elements of a dictionary are never indexed with integer indices. Instead, you use the keys to look up the corresponding values:\n\n```\n>>> print eng2sp['two']\n'dos'\n```\nThe key 'two' always maps to the value 'dos' so the order of the items doesn't matter.\n\nIf the key isn't in the dictionary, you get an exception:\n\n```\n>>> print eng2sp['four']\nKeyError: 'four'\n```\nThe len function works on dictionaries; it returns the number of key-value pairs:\n\n```\n>>> len(eng2sp)\n3\n```\nThe in operator works on dictionaries; it tells you whether something appears as a *key* in the dictionary (appearing as a value is not good enough).\n\n```\n>>> 'one' in eng2sp\nTrue\n>>> 'uno' in eng2sp\nFalse\n```\nTo see whether something appears as a value in a dictionary, you can use the method values, which returns the values as a list, and then use the in operator:\n\n```\n>>> vals = eng2sp.values()\n>>> 'uno' in vals\nTrue\n```\nThe in operator uses different algorithms for lists and dictionaries. For lists, it uses a search algorithm, as in Section [8.6.](#page-95-1) As the list gets longer, the search time gets longer in direct proportion. For dictionaries, Python uses an algorithm called a **hashtable** that has a remarkable property: the in operator takes about the same amount of time no matter how many items there are in a dictionary. I won't explain how that's possible, but you can read more about it at .\n\n**Exercise 11.1.** *Write a function that reads the words in* words.txt *and stores them as keys in a dictionary. It doesn't matter what the values are. Then you can use the* in *operator as a fast way to check whether a string is in the dictionary.*\n\n*If you did Exercise [10.11,](#page-119-1) you can compare the speed of this implementation with the list* in *operator and the bisection search.*\n\n#### **11.1 Dictionary as a set of counters**\n\nSuppose you are given a string and you want to count how many times each letter appears. There are several ways you could do it:\n\n- 1. You could create 26 variables, one for each letter of the alphabet. Then you could traverse the string and, for each character, increment the corresponding counter, probably using a chained conditional.\n- 2. You could create a list with 26 elements. Then you could convert each character to a number (using the built-in function ord), use the number as an index into the list, and increment the appropriate counter.\n\n- 3. You could create a dictionary with characters as keys and counters as the corresponding values. The first time you see a character, you would add an item to the dictionary. After that you would increment the value of an existing item.\nEach of these options performs the same computation, but each of them implements that computation in a different way.\n\nAn **implementation** is a way of performing a computation; some implementations are better than others. For example, an advantage of the dictionary implementation is that we don't have to know ahead of time which letters appear in the string and we only have to make room for the letters that do appear.\n\nHere is what the code might look like:\n\n```\ndef histogram(s):\n d = dict()\n for c in s:\n if c not in d:\n d[c] = 1\n else:\n d[c] += 1\n return d\n```\nThe name of the function is **histogram**, which is a statistical term for a set of counters (or frequencies).\n\nThe first line of the function creates an empty dictionary. The for loop traverses the string. Each time through the loop, if the character c is not in the dictionary, we create a new item with key c and the initial value 1 (since we have seen this letter once). If c is already in the dictionary we increment d[c].\n\nHere's how it works:\n\n```\n>>> h = histogram('brontosaurus')\n>>> print h\n{'a': 1, 'b': 1, 'o': 2, 'n': 1, 's': 2, 'r': 2, 'u': 2, 't': 1}\n```\nThe histogram indicates that the letters 'a' and 'b' appear once; 'o' appears twice, and so on.\n\n**Exercise 11.2.** *Dictionaries have a method called* get *that takes a key and a default value. If the key appears in the dictionary,* get *returns the corresponding value; otherwise it returns the default value. For example:*\n\n```\n>>> h = histogram('a')\n>>> print h\n{'a': 1}\n>>> h.get('a', 0)\n1\n>>> h.get('b', 0)\n0\n```\n*Use* get *to write* histogram *more concisely. You should be able to eliminate the* if *statement.*\n\n#### **11.2 Looping and dictionaries**\n\nIf you use a dictionary in a for statement, it traverses the keys of the dictionary. For example, print_hist prints each key and the corresponding value:\n\ndef print_hist(h): for c in h: print c, h[c]\n\nHere's what the output looks like:\n\n```\n>>> h = histogram('parrot')\n>>> print_hist(h)\na 1\np 1\nr 2\nt 1\no 1\n```\nAgain, the keys are in no particular order.\n\n**Exercise 11.3.** *Dictionaries have a method called* keys *that returns the keys of the dictionary, in no particular order, as a list.*\n\n*Modify* print_hist *to print the keys and their values in alphabetical order.*\n\n#### **11.3 Reverse lookup**\n\nGiven a dictionary d and a key k, it is easy to find the corresponding value $v = d[k]$. This operation is called a **lookup**.\n\nBut what if you have v and you want to find k? You have two problems: first, there might be more than one key that maps to the value v. Depending on the application, you might be able to pick one, or you might have to make a list that contains all of them. Second, there is no simple syntax to do a **reverse lookup**; you have to search.\n\nHere is a function that takes a value and returns the first key that maps to that value:\n\n```\ndef reverse_lookup(d, v):\n for k in d:\n if d[k] == v:\n return k\n raise ValueError\n```\nThis function is yet another example of the search pattern, but it uses a feature we haven't seen before, raise. The raise statement causes an exception; in this case it causes a ValueError, which generally indicates that there is something wrong with the value of a parameter.\n\nIf we get to the end of the loop, that means v doesn't appear in the dictionary as a value, so we raise an exception.\n\nHere is an example of a successful reverse lookup:\n\n```\n>>> h = histogram('parrot')\n>>> k = reverse_lookup(h, 2)\n>>> print k\nr\n```\nAnd an unsuccessful one:\n\n```\n>>> k = reverse_lookup(h, 3)\nTraceback (most recent call last):\n File \"\", line 1, in ?\n File \"\", line 5, in reverse_lookup\nValueError\n```\nThe result when you raise an exception is the same as when Python raises one: it prints a traceback and an error message.\n\nThe raise statement takes a detailed error message as an optional argument. For example:\n\n>>> raise ValueError('value does not appear in the dictionary') Traceback (most recent call last): File \"\", line 1, in ? ValueError: value does not appear in the dictionary\n\nA reverse lookup is much slower than a forward lookup; if you have to do it often, or if the dictionary gets big, the performance of your program will suffer.\n\n**Exercise 11.4.** *Modify* reverse_lookup *so that it builds and returns a list of* all *keys that map to* v*, or an empty list if there are none.*\n\n#### **11.4 Dictionaries and lists**\n\nLists can appear as values in a dictionary. For example, if you were given a dictionary that maps from letters to frequencies, you might want to invert it; that is, create a dictionary that maps from frequencies to letters. Since there might be several letters with the same frequency, each value in the inverted dictionary should be a list of letters.\n\nHere is a function that inverts a dictionary:\n\n```\ndef invert_dict(d):\n inverse = dict()\n for key in d:\n val = d[key]\n if val not in inverse:\n inverse[val] = [key]\n else:\n inverse[val].append(key)\n return inverse\n```\nEach time through the loop, key gets a key from d and val gets the corresponding value. If val is not in inverse, that means we haven't seen it before, so we create a new item and initialize it with a **singleton** (a list that contains a single element). Otherwise we have seen this value before, so we append the corresponding key to the list.\n\nHere is an example:\n\n```\n>>> hist = histogram('parrot')\n>>> print hist\n{'a': 1, 'p': 1, 'r': 2, 't': 1, 'o': 1}\n>>> inverse = invert_dict(hist)\n>>> print inverse\n{1: ['a', 'p', 't', 'o'], 2: ['r']}\n```\n\n\nFigure 11.1: State diagram.\n\nFigure [11.1](#page-127-1) is a state diagram showing hist and inverse. A dictionary is represented as a box with the type dict above it and the key-value pairs inside. If the values are integers, floats or strings, I usually draw them inside the box, but I usually draw lists outside the box, just to keep the diagram simple.\n\nLists can be values in a dictionary, as this example shows, but they cannot be keys. Here's what happens if you try:\n\n```\n>>> t = [1, 2, 3]\n>>> d = dict()\n>>> d[t] = 'oops'\nTraceback (most recent call last):\n File \"\", line 1, in ?\nTypeError: list objects are unhashable\n```\nI mentioned earlier that a dictionary is implemented using a hashtable and that means that the keys have to be **hashable**.\n\nA **hash** is a function that takes a value (of any kind) and returns an integer. Dictionaries use these integers, called hash values, to store and look up key-value pairs.\n\nThis system works fine if the keys are immutable. But if the keys are mutable, like lists, bad things happen. For example, when you create a key-value pair, Python hashes the key and stores it in the corresponding location. If you modify the key and then hash it again, it would go to a different location. In that case you might have two entries for the same key, or you might not be able to find a key. Either way, the dictionary wouldn't work correctly.\n\nThat's why the keys have to be hashable, and why mutable types like lists aren't. The simplest way to get around this limitation is to use tuples, which we will see in the next chapter.\n\nSince lists and dictionaries are mutable, they can't be used as keys, but they *can* be used as values.\n\n**Exercise 11.5.** *Read the documentation of the dictionary method* setdefault *and use it to write a more concise version of* invert_dict. *Solution:* [http://thinkpython.com/code/invert_](http://thinkpython.com/code/invert_dict.py) [dict. py](http://thinkpython.com/code/invert_dict.py) *.*\n\n#### **11.5 Memos**\n\nIf you played with the fibonacci function from Section [6.7,](#page-78-1) you might have noticed that the bigger the argument you provide, the longer the function takes to run. Furthermore,\n\n\n\nFigure 11.2: Call graph.\n\nthe run time increases very quickly.\n\nTo understand why, consider Figure [11.2,](#page-128-0) which shows the **call graph** for fibonacci with n=4:\n\nA call graph shows a set of function frames, with lines connecting each frame to the frames of the functions it calls. At the top of the graph, fibonacci with n=4 calls fibonacci with n=3 and n=2. In turn, fibonacci with n=3 calls fibonacci with n=2 and n=1. And so on.\n\nCount how many times fibonacci(0) and fibonacci(1) are called. This is an inefficient solution to the problem, and it gets worse as the argument gets bigger.\n\nOne solution is to keep track of values that have already been computed by storing them in a dictionary. A previously computed value that is stored for later use is called a **memo**. Here is a \"memoized\" version of fibonacci:\n\n```\nknown = {0:0, 1:1}\ndef fibonacci(n):\n if n in known:\n return known[n]\n res = fibonacci(n-1) + fibonacci(n-2)\n known[n] = res\n return res\n```\nknown is a dictionary that keeps track of the Fibonacci numbers we already know. It starts with two items: 0 maps to 0 and 1 maps to 1.\n\nWhenever fibonacci is called, it checks known. If the result is already there, it can return immediately. Otherwise it has to compute the new value, add it to the dictionary, and return it.\n\n**Exercise 11.6.** *Run this version of* fibonacci *and the original with a range of parameters and compare their run times.*\n\n**Exercise 11.7.** *Memoize the Ackermann function from Exercise [6.5](#page-82-1) and see if memoization makes it possible to evaluate the function with bigger arguments. Hint: no. Solution:* [http:](http://thinkpython.com/code/ackermann_memo.py) [// thinkpython. com/ code/ ackermann_ memo. py](http://thinkpython.com/code/ackermann_memo.py) *.*\n\n## **11.6 Global variables**\n\nIn the previous example, known is created outside the function, so it belongs to the special frame called __main__. Variables in __main__ are sometimes called **global** because they can be accessed from any function. Unlike local variables, which disappear when their function ends, global variables persist from one function call to the next.\n\nIt is common to use global variables for **flags**; that is, boolean variables that indicate (\"flag\") whether a condition is true. For example, some programs use a flag named verbose to control the level of detail in the output:\n\n```\nverbose = True\ndef example1():\n if verbose:\n print 'Running example1'\n```\nIf you try to reassign a global variable, you might be surprised. The following example is supposed to keep track of whether the function has been called:\n\n```\nbeen_called = False\ndef example2():\n been_called = True # WRONG\n```\nBut if you run it you will see that the value of been_called doesn't change. The problem is that example2 creates a new local variable named been_called. The local variable goes away when the function ends, and has no effect on the global variable.\n\nTo reassign a global variable inside a function you have to **declare** the global variable before you use it:\n\n```\nbeen_called = False\ndef example2():\n global been_called\n been_called = True\n```\nThe global statement tells the interpreter something like, \"In this function, when I say been_called, I mean the global variable; don't create a local one.\"\n\nHere's an example that tries to update a global variable:\n\n```\ncount = 0\ndef example3():\n count = count + 1 # WRONG\n```\n\n```\nIf you run it you get:\n```\nUnboundLocalError: local variable 'count' referenced before assignment\n\nPython assumes that count is local, which means that you are reading it before writing it. The solution, again, is to declare count global.\n\n```\ndef example3():\n global count\n count += 1\n```\nIf the global value is mutable, you can modify it without declaring it:\n\n```\nknown = {0:0, 1:1}\ndef example4():\n known[2] = 1\n```\nSo you can add, remove and replace elements of a global list or dictionary, but if you want to reassign the variable, you have to declare it:\n\n```\ndef example5():\n global known\n known = dict()\n```\n#### **11.7 Long integers**\n\nIf you compute fibonacci(50), you get:\n\n```\n>>> fibonacci(50)\n12586269025L\n```\nThe L at the end indicates that the result is a long integer, or type long. In Python 3, long is gone; all integers, even really big ones, are type int.\n\nValues with type int have a limited range; long integers can be arbitrarily big, but as they get bigger they consume more space and time.\n\nThe mathematical operators work on long integers, and the functions in the math module, too, so in general any code that works with int will also work with long.\n\nAny time the result of a computation is too big to be represented with an integer, Python converts the result as a long integer:\n\n```\n>>> 1000 * 1000\n1000000\n>>> 100000 * 100000\n10000000000L\n```\nIn the first case the result has type int; in the second case it is long.\n\n**Exercise 11.8.** *Exponentiation of large integers is the basis of common algorithms for public-key encryption. Read the Wikipedia page on the RSA algorithm (*[http: // en. wikipedia. org/ wiki/](http://en.wikipedia.org/wiki/RSA_(algorithm)) [RSA_ ( algorithm)](http://en.wikipedia.org/wiki/RSA_(algorithm)) *) and write functions to encode and decode messages.*\n\n#### **11.8 Debugging**\n\nAs you work with bigger datasets it can become unwieldy to debug by printing and checking data by hand. Here are some suggestions for debugging large datasets:\n\n- **Scale down the input:** If possible, reduce the size of the dataset. For example if the program reads a text file, start with just the first 10 lines, or with the smallest example you can find. You can either edit the files themselves, or (better) modify the program so it reads only the first n lines.\nIf there is an error, you can reduce n to the smallest value that manifests the error, and then increase it gradually as you find and correct errors.\n\n- **Check summaries and types:** Instead of printing and checking the entire dataset, consider printing summaries of the data: for example, the number of items in a dictionary or the total of a list of numbers.\nA common cause of runtime errors is a value that is not the right type. For debugging this kind of error, it is often enough to print the type of a value.\n\n- **Write self-checks:** Sometimes you can write code to check for errors automatically. For example, if you are computing the average of a list of numbers, you could check that the result is not greater than the largest element in the list or less than the smallest. This is called a \"sanity check\" because it detects results that are \"insane.\"\nAnother kind of check compares the results of two different computations to see if they are consistent. This is called a \"consistency check.\"\n\n- **Pretty print the output:** Formatting debugging output can make it easier to spot an error. We saw an example in Section [6.9.](#page-80-0) The pprint module provides a pprint function that displays built-in types in a more human-readable format.\nAgain, time you spend building scaffolding can reduce the time you spend debugging.\n\n#### **11.9 Glossary**\n\n**dictionary:** A mapping from a set of keys to their corresponding values.\n\n**key-value pair:** The representation of the mapping from a key to a value.\n\n**item:** Another name for a key-value pair.\n\n**key:** An object that appears in a dictionary as the first part of a key-value pair.\n\n- **value:** An object that appears in a dictionary as the second part of a key-value pair. This is more specific than our previous use of the word \"value.\"\n**implementation:** A way of performing a computation.\n\n**hashtable:** The algorithm used to implement Python dictionaries.\n\n**hash function:** A function used by a hashtable to compute the location for a key.\n\n- **hashable:** A type that has a hash function. Immutable types like integers, floats and strings are hashable; mutable types like lists and dictionaries are not.\n**lookup:** A dictionary operation that takes a key and finds the corresponding value.\n\n- **reverse lookup:** A dictionary operation that takes a value and finds one or more keys that map to it.\n**singleton:** A list (or other sequence) with a single element.\n\n- **call graph:** A diagram that shows every frame created during the execution of a program, with an arrow from each caller to each callee.\n**histogram:** A set of counters.\n\n**memo:** A computed value stored to avoid unnecessary future computation.\n\n- **global variable:** A variable defined outside a function. Global variables can be accessed from any function.\n- **flag:** A boolean variable used to indicate whether a condition is true.\n\n**declaration:** A statement like global that tells the interpreter something about a variable.\n\n#### **11.10 Exercises**\n\n**Exercise 11.9.** *If you did Exercise [10.8,](#page-119-2) you already have a function named* has_duplicates *that takes a list as a parameter and returns* True *if there is any object that appears more than once in the list.*\n\n*Use a dictionary to write a faster, simpler version of* has_duplicates*. Solution:* [http: //](http://thinkpython.com/code/has_duplicates.py) [thinkpython. com/ code/ has_ duplicates. py](http://thinkpython.com/code/has_duplicates.py) *.*\n\n**Exercise 11.10.** *Two words are \"rotate pairs\" if you can rotate one of them and get the other (see* rotate_word *in Exercise [8.12)](#page-101-0).*\n\n*Write a program that reads a wordlist and finds all the rotate pairs. Solution:* [http: //](http://thinkpython.com/code/rotate_pairs.py) [thinkpython. com/ code/ rotate_ pairs. py](http://thinkpython.com/code/rotate_pairs.py) *.*\n\n**Exercise 11.11.** *Here's another Puzzler from* Car Talk *(*[http: // www. cartalk. com/ content/](http://www.cartalk.com/content/puzzlers) [puzzlers](http://www.cartalk.com/content/puzzlers) *):*\n\n*This was sent in by a fellow named Dan O'Leary. He came upon a common one-syllable, five-letter word recently that has the following unique property. When you remove the first letter, the remaining letters form a homophone of the original word, that is a word that sounds exactly the same. Replace the first letter, that is, put it back and remove the second letter and the result is yet another homophone of the original word. And the question is, what's the word?*\n\n*Now I'm going to give you an example that doesn't work. Let's look at the five-letter word, 'wrack.' W-R-A-C-K, you know like to 'wrack with pain.' If I remove the first letter, I am left with a four-letter word, 'R-A-C-K.' As in, 'Holy cow, did you see the rack on that buck! It must have been a nine-pointer!' It's a perfect homophone. If you put the 'w' back, and remove the 'r,' instead, you're left with the word, 'wack,' which is a real word, it's just not a homophone of the other two words.*\n\n*But there is, however, at least one word that Dan and we know of, which will yield two homophones if you remove either of the first two letters to make two, new four-letter words. The question is, what's the word?*\n\n*You can use the dictionary from Exercise [11.1](#page-123-1) to check whether a string is in the word list.*\n\n*To check whether two words are homophones, you can use the CMU Pronouncing Dictionary. You can download it from* [http: // www. speech. cs. cmu. edu/ cgi-bin/ cmudict](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) *or from* [http: // thinkpython. com/ code/ c06d](http://thinkpython.com/code/c06d) *and you can also download* [http: // thinkpython.](http://thinkpython.com/code/pronounce.py) [com/ code/ pronounce. py](http://thinkpython.com/code/pronounce.py) *, which provides a function named* read_dictionary *that reads the pronouncing dictionary and returns a Python dictionary that maps from each word to a string that describes its primary pronunciation.*\n\n*Write a program that lists all the words that solve the Puzzler. Solution:* [http: // thinkpython.](http://thinkpython.com/code/homophone.py) [com/ code/ homophone. py](http://thinkpython.com/code/homophone.py) *.*\n\n# **Chapter 12**\n\n# **Tuples**\n\n#### **12.1 Tuples are immutable**\n\nA tuple is a sequence of values. The values can be any type, and they are indexed by integers, so in that respect tuples are a lot like lists. The important difference is that tuples are immutable.\n\nSyntactically, a tuple is a comma-separated list of values:\n\n>>> t = 'a', 'b', 'c', 'd', 'e'\n\nAlthough it is not necessary, it is common to enclose tuples in parentheses:\n\n```\n>>> t = ('a', 'b', 'c', 'd', 'e')\n```\nTo create a tuple with a single element, you have to include a final comma:\n\n```\n>>> t1 = 'a',\n>>> type(t1)\n\nA value in parentheses is not a tuple:\n>>> t2 = ('a')\n```\n\n```\n>>> type(t2)\n\n```\nAnother way to create a tuple is the built-in function tuple. With no argument, it creates an empty tuple:\n\n```\n>>> t = tuple()\n>>> print t\n()\n```\nIf the argument is a sequence (string, list or tuple), the result is a tuple with the elements of the sequence:\n\n```\n>>> t = tuple('lupins')\n>>> print t\n('l', 'u', 'p', 'i', 'n', 's')\n```\nBecause tuple is the name of a built-in function, you should avoid using it as a variable name.\n\nMost list operators also work on tuples. The bracket operator indexes an element:\n\n```\n>>> t = ('a', 'b', 'c', 'd', 'e')\n>>> print t[0]\n'a'\n```\nAnd the slice operator selects a range of elements.\n\n>>> print t[1:3] ('b', 'c')\n\nBut if you try to modify one of the elements of the tuple, you get an error:\n\n>>> t[0] = 'A' TypeError: object doesn't support item assignment\n\nYou can't modify the elements of a tuple, but you can replace one tuple with another:\n\n>>> t = ('A',) + t[1:] >>> print t ('A', 'b', 'c', 'd', 'e')\n\n#### **12.2 Tuple assignment**\n\nIt is often useful to swap the values of two variables. With conventional assignments, you have to use a temporary variable. For example, to swap a and b:\n\n>>> temp = a >>> a = b >>> b = temp\n\nThis solution is cumbersome; **tuple assignment** is more elegant:\n\n>>> a, b = b, a\n\nThe left side is a tuple of variables; the right side is a tuple of expressions. Each value is assigned to its respective variable. All the expressions on the right side are evaluated before any of the assignments.\n\nThe number of variables on the left and the number of values on the right have to be the same:\n\n```\n>>> a, b = 1, 2, 3\nValueError: too many values to unpack\n```\nMore generally, the right side can be any kind of sequence (string, list or tuple). For example, to split an email address into a user name and a domain, you could write:\n\n```\n>>> addr = 'monty@python.org'\n>>> uname, domain = addr.split('@')\n```\nThe return value from split is a list with two elements; the first element is assigned to uname, the second to domain.\n\n```\n>>> print uname\nmonty\n>>> print domain\npython.org\n```\n#### **12.3 Tuples as return values**\n\nStrictly speaking, a function can only return one value, but if the value is a tuple, the effect is the same as returning multiple values. For example, if you want to divide two integers and compute the quotient and remainder, it is inefficient to compute $x/y$ and then $x%y$. It is better to compute them both at the same time.\n\nThe built-in function divmod takes two arguments and returns a tuple of two values, the quotient and remainder. You can store the result as a tuple:\n\n```\n>>> t = divmod(7, 3)\n>>> print t\n(2, 1)\nOr use tuple assignment to store the elements separately:\n>>> quot, rem = divmod(7, 3)\n>>> print quot\n2\n>>> print rem\n1\n```\nHere is an example of a function that returns a tuple:\n\n```\ndef min_max(t):\n return min(t), max(t)\n```\nmax and min are built-in functions that find the largest and smallest elements of a sequence. min_max computes both and returns a tuple of two values.\n\n#### **12.4 Variable-length argument tuples**\n\nFunctions can take a variable number of arguments. A parameter name that begins with * **gathers** arguments into a tuple. For example, printall takes any number of arguments and prints them:\n\n```\ndef printall(*args):\n print args\n```\nThe gather parameter can have any name you like, but args is conventional. Here's how the function works:\n\n```\n>>> printall(1, 2.0, '3')\n(1, 2.0, '3')\n```\nThe complement of gather is **scatter**. If you have a sequence of values and you want to pass it to a function as multiple arguments, you can use the * operator. For example, divmod takes exactly two arguments; it doesn't work with a tuple:\n\n```\n>>> t = (7, 3)\n>>> divmod(t)\nTypeError: divmod expected 2 arguments, got 1\n```\nTypeError: atbmod expected 2 arguments, got\n\nBut if you scatter the tuple, it works:\n\n>>> divmod(*t)\n\n(2, 1)\n\n**Exercise 12.1.** *Many of the built-in functions use variable-length argument tuples. For example,* max *and* min *can take any number of arguments:*\n\n```\n>>> max(1,2,3)\n3\nBut sum does not.\n>>> sum(1,2,3)\nTypeError: sum expected at most 2 arguments, got 3\nWrite a function called sumall that takes any number of arguments and returns their sum.\n```\n## **12.5 Lists and tuples**\n\nzip is a built-in function that takes two or more sequences and “zips” them into a list of tuples where each tuple contains one element from each sequence. In Python 3, zip returns an iterator of tuples, but for most purposes, an iterator behaves like a list.\n\nThis example zips a string and a list:\n\n```\n>>> s = 'abc'\n>>> t = [0, 1, 2]\n>>> zip(s, t)\n[('a', 0), ('b', 1), ('c', 2)]\n```\nThe result is a list of tuples where each tuple contains a character from the string and the corresponding element from the list.\n\nIf the sequences are not the same length, the result has the length of the shorter one.\n\n>>> zip('Anne', 'Elk') [('A', 'E'), ('n', 'l'), ('n', 'k')]\n\nYou can use tuple assignment in a for loop to traverse a list of tuples:\n\n```\nt = [('a', 0), ('b', 1), ('c', 2)]\nfor letter, number in t:\n print number, letter\n```\nEach time through the loop, Python selects the next tuple in the list and assigns the elements to letter and number. The output of this loop is:\n\n0 a 1 b 2 c\n\nIf you combine zip, for and tuple assignment, you get a useful idiom for traversing two (or more) sequences at the same time. For example, has_match takes two sequences, t1 and t2, and returns True if there is an index i such that t1[i] == t2[i]:\n\n```\ndef has_match(t1, t2):\n for x, y in zip(t1, t2):\n if x == y:\n return True\n return False\n```\nIf you need to traverse the elements of a sequence and their indices, you can use the built-in function enumerate:\n\n```\nfor index, element in enumerate('abc'):\n print index, element\n```\nThe output of this loop is:\n\n0 a 1 b 2 c Again.\n\n#### **12.6 Dictionaries and tuples**\n\nDictionaries have a method called items that returns a list of tuples, where each tuple is a key-value pair.\n\n```\n>>> d = {'a':0, 'b':1, 'c':2}\n>>> t = d.items()\n>>> print t\n[('a', 0), ('c', 2), ('b', 1)]\n```\nAs you should expect from a dictionary, the items are in no particular order. In Python 3, items returns an iterator, but for many purposes, iterators behave like lists.\n\nGoing in the other direction, you can use a list of tuples to initialize a new dictionary:\n\n```\n>>> t = [('a', 0), ('c', 2), ('b', 1)]\n>>> d = dict(t)\n>>> print d\n{'a': 0, 'c': 2, 'b': 1}\n```\nCombining dict with zip yields a concise way to create a dictionary:\n\n```\n>>> d = dict(zip('abc', range(3)))\n>>> print d\n{'a': 0, 'c': 2, 'b': 1}\n```\nThe dictionary method update also takes a list of tuples and adds them, as key-value pairs, to an existing dictionary.\n\nCombining items, tuple assignment and for, you get the idiom for traversing the keys and values of a dictionary:\n\n```\nfor key, val in d.items():\n print val, key\n```\nThe output of this loop is:\n\n```\n0 a\n2 c\n1 b\n```\nAgain.\n\nIt is common to use tuples as keys in dictionaries (primarily because you can't use lists). For example, a telephone directory might map from last-name, first-name pairs to telephone numbers. Assuming that we have defined last, first and number, we could write:\n\ndirectory[last,first] = number\n\nThe expression in brackets is a tuple. We could use tuple assignment to traverse this dictionary.\n\ntuple\n\n| 0 → 'Cleese' |\n|--------------|\n| 1 → 'John' |\n\nFigure 12.1: State diagram.\n\n| dict |\n|-----------------------------------------|\n| ('Cleese', 'John') → '08700 100 222' |\n| ('Chapman', 'Graham') → '08700 100 222' |\n| ('Idle', 'Eric') → '08700 100 222' |\n| ('Gilliam', 'Terry') → '08700 100 222' |\n| ('Jones', 'Terry') → '08700 100 222' |\n| ('Palin', 'Michael') → '08700 100 222' |\n\nFigure 12.2: State diagram.\n\n```\nfor last, first in directory:\n print first, last, directory[last,first]\n```\nThis loop traverses the keys in directory, which are tuples. It assigns the elements of each tuple to last and first, then prints the name and corresponding telephone number.\n\nThere are two ways to represent tuples in a state diagram. The more detailed version shows the indices and elements just as they appear in a list. For example, the tuple ('Cleese', 'John') would appear as in Figure [12.1.](#page-139-1)\n\nBut in a larger diagram you might want to leave out the details. For example, a diagram of the telephone directory might appear as in Figure [12.2.](#page-139-2)\n\nHere the tuples are shown using Python syntax as a graphical shorthand.\n\nThe telephone number in the diagram is the complaints line for the BBC, so please don't call it.\n\n### **12.7 Comparing tuples**\n\nThe relational operators work with tuples and other sequences; Python starts by comparing the first element from each sequence. If they are equal, it goes on to the next elements, and so on, until it finds elements that differ. Subsequent elements are not considered (even if they are really big).\n\n```\n>>> (0, 1, 2) < (0, 3, 4)\nTrue\n>>> (0, 1, 2000000) < (0, 3, 4)\nTrue\n```\nThe sort function works the same way. It sorts primarily by first element, but in the case of a tie, it sorts by second element, and so on.\n\nThis feature lends itself to a pattern called **DSU** for\n\n- **Decorate** a sequence by building a list of tuples with one or more sort keys preceding the elements from the sequence,\n**Sort** the list of tuples, and\n\n**Undecorate** by extracting the sorted elements of the sequence.\n\nFor example, suppose you have a list of words and you want to sort them from longest to shortest:\n\n```\ndef sort_by_length(words):\n t = []\n for word in words:\n t.append((len(word), word))\n t.sort(reverse=True)\n res = []\n for length, word in t:\n res.append(word)\n return res\n```\nThe first loop builds a list of tuples, where each tuple is a word preceded by its length.\n\nsort compares the first element, length, first, and only considers the second element to break ties. The keyword argument reverse=True tells sort to go in decreasing order.\n\nThe second loop traverses the list of tuples and builds a list of words in descending order of length.\n\n**Exercise 12.2.** *In this example, ties are broken by comparing words, so words with the same length appear in reverse alphabetical order. For other applications you might want to break ties at random. Modify this example so that words with the same length appear in random order. Hint: see the* random *function in the* random *module. Solution:* [http: // thinkpython. com/ code/](http://thinkpython.com/code/unstable_sort.py) [unstable_ sort. py](http://thinkpython.com/code/unstable_sort.py) *.*\n\n#### **12.8 Sequences of sequences**\n\nI have focused on lists of tuples, but almost all of the examples in this chapter also work with lists of lists, tuples of tuples, and tuples of lists. To avoid enumerating the possible combinations, it is sometimes easier to talk about sequences of sequences.\n\nIn many contexts, the different kinds of sequences (strings, lists and tuples) can be used interchangeably. So how and why do you choose one over the others?\n\nTo start with the obvious, strings are more limited than other sequences because the elements have to be characters. They are also immutable. If you need the ability to change the characters in a string (as opposed to creating a new string), you might want to use a list of characters instead.\n\nLists are more common than tuples, mostly because they are mutable. But there are a few cases where you might prefer tuples:\n\n- 1. In some contexts, like a return statement, it is syntactically simpler to create a tuple than a list. In other contexts, you might prefer a list.\n- 2. If you want to use a sequence as a dictionary key, you have to use an immutable type like a tuple or string.\n- 3. If you are passing a sequence as an argument to a function, using tuples reduces the potential for unexpected behavior due to aliasing.\n\nBecause tuples are immutable, they don't provide methods like sort and reverse, which modify existing lists. But Python provides the built-in functions sorted and reversed, which take any sequence as a parameter and return a new list with the same elements in a different order.\n\n#### **12.9 Debugging**\n\nLists, dictionaries and tuples are known generically as **data structures**; in this chapter we are starting to see compound data structures, like lists of tuples, and dictionaries that contain tuples as keys and lists as values. Compound data structures are useful, but they are prone to what I call **shape errors**; that is, errors caused when a data structure has the wrong type, size or composition. For example, if you are expecting a list with one integer and I give you a plain old integer (not in a list), it won't work.\n\nTo help debug these kinds of errors, I have written a module called structshape that provides a function, also called structshape, that takes any kind of data structure as an argument and returns a string that summarizes its shape. You can download it from \n\nHere's the result for a simple list:\n\n```\n>>> from structshape import structshape\n>>> t = [1,2,3]\n>>> print structshape(t)\nlist of 3 int\nA fancier program might write \"list of 3 ints,\" but it was easier not to deal with plurals.\nHere's a list of lists:\n>>> t2 = [[1,2], [3,4], [5,6]]\n>>> print structshape(t2)\nlist of 3 list of 2 int\nIf the elements of the list are not the same type, structshape groups them, in order, by\ntype:\n>>> t3 = [1, 2, 3, 4.0, '5', '6', [7], [8], 9]\n>>> print structshape(t3)\nlist of (3 int, float, 2 str, 2 list of int, int)\nHere's a list of tuples:\n>>> s = 'abc'\n>>> lt = zip(t, s)\n>>> print structshape(lt)\nlist of 3 tuple of (int, str)\nAnd here's a dictionary with 3 items that map integers to strings.\n>>> d = dict(lt)\n>>> print structshape(d)\ndict of 3 int->str\n```\nIf you are having trouble keeping track of your data structures, structshape can help.\n\n#### **12.10 Glossary**\n\n**tuple:** An immutable sequence of elements.\n\n- **tuple assignment:** An assignment with a sequence on the right side and a tuple of variables on the left. The right side is evaluated and then its elements are assigned to the variables on the left.\n- **gather:** The operation of assembling a variable-length argument tuple.\n- **scatter:** The operation of treating a sequence as a list of arguments.\n- **DSU:** Abbreviation of \"decorate-sort-undecorate,\" a pattern that involves building a list of tuples, sorting, and extracting part of the result.\n- **data structure:** A collection of related values, often organized in lists, dictionaries, tuples, etc.\n- **shape (of a data structure):** A summary of the type, size and composition of a data structure.\n\n#### **12.11 Exercises**\n\n**Exercise 12.3.** *Write a function called* most_frequent *that takes a string and prints the letters in decreasing order of frequency. Find text samples from several different languages and see how letter frequency varies between languages. Compare your results with the tables at* [http: //](http://en.wikipedia.org/wiki/Letter_frequencies) [en. wikipedia. org/ wiki/ Letter_ frequencies](http://en.wikipedia.org/wiki/Letter_frequencies) *. Solution:* [http: // thinkpython. com/](http://thinkpython.com/code/most_frequent.py) [code/ most_ frequent. py](http://thinkpython.com/code/most_frequent.py) *.*\n\n**Exercise 12.4.** *More anagrams!*\n\n- *1. Write a program that reads a word list from a file (see Section [9.1)](#page-102-1) and prints all the sets of words that are anagrams.*\n*Here is an example of what the output might look like:*\n\n['deltas', 'desalt', 'lasted', 'salted', 'slated', 'staled'] ['retainers', 'ternaries'] ['generating', 'greatening'] ['resmelts', 'smelters', 'termless']\n\n*Hint: you might want to build a dictionary that maps from a set of letters to a list of words that can be spelled with those letters. The question is, how can you represent the set of letters in a way that can be used as a key?*\n\n- *2. Modify the previous program so that it prints the largest set of anagrams first, followed by the second largest set, and so on.*\n- *3. In Scrabble a \"bingo\" is when you play all seven tiles in your rack, along with a letter on the board, to form an eight-letter word. What set of 8 letters forms the most possible bingos? Hint: there are seven.*\n\n*Solution:* [http: // thinkpython. com/ code/ anagram_ sets. py](http://thinkpython.com/code/anagram_sets.py) *.*\n\n**Exercise 12.5.** *Two words form a \"metathesis pair\" if you can transform one into the other by swapping two letters; for example, \"converse\" and \"conserve.\" Write a program that finds all of the metathesis pairs in the dictionary. Hint: don't test all pairs of words, and don't test all possible swaps. Solution:* [http: // thinkpython. com/ code/ metathesis. py](http://thinkpython.com/code/metathesis.py) *. Credit: This exercise is inspired by an example at* [http: // puzzlers. org](http://puzzlers.org) *.*\n\n**Exercise 12.6.** *Here's another Car Talk Puzzler (*[http: // www. cartalk. com/ content/](http://www.cartalk.com/content/puzzlers) [puzzlers](http://www.cartalk.com/content/puzzlers) *):*\n\n*What is the longest English word, that remains a valid English word, as you remove its letters one at a time?*\n\n*Now, letters can be removed from either end, or the middle, but you can't rearrange any of the letters. Every time you drop a letter, you wind up with another English word. If you do that, you're eventually going to wind up with one letter and that too is going to be an English word—one that's found in the dictionary. I want to know what's the longest word and how many letters does it have?*\n\n*I'm going to give you a little modest example: Sprite. Ok? You start off with sprite, you take a letter off, one from the interior of the word, take the r away, and we're left with the word spite, then we take the e off the end, we're left with spit, we take the s off, we're left with pit, it, and I.*\n\n*Write a program to find all words that can be reduced in this way, and then find the longest one.*\n\n*This exercise is a little more challenging than most, so here are some suggestions:*\n\n- *1. You might want to write a function that takes a word and computes a list of all the words that can be formed by removing one letter. These are the \"children\" of the word.*\n- *2. Recursively, a word is reducible if any of its children are reducible. As a base case, you can consider the empty string reducible.*\n- *3. The wordlist I provided,* words.txt*, doesn't contain single letter words. So you might want to add \"I\", \"a\", and the empty string.*\n- *4. To improve the performance of your program, you might want to memoize the words that are known to be reducible.*\n\n*Solution:* [http: // thinkpython. com/ code/ reducible. py](http://thinkpython.com/code/reducible.py) *.*\n\n# **Chapter 13**\n\n# **Case study: data structure selection**\n\n### **13.1 Word frequency analysis**\n\nAs usual, you should at least attempt the following exercises before you read my solutions. **Exercise 13.1.** *Write a program that reads a file, breaks each line into words, strips whitespace and punctuation from the words, and converts them to lowercase.*\n\n*Hint: The* string *module provides strings named* whitespace*, which contains space, tab, newline, etc., and* punctuation *which contains the punctuation characters. Let's see if we can make Python swear:*\n\n>>> import string >>> print string.punctuation !\"#\\$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n\n*Also, you might consider using the string methods* strip*,* replace *and* translate*.* **Exercise 13.2.** *Go to Project Gutenberg (*[http: // gutenberg. org](http://gutenberg.org) *) and download your favorite out-of-copyright book in plain text format.*\n\n*Modify your program from the previous exercise to read the book you downloaded, skip over the header information at the beginning of the file, and process the rest of the words as before.*\n\n*Then modify the program to count the total number of words in the book, and the number of times each word is used.*\n\n*Print the number of different words used in the book. Compare different books by different authors, written in different eras. Which author uses the most extensive vocabulary?*\n\n**Exercise 13.3.** *Modify the program from the previous exercise to print the 20 most frequently-used words in the book.*\n\n**Exercise 13.4.** *Modify the previous program to read a word list (see Section [9.1)](#page-102-1) and then print all the words in the book that are not in the word list. How many of them are typos? How many of them are common words that* should *be in the word list, and how many of them are really obscure?*\n\n#### **13.2 Random numbers**\n\nGiven the same inputs, most computer programs generate the same outputs every time, so they are said to be **deterministic**. Determinism is usually a good thing, since we expect the same calculation to yield the same result. For some applications, though, we want the computer to be unpredictable. Games are an obvious example, but there are more.\n\nMaking a program truly nondeterministic turns out to be not so easy, but there are ways to make it at least seem nondeterministic. One of them is to use algorithms that generate **pseudorandom** numbers. Pseudorandom numbers are not truly random because they are generated by a deterministic computation, but just by looking at the numbers it is all but impossible to distinguish them from random.\n\nThe random module provides functions that generate pseudorandom numbers (which I will simply call \"random\" from here on).\n\nThe function random returns a random float between 0.0 and 1.0 (including 0.0 but not 1.0). Each time you call random, you get the next number in a long series. To see a sample, run this loop:\n\n```\nimport random\n```\n\n```\nfor i in range(10):\n x = random.random()\n print x\n```\nThe function randint takes parameters low and high and returns an integer between low and high (including both).\n\n```\n>>> random.randint(5, 10)\n5\n>>> random.randint(5, 10)\n9\n```\nTo choose an element from a sequence at random, you can use choice:\n\n```\n>>> t = [1, 2, 3]\n>>> random.choice(t)\n2\n>>> random.choice(t)\n3\n```\nThe random module also provides functions to generate random values from continuous distributions including Gaussian, exponential, gamma, and a few more.\n\n**Exercise 13.5.** *Write a function named* choose_from_hist *that takes a histogram as defined in Section [11.1](#page-123-0) and returns a random value from the histogram, chosen with probability in proportion to frequency. For example, for this histogram:*\n\n```\n>>> t = ['a', 'a', 'b']\n>>> hist = histogram(t)\n>>> print hist\n{'a': 2, 'b': 1}\n```\n*your function should return* 'a' *with probability* 2/3 *and* 'b' *with probability* 1/3*.*\n\n#### **13.3 Word histogram**\n\nYou should attempt the previous exercises before you go on. You can download my solution from . You will also need [http:](http://thinkpython.com/code/emma.txt) [//thinkpython.com/code/emma.txt](http://thinkpython.com/code/emma.txt).\n\nHere is a program that reads a file and builds a histogram of the words in the file:\n\n```\nimport string\ndef process_file(filename):\n hist = dict()\n fp = open(filename)\n for line in fp:\n process_line(line, hist)\n return hist\ndef process_line(line, hist):\n line = line.replace('-', ' ')\n for word in line.split():\n word = word.strip(string.punctuation + string.whitespace)\n word = word.lower()\n hist[word] = hist.get(word, 0) + 1\n```\nhist = process_file('emma.txt')\n\nThis program reads emma.txt, which contains the text of *Emma* by Jane Austen.\n\nprocess_file loops through the lines of the file, passing them one at a time to process_line. The histogram hist is being used as an accumulator.\n\nprocess_line uses the string method replace to replace hyphens with spaces before using split to break the line into a list of strings. It traverses the list of words and uses strip and lower to remove punctuation and convert to lower case. (It is a shorthand to say that strings are \"converted;\" remember that string are immutable, so methods like strip and lower return new strings.)\n\nFinally, process_line updates the histogram by creating a new item or incrementing an existing one.\n\nTo count the total number of words in the file, we can add up the frequencies in the histogram:\n\n```\ndef total_words(hist):\n return sum(hist.values())\n```\nThe number of different words is just the number of items in the dictionary:\n\ndef different_words(hist):\n\nreturn len(hist)\n\nHere is some code to print the results:\n\n```\nprint 'Total number of words:', total_words(hist)\nprint 'Number of different words:', different_words(hist)\n```\nAnd the results:\n\nTotal number of words: 161080 Number of different words: 7214\n\n#### **13.4 Most common words**\n\nTo find the most common words, we can apply the DSU pattern; most_common takes a histogram and returns a list of word-frequency tuples, sorted in reverse order by frequency:\n\n```\ndef most_common(hist):\n t = []\n for key, value in hist.items():\n t.append((value, key))\n t.sort(reverse=True)\n return t\n```\nHere is a loop that prints the ten most common words:\n\n```\nt = most_common(hist)\nprint 'The most common words are:'\nfor freq, word in t[0:10]:\n print word, '\\t', freq\n```\nAnd here are the results from *Emma*:\n\nThe most common words are: to 5242 the 5205 and 4897 of 4295 i 3191 a 3130 it 2529 her 2483 was 2400 she 2364\n\n#### **13.5 Optional parameters**\n\nWe have seen built-in functions and methods that take a variable number of arguments. It is possible to write user-defined functions with optional arguments, too. For example, here is a function that prints the most common words in a histogram\n\n```\ndef print_most_common(hist, num=10):\n t = most_common(hist)\n print 'The most common words are:'\n for freq, word in t[:num]:\n print word, '\\t', freq\n```\nThe first parameter is required; the second is optional. The **default value** of num is 10.\n\nIf you only provide one argument:\n\n```\nprint_most_common(hist)\n```\nnum gets the default value. If you provide two arguments:\n\n```\nprint_most_common(hist, 20)\n```\nnum gets the value of the argument instead. In other words, the optional argument **overrides** the default value.\n\nIf a function has both required and optional parameters, all the required parameters have to come first, followed by the optional ones.\n\n#### **13.6 Dictionary subtraction**\n\nFinding the words from the book that are not in the word list from words.txt is a problem you might recognize as set subtraction; that is, we want to find all the words from one set (the words in the book) that are not in another set (the words in the list).