diff --git a/.github/workflows/scripts.yml b/.github/workflows/scripts.yml new file mode 100644 index 00000000..217e4221 --- /dev/null +++ b/.github/workflows/scripts.yml @@ -0,0 +1,31 @@ +name: Test CLI scripts + +on: [push] + +env: + TORCH_DEVICE: "cpu" + OCR_ENGINE: "surya" + +jobs: + tests: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.11 + uses: actions/setup-python@v4 + with: + python-version: 3.11 + - name: Install python dependencies + run: | + pip install poetry + poetry install + - name: Download benchmark data + run: | + wget -O benchmark_data.zip "https://drive.google.com/uc?export=download&id=1NHrdYatR1rtqs2gPVfdvO0BAvocH8CJi" + unzip -o benchmark_data.zip + - name: Test single script + run: poetry run marker_single benchmark_data/pdfs/switch_trans.pdf --page_range 0 + - name: Test convert script + run: poetry run marker benchmark_data/pdfs --max_files 1 --workers 1 --page_range 0 + - name: Text convert script multiple workers + run: poetry run marker benchmark_data/pdfs --max_files 2 --workers 2 --page_range 0-5 \ No newline at end of file diff --git a/README.md b/README.md index 5d30ce23..5d952a0f 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,11 @@ # Marker -Marker converts PDFs to markdown, JSON, and HTML quickly and accurately. +Marker converts PDFs and images to markdown, JSON, and HTML quickly and accurately. -- Supports a wide range of documents -- Supports all languages -- Removes headers/footers/other artifacts -- Formats tables, forms, and code blocks +- Supports a range of documents in all languages +- Formats tables, forms, equations, links, references, and code blocks - Extracts and saves images along with the markdown -- Converts equations to latex +- Removes headers/footers/other artifacts - Easily extensible with your own formatting and logic - Optionally boost accuracy with an LLM - Works on GPU, CPU, or MPS @@ -18,7 +16,7 @@ Marker is a pipeline of deep learning models: - Extract text, OCR if necessary (heuristics, [surya](https://github.com/VikParuchuri/surya)) - Detect page layout and find reading order ([surya](https://github.com/VikParuchuri/surya)) -- Clean and format each block (heuristics, [texify](https://github.com/VikParuchuri/texify). [tabled](https://github.com/VikParuchuri/tabled)) +- Clean and format each block (heuristics, [texify](https://github.com/VikParuchuri/texify), [surya](https://github.com/VikParuchuri/surya)) - Optionally use an LLM to improve quality - Combine blocks and postprocess complete text @@ -63,11 +61,11 @@ There's a hosted API for marker available [here](https://www.datalab.to/): PDF is a tricky format, so marker will not always work perfectly. Here are some known limitations that are on the roadmap to address: - Marker will only convert block equations -- Tables are not always formatted 100% correctly - multiline cells are sometimes split into multiple rows. +- Tables are not always formatted 100% correctly - Forms are not converted optimally - Very complex layouts, with nested tables and forms, may not work -Note: Passing the `--use_llm` flag will mostly solve all of these issues. +Note: Passing the `--use_llm` flag will mostly solve these issues. # Installation @@ -84,7 +82,7 @@ pip install marker-pdf First, some configuration: - Your torch device will be automatically detected, but you can override this. For example, `TORCH_DEVICE=cuda`. -- Some PDFs, even digital ones, have bad text in them. Set the `force_ocr` flag on the CLI or via configuration to ensure your PDF runs through OCR. +- Some PDFs, even digital ones, have bad text in them. Set the `force_ocr` flag on the CLI or via configuration to ensure your PDF runs through OCR, or the `strip_existing_ocr` to keep all digital text, and only strip out any existing OCR text. ## Interactive App @@ -101,9 +99,12 @@ marker_gui marker_single /path/to/file.pdf ``` +You can pass in PDFs or images. + Options: - `--output_dir PATH`: Directory where output files will be saved. Defaults to the value specified in settings.OUTPUT_DIR. - `--output_format [markdown|json|html]`: Specify the format for the output results. +- `--paginate_output`: Paginates the output, using `\n\n{PAGE_NUMBER}` followed by `-` * 48, then `\n\n` - `--use_llm`: Uses an LLM to improve accuracy. You must set your Gemini API key using the `GOOGLE_API_KEY` env var. - `--disable_image_extraction`: Don't extract images from the PDF. If you also specify `--use_llm`, then images will be replaced with a description. - `--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. @@ -114,8 +115,9 @@ Options: - `--config_json PATH`: Path to a JSON configuration file containing additional settings. - `--languages TEXT`: Optionally specify which languages to use for OCR processing. Accepts a comma-separated list. Example: `--languages "en,fr,de"` for English, French, and German. - `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. +- `--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. -The list of supported languages for surya OCR is [here](https://github.com/VikParuchuri/surya/blob/master/surya/languages.py). If you don't need OCR, marker can work with any language. +The 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. ## Convert multiple files @@ -179,7 +181,7 @@ rendered = converter("FILEPATH") ### Extract blocks -Each document consists of one or more pages. Pages contain blocks, which can themselves contain other blocks. It's possible to programatically manipulate these blocks. +Each document consists of one or more pages. Pages contain blocks, which can themselves contain other blocks. It's possible to programmatically manipulate these blocks. Here's an example of extracting all forms from a document: @@ -197,6 +199,33 @@ forms = document.contained_blocks((BlockTypes.Form,)) Look at the processors for more examples of extracting and manipulating blocks. +## Other converters + +You can also use other converters that define different conversion pipelines: + +### Extract tables + +The `TableConverter` will only convert and extract tables: + +```python +from marker.converters.table import TableConverter +from marker.models import create_model_dict +from marker.output import text_from_rendered + +converter = TableConverter( + artifact_dict=create_model_dict(), +) +rendered = converter("FILEPATH") +text, _, images = text_from_rendered(rendered) +``` + +This 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. + +You can also run this via the CLI with +```shell +python convert_single.py FILENAME --use_llm --force_layout_block Table --converter_cls marker.converters.table.TableConverter +``` + # Output Formats ## Markdown @@ -348,7 +377,7 @@ There are some settings that you may find useful if things aren't working the wa Pass the `debug` option to activate debug mode. This will save images of each page with detected layout and text, as well as output a json file with additional bounding box information. # Benchmarks - +## Overall PDF Conversion Benchmarking PDF extraction quality is hard. I've created a test set by finding books and scientific papers that have a pdf version and a latex source. I convert the latex to text, and compare the reference to the output of text extraction methods. It's noisy, but at least directionally correct. **Speed** @@ -371,6 +400,18 @@ Marker takes about 6GB of VRAM on average per task, so you can convert 8 documen ![Benchmark results](data/images/per_doc.png) +## Table Conversion +Marker can extract tables from PDFs using `marker.converters.table.TableConverter`. The table extraction performance is measured by comparing the extracted HTML representation of tables against the original HTML representations using the test split of [FinTabNet](https://developer.ibm.com/exchanges/data/all/fintabnet/). The HTML representations are compared using a tree edit distance based metric to judge both structure and content. Marker detects and identifies the structure of all tables in a PDF page and achieves these scores: + +| Avg score | Total tables | use_llm | +|-----------|--------------|---------| +| 0.822 | 54 | False | +| 0.887 | 54 | True | + +The `--use_llm` flag can significantly improve table recognition performance, as you can see. + +We filter out tables that we cannot align with the ground truth, since fintabnet and our layout model have slightly different detection methods (this results in some tables being split/merged). + ## Running your own benchmarks You can benchmark the performance of marker on your machine. Install marker manually with: @@ -380,12 +421,21 @@ git clone https://github.com/VikParuchuri/marker.git poetry install ``` +### Overall PDF Conversion + Download the benchmark data [here](https://drive.google.com/file/d/1ZSeWDo2g1y0BRLT7KnbmytV2bjWARWba/view?usp=sharing) and unzip. Then run the overall benchmark like this: ```shell python benchmarks/overall.py data/pdfs data/references report.json ``` +### Table Conversion +The processed FinTabNet dataset is hosted [here](https://huggingface.co/datasets/datalab-to/fintabnet-test) and is automatically downloaded. Run the benchmark with: + +```shell +python benchmarks/table/table.py table_report.json --max_rows 1000 +``` + # Thanks This work would not have been possible without amazing open source models and datasets, including (but not limited to): @@ -395,4 +445,4 @@ This work would not have been possible without amazing open source models and da - Pypdfium2/pdfium - DocLayNet from IBM -Thank you to the authors of these models and datasets for making them available to the community! \ No newline at end of file +Thank you to the authors of these models and datasets for making them available to the community! diff --git a/benchmarks/table/gemini.py b/benchmarks/table/gemini.py new file mode 100644 index 00000000..9e2591ee --- /dev/null +++ b/benchmarks/table/gemini.py @@ -0,0 +1,49 @@ +import json +from PIL import Image +import google.generativeai as genai +from google.ai.generativelanguage_v1beta.types import content +from marker.settings import settings + +prompt = """ +You're an expert document analyst who is good at turning tables in documents into HTML. Analyze the provided image, and convert it to a faithful HTML representation. + +Guidelines: +- Keep the HTML simple and concise. +- Only include the
tags. Only use the colspan and rowspan attributes if necessary. Do not use | tags.
+- Make sure the table is as faithful to the image as possible with the given tags.
+
+**Instructions**
+1. Analyze the image, and determine the table structure.
+2. Convert the table image to HTML, following the guidelines above.
+3. Output only the HTML for the table, starting with the | which fintabnet data doesn't
+ #Fintabnet doesn't use th tags, need to be replaced for fair comparison
+ marker_table_soup = BeautifulSoup(marker_table.html, 'html.parser')
+ tbody = marker_table_soup.find('tbody')
+ if tbody:
+ tbody.unwrap()
+ for th_tag in marker_table_soup.find_all('th'):
+ th_tag.name = 'td'
+ marker_table_html = str(marker_table_soup)
+ marker_table_html = marker_table_html.replace("
---|
Method | MAE |
---|---|
AMDCN | 290.82 |
Hydra2s [18] | 333.73 |
MCNN [28] | 377.60 |
[27] | 467.00 |
[23] | 295.80 |
[3] | 318.10 |
Table 1. Mean absolute error of various methods on UCF crowds
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---|---|
AMDCN | 290.82 |
Hydra2s [18] | 333.73 |
MCNN [28] | 377.60 |
[27] | 467.00 |
[23] | 295.80 |
[3] | 318.10 |
Table 1. Mean absolute error of various methods on UCF crowds
", + "polygon": [ + [ + 49.23193359375, + 169.14794921875 + ], + [ + 283.587890625, + 169.14794921875 + ], + [ + 283.587890625, + 178.6640625 + ], + [ + 49.23193359375, + 178.6640625 + ] + ], + "bbox": [ + 49.23193359375, + 169.14794921875, + 283.587890625, + 178.6640625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/2/SectionHeader/4", + "2": "/page/3/SectionHeader/3", + "4": "/page/4/SectionHeader/10" + }, + "images": {} + } ], - "children": null, "section_hierarchy": { "1": "/page/2/SectionHeader/4", - "3": "/page/3/SectionHeader/3", + "2": "/page/3/SectionHeader/3", "4": "/page/4/SectionHeader/10" }, - "images": {} + "images": null }, { "id": "/page/5/TextInlineMath/2", "block_type": "TextInlineMath", - "html": "duce the final density map. We set σ = 0.2M(x) for Nh and σx = 0.2M(x), σy = 0.5M(x) for Σb in Nb.
", + "html": "duce the final density map. We set σ = 0.2M(x) for Nh and σx = 0.2M(x), σy = 0.5M(x) for Σb in Nb.
", "polygon": [ [ - 50.11195373535156, + 49.1572265625, 197.2218017578125 ], [ - 286.875, + 286.1279296875, 197.2218017578125 ], [ - 286.875, + 286.1279296875, 220.110595703125 ], [ - 50.11195373535156, + 49.1572265625, 220.110595703125 ] ], + "bbox": [ + 49.1572265625, + 197.2218017578125, + 286.1279296875, + 220.110595703125 + ], "children": null, "section_hierarchy": { "1": "/page/2/SectionHeader/4", - "3": "/page/3/SectionHeader/3", + "2": "/page/3/SectionHeader/3", "4": "/page/4/SectionHeader/10" }, "images": {} @@ -2380,22 +3380,28 @@ "html": "The 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 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.
", + "html": "The 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 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.
", "polygon": [ [ - 49.23193359375, - 269.349609375 + 49.4560546875, + 269.8974609375 ], [ - 286.875, - 269.349609375 + 286.4267578125, + 269.8974609375 ], [ - 286.875, - 352.107421875 + 286.4267578125, + 352.30078125 ], [ - 49.23193359375, - 352.107421875 + 49.4560546875, + 352.30078125 ] ], + "bbox": [ + 49.4560546875, + 269.8974609375, + 286.4267578125, + 352.30078125 + ], "children": null, "section_hierarchy": { "1": "/page/5/SectionHeader/3", - "3": "/page/5/SectionHeader/4" + "2": "/page/5/SectionHeader/4" }, "images": {} }, { "id": "/page/5/Text/6", "block_type": "Text", - "html": "We report a state of the art result on this dataset in Table 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]. 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.
", + "html": "We report a state of the art result on this dataset in Table 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]. 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.
", "polygon": [ [ - 49.60546875, + 49.68017578125, 353.5833740234375 ], [ @@ -2478,108 +3496,132 @@ 447.2319030761719 ], [ - 49.60546875, + 49.68017578125, 447.2319030761719 ] ], + "bbox": [ + 49.68017578125, + 353.5833740234375, + 286.36505126953125, + 447.2319030761719 + ], "children": null, "section_hierarchy": { "1": "/page/5/SectionHeader/3", - "3": "/page/5/SectionHeader/4" + "2": "/page/5/SectionHeader/4" }, "images": {} }, { "id": "/page/5/SectionHeader/7", "block_type": "SectionHeader", - "html": "Our 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] across all levels.
", + "html": "Our 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] across all levels.
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", + "html": "Results are shown in Table 3 and Figure 3. We see that the \"original\" split as defined by the creators of the dataset in [5] and used in [28] 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 and Figure 3 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
", "polygon": [ [ 49.68017578125, @@ -2598,98 +3640,998 @@ 713.4068832397461 ] ], + "bbox": [ + 49.68017578125, + 571.9372711181641, + 287.173828125, + 713.4068832397461 + ], "children": null, "section_hierarchy": { "1": "/page/5/SectionHeader/3", - "3": "/page/5/SectionHeader/9" + "2": "/page/5/SectionHeader/9" }, "images": {} }, { - "id": "/page/5/TableGroup/404", + "id": "/page/5/TableGroup/436", "block_type": "TableGroup", "html": "Method | GAME | GAME | GAME | GAME | |
---|---|---|---|---|---|
(L=0) | (L=1) | (L=2) | (L=3) | ||
AMDCN | 9.77 | 13.16 | 15.00 | 15.87 | |
[18] | 10.99 | 13.75 | 16.69 | 19.32 | |
[15] + | SIFT | 13.76 | 16.72 | 20.72 | 24.36 |
from [14] | |||||
[13] + | RGB | 17.68 | 19.97 | 23.54 | 25.84 |
Norm + Filters | |||||
from [14] | |||||
HOG-2 | 13.29 | 18.05 | 23.65 | 28.41 | |
from [14] |
Method | GAME (L=0) | GAME (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 |
Table 2. Mean absolute error of various methods on TRANCOS traffic
", + "html": "Table 2. Mean absolute error of various methods on TRANCOS traffic
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", "polygon": [ [ - 307.79296875, + 308.86199951171875, 269.58740234375 ], [ - 545.66015625, + 546.2578125, 269.58740234375 ], [ - 545.66015625, + 546.2578125, 291.5050048828125 ], [ - 307.79296875, + 308.86199951171875, 291.5050048828125 ] ], + "bbox": [ + 308.86199951171875, + 269.58740234375, + 546.2578125, + 291.5050048828125 + ], "children": null, "section_hierarchy": { "1": "/page/5/SectionHeader/3", - "3": "/page/5/SectionHeader/9" + "2": "/page/5/SectionHeader/9" }, "images": {} }, { "id": "/page/5/SectionHeader/14", "block_type": "SectionHeader", - "html": "Our 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, 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.
", + "html": "Our 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, 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.
", "polygon": [ [ - 307.79296875, + 308.86199951171875, 343.01953125 ], [ - 546.2578125, + 545.1151733398438, 343.01953125 ], [ - 546.2578125, + 545.1151733398438, 448.6579284667969 ], [ - 307.79296875, + 308.86199951171875, 448.6579284667969 ] ], + "bbox": [ + 308.86199951171875, + 343.01953125, + 545.1151733398438, + 448.6579284667969 + ], "children": null, "section_hierarchy": { "1": "/page/5/SectionHeader/3", - "3": "/page/5/SectionHeader/14" + "2": "/page/5/SectionHeader/14" }, "images": {} }, { "id": "/page/5/SectionHeader/16", "block_type": "SectionHeader", - "html": "We report the results of the ablation studies in Figure 4. 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.
", + "html": "We report the results of the ablation studies in Figure 4. 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.
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+ "/page/6/Figure/0": 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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.
", + "html": "(a) UCSD upscale split. (b) UCSD original split.
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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 |
Figure 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.
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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 |
Table 3. Mean absolute error of various methods on UCSD crowds
", + "html": "Table 3. Mean absolute error of various methods on UCSD crowds
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", + "html": "We have proposed the use of aggregated multicolumn dilated convolutions, the AMDCN, as an alternative to the HydraCNN [18] or multicolumn CNN [28] 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
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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.
", + "html": "Figure 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.
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---|---|
AMDCN (without perspective infor | 16.6 |
mation) | |
AMDCN (with perspective informa | 14.9 |
tion) | |
LBP+RR [28] (with perspective infor | 31.0 |
mation) | |
MCNN [28] (with perspective informa | 11.6 |
tion) | |
[27] (with perspective information) | 12.9 |
Method | MAE |
---|---|
AMDCN (without perspective information) | 16.6 |
AMDCN (with perspective information) | 14.9 |
LBP+RR [28] (with perspective information) | 31.0 |
MCNN [28] (with perspective information) | 11.6 |
[27] (with perspective information) | 12.9 |
Table 4. Mean absolute error of various methods on WorldExpo crowds
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", + "html": "In 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.12
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---|---|---|---|
2 | Switch Transformer | 4 | |
2.1 | Simplifying Sparse Routing | 5 | |
2.2 | Efficient Sparse Routing | 6 | |
2.3 | Putting It All Together: The Switch Transformer | 8 | |
2.4 | Improved Training and Fine-Tuning Techniques | 8 | |
3 | Scaling Properties | 11 | |
3.1 | Scaling Results on a Step-Basis | 12 | |
3.2 | Scaling Results on a Time-Basis | 13 | |
3.3 | Scaling Versus a Larger Dense Model | 13 | |
4 | Downstream Results | 14 | |
4.1 | Fine-Tuning | 14 | |
4.2 | Distillation | 16 | |
4.3 | Multilingual Learning | 17 | |
5 | Designing Models with Data, Model, and Expert-Parallelism | 18 | |
5.1 | Data Parallelism | 20 | |
5.2 | Model Parallelism | 20 | |
5.3 | Model and Data Parallelism | 21 | |
5.4 | Expert and Data Parallelism | 22 | |
5.5 | Expert, Model and Data Parallelism | 22 | |
5.6 | Towards Trillion Parameter Models | 22 | |
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 |
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 |
Large scale training has been an effective path towards flexible and powerful neural language models (Radford et al., 2018; Kaplan et al., 2020; Brown et al., 2020). Simple architectures backed by a generous computational budget, data set size and parameter count—surpass more complicated algorithms (Sutton, 2019). An approach followed in Radford et al. (2018); Raffel et al. (2019); Brown et al. (2020) expands the model size of a densely-activated Transformer (Vaswani et al., 2017). While effective, it is also extremely computationally intensive (Strubell et al., 2019). 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.
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Gmh6XauGhhkE+qKBkmNhtCfXBL49dlHI+4fWY/wAv4/8AAOl/t6x/6ef/AAFk/wDiaP7esf8Ap5/8BZP/AImu0hmjuII54XWSKRQ6OpyGUjIIp9HI+4fWY/y/j/wDiP7esf8Ap5/8BZP/AImj+3rH/p5/8BZP/ia7eijkfcPrMf5fx/4BxH9vWP8A08/+Asn/AMTR/b1j/wBPP/gLJ/8AE129FHI+4fWY/wAv4/8AAOI/t6x/6ef/AAFk/wDiaP7esf8Ap5/8BZP/AImu3oo5H3D6zH+X8f8AgHEf29Y/9PP/AICyf/E0f29Y/wDTz/4Cyf8AxNdvRRyPuH1mP8v4/wDAOI/t6x/6ef8AwFk/+Jo/t6x/6ef/AAFk/wDia7eijkfcPrMf5fx/4BxH9vWP/Tz/AOAsn/xNH9vWP/Tz/wCAsn/xNdvRRyPuH1mP8v4/8A4j+3rH/p5/8BZP/iaP7esf+nn/AMBZP/ia7eijkfcPrMf5fx/4BxH9vWP/AE8/+Asn/wATR/b1j/08/wDgLJ/8TXb0Ucj7h9Zj/L+P/AOI/t6x/wCnn/wFk/8AiaP7esf+nn/wFk/+Jrt6KOR9w+sx/l/H/gHEf29Y/wDTz/4Cyf8AxNH9vWP/AE8/+Asn/wATXb0Ucj7h9Zj/AC/j/wAA4j+3rH/p5/8AAWT/AOJo/t6x/wCnn/wFk/8Aia7eijkfcPrMf5fx/wCAcR/b1j/08/8AgLJ/8TR/b1j/ANPP/gLJ/wDE129FHI+4fWY/y/j/AMA4j+3rH/p5/wDAWT/4mj+3rH/p5/8AAWT/AOJrt6KOR9w+sx/l/H/gHEf29Y/9PP8A4Cyf/E0f29Y/9PP/AICyf/E129FHI+4fWY/y/j/wDiP7esf+nn/wFk/+Jo/t6x/6ef8AwFk/+JrtiQoJJAA5JNeQ+Dfi2viP4sapo5lX+yp08vTT6tHkk/8AAxuP0VRRyPuH1mP8v4/8A6j+3rH/AKef/AWT/wCJo/t6x/6ef/AWT/4mu3oo5H3D6zH+X8f+AcR/b1j/ANPP/gLJ/wDE0f29Y/8ATz/4Cyf/ABNdvRRyPuH1mP8AL+P/AADh/wC3bQ8Rx3cj9kS1kyx9BxW94Ysbiy0uRrpPLmuZ3uGjznZuPAPvgCtqinGFncipX548qVgoooqznCiiigAooooAKKKKACiiigAooooAKKKKAK9//wAg65/65P8AyNePeF/+RS0b/rxg/wDRa17Df/8AIOuf+uT/AMjXj3hf/kUtG/68YP8A0WtAHtNFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFNkcRRvI2cKCxwMnj2rIj8VaPLp2mX8d0Wg1OVYbUiNsu7Z4xjI6HOemOaANmiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqC7vLbT7SW7vLiK3t4l3SSyuFVR6knpQBPXNeK/Heg+Dok/tO6LXUuBDZwLvmlyccL6e5wKZ4V8daZ4yvNQj0iG7e0syqi9khKwzk5yEJ9OOuDzVyDwdoNv4mufEaadEdWuMbrh/mK4UL8oPCnA5I5NAE2u6HYeLPD8umX4nFndBWYIxjfAIYD1HTkGp9H0TTPD+nR6fpNlFaWsfSONcZPqT1J9zzV+igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACub+IX/JOPEn/AGDbj/0Wa6Sub+IX/JOPEn/YNuP/AEWaANbRf+QDp3/XtH/6CKvVR0X/AJAOnf8AXtH/AOgir1ABRRR0oAKw9U8WaVpbGNpTPMODHCNxH1PQVj6jq194jv30vRnMdqnE90O49j6fz+la2leHNP0lFMcQknHWaQZbPt6fhW6pxirz+4yc29Imb/wlmsXXzWWgSlOzPuIP6Cj/AISDxP8A9AFf1/xrqaKOaH8pPvdzlv8AhIPE/wD0AV/X/Gj/AISDxP8A9AFf1/xrqaKOeP8AKgtLuct/wkHif/oAr+v+NH/CQeJ/+gCv6/411NFHPH+VBaXc5b/hIPE//QBX9f8AGj/hIPE//QBX9f8AGupoo54/yoLS7nLf8JB4n/6AK/r/AI0f8JB4n/6AK/r/AI11NFHPH+VBaXc5b/hIPE//AEAV/X/Gj/hIPE//AEAV/X/Gupoo54/yoLS7nLf8JB4n/wCgCv6/40f8JB4n/wCgCv6/411NFHPH+VBaXc5b/hIPE/8A0AV/X/Gj/hIPE/8A0AV/X/Gupoo54/yoLS7nLf8ACQeJ/wDoAr+v+Nea+JdBWTxLpE954XjmudS1GRrl7gs7XGYX+TJPyqB0C4xtHcV7nXHeMf8AkafBX/YTk/8ASeShyj/KhpS7kOj3Wu6FpVvplloLi1t12QrI7OUXPC5JyQOgz2Aq9/wkHif/AKAK/r/jXU0Uc8f5UK0u5y3/AAkHif8A6AK/r/jR/wAJB4n/AOgCv6/411NFHPH+VBaXc5b/AISDxP8A9AFf1/xo/wCEg8T/APQBX9f8a6mijnj/ACoLS7nLf8JB4n/6AK/r/jR/wkHif/oAr+v+NdTRRzx/lQWl3OW/4SDxP/0AV/X/ABo/4SDxP/0AV/X/ABrqaKOeP8qC0u5y3/CQeJ/+gCv6/wCNH/CQeJ/+gCv6/wCNdTRRzx/lQWl3OW/4SDxP/wBAFf1/xo/4SDxP/wBAFf1/xrqaKOeP8qC0u5y3/CQeJ/8AoAr+v+NH/CQeJ/8AoAr+v+NdTRRzx/lQWl3OW/4SDxP/ANAFf1/xo/4SDxP/ANAFf1/xrqaKOeP8qC0u5y3/AAkHif8A6AK/r/jR/wAJB4n/AOgCv6/411NFHPH+VBaXc5b/AISDxP8A9AFf1/xo/wCEg8T/APQBX9f8a6mijnj/ACoLS7nLf8JB4n/6AK/r/jR/wkHif/oAr+v+NdTRRzx/lQWl3OW/4SDxP/0AV/X/ABo/4SDxP/0AV/X/ABrqaKOeP8qC0u5y3/CQeJ/+gCv6/wCNH/CQeJ/+gCv6/wCNdTRRzx/lQWl3OW/4SDxP/wBAFf1/xo/4SDxP/wBAFf1/xrqaKOeP8qC0u5xmpah4g1bTLnT7rQT9nuYzFKEZlLIeCMg5GRkcc815zpPhmCXxdrMdv4Vihk02ayktfILK9o6x7gQc85yCd2QTgmvea47wn8/jfxxL63tun/fNslDlH+VDSl3LH/CQeJ/+gCv6/wCNH/CQeJ/+gCv6/wCNdTRRzx/lQrS7nLf8JB4n/wCgCv6/40f8JB4n/wCgCv6/411NFHPH+VBaXc5b/hIPE/8A0AV/X/Gj/hIPE/8A0AV/X/Gupoo54/yoLS7nLf8ACQeJ/wDoAr+v+NH/AAkHif8A6AK/r/jXU0Uc8f5UFpdzlv8AhIPE/wD0AV/X/Gj/AISDxP8A9AFf1/xrqaKOeP8AKgtLuct/wkHif/oAr+v+NH/CQeJ/+gCv6/411NFHPH+VBaXc5b/hIPE//QBX9f8AGj/hIPE//QBX9f8AGupoo54/yoLS7nLf8JB4n/6AK/r/AI0f8JZrNr8174flCd2TcAP0NdTRRzQ/lD3u5k6X4t0rU2WNZTBMeBHMNuT7Hoa3awtV8O6fqyMZYRHMek0Yw2ff1/GsfT9VvvDV+mmaw5ls34guT2HufT27fSh04yV4fcUptaSO1ooByMjpRWBqV7//AJB1z/1yf+Rrx7wv/wAilo3/AF4wf+i1r2G//wCQdc/9cn/ka8e8L/8AIpaN/wBeMH/otaAPaaKKKACiiigAooooAKKKKACiiigAooooAK8x8MaNN/wsTULTKPo+hzSz2m05Cy3IDFf+ADf/AN9ivTutU9O0rT9It2t9Nsre0hZy7JBGEBY9ScdTQBcooooAKKKKACiiigAooooAKKKKACiiigAoqhrGtad4f0yXUtVu47W0iHzSSH8gB1JPoOawvBnjSTxm17cwaJeWekoVFpeXOF+1ddxC9QBxg8j6HigCDxV8SdI8NXselwRzatrcrBY9OsRvkz/tH+Hj8fbHNdFq2jad4k0hrDVrIT2k213hl4IIIYdDwQR2NOt9F0y01W61SCwt47+6wJ7hUG9wAAAT6YAq/QBDaWltY2sdraQRQW8S7Y4olCqo9AB0qaiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArm/iF/yTjxJ/2Dbj/0Wa6Sub+IX/JOPEn/AGDbj/0WaANbRf8AkA6d/wBe0f8A6CKvVR0X/kA6d/17R/8AoIq9QAVzPjLUpbawi0+1z9pvW8sAddvQ/nkD866auNlH2/4j7W5SyhBA98Z/m/6VrRS5rvpqZ1H7tu5t6NpcWkadHbRgFsZkf+83c1oUUUm23dkpWCiiigYUUUUAFFFFABRVFNZ0uTU/7Nj1G0e/CljbLMpkAHUlc5FUJ/EkkfiFdIi0LV5vmVXvVgAtkBAOd5YZxnnAPekBu0Vha5L4pFxFH4fttJaIrmSa/mkG056BUU549xVnXLLVr+1jj0jWF0uYPl5TarPuXB4AYgDnHPPSgDUorLk0q7ufDw0251i7F0UCvqFsEilJBB3AYKrnGOlGkaINK0yWybUtRvxIzMZr2fzJBkAYDADA4yMdyaANSisTQvCuneHppprOS+kkmUK7XV5JNwPQOxA/Cm2vg3QLPX31yCxK6k7vIZjPI3zPncdpbaM5PagDdrlPFfjzT/BeoWCazbzpp97uVb6Jd6RSDqrgcgEEEEZJ544zV7UfBvh3VtYi1a/0qGe/i27J3zldpyvfHBrnfiL8P7j4g3ulW0lzFZ6dZ75JZgu+Z2bA2IOgGF5J9RwcUArGj4i8XKPDlpP4aurW8vNUuUsrGVXDxrI+SWbHZVDMR7Yrg7TT/CVnDfw/ESCeTxFZqZJZ7q7kc3aE4V7fBHBOAFUAqeDW5qPw707wboVjf+FtNkmu9Lvo76ZSxea7RVZHUE8Z2uSAABkdOao2thb/ABMgbxPqWtxWMsGTpEFvOpOmkMCJJeeZCVGVPAHHXkAy94Jv9f0LWLHS9ajmTS9ZE0mlw3UxluLPYN3lSMeuUyQMkjGK9Ory3w1quqeOfFOly3VvD5Phx5/tN9bOHt7udk8tfKI7bSzH0JAr1KhCYUUUUwCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiigkAEk4AoAKK5W/+InhyzuTZ213Jql8P+XTS4jcyfQ7eB+JFVv7a8c6p/wAg3wzZ6ZEeVm1e73MR/wBcogSD7FqQWOzorjR4Z8XXvOpeN5YVbrDpljHEF+jvvaj/AIVvps3/ACENY8Q6gf8Ap51WUD8kKgflQB2VcVpfxP0G61260HUpDpOrW0xhaG7O1JCDwUfoQeCM4JzwKk/4VV4KPMmiLMfWe5ll/wDQnNcdpvwG0q41u61TXNi28kpaDS7IlI40/hVn+8xxjOMc55NGo9DU8bX+v67q99peipM+l6MIZNUhtZjFcXm8bvKjYdMJgkZGc4rDu7DwleQ2EPw8gnj8RXiiSGe1u5UNogOGe4yTwDkFWBLHgVpa1dXnw316/h0qxtorHXfs6WV1MwjtrKZE8thKew2qrD1IIqO7sLf4ZwL4n03W4r6afB1eC4nUHUiWJMkXPEgLHCjgjjryUM67w74uX/hHLufxLdWtneaVcvZX0rOEjaRMEOuezKVIHvin+FPHmn+NNQv49Gt53sLLarXsq7Flc9FQHkgAZJOCMjjnNc5p3w707xlod9f+KdNkhu9UvpL6FQxSa0RlVEUkcZ2oCQQRk9OKt/Dr4cyeANU1NUmhvLO6VTDcFdk6YPKMOhHQgjuDwM0xaHodFYVv4N8PWviBteg0qGPVGdnNyCdxZgQx645BNGqeDdB1nVYtUv7JpbyIKElW4kTAU5HCsAefagRu0Vj674bsvEKwC7mvYjAWKNaXckB5xnOwjPTvT73RBdaFFpUWp6jZiNUVbm3n/f4XHV2ByTjknrQBq0VlWWlXdhoctgms3dxdFXEV7dqkkiMR8pIAAbB7d6ZoNlrtks6a1rFvqWSvkvFZ/ZyvXO4BiD29OlAGxRWDpV74mk1SS21bRrKG0AZkvLa9L554UoVBBI757UP4tsIfEQ0S4ttRguHcJDK9lJ5MxIz8sgBX88dDQBvUU3enmGPcu8DO3POPXFOpgFFFFABRRRQAVQ1jS4tX06S1lABIyj/3W7Gr9FCbTuhNXOf8G6lLPZTabdZ+02TbDnrt6D8sEflXT1xq/wDEv+JCheEvYcsPwP8AVP1rsqdZLmuuupVN+7bsV7//AJB1z/1yf+Rrx7wv/wAilo3/AF4wf+i1r2G//wCQdc/9cn/ka8e8L/8AIpaN/wBeMH/otayND2miiigAooooAKKKKACiiigAooooAKKKKAIrq6gsrSa7upVit4I2klkc4CKoySfYAV4V8NvixNrXxR1S21CVksNYk/0FHPETIMIvoCyjn1YD1r3K+sbXUrOSzvYUntpRiSJxlXGc4I7j271wug6HpV3488Y20+nWzw2s9g9unlACBlt12lMfdI7EYxQB6FRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFe+sLTU7RrS+tYbm3cgtFMgdWwQRkHg8gGp1UKoVQAoGAAOAKWigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACub+IX/JOPEn/YNuP/RZrpK5v4hf8k48Sf8AYNuP/RZoA1tF/wCQDp3/AF7R/wDoIq9VHRf+QDp3/XtH/wCgir1ABXG6b/yUPVv+uP8A8RXZVx2m/wDJQ9W/64//ABFbUtpehlV6ep1VFFFSAUUUUAFR3FxDaW0tzcSLFDEhkkdjgKoGST7YonaVLeRoYxJKFJRGbaGbHAJ5x9ayfDlvrq2M0viK6glurl9/2aBAI7ZcY8sHq/uT3JxxSAXQvEdr4mtbi402K7S3Q7Yrm4t2jSfI+8mcFl9+KZoGk6xYST3Gs6/Lqc8wA8tYEhhixn7ijJ79STmtgzwKdpljBHGNw4pPtMH/AD3j/wC+xTsFyrY6HpOm3E1xY6baW08zs8ssUKq7sTkkkDJ5q/UX2mD/AJ7x/wDfYo+0wf8APeP/AL7FFhXJaKi+0wf894/++xT0kSTOx1bHXac0WAdRRVU6nYKSDfWwI6gyr/jQMtUVU/tTT/8An+tf+/y/41Dda9o9lbvcXWq2UMKDLPJOoA/WkBo1FcXMFpbvcXM0cMMY3PJIwVVHqSeBXCyfEK41uRrfwjZQyx9DqepyeRbL7qp+eT8AB70638KaVfXCXvizX01+6U7kimkVLWI/7MIO0/VsmgLHJ/EbxJc67psupeCr7XQNORpLi+tZmhsWRQcj5iPMb02A9ec8YPhf8O/7a08+J/GlvFqFzfqHt4rmNWOzqJHOMsW7Z7fXj1xoNO1LT3s9tvcWZXy2iTBTH90gcY9qu8AegFFh3Ira2t7O3S3tYI4IIxhI4kCqo9ABwKlqL7TB/wA9o/8AvsUfaYP+e8f/AH2KdiSWiovtMH/PeP8A77FH2mD/AJ7x/wDfYosBLRUX2mD/AJ7x/wDfYp6urjKMGHqDmgB1FBIAJJwB3qL7TB/z3j/77FAyWiovtMH/AD3j/wC+xR9pg/57x/8AfYosIloqL7TB/wA94/8AvsUfaYP+e8f/AH2KLAS0UisrqGVgwPcHNLQMKKKKACiimySRwxmSV1RB1ZjgD8aAHUVU/tTT/wDn+tf+/wAv+NH9qaf/AM/1r/3+X/GgC3TXdIo2kkZURRlmY4AHqTXO+IPHGi+H4E3XC3l7MdtvZWrB5ZW9hngerHAFYCaVP4skW68Z6raRWQIaPQrS6HlD085wcyn2GF470gsX7jx2+qXD2Pg3TjrVwp2veFvLsoT/ALUv8Z9kz9aQeBrzWyJPGGuT6kp5/s+0zbWi+xVTuk+rH8K6a3vNItLdLe2ubKGGMbUjjdFVR6ADgVeVldQysGUjIIOQaAK2n6ZYaTarbadZW9pAvSOCMIv5CrVIzqi7nYKPUnFR/aYP+e8f/fYpgS0VF9pg/wCe8f8A32KPtMH/AD3j/wC+xRYRLRUX2mD/AJ7x/wDfYo+0wf8APaP/AL7FFgC5tre8t3t7qCOeCQYeOVAysPQg8GsXT/BHhbSr0XljoGnQXKnKypAu5T/s+n4Vv013SMZdlUepOKBjqKi+0wf894/++xR9pg/57x/99iiwiWiovtMH/PeP/vsUfaYP+e8f/fYosBLRUX2mAnAmj/76FS0DCimPLHGcPIq5/vHFN+0wf894/wDvsUWES0VF9pg/57x/99ij7TB/z3j/AO+xRYDJ1fwjomuX9vqF9Z5vrcARXUUjRyKAcgblIJGSeDxyaPEM3iS2+z3GgWtheIm77RaXEjRvKOMeW/3VPX7wwc9q1vtMH/PeP/vsUqzwuwVZUJPYMDRYdxouUXyEnaOKeYfLEXGSQMkD1x7VNWR4h8NaZ4mslt9QibfE2+C4ibZNbv8A3o3HKnp+XOae+s2FjrNjoU88gvLmFngMinEoTG4buhbHOOuOaQGpRRRTAKKKKAOU1X/kf9I/65/1auyrjdV/5H/SP+uf9WrsqqrtH0Cn1K9//wAg65/65P8AyNePeF/+RS0b/rxg/wDRa17Df/8AIOuf+uT/AMjXj3hf/kUtG/68YP8A0WtYmp7NJLHDG0krqiKMszHAH1NUP+Eh0T/oMWH/AIEp/jWP4lUXmu6dYzDdbCKSdoz912BUDI74yab9htP+fWD/AL9is3N3sjrhQi4qUnubX/CRaJ/0GLD/AMCU/wAaP+Ei0T/oMWH/AIEp/jWL9itP+fWH/v2KPsVp/wA+sP8A37FLnZX1en3Ztf8ACRaJ/wBBiw/8CU/xo/4SLRP+gxYf+BKf41i/YrT/AJ9Yf+/Yo+xWn/PrD/37FHOw+r0+7Nr/AISLRP8AoMWH/gSn+NH/AAkWif8AQYsP/AlP8axfsVp/z6w/9+xR9itP+fWH/v2KOdh9Xp92bX/CRaJ/0GLD/wACU/xo/wCEi0T/AKDFh/4Ep/jWL9itP+fWH/v2KPsVp/z6w/8AfsUc7D6vT7s2v+Ei0T/oMWH/AIEp/jR/wkWif9Biw/8AAlP8axfsVp/z6w/9+xR9itP+fWH/AL9ijnYfV6fdm1/wkWif9Biw/wDAlP8AGsHSJdL07xT4i1WTXdMaLVHt2jRbldyeXEEOee5HapPsVp/z6w/9+xR9itP+fWH/AL9ijnYfV6fdm1/wkWif9Biw/wDAlP8AGj/hItE/6DFh/wCBKf41i/YrT/n1h/79ij7Faf8APrD/AN+xRzsPq9Puza/4SLRP+gxYf+BKf40f8JFon/QYsP8AwJT/ABrF+xWn/PrD/wB+xR9itP8An1h/79ijnYfV6fdm1/wkWif9Biw/8CU/xo/4SLRP+gxYf+BKf41i/YrT/n1h/wC/Yo+xWn/PrD/37FHOw+r0+7Nr/hItE/6DFh/4Ep/jR/wkWif9Biw/8CU/xrF+xWn/AD6w/wDfsUfYrT/n1h/79ijnYfV6fdm1/wAJFon/AEGLD/wJT/Gj/hItE/6DFh/4Ep/jWL9itP8An1h/79ij7Faf8+sP/fsUc7D6vT7s2v8AhItE/wCgxYf+BKf40f8ACRaJ/wBBiw/8CU/xrF+xWn/PrD/37FH2K0/59Yf+/Yo52H1en3Z0tre2t7GZLS5hnQHBaKQMB+VT1xSRR6f4j0ue1RYTPKYJggwHUqSMj2IFdrVxlcwrU1Bq3UKKKKoxCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArm/iF/yTjxJ/2Dbj/0Wa6Sub+IX/JOPEn/AGDbj/0WaANbRf8AkA6d/wBe0f8A6CKvVR0X/kA6d/17R/8AoIq9QAVx2m/8lD1b/rj/APEV2Ncdpv8AyUPVv+uP/wARW1LaXoZVenqdVRRRUgFFFY3i2DVbrwnqdvohK6nNA0duwk2FWPGQ3YgEkH2pAR6Vo9/D4j1bWNRvTKbgrDaW8bt5cMC8jK9C5YsSeewBxVnxFLJDoc5icozFE3KeQGYA4/AmtCCIwW8URd5CiBd7nLNgYyT3NZnib/kBS/8AXSL/ANGLVw+JClsyouh6WqhRYwHHcrk/nTv7F0z/AJ8Lf/vgVauCVtpSCQQhII+leZw/2knw8l1Rl1KO4bT1dbttZmk3sdvIQthSc9R0rVzZKij0L+xdM/58Lf8A74FH9i6Z/wA+Fv8A98CuM1az1a3t9PjsnvtMu579FQy6pLdK+I3YBt7HCkgAjv8AhWv4S1uTW9T1mRzKnlNDG1s7E+RIEIdcdvmH49aOdhyo3P7F0z/nwt/++BVa4s7fTruwuLOJYJDdRxN5YwGVjggjvVbw9PfvrGvW99d/aPIuYxFhNiopjU4AyfX1rQ1Xrp//AF/Q/wDoVNSbE0kZ/wASpJW0LT9PSWSKHUtUtrK4MbFWMTt8ygjkZAx9CaiX4e+D1UKPDem4AxzbqT+dP+I//Hr4c/7GCz/9CNXfE80tv4V1aaGR45Y7OVkdGwykKcEEdDWUEi2UP+Ff+EP+hb0z/wABl/wo/wCFf+EP+hb0z/wGX/CvN5jr1r8PjqbvrsE86WoSeTW2l87e652jP7skd/er2qxeJ9MbQ47GTVbO+mvJXFteao12JwkW4ITnGGwRj15p3XYLPud1/wAK/wDCH/Qt6Z/4DL/hR/wr/wAIf9C3pn/gMv8AhWb4C1uXxBomuagbqZFbUZhCbgljbr5aHbg9ApJ46dareAZ7y61a9mg1W91DRhAqLPeyZNxOCd8kakkrH29PSjQNSW60TTfCvjPwxdaFaR6eb27ezuo7cbUmjMTsNyjgkFQQa7LxDmX7BaMSIZ59soBxuUKTj6ZArnfFX/Iy+Df+wsf/AERJXRa7/wAfmk/9fDf+gNRH4xS+Eg/sXTP+fC3/AO+BS/2Lpn/Phb/98Cs/xpPPbeDtTmtpJI5lh+R43KMDkdGHI+tc7qttf2lpYIltqMElxqEcbRf25O5lXY5I3lspyB064q3Ni5Udl/Yumf8APhb/APfAo/sXTP8Anwt/++BXF3p1jTNZ057IXsfkWs9zPYSX0lyJ1DopG5yfm2klfQ/U1qaHqo1Dwbf3/wDarQxPcXPlXr/P5SeYwU4PYDGB2o52HKjoP7F0z/nwt/8AvgVHbW8WneILNLRBFHcpIJUXhTtAIOPWsjwvLdw6zrGnXMlyUg8p4VnuPPO1gw3bzz8xX7p6Y963JP8AkYtK/wB2b/0EU+ZtO4mkh2vILnUNNspcm3k8x5EzgOVAxn25qP8AsXTP+fC3/wC+BU2rf8h7Sf8Acn/ktY3jaW4i8Oj7M8yyPdW8f7mZomYNKoK71IIyDjNJNqKsO12an9i6Z/z4W/8A3wKP7F0z/nwt/wDvgVxuo21/Bc6Napa6jGbi4l8y2/tycmQLESP3m7IAPOPai9m1XSdfs5rY3ggtLBp7rT3unuPMQyYY7mJLMByPpjvRzsOVHZf2Lpn/AD4W/wD3wKP7F0z/AJ8Lf/vgVg6Rr0kfgMaugn1B2kmMWwNIzgzOE6AnaBj6AVL4H1K41HSrs3dzNczxXsyNJLA0XG7gAMBgD07d6Od9w5Ua+mwx2PiI21svlwTWxkaNfu7gwGQO3BroqwYP+Rqi/wCvJ/8A0Na3qipuOIUUUVmUFeeeIdPtvEXxPh0nVohdadZ6QLuO1c/uzM8rIWZejEKuBnpk16HXDTf8lkn/AOxfi/8ASiShbgP/AOFf+EP+hb0z/wABl/wo/wCFf+EP+hb0z/wGX/Csn4lT6gkeg2+nyXge51ARPHaXZtnlGxjt3jp0rmtQt9dTxBpGlxJrxZ7KaaWzXXmWQESABjLnDcdvetHbsJXO7/4V/wCEP+hb0z/wGX/Cj/hX/hD/AKFvTP8AwGX/AArgdY1nXPD+vavKt3fNptppsENxBJcNK8DSRtiUNnllcAFh1zmuyln1KX4U295a6jJb3o0mOdrhl8x2PlAnknqT3oVuwalz/hX/AIQ/6FvTP/AZf8Kg8D20ej+LvE+hWWY9MgW1ube33ErC0gfeFz0BKA46ZzWv4bmlufC2kTzSNJLJZQu7sclmKAkk+uazvDf/ACU7xb/16WH8paUkrAjTureLUfEN3HdoJY7aKMRxtyoLZJOPXgVJ/Yumf8+Fv/3wKVP+Rk1T/cg/k1c/4rN3Lr+i2tut1KkiXDPDBfPa79oTBLIQTjJ49615mkrEJJm//Yumf8+Fv/3wKP7F0z/nwt/++BXHNbX0viaWyW21GWKGxgfyRrk8flMzSZJYNlycDk+lUNU1fU9FPie4e9uW05ne1jLSsxs5RCpjZWzkBiSD/tYPc1POx8qPQP7F0z/nwt/++BSf2Lpn/Phb/wDfArJ1yX/iW6YJLy9XzCP9Gssie6bZwoYEFQOpOQOOSBWpoMWoQ6HaR6pJvvVT94SwY9TgEjqQMAnuafMw5UWPDw8l7+zUnyYJgIlJztBUHA9s1Ua1h1LWtQe8jEwhdYolflUG0E4HuTVzRP8AkIat/wBd0/8AQBUNp/yFtW/67r/6AtC+Jv8AroLog/sXTP8Anwt/++BR/Yumf8+Fv/3wK5zxALy58Y2tpBHd3EX2B5DDBqMloN3mKNxKEZ4OPxqnbWt5c+JtStzBqM0Fu1ugxrU8YhBiUkYDfPzk5PJo52PlR1/9i6Z/z4W//fAo/sXTP+fC3/74FedXOt6rpdjqrXF9cPbXuoSRWsxkO63kWbHlg9lZBx7gjvXWeLBcSqUs72cXMVtJKlnBceQ0hGBvL+i/3ehzzRzsOVGwdE0wjH2CD/vgVZ8OMwsriAszJb3MkUe45IUYwM+2araJdG+0HT7ppDI01vG5dl2liVBJx2qx4d/1Wof9f0n9KUm3HUErMzNNsLXULU3t5Ak880jlnkG7GGIAHoMCrn9i6Z/z4W//AHwKj0H/AJA8X+9J/wChtXNGK8v/ABjrEZhvri3hlgVTHq0tusQMak4RWAPrVSk09BRSaOp/sXTP+fC3/wC+BR/Yumf8+Fv/AN8CuI05b+bT9Uv5E1BvLlvNl1/a821drOFHk7tvGAOnal0HV9Si1Hw3o2oXc8k7BplmZz/pUDQMwLf3mVuDn0B71POx8qO2/sXTP+fC3/74FRz6JppgfbZxI20kMi7Sp9QRXN+KvEd1Z67YWkJu7e2iu4PPkS1kYXG5uUDBSMAdeckkDsa7OTmF/wDdNUpO+4nFE2izyXOiWU0rbpHiUsx7nHWl1LTbC+FvNfW6y/YpRcwtzmN1Bwwxz0J471F4d/5F6w/64rWnWEviZa2Kml6naazpltqVhN51pcxiSKTBG5T7HkfjVuszw9pdho2hW2n6XIZLKEMImLh+CxJ5HuTWnUjCiiimBymq/wDI/wCkf9c/6tXZVxuq/wDI/wCkf9c/6tXZVVXaPoFPqV7/AP5B1z/1yf8Aka8e8L/8ilo3/XjB/wCi1r2G/wD+Qdc/9cn/AJGvHvC//IpaN/14wf8AotaxNT0DW/8AkbLH/rzl/wDQlqSo9b/5Gyx/685f/QlqSsXuz0Yfw4+n6hRRRSKCiiigArkPEXiu70rxFa21tFE9hB5balIwOY1lbYmPTnJPtXX1xj+ArfU49Xm1hne+1CVyGgupVRExiMFQQGwADyDzmmiZX6Gjr/i200hbyCNLia6t7cyuYbdpEhyDtMhHQHH5c1TufG0FroiyNHPNfDT0upjBbtJHCWTKlyOgJ/SqUPh7xRaWt9DG2mXEup2qRXE0kzjypFj8ssBsO8EAHHy4OacnhnXtOtr