\n\nsubtract takes dictionaries d1 and d2 and returns a new dictionary that contains all the keys from d1 that are not in d2. Since we don't really care about the values, we set them all to None.\n\n```\ndef subtract(d1, d2):\n res = dict()\n for key in d1:\n if key not in d2:\n res[key] = None\n return res\n```\nTo find the words in the book that are not in words.txt, we can use process_file to build a histogram for words.txt, and then subtract:\n\n```\nwords = process_file('words.txt')\ndiff = subtract(hist, words)\n```\n\n```\nprint \"The words in the book that aren't in the word list are:\"\nfor word in diff.keys():\n```\nprint word,\n\nHere are some of the results from *Emma*:\n\n```\nThe words in the book that aren't in the word list are:\n rencontre jane's blanche woodhouses disingenuousness\nfriend's venice apartment ...\n```\nSome of these words are names and possessives. Others, like \"rencontre,\" are no longer in common use. But a few are common words that should really be in the list!\n\n```\nExercise 13.6. Python provides a data structure called set that provides many common set opera-\ntions. Read the documentation at http: // docs. python. org/ 2/ library/ stdtypes. html#\ntypes-set and write a program that uses set subtraction to find words in the book that are not in\nthe word list. Solution: http: // thinkpython. com/ code/ analyze_ book2. py .\n```\n#### **13.7 Random words**\n\nTo choose a random word from the histogram, the simplest algorithm is to build a list with multiple copies of each word, according to the observed frequency, and then choose from the list:\n\n```\ndef random_word(h):\n t = []\n for word, freq in h.items():\n t.extend([word] * freq)\n```\nreturn random.choice(t)\n\nThe expression [word] * freq creates a list with freq copies of the string word. The extend method is similar to append except that the argument is a sequence.\n\n**Exercise 13.7.** *This algorithm works, but it is not very efficient; each time you choose a random word, it rebuilds the list, which is as big as the original book. An obvious improvement is to build the list once and then make multiple selections, but the list is still big.*\n\n*An alternative is:*\n\n- *1. Use* keys *to get a list of the words in the book.*\n- *2. Build a list that contains the cumulative sum of the word frequencies (see Exercise [10.3)](#page-113-1). The last item in this list is the total number of words in the book, n.*\n- *3. Choose a random number from 1 to n. Use a bisection search (See Exercise [10.11)](#page-119-1) to find the index where the random number would be inserted in the cumulative sum.*\n- *4. Use the index to find the corresponding word in the word list.*\n\n*Write a program that uses this algorithm to choose a random word from the book. Solution:* [http:](http://thinkpython.com/code/analyze_book3.py) [// thinkpython. com/ code/ analyze_ book3. py](http://thinkpython.com/code/analyze_book3.py) *.*\n\n#### **13.8 Markov analysis**\n\nIf you choose words from the book at random, you can get a sense of the vocabulary, you probably won't get a sentence:\n\nthis the small regard harriet which knightley's it most things\n\nA series of random words seldom makes sense because there is no relationship between successive words. For example, in a real sentence you would expect an article like \"the\" to be followed by an adjective or a noun, and probably not a verb or adverb.\n\nOne way to measure these kinds of relationships is Markov analysis, which characterizes, for a given sequence of words, the probability of the word that comes next. For example, the song *Eric, the Half a Bee* begins:\n\nHalf a bee, philosophically, Must, ipso facto, half not be. But half the bee has got to be Vis a vis, its entity. D'you see?\n\nBut can a bee be said to be Or not to be an entire bee When half the bee is not a bee Due to some ancient injury?\n\nIn this text, the phrase \"half the\" is always followed by the word \"bee,\" but the phrase \"the bee\" might be followed by either \"has\" or \"is\".\n\nThe result of Markov analysis is a mapping from each prefix (like \"half the\" and \"the bee\") to all possible suffixes (like \"has\" and \"is\").\n\nGiven this mapping, you can generate a random text by starting with any prefix and choosing at random from the possible suffixes. Next, you can combine the end of the prefix and the new suffix to form the next prefix, and repeat.\n\nFor example, if you start with the prefix \"Half a,\" then the next word has to be \"bee,\" because the prefix only appears once in the text. The next prefix is \"a bee,\" so the next suffix might be \"philosophically,\" \"be\" or \"due.\"\n\nIn this example the length of the prefix is always two, but you can do Markov analysis with any prefix length. The length of the prefix is called the \"order\" of the analysis. **Exercise 13.8.** *Markov analysis:*\n\n- *1. Write a program to read a text from a file and perform Markov analysis. The result should be a dictionary that maps from prefixes to a collection of possible suffixes. The collection might be a list, tuple, or dictionary; it is up to you to make an appropriate choice. You can test your program with prefix length two, but you should write the program in a way that makes it easy to try other lengths.*\n- *2. Add a function to the previous program to generate random text based on the Markov analysis. Here is an example from* Emma *with prefix length 2:*\n\n*He was very clever, be it sweetness or be angry, ashamed or only amused, at such a stroke. She had never thought of Hannah till you were never meant for me?\" \"I cannot make speeches, Emma:\" he soon cut it all himself.*\n\n*For this example, I left the punctuation attached to the words. The result is almost syntactically correct, but not quite. Semantically, it almost makes sense, but not quite.*\n\n*What happens if you increase the prefix length? Does the random text make more sense?*\n\n- *3. Once your program is working, you might want to try a mash-up: if you analyze text from two or more books, the random text you generate will blend the vocabulary and phrases from the sources in interesting ways.*\n*Credit: This case study is based on an example from Kernighan and Pike,* The Practice of Programming*, Addison-Wesley, 1999.*\n\nYou should attempt this exercise before you go on; then you can can download my solution from . You will also need [http://](http://thinkpython.com/code/emma.txt) [thinkpython.com/code/emma.txt](http://thinkpython.com/code/emma.txt).\n\n#### **13.9 Data structures**\n\nUsing Markov analysis to generate random text is fun, but there is also a point to this exercise: data structure selection. In your solution to the previous exercises, you had to choose:\n\n- How to represent the prefixes.\n- How to represent the collection of possible suffixes.\n- How to represent the mapping from each prefix to the collection of possible suffixes.\n\nOk, the last one is easy; the only mapping type we have seen is a dictionary, so it is the natural choice.\n\nFor the prefixes, the most obvious options are string, list of strings, or tuple of strings. For the suffixes, one option is a list; another is a histogram (dictionary).\n\nHow should you choose? The first step is to think about the operations you will need to implement for each data structure. For the prefixes, we need to be able to remove words from the beginning and add to the end. For example, if the current prefix is \"Half a,\" and the next word is \"bee,\" you need to be able to form the next prefix, \"a bee.\"\n\nYour first choice might be a list, since it is easy to add and remove elements, but we also need to be able to use the prefixes as keys in a dictionary, so that rules out lists. With tuples, you can't append or remove, but you can use the addition operator to form a new tuple:\n\n```\ndef shift(prefix, word):\n return prefix[1:] + (word,)\n```\nshift takes a tuple of words, prefix, and a string, word, and forms a new tuple that has all the words in prefix except the first, and word added to the end.\n\nFor the collection of suffixes, the operations we need to perform include adding a new suffix (or increasing the frequency of an existing one), and choosing a random suffix.\n\nAdding a new suffix is equally easy for the list implementation or the histogram. Choosing a random element from a list is easy; choosing from a histogram is harder to do efficiently (see Exercise [13.7)](#page-149-1).\n\nSo far we have been talking mostly about ease of implementation, but there are other factors to consider in choosing data structures. One is run time. Sometimes there is a theoretical reason to expect one data structure to be faster than other; for example, I mentioned that the in operator is faster for dictionaries than for lists, at least when the number of elements is large.\n\nBut often you don't know ahead of time which implementation will be faster. One option is to implement both of them and see which is better. This approach is called **benchmarking**. A practical alternative is to choose the data structure that is easiest to implement, and then see if it is fast enough for the intended application. If so, there is no need to go on. If not, there are tools, like the profile module, that can identify the places in a program that take the most time.\n\nThe other factor to consider is storage space. For example, using a histogram for the collection of suffixes might take less space because you only have to store each word once, no matter how many times it appears in the text. In some cases, saving space can also make your program run faster, and in the extreme, your program might not run at all if you run out of memory. But for many applications, space is a secondary consideration after run time.\n\nOne final thought: in this discussion, I have implied that we should use one data structure for both analysis and generation. But since these are separate phases, it would also be possible to use one structure for analysis and then convert to another structure for generation. This would be a net win if the time saved during generation exceeded the time spent in conversion.\n\n#### **13.10 Debugging**\n\nWhen you are debugging a program, and especially if you are working on a hard bug, there are four things to try:\n\n- **reading:** Examine your code, read it back to yourself, and check that it says what you meant to say.\n- **running:** Experiment by making changes and running different versions. Often if you display the right thing at the right place in the program, the problem becomes obvious, but sometimes you have to spend some time to build scaffolding.\n- **ruminating:** Take some time to think! What kind of error is it: syntax, runtime, semantic? What information can you get from the error messages, or from the output of the program? What kind of error could cause the problem you're seeing? What did you change last, before the problem appeared?\n- **retreating:** At some point, the best thing to do is back off, undoing recent changes, until you get back to a program that works and that you understand. Then you can start rebuilding.\n\nBeginning programmers sometimes get stuck on one of these activities and forget the others. Each activity comes with its own failure mode.\n\nFor example, reading your code might help if the problem is a typographical error, but not if the problem is a conceptual misunderstanding. If you don't understand what your program does, you can read it 100 times and never see the error, because the error is in your head.\n\nRunning experiments can help, especially if you run small, simple tests. But if you run experiments without thinking or reading your code, you might fall into a pattern I call \"random walk programming,\" which is the process of making random changes until the program does the right thing. Needless to say, random walk programming can take a long time.\n\nYou have to take time to think. Debugging is like an experimental science. You should have at least one hypothesis about what the problem is. If there are two or more possibilities, try to think of a test that would eliminate one of them.\n\nTaking a break helps with the thinking. So does talking. If you explain the problem to someone else (or even yourself), you will sometimes find the answer before you finish asking the question.\n\nBut even the best debugging techniques will fail if there are too many errors, or if the code you are trying to fix is too big and complicated. Sometimes the best option is to retreat, simplifying the program until you get to something that works and that you understand.\n\nBeginning programmers are often reluctant to retreat because they can't stand to delete a line of code (even if it's wrong). If it makes you feel better, copy your program into another file before you start stripping it down. Then you can paste the pieces back in a little bit at a time.\n\nFinding a hard bug requires reading, running, ruminating, and sometimes retreating. If you get stuck on one of these activities, try the others.\n\n#### **13.11 Glossary**\n\n- **deterministic:** Pertaining to a program that does the same thing each time it runs, given the same inputs.\n- **pseudorandom:** Pertaining to a sequence of numbers that appear to be random, but are generated by a deterministic program.\n\n**default value:** The value given to an optional parameter if no argument is provided.\n\n**override:** To replace a default value with an argument.\n\n- **benchmarking:** The process of choosing between data structures by implementing alternatives and testing them on a sample of the possible inputs.\n#### **13.12 Exercises**\n\n**Exercise 13.9.** *The \"rank\" of a word is its position in a list of words sorted by frequency: the most common word has rank 1, the second most common has rank 2, etc.*\n\n*Zipf's law describes a relationship between the ranks and frequencies of words in natural languages* ([http://en.wikipedia.org/wiki/Zipf's_law](http://en.wikipedia.org/wiki/Zipf)). Specifically, it predicts that the frequency, *f , of the word with rank r is:*\n\n$f = cr^{-s}$\n\n*where s and c are parameters that depend on the language and the text. If you take the logarithm of both sides of this equation, you get:*\n\n$$\n\\log f = \\log c - s \\log r\n$$\n\n*So if you plot log f versus log r, you should get a straight line with slope* −*s and intercept log c.*\n\n*Write a program that reads a text from a file, counts word frequencies, and prints one line for each word, in descending order of frequency, with log f and log r. Use the graphing program of your choice to plot the results and check whether they form a straight line. Can you estimate the value of s?*\n\n*Solution:* [http: // thinkpython. com/ code/ zipf. py](http://thinkpython.com/code/zipf.py) *. To make the plots, you might have to* install matplotlib (see ).\n\n# **Chapter 14**\n\n# **Files**\n\n#### **14.1 Persistence**\n\nMost of the programs we have seen so far are transient in the sense that they run for a short time and produce some output, but when they end, their data disappears. If you run the program again, it starts with a clean slate.\n\nOther programs are **persistent**: they run for a long time (or all the time); they keep at least some of their data in permanent storage (a hard drive, for example); and if they shut down and restart, they pick up where they left off.\n\nExamples of persistent programs are operating systems, which run pretty much whenever a computer is on, and web servers, which run all the time, waiting for requests to come in on the network.\n\nOne of the simplest ways for programs to maintain their data is by reading and writing text files. We have already seen programs that read text files; in this chapter we will see programs that write them.\n\nAn alternative is to store the state of the program in a database. In this chapter I will present a simple database and a module, pickle, that makes it easy to store program data.\n\n#### **14.2 Reading and writing**\n\nA text file is a sequence of characters stored on a permanent medium like a hard drive, flash memory, or CD-ROM. We saw how to open and read a file in Section [9.1.](#page-102-1)\n\nTo write a file, you have to open it with mode 'w' as a second parameter:\n\n```\n>>> fout = open('output.txt', 'w')\n>>> print fout\n\n```\nIf the file already exists, opening it in write mode clears out the old data and starts fresh, so be careful! If the file doesn't exist, a new one is created.\n\nThe write method puts data into the file.\n\n>>> line1 = \"This here's the wattle,\\n\" >>> fout.write(line1)\n\nAgain, the file object keeps track of where it is, so if you call write again, it adds the new data to the end.\n\n>>> line2 = \"the emblem of our land.\\n\" >>> fout.write(line2)\n\nWhen you are done writing, you have to close the file.\n\n>>> fout.close()\n\n#### **14.3 Format operator**\n\nThe argument of write has to be a string, so if we want to put other values in a file, we have to convert them to strings. The easiest way to do that is with str:\n\n```\n>>> x = 52\n>>> fout.write(str(x))\n```\nAn alternative is to use the **format operator**, %. When applied to integers, % is the modulus operator. But when the first operand is a string, % is the format operator.\n\nThe first operand is the **format string**, which contains one or more **format sequences**, which specify how the second operand is formatted. The result is a string.\n\nFor example, the format sequence '%d' means that the second operand should be formatted as an integer (d stands for \"decimal\"):\n\n```\n>>> camels = 42\n>>> '%d' % camels\n'42'\n```\nThe result is the string '42', which is not to be confused with the integer value 42.\n\nA format sequence can appear anywhere in the string, so you can embed a value in a sentence:\n\n```\n>>> camels = 42\n>>> 'I have spotted %d camels.' % camels\n'I have spotted 42 camels.'\n```\nIf there is more than one format sequence in the string, the second argument has to be a tuple. Each format sequence is matched with an element of the tuple, in order.\n\nThe following example uses '%d' to format an integer, '%g' to format a floating-point number (don't ask why), and '%s' to format a string:\n\n```\n>>> 'In %d years I have spotted %g %s.' % (3, 0.1, 'camels')\n'In 3 years I have spotted 0.1 camels.'\n```\nThe number of elements in the tuple has to match the number of format sequences in the string. Also, the types of the elements have to match the format sequences:\n\n```\n>>> '%d %d %d' % (1, 2)\nTypeError: not enough arguments for format string\n>>> '%d' % 'dollars'\nTypeError: illegal argument type for built-in operation\n```\nIn the first example, there aren't enough elements; in the second, the element is the wrong type.\n\nThe format operator is powerful, but it can be difficult to use. You can read more about it at .\n\n#### **14.4 Filenames and paths**\n\nFiles are organized into **directories** (also called \"folders\"). Every running program has a \"current directory,\" which is the default directory for most operations. For example, when you open a file for reading, Python looks for it in the current directory.\n\nThe os module provides functions for working with files and directories (\"os\" stands for \"operating system\"). os.getcwd returns the name of the current directory:\n\n```\n>>> import os\n>>> cwd = os.getcwd()\n>>> print cwd\n/home/dinsdale\n```\ncwd stands for \"current working directory.\" The result in this example is /home/dinsdale, which is the home directory of a user named dinsdale.\n\nA string like cwd that identifies a file is called a **path**. A **relative path** starts from the current directory; an **absolute path** starts from the topmost directory in the file system.\n\nThe paths we have seen so far are simple filenames, so they are relative to the current directory. To find the absolute path to a file, you can use os.path.abspath:\n\n```\n>>> os.path.abspath('memo.txt')\n'/home/dinsdale/memo.txt'\n```\nos.path.exists checks whether a file or directory exists:\n\n```\n>>> os.path.exists('memo.txt')\n```\nTrue\n\nIf it exists, os.path.isdir checks whether it's a directory:\n\n```\n>>> os.path.isdir('memo.txt')\nFalse\n>>> os.path.isdir('music')\nTrue\n```\nSimilarly, os.path.isfile checks whether it's a file.\n\nos.listdir returns a list of the files (and other directories) in the given directory:\n\n```\n>>> os.listdir(cwd)\n['music', 'photos', 'memo.txt']\n```\nTo demonstrate these functions, the following example \"walks\" through a directory, prints the names of all the files, and calls itself recursively on all the directories.\n\n```\ndef walk(dirname):\n for name in os.listdir(dirname):\n path = os.path.join(dirname, name)\n```\n\n```\nif os.path.isfile(path):\n print path\nelse:\n walk(path)\n```\nos.path.join takes a directory and a file name and joins them into a complete path. **Exercise 14.1.** *The* os *module provides a function called* walk *that is similar to this one but more versatile. Read the documentation and use it to print the names of the files in a given directory and its subdirectories.*\n\n*Solution:* [http: // thinkpython. com/ code/ walk. py](http://thinkpython.com/code/walk.py) *.*\n\n## **14.5 Catching exceptions**\n\nA lot of things can go wrong when you try to read and write files. If you try to open a file that doesn't exist, you get an IOError:\n\n```\n>>> fin = open('bad_file')\nIOError: [Errno 2] No such file or directory: 'bad_file'\n```\nIf you don't have permission to access a file:\n\n>>> fout = open('/etc/passwd', 'w') IOError: [Errno 13] Permission denied: '/etc/passwd' And if you try to open a directory for reading, you get\n\n>>> fin = open('/home') IOError: [Errno 21] Is a directory\n\nTo avoid these errors, you could use functions like os.path.exists and os.path.isfile, but it would take a lot of time and code to check all the possibilities (if \"Errno 21\" is any indication, there are at least 21 things that can go wrong).\n\nIt is better to go ahead and try—and deal with problems if they happen—which is exactly what the try statement does. The syntax is similar to an if statement:\n\n```\ntry:\n fin = open('bad_file')\n for line in fin:\n print line\n fin.close()\nexcept:\n print 'Something went wrong.'\n```\nPython starts by executing the try clause. If all goes well, it skips the except clause and proceeds. If an exception occurs, it jumps out of the try clause and executes the except clause.\n\nHandling an exception with a try statement is called **catching** an exception. In this example, the except clause prints an error message that is not very helpful. In general, catching an exception gives you a chance to fix the problem, or try again, or at least end the program gracefully.\n\n**Exercise 14.2.** *Write a function called* sed *that takes as arguments a pattern string, a replacement string, and two filenames; it should read the first file and write the contents into the second file (creating it if necessary). If the pattern string appears anywhere in the file, it should be replaced with the replacement string.*\n\n*If an error occurs while opening, reading, writing or closing files, your program should catch the* exception, print an error message, and exit. Solution: [http://thinkpython.com/code/sed.](http://thinkpython.com/code/sed.py) [py](http://thinkpython.com/code/sed.py) *.*\n\n#### **14.6 Databases**\n\nA **database** is a file that is organized for storing data. Most databases are organized like a dictionary in the sense that they map from keys to values. The biggest difference is that the database is on disk (or other permanent storage), so it persists after the program ends.\n\nThe module anydbm provides an interface for creating and updating database files. As an example, I'll create a database that contains captions for image files.\n\nOpening a database is similar to opening other files:\n\n```\n>>> import anydbm\n>>> db = anydbm.open('captions.db', 'c')\n```\nThe mode 'c' means that the database should be created if it doesn't already exist. The result is a database object that can be used (for most operations) like a dictionary. If you create a new item, anydbm updates the database file.\n\n>>> db['cleese.png'] = 'Photo of John Cleese.'\n\nWhen you access one of the items, anydbm reads the file:\n\n```\n>>> print db['cleese.png']\nPhoto of John Cleese.\n```\nIf you make another assignment to an existing key, anydbm replaces the old value:\n\n```\n>>> db['cleese.png'] = 'Photo of John Cleese doing a silly walk.'\n>>> print db['cleese.png']\nPhoto of John Cleese doing a silly walk.\n```\nMany dictionary methods, like keys and items, also work with database objects. So does iteration with a for statement.\n\nfor key in db: print key\n\nAs with other files, you should close the database when you are done:\n\n```\n>>> db.close()\n```\n#### **14.7 Pickling**\n\nA limitation of anydbm is that the keys and values have to be strings. If you try to use any other type, you get an error.\n\nThe pickle module can help. It translates almost any type of object into a string suitable for storage in a database, and then translates strings back into objects.\n\npickle.dumps takes an object as a parameter and returns a string representation (dumps is short for \"dump string\"):\n\n```\n>>> import pickle\n>>> t = [1, 2, 3]\n>>> pickle.dumps(t)\n'(lp0\\nI1\\naI2\\naI3\\na.'\n```\nThe format isn't obvious to human readers; it is meant to be easy for pickle to interpret. pickle.loads (\"load string\") reconstitutes the object:\n\n```\n>>> t1 = [1, 2, 3]\n>>> s = pickle.dumps(t1)\n>>> t2 = pickle.loads(s)\n>>> print t2\n[1, 2, 3]\n```\nAlthough the new object has the same value as the old, it is not (in general) the same object:\n\n```\n>>> t1 == t2\nTrue\n>>> t1 is t2\nFalse\n```\nIn other words, pickling and then unpickling has the same effect as copying the object.\n\nYou can use pickle to store non-strings in a database. In fact, this combination is so common that it has been encapsulated in a module called shelve.\n\n**Exercise 14.3.** *If you download my solution to Exercise [12.4](#page-142-2) from* [http: // thinkpython. com/](http://thinkpython.com/code/anagram_sets.py) [code/ anagram_ sets. py](http://thinkpython.com/code/anagram_sets.py) *, you'll see that it creates a dictionary that maps from a sorted string of letters to the list of words that can be spelled with those letters. For example,* 'opst' *maps to the list* ['opts', 'post', 'pots', 'spot', 'stop', 'tops']*.*\n\n*Write a module that imports* anagram_sets *and provides two new functions:* store_anagrams *should store the anagram dictionary in a \"shelf;\"* read_anagrams *should look up a word and return a list of its anagrams. Solution:* [http: // thinkpython. com/ code/ anagram_ db. py](http://thinkpython.com/code/anagram_db.py)\n\n### **14.8 Pipes**\n\nMost operating systems provide a command-line interface, also known as a **shell**. Shells usually provide commands to navigate the file system and launch applications. For example, in Unix you can change directories with cd, display the contents of a directory with ls, and launch a web browser by typing (for example) firefox.\n\nAny program that you can launch from the shell can also be launched from Python using a **pipe**. A pipe is an object that represents a running program.\n\nFor example, the Unix command ls -l normally displays the contents of the current directory (in long format). You can launch ls with os.popen[1](#page-159-1) :\n\n```\n>>> cmd = 'ls -l'\n>>> fp = os.popen(cmd)\n```\nThe argument is a string that contains a shell command. The return value is an object that behaves just like an open file. You can read the output from the ls process one line at a time with readline or get the whole thing at once with read:\n\n1popen is deprecated now, which means we are supposed to stop using it and start using the subprocess module. But for simple cases, I find subprocess more complicated than necessary. So I am going to keep using popen until they take it away.\n\n>>> res = fp.read()\n\nWhen you are done, you close the pipe like a file:\n\n```\n>>> stat = fp.close()\n>>> print stat\nNone\n```\nThe return value is the final status of the ls process; None means that it ended normally (with no errors).\n\nFor example, most Unix systems provide a command called md5sum that reads the contents of a file and computes a \"checksum.\" You can read about MD5 at [http://en.wikipedia.](http://en.wikipedia.org/wiki/Md5) [org/wiki/Md5](http://en.wikipedia.org/wiki/Md5). This command provides an efficient way to check whether two files have the same contents. The probability that different contents yield the same checksum is very small (that is, unlikely to happen before the universe collapses).\n\nYou can use a pipe to run md5sum from Python and get the result:\n\n```\n>>> filename = 'book.tex'\n>>> cmd = 'md5sum ' + filename\n>>> fp = os.popen(cmd)\n>>> res = fp.read()\n>>> stat = fp.close()\n>>> print res\n1e0033f0ed0656636de0d75144ba32e0 book.tex\n>>> print stat\nNone\n```\n**Exercise 14.4.** *In a large collection of MP3 files, there may be more than one copy of the same song, stored in different directories or with different file names. The goal of this exercise is to search for duplicates.*\n\n- *1. Write a program that searches a directory and all of its subdirectories, recursively, and returns a list of complete paths for all files with a given suffix (like* .mp3*). Hint:* os.path *provides several useful functions for manipulating file and path names.*\n- *2. To recognize duplicates, you can use* md5sum *to compute a \"checksum\" for each files. If two files have the same checksum, they probably have the same contents.*\n- *3. To double-check, you can use the Unix command* diff*.*\n\n*Solution:* [http: // thinkpython. com/ code/ find_ duplicates. py](http://thinkpython.com/code/find_duplicates.py) *.*\n\n#### **14.9 Writing modules**\n\nAny file that contains Python code can be imported as a module. For example, suppose you have a file named wc.py with the following code:\n\n```\ndef linecount(filename):\n count = 0\n for line in open(filename):\n count += 1\n return count\n```\nprint linecount('wc.py')\n\nIf you run this program, it reads itself and prints the number of lines in the file, which is 7. You can also import it like this:\n\n```\n>>> import wc\n7\n```\nNow you have a module object wc:\n\n```\n>>> print wc\n\n```\nThat provides a function called linecount:\n\n```\n>>> wc.linecount('wc.py')\n7\n```\nSo that's how you write modules in Python.\n\nThe only problem with this example is that when you import the module it executes the test code at the bottom. Normally when you import a module, it defines new functions but it doesn't execute them.\n\nPrograms that will be imported as modules often use the following idiom:\n\n```\nif __name__ == '__main__':\n print linecount('wc.py')\n```\n__name__ is a built-in variable that is set when the program starts. If the program is running as a script, __name__ has the value __main__; in that case, the test code is executed. Otherwise, if the module is being imported, the test code is skipped.\n\n**Exercise 14.5.** *Type this example into a file named* wc.py *and run it as a script. Then run the Python interpreter and* import wc*. What is the value of* __name__ *when the module is being imported?*\n\n*Warning: If you import a module that has already been imported, Python does nothing. It does not re-read the file, even if it has changed.*\n\n*If you want to reload a module, you can use the built-in function* reload*, but it can be tricky, so the safest thing to do is restart the interpreter and then import the module again.*\n\n### **14.10 Debugging**\n\nWhen you are reading and writing files, you might run into problems with whitespace. These errors can be hard to debug because spaces, tabs and newlines are normally invisible:\n\n```\n>>> s = '1 2\\t 3\\n 4'\n>>> print s\n1 2 3\n 4\n```\nThe built-in function repr can help. It takes any object as an argument and returns a string representation of the object. For strings, it represents whitespace characters with backslash sequences:\n\n```\n>>> print repr(s)\n'1 2\\t 3\\n 4'\n```\nThis can be helpful for debugging.\n\nOne other problem you might run into is that different systems use different characters to indicate the end of a line. Some systems use a newline, represented \\n. Others use a return character, represented \\r. Some use both. If you move files between different systems, these inconsistencies might cause problems.\n\nFor most systems, there are applications to convert from one format to another. You can find them (and read more about this issue) at . Or, of course, you could write one yourself.\n\n#### **14.11 Glossary**\n\n- **persistent:** Pertaining to a program that runs indefinitely and keeps at least some of its data in permanent storage.\n- **format operator:** An operator, %, that takes a format string and a tuple and generates a string that includes the elements of the tuple formatted as specified by the format string.\n- **format string:** A string, used with the format operator, that contains format sequences.\n- **format sequence:** A sequence of characters in a format string, like %d, that specifies how a value should be formatted.\n- **text file:** A sequence of characters stored in permanent storage like a hard drive.\n- **directory:** A named collection of files, also called a folder.\n- **path:** A string that identifies a file.\n- **relative path:** A path that starts from the current directory.\n- **absolute path:** A path that starts from the topmost directory in the file system.\n- **catch:** To prevent an exception from terminating a program using the try and except statements.\n- **database:** A file whose contents are organized like a dictionary with keys that correspond to values.\n\n#### **14.12 Exercises**\n\n**Exercise 14.6.** *The* urllib *module provides methods for manipulating URLs and downloading information from the web. The following example downloads and prints a secret message from* thinkpython.com*:*\n\n```\nimport urllib\n```\n\n```\nconn = urllib.urlopen('http://thinkpython.com/secret.html')\nfor line in conn:\n print line.strip()\n```\n*Run this code and follow the instructions you see there. Solution:* [http: // thinkpython. com/](http://thinkpython.com/code/zip_code.py) [code/ zip_ code. py](http://thinkpython.com/code/zip_code.py) *.*\n\n# **Chapter 15**\n\n# **Classes and objects**\n\nCode examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Point1.py) [Point1.py](http://thinkpython.com/code/Point1.py); solutions to the exercises are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Point1_soln.py) [Point1_soln.py](http://thinkpython.com/code/Point1_soln.py).\n\n#### **15.1 User-defined types**\n\nWe have used many of Python's built-in types; now we are going to define a new type. As an example, we will create a type called Point that represents a point in two-dimensional space.\n\nIn mathematical notation, points are often written in parentheses with a comma separating the coordinates. For example, (0,0) represents the origin, and ($x$, $y$) represents the point $x $units to the right and *y* units up from the origin.\n\nThere are several ways we might represent points in Python:\n\n- We could store the coordinates separately in two variables, x and y.\n- We could store the coordinates as elements in a list or tuple.\n- We could create a new type to represent points as objects.\n\nCreating a new type is (a little) more complicated than the other options, but it has advantages that will be apparent soon.\n\nA user-defined type is also called a **class**. A class definition looks like this:\n\n```\nclass Point(object):\n \"\"\"Represents a point in 2-D space.\"\"\"\n```\nThis header indicates that the new class is a Point, which is a kind of object, which is a built-in type.\n\nThe body is a docstring that explains what the class is for. You can define variables and functions inside a class definition, but we will get back to that later.\n\nDefining a class named Point creates a class object.\n\n```\nx\n y\n 3.0\n 4.0\nblank\n Point\n```\nFigure 15.1: Object diagram.\n\n```\n>>> print Point\n\n```\nBecause Point is defined at the top level, its “full name” is __main__.Point.\n\nThe class object is like a factory for creating objects. To create a Point, you call Point as if it were a function.\n\n```\n>>> blank = Point()\n>>> print blank\n<__main__.Point instance at 0xb7e9d3ac>\n```\nThe return value is a reference to a Point object, which we assign to blank. Creating a new object is called **instantiation**, and the object is an **instance** of the class.\n\nWhen you print an instance, Python tells you what class it belongs to and where it is stored in memory (the prefix 0x means that the following number is in hexadecimal).\n\n### **15.2 Attributes**\n\nYou can assign values to an instance using dot notation:\n\n```\n>>> blank.x = 3.0\n>>> blank.y = 4.0\n```\nThis syntax is similar to the syntax for selecting a variable from a module, such as math.pi or string.whitespace. In this case, though, we are assigning values to named elements of an object. These elements are called **attributes**.\n\nAs a noun, \"AT-trib-ute\" is pronounced with emphasis on the first syllable, as opposed to \"a-TRIB-ute,\" which is a verb.\n\nThe following diagram shows the result of these assignments. A state diagram that shows an object and its attributes is called an **object diagram**; see Figure [15.1.](#page-165-1)\n\nThe variable blank refers to a Point object, which contains two attributes. Each attribute refers to a floating-point number.\n\nYou can read the value of an attribute using the same syntax:\n\n```\n>>> print blank.y\n4.0\n>>> x = blank.x\n>>> print x\n3.0\n```\nThe expression blank.x means, \"Go to the object blank refers to and get the value of x.\" In this case, we assign that value to a variable named x. There is no conflict between the variable x and the attribute x.\n\nYou can use dot notation as part of any expression. For example:\n\n```\n>>> print '(%g, %g)' % (blank.x, blank.y)\n(3.0, 4.0)\n>>> distance = math.sqrt(blank.x**2 + blank.y**2)\n>>> print distance\n5.0\n```\nYou can pass an instance as an argument in the usual way. For example:\n\ndef print_point(p): print '(%g, %g)' % (p.x, p.y)\n\nprint_point takes a point as an argument and displays it in mathematical notation. To invoke it, you can pass blank as an argument:\n\n>>> print_point(blank) (3.0, 4.0)\n\nInside the function, p is an alias for blank, so if the function modifies p, blank changes. **Exercise 15.1.** *Write a function called* distance_between_points *that takes two Points as arguments and returns the distance between them.*\n\n#### **15.3 Rectangles**\n\nSometimes it is obvious what the attributes of an object should be, but other times you have to make decisions. For example, imagine you are designing a class to represent rectangles. What attributes would you use to specify the location and size of a rectangle? You can ignore angle; to keep things simple, assume that the rectangle is either vertical or horizontal.\n\nThere are at least two possibilities:\n\n- You could specify one corner of the rectangle (or the center), the width, and the height.\n- You could specify two opposing corners.\n\nAt this point it is hard to say whether either is better than the other, so we'll implement the first one, just as an example.\n\nHere is the class definition:\n\n```\nclass Rectangle(object):\n \"\"\"Represents a rectangle.\n attributes: width, height, corner.\n \"\"\"\n```\nThe docstring lists the attributes: width and height are numbers; corner is a Point object that specifies the lower-left corner.\n\nTo represent a rectangle, you have to instantiate a Rectangle object and assign values to the attributes:\n\nbox = Rectangle() box.width = 100.0 box.height = 200.0\n\n\n\nFigure 15.2: Object diagram.\n\n```\nbox.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0\n```\nThe expression box.corner.x means, \"Go to the object box refers to and select the attribute named corner; then go to that object and select the attribute named x.\"\n\nFigure [15.2](#page-167-2) shows the state of this object. An object that is an attribute of another object is **embedded**.\n\n#### **15.4 Instances as return values**\n\nFunctions can return instances. For example, find_center takes a Rectangle as an argument and returns a Point that contains the coordinates of the center of the Rectangle:\n\n```\ndef find_center(rect):\n p = Point()\n p.x = rect.corner.x + rect.width/2.0\n p.y = rect.corner.y + rect.height/2.0\n return p\n```\nHere is an example that passes box as an argument and assigns the resulting Point to center:\n\n```\n>>> center = find_center(box)\n>>> print_point(center)\n(50.0, 100.0)\n```\n#### **15.5 Objects are mutable**\n\nYou can change the state of an object by making an assignment to one of its attributes. For example, to change the size of a rectangle without changing its position, you can modify the values of width and height:\n\n```\nbox.width = box.width + 50\nbox.height = box.width + 100\n```\nYou can also write functions that modify objects. For example, grow_rectangle takes a Rectangle object and two numbers, dwidth and dheight, and adds the numbers to the width and height of the rectangle:\n\n```\ndef grow_rectangle(rect, dwidth, dheight):\n rect.width += dwidth\n rect.height += dheight\n```\nHere is an example that demonstrates the effect:\n\n```\n>>> print box.width\n100.0\n>>> print box.height\n200.0\n>>> grow_rectangle(box, 50, 100)\n>>> print box.width\n150.0\n>>> print box.height\n300.0\n```\nInside the function, rect is an alias for box, so if the function modifies rect, box changes. **Exercise 15.2.** *Write a function named* move_rectangle *that takes a Rectangle and two numbers named* dx *and* dy*. It should change the location of the rectangle by adding* dx *to the* x *coordinate of* corner *and adding* dy *to the* y *coordinate of* corner*.*\n\n#### **15.6 Copying**\n\nAliasing can make a program difficult to read because changes in one place might have unexpected effects in another place. It is hard to keep track of all the variables that might refer to a given object.\n\nCopying an object is often an alternative to aliasing. The copy module contains a function called copy that can duplicate any object:\n\n```\n>>> p1 = Point()\n>>> p1.x = 3.0\n>>> p1.y = 4.0\n>>> import copy\n>>> p2 = copy.copy(p1)\n```\np1 and p2 contain the same data, but they are not the same Point.\n\n```\n>>> print_point(p1)\n(3.0, 4.0)\n>>> print_point(p2)\n(3.0, 4.0)\n>>> p1 is p2\nFalse\n>>> p1 == p2\nFalse\n```\nThe is operator indicates that p1 and p2 are not the same object, which is what we expected. But you might have expected == to yield True because these points contain the same data. In that case, you will be disappointed to learn that for instances, the default behavior of the == operator is the same as the is operator; it checks object identity, not object equivalence. This behavior can be changed—we'll see how later.\n\nIf you use copy.copy to duplicate a Rectangle, you will find that it copies the Rectangle object but not the embedded Point.\n\n\n\nFigure 15.3: Object diagram.\n\n```\n>>> box2 = copy.copy(box)\n>>> box2 is box\nFalse\n>>> box2.corner is box.corner\nTrue\n```\nFigure [15.3](#page-169-1) shows what the object diagram looks like. This operation is called a **shallow copy** because it copies the object and any references it contains, but not the embedded objects.\n\nFor most applications, this is not what you want. In this example, invoking grow_rectangle on one of the Rectangles would not affect the other, but invoking move_rectangle on either would affect both! This behavior is confusing and error-prone.\n\nFortunately, the copy module contains a method named deepcopy that copies not only the object but also the objects it refers to, and the objects *they* refer to, and so on. You will not be surprised to learn that this operation is called a **deep copy**.\n\n```\n>>> box3 = copy.deepcopy(box)\n>>> box3 is box\nFalse\n>>> box3.corner is box.corner\nFalse\n```\nbox3 and box are completely separate objects.\n\n**Exercise 15.3.** *Write a version of* move_rectangle *that creates and returns a new Rectangle instead of modifying the old one.*\n\n#### **15.7 Debugging**\n\nWhen you start working with objects, you are likely to encounter some new exceptions. If you try to access an attribute that doesn't exist, you get an AttributeError:\n\n```\n>>> p = Point()\n>>> print p.z\nAttributeError: Point instance has no attribute 'z'\n```\nIf you are not sure what type an object is, you can ask:\n\n```\n>>> type(p)\n\n```\nIf you are not sure whether an object has a particular attribute, you can use the built-in function hasattr:\n\n```\n>>> hasattr(p, 'x')\nTrue\n>>> hasattr(p, 'z')\nFalse\n```\nThe first argument can be any object; the second argument is a *string* that contains the name of the attribute.\n\n#### **15.8 Glossary**\n\n**class:** A user-defined type. A class definition creates a new class object.\n\n- **class object:** An object that contains information about a user-defined type. The class object can be used to create instances of the type.\n**instance:** An object that belongs to a class.\n\n**attribute:** One of the named values associated with an object.\n\nembedded (object): An object that is stored as an attribute of another object.\n\n- **shallow copy:** To copy the contents of an object, including any references to embedded objects; implemented by the copy function in the copy module.\n- **deep copy:** To copy the contents of an object as well as any embedded objects, and any objects embedded in them, and so on; implemented by the deepcopy function in the copy module.\n- **object diagram:** A diagram that shows objects, their attributes, and the values of the attributes.\n\n#### **15.9 Exercises**\n\n**Exercise 15.4.** *Swampy (see Chapter [4)](#page-52-0) provides a module named* World*, which defines a userdefined type also called* World*. You can import it like this:*\n\nfrom swampy.World import World\n\n*Or, depending on how you installed Swampy, like this:*\n\nfrom World import World\n\n*The following code creates a World object and calls the* mainloop *method, which waits for the user.*\n\n*A window should appear with a title bar and an empty square. We will use this window to draw Points, Rectangles and other shapes. Add the following lines before calling* mainloop *and run the program again.*\n\n```\ncanvas = world.ca(width=500, height=500, background='white')\nbbox = [[-150,-100], [150, 100]]\ncanvas.rectangle(bbox, outline='black', width=2, fill='green4')\n```\n*You should see a green rectangle with a black outline. The first line creates a Canvas, which appears in the window as a white square. The Canvas object provides methods like* rectangle *for drawing various shapes.*\n\nbbox *is a list of lists that represents the \"bounding box\" of the rectangle. The first pair of coordinates is the lower-left corner of the rectangle; the second pair is the upper-right corner.*\n\n*You can draw a circle like this:*\n\ncanvas.circle([-25,0], 70, outline=None, fill='red')\n\n*The first parameter is the coordinate pair for the center of the circle; the second parameter is the radius.*\n\n*If you add this line to the program, the result should resemble the national flag of Bangladesh (see* [http: // en. wikipedia. org/ wiki/ Gallery_ of_ sovereign-state_ flags](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) *).*\n\n- *1. Write a function called* draw_rectangle *that takes a Canvas and a Rectangle as arguments and draws a representation of the Rectangle on the Canvas.*\n- *2. Add an attribute named* color *to your Rectangle objects and modify* draw_rectangle *so that it uses the color attribute as the fill color.*\n- *3. Write a function called* draw_point *that takes a Canvas and a Point as arguments and draws a representation of the Point on the Canvas.*\n- *4. Define a new class called Circle with appropriate attributes and instantiate a few Circle objects. Write a function called* draw_circle *that draws circles on the canvas.*\n- *5. Write a program that draws the national flag of the Czech Republic. Hint: you can draw a polygon like this:*\n\npoints = [[-150,-100], [150, 100], [150, -100]] canvas.polygon(points, fill='blue')\n\n*I have written a small program that lists the available colors; you can download it from* [http:](http://thinkpython.com/code/color_list.py) [// thinkpython. com/ code/ color_ list. py](http://thinkpython.com/code/color_list.py) *.*\n\n# **Chapter 16**\n\n# **Classes and functions**\n\nCode examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Time1.py) [Time1.py](http://thinkpython.com/code/Time1.py).\n\n#### **16.1 Time**\n\nAs another example of a user-defined type, we'll define a class called Time that records the time of day. The class definition looks like this:\n\n```\nclass Time(object):\n \"\"\"Represents the time of day.\n attributes: hour, minute, second\n```\nWe can create a new Time object and assign attributes for hours, minutes, and seconds:\n\n```\ntime = Time()\ntime.hour = 11\ntime.minute = 59\ntime.second = 30\n```\n\"\"\"\n\nThe state diagram for the Time object looks like Figure [16.1.](#page-173-0)\n\n**Exercise 16.1.** *Write a function called* print_time *that takes a Time object and prints it in the form* hour:minute:second*. Hint: the format sequence* '%.2d' *prints an integer using at least two digits, including a leading zero if necessary.*\n\n**Exercise 16.2.** *Write a boolean function called* is_after *that takes two Time objects,* t1 *and* t2*, and returns* True *if* t1 *follows* t2 *chronologically and* False *otherwise. Challenge: don't use an* if *statement.*\n\n#### **16.2 Pure functions**\n\nIn the next few sections, we'll write two functions that add time values. They demonstrate two kinds of functions: pure functions and modifiers. They also demonstrate a development plan I'll call **prototype and patch**, which is a way of tackling a complex problem by starting with a simple prototype and incrementally dealing with the complications.\n\n\n\nFigure 16.1: Object diagram.