210/+z5k1CzihlknldfIkSIRkgBTvUgZA+WjQm8jrdJupL3RrG7lx5k9vHI+0YGWUE4/OnWGoW+pQyS2zFkjleFsjHzIxU/qKrW+kI3h200q9LMI4I4pDDK8eSoA4ZSGxke1ZvhbwlD4fE0jNI1w88zKRdyuvls5KgqxxuxjJxnPc0Fa6FO+8W3em+OzptzFF/Y/lQq04HzRSyFgpY/3SVx7EirVv4qSBdRN+GZotTeytYbeIvJLhFYAKOp5JJ6YFTT+Ghfa1rE16IpLHULOK38sE7gVL5J44+8MHPasHTPB2u6TDBc/arS+1C11CW4QSuyLPE8ax4Ztp2vhc5wR+dGhPvI3pPGmkRWUdw5ugz3Btfs/2dzMsuCdhTGc4HHrkVDP480e3eUSpfqsMohnf7I5WJyAQrEDqcgYGeaq2/hfUpNSg1S7e1S5fU/ts8UbsyoghMaqpKjcehJIHepLnwtezWeqQrLbhrvVY71CWbARWjJB4+98h9unNGgXkXk8YaY1rPM0d5HLDMsDWz2zCYuwyoCdTkc1XuPFazrYNYK6NJqaWVzFcxFHjypJGD0OMEHkc1S1jwzrE+pajd2U8Riuri3ka3FzJA00aRlWRnUZXkg8ZzjmqumeDtUtZ/nSxhiGqxagFjnkfChNrJllySOOT1yelGgXlsa//AAmFrZaOl7di7uYf3rPc29k/lqFdl567cYxyecZ71Yn8X6bCkLLFfTNLALlo4bV3aKI9GcAfL3468HisB/C3iJbXTbEJpd1p1rJLPLby3UkYmkaVnTdiJsqoIOO5+lP1fwlqN9q8mqfYbC4murZIpYnvpo1gdc/MpVfnXB6EKePejQLyO2tbqG9tYrq2kWSCZA8br0ZSMg1LVLSLBdL0i0sF2YgiWP5BheB2HOBV2kWgooooGFFFFAFK5/5DOi/9ff8A7I1dlXG3P/IZ0X/r7/8AZGrsqun1ObFfZ9P1CiiitDlCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArm/iF/yTjxJ/wBg24/9Fmukrm/iF/yTjxJ/2Dbj/wBFmgDW0X/kA6d/17R/+gir1UdF/wCQDp3/AF7R/wDoIq9QAVx2m/8AJQ9W/wCuP/xFdjXHab/yUPVv+uP/AMRW1LaXoZVenqdVRRRUgFYfjHUNS0rwlqN9pEXm38EYeKPyy+75hkbR1OM1uUHpx1pAIpDKGHQjPNZXiVWbQbgqpbYyOQB2DqT+gNReFPEH/CSaIL2S3+y3Mc0lvc22/cYZUYqyk4GegPToRW3VRdmmJroYgvrKWPIuoGRh/fHIqE/2SbIWR+xG0ChBB8mzaOg29MVqNpGmMxZtOtCT1JgXn9KP7G0v/oG2f/fhf8K054k2ZnyTadM0bSyWrmJt8ZZlOxsYyPQ4J596bE+mQTTTQtZxyzkNK6FQ0hAwCxHXjjmtL+xtL/6Btn/34X/Cj+xtL/6Btn/34X/CjniFmZ8c2nRSyyRyWqSSkGRlZQXIGASe/AxVW/uIbm506CCVJZTdxvtRgSFU5J47Yra/sbS/+gbZ/wDfhf8ACpbextLRi1tawQk8Exxhc/lR7SK2CzOS+Jh8jRNKv3B+zWOsWlzcuBny4lfDMfYZFXZNd8PXdu0cmq6ZNBKuGVriNldT2Izgg10zosiMjqGRhhlYZBHoaxD4J8JsST4X0Uk9SbCL/wCJrNSsWZ8moeFprNLOW80d7WPbsgaWIou37uFzgYwMelPl1bw5PNDNNqGlSSwEtE7zRloyRglSTxxxxVz/AIQfwl/0K+if+C+L/wCJrkvid4K8PRfDfW5tP0HTLS6hhEqTW9nGjrtdWOGAyMgEH2Jp84rG9FqHheCGeGG80eOK4ZnmRJYgsjNwxYA8k9yetV9PPgvSJml006BZSuu1ntvJjJHoSuOK84+EszeI4zbat4B0O9t4SqvqEdpbRyR7gGUvGcFgQchlA47GvXv+EH8Jf9Cvon/gvi/+Jo5/IbVjl9Y1Kx1jxp4SstNu4LyeG9e6mWCQP5cSxOCzEdBlgBnqTXV+IWEL6dcudsMVx87nouVIBPtmrmnaJpOjhxpml2ViJPv/AGa3SLd9doGauuiyIUdQykYIIyDQpWlcTV1YxJ5tOuoGhuJbWaJxhkkZWVh7g9aJZtOnMZlktZDG2+MuynY3TI9DyefetD+xtL/6Btn/AN+F/wAKP7G0v/oG2f8A34X/AAq+eJNmZ5m043C3BktTMqlFkLLuCnBIB644H5VHGukQ2r2sQsUt33b4lCBG3feyOhzk59a1P7G0v/oG2f8A34X/AAo/sbS/+gbZ/wDfhf8ACjniFmZFhBoulRNFp6WNqjHcyw7EBPqcUsc0d14ksRbyLL5McrSFDkKCABk1rf2Npf8A0DbP/vwv+FWILW3tVK28EUKnkiNAoP5Uc8baBZ9TH1x1g1XS7iUhIV81Gc8AFgMZPvg0TTadcoEnktZUDBwrsrAMDkHnuCAa2pIo5ozHKiujdVYZB/Cqv9jaX/0DbP8A78L/AIUlNWswad9DPebTpJYpZJLV5IiTG7MpKEjBwe2RxR52nG4+0eZa+ds2eZuXdtznGeuM9q0P7G0v/oG2f/fhf8KP7G0v/oG2f/fhf8KfPELMzraTTLO3W3tXtIIVztjiKqoycnAHHUk0sU2nwBhDLaxh2LsEZRuY8knHc+taH9jaX/0DbP8A78L/AIUf2Npf/QNs/wDvwv8AhRzxCzMuxljuvE5eB1kSK0KuynIBLAgZ9eDXQ1HBbwW0flwQxxJ12xqFH5CpKicuZlJWCiiipGFef6ve22kfFuK51GeO1t7zRVggmmYKjyJMzMm48ZwwOK9Aqtf6dY6pbfZtQsre7gJz5VxEsi59cEEULQDnp9W8N3TwvcahpUrwv5kTSTRsY26blyeD7ihtW8NteLeNqGlG6RDGsxmj3qp5Khs5x7Vc/wCEH8Jf9Cvon/gvi/8AiaP+EH8Jf9Cvon/gvi/+JqucVkUJL/wtK9w8l3o7tcoI5y0kRMqjgK3PzDk8H1p41bw4LMWY1DShaiPyhD50ezZjG3bnGMcYrn/iF8KdH17wvMmh6TY2GqW/723a2gSLzSBzG20DIPbPQ49684+G/hqy0DSY/EXifwrJqdhdb1+0bPPFlsdkbzLcrnqpO4bsDHA5o5/IfKj2qLXNAghSGHVNNjijUKiJcRhVUcAAA8CsrwZcw6n478WalZSLPYlLO2W4jOUeRFkLhT0ON65x61paZ4c8B6zYx3um6F4eu7aT7ssNlCw+n3eD7V0lraW1jbJbWlvFbwIMJFCgRV+gHApOVwtYw5Zo7TxLe/aHWITxRNGXOA23cDg/jUrTac88c7yWrTRghJCyllB64PbOB+VbE9rb3SBLiCKZRyBIgYD86r/2Npf/AEDbP/vwv+FaKatqRZmes2nLcPcLJaiZ1CtIGXcwGcAnqQMn86ikTR5obiGVbF4rk5nRghWU4Ayw/i4A6+lav9jaX/0DbP8A78L/AIUf2Npf/QNs/wDvwv8AhRzxCzMa9tdB1OOKO/g026SL/VrOiSBPoD0qe2k0yyt0t7V7SCBBhI4iqqo9gOBWl/Y2l/8AQNs/+/C/4Uf2Npf/AEDbP/vwv+FHPELMo+H2E0+pXMZ3QyTgI46NhQCR7ZqtHPFa63qcVxIkTPIsibzjcu0DIz15BrokRY0CIoVVGAqjAFR3Fna3YAubaGYL08xA2PzpKa5mw5dDI87TjcC48y188JsEm5dwXOcZ64z2oSbTopZZY5LVJJSDI6soLkDAye/HFaH9jaX/ANA2z/78L/hR/Y2l/wDQNs/+/C/4U+eIWZkvFo0tu1vIlg8LSea0bBCpfO7cR0znnPrTL+20PVRGNQi0+7EZynnhH2+uM1s/2Npf/QNs/wDvwv8AhR/Y2l/9A2z/AO/C/wCFHPELMoi8so0Crc26oowAHUACneG/ns7qYA+XNdySRt/eXgZ/Srn9jaWDkabZ/wDfhf8ACrgAVQqgADgAdqUppqyGk73Zy+j3MFvY/Zppo45oZHV0dgCDuJ7+xq4k2nRSyyxyWqSSkGR1ZQXIGBk9+OK1J9Psrp99xZ28r9N0kSsf1FR/2Npf/QNs/wDvwv8AhVOpF6i5WjNRtLjgeBGs1hcsXjUqFYsctkdDnJz65pv/ABKfMt5P9C32wKwN8mYgRghT/DxxxWp/Y2l/9A2z/wC/C/4Uf2Npf/QNs/8Avwv+FLniFmZ8s2nzhRNLayBGDqHZTtYdCM9x60lzqNlHbSO11DgKejg1o/2Npf8A0DbP/vwv+FKmladE4ePT7VHU5DLCoI/SjniFmR6FE8OhWMcilXEK5B6jir0gZo2CMFcghWIzg+uO9OrC8RaRqGs3GlQW92Lewhu1uL3a7LJIqfMiLjsWxnkcCsm7u5SRL4V0IeGvC+n6MJvPNrFsaXbt3tkknHOMkmtiiikMKKKKYHKar/yP+kf9c/6tXZVxuq/8j/pH/XP+rV2VVV2j6BT6le//AOQdc/8AXJ/5GvHvC/8AyKWjf9eMH/ota9hv/wDkHXP/AFyf+Rrx7wv/AMilo3/XjB/6LWsTU9B8Tq9nqllqrI7WqRvDMyqT5eSCGIHbjFUP+Eh0j/oIQf8AfVdvTPJi/wCeaf8AfIrNwu7o6oYhRioyWxxf/CQaR/0EIP8Avqj/AISDSP8AoIQf99V2nkxf88k/75FHkxf88k/75FLkfcr6zD+V/f8A8A4v/hINI/6CEH/fVH/CQaR/0EIP++q7TyYv+eSf98ijyYv+eSf98ijkfcPrMP5X9/8AwDi/+Eg0j/oIQf8AfVH/AAkGkf8AQQg/76rtPJi/55J/3yKPJi/55J/3yKOR9w+sw/lf3/8AAOL/AOEg0j/oIQf99Uf8JBpH/QQg/wC+q7TyYv8Ankn/AHyKPJi/55J/3yKOR9w+sw/lf3/8A4v/AISDSP8AoIQf99Uf8JBpH/QQg/76rtPJi/55J/3yKPJi/wCeSf8AfIo5H3D6zD+V/f8A8A4v/hINI/6CEH/fVJ/wkOkf9BCD/vqus1G5sdK0261C82R21rE00r7c7VUZJx34FeA/Db4i3d18UtQfV4ZIrDX5VSASIdsMmP3KjtynHHXKmjkfcPrMP5fx/wCAep/8JBpH/QQg/wC+qP8AhINI/wCghB/31XaeTF/zyT/vkUeTF/zyT/vkUcj7h9Zh/K/v/wCAcX/wkGkf9BCD/vqj/hINI/6CEH/fVdp5MX/PJP8AvkUeTF/zyT/vkUcj7h9Zh/K/v/4Bxf8AwkGkf9BCD/vqj/hINI/6CEH/AH1XaeTF/wA8k/75FHkxf88k/wC+RRyPuH1mH8r+/wD4Bxf/AAkGkf8AQQg/76o/4SDSP+ghB/31XaeTF/zyT/vkUeTF/wA8k/75FHI+4fWYfyv7/wDgHF/8JBpH/QQg/wC+qP8AhINI/wCghB/31XaeTF/zyT/vkUeTF/zyT/vkUcj7h9Zh/K/v/wCAcX/wkGkf9BCD/vqj/hINI/6CEH/fVdp5MX/PJP8AvkUeTF/zyT/vkUcj7h9Zh/K/v/4BxllMmta9p/2EmWC0kM004B2L8pAXPckmu2pAAowAAPQUtXGNjCrV9o1pawUUUVRkFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXN/EL/AJJx4k/7Btx/6LNdJXN/EL/knHiT/sG3H/os0Aa2i/8AIB07/r2j/wDQRV6qOi/8gHTv+vaP/wBBFXqACuO03/koerf9cf8A4iuxrjtN/wCSh6t/1x/+IraltL0MqvT1OqoooqQCiiigDB1/xBb+FjZTXNmV065uPLubtMBbZm+6zjHRm4Lds81vU10WRCrqGU9QwyDWHqV/r9hr9p9n0yO/0afbFKYW2z2zlsbyGOGTBGccjBPNIDeopNy7iuRuAyRnnFLTAKKKKACiiigAooooAKq6lp9vq2l3enXalre6heGUA4JVgQcHtwatUUAcR4l0ebw9d23inw7aAvYwC3vdPhXAubRegUf30HK+2R7V1mmalaaxplvqNhOs1rcIJI5F6EH+R9u1W64CbPw512S6AI8J6nNmYDpp1wx+/wC0Tnr2U+gNIDv6KQEMoZSCDyCO9LTAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACmxxpEpWNFRSxYhRjknJP4kk/jTqKAOS1PwLbtfSar4evJNC1Z+XltlBhnP/TWI/K/14PvVaLxre6DKlp4209bDJCpqttl7KU9snrET6Nx7121MlijnieKaNZI3BVkcZDA9iD1pBcIporiFJoZEkicBldGBVh6gjrT64mXwTeaFM934J1BdOJJZ9LucvZSnvhesRPqvHtVnTfHdv9uj0rxHZyaFqrnCR3LAwzn/AKZSj5W+nB9qAsdbRRRTAKKKKACiiigAooooAKKKhuLy2s1DXNxDAp6GVwoP50ATUVWm1CytrL7bPeW8VpgHz3lVUweh3E45zSW+p2F5ZteW19bT2q53TxSqyDHXLA44oAtUVQ0/XNI1d3TTdVsb1owC621wkhUHpnaTioovEuhz6udJh1ixk1EMym1SdWkBUEsCoOQQAc/SkBqUVg6l4z0HSNXj0q8vWW+k27YUt5JD8xwCSqkAfWpde1u70j7OlloOoarNPuwLXYETGPvs7DbnPHXoaANmorm5t7O2kuLqeOCCMbnklcKqj1JPArO1JNbvdGiGlz2+mX8m0yNcxef5QI+YABgCwPfOOKfb6R5uhpputTJq5x++kuIEAmO7cCUA28HHHsKAKv29PFnhqeXw3q4g84mKO+WEttw2GKhsZ4zg9O/NXtF0i30LSLfTbV5nihBG+aQu7kkksxPUkkn8auoixoqIoVFGFVRgAegp1ABRRRTAKKKKAOU1X/kf9I/65/1auyrjdV/5H/SP+uf9WrsqqrtH0Cn1K9//AMg65/65P/I1494X/wCRS0b/AK8YP/Ra17Df/wDIOuf+uT/yNePeF/8AkUtG/wCvGD/0WtYmp7TRWbqeozWssFraRLLd3GSgc4VFHVm9uRVbPiL/AJ7aX/36k/8AiqtQbVyXNJ2NuisXPiL/AJ7aX/36k/8AiqM+Iv8Antpf/fqT/wCKo5PMXOjaorFz4i/57aX/AN+pP/iqM+Iv+e2l/wDfqT/4qjk8w50bVFYufEX/AD20v/v1J/8AFUZ8Rf8APbS/+/Un/wAVRyeYc6NqisXPiL/ntpf/AH6k/wDiqM+Iv+e2l/8AfqT/AOKo5PMOdG1RWLnxF/z20v8A79Sf/FUZ8Rf89tL/AO/Un/xVHJ5hzo1bi3gu4WhuIY5omwSkihlODkcH3ArjNAtoLr4geOobiGOWLzrE7JFDDIt1I4Nb2fEX/PbS/wDv1J/8VWRp2h69p2v61qy3mnO+qNCzoYXwnlxhBj5u+M0cnmHOjsaKxc+Iv+e2l/8AfqT/AOKoz4i/57aX/wB+pP8A4qjk8w50bVFYufEX/PbS/wDv1J/8VRnxF/z20v8A79Sf/FUcnmHOjaorFz4i/wCe2l/9+pP/AIqjPiL/AJ7aX/36k/8AiqOTzDnRtUVi58Rf89tL/wC/Un/xVGfEX/PbS/8Av1J/8VRyeYc6NqisXPiL/ntpf/fqT/4qjPiL/ntpf/fqT/4qjk8w50bVFYufEX/PbS/+/Un/AMVSF/ESjIfTHx/DskXP45OKPZ+Yc6NuiqemX41GzE3lmKRWMcsZOSjg4Iq5UtNOzKTvqFFFFIYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc38Qv8AknHiT/sG3H/os10lc38Qv+SceJP+wbcf+izQBraL/wAgHTv+vaP/ANBFXqo6L/yAdO/69o//AEEVeoAK47Tf+Sh6t/1x/wDiK7GuO03/AJKHq3/XH/4itqW0vQyq9PU6qiiipAKKKKACioL29ttOspry8mSG2hUvJI5wFAqnpHiHTNdE39n3DO8OPMjkheJ1zyCVcA4PY4waQFXV/COlazqcGpyi4t9RgAVLu0naKTYDnYSDhl68EHqadr0viWB4JdAttMuo1DefDdyvG79MbGAIHfOR6Ual4v0LSL42V9feXOoUvthd1iDHCl2VSqA/7RFF74t0Ww1FtPnuZftKBSyRW0sgXdyMlVIGfrQBNqutNo2lw3lxpl/cu7KskGnwm4eMkEk4GCVGMZx3HFOTX7A+Hzrc7S2lisZkdrmJo2jUHB3KRkdK06KAM3RvEGk+IbR7rSNQgvII22O8TZCtjOD+BFJpviPQ9ZmaHS9Z0++lRd7R21ykjKucZIUkgZIrRWNEBCoq7jk4GM1XttM0+ylaW0sbaCRhtLRRKpI9MgUAV28Q6Imqf2Y+saeuobgn2U3KCXcRkDZnOcH0p1/rukaXMsOoarY2krruVLi4SNiOmQCRxUjaTpzXv2xtPtTdZDeeYV35HQ7sZpt7o2lanIsl/plnduo2q08CyED0BI6UATXd9Z2EayXl1Bbox2q00gQE+gJ71IlxBJAJ0mjaFhkSBgVI9c9Kr6jpOm6xAsGp6faXsKtuWO5hWRQcYyAwPOCaZLoumT6OdIksLc6aUEf2URgR7QcgbRwBQBeVlcZUgj1BqO6toL21ltbqJJoJkKSRuMqykYIIqjpHh7SdBsZbHSrGKztpXMjxxZALEAE/kB+VV9C8KaZ4clmfTmvAsqhTHNeSzKuPQOxxQBz2j3U3gXV4PDWpzPJot023R76U58s/8+0jeo/gJ6jjqMV3lcpf+DrjVbu6i1LXbi90W73mXTJ4Iyq5zt2SABl2nBHJPFZuh6/qHhvxBB4P8StJMJsjSdXfGLtQM+XJ6SAcZ/i+pGQZ3tFFFMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVTUtLsNYsZLLUrSG6tpPvRTIGB9+e/vVuigDhv+Ef8R+Evn8L3h1PTF/5g+oyncg9IZjyPZXyPetfQfGml65ctYHzrDVoxmXTr1PLmX3APDD3XIroqyde8M6R4ltlh1SzSYxndFKCVlib1Rxyp+hpAa1FcNjxd4P6eb4o0Zexwt/Cv6LN+jfWum0LXtO8R6d9u02ZpIg5jdXQo8bjqrKRkEZ6UAaMkkcMTyyuqRopZnY4CgdST2FZGheJ9M8Sm5bSnmnt7dgv2kwssUhOc7GIAfGOceo9ai0/WLrUvFWt6YbaNdP09IYxIyndLK6lmHptClR9Sa3I40ijWONFRFAVVUYAA7AUAYmiJ4qN7NNrs2krbFSIrexSQsDkYLOx54zwFHWhPDTL4hOrvrusyYcstkbkC2XIxjYAMjnPJPNbtFAGDq/gzw/r2pR6hqmni5uY1CIzSuAACSPlDAdSecVd1XQdI1xYl1bTLS+WEkxi5iWQKT1wD9BWjRQBTfStOk01dNewtXsEVVW2aFTEAvQBcYwMDH0p9tp1jZ2htLWyt4LZs5hiiVUOevygY5qzRTAp2Wk6bprO1jp9pas4wxghVCw98DmpE0+yiuWuY7SBJ2JJlWMBiT1OcZqxRQAUUUUAFFMl4hf8A3TXj3he8sZLfwrLpuq3Fz4hluwt9Ebp3fyPm3+YhOAgG3BwO2OtILHslFc14ruNTgNr/AGfLq8ed2/8As+zhnz0xu8zp+FTeGJtQuNPuDfS6k8u/CG/tooGAx2EfBHuaAJIPF2h3Orf2ZFfBrrzGiH7pwjOvVFkI2Mw9ASatavrunaHHC1/OyGZisUccTyvIQMnaiAscD0HFebaVd2svh7wx4djkQ65aawj3NqD+9i8uVmkdh1Cle54O4VqeL9UtbrxDoE9vrcOlW8L3KNq52sscgAVoTv8AkBPP3v7vFA7Hd6bqdnq9hHfWE6z20mdrrkdDggg8gg8YPNW65D4bZ/4RRhuEyC8n2XYBAuwXJ87H+0SenHpxXX0COU1X/kf9I/65/wBWrsq43Vf+R/0j/rn/AFauyq6u0fQKfUr3/wDyDrn/AK5P/I1494X/AORS0b/rxg/9FrXsN/8A8g65/wCuT/yNePeF/wDkUtG/68YP/Ra1ianp91/yNlp/15yf+hLWnWZdf8jZaf8AXnJ/6EtadavZGXVhRRRSAKz9V1vTdDgSXUruO3WRtkYbJZ29FUZJP0FaFcv4l0me+1rS73S9VtbPWbNZTDDdJ5iTRsAGBUEN2HzDpSA3NM1aw1mzF3p1ylxAWK707MOoIPIPsalvb2206zlu7yZYbeIbnkboory+78Z6tCIdSlRIpEnu9JmjtZC0E9ztBikXPqw288jOKo3ms6/DpmpabJqt2L3w/YTi5nWQq0zs6+SxPc7Mmi47Hr8F5bXMkscFxFK8RAkVHBKEjIDDtwQeajvdRtNPNsLqbyzczLBF8pO6Rs4HA46HrXld2J9Jl8a6tZ6hfRXUVxAMm4YoiyRxhpCh4O0MxGemPatfxJolvawaJbW+s6nP9o1a13PLfNM6DbJ8yFsld3PTjjjGKAsd/d39rYmAXMyxefKIYt38TnoB+VWK8w+3ahpusSaVFqV7JbQeIbWFDNO0j+VJFuZC7Ellz2JNVrS51O30fSNdXVtSuLybWntWhluWaJojJIuzZ07DBxn3oCx6xWdq2u6XoUUUmp3kdskz7Iy+fmbGcDHtXm+kak0sng++/wCEivJ9S1O7P262N8SmNjkr5OcKFYAcAdOa6TxzHdza74SSxuY7a5N/Jslki81V/cv1XcufzoFY1n8beG47SK6fVoVhlkaONiG+ZlAJAGM8AipbXxboN7cWtvb6pA812WFuhyDIV+9gEdq5bX7PXR4m8Jwtq9o2oGW7K3QsSEC+UOPL8zk9ed3eqniLQtS1PxVYWtxfQz6tDpk9xa3UUHkqkqSxlPl3N9DzyCaB2PRre+trua4igmWSS2fy5lHVGwDg/gQfxotr+1vJbmK3nSR7aTyplX+B8A4P4EfnXGfDbVRrT+ItQ8tonlv18yJhzG4hjDKfowI/CrfhKWOPXPF+91X/AImucE/9MY6BWOworyG31HULzWNGu7K41C10/WLqaBWuNYeaSRCj/MIMbY8EAgq2RwK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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 i=1 of N experts. The router variable Wr produces logits h(x) = Wr · 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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", + "polygon": [ + [ + 89.349609375, + 92.619140625 + ], + [ + 523.845703125, + 92.619140625 + ], + [ + 523.845703125, + 131.484375 + ], + [ + 89.349609375, + 131.484375 + ] + ], + "bbox": [ + 89.349609375, + 92.619140625, + 523.845703125, + 131.484375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/1/SectionHeader/1", + "3": "/page/2/SectionHeader/1" + }, + "images": {} + }, + { + "id": "/page/3/ListGroup/207", + "block_type": "ListGroup", + "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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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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", + "polygon": [ + [ + 90.3955078125, + 695.8544769287109 + ], + [ + 276.5654296875, + 695.8544769287109 + ], + [ + 276.5654296875, + 705.375 + ], + [ + 90.3955078125, + 705.375 + ] + ], + "bbox": [ + 90.3955078125, + 695.8544769287109, + 276.5654296875, + 705.375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/5/SectionHeader/5" + }, + "images": {} + } + ], + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/5/SectionHeader/5" + }, + "images": null + }, + { + "id": "/page/6/Page/372", + "block_type": "Page", + "html": "Distributed 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,
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", + "polygon": [ + [ + 90.6943359375, + 684.87890625 + ], + [ + 521.9830932617188, + 684.87890625 + ], + [ + 521.9830932617188, + 704.98828125 + ], + [ + 90.6943359375, + 704.98828125 + ] + ], + "bbox": [ + 90.6943359375, + 684.87890625, + 521.9830932617188, + 704.98828125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/5/SectionHeader/5" + }, + "images": {} + } + ], + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/5/SectionHeader/5" + }, + "images": null + }, + { + "id": "/page/7/Page/311", + "block_type": "Page", + "html": "the 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 PN i=1(fi · 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 α from 10−1 to 10−5 in powers of 10 and found 10−2 balanced load quickly without interfering with training loss.
", + "polygon": [ + [ + 88.6025390625, + 93.005859375 + ], + [ + 522.3515625, + 93.005859375 + ], + [ + 522.3515625, + 185.625 + ], + [ + 88.6025390625, + 185.625 + ] + ], + "bbox": [ + 88.6025390625, + 93.005859375, + 522.3515625, + 185.625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/5/SectionHeader/5" + }, + "images": {} + }, + { + "id": "/page/7/SectionHeader/2", + "block_type": "SectionHeader", + "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.
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---|---|---|---|---|
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 |
The top-k gate values are selected for routing the token x. If 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,
", + "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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", + "html": "To 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 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
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---|---|---|
(precision) | (Neg. Log Perp.) (↑) | (Examples/sec) (↑) |
Switch-Base (float32) | -1.718 | 1160 |
Switch-Base (bfloat16) | -3.780 [diverged] | 1390 |
Switch-Base (Selective precision) | -1.716 | 1390 |
Table 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.
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", + "html": "are broadcast through all-to-all communication operations, but we still benefit from the increased stability of float32.
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", + "html": "As 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 measures the improvement of the model quality and reduction of the variance early in training. We find that
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", + "id": "/page/9/Table/6", + "block_type": "Table", + "html": "Model (Initialization scale) | Average Quality (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 |
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.
", + "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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", + "html": "6. Values greater than two standard deviations from the mean are resampled.
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", + "html": "Regularizing 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. (2019) use dropout (Srivastava et al., 2014) 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.
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---|---|---|---|---|
T5-Base (d=0.1) | 82.9 | 19.6 | 83.5 | 72.4 |
Switch-Base (d=0.1) | 84.7 | 19.1 | 83.7 | 73.0 |
Switch-Base (d=0.2) | 84.4 | 19.2 | 83.9 | 73.2 |
Switch-Base (d=0.3) | 83.9 | 19.6 | 83.4 | 70.7 |
Switch-Base (d=0.1, ed=0.4) | 85.2 | 19.6 | 83.7 | 73.0 |
Table 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.
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", + "id": "/page/10/Text/4", + "block_type": "Text", + "html": "We 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 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.
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", + "html": "We present a study of the scaling properties of the Switch Transformer architecture during pre-training. Per Kaplan et al. (2020), 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., 2019) and we train until diminishing returns are observed.
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", - "polygon": [ - [ - 89.99993896484375, - 93.29522705078125 - ], - [ - 522.94921875, - 93.29522705078125 - ], - [ - 522.94921875, - 186.01171875 - ], - [ - 89.99993896484375, - 186.01171875 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/1/SectionHeader/1", - "3": "/page/5/SectionHeader/5" - }, - "images": {} - }, - { - "id": "/page/7/SectionHeader/2", - "block_type": "SectionHeader", - "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.
", + "id": "/page/11/PageHeader/0", + "block_type": "PageHeader", + "html": "", "polygon": [ [ - 89.4990234375, - 225.79119873046875 + 239.8095703125, + 37.992431640625 ], [ - 522.94921875, - 225.79119873046875 + 368.75390625, + 37.992431640625 ], [ - 522.94921875, - 345.0933837890625 + 368.75390625, + 50.080078125 ], [ - 89.4990234375, - 345.0933837890625 + 239.8095703125, + 50.080078125 ] ], + "bbox": [ + 239.8095703125, + 37.992431640625, + 368.75390625, + 50.080078125 + ], "children": null, "section_hierarchy": { - "1": "/page/1/SectionHeader/1", - "3": "/page/7/SectionHeader/2" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/10/SectionHeader/5" }, "images": {} }, { - "id": "/page/7/Text/4", + "id": "/page/11/Text/1", "block_type": "Text", - "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
", + "html": "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.
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", + "id": "/page/11/SectionHeader/2", + "block_type": "SectionHeader", + "html": "Figure 4 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. (2020), we find that larger models are also more sample efficient—learning more quickly for a fixed number of observed tokens.
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", + "id": "/page/11/FigureGroup/231", + "block_type": "FigureGroup", + "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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", + "id": "/page/12/SectionHeader/1", + "block_type": "SectionHeader", + "html": "Figure 4 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:
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---|---|---|---|---|
100k steps (↑) | (examples/sec) | |||
(Neg. Log Perp.) | (hours) | |||
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 |
For a fixed training duration and computational budget, should one train a dense or a sparse model?
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", + "html": "Figures 5 and 6 address this question. Figure 5 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.
", "polygon": [ [ - 88.154296875, - 472.5703125 + 88.9013671875, + 565.1682281494141 ], [ - 523.248046875, - 472.5703125 + 521.6076049804688, + 565.1682281494141 ], [ - 523.248046875, - 498.09375 + 521.6076049804688, + 617.203125 ], [ - 88.154296875, - 498.09375 + 88.9013671875, + 617.203125 ] ], + "bbox": [ + 88.9013671875, + 565.1682281494141, + 521.6076049804688, + 617.203125 + ], "children": null, "section_hierarchy": { - "1": "/page/1/SectionHeader/1", - "3": "/page/7/SectionHeader/2", - "4": "/page/7/SectionHeader/6" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/10/SectionHeader/5", + "4": "/page/12/SectionHeader/1" }, "images": {} }, { - "id": "/page/8/Text/4", - "block_type": "Text", - "html": "Selective precision with large sparse models. Model instability hinders the ability to train using efficient bfloat16 precision, and as a result, Lepikhin et al. (2020) 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. (2017). Table 2 shows that our approach permits nearly equal speed to bfloat16 training while conferring the training stability of float32.
", + "id": "/page/12/SectionHeader/7", + "block_type": "SectionHeader", + "html": "To 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 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
", + "html": "The above analysis shows that a computationally-matched dense model is outpaced by its Switch counterpart. Figure 6 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,
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---|---|---|
(precision) | (Neg. Log Perp.) (↑) | (Examples/sec) (↑) |
Switch-Base (float32) | -1.718 | 1160 |
Switch-Base (bfloat16) | -3.780 [diverged] | 1390 |
Switch-Base (Selective precision) | -1.716 | 1390 |
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.
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", + "id": "/page/13/FigureGroup/251", + "block_type": "FigureGroup", + "html": "are broadcast through all-to-all communication operations, but we still benefit from the increased stability of float32.
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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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", + "id": "/page/13/SectionHeader/4", + "block_type": "SectionHeader", + "html": "As 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 measures the improvement of the model quality and reduction of the variance early in training. We find that
", + "html": "Section 3 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.
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---|---|---|
(Neg. Log Perp.) | (Neg. Log Perp.) | |
Switch-Base (0.1x-init) | -2.72 | 0.01 |
Switch-Base (1.0x-init) | -3.60 | 0.68 |
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.
", + "html": "Baseline 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., 2019). For both versions, we design a FLOP-matched Switch Transformer, with many more parameters, which is summarized in Table 9. 7 Our baselines differ slightly from those in Raffel et al. (2019) 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.
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", + "html": "7. FLOPS are calculated for the forward pass as done in Kaplan et al. (2020).
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---|---|---|---|---|
T5-Base (d=0.1) | 82.9 | 19.6 | 83.5 | 72.4 |
Switch-Base (d=0.1) | 84.7 | 19.1 | 83.7 | 73.0 |
Switch-Base (d=0.2) | 84.4 | 19.2 | 83.9 | 73.2 |
Switch-Base (d=0.3) | 83.9 | 19.6 | 83.4 | 70.7 |
Switch-Base (d=0.1, ed=0.4) | 85.2 | 19.6 | 83.7 | 73.0 |
(2021). In our protocol we pre-train with 220 (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). 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.
", "polygon": [ [ - 89.4990234375, - 312.08203125 + 89.349609375, + 91.6331787109375 ], [ 522.3515625, - 312.08203125 + 91.6331787109375 ], [ 522.3515625, - 377.1973571777344 + 171.95037841796875 ], [ - 89.4990234375, - 377.1973571777344 + 89.349609375, + 171.95037841796875 ] ], + "bbox": [ + 89.349609375, + 91.6331787109375, + 522.3515625, + 171.95037841796875 + ], "children": null, "section_hierarchy": { - "1": "/page/1/SectionHeader/1", - "3": "/page/7/SectionHeader/2", - "4": "/page/7/SectionHeader/6" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/13/SectionHeader/6" }, "images": {} }, { - "id": "/page/10/Text/4", + "id": "/page/14/Text/2", "block_type": "Text", - "html": "We 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 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.
", + "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).
", "polygon": [ [ 89.6484375, - 420.9232482910156 + 178.6412353515625 ], [ - 521.8413696289062, - 420.9232482910156 + 521.76953125, + 178.6412353515625 ], [ - 521.8413696289062, - 526.7109375 + 521.76953125, + 420.36328125 ], [ 89.6484375, - 526.7109375 + 420.36328125 ] ], + "bbox": [ + 89.6484375, + 178.6412353515625, + 521.76953125, + 420.36328125 + ], "children": null, "section_hierarchy": { - "1": "/page/1/SectionHeader/1", - "3": "/page/7/SectionHeader/2", - "4": "/page/7/SectionHeader/6" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/13/SectionHeader/6" }, "images": {} }, { - "id": "/page/10/SectionHeader/5", - "block_type": "SectionHeader", - "html": "Fine-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. (2020) 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.
", "polygon": [ [ - 89.4990234375, - 547.7267608642578 + 89.6484375, + 426.578369140625 ], [ - 214.259765625, - 547.7267608642578 + 521.7088623046875, + 426.578369140625 ], [ - 214.259765625, - 560.35546875 + 521.7088623046875, + 518.9765625 ], [ - 89.4990234375, - 560.35546875 + 89.6484375, + 518.9765625 ] ], + "bbox": [ + 89.6484375, + 426.578369140625, + 521.7088623046875, + 518.9765625 + ], "children": null, "section_hierarchy": { - "1": "/page/1/SectionHeader/1", - "3": "/page/10/SectionHeader/5" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/13/SectionHeader/6" }, "images": {} }, { - "id": "/page/10/Text/6", + "id": "/page/14/Text/4", "block_type": "Text", - "html": "We present a study of the scaling properties of the Switch Transformer architecture during pre-training. Per Kaplan et al. (2020), 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., 2019) and we train until diminishing returns are observed.
", + "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 |
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.
", + "html": "Table 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.
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", + "html": "Deploying massive neural networks with billions, or trillions, of parameters is inconvenient. To alleviate this, we study distilling (Hinton et al., 2015) large sparse models into small dense models. Future work could additionally study distilling large models into smaller sparse models.
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", + "id": "/page/15/TextInlineMath/5", + "block_type": "TextInlineMath", + "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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- } - }, - { - "id": "/page/11/ListItem/5", - "block_type": "ListItem", - "html": "Figure 4 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:
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", + "id": "/page/16/Table/1", + "block_type": "Table", + "html": "Technique | Parameters | Quality (↑) |
---|---|---|
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 |
Image /page/12/Figure/4
", + "id": "/page/16/TableCell/191", + "block_type": "TableCell", + "html": "Figure 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.
", + "id": "/page/16/TableCell/209", + "block_type": "TableCell", + "html": "Figures 5 and 6 address this question. Figure 5 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.
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", + "id": "/page/16/TableCell/211", + "block_type": "TableCell", + "html": "Figure 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.
", + "id": "/page/16/TableCell/212", + "block_type": "TableCell", + "html": "Section 3 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.
", - "polygon": [ - [ - 89.4990234375, - 474.890625 - ], - [ - 522.94921875, - 474.890625 - ], - [ - 522.94921875, - 568.4765625 - ], - [ - 89.4990234375, - 568.4765625 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/13/SectionHeader/4" - }, - "images": {} - }, - { - "id": "/page/13/SectionHeader/6", - "block_type": "SectionHeader", - "html": "Baseline 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., 2019). For both versions, we design a FLOP-matched Switch Transformer, with many more parameters, which is summarized in Table 9. 7 Our baselines differ slightly from those in Raffel et al. (2019) 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.
", - "polygon": [ - [ - 89.6484375, - 604.44140625 - ], - [ - 522.94921875, - 604.44140625 - ], - [ - 522.94921875, - 683.5533218383789 - ], - [ - 89.6484375, - 683.5533218383789 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/13/SectionHeader/4", - "4": "/page/13/SectionHeader/6" - }, - "images": {} - }, - { - "id": "/page/13/PageFooter/9", - "block_type": "PageFooter", - "html": "", - "polygon": [ - [ - 300.919921875, - 724.9639129638672 - ], - [ - 311.677734375, - 724.9639129638672 - ], - [ - 311.677734375, - 735.15234375 - ], - [ - 300.919921875, - 735.15234375 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/13/SectionHeader/4", - "4": "/page/13/SectionHeader/6" - }, - "images": {} - }, - { - "id": "/page/13/Footnote/8", - "block_type": "Footnote", - "html": "7. FLOPS are calculated for the forward pass as done in Kaplan et al. (2020).
", - "polygon": [ - [ - 91.740234375, - 695.3203125 - ], - [ - 408.91265869140625, - 695.3203125 - ], - [ - 408.91265869140625, - 705.375 - ], - [ - 91.740234375, - 705.375 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/13/SectionHeader/4", - "4": "/page/13/SectionHeader/6" - }, - "images": {} - } - ], - "section_hierarchy": { - "1": "/page/13/SectionHeader/4", - "4": "/page/13/SectionHeader/6" - }, - "images": null - }, - { - "id": "/page/14/Page/184", - "block_type": "Page", - "html": "(2021). In our protocol we pre-train with 220 (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). 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.
", + "html": "the 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.
", "polygon": [ [ 90.0, - 91.6331787109375 + 93.19921875 ], [ - 523.546875, - 91.6331787109375 + 522.650390625, + 93.19921875 ], [ - 523.546875, - 171.95037841796875 + 522.650390625, + 117.75335693359375 ], [ 90.0, - 171.95037841796875 + 117.75335693359375 ] ], + "bbox": [ + 90.0, + 93.19921875, + 522.650390625, + 117.75335693359375 + ], "children": null, "section_hierarchy": { - "1": "/page/13/SectionHeader/4", - "3": "/page/14/SectionHeader/0" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/13/SectionHeader/6", + "4": "/page/15/SectionHeader/3" }, "images": {} }, { - "id": "/page/14/Text/2", - "block_type": "Text", - "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).
", + "id": "/page/16/ListItem/3", + "block_type": "ListItem", + "html": "Fine-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. (2020) 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.
", + "html": "Achievable compression rates. Using our best distillation technique described in Table 6, 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.
", "polygon": [ [ 89.4990234375, - 425.77734375 + 375.3381652832031 ], [ - 522.94921875, - 425.77734375 + 521.7330932617188, + 375.3381652832031 ], [ - 522.94921875, - 520.13671875 + 521.7330932617188, + 467.54296875 ], [ 89.4990234375, - 520.13671875 + 467.54296875 ] ], + "bbox": [ + 89.4990234375, + 375.3381652832031, + 521.7330932617188, + 467.54296875 + ], "children": null, "section_hierarchy": { - "1": "/page/13/SectionHeader/4", - "3": "/page/14/SectionHeader/0" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/13/SectionHeader/6", + "4": "/page/15/SectionHeader/3" }, "images": {} }, { - "id": "/page/14/Text/4", + "id": "/page/16/Text/5", "block_type": "Text", - "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.
", + "html": "Distilling a fine-tuned model. We conclude this with a study of distilling a finetuned sparse model into a dense model. Table 8 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.
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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 |
Table 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.
", + "html": "In 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., 2020), 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.
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", - "polygon": [ - [ - 90.0, - 513.17578125 - ], - [ - 522.3515625, - 513.17578125 - ], - [ - 522.3515625, - 564.8033142089844 - ], - [ - 90.0, - 564.8033142089844 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/13/SectionHeader/4", - "3": "/page/14/SectionHeader/0", - "4": "/page/15/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/15/TextInlineMath/5", - "block_type": "TextInlineMath", - "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
", + "html": "In Figure 7 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
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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 |
Dense | Sparse | |||||
---|---|---|---|---|---|---|
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 % |
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.
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", + "id": "/page/17/Table/3", + "block_type": "Table", + "html": "Model | Parameters | FLOPS | SuperGLUE (↑) |
---|---|---|---|
T5-Base | 223M | 124B | 74.6 |
Switch-Base | 7410M | 124B | 81.3 |
Distilled T5-Base | 223M | 124B | (30%) 76.6 |
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.
", + "id": "/page/17/ListItem/4", + "block_type": "ListItem", + "html": "Achievable compression rates. Using our best distillation technique described in Table 6, 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.
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", + "html": "pre-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 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.
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", + "html": "Arbitrarily increasing the number of experts is subject to diminishing returns (Figure 4). Here we describe complementary scaling strategies. The common way to scale a Transformer is to increase dimensions in tandem, like dmodel or df f . This increases both the parameters
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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 % |
Model | Parameters | FLOPS | SuperGLUE (↑) | |
---|---|---|---|---|
T5-Base | 223M | 124B | 74.6 | |
Switch-Base | 7410M | 124B | 81.3 | |
Distilled T5-Base | 223M | 124B | (30%) | 76.6 |
Figure 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.
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", + "html": "and 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.
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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. |
Image /page/18/Figure/1
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", + "id": "/page/19/TableCell/345", + "block_type": "TableCell", + "html": "Figure 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.
", + "id": "/page/19/TableCell/346", + "block_type": "TableCell", + "html": "and 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.
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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. |
When 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.
", + "id": "/page/19/TextInlineMath/5", + "block_type": "TextInlineMath", + "html": "When training data parallel models, which is the standard for distributed training, then all cores are allocated to the data-parallel dimension or . 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.