\n\nHere is a simple prototype of add_time:\n\n```\ndef add_time(t1, t2):\n sum = Time()\n sum.hour = t1.hour + t2.hour\n sum.minute = t1.minute + t2.minute\n sum.second = t1.second + t2.second\n return sum\n```\nThe function creates a new Time object, initializes its attributes, and returns a reference to the new object. This is called a **pure function** because it does not modify any of the objects passed to it as arguments and it has no effect, like displaying a value or getting user input, other than returning a value.\n\nTo test this function, I'll create two Time objects: start contains the start time of a movie, like *Monty Python and the Holy Grail*, and duration contains the run time of the movie, which is one hour 35 minutes.\n\nadd_time figures out when the movie will be done.\n\n```\n>>> start = Time()\n>>> start.hour = 9\n>>> start.minute = 45\n>>> start.second = 0\n>>> duration = Time()\n>>> duration.hour = 1\n>>> duration.minute = 35\n>>> duration.second = 0\n>>> done = add_time(start, duration)\n>>> print_time(done)\n10:80:00\n```\nThe result, 10:80:00 might not be what you were hoping for. The problem is that this function does not deal with cases where the number of seconds or minutes adds up to more than sixty. When that happens, we have to “carry” the extra seconds into the minute column or the extra minutes into the hour column.\n\nHere's an improved version:\n\n```\ndef add_time(t1, t2):\n sum = Time()\n sum.hour = t1.hour + t2.hour\n sum.minute = t1.minute + t2.minute\n sum.second = t1.second + t2.second\n```\n\n```\nif sum.second >= 60:\n sum.second -= 60\n sum.minute += 1\nif sum.minute >= 60:\n sum.minute -= 60\n sum.hour += 1\n```\nreturn sum\n\nAlthough this function is correct, it is starting to get big. We will see a shorter alternative later.\n\n#### **16.3 Modifiers**\n\nSometimes it is useful for a function to modify the objects it gets as parameters. In that case, the changes are visible to the caller. Functions that work this way are called **modifiers**.\n\nincrement, which adds a given number of seconds to a Time object, can be written naturally as a modifier. Here is a rough draft:\n\n```\ndef increment(time, seconds):\n time.second += seconds\n if time.second >= 60:\n time.second -= 60\n time.minute += 1\n if time.minute >= 60:\n time.minute -= 60\n time.hour += 1\n```\nThe first line performs the basic operation; the remainder deals with the special cases we saw before.\n\nIs this function correct? What happens if the parameter seconds is much greater than sixty?\n\nIn that case, it is not enough to carry once; we have to keep doing it until time.second is less than sixty. One solution is to replace the if statements with while statements. That would make the function correct, but not very efficient.\n\n**Exercise 16.3.** *Write a correct version of* increment *that doesn't contain any loops.*\n\nAnything that can be done with modifiers can also be done with pure functions. In fact, some programming languages only allow pure functions. There is some evidence that programs that use pure functions are faster to develop and less error-prone than programs that use modifiers. But modifiers are convenient at times, and functional programs tend to be less efficient.\n\nIn general, I recommend that you write pure functions whenever it is reasonable and resort to modifiers only if there is a compelling advantage. This approach might be called a **functional programming style**.\n\n**Exercise 16.4.** *Write a \"pure\" version of* increment *that creates and returns a new Time object rather than modifying the parameter.*\n\n### **16.4 Prototyping versus planning**\n\nThe development plan I am demonstrating is called \"prototype and patch.\" For each function, I wrote a prototype that performed the basic calculation and then tested it, patching errors along the way.\n\nThis approach can be effective, especially if you don't yet have a deep understanding of the problem. But incremental corrections can generate code that is unnecessarily complicated—since it deals with many special cases—and unreliable—since it is hard to know if you have found all the errors.\n\nAn alternative is **planned development**, in which high-level insight into the problem can make the programming much easier. In this case, the insight is that a Time object is really a three-digit number in base 60 (see .)! The second attribute is the \"ones column,\" the minute attribute is the \"sixties column,\" and the hour attribute is the \"thirty-six hundreds column.\"\n\nWhen we wrote add_time and increment, we were effectively doing addition in base 60, which is why we had to carry from one column to the next.\n\nThis observation suggests another approach to the whole problem—we can convert Time objects to integers and take advantage of the fact that the computer knows how to do integer arithmetic.\n\nHere is a function that converts Times to integers:\n\n```\ndef time_to_int(time):\n minutes = time.hour * 60 + time.minute\n seconds = minutes * 60 + time.second\n return seconds\n```\nAnd here is the function that converts integers to Times (recall that divmod divides the first argument by the second and returns the quotient and remainder as a tuple).\n\n```\ndef int_to_time(seconds):\n time = Time()\n minutes, time.second = divmod(seconds, 60)\n time.hour, time.minute = divmod(minutes, 60)\n return time\n```\nYou might have to think a bit, and run some tests, to convince yourself that these functions are correct. One way to test them is to check that time_to_int(int_to_time(x)) == x for many values of x. This is an example of a consistency check.\n\nOnce you are convinced they are correct, you can use them to rewrite add_time:\n\n```\ndef add_time(t1, t2):\n seconds = time_to_int(t1) + time_to_int(t2)\n return int_to_time(seconds)\n```\nThis version is shorter than the original, and easier to verify. **Exercise 16.5.** *Rewrite* increment *using* time_to_int *and* int_to_time*.*\n\nIn some ways, converting from base 60 to base 10 and back is harder than just dealing with times. Base conversion is more abstract; our intuition for dealing with time values is better.\n\nBut if we have the insight to treat times as base 60 numbers and make the investment of writing the conversion functions (time_to_int and int_to_time), we get a program that is shorter, easier to read and debug, and more reliable.\n\nIt is also easier to add features later. For example, imagine subtracting two Times to find the duration between them. The naive approach would be to implement subtraction with borrowing. Using the conversion functions would be easier and more likely to be correct.\n\nIronically, sometimes making a problem harder (or more general) makes it easier (because there are fewer special cases and fewer opportunities for error).\n\n#### **16.5 Debugging**\n\nA Time object is well-formed if the values of minute and second are between 0 and 60 (including 0 but not 60) and if hour is positive. hour and minute should be integral values, but we might allow second to have a fraction part.\n\nRequirements like these are called **invariants** because they should always be true. To put it a different way, if they are not true, then something has gone wrong.\n\nWriting code to check your invariants can help you detect errors and find their causes. For example, you might have a function like valid_time that takes a Time object and returns False if it violates an invariant:\n\n```\ndef valid_time(time):\n if time.hour < 0 or time.minute < 0 or time.second < 0:\n return False\n if time.minute >= 60 or time.second >= 60:\n return False\n return True\n```\nThen at the beginning of each function you could check the arguments to make sure they are valid:\n\n```\ndef add_time(t1, t2):\n if not valid_time(t1) or not valid_time(t2):\n raise ValueError('invalid Time object in add_time')\n seconds = time_to_int(t1) + time_to_int(t2)\n return int_to_time(seconds)\n```\nOr you could use an assert statement, which checks a given invariant and raises an exception if it fails:\n\n```\ndef add_time(t1, t2):\n assert valid_time(t1) and valid_time(t2)\n seconds = time_to_int(t1) + time_to_int(t2)\n return int_to_time(seconds)\n```\nassert statements are useful because they distinguish code that deals with normal conditions from code that checks for errors.\n\n#### **16.6 Glossary**\n\n**prototype and patch:** A development plan that involves writing a rough draft of a program, testing, and correcting errors as they are found.\n\n- **planned development:** A development plan that involves high-level insight into the problem and more planning than incremental development or prototype development.\n- **pure function:** A function that does not modify any of the objects it receives as arguments. Most pure functions are fruitful.\n- **modifier:** A function that changes one or more of the objects it receives as arguments. Most modifiers are fruitless.\n- **functional programming style:** A style of program design in which the majority of functions are pure.\n- **invariant:** A condition that should always be true during the execution of a program.\n\n#### **16.7 Exercises**\n\nCode examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Time1.py) [Time1.py](http://thinkpython.com/code/Time1.py); solutions to these exercises are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Time1_soln.py) [Time1_soln.py](http://thinkpython.com/code/Time1_soln.py).\n\n**Exercise 16.6.** *Write a function called* mul_time *that takes a Time object and a number and returns a new Time object that contains the product of the original Time and the number.*\n\n*Then use* mul_time *to write a function that takes a Time object that represents the finishing time in a race, and a number that represents the distance, and returns a Time object that represents the average pace (time per mile).*\n\n**Exercise 16.7.** *The* datetime *module provides* date *and* time *objects that are similar to the Date and Time objects in this chapter, but they provide a rich set of methods and operators. Read the documentation at* [http: // docs. python. org/ 2/ library/ datetime. html](http://docs.python.org/2/library/datetime.html) *.*\n\n- *1. Use the* datetime *module to write a program that gets the current date and prints the day of the week.*\n- *2. Write a program that takes a birthday as input and prints the user's age and the number of days, hours, minutes and seconds until their next birthday.*\n- *3. For two people born on different days, there is a day when one is twice as old as the other. That's their Double Day. Write a program that takes two birthdays and computes their Double Day.*\n- *4. For a little more challenge, write the more general version that computes the day when one person is n times older than the other.*\n\n# **Chapter 17**\n\n# **Classes and methods**\n\nCode examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Time2.py) [Time2.py](http://thinkpython.com/code/Time2.py).\n\n#### **17.1 Object-oriented features**\n\nPython is an **object-oriented programming language**, which means that it provides features that support object-oriented programming.\n\nIt is not easy to define object-oriented programming, but we have already seen some of its characteristics:\n\n- Programs are made up of object definitions and function definitions, and most of the computation is expressed in terms of operations on objects.\n- Each object definition corresponds to some object or concept in the real world, and the functions that operate on that object correspond to the ways real-world objects interact.\n\nFor example, the Time class defined in Chapter [16](#page-172-0) corresponds to the way people record the time of day, and the functions we defined correspond to the kinds of things people do with times. Similarly, the Point and Rectangle classes correspond to the mathematical concepts of a point and a rectangle.\n\nSo far, we have not taken advantage of the features Python provides to support objectoriented programming. These features are not strictly necessary; most of them provide alternative syntax for things we have already done. But in many cases, the alternative is more concise and more accurately conveys the structure of the program.\n\nFor example, in the Time program, there is no obvious connection between the class definition and the function definitions that follow. With some examination, it is apparent that every function takes at least one Time object as an argument.\n\nThis observation is the motivation for **methods**; a method is a function that is associated with a particular class. We have seen methods for strings, lists, dictionaries and tuples. In this chapter, we will define methods for user-defined types.\n\nMethods are semantically the same as functions, but there are two syntactic differences:\n\n- Methods are defined inside a class definition in order to make the relationship between the class and the method explicit.\n- The syntax for invoking a method is different from the syntax for calling a function.\n\nIn the next few sections, we will take the functions from the previous two chapters and transform them into methods. This transformation is purely mechanical; you can do it simply by following a sequence of steps. If you are comfortable converting from one form to another, you will be able to choose the best form for whatever you are doing.\n\n### **17.2 Printing objects**\n\nIn Chapter [16,](#page-172-0) we defined a class named Time and in Exercise [16.1,](#page-172-3) you wrote a function named print_time:\n\n```\nclass Time(object):\n \"\"\"Represents the time of day.\"\"\"\n```\n\n```\ndef print_time(time):\n print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)\n```\nTo call this function, you have to pass a Time object as an argument:\n\n```\n>>> start = Time()\n>>> start.hour = 9\n>>> start.minute = 45\n>>> start.second = 00\n>>> print_time(start)\n09:45:00\n```\nTo make print_time a method, all we have to do is move the function definition inside the class definition. Notice the change in indentation.\n\n```\nclass Time(object):\n def print_time(time):\n print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)\n```\nNow there are two ways to call print_time. The first (and less common) way is to use function syntax:\n\n```\n>>> Time.print_time(start)\n09:45:00\n```\nIn this use of dot notation, Time is the name of the class, and print_time is the name of the method. start is passed as a parameter.\n\nThe second (and more concise) way is to use method syntax:\n\n```\n>>> start.print_time()\n09:45:00\n```\nIn this use of dot notation, print_time is the name of the method (again), and start is the object the method is invoked on, which is called the **subject**. Just as the subject of a sentence is what the sentence is about, the subject of a method invocation is what the method is about.\n\nInside the method, the subject is assigned to the first parameter, so in this case start is assigned to time.\n\nBy convention, the first parameter of a method is called self, so it would be more common to write print_time like this:\n\n```\nclass Time(object):\n def print_time(self):\n print '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)\n```\nThe reason for this convention is an implicit metaphor:\n\n- The syntax for a function call, print_time(start), suggests that the function is the active agent. It says something like, \"Hey print_time! Here's an object for you to print.\"\n- In object-oriented programming, the objects are the active agents. A method invocation like start.print_time() says \"Hey start! Please print yourself.\"\n\nThis change in perspective might be more polite, but it is not obvious that it is useful. In the examples we have seen so far, it may not be. But sometimes shifting responsibility from the functions onto the objects makes it possible to write more versatile functions, and makes it easier to maintain and reuse code.\n\n**Exercise 17.1.** *Rewrite* time_to_int *(from Section [16.4)](#page-175-0) as a method. It is probably not appropriate to rewrite* int_to_time *as a method; what object you would invoke it on?*\n\n#### **17.3 Another example**\n\nHere's a version of increment (from Section [16.3)](#page-174-0) rewritten as a method:\n\n```\n# inside class Time:\n```\n\n```\ndef increment(self, seconds):\n seconds += self.time_to_int()\n return int_to_time(seconds)\n```\nThis version assumes that time_to_int is written as a method, as in Exercise [17.1.](#page-180-1) Also, note that it is a pure function, not a modifier.\n\nHere's how you would invoke increment:\n\n```\n>>> start.print_time()\n09:45:00\n>>> end = start.increment(1337)\n>>> end.print_time()\n10:07:17\n```\nThe subject, start, gets assigned to the first parameter, self. The argument, 1337, gets assigned to the second parameter, seconds.\n\nThis mechanism can be confusing, especially if you make an error. For example, if you invoke increment with two arguments, you get:\n\n```\n>>> end = start.increment(1337, 460)\nTypeError: increment() takes exactly 2 arguments (3 given)\n```\nThe error message is initially confusing, because there are only two arguments in parentheses. But the subject is also considered an argument, so all together that's three.\n\n#### **17.4 A more complicated example**\n\nis_after (from Exercise [16.2)](#page-172-4) is slightly more complicated because it takes two Time objects as parameters. In this case it is conventional to name the first parameter self and the second parameter other:\n\n```\ndef is_after(self, other):\n return self.time_to_int() > other.time_to_int()\n```\nTo use this method, you have to invoke it on one object and pass the other as an argument: >>> end.is_after(start)\n\nTrue\n\nOne nice thing about this syntax is that it almost reads like English: \"end is after start?\"\n\n### **17.5 The init method**\n\n# inside class Time:\n\nThe init method (short for \"initialization\") is a special method that gets invoked when an object is instantiated. Its full name is __init__ (two underscore characters, followed by init, and then two more underscores). An init method for the Time class might look like this:\n\n```\n# inside class Time:\n```\n\n```\ndef __init__(self, hour=0, minute=0, second=0):\n self.hour = hour\n self.minute = minute\n self.second = second\n```\nIt is common for the parameters of __init__ to have the same names as the attributes. The statement\n\n```\nself.hour = hour\n```\nstores the value of the parameter hour as an attribute of self.\n\nThe parameters are optional, so if you call Time with no arguments, you get the default values.\n\n```\n>>> time = Time()\n>>> time.print_time()\n00:00:00\n```\nIf you provide one argument, it overrides hour:\n\n```\n>>> time = Time (9)\n>>> time.print_time()\n09:00:00\n```\nIf you provide two arguments, they override hour and minute.\n\n```\n>>> time = Time(9, 45)\n>>> time.print_time()\n09:45:00\n```\nAnd if you provide three arguments, they override all three default values.\n\n**Exercise 17.2.** *Write an init method for the* Point *class that takes* x *and* y *as optional parameters and assigns them to the corresponding attributes.*\n\n## **17.6 The** __str__ **method**\n\n__str__ is a special method, like __init__, that is supposed to return a string representation of an object.\n\nFor example, here is a str method for Time objects:\n\n```\n# inside class Time:\n def __str__(self):\n return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)\n```\nWhen you print an object, Python invokes the str method:\n\n```\n>>> time = Time(9, 45)\n>>> print time\n09:45:00\n```\nWhen I write a new class, I almost always start by writing __init__, which makes it easier to instantiate objects, and __str__, which is useful for debugging.\n\n**Exercise 17.3.** *Write a* str *method for the* Point *class. Create a Point object and print it.*\n\n#### **17.7 Operator overloading**\n\nBy defining other special methods, you can specify the behavior of operators on userdefined types. For example, if you define a method named __add__ for the Time class, you can use the + operator on Time objects.\n\nHere is what the definition might look like:\n\n```\n# inside class Time:\n def __add__(self, other):\n seconds = self.time_to_int() + other.time_to_int()\n return int_to_time(seconds)\n```\nAnd here is how you could use it:\n\n```\n>>> start = Time(9, 45)\n>>> duration = Time(1, 35)\n>>> print start + duration\n11:20:00\n```\nWhen you apply the + operator to Time objects, Python invokes __add__. When you print the result, Python invokes __str__. So there is quite a lot happening behind the scenes!\n\nChanging the behavior of an operator so that it works with user-defined types is called **operator overloading**. For every operator in Python there is a corresponding special method, like __add__. For more details, see [http://docs.python.org/2/reference/datamodel.](http://docs.python.org/2/reference/datamodel.html#specialnames) [html#specialnames](http://docs.python.org/2/reference/datamodel.html#specialnames).\n\n**Exercise 17.4.** *Write an* add *method for the Point class.*\n\n#### **17.8 Type-based dispatch**\n\nIn the previous section we added two Time objects, but you also might want to add an integer to a Time object. The following is a version of __add__ that checks the type of other and invokes either add_time or increment:\n\n```\n# inside class Time:\n```\n\n```\ndef __add__(self, other):\n if isinstance(other, Time):\n return self.add_time(other)\n else:\n return self.increment(other)\ndef add_time(self, other):\n seconds = self.time_to_int() + other.time_to_int()\n return int_to_time(seconds)\ndef increment(self, seconds):\n seconds += self.time_to_int()\n return int_to_time(seconds)\n```\nThe built-in function isinstance takes a value and a class object, and returns True if the value is an instance of the class.\n\nIf other is a Time object, __add__ invokes add_time. Otherwise it assumes that the parameter is a number and invokes increment. This operation is called a **type-based dispatch** because it dispatches the computation to different methods based on the type of the arguments.\n\nHere are examples that use the + operator with different types:\n\n```\n>>> start = Time(9, 45)\n>>> duration = Time(1, 35)\n>>> print start + duration\n11:20:00\n>>> print start + 1337\n10:07:17\n```\nUnfortunately, this implementation of addition is not commutative. If the integer is the first operand, you get\n\n```\n>>> print 1337 + start\n```\nThe problem is, instead of asking the Time object to add an integer, Python is asking an integer to add a Time object, and it doesn't know how to do that. But there is a clever solution for this problem: the special method __radd__, which stands for \"right-side add.\" This method is invoked when a Time object appears on the right side of the + operator. Here's the definition:\n\n```\n# inside class Time:\n```\n\n```\ndef __radd__(self, other):\n return self.__add__(other)\n```\nAnd here's how it's used:\n\n```\n>>> print 1337 + start\n10:07:17\nExercise 17.5. Write an add method for Points that works with either a Point object or a tuple:\n```\n- *If the second operand is a Point, the method should return a new Point whose x coordinate is the sum of the x coordinates of the operands, and likewise for the y coordinates.*\n- *If the second operand is a tuple, the method should add the first element of the tuple to the x coordinate and the second element to the y coordinate, and return a new Point with the result.*\n\n#### **17.9 Polymorphism**\n\nType-based dispatch is useful when it is necessary, but (fortunately) it is not always necessary. Often you can avoid it by writing functions that work correctly for arguments with different types.\n\nMany of the functions we wrote for strings will actually work for any kind of sequence. For example, in Section [11.1](#page-123-0) we used histogram to count the number of times each letter appears in a word.\n\n```\ndef histogram(s):\n d = dict()\n for c in s:\n if c not in d:\n d[c] = 1\n else:\n d[c] = d[c]+1\n return d\n```\nThis function also works for lists, tuples, and even dictionaries, as long as the elements of s are hashable, so they can be used as keys in d.\n\n```\n>>> t = ['spam', 'egg', 'spam', 'spam', 'bacon', 'spam']\n>>> histogram(t)\n{'bacon': 1, 'egg': 1, 'spam': 4}\n```\nFunctions that can work with several types are called **polymorphic**. Polymorphism can facilitate code reuse. For example, the built-in function sum, which adds the elements of a sequence, works as long as the elements of the sequence support addition.\n\nSince Time objects provide an add method, they work with sum:\n\n```\n>>> t1 = Time(7, 43)\n>>> t2 = Time(7, 41)\n>>> t3 = Time(7, 37)\n>>> total = sum([t1, t2, t3])\n>>> print total\n23:01:00\n```\nIn general, if all of the operations inside a function work with a given type, then the function works with that type.\n\nThe best kind of polymorphism is the unintentional kind, where you discover that a function you already wrote can be applied to a type you never planned for.\n\n## **17.10 Debugging**\n\nIt is legal to add attributes to objects at any point in the execution of a program, but if you are a stickler for type theory, it is a dubious practice to have objects of the same type with different attribute sets. It is usually a good idea to initialize all of an object's attributes in the init method.\n\nIf you are not sure whether an object has a particular attribute, you can use the built-in function hasattr (see Section [15.7)](#page-169-0).\n\nAnother way to access the attributes of an object is through the special attribute __dict__, which is a dictionary that maps attribute names (as strings) and values:\n\n```\n>>> p = Point(3, 4)\n>>> print p.__dict__\n{'y': 4, 'x': 3}\n```\nFor purposes of debugging, you might find it useful to keep this function handy:\n\n```\ndef print_attributes(obj):\n for attr in obj.__dict__:\n print attr, getattr(obj, attr)\n```\nprint_attributes traverses the items in the object's dictionary and prints each attribute name and its corresponding value.\n\nThe built-in function getattr takes an object and an attribute name (as a string) and returns the attribute's value.\n\n## **17.11 Interface and implementation**\n\nOne of the goals of object-oriented design is to make software more maintainable, which means that you can keep the program working when other parts of the system change, and modify the program to meet new requirements.\n\nA design principle that helps achieve that goal is to keep interfaces separate from implementations. For objects, that means that the methods a class provides should not depend on how the attributes are represented.\n\nFor example, in this chapter we developed a class that represents a time of day. Methods provided by this class include time_to_int, is_after, and add_time.\n\nWe could implement those methods in several ways. The details of the implementation depend on how we represent time. In this chapter, the attributes of a Time object are hour, minute, and second.\n\nAs an alternative, we could replace these attributes with a single integer representing the number of seconds since midnight. This implementation would make some methods, like is_after, easier to write, but it makes some methods harder.\n\nAfter you deploy a new class, you might discover a better implementation. If other parts of the program are using your class, it might be time-consuming and error-prone to change the interface.\n\nBut if you designed the interface carefully, you can change the implementation without changing the interface, which means that other parts of the program don't have to change. Keeping the interface separate from the implementation means that you have to hide the attributes. Code in other parts of the program (outside the class definition) should use methods to read and modify the state of the object. They should not access the attributes directly. This principle is called **information hiding**; see [http://en.wikipedia.org/wiki/](http://en.wikipedia.org/wiki/Information_hiding) [Information_hiding](http://en.wikipedia.org/wiki/Information_hiding).\n\n**Exercise 17.6.** *Download the code from this chapter (*[http: // thinkpython. com/ code/](http://thinkpython.com/code/Time2.py) [Time2. py](http://thinkpython.com/code/Time2.py) *). Change the attributes of* Time *to be a single integer representing seconds since midnight. Then modify the methods (and the function* int_to_time*) to work with the new implementation. You should not have to modify the test code in* main*. When you are done, the output should be the same as before. Solution:* [http: // thinkpython. com/ code/ Time2_ soln. py](http://thinkpython.com/code/Time2_soln.py)\n\n#### **17.12 Glossary**\n\n- **object-oriented language:** A language that provides features, such as user-defined classes and method syntax, that facilitate object-oriented programming.\n- **object-oriented programming:** A style of programming in which data and the operations that manipulate it are organized into classes and methods.\n- **method:** A function that is defined inside a class definition and is invoked on instances of that class.\n- **subject:** The object a method is invoked on.\n- **operator overloading:** Changing the behavior of an operator like + so it works with a userdefined type.\n- **type-based dispatch:** A programming pattern that checks the type of an operand and invokes different functions for different types.\n- **polymorphic:** Pertaining to a function that can work with more than one type.\n- **information hiding:** The principle that the interface provided by an object should not depend on its implementation, in particular the representation of its attributes.\n\n#### **17.13 Exercises**\n\n**Exercise 17.7.** *This exercise is a cautionary tale about one of the most common, and difficult to find, errors in Python. Write a definition for a class named* Kangaroo *with the following methods:*\n\n- *1. An* __init__ *method that initializes an attribute named* pouch_contents *to an empty list.*\n- *2. A method named* put_in_pouch *that takes an object of any type and adds it to* pouch_contents*.*\n- 3. A __str__ method that returns a string representation of the Kangaroo object and the con*tents of the pouch.*\n\n*Test your code by creating two* Kangaroo *objects, assigning them to variables named* kanga *and* roo*, and then adding* roo *to the contents of* kanga*'s pouch.*\n\n*Download* [http: // thinkpython. com/ code/ BadKangaroo. py](http://thinkpython.com/code/BadKangaroo.py) *. It contains a solution to the previous problem with one big, nasty bug. Find and fix the bug.*\n\n*If you get stuck, you can download* [http: // thinkpython. com/ code/ GoodKangaroo. py](http://thinkpython.com/code/GoodKangaroo.py) *, which explains the problem and demonstrates a solution.*\n\n**Exercise 17.8.** *Visual is a Python module that provides 3-D graphics. It is not always included in a Python installation, so you might have to install it from your software repository or, if it's not there, from* [http: // vpython. org](http://vpython.org) *.*\n\n*The following example creates a 3-D space that is 256 units wide, long and high, and sets the \"center\" to be the point* (128, 128, 128)*. Then it draws a blue sphere.*\n\nfrom visual import *\n\nscene.range = (256, 256, 256) scene.center = (128, 128, 128)\n\ncolor = (0.1, 0.1, 0.9) # mostly blue sphere(pos=scene.center, radius=128, color=color)\n\ncolor *is an RGB tuple; that is, the elements are Red-Green-Blue levels between 0.0 and 1.0 (see* [http: // en. wikipedia. org/ wiki/ RGB_ color_ model](http://en.wikipedia.org/wiki/RGB_color_model) *).*\n\n*If you run this code, you should see a window with a black background and a blue sphere. If you drag the middle button up and down, you can zoom in and out. You can also rotate the scene by dragging the right button, but with only one sphere in the world, it is hard to tell the difference.*\n\n*The following loop creates a cube of spheres:*\n\n```\nt = range(0, 256, 51)\nfor x in t:\n for y in t:\n for z in t:\n pos = x, y, z\n sphere(pos=pos, radius=10, color=color)\n```\n- *1. Put this code in a script and make sure it works for you.*\n- *2. Modify the program so that each sphere in the cube has the color that corresponds to its position in RGB space. Notice that the coordinates are in the range 0–255, but the RGB tuples are in the range 0.0–1.0.*\n- *3. Download* [http: // thinkpython. com/ code/ color_ list. py](http://thinkpython.com/code/color_list.py) *and use the function* read_colors *to generate a list of the available colors on your system, their names and RGB values. For each named color draw a sphere in the position that corresponds to its RGB values.*\n\n*You can see my solution at* [http: // thinkpython. com/ code/ color_ space. py](http://thinkpython.com/code/color_space.py) *.*\n\n# **Chapter 18**\n\n# **Inheritance**\n\nIn this chapter I present classes to represent playing cards, decks of cards, and poker hands. If you don't play poker, you can read about it at , but you don't have to; I'll tell you what you need to know for the exercises. Code examples from this chapter are available from .\n\nIf you are not familiar with Anglo-American playing cards, you can read about them at .\n\n#### **18.1 Card objects**\n\nThere are fifty-two cards in a deck, each of which belongs to one of four suits and one of thirteen ranks. The suits are Spades, Hearts, Diamonds, and Clubs (in descending order in bridge). The ranks are Ace, 2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, and King. Depending on the game that you are playing, an Ace may be higher than King or lower than 2.\n\nIf we want to define a new object to represent a playing card, it is obvious what the attributes should be: rank and suit. It is not as obvious what type the attributes should be. One possibility is to use strings containing words like 'Spade' for suits and 'Queen' for ranks. One problem with this implementation is that it would not be easy to compare cards to see which had a higher rank or suit.\n\nAn alternative is to use integers to **encode** the ranks and suits. In this context, \"encode\" means that we are going to define a mapping between numbers and suits, or between numbers and ranks. This kind of encoding is not meant to be a secret (that would be \"encryption\").\n\nFor example, this table shows the suits and the corresponding integer codes:\n\n| Spades | → | 3 |\n|----------|---|---|\n| Hearts | → | 2 |\n| Diamonds | → | 1 |\n| Clubs | → | 0 |\n\nThis code makes it easy to compare cards; because higher suits map to higher numbers, we can compare suits by comparing their codes.\n\nThe mapping for ranks is fairly obvious; each of the numerical ranks maps to the corresponding integer, and for face cards:\n\nJack 7→ 11 Queen 7→ 12 King 7→ 13\n\nI am using the 7→ symbol to make it clear that these mappings are not part of the Python program. They are part of the program design, but they don't appear explicitly in the code.\n\nThe class definition for Card looks like this:\n\n```\nclass Card(object):\n \"\"\"Represents a standard playing card.\"\"\"\n def __init__(self, suit=0, rank=2):\n self.suit = suit\n self.rank = rank\n```\nAs usual, the init method takes an optional parameter for each attribute. The default card is the 2 of Clubs.\n\nTo create a Card, you call Card with the suit and rank of the card you want.\n\n```\nqueen_of_diamonds = Card(1, 12)\n```\n#### **18.2 Class attributes**\n\nIn order to print Card objects in a way that people can easily read, we need a mapping from the integer codes to the corresponding ranks and suits. A natural way to do that is with lists of strings. We assign these lists to **class attributes**:\n\n```\n# inside class Card:\n```\n\n```\nsuit_names = ['Clubs', 'Diamonds', 'Hearts', 'Spades']\nrank_names = [None, 'Ace', '2', '3', '4', '5', '6', '7',\n '8', '9', '10', 'Jack', 'Queen', 'King']\ndef __str__(self):\n return '%s of %s' % (Card.rank_names[self.rank],\n Card.suit_names[self.suit])\n```\nVariables like suit_names and rank_names, which are defined inside a class but outside of any method, are called class attributes because they are associated with the class object Card.\n\nThis term distinguishes them from variables like suit and rank, which are called **instance attributes** because they are associated with a particular instance.\n\nBoth kinds of attribute are accessed using dot notation. For example, in __str__, self is a Card object, and self.rank is its rank. Similarly, Card is a class object, and Card.rank_names is a list of strings associated with the class.\n\nEvery card has its own suit and rank, but there is only one copy of suit_names and rank_names.\n\n\n\nFigure 18.1: Object diagram.\n\nPutting it all together, the expression Card.rank_names[self.rank] means \"use the attribute rank from the object self as an index into the list rank_names from the class Card, and select the appropriate string.\"\n\nThe first element of rank_names is None because there is no card with rank zero. By including None as a place-keeper, we get a mapping with the nice property that the index 2 maps to the string '2', and so on. To avoid this tweak, we could have used a dictionary instead of a list.\n\nWith the methods we have so far, we can create and print cards:\n\n```\n>>> card1 = Card(2, 11)\n>>> print card1\nJack of Hearts\n```\nFigure [18.1](#page-190-1) is a diagram of the Card class object and one Card instance. Card is a class object, so it has type type. card1 has type Card. (To save space, I didn't draw the contents of suit_names and rank_names).\n\n#### **18.3 Comparing cards**\n\nFor built-in types, there are relational operators (<, >, ==, etc.) that compare values and determine when one is greater than, less than, or equal to another. For user-defined types, we can override the behavior of the built-in operators by providing a method named __cmp__.\n\n__cmp__ takes two parameters, self and other, and returns a positive number if the first object is greater, a negative number if the second object is greater, and 0 if they are equal to each other.\n\nThe correct ordering for cards is not obvious. For example, which is better, the 3 of Clubs or the 2 of Diamonds? One has a higher rank, but the other has a higher suit. In order to compare cards, you have to decide whether rank or suit is more important.\n\nThe answer might depend on what game you are playing, but to keep things simple, we'll make the arbitrary choice that suit is more important, so all of the Spades outrank all of the Diamonds, and so on.\n\nWith that decided, we can write __cmp__:\n\n```\n# inside class Card:\n def __cmp__(self, other):\n # check the suits\n if self.suit > other.suit: return 1\n if self.suit < other.suit: return -1\n # suits are the same... check ranks\n if self.rank > other.rank: return 1\n if self.rank < other.rank: return -1\n # ranks are the same... it's a tie\n return 0\n```\nYou can write this more concisely using tuple comparison:\n\n```\n# inside class Card:\n```\n\n```\ndef __cmp__(self, other):\n t1 = self.suit, self.rank\n t2 = other.suit, other.rank\n return cmp(t1, t2)\n```\nThe built-in function cmp has the same interface as the method __cmp__: it takes two values and returns a positive number if the first is larger, a negative number if the second is larger, and 0 if they are equal.\n\nIn Python 3, cmp no longer exists, and the __cmp__ method is not supported. Instead you should provide __lt__, which returns True if self is less than other. You can implement __lt__ using tuples and the < operator.\n\n**Exercise 18.1.** *Write a* __cmp__ *method for Time objects. Hint: you can use tuple comparison, but you also might consider using integer subtraction.*\n\n#### **18.4 Decks**\n\nNow that we have Cards, the next step is to define Decks. Since a deck is made up of cards, it is natural for each Deck to contain a list of cards as an attribute.\n\nThe following is a class definition for Deck. The init method creates the attribute cards and generates the standard set of fifty-two cards:\n\n```\ndef __init__(self):\n self.cards = []\n for suit in range(4):\n for rank in range(1, 14):\n card = Card(suit, rank)\n self.cards.append(card)\n```\nThe easiest way to populate the deck is with a nested loop. The outer loop enumerates the suits from 0 to 3. The inner loop enumerates the ranks from 1 to 13. Each iteration creates a new Card with the current suit and rank, and appends it to self.cards.\n\n#### **18.5 Printing the deck**\n\nHere is a __str__ method for Deck: #inside class Deck: def __str__(self): res = [] for card in self.cards: res.append(str(card)) return '\\n'.join(res)\n\nThis method demonstrates an efficient way to accumulate a large string: building a list of strings and then using join. The built-in function str invokes the __str__ method on each card and returns the string representation.\n\nSince we invoke join on a newline character, the cards are separated by newlines. Here's what the result looks like:\n\n```\n>>> deck = Deck()\n>>> print deck\nAce of Clubs\n2 of Clubs\n3 of Clubs\n...\n10 of Spades\nJack of Spades\nQueen of Spades\nKing of Spades\n```\nEven though the result appears on 52 lines, it is one long string that contains newlines.\n\n#### **18.6 Add, remove, shuffle and sort**\n\nTo deal cards, we would like a method that removes a card from the deck and returns it. The list method pop provides a convenient way to do that:\n\n#inside class Deck:\n\n```\ndef pop_card(self):\n return self.cards.pop()\n```\nSince pop removes the *last* card in the list, we are dealing from the bottom of the deck. In real life \"bottom dealing\" is frowned upon, but in this context it's ok.\n\nTo add a card, we can use the list method append:\n\n#inside class Deck: def add_card(self, card): self.cards.append(card)\n\nA method like this that uses another function without doing much real work is sometimes called a **veneer**. The metaphor comes from woodworking, where it is common to glue a thin layer of good quality wood to the surface of a cheaper piece of wood.\n\nIn this case we are defining a \"thin\" method that expresses a list operation in terms that are appropriate for decks.\n\nAs another example, we can write a Deck method named shuffle using the function shuffle from the random module:\n\n```\ndef shuffle(self):\n random.shuffle(self.cards)\n```\nDon't forget to import random.\n\n# inside class Deck:\n\n**Exercise 18.2.** *Write a Deck method named* sort *that uses the list method* sort *to sort the cards in a* Deck*.* sort *uses the* __cmp__ *method we defined to determine sort order.*\n\n#### **18.7 Inheritance**\n\nThe language feature most often associated with object-oriented programming is **inheritance**. Inheritance is the ability to define a new class that is a modified version of an existing class.\n\nIt is called \"inheritance\" because the new class inherits the methods of the existing class. Extending this metaphor, the existing class is called the **parent** and the new class is called the **child**.\n\nAs an example, let's say we want a class to represent a \"hand,\" that is, the set of cards held by one player. A hand is similar to a deck: both are made up of a set of cards, and both require operations like adding and removing cards.\n\nA hand is also different from a deck; there are operations we want for hands that don't make sense for a deck. For example, in poker we might compare two hands to see which one wins. In bridge, we might compute a score for a hand in order to make a bid.\n\nThis relationship between classes—similar, but different—lends itself to inheritance.\n\nThe definition of a child class is like other class definitions, but the name of the parent class appears in parentheses:\n\n```\nclass Hand(Deck):\n```\n\"\"\"Represents a hand of playing cards.\"\"\"\n\nThis definition indicates that Hand inherits from Deck; that means we can use methods like pop_card and add_card for Hands as well as Decks.\n\nHand also inherits __init__ from Deck, but it doesn't really do what we want: instead of populating the hand with 52 new cards, the init method for Hands should initialize cards with an empty list.\n\nIf we provide an init method in the Hand class, it overrides the one in the Deck class:\n\n# inside class Hand:\n\n```\ndef __init__(self, label=''):\n self.cards = []\n self.label = label\n```\nSo when you create a Hand, Python invokes this init method:\n\n```\n>>> hand = Hand('new hand')\n>>> print hand.cards\n[]\n>>> print hand.label\nnew hand\n```\nBut the other methods are inherited from Deck, so we can use pop_card and add_card to deal a card:\n\n```\n>>> deck = Deck()\n>>> card = deck.pop_card()\n>>> hand.add_card(card)\n>>> print hand\nKing of Spades\n```\nA natural next step is to encapsulate this code in a method called move_cards:\n\n#inside class Deck:\n\n```\ndef move_cards(self, hand, num):\n for i in range(num):\n hand.add_card(self.pop_card())\n```\nmove_cards takes two arguments, a Hand object and the number of cards to deal. It modifies both self and hand, and returns None.\n\nIn some games, cards are moved from one hand to another, or from a hand back to the deck. You can use move_cards for any of these operations: self can be either a Deck or a Hand, and hand, despite the name, can also be a Deck.\n\n**Exercise 18.3.** *Write a Deck method called* deal_hands *that takes two parameters, the number of hands and the number of cards per hand, and that creates new Hand objects, deals the appropriate number of cards per hand, and returns a list of Hand objects.*\n\nInheritance is a useful feature. Some programs that would be repetitive without inheritance can be written more elegantly with it. Inheritance can facilitate code reuse, since you can customize the behavior of parent classes without having to modify them. In some cases, the inheritance structure reflects the natural structure of the problem, which makes the program easier to understand.\n\nOn the other hand, inheritance can make programs difficult to read. When a method is invoked, it is sometimes not clear where to find its definition. The relevant code may be scattered among several modules. Also, many of the things that can be done using inheritance can be done as well or better without it.\n\n#### **18.8 Class diagrams**\n\nSo far we have seen stack diagrams, which show the state of a program, and object diagrams, which show the attributes of an object and their values. These diagrams represent a snapshot in the execution of a program, so they change as the program runs.\n\nThey are also highly detailed; for some purposes, too detailed. A class diagram is a more abstract representation of the structure of a program. Instead of showing individual objects, it shows classes and the relationships between them.\n\n\n\nFigure 18.2: Class diagram.\n\nThere are several kinds of relationship between classes:\n\n- Objects in one class might contain references to objects in another class. For example, each Rectangle contains a reference to a Point, and each Deck contains references to many Cards. This kind of relationship is called **HAS-A**, as in, \"a Rectangle has a Point.\"\n- One class might inherit from another. This relationship is called **IS-A**, as in, \"a Hand is a kind of a Deck.\"\n- One class might depend on another in the sense that changes in one class would require changes in the other.\n\nA **class diagram** is a graphical representation of these relationships. For example, Figure [18.2](#page-195-1) shows the relationships between Card, Deck and Hand.\n\nThe arrow with a hollow triangle head represents an IS-A relationship; in this case it indicates that Hand inherits from Deck.\n\nThe standard arrow head represents a HAS-A relationship; in this case a Deck has references to Card objects.\n\nThe star (*) near the arrow head is a **multiplicity**; it indicates how many Cards a Deck has. A multiplicity can be a simple number, like 52, a range, like 5..7 or a star, which indicates that a Deck can have any number of Cards.\n\nA more detailed diagram might show that a Deck actually contains a *list* of Cards, but built-in types like list and dict are usually not included in class diagrams.\n\n**Exercise 18.4.** *Read* TurtleWorld.py*,* World.py *and* Gui.py *and draw a class diagram that shows the relationships among the classes defined there.*\n\n### **18.9 Debugging**\n\nInheritance can make debugging a challenge because when you invoke a method on an object, you might not know which method will be invoked.\n\nSuppose you are writing a function that works with Hand objects. You would like it to work with all kinds of Hands, like PokerHands, BridgeHands, etc. If you invoke a method like shuffle, you might get the one defined in Deck, but if any of the subclasses override this method, you'll get that version instead.\n\nAny time you are unsure about the flow of execution through your program, the simplest solution is to add print statements at the beginning of the relevant methods. If Deck.shuffle prints a message that says something like Running Deck.shuffle, then as the program runs it traces the flow of execution.\n\nAs an alternative, you could use this function, which takes an object and a method name (as a string) and returns the class that provides the definition of the method:\n\ndef find_defining_class(obj, meth_name): for ty in type(obj).mro(): if meth_name in ty.__dict__: return ty\n\nHere's an example:\n\n```\n>>> hand = Hand()\n>>> print find_defining_class(hand, 'shuffle')\n\n```\nSo the shuffle method for this Hand is the one in Deck.\n\nfind_defining_class uses the mro method to get the list of class objects (types) that will be searched for methods. \"MRO\" stands for \"method resolution order.\"\n\nHere's a program design suggestion: whenever you override a method, the interface of the new method should be the same as the old. It should take the same parameters, return the same type, and obey the same preconditions and postconditions. If you obey this rule, you will find that any function designed to work with an instance of a superclass, like a Deck, will also work with instances of subclasses like a Hand or PokerHand.\n\nIf you violate this rule, your code will collapse like (sorry) a house of cards.\n\n#### **18.10 Data encapsulation**\n\nChapter [16](#page-172-0) demonstrates a development plan we might call \"object-oriented design.\" We identified objects we needed—Time, Point and Rectangle—and defined classes to represent them. In each case there is an obvious correspondence between the object and some entity in the real world (or at least a mathematical world).\n\nBut sometimes it is less obvious what objects you need and how they should interact. In that case you need a different development plan. In the same way that we discovered function interfaces by encapsulation and generalization, we can discover class interfaces by **data encapsulation**.\n\nMarkov analysis, from Section [13.8,](#page-149-0) provides a good example. If you download my code from , you'll see that it uses two global variables—suffix_map and prefix—that are read and written from several functions.\n\nsuffix_map = {} prefix = ()\n\nBecause these variables are global we can only run one analysis at a time. If we read two texts, their prefixes and suffixes would be added to the same data structures (which makes for some interesting generated text).\n\nTo run multiple analyses, and keep them separate, we can encapsulate the state of each analysis in an object. Here's what that looks like:\n\n```\nclass Markov(object):\n```\n\n```\ndef __init__(self):\n self.suffix_map = {}\n self.prefix = ()\n```\nNext, we transform the functions into methods. For example, here's process_word:\n\n```\ndef process_word(self, word, order=2):\n if len(self.prefix) < order:\n self.prefix += (word,)\n return\n try:\n self.suffix_map[self.prefix].append(word)\n except KeyError:\n # if there is no entry for this prefix, make one\n self.suffix_map[self.prefix] = [word]\n self.prefix = shift(self.prefix, word)\n```\nTransforming a program like this—changing the design without changing the function—is another example of refactoring (see Section [4.7)](#page-57-0).\n\nThis example suggests a development plan for designing objects and methods:\n\n- 1. Start by writing functions that read and write global variables (when necessary).\n- 2. Once you get the program working, look for associations between global variables and the functions that use them.\n- 3. Encapsulate related variables as attributes of an object.\n- 4. Transform the associated functions into methods of the new class.\n\n**Exercise 18.5.** *Download my code from Section [13.8](#page-149-0) (*[http: // thinkpython. com/ code/](http://thinkpython.com/code/markov.py) [markov. py](http://thinkpython.com/code/markov.py) *), and follow the steps described above to encapsulate the global variables as attributes of a new class called* Markov*. Solution:* [http: // thinkpython. com/ code/ Markov. py](http://thinkpython.com/code/Markov.py) *(note the capital M).*\n\n#### **18.11 Glossary**\n\n- **encode:** To represent one set of values using another set of values by constructing a mapping between them.\n- **class attribute:** An attribute associated with a class object. Class attributes are defined inside a class definition but outside any method.\n- **instance attribute:** An attribute associated with an instance of a class.\n- **veneer:** A method or function that provides a different interface to another function without doing much computation.\n- **inheritance:** The ability to define a new class that is a modified version of a previously defined class.\n\n**parent class:** The class from which a child class inherits.\n\n- **child class:** A new class created by inheriting from an existing class; also called a \"subclass.\"\n- **IS-A relationship:** The relationship between a child class and its parent class.\n- **HAS-A relationship:** The relationship between two classes where instances of one class contain references to instances of the other.\n- **class diagram:** A diagram that shows the classes in a program and the relationships between them.\n- **multiplicity:** A notation in a class diagram that shows, for a HAS-A relationship, how many references there are to instances of another class.\n\n#### **18.12 Exercises**\n\n**Exercise 18.6.** *The following are the possible hands in poker, in increasing order of value (and decreasing order of probability):*\n\n**pair:** *two cards with the same rank*\n\n**two pair:** *two pairs of cards with the same rank*\n\n**three of a kind:** *three cards with the same rank*\n\n**straight:** *five cards with ranks in sequence (aces can be high or low, so* Ace-2-3-4-5 *is a straight and so is* 10-Jack-Queen-King-Ace*, but* Queen-King-Ace-2-3 *is not.)*\n\n**flush:** *five cards with the same suit*\n\n**full house:** *three cards with one rank, two cards with another*\n\n**four of a kind:** *four cards with the same rank*\n\n**straight flush:** *five cards in sequence (as defined above) and with the same suit*\n\n*The goal of these exercises is to estimate the probability of drawing these various hands.*\n\n- *1. Download the following files from* [http: // thinkpython. com/ code](http://thinkpython.com/code) *:*\nCard.py *: A complete version of the* Card*,* Deck *and* Hand *classes in this chapter.*\n\n- PokerHand.py *: An incomplete implementation of a class that represents a poker hand, and some code that tests it.*\n- *2. If you run* PokerHand.py*, it deals seven 7-card poker hands and checks to see if any of them contains a flush. Read this code carefully before you go on.*\n- *3. Add methods to* PokerHand.py *named* has_pair*,* has_twopair*, etc. that return True or False according to whether or not the hand meets the relevant criteria. Your code should work correctly for \"hands\" that contain any number of cards (although 5 and 7 are the most common sizes).*\n- *4. Write a method named* classify *that figures out the highest-value classification for a hand and sets the* label *attribute accordingly. For example, a 7-card hand might contain a flush and a pair; it should be labeled \"flush\".*\n- *5. When you are convinced that your classification methods are working, the next step is to estimate the probabilities of the various hands. Write a function in* PokerHand.py *that shuffles a deck of cards, divides it into hands, classifies the hands, and counts the number of times various classifications appear.*\n- *6. Print a table of the classifications and their probabilities. Run your program with larger and larger numbers of hands until the output values converge to a reasonable degree of accuracy. Compare your results to the values at* [http: // en. wikipedia. org/ wiki/ Hand_](http://en.wikipedia.org/wiki/Hand_rankings) [rankings](http://en.wikipedia.org/wiki/Hand_rankings) *.*\n\n*Solution:* [http: // thinkpython. com/ code/ PokerHandSoln. py](http://thinkpython.com/code/PokerHandSoln.py) *.*\n\n**Exercise 18.7.** *This exercise uses TurtleWorld from Chapter [4.](#page-52-0) You will write code that makes Turtles play tag. If you are not familiar with the rules of tag, see* [http: // en. wikipedia. org/](http://en.wikipedia.org/wiki/Tag_(game)) [wiki/ Tag_ ( game)](http://en.wikipedia.org/wiki/Tag_(game)) *.*\n\n- 1. Download and run it. You should see a *TurtleWorld with three Turtles. If you press the* Run *button, the Turtles wander at random.*\n- *2. Read the code and make sure you understand how it works. The* Wobbler *class inherits from* Turtle*, which means that the* Turtle *methods* lt*,* rt*,* fd *and* bk *work on Wobblers.*\n\n*The* step *method gets invoked by TurtleWorld. It invokes* steer*, which turns the Turtle in the desired direction,* wobble*, which makes a random turn in proportion to the Turtle's clumsiness, and* move*, which moves forward a few pixels, depending on the Turtle's speed.*\n\n- *3. Create a file named* Tagger.py*. Import everything from* Wobbler*, then define a class named* Tagger *that inherits from* Wobbler*. Call* make_world *passing the* Tagger *class object as an argument.*\n- *4. Add a* steer *method to* Tagger *to override the one in* Wobbler*. As a starting place, write a version that always points the Turtle toward the origin. Hint: use the math function* atan2 *and the Turtle attributes* x*,* y *and* heading*.*\n- *5. Modify* steer *so that the Turtles stay in bounds. For debugging, you might want to use the* Step *button, which invokes* step *once on each Turtle.*\n- *6. Modify* steer *so that each Turtle points toward its nearest neighbor. Hint: Turtles have an attribute,* world*, that is a reference to the TurtleWorld they live in, and the TurtleWorld has an attribute,* animals*, that is a list of all Turtles in the world.*\n- *7. Modify* steer *so the Turtles play tag. You can add methods to* Tagger *and you can override* steer *and* __init__*, but you may not modify or override* step*,* wobble *or* move*. Also,* steer *is allowed to change the heading of the Turtle but not the position.*\n\n*Adjust the rules and your* steer *method for good quality play; for example, it should be possible for the slow Turtle to tag the faster Turtles eventually.*\n\n*Solution:* [http: // thinkpython. com/ code/ Tagger. py](http://thinkpython.com/code/Tagger.py) *.*\n\n# **Chapter 19**\n\n# **Case study: Tkinter**\n\n#### **19.1 GUI**\n\nMost of the programs we have seen so far are text-based, but many programs use **graphical user interfaces**, also known as **GUIs**.\n\nPython provides several choices for writing GUI-based programs, including wxPython, Tkinter, and Qt. Each has pros and cons, which is why Python has not converged on a standard.\n\nThe one I will present in this chapter is Tkinter because I think it is the easiest to get started with. Most of the concepts in this chapter apply to the other GUI modules, too.\n\nThere are several books and web pages about Tkinter. One of the best online resources is *An Introduction to Tkinter* by Fredrik Lundh.\n\nI have written a module called Gui.py that comes with Swampy. It provides a simplified interface to the functions and classes in Tkinter. The examples in this chapter are based on this module.\n\nHere is a simple example that creates and displays a Gui:\n\nTo create a GUI, you have to import Gui from Swampy:\n\nfrom swampy.Gui import *\n\nOr, depending on how you installed Swampy, like this:\n\nfrom Gui import *\n\nThen instantiate a Gui object:\n\n```\ng = Gui()\ng.title('Gui')\ng.mainloop()\n```\nWhen you run this code, a window should appear with an empty gray square and the title Gui. mainloop runs the **event loop**, which waits for the user to do something and responds accordingly. It is an infinite loop; it runs until the user closes the window, or presses Control-C, or does something that causes the program to quit.\n\nThis Gui doesn't do much because it doesn't have any **widgets**. Widgets are the elements that make up a GUI; they include:\n\n**Button:** A widget, containing text or an image, that performs an action when pressed.\n\n**Canvas:** A region that can display lines, rectangles, circles and other shapes.\n\n**Entry:** A region where users can type text.\n\n**Scrollbar:** A widget that controls the visible part of another widget.\n\n**Frame:** A container, often invisible, that contains other widgets.\n\nThe empty gray square you see when you create a Gui is a Frame. When you create a new widget, it is added to this Frame.\n\n#### **19.2 Buttons and callbacks**\n\nThe method bu creates a Button widget:\n\nbutton = g.bu(text='Press me.')\n\nThe return value from bu is a Button object. The button that appears in the Frame is a graphical representation of this object; you can control the button by invoking methods on it.\n\nbu takes up to 32 parameters that control the appearance and function of the button. These parameters are called **options**. Instead of providing values for all 32 options, you can use keyword arguments, like text='Press me.', to specify only the options you need and use the default values for the rest.\n\nWhen you add a widget to the Frame, it gets \"shrink-wrapped;\" that is, the Frame shrinks to the size of the Button. If you add more widgets, the Frame grows to accommodate them.\n\nThe method la creates a Label widget:\n\nlabel = g.la(text='Press the button.')\n\nBy default, Tkinter stacks the widgets top-to-bottom and centers them. We'll see how to override that behavior soon.\n\nIf you press the button, you will see that it doesn't do much. That's because you haven't \"wired it up;\" that is, you haven't told it what to do!\n\nThe option that controls the behavior of a button is command. The value of command is a function that gets executed when the button is pressed. For example, here is a function that creates a new Label:\n\n```\ndef make_label():\n g.la(text='Thank you.')\n```\nNow we can create a button with this function as its command:\n\nbutton2 = g.bu(text='No, press me!', command=make_label)\n\nWhen you press this button, it should execute make_label and a new label should appear.\n\nThe value of the command option is a function object, which is known as a **callback** because after you call bu to create the button, the flow of execution \"calls back\" when the user presses the button.\n\nThis kind of flow is characteristic of **event-driven programming**. User actions, like button presses and key strokes, are called **events**. In event-driven programming, the flow of execution is determined by user actions rather than by the programmer.\n\nThe challenge of event-driven programming is to construct a set of widgets and callbacks that work correctly (or at least generate appropriate error messages) for any sequence of user actions.\n\n**Exercise 19.1.** *Write a program that creates a GUI with a single button. When the button is pressed it should create a second button. When* that *button is pressed, it should create a label that says, \"Nice job!\".*\n\n*What happens if you press the buttons more than once? Solution:* [http: // thinkpython. com/](http://thinkpython.com/code/button_demo.py) [code/ button_ demo. py](http://thinkpython.com/code/button_demo.py)\n\n#### **19.3 Canvas widgets**\n\nOne of the most versatile widgets is the Canvas, which creates a region for drawing lines, circles and other shapes. If you did Exercise [15.4](#page-170-2) you are already familiar with canvases.\n\nThe method ca creates a new Canvas:\n\ncanvas = g.ca(width=500, height=500)\n\nwidth and height are the dimensions of the canvas in pixels.\n\nAfter you create a widget, you can still change the values of the options with the config method. For example, the bg option changes the background color:\n\n```\ncanvas.config(bg='white')\n```\nThe value of bg is a string that names a color. The set of legal color names is different for different implementations of Python, but all implementations provide at least:\n\nwhite black red green blue cyan yellow magenta\n\nShapes on a Canvas are called **items**. For example, the Canvas method circle draws (you guessed it) a circle:\n\nitem = canvas.circle([0,0], 100, fill='red')\n\nThe first argument is a coordinate pair that specifies the center of the circle; the second is the radius.\n\nGui.py provides a standard Cartesian coordinate system with the origin at the center of the Canvas and the positive *y* axis pointing up. This is different from some other graphics systems where the origin is in the upper left corner, with the *y* axis pointing down.\n\nThe fill option specifies that the circle should be filled in with red.\n\nThe return value from circle is an Item object that provides methods for modifying the item on the canvas. For example, you can use config to change any of the circle's options: item.config(fill='yellow', outline='orange', width=10)\n\nwidth is the thickness of the outline in pixels; outline is the color. **Exercise 19.2.** *Write a program that creates a Canvas and a Button. When the user presses the Button, it should draw a circle on the canvas.*\n\n#### **19.4 Coordinate sequences**\n\nThe rectangle method takes a sequence of coordinates that specify opposite corners of the rectangle. This example draws a blue rectangle with the lower left corner at the origin and the upper right corner at (200, 100):\n\n```\ncanvas.rectangle([[0, 0], [200, 100]],\n fill='blue', outline='orange', width=10)\n```\nThis way of specifying corners is called a **bounding box** because the two points bound the rectangle.\n\noval takes a bounding box and draws an oval within the specified rectangle:\n\ncanvas.oval([[0, 0], [200, 100]], outline='orange', width=10)\n\nline takes a sequence of coordinates and draws a line that connects the points. This example draws two legs of a triangle:\n\ncanvas.line([[0, 100], [100, 200], [200, 100]], width=10)\n\npolygon takes the same arguments, but it draws the last leg of the polygon (if necessary) and fills it in:\n\ncanvas.polygon([[0, 100], [100, 200], [200, 100]], fill='red', outline='orange', width=10)\n\n#### **19.5 More widgets**\n\nTkinter provides two widgets that let users type text: an Entry, which is a single line, and a Text widget, which has multiple lines.\n\nen creates a new Entry:\n\nentry = g.en(text='Default text.')\n\nThe text option allows you to put text into the entry when it is created. The get method returns the contents of the Entry (which may have been changed by the user):\n\n```\n>>> entry.get()\n'Default text.'\n```\nte creates a Text widget:\n\ntext = g.te(width=100, height=5)\n\nwidth and height are the dimensions of the widget in characters and lines.\n\ninsert puts text into the Text widget:\n\n```\ntext.insert(END, 'A line of text.')\n```\nEND is a special index that indicates the last character in the Text widget.\n\nYou can also specify a character using a dotted index, like 1.1, which has the line number before the dot and the column number after. The following example adds the letters 'nother' after the first character of the first line.\n\n>>> text.insert(1.1, 'nother')\n\nThe get method reads the text in the widget; it takes a start and end index as arguments. The following example returns all the text in the widget, including the newline character:\n\n```\n>>> text.get(0.0, END)\n'Another line of text.\\n'\n```\nThe delete method removes text from the widget; the following example deletes all but the first two characters:\n\n```\n>>> text.delete(1.2, END)\n>>> text.get(0.0, END)\n'An\\n'\n```\n**Exercise 19.3.** *Modify your solution to Exercise [19.2](#page-203-2) by adding an Entry widget and a second button. When the user presses the second button, it should read a color name from the Entry and use it to change the fill color of the circle. Use* config *to modify the existing circle; don't create a new one.*\n\n*Your program should handle the case where the user tries to change the color of a circle that hasn't been created, and the case where the color name is invalid.*\n\n*You can see my solution at* [http: // thinkpython. com/ code/ circle_ demo. py](http://thinkpython.com/code/circle_demo.py) *.*\n\n#### **19.6 Packing widgets**\n\nSo far we have been stacking widgets in a single column, but in most GUIs the layout is more complicated. For example, Figure [19.1](#page-205-0) shows a simplified version of TurtleWorld (see Chapter [4)](#page-52-0).\n\nThis section presents the code that creates this GUI, broken into a series of steps. You can download the complete example from [http://thinkpython.com/code/](http://thinkpython.com/code/SimpleTurtleWorld.py) [SimpleTurtleWorld.py](http://thinkpython.com/code/SimpleTurtleWorld.py).\n\nAt the top level, this GUI contains two widgets—a Canvas and a Frame—arranged in a row. So the first step is to create the row.\n\n```\nclass SimpleTurtleWorld(TurtleWorld):\n \"\"\"This class is identical to TurtleWorld, but the code that\n lays out the GUI is simplified for explanatory purposes.\"\"\"\n def setup(self):\n self.row()\n ...\n```\nsetup is the function that creates and arranges the widgets. Arranging widgets in a GUI is called **packing**.\n\nrow creates a row Frame and makes it the \"current Frame.\" Until this Frame is closed or another Frame is created, all subsequent widgets are packed in a row.\n\nHere is the code that creates the Canvas and the column Frame that hold the other widgets:\n\n\n\nFigure 19.1: TurtleWorld after running the snowflake code.\n\n```\nself.canvas = self.ca(width=400, height=400, bg='white')\nself.col()\n```\nThe first widget in the column is a grid Frame, which contains four buttons arranged twoby-two:\n\n```\nself.gr(cols=2)\nself.bu(text='Print canvas', command=self.canvas.dump)\nself.bu(text='Quit', command=self.quit)\nself.bu(text='Make Turtle', command=self.make_turtle)\nself.bu(text='Clear', command=self.clear)\nself.endgr()\n```\ngr creates the grid; the argument is the number of columns. Widgets in the grid are laid out left-to-right, top-to-bottom.\n\nThe first button uses self.canvas.dump as a callback; the second uses self.quit. These are **bound methods**, which means they are associated with a particular object. When they are invoked, they are invoked on the object.\n\nThe next widget in the column is a row Frame that contains a Button and an Entry:\n\n```\nself.row([0,1], pady=30)\nself.bu(text='Run file', command=self.run_file)\nself.en_file = self.en(text='snowflake.py', width=5)\nself.endrow()\n```\nThe first argument to row is a list of weights that determines how extra space is allocated between widgets. The list [0,1] means that all extra space is allocated to the second widget, which is the Entry. If you run this code and resize the window, you will see that the Entry grows and the Button doesn't.\n\nThe option pady \"pads\" this row in the *y* direction, adding 30 pixels of space above and below.\n\nendrow ends this row of widgets, so subsequent widgets are packed in the column Frame. Gui.py keeps a stack of Frames:\n\n- When you use row, col or gr to create a Frame, it goes on top of the stack and becomes the current Frame.\n- When you use endrow, endcol or endgr to close a Frame, it gets popped off the stack and the previous Frame on the stack becomes the current Frame.\n\nThe method run_file reads the contents of the Entry, uses it as a filename, reads the contents and passes it to run_code. self.inter is an Interpreter object that knows how to take a string and execute it as Python code.\n\n```\ndef run_file(self):\n filename = self.en_file.get()\n fp = open(filename)\n source = fp.read()\n self.inter.run_code(source, filename)\n```\nThe last two widgets are a Text widget and a Button:\n\n```\nself.te_code = self.te(width=25, height=10)\nself.te_code.insert(END, 'world.clear()\\n')\nself.te_code.insert(END, 'bob = Turtle(world)\\n')\n```\n\n```\nself.bu(text='Run code', command=self.run_text)\n```\nrun_text is similar to run_file except that it takes the code from the Text widget instead of from a file:\n\n```\ndef run_text(self):\n source = self.te_code.get(1.0, END)\n self.inter.run_code(source, '')\n```\nUnfortunately, the details of widget layout are different in other languages, and in different Python modules. Tkinter alone provides three different mechanisms for arranging widgets. These mechanisms are called **geometry managers**. The one I demonstrated in this section is the \"grid\" geometry manager; the others are called \"pack\" and \"place\".\n\nFortunately, most of the concepts in this section apply to other GUI modules and other languages.\n\n#### **19.7 Menus and Callables**\n\nA Menubutton is a widget that looks like a button, but when pressed it pops up a menu. After the user selects an item, the menu disappears.\n\nHere is code that creates a color selection Menubutton (you can download it from [http:](http://thinkpython.com/code/menubutton_demo.py) [//thinkpython.com/code/menubutton_demo.py](http://thinkpython.com/code/menubutton_demo.py)):\n\n```\ng = Gui()\ng.la('Select a color:')\ncolors = ['red', 'green', 'blue']\nmb = g.mb(text=colors[0])\n```\nmb creates the Menubutton. Initially, the text on the button is the name of the default color. The following loop creates one menu item for each color:\n\n```\nfor color in colors:\n g.mi(mb, text=color, command=Callable(set_color, color))\n```\nThe first argument of mi is the Menubutton these items are associated with.\n\nThe command option is a Callable object, which is something new. So far we have seen functions and bound methods used as callbacks, which works fine if you don't have to pass any arguments to the function. Otherwise you have to construct a Callable object that contains a function, like set_color, and its arguments, like color.\n\nThe Callable object stores a reference to the function and the arguments as attributes. Later, when the user clicks on a menu item, the callback calls the function and passes the stored arguments.\n\nHere is what set_color might look like:\n\n```\ndef set_color(color):\n mb.config(text=color)\n print color\n```\nWhen the user selects a menu item and set_color is called, it configures the Menubutton to display the newly-selected color. It also print the color; if you try this example, you can confirm that set_color is called when you select an item (and *not* called when you create the Callable object).\n\n#### **19.8 Binding**\n\nA **binding** is an association between a widget, an event and a callback: when an event (like a button press) happens on a widget, the callback is invoked.\n\nMany widgets have default bindings. For example, when you press a button, the default binding changes the relief of the button to make it look depressed. When you release the button, the binding restores the appearance of the button and invokes the callback specified with the command option.\n\nYou can use the bind method to override these default bindings or to add new ones. For example, this code creates a binding for a canvas (you can download the code in this section from ):\n\n```\nca.bind('', make_circle)\n```\nThe first argument is an event string; this event is triggered when the user presses the left mouse button. Other mouse events include ButtonMotion, ButtonRelease and Double-Button.\n\nThe second argument is an event handler. An event handler is a function or bound method, like a callback, but an important difference is that an event handler takes an Event object as a parameter. Here is an example:\n\n```\ndef make_circle(event):\n pos = ca.canvas_coords([event.x, event.y])\n item = ca.circle(pos, 5, fill='red')\n```\nThe Event object contains information about the type of event and details like the coordinates of the mouse pointer. In this example the information we need is the location of the mouse click. These values are in \"pixel coordinates,\" which are defined by the underlying graphical system. The method canvas_coords translates them to \"Canvas coordinates,\" which are compatible with Canvas methods like circle.\n\nFor Entry widgets, it is common to bind the event, which is triggered when the user presses the Return or Enter key. For example, the following code creates a Button and an Entry.\n\n```\nbu = g.bu('Make text item:', make_text)\nen = g.en()\nen.bind('', make_text)\n```\nmake_text is called when the Button is pressed or when the user hits Return while typing in the Entry. To make this work, we need a function that can be called as a command (with no arguments) or as an event handler (with an Event as an argument):\n\n```\ndef make_text(event=None):\n text = en.get()\n item = ca.text([0,0], text)\n```\nmake_text gets the contents of the Entry and displays it as a Text item in the Canvas.\n\nIt is also possible to create bindings for Canvas items. The following is a class definition for Draggable, which is a child class of Item that provides bindings that implement dragand-drop capability.\n\n```\nclass Draggable(Item):\n```\n\n```\ndef __init__(self, item):\n self.canvas = item.canvas\n self.tag = item.tag\n self.bind('', self.select)\n self.bind('', self.drag)\n self.bind('', self.drop)\n```\nThe init method takes an Item as a parameter. It copies the attributes of the Item and then creates bindings for three events: a button press, button motion, and button release.\n\nThe event handler select stores the coordinates of the current event and the original color of the item, then changes the color to yellow:\n\n```\ndef select(self, event):\n self.dragx = event.x\n self.dragy = event.y\n self.fill = self.cget('fill')\n self.config(fill='yellow')\n```\ncget stands for \"get configuration;\" it takes the name of an option as a string and returns the current value of that option.\n\ndrag computes how far the object has moved relative to the starting place, updates the stored coordinates, and then moves the item.\n\n```\ndef drag(self, event):\n dx = event.x - self.dragx\n```\n\n```\ndy = event.y - self.dragy\nself.dragx = event.x\nself.dragy = event.y\nself.move(dx, dy)\n```\nThis computation is done in pixel coordinates; there is no need to convert to Canvas coordinates.\n\nFinally, drop restores the original color of the item:\n\n```\ndef drop(self, event):\n self.config(fill=self.fill)\n```\nYou can use the Draggable class to add drag-and-drop capability to an existing item. For example, here is a modified version of make_circle that uses circle to create an Item and Draggable to make it draggable:\n\n```\ndef make_circle(event):\n pos = ca.canvas_coords([event.x, event.y])\n item = ca.circle(pos, 5, fill='red')\n item = Draggable(item)\n```\nThis example demonstrates one of the benefits of inheritance: you can modify the capabilities of a parent class without modifying its definition. This is particularly useful if you want to change behavior defined in a module you did not write.\n\n#### **19.9 Debugging**\n\nOne of the challenges of GUI programming is keeping track of which things happen while the GUI is being built and which things happen later in response to user events.\n\nFor example, when you are setting up a callback, it is a common error to call the function rather than passing a reference to it:\n\n```\ndef the_callback():\n print 'Called.'\n```\ng.bu(text='This is wrong!', command=the_callback())\n\nIf you run this code, you will see that it calls the_callback immediately, and *then* creates the button. When you press the button, it does nothing because the return value from the_callback is None. Usually you do not want to invoke a callback while you are setting up the GUI; it should only be invoked later in response to a user event.\n\nAnother challenge of GUI programming is that you don't have control of the flow of execution. Which parts of the program execute and their order are determined by user actions. That means that you have to design your program to work correctly for any possible sequence of events.\n\nFor example, the GUI in Exercise [19.3](#page-204-1) has two widgets: one creates a Circle item and the other changes the color of the Circle. If the user creates the circle and then changes its color, there's no problem. But what if the user changes the color of a circle that doesn't exist yet? Or creates more than one circle?\n\nAs the number of widgets grows, it is increasingly difficult to imagine all possible sequences of events. One way to manage this complexity is to encapsulate the state of the system in an object and then consider:\n\n- What are the possible states? In the Circle example, we might consider two states: before and after the user creates the first circle.\n- In each state, what events can occur? In the example, the user can press either of the buttons, or quit.\n- For each state-event pair, what is the desired outcome? Since there are two states and two buttons, there are four state-event pairs to consider.\n- What can cause a transition from one state to another? In this case, there is a transition when the user creates the first circle.\n\nYou might also find it useful to define, and check, invariants that should hold regardless of the sequence of events.\n\nThis approach to GUI programming can help you write correct code without taking the time to test every possible sequence of user events!\n\n#### **19.10 Glossary**\n\n**GUI:** A graphical user interface.\n\n- **widget:** One of the elements that makes up a GUI, including buttons, menus, text entry fields, etc.\n- **option:** A value that controls the appearance or function of a widget.\n- **keyword argument:** An argument that indicates the parameter name as part of the function call.\n- **callback:** A function associated with a widget that is called when the user performs an action.\n- **bound method:** A method associated with a particular instance.\n- **event-driven programming:** A style of programming in which the flow of execution is determined by user actions.\n- **event:** A user action, like a mouse click or key press, that causes a GUI to respond.\n- **event loop:** An infinite loop that waits for user actions and responds.\n- **item:** A graphical element on a Canvas widget.\n- **bounding box:** A rectangle that encloses a set of items, usually specified by two opposing corners.\n- **pack:** To arrange and display the elements of a GUI.\n- **geometry manager:** A system for packing widgets.\n- **binding:** An association between a widget, an event, and an event handler. The event handler is called when the event occurs in the widget.\n\n## **19.11 Exercises**\n\n**Exercise 19.4.** *For this exercise, you will write an image viewer. Here is a simple example:*\n\n```\ng = Gui()\ncanvas = g.ca(width=300)\nphoto = PhotoImage(file='danger.gif')\ncanvas.image([0,0], image=photo)\ng.mainloop()\n```\nPhotoImage *reads a file and returns a* PhotoImage *object that Tkinter can display.* Canvas.image *puts the image on the canvas, centered on the given coordinates. You can also put images on labels, buttons, and some other widgets:*\n\n```\ng.la(image=photo)\ng.bu(image=photo)\n```\n*PhotoImage can only handle a few image formats, like GIF and PPM, but we can use the Python Imaging Library (PIL) to read other files.*\n\n*The name of the PIL module is* Image*, but Tkinter defines an object with the same name. To avoid the conflict, you can use* import...as *like this:*\n\nimport Image as PIL import ImageTk\n\n*The first line imports* Image *and gives it the local name* PIL*. The second line imports* ImageTk*, which can translate a PIL image into a Tkinter PhotoImage. Here's an example:*\n\n```\nimage = PIL.open('allen.png')\nphoto2 = ImageTk.PhotoImage(image)\ng.la(image=photo2)\n```\n- *1. Download* image_demo.py*,* danger.gif *and* allen.png *from* [http: // thinkpython.](http://thinkpython.com/code) [com/ code](http://thinkpython.com/code) *. Run* image_demo.py*. You might have to install* PIL *and* ImageTk*. They are probably in your software repository, but if not you can get them from* [http: //](http://pythonware.com/products/pil) [pythonware. com/ products/ pil](http://pythonware.com/products/pil) *.*\n- *2. In* image_demo.py *change the name of the second PhotoImage from* photo2 *to* photo *and run the program again. You should see the second PhotoImage but not the first.*\n\n*The problem is that when you reassign* photo *it overwrites the reference to the first PhotoImage, which then disappears. The same thing happens if you assign a PhotoImage to a local variable; it disappears when the function ends.*\n\n*To avoid this problem, you have to store a reference to each PhotoImage you want to keep. You can use a global variable, or store PhotoImages in a data structure or as an attribute of an object.*\n\n*This behavior can be frustrating, which is why I am warning you (and why the example image says \"Danger!\").*\n\n- *3. Starting with this example, write a program that takes the name of a directory and loops through all the files, displaying any files that PIL recognizes as images. You can use a* try *statement to catch the files PIL doesn't recognize.*\n*When the user clicks on the image, the program should display the next one.*\n\n- *4. PIL provides a variety of methods for manipulating images. You can read about them at* . As a challenge, choose a few of *these methods and provide a GUI for applying them to images.*\n*Solution:* [http: // thinkpython. com/ code/ ImageBrowser. py](http://thinkpython.com/code/ImageBrowser.py) *.* **Exercise 19.5.** *A vector graphics editor is a program that allows users to draw and edit shapes on the screen and generate output files in vector graphics formats like Postscript and SVG.*\n\n*Write a simple vector graphics editor using Tkinter. At a minimum, it should allow users to draw lines, circles and rectangles, and it should use* Canvas.dump *to generate a Postscript description of the contents of the Canvas.*\n\n*As a challenge, you could allow users to select and resize items on the Canvas.* **Exercise 19.6.** *Use Tkinter to write a basic web browser. It should have a Text widget where the user can enter a URL and a Canvas to display the contents of the page.*\n\n*You can use the* urllib *module to download files (see Exercise [14.6)](#page-162-2) and the* HTMLParser *module to parse the HTML tags (see* [http: // docs. python. org/ 2/ library/ htmlparser. html](http://docs.python.org/2/library/htmlparser.html) *).*\n\n*At a minimum your browser should handle plain text and hyperlinks. As a challenge you could handle background colors, text formatting tags and images.*\n\n**192 Chapter 19. Case study: Tkinter**\n\n# **Appendix A**\n\n# **Debugging**\n\nDifferent kinds of errors can occur in a program, and it is useful to distinguish among them in order to track them down more quickly:\n\n- Syntax errors are produced by Python when it is translating the source code into byte code. They usually indicate that there is something wrong with the syntax of the program. Example: Omitting the colon at the end of a def statement yields the somewhat redundant message SyntaxError: invalid syntax.\n- Runtime errors are produced by the interpreter if something goes wrong while the program is running. Most runtime error messages include information about where the error occurred and what functions were executing. Example: An infinite recursion eventually causes the runtime error \"maximum recursion depth exceeded.\"\n- Semantic errors are problems with a program that runs without producing error messages but doesn't do the right thing. Example: An expression may not be evaluated in the order you expect, yielding an incorrect result.\n\nThe first step in debugging is to figure out which kind of error you are dealing with. Although the following sections are organized by error type, some techniques are applicable in more than one situation.\n\n#### **A.1 Syntax errors**\n\nSyntax errors are usually easy to fix once you figure out what they are. Unfortunately, the error messages are often not helpful. The most common messages are SyntaxError: invalid syntax and SyntaxError: invalid token, neither of which is very informative.\n\nOn the other hand, the message does tell you where in the program the problem occurred. Actually, it tells you where Python noticed a problem, which is not necessarily where the error is. Sometimes the error is prior to the location of the error message, often on the preceding line.\n\nIf you are building the program incrementally, you should have a good idea about where the error is. It will be in the last line you added.\n\nIf you are copying code from a book, start by comparing your code to the book's code very carefully. Check every character. At the same time, remember that the book might be wrong, so if you see something that looks like a syntax error, it might be.\n\nHere are some ways to avoid the most common syntax errors:\n\n- 1. Make sure you are not using a Python keyword for a variable name.\n- 2. Check that you have a colon at the end of the header of every compound statement, including for, while, if, and def statements.\n- 3. Make sure that any strings in the code have matching quotation marks.\n- 4. If you have multiline strings with triple quotes (single or double), make sure you have terminated the string properly. An unterminated string may cause an invalid token error at the end of your program, or it may treat the following part of the program as a string until it comes to the next string. In the second case, it might not produce an error message at all!\n- 5. An unclosed opening operator—(, {, or [—makes Python continue with the next line as part of the current statement. Generally, an error occurs almost immediately in the next line.\n- 6. Check for the classic = instead of == inside a conditional.\n- 7. Check the indentation to make sure it lines up the way it is supposed to. Python can handle space and tabs, but if you mix them it can cause problems. The best way to avoid this problem is to use a text editor that knows about Python and generates consistent indentation.\n\nIf nothing works, move on to the next section...\n\n#### **A.1.1 I keep making changes and it makes no difference.**\n\nIf the interpreter says there is an error and you don't see it, that might be because you and the interpreter are not looking at the same code. Check your programming environment to make sure that the program you are editing is the one Python is trying to run.\n\nIf you are not sure, try putting an obvious and deliberate syntax error at the beginning of the program. Now run it again. If the interpreter doesn't find the new error, you are not running the new code.\n\nThere are a few likely culprits:\n\n- You edited the file and forgot to save the changes before running it again. Some programming environments do this for you, but some don't.\n- You changed the name of the file, but you are still running the old name.\n- Something in your development environment is configured incorrectly.\n- If you are writing a module and using import, make sure you don't give your module the same name as one of the standard Python modules.\n- If you are using import to read a module, remember that you have to restart the interpreter or use reload to read a modified file. If you import the module again, it doesn't do anything.\n\nIf you get stuck and you can't figure out what is going on, one approach is to start again with a new program like \"Hello, World!,\" and make sure you can get a known program to run. Then gradually add the pieces of the original program to the new one.\n\n#### **A.2 Runtime errors**\n\nOnce your program is syntactically correct, Python can compile it and at least start running it. What could possibly go wrong?\n\n#### **A.2.1 My program does absolutely nothing.**\n\nThis problem is most common when your file consists of functions and classes but does not actually invoke anything to start execution. This may be intentional if you only plan to import this module to supply classes and functions.\n\nIf it is not intentional, make sure that you are invoking a function to start execution, or execute one from the interactive prompt. Also see the \"Flow of Execution\" section below.\n\n#### **A.2.2 My program hangs.**\n\nIf a program stops and seems to be doing nothing, it is \"hanging.\" Often that means that it is caught in an infinite loop or infinite recursion.\n\n- If there is a particular loop that you suspect is the problem, add a print statement immediately before the loop that says \"entering the loop\" and another immediately after that says \"exiting the loop.\"\nRun the program. If you get the first message and not the second, you've got an infinite loop. Go to the \"Infinite Loop\" section below.\n\n- Most of the time, an infinite recursion will cause the program to run for a while and then produce a \"RuntimeError: Maximum recursion depth exceeded\" error. If that happens, go to the \"Infinite Recursion\" section below.\nIf you are not getting this error but you suspect there is a problem with a recursive method or function, you can still use the techniques in the \"Infinite Recursion\" section.\n\n- If neither of those steps works, start testing other loops and other recursive functions and methods.\n- If that doesn't work, then it is possible that you don't understand the flow of execution in your program. Go to the \"Flow of Execution\" section below.\n\n#### **Infinite Loop**\n\nIf you think you have an infinite loop and you think you know what loop is causing the problem, add a print statement at the end of the loop that prints the values of the variables in the condition and the value of the condition.\n\nFor example:\n\n```\nwhile x > 0 and y < 0 :\n # do something to x\n # do something to y\n print \"x: \", x\n print \"y: \", y\n print \"condition: \", (x > 0 and y < 0)\n```\nNow when you run the program, you will see three lines of output for each time through the loop. The last time through the loop, the condition should be false. If the loop keeps going, you will be able to see the values of x and y, and you might figure out why they are not being updated correctly.\n\n#### **Infinite Recursion**\n\nMost of the time, an infinite recursion will cause the program to run for a while and then produce a Maximum recursion depth exceeded error.\n\nIf you suspect that a function or method is causing an infinite recursion, start by checking to make sure that there is a base case. In other words, there should be some condition that will cause the function or method to return without making a recursive invocation. If not, then you need to rethink the algorithm and identify a base case.\n\nIf there is a base case but the program doesn't seem to be reaching it, add a print statement at the beginning of the function or method that prints the parameters. Now when you run the program, you will see a few lines of output every time the function or method is invoked, and you will see the parameters. If the parameters are not moving toward the base case, you will get some ideas about why not.\n\n#### **Flow of Execution**\n\nIf you are not sure how the flow of execution is moving through your program, add print statements to the beginning of each function with a message like “entering function foo,” where foo is the name of the function.\n\nNow when you run the program, it will print a trace of each function as it is invoked.\n\n#### **A.2.3 When I run the program I get an exception.**\n\nIf something goes wrong during runtime, Python prints a message that includes the name of the exception, the line of the program where the problem occurred, and a traceback.\n\nThe traceback identifies the function that is currently running, and then the function that invoked it, and then the function that invoked that, and so on. In other words, it traces the sequence of function invocations that got you to where you are. It also includes the line number in your file where each of these calls occurs.\n\nThe first step is to examine the place in the program where the error occurred and see if you can figure out what happened. These are some of the most common runtime errors:\n\n- **NameError:** You are trying to use a variable that doesn't exist in the current environment. Remember that local variables are local. You cannot refer to them from outside the function where they are defined.\n**TypeError:** There are several possible causes:\n\n- You are trying to use a value improperly. Example: indexing a string, list, or tuple with something other than an integer.\n- There is a mismatch between the items in a format string and the items passed for conversion. This can happen if either the number of items does not match or an invalid conversion is called for.\n- You are passing the wrong number of arguments to a function or method. For methods, look at the method definition and check that the first parameter is self. Then look at the method invocation; make sure you are invoking the method on an object with the right type and providing the other arguments correctly.\n- **KeyError:** You are trying to access an element of a dictionary using a key that the dictionary does not contain.\n- **AttributeError:** You are trying to access an attribute or method that does not exist. Check the spelling! You can use dir to list the attributes that do exist.\n\nIf an AttributeError indicates that an object has NoneType, that means that it is None. One common cause is forgetting to return a value from a function; if you get to the end of a function without hitting a return statement, it returns None. Another common cause is using the result from a list method, like sort, that returns None.\n\n- **IndexError:** The index you are using to access a list, string, or tuple is greater than its length minus one. Immediately before the site of the error, add a print statement to display the value of the index and the length of the array. Is the array the right size? Is the index the right value?\nThe Python debugger (pdb) is useful for tracking down Exceptions because it allows you to examine the state of the program immediately before the error. You can read about pdb at .\n\n#### **A.2.4 I added so many** print **statements I get inundated with output.**\n\nOne of the problems with using print statements for debugging is that you can end up buried in output. There are two ways to proceed: simplify the output or simplify the program.\n\nTo simplify the output, you can remove or comment out print statements that aren't helping, or combine them, or format the output so it is easier to understand.\n\nTo simplify the program, there are several things you can do. First, scale down the problem the program is working on. For example, if you are searching a list, search a *small* list. If the program takes input from the user, give it the simplest input that causes the problem.\n\nSecond, clean up the program. Remove dead code and reorganize the program to make it as easy to read as possible. For example, if you suspect that the problem is in a deeply nested part of the program, try rewriting that part with simpler structure. If you suspect a large function, try splitting it into smaller functions and testing them separately.\n\nOften the process of finding the minimal test case leads you to the bug. If you find that a program works in one situation but not in another, that gives you a clue about what is going on.\n\nSimilarly, rewriting a piece of code can help you find subtle bugs. If you make a change that you think shouldn't affect the program, and it does, that can tip you off.\n\n### **A.3 Semantic errors**\n\nIn some ways, semantic errors are the hardest to debug, because the interpreter provides no information about what is wrong. Only you know what the program is supposed to do.\n\nThe first step is to make a connection between the program text and the behavior you are seeing. You need a hypothesis about what the program is actually doing. One of the things that makes that hard is that computers run so fast.