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" + "/page/20/Figure/4": 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siE41tbhNl1575RfMYBVGcADGeB1rsV02z8efGO803xSZJ7Sy0uKS0tWlZFZmVCzjaQc/MT/+qvT5fAvhufwtF4Zk07do8TBkt/PkGCCW+9u3dSe9QeIPh14X8TTW0+p6bvmtkEcckcrxtsH8JKkZH1oA4f4GQQ2t94zt7adp4ItSCRys24uo3gEnv9al8bSxw/HnwXJLIsaLbyZZjgD7/evQvD3hLQvCouhomnrZrdOHmVZHYMR0wGJx16DFVPEvgDwx4vuobnXdM+1zQp5cbefJHtXOcYRh3oA8+8dX1prfxk8E2mkzxXF3aSNLcPCwby48g4JHsrHHv71wHiK0tNZ0fxR4kstJnu0jvCBreo6jskjYMPkihUdORjPb6V9B+HPAnhjwlJJLomkRWssg2tJvaRyPTc5JA9qypPhF4Hmvbu6l0RXa63GRDNIEBPUqobCn3HTtigDzC70XTdW8c/DmbUYPPfVNNSS8d5GzMyxjaSc9eB0qn4rs7LxFqvjTUrXR5tT+wFo5NR1DUfJSzZQflijUZbpxk9vevatQ+HfhTVbPTrS+0kTRaagS0zPIGjUYwNwbJHA6k1VvPhV4Mv8AWLjVLrRkkuLjJlBlcIzHq20HGff1560AO+FdzPefDHQZrmV5ZTbkF3OSQGIHP0ArsKoaNo1h4f0qDTNMgMFnACI4zIz7QTnqxJ6n1q/QAUUUUAFFFFABRRRQAUUUUAFfOvg3TddvvEfi9tI8ZxeH0XVHEkbwJJ5p3Ng/MRjFfRVcNefB3wHf3s95c6F5k88jSSP9rnG5ick4D4HNAGN4x0S2uPhFdp4l8RRahd2O6aLVI0VCJc/IAqnvkLjvmuQ+GUi+JLXxR4h1u8a58RQWDW6RSrtMMPlYDgf7Xr/jXqKfC3wbHo66QukEaeLj7T5H2ubaZMY3H5+eB0PFWNT8EaNJc6hrFlp4TWZrJ7ZZY5nQOChUKVDBD26jsPSgDwqy8MaE3wGbxS1xKNctpD5FwLlgYmEuBGq5wODnpnnNWrmTWPG/jexsdU0lda8nRoJo7C4vzaIWaNS0mR1bJP8AkV3Hgn4M6LH4c01/FGiqdXgZjKguCUf5yVLBW2txj+RrtfEnw/8ADPitoJNV01XmgXZHLE7ROq/3cqRkexoA8O1W31TT/grqtheXlrLDBq0QtUt75Lr7OpJzGWU8YPY4rU+JXgHRfDnw80+7hE9xez38TTXNxMWZi6/P7c7R+Veun4feFm8Mr4d/slBpSyCXyFkdSzj+IsGDE/U1o614b0jxFpH9latZLc2QKkRszDBHQgggg/jQA+xtbHQvDsdvaJ9nsrWA7ApLbFAzxnJNfL2tWkMvg2fxTp+kzrvv8xa7falm6lbd0WJcAfjX1NpukWOkaRBpVjbiOxgj8uOIsXAX0yxJP41yK/BzwGPtAbQ1dZzkq1xLhOc/J83y9O306UAcPNoVl4s+OC2WsCSe1fQ4ZZYhKyCU4X7xUgkZOceoFck19faB8PfGuk6bczxWUGuJaLhyTFCxYMAe2dqg/X3r6HtfCmi2euLrUFmV1FbZbQTmZ2PlDGFwWx2HOM+9V4vA/huG11a2XS0MOrSGW9R5HcStyc8k7eT2xQB5B4g8IeHfC3i/4cnQmZDdX8LSp57OJcMmJcEnBOT04r1f4k/8k28Q/wDXjJ/KqemfCbwXpN1a3VtpB+02syzwSvcysyMpyv8AFyB6Hiur1LTrTV9NuNOvovNtLlDHLHuK7lPUZBBH4UAeWaVrmkaf+zzEt9eWwLaXJEITINzO24BQvXOSK4mC2SXwL4D8P31lf6jd3bzXEGlpci3imQuSDI5GQMcjHqa9Yt/g18P7WdJo/DsZdDkCS5mdfxVnIP4itnxJ4G8O+LIrWPVrASfZP9Q0btGyD0BUjjgcUAeE6NpcRsviTo11ZxQWtlbC5isYbp5YreZQ2NrE5JHerz2mjWnwh8Jaeltey3Gs3Su9naXIiF5IOMSO2dq/d6V7Jpnw+8K6Ob37BpEcK3sAt7lBI5SSPGMFS2PxxnrzVGT4T+Cn0U6SujBLTz/tAC3Em5ZMAEhixI4A46UAeffDC2m0X4x6zpKWEOlQfYBI+n2921xHG2Uxlj1bk/TNe61zWh+APDHhvUl1DSdLFrdiHyDIs0h3JnJ3AtgnPcjNdLQAUUUUAFFFFABRRRQB4f8AEi3vbr43eHodO1hdIumsG2XrRq4j5fPB45HH412fh3QtSaPUbPxL4zt/EVndW5jNv5Ecewd2ypPb8q1vEnw98LeLr2O81zS/tdxFH5aP9oljwuScYRgOpNUtP+E3gnSpJnsdGaB54WgkZbyfJRvvDO/jPtQB4r4ISx1b4g2HhfUNWe68OabdTyaUsi4S5cEYUnuO/wD+uum0XwroPiv4n+PbfXi0kUEweODz2jUdQZMAjJX34Ga9Pb4ceEm0/TrH+yFSDTZDLaeXPIjRMTkneGDHn1JrjLP4Twax408VXvifTN9ld3CyWMqXG1yOd33WyAeODQB5odc1mT4V6fpC3M02nya69mHaby/MhABWMv2Ukn6fhXVaLoOqeHPFl6lvY2Gh2NxpcwuNLj1hbl3xGxWQITu6gc49a9efwT4bk8ML4cfSYDpK/dt+eD/eDZ3bvfOaqaD8OPC3hsXR07Tdsl1GYZZZJXdyh4KhicgfTFAHkngbwJpGo/CG98Q6j9ours2VykSPKRHEqEsAoH+0u6u2+DMejaL8M9OvGlt7Sa/ZjK8s23zXDMB944zgdBXdaV4b0jRNC/sTT7NYtNw6+Qzs4IbO4EsSTnJ71yOp/CXw/wD2bb6fo2lw2kZvo7uSRrmUmMr1KqSQSR8uDgDOe1AHcar/AMgi9/64Sf8AoJr5d8M6o3g/w9Ya6mVXUdOv7Ekf89Vbcn6kV9VSxJPC8Ug3I6lWGcZB4Ncs/wANPCEmiWmjvo4awtJmngiNxL8jnqd27J+hOKAPENLS+8H6j4qXT9yXtp4bgO5eqMwiLt9RuY/hVrWfDmi+HvhPofjLR7mWPxG8kMv2xbhi80jH51Izjjnt25r3iPwpocWt3usLYKb69hFvcSM7MJIwANpQnbjAHQVg2Xwj8E6fqqajBow82N/MjR5naNG9QhOKAOGGnWvj34y3uneLFeS3s9MiktbIytGu5lQswwQc5Y/l7VwgvLjRPBXjW30i8laCTW4rVrkSfM0X7zq/vgAn3r6E8S/D7wz4tvIbzV9O825hG1ZY5WjYr/dJUjIpbL4feFNO03UNOtdGhSy1AhrmBndlcjpgEnbj2xQB5T4a8Oan4d8faFPYadp2g288Wy5tRraztexkffCNgk9+P0rqPgN/yKGqf9hWb+S11Hh34a+FPC1+b/S9M2XW0osssryFFPZdxOPw5rX0Hw3pPhizltNHtPs0EsrTOvmO+XPU5Yk9qAPKPH1rZ+JfigdLTSLjXbmzsd72txfi1tLcHnfkDcx5GcH09K4FZp5vgc1u87bIfEaxRbZCwjXbnCk9skkV9A698OvC3iXWI9V1XTBNdooUuJXQOo6BgpAYfWmD4aeEF0mTSl0gLYyXQu2hW4lC+aBgMPm4+g49qAPOb3w9Y+CfjF4SGgpNCb63l+1AzO5nYKeW3E5JNc9o+gaT4m+G/izxdrdxK3iCOaf/AEg3DK0OANqYzjBzjBHTgV77e+GdH1DW9P1m6s/M1DTwRay+a48sHrwDg/iDWDe/CfwVqGrz6lc6MrTXBJlUTOqOx6ttBAzz19eetAB8JP8Aklfh/wD64H/0Nq5PwBeW1p8WfiC1zcQwqbhMGRwueW9a9T0jSbHQtKt9M02DyLO3XbFHvZtoznqxJPJ7muX1P4S+B9Z1O41K/wBE867uXMksn2qZdzHvgOAPwFAHmMGrWUnjP4k+Iba7uI9HWyELXVkRuaQlVyhPGSQ3PvmsG2006Hr3gS/s9EGji9ukxOdSM9xeRsVyzqMKoIPQDvX0La+DvD1l4em0C20q3j0udSssAB+fPcnOSffOeKxLH4R+CdPaB4NHPm28yzRStcy70YdMHdnA9OlAHnfhbSdC0T4gfEG7MT2yaREWtpIWJeBWRtxUE8n65rir2xGnaJ4f8Q6do8lgbjUFaHVbjUzLdXWWOSY1AVRxz3/OvoybwD4XuNfm12XSIm1GdGjll3vh1ZdrArnacjjpWRB8HPAkEbINE3AuJFLXMu5CDkBTuyB9OvegDux0opAAqgDOAMcnNLQAUUUUAFFFFABUN3I0NnPKn3kjZhn1Aqaq2of8g26/64v/ACNAHiWgeLfilr/hK78T2V9oRtLQyb4JoWV2CDJxgY6e4rttK+LGhnwNpfiDXp0sJb3cggRWkLOpw20AE4/x615t8Lfh0PF3giWa48S63Z2kl1JFJY2lxthcDGSVORk59K2PHIsvBOveE9Fsng0TTbeCQprMtkLqWI5OVXIOCTyTj+KgD0ez+I/hO/8AD93rkGrKbCzIFy5hcNEScDKbd36U2H4l+EJ7PULyPWYja6eENxNscKu/7oHGWPHQZNeD28wk8L/E8m6uLhpGt5BLdR+XLKDIfnK4GM5B6dxXceLvs/h74J6A9jpFiUmFr580tqJVhymTKVx8xye+etAHoXh34jeF/Fd5JZaRqXmXaIX8mSF42KjuNwGfwrzc/FLxKPhtNrnm2321db+xA+SNvlY6Y9fes3wvqEd98cNIuV1651qN9PlRb2e1FsrkK3yxrgfKP55rnW/5Ircf9jR/SgD33xD8QvDPhSSC31nUhFcyoHEMcbSMF9SFBwPrWB44+IT2fhnQ9Z8L3tvcW9/qEduZSm4FDnIweQeO/NcF4heXw98XNXvtV8QXWgW19aJ9lvUshcLKoVQY+QcdO3pzWVf6da2Pw10aawl1GS0u/EUc0bX1ssBb5cFlVWI2nHt9KAPaLfxHOPiRqulXGsaf9gtbIT/ZPKdZouFJdnK7NvJ6MetJpnxW8F6vq8el2esq1zK+yLfC6JI3orFQD/WuE1COKb4yeNo57a5uYX0Ha8NtjzXUqmQue/pXEaZrf2VvDthpGrReIrdLtRFoeoaZie0OeSHAIyPUN+FAHuerfFTwbol3d2l7q+26tJBHNCtvIzK34LyPccU1Piz4Il1C1so9dieW62+WwifZlugLYwp56Hp3xXCaNGj+Lvi2XRWP2dhyM8bH/wAK5e/ijX9njwqyooY6qDkDnO+TmgD6Xopsf+qT6CnUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXkereJvHWpfFPU/C3hu+0u2itLdJwbyEnghc8gE5y3pXrleD3vh2XxL+0Dr1nDrWo6SyWUcnn2EpjdvljG0kduf0oA6vwb8Q9Q+2eItK8aNY29zoQDzXlvkRMh46Hv06dc9M1vaB8T/CPiXVF03TNU33bgmOOSF4/MA/ulgAf51xfinwdYfDr4b65eafbSaze3jxm6n1IeeSN33iMAEAnPPfrmuQ0zUor/wCJ3gadPEM+sRJmLzmshawQtt/1UYCgHGRnr1FAHsyfEvwjLqyaXHq6vfPdG0EIicESDqDlcYHr096pr8XvA76oNPGtr5hk8oSeTJ5Rbpjft2/jnHvXF/DazthcfEPUv7NhvL2C/mEQeMMzY3kKD1GT6V5nq2tLqXw+t4/7YIkjvBJJotnpogt7MbiNzOF5J4xk9/agD3S+8Y6tB8UtQ0CN4fsEGjteIPLBbzAMg59Pajwb8RIpfhnb+J/Fl/Bbl5ZI2kVCAxDEAKoyScDtXNXTB/jbqLKQVPhhiCO421wf2O7b4UeC9TV5otPsdSma6nihEpgy4xIVPBxg9f60Ae52/wARdD1/w7rF34e1FJruxtZJjFJGyMhCkglWAJGRXMWPjzWbrwT4R1WfV9OsrrU7wxT+fbuwmG8jagRW2tgdTge9cvpSadrGt+ItZsvEOq65PDo08c962npBbsDGcKWBBLD/AHe3Xisxv+SZ/DH/ALC//tQ0Aey658UPCHhzVW0zUtWCXUePNSOF5BFnpuKggfSruqeO/DGjWun3V/q8MVvqClrWUKzrIAAc5UEDqOuK8P8AFV/Z6N4s8VtY6/c6Hd3DE3Om6nYCeG/6/cYBvlPbI/i61oX80t9b/CeS70uHT2e8b/RY49iAb1wQp6A9ce9AHoy/GXwI1nLc/wBtYET7DGbeQSE+oXbkjjr0Heut0bWdP8QaVDqel3S3NnMMpIoIzjggg8g+xryzSYIj8b/HhMSH/iWL1Ud0TP51qfAT/kmUQ/6e5v5igD06iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKbISsbMOoBNOpkv+pf/dNAHh/h3xX8UvFGiajrWm3+hi2sppEMM8JV32DdxgY6H1FdfoHxZ0ifwDY+IvEM0enyTyPAYo1Z98innYoycdD7Z615x8L/AAA3i7w5qcsniXWrC2N9JDJaWdxsikGBklehJzjp2ra8eW1j4Dn8IaLp4h0vTomkb+2bi0F1JbtkE7QQRuJ56emMYoA9K0n4h+F9a0i+1Oy1MPbWCb7rdE6vEvqVIz2PQGorL4l+ENRF01rrMTx2lsLqeQo6rHGSAMkgc8jjrz0rxfQbhZpPifJ9uubwzaZ5iXF1F5Uk64Pz7MDAOQRx0IrogkGh/s32t9ZaLZ3U00CGbzbcODmTl3H8WOOvHSgD0PQPif4R8S6oum6Zqm+7cExxyQvH5gH90sAD/OvP5/if4kj8BeItYWW2+12OsizhPkjAjzjkdz71zmmalFf/ABO8DTp4hn1iJMxec1kLWCFtv+qjAUA4yM9eoqhdf8km8Y/9jIP/AEKgD3fWviD4d8L21l/bmorDc3MSyLDHG0jkY5O1QcDr1rA8c/EUWvgOz8Q+FL63uI572ODzCm4YOcgqcEHgda4PxGZtC+Kg1TUtdudBs7vTIltdRjsxcKcIoaPkHHQ9P61j6pYWdt8J5rzT7jUp7W916KVZL21SAOcNlkVWPyn8OnSgD2uHxDdt8TW0Z9WsfsgsBP8AYfJkE4bj59+3Zt9t2famR/FrwRLq40xNbTzjJ5SuYnERfpgPjb+Oce9cjKA3x/vg0UkwPh4gxx/efgcD3NeaLq0Ol6PBYaNq/wDaUAvePC+r6XulR9x6MARn6FetAH0FrfxL8JeHr+5sNS1URXlsqtJAIJGbBGRjC4PB7HiqTfGHwIptf+J6hFyAVIhkITJx8/y/KfY/XpXLaGom+PHiVpoFRzo0eUPOzKx5H9K4a2hjH7NOqsI13f2tnOOfvIP5UAfTSsrqGUgqRkEdCKWs/Qf+Rd0z/r0i/wDQBWhQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVieL9TudG8H6vqdmVFza2ryxlhkbgOMituuY+Iv/JOfEP8A14y/+g0AeZR+Mfihb+BYvG0l1oNzpmwSvbGJlk27tvYAdfQ131z8VPDGmaRpV5qt6babUbZLlLZI2lcKwzyFBwOvJ64rgPAvwqg8UeAtIudT8S661jOm86clziAYY8BSCAOKTxXqkGhfE5NLW7j8NWdtpqRW99BpwuLi5XAAjViDj049PegD1D/hYPhb/hGV8Rf2sh0oyCLzxE5w5/hKhdwP1FQH4m+EBpV5qf8AbEf2K0n+zySiNyGkxnaoxlvwzXgcRB+A+txMX3rr67lk4ccD7w7HrXo3xOktvDfhvwtBZWFhZWj3Me+/exWYWeFGHCYwW69R2oA663+JGg6/oWsT+HtQWW9sbSScxSxMjLhSQdrAZGcdK5C1+JHiGbRvAN08lv5ut3jw3mIRgqHCjb6cVyei3iXfj3xZcDVrnVFn8OzGO9uYBAZ8KOVTA+XggcdqbYf8i18JP+wlJ/6NFAHsmu/E7wh4b1Q6bqerLHdLjzEjieTy89NxUED6dazPEvjW7tfF/gu00i5t5dM1qRvNcKH3p8uCp7da8nmdfD3iTxnZeIPEl7oj3lw8ghj05bj7fExbG1mHofUDnrxWvDpselat8KraE33k/aJnjF9EI5QrMCMqGIHXjmgD0rQ/F3/E18Vtq+u6dJYaVMAFiikRrVctxIWUBjwPuk1b8P8AxL8J+J9S/s7S9U8y7ILJFJC8ZkA7ruAz/OvJTDaTj4rx31tfT2xvoy4sQDKmHY7gDwQCMn2qPwhrs0vj3wzZ2mrWviy2VdiPLpxiuNOjx/fx2/3iOPcUAeqX/wAXfA+nSyxT61+9hmaCVFt5SUYHBz8vT36elWLT4oeDb/XIdHtdailu5yFi2xvsdj0UPjbn8fbrXlWkRRt4H+LLMik/a5eSPQsR+tGrxRx+DfhKURVP2uLkDHUqT+tAH0JRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeM2PiX4j+J/FniTT9A1DRoINJujEFu4TkrlgvIBz92vZq+ffC3hCbxV8QPHKx+IdW0lYb8hhp85j80Fn+964x+poA7fwd8U4rvwrqWoeLXttPn0u7NpcSRBikjdtqjJJ4PAz0zXReHfiJ4X8Uyzw6VqXmTwIZJIpInjcIP4gGAyPpXnXjzw/p/w38C6dYaXapLBPqSvc6jfwC5aBsf60rjBPpx+prI8K3yXvxkmuRrdzq6TaNKEvri3EHnYH8CYHyjBHTsaAPWdN+JnhDWL2G0sNZjmlljklA8t0CogJYsWA29D1qtp3xa8Fapq0em2usgzyv5cReCREkbpgMVxXAfDa2t7H4HazqsGkW17fZucq8W4ygDG09yuO31rhrnWI9R03wgw157sQahEZbKHT1trawy2du4KMt179ATQB6nefEDXYb74hxJJBt0KJXssxDgn+9610dn8QtM0vwJomt+JtQjgnv7dH2pGS0jkZO1FBP9K8z1H/kKfGH/AK90rO1OK5sP+Fea5cahcaZpcelLD/aMVsJxbSYPVSCOcjt9OlAHqmv/ABHsbn4b6v4i8KahDcTWaqPnjOY2LAYZGAPQmmQeLNSl1fwXDJq1hANVsxNcWrwSGSdiucxlVKqPqRXmEttYXHgrx7rWn6pqeprcRxRzXlxZJbwzuJFO5NpyT65Udfet1/8AkePhRxn/AIlnQd/3dAHoF38WfBNjrD6Zca0izxyeU7CF2jR/QuBt/XFaOueP/DHhu7jtdV1VIJ5YfPjTynfenqCoIOcdM5r55vdTtdFs9XtNJ1qWJXvS0vhjWdNErSvuHRgCP/QTxXoOXuPjV4HkubJbSU6Jua2A4hbY/wAoB6Y6UAdafjL4DFlFdnWxskYrsFvIXXHUsoXIHPU9e1drZ3lvqFlDeWkyzW86CSORDkMp5BFeC6PBCPDvxfIiTi4lA+UcAFyB+deo/Cv/AJJf4f8A+vUfzNAHYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFMkijlAEiK4ByAwzg+tPooAKRlV1KuoZSMEEZBpaKAEVVRQqqFUDAAGAKWiigBkkUcy7ZY0dc5wygjNPoooAKjWCFJWlWKNZG+84UAn6mpKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBCAQQRkHqDSJGkSBI0VFHRVGAKdRQAUxIo4yxRFUscsVGMn1NPooAKCAQQRkHqKKKAGRxRwpsijVFH8KjAp9FFAEckEMrK0kUbsv3SygkfSpKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApskUcq7ZEV1znDDIzTqKACkIBBBGQeoNLRQA1I0iQJGioo6KowBTqKKAGSRRzJsljV1/usMingADAGAKKKACo/Ih87zvKj83GN+0bvzqSigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApjRRu6uyKzJypIyR9KfRQAU2SNJUKSIrqeqsMg06igAAAAAGAO1FFFADHhilZWkjRyhypZQcH2p9FFABUccEMJYxRRoWOWKqBn61JRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUANdEkQo6qyngqwyDSgBVCqAAOAB2paKACmxxRxAiNFQE5IUY5p1FABTXRZEKOoZTwVYZBp1FACIixoERQqjgADAFLRRQBGYIWmEzRRmUdHKjI/GpKKKACiiigAooooAKKKKACiiigAooooA5ifxPqTa5qOm6b4dmvlsGjSSYXUcYLOgfADHPAYU7+3fEv/AEJ03/gwh/xo8P8A/I3eLf8Ar7t//SaOuloA5r+3fEv/AEJ03/gwh/xo/t3xL/0J03/gwh/xrpaKAOa/t3xL/wBCdN/4MIf8aP7d8S/9CdN/4MIf8a6WigDkbXxXrl6ZxbeFJJDbymCXGoQ/K4wSOvuKs/274l/6E6b/AMGEP+NaGiS6dK2p/wBnwtGUvpFud2fmmAXcRyeMYrVoA5r+3fEv/QnTf+DCH/Gj+3fEv/QnTf8Agwh/xrpaKAOa/t3xL/0J03/gwh/xo/t3xL/0J03/AIMIf8a6WigDlLbxPrt7bpcWvhRpoXztkj1KAqeccHNTf274l/6E6b/wYQ/41oeGxpo8P2g0dmaww3kls5xuOevPXNatAHNf274l/wChOm/8GEP+NH9u+Jf+hOm/8GEP+NdLRQBzX9u+Jf8AoTpv/BhD/jUN14m1+ytJrq48JSxwQo0kjnUIflUDJPX0FdXVHWpLSLQtQk1CMyWS20jXCL1aMKdwH4ZoAxYvEXiKeFJovCErRyKGVv7Qh5B5B60/+3fEv/QnTf8Agwh/xresTC1hbNbqVgMSmNT2XAwPyqegDmv7d8S/9CdN/wCDCH/Gj+3fEv8A0J03/gwh/wAa6WigDmv7d8S/9CdN/wCDCH/GoJvFer2M9kNQ8Lz21vc3UVr5wvIn2NIwUEgHJGT2rrK5rxr/AMeWkf8AYZsv/Ry0AdLRRRQAUUUUAFc94j1bVbPUdI0/SI7Jp7+SVWe73bUCIW42854xXQ1zWu/8jn4U/wCut1/6JNABjxx/e8PflN/jRjxx/e8PflN/jXS1Wn1GxtpPLnvLeJ+u2SVVP5E00m9gMPHjj+94e/Kb/GjHjj+94e/Kb/Gtj+2dL/6CVn/3/X/GnR6pp80gjiv7V3bgKsykn8M0cr7CujFx44/veHvym/xqjJqnjGPXLfSW/sH7RPby3KEJNt2xsitk565kXHHrXZ1ly30aeKbTTzZhpJbKeYXX9wK8QKdP4t4PX+D8kMzseOP73h78pv8AGjHjj+94e/Kb/GulqpJqmnwyGOW/tUdeCrTKCPwzTSb2C9jFx44/veHvym/xox44/veHvym/xrY/tnS/+glZ/wDf9f8AGpINRsrmTy4Ly3lfrtjlVj+QNHK+wrow8eOP73h78pv8aqQ6j4vn1O606OTw+bm1jjklXZPgLJu289/uNXYVlWl3ZS+JtUtIrXZewwW7Tz7R+8VjJsGepxtbr/epDM/Hjj+94e/Kb/GjHjj+94e/Kb/GulqkdX0xSQdRtAR1BnX/ABppN7CvYx8eOP73h78pv8aMeOP73h78pv8AGtj+2dL/AOglZ/8Af9f8amt720uyRbXUMxXqI5A2Pyo5WugXRg48cf3vD35Tf41R0/VPGOpyXyQf2CDZ3LW0m9Jhlwqscc9MMK7OsvR76O9l1RUsxbG3vWhYj/lsQqnf0HXOO/SkMzseOP73h78pv8aMeOP73h78pv8AGukd1RC7sFUDJJOAKp/2zpf/AEErP/v+v+NNJvYV0jHx44/veHvym/xox44/veHvym/xrY/tnS/+glZ/9/1/xqxBdW90he3nimUHBMbhgPyocWtwujn8eOP73h78pv8AGr/hjVpNd8LaVq00aRy3lrHOyJnCllBwM1rVzXw7/wCSceHP+wdB/wCgCkM6WiiigAooooAy/EuqSaH4X1XVoY1kksrSW4VH6MVUkA47cVmr/wAJwyg58PcjPSb/ABp/xB/5Jz4k/wCwZcf+i2roU/1a/QUAc5jxx/e8PflN/jRjxx/e8PflN/jXQT3NvaoHuJ4oVJwDI4UfrVf+2dL/AOglZ/8Af9f8aaTewrox8eOP73h78pv8aMeOP73h78pv8a2P7Z0v/oJWf/f9f8auI6yIHRgykZBByDQ01uF0zkNR1HxfpdstxdyeH1jaaKAEJOfnkdUUcf7TCrePHH97w9+U3+NaHiG7srLTopb+1+0wteW0SptBxI8yKjc/3WKt+FatIZzWPHH97w9+U3+NGPHH97w9+U3+Nb1xe2toQLm5hhLdPMkC5/Oof7Z0v/oJWf8A3/X/ABp8rfQV0Y+PHH97w9+U3+NGPHH97w9+U3+NbA1jTGIA1G0JPQCdf8au9aGmtwumcfe6j4vsJbOK4k8Pq15P9nhASc5faz49uEarePHH97w9+U3+NaGsXdla3ejpd2vnyXF8IrZtoPlSeVI2/np8qsOP71atIZzWPHH97w9+U3+NGPHH97w9+U3+NdG0saHDuqn3OKb9oh/57R/99Ci4HPY8cf3vD35Tf40Y8cf3vD35Tf410SzRMcLIhPoGFPoA4xtU8YrrkWkH+wftEls9yDsm27FZVPOeuXHb1q9jxx/e8PflN/jWi99GvimHT/sYMj2Ukwuv7oDouzp33A9f4a1KAOax44/veHvym/xox44/veHvym/xrommiQ4aRFPoWApPtEP/AD2j/wC+hRcDnseOP73h78pv8aMeOP73h78pv8a6JZY3OEdWPsc0+gDnvDurateanq+navHZLNYPEFe037XDpu53c5FdDXNaJ/yO3ir/AHrX/wBFV0tABRRRQAUUUUAcsvjRprm8is/DWuXiWtw9s80KQBGdDhtu6UHGfapP+Ervf+hO8Q/9823/AMeo8F/8e2s/9hm8/wDRhrpaAOa/4Su9/wChO8Q/9823/wAeo/4Su9/6E7xD/wB823/x6ulooA5r/hK73/oTvEP/AHzbf/HqP+Ervf8AoTvEP/fNt/8AHq6WigDlLbxrNeQCe28Ka9NESQHj+ysCQSDyJ+xBH4VN/wAJXe/9Cd4h/wC+bb/49Wh4cGmjQ4RpDM1l5kuwtnO7zG39efvbq1aAOa/4Su9/6E7xD/3zbf8Ax6j/AISu9/6E7xD/AN823/x6ulooA5r/AISu9/6E7xD/AN823/x6mt4uu0Rnfwf4gVVGSxFsAB/3+rp6q6mlvLpV5HdyGO2aB1lcHlUKnJ/LNAGEni67kRXTwh4gZGGVZRbEEeo/fU7/AISu9/6E7xD/AN823/x6trS47eHSLKKzkMlqkCLC5OSyBRtP5Yq3QBzX/CV3v/QneIf++bb/AOPUf8JXe/8AQneIf++bb/49XS0UAc1/wld7/wBCd4h/75tv/j1Q3PjWazgae58Ka9DCpALyfZVAycDkz+pArq6yvEg006FONXZlsd0e8rnOfMXb05+9toAz/wDhK73/AKE7xD/3zbf/AB6j/hK73/oTvEP/AHzbf/Hq6WigDmv+Ervf+hO8Q/8AfNt/8eo/4Su9/wChO8Q/9823/wAerpaKAOa/4Su9/wChO8Q/9823/wAeotvF5k1iw0278P6xYPfM6Qy3KQ+WWVGcglJGI4U9q6Wua8Q/8jX4Q/6/p/8A0lmoA6WiiigAooooAK5jWL3XJvFdto+k31lZo1jJdSSXFm05JV1UAASJj73v0rp65qX/AJKda/8AYGm/9HR0AH9m+M/+hm0j/wAEr/8AyRR/ZvjP/oZtI/8ABK//AMkV0tZ8muaZFI0b3sW5ThgDnB9OKaTewm0tzK/s3xn/ANDNpH/glf8A+SKP7N8Z/wDQzaR/4JX/APkitP8A4SDSv+f2P9akg1nTbiZYYryJpG4Vc4J+mafJLsHMu5kf2b4z/wChm0j/AMEr/wDyRVG3fxZc6ve6YniTSxPZpFJIToj7SJN23B+0/wCwc/hXZ1l2d9HN4h1OyWzEb28UDNcf89t+/A6fw7T3P3qkZnf2b4z/AOhm0j/wSv8A/JFH9m+M/wDoZtI/8Er/APyRXSEhQSSABySazjr+lZ/4/oj7g5H501FvYTaW5mf2b4z/AOhm0j/wSv8A/JFH9m+M/wDoZtI/8Er/APyRWn/wkGlf8/sf61Na6rYXkpit7qOSTGdgPOPpTcZLoHMu5jf2b4z/AOhm0j/wSv8A/JFUbB/Fmo3GoQw+JNLDWFz9ml3aI4Bfy0k4/wBJ5GJF/HNdnWXpN9HeXerxpZi3Nre+S7j/AJbnyo239B2YL3+7UjM7+zfGf/QzaR/4JX/+SKP7N8Z/9DNpH/glf/5IrpGYKpZiABySe1Vf7V07/n/tf+/y/wCNJtLcLmL/AGb4z/6GbSP/AASv/wDJFH9m+M/+hm0j/wAEr/8AyRW1/aunf8/9r/3+X/GpobmC5UtBNHKB1KMGx+VCaYro5/8As3xn/wBDNpH/AIJX/wDkis7Q5PF+uaPb6lF4h0qNJgxCPor5GGI/5+Pau2rO0K+uNS0W2u7q0a0nkBLQMCCmGI789Bn8aYzL/s3xn/0M2kf+CV//AJIo/s3xn/0M2kf+CV//AJIro5JY4Yy8rqiDqzHAFVv7V07/AJ/7X/v8v+NJtLcV0Yv9m+M/+hm0j/wSv/8AJFH9m+M/+hm0j/wSv/8AJFbX9q6d/wA/9r/3+X/GrEU0U8YeGRJEPRkYEfpQmnsF0cdrf/CZ6PoOo6p/wkGkTfY7WW48v+x3XfsUtjP2g4zjGcGuvtZGmtIZWxueNWOPUisfxt/yIXiL/sGXP/opq1dP/wCQba/9cU/kKYyxRRRQBzXh/wD5G7xb/wBfdv8A+k0ddLXNeH/+Ru8W/wDX3b/+k0ddLQBBd3kFjbmed9qDgADJY9gB3NY8lzqN/wAmQ2MB6JHgyke7dB9B+dRJIdTuzfvzCpK2qnoF7v8AVv5Y96t10Rgo77mMpN7FM6XaPzKskzf3pZWc/qaT+yNP/wCfVP1q7RWl2RZGBpNtpd8b8RaZHB9nvHgb/powAy345/StL+yNP/59U/Wm6Zd3d2b37VbGDybp4osgjzIxjD8+uT+VX6Sk+47Ipf2Rp/8Az6p+tH9kaf8A8+qfrV2inzMVkUv7I0//AJ9U/Wj+yNP/AOfVP1q7RRzMLIwNBs9Ev9Etbqx08Q20gJSNhgryR2J75rS/sjT/APn1T9aj0K9Oo6LbXbWn2QyKT5H9zkj0H8q0aSk+47Ipf2Rp/wDz6p+tH9kaf/z6p+tXaKfMxWRS/sjT/wDn1T9ap6tY6ZZaPfXcmnxzxwW8kjRf3wqklfxxitmqup3E9rpN5cWsJnuIoHeKIAnzHCkhcDnk4FHM+4WRWtNM06azglWyRFeNWCf3QRnFTDSrJeUiMZ9Y3ZT+YNWLZ3ltYpJU2SMgZl/ukjkVLRdhZFdH1Cy5t7g3UY6w3Byfwfr+ea17DUIdQiZo9yuh2yROMMh9CP696oVVuRJbSrqFspM0Q+dR/wAtU7r9e496iUFL1LUmjo65rxr/AMeWkf8AYZsv/Ry10UMyXEEc0Tbo5FDKR3B5Fc741/48tI/7DNl/6OWuY2OlooooAKKKKACua13/AJHPwp/11uv/AESa6Wua13/kc/Cn/XW6/wDRJoA6WuW0uKN7BJnRWklJd2IyWJJ5NdTXM6R/yCrf/d/rW1LZmVTdFryYv+eaf98iobu1gktJVeJCCh/h9qs1HP8A8e8n+4f5VqmQaemSPNpNnLIxZ3gRmJ7kqM1BJd3q+I7azS2zYPaSyyXG0/LKrxhEz05DOf8AgNSaP/yBLD/r2j/9BFMk/tL/AISO22bf7K+yS+d0z52+PZ742+Z7fpXLLdm62JdUkeHSb2WNirpA7KR2IU4rItbWCK1iRIUACj+GtXWP+QJf/wDXtJ/6CaoQ/wCoj/3R/KtqXwmdTcPJi/55p/3yKpapFHHYvMiKskRDowGCpBHIrQqlq/8AyCrj/d/qK1W5m9jpqzbW+lm1/UbJrMxxW8MDpcYP70vvyvT+HaO/8VaVZ1tNqL67qEM8Krp8cUJtpAOXc7/MB57YTt3rjOkb4gJ/saVQSA8kUbY7q0igj8QSKqrbwqoVYowB0AUVZ8Q/8gg/9d4P/RqVFXRT+Exn8QzyYv8Anmn/AHyKrTRpDe2E0aKkguFTcowdpBBH0q5Va7/19h/19x/1qyToqztKu726k1AXlt5Cw3bRW52keZEFUh+euSWHHpWjWdpX9pb9Q/tHbt+1t9l24/1O1ducd87uvNch0EGvgSf2fA4zFJc/Op6NhGYA/iAfwqPyYv8Anmn/AHyKl1z/AI+NM/6+W/8ART02umHwoxl8QzyYv+eaf98io7dFh1+1MShDLFIr7RjcBgjP0/rU9Qx/8h6w/wByX+QpvZkrdG/XNfDv/knHhz/sHQf+gCulrmvh3/yTjw5/2DoP/QBXKdB0tFFFABRRRQBznxB/5Jz4k/7Blx/6LauhT/Vr9BXPfEH/AJJz4k/7Blx/6LauhT/Vr9BQBg3KLN4guTKofyoY1TcM7c7icfXj8qk8mL/nmn/fIpsv/Ievv+ucX/s1S11LZHO92M8mL/nmn/fIqTQQI31CBBtijuBsUdFyik4/Ek/jSUuif8fWp/8AXdf/AEWlKfwsqPxE2t30un2MU0Nmbtnu7eExgH5VeVUL9D90MW/DtWlWfrM2owWcLaZCs05uoEdWGcRGVRI3UdELH8O9aFcxsc3FGkt/qE0iK8n2gpuYZIUAYH0qx5MX/PNP++RUNt/x83//AF9P/IVZrrOcjMELAhooyD1BUVZ8Pk/2QiZJEcssa57KsjAD8AAKiqXw/wD8gs/9fE//AKNaoqfCVD4h2qX0tnc6VHHZm4F1eeS7gH9wvlyNv6eqhe33q0qz9Tm1GK50tbGFZIpLvZdkj/Vw+XIdw567wg79a0K5zY5O3ghuYzcTxJLLIxZndQT1Pr2qb7Faf8+sP/fsU2w/48o/x/mas1zRSsjnRVlsbUxNiCNTjIZVAIPqCOldBp0rz6ZaSyHLvCjMfUlQTWO/+rb6GtXSf+QNY/8AXvH/AOgitIK0tC4bkTXd6PEcVmLbNg1o8rXG08Sh1ATPTkFj+FWr6RobC5lQ4dImZT7gGqrf2l/wkce3b/ZX2R9/TPnb12++Nu72qfU/+QVef9cH/wDQTWktmaPY5+GytjChaCN2IBZnUEsfUk1J9itP+fWH/v2Kkh/1Ef8Auj+VPrBRVjCxRu4Iba3a4giSKWL50dFAII+ldbXLaj/yDp/9yupq6ejZpT6nNaJ/yO3ir/etf/RVdLXNaJ/yO3ir/etf/RVdLWpoFFFFABRRRQBzXgv/AI9tZ/7DN5/6MNdLXNeC/wDj21n/ALDN5/6MNdHI6xRtI7BUUFmJ7AUAQ3l7BYQebO2BnCqBlmPYAdzWQ91qV7zvFjCeioA0pHuTkD6AH61FAXv5/wC0ZwQWGIIz/wAs0/8Aij1P5dqt10Rgo77mLk3sVDYI3MlzeSH1a6kH6AgUf2bB/wA9Lr/wKl/+Kq3RV3ZJg6Bb6XeaNFPp8V1b2zPIFj+0yLgh2DHAbHJBP41p/wBmwf8APS6/8Cpf/iqh0O9OoaRFctafZCzOPJ/u4dhnoOuM9O9aNCbCxU/s2D/npdf+BUv/AMVR/ZsH/PS6/wDAqX/4qrdFF2Fip/ZsH/PS6/8AAqX/AOKqtqFhYR6bdPdm6a2WFzKv2qU5TByPvema1Kq6lNFbaVeTzxCWKKB3eMgEOoUkjn1FF2Firp9hYSabavaG6W2aFDEv2qUYTAwPvemKs/2bB/z0uv8AwKl/+Kp2nTRXGmWk0MQiikhR0jAACKVBA49Ks0XYWKn9mwf89Lr/AMCpf/iqP7Ng/wCel1/4FS//ABVW6KLsLFT+zYP+el1/4FS//FVma/b6XZ6NLPqEV1cWyvGGj+0yNkl1C8FscEg/hW9Wdrl6dP0mW5W0+1lXjXyfXc6rnoemc9O1DbCxN/ZsH/PS6/8AAqX/AOKoFiEOYbq9iPqLl2/RiRVuii4EUd/qFlzP/psA6sqhZV98DhvwwfrWzbXMN5brPbyB426MKy6q+Z/ZV59sTi2lYC5TsM8CT6jv7fSolBPbcqM2tzo65rxD/wAjX4Q/6/p//SWaulrmvEP/ACNfhD/r+n/9JZq5zY6WiiigAooooAK5qX/kp1r/ANgab/0dHXS1zUv/ACU61/7A03/o6OgDc1BmTTLp1JDLC5BHY4NY9iix2FuiKFURrgD6Vr6n/wAgq8/64P8A+gmsq0/484P+ua/yrel8JlU3Jqo6wP8AiUXLfxIhdT6Ecg/nV6qOsf8AIGvP+uTfyrWO6M3sdPWda3d7LrmoWs1tss4Y4Wt5tp/eM27eM9DjC9PWtGs+1/tL+29Q+0bf7O2Q/ZMYzuw3mZ7/AN3rXGdIzxD/AMgScdmKKfcF1BH5GoAAAABgDoBU3iH/AJAsv+/H/wCjFqKuin8JjP4gqreDE1i44dbqMA9xk4P6E1aqre/fs/8Ar7i/9CFWiTo6ztMu725udTS8tvJjgu/Ktm2kebF5aNv56/Mzjj+7WjWfpn9pfaNT/tDb5X2s/Y8Y/wBT5adcd9+/rz+lch0EOvfNb2sZ5SS4AdezAKxwfxAqp5Uf9xfyq3rv3LL/AK+R/wCgPVasX8TMZbjfLj/uL+VFqoi1q1MYCmRXV8DG4Yzz+Ip1JB/yGbL/ALaf+g0uwludBWdocmozaLbSavEsV+QfNRcYBycdCe2K0az9Ch1G30W2i1WZZ75VPmyKchjk47DtjtW5uVdaAk1CyjcbkCSPtPTcNoB/U/nUHlx/3F/Kp9X/AOQpZ/8AXGX+aVFWL+JmMviY3y4/7i/lUmkgRavOiAKrwhmA6EgkZptP03/kNSf9e4/9CpLdAt0J42/5ELxF/wBgy5/9FNWrp/8AyDbX/rin8hWV42/5ELxF/wBgy5/9FNWrp/8AyDbX/rin8hW5sWKKKKAOa8P/API3eLf+vu3/APSaOtTXZWi0W52Eh3AiUjsXIX+tZfh//kbvFv8A192//pNHWj4g/wCQWv8A18Qf+jVqofEiZfCyBEWONY0GFUAAegFOoorpMQooooAoaWdRP23+0FVcXTi3xjmHjaTj8evNX6oaXb3tv9s+23Am8y6eSDBJ2RHG1eR25q/SAKKKKYBRRRQBQ0We+utHtptSgWC8dSZY1GApyfc9sd6v1Q0VdRTR7ZdWdXvwv75lxgnJ9AB0xV+kAUUUUwCqmpm8Gk3h08Br0QP9nBxgybTt68dcdat1U1SG6uNJvYLKYQ3ckDpBKTgI5UhTkehwaQE9v5ptovPAE2wb8f3sc/rUlR26yJbRJK26RUAdvU45NSUwCiiigB/h9ttnPbdre4eNfZThgPyYCqHjX/jy0j/sM2X/AKOWr2h/67U/+vkf+ikqj41/48tI/wCwzZf+jlrnqfEzaHwnS0UUVBQUUUUAFc1rv/I5+FP+ut1/6JNdLXNa7/yOfhT/AK63X/ok0AdLXJ2t1Fp9utpdsYZoiVIZTzzwQe4NdZRVwnykyjc5n+1rD/n4X8jTJdTtZIXSF2mlZSFjjQksfTpXU0VftV2I9m+5W0+F7bTbWB8b4oURseoAFV5LS9bxHb3i3OLBLSWKS33H5pWeMq+OnAVx/wACrRrLksY28U22oG8Cyx2U0Ite7hnjYv17bAOn8VYt31NS3qED3Om3UEeN8sLoufUggVz8WqWscSJM7QyqoDRyIQVPp0rqaK0hPlViJRucz/a1h/z8L+RqG6uotRt2tLRjNNKQoCqcDnkk9gK6yir9suiJ9n5hWfbR6iuu6hJPKrae8UItYxjKuN/mE8d8p37VoVnWtjcQ67qN6920kFxHCsduScRFN+446fNuH/fNYGoa5DJPpMqwoXdXjk2r1IV1Yge+Aayv7Wse84B9GUgj8MV01FaQqcqtYiULu5zP9rWH/Pwv5GkSdNRvbOO13SeXMJZGCnaijPU/piunoqnVXRC9n5hWdpVpe2r6gb2588TXbywDcT5cRCgJz0wQfzrRrL0axjsn1Ix3guftF68zAf8ALIkKNnU9Me3WsTQbrqOEs7lUZ0t598gQZIUoy5wOuCwrP/taw/5+F/I101FaxqWVmiJQu7nM/wBrWH/Pwv5GpbCQX2sQTW4ZoYI33ybSFy2AACep610NFN1U1ZIShruFc18O/wDknHhz/sHQf+gCulrmvh3/AMk48Of9g6D/ANAFYmh0tFFFABRRRQBznxB/5Jz4k/7Blx/6LauhT/Vr9BXPfEH/AJJz4k/7Blx/6LauhT/Vr9BQBgag4sdZmmnDLDPGgSTaSuVzkEjoeRUX9rWH/Pwv5GumorZVUlZozcNdzmf7WsP+fhfyNaGhI7LeXJRkSeYNGHGCVCKucHpkg1rUUpVLqyQ4ws7mfrMeoy2kK6XKscwuoGkLY5hEqmUcg8lNw/HtWhWdrNjcX9pBFbXbWrpd28zOCRuRJVdk4/vAFfxrRrIs5mSdNOvryO63RiSYyxuVO11IHQ+o54o/taw/5+F/I101FbKquqM/Z+ZzP9rWPacE+gUkn9K1dDhkh0pBKhjd3kk2N1UM5YA++CK0aKmdTmVkhxhZ3M7U4tRkutLNhKqQpd7rwHHzw+XIMDjrvMZ7dK0aztTsbi7utKkgu2gS1u/OmQEjzk8qRdhx15dW5/u1o1mWcnbzw2sZt7iVIpY2KsrsFPU889ql+3Wn/P1B/wB/BXStGjnLIrfUZpPJi/55J/3yKyVNrS5n7NnMyX1r5bbbiN2IwFRgxJ9AB1roNOieDTLSGQYdIUVh6EKAamWKNTlUUH1Ap9VGLTuxxjYzmtL0+I47wXOLBbRomt9x5lLqQ+OnABH41avY2msLmJBl3iZVHuQapNYxnxTHqH2wCRbJ4fsvqC6tv69sY6d61Ktq5ZysN9aiFFeeNHUAMjsFZT6EGn/brT/n6g/7+CulaKNjlkUn1IpPJi/55J/3yKy5H3M+R9zlbqeG6t2t7eVJZpfkREYMST9O1dbTVjRDlUVT7DFOqoxtqyoxsc1on/I7eKv961/9FV0tc1on/I7eKv8Aetf/AEVXS1ZQUUUUAFFFFAHNeC/+PbWf+wzef+jDWh4gbOnpbf8APzMkR/3c5Yf98gis/wAF/wDHtrP/AGGbz/0Yava79/Tf+vv/ANpyVdP4kTP4RtFFFdBiFFFFAFDRp7650qKXUoFgu2Zw8ajAADEL3PUAH8av1Q0ZdRTSol1V1e9BfzGXGCNx29AB93FX6QBRRRTAKr388ttp1zPBEZpoondIwCS7AEgcepqxVe/a5XTrprNQ10InMKnGC+DtHPvikAtlNJcWFvNNEYpZIld4yMFGIBI59KnqCyM7WNubpQtwYlMoHQPjn9c1PTAKKKKACqGsz31tpckumwLPdB4wsbDIILqGPUdFJP4VfqhrK6i+mOulOqXm+PazYxt3rv6gj7u6kBfooopgFNljWaJ4nGUdSrD1Bp1FAFnQ5nn0a3Mh3SIDExPcqSpP6VleIf8Aka/CH/X9P/6SzVoeH/8AkHSf9fM//oxqz/EP/I1+EP8Ar+n/APSWauaekmbR+FHS0UUVJQUUUUAFc1L/AMlOtf8AsDTf+jo66Wual/5Kda/9gab/ANHR0Ab93CbizngBAMkbICe2Riudgu/IgjhuLa7jlRQrKLd2GR6FQQRXUUVcJ8uhMo3Ob/tGH/nld/8AgJL/APE1DdyNqNrJZ21vcmSYbMvA6KoPUksAOK6qir9r2RPs/MKzrW0vYtb1G5mud9nMkIt4dx/dlQ2846DOR09K0ay7Oxjg8QapeLeCR7hIFa3/AOeO0Ng9f4s+g6ViaE2s20t3pU8UK7pPlZVzjcVYNj8cVk/2jH/FBeK3dTaScfktdJRWkanKrEShd3Ob/tGH/nld/wDgJL/8TSBn1G6tY4ILgKkyyySSQtGqhTn+IDJPTiuloqva9kL2YVnaZaXttcam95c+ck92ZbZdxPlReWi7OenzKx4/vVo1l6RYx2dzqzpeC4NzemZ1H/LE+XGuzqeyg9vvViaC65G7W0EqIziGYSOFGTtwRnH41lf2ha/89h+RrqKKhwbd0yHC7ucv/aFr/wA9h+RqfTz9r1WCSEM0UIYu+0gZIwBnua6GilyPqxKAVm6BYy6bodrZzXhvJIlIa4JPz8k55J9fWtKsrw1aWVj4dsrbTrr7VaRoRFNuB3jJ5yOK0NCPWlaO5tboqxiRXR2UZ252kE47fKaof2ha/wDPYfka6iis3B3umQ4Xdzl/7Qtf+ew/I1c0cGe/mukVvJEQjDkEBjnJxW5RQoO92wULO5heNv8AkQvEX/YMuf8A0U1aun/8g21/64p/IVleNv8AkQvEX/YMuf8A0U1aun/8g21/64p/IVoWWKKKKAOM0b+1f+Fg+Jvs5s/7P+02/n+YG83P2aP7uOPTrV7W/wC2fNfzjYf2X58Hl7A/nZ8xOuflx1/Sjw//AMjd4t/6+7f/ANJo60fEH/IMX/r4g/8ARq1dP4kTL4WZVn/a/wBpuftxsfIyfs/kB92Mn7+eOmOlMtP7d+wXH2w6d9swfI8kP5eccbs89fStOitzEzB/bv8AZDbjp39p5+XAfycZ7/xdM/jRcf27/ZkH2Y6d9vyPO80P5WMHO3HPXHX3rTooA521stZsvM+wXlhcia8kmufPD/u9235EwT056+3Fadx/a39qQfZjZf2fged5gfzc5OduOOmOvvUejW9jbi/+wztMJL2WSbJB2SkjcvQdK06BlBf7W/td9xsv7Mx8uA/nZwOv8PXP4UWn9rfbrj7abL7Hk+R5IfzMZ43Z46ener9FAjMs/wC3Pstz9uOnfacH7P5AfZnHG/PPXHSiP+3P7Jk8w6d/aWfk2h/Jxkdf4umf0rTooAwLGz8Q2HhiztftVnc6rHxNNcNIyOMnnP3ienX3q7e/235Nt9hOn+bj/SPPD7e33Mfj19qTw9Z2+n6BZ2lpdLdW8Ue1J1IIcZPORxWnQMoXX9rf2jb/AGQ2X2Ljz/ND+Z152446etH/ABNv7X62X9mY9H87OP8Avnr+lX6KBFC2/tb+0Z/tZsvsPPk+UH83rxuzx0z0qjcW2sXmh6va6jcWFuZreSOCa33jy8qw3NuPbIPHoa3ao63DaXGg6jDfzGGzktZUnlU4KRlSGYHB6DJ6UDK1jHrcWhGOZ9Pe/UAQsm/yiABgtnnPXp7VI/8Abv8AZCbDp39p5+fcH8nGT0/i6Y/Wr1sqJawrE26MIApPcY4qWgRmXn9ufY7b7CdO+1YH2jzw+zOOduOeuetSXn9rfbbb7EbL7JkfaPPD+ZjPO3HHT171fooAo6P/AGv/AG/feUbH+y/PHmbg/nbvKXp/DjOOvvVDxb/bP2nT/tJsP7N/tey8nyw/nZ89Mbs/Ljr09q39D/1+p/8AXyP/AEUlUfGv/HlpH/YZsv8A0ctYVPiNobHS0UUVBQUUUUAFcf4utrq68TeF4rO+eynM1zidI1kK/uTnhgRzXYVzWu/8jn4U/wCut1/6JNAGlfafqVxHaLa61LaNF/rmW3jfz+nXcPl6Hp6066stQm1O3uINWkt7WPHmWogRhLzzliNw4449K0azptc0+GZ4mlkd0OG8qF5AD6EqCM00m9hNpbh9i1D+2vtf9rSfYsY+w+Qm3OMZ343deetLaWWoQalcXFxq0lzbSZ8q1aBFEXPGGAyeOOaj/wCEh0/1uf8AwEl/+JpR4g03cA0ssYJxukt5EUfUlQBT5Jdhc0e4ljp+p28N2l1rUt28o/cu1vGnkcHoFGG6jr6Vkro7pqsaz+I55NfNlMsE5t4xthMkRY7Au04ZVGT/AHj+HVVlSSab/wAJZbRvEx1Q2MrRyc4EPmR7x1xnds7dqkoJNO1R9HjtU1yVL1Wy16LaMs4yeNhG0dQOB2ovtP1O4s7WK11yW0miAEsy28bmY4HJDDC85PHrWrWWfEGm5IWWWQA43R28jqfoQpBpqLeyE2luPvbLULjULae21aS1t4yDLbrAjiYZyQWIyMjjihrLUDrSXY1aRbIDDWPkJtY4Izvxu64PXtTP+Eh0/wBbn/wEl/8AiafDrmnzzJEssiO5wvmwvGCfQFgBmnyS7C5o9xbayv4tWnuZtWkntHUiO0MCKsZyOQwG49D1PeszR9Iez13XLh9dN7e3UcKshijVrZV8zZwvXO44yOdtdHWVZLpo8R6q1u7HUTHB9rU5wFw/l47f3ulSUFtp2qQ6VPbT65LPdyEmO7NtGpiGBwFA2noTyO9Dadqh0VbQa5KL0Nk332aPcRnONmNvTjp2rTkkSGNpJHVEUZZmOABWb/wkOnHkPcMPVbWUg/jtpqLeyE2luJeadqk+nW0FvrkttcRgebcrbRsZuMHKkYGTzxUl9Y6hcXVrJa6vJaRREGaJYEcTcjglhle449aZ/wAJDp/rc/8AgJL/APE1Na6vZXk4hilYSkZCSRNGSPbcBn8KbhJdBcy7jJbLUH1iK6j1aSOzUYeyECFXPPO8jcOo6HtWZoOmrb6zq11Za1JPaS3chnszEmI5yF3fNjdwAOOnNdJWVokmmyNqf9nRNGVvpFud2fmmwu4jJ6dKkofY2OoW9xdPdavJdxyn9zG0EaCHk9Coy3br6Uy007VINOuYLjXJbm5kz5Vy1tGpi4wMKBg8881oXNzDaQNNcSCONerGqH/CQ6f63P8A4CS//E1SjJ7ITkluINO1QaKbQ65Kb0tkX32aPcBnONmNvTjpRdadqk2l29tBrktvdRkeZdi2jYy8HqpG0djx6Uv/AAkOn+tz/wCAkv8A8TVmz1O0v2ZLeUl0GWR0ZGA9cMAcUOElq0Ckn1IL6w1G4e0NrrMloIv9cFgjfz+nXcPl6Hp61mfDv/knHhz/ALB0H/oArpa5r4d/8k48Of8AYOg/9AFSM6WiiigAooooA5z4g/8AJOfEn/YMuP8A0W1XLPTtUi0aW2m1yWa7k5juzbxqYhgcBQNp6HqO9U/iD/yTnxJ/2DLj/wBFtXQp/q1+goAzG07VDoqWi65Kt6Gy199mj3MMk42Y29MDp2ovdO1S40+2gttcltbiMAS3C20bmY4wSVIwMnnirV5qVpYMi3EpDvyqIjOxHrhQTiq3/CQ6f63P/gJL/wDE1ShJ6pCckuo++stQuLy1ltdXktIYiDLCsCOJhkcEsMjjjj1okstQbWY7tNWkSyUYayECFXODzvxuHY9e1M/4SHT/AFuf/ASX/wCJq/bXUN5As1vIJI26Efy9jQ4yW6BST2Of1nRpLq6jm1HxA0dh9stpYbVoo0USpMjIofG4lmULjPO76Vp2NjqNvNdvdaxJdpKf3KNBGnkdehUfN1HX0pmvrprWdqNUdlh+3WxiK55mEq+UOO2/b7etatSMyrTTtUh024t7jXJbi5kz5V01tGpi44woGDzzzR/Z2qf2L9k/tyX7buz9u+zR7sZzjZjb046VPdavZWc3kyysZQMlI42kIHvtBx+NQ/8ACQ6f63P/AICS/wDxNUoSeyFzLuJdadqk2mW9vBrktvdR48y6W2jYy8c5UjaOeePSn31jqNxLaNa6xJaLF/rlWCN/P6ddw+XoenrTf+Eh04dXuAPU2soA/wDHa0YpUmiWWJ1eNxlWU5BFJxa3QJp7GBrWkm88Q6JeT639mhtrnfBZGNMTTeXKDhj82djNwOyk471pR2WoLrMl2+rSPZMuFsjAgVTgc78bjzk9e9Rauumtf6L9vdlnF6TZAZ+abyZcg47bPM68ceuK1aQzOsbLULe9uprrVpLqCUkxQNAiCEZzgFRk8cc+lR2Wnapb2F1Dc65LdXEoIiuGto0MJxgEKowcHnmrM+q2FtKYpbqNZB1XOSPrUf8Abmmf8/kf61PPHuLmREmnaouivaNrkrXrNlb77NGGUZBxsxtPGR070XGnapLpMFtDrksN2jAyXgto2aQc8FSNo6joO1TprOmu4VbyLJOBk4/nV6mmnsCaexzF/pZufE1jLba9LaarFZMkirAj+dDvXJIYYX5gOnrWtdWWoTapb3MGrSW9rGB5loIEYS8nOWI3DsOPSo2k03/hLEjMTf2p9hZlk5x5PmLkdcZ3Y7Vq0xmcLLUBrRuzq0hsiMCx8hNoOMZ343deetFpZahBqNzPcatJc20mfKtmgRRFzkYYDJwOOae+s6dG7I15FuU4IBzg/hTf7c0z/n8j/Wp549xcy7kdjp+p29vdJda3LdySj9zI1vGhh4PQKMN26+lJDp2qJo8trJrksl47ZS8NtGGQccbANp6HqO9WYdVsLiURRXUbO3Rc4J+lXKaaewJ3OR8KwXFt4p8Sw3V213Optd07IqF/3Z7LwPTiuurmtE/5HbxV/vWv/oqulpjCiiigAooooA4zwVNqn27W4ms7Uab/AGteEXAuW80t5h48vZjHvu/Crd/PrEl9bLfWFnBbLeH7PJDdtI0g8uTG5TGoXjHQtT/Bf/HtrP8A2Gbz/wBGGr2u/f0z/r7/APaclXT+JEz2Mq0uNbexuHvNOsobtQfIiivGkRzjjcxjBXn0BoW41s6Q0radZDUgfltxeMYiMjrJ5eRxk/d9vetOitzEzLi41tdMgkt9OspL9iPNge8ZI0GDnDiMlucfwjqfSnXs+sR/ZfsNhZz7v+Pjzrto/L6fdxG27v129B68aNFAGDp0Ov6ctnZSJbX0A3G4vJbtxKMuxwEKHdhSvVh+lXxNqv8Aa7RNZWg03Hy3Aum80nH/ADz2Y65H3vf2qPw/Z2+n6Jb21pdLdQIX2zKQQ2XJPTjgkj8K06EMoWk2qvf3CXllaQ2ik+RLFdNI7jPG5CgC8ejGmWc+tSW1y17p9lDOoPkJFeNIshwfvMY12846A1pUUCMyO41s6TJLLp1kuog/u7dbxjEw46yeWCO/8J6VDqD6/L4eYWdpZx6rIrIUN2wjiyCAyv5eWI4ONorZqrqUButLu7dZ/IaWF0Ev/PPKkbvw60AUnk16202wWCzs7u68tRdma7aIK2BkqRG27nPUCrF1NqqahbpaWVpLZtjzppLpkdOedqBCG4/2hViyiNvYW8Jl80xxKhk/v4GM/jU9AFAzar/a4iFlaf2bjm4+1N5ucf8APPZjrx9739qLabVX1KeO5srSKxUHyZo7pnkfkY3IUAXjP8Rq/RQBnWU+syJdG9sLOBl/49xFdtIJOv3sxrt7dN3U+nNC/i8Raj4buIxDa2OqeahiEN67IVV1Y5k8tSMgMMbT+tdBWZr9nb3+jvb3V0trCZYmMrEAArIrAc+pAH40DB7jWxpCSpp1kdSJ+e3N4wiAyeknl5PGD90f1ovLjW47K2ez06ymumA8+KW8aNIzjnawjYtzxyB/StOigRQvJtVjvLZLKytJrZiPPklumjeMZ52qEYNxnqRQ82qjV0iSytG04j57g3TCUHB6R7MHnH8Q61fooAqeGptVaa6imsrRNNE85juFumaVm8w8GPYAB153HoOOeM3UJ9Xl8ZeGV1Kxs7aFb+fyHgu2maQfZZ+WBjXbxjoT1Ppz0Ph//kHSf9fU/wD6Mas/xD/yNfhD/r+n/wDSWasKnxM2h8KOloooqCgooooAK4zV9Pk1H4k2KR6jeWJTSZXL2pQFx50fyncrcfTn3rs65qX/AJKda/8AYGm/9HR0Aal3pct1qdveLqt/bpDjNtCyCKXBz8wKk+3BFL/Zcv8AbX9of2rf+Xtx9i3J5HTGcbd2e/3utaBOBk9Kyf8AhILVuYYLqZO0kcJKt9Ceo96ai3sJtLcltNLlttTuLxtVv7hJs4tpmQxRZOflAUN7ck0yx0iaziu0fWdRujcD5XuGjJh6/c2oPXvnoKb/AG/F/wA+V9/35/8Ar05NetWkRJYrmAMQoeWIhcnoCe341Xs5dhc8e4kGjTQ6RNYtrWpyySMSLuRo/Oj6cKQgXHHdT1NZVr4ftftOpWtt4h1X+0iIPtVwHj84Ab9n/LPbyCe3YV1VZVjJpreIdWS2iZb9Vg+1uc4YFW2Y5xwM9KgoL7RpryytbdNa1O1aAANNA0YebjGX3IR78Acmpb3S5bu/trpNVv7ZYCC1vAyCObBzh9yk89OCOKuzTR28LzSuEjQFmY9AKzP+EggPK2l6ynoRARn86pRb2E5Jbkz6XK2tJqA1W/WNVwbIMnkNwRkjbuzzn73UCi30uWDVp75tVv5o5VIFpIyeTH05UBQ2eO7Hqah/t+L/AJ8r7/vz/wDXqW21q2uLhICk8Mj/AHBNGVDH0B6Z9qbhJdBcy7iWOky2TXZfV9QuvtH3RcNGRD1+5tQevfPQVmaFotpbWWs2dlreoXE814xurqRkM0U3loCAQgX7oQ9D1rpaytGk02S41cafEySLfMt2Tn5pvLTJGT027OmOlQUB0aY6KNP/ALa1MSBs/bQ0fnnnOM7NuO33elF5o011p1taJrWp27w43XMLRiWXAx8+UI9+AK0Z54raFppnCRr1Y1R/t2w/vz/+A0n/AMTSckt2JtLcW+0qW8uLWVNW1C1EBy0cDRhZuQfn3IT27EdTRNpcsusRX41W/jjjGDZoyeS/Xkgruzz2YdBSf27Yf3p//AaT/wCJqxa6ja3rskEpLqMlWQq2PXBAOKSlF7MOZPqQRaXLFrEt+dVv5I3GBZuyeSnTkALuzx3Y9TWX4P0yytNISfStV1C602dM28VyV2wrk/c+QN3/AIieldLWV4aurG98OWNzptsbazkj3RQkAbBk8YHFUMLPRprTTrm0fWtTuXmBC3EzRmSLIx8mEA9+QeaBo0w0VtP/ALa1MyFs/bS0fnjnOAdm3Hb7vSr11dwWcQkuJAik4HBJJ9AByaqf27Yf3p//AAGk/wDialyit2JtLcZc6NNcaVb2S61qcDxEE3UTR+bJgEYYlCvfPAHQU6+0ma9NoU1fUbX7P94W7Rjz+n39yH07Y6ml/t2w/vT/APgNJ/8AE1atb23vUZreTdtOGBBUj6g8ihSi9mCknscz4+0uW48Ma3fLqt/DHFpVwDaRMnkyfu35YFS2eezDoK6bT/8AkG2v/XFP5Csrxt/yIXiL/sGXP/opq1dP/wCQba/9cU/kKoZYooooA5rw/wD8jd4t/wCvu3/9Jo60fEH/ACDF/wCviD/0atZ3h/8A5G7xb/192/8A6TR1o+IP+QYv/XxB/wCjVq6fxImXwsioooroMQooooAzNFbTmW//ALORlAvZRPuzzNn5zz/TitOszRbu1u1vja2otxFeyxSYAG+RThn49a06SBhRRRTAKKKKAMzw8mnx+H7JNKkaSwEf7l2zkrn3ANadZnh2awuPD9jNpkLQ2LxAwxsMFV9Op/nWnSQBRRRTAKoa21kugai2pKWsBaym5Vc5Me07gMc9M9Kv1Q1y5t7Lw/qV1d24uLaG1lklhIBEiBCWXB4OQCOaQFu38v7NF5QxHsGwe2OKkqOBle3idF2qyAhfQY6VJTAKKKKAHaH/AK/U/wDr5H/opKo+Nf8Ajy0j/sM2X/o5avaH/r9T/wCvkf8AopKo+Nf+PLSP+wzZf+jlrnqfEbQ2OloooqCgooooAK5rXf8Akc/Cn/XW6/8ARJrpa5rXf+Rz8Kf9dbr/ANEmgDpa5nSP+QXAe5BJ9ySc101czpH/ACCrf/d/rW1LZmVTdF2orgBrWVWAIKEEH6VLUc//AB7yf7h/lWqINHSCW0axJOSbeMkn/dFVpL62XxZb2BtFN09jLMtzgZVFeMFM9eSyn/gNWNH/AOQJYf8AXtH/AOgio3vbtfEcNiLRjZPaSTNc7ThZA6AJnpyGY46/LXLLdm62JdXJXRb8g4It5CCP901nW6hbaJVAACAAD6Voax/yBL//AK9pP/QTVCH/AFEf+6P5VtS+EznuPqlq/wDyC5z3ABHsQRirtUtX/wCQVcf7v9RWsdzN7HTVlWVzYSeI9Vt4Ldkvoo4DcykDEisH2AHPOMN2HWtWsyzvvP1/U7P7F5X2ZIW+0f8APbcG46fw49T17VxnSJ4h50dx2M0II9QZVzUVS+If+QQf+u8H/o1Kirop/AYz+IKq3X/Hzp7dxdJg/XIq1Va7/wBfYf8AX3H/AFqyToqytEvra9OpfZrRbfyL6SGXAA81xjLnHrkdfStWs/Sr27vft32u0a38m7khiypHmRjGH565yenHFch0FfXOZtMU9Dck4+kb4pKdrn/Hxpn/AF8t/wCinptdMPhRjL4mFQx8a/YkdTHKD9MLU1Qx/wDIesP9yX+QpvZkrdG/XNfDv/knHhz/ALB0H/oArpa5r4d/8k48Of8AYOg/9AFcp0HS0UUUAFFFFAHOfEH/AJJz4k/7Blx/6LauhT/Vr9BXPfEH/knPiT/sGXH/AKLauhT/AFa/QUAYUvPiC9J6iKID2HzVNUUv/Ievv+ucX/s1S11LZGD3Cl0Pi41NRwPtCnHuY0zSUuif8fWp/wDXdf8A0WlKfwscfiQ/X7mwtbS1bUbdp43vraONVAO2VpVEbckcBip/DvWrWZrl9/Z9tayfYvtfmXtvBs/ub5FXf0P3c7vw6jrWnXMbHO23/H3qDdzdNk/QAVZqtbf8fN//ANfT/wAhVmus5wqXw9xpIUdFnmAHoPNaoql8P/8AILP/AF8T/wDo1qip8JUPiE1a5sINQ0WO8t2lmnvClowAPlS+TKxY88fIrjv978a1azNUvvsmoaPD9i+0fa7tovM/598QyPv6H+5t7fe69jp1zmxy9hzaK38TszMfUknmrNVrD/jyj/H+ZqzXPHZHOthkyq8LqwBUqQQa19LZn0iyZiSzQIST3O0Vkv8A6tvoa1dJ/wCQNY/9e8f/AKCKuPxFw3KxvrYeK0sPsi/ajYtMLnAyE3gbM9epB/CrmoMyabdMpIZYXII7HBqub27/AOEjWx+yN9iNoZjc7TgSbwNmenTJ9an1P/kFXn/XB/8A0E1pLZmj2MW2VUtolUAKEGAPpUtMh/1Ef+6P5U+sVsYlXUeLCVv4lG5T6EHINdTXLaj/AMg6f/crqaqG7LhuzmtE/wCR28Vf71r/AOiq6Wua0T/kdvFX+9a/+iq6WtTQKKKKACiiigDmvBf/AB7az/2Gbz/0Yava79/TP+vv/wBpyVR8F/8AHtrP/YZvP/Rhq9rv39M/6+//AGnJV0/iRM9htFFFdBiFFFFAGZ4eTT49Dt10qRpLIbvLZs5PzHPUDvmtOszw9NYXGh20umQtDZtu8uNhgj5jnue+a06SAKKKKYBVTVYrefR72G6l8q2kt5FlkzjYhUgn8BmrdU9WNqNGvjfZ+x/Z5PPxnPl7Tu6c9M0gJLGOKLT7aOB/MhSJVR8/eUAYP5VYqCx8n7BbfZs+R5S+Xn+7gY6+1T0AFFFFMArM8QJp8mkOuqSNHaebCSy5zu8xdnQHq20Vp1ma/NYW+kNJqULTWvnQqUUZO4yqEPUdGKn8KTBGnRRRTAKKKKAJPD//ACDpP+vqf/0Y1Z/iH/ka/CH/AF/T/wDpLNWh4f8A+QdJ/wBfU/8A6Mas/wAQ/wDI1+EP+v6f/wBJZq56nxM2h8KOloooqCgooooAK5qX/kp1r/2Bpv8A0dHXS1zUv/JTrX/sDTf+jo6ANvUuNKvCP+eD/wDoJrKtBiygA6eWv8q1dT/5BV5/1wf/ANBNZVp/x5wf9c1/lW9L4TKpuTVR1j/kDXf/AFyar1UdY/5A15/1yb+Vax3Rm9jp6yrG+tp/EGrWkdosdxbLCZZwBmXcpK5PXjBHPrWrWdaXt3PrOpWk1o0dtbiIwTlSBLuUlsHocEAcetcZ0jPEP/IEm/34/wD0NaiqXxD/AMgWX/fj/wDRi1FXRT+Exn8QVVvfv2X/AF9xf+hCrVVb379n/wBfcX/oQq0SdHWVo19bXk+rLb2i27W980MzAAec4RDvOOvDAc/3a1aztLvbu8l1Fbq0a3W3u2hgJUjzYwqkOM9eWYcccVyHQRa7/q7IdjcjP/fLGq1Wdd+5Zf8AXyP/AEB6rVi/iZjLcKSDjWbIjqRIPw20tJB/yGbL/tp/6DSYludBWb4fvn1LQbO9kszZPNHuNuf+WfPToP5VpVnaFPqF1odnPqsCwX7xgzxKMBW9Op/nW5uVtX51SyB7RSn8cpUVS6v/AMhSz/64y/zSoqxfxMxl8TCn6ZxrUuO9uM+/zGmU/Tf+Q1J/17j/ANCo6oFuhPG3/IheIv8AsGXP/opq1dP/AOQba/8AXFP5Csrxt/yIXiL/ALBlz/6KatXT/wDkG2v/AFxT+QrY2LFFFFAHNeH/APkbvFv/AF92/wD6TR1o+IP+QYv/AF8Qf+jVrO8P/wDI3eLf+vu3/wDSaOtHxB/yDF/6+IP/AEatXT+JEy+FkVFFFdBiFFFFAFDSrye8W7Nxam3MV1JCgII3opwH59av1Q0uTUJEu/7QjWNlupFg245hB+Q8HuKv0gCiiimAUUUUAZugXqajoFleR2gtEmiDC3H/ACz9ug/lWlVDRbq8vdFtLnULf7NeSRhpYcEbG9MHmr9JAFFFFMAqnq91NY6Lf3dvbm5ngt5JY4ACTKyqSF455Ix+NXKp6u97Hot9JpsYkv1t5DbI2MNKFO0HOO+KQFmJi8KOy7WZQSvp7U+mRFzChkGH2jcPfvT6YBRRRQA7Q/8AX6n/ANfI/wDRSVR8a/8AHlpH/YZsv/Ry1e0P/X6n/wBfI/8ARSVR8a/8eWkf9hmy/wDRy1z1PiNobHS0UUVBQUUUUAFc1rv/ACOfhT/rrdf+iTXS1zWu/wDI5+FP+ut1/wCiTQB0tcxAl1p0QtJLG5k8skLJEoZXXPB68fSunoq4T5SZRuc79ql/6B19/wB+f/r0ySa6miaKHTbvzHBVd6BVBPqSeldLRV+18ifZ+ZBZQG1sbe3LbjFEqZ9cDFVXbUv+EjhRY1/sr7I5kfjIm3ptHXONu/titGs5rfUD4jiuVuFGmraPG8GTkyl1KtjHQKGHXv0rF6mhavbc3dhcWwbaZYmjz6ZGKwY5rqGNY5tNuxIgAbYgZSfYg8iuloq4z5dCZRuc79ql/wCgdff9+f8A69RTpdajF9kjsbiPzCA0kqhVRc8nrz9K6eir9r5E+z8wrOtLm/k1vUbee3VLKFYjbSgHMhYNvyc84IHYda0azrRdSGtaibllOnlYvsijGQcHzM4567etYmg/V7WW80ySKEAyhkdQTgEqwbH44xWX9qnH3tNvge48oH+RroqK0jU5VYiULu5zv2qX/oHX3/fn/wCvRHHc315agWk8MUMoleSYBemcADOSSa6Kim6vkL2YVnaS2pN9u/tKNUxdyC2245h42E4PXr15rRrO0m31C3F79vuFm8y7kkg2knZEcbVPA5HP+NZGg3WbaaaO2mt4/Ne3m8wxggFgVZTjPf5s/hVD7VN/0Dr7/vz/APXroqK0jUsrWIcLu5zv2qX/AKB19/35/wDr1LYQ3FzqkVy9tLBDAjAGUAM7NjoPQAda3aKbq3VrCUNQrmvh3/yTjw5/2DoP/QBXS1zXw7/5Jx4c/wCwdB/6AKyNDpaKKKACiiigDnPiD/yTnxJ/2DLj/wBFtXQp/q1+grnviD/yTnxJ/wBgy4/9FtXQp/q1+goAxL+G4ttUkuktpJ4Zo0U+UAWRlz1HoQf0qL7VL/0Dr7/vz/8AXroqK1VWytYhw1Od+1Tf9A6+/wC/P/16v6NbTxJcz3EZie4l3iMkEqoUKM478Z/GtOilKpdWsChZ3M7WLm/tbe2bT7dZ5Hu4I5VIJ2xNIBI3BHRST+HetGs7WF1JoLb+y3VZBdwmbdjmHePMHPfbn39K0azLOeljubG9us2k00U0plR4QG6gZBGcg5FJ9ql/6B19/wB+f/r10VFaqr5GfsznftU3bTb4nsPKx/M1qaRay2mmpHOAJWZ5GUHIUsxbGfbOKvUUpVOZWHGFncz9Rub+C+0qO0t1lgnuWS7cgnyo/KkYMOePnVBzn71aFZ+oLqRv9KNi6i2FyxvgcZMXlPgDP/TTy+nP4ZrQrMs5eMSWKm3mgn3IxAZIWdWGeCCAad9qX/njdf8AgNJ/8TXTUVl7NrZmfIcw07OhSK2unkIwq+Q65P1IAFdBYwNbWFtAxBaKJUJHqABU9FVGNncqMbGcW1L/AISMII1/sr7ISX4z528YHXONue2Kt3cJuLOeFSAZI2QE+4xVT7PqH/CRi5+0L/Zv2Qx+Rk583fndjHTbx1/CtGrepRy8czRRrHNbXKSKAGAgdhn2IBBp32pf+eN1/wCA0n/xNdNRWfI+5nyeZy0wkvojbQQXG6T5dzwsqqO5JIFdTRRVRjYqMbHNaJ/yO3ir/etf/RVdLXNaJ/yO3ir/AHrX/wBFV0tUUFFFFABRRRQBzXgv/j21n/sM3n/ow1e137+mf9ff/tOSqPgv/j21n/sM3n/ow1e137+mf9ff/tOSrp/EiZ7DaKKK6DEKKKKAM3Qb1NQ0S2uo7QWiSBsQD+HDEeg9M9O9aVUNFury80i3uNQt/s104PmRYI28kDg89MVfpIAooopgFU9WngttGvp7qHzreK3keWLAO9ApJHPqMirlVdTuHtNJvLmOA3DwwPIsI/5aEKSF/HpSAfZSRy2FvJCnlxPErIn90EDAqeobWRprOCVo/LZ41Yp/dJHSpqYBRRRQAVm67epp+lNcSWgu1E0KeUe5aVVB6HoTu/CtKqGsXV5Z6cZrC3+0XHmxKI8E/K0iq549FLH8KTAv0UUUwCiiigCTw/8A8g6T/r6n/wDRjVn+If8Aka/CH/X9P/6SzVoeH/8AkHSf9fU//oxqz/EP/I1+EP8Ar+n/APSWauep8TNofCjpaKKKgoKKKKACual/5Kda/wDYGm/9HR10tc1L/wAlOtf+wNN/6OjoA6G4hFxbSwE4EiFCR2yMVgRR6nbxJC+mSStGoXfFLHtbHcbmB/Sujoq4zcSZRTOf36h/0CLn/v5F/wDF1FPa6hqELWh0+S3SX5XllkQhV74CsSTiuloqvbPsT7NBWdaNqR1nUluo1Fgoi+yMMZb5Tvzg56461o1nWdvqEes6lPcXCvZS+V9liBOY8KQ+RjjJx3NZGhLqlo99ps1vGwWRsFS3TIIIz7ZFZW7URw2kT577ZYiPwywroaKuNRxViZQT1Of36h/0CLn/AL+Rf/F0Ja317dW4ls3toYpVlZpHQk7eQAFJ74610FFV7V9ifZoKztLbUml1H+0Y1RBdsLTGPmh2rgnB67t3XmtGs7S7fULeTUTfXCzLLdtJbBST5cRVQFPA5yGPfr1rI0F1i2muLaJoE3vDKJNmcFhggge/NZfmT/8APhd/9+//AK9dJRUOF3chwu7nN+ZP/wA+N3/37qewt7ibUYp3t5IYoQ3MgwWJGMAVu0UuTuwUArO0JNSj0OzTWJFk1ERgXDrjBb8ABWjWdoVldaboVnZ3t213cwxBJLhiSZD65PNaFker287TW91DE0oiDo6L97DY5Hr939aoeZP/AM+N3/37rpKKhwu73IcLu5zfmT/8+N3/AN+6uaVbzm8lu5YWhQxiNFf7x5yTjtWxRQoa3uChZ3MLxt/yIXiL/sGXP/opq1dP/wCQba/9cU/kKyvG3/IheIv+wZc/+imrV0//AJBtr/1xT+QqyyxRRRQBxmjWNzL8QfE11HqlzDBFc24e0RIzHL/o0fLEoWH4MOlXtb0+8jle8fWLuS2eeALYtHEIkPmJyGCB+x6sev0o8P8A/I3eLf8Ar7t//SaOtHxB/wAgxf8Ar4g/9GrV0/iRMvhZlWdhd21zcyz6vdXaSkmOKWOJVh5PClUBPp8xPSmWmm31vYXFvNrl5czSg+XcyRQh4eMfKFQKcdfmBrTorcxMwabfDSGszrl4bonIvjFD5gGc427NnTj7vf1ouNNv5tMgtotcu4LiMgvdpFCXl4PBDIVHUHgDpWnRRYDn7fStcdf9I1+4hKXUrjyY4m82IsNituQ4wAfu46n2rRuLG8l1SC6i1a4gt4wA9okcRSQ5PJYqWHUdCOlJpNlJZR3ayXZuTLdyzKT/AABmyE6np0rQoGUFsbwau12dWuGtSMCyMUflqcAZ3bd/Xn73ei0sby3vriefVri6hlJMdvJFGqw85wCqhjgcck1fooEZlnp1/b2tzFPrdzdSygiOaSGJWhODyAqAH15B6UR6dqCaTJaNrdy90xyt6YYg6DI4ChdvYjkd606KAMGxsdduPC9jBfatLa6sFDXNxFFCxJ5yuCpXHI6DtVy90+/uIbVLfWbi1eIfvXSGJjN06hlIHQ9MdafolreWWiWdtqFz9pvIogs02Sd7dzk81foGULqyvZtQt7iHVZre3jx5lskUbLLz3ZlLD04Io+xXv9r/AGv+1ZvsmMfYvKj2ZxjO7bu689av0UCKFtZXsOoz3E2qzXFvJny7Voo1WLnswAY+nJPWqU2m61Ho2qxQ61Jc308DizeaKOMQSbW2n5V5GSOuelblUdZtHv8AQtQs4rk2slxbSRLcD/lkWUgN1HTOevagZBa6fqcWjyW0+tTS3j8rdmCMNH04Chdp6HqO9OfT9RbSEtV1qZbtTlrzyIyzDJ427dvTA6dq0Y1KRIpbcQoGfWnUCMy80/UZ7O2it9amtZowBLOsEbGY4xkhhgc88VJeWd9Pe201vqkltBGQZYFhRhKM5wSwyOOOKv0UAUdHs75tfvrpNUkSzWcB7IQoVc+UvO8jcOoPB7VQ8W2V/Fc6fczatJNaPq9kI7MwIqxnz05DAbj0PU9639D/ANfqf/XyP/RSVR8a/wDHlpH/AGGbL/0ctYVPiNobHS0UUVBQUUUUAFcf4u0631XxN4XtLkziJprkkwXDwtxCTwyEMPzrsK5rXf8Akc/Cn/XW6/8ARJoA0r7QLHUY7SOdr0LajEXk300RPT7xRwX6D72e/qafdaLaXmp2+oSvdie3xsEd5LHGcHPzRqwVuvcGtCsl9dUyOttZXNyisV8xNiqSODjcwz9apRcthNpbk39i2n9tf2tvu/tONu37ZL5WMY/1W7Z0/wBnrz1otNFtLLUri/ie8M1xnest5LJGMnPyxsxVf+AgVX/tub/oE3f/AH3F/wDF0HXzGN8+m3cUQ5Z8o20epCsT+Qp+zkLniSWOgWenw3UUEt8y3QxIZr6aUjgj5S7kp1P3cdvSsuPwhaQ6hsTUtQ+zPaSRNbvfztKSzo28SmTeuNmMD+8fU56dWV0V1IKsMgjuKy3tbA+LIrs3JGorYvGsGRgxGRSWxjPDBR171BQSeH7OXR49LabUBbxtuV1v51mPJPMofeevc+npRfeH7TULO1tZZ9QSO2ACGC/midsAD5mVgX6fxE1qO6ojO5CqoySewrIGvmQb4NNu5Yjyr5Rdw9cMwP5iqUXLYTkluWL3Rra+1C2vZZr1JbcgosN5LFG2Dn5kVgr/APAgeOKRtGt21pNVM98J1G0Ri8lEJ4I5i3bD19OvPWoP7bm/6BN3/wB9xf8AxdOTXVEiLc2VzbIzBRI+xlBPAztY4+tP2chc8Sa20a3tdWn1FJ75pp1KtHJeSvEMkH5Y2Yop47Ad/U1R0nw9Lpup6vK2oXs9neiPyopryWUwkBt+0sx25LD7uOg9BW9WXY2MEHiDVbuO8Es1ysIkgBGYdqkDvnnOefSoKEttAtrXSp9OS61JopyS0kuoTPKuQB8sjMWXp2I7+tDaBbNoq6V9r1MQq24SjUJhP1zzLu3kc9M9OOlaFzcxWltJcTNtjQZJxmsz+3JTyuk3hHbLRj9N9UoN7Ccktx95oFte6dbWUl3qccduAFeDUJo5GwMfO6sGf8SeeakvtGgv7q1uJLm/ja2IKrBeyxI/IPzqrAP0/iz3qD+25v8AoE3f/fcX/wAXUttrKTXKQT2s9s8nEZk2lWOM4ypPPXrTdOSFzofLo8M2sRambm/WWNdoiS8kWE9esQbYTz3Hp6Vn6L4ej0/VdQ1E6peXUtxcyv5RuXMMQYg7PL3FcjHXANdBWVodrYWy6h9guTOJb6WSfJB2Sk/MvA7VBQ+x0WGwuLqaO61CVrk5Zbi8klVOSfkViQnXtjt6Uy00CCy065sUvNTkS4zukmvpZJVyMfK7MWX8DVy9vYrCDzZdxywVUQZZ2PQAVQ/tyb/oE3f/AH3F/wDF1ShJ6olyS3HjQIBop0v7bqflFt3nG+l8/rn/AFu7dj8enFF1oMF3pdvYNe6nHHAQRLFfSpK2AR80gO5uvc0z+25v+gTd/wDfcX/xdWLLVY7uc27wTW84XeElx8w7kEEg4yPzpuEkrgpJjb7RYb97RnvNQi+zfdFveSRB+n39pG/p3z1PrWZ8O/8AknHhz/sHQf8AoArpa5r4d/8AJOPDn/YOg/8AQBUFHS0UUUAFFFFAHOfEH/knPiT/ALBlx/6Larln4esLXRpdNia9+zz/ADOXv53kyQB8sjOXXoOAR39TVP4g/wDJOfEn/YMuP/RbV0Kf6tfoKAMxvD1g2ippJe++yo24EX84lzknmXfvPJ7t7dKL3w9Y39hbWU0l8IbYARmK/nic4GPmdXDP/wACJ9atXeowWkixMJJJWG4JGm449T6CoP7aj/59Lz/v1/8AXrGWIpxdm9S1Tm1dIW+0W01C8tbqd7xZLYgxiG8liQ4IPzKjBX6fxA0smi2susx6q0l4LiMbVVbyURdCOYg2w9e4/lTf7aj/AOfS8/79f/Xq5a3UV5D5sLErkggjBUjqCD0NOFanN2i9QlCUVdoxNR8MtLqCahp9/e2901zDJMGvpjE0SurOgj3bBuC44A6n1NX7HQ7XT5ruWGW+Zro5kE17NKB1+4HYhOv8OO3oKbrtjBf29mlxdi1WK+t5kYkDe6SBlTk/xEY/GtStSDKtPD9nZabcWEU+oNDcZ3tLqE0kgyMfLIzll/Aij/hH7T+xf7K+0aj9n3bvM/tCbzuuf9bv3/r046VPcarBbztCEmlkXG4RJu256ZNR/wBtR/8APpef9+v/AK9YvEUk7ORapzavYZdeH7S80y3sJLjUUigxseLUJo5TgY+aRWDN17k0++0S21CW0kluL+M2v3BBeyxBun3wrAP0/iz39TR/bUQ5NreAdz5NX4Zo7iFJonDxuMqw71UK0Ju0WKUJR3RjanoM154i0rVbe/u4VtpD9pgW6lWKaPY4A8sHYTvZTkjouKuR6NBHrMmqC4vjM42mJryUwjgDiItsB46gevrUeqWMF1quizy3awyWl08sMRI/fsYZEKjnsGLcZ+7WpWhJnWWjQWF7dXcdxfyPcklknvZZUXJz8iMxVOv8IHHFR2WgW1hYXVnHd6nIlyCrPPqE0si5GPkdmLIf90jnmoJfEsfnSJa2F3dIjFDLHsVSQcEDcwJwe/Sm/wDCSS/9AW+/77h/+LqedGvsKnYspoFsmivpYu9TMLtuMrX8xnHIPEu7eBx2PTI70XGgW9zpMGnNeamkULBlljv5kmbGfvSBtzDnufT0qqfE4jBefSr6KIctJ+7baPXCuT+QrdjdJY1kjYMjAMrDoQe9NNPYmVOUPiRz174Ziv8AX7W5k1a/jS3tBEttBeSRM5DZ3uysC3pyDmtO60eG71S31B7q/SSAALFFdyJE2CT80YO1uvcVF9lsP+Es+1/aT/aP2Hy/IyMeV5md2MZ+9x1rUZlRGdiFVRkk9AKZBnjR4RrR1T7Vf+aV2+SbuTyOmP8AVZ25/DrzRZ6PDZajc3qXV/I9xndHNdySRLk5+VGJVfwFUR4nEo32+l3s0J5WQeWoYeoDMDj6il/4SSX/AKAt9/33D/8AF1POjb2FTsWrHQoLC3uoUvNSlFyMM1xeySsnBHyFiSnXtjtSQ6DBDo8umi91No5W3GZ76Rpl6fdkJ3Acdj6+tQJ4mjEqLdafd2sbsF82TYygngZ2sSOe+MVuU009iJQlD4jkfCtmmn+KfEtqks8qxm1Ae4laVz+7J5ZiSfxrrq5rRP8AkdvFX+9a/wDoqulpkBRRRQAUUUUAcZ4KttQF9rdwdQU2J1a8AtPs4yG8w/Nvzn8MVbv7TVIL62kvNUS5gkvCYIlthGYh5cnBbJ3cYHbpT/Bf/HtrP/YZvP8A0Yava79/TP8Ar7/9pyVdP4kTPYyrSz1aKxuIrrVo7i5cHyZxaBBEccZXcd3PPUULZ6sNIa3OrRm/Jyt39kG0DI42bvTI69606K3MTMuLPVpNMghg1aKK8Ugy3JtAwkGDkBNwx279qde2uqTfZfsepxW3l/6/daiTzenT5ht6H16+1aNFAGDptt4jkjsJr7UYYmVf9Kt/sysZDuPRwwA429j0q+LbUhq7TnUYzYEYFr9m+YHHXzN3rk9KNFtbyy0i3t9QuftN0gIklyTuOSep56Yq/QMoWltqUV/cS3WoxT2rk+TAttsMfPGW3Hdxx0FMs7TV4ra5S71WG4mcHyZEtPLERweo3HdzjuOlaVFAjMjtNYXSZIJNVge/Jyl0LPCqOODHv579+9QanDr0fhqQWN/C+rRRs4k+y/LMQCQoTf8ALk4GcmtqquppdyaVeJYOEvGgcQOeiybTtP54oGU5bfWriwsfI1GC0uVRTcl7TzA7YGcDeNvOfWp7q31OTULeW11CGC0THnQPbb2k55w+4beOOhq1bCVbWETkNMEUOR3bHP61LQIoG31P+1xONQh/s/GDa/Zvnzjr5m715+77UW1vqcepTy3OoQTWTA+Vbra7Gj5GMvvO7jPYVfooAzrK21aJLoXmpW9wz/6gx2hj8rr975zu7enT3qjeW3iWLw9NFb6jbT6s00flTraiNEj3puyjOc4Xeeoz2wa36oaxa3l5p3k2Fz9nn86F/MyR8qyKzjj1UMPxosMje11k6QkKaparqAOWujZEowyePL8zjjA+92ovLXWZLK2js9UtYLpABPLJZGRZDjkhfMG3nnqa06KLCKF5b6pLeWz2eoW8FshHnxSWpkaQZ5CtvG3jjoaHg1Q6ukyX9uunAfPbG1JkY4PIk34HOD909Kv0UAVPDUGqLNdTTX9s+nNPOI7ZbUrIjeYeTJvII68bR19uc3ULfVofGXhltR1C1uonv5zAkNoYTGPss/DEyNu4x2HT346Hw/8A8g6T/r6n/wDRjVn+If8Aka/CH/X9P/6SzVhU+Jm0PhR0tFFFQUFFFFABXGavo+n6x8SbGLULVLhItJlkQNn5WE0fPH1rs65qX/kp1r/2Bpv/AEdHQBqXehaZfanb6jc2iSXltjyZSTlMHIxz6ml/sLTP7a/tj7In9obdvn5OcYx646VfJCgkkADkk1mDWvMG6CwuZYz91xsUMPUAsDis51YQ+JlRhKWw+00LTLHU7jUba0SO8uc+bKCcvk5PfHUUyx8O6TpsV3FZ2SRJdjE4DE7+vqfc/nR/a8v/AEDLr/vuP/4qlGshWX7RZ3ECEgeY20qCfXDHH1qPrNLv+D/yK9lPsNg8N6RbaRNpUNkiWMzFpIQzYY8d857Csy28GaSjahZybZtPlaJkst7YtyoPfdnkkntXUVlWFrYRa/q9xb3JkvJjD9piJBEeEwvGOMjmtzML7w3pGo2VrZ3dmstvagLChdhsAGB0PoB1qW90PTdRv7a+u7YSXNqQ0L72Gwg5HAODz61cnnjtoHmlbbGgyxrP/tiQ8rpt0QehJjH6FqynWhB2k/1/IuMJS2JH0PTX1pNYa2Bv0Xas29uBgjpnHQntSW+h6baatPqkFtsvZ1KyS72O4HB6E47DtTP7Xl/6Bl1/33H/APFVJDqyyTpDNbTW7SHCGTaQx9MqTg/WpWJpt2v+D/yG6U10G2Ph/S9Na7a0tfLN3/r/AN4x39fU8fePSs3Q/CmnaZp2racjiezvLt5WiDN+7BVRszuJ425zx1rpKytFtbG2k1Q2NyZmmvnkuASD5cpVQV4HGAF/OtzMD4b0k6KNINqfsAbcIvNfrnP3s56+9F54b0m/0620+5tS9rbYESCVxtwMDkHJ49TVnUtRj0y1E0iPIzMEjjTq7HoBn8fyrK/4SDUP+gOv/gUP/ialyS3NI0pyV0jQvtB03Uri1uLu3MktqcwsJXXbyD0BGeg60s2h6dPrEWqyQE3sS7Uk81wAOf4c4PU9qzv+Eg1D/oDr/wCBQ/8AiatafrhurwWlzaNazMpaP5w6uB1AI7jPTFCmmN0JpXaJotD06DWJdWjgYXso2vJ5rkEcfw52joO1ZnhDwxo2g6NCNKl+0pLCqm6ExdZgM4I5Kjr/AA10lZXhpNNj8NacmjyNJpogX7O7ZyydicgH8xVGQWfhvSrDTrmwtrd0trkESqZ5GLZGDyWJHHoRQPDmlLoraQLd/sLNuMfnyZznP3t27qPWpNU1ZdNEKLC89xMT5cSkDIHUknoBkfmKz/8AhINQ/wCgOv8A4FD/AOJqXJI1jRnJXSLlz4c0u80q302eCRrS3IMaCeRSCAR94NuPU9TTr7QNN1I2huoZHNp/qds8ibenXaw3fdHXNUf+Eg1D/oDr/wCBQ/8AiavaXq41CSWCW3e3uYgGaNmDAqc4II6jg0KSYSozirtGJ4+0PT7vwxrepzRSNdwaVcLG4mdQAI3PKg7T1PUV02n/APINtf8Arin8hWV42/5ELxF/2DLn/wBFNWrp/wDyDbX/AK4p/IVRkWKKKKAOa8P/API3eLf+vu3/APSaOtHxB/yDF/6+IP8A0atZ3h//AJG7xb/192//AKTR1o+IP+QYv/XxB/6NWrp/EiZfCyKiiiugxCiiigDM0W1s7WK9Fnc+esl7NLIcg7JGYll49DxWnWZon9neTef2cW2fbZvO3Z/1287+v+1n2rTpIAooopgFFFFAGb4fso9O8P2FnFdi7jhhVFuB/wAtAB97qf51pVmeHYbC38OafDpczTWCQKIJGOSyY4PQfyrTpIAooopgFZ2v29reeHdTtr64+z2c1rLHPNkDy0KEM2TwMDJrRrO1/wCwf8I7qf8Aam7+z/ssv2rbnPlbTuxjnpnpSYF+IBYkVTlQoAPqKdTY9vlps+7gY+lOpgFFFFADtD/1+p/9fI/9FJVHxr/x5aR/2GbL/wBHLV7Q/wDX6n/18j/0UlUfGv8Ax5aR/wBhmy/9HLXPU+I2hsdLRRRUFBRRRQAVzWu/8jn4U/663X/ok10tc1rv/I5+FP8Arrdf+iTQB0tczpH/ACCrf/d/rXTVzOkf8gq3/wB3+tbUtmZVN0Xajn/495f9w/yqSo5/+PeT/cP8q1INDR/+QJYf9e0f/oIqux03/hLIgyt/av2Fyp5x5PmLu9s7tvvVjR/+QJYf9e0f/oIqu15ZjxZHZG1BvjYvKLjaOIxIoKZ68kg/hXLLdm62LGsf8gS//wCvaT/0E1Qh/wBRH/uj+VX9Y/5Al/8A9e0n/oJqhD/qI/8AdH8q2pfCZz3H1S1f/kFXH+6P5irtUtX/AOQVcf7v9RWsdzN7HTVlWMWnL4g1aS2lZr5xD9rQ5wmFOzHHcZ9a1ayrCbTn8QavFbQst9GIftchHD5U7Mc9hnsK4zpF8Q/8ghv+u8H/AKNSoql8Q/8AIIP/AF3g/wDRqVFXRT+Axn8QVVuv9fYf9faf1q1Va7/19h/19x/1qyToqytDOmlNQ/sxWA+3TC43Z5mz8/Xtn04rVrK0K8s7xL82dqLcRX00UoCgb5FbDPx6n8a5DoGa5/x8aZ/18t/6Kekp2uf8fGmf9fLf+inptdMPhRjL4mFQx/8AIesP9yX+QqaoY/8AkPWH+5L/ACFN7Mlbo365r4d/8k48Of8AYOg/9AFdLXNfDv8A5Jx4c/7B0H/oArlOg6WiiigAooooA5z4g/8AJOfEn/YMuP8A0W1dCn+rX6Cue+IP/JOfEn/YMuP/AEW1dCn+rX6CgDIf/kNXn/XOL/2apaif/kNXn/XOL/2apa86PX1f5s7VsvRBRpH/AB8aiP8Apuv/AKLWijSf+PnUf+uy/wDotaqP8SHr+jJn8D/rqJr0WnSwWQ1KVo0W+t2hK5+aYODGOh4LYrVrK16bToYLI6lC0qNfQJCFH3Zi48tuo4DYNatd5yGHbf8AHxe/9fLfyFWKr23+vvf+vlv5CrFebT+H7/zO5hT9E/5BuPSab/0Y1Mp+if8AIOP/AF3m/wDRjVpS/jL0f5ozq/B8/wDMj1SLTpNW0R7yVkuo7qRrJRnDyeRIGB4/55lz26Vq1lapNp0er6JHeQs91JcyLZOBxHIIZCxPP9wOO/WtWu45TjNH/wCQVD/wL/0I1eqjo/8AyCof+Bf+hGr1c62PUl8TGS/6l/8AdNa/h/8A5FvS/wDr0i/9AFZEv+pf/dNa/h//AJFvS/8Ar0i/9AFXDc58R8HzI86b/wAJYRtb+1fsOc848nzPyzu/Gp9a/wCQFqP/AF7Sf+gmoBeWf/CWGy+yj7cLES/aNo/1fmEbM9evNT61/wAgHUf+vaT/ANBNaPY5YfEjDtf+PSH/AHF/lUtRW3/HpD/uL/KpaxPRe5Q1r/kC3f8A1zNdpXF61/yBbv8A65mu0q4bs58T8Mfn+hzWif8AI7eKv961/wDRVdLXNaJ/yO3ir/etf/RVdLWhyBRRRQAUUUUAc14L/wCPbWf+wzef+jDV7Xfv6Z/19/8AtOSqPgv/AI9tZ/7DN5/6MNXtd+/pn/X3/wC05Kun8SJnsNoooroMQooooAzdAso9P0O1tIrsXaRqQJx/HyTnqf51pVmeHobC30Czi0yZprJUPlSMclhk+w757Vp0kAUUUUwCqmqW0t5pF7awTm3mmgeNJgSDGxUgNx6E5q3VHWbeC70PULa6nEFvNbSRyzEgCNCpBbJ44GTSAs20bRWsMbvvZECl/wC8QOtS1FaokVpDHG+9FjVVb1AHBqWmAUUUUAFZuu2Ud/pf2eS7FqvnwP5p9VlRgvUfeKhfxrSrM1+Gwn0ry9SmaG2+0W7b1ODvEyGMdD1cKPxpMDTooopgFFFFAEnh/wD5B0n/AF9T/wDoxqz/ABD/AMjX4Q/6/p//AElmrQ8P/wDIOk/6+p//AEY1Z/iH/ka/CH/X9P8A+ks1c9T4mbQ+FHS0UUVBQUUUUAFc1L/yU61/7A03/o6Oulrmpf8Akp1r/wBgab/0dHQBuah/yDbr/ri/8jWfbf8AHpD/ALi/yrQ1D/kG3X/XF/5Gs+2/49Yf9xf5VxVv4q9P1Omj8LJap6r/AMgq6/65n+VXKp6r/wAgm6/65N/KsK38OXozaPxI6CsqwOmnXtXFqrC+Bh+2E5wfk+TGePu+latZWn3lnPr2sW0FqI7m3aEXE20DzdyZXnqcDjmvUOAk1z/kEy/78f8A6GtMqTXP+QTL/vx/+hrUdcNX+M/RfmzqpfB8/wDIKrXf3rX/AK+Yv/QhVmq13961/wCvmL/0IVlU+Bmi3N2srRTppk1T+zlYML5xdbs8zbV3Yz2xt6cVq1laJeWd2+qC0tRAYb6SKchQPMkAXL8dcgjrzxXpnCV/E33dL/6/R/6LkqvVjxN93S/+v0f+i5Kr1jLc76P8NBUUP/IxaX9Zf/QDUtRQ/wDIxaX9Zf8A0A0luW/hfo/yOqrK8M3Gn3XhnTrjSrdrfT5IFa3iYYKIRwCMn+datZnhy+XU/Dmn3yWX2JZ4FkFt/wA8sj7vQdPoK3PNKGuf8h/Tv+vef/0KKmU/XP8AkP6d/wBe0/8A6FFTKxl8TPQp/wAOP9dWFLpP/IzS/wDXmP8A0OkpdJ/5GaX/AK8x/wCh0LdDn8DJ/G3/ACIXiL/sGXP/AKKatXT/APkG2v8A1xT+QrK8bf8AIheIv+wZc/8Aopq1dP8A+Qba/wDXFP5CtjzixRRRQBzXh/8A5G7xb/192/8A6TR1o+IP+QYv/XxB/wCjVrO8P/8AI3eLf+vu3/8ASaOtHxB/yDF/6+IP/Rq1dP4kTL4WRUUUV0GIUUUUAZmiXNndQ3jWVuYFS9njkGAN0iuQ7cepya06z9IvXvobp3tPsxiu5oQv98K5AfoPvYz+NaFJAFFFFMAooooAzPDsmny+HNOk0mNotPa3Q26NnKpjgHJJ6e9adZnh28t9R8OadeWlqtpbz26SRwKABGpGQuBxxWnSQBRRRTAKzvEFzaWfhzU7m/t/tFnFayPPCAD5iBSWXB45GRWjVDXLx9P0HULyO1+1vBbySLbj/lqQpO3oevToaQF1CDGpUYGBgU6kU5QHGMjpS0wCiiigB2h/6/U/+vkf+ikqj41/48tI/wCwzZf+jlq9of8Ar9T/AOvkf+ikqj41/wCPLSP+wzZf+jlrnqfEbQ2OloooqCgooooAK5rXf+Rz8Kf9dbr/ANEmulrmtd/5HPwp/wBdbr/0SaAOlrno7DUrFfs8NvFcQqT5b+btO3PAII6iuhoqozcSZRTMDy9V/wCgfH/4ED/CmvbatPG0QtIYd42+Y0+4L74A5roaKv2r7E+zRFbQLa2sNuhJWJFQE+gGKpm+mHiRLD7GTAbNpjdYOAwcDZ0x0OevatGs4y6j/wAJGsIhX+zPshYy9/O3gBevTbk9KyNC5cwLdWk1u5IWVGQkehGKxEttWhRYjaQylBjzFn2hvfBHFdBRVxm4kyimYHl6r/0D4/8AwIH+FNksNRvlFvNbxW8LEeY/m7ztByQAB1NdDRVe1YvZoKy7G+in17VrRLMRSW3k758D99uUkdu3TvWpWdZ3d7NrGpW09r5drB5X2ebaR5u5ctz0ODxxWRZNqdmb/T5LdXCOSrIxGQGVgwz7ZArL8vVhwbCIn1W44/lW/RVxm4qxLinqYHl6r/0D4/8AwIH+FOhsL65u7d7mGKCGF/MIEm9nIBwOnA5zW7RTdVi9mgrO0e+mvkvDNZm2MN5LCgII8xVOA/I79a0aztIl1GaO7OpQrEy3cqwBf4oQ3yN1PJFZli6rZS3cUL25QTwSeYgc4VuCpBPbgnmqHl6r/wBA+P8A8CB/hW/RVxqNKxLgm7mB5eq/9A+P/wACB/hU9jY3j6gl3dpHCsSMscaPvJLYyScDsOnvWxRTdVtWEoJBXNfDv/knHhz/ALB0H/oArpa5r4d/8k48Of8AYOg/9AFZlnS0UUUAFFFFAHOfEH/knPiT/sGXH/otq6FP9Wv0Fc98Qf8AknPiT/sGXH/otq3Ibm3e281Z4mjUYZw4IH1NAFC8t7qK/e5ggM6SoqsqsAykZ55IBHP6VFvvv+gXcf8AfyP/AOKrW+0wCETefH5R6PvG386HuYI0V3mjVG+6xYAH6VzPCpttSa+7/I2VZpWsZO++/wCgXcf9/I//AIqrml200CTyzgLLPJvKA52gAADPrxVt54o2VXlRWb7oZgCfpSmaISiIyIJD0TcMn8KcMOoyUnJu3p+iQpVXJWsZ2uX0NjDZNNZi6Et7BCoIH7tmcAPyD93rWpWXq2o3Fr9lSwhW5le7iimQcmOJmAd8Dpgc56VopNFIWCSIxX7wDA4+tdBkZElvd2t3cGK1a4ilfzAUdQVJAyCGI9KTfff9Au4/7+R//FVrrPE6F1lRkXqwYECl86Ly/M81Nn97cMfnXL9VV9JNfd/kbKu7bIx99920ufPvJHj/ANCrQ022e0sUikIMhZnfb0yzFiB+dWDNEqB2kQKejFhg0rSxoQGdQW6ZPWrp0FCXNdt+dv0SJnVclaxm6lfQ22raNbyWYme6uJI45SB+4Ihkct07hSvb71alZmoX9zbarpNrbwCWK5mdLl8HMSCJ2De2WVV59a0fMQvs3ruH8Oea3Mzl10nVdP3W9vbQ3NurMYn87Y20nOCCOozjI6077NrX/QMi/wDAof4V0wdGJCspI6gHpQJEYEh1IHUg9KjkR0fWZdUjl3sdbmRolsreEuMeY9xuC++AOfpXR2VstlY29ohJSCJY1J6kKMf0qXehXduXb654o3qFDFhg9801FIipVc1ZlAX03/CSGw+xnyBZib7Vg43byNnTHTnrVy6t1u7Sa2ckJLG0bEdcEYql9ovx4iMLRoNL+yB1l4yZt5BXr024PStEsAcEjPpVGSdtTlY7DW7aNYTZ28/lgKJVn2hgO+COPpT/ALNrX/QMi/8AAof4V0+RnGRn0oyCcZqORHR9Zl2Rysmk6rqCfZZ7aG2gcgSyedvO3PIAA6muroyPWimopGdSq57nNaJ/yO3ir/etf/RVdLXM6J/yO3in/etf/RVdNVGYUUUUAFFFFAHNeC/+PbWf+wzef+jDV7Xfv6Z/19/+05Ko+C/+PbWf+wzef+jDV7Xfv6Z/19/+05Kun8SJnsNoooroMQooooAzPD0mny6BZvpUbR2JTMKNnIGT6k1p1meHry31DQLO7tbVbWCWPckCgAIMnjjitOkgCiiimAVR1oWZ0LUBqLFbE20n2hlzkR7TuIxz0z0q9VHWprW20HUZ76EzWcdrI88QGS8YUllwSOoyKQFm1EYtIRCSYti7Cf7uOKlqK2ZHtYWiXbGUUqvoMcCpaYBRRRQAVma/Jp8elhtTjaS2+0242rnPmGZBGeCOjlT+FadZmvXlvYaYs91arcxm5t4/LYAjc8yKrc/3WYN+FJgadFFFMAooooAk8P8A/IOk/wCvqf8A9GNWf4h/5Gvwh/1/T/8ApLNWh4f/AOQdJ/19T/8Aoxqz/EP/ACNfhD/r+n/9JZq56nxM2h8KOloooqCgooooAK5qX/kp1r/2Bpv/AEdHXS1yGpalYaZ8S7KS/vba0STSZURp5VjDN50fAJPJ9qAOsmiWaCSJs7XUqcehGKxo7fUoI1h+zRS7BtEiy7dwHfBHFaFxq2m2l7DZXOoWkN3PjyoJJlWSTJwNqk5PPpR/a2nf2l/Zv9oWv2/GfsvnL5uMZzsznpz0rGpRVRp3szSFRx0KWzUv+fFP+/4/wpktnf3sZt5II4Yn4d/N3Hb3wMda0INV066vZrK31C1mu4c+bBHMrPHg4O5Qcjn1pLXWdLvUne01KzuEt/8AXNFOriLr97B46Hr6Vm8ImrOTa+X+RXt5dEXazrK+muNY1S1kszFFbNEI5iD++3Jk9ux4p8WtaVPYSX8Wp2UllEcSXCTqY0PHBbOB1H51nWuvx+dqF7eXtjHooeNbG889BHLlfn+bdg4YEfhXUYmtf2pvLKWANtZgCrYzgg5H6is7ZqY4NlET3Kz8H9KuXOs6XZ28NxdalZwQTgGGSWdVWQEZ+Uk4PHPFPuNU0+zuobW5vraC4nIEUUsyq8hJwNoJyeeOKwqUFOXNdp/13NIVHFWKOzUv+fFP+/4/woSzvbm4hNxFHDFE4kOJNzMR0HTjmr7anp66guntfWwvWGVtjKvmEYzkLnPQE/hRFqdhPfSWMN9bSXcQzJAkqmRBxyVByOo/Oo+qrrJv7v8AIr2z7Fqs7Sb6a9bUBNZm28i8eFCQR5qgDD8jvk/lU1tqunXhnFrf2s5t/wDXCKZW8vr97B46Hr6Gs3RNdW9tNTvLq7svslveSJFPFMhQQgKQWYEgHk9cV1GJd1jTm1K0jWKQRzwyCWJmGV3AEYI9CCR+NZP9m69/zz03/v8Av/8AEVtf2vpgsBfnUbT7GTgXHnr5ZOcfezjrxRPq+m21rFdT6haRW82PLlkmVUfIzwScHj0qXFM1hWlBWRi/2brv/PLTf+/7/wDxFWdO0e9XUY72/eAGFWEUUBLDLcEkkDtxjHetO41KwtJYYrm9toZJuIkklVTJ/ugnnqOlK+o2Md6lk95brduMpA0qh2HsucnofyoUEVKvJqxZrP0G6vr3QbG61O2FtfSwq88ABHluRyMHJqdNRsZL17JLy3a7QZaBZVLqPdc5HUfnWd4a1GW50W2h1G+tZ9XhhUXywyo2yTGGyF4HINUYEms6XPeyW9zaSRpcwblAlztdWxkHHI5UHPtWf/Zuvf8APPTf+/7/APxFbcOq6dc20tzBf2ssEX+slSZWVOM8kHA4oGqaebI3ov7U2gODP5y+WDnH3s468VLgm7m0a8oqxif2brv/ADy03/v+/wD8RV7SNKuLW6mvL2SJp5EEapDnaigk9TySSfQdKuy6pp8NrHdS31tHbyHCTPMoRvoc4PQ06fUbG1MIuLy3i87/AFXmSqu/p93J56jp60KCQSrykrGX42/5ELxF/wBgy5/9FNWrp/8AyDbX/rin8hWF46v7OHwbr9rLdwJcyaXclIWkAdh5T9B1PQ/lW7p//INtf+uKfyFUYliiiigDjNG0uO4+IPia/a5vEeC5twIo7l1ib/Ro/vIDtbr3FXtb0eKKV9SF3ftJJPAphe7doR+8TpGTtB47D19aPD//ACN3i3/r7t//AEmjrR8Qf8goHsLiAn/v6tXT+JEy+FmVZ6TFZXNzOl1fSm4JLLPdPIqck/IpOF69qZaaJDZ2FxaLeajIs4IMk15I8i5GPlcnK/hWnRW9jEzBokI0htN+2aiY2OfON5J5w5zxJnd+vTii40SG50yCxa81FEhIIlivJElbAI+Zwdzde59K06KLAc/babdarEG1KS9tXtLqZIBbXUkXmxB8Iz4b58qoPPqfWtG40qO41OG/a6vUeEACKO6dImwSfmQHa3XuPT0o0pNQSG4GoyK8hupjEVxxCXPljgdlxV+gZQXSo11d9S+1XpkYY8k3TmEcAcR52jp6deaLTSo7O+uLtLq9kackmOa6eSNMnPyqThfw7VfooEZlnokNla3Nul5qMq3AIZ57ySR04x8jE5Xr2oj0SGPSZNOF5qJjc5MzXkhmHIPEmdw6evrWnRRYDndK0+TVvB2mR376lZztEksqi7lSZHIyVZ87zjPQ+g9K0L3RYr6G1je81CIW4wpgu5I2fp98g/N07+9P0RtRfQ7FtXRU1EwIblVxgSY+YDBI6+lX6LDKF1pUd3qFvetdXsbwYxHFdOkbYOfmQHDfjR/ZUf8Aa/8AaX2q98zGPJ+1P5PTH+rzt/TrzV+iiwihbaVHa6jPerdXsjzZzHLdO8S5OflQnC9OwrOvdMuNJ0DWJNJlvru+lt3aCO5upJf3gU7QuW+XJPYjt6V0FUdaTUJNDvk0qRY9RaBxbO2MLJg7ScgjrjrQMht9Eig0mWw+2aiyync0r3kjSqePuuTuA47H19aV9EhfSE043moiNDkTLeSCY8k8yZ3Hr6+laQzgZ60tFhGZeaJDe2dtbPeajEtuAFeC8kjd8DHzMDlvxqS80qO9vba6e6vY2gIKpBdPHG+Dn5lBw349qv0UWAo6PpMUmv32pG6vlkjnCiFbpxC37pRkx52k89SOw9KoeLdHitrnT9RW7v3km1eyUwyXTvCuZ0+7GTtU8dh3PrW/ofMupnt9qA/8hpVHxr/x5aR/2GbL/wBHLWFT4jaGx0tFFFQUFFFFABXH+LtMsdX8TeF7PUrOC7tXmuS0M6B1JEJIyD712Fc1rv8AyOfhT/rrdf8Aok0AaV94c0TU47SO+0qzuUs+LZZYVYQ9Pu5HHQdPQU660HSL7U7fU7vTbWe+tsCG4kiDSR4ORtY8jkk1o1hjWby5LSWdrAbfJCPNKQXwcZwFOBVRi5bCckty3/YOk/21/bP9m2v9pkbftflDzcY243denFFpoOk2GpXGpWmm2sF9c58+4jiCvJk5O4jk881V/tHVv+fWy/7/AD//ABNI2ralCpklsrZ41GWEUzbse2VwTVeykT7SJYsfDeiaZDdw2Ok2dtFdjFwkUKqJRgj5gBz1PX1rNi8KWdveSafBp1hH4amtmE1gsKhHnLqdxXHPyrj8BXRwypPDHNGcpIoZT6gjIqibGY+JFv8A7YfIFmYfsuTgtvB39cdBjpWZZHJ4Z0KbR49Hk0iyfTYm3JaNCpiU5JyFxgck/nRfeGtD1KztbS+0iyuba0AW3ilhVliAAACgjjgAfhWlNKkEEk0hwkalmPoAMmsZdW1KZRJFZWyRsMqJZm3Y98LgGqjBy2Jckty3e6DpGo6hbX97ptrcXlqQYJ5YgzxEHIKk8jnmhtB0l9aTWW021OpoNq3ZiHmgYK4DdehI/Gqv9o6t/wA+tl/3+f8A+JpP7ZvLcq95awC33AO8MpJTJxnBUZFV7KQueJcttB0mz1afVbbTbWHULhSs1ykQEkgJBILdTyB+QrM0Xw9Bpms675ek2NrY3hi2GGFFM3ynfvxyfmY9fU10dZ1na30WsalcXFyJLScxfZosk+VhcNxjjJ54rMsjtvDOhWelT6XbaRZQ2FwSZraOFRHISACSuMHgD8qG8M6E2irozaRZHTFbctoYV8oHO7O3GOpJq7fXaWFnJcurMEwAq9WJOAB9SQKzP7S1U8/Y7Nc9jOxI/wDHauMHLVEuSW5NeeGdC1DTrbT7zSLK4s7UAQQSwqyRADA2gjA44qS+0DSNTurW5vtMtLme0INvJLEGaIgg/KT05A/Kqv8AaOrf8+tl/wB/n/8AiafBq1yt1DDe20SJM2xJIpCwDYyAQQOuOtN05C50WJdB0mfWItXl021fUol2x3TRAyKORgN1HU/nWdofhyKy1TUNWvLOzOqXFzMUu44lEhgLfIrMACcAAfhXQ1naPYzWMV2s94bky3k0ykkny1ZshOT/AAjisywsfD+j6ZcXVxY6ZaW012c3EkUQVpTkn5iOvJP51HaeGdC0/TrnTrPSLKCyuc+fbxwqqSZGDuAGDxxVnUb/AOwQoyxGWWVxHHHnG5sE8nsMAn8Kof2jq3/PrZf9/wB//iatQk1dEuSWhMPDOhDRTow0iyGmFtxtPJXyic7s7cY680XXhnQr3S7fTLrSLKawtyDDbSQq0cZAIG1cYHBP51D/AGjq3/PrZf8Af5//AImp7LVJpbwWl3bpFI6l42jcsrAYyOQCDyKHTklcFNMdfeHtG1NrRr7S7S5az/49jLCreT0+7np90dPQVmfDv/knHhz/ALB0H/oArpa5r4d/8k48Of8AYOg/9AFQUdLRRRQAUUUUAc58Qf8AknPiT/sGXH/otq0bbQtIt9LfT4NKsYrKb5pbZLdFjkJABLKBg9B19BWd8Qf+Sc+JP+wZcf8Aotq6FP8AVr9BQBRbQdHfS10ttJsTp6nK2ht08oHOchMY6knp3outB0e+s4LO70mwuLW3AEMMtsjpEAMAKpGBxxxS3epGC4+zwW7TyhQzDcFCg9Mn1ODUP9p3v/QOX/v+P8KwliacXZ/k3+hoqU2rosXejaXf3Nvc3mm2dxcWxBglmgV2iIOQVJGV5APFD6PpcuqJqkmm2b6hGNqXbQKZVGCMB8ZHBPfvVf8AtO9/6By/9/x/hVyyvFvYmYI0bo2x0bqrf/qIP404V4TfKt/Rr8xSpyirsytR0dbe/g1PRtMsU1Ka6iW7uhBGsrwbh5mXIyeAO/YVftNE0nT5bmWy0uytpLrm4eG3RDL1+8QPm6nr6mm6va311HaCwuRA0d3FLMSSN8Stl14HccVo1sQZ1t4f0WzsJ7C10fT4LO4z51vFbIscmRg7lAweOOaT/hHtF/sn+yv7H0/+zc7vsf2ZPJznOdmNvXnp1on1R1uJIba1M/lnDsXCqDjOB60z+073/oHL/wB/x/hWDxNNO2v3P/I0VGbVx9z4e0S80+DT7rR9PnsrfHk20tsjRx4GBtUjA4JHFOvNC0jUZLaS90qyuXtebdprdHMPT7hI+XoOnoKi/tS8HJ04Y9pxn+VX7W5jvLZJ4s7W7MMEEcEH3BqoV4Tdo7+jX5ilTlFXZjaroqXHifRdTh0uxlkildbm7kgjM0cQjfYFcjcPnK9PU+9aCaJpUerPqyaZZrqUg2vdrAolYYAwXxk8ADr2pt/a30+p6VNbXIitoJna6jJI81DGyqOnOGKnn0rRrUgoWeiaTp15c3llplnbXNyS080MCo8pJySxAyeSTzUdn4c0TT7K5srLR7C2tLoFZ4IbdESUEYIZQMHjjms8+IL64d3sbK3NsGKpJPMVL4OMgBTgccc0f2zrP/PlYf8AgQ//AMRUc6N/q9Q0F8OaGmkPpCaPYLprnc9oLdBExyDkpjHUA/hRceG9DutLh0u40ewl0+AhorV7dTHGRnBVSMDqfzNZza7qsKmSXT7V41GWWK4beR7ZUAn8RXQW88d1bRXER3RyoHQ+oIyKpST2InSlDcwbzwzb6l4gt5L/AE+wudKtLQJawzQI5hm38lcj5RtCjg9hWlc6HpN7qcGpXWm2k1/bgCG5khVpIwCSNrEZHJP50gsZh4ke/wDthMBs1hFrk4DByd/XHQ46dqvTSpBBJNI22ONSzH0AGTTMykNC0kaydYGm2g1MjabvyV80jGMbsZ6cUWmhaTYajcahZ6baQXtznz7iKFVeTJydzAZPPNZSa/qlwglh0+1SJxlBNOwfHbICkA+2TTv7Z1n/AJ8rD/wIf/4io50b/V6n9Mv2XhvRNNt7q3sdJsraG7GLiOKBVWUYI+YAc8E9fWki8M6FBpEukQ6RZR6dM26S1WBRG545K4weg/KqH/CQ31qVkvrK3FtkB3hmLMgJxuwVGR6810lUpJ7Gc6cofEcj4VsLTS/FPiWysLaK2tYjaiOGFAqrmMngDpyTXXVzWif8jt4q/wB61/8ARVdLTICiiigAooooA4zwVp0gvtbv/wC0bwxnVrxPsZZPJH7w/Njbuz/wKrd/pctnfW076rf3Sz3hKwzshSH93IcJtUHvjknpT/Bf/HtrP/YZvP8A0Yava797TT/09/8AtOSrp/EiZ/CZVppE1rY3Fs2sajcNMCBPM0ZkiyMfLhAPfkGhdImXSGsP7Y1EyMc/bC0fnDnOAdm32+70NadFb2MTMuNImn0yCzXWNRheIgm6jaPzZODwxKFe/YDoKde6XLd/Zdmq39t5H3vIZB53T7+5T6dsdTWjRRYDB02G+1eHTtWu7m/0+bywZdPTasZOT94MpbPPqOgq+NNkGrtf/wBp3pjIx9jLJ5I4xkDbu9/vdaNFbUX0a0bVkVL8p+/VcYDfgSKv0DKFppslrf3Fy2p3twsxJEEzIY4snPy4UH25JplnpMtpbXML6vqFyZgQss7RlouCMphAO+eQelaVFAjMj0iaPSZLE6xqLu5yLtmj85OnAITb29O5qlr1lLbeDrtRf6jM9pBJMXRo/OuNqsdhJQrz04X0roKp6tNd22j309hCJryK3keCIgkPIFJVcAjqcCgZVl0yW/sLEJqmoWZiRSxgaMNJwPv5Qjt2A6mp7rTZLnULe6XU723SHGbeFkEcuDn5sqT7cEVchZ2gjaRdrlQWHocc0+gRQOmyHVxf/wBp3ojAx9j3J5J4xnG3d7/e60W2myW+pT3banezpKCBbSsnlR8g/KAoPbHJPWr9FAGdZaVLaJdK+rahc+f90zshMPX7mEHr3z0FZ2o2moaX4dkgtb7Ur64luYU85ijTRo8iIxXamMKpZuQe+eK6KqGsNqK2KnS0V7j7RCGDYx5Xmr5h5PZNx/lzQMjfSJm0hLAaxqKyKcm7DR+c3JOCdm3vj7vQCi80ia7sra3TWNRtmhABngaMSS4GMtlCPfgDmtOiiwiheabJd3ltcJqd7bLCQTDAyBJec4bKk+3BFD6bI+rpfDU71Y1GDaKyeS3BGSNu7vn73YVfooAqeGtNkimur86nfSRyTzqLR2TyU/eHlQF3Z47seprN1DTJbDxl4Zlk1S/vBNfzlY7lkKxf6LPwm1Qcc989BXQ+H/8AkGyHsbmfH/fxqz/EP/I1+EP+v6f/ANJZqwqfEzaPwo6WiiioKCiiigArlrqCGf4m2gmiSQDR5iA6g4PnR8811Nc1L/yU61/7A03/AKOjoA6B7aCSVZXhjaRPuuygkfQ0fZoPtHn+TH53TzNg3fn1p7uscbSOcKoJJ9AKyU1O/nQSxWkCxsMqJJSGx2yAvFZVK0KbSkXGnKWxppbQRzNNHBGsr/edUALfU0kdpbQq6xW8SCT74VAN319az/t2p/8APvaf9/m/+Jprard248y6tofIB+dopSSo9cEDIrP61TW9/uZXsZmitlapA0C20Kwty0YjAU/UdKzrTTZhfalHdx28mmM8ZsrfYpEYCfPxju2T361sVnWNjNb6vql1JeGWK6eNo4STiEKgUjr3Iz+NdJkWpbK0njSOW1hkjj4RXjBC/QdqWW0tppUllt4pJIzlHdASvfg9qLu5SztZLhwSqDOB1J7AfjWd9v1I8/ZbVc9jMxI/8drGpXhB8r39C405SV0aJtLY3IuTbxG4AwJSg3Dt160JZ20dw9wlvCs7jDSKgDN9T1PSs/7dqf8Az72n/f5v/iaWLU7hLiKO7t4lSVtivFIWwx6AggdalYqnfr9zKdGZeisrSAyGG1hjMv8ArNkYG/6469TWbo2lS2sGpW95DbGCa7keGOONQvkkDaCAAM8Hrmtms7SLGayN/wCdeG58+8kmTJJ8pTjCcntj9a6DIsHT7I2gtTZ2/wBmBz5PlDZnOemMdaJdPsp4I4JrO3khj+5G8SlV7cAjioNY1FtNtEaKISTzSCKJWOBuIJyT6AAn8Kyf7S13+/p3/fl//i6lySNYUZTV0b01jaXEkck9rBK8XMbPGGKfQnpStY2j3S3T2sDXKDCzGMFwPY9e5rA/tLXf7+nf9+X/APi6s6drF42ox2V/HATMrGKSAEDK8lSCT25zntQpoqVCaVzWWxtEu2u0tYFuWGGmEYDke7dewrO8N6KdH0S0guhBLqAgVLu6ROZ3A5YnGTk5PPrWxWX4bsYtM8M6ZYwXgvIbe2jjS5BBEoCgBuCevXrVGBah02wt7eS3hsraOGX/AFkaRKFftyAMGgaZYCzNmLG2+yk5MHlLsJzn7uMdap6xqk1lJb21pHG9zPuYGQnairjJOOT94DHvWf8A2lrv9/Tv+/L/APxdS5pOxtGhKSubkumafPax2stjbSW8ZykTxKUX6DGB1NLPp1jdGE3FlbzGH/VeZEreX0+7kcdB09Kwv7S13+/p3/fl/wD4ur2karcXVzNZ3scS3EaCRXiztdSSOh5BBH6ihTTCVCUVco+OtPspvBuv3ctpbvcx6XchJmjBdR5T9GxkdT+db2n/APINtf8Arin8hWV42/5ELxF/2DLn/wBFNWrp/wDyDbX/AK4p/IVRiWKKKKAOa8P/API3eLf+vu3/APSaOtrVLVr3TLm3Q4d0Ow+jDkfqBWL4f/5G7xb/ANfdv/6TR10tNOzuDV9DAtbgXVrHMBjeuSD2PcfgeKmqG/gbS7qS6RSbKY7pQB/qX7t/unv6HnualVldQykFSMgg8Guq6aujntbRi0UUUAUNJs7myguEubprhpLqaZGJJ2IzllTn+6CB+FX6zNDs7aytrpLW6W4WS9uJnZSDtdpGZk4/ukkevFadJAFFFFMAoopDyDQBR0S3vrXQ7G31Odbi/jgRbiVSSHcD5iCQOp9hV+s/QrM6doGn2Ru/thgt0jNz/wA9cKBu6nr16mtCkAUUUUwCqGt2dzqGh31nZ3TWtzPA8cU6kgxsQQGBHPB54q/WZ4is7bUPDepWd7dLa2s9tJHLcMwAjUqQWJPAwPWkBpjoKKKKYBTXdY42kcgKoJJPYU6qsMX9sziNObCNv3snaUj+Aeoz1P4etG2rDyNHQoWj0xZZFKyXDtOwPbccgfgMCszxr/x5aR/2GbL/ANHLXS1zXjX/AI8tI/7DNl/6OWuWTu7m6VlY6WiiikMKKKKACua13/kc/Cn/AF1uv/RJrpa5rXf+Rz8Kf9dbr/0SaAOlrmdI/wCQVb/7v9a6auZ0j/kFW/8Au/1ralszKpui7Uc//HvJ/uH+VSVHP/x7yf7h/lWpBoaP/wAgSw/69o//AEEVXNnZ/wDCWLe/ah9uFiYvs+4f6vzAd+OvXj0qxo//ACBLD/r2j/8AQRVfGm/8JYDub+1fsJG3nHk+Z19M7vxrlluzdbFjWP8AkCX/AP17Sf8AoJqhD/qI/wDdH8qv6x/yBL//AK9pP/QTVCH/AFEf+6P5VtS+EznuPqlq/wDyCrj/AHf6irtUtX/5BVx/u/1Fax3M3sdNWZY2K2+t6tdi981rloiYP+eG1Mevfr0FadZWn2+nx67rE1tcM95K0P2qMniMhAExx3Xnqa4zpF8Q/wDIIP8A13g/9GpUVS+If+QQf+u8H/o1Kirop/AYz+IKrXf+vsP+vuP+tWarXf8Ar7D/AK+4/wCtWSdFWVoVnZ2cN6tldC4WW+nlkIYHZIzksnHoePWtWsrQRpogvf7MZmQ305n3Z4m3nzBz23Z9q5DoG65/x8aZ/wBfLf8Aop6bTtc/4+NM/wCvlv8A0U9Nrph8KMZfEwqGP/kPWH+5L/IVNUMf/IesP9yX+QpvZkrdG/XNfDv/AJJx4c/7B0H/AKAK6Wua+Hf/ACTjw5/2DoP/AEAVynQdLRRRQAUUUUAc58Qf+Sc+JP8AsGXH/otq6FP9Wv0Fc98Qf+Sc+JP+wZcf+i2roU/1a/QUAZD/APIavP8ArnF/7NUtRP8A8hq8/wCucX/s1S150evq/wA2dq2Xogo0n/j51H/rsv8A6LWijSf+PnUf+uy/+i1qo/xIev6MmfwP+uoutWK3yWIa9+y+Tewzg/8APQq2dnUden9K06ytdt9PuE08ahcNCEvoZINpxvmB+Reh4J//AF1q13nIYdt/r73/AK+W/kKsVXtv9fe/9fLfyFWK82n8P3/mdzCn6J/yDj/13m/9GNTKfon/ACDj/wBd5v8A0Y1aUv4y9H+aM6vwfP8AzG6jYrc6vo9yb3yTazSOsP8Az3zE67evbdu79PxrTrK1K30+XWdFlurho7qGaRrSMHiRzE4YHjnCFj26Vq13HKcZo/8AyCof+Bf+hGr1UdH/AOQVD/wL/wBCNXq51sepL4mMl/1L/wC6a1/D/wDyLel/9ekX/oArIl/1L/7prX8P/wDIt6X/ANekX/oAq4bnPiPg+ZGtnZjxZJei6BvjYrEbfcOIxIxD469SR6cVPrX/ACAdR/69pP8A0E1Ao03/AISyQhm/tX7CgZeceT5jYPpndu96n1r/AJAOo/8AXtJ/6Ca0exyw+JGHbf8AHpD/ALi/yqWorb/j0h/3F/lUtYnovcoa1/yBbv8A65mu0ri9a/5At3/1zNdpVw3Zz4n4Y/P9DmtE/wCR28Vf71r/AOiq6Wua0T/kdvFX+9a/+iq6WtDkCiiigAooooA5rwX/AMe2s/8AYZvP/RhrS16Nm0wzIpZrZ1nAHcKfm/8AHc1m+C/+PbWf+wzef+jDXSkAjBGRTi7O4mrqxiqyugZSCrDII7ilqoYzpFwLWTi0kb/RpD0X/pmfT29Rx2q3XV5owCiiigChotvfWui2kGpzrPepGBNKpJDN65IH8qv1naDZHTtCsrM3n2wwxBftH/PT36n+daNJAFFFFMAqnqwvW0a+GmMFvzbyC2LYwJdp2Zzx1x1q5VPVra5vNGvrWzuDb3U1vJHDMCQY3KkK2RyMEg8UgLMO/wAmPzTmTaN31xzT6ZErJCiu25goBPqafTAKKKKACqGr299c2SR6fOsE4uIXZ2JGY1kUyL0PVAw/HtV+s7WrI39jHCLv7LtuYJfM9dkqts6j723b+PfpSA0aKKKYBUVxOltbSTv92NSx96lqvaRHV7tHA/0CB927tM46Aeqg857ke1F0tWG+iNTSLZ7TSbaGTiQLuf8A3jyf1JrH8Q/8jX4Q/wCv6f8A9JZq6Wua8Q/8jX4Q/wCv6f8A9JZq5W7u5ulZWOlooopDCiiigArmpf8Akp1r/wBgab/0dHXS1zUv/JTrX/sDTf8Ao6OgDc1D/kG3X/XF/wCRrPtv+PWH/cX+VaGof8g26/64v/I1n23/AB6w/wC4v8q4q38Ven6nTR+FktU9V/5BN1/1yb+VXKp6r/yCbr/rk38qwrfw5ejNo/EjoKytOs7ODXNZuILoS3NxJEbiLcD5JWMBRjqMqAefWtWsrThpo1vWTaMxvTJF9tBzgN5a7MZ4+7jpXqHAS65/yCZf9+P/ANDWo6k1z/kEy/78f/oa1HXDV/jP0X5s6qXwfP8AyCq13961/wCvmL/0IVZqtd/etf8Ar5i/9CFZVPgZotzdrK0Ozs7T+0vsd0Ljzr6WWbDA+XIcbk49MD3rVrK0MaaBqP8AZrMc30p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With a total of N = n × m cores, now each core will be responsible for B/n tokens and df f /m of both the weights and intermediate activation. In the forward and backward pass each core communicates a tensor of size [B/n, dmodel] in an all-reduce operation.
", + "html": "It is common to mix both model and data parallelism for large scale models, which was done in the largest T5 models (Raffel et al., 2019; Xue et al., 2020) and in GPT-3 (Brown et al., 2020). With a total of N = n × m cores, now each core will be responsible for B/n tokens and df f /m of both the weights and intermediate activation. In the forward and backward pass each core communicates a tensor of size [B/n, dmodel] in an all-reduce operation.
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", + "html": "Next 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\", \"dmodel\"].
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", + "html": "resulting in the final tensor of shape [n, E, C, dmodel], 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, dmodel] to now shard the E dimension instead of the n-dimension. There are additional communication costs of bfloat16 tensors of size E \n× C \n× dmodel in the forward pass to analogously receive the tokens from each expert located on different cores. See Appendix F for a detailed analysis of the expert partitioning code.
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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 |
Combining 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 dmodel, df f , dkv, number of heads and number of layers are described, as well as a less common feature, F F NGEGLU , 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).
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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 |
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.
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", + "html": "Sample efficiency versus T5-XXL. In the final two columns of Table 9 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 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.
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", + "html": "Training 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., 2019).
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", + "html": "10. 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.
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", + "html": "Reasoning 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., 2019). On ANLI (Nie et al., 2019), 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., 2020). 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.
", "polygon": [ [ - 89.947265625, - 92.8125 + 89.349609375, + 93.29522705078125 ], [ - 523.845703125, - 92.8125 + 522.052734375, + 93.29522705078125 ], [ - 523.845703125, + 522.052734375, 253.24542236328125 ], [ - 89.947265625, + 89.349609375, 253.24542236328125 ] ], + "bbox": [ + 89.349609375, + 93.29522705078125, + 522.052734375, + 253.24542236328125 + ], "children": null, "section_hierarchy": { - "1": "/page/17/SectionHeader/6", - "3": "/page/22/SectionHeader/0" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/19/SectionHeader/4", + "4": "/page/21/SectionHeader/8" }, "images": {} }, { "id": "/page/23/Text/2", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 89.4990234375, - 257.16796875 + 89.349609375, + 257.3043212890625 ], [ - 522.94921875, - 257.16796875 + 521.8300170898438, + 257.3043212890625 ], [ - 522.94921875, - 349.98046875 + 521.8300170898438, + 349.787109375 ], [ - 89.4990234375, - 349.98046875 + 89.349609375, + 349.787109375 ] ], + "bbox": [ + 89.349609375, + 257.3043212890625, + 521.8300170898438, + 349.787109375 + ], "children": null, "section_hierarchy": { - "1": "/page/17/SectionHeader/6", - "3": "/page/22/SectionHeader/0" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/19/SectionHeader/4", + "4": "/page/21/SectionHeader/8" }, "images": {} }, { "id": "/page/23/Text/3", "block_type": "Text", - "html": "Summing 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). 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.
", + "html": "Summing 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). 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.
", "polygon": [ [ - 89.349609375, + 89.2001953125, 353.5673522949219 ], [ - 523.248046875, + 521.6235961914062, 353.5673522949219 ], [ - 523.248046875, - 432.3515625 + 521.6235961914062, + 432.2225036621094 ], [ - 89.349609375, - 432.3515625 + 89.2001953125, + 432.2225036621094 ] ], + "bbox": [ + 89.2001953125, + 353.5673522949219, + 521.6235961914062, + 432.2225036621094 + ], "children": null, "section_hierarchy": { - "1": "/page/17/SectionHeader/6", - "3": "/page/22/SectionHeader/0" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/19/SectionHeader/4", + "4": "/page/21/SectionHeader/8" }, "images": {} }, { "id": "/page/23/SectionHeader/4", "block_type": "SectionHeader", - "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.