\n\nYou will often wish that you could slow the program down to human speed, and with some debuggers you can. But the time it takes to insert a few well-placed print statements is often short compared to setting up the debugger, inserting and removing breakpoints, and \"stepping\" the program to where the error is occurring.\n\n#### **A.3.1 My program doesn't work.**\n\nYou should ask yourself these questions:\n\n- Is there something the program was supposed to do but which doesn't seem to be happening? Find the section of the code that performs that function and make sure it is executing when you think it should.\n- Is something happening that shouldn't? Find code in your program that performs that function and see if it is executing when it shouldn't.\n- Is a section of code producing an effect that is not what you expected? Make sure that you understand the code in question, especially if it involves invocations to functions or methods in other Python modules. Read the documentation for the functions you invoke. Try them out by writing simple test cases and checking the results.\n\nIn order to program, you need to have a mental model of how programs work. If you write a program that doesn't do what you expect, very often the problem is not in the program; it's in your mental model.\n\nThe best way to correct your mental model is to break the program into its components (usually the functions and methods) and test each component independently. Once you find the discrepancy between your model and reality, you can solve the problem.\n\nOf course, you should be building and testing components as you develop the program. If you encounter a problem, there should be only a small amount of new code that is not known to be correct.\n\n#### **A.3.2 I've got a big hairy expression and it doesn't do what I expect.**\n\nWriting complex expressions is fine as long as they are readable, but they can be hard to debug. It is often a good idea to break a complex expression into a series of assignments to temporary variables.\n\nFor example:\n\n```\nself.hands[i].addCard(self.hands[self.findNeighbor(i)].popCard())\n```\nThis can be rewritten as:\n\n```\nneighbor = self.findNeighbor(i)\npickedCard = self.hands[neighbor].popCard()\nself.hands[i].addCard(pickedCard)\n```\nThe explicit version is easier to read because the variable names provide additional documentation, and it is easier to debug because you can check the types of the intermediate variables and display their values.\n\nAnother problem that can occur with big expressions is that the order of evaluation may not be what you expect. For example, if you are translating the expression *x* 2*π* into Python, you might write:\n\n$y = x / 2 * math.pi $\n\nThat is not correct because multiplication and division have the same precedence and are evaluated from left to right. So this expression computes *xπ*/2.\n\nA good way to debug expressions is to add parentheses to make the order of evaluation explicit:\n\n$y = x / (2 * math.pi) $\n\nWhenever you are not sure of the order of evaluation, use parentheses. Not only will the program be correct (in the sense of doing what you intended), it will also be more readable for other people who haven't memorized the rules of precedence.\n\n#### **A.3.3 I've got a function or method that doesn't return what I expect.**\n\nIf you have a return statement with a complex expression, you don't have a chance to print the return value before returning. Again, you can use a temporary variable. For example, instead of:\n\n```\nreturn self.hands[i].removeMatches()\nyou could write:\ncount = self.hands[i].removeMatches()\nreturn count\n```\nNow you have the opportunity to display the value of count before returning.\n\n#### **A.3.4 I'm really, really stuck and I need help.**\n\nFirst, try getting away from the computer for a few minutes. Computers emit waves that affect the brain, causing these symptoms:\n\n- Frustration and rage.\n- Superstitious beliefs (\"the computer hates me\") and magical thinking (\"the program only works when I wear my hat backward\").\n- Random walk programming (the attempt to program by writing every possible program and choosing the one that does the right thing).\n\nIf you find yourself suffering from any of these symptoms, get up and go for a walk. When you are calm, think about the program. What is it doing? What are some possible causes of that behavior? When was the last time you had a working program, and what did you do next?\n\nSometimes it just takes time to find a bug. I often find bugs when I am away from the computer and let my mind wander. Some of the best places to find bugs are trains, showers, and in bed, just before you fall asleep.\n\n#### **A.3.5 No, I really need help.**\n\nIt happens. Even the best programmers occasionally get stuck. Sometimes you work on a program so long that you can't see the error. A fresh pair of eyes is just the thing.\n\nBefore you bring someone else in, make sure you are prepared. Your program should be as simple as possible, and you should be working on the smallest input that causes the error. You should have print statements in the appropriate places (and the output they produce should be comprehensible). You should understand the problem well enough to describe it concisely.\n\nWhen you bring someone in to help, be sure to give them the information they need:\n\n- If there is an error message, what is it and what part of the program does it indicate?\n- What was the last thing you did before this error occurred? What were the last lines of code that you wrote, or what is the new test case that fails?\n- What have you tried so far, and what have you learned?\n\nWhen you find the bug, take a second to think about what you could have done to find it faster. Next time you see something similar, you will be able to find the bug more quickly.\n\nRemember, the goal is not just to make the program work. The goal is to learn how to make the program work.\n\n# **Appendix B**\n\n# **Analysis of Algorithms**\n\nThis appendix is an edited excerpt from *Think Complexity*, by Allen B. Downey, also published by O'Reilly Media (2011). When you are done with this book, you might want to move on to that one.\n\n**Analysis of algorithms** is a branch of computer science that studies the performance of algorithms, especially their run time and space requirements. See [http://en.wikipedia.](http://en.wikipedia.org/wiki/Analysis_of_algorithms) [org/wiki/Analysis_of_algorithms](http://en.wikipedia.org/wiki/Analysis_of_algorithms).\n\nThe practical goal of algorithm analysis is to predict the performance of different algorithms in order to guide design decisions.\n\nDuring the 2008 United States Presidential Campaign, candidate Barack Obama was asked to perform an impromptu analysis when he visited Google. Chief executive Eric Schmidt jokingly asked him for \"the most efficient way to sort a million 32-bit integers.\" Obama had apparently been tipped off, because he quickly replied, \"I think the bubble sort would be the wrong way to go.\" See .\n\nThis is true: bubble sort is conceptually simple but slow for large datasets. The answer Schmidt was probably looking for is \"radix sort\" ([http://en.wikipedia.org/wiki/](http://en.wikipedia.org/wiki/Radix_sort) [Radix_sort](http://en.wikipedia.org/wiki/Radix_sort)) [1](#page-222-1) .\n\nThe goal of algorithm analysis is to make meaningful comparisons between algorithms, but there are some problems:\n\n- The relative performance of the algorithms might depend on characteristics of the hardware, so one algorithm might be faster on Machine A, another on Machine B. The general solution to this problem is to specify a **machine model** and analyze the number of steps, or operations, an algorithm requires under a given model.\n- Relative performance might depend on the details of the dataset. For example, some sorting algorithms run faster if the data are already partially sorted; other algorithms\n\n1 But if you get a question like this in an interview, I think a better answer is, \"The fastest way to sort a million integers is to use whatever sort function is provided by the language I'm using. Its performance is good enough for the vast majority of applications, but if it turned out that my application was too slow, I would use a profiler to see where the time was being spent. If it looked like a faster sort algorithm would have a significant effect on performance, then I would look around for a good implementation of radix sort.\"\n\nrun slower in this case. A common way to avoid this problem is to analyze the **worst case** scenario. It is sometimes useful to analyze average case performance, but that's usually harder, and it might not be obvious what set of cases to average over.\n\n- Relative performance also depends on the size of the problem. A sorting algorithm that is fast for small lists might be slow for long lists. The usual solution to this problem is to express run time (or number of operations) as a function of problem size, and to compare the functions **asymptotically** as the problem size increases.\nThe good thing about this kind of comparison that it lends itself to simple classification of algorithms. For example, if I know that the run time of Algorithm A tends to be proportional to the size of the input, *n*, and Algorithm B tends to be proportional to *n* 2 , then I expect A to be faster than B for large values of *n*.\n\nThis kind of analysis comes with some caveats, but we'll get to that later.\n\n### **B.1 Order of growth**\n\nSuppose you have analyzed two algorithms and expressed their run times in terms of the size of the input: Algorithm A takes $100n + 1$ steps to solve a problem with size $n$; Algorithm B takes $n^2 + n + 1$ steps.\n\nThe following table shows the run time of these algorithms for different problem sizes:\n\n| Input
size | Run time of
Algorithm A | Run time of
Algorithm B |\n|---------------|----------------------------|----------------------------|\n| 10 | 1 001 | 111 |\n| 100 | 10 001 | 10 101 |\n| 1 000 | 100 001 | 1 001 001 |\n| 10 000 | 1 000 001 | > 1010 |\n\nAt $n = 10$, Algorithm A looks pretty bad; it takes almost 10 times longer than Algorithm B. But for $n$ = 100 they are about the same, and for larger values A is much better.\n\nThe fundamental reason is that for large values of *n*, any function that contains an *n* 2 term will grow faster than a function whose leading term is *n*. The **leading term** is the term with the highest exponent.\n\nFor Algorithm A, the leading term has a large coefficient, 100, which is why B does better than A for small *n*. But regardless of the coefficients, there will always be some value of *n* where $an^2 > bn$.\n\nThe same argument applies to the non-leading terms. Even if the run time of Algorithm A were $n$ + 1000000, it would still be better than Algorithm B for sufficiently large $n$.\n\nIn general, we expect an algorithm with a smaller leading term to be a better algorithm for large problems, but for smaller problems, there may be a **crossover point** where another algorithm is better. The location of the crossover point depends on the details of the algorithms, the inputs, and the hardware, so it is usually ignored for purposes of algorithmic analysis. But that doesn't mean you can forget about it.\n\nIf two algorithms have the same leading order term, it is hard to say which is better; again, the answer depends on the details. So for algorithmic analysis, functions with the same leading term are considered equivalent, even if they have different coefficients.\n\nAn **order of growth** is a set of functions whose asymptotic growth behavior is considered equivalent. For example, $2n, 100n$ and $n$ + 1 belong to the same order of growth, which is written $O(n)$ in **Big-Oh notation** and often called **linear** because every function in the set grows linearly with *n*.\n\nAll functions with the leading term $n^2$ belong to $O(n^2)$; they are **quadratic**, which is a fancy word for functions with the leading term *n* 2 .\n\nThe following table shows some of the orders of growth that appear most commonly in algorithmic analysis, in increasing order of badness.\n\n| Order of growth | Name |\n|-----------------|-------------------------|\n| $O(1)$ | constant |\n| $O(log_b n)$ | logarithmic (for any b) |\n| $O(n)$ | linear |\n| $O(n log_b n)$ | \"en log en\" |\n| $O(n^2)$ | quadratic |\n| $O(n^3)$ | cubic |\n| $O(c^n)$ | exponential (for any c) |\n\nFor the logarithmic terms, the base of the logarithm doesn't matter; changing bases is the equivalent of multiplying by a constant, which doesn't change the order of growth. Similarly, all exponential functions belong to the same order of growth regardless of the base of the exponent. Exponential functions grow very quickly, so exponential algorithms are only useful for small problems.\n\n**Exercise B.1.** *Read the Wikipedia page on Big-Oh notation at* [http://en.wikipedia.org/](http://en.wikipedia.org/wiki/Big_O_notation) [wiki/Big_O_notation](http://en.wikipedia.org/wiki/Big_O_notation) *and answer the following questions:* \n\n- *1. What is the order of growth of n^3 + n^2? What about 1000000n^3 + n^2? What about n^3 +* 1000000*n* 2*?*\n- *2. What is the order of growth of* ($n^2$ + $n$) · ($n$ + 1)*? Before you start multiplying, remember that you only need the leading term.*\n- 3. If $f$ is in $O(g)$, for some unspecified function $g$, what can we say about $af + b$?\n- 4. If $f_1$ and $f_2$ are in $O(g)$, what can we say about $f_1 + f_2$?\n- 5. If $f_1$ is in $O(g)$ and $f_2$ is in $O(h)$, what can we say about $f_1 + f_2$?\n- 6. If $f_1$ is in $O(g)$ and $f_2$ is $O(h)$, what can we say about $f_1 \n\nf_2$?\n\nProgrammers who care about performance often find this kind of analysis hard to swallow. They have a point: sometimes the coefficients and the non-leading terms make a real difference. Sometimes the details of the hardware, the programming language, and the characteristics of the input make a big difference. And for small problems asymptotic behavior is irrelevant.\n\nBut if you keep those caveats in mind, algorithmic analysis is a useful tool. At least for large problems, the \"better\" algorithms is usually better, and sometimes it is *much* better. The difference between two algorithms with the same order of growth is usually a constant factor, but the difference between a good algorithm and a bad algorithm is unbounded!\n\n### **B.2 Analysis of basic Python operations**\n\nMost arithmetic operations are constant time; multiplication usually takes longer than addition and subtraction, and division takes even longer, but these run times don't depend on the magnitude of the operands. Very large integers are an exception; in that case the run time increases with the number of digits.\n\nIndexing operations—reading or writing elements in a sequence or dictionary—are also constant time, regardless of the size of the data structure.\n\nA for loop that traverses a sequence or dictionary is usually linear, as long as all of the operations in the body of the loop are constant time. For example, adding up the elements of a list is linear:\n\n```\ntotal = 0\nfor x in t:\n total += x\n```\nThe built-in function sum is also linear because it does the same thing, but it tends to be faster because it is a more efficient implementation; in the language of algorithmic analysis, it has a smaller leading coefficient.\n\nIf you use the same loop to \"add” a list of strings, the run time is quadratic because string concatenation is linear.\n\nThe string method join is usually faster because it is linear in the total length of the strings.\n\nAs a rule of thumb, if the body of a loop is in $O(n^a)$ then the whole loop is in $O(n^{a+1})$. The exception is if you can show that the loop exits after a constant number of iterations. If a loop runs $k$ times regardless of $n$, then the loop is in $O(n^a)$, even for large $k$.\n\nMultiplying by *k* doesn't change the order of growth, but neither does dividing. So if the body of a loop is in $O(n^a)$ and it runs $n/k$ times, the loop is in $O(n^{a+1})$, even for large $k$.\n\nMost string and tuple operations are linear, except indexing and len, which are constant time. The built-in functions min and max are linear. The run-time of a slice operation is proportional to the length of the output, but independent of the size of the input.\n\nAll string methods are linear, but if the lengths of the strings are bounded by a constant for example, operations on single characters—they are considered constant time.\n\nMost list methods are linear, but there are some exceptions:\n\n- Adding an element to the end of a list is constant time on average; when it runs out of room it occasionally gets copied to a bigger location, but the total time for *n* operations is $O(n)$, so we say that the “amortized” time for one operation is $O(1)$.\n- Removing an element from the end of a list is constant time.\n- Sorting is $O(n \\log n)$.\n\nMost dictionary operations and methods are constant time, but there are some exceptions:\n\n- The run time of copy is proportional to the number of elements, but not the size of the elements (it copies references, not the elements themselves).\n- The run time of update is proportional to the size of the dictionary passed as a parameter, not the dictionary being updated.\n- keys, values and items are linear because they return new lists; iterkeys, itervalues and iteritems are constant time because they return iterators. But if you loop through the iterators, the loop will be linear. Using the \"iter\" functions saves some overhead, but it doesn't change the order of growth unless the number of items you access is bounded.\n\nThe performance of dictionaries is one of the minor miracles of computer science. We will see how they work in Section [B.4.](#page-227-0)\n\n**Exercise B.2.** Read the Wikipedia page on sorting algorithms at [http://en.wikipedia.org/](http://en.wikipedia.org/wiki/Sorting_algorithm) [wiki/ Sorting_ algorithm](http://en.wikipedia.org/wiki/Sorting_algorithm) *and answer the following questions:*\n\n- *1. What is a \"comparison sort?\" What is the best worst-case order of growth for a comparison sort? What is the best worst-case order of growth for any sort algorithm?*\n- *2. What is the order of growth of bubble sort, and why does Barack Obama think it is \"the wrong way to go?\"*\n- *3. What is the order of growth of radix sort? What preconditions do we need to use it?*\n- *4. What is a stable sort and why might it matter in practice?*\n- *5. What is the worst sorting algorithm (that has a name)?*\n- *6. What sort algorithm does the C library use? What sort algorithm does Python use? Are these algorithms stable? You might have to Google around to find these answers.*\n- 7. Many of the non-comparison sorts are linear, so why does Python use an $O(n \\log n) $*comparison sort?*\n\n#### **B.3 Analysis of search algorithms**\n\nA **search** is an algorithm that takes a collection and a target item and determines whether the target is in the collection, often returning the index of the target.\n\nThe simplest search algorithm is a \"linear search,\" which traverses the items of the collection in order, stopping if it finds the target. In the worst case it has to traverse the entire collection, so the run time is linear.\n\nThe in operator for sequences uses a linear search; so do string methods like find and count.\n\nIf the elements of the sequence are in order, you can use a **bisection search**, which is $O(\\log n)$. Bisection search is similar to the algorithm you probably use to look a word up in a dictionary (a real dictionary, not the data structure). Instead of starting at the beginning and checking each item in order, you start with the item in the middle and check whether the word you are looking for comes before or after. If it comes before, then you search the first half of the sequence. Otherwise you search the second half. Either way, you cut the number of remaining items in half.\n\nIf the sequence has 1,000,000 items, it will take about 20 steps to find the word or conclude that it's not there. So that's about 50,000 times faster than a linear search.\n\n**Exercise B.3.** *Write a function called* bisection *that takes a sorted list and a target value and returns the index of the value in the list, if it's there, or* None *if it's not.*\n\n*Or you could read the documentation of the* bisect *module and use that!*\n\nBisection search can be much faster than linear search, but it requires the sequence to be in order, which might require extra work.\n\nThere is another data structure, called a **hashtable** that is even faster—it can do a search in constant time—and it doesn't require the items to be sorted. Python dictionaries are implemented using hashtables, which is why most dictionary operations, including the in operator, are constant time.\n\n#### **B.4 Hashtables**\n\nTo explain how hashtables work and why their performance is so good, I start with a simple implementation of a map and gradually improve it until it's a hashtable.\n\nI use Python to demonstrate these implementations, but in real life you wouldn't write code like this in Python; you would just use a dictionary! So for the rest of this chapter, you have to imagine that dictionaries don't exist and you want to implement a data structure that maps from keys to values. The operations you have to implement are:\n\n- add(k, v)**:** Add a new item that maps from key k to value v. With a Python dictionary, d, this operation is written d[k] = v.\n- get(target)**:** Look up and return the value that corresponds to key target. With a Python dictionary, d, this operation is written d[target] or d.get(target).\n\nFor now, I assume that each key only appears once. The simplest implementation of this interface uses a list of tuples, where each tuple is a key-value pair.\n\nclass LinearMap(object):\n\n```\ndef __init__(self):\n self.items = []\ndef add(self, k, v):\n self.items.append((k, v))\ndef get(self, k):\n for key, val in self.items:\n if key == k:\n return val\n raise KeyError\n```\nadd appends a key-value tuple to the list of items, which takes constant time.\n\nget uses a for loop to search the list: if it finds the target key it returns the corresponding value; otherwise it raises a KeyError. So get is linear.\n\nAn alternative is to keep the list sorted by key. Then get could use a bisection search, which is $O$(log $n$). But inserting a new item in the middle of a list is linear, so this might not be the best option. There are other data structures (see [http://en.wikipedia.org/](http://en.wikipedia.org/wiki/Red-black_tree) [wiki/Red-black_tree](http://en.wikipedia.org/wiki/Red-black_tree)) that can implement add and get in log time, but that's still not as good as constant time, so let's move on.\n\nOne way to improve LinearMap is to break the list of key-value pairs into smaller lists. Here's an implementation called BetterMap, which is a list of 100 LinearMaps. As we'll see in a second, the order of growth for get is still linear, but BetterMap is a step on the path toward hashtables:\n\nclass BetterMap(object):\n\n```\ndef __init__(self, n=100):\n self.maps = []\n for i in range(n):\n self.maps.append(LinearMap())\n def find_map(self, k):\n index = hash(k) % len(self.maps)\n return self.maps[index]\n def add(self, k, v):\n m = self.find_map(k)\n m.add(k, v)\n def get(self, k):\n m = self.find_map(k)\n return m.get(k)\n__init__ makes a list of n LinearMaps.\n```\nfind_map is used by add and get to figure out which map to put the new item in, or which map to search.\n\nfind_map uses the built-in function hash, which takes almost any Python object and returns an integer. A limitation of this implementation is that it only works with hashable keys. Mutable types like lists and dictionaries are unhashable.\n\nHashable objects that are considered equal return the same hash value, but the converse is not necessarily true: two different objects can return the same hash value.\n\nfind_map uses the modulus operator to wrap the hash values into the range from 0 to len(self.maps), so the result is a legal index into the list. Of course, this means that many different hash values will wrap onto the same index. But if the hash function spreads things out pretty evenly (which is what hash functions are designed to do), then we expect *n*/100 items per LinearMap.\n\nSince the run time of LinearMap.get is proportional to the number of items, we expect BetterMap to be about 100 times faster than LinearMap. The order of growth is still linear, but the leading coefficient is smaller. That's nice, but still not as good as a hashtable.\n\nHere (finally) is the crucial idea that makes hashtables fast: if you can keep the maximum length of the LinearMaps bounded, LinearMap.get is constant time. All you have to do is keep track of the number of items and when the number of items per LinearMap exceeds a threshold, resize the hashtable by adding more LinearMaps.\n\nHere is an implementation of a hashtable:\n\n```\nclass HashMap(object):\n def __init__(self):\n self.maps = BetterMap(2)\n self.num = 0\n def get(self, k):\n return self.maps.get(k)\n def add(self, k, v):\n if self.num == len(self.maps.maps):\n self.resize()\n self.maps.add(k, v)\n self.num += 1\n def resize(self):\n new_maps = BetterMap(self.num * 2)\n for m in self.maps.maps:\n for k, v in m.items:\n new_maps.add(k, v)\n self.maps = new_maps\n```\nEach HashMap contains a BetterMap; __init__ starts with just 2 LinearMaps and initializes num, which keeps track of the number of items.\n\nget just dispatches to BetterMap. The real work happens in add, which checks the number of items and the size of the BetterMap: if they are equal, the average number of items per LinearMap is 1, so it calls resize.\n\nresize make a new BetterMap, twice as big as the previous one, and then \"rehashes\" the items from the old map to the new.\n\nRehashing is necessary because changing the number of LinearMaps changes the denominator of the modulus operator in find_map. That means that some objects that used to wrap into the same LinearMap will get split up (which is what we wanted, right?).\n\nRehashing is linear, so resize is linear, which might seem bad, since I promised that add would be constant time. But remember that we don't have to resize every time, so add is usually constant time and only occasionally linear. The total amount of work to run add *n* times is proportional to *n*, so the average time of each add is constant time!\n\nTo see how this works, think about starting with an empty HashTable and adding a sequence of items. We start with 2 LinearMaps, so the first 2 adds are fast (no resizing required). Let's say that they take one unit of work each. The next add requires a resize, so we have to rehash the first two items (let's call that 2 more units of work) and then add the third item (one more unit). Adding the next item costs 1 unit, so the total so far is 6 units of work for 4 items.\n\nThe next add costs 5 units, but the next three are only one unit each, so the total is 14 units for the first 8 adds.\n\n\n\nFigure B.1: The cost of a hashtable add.\n\nThe next add costs 9 units, but then we can add 7 more before the next resize, so the total is 30 units for the first 16 adds.\n\nAfter 32 adds, the total cost is 62 units, and I hope you are starting to see a pattern. After *n* adds, where $n$ is a power of two, the total cost is $2n - 2$ units, so the average work per add is a little less than 2 units. When *n* is a power of two, that's the best case; for other values of *n* the average work is a little higher, but that's not important. The important thing is that it is $O$(1).\n\nFigure [B.1](#page-230-0) shows how this works graphically. Each block represents a unit of work. The columns show the total work for each add in order from left to right: the first two adds cost 1 units, the third costs 3 units, etc.\n\nThe extra work of rehashing appears as a sequence of increasingly tall towers with increasing space between them. Now if you knock over the towers, amortizing the cost of resizing over all adds, you can see graphically that the total cost after $n$ adds is $2n - 2$.\n\nAn important feature of this algorithm is that when we resize the HashTable it grows geometrically; that is, we multiply the size by a constant. If you increase the size arithmetically—adding a fixed number each time—the average time per add is linear.\n\nYou can download my implementation of HashMap from [http://thinkpython/code/](http://thinkpython/code/Map.py) [Map.py](http://thinkpython/code/Map.py), but remember that there is no reason to use it; if you want a map, just use a Python dictionary.\n\n**210 Appendix B. Analysis of Algorithms**\n\n# **Appendix C**\n\n# **Lumpy**\n\nThroughout the book, I have used diagrams to represent the state of running programs.\n\nIn Section [2.2,](#page-33-0) we used a state diagram to show the names and values of variables. In Section [3.10](#page-46-0) I introduced a stack diagram, which shows one frame for each function call. Each frame shows the parameters and local variables for the function or method. Stack diagrams for recursive functions appear in Section [5.9](#page-66-0) and Section [6.5.](#page-76-0)\n\nSection [10.2](#page-108-2) shows what a list looks like in a state diagram, Section [11.4](#page-126-0) shows what a dictionary looks like, and Section [12.6](#page-138-0) shows two ways to represent tuples.\n\nSection [15.2](#page-165-0) introduces object diagrams, which show the state of an object's attributes, and their attributes, and so on. Section [15.3](#page-166-0) has object diagrams for Rectangles and their embedded Points. Section [16.1](#page-172-1) shows the state of a Time object. Section [18.2](#page-189-0) has a diagram that includes a class object and an instance, each with their own attributes.\n\nFinally, Section [18.8](#page-194-0) introduces class diagrams, which show the classes that make up a program and the relationships between them.\n\nThese diagrams are based on the Unified Modeling Language (UML), which is a standardized graphical language used by software engineers to communicate about program design, especially for object-oriented programs.\n\nUML is a rich language with many kinds of diagrams that represent many kinds of relationship between objects and classes. What I presented in this book is a small subset of the language, but it is the subset most commonly used in practice.\n\nThe purpose of this appendix is to review the diagrams presented in the previous chapters, and to introduce Lumpy. Lumpy, which stands for \"UML in Python,\" with some of the letters rearranged, is part of Swampy, which you already installed if you worked on the case study in Chapter [4](#page-52-0) or Chapter [19,](#page-200-0) or if you did Exercise [15.4,](#page-170-2)\n\nLumpy uses Python's inspect module to examine the state of a running program and generate object diagrams (including stack diagrams) and class diagrams.\n\n#### **C.1 State diagram**\n\nHere's an example that uses Lumpy to generate a state diagram.\n\n\n\nFigure C.1: State diagram generated by Lumpy.\n\n\n\nFigure C.2: Stack diagram.\n\nfrom swampy.Lumpy import Lumpy\n\n```\nlumpy = Lumpy()\nlumpy.make_reference()\n```\n\n```\nmessage = 'And now for something completely different'\nn = 17\npi = 3.1415926535897932\n```\n\n```\nlumpy.object_diagram()\n```\nThe first line imports the Lumpy class from swampy.Lumpy. If you don't have Swampy installed as a package, make sure the Swampy files are in Python's search path and use this import statement instead:\n\nfrom Lumpy import Lumpy\n\nThe next lines create a Lumpy object and make a \"reference\" point, which means that Lumpy records the objects that have been defined so far.\n\nNext we define new variables and invoke object_diagram, which draws the objects that have been defined since the reference point, in this case message, n and pi.\n\nFigure [C.1](#page-233-1) shows the result. The graphical style is different from what I showed earlier; for example, each reference is represented by a circle next to the variable name and a line to the value. And long strings are truncated. But the information conveyed by the diagram is the same.\n\nThe variable names are in a frame labeled , which indicates that these are modulelevel variables, also known as global.\n\nYou can download this example from . Try adding some additional assignments and see what the diagram looks like.\n\n## **C.2 Stack diagram**\n\nHere's an example that uses Lumpy to generate a stack diagram. You can download it from .\n\n\n\nFigure C.3: Object diagram.\n\n```\nfrom swampy.Lumpy import Lumpy\ndef countdown(n):\n if n <= 0:\n print 'Blastoff!'\n lumpy.object_diagram()\n else:\n print n\n countdown(n-1)\nlumpy = Lumpy()\nlumpy.make_reference()\n```\ncountdown(3)\n\nFigure [C.2](#page-233-2) shows the result. Each frame is represented with a box that has the function's name outside and variables inside. Since this function is recursive, there is one frame for each level of recursion.\n\nRemember that a stack diagram shows the state of the program at a particular point in its execution. To get the diagram you want, sometimes you have to think about where to invoke object_diagram.\n\nIn this case I invoke object_diagram after executing the base case of the recursion; that way the stack diagram shows each level of the recursion. You can call object_diagram more than once to get a series of snapshots of the program's execution.\n\n#### **C.3 Object diagrams**\n\nThis example generates an object diagram showing the lists from Section [10.1.](#page-108-1) You can download it from .\n\nfrom swampy.Lumpy import Lumpy\n\n```\nlumpy = Lumpy()\nlumpy.make_reference()\ncheeses = ['Cheddar', 'Edam', 'Gouda']\n```\n\n\nFigure C.4: Object diagram.\n\n```\nnumbers = [17, 123]\nempty = []\n```\n\n```\nlumpy.object_diagram()\n```\nFigure [C.3](#page-234-1) shows the result. Lists are represented by a box that shows the indices mapping to the elements. This representation is slightly misleading, since indices are not actually part of the list, but I think they make the diagram easier to read. The empty list is represented by an empty box.\n\nAnd here's an example showing the dictionaries from Section [11.4.](#page-126-0) You can download it from .\n\nfrom swampy.Lumpy import Lumpy\n\nlumpy = Lumpy() lumpy.make_reference()\n\nhist = histogram('parrot') inverse = invert_dict(hist)\n\nlumpy.object_diagram()\n\nFigure [C.4](#page-235-0) shows the result. hist is a dictionary that maps from characters (single-letter strings) to integers; inverse maps from integers to lists of strings.\n\nThis example generates an object diagram for Point and Rectangle objects, as in Section [15.6.](#page-168-0) You can download it from .\n\nimport copy from swampy.Lumpy import Lumpy\n\n\n\nFigure C.5: Object diagram.\n\n\n\nFigure C.6: Object diagram.\n\n```\nlumpy = Lumpy()\nlumpy.make_reference()\nbox = Rectangle()\nbox.width = 100.0\nbox.height = 200.0\nbox.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0\nbox2 = copy.copy(box)\n```\n\n```\nlumpy.object_diagram()\n```\nFigure [C.5](#page-236-1) shows the result. copy.copy make a shallow copy, so box and box2 have their own width and height, but they share the same embedded Point object. This kind of sharing is usually fine with immutable objects, but with mutable types, it is highly errorprone.\n\n#### **C.4 Function and class objects**\n\nWhen I use Lumpy to make object diagrams, I usually define the functions and classes before I make the reference point. That way, function and class objects don't appear in the diagram.\n\n\n\nFigure C.7: Class diagram.\n\nBut if you are passing functions and classes as parameters, you might want them to appear. This example shows what that looks like; you can download it from [http://thinkpython.](http://thinkpython.com/code/lumpy_demo6.py) [com/code/lumpy_demo6.py](http://thinkpython.com/code/lumpy_demo6.py).\n\n```\nimport copy\nfrom swampy.Lumpy import Lumpy\nlumpy = Lumpy()\nlumpy.make_reference()\nclass Point(object):\n \"\"\"Represents a point in 2-D space.\"\"\"\nclass Rectangle(object):\n \"\"\"Represents a rectangle.\"\"\"\ndef instantiate(constructor):\n \"\"\"Instantiates a new object.\"\"\"\n obj = constructor()\n lumpy.object_diagram()\n return obj\n```\n\n```\npoint = instantiate(Point)\n```\nFigure [C.6](#page-236-2) shows the result. Since we invoke object_diagram inside a function, we get a stack diagram with a frame for the module-level variables and for the invocation of instantiate.\n\nAt the module level, Point and Rectangle refer to class objects (which have type type); instantiate refers to a function object.\n\nThis diagram might clarify two points of common confusion: (1) the difference between the class object, Point, and the instance of Point, obj, and (2) the difference between the function object created when instantiate is defined, and the frame created with it is called.\n\n## **C.5 Class Diagrams**\n\nAlthough I distinguish between state diagrams, stack diagrams and object diagrams, they are mostly the same thing: they show the state of a running program at a point in time.\n\n\n\nFigure C.8: Class diagram.\n\nClass diagrams are different. They show the classes that make up a program and the relationships between them. They are timeless in the sense that they describe the program as a whole, not any particular point in time. For example, if an instance of Class A generally contains a reference to an instance of Class B, we say there is a \"HAS-A relationship\" between those classes.\n\nHere's an example that shows a HAS-A relationship. You can download it from [http:](http://thinkpython.com/code/lumpy_demo7.py) [//thinkpython.com/code/lumpy_demo7.py](http://thinkpython.com/code/lumpy_demo7.py).\n\n```\nfrom swampy.Lumpy import Lumpy\n```\nlumpy = Lumpy() lumpy.make_reference()\n\n```\nbox = Rectangle()\nbox.width = 100.0\nbox.height = 200.0\nbox.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0\n```\n\n```\nlumpy.class_diagram()\n```\nFigure [C.7](#page-237-1) shows the result. Each class is represented with a box that contains the name of the class, any methods the class provides, any class variables, and any instance variables. In this example, Rectangle and Point have instance variables, but no methods or class variables.\n\nThe arrow from Rectangle to Point shows that Rectangles contain an embedded Point. In addition, Rectangle and Point both inherit from object, which is represented in the diagram with a triangle-headed arrow.\n\nHere's a more complex example using my solution to Exercise [18.6.](#page-198-1) You can download the code from ; you will also need [http:](http://thinkpython.com/code/PokerHand.py) [//thinkpython.com/code/PokerHand.py](http://thinkpython.com/code/PokerHand.py).\n\n```\nfrom swampy.Lumpy import Lumpy\nfrom PokerHand import *\nlumpy = Lumpy()\nlumpy.make_reference()\ndeck = Deck()\nhand = PokerHand()\ndeck.move_cards(hand, 7)\n```\n\n```\nlumpy.class_diagram()\n```\nFigure [C.8](#page-238-0) shows the result. PokerHand inherits from Hand, which inherits from Deck. Both Deck and PokerHand have Cards.\n\nThis diagram does not show that Hand also has cards, because in the program there are no instances of Hand. This example demonstrates a limitation of Lumpy; it only knows about the attributes and HAS-A relationships of objects that are instantiated."
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\"heading_level\": null,\n \"page_id\": 2,\n \"polygon\": [\n [\n 267.451171875,\n 306.66796875\n ],\n [\n 525.6033325195312,\n 306.66796875\n ],\n [\n 525.6033325195312,\n 323.7117614746094\n ],\n [\n 267.451171875,\n 323.7117614746094\n ]\n ]\n },\n {\n \"title\": \"Preface\",\n \"heading_level\": null,\n \"page_id\": 4,\n \"polygon\": [\n [\n 128.49609375,\n 165.322265625\n ],\n [\n 213.662109375,\n 165.322265625\n ],\n [\n 213.662109375,\n 190.65838623046875\n ],\n [\n 128.49609375,\n 190.65838623046875\n ]\n ]\n },\n {\n \"title\": \"The strange history of this book\",\n \"heading_level\": null,\n \"page_id\": 4,\n \"polygon\": [\n [\n 129.31787109375,\n 237.638671875\n ],\n [\n 338.8106384277344,\n 237.638671875\n ],\n [\n 338.8106384277344,\n 253.55902099609375\n ],\n [\n 129.31787109375,\n 253.55902099609375\n ]\n ]\n },\n {\n \"title\": \"Acknowledgments\",\n \"heading_level\": null,\n \"page_id\": 5,\n \"polygon\": [\n [\n 85.83837890625,\n 510.08203125\n ],\n [\n 211.5703125,\n 510.08203125\n ],\n [\n 211.5703125,\n 526.0249328613281\n ],\n [\n 85.83837890625,\n 526.0249328613281\n ]\n ]\n },\n {\n \"title\": \"Contributor List\",\n \"heading_level\": null,\n \"page_id\": 6,\n \"polygon\": [\n [\n 128.0478515625,\n 84.498046875\n ],\n [\n 235.97708129882812,\n 84.498046875\n ],\n [\n 235.97708129882812,\n 100.29998779296875\n ],\n [\n 128.0478515625,\n 100.29998779296875\n ]\n ]\n },\n {\n \"title\": \"Contents\",\n \"heading_level\": null,\n \"page_id\": 12,\n \"polygon\": [\n [\n 128.0478515625,\n 165.40826416015625\n ],\n [\n 231.47499084472656,\n 165.40826416015625\n ],\n [\n 231.47499084472656,\n 190.1953125\n ],\n [\n 128.0478515625,\n 190.1953125\n ]\n ]\n },\n {\n \"title\": \"Chapter 1\",\n \"heading_level\": null,\n \"page_id\": 22,\n \"polygon\": [\n [\n 128.197265625,\n 164.801513671875\n ],\n [\n 220.84561157226562,\n 164.801513671875\n ],\n [\n 220.84561157226562,\n 185.4639892578125\n ],\n [\n 128.197265625,\n 185.4639892578125\n ]\n ]\n },\n {\n \"title\": \"The way of the program\",\n \"heading_level\": null,\n \"page_id\": 22,\n \"polygon\": [\n [\n 127.97314453125,\n 220.236328125\n ],\n [\n 400.91961669921875,\n 220.236328125\n ],\n [\n 400.91961669921875,\n 245.6234130859375\n ],\n [\n 127.97314453125,\n 245.6234130859375\n ]\n ]\n },\n {\n \"title\": \"1.1 The Python programming language\",\n \"heading_level\": null,\n \"page_id\": 22,\n \"polygon\": [\n [\n 128.9443359375,\n 498.8671875\n ],\n [\n 395.05078125,\n 498.8671875\n ],\n [\n 395.05078125,\n 513.9990234375\n ],\n [\n 128.9443359375,\n 513.9990234375\n ]\n ]\n },\n {\n \"title\": \"1.2 What is a program?\",\n \"heading_level\": null,\n \"page_id\": 24,\n \"polygon\": [\n [\n 128.3466796875,\n 85.95379638671875\n ],\n [\n 288.8173828125,\n 85.95379638671875\n ],\n [\n 288.8173828125,\n 100.29998779296875\n ],\n [\n 128.3466796875,\n 100.29998779296875\n ]\n ]\n },\n {\n \"title\": \"1.3 What is debugging?\",\n \"heading_level\": null,\n \"page_id\": 24,\n \"polygon\": [\n [\n 128.0478515625,\n 439.2318420410156\n ],\n [\n 292.5728454589844,\n 439.2318420410156\n ],\n [\n 292.5728454589844,\n 453.57806396484375\n ],\n [\n 128.0478515625,\n 453.57806396484375\n ]\n ]\n },\n {\n \"title\": \"1.3.1 Syntax errors\",\n \"heading_level\": null,\n \"page_id\": 24,\n \"polygon\": [\n [\n 129.09375,\n 548.9158477783203\n ],\n [\n 237.53152465820312,\n 548.9158477783203\n ],\n [\n 237.53152465820312,\n 560.8710479736328\n ],\n [\n 129.09375,\n 560.8710479736328\n ]\n ]\n },\n {\n \"title\": \"1.3.2 Runtime errors\",\n \"heading_level\": null,\n \"page_id\": 25,\n \"polygon\": [\n [\n 85.53955078125,\n 86.3349609375\n ],\n [\n 207.685546875,\n 86.3349609375\n ],\n [\n 207.685546875,\n 99.24493408203125\n ],\n [\n 85.53955078125,\n 99.24493408203125\n ]\n ]\n },\n {\n \"title\": \"1.3.3 Semantic errors\",\n \"heading_level\": null,\n \"page_id\": 25,\n \"polygon\": [\n [\n 85.9130859375,\n 206.12109375\n ],\n [\n 208.1337890625,\n 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\"block_counts\": [\n [\n \"Span\",\n 117\n ],\n [\n \"Line\",\n 44\n ],\n [\n \"Reference\",\n 3\n ],\n [\n \"PageHeader\",\n 2\n ],\n [\n \"Figure\",\n 2\n ],\n [\n \"Caption\",\n 2\n ],\n [\n \"Code\",\n 2\n ],\n [\n \"Text\",\n 2\n ],\n [\n \"FigureGroup\",\n 2\n ],\n [\n \"SectionHeader\",\n 1\n ]\n ],\n \"block_metadata\": {\n \"llm_request_count\": 1,\n \"llm_error_count\": 0,\n \"llm_tokens_used\": 3794\n }\n },\n {\n \"page_id\": 237,\n \"text_extraction_method\": \"pdftext\",\n \"block_counts\": [\n [\n \"Span\",\n 109\n ],\n [\n \"Line\",\n 37\n ],\n [\n \"Text\",\n 5\n ],\n [\n \"PageHeader\",\n 2\n ],\n [\n \"Code\",\n 2\n ],\n [\n \"Reference\",\n 2\n ],\n [\n \"Figure\",\n 1\n ],\n [\n \"Caption\",\n 1\n ],\n [\n \"SectionHeader\",\n 1\n ],\n [\n \"FigureGroup\",\n 1\n ]\n ],\n \"block_metadata\": {\n \"llm_request_count\": 0,\n \"llm_error_count\": 0,\n \"llm_tokens_used\": 0\n }\n },\n {\n \"page_id\": 238,\n \"text_extraction_method\": \"pdftext\",\n \"block_counts\": [\n [\n \"Span\",\n 140\n ],\n [\n \"Line\",\n 51\n ],\n [\n \"Text\",\n 5\n ],\n [\n \"Code\",\n 3\n ],\n [\n \"PageHeader\",\n 2\n ],\n [\n \"Figure\",\n 1\n ],\n [\n \"Caption\",\n 1\n ],\n [\n \"FigureGroup\",\n 1\n ],\n [\n \"Reference\",\n 1\n ]\n ],\n \"block_metadata\": {\n \"llm_request_count\": 1,\n \"llm_error_count\": 0,\n \"llm_tokens_used\": 3419\n }\n },\n {\n \"page_id\": 239,\n \"text_extraction_method\": \"pdftext\",\n \"block_counts\": [\n [\n \"Span\",\n 63\n ],\n [\n \"Line\",\n 17\n ],\n [\n \"Text\",\n 3\n ],\n [\n \"PageHeader\",\n 2\n ],\n [\n \"Code\",\n 2\n ]\n ],\n \"block_metadata\": {\n \"llm_request_count\": 0,\n \"llm_error_count\": 0,\n \"llm_tokens_used\": 0\n }\n }\n ],\n \"debug_data_path\": \"debug_data/thinkpython\"\n}"
},
{
"path": "data/latex_to_md.sh",
"content": "#!/bin/bash\n\n# List all .tex files in the latex folder\nFILES=$(find latex -name \"*.tex\")\n\nfor f in $FILES\ndo\n echo \"Processing $f file...\"\n base_name=$(basename \"$f\" .tex)\n out_file=\"references/${base_name}.md\"\n\n pandoc --wrap=none \\\n --no-highlight \\\n --strip-comments \\\n --from=latex \\\n --to=commonmark_x+pipe_tables \\\n \"$f\" \\\n -o \"$out_file\"\n # Replace non-breaking spaces\n sed -i .bak 's/ / /g' \"$out_file\"\n sed -i .bak 's/ / /g' \"$out_file\"\n sed -i .bak 's/ / /g' \"$out_file\"\n sed -i .bak 's/ / /g' \"$out_file\"\n sed -i.bak -E 's/`\\\\cite`//g; s/<[^>]*>//g; s/\\{[^}]*\\}//g; s/\\\\cite\\{[^}]*\\}//g' \"$out_file\"\n sed -i.bak -E '\n s/`\\\\cite`//g; # Remove \\cite commands inside backticks\n s/::: //g; # Remove the leading ::: for content markers\n s/\\[//g; # Remove opening square bracket\n s/\\]//g; # Remove closing square bracket\n ' \"$out_file\"\n # Remove .bak file\n rm \"$out_file.bak\"\ndone\n\n"
},
{
"path": "examples/README.md",
"content": "## Usage Examples\n\nThis directory contains examples of running `marker` in different contexts.\n\n### Usage with Modal\n\nWe have a [self-contained example](./marker_modal_deployment.py) that shows how you can quickly use [Modal](https://modal.com) to deploy `marker` by provisioning a container with a GPU, and expose that with an API so you can submit PDFs for conversion into Markdown, HTML, or JSON.\n\nIt's a limited example that you can extend into different use cases.\n\n#### Pre-requisites\n\nMake sure you have the `modal` client installed by [following their instructions here](https://modal.com/docs/guide#getting-started).\n\nModal's [Starter Plan](https://modal.com/pricing) includes $30 of free compute each month.\nModal is [serverless](https://arxiv.org/abs/1902.03383), so you only pay for resources when you are using them.\n\n#### Running the example\n\nOnce `modal` is configured, you can deploy it to your workspace by running:\n\n> modal deploy marker_modal_deployment.py\n\nNotes:\n- `marker` has a few models it uses. By default, the endpoint will check if these models are loaded and download them if not (first request will be slow). You can avoid this by running\n\n> modal run marker_modal_deployment.py::download_models\n\nWhich will create a [`Modal Volume`](https://modal.com/docs/guide/Volumes) to store them for re-use.\n\nOnce the deploy is finished, you can:\n- Test a file upload locally through your CLI using an `invoke_conversion` command we expose through Modal's [`local_entrypoint`](https://modal.com/docs/reference/modal.App#local_entrypoint)\n- Get the URL of your endpoint and make a request through a client of your choice.\n\n**Test from your CLI with `invoke_conversion`**\n\nIf your endpoint is live, simply run this command:\n\n```\n$ modal run marker_modal_deployment.py::invoke_conversion --pdf-file --output-format markdown\n```\n\nAnd it'll automatically detect the URL of your new endpoint using [`.get_web_url()`](https://modal.com/docs/guide/webhook-urls#determine-the-url-of-a-web-endpoint-from-code), make sure it's healthy, submit your file, and store its output on your machine (in the same directory).\n\n**Making a request using your own client**\n\nIf you want to make requests elsewhere e.g. with cURL or a client like Insomnia, you'll need to get the URL.\n\nWhen your `modal deploy` command from earlier finishes, it'll include your endpoint URL at the end. For example:\n\n```\n$ modal deploy marker_modal_deployment.py\n...\n✓ Created objects.\n├── 🔨 Created mount /marker/examples/marker_modal_deployment.py\n├── 🔨 Created function download_models.\n├── 🔨 Created function MarkerModalDemoService.*.\n└── 🔨 Created web endpoint for MarkerModalDemoService.fastapi_app => \n✓ App deployed in 149.877s! 🎉\n```\n\nIf you accidentally close your terminal session, you can also always go into Modal's dashboard and:\n - Find the app (default name: `datalab-marker-modal-demo`)\n - Click on `MarkerModalDemoService`\n - Find your endpoint URL\n\nOnce you have your URL, make a request to `{YOUR_ENDPOINT_URL}/convert` like this (you can also use Insomnia, etc.):\n```\ncurl --request POST \\\n --url {BASE_URL}/convert \\\n --header 'Content-Type: multipart/form-data' \\\n --form file=@/Users/cooldev/sample.pdf \\\n --form output_format=html\n ```\n\nYou should get a response like this\n\n```\n{\n\t\"success\": true,\n\t\"filename\": \"sample.pdf\",\n\t\"output_format\": \"html\",\n\t\"json\": null,\n\t\"html\": \"\",\n\t\"markdown\": null,\n\t\"images\": {},\n\t\"metadata\": {... page level metadata ...},\n\t\"page_count\": 2\n}\n```\n\n[Modal](https://modal.com) makes deploying and scaling models and inference workloads much easier.\n\nIf you're interested in Datalab's managed API or on-prem document intelligence solution, check out [our platform here](https://datalab.to/?utm_source=gh-marker).\n"
},
{