", + "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.
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", + "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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", + "html": "Mixture of Experts (MoE), in the context of modern deep learning architectures, was proven effective in Shazeer et al. (2017). That work added an MoE layer which was stacked between LSTM (Hochreiter and Schmidhuber, 1997) 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., 2018) 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., 2020), which extended the XLA compiler, used the MoE Transformer to dramatically improve machine translation across 100 languages. Finally Fan et al. (2021) chooses a different deterministic MoE strategy to split the model parameters into non-overlapping groups of languages.
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", + "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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", + "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.
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", + "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.
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", + "html": "Why 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). Also, we point out that this decision is not mutually exclusive—we
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", + "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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", + "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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", + "html": "Table 10 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.
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suggests a future promising direction. |
Model | Precision | Quality @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] | - |
Image /page/27/Figure/1
", + "id": "/page/27/TableCell/287", + "block_type": "TableCell", + "html": "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, 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. 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.
", + "id": "/page/27/TableCell/303", + "block_type": "TableCell", + "html": "However, when these layers do train stably, we believe the preliminary positive results |
---|
suggests a future promising direction. |
Model | Precision | Quality | Quality | Speed |
---|---|---|---|---|
@100k Steps (↑) | @16H (↑) | (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] | – |
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.
", + "id": "/page/27/ListItem/5", + "block_type": "ListItem", + "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.
", + "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.
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", + "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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", + "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.
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", + "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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---|---|
Argmax | -1.471 |
Sample softmax | -1.570 |
Input dropout | -1.480 |
Input jitter | -1.468 |
Image /page/29/Figure/1
", + "id": "/page/29/ListItem/4", + "block_type": "ListItem", + "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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---|---|
Argmax | -1.471 |
Sample softmax | -1.570 |
Input dropout | -1.480 |
Input jitter | -1.468 |
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.
", + "id": "/page/30/FigureGroup/70", + "block_type": "FigureGroup", + "html": "Figure 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.
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+ "/page/31/Figure/3": 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} }, { - "id": "/page/30/Caption/2", + "id": "/page/31/Caption/4", "block_type": "Caption", - "html": "Figure 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.
", + "html": "Figure 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.
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", + "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).
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", + "id": "/page/32/Code/3", + "block_type": "Code", + "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", "polygon": [ [ - 99.9580078125, - 210.568359375 + 90.0, + 156.5263671875 ], [ - 500.6169128417969, - 210.568359375 + 477.30706787109375, + 156.5263671875 ], [ - 500.6169128417969, - 354.041015625 + 477.30706787109375, + 360.80859375 ], [ - 99.9580078125, - 354.041015625 + 90.0, + 360.80859375 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/28/SectionHeader/1", - "3": "/page/31/SectionHeader/1" - }, - "images": { - "/page/31/Figure/3": 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We 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, 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.
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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]", + "polygon": [ + [ + 90.0, + 110.21484375 + ], + [ + 384.9657287597656, + 110.21484375 + ], + [ + 384.9657287597656, + 156.234375 + ], + [ + 90.0, + 156.234375 + ] + ], + "bbox": [ + 90.0, + 110.21484375, + 384.9657287597656, + 156.234375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/32/SectionHeader/1" + }, + "images": {} + }, + { + "id": "/page/33/SectionHeader/3", "block_type": "SectionHeader", - "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).
", + "id": "/page/33/Code/4", + "block_type": "Code", + "html": "# router logits shape: [num cores, tokens per core, num experts]\nrouter logits = mtf.einsum([inputs, router weights], reduced dim=d model)", "polygon": [ [ - 89.947265625, - 113.888671875 + 103.76806640625, + 180.8876953125 ], [ - 523.845703125, - 113.888671875 + 389.60577392578125, + 180.8876953125 + ], + [ + 389.60577392578125, + 197.20843505859375 + ], + [ + 103.76806640625, + 197.20843505859375 + ] + ], + "bbox": [ + 103.76806640625, + 180.8876953125, + 389.60577392578125, + 197.20843505859375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/32/SectionHeader/1", + "4": "/page/33/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/33/SectionHeader/5", + "block_type": "SectionHeader", + "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", + "id": "/page/33/Text/6", + "block_type": "Text", + "html": "
# Add noise for exploration across experts. router logits += mtf.random uniform(shape=router logits.shape, minval=1−eps, maxval=1+eps)
", "polygon": [ [ - 89.4990234375, - 154.0107421875 + 111.462890625, + 214.2421875 ], [ - 500.23828125, - 154.0107421875 + 472.4738464355469, + 214.2421875 ], [ - 500.23828125, - 358.7674255371094 + 472.4738464355469, + 231.64453125 ], [ - 89.4990234375, - 358.7674255371094 + 111.462890625, + 231.64453125 ] ], + "bbox": [ + 111.462890625, + 214.2421875, + 472.4738464355469, + 231.64453125 + ], "children": null, "section_hierarchy": { - "1": "/page/32/SectionHeader/1" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/32/SectionHeader/1", + "4": "/page/33/SectionHeader/5" }, "images": {} }, { - "id": "/page/32/Text/4", + "id": "/page/33/Text/7", "block_type": "Text", - "html": "Figure 14: Pseudo code for the load balance loss for Switch Transformers in Mesh Tensorflow.
", + "html": "# Convert input to softmax operation from bfloat16 to float32 for stability. router logits = mtf.to float32(router logits)
", "polygon": [ [ - 89.4990234375, - 380.53125 + 104.28999328613281, + 240.54638671875 ], [ - 522.3515625, - 380.53125 + 405.8747863769531, + 240.54638671875 ], [ - 522.3515625, - 406.73431396484375 + 405.8747863769531, + 256.4864501953125 ], [ - 89.4990234375, - 406.73431396484375 + 104.28999328613281, + 256.4864501953125 ] ], + "bbox": [ + 104.28999328613281, + 240.54638671875, + 405.8747863769531, + 256.4864501953125 + ], "children": null, "section_hierarchy": { - "1": "/page/32/SectionHeader/1" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/32/SectionHeader/1", + "4": "/page/33/SectionHeader/5" }, "images": {} }, { - "id": "/page/32/PageFooter/5", - "block_type": "PageFooter", - "html": "", + "id": "/page/33/Code/8", + "block_type": "Code", + "html": "# Probabilities for each token of what expert it should be sent to.\nrouter probs = mtf.softmax(router logits, axis=−1)", "polygon": [ [ - 300.322265625, - 724.9639129638672 + 104.28999328613281, + 265.950439453125 ], [ - 311.37890625, - 724.9639129638672 + 371.443359375, + 265.950439453125 ], [ - 311.37890625, - 735.92578125 + 371.443359375, + 281.8904113769531 ], [ - 300.322265625, - 735.92578125 + 104.28999328613281, + 281.8904113769531 ] ], + "bbox": [ + 104.28999328613281, + 265.950439453125, + 371.443359375, + 281.8904113769531 + ], "children": null, "section_hierarchy": { - "1": "/page/32/SectionHeader/1" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/32/SectionHeader/1", + "4": "/page/33/SectionHeader/5" }, "images": {} - } - ], - "section_hierarchy": { - "1": "/page/32/SectionHeader/1" - }, - "images": null - }, - { - "id": "/page/33/Page/863", - "block_type": "Page", - "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)", "polygon": [ [ - 238.9130859375, - 37.992431640625 + 97.5673828125, + 290.9898986816406 ], [ - 369.94921875, - 37.992431640625 + 481.2746887207031, + 290.9898986816406 ], [ - 369.94921875, - 49.74169921875 + 481.2746887207031, + 627.64453125 ], [ - 238.9130859375, - 49.74169921875 + 97.5673828125, + 627.64453125 ] ], + "bbox": [ + 97.5673828125, + 290.9898986816406, + 481.2746887207031, + 627.64453125 + ], "children": null, "section_hierarchy": { - "1": "/page/32/SectionHeader/1" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/32/SectionHeader/1", + "4": "/page/33/SectionHeader/5" }, "images": {} }, { - "id": "/page/33/Code/1", + "id": "/page/33/Code/10", "block_type": "Code", - "html": "
import mesh tensorflow as mtf\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 router weights = mtf.Variable(shape=[d model, num experts])\n # router logits shape: [num cores, tokens per core, num experts]\n router logits = mtf.einsum([inputs, router weights], reduced dim=d model)\n if is training:\n # Add noise for exploration across experts.\n router logits += mtf.random uniform(shape=router logits.shape, minval=1−eps, maxval=1+eps)\n # Convert input to softmax operation from bfloat16 to float32 for stability.\n router logits = mtf.to float32(router logits)\n # Probabilities for each token of what expert it should be sent to.\n router probs = mtf.softmax(router logits, axis=−1)\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]\n expert gate, expert index = mtf.top 1(router probs, reduced dim=num experts)\n # expert mask shape: [num cores, tokens per core, num experts]\n expert mask = mtf.one hot(expert index, dimension=num experts)\n # Compute load balancing loss.\n aux 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.\n position in expert = mtf.cumsum(expert mask, dimension=tokens per core) ∗ expert mask\n # Keep only tokens that fit within expert capacity.\n expert mask ∗= mtf.less(position in expert, expert capacity)\n expert mask flat = mtf.reduce sum(expert mask, reduced dim=experts dim)\n # Mask out the experts that have overflowed the expert capacity.\n expert 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]\n combine 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.\n combine 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]\n dispatch tensor = mtf.cast(combine tensor, tf.bool)\n return dispatch tensor, combine tensor, aux loss", + "html": "
return dispatch tensor, combine tensor, aux loss", "polygon": [ [ - 90.0, - 93.005859375 + 104.28999328613281, + 630.0844573974609 ], [ - 492.46875, - 93.005859375 + 291.4835205078125, + 630.0844573974609 ], [ - 492.46875, + 291.4835205078125, 637.5564575195312 ], [ - 90.0, + 104.28999328613281, 637.5564575195312 ] ], + "bbox": [ + 104.28999328613281, + 630.0844573974609, + 291.4835205078125, + 637.5564575195312 + ], "children": null, "section_hierarchy": { - "1": "/page/32/SectionHeader/1" + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/32/SectionHeader/1", + "4": "/page/33/SectionHeader/5" }, "images": {} }, { - "id": "/page/33/Text/2", + "id": "/page/33/Text/11", "block_type": "Text", - "html": "
Figure 15: Pseudo code for the router for Switch Transformers in Mesh Tensorflow.
", + "html": "Figure 15: Pseudo code for the router for Switch Transformers in Mesh Tensorflow.
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import mesh tensorflow as mtf", + "polygon": [ + [ + 89.7978515625, + 96.58636474609375 + ], + [ + 209.77734375, + 96.58636474609375 + ], + [ + 209.77734375, + 104.220703125 + ], + [ + 89.7978515625, + 104.220703125 + ] + ], + "bbox": [ + 89.7978515625, + 96.58636474609375, + 209.77734375, + 104.220703125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/0/SectionHeader/2", + "2": "/page/3/SectionHeader/8", + "3": "/page/32/SectionHeader/1", + "4": "/page/33/SectionHeader/5" + }, + "images": {} + }, + { + "id": "/page/34/Text/5", + "block_type": "Text", + "html": "
def switch layer(inputs, n, capacity factor, num experts): \"\"\"Distributed switch transformer feed−forward layer.\"\"\"
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Figure 16: Pseudo code of the Switch Transformer layer in Mesh Tensorflow.
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---|---|---|---|
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 | 11 | |
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 |
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 |
xiv Contents
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---|---|---|---|
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 |
Contents | xv | ||
---|---|---|---|
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 |
6.10 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
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---|---|---|---|
7.1 | Multiple assignment | 63 | |
7.2 | Updating variables | 64 | |
7.3 | The while statement | 64 | |
7.4 | break | 65 | |
7.5 | Square roots | 66 | |
7.6 | Algorithms | 67 | |
7.7 | Debugging | 68 | |
7.8 | Glossary | 68 | |
7.9 | Exercises | 69 | |
8 | Strings | 71 | |
8.1 | A string is a sequence | 71 | |
8.2 | len | 71 | |
8.3 | Traversal with a for loop | 72 | |
8.4 | String slices | 73 | |
8.5 | Strings are immutable | 74 | |
8.6 | Searching | 74 | |
8.7 | Looping and counting | 75 | |
8.8 | String methods | 75 | |
8.9 | The in operator | 76 | |
8.10 | String comparison | 76 | |
8.11 | Debugging | 77 | |
8.12 | Glossary | 78 | |
8.13 | Exercises | 79 | |
9 | Case study: word play | 81 | |
9.1 | Reading word lists | 81 | |
9.2 | Exercises | 82 | |
9.3 | Search | 82 | |
9.4 | Looping with indices | 83 | |
9.5 | Debugging | 85 | |
9.6 | Glossary | 85 | |
9.7 | Exercises | 86 |
Contents | xvii | |
---|---|---|
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 |
xviii Contents
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---|---|---|
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 |
Contents | xix | |
---|---|---|
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 | 143 | |
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 | 151 | |
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 |
xx Contents
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---|---|---|
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 |
18.12 | Exercises | 177 |
Contents | xxi | ||
---|---|---|---|
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 |
xxii Contents
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", + "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.
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This 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
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The parentheses indicate that print is a function. We'll get to functions in Chapter 3.
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What about values like '17' and '3.2'? They look like numbers, but they are in quotation marks like strings.
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They're strings.
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- } - }, - { - "id": "/page/33/Caption/2", - "block_type": "Caption", - "html": "Figure 2.1: State diagram.
", - "polygon": [ - [ - 227.70703125, - 148.11328125 - ], - [ - 340.962890625, - 148.11328125 - ], - [ - 340.962890625, - 158.7529296875 - ], - [ - 227.70703125, - 158.7529296875 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/32/SectionHeader/1", - "3": "/page/32/SectionHeader/2" - }, - "images": {} - } + "bbox": [ + 159.275390625, + 88.4619140625, + 403.716796875, + 133.1279296875 ], + "children": null, "section_hierarchy": { "1": "/page/32/SectionHeader/1", - "3": "/page/32/SectionHeader/2" + "4": "/page/32/SectionHeader/2" }, - "images": null + "images": {} }, { - "id": "/page/33/Text/3", + "id": "/page/33/Text/2", "block_type": "Text", - "html": ">>> 1,000,000
", + "html": "Figure 2.1: State diagram.
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", + "html": ">>> 1,000,000 (1, 0, 0)
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This 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.
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", + "html": "A 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 shows the result of the previous example.
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Programmers generally choose names for their variables that are meaningful—they document what the variable is used for.
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76trombones 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?
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---|---|---|---|---|
as | elif | global | or | with |
assert | else | if | pass | yield |
break | except | import | ||
class | exec | in | raise | |
continue | finally | is | return | |
def | for | lambda | try |
and | del | from | not | while |
---|---|---|---|---|
as | elif | global | or | with |
assert | else | if | pass | yield |
break | except | import | ||
class | exec | in | raise | |
continue | finally | is | return | |
def | for | lambda | try |
In Python 3, exec is no longer a keyword, but nonlocal is.
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20+32 hour-1 hour*60+minute minute/60 5**2 (5+9)*(15-7)
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", + "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.
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14 Chapter 2. Variables, expressions and statements
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The 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.
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This behavior can be confusing at first.
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", + "html": "'2' - '1' 'eggs' / 'easy' 'third' * 'a charm'
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", - "polygon": [ - [ - 85.9130859375, - 115.435546875 - ], - [ - 243.9931640625, - 115.435546875 - ], - [ - 243.9931640625, - 155.267578125 - ], - [ - 85.9130859375, - 155.267578125 - ] + "bbox": [ + 85.46484375, + 88.31689453125, + 482.90625, + 111.2783203125 ], "children": null, "section_hierarchy": { "1": "/page/32/SectionHeader/1", - "3": "/page/36/SectionHeader/15" + "2": "/page/36/SectionHeader/4", + "3": "/page/36/SectionHeader/11" }, "images": {} }, { - "id": "/page/37/Text/3", - "block_type": "Text", - "html": "The output of this program is throatwarbler.
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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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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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", + "id": "/page/37/Code/10", + "block_type": "Code", + "html": "percentage = (minute * 100) / 60 # percentage of an hour", "polygon": [ [ - 85.0166015625, - 454.041748046875 + 85.6142578125, + 453.234375 ], [ - 401.625, - 454.041748046875 + 400.4296875, + 453.234375 ], [ - 401.625, - 464.8359375 + 400.4296875, + 464.00433349609375 ], [ - 85.0166015625, - 464.8359375 + 85.6142578125, + 464.00433349609375 ] ], + "bbox": [ + 85.6142578125, + 453.234375, + 400.4296875, + 464.00433349609375 + ], "children": null, "section_hierarchy": { "1": "/page/32/SectionHeader/1", - "3": "/page/37/SectionHeader/6" + "2": "/page/36/SectionHeader/4", + "3": "/page/37/SectionHeader/5" }, "images": {} }, { - "id": "/page/37/Text/12", + "id": "/page/37/Text/11", "block_type": "Text", "html": "
Everything from the # to the end of the line is ignored—it has no effect on the program.
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", + "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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", "polygon": [ [ - 85.83837890625, - 559.58203125 + 85.6142578125, + 560.35546875 ], [ - 375.626953125, - 559.58203125 + 373.53515625, + 560.35546875 ], [ - 375.626953125, + 373.53515625, 571.2558898925781 ], [ - 85.83837890625, + 85.6142578125, 571.2558898925781 ] ], + "bbox": [ + 85.6142578125, + 560.35546875, + 373.53515625, + 571.2558898925781 + ], "children": null, "section_hierarchy": { "1": "/page/32/SectionHeader/1", - "3": "/page/37/SectionHeader/6" + "2": "/page/36/SectionHeader/4", + "3": "/page/37/SectionHeader/5" }, "images": {} }, { - "id": "/page/37/Text/17", + "id": "/page/37/Text/208", "block_type": "Text", "html": "v = 5 # velocity in meters/second.
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Variables names are case sensitive, so LaTeX is not the same as latex.
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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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Use the Python interpreter to check your answers. Exercise 2.3. Practice using the Python interpreter as a calculator:
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>>> type(32) <type 'int'>
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>>> int('32')\n32\n>>> int('Hello')\nValueError: invalid literal for int(): Hello", "polygon": [ [ - 129.16845703125, + 129.60003662109375, 553.6027374267578 ], [ - 525.6051025390625, + 359.7460021972656, 553.6027374267578 ], [ - 525.6051025390625, - 683.4323425292969 + 359.7460021972656, + 602.12109375 ], [ - 129.16845703125, - 683.4323425292969 + 129.60003662109375, + 602.12109375 ] ], + "bbox": [ + 129.60003662109375, + 553.6027374267578, + 359.7460021972656, + 602.12109375 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", @@ -17096,25 +55380,101 @@ { "id": "/page/40/Text/10", "block_type": "Text", + "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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float converts integers and strings to floating-point numbers:
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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.
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>>> print math <module 'math' (built-in)>
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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.
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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.
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", + "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:
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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.
", + "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.
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>>> math.sqrt(2) / 2.0 0.707106781187
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x = math.exp(math.log(x+1))
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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.
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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.
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", + "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.
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To end the function, you have to enter an empty line (this is not necessary in a script).
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The syntax for calling the new function is the same as for built-in functions:
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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:
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def repeat_lyrics():\n print_lyrics()\n print_lyrics()", "polygon": [ [ - 85.166015625, + 85.9130859375, 329.8318176269531 ], [ - 218.7967071533203, + 192.4453125, 329.8318176269531 ], [ - 218.7967071533203, - 379.7649841308594 + 192.4453125, + 364.18341064453125 ], [ - 85.166015625, - 379.7649841308594 + 85.9130859375, + 364.18341064453125 ] ], + "bbox": [ + 85.9130859375, + 329.8318176269531, + 192.4453125, + 364.18341064453125 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/42/SectionHeader/9" + "3": "/page/41/SectionHeader/2", + "4": "/page/42/SectionHeader/9" }, "images": {} }, { "id": "/page/43/Text/9", "block_type": "Text", - "html": "
>>> repeat_lyrics()
", + "html": "And then call repeat_lyrics:
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", - "polygon": [ - [ - 85.53955078125, - 393.873046875 - ], - [ - 279.92352294921875, - 393.873046875 - ], - [ - 279.92352294921875, - 449.3671875 - ], - [ - 85.53955078125, - 449.3671875 - ] + "bbox": [ + 86.361328125, + 369.31640625, + 219.638671875, + 379.7649841308594 ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/42/SectionHeader/9" + "3": "/page/41/SectionHeader/2", + "4": "/page/42/SectionHeader/9" }, "images": {} }, { - "id": "/page/43/Text/11", - "block_type": "Text", - "html": "But that's not really how the song goes.
", + "id": "/page/43/Code/10", + "block_type": "Code", + "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.", "polygon": [ [ - 85.763671875, - 446.66015625 + 85.53955078125, + 384.78515625 ], [ - 259.681640625, - 446.66015625 + 281.794921875, + 384.78515625 ], [ - 259.681640625, + 281.794921875, 459.406005859375 ], [ - 85.763671875, + 85.53955078125, 459.406005859375 ] ], + "bbox": [ + 85.53955078125, + 384.78515625, + 281.794921875, + 459.406005859375 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/42/SectionHeader/9" + "3": "/page/41/SectionHeader/2", + "4": "/page/42/SectionHeader/9" }, "images": {} }, { - "id": "/page/43/SectionHeader/12", + "id": "/page/43/SectionHeader/11", "block_type": "SectionHeader", - "html": "
Pulling together the code fragments from the previous section, the whole program looks like this:
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repeat_lyrics()
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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.
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", + "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.
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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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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.
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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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This function takes two arguments, concatenates them, and prints the result twice. Here is an example that uses it:
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When cat_twice terminates, the variable cat is destroyed. If we try to print it, we get an exception:
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3.10. Stack diagrams 25
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+ "/page/46/Figure/1": 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} }, { "id": "/page/46/Caption/2", "block_type": "Caption", - "html": "Figure 3.1: Stack diagram.
", + "html": "Figure 3.1: Stack diagram.
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", + "html": "Each 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.
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This 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.
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>>> math.sqrt(5)
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>>> math.sqrt(5)\n2.2360679774997898", "polygon": [ [ - 84.64306640625, - 290.619140625 + 85.46484375, + 281.810791015625 ], [ - 180.5565185546875, - 290.619140625 + 181.986328125, + 281.810791015625 ], [ - 180.5565185546875, + 181.986328125, 303.9683837890625 ], [ - 84.64306640625, + 85.46484375, 303.9683837890625 ] ], + "bbox": [ + 85.46484375, + 281.810791015625, + 181.986328125, + 303.9683837890625 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", @@ -20589,22 +59755,28 @@ "html": "
But in a script, if you call a fruitful function all by itself, the return value is lost forever!
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The value None is not the same as the string 'None'. It is a special value that has its own type:
", "polygon": [ [ - 85.3154296875, + 86.0625, 478.0097961425781 ], [ @@ -20746,10 +59942,16 @@ 500.4140625 ], [ - 85.3154296875, + 86.0625, 500.4140625 ] ], + "bbox": [ + 86.0625, + 478.0097961425781, + 482.399169921875, + 500.4140625 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", @@ -20760,53 +59962,30 @@ { "id": "/page/47/Code/14", "block_type": "Code", - "html": ">>> print type(None)", + "html": "
>>> print type(None)\n<type 'NoneType'>", "polygon": [ [ - 85.763671875, - 505.828125 + 85.46484375, + 505.9827880859375 ], [ 191.0172119140625, - 505.828125 + 505.9827880859375 ], [ 191.0172119140625, - 516.65625 + 528.1393737792969 ], [ - 85.763671875, - 516.65625 + 85.46484375, + 528.1393737792969 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/40/SectionHeader/1", - "3": "/page/47/SectionHeader/2" - }, - "images": {} - }, - { - "id": "/page/47/Text/15", - "block_type": "Text", - "html": "
<type 'NoneType'>
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If you import math, you get a module object named math. The module object contains constants like pi and functions like sin and exp.
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", "polygon": [ [ - 127.4501953125, + 129.2431640625, 307.28668212890625 ], [ - 355.0078125, + 354.41015625, 307.28668212890625 ], [ - 355.0078125, - 317.689453125 + 354.41015625, + 317.3988342285156 ], [ - 127.4501953125, - 317.689453125 + 129.2431640625, + 317.3988342285156 ] ], + "bbox": [ + 129.2431640625, + 307.28668212890625, + 354.41015625, + 317.3988342285156 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/48/SectionHeader/3" + "2": "/page/48/SectionHeader/3" }, "images": {} }, { "id": "/page/48/Code/8", "block_type": "Code", - "html": ">>> print pi\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nNameError: name 'pi' is not defined\nAs an alternative, you can import an object from a module like this:\n>>> from math import pi\nNow you can access pi directly, without dot notation.\n>>> print pi", + "html": "
>>> print pi\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nNameError: name 'pi' is not defined", "polygon": [ [ - 128.42138671875, - 322.5234375 + 129.16845703125, + 322.5906677246094 ], [ - 425.95745849609375, - 322.5234375 + 324.228515625, + 322.5906677246094 ], [ - 425.95745849609375, - 430.3512268066406 + 324.228515625, + 369.1362609863281 ], [ - 128.42138671875, - 430.3512268066406 + 129.16845703125, + 369.1362609863281 ] ], + "bbox": [ + 129.16845703125, + 322.5906677246094, + 324.228515625, + 369.1362609863281 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/48/SectionHeader/3" + "2": "/page/48/SectionHeader/3" }, "images": {} }, { "id": "/page/48/Text/9", "block_type": "Text", - "html": "
3.14159265359
", + "html": "As an alternative, you can import an object from a module like this:
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Now you can access pi directly, without dot notation.
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Or you can use the star operator to import everything from the module:
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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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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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Here's an example that uses do_twice to call a function named print_spam twice.
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do_twice(print_spam)
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Solution: http: // thinkpython. com/ code/ do_ four. py .
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.37646484375 + 61.05322265625 ], [ - 483.802734375, - 60.37646484375 + 482.4034118652344, + 61.05322265625 ], [ - 483.802734375, + 482.4034118652344, 71.13372802734375 ], [ @@ -23108,68 +62891,89 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 61.05322265625, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, { "id": "/page/51/PageHeader/10", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 86.0625, - 60.66650390625 + 85.166015625, + 60.76318359375 ], [ - 97.41796875, - 60.66650390625 + 96.521484375, + 60.76318359375 ], [ - 96.22265625, - 69.65771484375 + 96.521484375, + 70.23779296875 ], [ - 84.8671875, - 69.65771484375 + 85.166015625, + 70.23779296875 ] ], + "bbox": [ + 85.166015625, + 60.76318359375, + 96.521484375, + 70.23779296875 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, { - "id": "/page/51/TextInlineMath/1", - "block_type": "TextInlineMath", - "html": "+ - - - - + - - - - + | | | | | | | | | | | | + - - - - + - - - - + | | | | | | | | | | | | + - - - - + - - - - +
", + "id": "/page/51/Code/1", + "block_type": "Code", + "html": "+ - - - - + - - - - +\n| | |\n| | |\n| | |\n| | |\n+ - - - - + - - - - +\n| | |\n| | |\n| | |\n| | |\n+ - - - - + - - - - +", "polygon": [ [ - 110.56640625, + 111.3070068359375, 88.68572998046875 ], [ - 221.1346893310547, - 87.44677734375 + 223.6728515625, + 88.68572998046875 ], [ - 221.1346893310547, + 223.6728515625, 220.59130859375 ], [ - 110.56640625, - 221.203125 + 111.3070068359375, + 220.59130859375 ] ], + "bbox": [ + 111.3070068359375, + 88.68572998046875, + 223.6728515625, + 220.59130859375 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, @@ -23179,55 +62983,69 @@ "html": "
Hint: to print more than one value on a line, you can print a comma-separated sequence:
", "polygon": [ [ - 109.5205078125, - 229.32421875 + 110.56640625, + 229.7109375 ], [ - 465.5771179199219, - 229.32421875 + 466.76953125, + 229.7109375 ], [ - 465.5771179199219, + 466.76953125, 240.74615478515625 ], [ - 109.5205078125, + 110.56640625, 240.74615478515625 ] ], + "bbox": [ + 110.56640625, + 229.7109375, + 466.76953125, + 240.74615478515625 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, { - "id": "/page/51/TextInlineMath/3", - "block_type": "TextInlineMath", - "html": "print '+', '-'
", + "id": "/page/51/Code/3", + "block_type": "Code", + "html": "print '+', '-'", "polygon": [ [ - 110.19287109375, - 250.20703125 + 109.5205078125, + 249.626953125 ], [ 184.51536560058594, - 250.20703125 + 249.626953125 ], [ 184.51536560058594, 260.947265625 ], [ - 110.19287109375, + 109.5205078125, 260.947265625 ] ], + "bbox": [ + 109.5205078125, + 249.626953125, + 184.51536560058594, + 260.947265625 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, @@ -23237,123 +63055,151 @@ "html": "
If the sequence ends with a comma, Python leaves the line unfinished, so the value printed next appears on the same line.
", "polygon": [ [ - 110.1181640625, - 270.31640625 + 109.5205078125, + 270.896484375 ], [ - 482.90625, - 270.31640625 + 483.50390625, + 270.896484375 ], [ - 482.90625, - 293.51953125 + 483.50390625, + 293.2961120605469 ], [ - 110.1181640625, - 293.51953125 + 109.5205078125, + 293.2961120605469 ] ], + "bbox": [ + 109.5205078125, + 270.896484375, + 483.50390625, + 293.2961120605469 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, { - "id": "/page/51/Text/5", - "block_type": "Text", - "html": "print '+', print '-'
", + "id": "/page/51/Code/5", + "block_type": "Code", + "html": "print '+',\nprint '-'", "polygon": [ [ - 109.37109375, - 303.1875 + 109.74462890625, + 303.53466796875 ], [ - 163.599365234375, - 303.1875 + 171.52734375, + 303.53466796875 ], [ - 163.599365234375, - 325.6922607421875 + 171.52734375, + 329.291015625 ], [ - 109.37109375, - 325.6922607421875 + 109.74462890625, + 329.291015625 ] ], + "bbox": [ + 109.74462890625, + 303.53466796875, + 171.52734375, + 329.291015625 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, { "id": "/page/51/Text/6", "block_type": "Text", - "html": "
The output of these statements is '+ -'.
", + "html": "The output of these statements is '+ -'.
", "polygon": [ [ - 109.37109375, - 335.478515625 + 111.08935546875, + 335.865234375 ], [ 273.878662109375, - 335.478515625 + 335.865234375 ], [ 273.878662109375, - 345.919921875 + 345.8702697753906 ], [ - 109.37109375, - 345.919921875 + 111.08935546875, + 345.8702697753906 ] ], + "bbox": [ + 111.08935546875, + 335.865234375, + 273.878662109375, + 345.8702697753906 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, { "id": "/page/51/Text/7", "block_type": "Text", - "html": "A print statement all by itself ends the current line and goes to the next line.
", + "html": "A print statement all by itself ends the current line and goes to the next line.
", "polygon": [ [ - 110.4169921875, - 351.9140625 + 109.96875, + 351.52734375 ], [ 422.7018737792969, - 351.9140625 + 351.52734375 ], [ 422.7018737792969, 362.04925537109375 ], [ - 110.4169921875, + 109.96875, 362.04925537109375 ] ], + "bbox": [ + 109.96875, + 351.52734375, + 422.7018737792969, + 362.04925537109375 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} }, { - "id": "/page/51/Text/8", - "block_type": "Text", - "html": "2. Write a function that draws a similar grid with four rows and four columns.
", + "id": "/page/51/ListItem/8", + "block_type": "ListItem", + "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.
", + "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.
", "polygon": [ [ 85.763671875, 395.806640625 ], [ - 482.90625, + 482.403076171875, 395.806640625 ], [ - 482.90625, - 418.6938171386719 + 482.403076171875, + 418.81640625 ], [ 85.763671875, - 418.6938171386719 + 418.81640625 ] ], + "bbox": [ + 85.763671875, + 395.806640625, + 482.403076171875, + 418.81640625 + ], "children": null, "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": {} } ], "section_hierarchy": { "1": "/page/40/SectionHeader/1", - "3": "/page/50/SectionHeader/4" + "2": "/page/48/SectionHeader/3", + "4": "/page/50/SectionHeader/4" }, "images": null }, { - "id": "/page/52/Page/114", + "id": "/page/52/Page/115", "block_type": "Page", - "html": "Code examples from this chapter are available from http://thinkpython.com/code/ polygon.py.
", + "html": "Code examples from this chapter are available from http://thinkpython.com/code/ polygon.py.
", "polygon": [ [ - 128.6455078125, - 296.4827880859375 + 128.3466796875, + 296.2265625 ], [ - 525.9375, - 296.4827880859375 + 526.53515625, + 296.2265625 ], [ - 525.9375, - 319.81640625 + 526.53515625, + 318.7899475097656 ], [ - 128.6455078125, - 319.81640625 + 128.3466796875, + 318.7899475097656 ] ], + "bbox": [ + 128.3466796875, + 296.2265625, + 526.53515625, + 318.7899475097656 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1" @@ -23523,58 +63408,70 @@ { "id": "/page/52/SectionHeader/3", "block_type": "SectionHeader", - "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.
", + "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.
", "polygon": [ [ - 128.9443359375, - 380.53125 + 128.794921875, + 380.337890625 ], [ - 527.1328125, - 380.53125 + 526.236328125, + 380.337890625 ], [ - 527.1328125, + 526.236328125, 414.95196533203125 ], [ - 128.9443359375, + 128.794921875, 414.95196533203125 ] ], + "bbox": [ + 128.794921875, + 380.337890625, + 526.236328125, + 414.95196533203125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -23584,26 +63481,32 @@ "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.
", "polygon": [ [ - 128.794921875, - 426.2898254394531 + 129.09375, + 426.1640625 ], [ - 526.833984375, - 426.2898254394531 + 525.9375, + 426.1640625 ], [ - 526.833984375, + 525.9375, 448.59698486328125 ], [ - 128.794921875, + 129.09375, 448.59698486328125 ] ], + "bbox": [ + 129.09375, + 426.1640625, + 525.9375, + 448.59698486328125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -23613,55 +63516,67 @@ "html": "If Swampy is installed as a package on your system, you can import TurtleWorld like this:
", "polygon": [ [ - 128.794921875, - 459.03515625 + 128.0478515625, + 459.80859375 ], [ - 526.833984375, - 459.03515625 + 525.9375, + 459.80859375 ], [ - 526.833984375, + 525.9375, 470.0469970703125 ], [ - 128.794921875, + 128.0478515625, 470.0469970703125 ] ], + "bbox": [ + 128.0478515625, + 459.80859375, + 525.9375, + 470.0469970703125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, { - "id": "/page/52/Text/7", - "block_type": "Text", - "html": "from swampy.TurtleWorld import *
", + "id": "/page/52/Code/7", + "block_type": "Code", + "html": "from swampy.TurtleWorld import *", "polygon": [ [ - 128.72021484375, - 476.82421875 + 128.27197265625, + 477.4138488769531 ], [ - 298.828125, - 476.82421875 + 296.9815979003906, + 477.4138488769531 ], [ - 298.828125, + 296.9815979003906, 487.3764343261719 ], [ - 128.72021484375, + 128.27197265625, 487.3764343261719 ] ], + "bbox": [ + 128.27197265625, + 477.4138488769531, + 296.9815979003906, + 487.3764343261719 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -23671,26 +63586,32 @@ "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:
", "polygon": [ [ - 128.9443359375, + 129.09375, 495.04241943359375 ], [ - 527.1328125, + 525.9375, 495.04241943359375 ], [ - 527.1328125, - 529.3940124511719 + 525.9375, + 529.41796875 ], [ - 128.9443359375, - 529.3940124511719 + 129.09375, + 529.41796875 ] ], + "bbox": [ + 129.09375, + 495.04241943359375, + 525.9375, + 529.41796875 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -23700,55 +63621,67 @@ "html": "from TurtleWorld import *
", "polygon": [ [ - 129.392578125, - 536.37890625 + 127.30078125, + 536.7598571777344 ], [ 260.578125, - 536.37890625 + 536.7598571777344 ], [ 260.578125, 546.7224578857422 ], [ - 129.392578125, + 127.30078125, 546.7224578857422 ] ], + "bbox": [ + 127.30078125, + 536.7598571777344, + 260.578125, + 546.7224578857422 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, { "id": "/page/52/Text/10", "block_type": "Text", - "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
", + "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
", "polygon": [ [ - 129.09375, - 554.16796875 + 128.49609375, + 554.3884124755859 ], [ 526.53515625, - 554.16796875 + 554.3884124755859 ], [ 526.53515625, 588.7400207519531 ], [ - 129.09375, + 128.49609375, 588.7400207519531 ] ], + "bbox": [ + 128.49609375, + 554.3884124755859, + 526.53515625, + 588.7400207519531 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -23758,94 +63691,77 @@ "html": "Create a file named mypolygon.py and type in the following code:
", "polygon": [ [ - 128.49609375, + 127.52490234375, 600.0778656005859 ], [ - 421.34765625, + 419.64520263671875, 600.0778656005859 ], [ - 421.34765625, + 419.64520263671875, 610.1900177001953 ], [ - 128.49609375, + 127.52490234375, 610.1900177001953 ] ], + "bbox": [ + 127.52490234375, + 600.0778656005859, + 419.64520263671875, + 610.1900177001953 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, { - "id": "/page/52/Text/12", - "block_type": "Text", - "html": "from swampy.TurtleWorld import *
", + "id": "/page/52/Code/12", + "block_type": "Code", + "html": "from swampy.TurtleWorld import *\nworld = TurtleWorld()\nbob = Turtle()\nprint bob", "polygon": [ [ - 128.42138671875, + 128.49609375, 617.5568695068359 ], [ - 297.03515625, + 296.98162841796875, 617.5568695068359 ], [ - 297.03515625, - 627.5194702148438 + 296.98162841796875, + 690.29296875 ], [ - 128.42138671875, - 627.5194702148438 + 128.49609375, + 690.29296875 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/52/Code/13", - "block_type": "Code", - "html": "
world = TurtleWorld()\nbob = Turtle()\nprint bob", - "polygon": [ - [ - 128.42138671875, - 641.56640625 - ], - [ - 239.4476318359375, - 641.56640625 - ], - [ - 239.4476318359375, - 676.2974548339844 - ], - [ - 128.42138671875, - 676.2974548339844 - ] + "bbox": [ + 128.49609375, + 617.5568695068359, + 296.98162841796875, + 690.29296875 ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, { - "id": "/page/52/Text/14", + "id": "/page/52/Text/13", "block_type": "Text", "html": "
wait_for_user()
", "polygon": [ [ - 128.3466796875, + 129.6000213623047, 690.7228546142578 ], [ @@ -23857,28 +63773,34 @@ 700.6854553222656 ], [ - 128.3466796875, + 129.6000213623047, 700.6854553222656 ] ], + "bbox": [ + 129.6000213623047, + 690.7228546142578, + 208.06546020507812, + 700.6854553222656 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} } ], "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": null }, { - "id": "/page/53/Page/201", + "id": "/page/53/Page/204", "block_type": "Page", - "html": "32 Chapter 4. Case study: interface design
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.71484375 + 60.85986328125 ], [ - 483.50390625, - 60.71484375 + 482.90625, + 60.85986328125 ], [ - 483.50390625, + 482.90625, 71.13372802734375 ], [ @@ -23920,39 +63848,16 @@ 71.13372802734375 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/53/PageHeader/16", - "block_type": "PageHeader", - "html": "", - "polygon": [ - [ - 85.24072265625, - 60.134765625 - ], - [ - 96.14794921875, - 60.134765625 - ], - [ - 96.14794921875, - 69.8994140625 - ], - [ - 85.24072265625, - 69.8994140625 - ] + "bbox": [ + 86.4000015258789, + 60.85986328125, + 482.90625, + 71.13372802734375 ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -23962,26 +63867,32 @@ "html": "The first line imports everything from the TurtleWorld module in the swampy package.
", "polygon": [ [ - 85.6142578125, - 88.02685546875 + 85.46484375, + 87.978515625 ], [ - 469.16015625, - 88.02685546875 + 468.481689453125, + 87.978515625 ], [ - 469.16015625, - 99.4833984375 + 468.481689453125, + 98.79791259765625 ], [ - 85.6142578125, - 99.4833984375 + 85.46484375, + 98.79791259765625 ] ], + "bbox": [ + 85.46484375, + 87.978515625, + 468.481689453125, + 98.79791259765625 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -23991,26 +63902,32 @@ "html": "The next lines create a TurtleWorld assigned to world and a Turtle assigned to bob. Printing bob yields something like:
", "polygon": [ [ - 85.3154296875, - 107.7978515625 + 85.6142578125, + 108.5712890625 ], [ - 484.69921875, - 107.7978515625 + 482.90625, + 108.5712890625 ], [ - 484.69921875, + 482.90625, 131.42388916015625 ], [ - 85.3154296875, + 85.6142578125, 131.42388916015625 ] ], + "bbox": [ + 85.6142578125, + 108.5712890625, + 482.90625, + 131.42388916015625 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -24020,26 +63937,32 @@ "html": "<TurtleWorld.Turtle instance at 0xb7bfbf4c>
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", "polygon": [ [ - 84.26953125, - 356.5546875 + 85.763671875, + 357.71484375 ], [ - 461.091796875, - 356.5546875 + 460.2214660644531, + 357.71484375 ], [ - 461.091796875, + 460.2214660644531, 368.3109130859375 ], [ - 84.26953125, + 85.763671875, 368.3109130859375 ] ], + "bbox": [ + 85.763671875, + 357.71484375, + 460.2214660644531, + 368.3109130859375 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -24223,26 +64182,32 @@ "html": "When you run this program, you should see bob move east and then north, leaving two line segments behind.
", "polygon": [ [ - 85.166015625, - 378.017578125 + 85.46484375, + 378.2109375 ], [ - 484.400390625, - 378.017578125 + 482.39788818359375, + 378.2109375 ], [ - 484.400390625, + 482.39788818359375, 400.9369201660156 ], [ - 85.166015625, + 85.46484375, 400.9369201660156 ] ], + "bbox": [ + 85.46484375, + 378.2109375, + 482.39788818359375, + 400.9369201660156 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, @@ -24252,55 +64217,67 @@ "html": "Now modify the program to draw a square. Don't go on until you've got it working!
", "polygon": [ [ - 85.46484375, + 85.6142578125, 410.6953125 ], [ - 458.701171875, + 458.40234375, 410.6953125 ], [ - 458.701171875, + 458.40234375, 421.3689270019531 ], [ - 85.46484375, + 85.6142578125, 421.3689270019531 ] ], + "bbox": [ + 85.6142578125, + 410.6953125, + 458.40234375, + 421.3689270019531 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/52/SectionHeader/3" + "4": "/page/52/SectionHeader/3" }, "images": {} }, { "id": "/page/53/SectionHeader/12", "block_type": "SectionHeader", - "html": "Chances are you wrote something like this (leaving out the code that creates TurtleWorld and waits for the user):
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fd(bob, 100)\nlt(bob)\nfd(bob, 100)\nlt(bob)\nfd(bob, 100)\nlt(bob)\nfd(bob, 100)", "polygon": [ [ - 85.09130859375, + 85.763671875, 507.2037353515625 ], [ - 482.3988037109375, + 157.6318359375, 507.2037353515625 ], [ - 482.3988037109375, + 157.6318359375, + 626.9153594970703 + ], + [ + 85.763671875, + 626.9153594970703 + ] + ], + "bbox": [ + 85.763671875, + 507.2037353515625, + 157.6318359375, + 626.9153594970703 + ], + "children": null, + "section_hierarchy": { + "1": "/page/52/SectionHeader/1", + "4": "/page/53/SectionHeader/12" + }, + "images": {} + }, + { + "id": "/page/53/Text/15", + "block_type": "Text", + "html": "
We can do the same thing more concisely with a for statement. Add this example to mypolygon.py and run it again:
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You should see something like this:
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\n", + "polygon": [ + [ + 85.09130859375, + 60.521484375 + ], + [ + 96.74560546875, + 60.521484375 + ], + [ + 96.74560546875, + 69.5126953125 + ], + [ + 85.09130859375, + 69.5126953125 + ] + ], + "bbox": [ + 85.09130859375, + 60.521484375, + 96.74560546875, + 69.5126953125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} } ], "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": null }, { - "id": "/page/54/Page/231", + "id": "/page/54/Page/236", "block_type": "Page", - "html": "4.3. Exercises 33
", + "html": "", "polygon": [ [ - 127.7490234375, + 129.60000610351562, 61.171142578125 ], [ @@ -24439,293 +64545,388 @@ 71.13372802734375 ], [ - 127.7490234375, + 129.60000610351562, 71.13372802734375 ] ], + "bbox": [ + 129.60000610351562, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} }, { - "id": "/page/54/PageHeader/17", + "id": "/page/54/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 515.77734375, - 60.6181640625 + 514.880859375, + 60.85986328125 ], [ - 526.53515625, - 60.6181640625 + 526.236328125, + 60.85986328125 ], [ - 526.53515625, - 70.0927734375 + 526.236328125, + 70.33447265625 ], [ - 515.77734375, - 70.0927734375 + 514.880859375, + 70.33447265625 ] ], + "bbox": [ + 514.880859375, + 60.85986328125, + 526.236328125, + 70.33447265625 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} }, { "id": "/page/54/Text/1", "block_type": "Text", - "html": "Hello! Hello! Hello! Hello!
", + "html": "Hello! Hello! Hello!
", "polygon": [ [ - 129.16845703125, - 88.2685546875 + 129.01904296875, + 87.78515625 ], [ - 161.8154296875, - 88.2685546875 + 161.666015625, + 87.78515625 ], [ - 161.8154296875, - 135.2313232421875 + 161.666015625, + 124.13671875 ], [ - 129.16845703125, - 135.2313232421875 + 129.01904296875, + 124.13671875 ] ], + "bbox": [ + 129.01904296875, + 87.78515625, + 161.666015625, + 124.13671875 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} }, { "id": "/page/54/Text/2", "block_type": "Text", + "html": "Hello!
", + "polygon": [ + [ + 128.3466796875, + 125.26873779296875 + ], + [ + 160.98219299316406, + 125.26873779296875 + ], + [ + 160.98219299316406, + 136.2216796875 + ], + [ + 128.3466796875, + 136.2216796875 + ] + ], + "bbox": [ + 128.3466796875, + 125.26873779296875, + 160.98219299316406, + 136.2216796875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/52/SectionHeader/1", + "4": "/page/53/SectionHeader/12" + }, + "images": {} + }, + { + "id": "/page/54/Text/3", + "block_type": "Text", "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.
", "polygon": [ [ - 128.794921875, - 140.37890625 + 128.49609375, + 141.0556640625 ], [ - 526.833984375, - 140.37890625 + 525.5963134765625, + 141.0556640625 ], [ - 526.833984375, + 525.5963134765625, 163.5699462890625 ], [ - 128.794921875, + 128.49609375, 163.5699462890625 ] ], + "bbox": [ + 128.49609375, + 141.0556640625, + 525.5963134765625, + 163.5699462890625 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} }, { - "id": "/page/54/Text/3", + "id": "/page/54/Text/4", "block_type": "Text", "html": "Here is a for statement that draws a square:
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for i in range(4): fd(bob, 100) lt(bob)
", "polygon": [ [ - 128.49609375, - 188.138671875 + 129.5999755859375, + 189.41876220703125 ], [ - 223.822265625, - 188.138671875 + 223.7465362548828, + 189.41876220703125 ], [ - 223.822265625, - 225.263671875 + 223.7465362548828, + 225.650390625 ], [ - 128.49609375, - 225.263671875 + 129.5999755859375, + 225.650390625 ] ], + "bbox": [ + 129.5999755859375, + 189.41876220703125, + 223.7465362548828, + 225.650390625 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} }, { - "id": "/page/54/Text/5", + "id": "/page/54/Text/6", "block_type": "Text", "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.
", "polygon": [ [ - 129.5419921875, - 229.32421875 + 129.09375, + 229.7109375 ], [ - 525.9375, - 229.32421875 + 525.6016845703125, + 229.7109375 ], [ - 525.9375, + 525.6016845703125, 252.10784912109375 ], [ - 129.5419921875, + 129.09375, 252.10784912109375 ] ], + "bbox": [ + 129.09375, + 229.7109375, + 525.6016845703125, + 252.10784912109375 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} }, { - "id": "/page/54/Text/6", + "id": "/page/54/Text/7", "block_type": "Text", "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.
", "polygon": [ [ - 128.9443359375, - 261.228515625 + 129.09375, + 261.80859375 ], [ - 525.9375, - 261.228515625 + 525.6040649414062, + 261.80859375 ], [ - 525.9375, + 525.6040649414062, 284.2688293457031 ], [ - 128.9443359375, + 129.09375, 284.2688293457031 ] ], + "bbox": [ + 129.09375, + 261.80859375, + 525.6040649414062, + 284.2688293457031 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} }, { - "id": "/page/54/Text/7", + "id": "/page/54/Text/8", "block_type": "Text", "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.
", "polygon": [ [ - 129.09375, + 128.794921875, 293.90625 ], [ - 525.9375, + 525.638671875, 293.90625 ], [ - 525.9375, - 353.0118408203125 + 525.638671875, + 353.07421875 ], [ - 129.09375, - 353.0118408203125 + 128.794921875, + 353.07421875 ] ], + "bbox": [ + 128.794921875, + 293.90625, + 525.638671875, + 353.07421875 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/53/SectionHeader/12" + "4": "/page/53/SectionHeader/12" }, "images": {} }, { - "id": "/page/54/SectionHeader/8", + "id": "/page/54/SectionHeader/9", "block_type": "SectionHeader", - "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.
", "polygon": [ [ 129.2431640625, - 406.828125 + 407.98828125 ], [ - 526.53515625, - 406.828125 + 525.9375, + 407.98828125 ], [ - 526.53515625, + 525.9375, 430.79083251953125 ], [ @@ -24733,257 +64934,311 @@ 430.79083251953125 ] ], + "bbox": [ + 129.2431640625, + 407.98828125, + 525.9375, + 430.79083251953125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/54/SectionHeader/8" + "4": "/page/54/SectionHeader/9" }, "images": {} }, { - "id": "/page/54/Text/10", + "id": "/page/54/Text/11", "block_type": "Text", "html": "The following sections have solutions to the exercises, so don't look until you have finished (or at least tried).
", "polygon": [ [ - 129.392578125, + 128.794921875, 440.0859375 ], [ - 526.833984375, + 525.638671875, 440.0859375 ], [ - 526.833984375, + 525.638671875, 462.95184326171875 ], [ - 129.392578125, + 128.794921875, 462.95184326171875 ] ], + "bbox": [ + 128.794921875, + 440.0859375, + 525.638671875, + 462.95184326171875 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/54/SectionHeader/8" + "4": "/page/54/SectionHeader/9" }, "images": {} }, { - "id": "/page/54/ListItem/11", + "id": "/page/54/ListItem/12", "block_type": "ListItem", "html": "Write a function call that passes bob as an argument to square, and then run the program again.
", "polygon": [ [ - 153.0, - 504.28125 + 153.4482421875, + 504.81268310546875 ], [ - 526.53515625, - 504.28125 + 525.6010131835938, + 504.81268310546875 ], [ - 526.53515625, + 525.6010131835938, 527.1188354492188 ], [ - 153.0, + 153.4482421875, 527.1188354492188 ] ], + "bbox": [ + 153.4482421875, + 504.81268310546875, + 525.6010131835938, + 527.1188354492188 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/54/SectionHeader/8" + "4": "/page/54/SectionHeader/9" }, "images": {} }, { - "id": "/page/54/ListGroup/231", + "id": "/page/54/ListGroup/232", "block_type": "ListGroup", - "html": "Make 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.
", + "html": "Make 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.
", "polygon": [ [ - 153.0, + 152.5517578125, 621.84375 ], [ - 526.53515625, + 525.9375, 621.84375 ], [ - 526.53515625, - 656.6484375 + 525.9375, + 656.4278259277344 ], [ - 153.0, - 656.6484375 + 152.5517578125, + 656.4278259277344 ] ], + "bbox": [ + 152.5517578125, + 621.84375, + 525.9375, + 656.4278259277344 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/54/SectionHeader/8" + "4": "/page/54/SectionHeader/9" }, "images": {} }, { - "id": "/page/54/ListItem/16", + "id": "/page/54/ListItem/17", "block_type": "ListItem", "html": "34 Chapter 4. Case study: interface design
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def square(t):", "polygon": [ [ - 86.13720703125, - 278.630859375 + 85.46484375, + 280.1846618652344 + ], + [ + 173.4697265625, + 280.1846618652344 + ], + [ + 173.4697265625, + 295.259765625 + ], + [ + 85.46484375, + 295.259765625 + ] + ], + "bbox": [ + 85.46484375, + 280.1846618652344, + 173.4697265625, + 295.259765625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/52/SectionHeader/1", + "3": "/page/55/SectionHeader/4" + }, + "images": {} + }, + { + "id": "/page/55/Code/7", + "block_type": "Code", + "html": "
for i in range(4):\n fd(t, 100)\n lt(t)", + "polygon": [ + [ + 96.0732421875, + 292.378662109375 ], [ 201.4625244140625, - 278.630859375 + 292.378662109375 ], [ 201.4625244140625, - 329.291015625 + 327.55078125 ], [ - 86.13720703125, - 329.291015625 + 96.0732421875, + 327.55078125 ] ], + "bbox": [ + 96.0732421875, + 292.378662109375, + 201.4625244140625, + 327.55078125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", @@ -25236,27 +65580,33 @@ "images": {} }, { - "id": "/page/55/Text/7", + "id": "/page/55/Text/8", "block_type": "Text", "html": "
square(bob)
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", + "id": "/page/55/Code/11", + "block_type": "Code", + "html": "ray = Turtle()\nsquare(ray)", "polygon": [ [ - 85.09130859375, - 440.859375 + 85.83837890625, + 442.01953125 ], [ - 161.2177734375, - 440.859375 + 159.6250762939453, + 442.01953125 ], [ - 161.2177734375, - 464.4482727050781 + 159.6250762939453, + 464.44921875 ], [ - 85.09130859375, - 464.4482727050781 + 85.83837890625, + 464.44921875 ] ], + "bbox": [ + 85.83837890625, + 442.01953125, + 159.6250762939453, + 464.44921875 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", @@ -25352,17 +65720,17 @@ "images": {} }, { - "id": "/page/55/Text/11", + "id": "/page/55/Text/12", "block_type": "Text", "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!
", "polygon": [ [ 85.6142578125, - 469.4765625 + 470.63671875 ], [ 482.90625, - 469.4765625 + 470.63671875 ], [ 482.90625, @@ -25373,6 +65741,12 @@ 517.3098449707031 ] ], + "bbox": [ + 85.6142578125, + 470.63671875, + 482.90625, + 517.3098449707031 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", @@ -25381,41 +65755,48 @@ "images": {} }, { - "id": "/page/55/SectionHeader/12", + "id": "/page/55/SectionHeader/13", "block_type": "SectionHeader", - "html": "The next step is to add a length parameter to square. Here is a solution:
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square(bob, 100)
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", + "html": "", "polygon": [ [ - 127.7490234375, + 128.197265625, 61.171142578125 ], [ @@ -25573,43 +65989,57 @@ 71.13372802734375 ], [ - 127.7490234375, + 128.197265625, 71.13372802734375 ] ], + "bbox": [ + 128.197265625, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/55/SectionHeader/12" + "3": "/page/55/SectionHeader/4", + "4": "/page/55/SectionHeader/13" }, "images": {} }, { "id": "/page/56/PageHeader/16", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 515.1796875, - 61.43994140625 + 514.880859375, + 61.0048828125 ], [ - 525.9375, - 61.43994140625 + 525.638671875, + 61.0048828125 ], [ - 525.9375, - 70.72119140625 + 525.638671875, + 70.189453125 ], [ - 515.1796875, - 70.72119140625 + 514.880859375, + 70.189453125 ] ], + "bbox": [ + 514.880859375, + 61.0048828125, + 525.638671875, + 70.189453125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/55/SectionHeader/12" + "3": "/page/55/SectionHeader/4", + "4": "/page/55/SectionHeader/13" }, "images": {} }, @@ -25619,26 +66049,33 @@ "html": "The next step is also a generalization. Instead of drawing squares, polygon draws regular polygons with any number of sides. Here is a solution :rule
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polygon(bob, 7, 70)
", + "id": "/page/56/Code/3", + "block_type": "Code", + "html": "polygon(bob, 7, 70)", "polygon": [ [ - 126.703125, - 193.2626953125 + 128.9443359375, + 193.3607177734375 ], [ - 228.98692321777344, - 193.2626953125 + 230.2470703125, + 193.3607177734375 ], [ - 228.98692321777344, - 203.32330322265625 + 230.2470703125, + 203.4140625 ], [ - 126.703125, - 203.32330322265625 + 128.9443359375, + 203.4140625 ] ], + "bbox": [ + 128.9443359375, + 193.3607177734375, + 230.2470703125, + 203.4140625 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/55/SectionHeader/12" + "3": "/page/55/SectionHeader/4", + "4": "/page/55/SectionHeader/13" }, "images": {} }, @@ -25706,55 +66157,69 @@ "html": "
This 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:
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", + "id": "/page/56/Code/5", + "block_type": "Code", + "html": "polygon(bob, n=7, length=70)", "polygon": [ [ - 129.6000213623047, - 251.947265625 + 128.197265625, + 252.40679931640625 ], [ 276.0601806640625, - 251.947265625 + 252.40679931640625 ], [ 276.0601806640625, 262.369384765625 ], [ - 129.16845703125, + 128.197265625, 262.369384765625 ] ], + "bbox": [ + 128.197265625, + 252.40679931640625, + 276.0601806640625, + 262.369384765625 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/55/SectionHeader/12" + "3": "/page/55/SectionHeader/4", + "4": "/page/55/SectionHeader/13" }, "images": {} }, @@ -25764,26 +66229,33 @@ "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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", + "html": "In 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.
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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.
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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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def polyline(t, n, length, angle):\n for i in range(n):\n fd(t, length)\n lt(t, angle)", "polygon": [ [ - 84.7177734375, + 85.68896484375, 512.005615234375 ], [ - 332.89453125, + 264.2424011230469, 512.005615234375 ], [ - 332.89453125, - 700.6852111816406 + 264.2424011230469, + 558.5512237548828 ], [ - 84.7177734375, - 700.6852111816406 + 85.68896484375, + 558.5512237548828 ] ], + "bbox": [ + 85.68896484375, + 512.005615234375, + 264.2424011230469, + 558.5512237548828 + ], + "children": null, + "section_hierarchy": { + "1": "/page/52/SectionHeader/1", + "3": "/page/57/SectionHeader/4" + }, + "images": {} + }, + { + "id": "/page/57/Text/10", + "block_type": "Text", + "html": "
Now we can rewrite polygon and arc to use polyline:
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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 polyline(t, n, step_length, step_angle)", + "polygon": [ + [ + 84.64306640625, + 628.8046875 + ], + [ + 328.11328125, + 628.8046875 + ], + [ + 328.11328125, + 701.5078125 + ], + [ + 84.64306640625, + 701.5078125 + ] + ], + "bbox": [ + 84.64306640625, + 628.8046875, + 328.11328125, + 701.5078125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", @@ -26405,9 +67123,9 @@ "images": null }, { - "id": "/page/58/Page/150", + "id": "/page/58/Page/152", "block_type": "Page", - "html": "
4.8. A development plan 37
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", + "id": "/page/58/Text/2", + "block_type": "Text", + "html": "def circle(t, r): arc(t, r, 360)
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38 Chapter 4. Case study: interface design
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+ "/page/60/Figure/1": 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" + "/page/60/Figure/3": 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", + "html": "Exercise 4.1. Download the code in this chapter from http: // thinkpython. com/ code/ polygon. py .
", "polygon": [ [ 129.392578125, @@ -27997,62 +69040,82 @@ 495.4031982421875 ] ], + "bbox": [ + 129.392578125, + 473.1658935546875, + 525.041015625, + 495.4031982421875 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/60/SectionHeader/9" + "3": "/page/58/SectionHeader/5", + "4": "/page/60/SectionHeader/9" }, "images": {} }, { - "id": "/page/60/ListGroup/140", + "id": "/page/60/ListGroup/187", "block_type": "ListGroup", "html": "Exercise 4.2. Write an appropriately general set of functions that can draw flowers as in Figure 4.1.
", + "html": "Exercise 4.2. Write an appropriately general set of functions that can draw flowers as in Figure 4.1.
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", + "html": "Solution: http: // thinkpython. com/ code/ flower. py , also requires http: // thinkpython. com/ code/ polygon. py .
", "polygon": [ [ - 128.794921875, - 642.7779235839844 + 129.09375, + 642.7265625 ], [ - 524.443359375, - 642.7779235839844 + 524.14453125, + 642.7265625 ], [ - 524.443359375, - 668.25 + 524.14453125, + 666.703125 ], [ - 128.794921875, - 668.25 + 129.09375, + 666.703125 ] ], + "bbox": [ + 129.09375, + 642.7265625, + 524.14453125, + 666.703125 + ], "children": null, "section_hierarchy": { "1": "/page/52/SectionHeader/1", - "3": "/page/60/SectionHeader/9" + "3": "/page/58/SectionHeader/5", + "4": "/page/60/SectionHeader/9" }, "images": {} }, { "id": "/page/60/Text/16", "block_type": "Text", - "html": "Exercise 4.3. Write an appropriately general set of functions that can draw shapes as in Figure 4.2.
", + "html": "Exercise 4.3. Write an appropriately general set of functions that can draw shapes as in Figure 4.2.
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", + "html": "Solution: http: // thinkpython. com/ code/ pie. py .
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", + "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.
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", + "html": "Solution: http: // thinkpython. com/ code/ letters. py , also requires http: // thinkpython. com/ code/ polygon. py .
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", + "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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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.
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>>> 5 == 5\nTrue\n>>> 5 == 6\nFalse", + "polygon": [ + [ + 128.49609375, + 587.0390625 + ], + [ + 186.46875, + 587.0390625 + ], + [ + 186.46875, + 635.2594299316406 + ], + [ + 128.49609375, + 635.2594299316406 + ] + ], + "bbox": [ + 128.49609375, + 587.0390625, + 186.46875, + 635.2594299316406 + ], + "children": null, + "section_hierarchy": { + "1": "/page/62/SectionHeader/1", + "4": "/page/62/SectionHeader/7" + }, + "images": {} + }, + { + "id": "/page/62/Text/10", + "block_type": "Text", + "html": "
True and False are special values that belong to the type bool; they are not strings:
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42 Chapter 5. Conditionals and recursion
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", + "id": "/page/63/Table/2", + "block_type": "Table", + "html": "x != y | # x is not equal to y | ||||
x > y | # x is greater than y | ||||
x < y | # x is less than y | ||||
x >= y | # x is greater than or equal to y | ||||
x <= y | # x is less than or equal to y |
Although 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 =>.
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", + "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.
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", + "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.
", "polygon": [ [ - 85.0166015625, - 307.634765625 + 85.46484375, + 307.828125 ], [ - 483.50390625, - 307.634765625 + 482.607421875, + 307.828125 ], [ - 483.50390625, + 482.607421875, 330.69293212890625 ], [ - 85.0166015625, + 85.46484375, 330.69293212890625 ] ], + "bbox": [ + 85.46484375, + 307.828125, + 482.607421875, + 330.69293212890625 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/63/SectionHeader/4" + "4": "/page/63/SectionHeader/4" }, "images": {} }, @@ -29028,26 +71425,32 @@ "html": "Finally, 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.
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True
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if x > 0:\n print 'x is positive'", "polygon": [ [ 86.40007019042969, - 536.37890625 + 535.9921875 ], [ - 133.47335815429688, - 536.37890625 + 222.328125, + 535.9921875 ], [ - 133.47335815429688, - 546.8114013671875 + 222.328125, + 559.96875 ], [ 86.40007019042969, - 546.8114013671875 + 559.96875 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/62/SectionHeader/1", - "3": "/page/63/SectionHeader/12" - }, - "images": {} - }, - { - "id": "/page/63/Code/15", - "block_type": "Code", - "html": "
print 'x is positive'", - "polygon": [ - [ - 103.5439453125, - 548.3671875 - ], - [ - 217.1294403076172, - 548.3671875 - ], - [ - 217.1294403076172, - 559.0064086914062 - ], - [ - 103.5439453125, - 559.0064086914062 - ] + "bbox": [ + 86.40007019042969, + 535.9921875, + 222.328125, + 559.96875 ], "children": null, "section_hierarchy": { @@ -29284,27 +71700,33 @@ "images": {} }, { - "id": "/page/63/Text/16", + "id": "/page/63/Text/15", "block_type": "Text", "html": "
The boolean expression after if is called the condition. If it is true, then the indented statement gets executed. If not, nothing happens.
", "polygon": [ [ - 85.46484375, - 562.67578125 + 85.9130859375, + 563.0625 ], [ - 482.39599609375, - 562.67578125 + 482.90625, + 563.0625 ], [ - 482.39599609375, + 482.90625, 585.93896484375 ], [ - 85.46484375, + 85.9130859375, 585.93896484375 ] ], + "bbox": [ + 85.9130859375, + 563.0625, + 482.90625, + 585.93896484375 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", @@ -29313,27 +71735,33 @@ "images": {} }, { - "id": "/page/63/Text/17", + "id": "/page/63/Text/16", "block_type": "Text", "html": "if statements have the same structure as function definitions: a header followed by an indented body. Statements like this are called compound statements.
", "polygon": [ [ - 85.6142578125, - 593.2265625 + 85.3154296875, + 594.0 ], [ - 482.40435791015625, - 593.2265625 + 482.90625, + 594.0 ], [ - 482.40435791015625, + 482.90625, 616.6939697265625 ], [ - 85.6142578125, + 85.3154296875, 616.6939697265625 ] ], + "bbox": [ + 85.3154296875, + 594.0, + 482.90625, + 616.6939697265625 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", @@ -29342,27 +71770,33 @@ "images": {} }, { - "id": "/page/63/Text/18", + "id": "/page/63/Text/17", "block_type": "Text", "html": "There 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.
", "polygon": [ [ - 86.0625, + 85.3154296875, 624.55078125 ], [ - 482.607421875, + 482.40350341796875, 624.55078125 ], [ - 482.607421875, + 482.40350341796875, 671.8379898071289 ], [ - 86.0625, + 85.3154296875, 671.8379898071289 ] ], + "bbox": [ + 85.3154296875, + 624.55078125, + 482.40350341796875, + 671.8379898071289 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", @@ -29371,27 +71805,33 @@ "images": {} }, { - "id": "/page/63/Text/19", - "block_type": "Text", - "html": "if x < 0: pass # need to handle negative values!
", + "id": "/page/63/Code/247", + "block_type": "Code", + "html": "if x < 0:\n pass # need to handle negative values!", "polygon": [ [ - 85.68896484375, - 676.3158264160156 + 86.361328125, + 674.82421875 ], [ 353.1431579589844, - 676.3158264160156 + 674.82421875 ], [ 353.1431579589844, - 700.734375 + 699.57421875 ], [ - 85.68896484375, - 700.734375 + 86.361328125, + 699.57421875 ] ], + "bbox": [ + 86.361328125, + 674.82421875, + 353.1431579589844, + 699.57421875 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", @@ -29407,9 +71847,9 @@ "images": null }, { - "id": "/page/64/Page/184", + "id": "/page/64/Page/191", "block_type": "Page", - "html": "
5.5. Alternative execution 43
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if x%2 == 0: print 'x is even' else: print 'x is odd'
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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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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.
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44 Chapter 5. Conditionals and recursion
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print 'x is less than y' else: print 'x is greater than y'
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", + "id": "/page/65/Code/5", + "block_type": "Code", + "html": "if 0 < x:\n if x < 10:\n print 'x is a positive single-digit number.'", "polygon": [ [ - 86.2119140625, + 86.4000244140625, 254.84765625 ], [ @@ -30083,13 +72665,19 @@ ], [ 358.3153991699219, - 293.90625 + 290.0390625 ], [ - 86.2119140625, - 293.90625 + 86.4000244140625, + 290.0390625 ] ], + "bbox": [ + 86.4000244140625, + 254.84765625, + 358.3153991699219, + 290.0390625 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", @@ -30103,22 +72691,28 @@ "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:
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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:
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The execution of countdown begins with n=3, and since n is greater than 0, it outputs the value 3, and then calls itself...
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5.9. Stack diagrams for recursive functions 45
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A function that calls itself is recursive; the process is called recursion.
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If 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.
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", + "html": "The 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.
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", + "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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", + "html": "Figure 5.1 shows a stack diagram for countdown called with n = 3.
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+ "/page/67/Figure/1": 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", + "html": "Figure 5.1: Stack diagram.
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In 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:
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File \"<stdin>\", line 2, in recurse\n File \"<stdin>\", line 2, in recurse\n File \"<stdin>\", line 2, in recurse\n .\n .\n .\n File \"<stdin>\", line 2, in recurse\nRuntimeError: Maximum recursion depth exceeded", "polygon": [ [ - 95.4755859375, - 380.53125 + 86.4000244140625, + 380.6177673339844 ], [ - 282.2431640625, - 380.53125 + 327.01666259765625, + 380.6177673339844 ], [ - 282.2431640625, - 464.8359375 + 327.01666259765625, + 475.94036865234375 ], [ - 95.4755859375, - 464.8359375 + 86.4000244140625, + 475.94036865234375 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/62/SectionHeader/1", - "3": "/page/67/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/67/Text/8", - "block_type": "Text", - "html": "
RuntimeError: Maximum recursion depth exceeded
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", "polygon": [ @@ -31516,138 +74294,174 @@ 505.6289367675781 ] ], + "bbox": [ + 85.46484375, + 483.01171875, + 482.40350341796875, + 505.6289367675781 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/67/SectionHeader/3" + "3": "/page/64/SectionHeader/11", + "4": "/page/67/SectionHeader/3" }, "images": {} }, { - "id": "/page/67/SectionHeader/10", + "id": "/page/67/SectionHeader/9", "block_type": "SectionHeader", - "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.
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5.12. Debugging 47
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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.
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But if the user types something other than a string of digits, you get an error:
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We will see how to handle this kind of error later.
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IndentationError: unexpected indent
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", + "html": "", "polygon": [ [ 86.4000015258789, 60.66650390625 ], [ - 482.90625, + 482.607421875, 60.66650390625 ], [ - 482.90625, + 482.607421875, 71.13372802734375 ], [ @@ -32273,39 +75233,53 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.66650390625, + 482.607421875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/68/SectionHeader/9" + "3": "/page/64/SectionHeader/11", + "4": "/page/68/SectionHeader/9" }, "images": {} }, { - "id": "/page/69/PageHeader/17", + "id": "/page/69/PageHeader/19", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.83837890625, - 60.71484375 + 85.68896484375, + 61.05322265625 ], [ - 95.69970703125, - 60.71484375 + 97.64208984375, + 61.05322265625 ], [ - 95.69970703125, - 69.609375 + 97.64208984375, + 70.14111328125 ], [ - 85.83837890625, - 69.609375 + 85.68896484375, + 70.14111328125 ] ], + "bbox": [ + 85.68896484375, + 61.05322265625, + 97.64208984375, + 70.14111328125 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/68/SectionHeader/9" + "3": "/page/64/SectionHeader/11", + "4": "/page/68/SectionHeader/9" }, "images": {} }, @@ -32316,324 +75290,472 @@ "polygon": [ [ 86.2119140625, - 88.83526611328125 + 88.365234375 ], [ - 238.48902893066406, - 88.83526611328125 + 238.6142578125, + 88.365234375 ], [ - 238.48902893066406, - 99.2900390625 + 238.6142578125, + 98.79791259765625 ], [ 86.2119140625, - 99.2900390625 + 98.79791259765625 ] ], + "bbox": [ + 86.2119140625, + 88.365234375, + 238.6142578125, + 98.79791259765625 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/68/SectionHeader/9" + "3": "/page/64/SectionHeader/11", + "4": "/page/68/SectionHeader/9" }, "images": {} }, { "id": "/page/69/TextInlineMath/2", "block_type": "TextInlineMath", - "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:
", + "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:
", "polygon": [ [ - 85.166015625, - 108.087890625 + 85.3154296875, + 108.861328125 ], [ - 483.205078125, - 108.087890625 + 484.1015625, + 108.861328125 ], [ - 483.205078125, + 484.1015625, 134.007568359375 ], [ - 85.166015625, + 85.3154296875, 134.007568359375 ] ], + "bbox": [ + 85.3154296875, + 108.861328125, + 484.1015625, + 134.007568359375 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/68/SectionHeader/9" + "3": "/page/64/SectionHeader/11", + "4": "/page/68/SectionHeader/9" }, "images": {} }, { "id": "/page/69/Code/3", "block_type": "Code", - "html": "import math\nsignal_power = 9\nnoise_power = 10\nratio = signal_power / noise_power\ndecibels = 10 * math.log10(ratio)\nprint decibels\nBut when you run it in Python 2, you get an error message.\nTraceback (most recent call last):\n File \"snr.py\", line 5, in ?\n decibels = 10 * math.log10(ratio)\nValueError: math domain error", + "html": "
import math\nsignal_power = 9\nnoise_power = 10\nratio = signal_power / noise_power\ndecibels = 10 * math.log10(ratio)\nprint decibels", "polygon": [ [ - 84.79248046875, - 137.916748046875 + 85.763671875, + 137.5751953125 ], [ - 346.939453125, - 137.916748046875 + 265.359375, + 137.5751953125 ], [ - 346.939453125, + 265.359375, + 209.98828125 + ], + [ + 85.763671875, + 209.98828125 + ] + ], + "bbox": [ + 85.763671875, + 137.5751953125, + 265.359375, + 209.98828125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/62/SectionHeader/1", + "3": "/page/64/SectionHeader/11", + "4": "/page/68/SectionHeader/9" + }, + "images": {} + }, + { + "id": "/page/69/Text/4", + "block_type": "Text", + "html": "
But when you run it in Python 2, you get an error message.
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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.
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The following exercises use TurtleWorld from Chapter 4:
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+ "/page/71/Figure/1": 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} }, { "id": "/page/71/Caption/2", "block_type": "Caption", - "html": "Figure 5.2: A Koch curve.
", + "html": "Figure 5.2: A Koch curve.
", "polygon": [ [ - 228.005859375, - 163.001953125 + 228.4399871826172, + 162.615234375 ], [ 340.6640625, - 163.001953125 + 162.615234375 ], [ 340.6640625, 173.1529541015625 ], [ - 228.005859375, + 228.4399871826172, 173.1529541015625 ] ], + "bbox": [ + 228.4399871826172, + 162.615234375, + 340.6640625, + 173.1529541015625 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/70/SectionHeader/5" + "3": "/page/64/SectionHeader/11", + "4": "/page/70/SectionHeader/5" }, "images": {} } ], "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/70/SectionHeader/5" + "3": "/page/64/SectionHeader/11", + "4": "/page/70/SectionHeader/5" }, "images": null }, @@ -33627,107 +76988,134 @@ "html": "def 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)", "polygon": [ [ - 83.671875, - 195.486328125 + 85.46484375, + 195.29296875 ], [ - 240.556640625, - 195.486328125 + 216.052734375, + 195.29296875 ], [ - 240.556640625, - 328.904296875 + 216.052734375, + 327.64434814453125 ], [ - 83.671875, - 328.904296875 + 85.46484375, + 327.64434814453125 ] ], + "bbox": [ + 85.46484375, + 195.29296875, + 216.052734375, + 327.64434814453125 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/70/SectionHeader/5" + "3": "/page/64/SectionHeader/11", + "4": "/page/70/SectionHeader/5" }, "images": {} }, { "id": "/page/71/Text/4", "block_type": "Text", - "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
", + "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
", "polygon": [ [ - 85.46484375, - 329.291015625 + 86.4000015258789, + 328.904296875 ], [ - 482.90625, - 329.291015625 + 482.4046325683594, + 328.904296875 ], [ - 482.90625, + 482.4046325683594, 352.00921630859375 ], [ - 85.46484375, + 86.4000015258789, 352.00921630859375 ] ], + "bbox": [ + 86.4000015258789, + 328.904296875, + 482.4046325683594, + 352.00921630859375 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/70/SectionHeader/5" + "3": "/page/64/SectionHeader/11", + "4": "/page/70/SectionHeader/5" }, "images": {} }, { - "id": "/page/71/ListGroup/134", + "id": "/page/71/ListGroup/160", "block_type": "ListGroup", "html": "The exception is if x is less than 3: in that case, you can just draw a straight line with length x.
", "polygon": [ [ - 85.3154296875, - 510.08203125 + 85.6142578125, + 510.85546875 ], [ 467.16058349609375, - 510.08203125 + 510.85546875 ], [ 467.16058349609375, 521.2682495117188 ], [ - 85.3154296875, + 85.6142578125, 521.2682495117188 ] ], + "bbox": [ + 85.6142578125, + 510.85546875, + 467.16058349609375, + 521.2682495117188 + ], "children": null, "section_hierarchy": { "1": "/page/62/SectionHeader/1", - "3": "/page/70/SectionHeader/5" + "3": "/page/64/SectionHeader/11", + "4": "/page/70/SectionHeader/5" }, "images": {} }, { - "id": "/page/71/ListGroup/135", + "id": "/page/71/ListGroup/161", "block_type": "ListGroup", "html": "Solution: http: // thinkpython. com/ code/ koch. py .
", + "html": "Solution: http: // thinkpython. com/ code/ koch. py .
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On the other hand, temporary variables like temp often make debugging easier.
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52 Chapter 6. Fruitful functions
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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.
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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.
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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.
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If that works correctly, you are done. Otherwise, you might want to print the value of result before the return statement.