"path": "examples/marker_modal_deployment.py",
"content": "\"\"\"\nModal deployment for Datalab Marker PDF conversion service.\n\"\"\"\n\nimport modal\nimport os\nfrom typing import Optional\n\n# Define the Modal app\napp = modal.App(\"datalab-marker-modal-demo\")\nGPU_TYPE = \"L40S\"\nMODEL_PATH_PREFIX = \"/root/.cache/datalab/models\"\n\n# Define the container image with all dependencies\nimage = (\n modal.Image.debian_slim(python_version=\"3.10\")\n .apt_install([\"git\", \"wget\"])\n .env({\"TORCH_DEVICE\": \"cuda\"})\n .pip_install([\n \"marker-pdf[full]\",\n \"fastapi==0.104.1\",\n \"uvicorn==0.24.0\",\n \"python-multipart==0.0.6\",\n \"torch>=2.2.2,<3.0.0\",\n \"torchvision>=0.17.0\",\n \"torchaudio>=2.2.0\",\n ])\n)\n\n# Create a persistent volume for model caching\nmodels_volume = modal.Volume.from_name(\"marker-models-modal-demo\", create_if_missing=True)\n\ndef setup_models_with_cache_check(logger, commit_volume=False):\n \"\"\"\n Shared function to create models and handle cache checking/logging.\n \"\"\"\n import os\n import gc\n from marker.models import create_model_dict\n\n # Check if models exist in cache\n models_dir_exists = os.path.exists(MODEL_PATH_PREFIX)\n models_dir_contents = os.listdir(MODEL_PATH_PREFIX) if models_dir_exists else []\n\n logger.info(f\"Models cache directory exists: {models_dir_exists}\")\n logger.info(f\"Models cache directory contents: {models_dir_contents}\")\n\n if models_dir_exists and models_dir_contents:\n logger.info(\"Found existing models in volume cache, loading from cache...\")\n else:\n logger.warning(\"No models found in volume cache. Models will be downloaded now (this may take several minutes).\")\n\n # Create/load models\n models = create_model_dict()\n logger.info(f\"Successfully loaded {len(models)} models\")\n\n # Check what was downloaded/cached\n if os.path.exists(MODEL_PATH_PREFIX):\n contents = os.listdir(MODEL_PATH_PREFIX)\n logger.info(f\"Models in cache: {contents}\")\n\n # Commit volume if requested (for download function)\n if commit_volume:\n gc.collect()\n logger.info(\"Attempting to commit volume...\")\n models_volume.commit()\n logger.info(\"Volume committed successfully\")\n\n return models\n\n@app.function(\n image=image,\n volumes={MODEL_PATH_PREFIX: models_volume},\n gpu=GPU_TYPE,\n timeout=600,\n)\ndef download_models():\n \"\"\"\n Helper function to download models used in marker into a Modal volume.\n \"\"\"\n import logging\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n logger.info(\"Downloading models to persistent volume...\")\n logger.info(f\"Volume mounted at: {MODEL_PATH_PREFIX}\")\n\n try:\n models = setup_models_with_cache_check(logger, commit_volume=True)\n return f\"Models downloaded successfully: {list(models.keys())}\"\n except Exception as e:\n logger.error(f\"Failed to download models: {e}\")\n raise\n\n@app.cls(\n image=image,\n gpu=GPU_TYPE,\n memory=16384,\n timeout=600, # 10 minute timeout for large documents\n volumes={MODEL_PATH_PREFIX: models_volume},\n scaledown_window=300,\n)\nclass MarkerModalDemoService:\n @modal.enter()\n def load_models(self):\n \"\"\"Load models once per container using @modal.enter() for efficiency.\"\"\"\n import logging\n import traceback\n\n logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n logger = logging.getLogger(__name__)\n\n logger.info(\"Loading Marker models using @modal.enter()...\")\n try:\n self.models = setup_models_with_cache_check(logger, commit_volume=True)\n except Exception as e:\n logger.error(f\"Error loading models: {e}\")\n traceback.print_exc()\n self.models = None\n\n @modal.asgi_app()\n def marker_api(self):\n import traceback\n import io\n import base64\n import logging\n from contextlib import asynccontextmanager\n from typing import Optional\n from pathlib import Path\n\n from fastapi import FastAPI, Form, File, UploadFile, HTTPException\n from fastapi.responses import JSONResponse\n\n from marker.converters.pdf import PdfConverter\n from marker.config.parser import ConfigParser\n from marker.settings import settings\n\n logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n logger = logging.getLogger(__name__)\n\n @asynccontextmanager\n async def lifespan(app: FastAPI):\n # Models are already loaded in @modal.enter()\n logger.info(\"Datalab Marker / Modal demo app starting up...\")\n yield\n logger.info(\"Datalab Marker / Modal demo app shutting down...\")\n\n # Create FastAPI app\n web_app = FastAPI(\n title=\"Datalab Marker PDF Conversion Service - Modal Demo\",\n description=\"Convert PDFs and documents to markdown, JSON, or HTML using Marker, deployed on Modal\",\n version=\"1.0.0\",\n lifespan=lifespan\n )\n\n @web_app.get(\"/health\")\n async def health_check():\n models_loaded = hasattr(self, 'models') and self.models is not None\n model_count = len(self.models) if models_loaded else 0\n\n # Check volume contents for debugging\n cache_exists = os.path.exists(MODEL_PATH_PREFIX)\n cache_contents = os.listdir(MODEL_PATH_PREFIX) if cache_exists else []\n\n return {\n \"status\": \"healthy\" if models_loaded else \"loading\",\n \"models_loaded\": models_loaded,\n \"model_count\": model_count,\n \"cache_dir\": MODEL_PATH_PREFIX,\n \"cache_exists\": cache_exists,\n \"cache_contents\": cache_contents[:10]\n }\n\n @web_app.post(\"/convert\")\n async def convert_document(\n file: UploadFile = File(..., description=\"Document to convert\"),\n page_range: Optional[str] = Form(None),\n force_ocr: bool = Form(False),\n paginate_output: bool = Form(False),\n output_format: str = Form(\"markdown\"),\n use_llm: bool = Form(False),\n ):\n \"\"\"Convert uploaded document to specified format.\"\"\"\n\n if not hasattr(self, 'models') or self.models is None:\n logger.error(\"Models not available for conversion\")\n raise HTTPException(status_code=503, detail=\"Models not loaded yet. Please wait for model initialization.\")\n\n # Validate file type\n allowed_extensions = {'.pdf', '.png', '.jpg', '.jpeg', '.tiff', '.bmp'}\n file_ext = Path(file.filename).suffix.lower()\n if file_ext not in allowed_extensions:\n raise HTTPException(\n status_code=400,\n detail=f\"Unsupported file type: {file_ext}. Supported: {allowed_extensions}\"\n )\n\n # Validate output format\n if output_format not in [\"markdown\", \"json\", \"html\", \"chunks\"]:\n raise HTTPException(\n status_code=400,\n detail=\"Output format must be one of: markdown, json, html, chunks\"\n )\n\n try:\n # Read file content\n file_content = await file.read()\n\n # Save to temporary file\n temp_path = f\"/tmp/{file.filename}\"\n with open(temp_path, \"wb\") as temp_file:\n temp_file.write(file_content)\n\n # Configure conversion parameters\n config = {\n \"filepath\": temp_path,\n \"page_range\": page_range,\n \"force_ocr\": force_ocr,\n \"paginate_output\": paginate_output,\n \"output_format\": output_format,\n \"use_llm\": use_llm,\n }\n\n # Create converter\n config_parser = ConfigParser(config)\n config_dict = config_parser.generate_config_dict()\n config_dict[\"pdftext_workers\"] = 1\n\n converter = PdfConverter(\n config=config_dict,\n artifact_dict=self.models,\n processor_list=config_parser.get_processors(),\n renderer=config_parser.get_renderer(),\n llm_service=config_parser.get_llm_service() if use_llm else None,\n )\n\n # Convert document - converter already applies the appropriate renderer\n logger.info(f\"Converting {file.filename} to {output_format}...\")\n rendered_output = converter(temp_path)\n\n # Extract content based on output format\n json_content = None\n html_content = None\n markdown_content = None\n encoded_images = {}\n\n if output_format == \"json\":\n # For JSON, return the structured data directly\n json_content = rendered_output.model_dump()\n else:\n from marker.output import text_from_rendered\n text, _, images = text_from_rendered(rendered_output)\n\n # Assign to appropriate content field\n if output_format == \"html\":\n html_content = text\n else:\n markdown_content = text\n\n # Encode images as base64\n for img_name, img_obj in images.items():\n byte_stream = io.BytesIO()\n img_obj.save(byte_stream, format=settings.OUTPUT_IMAGE_FORMAT)\n encoded_images[img_name] = base64.b64encode(byte_stream.getvalue()).decode('utf-8')\n\n metadata = rendered_output.metadata\n\n logger.info(f\"Conversion completed for {file.filename}\")\n\n # Clean up temp file\n os.unlink(temp_path)\n\n return JSONResponse({\n \"success\": True,\n \"filename\": file.filename,\n \"output_format\": output_format,\n \"json\": json_content,\n \"html\": html_content,\n \"markdown\": markdown_content,\n \"images\": encoded_images,\n \"metadata\": metadata,\n \"page_count\": len(metadata.get(\"page_stats\", [])),\n })\n\n except Exception as e:\n # Clean up temp file if it exists\n if os.path.exists(temp_path):\n os.unlink(temp_path)\n\n logger.error(f\"Conversion error for {file.filename}: {str(e)}\")\n traceback.print_exc()\n\n raise HTTPException(\n status_code=500,\n detail=f\"Conversion failed: {str(e)}\"\n )\n\n return web_app\n\n\n#\n# This does not get deployed. It's a useful entrypoint from your local CLI\n# that you can use to test your deployment. It'll store the\n# API response in a new file on your machine.\n#\n@app.local_entrypoint()\nasync def invoke_conversion(\n pdf_file: Optional[str] = None,\n output_format: str = \"markdown\",\n env: str = 'main'\n):\n \"\"\"\n Local entrypoint to test your deployed Marker endpoint in Modal.\n\n Usage:\n modal run marker_modal_deployment.py::invoke_conversion --pdf-file /path/to/file.pdf --output-format markdown\n \"\"\"\n import requests\n import json\n from pathlib import Path\n\n if not pdf_file:\n print(\"No PDF file specified. Use --pdf-file /path/to/your.pdf\")\n return\n\n pdf_path = Path(pdf_file)\n if not pdf_path.exists():\n print(f\"File not found: {pdf_file}\")\n return\n\n #\n # Get the web URL for our deployed service\n #\n try:\n service = modal.Cls.from_name(\n \"datalab-marker-modal-demo\",\n \"MarkerModalDemoService\",\n environment_name=env\n )\n web_url = service().marker_api.get_web_url()\n print(f\"Found deployed service at: {web_url}\")\n except Exception as e:\n print(f\"Error getting web URL: {e}\")\n print(\"Make sure you've deployed the service first with: modal deploy marker_modal_deployment.py\")\n return\n\n print(f\"Testing conversion of: {pdf_path.name}\")\n print(f\"Output format: {output_format}\")\n\n #\n # Test health endpoint first\n #\n try:\n health_response = requests.get(f\"{web_url}/health\")\n health_data = health_response.json()\n print(f\"Service health: {health_data['status']}\")\n print(f\"Models loaded: {health_data['models_loaded']} ({health_data['model_count']} models)\")\n\n if not health_data['models_loaded']:\n print(\"Warning: Models not loaded yet. First request may be slow.\")\n\n except Exception as e:\n print(f\"Health check failed: {e}\")\n\n #\n # Make conversion request\n #\n try:\n with open(pdf_path, 'rb') as f:\n files = {'file': (pdf_path.name, f, 'application/pdf')}\n data = {'output_format': output_format}\n\n print(f\"Sending request to {web_url}/convert...\")\n response = requests.post(f\"{web_url}/convert\", files=files, data=data)\n\n if response.status_code == 200:\n result = response.json()\n print(f\"✅ Conversion successful!\")\n print(f\"Filename: {result['filename']}\")\n print(f\"Format: {result['output_format']}\")\n print(f\"Pages: {result['page_count']}\")\n\n output_file = f\"{pdf_path.stem}_response.json\"\n with open(output_file, 'w', encoding='utf-8') as f:\n json.dump(result, f, indent=2, ensure_ascii=False)\n print(f\"Full API response saved to: {output_file}\")\n\n if result['images']:\n print(f\"Images extracted: {len(result['images'])}\")\n\n else:\n print(f\"❌ Conversion failed: {response.status_code}\")\n print(f\"Error: {response.text}\")\n\n except Exception as e:\n print(f\"Request failed: {e}\")\n"
},
{
"path": "extraction_app.py",
"content": "from marker.scripts.run_streamlit_app import extraction_app_cli\n\nif __name__ == \"__main__\":\n extraction_app_cli()\n"
},
{
"path": "marker/builders/__init__.py",
"content": "from typing import Optional\n\nfrom pydantic import BaseModel\n\nfrom marker.util import assign_config\n\n\nclass BaseBuilder:\n def __init__(self, config: Optional[BaseModel | dict] = None):\n assign_config(self, config)\n\n def __call__(self, data, *args, **kwargs):\n raise NotImplementedError\n"
},
{
"path": "marker/builders/document.py",
"content": "from typing import Annotated\n\nfrom marker.builders import BaseBuilder\nfrom marker.builders.layout import LayoutBuilder\nfrom marker.builders.line import LineBuilder\nfrom marker.builders.ocr import OcrBuilder\nfrom marker.providers.pdf import PdfProvider\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\nfrom marker.schema.groups.page import PageGroup\nfrom marker.schema.registry import get_block_class\n\n\nclass DocumentBuilder(BaseBuilder):\n \"\"\"\n Constructs a Document given a PdfProvider, LayoutBuilder, and OcrBuilder.\n \"\"\"\n lowres_image_dpi: Annotated[\n int,\n \"DPI setting for low-resolution page images used for Layout and Line Detection.\",\n ] = 96\n highres_image_dpi: Annotated[\n int,\n \"DPI setting for high-resolution page images used for OCR.\",\n ] = 192\n disable_ocr: Annotated[\n bool,\n \"Disable OCR processing.\",\n ] = False\n\n def __call__(self, provider: PdfProvider, layout_builder: LayoutBuilder, line_builder: LineBuilder, ocr_builder: OcrBuilder):\n document = self.build_document(provider)\n layout_builder(document, provider)\n line_builder(document, provider)\n if not self.disable_ocr:\n ocr_builder(document, provider)\n return document\n\n def build_document(self, provider: PdfProvider):\n PageGroupClass: PageGroup = get_block_class(BlockTypes.Page)\n lowres_images = provider.get_images(provider.page_range, self.lowres_image_dpi)\n highres_images = provider.get_images(provider.page_range, self.highres_image_dpi)\n initial_pages = [\n PageGroupClass(\n page_id=p,\n lowres_image=lowres_images[i],\n highres_image=highres_images[i],\n polygon=provider.get_page_bbox(p),\n refs=provider.get_page_refs(p)\n ) for i, p in enumerate(provider.page_range)\n ]\n DocumentClass: Document = get_block_class(BlockTypes.Document)\n return DocumentClass(filepath=provider.filepath, pages=initial_pages)\n"
},
{
"path": "marker/builders/layout.py",
"content": "from typing import Annotated, List\n\nfrom surya.layout import LayoutPredictor\nfrom surya.layout.schema import LayoutResult, LayoutBox\n\nfrom marker.builders import BaseBuilder\nfrom marker.providers.pdf import PdfProvider\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\nfrom marker.schema.groups.page import PageGroup\nfrom marker.schema.polygon import PolygonBox\nfrom marker.schema.registry import get_block_class\nfrom marker.settings import settings\n\n\nclass LayoutBuilder(BaseBuilder):\n \"\"\"\n A builder for performing layout detection on PDF pages and merging the results into the document.\n \"\"\"\n\n layout_batch_size: Annotated[\n int,\n \"The batch size to use for the layout model.\",\n \"Default is None, which will use the default batch size for the model.\",\n ] = None\n force_layout_block: Annotated[\n str,\n \"Skip layout and force every page to be treated as a specific block type.\",\n ] = None\n disable_tqdm: Annotated[\n bool,\n \"Disable tqdm progress bars.\",\n ] = False\n expand_block_types: Annotated[\n List[BlockTypes],\n \"Block types whose bounds should be expanded to accomodate missing regions\",\n ] = [\n BlockTypes.Picture,\n BlockTypes.Figure,\n BlockTypes.ComplexRegion,\n ] # Does not include groups since they are only injected later\n max_expand_frac: Annotated[\n float, \"The maximum fraction to expand the layout box bounds by\"\n ] = 0.05\n\n def __init__(self, layout_model: LayoutPredictor, config=None):\n self.layout_model = layout_model\n\n super().__init__(config)\n\n def __call__(self, document: Document, provider: PdfProvider):\n if self.force_layout_block is not None:\n # Assign the full content of every page to a single layout type\n layout_results = self.forced_layout(document.pages)\n else:\n layout_results = self.surya_layout(document.pages)\n self.add_blocks_to_pages(document.pages, layout_results)\n self.expand_layout_blocks(document)\n\n def get_batch_size(self):\n if self.layout_batch_size is not None:\n return self.layout_batch_size\n elif settings.TORCH_DEVICE_MODEL == \"cuda\":\n return 12\n return 6\n\n def forced_layout(self, pages: List[PageGroup]) -> List[LayoutResult]:\n layout_results = []\n for page in pages:\n layout_results.append(\n LayoutResult(\n image_bbox=page.polygon.bbox,\n bboxes=[\n LayoutBox(\n label=self.force_layout_block,\n position=0,\n top_k={self.force_layout_block: 1},\n polygon=page.polygon.polygon,\n ),\n ],\n sliced=False,\n )\n )\n return layout_results\n\n def surya_layout(self, pages: List[PageGroup]) -> List[LayoutResult]:\n self.layout_model.disable_tqdm = self.disable_tqdm\n layout_results = self.layout_model(\n [p.get_image(highres=False) for p in pages],\n batch_size=int(self.get_batch_size()),\n )\n return layout_results\n\n def expand_layout_blocks(self, document: Document):\n for page in document.pages:\n # Collect all blocks on this page as PolygonBox for easy access\n page_blocks = [document.get_block(bid) for bid in page.structure]\n page_size = page.polygon.size\n\n for block_id in page.structure:\n block = document.get_block(block_id)\n if block.block_type in self.expand_block_types:\n other_blocks = [b for b in page_blocks if b != block]\n if not other_blocks:\n block.polygon = block.polygon.expand(\n self.max_expand_frac, self.max_expand_frac\n ).fit_to_bounds((0, 0, *page_size))\n continue\n\n min_gap = min(\n block.polygon.minimum_gap(other.polygon)\n for other in other_blocks\n )\n if min_gap <= 0:\n continue\n\n x_expand_frac = (\n min_gap / block.polygon.width if block.polygon.width > 0 else 0\n )\n y_expand_frac = (\n min_gap / block.polygon.height\n if block.polygon.height > 0\n else 0\n )\n\n block.polygon = block.polygon.expand(\n min(self.max_expand_frac, x_expand_frac),\n min(self.max_expand_frac, y_expand_frac),\n ).fit_to_bounds((0, 0, *page_size))\n\n def add_blocks_to_pages(\n self, pages: List[PageGroup], layout_results: List[LayoutResult]\n ):\n for page, layout_result in zip(pages, layout_results):\n layout_page_size = PolygonBox.from_bbox(layout_result.image_bbox).size\n provider_page_size = page.polygon.size\n page.layout_sliced = (\n layout_result.sliced\n ) # This indicates if the page was sliced by the layout model\n for bbox in sorted(layout_result.bboxes, key=lambda x: x.position):\n block_cls = get_block_class(BlockTypes[bbox.label])\n layout_block = page.add_block(\n block_cls, PolygonBox(polygon=bbox.polygon)\n )\n layout_block.polygon = layout_block.polygon.rescale(\n layout_page_size, provider_page_size\n ).fit_to_bounds((0, 0, *provider_page_size))\n layout_block.top_k = {\n BlockTypes[label]: prob\n for (label, prob) in bbox.top_k.items()\n if label in BlockTypes.__members__\n }\n page.add_structure(layout_block)\n\n # Ensure page has non-empty structure\n if page.structure is None:\n page.structure = []\n\n # Ensure page has non-empty children\n if page.children is None:\n page.children = []\n"
},
{
"path": "marker/builders/line.py",
"content": "from copy import deepcopy\nfrom typing import Annotated, List, Tuple\n\nimport numpy as np\nfrom PIL import Image\nimport cv2\n\nfrom surya.detection import DetectionPredictor\nfrom surya.ocr_error import OCRErrorPredictor\n\nfrom marker.builders import BaseBuilder\nfrom marker.providers import ProviderOutput, ProviderPageLines\nfrom marker.providers.pdf import PdfProvider\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\nfrom marker.schema.groups.page import PageGroup\nfrom marker.schema.polygon import PolygonBox\nfrom marker.schema.registry import get_block_class\nfrom marker.schema.text.line import Line\nfrom marker.settings import settings\nfrom marker.util import matrix_intersection_area, sort_text_lines\nfrom marker.utils.image import is_blank_image\n\n\nclass LineBuilder(BaseBuilder):\n \"\"\"\n A builder for detecting text lines. Merges the detected lines with the lines from the provider\n \"\"\"\n\n detection_batch_size: Annotated[\n int,\n \"The batch size to use for the detection model.\",\n \"Default is None, which will use the default batch size for the model.\",\n ] = None\n ocr_error_batch_size: Annotated[\n int,\n \"The batch size to use for the ocr error detection model.\",\n \"Default is None, which will use the default batch size for the model.\",\n ] = None\n layout_coverage_min_lines: Annotated[\n int,\n \"The minimum number of PdfProvider lines that must be covered by the layout model\",\n \"to consider the lines from the PdfProvider valid.\",\n ] = 1\n layout_coverage_threshold: Annotated[\n float,\n \"The minimum coverage ratio required for the layout model to consider\",\n \"the lines from the PdfProvider valid.\",\n ] = 0.25\n min_document_ocr_threshold: Annotated[\n float,\n \"If less pages than this threshold are good, OCR will happen in the document. Otherwise it will not.\",\n ] = 0.85\n provider_line_provider_line_min_overlap_pct: Annotated[\n float,\n \"The percentage of a provider line that has to be covered by a detected line\",\n ] = 0.1\n excluded_for_coverage: Annotated[\n Tuple[BlockTypes],\n \"A list of block types to exclude from the layout coverage check.\",\n ] = (\n BlockTypes.Figure,\n BlockTypes.Picture,\n BlockTypes.Table,\n BlockTypes.FigureGroup,\n BlockTypes.TableGroup,\n BlockTypes.PictureGroup,\n )\n ocr_remove_blocks: Tuple[BlockTypes, ...] = (\n BlockTypes.Table,\n BlockTypes.Form,\n BlockTypes.TableOfContents,\n )\n disable_tqdm: Annotated[\n bool,\n \"Disable tqdm progress bars.\",\n ] = False\n disable_ocr: Annotated[\n bool,\n \"Disable OCR for the document. This will only use the lines from the provider.\",\n ] = False\n keep_chars: Annotated[bool, \"Keep individual characters.\"] = False\n detection_line_min_confidence: Annotated[float, \"Minimum confidence for a detected line to be included\"] = 0.8\n\n def __init__(\n self,\n detection_model: DetectionPredictor,\n ocr_error_model: OCRErrorPredictor,\n config=None,\n ):\n super().__init__(config)\n\n self.detection_model = detection_model\n self.ocr_error_model = ocr_error_model\n\n def __call__(self, document: Document, provider: PdfProvider):\n # Disable inline detection for documents where layout model doesn't detect any equations\n # Also disable if we won't use the inline detections (if we aren't using the LLM)\n provider_lines, ocr_lines = self.get_all_lines(document, provider)\n self.merge_blocks(document, provider_lines, ocr_lines)\n\n def get_detection_batch_size(self):\n if self.detection_batch_size is not None:\n return self.detection_batch_size\n elif settings.TORCH_DEVICE_MODEL == \"cuda\":\n return 10\n return 4\n\n def get_ocr_error_batch_size(self):\n if self.ocr_error_batch_size is not None:\n return self.ocr_error_batch_size\n elif settings.TORCH_DEVICE_MODEL == \"cuda\":\n return 14\n return 4\n\n def get_detection_results(\n self, page_images: List[Image.Image], run_detection: List[bool]\n ):\n self.detection_model.disable_tqdm = self.disable_tqdm\n page_detection_results = self.detection_model(\n images=page_images, batch_size=self.get_detection_batch_size()\n )\n\n assert len(page_detection_results) == sum(run_detection)\n detection_results = []\n idx = 0\n for good in run_detection:\n if good:\n detection_results.append(page_detection_results[idx])\n idx += 1\n else:\n detection_results.append(None)\n assert idx == len(page_images)\n\n assert len(run_detection) == len(detection_results)\n return detection_results\n\n def get_all_lines(self, document: Document, provider: PdfProvider):\n ocr_error_detection_results = self.ocr_error_detection(\n document.pages, provider.page_lines\n )\n\n boxes_to_ocr = {page.page_id: [] for page in document.pages}\n page_lines = {page.page_id: [] for page in document.pages}\n\n LineClass: Line = get_block_class(BlockTypes.Line)\n\n layout_good = []\n for document_page, ocr_error_detection_label in zip(\n document.pages, ocr_error_detection_results.labels\n ):\n document_page.ocr_errors_detected = ocr_error_detection_label == \"bad\"\n provider_lines: List[ProviderOutput] = provider.page_lines.get(\n document_page.page_id, []\n )\n provider_lines_good = all(\n [\n bool(provider_lines),\n not document_page.ocr_errors_detected,\n self.check_layout_coverage(document_page, provider_lines),\n self.check_line_overlaps(\n document_page, provider_lines\n ), # Ensure provider lines don't overflow the page or intersect\n ]\n )\n if self.disable_ocr:\n provider_lines_good = True\n\n layout_good.append(provider_lines_good)\n\n run_detection = [not good for good in layout_good]\n page_images = [\n page.get_image(highres=False, remove_blocks=self.ocr_remove_blocks)\n for page, bad in zip(document.pages, run_detection)\n if bad\n ]\n\n # Note: run_detection is longer than page_images, since it has a value for each page, not just good ones\n # Detection results and inline detection results are for every page (we use run_detection to make the list full length)\n detection_results = self.get_detection_results(page_images, run_detection)\n\n assert len(detection_results) == len(layout_good) == len(document.pages)\n for document_page, detection_result, provider_lines_good in zip(\n document.pages, detection_results, layout_good\n ):\n provider_lines: List[ProviderOutput] = provider.page_lines.get(\n document_page.page_id, []\n )\n\n # Setup detection results\n detection_boxes = []\n if detection_result:\n detection_boxes = [\n PolygonBox(polygon=box.polygon) for box in detection_result.bboxes if box.confidence > self.detection_line_min_confidence\n ]\n\n detection_boxes = sort_text_lines(detection_boxes)\n\n if provider_lines_good:\n document_page.text_extraction_method = \"pdftext\"\n\n # Mark extraction method as pdftext, since all lines are good\n for provider_line in provider_lines:\n provider_line.line.text_extraction_method = \"pdftext\"\n\n page_lines[document_page.page_id] = provider_lines\n else:\n document_page.text_extraction_method = \"surya\"\n boxes_to_ocr[document_page.page_id].extend(detection_boxes)\n\n # Dummy lines to merge into the document - Contains no spans, will be filled in later by OCRBuilder\n ocr_lines = {document_page.page_id: [] for document_page in document.pages}\n for page_id, page_ocr_boxes in boxes_to_ocr.items():\n page_size = provider.get_page_bbox(page_id).size\n image_size = document.get_page(page_id).get_image(highres=False).size\n for box_to_ocr in page_ocr_boxes:\n line_polygon = PolygonBox(polygon=box_to_ocr.polygon).rescale(\n image_size, page_size\n )\n ocr_lines[page_id].append(\n ProviderOutput(\n line=LineClass(\n polygon=line_polygon,\n page_id=page_id,\n text_extraction_method=\"surya\",\n ),\n spans=[],\n chars=[],\n )\n )\n\n return page_lines, ocr_lines\n\n def ocr_error_detection(\n self, pages: List[PageGroup], provider_page_lines: ProviderPageLines\n ):\n page_texts = []\n for document_page in pages:\n provider_lines = provider_page_lines.get(document_page.page_id, [])\n page_text = \"\\n\".join(\n \" \".join(s.text for s in line.spans) for line in provider_lines\n )\n page_texts.append(page_text)\n\n self.ocr_error_model.disable_tqdm = self.disable_tqdm\n ocr_error_detection_results = self.ocr_error_model(\n page_texts, batch_size=int(self.get_ocr_error_batch_size())\n )\n return ocr_error_detection_results\n\n def check_line_overlaps(\n self, document_page: PageGroup, provider_lines: List[ProviderOutput]\n ) -> bool:\n provider_bboxes = [line.line.polygon.bbox for line in provider_lines]\n # Add a small margin to account for minor overflows\n page_bbox = document_page.polygon.expand(5, 5).bbox\n\n for bbox in provider_bboxes:\n if bbox[0] < page_bbox[0]:\n return False\n if bbox[1] < page_bbox[1]:\n return False\n if bbox[2] > page_bbox[2]:\n return False\n if bbox[3] > page_bbox[3]:\n return False\n\n intersection_matrix = matrix_intersection_area(provider_bboxes, provider_bboxes)\n for i, line in enumerate(provider_lines):\n intersect_counts = np.sum(\n intersection_matrix[i]\n > self.provider_line_provider_line_min_overlap_pct\n )\n\n # There should be one intersection with itself\n if intersect_counts > 2:\n return False\n\n return True\n\n def check_layout_coverage(\n self,\n document_page: PageGroup,\n provider_lines: List[ProviderOutput],\n ):\n covered_blocks = 0\n total_blocks = 0\n large_text_blocks = 0\n\n layout_blocks = [\n document_page.get_block(block) for block in document_page.structure\n ]\n layout_blocks = [\n b for b in layout_blocks if b.block_type not in self.excluded_for_coverage\n ]\n\n layout_bboxes = [block.polygon.bbox for block in layout_blocks]\n provider_bboxes = [line.line.polygon.bbox for line in provider_lines]\n\n if len(layout_bboxes) == 0:\n return True\n\n if len(provider_bboxes) == 0:\n return False\n\n intersection_matrix = matrix_intersection_area(layout_bboxes, provider_bboxes)\n\n for idx, layout_block in enumerate(layout_blocks):\n total_blocks += 1\n intersecting_lines = np.count_nonzero(intersection_matrix[idx] > 0)\n\n if intersecting_lines >= self.layout_coverage_min_lines:\n covered_blocks += 1\n\n if (\n layout_block.polygon.intersection_pct(document_page.polygon) > 0.8\n and layout_block.block_type == BlockTypes.Text\n ):\n large_text_blocks += 1\n\n coverage_ratio = covered_blocks / total_blocks if total_blocks > 0 else 1\n text_okay = coverage_ratio >= self.layout_coverage_threshold\n\n # Model will sometimes say there is a single block of text on the page when it is blank\n if not text_okay and (total_blocks == 1 and large_text_blocks == 1):\n text_okay = True\n return text_okay\n\n def filter_blank_lines(self, page: PageGroup, lines: List[ProviderOutput]):\n page_size = (page.polygon.width, page.polygon.height)\n page_image = page.get_image()\n image_size = page_image.size\n\n good_lines = []\n for line in lines:\n line_polygon_rescaled = deepcopy(line.line.polygon).rescale(\n page_size, image_size\n )\n line_bbox = line_polygon_rescaled.fit_to_bounds((0, 0, *image_size)).bbox\n\n if not is_blank_image(page_image.crop(line_bbox)):\n good_lines.append(line)\n\n return good_lines\n\n def merge_blocks(\n self,\n document: Document,\n page_provider_lines: ProviderPageLines,\n page_ocr_lines: ProviderPageLines,\n ):\n for document_page in document.pages:\n provider_lines: List[ProviderOutput] = page_provider_lines[\n document_page.page_id\n ]\n ocr_lines: List[ProviderOutput] = page_ocr_lines[document_page.page_id]\n\n # Only one or the other will have lines\n # Filter out blank lines which come from bad provider boxes, or invisible text\n merged_lines = self.filter_blank_lines(\n document_page, provider_lines + ocr_lines\n )\n\n # Text extraction method is overridden later for OCRed documents\n document_page.merge_blocks(\n merged_lines,\n text_extraction_method=\"pdftext\" if provider_lines else \"surya\",\n keep_chars=self.keep_chars,\n )\n"
},
{
"path": "marker/builders/ocr.py",
"content": "import copy\nfrom typing import Annotated, List\n\nfrom ftfy import fix_text\nfrom PIL import Image\nfrom surya.common.surya.schema import TaskNames\nfrom surya.recognition import RecognitionPredictor, OCRResult, TextChar\n\nfrom marker.builders import BaseBuilder\nfrom marker.providers.pdf import PdfProvider\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import BlockId\nfrom marker.schema.blocks.base import Block\nfrom marker.schema.document import Document\nfrom marker.schema.groups import PageGroup\nfrom marker.schema.registry import get_block_class\nfrom marker.schema.text.char import Char\nfrom marker.schema.text.line import Line\nfrom marker.schema.text.span import Span\nfrom marker.settings import settings\nfrom marker.schema.polygon import PolygonBox\nfrom marker.util import get_opening_tag_type, get_closing_tag_type\n\n\nclass OcrBuilder(BaseBuilder):\n \"\"\"\n A builder for performing OCR on PDF pages and merging the results into the document.\n \"\"\"\n\n recognition_batch_size: Annotated[\n int,\n \"The batch size to use for the recognition model.\",\n \"Default is None, which will use the default batch size for the model.\",\n ] = None\n disable_tqdm: Annotated[\n bool,\n \"Disable tqdm progress bars.\",\n ] = False\n # We can skip tables here, since the TableProcessor will re-OCR\n skip_ocr_blocks: Annotated[\n List[BlockTypes],\n \"Blocktypes to skip OCRing by the model in this stage.\"\n \"By default, this avoids recognizing lines inside equations/tables (handled later), figures, and pictures\",\n \"Note that we **do not** have to skip group types, since they are not built by this point\"\n ] = [\n BlockTypes.Equation,\n BlockTypes.Figure,\n BlockTypes.Picture,\n BlockTypes.Table,\n BlockTypes.Form,\n BlockTypes.TableOfContents,\n ]\n full_ocr_block_types: Annotated[\n List[BlockTypes],\n \"Blocktypes for which OCR is done at the **block level** instead of line-level.\"\n \"This feature is still in beta, and should be used sparingly.\"\n ] = [\n BlockTypes.SectionHeader,\n BlockTypes.ListItem,\n BlockTypes.Footnote,\n BlockTypes.Text,\n BlockTypes.TextInlineMath,\n BlockTypes.Code,\n BlockTypes.Caption,\n ]\n ocr_task_name: Annotated[\n str,\n \"The OCR mode to use, see surya for details. Set to 'ocr_without_boxes' for potentially better performance, at the expense of formatting.\",\n ] = TaskNames.ocr_with_boxes\n keep_chars: Annotated[bool, \"Keep individual characters.\"] = False\n disable_ocr_math: Annotated[bool, \"Disable inline math recognition in OCR\"] = False\n drop_repeated_text: Annotated[bool, \"Drop repeated text in OCR results.\"] = False\n block_mode_intersection_thresh: Annotated[float, \"Max intersection before falling back to line mode\"] = 0.5\n block_mode_max_lines: Annotated[int, \"Max lines within a block before falling back to line mode\"] = 15\n block_mode_max_height_frac: Annotated[float, \"Max height of a block as a percentage of the page before falling back to line mode\"] = 0.5\n\n def __init__(self, recognition_model: RecognitionPredictor, config=None):\n super().__init__(config)\n\n self.recognition_model = recognition_model\n\n def __call__(self, document: Document, provider: PdfProvider):\n pages_to_ocr = [page for page in document.pages if page.text_extraction_method == 'surya']\n ocr_page_images, block_polygons, block_ids, block_original_texts = (\n self.get_ocr_images_polygons_ids(document, pages_to_ocr, provider)\n )\n self.ocr_extraction(\n document,\n pages_to_ocr,\n ocr_page_images,\n block_polygons,\n block_ids,\n block_original_texts,\n )\n\n def get_recognition_batch_size(self):\n if self.recognition_batch_size is not None:\n return self.recognition_batch_size\n elif settings.TORCH_DEVICE_MODEL == \"cuda\":\n return 48\n elif settings.TORCH_DEVICE_MODEL == \"mps\":\n return 16\n return 32\n\n def select_ocr_blocks_by_mode(\n self, page: PageGroup, block: Block, block_lines: List[Block], page_max_intersection_pct: float\n ):\n if any([\n page_max_intersection_pct > self.block_mode_intersection_thresh,\n block.block_type not in self.full_ocr_block_types,\n len(block_lines) > self.block_mode_max_lines,\n block.polygon.height >= self.block_mode_max_height_frac * page.polygon.height\n ]):\n # Line mode\n return block_lines\n\n # Block mode\n return [block]\n\n def get_ocr_images_polygons_ids(\n self, document: Document, pages: List[PageGroup], provider: PdfProvider\n ):\n highres_images, highres_polys, block_ids, block_original_texts = [], [], [], []\n for document_page in pages:\n page_highres_image = document_page.get_image(highres=True)\n page_highres_polys = []\n page_block_ids = []\n page_block_original_texts = []\n\n page_size = provider.get_page_bbox(document_page.page_id).size\n image_size = page_highres_image.size\n max_intersection_pct = document_page.compute_max_structure_block_intersection_pct()\n for block in document_page.structure_blocks(document):\n if block.block_type in self.skip_ocr_blocks:\n # Skip OCR\n continue\n\n block_lines = block.contained_blocks(document, [BlockTypes.Line])\n blocks_to_ocr = self.select_ocr_blocks_by_mode(document_page, block, block_lines, max_intersection_pct)\n\n block.text_extraction_method = \"surya\"\n for block in blocks_to_ocr:\n # Fit the polygon to image bounds since PIL image crop expands by default which might create bad images for the OCR model.\n block_polygon_rescaled = (\n copy.deepcopy(block.polygon)\n .rescale(page_size, image_size)\n .fit_to_bounds((0, 0, *image_size))\n )\n block_bbox_rescaled = block_polygon_rescaled.polygon\n block_bbox_rescaled = [\n [int(x) for x in point] for point in block_bbox_rescaled\n ]\n\n page_highres_polys.append(block_bbox_rescaled)\n page_block_ids.append(block.id)\n page_block_original_texts.append(\"\")\n\n highres_images.append(page_highres_image)\n highres_polys.append(page_highres_polys)\n block_ids.append(page_block_ids)\n block_original_texts.append(page_block_original_texts)\n\n return highres_images, highres_polys, block_ids, block_original_texts\n\n def ocr_extraction(\n self,\n document: Document,\n pages: List[PageGroup],\n images: List[any],\n block_polygons: List[List[List[List[int]]]], # polygons\n block_ids: List[List[BlockId]],\n block_original_texts: List[List[str]],\n ):\n if sum(len(b) for b in block_polygons) == 0:\n return\n\n self.recognition_model.disable_tqdm = self.disable_tqdm\n recognition_results: List[OCRResult] = self.recognition_model(\n images=images,\n task_names=[self.ocr_task_name] * len(images),\n polygons=block_polygons,\n input_text=block_original_texts,\n recognition_batch_size=int(self.get_recognition_batch_size()),\n sort_lines=False,\n math_mode=not self.disable_ocr_math,\n drop_repeated_text=self.drop_repeated_text,\n max_sliding_window=2148,\n max_tokens=2048\n )\n\n assert len(recognition_results) == len(images) == len(pages) == len(block_ids), (\n f\"Mismatch in OCR lengths: {len(recognition_results)}, {len(images)}, {len(pages)}, {len(block_ids)}\"\n )\n for document_page, page_recognition_result, page_block_ids, image in zip(\n pages, recognition_results, block_ids, images\n ):\n for block_id, block_ocr_result in zip(\n page_block_ids, page_recognition_result.text_lines\n ):\n if block_ocr_result.original_text_good:\n continue\n if not fix_text(block_ocr_result.text):\n continue\n \n block = document_page.get_block(block_id)\n # This is a nested list of spans, so multiple lines are supported\n all_line_spans = self.spans_from_html_chars(\n block_ocr_result.chars, document_page, image\n )\n if block.block_type == BlockTypes.Line:\n # flatten all spans across lines\n flat_spans = [s for line_spans in all_line_spans for s in line_spans]\n self.replace_line_spans(document, document_page, block, flat_spans)\n else:\n # Clear out any old lines. Mark as removed for the json ocr renderer\n for line in block.contained_blocks(document_page, block_types=[BlockTypes.Line]):\n line.removed = True\n block.structure = []\n\n for line_spans in all_line_spans:\n # TODO Replace this polygon with the polygon for each line, constructed from the spans\n # This needs the OCR model bbox predictions to improve first\n new_line = Line(\n polygon=block.polygon,\n page_id=block.page_id,\n text_extraction_method=\"surya\"\n )\n document_page.add_full_block(new_line)\n block.add_structure(new_line)\n self.replace_line_spans(document, document_page, new_line, line_spans)\n\n # TODO Fix polygons when we cut the span into multiple spans\n def link_and_break_span(self, span: Span, text: str, match_text, url: str):\n before_text, _, after_text = text.partition(match_text)\n before_span, after_span = None, None\n if before_text:\n before_span = copy.deepcopy(span)\n before_span.structure = [] # Avoid duplicate characters\n before_span.text = before_text\n if after_text:\n after_span = copy.deepcopy(span)\n after_span.text = after_text\n after_span.structure = [] # Avoid duplicate characters\n\n match_span = copy.deepcopy(span)\n match_span.text = match_text\n match_span.url = url\n\n return before_span, match_span, after_span\n\n # Pull all refs from old spans and attempt to insert back into appropriate place in new spans\n def replace_line_spans(\n self, document: Document, page: PageGroup, line: Line, new_spans: List[Span]\n ):\n old_spans = line.contained_blocks(document, [BlockTypes.Span])\n text_ref_matching = {span.text: span.url for span in old_spans if span.url}\n\n # Insert refs into new spans, since the OCR model does not (cannot) generate these\n final_new_spans = []\n for span in new_spans:\n # Use for copying attributes into new spans\n original_span = copy.deepcopy(span)\n remaining_text = span.text\n while remaining_text:\n matched = False\n for match_text, url in text_ref_matching.items():\n if match_text in remaining_text:\n matched = True\n before, current, after = self.link_and_break_span(\n original_span, remaining_text, match_text, url\n )\n if before:\n final_new_spans.append(before)\n final_new_spans.append(current)\n if after:\n remaining_text = after.text\n else:\n remaining_text = \"\" # No more text left\n # Prevent repeat matches\n del text_ref_matching[match_text]\n break\n if not matched:\n remaining_span = copy.deepcopy(original_span)\n remaining_span.text = remaining_text\n final_new_spans.append(remaining_span)\n break\n\n # Clear the old spans from the line\n line.structure = []\n for span in final_new_spans:\n page.add_full_block(span)\n line.structure.append(span.id)\n\n def assign_chars(self, span: Span, current_chars: List[Char]):\n if self.keep_chars:\n span.structure = [c.id for c in current_chars]\n\n return []\n\n def store_char(self, char: Char, current_chars: List[Char], page: PageGroup):\n if self.keep_chars:\n current_chars.append(char)\n page.add_full_block(char)\n\n def spans_from_html_chars(\n self, chars: List[TextChar], page: PageGroup, image: Image.Image\n ) -> List[List[Span]]:\n # Turn input characters from surya into spans - also store the raw characters\n SpanClass: Span = get_block_class(BlockTypes.Span)\n CharClass: Char = get_block_class(BlockTypes.Char)\n\n all_line_spans = []\n current_line_spans = []\n formats = {\"plain\"}\n current_span = None\n current_chars = []\n image_size = image.size\n\n for idx, char in enumerate(chars):\n char_box = PolygonBox(polygon=char.polygon).rescale(\n image_size, page.polygon.size\n )\n marker_char = CharClass(\n text=char.text,\n idx=idx,\n page_id=page.page_id,\n polygon=char_box,\n )\n\n if char.text == \"
\":\n if current_span:\n current_chars = self.assign_chars(current_span, current_chars)\n current_line_spans.append(current_span)\n current_span = None\n if current_line_spans:\n current_line_spans[-1].text += \"\\n\"\n all_line_spans.append(current_line_spans)\n current_line_spans = []\n continue\n\n is_opening_tag, format = get_opening_tag_type(char.text)\n if is_opening_tag and format not in formats:\n formats.add(format)\n if current_span:\n current_chars = self.assign_chars(current_span, current_chars)\n current_line_spans.append(current_span)\n current_span = None\n\n if format == \"math\":\n current_span = SpanClass(\n text=\"\",\n formats=list(formats),\n page_id=page.page_id,\n polygon=char_box,\n minimum_position=0,\n maximum_position=0,\n font=\"Unknown\",\n font_weight=0,\n font_size=0,\n )\n self.store_char(marker_char, current_chars, page)\n continue\n\n is_closing_tag, format = get_closing_tag_type(char.text)\n if is_closing_tag:\n # Useful since the OCR model sometimes returns closing tags without an opening tag\n try:\n formats.remove(format)\n except Exception:\n continue\n if current_span:\n current_chars = self.assign_chars(current_span, current_chars)\n current_line_spans.append(current_span)\n current_span = None\n continue\n\n if not current_span:\n current_span = SpanClass(\n text=fix_text(char.text),\n formats=list(formats),\n page_id=page.page_id,\n polygon=char_box,\n minimum_position=0,\n maximum_position=0,\n font=\"Unknown\",\n font_weight=0,\n font_size=0,\n )\n self.store_char(marker_char, current_chars, page)\n continue\n\n current_span.text = fix_text(current_span.text + char.text)\n self.store_char(marker_char, current_chars, page)\n\n # Tokens inside a math span don't have valid boxes, so we skip the merging\n if \"math\" not in formats:\n current_span.polygon = current_span.polygon.merge([char_box])\n\n # Add the last span to the list\n if current_span:\n self.assign_chars(current_span, current_chars)\n current_line_spans.append(current_span)\n\n # flush last line\n if current_line_spans:\n current_line_spans[-1].text += \"\\n\"\n all_line_spans.append(current_line_spans)\n\n return all_line_spans\n"
},
{
"path": "marker/builders/structure.py",
"content": "from typing import Annotated\n\nfrom marker.builders import BaseBuilder\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import Text\nfrom marker.schema.document import Document\nfrom marker.schema.groups import ListGroup\nfrom marker.schema.groups.page import PageGroup\nfrom marker.schema.registry import get_block_class\n\n\nclass StructureBuilder(BaseBuilder):\n \"\"\"\n A builder for grouping blocks together based on their structure.\n \"\"\"\n gap_threshold: Annotated[\n float,\n \"The minimum gap between blocks to consider them part of the same group.\",\n ] = 0.05\n list_gap_threshold: Annotated[\n float,\n \"The minimum gap between list items to consider them part of the same group.\",\n ] = 0.1\n\n def __init__(self, config=None):\n super().__init__(config)\n\n def __call__(self, document: Document):\n for page in document.pages:\n self.group_caption_blocks(page)\n self.group_lists(page)\n self.unmark_lists(page)\n\n def group_caption_blocks(self, page: PageGroup):\n gap_threshold_px = self.gap_threshold * page.polygon.height\n static_page_structure = page.structure.copy()\n remove_ids = list()\n\n for i, block_id in enumerate(static_page_structure):\n block = page.get_block(block_id)\n if block.block_type not in [BlockTypes.Table, BlockTypes.Figure, BlockTypes.Picture]:\n continue\n\n if block.id in remove_ids:\n continue\n\n block_structure = [block_id]\n selected_polygons = [block.polygon]\n caption_types = [BlockTypes.Caption, BlockTypes.Footnote]\n\n prev_block = page.get_prev_block(block)\n next_block = page.get_next_block(block)\n\n if prev_block and \\\n prev_block.block_type in caption_types and \\\n prev_block.polygon.minimum_gap(block.polygon) < gap_threshold_px and \\\n prev_block.id not in remove_ids:\n block_structure.insert(0, prev_block.id)\n selected_polygons.append(prev_block.polygon)\n\n if next_block and \\\n next_block.block_type in caption_types and \\\n next_block.polygon.minimum_gap(block.polygon) < gap_threshold_px:\n block_structure.append(next_block.id)\n selected_polygons.append(next_block.polygon)\n\n if len(block_structure) > 1:\n # Create a merged block\n new_block_cls = get_block_class(BlockTypes[block.block_type.name + \"Group\"])\n new_polygon = block.polygon.merge(selected_polygons)\n group_block = page.add_block(new_block_cls, new_polygon)\n group_block.structure = block_structure\n\n # Update the structure of the page to reflect the new block\n page.update_structure_item(block_id, group_block.id)\n remove_ids.extend(block_structure)\n page.remove_structure_items(remove_ids)\n\n def group_lists(self, page: PageGroup):\n gap_threshold_px = self.list_gap_threshold * page.polygon.height\n static_page_structure = page.structure.copy()\n remove_ids = list()\n for i, block_id in enumerate(static_page_structure):\n block = page.get_block(block_id)\n if block.block_type not in [BlockTypes.ListItem]:\n continue\n\n if block.id in remove_ids:\n continue\n\n block_structure = [block_id]\n selected_polygons = [block.polygon]\n\n for j, next_block_id in enumerate(page.structure[i + 1:]):\n next_block = page.get_block(next_block_id)\n if all([\n next_block.block_type == BlockTypes.ListItem,\n next_block.polygon.minimum_gap(selected_polygons[-1]) < gap_threshold_px\n ]):\n block_structure.append(next_block_id)\n selected_polygons.append(next_block.polygon)\n else:\n break\n\n if len(block_structure) > 1:\n new_polygon = block.polygon.merge(selected_polygons)\n group_block = page.add_block(ListGroup, new_polygon)\n group_block.structure = block_structure\n\n # Update the structure of the page to reflect the new block\n page.update_structure_item(block_id, group_block.id)\n remove_ids.extend(block_structure)\n\n page.remove_structure_items(remove_ids)\n\n def unmark_lists(self, page: PageGroup):\n # If lists aren't grouped, unmark them as list items\n for block_id in page.structure:\n block = page.get_block(block_id)\n if block.block_type == BlockTypes.ListItem:\n generated_block = Text(\n polygon=block.polygon,\n page_id=block.page_id,\n structure=block.structure,\n )\n page.replace_block(block, generated_block)\n"
},
{
"path": "marker/config/__init__.py",
"content": ""
},
{
"path": "marker/config/crawler.py",