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", + "id": "/page/75/Code/10", + "block_type": "Code", + "html": "result = area(radius)", "polygon": [ [ - 85.98779296875, - 398.70703125 + 85.39013671875, + 400.8698425292969 ], [ - 196.24758911132812, - 398.70703125 + 197.82421875, + 400.8698425292969 ], [ - 196.24758911132812, + 197.82421875, 410.8324279785156 ], [ - 85.98779296875, + 85.39013671875, 410.8324279785156 ] ], + "bbox": [ + 85.39013671875, + 400.8698425292969, + 197.82421875, + 410.8324279785156 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/75/SectionHeader/4" + "4": "/page/75/SectionHeader/4" }, "images": {} }, @@ -36011,26 +79881,32 @@ "html": "
Encapsulating these steps in a function, we get:
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def circle_area(xc, yc, xp, yp): return area(distance(xc, yc, xp, yp))
", + "id": "/page/75/Code/14", + "block_type": "Code", + "html": "def circle_area(xc, yc, xp, yp):\n return area(distance(xc, yc, xp, yp))", "polygon": [ [ - 85.166015625, + 86.28662109375, 530.287841796875 ], [ @@ -36110,43 +79998,55 @@ 552.4454498291016 ], [ - 85.166015625, + 86.28662109375, 552.4454498291016 ] ], + "bbox": [ + 86.28662109375, + 530.287841796875, + 300.8493957519531, + 552.4454498291016 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/75/SectionHeader/4" + "4": "/page/75/SectionHeader/4" }, "images": {} }, { "id": "/page/75/SectionHeader/15", "block_type": "SectionHeader", - "html": "
Functions can return booleans, which is often convenient for hiding complicated tests inside functions. For example:
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6.5. More recursion 55
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Here is an example:
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", + "id": "/page/76/Code/3", + "block_type": "Code", + "html": ">>> is_divisible(6, 4)\nFalse\n>>> is_divisible(6, 3)\nTrue", "polygon": [ [ - 150.51600646972656, - 226.23870849609375 + 128.3466796875, + 135.0587158203125 ], [ - 239.4322052001953, - 226.23870849609375 + 255.3486328125, + 135.0587158203125 ], [ - 239.4322052001953, - 236.2012939453125 + 255.3486328125, + 181.604248046875 ], [ - 150.51600646972656, - 236.2012939453125 + 128.3466796875, + 181.604248046875 ] ], + "bbox": [ + 128.3466796875, + 135.0587158203125, + 255.3486328125, + 181.604248046875 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/75/SectionHeader/15" + "4": "/page/75/SectionHeader/15" }, "images": {} }, { - "id": "/page/76/Text/3", + "id": "/page/76/Text/4", "block_type": "Text", - "html": "
Boolean functions are often used in conditional statements:
", + "html": "The result of the == operator is a boolean, so we can write the function more concisely by returning it directly:
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def is_divisible(x, y):\n return x % y == 0", "polygon": [ [ - 129.2431640625, - 252.9140625 + 128.6455078125, + 213.46875 ], [ - 331.1015625, - 252.9140625 + 249.90834045410156, + 213.46875 ], [ - 329.90625, - 280.7578125 + 249.90834045410156, + 236.2012939453125 ], [ - 128.0478515625, - 280.7578125 + 128.6455078125, + 236.2012939453125 ] ], + "bbox": [ + 128.6455078125, + 213.46875, + 249.90834045410156, + 236.2012939453125 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/75/SectionHeader/15" + "4": "/page/75/SectionHeader/15" }, "images": {} }, { - "id": "/page/76/Text/5", + "id": "/page/76/Text/6", "block_type": "Text", - "html": "
It might be tempting to write something like:
", + "html": "Boolean functions are often used in conditional statements:
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if is_divisible(x, y):
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", + "id": "/page/76/TextInlineMath/8", + "block_type": "TextInlineMath", + "html": "print 'x is divisible by y'
", "polygon": [ [ - 147.7705078125, - 311.0427551269531 + 147.62109375, + 268.6407470703125 ], [ - 291.7033996582031, - 311.0427551269531 + 291.955078125, + 268.6407470703125 ], [ - 291.7033996582031, - 322.330078125 + 291.955078125, + 282.69140625 ], [ - 147.7705078125, - 322.330078125 + 147.62109375, + 282.69140625 ] ], + "bbox": [ + 147.62109375, + 268.6407470703125, + 291.955078125, + 282.69140625 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/75/SectionHeader/15" + "4": "/page/75/SectionHeader/15" }, "images": {} }, { - "id": "/page/76/Text/8", + "id": "/page/76/Text/9", "block_type": "Text", - "html": "But the extra comparison is unnecessary.
", + "html": "It might be tempting to write something like:
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", + "polygon": [ + [ + 129.60000610351562, + 298.8477478027344 + ], + [ + 291.7033996582031, + 298.8477478027344 + ], + [ + 291.7033996582031, + 321.0053405761719 + ], + [ + 129.60000610351562, + 321.0053405761719 + ] + ], + "bbox": [ + 129.60000610351562, + 298.8477478027344, + 291.7033996582031, + 321.0053405761719 + ], + "children": null, + "section_hierarchy": { + "1": "/page/72/SectionHeader/1", + "4": "/page/75/SectionHeader/15" + }, + "images": {} + }, + { + "id": "/page/76/Text/11", "block_type": "Text", - "html": "Exercise 6.3. Write a function is_between(x, y, z) that returns True if x ≤ y ≤ z or False otherwise.
", + "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.
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56 Chapter 6. Fruitful functions
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", + "html": "This 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.
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If the argument happens to be 0, all we have to do is return 1:
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Otherwise, 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:
", + "html": "Otherwise, 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:
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The flow of execution for this program is similar to the flow of countdown in Section 5.8. If we call factorial with the value 3:
", + "html": "The flow of execution for this program is similar to the flow of countdown in Section 5.8. If we call factorial with the value 3:
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The return value (1) is multiplied by n, which is 1, and the result is returned.
", + "html": "The return value (1) is multiplied by n, which is 1, and the result is returned.
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", + "html": "The return value (1) is multiplied by n, which is 2, and the result is returned.
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", + "html": "The 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.
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", + "html": "Figure 6.1 shows what the stack diagram looks like for this sequence of function calls.
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+ "/page/78/Figure/1": 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", + "html": "Figure 6.1: Stack diagram.
", "polygon": [ [ 270.27001953125, - 220.790283203125 + 220.4296875 ], [ - 385.189453125, - 220.790283203125 + 385.787109375, + 220.4296875 ], [ - 385.189453125, - 231.064453125 + 385.787109375, + 230.7529296875 ], [ 270.27001953125, - 231.064453125 + 230.7529296875 ] ], + "bbox": [ + 270.27001953125, + 220.4296875, + 385.787109375, + 230.7529296875 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/76/SectionHeader/10" + "4": "/page/76/SectionHeader/12" }, "images": {} } ], "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/76/SectionHeader/10" + "4": "/page/76/SectionHeader/12" }, "images": null }, { "id": "/page/78/SectionHeader/3", "block_type": "SectionHeader", - "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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", "polygon": [ [ - 128.197265625, + 128.49609375, 336.251953125 ], [ - 526.833984375, + 526.53515625, 336.251953125 ], [ - 526.833984375, + 526.53515625, 383.3229064941406 ], [ - 128.197265625, + 128.49609375, 383.3229064941406 ] ], + "bbox": [ + 128.49609375, + 336.251953125, + 526.53515625, + 383.3229064941406 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/78/SectionHeader/3" + "4": "/page/78/SectionHeader/3" }, "images": {} }, { "id": "/page/78/Text/6", "block_type": "Text", - "html": "The same is true when you call one of your own functions. For example, in Section 6.4, 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.
", + "html": "The same is true when you call one of your own functions. For example, in Section 6.4, 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.
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", + "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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", + "html": "After factorial, the most common example of a recursively defined mathematical function is fibonacci, which has the following definition (see http://en.wikipedia.org/ wiki/Fibonacci_number):
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Translated into Python, it looks like this:
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", + "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.
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", "polygon": [ [ - 85.763671875, - 263.162109375 + 85.98779296875, + 264.708984375 ], [ - 381.005859375, - 263.162109375 + 381.00238037109375, + 264.708984375 ], [ - 381.005859375, + 381.00238037109375, 275.0599365234375 ], [ - 85.763671875, + 85.98779296875, 275.0599365234375 ] ], + "bbox": [ + 85.98779296875, + 264.708984375, + 381.00238037109375, + 275.0599365234375 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/79/SectionHeader/3" + "3": "/page/78/SectionHeader/9", + "4": "/page/79/SectionHeader/3" }, "images": {} }, { "id": "/page/79/Code/5", "block_type": "Code", - "html": ">>> factorial(1.5)\nRuntimeError: Maximum recursion depth exceeded", + "html": "
>>> factorial(1.5)", "polygon": [ [ - 84.64306640625, - 277.083984375 + 85.53955078125, + 279.791015625 + ], + [ + 180.55654907226562, + 279.791015625 ], [ - 336.181640625, - 277.083984375 + 180.55654907226562, + 290.059326171875 ], [ - 336.181640625, + 85.53955078125, + 290.059326171875 + ] + ], + "bbox": [ + 85.53955078125, + 279.791015625, + 180.55654907226562, + 290.059326171875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/72/SectionHeader/1", + "3": "/page/78/SectionHeader/9", + "4": "/page/79/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/79/Text/6", + "block_type": "Text", + "html": "
RuntimeError: Maximum recursion depth exceeded
", + "polygon": [ + [ + 85.763671875, + 292.291748046875 + ], + [ + 327.515625, + 292.291748046875 + ], + [ + 327.515625, 302.25433349609375 ], [ - 84.64306640625, + 85.763671875, 302.25433349609375 ] ], + "bbox": [ + 85.763671875, + 292.291748046875, + 327.515625, + 302.25433349609375 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/79/SectionHeader/3" + "3": "/page/78/SectionHeader/9", + "4": "/page/79/SectionHeader/3" }, "images": {} }, { - "id": "/page/79/Text/6", + "id": "/page/79/Text/7", "block_type": "Text", "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.
", "polygon": [ [ - 85.166015625, - 306.28125 + 85.763671875, + 307.44073486328125 ], [ - 483.50390625, - 306.28125 + 482.3996887207031, + 307.44073486328125 ], [ - 483.50390625, + 482.3996887207031, 329.7478942871094 ], [ - 85.166015625, + 85.763671875, 329.7478942871094 ] ], + "bbox": [ + 85.763671875, + 307.44073486328125, + 482.3996887207031, + 329.7478942871094 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/79/SectionHeader/3" + "3": "/page/78/SectionHeader/9", + "4": "/page/79/SectionHeader/3" }, "images": {} }, { - "id": "/page/79/Text/7", + "id": "/page/79/Text/8", "block_type": "Text", "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.
", "polygon": [ [ - 85.0166015625, - 336.638671875 + 85.6142578125, + 338.7567443847656 ], [ - 482.3966979980469, - 336.638671875 + 482.90625, + 338.7567443847656 ], [ - 482.3966979980469, + 482.90625, 361.0628967285156 ], [ - 85.0166015625, + 85.6142578125, 361.0628967285156 ] ], + "bbox": [ + 85.6142578125, + 338.7567443847656, + 482.90625, + 361.0628967285156 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/79/SectionHeader/3" + "3": "/page/78/SectionHeader/9", + "4": "/page/79/SectionHeader/3" }, "images": {} }, { - "id": "/page/79/Text/8", + "id": "/page/79/Text/9", "block_type": "Text", "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.
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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:
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6.9. Debugging 59
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", + "html": "If we get past both checks, then we know that n is positive or zero, so we can prove that the recursion terminates.
", "polygon": [ [ - 128.9443359375, + 128.49609375, 88.66259765625 ], [ @@ -38438,14 +82890,21 @@ 110.99188232421875 ], [ - 128.9443359375, + 128.49609375, 110.99188232421875 ] ], + "bbox": [ + 128.49609375, + 88.66259765625, + 525.6028442382812, + 110.99188232421875 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/79/SectionHeader/3" + "3": "/page/78/SectionHeader/9", + "4": "/page/79/SectionHeader/3" }, "images": {} }, @@ -38455,84 +82914,105 @@ "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.
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", + "html": "In Section 11.3 we will see a more flexible alternative to printing an error message: raising an exception.
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", + "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).
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.4248046875 + 60.47314453125 ], [ - 484.1015625, - 60.4248046875 + 482.607421875, + 60.47314453125 ], [ - 484.1015625, + 482.607421875, 71.13372802734375 ], [ @@ -38908,39 +83472,53 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.47314453125, + 482.607421875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/80/SectionHeader/4" + "3": "/page/78/SectionHeader/9", + "4": "/page/80/SectionHeader/4" }, "images": {} }, { - "id": "/page/81/PageHeader/13", + "id": "/page/81/PageHeader/14", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.6142578125, - 60.328125 + 84.94189453125, + 59.748046875 ], [ - 96.8203125, - 60.328125 + 96.29736328125, + 59.748046875 ], [ - 96.8203125, - 69.7060546875 + 96.29736328125, + 70.0927734375 ], [ - 85.6142578125, - 69.7060546875 + 84.94189453125, + 70.0927734375 ] ], + "bbox": [ + 84.94189453125, + 59.748046875, + 96.29736328125, + 70.0927734375 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/80/SectionHeader/4" + "3": "/page/78/SectionHeader/9", + "4": "/page/80/SectionHeader/4" }, "images": {} }, @@ -38950,26 +83528,33 @@ "html": "factorial 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", "polygon": [ [ - 90.24609375, - 88.68572998046875 + 91.21728515625, + 85.89990234375 ], [ - 264.20574951171875, - 88.6552734375 + 270.73828125, + 85.89990234375 ], [ - 264.20574951171875, - 232.78631591796875 + 270.73828125, + 238.60546875 ], [ - 90.24609375, - 232.8046875 + 91.21728515625, + 238.60546875 ] ], + "bbox": [ + 91.21728515625, + 85.89990234375, + 270.73828125, + 238.60546875 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/80/SectionHeader/4" + "3": "/page/78/SectionHeader/9", + "4": "/page/80/SectionHeader/4" }, "images": {} }, @@ -38979,80 +83564,99 @@ "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.
", "polygon": [ [ - 85.6142578125, + 86.2119140625, 238.21875 ], [ - 483.50390625, + 482.4032897949219, 238.21875 ], [ - 483.50390625, - 273.37384033203125 + 482.4032897949219, + 273.41015625 ], [ - 85.6142578125, - 273.37384033203125 + 86.2119140625, + 273.41015625 ] ], + "bbox": [ + 86.2119140625, + 238.21875, + 482.4032897949219, + 273.41015625 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/80/SectionHeader/4" + "3": "/page/78/SectionHeader/9", + "4": "/page/80/SectionHeader/4" }, "images": {} }, { "id": "/page/81/SectionHeader/3", "block_type": "SectionHeader", - "html": "Exercise 6.4. Draw a stack diagram for the following program. What does the program print? Solution: http: // thinkpython. com/ code/ stack_ diagram. py .
", + "html": "Exercise 6.4. Draw a stack diagram for the following program. What does the program print? Solution: http: // thinkpython. com/ code/ stack_ diagram. py .
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def b(z):\n prod = a(z, z)\n print z, prod\n return prod\ndef a(x, y):", "polygon": [ [ - 84.19482421875, - 592.83984375 + 85.3154296875, + 592.06640625 ], [ 180.54110717773438, - 592.83984375 + 592.06640625 ], [ 180.54110717773438, - 688.4913330078125 + 664.1023330688477 + ], + [ + 85.3154296875, + 664.1023330688477 + ] + ], + "bbox": [ + 85.3154296875, + 592.06640625, + 180.54110717773438, + 664.1023330688477 + ], + "children": null, + "section_hierarchy": { + "1": "/page/72/SectionHeader/1", + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/81/Code/13", + "block_type": "Code", + "html": "
x = x + 1\nreturn x * y", + "polygon": [ + [ + 85.68896484375, + 658.96875 + ], + [ + 170.0803680419922, + 658.96875 ], [ - 84.19482421875, - 688.4913330078125 + 170.0803680419922, + 689.90625 + ], + [ + 85.68896484375, + 689.90625 ] ], + "bbox": [ + 85.68896484375, + 658.96875, + 170.0803680419922, + 689.90625 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/81/SectionHeader/10" + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" }, "images": {} } ], "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/81/SectionHeader/10" + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" }, "images": null }, { - "id": "/page/82/Page/324", + "id": "/page/82/Page/367", "block_type": "Page", - "html": "
6.11. Exercises 61
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See http: // en. wikipedia. org/ wiki/ Ackermann_ function . 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/ ackermann. py .
", + "id": "/page/82/Text/3", + "block_type": "Text", + "html": "See http: // en. wikipedia. org/ wiki/ Ackermann_ function . 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/ ackermann. py .
", "polygon": [ [ 128.0478515625, @@ -39519,107 +84222,135 @@ 325.3742370605469 ] ], + "bbox": [ + 128.0478515625, + 278.7479248046875, + 525.6039428710938, + 325.3742370605469 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/81/SectionHeader/10" + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" }, "images": {} }, { - "id": "/page/82/Text/5", + "id": "/page/82/Text/4", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 128.0478515625, - 327.6066589355469 + 129.2431640625, + 327.357421875 ], [ - 526.53515625, - 327.6066589355469 + 525.6033325195312, + 327.357421875 ], [ - 526.53515625, + 525.6033325195312, 361.9572448730469 ], [ - 128.0478515625, + 129.2431640625, 361.9572448730469 ] ], + "bbox": [ + 129.2431640625, + 327.357421875, + 525.6033325195312, + 361.9572448730469 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/81/SectionHeader/10" + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" }, "images": {} }, { - "id": "/page/82/Text/6", + "id": "/page/82/Text/5", "block_type": "Text", "html": "The following are functions that take a string argument and return the first, last, and middle letters:
", "polygon": [ [ - 128.0478515625, - 370.669921875 + 129.392578125, + 371.056640625 ], [ - 525.9375, - 370.669921875 + 525.6033325195312, + 371.056640625 ], [ - 525.9375, + 525.6033325195312, 381.1552429199219 ], [ - 128.0478515625, + 129.392578125, 381.1552429199219 ] ], + "bbox": [ + 129.392578125, + 371.056640625, + 525.6033325195312, + 381.1552429199219 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/81/SectionHeader/10" + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" }, "images": {} }, { - "id": "/page/82/Text/7", - "block_type": "Text", - "html": "def first(word): return word[0]
", + "id": "/page/82/Code/6", + "block_type": "Code", + "html": "def first(word):\n return word[0]", "polygon": [ [ - 128.42138671875, - 384.978515625 + 129.59988403320312, + 385.55859375 ], [ 223.75096130371094, - 384.978515625 + 385.55859375 ], [ 223.75096130371094, - 409.53515625 + 412.62890625 ], [ - 128.42138671875, - 409.53515625 + 129.59988403320312, + 412.62890625 ] ], + "bbox": [ + 129.59988403320312, + 385.55859375, + 223.75096130371094, + 412.62890625 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/81/SectionHeader/10" + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" }, "images": {} }, { - "id": "/page/82/Text/8", - "block_type": "Text", - "html": "
def last(word): return word[-1]
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def middle(word): return word[1:-1]
", + "id": "/page/82/Code/362", + "block_type": "Code", + "html": "def middle(word):\n return word[1:-1]", "polygon": [ [ - 128.42138671875, + 128.197265625, 459.6087951660156 ], [ - 252.2109375, + 239.44204711914062, 459.6087951660156 ], [ - 252.2109375, - 485.33203125 + 239.44204711914062, + 481.765380859375 ], [ - 128.42138671875, - 485.33203125 + 128.197265625, + 481.765380859375 ] ], + "bbox": [ + 128.197265625, + 459.6087951660156, + 239.44204711914062, + 481.765380859375 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/81/SectionHeader/10" + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" }, "images": {} }, { - "id": "/page/82/Text/10", + "id": "/page/82/Text/9", "block_type": "Text", - "html": "
We'll see how they work in Chapter 8.
", + "html": "We'll see how they work in Chapter 8.
", "polygon": [ [ - 129.59988403320312, - 486.10546875 + 129.09375, + 486.87890625 ], [ 281.5992736816406, - 486.10546875 + 486.87890625 ], [ 281.5992736816406, 496.96923828125 ], [ - 129.59988403320312, + 129.09375, 496.96923828125 ] ], + "bbox": [ + 129.09375, + 486.87890625, + 281.5992736816406, + 496.96923828125 + ], "children": null, "section_hierarchy": { "1": "/page/72/SectionHeader/1", - "3": "/page/81/SectionHeader/10" + "2": "/page/81/SectionHeader/3", + "4": "/page/81/SectionHeader/10" }, "images": {} }, { - "id": "/page/82/ListGroup/324", + "id": "/page/82/ListGroup/363", "block_type": "ListGroup", - "html": "Solution: http: // thinkpython. com/ code/ palindrome_ soln. py .
", + "html": "Solution: http: // thinkpython. com/ code/ palindrome_ soln. py .
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", + "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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", + "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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", + "html": "Write a function called gcd that takes parameters a and b and returns their greatest common divisor.
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", + "html": "Credit: This exercise is based on an example from Abelson and Sussman's Structure and Interpretation of Computer Programs.
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", + "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.
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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!\"
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", "polygon": [ [ - 86.28662109375, - 665.15625 + 85.53955078125, + 665.9296875 ], [ - 378.017578125, - 665.15625 + 376.88201904296875, + 665.9296875 ], [ - 378.017578125, + 376.88201904296875, 676.2609329223633 ], [ - 86.28662109375, + 85.53955078125, 676.2609329223633 ] ], + "bbox": [ + 85.53955078125, + 665.9296875, + 376.88201904296875, + 676.2609329223633 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, @@ -41104,7 +86157,7 @@ "html": "7.4. break 65
", + "html": "", "polygon": [ [ - 127.8984375, + 128.42138671875, 61.11871337890625 ], [ @@ -41175,68 +86240,86 @@ 71.13372802734375 ], [ - 127.8984375, + 128.42138671875, 71.13372802734375 ] ], + "bbox": [ + 128.42138671875, + 61.11871337890625, + 525.5996704101562, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { - "id": "/page/86/PageHeader/15", + "id": "/page/86/PageHeader/16", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 515.478515625, - 61.48828125 + 514.880859375, + 61.1982421875 ], [ - 526.236328125, - 61.48828125 + 525.041015625, + 61.1982421875 ], [ - 526.236328125, - 70.189453125 + 525.041015625, + 70.2861328125 ], [ - 515.478515625, - 70.189453125 + 514.880859375, + 70.2861328125 ] ], + "bbox": [ + 514.880859375, + 61.1982421875, + 525.041015625, + 70.2861328125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { - "id": "/page/86/ListGroup/230", + "id": "/page/86/ListGroup/233", "block_type": "ListGroup", "html": "This type of flow is called a loop because the third step loops back around to the top.
", "polygon": [ [ - 128.9443359375, - 149.5634765625 + 129.2431640625, + 149.3701171875 ], [ - 502.4393310546875, - 149.5634765625 + 502.62890625, + 149.3701171875 ], [ - 502.4393310546875, + 502.62890625, 160.0938720703125 ], [ - 128.9443359375, + 129.2431640625, 160.0938720703125 ] ], + "bbox": [ + 129.2431640625, + 149.3701171875, + 502.62890625, + 160.0938720703125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, @@ -41338,26 +86439,32 @@ "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.
", "polygon": [ [ - 128.49609375, - 170.9296875 + 128.9443359375, + 171.4130859375 ], [ - 525.9375, - 170.9296875 + 525.6033935546875, + 171.4130859375 ], [ - 525.9375, - 230.484375 + 525.6033935546875, + 230.35784912109375 ], [ - 128.49609375, - 230.484375 + 128.9443359375, + 230.35784912109375 ] ], + "bbox": [ + 128.9443359375, + 171.4130859375, + 525.6033935546875, + 230.35784912109375 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, @@ -41367,210 +86474,287 @@ "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:
", "polygon": [ [ - 129.09375, - 241.69921875 + 128.3466796875, + 241.732666015625 ], [ - 526.53515625, - 241.69921875 + 525.59765625, + 241.732666015625 ], [ - 526.53515625, - 276.50390625 + 525.59765625, + 276.23284912109375 ], [ - 129.09375, - 276.50390625 + 128.3466796875, + 276.23284912109375 ] ], + "bbox": [ + 128.3466796875, + 241.732666015625, + 525.59765625, + 276.23284912109375 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { "id": "/page/86/Code/6", "block_type": "Code", - "html": "def sequence(n):\n 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", + "html": "
def sequence(n):", "polygon": [ [ - 129.6000518798828, + 127.8984375, 283.63665771484375 ], [ - 333.56439208984375, + 213.36328125, 283.63665771484375 ], [ - 333.56439208984375, - 367.76953125 + 213.36328125, + 293.5992431640625 ], [ - 129.6000518798828, - 367.76953125 + 127.8984375, + 293.5992431640625 + ] + ], + "bbox": [ + 127.8984375, + 283.63665771484375, + 213.36328125, + 293.5992431640625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/84/SectionHeader/1", + "2": "/page/85/SectionHeader/12" + }, + "images": {} + }, + { + "id": "/page/86/Code/7", + "block_type": "Code", + "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", + "polygon": [ + [ + 147.99462890625, + 295.259765625 + ], + [ + 335.583984375, + 295.259765625 + ], + [ + 335.583984375, + 366.7652587890625 + ], + [ + 147.99462890625, + 366.7652587890625 ] ], + "bbox": [ + 147.99462890625, + 295.259765625, + 335.583984375, + 366.7652587890625 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { - "id": "/page/86/Text/7", + "id": "/page/86/Text/8", "block_type": "Text", "html": "
The condition for this loop is n != 1, so the loop will continue until n is 1, which makes the condition false.
", "polygon": [ [ - 129.09375, - 374.31768798828125 + 129.392578125, + 374.150390625 ], [ - 525.9375, - 374.31768798828125 + 525.5995483398438, + 374.150390625 ], [ - 525.9375, + 525.5995483398438, 396.62384033203125 ], [ - 129.09375, + 129.392578125, 396.62384033203125 ] ], + "bbox": [ + 129.392578125, + 374.150390625, + 525.5995483398438, + 396.62384033203125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { - "id": "/page/86/Text/8", + "id": "/page/86/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 129.392578125, - 407.6015625 + 128.6455078125, + 407.98828125 ], [ - 526.236328125, - 407.6015625 + 525.6026611328125, + 407.98828125 ], [ - 526.236328125, + 525.6026611328125, 454.69384765625 ], [ - 129.392578125, + 128.6455078125, 454.69384765625 ] ], + "bbox": [ + 128.6455078125, + 407.98828125, + 525.6026611328125, + 454.69384765625 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { - "id": "/page/86/Text/9", + "id": "/page/86/Text/10", "block_type": "Text", "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.
", "polygon": [ [ - 129.2431640625, + 128.6455078125, 465.609375 ], [ - 526.53515625, + 525.9375, 465.609375 ], [ - 526.53515625, + 525.9375, 524.9578552246094 ], [ - 129.2431640625, + 128.6455078125, 524.9578552246094 ] ], + "bbox": [ + 128.6455078125, + 465.609375, + 525.9375, + 524.9578552246094 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { - "id": "/page/86/Text/10", + "id": "/page/86/Text/11", "block_type": "Text", - "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.)
", + "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.)
", "polygon": [ [ - 129.2431640625, - 535.9921875 + 129.09375, + 535.60546875 ], [ 525.6057739257812, - 535.9921875 + 535.60546875 ], [ 525.6057739257812, - 571.5703125 + 570.8328552246094 ], [ - 129.2431640625, - 571.5703125 + 129.09375, + 570.8328552246094 ] ], + "bbox": [ + 129.09375, + 535.60546875, + 525.6057739257812, + 570.8328552246094 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { - "id": "/page/86/Text/11", + "id": "/page/86/Text/12", "block_type": "Text", - "html": "Exercise 7.1. Rewrite the function print_n from Section 5.8 using iteration instead of recursion.
", + "html": "Exercise 7.1. Rewrite the function print_n from Section 5.8 using iteration instead of recursion.
", "polygon": [ [ - 129.60000610351562, - 572.8915557861328 + 128.6455078125, + 572.73046875 ], [ 524.7421875, - 572.8915557861328 + 572.73046875 ], [ 524.7421875, 582.9297485351562 ], [ - 129.60000610351562, + 128.6455078125, 582.9297485351562 ] ], + "bbox": [ + 128.6455078125, + 572.73046875, + 524.7421875, + 582.9297485351562 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/85/SectionHeader/12" + "2": "/page/85/SectionHeader/12" }, "images": {} }, { - "id": "/page/86/SectionHeader/12", + "id": "/page/86/SectionHeader/13", "block_type": "SectionHeader", - "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.
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.56982421875 + 60.8115234375 ], [ - 482.607421875, - 60.56982421875 + 482.4033508300781, + 60.8115234375 ], [ - 482.607421875, + 482.4033508300781, 71.13372802734375 ], [ @@ -41703,39 +86911,51 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.8115234375, + 482.4033508300781, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/86/SectionHeader/12" + "2": "/page/86/SectionHeader/13" }, "images": {} }, { "id": "/page/87/PageHeader/16", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 84.8671875, - 60.521484375 + 85.39013671875, + 60.908203125 ], [ - 96.22265625, - 60.521484375 + 96.89501953125, + 60.908203125 ], [ - 96.22265625, - 69.99609375 + 96.89501953125, + 70.2861328125 ], [ - 84.8671875, - 69.99609375 + 85.39013671875, + 70.2861328125 ] ], + "bbox": [ + 85.39013671875, + 60.908203125, + 96.89501953125, + 70.2861328125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/86/SectionHeader/12" + "2": "/page/86/SectionHeader/13" }, "images": {} }, @@ -41745,26 +86965,32 @@ "html": "while True:\n line = raw_input('> ')\n if line == 'done':\n break\n print line", "polygon": [ [ - 86.4000015258789, - 88.68572998046875 + 86.2119140625, + 88.55859375 ], [ - 222.35836791992188, - 88.68572998046875 + 223.0751953125, + 88.55859375 ], [ - 222.35836791992188, - 148.693359375 + 223.0751953125, + 152.947265625 ], [ - 86.4000015258789, - 150.240234375 + 86.2119140625, + 152.947265625 ] ], + "bbox": [ + 86.2119140625, + 88.55859375, + 223.0751953125, + 152.947265625 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/86/SectionHeader/12" + "2": "/page/86/SectionHeader/13" }, "images": {} }, @@ -41774,26 +87000,32 @@ "html": "
print 'Done!'
", "polygon": [ [ - 86.13720703125, - 161.5517578125 + 86.2119140625, + 161.26171875 ], [ 154.37936401367188, - 161.5517578125 + 161.26171875 ], [ 154.37936401367188, 171.81427001953125 ], [ - 86.13720703125, + 86.2119140625, 171.81427001953125 ] ], + "bbox": [ + 86.2119140625, + 161.26171875, + 154.37936401367188, + 171.81427001953125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/86/SectionHeader/12" + "2": "/page/86/SectionHeader/13" }, "images": {} }, @@ -41804,14 +87036,14 @@ "polygon": [ [ 85.3154296875, - 178.083984375 + 179.05078125 ], [ - 482.90625, - 178.083984375 + 482.4029235839844, + 179.05078125 ], [ - 482.90625, + 482.4029235839844, 201.81085205078125 ], [ @@ -41819,10 +87051,16 @@ 201.81085205078125 ] ], + "bbox": [ + 85.3154296875, + 179.05078125, + 482.4029235839844, + 201.81085205078125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/86/SectionHeader/12" + "2": "/page/86/SectionHeader/13" }, "images": {} }, @@ -41832,55 +87070,67 @@ "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:
", "polygon": [ [ - 85.0166015625, - 211.921875 + 85.46484375, + 212.501953125 ], [ 482.40045166015625, - 211.921875 + 212.501953125 ], [ 482.40045166015625, 247.8248291015625 ], [ - 85.0166015625, + 85.46484375, 247.8248291015625 ] ], + "bbox": [ + 85.46484375, + 212.501953125, + 482.40045166015625, + 247.8248291015625 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/86/SectionHeader/12" + "2": "/page/86/SectionHeader/13" }, "images": {} }, { - "id": "/page/87/TextInlineMath/5", - "block_type": "TextInlineMath", - "html": "> not done not done > done Done!
", + "id": "/page/87/Code/5", + "block_type": "Code", + "html": "> not done\nnot done\n> done\nDone!", "polygon": [ [ - 85.166015625, - 254.4609375 + 85.98779296875, + 255.36566162109375 ], [ 138.70364379882812, - 254.4609375 + 255.36566162109375 ], [ 138.70364379882812, - 302.80078125 + 301.9112243652344 ], [ - 85.166015625, - 302.80078125 + 85.98779296875, + 301.9112243652344 ] ], + "bbox": [ + 85.98779296875, + 255.36566162109375, + 138.70364379882812, + 301.9112243652344 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/86/SectionHeader/12" + "2": "/page/86/SectionHeader/13" }, "images": {} }, @@ -41890,55 +87140,68 @@ "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.\").
", "polygon": [ [ - 85.3154296875, - 308.98828125 + 86.0625, + 309.375 ], [ - 483.50390625, - 308.98828125 + 482.40447998046875, + 309.375 ], [ - 483.50390625, - 344.1017761230469 + 482.40447998046875, + 344.1796875 ], [ - 85.3154296875, - 344.1017761230469 + 86.0625, + 344.1796875 ] ], + "bbox": [ + 86.0625, + 309.375, + 482.40447998046875, + 344.1796875 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/86/SectionHeader/12" + "2": "/page/86/SectionHeader/13" }, "images": {} }, { "id": "/page/87/SectionHeader/7", "block_type": "SectionHeader", - "html": "Loops are often used in programs that compute numerical results by starting with an approximate answer and iteratively improving it.
", "polygon": [ [ - 85.3154296875, - 405.66796875 + 86.2119140625, + 406.44140625 ], [ 482.40338134765625, - 405.66796875 + 406.44140625 ], [ 482.40338134765625, - 428.87109375 + 428.83575439453125 ], [ - 85.3154296875, - 428.87109375 + 86.2119140625, + 428.83575439453125 ] ], + "bbox": [ + 86.2119140625, + 406.44140625, + 482.40338134765625, + 428.83575439453125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/87/SectionHeader/7" + "2": "/page/86/SectionHeader/13", + "4": "/page/87/SectionHeader/7" }, "images": {} }, { "id": "/page/87/Text/9", "block_type": "Text", - "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:
", + "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:
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", + "html": "For example, if a is 4 and x is 3:
", "polygon": [ [ - 84.7177734375, - 521.296875 + 86.361328125, + 522.0703125 ], [ 223.673828125, - 521.296875 + 522.0703125 ], [ 223.673828125, 532.6678771972656 ], [ - 84.7177734375, + 86.361328125, 532.6678771972656 ] ], + "bbox": [ + 86.361328125, + 522.0703125, + 223.673828125, + 532.6678771972656 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/87/SectionHeader/7" + "2": "/page/86/SectionHeader/13", + "4": "/page/87/SectionHeader/7" }, "images": {} }, @@ -42065,14 +87356,14 @@ "polygon": [ [ 85.46484375, - 540.2087249755859 + 539.859375 ], [ - 197.3759765625, - 540.2087249755859 + 196.23760986328125, + 539.859375 ], [ - 197.3759765625, + 196.23760986328125, 598.9493408203125 ], [ @@ -42080,17 +87371,24 @@ 598.9493408203125 ] ], + "bbox": [ + 85.46484375, + 539.859375, + 196.23760986328125, + 598.9493408203125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/87/SectionHeader/7" + "2": "/page/86/SectionHeader/13", + "4": "/page/87/SectionHeader/7" }, "images": {} }, { - "id": "/page/87/TextInlineMath/13", - "block_type": "TextInlineMath", - "html": "Which is closer to the correct answer (√ 4 = 2). If we repeat the process with the new estimate, it gets even closer:
", + "id": "/page/87/Text/13", + "block_type": "Text", + "html": "Which is closer to the correct answer (√ 4 = 2). If we repeat the process with the new estimate, it gets even closer:
", "polygon": [ [ 85.46484375, @@ -42109,10 +87407,17 @@ 628.9459075927734 ] ], + "bbox": [ + 85.46484375, + 597.00830078125, + 482.4031677246094, + 628.9459075927734 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/87/SectionHeader/7" + "2": "/page/86/SectionHeader/13", + "4": "/page/87/SectionHeader/7" }, "images": {} }, @@ -42122,26 +87427,33 @@ "html": ">>> x = y\n>>> y = (x + a/x) / 2\n>>> print y\n2.00641025641", "polygon": [ [ - 84.64306640625, - 634.9921875 + 85.166015625, + 636.4867553710938 ], [ - 198.720703125, - 634.9921875 + 197.525390625, + 636.4867553710938 ], [ - 198.720703125, - 683.71875 + 197.525390625, + 683.032356262207 ], [ - 84.64306640625, - 683.71875 + 85.166015625, + 683.032356262207 ] ], + "bbox": [ + 85.166015625, + 636.4867553710938, + 197.525390625, + 683.032356262207 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/87/SectionHeader/7" + "2": "/page/86/SectionHeader/13", + "4": "/page/87/SectionHeader/7" }, "images": {} }, @@ -42151,40 +87463,48 @@ "html": "
After a few more updates, the estimate is almost exact:
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", + "html": "", "polygon": [ [ - 127.52490234375, + 128.0478515625, 60.8115234375 ], [ @@ -42222,72 +87548,93 @@ 71.13372802734375 ], [ - 127.52490234375, + 128.0478515625, 71.13372802734375 ] ], + "bbox": [ + 128.0478515625, + 60.8115234375, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/87/SectionHeader/7" + "2": "/page/86/SectionHeader/13", + "4": "/page/87/SectionHeader/7" }, "images": {} }, { "id": "/page/88/PageHeader/13", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 515.1796875, - 60.37646484375 + 514.880859375, + 60.76318359375 ], [ - 525.9375, - 60.37646484375 + 525.638671875, + 60.76318359375 ], [ - 525.9375, - 70.14111328125 + 525.638671875, + 70.43115234375 ], [ - 515.1796875, - 70.14111328125 + 514.880859375, + 70.43115234375 ] ], + "bbox": [ + 514.880859375, + 60.76318359375, + 525.638671875, + 70.43115234375 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/87/SectionHeader/7" + "2": "/page/86/SectionHeader/13", + "4": "/page/87/SectionHeader/7" }, "images": {} }, { - "id": "/page/88/TextInlineMath/1", - "block_type": "TextInlineMath", - "html": ">>> x = y >>> y = (x + a/x) / 2 >>> print y 2.00001024003 >>> x = y >>> y = (x + a/x) / 2 >>> print y 2.00000000003
", + "id": "/page/88/Code/1", + "block_type": "Code", + "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", "polygon": [ [ - 129.09375, - 88.68572998046875 + 129.60000610351562, + 87.8818359375 ], [ - 240.85546875, - 88.68572998046875 + 242.2001953125, + 87.8818359375 ], [ - 240.85546875, + 242.2001953125, 184.00830078125 ], [ - 129.09375, + 129.60000610351562, 184.00830078125 ] ], + "bbox": [ + 129.60000610351562, + 87.8818359375, + 242.2001953125, + 184.00830078125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/87/SectionHeader/7" + "2": "/page/86/SectionHeader/13", + "4": "/page/87/SectionHeader/7" }, "images": {} }, @@ -42297,26 +87644,33 @@ "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:
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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.
", + "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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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.
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Write a function called eval_loop that iteratively prompts the user, takes the resulting input and evaluates it using eval, and prints the result.
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", + "html": "Exercise 7.5. The mathematician Srinivasa Ramanujan found an infinite series that can be used to generate a numerical approximation of 1/π:
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.134765625 + 60.8115234375 ], [ - 484.400390625, - 60.134765625 + 482.90625, + 60.8115234375 ], [ - 484.400390625, + 482.90625, 71.13372802734375 ], [ @@ -43708,111 +89373,140 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.8115234375, + 482.90625, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/90/SectionHeader/7" + "2": "/page/89/SectionHeader/13", + "4": "/page/90/SectionHeader/7" }, "images": {} }, { "id": "/page/91/PageHeader/3", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.09130859375, - 61.0048828125 + 85.6142578125, + 60.6181640625 ], [ - 95.69970703125, - 61.0048828125 + 96.9697265625, + 60.6181640625 ], [ - 95.69970703125, - 70.189453125 + 96.9697265625, + 70.3828125 ], [ - 85.09130859375, - 70.189453125 + 85.6142578125, + 70.3828125 ] ], + "bbox": [ + 85.6142578125, + 60.6181640625, + 96.9697265625, + 70.3828125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/90/SectionHeader/7" + "2": "/page/89/SectionHeader/13", + "4": "/page/90/SectionHeader/7" }, "images": {} }, { "id": "/page/91/Text/1", "block_type": "Text", - "html": "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.
", + "html": "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.
", "polygon": [ [ - 86.361328125, - 87.78515625 + 85.166015625, + 88.55859375 ], [ - 483.205078125, - 87.78515625 + 482.90625, + 88.55859375 ], [ - 483.205078125, + 482.90625, 123.1868896484375 ], [ - 86.361328125, + 85.166015625, 123.1868896484375 ] ], + "bbox": [ + 85.166015625, + 88.55859375, + 482.90625, + 123.1868896484375 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/90/SectionHeader/7" + "2": "/page/89/SectionHeader/13", + "4": "/page/90/SectionHeader/7" }, "images": {} }, { "id": "/page/91/Text/2", "block_type": "Text", - "html": "Solution: http: // thinkpython. com/ code/ pi. py .
", + "html": "Solution: http: // thinkpython. com/ code/ pi. py .
", "polygon": [ [ - 86.39996337890625, - 132.7412109375 + 86.2119140625, + 132.5478515625 ], [ 313.45562744140625, - 132.7412109375 + 132.5478515625 ], [ 313.45562744140625, 143.15118408203125 ], [ - 86.39996337890625, + 86.2119140625, 143.15118408203125 ] ], + "bbox": [ + 86.2119140625, + 132.5478515625, + 313.45562744140625, + 143.15118408203125 + ], "children": null, "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/90/SectionHeader/7" + "2": "/page/89/SectionHeader/13", + "4": "/page/90/SectionHeader/7" }, "images": {} } ], "section_hierarchy": { "1": "/page/84/SectionHeader/1", - "3": "/page/90/SectionHeader/7" + "2": "/page/89/SectionHeader/13", + "4": "/page/90/SectionHeader/7" }, "images": null }, { - "id": "/page/92/Page/139", + "id": "/page/92/Page/140", "block_type": "Page", - "html": ">>> fruit = 'banana'
", - "polygon": [ - [ - 128.27197265625, - 349.787109375 - ], - [ - 234.18336486816406, - 349.787109375 - ], - [ - 234.18336486816406, - 366.416015625 - ], - [ - 128.27197265625, - 366.416015625 - ] + "bbox": [ + 129.2431640625, + 321.9742431640625, + 526.53515625, + 344.22796630859375 ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/Text/5", + "id": "/page/92/Text/4", "block_type": "Text", - "html": ">>> letter = fruit[1]
", + "html": ">>> fruit = 'banana' >>> letter = fruit[1]
", "polygon": [ [ - 128.49609375, - 362.9818115234375 + 128.57080078125, + 350.7878112792969 ], [ 239.43765258789062, - 362.9818115234375 + 350.7878112792969 ], [ 239.43765258789062, - 376.857421875 + 372.990234375 ], [ - 128.49609375, - 376.857421875 + 128.57080078125, + 372.990234375 ] ], + "bbox": [ + 128.57080078125, + 350.7878112792969, + 239.43765258789062, + 372.990234375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/Text/6", + "id": "/page/92/Text/5", "block_type": "Text", "html": "The second statement selects character number 1 from fruit and assigns it to letter.
", "polygon": [ [ - 128.6455078125, - 379.37109375 + 129.09375, + 379.65380859375 ], [ - 508.0078125, - 379.37109375 + 506.2596740722656, + 379.65380859375 ], [ - 508.0078125, - 390.19921875 + 506.2596740722656, + 389.7659606933594 ], [ - 128.6455078125, - 390.19921875 + 129.09375, + 389.7659606933594 ] ], + "bbox": [ + 129.09375, + 379.65380859375, + 506.2596740722656, + 389.7659606933594 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/Text/7", + "id": "/page/92/Text/6", "block_type": "Text", "html": "The expression in brackets is called an index. The index indicates which character in the sequence you want (hence the name).
", "polygon": [ [ - 129.09375, - 398.3203125 + 129.2431640625, + 399.8671875 ], [ - 527.73046875, - 398.3203125 + 526.53515625, + 399.8671875 ], [ - 527.73046875, + 526.53515625, 422.6039733886719 ], [ - 129.09375, + 129.2431640625, 422.6039733886719 ] ], + "bbox": [ + 129.2431640625, + 399.8671875, + 526.53515625, + 422.6039733886719 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/Text/8", + "id": "/page/92/Text/7", "block_type": "Text", "html": "But you might not get what you expect:
", "polygon": [ [ - 128.86962890625, - 431.19140625 + 129.46728515625, + 433.125 ], [ - 305.701171875, - 431.19140625 + 304.505859375, + 433.125 ], [ - 305.701171875, + 304.505859375, 443.24798583984375 ], [ - 128.86962890625, + 129.46728515625, 443.24798583984375 ] ], + "bbox": [ + 129.46728515625, + 433.125, + 304.505859375, + 443.24798583984375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/TextInlineMath/9", - "block_type": "TextInlineMath", - "html": ">>> print letter
", + "id": "/page/92/Text/8", + "block_type": "Text", + "html": ">>> print letter a
", "polygon": [ [ 129.5999755859375, - 447.8203125 + 449.8078308105469 ], [ 213.2858123779297, - 447.8203125 + 449.8078308105469 ], [ 213.2858123779297, - 459.7704162597656 + 477.2109375 ], [ 129.5999755859375, - 459.7704162597656 + 477.2109375 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" - }, - "images": {} - }, - { - "id": "/page/92/Text/10", - "block_type": "Text", - "html": "a
", - "polygon": [ - [ - 129.5999755859375, - 462.0018310546875 - ], - [ - 139.32861328125, - 460.96875 - ], - [ - 139.32861328125, - 471.96441650390625 - ], - [ - 129.5999755859375, - 473.34375 - ] + "bbox": [ + 129.5999755859375, + 449.8078308105469, + 213.2858123779297, + 477.2109375 ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/Text/11", + "id": "/page/92/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 128.9443359375, - 477.59765625 + 129.59994506835938, + 478.673828125 ], [ - 527.1328125, - 477.59765625 + 525.9375, + 478.673828125 ], [ - 527.1328125, - 500.9809875488281 + 525.9375, + 501.1875 ], [ - 128.9443359375, - 500.9809875488281 + 129.59994506835938, + 501.1875 ] ], + "bbox": [ + 129.59994506835938, + 478.673828125, + 525.9375, + 501.1875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/Code/12", + "id": "/page/92/Code/10", "block_type": "Code", "html": ">>> letter = fruit[0]\n>>> print letter\nb", "polygon": [ [ - 128.9443359375, - 505.828125 + 128.42138671875, + 507.54083251953125 ], [ 239.43759155273438, - 505.828125 + 507.54083251953125 ], [ 239.43759155273438, 541.8914337158203 ], [ - 128.9443359375, + 128.42138671875, 541.8914337158203 ] ], + "bbox": [ + 128.42138671875, + 507.54083251953125, + 239.43759155273438, + 541.8914337158203 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/Text/13", + "id": "/page/92/Text/11", "block_type": "Text", "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.
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", "polygon": [ [ - 128.3466796875, - 580.078125 + 128.49609375, + 581.23828125 ], [ - 527.1328125, - 580.078125 + 526.53515625, + 581.23828125 ], [ - 527.1328125, + 526.53515625, 603.7460021972656 ], [ - 128.3466796875, + 128.49609375, 603.7460021972656 ] ], + "bbox": [ + 128.49609375, + 581.23828125, + 526.53515625, + 603.7460021972656 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/Code/15", + "id": "/page/92/Code/13", "block_type": "Code", "html": ">>> letter = fruit[1.5]\nTypeError: string indices must be integers, not float", "polygon": [ [ - 127.37548828125, + 128.57080078125, 610.3058471679688 ], [ - 407.302734375, - 609.08203125 + 406.8192138671875, + 610.3058471679688 ], [ - 407.302734375, + 406.8192138671875, 632.4624481201172 ], [ - 127.37548828125, - 633.05859375 + 128.57080078125, + 632.4624481201172 ] ], + "bbox": [ + 128.57080078125, + 610.3058471679688, + 406.8192138671875, + 632.4624481201172 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/2" + "4": "/page/92/SectionHeader/2" }, "images": {} }, { - "id": "/page/92/SectionHeader/16", + "id": "/page/92/SectionHeader/14", "block_type": "SectionHeader", - "html": "
len is a built-in function that returns the number of characters in a string:
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.8115234375 + 60.66650390625 ], [ - 483.50390625, - 60.8115234375 + 482.4034118652344, + 60.66650390625 ], [ - 483.50390625, - 71.15625 + 482.4034118652344, + 71.13372802734375 ], [ 86.4000015258789, - 71.15625 + 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.66650390625, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/16" + "2": "/page/92/SectionHeader/14" }, "images": {} }, { - "id": "/page/93/PageHeader/12", + "id": "/page/93/PageHeader/16", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.0166015625, - 60.8115234375 + 85.68896484375, + 60.95654296875 ], [ - 96.9697265625, - 60.8115234375 + 96.89501953125, + 60.95654296875 ], [ - 96.9697265625, - 71.349609375 + 96.89501953125, + 70.52783203125 ], [ - 85.0166015625, - 71.349609375 + 85.68896484375, + 70.52783203125 ] ], + "bbox": [ + 85.68896484375, + 60.95654296875, + 96.89501953125, + 70.52783203125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/16" + "2": "/page/92/SectionHeader/14" }, "images": {} }, { "id": "/page/93/Code/1", "block_type": "Code", - "html": ">>> fruit = 'banana'\n>>> len(fruit)\n6\nTo get the last letter of a string, you might be tempted to try something like this:\n>>> length = len(fruit)\n>>> last = fruit[length]\nIndexError: string index out of range\nThe reason for the IndexError is that there is no letter in 'banana' with the index 6. Since\nwe started counting at zero, the six letters are numbered 0 to 5. To get the last character,\nyou have to subtract 1 from length:\n>>> last = fruit[length-1]\n>>> print last\na", + "html": "
>>> fruit = 'banana'\n>>> len(fruit)\n6", "polygon": [ [ - 86.4000015258789, + 86.2119140625, 88.68572998046875 ], [ - 482.40338134765625, + 191.548828125, 88.68572998046875 ], [ - 482.40338134765625, - 267.99609375 + 191.548828125, + 123.03729248046875 ], [ - 86.4000015258789, - 269.54296875 + 86.2119140625, + 123.03729248046875 ] ], + "bbox": [ + 86.2119140625, + 88.68572998046875, + 191.548828125, + 123.03729248046875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/16" + "2": "/page/92/SectionHeader/14" }, "images": {} }, { "id": "/page/93/Text/2", "block_type": "Text", + "html": "
To get the last letter of a string, you might be tempted to try something like this:
", + "polygon": [ + [ + 86.4000015258789, + 130.2275390625 + ], + [ + 438.08203125, + 130.2275390625 + ], + [ + 438.08203125, + 140.8623046875 + ], + [ + 86.4000015258789, + 140.8623046875 + ] + ], + "bbox": [ + 86.4000015258789, + 130.2275390625, + 438.08203125, + 140.8623046875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/92/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/93/Code/3", + "block_type": "Code", + "html": ">>> length = len(fruit)\n>>> last = fruit[length]\nIndexError: string index out of range", + "polygon": [ + [ + 85.46484375, + 148.13671875 + ], + [ + 279.93341064453125, + 148.13671875 + ], + [ + 279.93341064453125, + 182.48834228515625 + ], + [ + 85.46484375, + 182.48834228515625 + ] + ], + "bbox": [ + 85.46484375, + 148.13671875, + 279.93341064453125, + 182.48834228515625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/92/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/93/Text/4", + "block_type": "Text", + "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:
", + "polygon": [ + [ + 86.361328125, + 189.2021484375 + ], + [ + 482.40338134765625, + 189.2021484375 + ], + [ + 482.40338134765625, + 224.5579833984375 + ], + [ + 86.361328125, + 224.5579833984375 + ] + ], + "bbox": [ + 86.361328125, + 189.2021484375, + 482.40338134765625, + 224.5579833984375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/92/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/93/Code/5", + "block_type": "Code", + "html": ">>> last = fruit[length-1]\n>>> print last\na", + "polygon": [ + [ + 85.9130859375, + 231.976806640625 + ], + [ + 222.39944458007812, + 231.976806640625 + ], + [ + 222.39944458007812, + 266.32843017578125 + ], + [ + 85.9130859375, + 266.32843017578125 + ] + ], + "bbox": [ + 85.9130859375, + 231.976806640625, + 222.39944458007812, + 266.32843017578125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/92/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/93/Text/6", + "block_type": "Text", "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.