"content": "import importlib\nimport inspect\nimport pkgutil\nfrom functools import cached_property\nfrom typing import Annotated, Dict, Set, Type, get_args, get_origin\n\nfrom marker.builders import BaseBuilder\nfrom marker.converters import BaseConverter\nfrom marker.extractors import BaseExtractor\nfrom marker.processors import BaseProcessor\nfrom marker.providers import BaseProvider\nfrom marker.renderers import BaseRenderer\nfrom marker.services import BaseService\n\n\nclass ConfigCrawler:\n def __init__(\n self,\n base_classes=(\n BaseBuilder,\n BaseProcessor,\n BaseConverter,\n BaseProvider,\n BaseRenderer,\n BaseService,\n BaseExtractor,\n ),\n ):\n self.base_classes = base_classes\n self.class_config_map: Dict[str, dict] = {}\n\n self._crawl_config()\n\n def _crawl_config(self):\n for base in self.base_classes:\n base_class_type = base.__name__.removeprefix(\"Base\")\n self.class_config_map.setdefault(base_class_type, {})\n for class_name, class_type in self._find_subclasses(base).items():\n if class_name.startswith(\"Base\"):\n continue\n\n self.class_config_map[base_class_type].setdefault(\n class_name, {\"class_type\": class_type, \"config\": {}}\n )\n for attr, attr_type in self._gather_super_annotations(\n class_type\n ).items():\n default = getattr(class_type, attr)\n metadata = (f\"Default is {default}.\",)\n\n if get_origin(attr_type) is Annotated:\n if any(\"Default\" in desc for desc in attr_type.__metadata__):\n metadata = attr_type.__metadata__\n else:\n metadata = attr_type.__metadata__ + metadata\n attr_type = get_args(attr_type)[0]\n\n formatted_type = self._format_type(attr_type)\n self.class_config_map[base_class_type][class_name][\"config\"][\n attr\n ] = (attr_type, formatted_type, default, metadata)\n\n @staticmethod\n def _gather_super_annotations(cls: Type) -> Dict[str, Type]:\n \"\"\"\n Collect all annotated attributes from `cls` and its superclasses, bottom-up.\n Subclass attributes overwrite superclass attributes with the same name.\n \"\"\"\n # We'll walk the MRO from base -> derived so subclass attributes overwrite\n # the same attribute name from superclasses.\n annotations = {}\n for base in reversed(cls.__mro__):\n if base is object:\n continue\n if hasattr(base, \"__annotations__\"):\n for name, annotation in base.__annotations__.items():\n annotations[name] = annotation\n return annotations\n\n @cached_property\n def attr_counts(self) -> Dict[str, int]:\n counts: Dict[str, int] = {}\n for base_type_dict in self.class_config_map.values():\n for class_map in base_type_dict.values():\n for attr in class_map[\"config\"].keys():\n counts[attr] = counts.get(attr, 0) + 1\n return counts\n\n @cached_property\n def attr_set(self) -> Set[str]:\n attr_set: Set[str] = set()\n for base_type_dict in self.class_config_map.values():\n for class_name, class_map in base_type_dict.items():\n for attr in class_map[\"config\"].keys():\n attr_set.add(attr)\n attr_set.add(f\"{class_name}_{attr}\")\n return attr_set\n\n def _find_subclasses(self, base_class):\n subclasses = {}\n module_name = base_class.__module__\n package = importlib.import_module(module_name)\n if hasattr(package, \"__path__\"):\n for _, module_name, _ in pkgutil.walk_packages(\n package.__path__, module_name + \".\"\n ):\n try:\n module = importlib.import_module(module_name)\n for name, obj in inspect.getmembers(module, inspect.isclass):\n if issubclass(obj, base_class) and obj is not base_class:\n subclasses[name] = obj\n except ImportError:\n pass\n return subclasses\n\n def _format_type(self, t: Type) -> str:\n \"\"\"Format a typing type like Optional[int] into a readable string.\"\"\"\n\n if get_origin(t): # Handle Optional and types with origins separately\n return f\"{t}\".removeprefix(\"typing.\")\n else: # Regular types like int, str\n return t.__name__\n\n\ncrawler = ConfigCrawler()\n"
},
{
"path": "marker/config/parser.py",
"content": "import json\nimport os\nfrom typing import Dict\n\nimport click\n\nfrom marker.converters.pdf import PdfConverter\nfrom marker.logger import get_logger\nfrom marker.renderers.chunk import ChunkRenderer\nfrom marker.renderers.html import HTMLRenderer\nfrom marker.renderers.json import JSONRenderer\nfrom marker.renderers.markdown import MarkdownRenderer\nfrom marker.settings import settings\nfrom marker.util import classes_to_strings, parse_range_str, strings_to_classes\n\nlogger = get_logger()\n\n\nclass ConfigParser:\n def __init__(self, cli_options: dict):\n self.cli_options = cli_options\n\n @staticmethod\n def common_options(fn):\n fn = click.option(\n \"--output_dir\",\n type=click.Path(exists=False),\n required=False,\n default=settings.OUTPUT_DIR,\n help=\"Directory to save output.\",\n )(fn)\n fn = click.option(\"--debug\", \"-d\", is_flag=True, help=\"Enable debug mode.\")(fn)\n fn = click.option(\n \"--output_format\",\n type=click.Choice([\"markdown\", \"json\", \"html\", \"chunks\"]),\n default=\"markdown\",\n help=\"Format to output results in.\",\n )(fn)\n fn = click.option(\n \"--processors\",\n type=str,\n default=None,\n help=\"Comma separated list of processors to use. Must use full module path.\",\n )(fn)\n fn = click.option(\n \"--config_json\",\n type=str,\n default=None,\n help=\"Path to JSON file with additional configuration.\",\n )(fn)\n fn = click.option(\n \"--disable_multiprocessing\",\n is_flag=True,\n default=False,\n help=\"Disable multiprocessing.\",\n )(fn)\n fn = click.option(\n \"--disable_image_extraction\",\n is_flag=True,\n default=False,\n help=\"Disable image extraction.\",\n )(fn)\n # these are options that need a list transformation, i.e splitting/parsing a string\n fn = click.option(\n \"--page_range\",\n type=str,\n default=None,\n help=\"Page range to convert, specify comma separated page numbers or ranges. Example: 0,5-10,20\",\n )(fn)\n\n # we put common options here\n fn = click.option(\n \"--converter_cls\",\n type=str,\n default=None,\n help=\"Converter class to use. Defaults to PDF converter.\",\n )(fn)\n fn = click.option(\n \"--llm_service\",\n type=str,\n default=None,\n help=\"LLM service to use - should be full import path, like marker.services.gemini.GoogleGeminiService\",\n )(fn)\n return fn\n\n def generate_config_dict(self) -> Dict[str, any]:\n config = {}\n output_dir = self.cli_options.get(\"output_dir\", settings.OUTPUT_DIR)\n for k, v in self.cli_options.items():\n if not v:\n continue\n\n match k:\n case \"debug\":\n config[\"debug_pdf_images\"] = True\n config[\"debug_layout_images\"] = True\n config[\"debug_json\"] = True\n config[\"debug_data_folder\"] = output_dir\n case \"page_range\":\n config[\"page_range\"] = parse_range_str(v)\n case \"config_json\":\n with open(v, \"r\", encoding=\"utf-8\") as f:\n config.update(json.load(f))\n case \"disable_multiprocessing\":\n config[\"pdftext_workers\"] = 1\n case \"disable_image_extraction\":\n config[\"extract_images\"] = False\n case _:\n config[k] = v\n\n # Backward compatibility for google_api_key\n if settings.GOOGLE_API_KEY:\n config[\"gemini_api_key\"] = settings.GOOGLE_API_KEY\n\n return config\n\n def get_llm_service(self):\n # Only return an LLM service when use_llm is enabled\n if not self.cli_options.get(\"use_llm\", False):\n return None\n\n service_cls = self.cli_options.get(\"llm_service\", None)\n if service_cls is None:\n service_cls = \"marker.services.gemini.GoogleGeminiService\"\n return service_cls\n\n def get_renderer(self):\n match self.cli_options[\"output_format\"]:\n case \"json\":\n r = JSONRenderer\n case \"markdown\":\n r = MarkdownRenderer\n case \"html\":\n r = HTMLRenderer\n case \"chunks\":\n r = ChunkRenderer\n case _:\n raise ValueError(\"Invalid output format\")\n return classes_to_strings([r])[0]\n\n def get_processors(self):\n processors = self.cli_options.get(\"processors\", None)\n if processors is not None:\n processors = processors.split(\",\")\n for p in processors:\n try:\n strings_to_classes([p])\n except Exception as e:\n logger.error(f\"Error loading processor: {p} with error: {e}\")\n raise\n\n return processors\n\n def get_converter_cls(self):\n converter_cls = self.cli_options.get(\"converter_cls\", None)\n if converter_cls is not None:\n try:\n return strings_to_classes([converter_cls])[0]\n except Exception as e:\n logger.error(\n f\"Error loading converter: {converter_cls} with error: {e}\"\n )\n raise\n\n return PdfConverter\n\n def get_output_folder(self, filepath: str):\n output_dir = self.cli_options.get(\"output_dir\", settings.OUTPUT_DIR)\n fname_base = os.path.splitext(os.path.basename(filepath))[0]\n output_dir = os.path.join(output_dir, fname_base)\n os.makedirs(output_dir, exist_ok=True)\n return output_dir\n\n def get_base_filename(self, filepath: str):\n basename = os.path.basename(filepath)\n return os.path.splitext(basename)[0]\n"
},
{
"path": "marker/config/printer.py",
"content": "from typing import Optional\n\nimport click\n\nfrom marker.config.crawler import crawler\n\n\nclass CustomClickPrinter(click.Command):\n def parse_args(self, ctx, args):\n display_help = \"config\" in args and \"--help\" in args\n if display_help:\n click.echo(\n \"Here is a list of all the Builders, Processors, Converters, Providers and Renderers in Marker along with their attributes:\"\n )\n\n # Keep track of shared attributes and their types\n shared_attrs = {}\n\n # First pass: identify shared attributes and verify compatibility\n for base_type, base_type_dict in crawler.class_config_map.items():\n for class_name, class_map in base_type_dict.items():\n for attr, (attr_type, formatted_type, default, metadata) in class_map[\n \"config\"\n ].items():\n if attr not in shared_attrs:\n shared_attrs[attr] = {\n \"classes\": [],\n \"type\": attr_type,\n \"is_flag\": attr_type in [bool, Optional[bool]]\n and not default,\n \"metadata\": metadata,\n \"default\": default,\n }\n shared_attrs[attr][\"classes\"].append(class_name)\n\n # These are the types of attrs that can be set from the command line\n attr_types = [\n str,\n int,\n float,\n bool,\n Optional[int],\n Optional[float],\n Optional[str],\n ]\n\n # Add shared attribute options first\n for attr, info in shared_attrs.items():\n if info[\"type\"] in attr_types:\n ctx.command.params.append(\n click.Option(\n [\"--\" + attr],\n type=info[\"type\"],\n help=\" \".join(info[\"metadata\"])\n + f\" (Applies to: {', '.join(info['classes'])})\",\n default=None, # This is important, or it sets all the default keys again in config\n is_flag=info[\"is_flag\"],\n flag_value=True if info[\"is_flag\"] else None,\n )\n )\n\n # Second pass: create class-specific options\n for base_type, base_type_dict in crawler.class_config_map.items():\n if display_help:\n click.echo(f\"{base_type}s:\")\n for class_name, class_map in base_type_dict.items():\n if display_help and class_map[\"config\"]:\n click.echo(\n f\"\\n {class_name}: {class_map['class_type'].__doc__ or ''}\"\n )\n click.echo(\" \" * 4 + \"Attributes:\")\n for attr, (attr_type, formatted_type, default, metadata) in class_map[\n \"config\"\n ].items():\n class_name_attr = class_name + \"_\" + attr\n\n if display_help:\n click.echo(\" \" * 8 + f\"{attr} ({formatted_type}):\")\n click.echo(\n \"\\n\".join([f\"{' ' * 12}\" + desc for desc in metadata])\n )\n\n if attr_type in attr_types:\n is_flag = attr_type in [bool, Optional[bool]] and not default\n\n # Only add class-specific options\n ctx.command.params.append(\n click.Option(\n [\"--\" + class_name_attr, class_name_attr],\n type=attr_type,\n help=\" \".join(metadata),\n is_flag=is_flag,\n default=None, # This is important, or it sets all the default keys again in config\n )\n )\n\n if display_help:\n ctx.exit()\n\n super().parse_args(ctx, args)\n"
},
{
"path": "marker/converters/__init__.py",
"content": "import inspect\nfrom typing import Optional, List, Type\n\nfrom pydantic import BaseModel\n\nfrom marker.processors import BaseProcessor\nfrom marker.processors.llm import BaseLLMSimpleBlockProcessor\nfrom marker.processors.llm.llm_meta import LLMSimpleBlockMetaProcessor\nfrom marker.util import assign_config, download_font\n\n\nclass BaseConverter:\n def __init__(self, config: Optional[BaseModel | dict] = None):\n assign_config(self, config)\n self.config = config\n self.llm_service = None\n\n # Download render font, needed for some providers\n download_font()\n\n def __call__(self, *args, **kwargs):\n raise NotImplementedError\n\n def resolve_dependencies(self, cls):\n init_signature = inspect.signature(cls.__init__)\n parameters = init_signature.parameters\n\n resolved_kwargs = {}\n for param_name, param in parameters.items():\n if param_name == 'self':\n continue\n elif param_name == 'config':\n resolved_kwargs[param_name] = self.config\n elif param.name in self.artifact_dict:\n resolved_kwargs[param_name] = self.artifact_dict[param_name]\n elif param.default != inspect.Parameter.empty:\n resolved_kwargs[param_name] = param.default\n else:\n raise ValueError(f\"Cannot resolve dependency for parameter: {param_name}\")\n\n return cls(**resolved_kwargs)\n\n def initialize_processors(self, processor_cls_lst: List[Type[BaseProcessor]]) -> List[BaseProcessor]:\n processors = []\n for processor_cls in processor_cls_lst:\n processors.append(self.resolve_dependencies(processor_cls))\n\n simple_llm_processors = [p for p in processors if issubclass(type(p), BaseLLMSimpleBlockProcessor)]\n other_processors = [p for p in processors if not issubclass(type(p), BaseLLMSimpleBlockProcessor)]\n\n if not simple_llm_processors:\n return processors\n\n llm_positions = [i for i, p in enumerate(processors) if issubclass(type(p), BaseLLMSimpleBlockProcessor)]\n insert_position = max(0, llm_positions[-1] - len(simple_llm_processors) + 1)\n\n meta_processor = LLMSimpleBlockMetaProcessor(\n processor_lst=simple_llm_processors,\n llm_service=self.llm_service,\n config=self.config,\n )\n other_processors.insert(insert_position, meta_processor)\n return other_processors"
},
{
"path": "marker/converters/extraction.py",
"content": "import re\nfrom typing import Annotated\n\nfrom marker.builders.document import DocumentBuilder\nfrom marker.builders.line import LineBuilder\nfrom marker.builders.ocr import OcrBuilder\nfrom marker.builders.structure import StructureBuilder\nfrom marker.converters.pdf import PdfConverter\nfrom marker.extractors.document import DocumentExtractor\nfrom marker.extractors.page import PageExtractor\nfrom marker.providers.registry import provider_from_filepath\n\nfrom marker.renderers.extraction import ExtractionRenderer, ExtractionOutput\nfrom marker.renderers.markdown import MarkdownRenderer\n\nfrom marker.logger import get_logger\n\nlogger = get_logger()\n\n\nclass ExtractionConverter(PdfConverter):\n pattern: str = r\"{\\d+\\}-{48}\\n\\n\"\n existing_markdown: Annotated[\n str, \"Markdown that was already converted for extraction.\"\n ] = None\n\n def build_document(self, filepath: str):\n provider_cls = provider_from_filepath(filepath)\n layout_builder = self.resolve_dependencies(self.layout_builder_class)\n line_builder = self.resolve_dependencies(LineBuilder)\n ocr_builder = self.resolve_dependencies(OcrBuilder)\n provider = provider_cls(filepath, self.config)\n document = DocumentBuilder(self.config)(\n provider, layout_builder, line_builder, ocr_builder\n )\n structure_builder_cls = self.resolve_dependencies(StructureBuilder)\n structure_builder_cls(document)\n\n for processor in self.processor_list:\n processor(document)\n\n return document, provider\n\n def __call__(self, filepath: str) -> ExtractionOutput:\n self.config[\"paginate_output\"] = True # Ensure we can split the output properly\n self.config[\"output_format\"] = (\n \"markdown\" # Output must be markdown for extraction\n )\n markdown = self.existing_markdown\n\n if not markdown:\n document, provider = self.build_document(filepath)\n self.page_count = len(document.pages)\n renderer = self.resolve_dependencies(MarkdownRenderer)\n output = renderer(document)\n markdown = output.markdown\n\n output_pages = re.split(self.pattern, markdown)[1:] # Split output into pages\n\n # This needs an LLM service for extraction, this sets it in the extractor\n if self.artifact_dict.get(\"llm_service\") is None:\n self.artifact_dict[\"llm_service\"] = self.resolve_dependencies(\n self.default_llm_service\n )\n\n page_extractor = self.resolve_dependencies(PageExtractor)\n document_extractor = self.resolve_dependencies(DocumentExtractor)\n renderer = self.resolve_dependencies(ExtractionRenderer)\n\n # Inference in parallel\n notes = page_extractor(output_pages)\n document_output = document_extractor(notes)\n\n merged = renderer(document_output, markdown)\n return merged\n"
},
{
"path": "marker/converters/ocr.py",
"content": "from typing import Tuple\n\nfrom marker.builders.document import DocumentBuilder\nfrom marker.builders.line import LineBuilder\nfrom marker.builders.ocr import OcrBuilder\nfrom marker.converters.pdf import PdfConverter\nfrom marker.processors import BaseProcessor\nfrom marker.processors.equation import EquationProcessor\nfrom marker.providers.registry import provider_from_filepath\nfrom marker.renderers.ocr_json import OCRJSONRenderer\n\n\nclass OCRConverter(PdfConverter):\n default_processors: Tuple[BaseProcessor, ...] = (EquationProcessor,)\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n if not self.config:\n self.config = {}\n\n self.config[\"force_ocr\"] = True\n self.renderer = OCRJSONRenderer\n\n def build_document(self, filepath: str):\n provider_cls = provider_from_filepath(filepath)\n layout_builder = self.resolve_dependencies(self.layout_builder_class)\n line_builder = self.resolve_dependencies(LineBuilder)\n ocr_builder = self.resolve_dependencies(OcrBuilder)\n document_builder = DocumentBuilder(self.config)\n\n provider = provider_cls(filepath, self.config)\n document = document_builder(provider, layout_builder, line_builder, ocr_builder)\n\n for processor in self.processor_list:\n processor(document)\n\n return document\n\n def __call__(self, filepath: str):\n document = self.build_document(filepath)\n self.page_count = len(document.pages)\n renderer = self.resolve_dependencies(self.renderer)\n return renderer(document)\n"
},
{
"path": "marker/converters/pdf.py",
"content": "import os\n\nfrom marker.schema.document import Document\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disables a tokenizers warning\n\nfrom collections import defaultdict\nfrom typing import Annotated, Any, Dict, List, Optional, Type, Tuple, Union\nimport io\nfrom contextlib import contextmanager\nimport tempfile\n\nfrom marker.processors import BaseProcessor\nfrom marker.services import BaseService\nfrom marker.processors.llm.llm_table_merge import LLMTableMergeProcessor\nfrom marker.providers.registry import provider_from_filepath\nfrom marker.builders.document import DocumentBuilder\nfrom marker.builders.layout import LayoutBuilder\nfrom marker.builders.line import LineBuilder\nfrom marker.builders.ocr import OcrBuilder\nfrom marker.builders.structure import StructureBuilder\nfrom marker.converters import BaseConverter\nfrom marker.processors.blockquote import BlockquoteProcessor\nfrom marker.processors.code import CodeProcessor\nfrom marker.processors.debug import DebugProcessor\nfrom marker.processors.document_toc import DocumentTOCProcessor\nfrom marker.processors.equation import EquationProcessor\nfrom marker.processors.footnote import FootnoteProcessor\nfrom marker.processors.ignoretext import IgnoreTextProcessor\nfrom marker.processors.line_numbers import LineNumbersProcessor\nfrom marker.processors.list import ListProcessor\nfrom marker.processors.llm.llm_complex import LLMComplexRegionProcessor\nfrom marker.processors.llm.llm_form import LLMFormProcessor\nfrom marker.processors.llm.llm_image_description import LLMImageDescriptionProcessor\nfrom marker.processors.llm.llm_table import LLMTableProcessor\nfrom marker.processors.page_header import PageHeaderProcessor\nfrom marker.processors.reference import ReferenceProcessor\nfrom marker.processors.sectionheader import SectionHeaderProcessor\nfrom marker.processors.table import TableProcessor\nfrom marker.processors.text import TextProcessor\nfrom marker.processors.block_relabel import BlockRelabelProcessor\nfrom marker.processors.blank_page import BlankPageProcessor\nfrom marker.processors.llm.llm_equation import LLMEquationProcessor\nfrom marker.renderers.markdown import MarkdownRenderer\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import Block\nfrom marker.schema.registry import register_block_class\nfrom marker.util import strings_to_classes\nfrom marker.processors.llm.llm_handwriting import LLMHandwritingProcessor\nfrom marker.processors.order import OrderProcessor\nfrom marker.services.gemini import GoogleGeminiService\nfrom marker.processors.line_merge import LineMergeProcessor\nfrom marker.processors.llm.llm_mathblock import LLMMathBlockProcessor\nfrom marker.processors.llm.llm_page_correction import LLMPageCorrectionProcessor\nfrom marker.processors.llm.llm_sectionheader import LLMSectionHeaderProcessor\n\n\nclass PdfConverter(BaseConverter):\n \"\"\"\n A converter for processing and rendering PDF files into Markdown, JSON, HTML and other formats.\n \"\"\"\n\n override_map: Annotated[\n Dict[BlockTypes, Type[Block]],\n \"A mapping to override the default block classes for specific block types.\",\n \"The keys are `BlockTypes` enum values, representing the types of blocks,\",\n \"and the values are corresponding `Block` class implementations to use\",\n \"instead of the defaults.\",\n ] = defaultdict()\n use_llm: Annotated[\n bool,\n \"Enable higher quality processing with LLMs.\",\n ] = False\n default_processors: Tuple[BaseProcessor, ...] = (\n OrderProcessor,\n BlockRelabelProcessor,\n LineMergeProcessor,\n BlockquoteProcessor,\n CodeProcessor,\n DocumentTOCProcessor,\n EquationProcessor,\n FootnoteProcessor,\n IgnoreTextProcessor,\n LineNumbersProcessor,\n ListProcessor,\n PageHeaderProcessor,\n SectionHeaderProcessor,\n TableProcessor,\n LLMTableProcessor,\n LLMTableMergeProcessor,\n LLMFormProcessor,\n TextProcessor,\n LLMComplexRegionProcessor,\n LLMImageDescriptionProcessor,\n LLMEquationProcessor,\n LLMHandwritingProcessor,\n LLMMathBlockProcessor,\n LLMSectionHeaderProcessor,\n LLMPageCorrectionProcessor,\n ReferenceProcessor,\n BlankPageProcessor,\n DebugProcessor,\n )\n default_llm_service: BaseService = GoogleGeminiService\n\n def __init__(\n self,\n artifact_dict: Dict[str, Any],\n processor_list: Optional[List[str]] = None,\n renderer: str | None = None,\n llm_service: str | None = None,\n config=None,\n ):\n super().__init__(config)\n\n if config is None:\n config = {}\n\n for block_type, override_block_type in self.override_map.items():\n register_block_class(block_type, override_block_type)\n\n if processor_list is not None:\n processor_list = strings_to_classes(processor_list)\n else:\n processor_list = self.default_processors\n\n if renderer:\n renderer = strings_to_classes([renderer])[0]\n else:\n renderer = MarkdownRenderer\n\n # Put here so that resolve_dependencies can access it\n self.artifact_dict = artifact_dict\n\n if llm_service:\n llm_service_cls = strings_to_classes([llm_service])[0]\n llm_service = self.resolve_dependencies(llm_service_cls)\n elif config.get(\"use_llm\", False):\n llm_service = self.resolve_dependencies(self.default_llm_service)\n\n # Inject llm service into artifact_dict so it can be picked up by processors, etc.\n self.artifact_dict[\"llm_service\"] = llm_service\n self.llm_service = llm_service\n\n self.renderer = renderer\n\n processor_list = self.initialize_processors(processor_list)\n self.processor_list = processor_list\n\n self.layout_builder_class = LayoutBuilder\n self.page_count = None # Track how many pages were converted\n\n @contextmanager\n def filepath_to_str(self, file_input: Union[str, io.BytesIO]):\n temp_file = None\n try:\n if isinstance(file_input, str):\n yield file_input\n else:\n with tempfile.NamedTemporaryFile(\n delete=False, suffix=\".pdf\"\n ) as temp_file:\n if isinstance(file_input, io.BytesIO):\n file_input.seek(0)\n temp_file.write(file_input.getvalue())\n else:\n raise TypeError(\n f\"Expected str or BytesIO, got {type(file_input)}\"\n )\n\n yield temp_file.name\n finally:\n if temp_file is not None and os.path.exists(temp_file.name):\n os.unlink(temp_file.name)\n\n def build_document(self, filepath: str) -> Document:\n provider_cls = provider_from_filepath(filepath)\n layout_builder = self.resolve_dependencies(self.layout_builder_class)\n line_builder = self.resolve_dependencies(LineBuilder)\n ocr_builder = self.resolve_dependencies(OcrBuilder)\n provider = provider_cls(filepath, self.config)\n document = DocumentBuilder(self.config)(\n provider, layout_builder, line_builder, ocr_builder\n )\n structure_builder_cls = self.resolve_dependencies(StructureBuilder)\n structure_builder_cls(document)\n\n for processor in self.processor_list:\n processor(document)\n\n return document\n\n def __call__(self, filepath: str | io.BytesIO):\n with self.filepath_to_str(filepath) as temp_path:\n document = self.build_document(temp_path)\n self.page_count = len(document.pages)\n renderer = self.resolve_dependencies(self.renderer)\n rendered = renderer(document)\n return rendered\n"
},
{
"path": "marker/converters/table.py",
"content": "from typing import Tuple, List\n\nfrom marker.builders.document import DocumentBuilder\nfrom marker.builders.line import LineBuilder\nfrom marker.builders.ocr import OcrBuilder\nfrom marker.converters.pdf import PdfConverter\nfrom marker.processors import BaseProcessor\nfrom marker.processors.llm.llm_complex import LLMComplexRegionProcessor\nfrom marker.processors.llm.llm_form import LLMFormProcessor\nfrom marker.processors.llm.llm_table import LLMTableProcessor\nfrom marker.processors.llm.llm_table_merge import LLMTableMergeProcessor\nfrom marker.processors.table import TableProcessor\nfrom marker.providers.registry import provider_from_filepath\nfrom marker.schema import BlockTypes\n\n\nclass TableConverter(PdfConverter):\n default_processors: Tuple[BaseProcessor, ...] = (\n TableProcessor,\n LLMTableProcessor,\n LLMTableMergeProcessor,\n LLMFormProcessor,\n LLMComplexRegionProcessor,\n )\n converter_block_types: List[BlockTypes] = (\n BlockTypes.Table,\n BlockTypes.Form,\n BlockTypes.TableOfContents,\n )\n\n def build_document(self, filepath: str):\n provider_cls = provider_from_filepath(filepath)\n layout_builder = self.resolve_dependencies(self.layout_builder_class)\n line_builder = self.resolve_dependencies(LineBuilder)\n ocr_builder = self.resolve_dependencies(OcrBuilder)\n document_builder = DocumentBuilder(self.config)\n document_builder.disable_ocr = True\n\n provider = provider_cls(filepath, self.config)\n document = document_builder(provider, layout_builder, line_builder, ocr_builder)\n\n for page in document.pages:\n page.structure = [\n p for p in page.structure if p.block_type in self.converter_block_types\n ]\n\n for processor in self.processor_list:\n processor(document)\n\n return document\n\n def __call__(self, filepath: str):\n document = self.build_document(filepath)\n self.page_count = len(document.pages)\n\n renderer = self.resolve_dependencies(self.renderer)\n return renderer(document)\n"
},
{
"path": "marker/extractors/__init__.py",
"content": "from typing import Annotated, Sequence\n\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\nfrom marker.schema.groups import PageGroup\nfrom PIL import Image\n\nfrom marker.services import BaseService\nfrom marker.util import assign_config\n\n\nclass BaseExtractor:\n \"\"\"\n An extractor that uses a provided service to extract structured data from documents.\n \"\"\"\n\n max_concurrency: Annotated[\n int,\n \"The maximum number of concurrent requests to make to the Gemini model.\",\n ] = 3\n disable_tqdm: Annotated[\n bool,\n \"Whether to disable the tqdm progress bar.\",\n ] = False\n\n def __init__(self, llm_service: BaseService, config=None):\n assign_config(self, config)\n self.llm_service = llm_service\n\n def extract_image(\n self,\n document: Document,\n page: PageGroup,\n remove_blocks: Sequence[BlockTypes] | None = None,\n highres: bool = False, # Default False to save tokens\n ) -> Image.Image:\n return page.get_image(\n document,\n highres=highres,\n remove_blocks=remove_blocks,\n )\n\n def __call__(self, document: Document, *args, **kwargs):\n raise NotImplementedError\n"
},
{
"path": "marker/extractors/document.py",
"content": "import json\n\nfrom pydantic import BaseModel\nfrom typing import Annotated, Optional, List\n\nfrom marker.extractors import BaseExtractor\nfrom marker.extractors.page import PageExtractionSchema\nfrom marker.logger import get_logger\n\nlogger = get_logger()\n\n\nclass DocumentExtractionSchema(BaseModel):\n analysis: str\n document_json: str\n\n\nclass DocumentExtractor(BaseExtractor):\n \"\"\"\n An extractor that combines data from across all pages.\n \"\"\"\n\n page_schema: Annotated[\n str,\n \"The JSON schema to be extracted from the page.\",\n ] = \"\"\n\n page_extraction_prompt = \"\"\"You are an expert document analyst who reads documents and pulls data out in JSON format. You will receive your detailed notes from all the pages of a document, and a JSON schema that we want to extract from the document. Your task is to extract all the information properly into the JSON schema.\n\nSome notes:\n- The schema may contain a single object to extract from the entire document, or an array of objects. \n- The schema may contain nested objects, arrays, and other complex structures.\n\nSome guidelines:\n- Some entities will span multiple pages, so make sure to consult your notes thoroughly.\n- In the case of potential conflicting values, pull out the values you have the most confidence in, from your notes.\n- If you cannot find a value for a field, leave it blank in the JSON.\n\n**Instructions:**\n1. Analyze your provided notes.\n2. Analyze the JSON schema.\n3. Write a detailed analysis of the notes, and the associated values in the schema. Make sure to reference which page each piece of information comes from.\n4. Write the output in the JSON schema format, ensuring all required fields are filled out. Output only the json data, without any additional text or formatting.\n\n**Example:**\nInput:\n\nDetailed Notes\nPage 0\nOn this page, I see a table with car makes and sales. The makes are Honda and Toyota, with sales of 100 and 200 respectively. The color is not present in the table, so I will leave it blank in the JSON. That information may be present on another page. Some JSON snippets I may find useful later are:\n```json\n{\n \"make\": \"Honda\",\n \"sales\": 100,\n}\n```\n```json\n{\n \"make\": \"Toyota\",\n \"sales\": 200,\n}\n```\n\nHonda is the first row in the table, and Toyota is the second row. Make is the first column, and sales is the second.\n\nPage 1\nI see a table that contains 2 rows, and has a color header. The first row has the color red, and the second row has the color blue. Here are some useful snippets:\n\nSchema\n\n```json\n{'$defs': {'Cars': {'properties': {'make': {'title': 'Make', 'type': 'string'}, 'sales': {'title': 'Sales', 'type': 'integer'}, 'color': {'title': 'Color', 'type': 'string'}}, 'required': ['make', 'sales', 'color'], 'title': 'Cars', 'type': 'object'}}, 'properties': {'cars': {'items': {'$ref': '#/$defs/Cars'}, 'title': 'Cars', 'type': 'array'}}, 'required': ['cars'], 'title': 'CarsList', 'type': 'object'}\n```\n\nOutput:\n\nAnalysis: From the notes, it looks like the information I need is in a table that spans 2 pages. The first page has the makes and sales, while the second page has the colors. I will combine this information into the JSON schema.\nJSON\n\n{\n \"cars\": [\n {\n \"make\": \"Honda\",\n \"sales\": 100,\n \"color\": \"red\"\n },\n {\n \"make\": \"Toyota\",\n \"sales\": 200,\n \"color\": \"blue\"\n }\n ]\n}\n\n**Input:**\n\nDetailed Notes\n{{document_notes}}\n\nSchema\n```json\n{{schema}}\n```\n\"\"\"\n\n def assemble_document_notes(self, page_notes: List[PageExtractionSchema]) -> str:\n notes = \"\"\n for i, page_schema in enumerate(page_notes):\n if not page_notes:\n continue\n notes += f\"Page {i + 1}\\n{page_schema.detailed_notes}\\n\\n\"\n return notes.strip()\n\n def __call__(\n self,\n page_notes: List[PageExtractionSchema],\n **kwargs,\n ) -> Optional[DocumentExtractionSchema]:\n if not self.page_schema:\n raise ValueError(\n \"Page schema must be defined for structured extraction to work.\"\n )\n\n prompt = self.page_extraction_prompt.replace(\n \"{{document_notes}}\", self.assemble_document_notes(page_notes)\n ).replace(\"{{schema}}\", json.dumps(self.page_schema))\n response = self.llm_service(prompt, None, None, DocumentExtractionSchema)\n\n logger.debug(f\"Document extraction response: {response}\")\n\n if not response or any(\n [\n key not in response\n for key in [\n \"analysis\",\n \"document_json\",\n ]\n ]\n ):\n return None\n\n json_data = response[\"document_json\"].strip().lstrip(\"```json\").rstrip(\"```\")\n\n return DocumentExtractionSchema(\n analysis=response[\"analysis\"], document_json=json_data\n )\n"
},
{
"path": "marker/extractors/page.py",
"content": "import json\nfrom concurrent.futures import ThreadPoolExecutor\n\nfrom pydantic import BaseModel\nfrom typing import Annotated, Optional, List\n\nfrom tqdm import tqdm\n\nfrom marker.extractors import BaseExtractor\nfrom marker.logger import get_logger\n\nlogger = get_logger()\n\n\nclass PageExtractionSchema(BaseModel):\n description: str\n detailed_notes: str\n\n\nclass PageExtractor(BaseExtractor):\n \"\"\"\n An extractor that pulls data from a single page.\n \"\"\"\n\n extraction_page_chunk_size: Annotated[\n int, \"The number of pages to chunk together for extraction.\"\n ] = 3\n\n page_schema: Annotated[\n str,\n \"The JSON schema to be extracted from the page.\",\n ] = \"\"\n\n page_extraction_prompt = \"\"\"You are an expert document analyst who reads documents and pulls data out in JSON format. You will receive the markdown representation of a document page, and a JSON schema that we want to extract from the document. Your task is to write detailed notes on this page, so that when you look at all your notes from across the document, you can fill in the schema.\n \nSome notes:\n- The schema may contain a single object to extract from the entire document, or an array of objects. \n- The schema may contain nested objects, arrays, and other complex structures.\n\nSome guidelines:\n- Write very thorough notes, and include specific JSON snippets that can be extracted from the page.\n- You may need information from prior or subsequent pages to fully fill in the schema, so make sure to write detailed notes that will let you join entities across pages later on.\n- Estimate your confidence in the values you extract, so you can reconstruct the JSON later when you only have your notes.\n- Some tables and other data structures may continue on a subsequent page, so make sure to store the positions that data comes from where appropriate.\n\n**Instructions:**\n1. Analyze the provided markdown representation of the page.\n2. Analyze the JSON schema.\n3. Write a short description of the fields in the schema, and the associated values in the markdown.\n4. Write detailed notes on the page, including any values that can be extracted from the markdown. Include snippets of JSON that can be extracted from the page where possible.\n\n**Example:**\nInput:\n\nMarkdown\n```markdown\n| Make | Sales |\n|--------|-------|\n| Honda | 100 |\n| Toyota | 200 |\n```\n\nSchema\n\n```json\n{'$defs': {'Cars': {'properties': {'make': {'title': 'Make', 'type': 'string'}, 'sales': {'title': 'Sales', 'type': 'integer'}, 'color': {'title': 'Color', 'type': 'string'}}, 'required': ['make', 'sales', 'color'], 'title': 'Cars', 'type': 'object'}}, 'properties': {'cars': {'items': {'$ref': '#/$defs/Cars'}, 'title': 'Cars', 'type': 'array'}}, 'required': ['cars'], 'title': 'CarsList', 'type': 'object'}\n```\n\nOutput:\n\nDescription: The schema has a list of cars, each with a make, sales, and color. The image and markdown contain a table with 2 cars: Honda with 100 sales and Toyota with 200 sales. The color is not present in the table.\nDetailed Notes: On this page, I see a table with car makes and sales. The makes are Honda and Toyota, with sales of 100 and 200 respectively. The color is not present in the table, so I will leave it blank in the JSON. That information may be present on another page. Some JSON snippets I may find useful later are:\n```json\n{\n \"make\": \"Honda\",\n \"sales\": 100,\n}\n```\n```json\n{\n \"make\": \"Toyota\",\n \"sales\": 200,\n}\n```\n\nHonda is the first row in the table, and Toyota is the second row. Make is the first column, and sales is the second.\n\n**Input:**\n\nMarkdown\n```markdown\n{{page_md}}\n```\n\nSchema\n```json\n{{schema}}\n```\n\"\"\"\n\n def chunk_page_markdown(self, page_markdown: List[str]) -> List[str]:\n \"\"\"\n Chunk the page markdown into smaller pieces for processing.\n \"\"\"\n\n chunks = []\n for i in range(0, len(page_markdown), self.extraction_page_chunk_size):\n chunk = page_markdown[i : i + self.extraction_page_chunk_size]\n chunks.append(\"\\n\\n\".join(chunk))\n\n return chunks\n\n def inference_single_chunk(\n self, page_markdown: str\n ) -> Optional[PageExtractionSchema]:\n prompt = self.page_extraction_prompt.replace(\n \"{{page_md}}\", page_markdown\n ).replace(\"{{schema}}\", json.dumps(self.page_schema))\n response = self.llm_service(prompt, None, None, PageExtractionSchema)\n logger.debug(f\"Page extraction response: {response}\")\n\n if not response or any(\n [\n key not in response\n for key in [\n \"description\",\n \"detailed_notes\",\n ]\n ]\n ):\n return None\n\n return PageExtractionSchema(\n description=response[\"description\"],\n detailed_notes=response[\"detailed_notes\"],\n )\n\n def __call__(\n self,\n page_markdown: List[str],\n **kwargs,\n ) -> List[PageExtractionSchema]:\n if not self.page_schema:\n raise ValueError(\n \"Page schema must be defined for structured extraction to work.\"\n )\n\n chunks = self.chunk_page_markdown(page_markdown)\n results = []\n pbar = tqdm(\n desc=\"Running page extraction\",\n disable=self.disable_tqdm,\n total=len(chunks),\n )\n\n with ThreadPoolExecutor(max_workers=self.max_concurrency) as executor:\n for future in [\n executor.submit(self.inference_single_chunk, chunk) for chunk in chunks\n ]:\n results.append(future.result()) # Raise exceptions if any occurred\n pbar.update(1)\n\n pbar.close()\n return results\n"
},
{
"path": "marker/logger.py",
"content": "import logging\nimport warnings\n\nfrom marker.settings import settings\n\n\ndef configure_logging():\n # Setup marker logger\n logger = get_logger()\n\n if not logger.handlers:\n handler = logging.StreamHandler()\n formatter = logging.Formatter(\n \"%(asctime)s [%(levelname)s] %(name)s: %(message)s\"\n )\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n\n logger.setLevel(settings.LOGLEVEL)\n\n # Ignore future warnings\n warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n\n # Set component loglevels\n logging.getLogger(\"PIL\").setLevel(logging.ERROR)\n logging.getLogger(\"fontTools.subset\").setLevel(logging.ERROR)\n logging.getLogger(\"fontTools.ttLib.ttFont\").setLevel(logging.ERROR)\n logging.getLogger(\"weasyprint\").setLevel(logging.CRITICAL)\n\n\ndef get_logger():\n return logging.getLogger(\"marker\")\n"
},
{
"path": "marker/models.py",
"content": "import os\n\nos.environ[\"PYTORCH_ENABLE_MPS_FALLBACK\"] = (\n \"1\" # Transformers uses .isin for an op, which is not supported on MPS\n)\n\nfrom surya.foundation import FoundationPredictor\nfrom surya.detection import DetectionPredictor\nfrom surya.layout import LayoutPredictor\nfrom surya.ocr_error import OCRErrorPredictor\nfrom surya.recognition import RecognitionPredictor\nfrom surya.table_rec import TableRecPredictor\nfrom surya.settings import settings as surya_settings\n\n\ndef create_model_dict(\n device=None, dtype=None, attention_implementation: str | None = None\n) -> dict:\n return {\n \"layout_model\": LayoutPredictor(FoundationPredictor(checkpoint=surya_settings.LAYOUT_MODEL_CHECKPOINT, attention_implementation=attention_implementation, device=device, dtype=dtype)),\n \"recognition_model\": RecognitionPredictor(FoundationPredictor(checkpoint=surya_settings.RECOGNITION_MODEL_CHECKPOINT, attention_implementation=attention_implementation, device=device, dtype=dtype)),\n \"table_rec_model\": TableRecPredictor(device=device, dtype=dtype),\n \"detection_model\": DetectionPredictor(device=device, dtype=dtype),\n \"ocr_error_model\": OCRErrorPredictor(device=device, dtype=dtype),\n }\n"
},
{
"path": "marker/output.py",
"content": "import json\nimport os\n\nfrom bs4 import BeautifulSoup, Tag\nfrom pydantic import BaseModel\nfrom PIL import Image\n\nfrom marker.renderers.extraction import ExtractionOutput\nfrom marker.renderers.html import HTMLOutput\nfrom marker.renderers.json import JSONOutput, JSONBlockOutput\nfrom marker.renderers.markdown import MarkdownOutput\nfrom marker.renderers.ocr_json import OCRJSONOutput\nfrom marker.schema.blocks import BlockOutput\nfrom marker.settings import settings\n\n\ndef unwrap_outer_tag(html: str):\n soup = BeautifulSoup(html, \"html.parser\")\n contents = list(soup.contents)\n if len(contents) == 1 and isinstance(contents[0], Tag) and contents[0].name == \"p\":\n # Unwrap the p tag\n soup.p.unwrap()\n\n return str(soup)\n\n\ndef json_to_html(block: JSONBlockOutput | BlockOutput):\n # Utility function to take in json block output and give html for the block.\n if not getattr(block, \"children\", None):\n return block.html\n else:\n child_html = [json_to_html(child) for child in block.children]\n child_ids = [child.id for child in block.children]\n\n soup = BeautifulSoup(block.html, \"html.parser\")\n content_refs = soup.find_all(\"content-ref\")\n for ref in content_refs:\n src_id = ref.attrs[\"src\"]\n if src_id in child_ids:\n child_soup = BeautifulSoup(\n child_html[child_ids.index(src_id)], \"html.parser\"\n )\n ref.replace_with(child_soup)\n return str(soup)\n\n\ndef output_exists(output_dir: str, fname_base: str):\n exts = [\"md\", \"html\", \"json\"]\n for ext in exts:\n if os.path.exists(os.path.join(output_dir, f\"{fname_base}.{ext}\")):\n return True\n return False\n\n\ndef text_from_rendered(rendered: BaseModel):\n from marker.renderers.chunk import ChunkOutput # Has an import from this file\n\n if isinstance(rendered, MarkdownOutput):\n return rendered.markdown, \"md\", rendered.images\n elif isinstance(rendered, HTMLOutput):\n return rendered.html, \"html\", rendered.images\n elif isinstance(rendered, JSONOutput):\n return rendered.model_dump_json(exclude=[\"metadata\"], indent=2), \"json\", {}\n elif isinstance(rendered, ChunkOutput):\n return rendered.model_dump_json(exclude=[\"metadata\"], indent=2), \"json\", {}\n elif isinstance(rendered, OCRJSONOutput):\n return rendered.model_dump_json(exclude=[\"metadata\"], indent=2), \"json\", {}\n elif isinstance(rendered, ExtractionOutput):\n return rendered.document_json, \"json\", {}\n else:\n raise ValueError(\"Invalid output type\")\n\n\ndef convert_if_not_rgb(image: Image.Image) -> Image.Image:\n if image.mode != \"RGB\":\n image = image.convert(\"RGB\")\n return image\n\n\ndef save_output(rendered: BaseModel, output_dir: str, fname_base: str):\n text, ext, images = text_from_rendered(rendered)\n text = text.encode(settings.OUTPUT_ENCODING, errors=\"replace\").decode(\n settings.OUTPUT_ENCODING\n )\n\n with open(\n os.path.join(output_dir, f\"{fname_base}.{ext}\"),\n \"w+\",\n encoding=settings.OUTPUT_ENCODING,\n ) as f:\n f.write(text)\n with open(\n os.path.join(output_dir, f\"{fname_base}_meta.json\"),\n \"w+\",\n encoding=settings.OUTPUT_ENCODING,\n ) as f:\n f.write(json.dumps(rendered.metadata, indent=2))\n\n for img_name, img in images.items():\n img = convert_if_not_rgb(img) # RGBA images can't save as JPG\n img.save(os.path.join(output_dir, img_name), settings.OUTPUT_IMAGE_FORMAT)\n"
},
{
"path": "marker/processors/__init__.py",
"content": "from typing import Optional, Tuple\n\nfrom pydantic import BaseModel\n\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\nfrom marker.util import assign_config\n\n\nclass BaseProcessor:\n block_types: Tuple[BlockTypes] | None = None # What block types this processor is responsible for\n\n def __init__(self, config: Optional[BaseModel | dict] = None):\n assign_config(self, config)\n\n def __call__(self, document: Document, *args, **kwargs):\n raise NotImplementedError\n"
},
{
"path": "marker/processors/blank_page.py",
"content": "from typing import Annotated\n\nfrom PIL import Image\nimport numpy as np\nimport cv2\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import Block\nfrom marker.schema.document import Document\n\nfrom marker.logger import get_logger\n\nlogger = get_logger()\n\n\nclass BlankPageProcessor(BaseProcessor):\n \"\"\"\n A processor to filter out blank pages detected as a single layout block\n \"\"\"\n\n full_page_block_intersection_threshold: Annotated[\n float, \"Threshold to detect blank pages at\"\n ] = 0.8\n filter_blank_pages: Annotated[bool, \"Remove blank pages detected as images.\"] = (\n False\n )\n\n def is_blank(self, image: Image.Image):\n image = np.asarray(image)\n if image.size == 0 or image.shape[0] == 0 or image.shape[1] == 0:\n # Handle empty image case\n return True\n\n gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n gray = cv2.GaussianBlur(gray, (7, 7), 0)\n\n # Adaptive threshold (inverse for text as white)\n binarized = cv2.adaptiveThreshold(\n gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 31, 15\n )\n\n num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(\n binarized, connectivity=8\n )\n cleaned = np.zeros_like(binarized)\n for i in range(1, num_labels): # skip background\n cleaned[labels == i] = 255\n\n kernel = np.ones((1, 5), np.uint8)\n dilated = cv2.dilate(cleaned, kernel, iterations=3)\n b = dilated / 255\n return b.sum() == 0\n\n def __call__(self, document: Document):\n if not self.filter_blank_pages:\n return\n\n for page in document.pages:\n structure_blocks = page.structure_blocks(document)\n if not structure_blocks or len(structure_blocks) > 1:\n continue\n\n full_page_block: Block = structure_blocks[0]\n\n conditions = [\n full_page_block.block_type in [BlockTypes.Picture, BlockTypes.Figure],\n self.is_blank(full_page_block.get_image(document)),\n page.polygon.intersection_area(full_page_block.polygon)\n > self.full_page_block_intersection_threshold,\n ]\n\n if all(conditions):\n logger.debug(f\"Removing blank block {full_page_block.id}\")\n page.remove_structure_items([full_page_block.id])\n full_page_block.removed = True\n"
},
{
"path": "marker/processors/block_relabel.py",
"content": "from copy import deepcopy\nfrom typing import Annotated\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import BlockId\nfrom marker.schema.document import Document\nfrom marker.schema.registry import get_block_class\n\nfrom marker.logger import get_logger\nlogger = get_logger()\n\nclass BlockRelabelProcessor(BaseProcessor):\n \"\"\"\n A processor to heuristically relabel blocks based on a confidence threshold.\n \n Each rule in the relabel string maps an original block label to a new one\n if the confidence exceeds a given threshold.\n \"\"\"\n \n block_relabel_str: Annotated[\n str,\n \"Comma-separated relabeling rules in the format '::'.\",\n \"Each rule defines how blocks of a certain type should be relabeled when the confidence exceeds the threshold.\",\n \"Example: 'Table:Picture:0.85,Form:Picture:0.9'\"\n ] = \"\"\n\n def __init__(self, config=None):\n super().__init__(config)\n self.block_relabel_map = {}\n\n if not self.block_relabel_str:\n return\n\n for i, block_config_str in enumerate(self.block_relabel_str.split(',')):\n block_config_str = block_config_str.strip()\n if not block_config_str:\n continue # Skip empty segments\n\n try:\n parts = block_config_str.split(':')\n if len(parts) != 3:\n raise ValueError(f\"Expected 3 parts, got {len(parts)}\")\n\n block_label, block_relabel, confidence_str = parts\n confidence_thresh = float(confidence_str)\n\n block_type = BlockTypes[block_label]\n relabel_block_type = BlockTypes[block_relabel]\n\n self.block_relabel_map[block_type] = (\n confidence_thresh,\n relabel_block_type\n )\n except Exception as e:\n logger.warning(f\"Failed to parse relabel rule '{block_config_str}' at index {i}: {e}. Expected format is ::\")\n\n def __call__(self, document: Document):\n if len(self.block_relabel_map) == 0:\n return\n\n for page in document.pages:\n for block in page.structure_blocks(document):\n if block.block_type not in self.block_relabel_map:\n continue\n \n block_id = BlockId(page_id=page.page_id, block_id=block.block_id, block_type=block.block_type)\n confidence_thresh, relabel_block_type = self.block_relabel_map[block.block_type]\n confidence = block.top_k.get(block.block_type)\n if confidence > confidence_thresh:\n logger.debug(f\"Skipping relabel for {block_id}; Confidence: {confidence} > Confidence Threshold {confidence_thresh} for re-labelling\")\n continue\n\n new_block_cls = get_block_class(relabel_block_type)\n new_block = new_block_cls(\n polygon=deepcopy(block.polygon),\n page_id=block.page_id,\n structure=deepcopy(block.structure),\n text_extraction_method=block.text_extraction_method,\n source=\"heuristics\",\n top_k=block.top_k,\n metadata=block.metadata\n )\n page.replace_block(block, new_block)\n logger.debug(f\"Relabelled {block_id} to {relabel_block_type}\")"
},
{
"path": "marker/processors/blockquote.py",
"content": "from typing import Annotated, Tuple\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\n\n\nclass BlockquoteProcessor(BaseProcessor):\n \"\"\"\n A processor for tagging blockquotes.\n \"\"\"\n block_types: Annotated[\n Tuple[BlockTypes],\n \"The block types to process.\",\n ] = (BlockTypes.Text, BlockTypes.TextInlineMath)\n min_x_indent: Annotated[\n float,\n \"The minimum horizontal indentation required to consider a block as part of a blockquote.\",\n \"Expressed as a percentage of the block width.\",\n ] = 0.1\n x_start_tolerance: Annotated[\n float,\n \"The maximum allowable difference between the starting x-coordinates of consecutive blocks to consider them aligned.\",\n \"Expressed as a percentage of the block width.\",\n ] = 0.01\n x_end_tolerance: Annotated[\n float,\n \"The maximum allowable difference between the ending x-coordinates of consecutive blocks to consider them aligned.\",\n \"Expressed as a percentage of the block width.\",\n ] = 0.01\n\n def __init__(self, config):\n super().__init__(config)\n\n def __call__(self, document: Document):\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n if block.structure is None:\n continue\n\n if not len(block.structure) >= 2:\n continue\n\n next_block = page.get_next_block(block)\n if next_block is None:\n continue\n if next_block.block_type not in self.block_types:\n continue\n if next_block.structure is None:\n continue\n if next_block.ignore_for_output:\n continue\n\n matching_x_end = abs(next_block.polygon.x_end - block.polygon.x_end) < self.x_end_tolerance * block.polygon.width\n matching_x_start = abs(next_block.polygon.x_start - block.polygon.x_start) < self.x_start_tolerance * block.polygon.width\n x_indent = next_block.polygon.x_start > block.polygon.x_start + (self.min_x_indent * block.polygon.width)\n y_indent = next_block.polygon.y_start > block.polygon.y_end\n\n if block.blockquote:\n next_block.blockquote = (matching_x_end and matching_x_start) or (x_indent and y_indent)\n next_block.blockquote_level = block.blockquote_level\n if (x_indent and y_indent):\n next_block.blockquote_level += 1\n elif len(next_block.structure) >= 2 and (x_indent and y_indent):\n next_block.blockquote = True\n next_block.blockquote_level = 1"
},
{
"path": "marker/processors/code.py",
"content": "from marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import Code\nfrom marker.schema.document import Document\n\n\nclass CodeProcessor(BaseProcessor):\n \"\"\"\n A processor for formatting code blocks.\n \"\"\"\n block_types = (BlockTypes.Code, )\n\n def __call__(self, document: Document):\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n self.format_block(document, block)\n\n\n def format_block(self, document: Document, block: Code):\n min_left = 9999 # will contain x- coord of column 0\n total_width = 0\n total_chars = 0\n \n contained_lines = block.contained_blocks(document, (BlockTypes.Line,))\n for line in contained_lines:\n min_left = min(line.polygon.bbox[0], min_left)\n total_width += line.polygon.width\n total_chars += len(line.raw_text(document))\n\n avg_char_width = total_width / max(total_chars, 1)\n code_text = \"\"\n is_new_line = False\n for line in contained_lines:\n text = line.raw_text(document)\n if avg_char_width == 0:\n prefix = \"\"\n else:\n total_spaces = int((line.polygon.bbox[0] - min_left) / avg_char_width)\n prefix = \" \" * max(0, total_spaces)\n\n if is_new_line:\n text = prefix + text\n\n code_text += text\n is_new_line = text.endswith(\"\\n\")\n\n block.code = code_text.rstrip()\n"
},
{
"path": "marker/processors/debug.py",
"content": "import json\nimport os\nfrom typing import Annotated\n\nfrom PIL import Image, ImageDraw, ImageFont\nfrom marker.logger import get_logger\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\nfrom marker.settings import settings\n\nlogger = get_logger()\n\n\nclass DebugProcessor(BaseProcessor):\n \"\"\"\n A processor for debugging the document.\n \"\"\"\n\n block_types: Annotated[\n tuple, \"The block types to process.\", \"Default is an empty tuple.\"\n ] = tuple()\n debug_data_folder: Annotated[\n str,\n \"The folder to dump debug data to.\",\n ] = \"debug_data\"\n debug_layout_images: Annotated[\n bool,\n \"Whether to dump layout debug images.\",\n ] = False\n debug_pdf_images: Annotated[\n bool,\n \"Whether to dump PDF debug images.\",\n ] = False\n debug_json: Annotated[\n bool,\n \"Whether to dump block debug data.\",\n ] = False\n\n def __call__(self, document: Document):\n # Remove extension from doc name\n doc_base = os.path.basename(document.filepath).rsplit(\".\", 1)[0]\n self.debug_folder = os.path.join(self.debug_data_folder, doc_base)\n if any([self.debug_layout_images, self.debug_pdf_images, self.debug_json]):\n os.makedirs(self.debug_folder, exist_ok=True)\n\n document.debug_data_path = self.debug_folder\n\n if self.debug_layout_images:\n self.draw_layout_debug_images(document)\n logger.info(f\"Dumped layout debug images to {self.debug_data_folder}\")\n\n if self.debug_pdf_images:\n self.draw_pdf_debug_images(document)\n logger.info(f\"Dumped PDF debug images to {self.debug_data_folder}\")\n\n if self.debug_json:\n self.dump_block_debug_data(document)\n logger.info(f\"Dumped block debug data to {self.debug_data_folder}\")\n\n def draw_pdf_debug_images(self, document: Document):\n for page in document.pages:\n png_image = page.get_image(highres=True).copy()\n\n line_bboxes = []\n span_bboxes = []\n line_ids = []\n for child in page.children:\n # Skip any blocks that have been removed\n if child.removed:\n continue\n\n if child.block_type == BlockTypes.Line:\n bbox = child.polygon.rescale(page.polygon.size, png_image.size).bbox\n line_bboxes.append(bbox)\n line_ids.append(child.block_id)\n elif child.block_type == BlockTypes.Span:\n bbox = child.polygon.rescale(page.polygon.size, png_image.size).bbox\n span_bboxes.append(bbox)\n\n self.render_on_image(\n line_bboxes,\n png_image,\n color=\"blue\",\n draw_bbox=True,\n label_font_size=24,\n labels=[str(i) for i in line_ids],\n )\n\n png_image = self.render_layout_boxes(page, png_image)\n\n debug_file = os.path.join(self.debug_folder, f\"pdf_page_{page.page_id}.png\")\n png_image.save(debug_file)\n\n def draw_layout_debug_images(self, document: Document, pdf_mode=False):\n for page in document.pages:\n img_size = page.get_image(highres=True).size\n png_image = Image.new(\"RGB\", img_size, color=\"white\")\n\n line_bboxes = []\n line_text = []\n for child in page.children:\n if child.removed:\n continue\n\n if child.block_type != BlockTypes.Line:\n continue\n\n bbox = child.polygon.rescale(page.polygon.size, img_size).bbox\n line_bboxes.append(bbox)\n line_text.append(child.raw_text(document))\n\n self.render_on_image(\n line_bboxes,\n png_image,\n labels=line_text,\n color=\"black\",\n draw_bbox=False,\n label_font_size=24,\n )\n\n png_image = self.render_layout_boxes(page, png_image)\n\n debug_file = os.path.join(\n self.debug_folder, f\"layout_page_{page.page_id}.png\"\n )\n png_image.save(debug_file)\n\n def render_layout_boxes(self, page, png_image):\n layout_bboxes = []\n layout_labels = []\n for block_id in page.structure:\n child = page.get_block(block_id)\n if child.block_type in [BlockTypes.Line, BlockTypes.Span]:\n continue\n\n bbox = child.polygon.rescale(page.polygon.size, png_image.size).bbox\n layout_bboxes.append(bbox)\n layout_labels.append(str(child.block_type))\n\n self.render_on_image(\n layout_bboxes,\n png_image,\n labels=layout_labels,\n color=\"red\",\n label_font_size=24,\n )\n\n order_labels = [str(i) for i in range(len(layout_bboxes))]\n self.render_on_image(\n layout_bboxes,\n png_image,\n labels=order_labels,\n color=\"green\",\n draw_bbox=False,\n label_offset=5,\n label_font_size=24,\n )\n return png_image\n\n def dump_block_debug_data(self, document: Document):\n debug_file = os.path.join(self.debug_folder, \"blocks.json\")\n debug_data = []\n for page in document.pages:\n page_data = page.model_dump(\n exclude={\n \"lowres_image\": True,\n \"highres_image\": True,\n \"children\": {\n \"__all__\": {\"lowres_image\": True, \"highres_image\": True}\n },\n }\n )\n debug_data.append(page_data)\n\n with open(debug_file, \"w+\") as f:\n json.dump(debug_data, f)\n\n def get_text_size(self, text, font):\n im = Image.new(mode=\"P\", size=(0, 0))\n draw = ImageDraw.Draw(im)\n _, _, width, height = draw.textbbox((0, 0), text=text, font=font)\n return width, height\n\n def render_on_image(\n self,\n bboxes,\n image,\n labels=None,\n label_offset=1,\n label_font_size=10,\n color: str | list = \"red\",\n draw_bbox=True,\n ):\n draw = ImageDraw.Draw(image)\n font_path = settings.FONT_PATH\n label_font = ImageFont.truetype(font_path, label_font_size)\n\n for i, bbox in enumerate(bboxes):\n bbox = [int(p) for p in bbox]\n if draw_bbox:\n draw.rectangle(\n bbox,\n outline=color[i] if isinstance(color, list) else color,\n width=1,\n )\n\n if labels is not None:\n label = labels[i]\n text_position = (bbox[0] + label_offset, bbox[1] + label_offset)\n text_size = self.get_text_size(label, label_font)\n if text_size[0] <= 0 or text_size[1] <= 0:\n continue\n box_position = (\n text_position[0],\n text_position[1],\n text_position[0] + text_size[0],\n text_position[1] + text_size[1],\n )\n draw.rectangle(box_position, fill=\"white\")\n draw.text(\n text_position,\n label,\n fill=color[i] if isinstance(color, list) else color,\n font=label_font,\n )\n\n return image\n"
},
{
"path": "marker/processors/document_toc.py",
"content": "from marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\n\n\nclass DocumentTOCProcessor(BaseProcessor):\n \"\"\"\n A processor for generating a table of contents for the document.\n \"\"\"\n block_types = (BlockTypes.SectionHeader, )\n\n def __call__(self, document: Document):\n toc = []\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n toc.append({\n \"title\": block.raw_text(document).strip(),\n \"heading_level\": block.heading_level,\n \"page_id\": page.page_id,\n \"polygon\": block.polygon.polygon\n })\n document.table_of_contents = toc\n"
},
{
"path": "marker/processors/equation.py",
"content": "from typing import Annotated, List, Tuple\nfrom PIL import Image\nimport re\nfrom bs4 import BeautifulSoup\n\nfrom ftfy import fix_text, TextFixerConfig\nfrom surya.recognition import RecognitionPredictor, OCRResult\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\nfrom marker.settings import settings\n\nMATH_TAG_PATTERN = re.compile(r\"]*>(.*?)\")\n\n\nclass EquationProcessor(BaseProcessor):\n \"\"\"\n A processor for recognizing equations in the document.\n \"\"\"\n\n block_types: Annotated[\n Tuple[BlockTypes],\n \"The block types to process.\",\n ] = (BlockTypes.Equation,)\n model_max_length: Annotated[\n int,\n \"The maximum number of tokens to allow for the Recognition model.\",\n ] = 1024\n equation_batch_size: Annotated[\n int,\n \"The batch size to use for the recognition model while processing equations.\",\n \"Default is None, which will use the default batch size for the model.\",\n ] = None\n disable_tqdm: Annotated[\n bool,\n \"Whether to disable the tqdm progress bar.\",\n ] = False\n drop_repeated_text: Annotated[bool, \"Drop repeated text in OCR results.\"] = False\n\n def __init__(self, recognition_model: RecognitionPredictor, config=None):\n super().__init__(config)\n\n self.recognition_model = recognition_model\n\n def get_batch_size(self):\n # Set to 1/4th of OCR batch size due to sequence length with tiling\n if self.equation_batch_size is not None:\n return self.equation_batch_size\n elif settings.TORCH_DEVICE_MODEL == \"cuda\":\n return 32\n elif settings.TORCH_DEVICE_MODEL == \"mps\":\n return 6\n return 6\n\n def __call__(self, document: Document):\n images = []\n equation_boxes = []\n equation_block_ids = []\n total_equation_blocks = 0\n\n for page in document.pages:\n page_image = page.get_image(highres=True)\n page_size = page.polygon.width, page.polygon.height\n image_size = page_image.size\n\n page_equation_boxes = []\n page_equation_block_ids = []\n equation_blocks = page.contained_blocks(document, self.block_types)\n for block in equation_blocks:\n page_equation_boxes.append(\n block.polygon.rescale(page_size, image_size).bbox\n )\n page_equation_block_ids.append(block.id)\n total_equation_blocks += 1\n\n images.append(page_image)\n equation_boxes.append(page_equation_boxes)\n equation_block_ids.append(page_equation_block_ids)\n\n if total_equation_blocks == 0:\n return\n\n predictions = self.get_latex_batched(images, equation_boxes)\n for page_predictions, page_equation_block_ids in zip(\n predictions, equation_block_ids\n ):\n assert len(page_predictions) == len(page_equation_block_ids), (\n \"Every equation block should have a corresponding prediction\"\n )\n for block_prediction, block_id in zip(\n page_predictions, page_equation_block_ids\n ):\n block = document.get_block(block_id)\n block.html = self.fix_latex(block_prediction)\n\n def fix_latex(self, math_html: str):\n math_html = math_html.strip()\n soup = BeautifulSoup(math_html, \"html.parser\")\n opening_math_tag = soup.find(\"math\")\n\n # No math block found\n if not opening_math_tag:\n return \"\"\n\n # Force block format\n opening_math_tag.attrs[\"display\"] = \"block\"\n fixed_math_html = str(soup)\n\n # Sometimes model outputs newlines at the beginning/end of tags\n fixed_math_html = re.sub(\n r\"^\", fixed_math_html)\n fixed_math_html = re.sub(r\"
\", \"\", fixed_math_html)\n fixed_math_html = fix_text(\n fixed_math_html, config=TextFixerConfig(unescape_html=True)\n )\n return fixed_math_html\n\n def get_latex_batched(\n self,\n page_images: List[Image.Image],\n bboxes: List[List[List[float]]],\n ):\n self.recognition_model.disable_tqdm = self.disable_tqdm\n predictions: List[OCRResult] = self.recognition_model(\n images=page_images,\n bboxes=bboxes,\n task_names=[\"ocr_with_boxes\"] * len(page_images),\n recognition_batch_size=self.get_batch_size(),\n sort_lines=False,\n drop_repeated_text=self.drop_repeated_text,\n max_tokens=2048,\n max_sliding_window=2148,\n )\n\n equation_predictions = [\n [line.text.strip() for line in page_prediction.text_lines]\n for page_prediction in predictions\n ]\n\n return equation_predictions\n"
},
{
"path": "marker/processors/footnote.py",
"content": "import re\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\nfrom marker.schema.groups import PageGroup\n\n\nclass FootnoteProcessor(BaseProcessor):\n \"\"\"\n A processor for pushing footnotes to the bottom, and relabeling mislabeled text blocks.\n \"\"\"\n block_types = (BlockTypes.Footnote,)\n\n def __call__(self, document: Document):\n for page in document.pages:\n self.push_footnotes_to_bottom(page, document)\n self.assign_superscripts(page, document)\n\n def push_footnotes_to_bottom(self, page: PageGroup, document: Document):\n footnote_blocks = page.contained_blocks(document, self.block_types)\n\n # Push footnotes to the bottom\n for block in footnote_blocks:\n # Check if it is top-level\n if block.id in page.structure:\n # Move to bottom if it is\n page.structure.remove(block.id)\n page.add_structure(block)\n\n def assign_superscripts(self, page: PageGroup, document: Document):\n footnote_blocks = page.contained_blocks(document, self.block_types)\n\n for block in footnote_blocks:\n for span in block.contained_blocks(document, (BlockTypes.Span,)):\n if re.match(r\"^[0-9\\W]+\", span.text):\n span.has_superscript = True\n break\n"
},
{
"path": "marker/processors/ignoretext.py",
"content": "import re\nfrom collections import Counter\nfrom itertools import groupby\nfrom typing import Annotated, List\n\nfrom rapidfuzz import fuzz\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import Block\nfrom marker.schema.document import Document\n\n\nclass IgnoreTextProcessor(BaseProcessor):\n \"\"\"\n A processor for identifying and ignoring common text blocks in a document. \n These blocks often represent repetitive or non-essential elements, such as headers, footers, or page numbers.\n \"\"\"\n block_types = (\n BlockTypes.Text, BlockTypes.SectionHeader,\n BlockTypes.TextInlineMath\n )\n common_element_threshold: Annotated[\n float,\n \"The minimum ratio of pages a text block must appear on to be considered a common element.\",\n \"Blocks that meet or exceed this threshold are marked as common elements.\",\n ] = 0.2\n common_element_min_blocks: Annotated[\n int,\n \"The minimum number of occurrences of a text block within a document to consider it a common element.\",\n \"This ensures that rare blocks are not mistakenly flagged.\",\n ] = 3\n max_streak: Annotated[\n int,\n \"The maximum number of consecutive occurrences of a text block allowed before it is classified as a common element.\",\n \"Helps to identify patterns like repeated headers or footers.\",\n ] = 3\n text_match_threshold: Annotated[\n int,\n \"The minimum fuzzy match score (0-100) required to classify a text block as similar to a common element.\",\n \"Higher values enforce stricter matching.\",\n ] = 90\n\n def __call__(self, document: Document):\n first_blocks = []\n last_blocks = []\n for page in document.pages:\n initial_block = None\n last_block = None\n for block in page.contained_blocks(document, self.block_types):\n if block.structure is not None:\n if initial_block is None:\n initial_block = block\n\n last_block = block\n\n if initial_block is not None:\n first_blocks.append(initial_block)\n if last_block is not None:\n last_blocks.append(last_block)\n\n self.filter_common_elements(document, first_blocks)\n self.filter_common_elements(document, last_blocks)\n\n @staticmethod\n def clean_text(text):\n text = text.replace(\"\\n\", \"\").strip()\n text = re.sub(r\"^\\d+\\s*\", \"\", text) # remove numbers at the start of the line\n text = re.sub(r\"\\s*\\d+$\", \"\", text) # remove numbers at the end of the line\n return text\n\n def filter_common_elements(self, document, blocks: List[Block]):\n # We can't filter if we don't have enough pages to find common elements\n if len(blocks) < self.common_element_min_blocks:\n return\n\n text = [self.clean_text(b.raw_text(document)) for b in blocks]\n\n streaks = {}\n for key, group in groupby(text):\n streaks[key] = max(streaks.get(key, 0), len(list(group)))\n\n counter = Counter(text)\n common = [\n k for k, v in counter.items()\n if (v >= len(blocks) * self.common_element_threshold or streaks[k] >= self.max_streak)\n and v > self.common_element_min_blocks\n ]\n if len(common) == 0:\n return\n\n for t, b in zip(text, blocks):\n # Check against all common elements\n if any(fuzz.ratio(t, common_element) > self.text_match_threshold for common_element in common):\n b.ignore_for_output = True\n"
},
{
"path": "marker/processors/line_merge.py",
"content": "from typing import Annotated, List\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import Block\nfrom marker.schema.document import Document\nfrom marker.schema.text import Line\nfrom marker.util import matrix_intersection_area\n\n\nclass LineMergeProcessor(BaseProcessor):\n \"\"\"\n A processor for merging inline math lines.\n \"\"\"\n block_types = (BlockTypes.Text, BlockTypes.TextInlineMath, BlockTypes.Caption, BlockTypes.Footnote, BlockTypes.SectionHeader)\n min_merge_pct: Annotated[\n float,\n \"The minimum percentage of intersection area to consider merging.\"\n ] = .015\n block_expand_threshold: Annotated[\n float,\n \"The percentage of the block width to expand the bounding box.\"\n ] = .05\n min_merge_ydist: Annotated[\n float,\n \"The minimum y distance between lines to consider merging.\"\n ] = 5\n intersection_pct_threshold: Annotated[\n float,\n \"The total amount of intersection area concentrated in the max intersection block.\"\n ] = .5\n vertical_overlap_pct_threshold: Annotated[\n float,\n \"The minimum percentage of vertical overlap to consider merging.\"\n ] = .8\n use_llm: Annotated[\n bool,\n \"Whether to use LLMs to improve accuracy.\"\n ] = False\n\n def __init__(self, config):\n super().__init__(config)\n\n def merge_lines(self, lines: List[Line], block: Block):\n lines = [l for l in lines if l.polygon.width * 5 > l.polygon.height] # Skip vertical lines\n line_bboxes = [l.polygon.expand(self.block_expand_threshold, 0).bbox for l in lines] # Expand horizontally\n intersections = matrix_intersection_area(line_bboxes, line_bboxes)\n\n merges = []\n merge = []\n for i in range(len(line_bboxes)):\n intersection_row = intersections[i]\n intersection_row[i] = 0 # Zero out the current idx\n\n if i < len(line_bboxes) - 1:\n intersection_row[i+1] = 0 # Zero out the next idx, so we only evaluate merge from the left\n\n if len(merge) == 0:\n merge.append(i)\n continue\n\n # Zero out previous merge segments\n merge_intersection = sum([intersection_row[m] for m in merge])\n line_area = lines[i].polygon.area\n intersection_pct = merge_intersection / max(1, line_area)\n\n total_intersection = max(1, sum(intersection_row))\n\n line_start = lines[merge[0]].polygon.y_start\n line_end = lines[merge[0]].polygon.y_end\n\n vertical_overlap_start = max(line_start, lines[i].polygon.y_start)\n vertical_overlap_end = min(line_end, lines[i].polygon.y_end)\n vertical_overlap = max(0, vertical_overlap_end - vertical_overlap_start)\n vertical_overlap_pct = vertical_overlap / max(1, lines[i].polygon.height)\n\n if all([\n # Overlaps enough\n intersection_pct >= self.min_merge_pct,\n # Within same line\n vertical_overlap_pct > self.vertical_overlap_pct_threshold,\n # doesn't overlap with anything else\n merge_intersection / total_intersection > self.intersection_pct_threshold\n ]):\n merge.append(i)\n else:\n merges.append(merge)\n merge = []\n\n if merge:\n merges.append(merge)\n\n merges = [m for m in merges if len(m) > 1]\n merged = set()\n for merge in merges:\n merge = [m for m in merge if m not in merged]\n if len(merge) < 2:\n continue\n\n line: Line = lines[merge[0]]\n merged.add(merge[0])\n for idx in merge[1:]:\n other_line: Line = lines[idx]\n line.merge(other_line)\n block.structure.remove(other_line.id)\n other_line.removed = True # Mark line as removed\n merged.add(idx)\n\n # It is probably math if we are merging provider lines like this\n if not line.formats:\n line.formats = [\"math\"]\n elif \"math\" not in line.formats:\n line.formats.append(\"math\")\n\n\n def __call__(self, document: Document):\n # Merging lines only needed for inline math\n if not self.use_llm:\n return\n\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n if block.structure is None:\n continue\n\n if not len(block.structure) >= 2: # Skip single lines\n continue\n\n lines = block.contained_blocks(document, (BlockTypes.Line,))\n self.merge_lines(lines, block)\n"
},
{
"path": "marker/processors/line_numbers.py",
"content": "from typing import Annotated\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\n\n\nclass LineNumbersProcessor(BaseProcessor):\n \"\"\"\n A processor for ignoring line numbers.\n \"\"\"\n block_types = (BlockTypes.Text, BlockTypes.TextInlineMath)\n strip_numbers_threshold: Annotated[\n float,\n \"The fraction of lines or tokens in a block that must be numeric to consider them as line numbers.\",\n ] = 0.6\n min_lines_in_block: Annotated[\n int,\n \"The minimum number of lines required in a block for it to be considered during processing.\",\n \"Ensures that small blocks are ignored as they are unlikely to contain meaningful line numbers.\",\n ] = 4\n min_line_length: Annotated[\n int,\n \"The minimum length of a line (in characters) to consider it significant when checking for\",\n \"numeric prefixes or suffixes. Prevents false positives for short lines.\",\n ] = 10\n min_line_number_span_ratio: Annotated[\n float,\n \"The minimum ratio of detected line number spans to total lines required to treat them as line numbers.\",\n ] = .6\n\n def __init__(self, config):\n super().__init__(config)\n\n def __call__(self, document: Document):\n self.ignore_line_number_spans(document)\n self.ignore_line_starts_ends(document)\n self.ignore_line_number_blocks(document)\n\n def ignore_line_number_spans(self, document: Document):\n for page in document.pages:\n line_count = 0\n line_number_spans = []\n for block in page.contained_blocks(document, (BlockTypes.Line,)):\n if block.structure is None:\n continue\n\n line_count += 1\n leftmost_span = None\n for span in block.contained_blocks(document, (BlockTypes.Span,)):\n if leftmost_span is None or span.polygon.x_start < leftmost_span.polygon.x_start:\n leftmost_span = span\n\n if leftmost_span is not None and leftmost_span.text.strip().isnumeric():\n line_number_spans.append(leftmost_span)\n\n if line_count > 0 and len(line_number_spans) / line_count > self.min_line_number_span_ratio:\n for span in line_number_spans:\n span.ignore_for_output = True\n\n def ignore_line_number_blocks(self, document: Document):\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n raw_text = block.raw_text(document)\n tokens = raw_text.strip().split()\n if len(tokens) < 4:\n continue\n\n tokens_are_numbers = [token.isdigit() for token in tokens]\n if all([\n sum(tokens_are_numbers) / len(tokens) > self.strip_numbers_threshold,\n block.polygon.height > block.polygon.width # Ensure block is taller than it is wide, like vertical page numbers\n ]):\n block.ignore_for_output = True\n\n def ignore_line_starts_ends(self, document: Document):\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n if block.structure is None:\n continue\n\n all_lines = block.structure_blocks(document)\n if len(all_lines) < self.min_lines_in_block:\n continue\n\n starts_with_number = []\n ends_with_number = []\n for line in all_lines:\n spans = line.structure_blocks(document)\n if len(spans) < 2:\n starts_with_number.append(False)\n ends_with_number.append(False)\n continue\n\n raw_text = line.raw_text(document)\n starts = all([\n spans[0].text.strip().isdigit(),\n len(raw_text) - len(spans[0].text.strip()) > self.min_line_length\n ])\n\n ends = all([\n spans[-1].text.strip().isdigit(),\n len(raw_text) - len(spans[-1].text.strip()) > self.min_line_length\n ])\n\n starts_with_number.append(starts)\n ends_with_number.append(ends)\n\n if sum(starts_with_number) / len(starts_with_number) > self.strip_numbers_threshold:\n for starts, line in zip(starts_with_number, all_lines):\n if starts:\n span = page.get_block(line.structure[0])\n span.ignore_for_output = True\n\n if sum(ends_with_number) / len(ends_with_number) > self.strip_numbers_threshold:\n for ends, line in zip(ends_with_number, all_lines):\n if ends:\n span = page.get_block(line.structure[-1])\n span.ignore_for_output = True\n"
},
{
"path": "marker/processors/list.py",
"content": "from typing import Annotated, List, Tuple\n\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import ListItem\nfrom marker.schema.document import Document\n\n\nclass ListProcessor(BaseProcessor):\n \"\"\"\n A processor for merging lists across pages and columns\n \"\"\"\n block_types = (BlockTypes.ListGroup,)\n ignored_block_types: Annotated[\n Tuple[BlockTypes],\n \"The list of block types to ignore when merging lists.\",\n ] = (BlockTypes.PageHeader, BlockTypes.PageFooter)\n min_x_indent: Annotated[\n float, \"The minimum horizontal indentation required to consider a block as a nested list item.\",\n \"This is expressed as a percentage of the page width and is used to determine hierarchical relationships within a list.\",\n ] = 0.01\n\n def __init__(self, config):\n super().__init__(config)\n\n def __call__(self, document: Document):\n self.list_group_continuation(document)\n self.list_group_indentation(document)\n\n def list_group_continuation(self, document: Document):\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n next_block = document.get_next_block(block, self.ignored_block_types)\n if next_block is None:\n continue\n if next_block.block_type not in self.block_types:\n continue\n if next_block.structure is None:\n continue\n if next_block.ignore_for_output:\n continue\n\n column_break, page_break = False, False\n next_block_in_first_quadrant = False\n\n if next_block.page_id == block.page_id: # block on the same page\n # we check for a column break\n column_break = next_block.polygon.y_start <= block.polygon.y_end\n else:\n page_break = True\n next_page = document.get_page(next_block.page_id)\n next_block_in_first_quadrant = (next_block.polygon.x_start < next_page.polygon.width // 2) and \\\n (next_block.polygon.y_start < next_page.polygon.height // 2)\n\n block.has_continuation = column_break or (page_break and next_block_in_first_quadrant)\n\n def list_group_indentation(self, document: Document):\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n if block.structure is None:\n continue\n if block.ignore_for_output:\n continue\n\n stack: List[ListItem] = [block.get_next_block(page, None)]\n for list_item_id in block.structure:\n list_item_block: ListItem = page.get_block(list_item_id)\n\n # This can be a line sometimes\n if list_item_block.block_type != BlockTypes.ListItem:\n continue\n\n while stack and list_item_block.polygon.x_start <= stack[-1].polygon.x_start + (self.min_x_indent * page.polygon.width):\n stack.pop()\n\n if stack and list_item_block.polygon.y_start > stack[-1].polygon.y_start:\n list_item_block.list_indent_level = stack[-1].list_indent_level\n if list_item_block.polygon.x_start > stack[-1].polygon.x_start + (self.min_x_indent * page.polygon.width):\n list_item_block.list_indent_level += 1\n\n next_list_item_block = block.get_next_block(page, list_item_block)\n if next_list_item_block is not None and next_list_item_block.polygon.x_start > list_item_block.polygon.x_end:\n stack = [next_list_item_block] # reset stack on column breaks\n else:\n stack.append(list_item_block)\n\n stack: List[ListItem] = [block.get_next_block(page, None)]\n for list_item_id in block.structure.copy():\n list_item_block: ListItem = page.get_block(list_item_id)\n\n while stack and list_item_block.list_indent_level <= stack[-1].list_indent_level:\n stack.pop()\n\n if stack:\n current_parent = stack[-1]\n current_parent.add_structure(list_item_block)\n current_parent.polygon = current_parent.polygon.merge([list_item_block.polygon])\n\n block.remove_structure_items([list_item_id])\n stack.append(list_item_block)\n"
},
{
"path": "marker/processors/llm/__init__.py",
"content": "import json\nimport traceback\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\nfrom typing import Annotated, TypedDict, List, Sequence\n\nfrom pydantic import BaseModel\nfrom tqdm import tqdm\nfrom PIL import Image\n\nfrom marker.output import json_to_html\nfrom marker.processors import BaseProcessor\nfrom marker.schema import BlockTypes\nfrom marker.schema.blocks import Block, BlockId\nfrom marker.schema.document import Document\nfrom marker.schema.groups import PageGroup\nfrom marker.services import BaseService\nfrom marker.util import assign_config\nfrom marker.logger import get_logger\n\nlogger = get_logger()\n\n\nclass PromptData(TypedDict):\n prompt: str\n image: Image.Image\n block: Block\n schema: BaseModel\n page: PageGroup\n additional_data: dict | None\n\n\nclass BlockData(TypedDict):\n page: PageGroup\n block: Block\n\n\nclass BaseLLMProcessor(BaseProcessor):\n \"\"\"\n A processor for using LLMs to convert blocks.\n \"\"\"\n\n max_concurrency: Annotated[\n int,\n \"The maximum number of concurrent requests to make to the Gemini model.\",\n ] = 3\n image_expansion_ratio: Annotated[\n float,\n \"The ratio to expand the image by when cropping.\",\n ] = 0.01\n use_llm: Annotated[\n bool,\n \"Whether to use the LLM model.\",\n ] = False\n disable_tqdm: Annotated[\n bool,\n \"Whether to disable the tqdm progress bar.\",\n ] = False\n block_types = None\n\n def __init__(self, llm_service: BaseService, config=None):\n super().__init__(config)\n\n self.llm_service = None\n if not self.use_llm:\n return\n\n self.llm_service = llm_service\n\n def extract_image(\n self,\n document: Document,\n image_block: Block,\n remove_blocks: Sequence[BlockTypes] | None = None,\n ) -> Image.Image:\n return image_block.get_image(\n document,\n highres=True,\n expansion=(self.image_expansion_ratio, self.image_expansion_ratio),\n remove_blocks=remove_blocks,\n )\n\n def normalize_block_json(self, block: Block, document: Document, page: PageGroup):\n \"\"\"\n Get the normalized JSON representation of a block for the LLM.\n \"\"\"\n page_width = page.polygon.width\n page_height = page.polygon.height\n block_bbox = block.polygon.bbox\n\n # Normalize bbox to 0-1000 range\n normalized_bbox = [\n (block_bbox[0] / page_width) * 1000,\n (block_bbox[1] / page_height) * 1000,\n (block_bbox[2] / page_width) * 1000,\n (block_bbox[3] / page_height) * 1000,\n ]\n\n block_json = {\n \"id\": str(block.id),\n \"block_type\": str(block.id.block_type),\n \"bbox\": normalized_bbox,\n \"html\": json_to_html(block.render(document)),\n }\n\n return block_json\n\n def load_blocks(self, response: dict):\n return [json.loads(block) for block in response[\"blocks\"]]\n\n def handle_rewrites(self, blocks: list, document: Document):\n for block_data in blocks:\n try:\n block_id = block_data[\"id\"].strip().lstrip(\"/\")\n _, page_id, block_type, block_id = block_id.split(\"/\")\n block_id = BlockId(\n page_id=page_id,\n block_id=block_id,\n block_type=getattr(BlockTypes, block_type),\n )\n block = document.get_block(block_id)\n if not block:\n logger.debug(f\"Block {block_id} not found in document\")\n continue\n\n if hasattr(block, \"html\"):\n block.html = block_data[\"html\"]\n except Exception as e:\n logger.debug(f\"Error parsing block ID {block_data['id']}: {e}\")\n continue\n\n\nclass BaseLLMComplexBlockProcessor(BaseLLMProcessor):\n \"\"\"\n A processor for using LLMs to convert blocks with more complex logic.\n \"\"\"\n\n def __call__(self, document: Document):\n if not self.use_llm or self.llm_service is None:\n return\n\n try:\n self.rewrite_blocks(document)\n except Exception as e:\n logger.warning(f\"Error rewriting blocks in {self.__class__.__name__}: {e}\")\n\n def process_rewriting(self, document: Document, page: PageGroup, block: Block):\n raise NotImplementedError()\n\n def rewrite_blocks(self, document: Document):\n # Don't show progress if there are no blocks to process\n total_blocks = sum(\n len(page.contained_blocks(document, self.block_types))\n for page in document.pages\n )\n if total_blocks == 0:\n return\n\n pbar = tqdm(\n total=total_blocks,\n desc=f\"{self.__class__.__name__} running\",\n disable=self.disable_tqdm\n )\n with ThreadPoolExecutor(max_workers=self.max_concurrency) as executor:\n for future in as_completed(\n [\n executor.submit(self.process_rewriting, document, page, block)\n for page in document.pages\n for block in page.contained_blocks(document, self.block_types)\n ]\n ):\n future.result() # Raise exceptions if any occurred\n pbar.update(1)\n\n pbar.close()\n\n\nclass BaseLLMSimpleBlockProcessor(BaseLLMProcessor):\n \"\"\"\n A processor for using LLMs to convert single blocks.\n \"\"\"\n\n # Override init since we don't need an llmservice here\n def __init__(self, config=None):\n assign_config(self, config)\n\n def __call__(self, result: dict, prompt_data: PromptData, document: Document):\n try:\n self.rewrite_block(result, prompt_data, document)\n except Exception as e:\n logger.warning(f\"Error rewriting block in {self.__class__.__name__}: {e}\")\n traceback.print_exc()\n\n def inference_blocks(self, document: Document) -> List[BlockData]:\n blocks = []\n for page in document.pages:\n for block in page.contained_blocks(document, self.block_types):\n blocks.append({\"page\": page, \"block\": block})\n return blocks\n\n def block_prompts(self, document: Document) -> List[PromptData]:\n raise NotImplementedError()\n\n def rewrite_block(\n self, response: dict, prompt_data: PromptData, document: Document\n ):\n raise NotImplementedError()\n"
},
{
"path": "marker/processors/llm/llm_complex.py",
"content": "from typing import List\n\nimport markdown2\nfrom pydantic import BaseModel\n\nfrom marker.processors.llm import PromptData, BaseLLMSimpleBlockProcessor\n\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\n\n\nclass LLMComplexRegionProcessor(BaseLLMSimpleBlockProcessor):\n block_types = (BlockTypes.ComplexRegion,)\n complex_region_prompt = \"\"\"You are a text correction expert specializing in accurately reproducing text from images.\nYou will receive an image of a text block and the text that can be extracted from the image.\nYour task is to generate markdown to properly represent the content of the image. Do not omit any text present in the image - make sure everything is included in the markdown representation. The markdown representation should be as faithful to the original image as possible.\n\nFormatting should be in markdown, with the following rules:\n- * for italics, ** for bold, and ` for inline code.\n- Use ... for superscripts.\n- Headers should be formatted with #, with one # for the largest header, and up to 6 for the smallest.\n- Lists should be formatted with either - or 1. for unordered and ordered lists, respectively.\n- Links should be formatted with [text](url).\n- Use ``` for code blocks.\n- Inline math should be formatted with .\n- Display math should be formatted with .\n- Values and labels should be extracted from forms, and put into markdown tables, with the labels on the left side, and values on the right. The headers should be \"Labels\" and \"Values\". Other text in the form can appear between the tables.\n- Tables should be formatted with markdown tables, with the headers bolded.\n\n**Instructions:**\n1. Carefully examine the provided block image.\n2. Analyze the existing text representation.\n3. Generate the markdown representation of the content in the image.\n**Example:**\nInput:\n```text\nTable 1: Car Sales\n```\nOutput:\n```markdown\n## Table 1: Car Sales\n\n| Car | Sales |\n| --- | --- |\n| Honda | 100 |\n| Toyota | 200 |\n```\n**Input:**\n```text\n{extracted_text}\n```\n\"\"\"\n\n def block_prompts(self, document: Document) -> List[PromptData]:\n prompt_data = []\n for block in self.inference_blocks(document):\n text = block[\"block\"].raw_text(document)\n prompt = self.complex_region_prompt.replace(\"{extracted_text}\", text)\n image = self.extract_image(document, block[\"block\"])\n prompt_data.append({\n \"prompt\": prompt,\n \"image\": image,\n \"block\": block[\"block\"],\n \"schema\": ComplexSchema,\n \"page\": block[\"page\"]\n })\n return prompt_data\n\n def rewrite_block(self, response: dict, prompt_data: PromptData, document: Document):\n block = prompt_data[\"block\"]\n text = block.raw_text(document)\n\n if not response or \"corrected_markdown\" not in response:\n block.update_metadata(llm_error_count=1)\n return\n\n corrected_markdown = response[\"corrected_markdown\"]\n\n # The original table is okay\n if \"no corrections\" in corrected_markdown.lower():\n return\n\n # Potentially a partial response\n if len(corrected_markdown) < len(text) * .5:\n block.update_metadata(llm_error_count=1)\n return\n\n # Convert LLM markdown to html\n corrected_markdown = corrected_markdown.strip().lstrip(\"```markdown\").rstrip(\"```\").strip()\n block.html = markdown2.markdown(corrected_markdown, extras=[\"tables\"])\n\nclass ComplexSchema(BaseModel):\n corrected_markdown: str"
},
{
"path": "marker/processors/llm/llm_equation.py",
"content": "from pydantic import BaseModel\n\nfrom marker.processors.llm import BaseLLMSimpleBlockProcessor, PromptData, BlockData\nfrom marker.schema import BlockTypes\nfrom marker.schema.document import Document\n\nfrom typing import Annotated, List\n\n\nclass LLMEquationProcessor(BaseLLMSimpleBlockProcessor):\n block_types = (BlockTypes.Equation,)\n min_equation_height: Annotated[\n float,\n \"The minimum ratio between equation height and page height to consider for processing.\",\n ] = 0.06\n image_expansion_ratio: Annotated[\n float,\n \"The ratio to expand the image by when cropping.\",\n ] = 0.05 # Equations sometimes get bboxes that are too tight\n redo_inline_math: Annotated[\n bool,\n \"Whether to redo inline math blocks.\",\n ] = False\n equation_latex_prompt: Annotated[\n str,\n \"The prompt to use for generating LaTeX from equations.\",\n \"Default is a string containing the Gemini prompt.\"\n ] = r\"\"\"You're an expert mathematician who is good at writing LaTeX code and html for equations.\nYou'll receive an image of a math block, along with the text extracted from the block. It may contain one or more equations. Your job is to write html that represents the content of the image, with the equations in LaTeX format.\n\nSome guidelines:\n- Output valid html, where all the equations can render properly.\n- Use ![](\"data/images/overall.png\")
\n\nMarker benchmarks favorably compared to cloud services like Llamaparse and Mathpix, as well as other open source tools.\n\nThe above results are running single PDF pages serially. Marker is significantly faster when running in batch mode, with a projected throughput of 25 pages/second on an H100.\n\nSee [below](#benchmarks) for detailed speed and accuracy benchmarks, and instructions on how to run your own benchmarks.\n\n## Hybrid Mode\n\nFor the highest accuracy, pass the `--use_llm` flag to use an LLM alongside marker. This will do things like merge tables across pages, handle inline math, format tables properly, and extract values from forms. It can use any gemini or ollama model. By default, it uses `gemini-2.0-flash`. See [below](#llm-services) for details.\n\nHere is a table benchmark comparing marker, gemini flash alone, and marker with use_llm:\n\n![](\"data/images/table.png\")
\n\nAs you can see, the use_llm mode offers higher accuracy than marker or gemini alone.\n\n## Examples\n\n| PDF | File type | Markdown | JSON |\n|-----|-----------|------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|\n| [Think Python](https://greenteapress.com/thinkpython/thinkpython.pdf) | Textbook | [View](https://github.com/VikParuchuri/marker/blob/master/data/examples/markdown/thinkpython/thinkpython.md) | [View](https://github.com/VikParuchuri/marker/blob/master/data/examples/json/thinkpython.json) |\n| [Switch Transformers](https://arxiv.org/pdf/2101.03961.pdf) | arXiv paper | [View](https://github.com/VikParuchuri/marker/blob/master/data/examples/markdown/switch_transformers/switch_trans.md) | [View](https://github.com/VikParuchuri/marker/blob/master/data/examples/json/switch_trans.json) |\n| [Multi-column CNN](https://arxiv.org/pdf/1804.07821.pdf) | arXiv paper | [View](https://github.com/VikParuchuri/marker/blob/master/data/examples/markdown/multicolcnn/multicolcnn.md) | [View](https://github.com/VikParuchuri/marker/blob/master/data/examples/json/multicolcnn.json) |\n\n# Commercial usage\n\nOur model weights use a modified AI Pubs Open Rail-M license (free for research, personal use, and startups under $2M funding/revenue) and our code is GPL. For broader commercial licensing or to remove GPL requirements, visit our pricing page [here](https://www.datalab.to/pricing?utm_source=gh-marker).\n\n# Hosted API & On-prem\n\nThere's a [hosted API](https://www.datalab.to?utm_source=gh-marker) and [painless on-prem solution](https://www.datalab.to/blog/self-serve-on-prem-licensing) for marker - it's free to sign up, and we'll throw in credits for you to test it out.\n\nThe API:\n- Supports PDF, image, PPT, PPTX, DOC, DOCX, XLS, XLSX, HTML, EPUB files\n- Is 1/4th the price of leading cloud-based competitors\n- Fast - ~15s for a 250 page PDF\n- Supports LLM mode\n- High uptime (99.99%)\n\n# Community\n\n[Discord](https://discord.gg//KuZwXNGnfH) is where we discuss future development.\n\n# Installation\n\nYou'll need python 3.10+ and [PyTorch](https://pytorch.org/get-started/locally/).\n\nInstall with:\n\n```shell\npip install marker-pdf\n```\n\nIf you want to use marker on documents other than PDFs, you will need to install additional dependencies with:\n\n```shell\npip install marker-pdf[full]\n```\n\n# Usage\n\nFirst, some configuration:\n\n- Your torch device will be automatically detected, but you can override this. For example, `TORCH_DEVICE=cuda`.\n- Some PDFs, even digital ones, have bad text in them. Set `--force_ocr` to force OCR on all lines, or the `strip_existing_ocr` to keep all digital text, and strip out any existing OCR text.\n- If you care about inline math, set `force_ocr` to convert inline math to LaTeX.\n\n## Interactive App\n\nI've included a streamlit app that lets you interactively try marker with some basic options. Run it with:\n\n```shell\npip install streamlit streamlit-ace\nmarker_gui\n```\n\n## Convert a single file\n\n```shell\nmarker_single /path/to/file.pdf\n```\n\nYou can pass in PDFs or images.\n\nOptions:\n- `--page_range TEXT`: Specify which pages to process. Accepts comma-separated page numbers and ranges. Example: `--page_range \"0,5-10,20\"` will process pages 0, 5 through 10, and page 20.\n- `--output_format [markdown|json|html|chunks]`: Specify the format for the output results.\n- `--output_dir PATH`: Directory where output files will be saved. Defaults to the value specified in settings.OUTPUT_DIR.\n- `--paginate_output`: Paginates the output, using `\\n\\n{PAGE_NUMBER}` followed by `-` * 48, then `\\n\\n`\n- `--use_llm`: Uses an LLM to improve accuracy. You will need to configure the LLM backend - see [below](#llm-services).\n- `--force_ocr`: Force OCR processing on the entire document, even for pages that might contain extractable text. This will also format inline math properly.\n- `--block_correction_prompt`: if LLM mode is active, an optional prompt that will be used to correct the output of marker. This is useful for custom formatting or logic that you want to apply to the output.\n- `--strip_existing_ocr`: Remove all existing OCR text in the document and re-OCR with surya.\n- `--redo_inline_math`: If you want the absolute highest quality inline math conversion, use this along with `--use_llm`.\n- `--disable_image_extraction`: Don't extract images from the PDF. If you also specify `--use_llm`, then images will be replaced with a description.\n- `--debug`: Enable debug mode for additional logging and diagnostic information.\n- `--processors TEXT`: Override the default processors by providing their full module paths, separated by commas. Example: `--processors \"module1.processor1,module2.processor2\"`\n- `--config_json PATH`: Path to a JSON configuration file containing additional settings.\n- `config --help`: List all available builders, processors, and converters, and their associated configuration. These values can be used to build a JSON configuration file for additional tweaking of marker defaults.\n- `--converter_cls`: One of `marker.converters.pdf.PdfConverter` (default) or `marker.converters.table.TableConverter`. The `PdfConverter` will convert the whole PDF, the `TableConverter` will only extract and convert tables.\n- `--llm_service`: Which llm service to use if `--use_llm` is passed. This defaults to `marker.services.gemini.GoogleGeminiService`.\n- `--help`: see all of the flags that can be passed into marker. (it supports many more options then are listed above)\n\nThe list of supported languages for surya OCR is [here](https://github.com/VikParuchuri/surya/blob/master/surya/recognition/languages.py). If you don't need OCR, marker can work with any language.\n\n## Convert multiple files\n\n```shell\nmarker /path/to/input/folder\n```\n\n- `marker` supports all the same options from `marker_single` above.\n- `--workers` is the number of conversion workers to run simultaneously. This is automatically set by default, but you can increase it to increase throughput, at the cost of more CPU/GPU usage. Marker will use 5GB of VRAM per worker at the peak, and 3.5GB average.\n\n## Convert multiple files on multiple GPUs\n\n```shell\nNUM_DEVICES=4 NUM_WORKERS=15 marker_chunk_convert ../pdf_in ../md_out\n```\n\n- `NUM_DEVICES` is the number of GPUs to use. Should be `2` or greater.\n- `NUM_WORKERS` is the number of parallel processes to run on each GPU.\n\n## Use from python\n\nSee the `PdfConverter` class at `marker/converters/pdf.py` function for additional arguments that can be passed.\n\n```python\nfrom marker.converters.pdf import PdfConverter\nfrom marker.models import create_model_dict\nfrom marker.output import text_from_rendered\n\nconverter = PdfConverter(\n artifact_dict=create_model_dict(),\n)\nrendered = converter(\"FILEPATH\")\ntext, _, images = text_from_rendered(rendered)\n```\n\n`rendered` will be a pydantic basemodel with different properties depending on the output type requested. With markdown output (default), you'll have the properties `markdown`, `metadata`, and `images`. For json output, you'll have `children`, `block_type`, and `metadata`.\n\n### Custom configuration\n\nYou can pass configuration using the `ConfigParser`. To see all available options, do `marker_single --help`.\n\n```python\nfrom marker.converters.pdf import PdfConverter\nfrom marker.models import create_model_dict\nfrom marker.config.parser import ConfigParser\n\nconfig = {\n \"output_format\": \"json\",\n \"ADDITIONAL_KEY\": \"VALUE\"\n}\nconfig_parser = ConfigParser(config)\n\nconverter = PdfConverter(\n config=config_parser.generate_config_dict(),\n artifact_dict=create_model_dict(),\n processor_list=config_parser.get_processors(),\n renderer=config_parser.get_renderer(),\n llm_service=config_parser.get_llm_service()\n)\nrendered = converter(\"FILEPATH\")\n```\n\n### Extract blocks\n\nEach document consists of one or more pages. Pages contain blocks, which can themselves contain other blocks. It's possible to programmatically manipulate these blocks.\n\nHere's an example of extracting all forms from a document:\n\n```python\nfrom marker.converters.pdf import PdfConverter\nfrom marker.models import create_model_dict\nfrom marker.schema import BlockTypes\n\nconverter = PdfConverter(\n artifact_dict=create_model_dict(),\n)\ndocument = converter.build_document(\"FILEPATH\")\nforms = document.contained_blocks((BlockTypes.Form,))\n```\n\nLook at the processors for more examples of extracting and manipulating blocks.\n\n## Other converters\n\nYou can also use other converters that define different conversion pipelines:\n\n### Extract tables\n\nThe `TableConverter` will only convert and extract tables:\n\n```python\nfrom marker.converters.table import TableConverter\nfrom marker.models import create_model_dict\nfrom marker.output import text_from_rendered\n\nconverter = TableConverter(\n artifact_dict=create_model_dict(),\n)\nrendered = converter(\"FILEPATH\")\ntext, _, images = text_from_rendered(rendered)\n```\n\nThis takes all the same configuration as the PdfConverter. You can specify the configuration `force_layout_block=Table` to avoid layout detection and instead assume every page is a table. Set `output_format=json` to also get cell bounding boxes.\n\nYou can also run this via the CLI with\n```shell\nmarker_single FILENAME --use_llm --force_layout_block Table --converter_cls marker.converters.table.TableConverter --output_format json\n```\n\n### OCR Only\n\nIf you only want to run OCR, you can also do that through the `OCRConverter`. Set `--keep_chars` to keep individual characters and bounding boxes.\n\n```python\nfrom marker.converters.ocr import OCRConverter\nfrom marker.models import create_model_dict\n\nconverter = OCRConverter(\n artifact_dict=create_model_dict(),\n)\nrendered = converter(\"FILEPATH\")\n```\n\nThis takes all the same configuration as the PdfConverter.\n\nYou can also run this via the CLI with\n```shell\nmarker_single FILENAME --converter_cls marker.converters.ocr.OCRConverter\n```\n\n### Structured Extraction (beta)\n\nYou can run structured extraction via the `ExtractionConverter`. This requires an llm service to be setup first (see [here](#llm-services) for details). You'll get a JSON output with the extracted values.\n\n```python\nfrom marker.converters.extraction import ExtractionConverter\nfrom marker.models import create_model_dict\nfrom marker.config.parser import ConfigParser\nfrom pydantic import BaseModel\n\nclass Links(BaseModel):\n links: list[str]\n\nschema = Links.model_json_schema()\nconfig_parser = ConfigParser({\n \"page_schema\": schema\n})\n\nconverter = ExtractionConverter(\n artifact_dict=create_model_dict(),\n config=config_parser.generate_config_dict(),\n llm_service=config_parser.get_llm_service(),\n)\nrendered = converter(\"FILEPATH\")\n```\n\nRendered will have an `original_markdown` field. If you pass this back in next time you run the converter, as the `existing_markdown` config key, you can skip re-parsing the document.\n\n# Output Formats\n\n## Markdown\n\nMarkdown output will include:\n\n- image links (images will be saved in the same folder)\n- formatted tables\n- embedded LaTeX equations (fenced with `$$`)\n- Code is fenced with triple backticks\n- Superscripts for footnotes\n\n## HTML\n\nHTML output is similar to markdown output:\n\n- Images are included via `img` tags\n- equations are fenced with `