", "polygon": [ [ - 85.6142578125, - 273.216796875 + 85.9130859375, + 273.41015625 ], [ - 483.50390625, - 273.216796875 + 482.40338134765625, + 273.41015625 ], [ - 483.50390625, + 482.40338134765625, 308.3970031738281 ], [ - 85.6142578125, + 85.9130859375, 308.3970031738281 ] ], + "bbox": [ + 85.9130859375, + 273.41015625, + 482.40338134765625, + 308.3970031738281 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/92/SectionHeader/16" + "2": "/page/92/SectionHeader/14" }, "images": {} }, { - "id": "/page/93/SectionHeader/3", + "id": "/page/93/SectionHeader/7", "block_type": "SectionHeader", - "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:
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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.
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", "polygon": [ [ 85.6142578125, - 537.92578125 + 539.0859375 ], [ - 482.90625, - 537.92578125 + 482.4005126953125, + 539.0859375 ], [ - 482.90625, + 482.4005126953125, 561.6233520507812 ], [ @@ -44637,140 +90569,170 @@ 561.6233520507812 ] ], + "bbox": [ + 85.6142578125, + 539.0859375, + 482.4005126953125, + 561.6233520507812 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" + "2": "/page/93/SectionHeader/7" }, "images": {} }, { - "id": "/page/93/Text/8", + "id": "/page/93/Text/12", "block_type": "Text", "html": "Another way to write a traversal is with a for loop:
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", + "id": "/page/93/Code/13", + "block_type": "Code", + "html": "for char in fruit:\n print char", "polygon": [ [ - 85.46484375, - 590.7178955078125 + 85.6142578125, + 590.51953125 ], [ - 180.54653930664062, - 590.7178955078125 + 181.6875, + 590.51953125 ], [ - 180.54653930664062, - 612.8744964599609 + 181.6875, + 612.94921875 ], [ - 85.46484375, - 612.8744964599609 + 85.6142578125, + 612.94921875 ] ], + "bbox": [ + 85.6142578125, + 590.51953125, + 181.6875, + 612.94921875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" + "2": "/page/93/SectionHeader/7" }, "images": {} }, { - "id": "/page/93/Text/10", + "id": "/page/93/Text/14", "block_type": "Text", "html": "
Each time through the loop, the next character in the string is assigned to the variable char. The loop continues until no characters are left.
", "polygon": [ [ - 86.0625, - 618.75 + 86.39997863769531, + 620.296875 ], [ 482.399658203125, - 618.75 + 620.296875 ], [ 482.399658203125, 642.7500610351562 ], [ - 86.0625, + 86.39997863769531, 642.7500610351562 ] ], + "bbox": [ + 86.39997863769531, + 620.296875, + 482.399658203125, + 642.7500610351562 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" + "2": "/page/93/SectionHeader/7" }, "images": {} }, { - "id": "/page/93/Text/11", + "id": "/page/93/Text/15", "block_type": "Text", "html": "The 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:
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", + "html": "", "polygon": [ [ 127.7490234375, - 61.1015625 + 61.05322265625 ], [ 525.6033935546875, - 61.1015625 + 61.05322265625 ], [ 525.6033935546875, @@ -44812,340 +90780,276 @@ 71.13372802734375 ] ], + "bbox": [ + 127.7490234375, + 61.05322265625, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" + "2": "/page/93/SectionHeader/7" }, "images": {} }, { - "id": "/page/94/PageHeader/16", + "id": "/page/94/PageHeader/12", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 515.1796875, - 60.4248046875 + 515.77734375, + 60.71484375 ], [ - 525.9375, - 60.4248046875 + 526.53515625, + 60.71484375 ], [ - 525.9375, - 69.8994140625 + 526.53515625, + 70.4794921875 ], [ - 515.1796875, - 69.8994140625 + 515.77734375, + 70.4794921875 ] ], + "bbox": [ + 515.77734375, + 60.71484375, + 526.53515625, + 70.4794921875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" + "2": "/page/93/SectionHeader/7" }, "images": {} }, { - "id": "/page/94/TableGroup/173", - "block_type": "TableGroup", - "html": "fruit | ' | b a n | a | n | a ' | ||
---|---|---|---|---|---|---|---|
index | 0 | 1 2 3 | 4 | 5 | 6 |
Figure 8.1: Slice indices.
", + "html": "Figure 8.1: Slice indices.
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for letter in prefixes:\n print letter + suffix", - "polygon": [ - [ - 128.9443359375, - 209.34375 - ], - [ - 260.36358642578125, - 209.34375 - ], - [ - 260.36358642578125, - 236.671875 - ], - [ - 128.9443359375, - 236.671875 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/94/Text/5", - "block_type": "Text", - "html": "
The output is:
", + "html": "prefixes = '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.", "polygon": [ [ - 129.46728515625, - 236.57733154296875 - ], - [ - 190.8101806640625, - 236.57733154296875 - ], - [ - 190.8101806640625, - 252.140625 - ], - [ - 129.46728515625, - 252.140625 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/94/Text/6", - "block_type": "Text", - "html": "
Jack Kack Lack Mack Nack Oack Pack Qack
", - "polygon": [ - [ - 128.42138671875, - 251.3167724609375 - ], - [ - 153.1494140625, - 251.3167724609375 - ], - [ - 153.1494140625, - 346.640380859375 - ], - [ - 128.42138671875, - 346.640380859375 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/94/Text/7", - "block_type": "Text", - "html": "Of course, that's not quite right because \"Ouack\" and \"Quack\" are misspelled. Exercise 8.2. Modify the program to fix this error.
", - "polygon": [ - [ - 129.5419921875, - 349.400390625 + 107.4287109375, + 172.4765625 ], [ 474.7243347167969, - 349.400390625 + 172.4765625 ], [ 474.7243347167969, 373.77685546875 ], [ - 129.5419921875, + 107.4287109375, 373.77685546875 ] ], + "bbox": [ + 107.4287109375, + 172.4765625, + 474.7243347167969, + 373.77685546875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/93/SectionHeader/3" + "2": "/page/93/SectionHeader/7" }, "images": {} }, { - "id": "/page/94/SectionHeader/8", + "id": "/page/94/SectionHeader/4", "block_type": "SectionHeader", - "html": "A segment of a string is called a slice. Selecting a slice is similar to selecting a character:
", "polygon": [ [ - 128.794921875, - 426.7462463378906 + 129.5419921875, + 426.55078125 ], [ - 515.478515625, - 426.7462463378906 + 514.7135620117188, + 426.55078125 ], [ - 515.478515625, - 438.15234375 + 514.7135620117188, + 436.80596923828125 ], [ - 128.794921875, - 438.15234375 + 129.5419921875, + 436.80596923828125 ] ], + "bbox": [ + 129.5419921875, + 426.55078125, + 514.7135620117188, + 436.80596923828125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, { - "id": "/page/94/Code/10", + "id": "/page/94/Code/6", "block_type": "Code", "html": ">>> s = 'Monty Python'\n>>> print s[0:5]\nMonty\n>>> print s[6:12]\nPython", "polygon": [ [ - 129.09375, + 129.2431640625, 441.5838317871094 ], [ @@ -45154,51 +91058,65 @@ ], [ 244.64134216308594, - 502.734375 + 501.1875 ], [ - 129.09375, - 502.734375 + 129.2431640625, + 501.1875 ] ], + "bbox": [ + 129.2431640625, + 441.5838317871094, + 244.64134216308594, + 501.1875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, { - "id": "/page/94/Text/11", + "id": "/page/94/Text/7", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 128.794921875, + 129.392578125, 505.2508544921875 ], [ - 526.236328125, + 525.638671875, 505.2508544921875 ], [ - 526.236328125, - 540.24609375 + 525.638671875, + 539.7510070800781 ], [ - 128.794921875, - 540.24609375 + 129.392578125, + 539.7510070800781 ] ], + "bbox": [ + 129.392578125, + 505.2508544921875, + 525.638671875, + 539.7510070800781 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, { - "id": "/page/94/Text/12", + "id": "/page/94/Text/8", "block_type": "Text", "html": "If 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:
", "polygon": [ @@ -45207,95 +91125,116 @@ 547.98046875 ], [ - 525.9375, + 525.603271484375, 547.98046875 ], [ - 525.9375, - 571.18359375 + 525.603271484375, + 570.8070068359375 ], [ 129.09375, - 571.18359375 + 570.8070068359375 ] ], + "bbox": [ + 129.09375, + 547.98046875, + 525.603271484375, + 570.8070068359375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, { - "id": "/page/94/Code/13", + "id": "/page/94/Code/9", "block_type": "Code", "html": ">>> fruit = 'banana'\n>>> fruit[:3]\n'ban'\n>>> fruit[3:]\n'ana'", "polygon": [ [ - 129.59994506835938, - 575.4375 + 129.392578125, + 575.05078125 ], [ 234.1833038330078, - 575.4375 + 575.05078125 ], [ 234.1833038330078, - 638.859375 + 634.60546875 ], [ - 129.59994506835938, - 638.859375 + 129.392578125, + 634.60546875 ] ], + "bbox": [ + 129.392578125, + 575.05078125, + 234.1833038330078, + 634.60546875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, { - "id": "/page/94/Text/14", + "id": "/page/94/Text/10", "block_type": "Text", "html": "
If the first index is greater than or equal to the second the result is an empty string, represented by two quotation marks:
", "polygon": [ [ - 128.3466796875, - 639.3033142089844 + 129.5419921875, + 639.24609375 ], [ - 525.9375, - 639.3033142089844 + 525.6005249023438, + 639.24609375 ], [ - 525.9375, - 662.44921875 + 525.6005249023438, + 661.5570220947266 ], [ - 128.3466796875, - 662.44921875 + 129.5419921875, + 661.5570220947266 ] ], + "bbox": [ + 129.5419921875, + 639.24609375, + 525.6005249023438, + 661.5570220947266 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, { - "id": "/page/94/Code/15", + "id": "/page/94/Code/11", "block_type": "Code", "html": ">>> fruit = 'banana'\n>>> fruit[3:3]\n''", "polygon": [ [ 129.59994506835938, - 666.31640625 + 665.54296875 ], [ 234.1833038330078, - 666.31640625 + 665.54296875 ], [ 234.1833038330078, @@ -45306,24 +91245,32 @@ 697.25390625 ] ], + "bbox": [ + 129.59994506835938, + 665.54296875, + 234.1833038330078, + 697.25390625 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} } ], "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": null }, { - "id": "/page/95/Page/214", + "id": "/page/95/Page/216", "block_type": "Page", - "html": "
74 Chapter 8. Strings
", + "html": "", "polygon": [ [ 86.4000015258789, - 61.171142578125 + 60.95654296875 ], [ - 483.205078125, - 61.171142578125 + 482.4034118652344, + 60.95654296875 ], [ - 483.205078125, - 71.15625 + 482.4034118652344, + 71.13372802734375 ], [ 86.4000015258789, - 71.15625 + 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.95654296875, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, { "id": "/page/95/PageHeader/19", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.46484375, - 60.37646484375 + 85.53955078125, + 60.8115234375 ], [ - 95.625, - 60.37646484375 + 96.59619140625, + 60.8115234375 ], [ - 95.625, - 70.33447265625 + 96.59619140625, + 70.8662109375 ], [ - 85.46484375, - 70.33447265625 + 85.53955078125, + 70.8662109375 ] ], + "bbox": [ + 85.53955078125, + 60.8115234375, + 96.59619140625, + 70.8662109375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, @@ -45407,26 +91374,33 @@ "html": "An empty string contains no characters and has length 0, but other than that, it is the same as any other string.
", "polygon": [ [ - 85.9130859375, - 88.22021484375 + 85.763671875, + 88.55859375 ], [ 482.90625, - 88.22021484375 + 88.55859375 ], [ 482.90625, 110.99188232421875 ], [ - 85.9130859375, + 85.763671875, 110.99188232421875 ] ], + "bbox": [ + 85.763671875, + 88.55859375, + 482.90625, + 110.99188232421875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, @@ -45436,54 +91410,68 @@ "html": "Exercise 8.3. Given that fruit is a string, what does fruit[:] mean?
", "polygon": [ [ - 86.4000015258789, - 112.1484375 + 85.53955078125, + 112.341796875 ], [ 378.8342590332031, - 112.1484375 + 112.341796875 ], [ 378.8342590332031, 123.0897216796875 ], [ - 86.4000015258789, + 85.53955078125, 123.0897216796875 ] ], + "bbox": [ + 85.53955078125, + 112.341796875, + 378.8342590332031, + 123.0897216796875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/94/SectionHeader/8" + "2": "/page/93/SectionHeader/7", + "4": "/page/94/SectionHeader/4" }, "images": {} }, { "id": "/page/95/SectionHeader/3", "block_type": "SectionHeader", - "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:
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", + "id": "/page/95/Code/5", + "block_type": "Code", + "html": ">>> greeting = 'Hello, world!'\n>>> greeting[0] = 'J'\nTypeError: 'str' object does not support item assignment", "polygon": [ [ - 85.83837890625, - 203.607421875 + 85.53955078125, + 203.02734375 ], [ - 244.740234375, - 202.060546875 + 379.2892150878906, + 203.02734375 ], [ - 244.740234375, - 226.11737060546875 + 379.2892150878906, + 238.3114013671875 ], [ - 85.83837890625, - 226.810546875 + 85.53955078125, + 238.3114013671875 ] ], + "bbox": [ + 85.53955078125, + 203.02734375, + 379.2892150878906, + 238.3114013671875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", + "2": "/page/93/SectionHeader/7", "3": "/page/95/SectionHeader/3" }, "images": {} @@ -45549,28 +91551,35 @@ { "id": "/page/95/Text/6", "block_type": "Text", - "html": "
TypeError: 'str' object does not support item assignment
", + "html": "The \"object\" in this case is the string and the \"item\" is the character you tried to assign. For
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", + "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.
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", + "id": "/page/95/Code/9", + "block_type": "Code", + "html": ">>> greeting = 'Hello, world!'\n>>> new_greeting = 'J' + greeting[1:]\n>>> print new_greeting\nJello, world!", "polygon": [ [ - 85.9130859375, + 85.763671875, 314.33880615234375 ], [ @@ -45648,16 +91671,23 @@ ], [ 279.8952331542969, - 361.775390625 + 362.548828125 ], [ - 85.9130859375, - 361.775390625 + 85.763671875, + 362.548828125 ] ], + "bbox": [ + 85.763671875, + 314.33880615234375, + 279.8952331542969, + 362.548828125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", + "2": "/page/93/SectionHeader/7", "3": "/page/95/SectionHeader/3" }, "images": {} @@ -45669,14 +91699,14 @@ "polygon": [ [ 86.0625, - 364.67578125 + 365.8359375 ], [ - 483.205078125, - 364.67578125 + 482.90625, + 365.8359375 ], [ - 483.205078125, + 482.90625, 388.3729553222656 ], [ @@ -45684,9 +91714,16 @@ 388.3729553222656 ] ], + "bbox": [ + 86.0625, + 365.8359375, + 482.90625, + 388.3729553222656 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", + "2": "/page/93/SectionHeader/7", "3": "/page/95/SectionHeader/3" }, "images": {} @@ -45694,29 +91731,37 @@ { "id": "/page/95/SectionHeader/11", "block_type": "SectionHeader", - "html": "
What does the following function do?
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", + "html": "", "polygon": [ [ - 127.599609375, - 61.14990234375 + 128.9443359375, + 61.171142578125 ], [ 525.6033935546875, - 61.14990234375 + 61.171142578125 ], [ 525.6033935546875, 71.13372802734375 ], [ - 127.599609375, + 128.9443359375, 71.13372802734375 ] ], + "bbox": [ + 128.9443359375, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/95/SectionHeader/11" + "2": "/page/93/SectionHeader/7", + "3": "/page/95/SectionHeader/3", + "4": "/page/95/SectionHeader/11" }, "images": {} }, { "id": "/page/96/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ 515.1796875, - 60.521484375 + 60.908203125 ], [ - 526.53515625, - 60.521484375 + 525.9375, + 60.908203125 ], [ - 526.53515625, - 70.189453125 + 525.9375, + 70.0927734375 ], [ 515.1796875, - 70.189453125 + 70.0927734375 ] ], + "bbox": [ + 515.1796875, + 60.908203125, + 525.9375, + 70.0927734375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/95/SectionHeader/11" + "2": "/page/93/SectionHeader/7", + "3": "/page/95/SectionHeader/3", + "4": "/page/95/SectionHeader/11" }, "images": {} }, { "id": "/page/96/SectionHeader/1", "block_type": "SectionHeader", - "html": "The following program counts the number of times the letter a appears in a string:
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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.
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", "polygon": [ [ - 129.2431640625, - 241.892578125 + 128.3466796875, + 242.6566162109375 ], [ 525.6008911132812, - 241.892578125 + 242.6566162109375 ], [ 525.6008911132812, 264.81317138671875 ], [ - 129.2431640625, + 128.3466796875, 264.81317138671875 ] ], + "bbox": [ + 128.3466796875, + 242.6566162109375, + 525.6008911132812, + 264.81317138671875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/96/SectionHeader/1" + "2": "/page/93/SectionHeader/7", + "3": "/page/95/SectionHeader/3", + "4": "/page/96/SectionHeader/1" }, "images": {} }, @@ -46163,14 +92328,14 @@ "polygon": [ [ 128.49609375, - 266.8359375 + 267.04559326171875 ], [ - 525.9375, - 266.8359375 + 525.6040649414062, + 267.04559326171875 ], [ - 525.9375, + 525.6040649414062, 289.2253112792969 ], [ @@ -46178,39 +92343,55 @@ 289.2253112792969 ] ], + "bbox": [ + 128.49609375, + 267.04559326171875, + 525.6040649414062, + 289.2253112792969 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/96/SectionHeader/1" + "2": "/page/93/SectionHeader/7", + "3": "/page/95/SectionHeader/3", + "4": "/page/96/SectionHeader/1" }, "images": {} }, { "id": "/page/96/SectionHeader/7", "block_type": "SectionHeader", - "html": "Instead of the function syntax upper(word), it uses the method syntax word.upper().
", "polygon": [ [ - 128.9443359375, - 389.8125 + 128.49609375, + 390.2567443847656 ], [ 505.4187316894531, - 389.8125 + 390.2567443847656 ], [ 505.4187316894531, 400.368896484375 ], [ - 128.9443359375, + 128.49609375, 400.368896484375 ] ], + "bbox": [ + 128.49609375, + 390.2567443847656, + 505.4187316894531, + 400.368896484375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/96/SectionHeader/7" + "2": "/page/93/SectionHeader/7", + "3": "/page/95/SectionHeader/3", + "4": "/page/96/SectionHeader/7" }, "images": {} }, @@ -46278,26 +92475,34 @@ "html": ">>> word = 'banana'\n>>> new_word = word.upper()\n>>> print new_word\nBANANA", "polygon": [ [ - 129.60009765625, - 405.66796875 + 128.86962890625, + 406.5097351074219 ], [ 270.8299255371094, - 405.66796875 + 406.5097351074219 ], [ 270.8299255371094, 453.0553283691406 ], [ - 129.60009765625, + 128.86962890625, 453.0553283691406 ] ], + "bbox": [ + 128.86962890625, + 406.5097351074219, + 270.8299255371094, + 453.0553283691406 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/96/SectionHeader/7" + "2": "/page/93/SectionHeader/7", + "3": "/page/95/SectionHeader/3", + "4": "/page/96/SectionHeader/7" }, "images": {} }, @@ -46307,26 +92512,34 @@ "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.
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", "polygon": [ [ - 128.9443359375, + 128.6455078125, 536.3787536621094 ], [ @@ -46374,17 +92595,25 @@ ], [ 525.9375, - 558.80859375 + 558.6849060058594 ], [ - 128.9443359375, - 558.80859375 + 128.6455078125, + 558.6849060058594 ] ], + "bbox": [ + 128.6455078125, + 536.3787536621094, + 525.9375, + 558.6849060058594 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/96/SectionHeader/7" + "2": "/page/93/SectionHeader/7", + "3": "/page/95/SectionHeader/3", + "4": "/page/96/SectionHeader/7" }, "images": {} }, @@ -46394,26 +92623,34 @@ "html": ">>> word = 'banana'\n>>> index = word.find('a')\n>>> print index\n1", "polygon": [ [ - 128.6455078125, - 564.22265625 + 129.09375, + 564.8257598876953 ], [ 265.5585021972656, - 564.22265625 + 564.8257598876953 ], [ 265.5585021972656, - 612.94921875 + 611.7890625 ], [ - 128.6455078125, - 612.94921875 + 129.09375, + 611.7890625 ] ], + "bbox": [ + 129.09375, + 564.8257598876953, + 265.5585021972656, + 611.7890625 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/96/SectionHeader/7" + "2": "/page/93/SectionHeader/7", + "3": "/page/95/SectionHeader/3", + "4": "/page/96/SectionHeader/7" }, "images": {} }, @@ -46423,26 +92660,34 @@ "html": "
In this example, we invoke find on word and pass the letter we are looking for as a parameter.
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76 Chapter 8. Strings
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", + "html": "It can take as a second argument the index where it should start:
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And as a third argument the index where it should stop:
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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'.
", + "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'.
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", + "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.
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>>> 'a' in 'banana'\nTrue\n>>> 'seed' in 'banana'", "polygon": [ [ 85.6142578125, - 390.779296875 + 397.52166748046875 ], [ - 484.69921875, - 390.779296875 + 201.44131469726562, + 397.52166748046875 ], [ - 484.69921875, - 524.77734375 + 201.44131469726562, + 432.3515625 ], [ 85.6142578125, - 524.77734375 + 432.3515625 ] ], + "bbox": [ + 85.6142578125, + 397.52166748046875, + 201.44131469726562, + 432.3515625 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/97/SectionHeader/5" + "2": "/page/97/SectionHeader/8" }, "images": {} }, { - "id": "/page/97/Text/8", + "id": "/page/97/Text/11", + "block_type": "Text", + "html": "
False
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", + "polygon": [ + [ + 85.6142578125, + 448.98046875 + ], + [ + 482.398681640625, + 448.98046875 + ], + [ + 482.398681640625, + 472.0468444824219 + ], + [ + 85.6142578125, + 472.0468444824219 + ] + ], + "bbox": [ + 85.6142578125, + 448.98046875, + 482.398681640625, + 472.0468444824219 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/97/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/97/Code/13", + "block_type": "Code", + "html": "def in_both(word1, word2):\n for letter in word1:\n if letter in word2:\n print letter", + "polygon": [ + [ + 85.6142578125, + 477.5697021484375 + ], + [ + 227.60986328125, + 477.5697021484375 + ], + [ + 227.60986328125, + 524.1152954101562 + ], + [ + 85.6142578125, + 524.1152954101562 + ] + ], + "bbox": [ + 85.6142578125, + 477.5697021484375, + 227.60986328125, + 524.1152954101562 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/97/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/97/Text/14", "block_type": "Text", "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.\"
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The relational operators work on strings. To see if two strings are equal:
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if word == 'banana': print 'All right, bananas.'
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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:
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", + "id": "/page/98/Text/5", + "block_type": "Text", + "html": "Your word, Pineapple, comes before banana.
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def 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", "polygon": [ [ 129.60006713867188, 386.254638671875 ], [ - 299.42578125, + 301.9658203125, 386.254638671875 ], [ - 299.42578125, - 538.69921875 - ], - [ - 129.60006713867188, - 538.69921875 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" - }, - "images": {} - }, - { - "id": "/page/98/Text/10", - "block_type": "Text", - "html": "
return True
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", + "html": "The 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.
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", "polygon": [ [ - 128.3466796875, + 128.6455078125, 605.5576782226562 ], [ - 526.53515625, - 604.0546875 + 525.9375, + 605.5576782226562 ], [ - 526.53515625, + 525.9375, 640.058837890625 ], [ - 128.3466796875, - 641.1796875 + 128.6455078125, + 640.058837890625 ] ], + "bbox": [ + 128.6455078125, + 605.5576782226562, + 525.9375, + 640.058837890625 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, { - "id": "/page/98/Text/13", + "id": "/page/98/Text/12", "block_type": "Text", "html": "If we test this function with the words \"pots\" and \"stop\", we expect the return value True, but we get an IndexError:
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>>> is_reverse('pots', 'stop') ...
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} }, { "id": "/page/99/Caption/2", "block_type": "Caption", - "html": "Figure 8.2: State diagram.
", + "html": "Figure 8.2: State diagram.
", "polygon": [ [ - 225.7646484375, - 144.439453125 + 227.5576171875, + 145.01953125 ], [ - 342.45703125, - 144.439453125 + 341.859375, + 145.01953125 ], [ - 342.45703125, - 155.84765625 + 341.859375, + 155.55291748046875 ], [ - 225.7646484375, - 155.84765625 + 227.5576171875, + 155.55291748046875 ] ], + "bbox": [ + 227.5576171875, + 145.01953125, + 341.859375, + 155.55291748046875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} } ], "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": null }, { - "id": "/page/99/TextInlineMath/3", - "block_type": "TextInlineMath", - "html": "File \"reverse.py\", line 15, in is_reverse if word1[i] != word2[j]: IndexError: string index out of range
", + "id": "/page/99/Code/3", + "block_type": "Code", + "html": "File \"reverse.py\", line 15, in is_reverse\n if word1[i] != word2[j]:\nIndexError: string index out of range", "polygon": [ [ 86.39998626708984, - 177.890625 + 178.27734375 ], [ - 476.9296875, - 177.890625 + 311.3128356933594, + 178.27734375 ], [ - 476.9296875, - 214.435546875 + 311.3128356933594, + 214.048828125 ], [ 86.39998626708984, - 214.435546875 + 214.048828125 ] ], + "bbox": [ + 86.39998626708984, + 178.27734375, + 311.3128356933594, + 214.048828125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, @@ -47647,26 +94362,33 @@ "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.
", "polygon": [ [ - 85.6142578125, - 216.755859375 + 84.7177734375, + 218.689453125 ], [ 482.40338134765625, - 216.755859375 + 218.689453125 ], [ 482.40338134765625, - 241.50201416015625 + 241.505859375 ], [ - 85.6142578125, - 241.50201416015625 + 84.7177734375, + 241.505859375 ] ], + "bbox": [ + 84.7177734375, + 218.689453125, + 482.40338134765625, + 241.505859375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, @@ -47676,26 +94398,33 @@ "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", "polygon": [ [ - 106.681640625, - 244.212890625 + 105.56103515625, + 247.82684326171875 ], [ - 287.47265625, - 244.212890625 + 285.1339416503906, + 247.82684326171875 ], [ - 287.47265625, - 334.705078125 + 285.1339416503906, + 334.51171875 ], [ - 106.681640625, - 334.705078125 + 105.56103515625, + 334.51171875 ] ], + "bbox": [ + 105.56103515625, + 247.82684326171875, + 285.1339416503906, + 334.51171875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, @@ -47705,84 +94434,105 @@ "html": "
Now when I run the program again, I get more information:
", "polygon": [ [ - 85.39013671875, - 337.5804138183594 + 85.166015625, + 337.21875 ], [ - 350.82421875, - 337.5804138183594 + 350.6977233886719, + 337.21875 ], [ - 350.82421875, - 351.720703125 + 350.6977233886719, + 347.5429992675781 ], [ - 85.39013671875, - 351.720703125 + 85.166015625, + 347.5429992675781 ] ], + "bbox": [ + 85.166015625, + 337.21875, + 350.6977233886719, + 347.5429992675781 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, { - "id": "/page/99/TextInlineMath/7", - "block_type": "TextInlineMath", - "html": ">>> is_reverse('pots', 'stop') 0 4 ... IndexError: string index out of range
", + "id": "/page/99/Code/7", + "block_type": "Code", + "html": ">>> is_reverse('pots', 'stop')\n0 4\n...\nIndexError: string index out of range", "polygon": [ [ - 86.39999389648438, - 352.494140625 + 84.7177734375, + 353.86785888671875 ], [ 279.9334411621094, - 352.494140625 + 353.86785888671875 ], [ 279.9334411621094, 400.4134521484375 ], [ - 85.3154296875, + 84.7177734375, 400.4134521484375 ] ], + "bbox": [ + 84.7177734375, + 353.86785888671875, + 279.9334411621094, + 400.4134521484375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, { - "id": "/page/99/TextInlineMath/8", - "block_type": "TextInlineMath", - "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.
", + "id": "/page/99/Text/8", + "block_type": "Text", + "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.
", "polygon": [ [ - 85.9130859375, - 404.12109375 + 85.0166015625, + 406.0546875 ], [ - 483.50390625, - 402.57421875 + 482.40484619140625, + 406.0546875 ], [ - 483.50390625, - 441.3890380859375 + 482.40484619140625, + 441.6328125 ], [ - 85.9130859375, - 442.79296875 + 85.0166015625, + 441.6328125 ] ], + "bbox": [ + 85.0166015625, + 406.0546875, + 482.40484619140625, + 441.6328125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, @@ -47792,26 +94542,33 @@ "html": "If I fix that error and run the program again, I get:
", "polygon": [ [ - 85.53955078125, - 450.9140625 + 85.3154296875, + 451.30078125 ], [ - 307.79296875, - 450.9140625 + 304.9893493652344, + 451.30078125 ], [ - 307.79296875, - 462.515625 + 304.9893493652344, + 461.79803466796875 ], [ - 85.53955078125, - 462.515625 + 85.3154296875, + 461.79803466796875 ] ], + "bbox": [ + 85.3154296875, + 451.30078125, + 304.9893493652344, + 461.79803466796875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, @@ -47821,156 +94578,228 @@ "html": ">>> is_reverse('pots', 'stop')\n0 3\n1 2\n2 1\nTrue", "polygon": [ [ - 84.568359375, + 85.9130859375, 468.1228942871094 ], [ - 244.44140625, + 250.2685546875, 468.1228942871094 ], [ - 244.44140625, + 250.2685546875, 526.8624877929688 ], [ - 84.568359375, + 85.9130859375, 526.8624877929688 ] ], + "bbox": [ + 85.9130859375, + 468.1228942871094, + 250.2685546875, + 526.8624877929688 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, { "id": "/page/99/Text/11", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ 85.46484375, - 531.73828125 + 532.8984375 ], [ - 483.205078125, - 531.73828125 + 482.607421875, + 532.8984375 ], [ - 483.205078125, - 568.08984375 + 482.607421875, + 567.8380584716797 ], [ 85.46484375, - 568.08984375 + 567.8380584716797 ] ], + "bbox": [ + 85.46484375, + 532.8984375, + 482.607421875, + 567.8380584716797 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, { "id": "/page/99/Text/12", "block_type": "Text", - "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. 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.
", + "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.
", "polygon": [ [ - 85.166015625, - 576.984375 + 85.3154296875, + 577.7578125 ], [ - 482.607421875, - 576.984375 + 482.4034423828125, + 577.7578125 ], [ - 482.607421875, + 482.4034423828125, + 601.734375 + ], + [ + 85.3154296875, + 601.734375 + ] + ], + "bbox": [ + 85.3154296875, + 577.7578125, + 482.4034423828125, + 601.734375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" + }, + "images": {} + }, + { + "id": "/page/99/Text/13", + "block_type": "Text", + "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.
", + "polygon": [ + [ + 84.8671875, + 602.5007629394531 + ], + [ + 482.4013977050781, + 602.5007629394531 + ], + [ + 482.4013977050781, 624.6805114746094 ], [ - 85.166015625, + 84.8671875, 624.6805114746094 ] ], + "bbox": [ + 84.8671875, + 602.5007629394531, + 482.4013977050781, + 624.6805114746094 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/98/SectionHeader/7" + "2": "/page/97/SectionHeader/8", + "4": "/page/98/SectionHeader/7" }, "images": {} }, { - "id": "/page/99/SectionHeader/13", + "id": "/page/99/SectionHeader/14", "block_type": "SectionHeader", - "html": "object: Something a variable can refer to. For now, you can use \"object\" and \"value\" interchangeably.
", "polygon": [ [ - 85.763671875, - 675.2109375 + 86.2119140625, + 677.53125 ], [ 482.4032287597656, - 675.2109375 + 677.53125 ], [ 482.4032287597656, 700.835075378418 ], [ - 85.763671875, + 86.2119140625, 700.835075378418 ] ], + "bbox": [ + 86.2119140625, + 677.53125, + 482.4032287597656, + 700.835075378418 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/99/SectionHeader/13" + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" }, "images": {} } ], "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/99/SectionHeader/13" + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" }, "images": null }, { - "id": "/page/100/Page/176", + "id": "/page/100/Page/177", "block_type": "Page", - "html": "8.13. Exercises 79
", + "html": "", "polygon": [ [ - 127.8984375, + 129.01904296875, 61.171142578125 ], [ @@ -48008,72 +94843,93 @@ 71.13372802734375 ], [ - 127.8984375, + 129.01904296875, 71.13372802734375 ] ], + "bbox": [ + 129.01904296875, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/99/SectionHeader/13" + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" }, "images": {} }, { "id": "/page/100/PageHeader/19", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 516.375, - 61.1982421875 + 515.77734375, + 60.71484375 ], [ - 525.9375, - 61.1982421875 + 525.33984375, + 60.71484375 ], [ - 525.9375, - 69.99609375 + 525.33984375, + 69.609375 ], [ - 516.375, - 69.99609375 + 515.77734375, + 69.609375 ] ], + "bbox": [ + 515.77734375, + 60.71484375, + 525.33984375, + 69.609375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/99/SectionHeader/13" + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" }, "images": {} }, { - "id": "/page/100/Text/1", - "block_type": "Text", - "html": "sequence: An ordered set; that is, a set of values where each value is identified by an integer index.
", + "id": "/page/100/ListItem/1", + "block_type": "ListItem", + "html": "item: One of the values in a sequence.
", "polygon": [ [ - 128.794921875, - 120.849609375 + 128.197265625, + 120.953125 ], [ - 298.529296875, - 120.849609375 + 298.49591064453125, + 120.953125 ], [ - 298.529296875, + 298.49591064453125, 131.01287841796875 ], [ - 128.794921875, + 128.197265625, 131.01287841796875 ] ], + "bbox": [ + 128.197265625, + 120.953125, + 298.49591064453125, + 131.01287841796875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/99/SectionHeader/13" + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" }, "images": {} }, @@ -48112,7 +94975,7 @@ "html": "index: An integer value used to select an item in a sequence, such as a character in a string.
", "polygon": [ [ - 128.49609375, + 127.7490234375, 140.97412109375 ], [ @@ -48124,220 +94987,239 @@ 151.03387451171875 ], [ - 128.49609375, + 127.7490234375, 151.03387451171875 ] ], + "bbox": [ + 127.7490234375, + 140.97412109375, + 525.6030883789062, + 151.03387451171875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/100/Text/4", + "block_type": "Text", + "html": "slice: A part of a string specified by a range of indices.
", + "polygon": [ + [ + 127.97314453125, + 173.18914794921875 + ], + [ + 370.3261413574219, + 173.18914794921875 + ], + [ + 370.3261413574219, + 183.2489013671875 + ], + [ + 127.97314453125, + 183.2489013671875 + ] + ], + "bbox": [ + 127.97314453125, + 173.18914794921875, + 370.3261413574219, + 183.2489013671875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/100/ListItem/5", + "block_type": "ListItem", + "html": "immutable: The property of a sequence whose items cannot be assigned.
", + "polygon": [ + [ + 128.0478515625, + 225.4251708984375 + ], + [ + 452.1889343261719, + 225.4251708984375 + ], + [ + 452.1889343261719, + 235.48492431640625 + ], + [ + 128.0478515625, + 235.48492431640625 + ] + ], + "bbox": [ + 128.0478515625, + 225.4251708984375, + 452.1889343261719, + 235.48492431640625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/92/SectionHeader/1", + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/100/ListItem/7", + "block_type": "ListItem", + "html": "slice: A part of a string specified by a range of indices.
", + "html": "search: A pattern of traversal that stops when it finds what it is looking for.
", "polygon": [ [ - 128.86962890625, - 173.18914794921875 + 128.6455078125, + 277.6611328125 ], [ - 370.3261413574219, - 173.18914794921875 + 462.2711486816406, + 277.6611328125 ], [ - 370.3261413574219, - 183.2489013671875 + 462.2711486816406, + 287.7208557128906 ], [ - 128.86962890625, - 183.2489013671875 + 128.6455078125, + 287.7208557128906 ] ], + "bbox": [ + 128.6455078125, + 277.6611328125, + 462.2711486816406, + 287.7208557128906 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/99/SectionHeader/13" + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" }, "images": {} }, { - "id": "/page/100/ListGroup/176", - "block_type": "ListGroup", - "html": "method: A function that is associated with an object and called using dot notation.
", "polygon": [ [ - 128.3466796875, + 129.2431640625, 329.8971252441406 ], [ @@ -48357,14 +95239,21 @@ 339.95684814453125 ], [ - 128.3466796875, + 129.2431640625, 339.95684814453125 ] ], + "bbox": [ + 129.2431640625, + 329.8971252441406, + 494.46026611328125, + 339.95684814453125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/99/SectionHeader/13" + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" }, "images": {} }, @@ -48374,36 +95263,43 @@ "html": "invocation: A statement that calls a method.
", "polygon": [ [ - 129.01904296875, + 129.392578125, 349.9181213378906 ], [ - 327.515625, + 326.82940673828125, 349.9181213378906 ], [ - 327.515625, + 326.82940673828125, 359.97784423828125 ], [ - 129.01904296875, + 129.392578125, 359.97784423828125 ] ], + "bbox": [ + 129.392578125, + 349.9181213378906, + 326.82940673828125, + 359.97784423828125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/99/SectionHeader/13" + "2": "/page/97/SectionHeader/8", + "4": "/page/99/SectionHeader/14" }, "images": {} }, { "id": "/page/100/SectionHeader/12", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 129.09375, + 128.6455078125, 415.4345397949219 ], [ - 526.53515625, + 525.6035766601562, 415.4345397949219 ], [ - 526.53515625, + 525.6035766601562, 449.7861328125 ], [ - 129.09375, + 128.6455078125, 449.7861328125 ] ], + "bbox": [ + 128.6455078125, + 415.4345397949219, + 525.6035766601562, + 449.7861328125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, @@ -48461,7 +95371,7 @@ "html": ">>> fruit = 'banana'\n>>> fruit[0:5:2]\n'bnn'", "polygon": [ [ - 128.86962890625, + 127.97314453125, 455.84368896484375 ], [ @@ -48470,17 +95380,24 @@ ], [ 234.1833953857422, - 492.29296875 + 490.74609375 ], [ - 128.86962890625, - 492.29296875 + 127.97314453125, + 490.74609375 ] ], + "bbox": [ + 127.97314453125, + 455.84368896484375, + 234.1833953857422, + 490.74609375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, @@ -48490,7 +95407,7 @@ "html": "
A step size of -1 goes through the word backwards, so the slice [::-1] generates a reversed string.
", "polygon": [ [ - 128.9443359375, + 129.5419921875, 496.2065734863281 ], [ @@ -48499,75 +95416,96 @@ ], [ 522.5155029296875, - 506.21484375 + 506.1922912597656 ], [ - 128.9443359375, - 506.21484375 + 129.5419921875, + 506.1922912597656 ] ], + "bbox": [ + 129.5419921875, + 496.2065734863281, + 522.5155029296875, + 506.1922912597656 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, { "id": "/page/100/Text/16", "block_type": "Text", - "html": "Use this idiom to write a one-line version of is_palindrome from Exercise 6.6.
", + "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
", "polygon": [ [ - 129.09375, + 126.8525390625, 516.1765747070312 ], [ - 449.736328125, + 525.5995483398438, 516.1765747070312 ], [ - 449.2415771484375, - 526.1622924804688 + 525.5995483398438, + 545.66015625 ], [ - 127.8984375, - 526.1622924804688 + 126.8525390625, + 545.66015625 ] ], + "bbox": [ + 126.8525390625, + 516.1765747070312, + 525.5995483398438, + 545.66015625 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, { "id": "/page/100/Text/17", "block_type": "Text", - "html": "Exercise 8.11. The following functions are all intended to check whether a string contains any 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).
", + "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).
", "polygon": [ [ - 128.6455078125, - 528.3705749511719 + 129.392578125, + 540.5645599365234 ], [ - 526.53515625, - 528.3705749511719 + 525.6033935546875, + 540.5645599365234 ], [ - 526.53515625, - 563.0625 + 525.6033935546875, + 566.9296875 ], [ - 128.6455078125, - 563.0625 + 129.392578125, + 566.9296875 ] ], + "bbox": [ + 129.392578125, + 540.5645599365234, + 525.6033935546875, + 566.9296875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, @@ -48577,40 +95515,48 @@ "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'", "polygon": [ [ - 128.49609375, + 128.27197265625, 568.7797088623047 ], [ - 260.35101318359375, + 262.669921875, 568.7797088623047 ], [ - 260.35101318359375, + 262.669921875, 700.6853179931641 ], [ - 128.49609375, + 128.27197265625, 700.6853179931641 ] ], + "bbox": [ + 128.27197265625, + 568.7797088623047, + 262.669921875, + 700.6853179931641 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} } ], "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": null }, { - "id": "/page/101/Page/108", + "id": "/page/101/Page/119", "block_type": "Page", - "html": "
80 Chapter 8. Strings
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.37646484375 + 60.6181640625 ], [ - 483.802734375, - 60.37646484375 + 482.4034118652344, + 60.6181640625 ], [ - 483.802734375, + 482.4034118652344, 71.13372802734375 ], [ @@ -48652,39 +95604,53 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.6181640625, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, { "id": "/page/101/PageHeader/7", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.39013671875, - 60.66650390625 + 85.53955078125, + 61.1982421875 ], [ - 96.74560546875, - 60.66650390625 + 96.89501953125, + 61.1982421875 ], [ - 96.74560546875, - 70.33447265625 + 96.89501953125, + 70.2861328125 ], [ - 85.39013671875, - 70.33447265625 + 85.53955078125, + 70.2861328125 ] ], + "bbox": [ + 85.53955078125, + 61.1982421875, + 96.89501953125, + 70.2861328125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, @@ -48694,55 +95660,69 @@ "html": "else:\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", "polygon": [ [ - 85.68896484375, - 88.68572998046875 + 85.9130859375, + 86.431640625 ], [ - 264.2324523925781, - 88.68572998046875 + 271.1865234375, + 86.431640625 ], [ - 264.2324523925781, - 326.390625 + 271.1865234375, + 318.1463623046875 ], [ - 85.68896484375, - 327.9375 + 85.9130859375, + 318.1463623046875 ] ], + "bbox": [ + 85.9130859375, + 86.431640625, + 271.1865234375, + 318.1463623046875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, { "id": "/page/101/Text/2", "block_type": "Text", - "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'.
", + "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'.
", "polygon": [ [ - 85.166015625, - 320.3546447753906 + 85.6142578125, + 319.81640625 ], [ 482.40338134765625, - 320.3546447753906 + 319.81640625 ], [ 482.40338134765625, - 355.587890625 + 354.70623779296875 ], [ - 85.166015625, - 355.587890625 + 85.6142578125, + 354.70623779296875 ] ], + "bbox": [ + 85.6142578125, + 319.81640625, + 482.40338134765625, + 354.70623779296875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, @@ -48752,127 +95732,156 @@ "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.
", "polygon": [ [ - 84.8671875, - 363.90234375 + 85.6142578125, + 364.482421875 ], [ - 483.205078125, - 363.90234375 + 482.4034423828125, + 364.482421875 ], [ - 483.205078125, - 387.10546875 + 482.4034423828125, + 387.0372314453125 ], [ - 84.8671875, - 387.10546875 + 85.6142578125, + 387.0372314453125 ] ], + "bbox": [ + 85.6142578125, + 364.482421875, + 482.4034423828125, + 387.0372314453125 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, { - "id": "/page/101/TextInlineMath/4", - "block_type": "TextInlineMath", - "html": "For example, \"cheer\" rotated by 7 is \"jolly\" and \"melon\" rotated by -10 is \"cubed\".
", + "id": "/page/101/Text/4", + "block_type": "Text", + "html": "For example, \"cheer\" rotated by 7 is \"jolly\" and \"melon\" rotated by -10 is \"cubed\".
", "polygon": [ [ - 85.763671875, - 395.806640625 + 85.9130859375, + 396.0 ], [ - 420.51568603515625, - 395.806640625 + 420.75, + 396.0 ], [ - 420.51568603515625, + 420.75, 407.17523193359375 ], [ - 85.763671875, + 85.9130859375, 407.17523193359375 ] ], + "bbox": [ + 85.9130859375, + 396.0, + 420.75, + 407.17523193359375 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, { "id": "/page/101/Text/5", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 85.9130859375, - 415.3359375 + 85.166015625, + 416.49609375 ], [ 482.4031677246094, - 415.3359375 + 416.49609375 ], [ 482.4031677246094, 439.5303649902344 ], [ - 85.9130859375, + 85.166015625, 439.5303649902344 ] ], + "bbox": [ + 85.166015625, + 416.49609375, + 482.4031677246094, + 439.5303649902344 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} }, { "id": "/page/101/Text/6", "block_type": "Text", - "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 .
", + "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 .
", "polygon": [ [ - 85.763671875, - 448.20703125 + 85.3154296875, + 448.59375 ], [ - 482.90625, - 448.20703125 + 482.4035339355469, + 448.59375 ], [ - 482.90625, - 484.0332336425781 + 482.4035339355469, + 484.171875 ], [ - 85.763671875, - 484.0332336425781 + 85.3154296875, + 484.171875 ] ], + "bbox": [ + 85.3154296875, + 448.59375, + 482.4035339355469, + 484.171875 + ], "children": null, "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": {} } ], "section_hierarchy": { "1": "/page/92/SectionHeader/1", - "3": "/page/100/SectionHeader/12" + "2": "/page/97/SectionHeader/8", + "4": "/page/100/SectionHeader/12" }, "images": null }, { - "id": "/page/102/Page/156", + "id": "/page/102/Page/154", "block_type": "Page", - "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.
", + "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.
", "polygon": [ [ - 128.794921875, - 313.822265625 + 127.8984375, + 314.2833557128906 ], [ - 526.236328125, - 313.822265625 + 525.9375, + 314.2833557128906 ], [ - 526.236328125, - 397.546875 + 525.9375, + 397.4119567871094 ], [ - 128.794921875, - 397.546875 + 127.8984375, + 397.4119567871094 ] ], + "bbox": [ + 127.8984375, + 314.2833557128906, + 525.9375, + 397.4119567871094 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, @@ -49016,402 +96052,326 @@ "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.
", "polygon": [ [ - 129.392578125, - 406.0546875 + 127.8984375, + 405.28125 ], [ - 527.431640625, - 406.0546875 + 525.9375, + 405.28125 ], [ - 527.431640625, + 525.9375, 440.5479431152344 ], [ - 129.392578125, + 127.8984375, 440.5479431152344 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" - }, - "images": {} - }, - { - "id": "/page/102/Text/5", - "block_type": "Text", - "html": ">>> fin = open('words.txt')
", - "polygon": [ - [ - 129.01904296875, - 443.953125 - ], - [ - 271.037109375, - 443.953125 - ], - [ - 271.037109375, - 455.17437744140625 - ], - [ - 129.01904296875, - 455.17437744140625 - ] + "bbox": [ + 127.8984375, + 405.28125, + 525.9375, + 440.5479431152344 ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/102/Text/6", - "block_type": "Text", - "html": ">>> print fin
", + "id": "/page/102/Code/5", + "block_type": "Code", + "html": ">>> fin = open('words.txt')\n>>> print fin\n<open file 'words.txt', mode 'r' at 0xb7f4b380>", "polygon": [ [ - 129.392578125, - 456.328125 + 128.86962890625, + 443.56640625 ], [ - 197.82421875, - 456.328125 + 375.3861999511719, + 443.56640625 ], [ - 197.82421875, - 467.3683776855469 + 375.3861999511719, + 482.23828125 ], [ - 129.392578125, - 467.3683776855469 + 128.86962890625, + 482.23828125 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" - }, - "images": {} - }, - { - "id": "/page/102/Text/7", - "block_type": "Text", - "html": "
<open file 'words.txt', mode 'r' at 0xb7f4b380>
", - "polygon": [ - [ - 129.2431640625, - 469.6007995605469 - ], - [ - 376.5234375, - 469.6007995605469 - ], - [ - 376.5234375, - 479.5633850097656 - ], - [ - 129.2431640625, - 479.5633850097656 - ] + "bbox": [ + 128.86962890625, + 443.56640625, + 375.3861999511719, + 482.23828125 ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/102/Text/8", + "id": "/page/102/Text/6", "block_type": "Text", "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).
", "polygon": [ [ - 129.2431640625, + 129.5419921875, 484.37579345703125 ], [ - 527.1328125, + 525.9375, 484.37579345703125 ], [ - 527.1328125, - 506.6829528808594 + 525.9375, + 506.98828125 ], [ - 129.2431640625, - 506.6829528808594 + 129.5419921875, + 506.98828125 ] ], + "bbox": [ + 129.5419921875, + 484.37579345703125, + 525.9375, + 506.98828125 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/102/Text/9", + "id": "/page/102/Text/7", "block_type": "Text", "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:
", "polygon": [ [ - 128.794921875, - 515.3178100585938 + 128.6455078125, + 514.72265625 ], [ - 527.431640625, - 515.3178100585938 + 525.9375, + 514.72265625 ], [ - 527.431640625, + 525.9375, 537.6239624023438 ], [ - 128.794921875, + 128.6455078125, 537.6239624023438 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" - }, - "images": {} - }, - { - "id": "/page/102/Code/10", - "block_type": "Code", - "html": ">>> fin.readline()", - "polygon": [ - [ - 129.5419921875, - 541.01953125 - ], - [ - 223.9716796875, - 541.01953125 - ], - [ - 223.9716796875, - 552.2504119873047 - ], - [ - 129.5419921875, - 552.2504119873047 - ] + "bbox": [ + 128.6455078125, + 514.72265625, + 525.9375, + 537.6239624023438 ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/102/Text/11", - "block_type": "Text", - "html": "
'aa\\r\\n'
", + "id": "/page/102/Code/8", + "block_type": "Code", + "html": ">>> fin.readline()\n'aa\\r\\n'", "polygon": [ [ - 129.31787109375, - 553.78125 + 128.72021484375, + 542.1796875 ], [ - 171.4345245361328, - 553.78125 + 223.75668334960938, + 542.1796875 ], [ - 171.4345245361328, + 223.75668334960938, 564.4454193115234 ], [ - 129.31787109375, + 128.72021484375, 564.4454193115234 ] ], + "bbox": [ + 128.72021484375, + 542.1796875, + 223.75668334960938, + 564.4454193115234 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/102/Text/12", + "id": "/page/102/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 128.6455078125, - 569.2578125 + 128.49609375, + 568.86328125 ], [ - 527.1328125, - 569.2578125 + 525.9375, + 568.86328125 ], [ - 527.1328125, + 525.9375, 603.7589721679688 ], [ - 128.6455078125, + 128.49609375, 603.7589721679688 ] ], + "bbox": [ + 128.49609375, + 568.86328125, + 525.9375, + 603.7589721679688 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/102/Text/13", + "id": "/page/102/Text/10", "block_type": "Text", "html": "The file object keeps track of where it is in the file, so if you call readline again, you get the next word:
", "polygon": [ [ - 128.3466796875, - 612.3938293457031 + 128.49609375, + 612.17578125 ], [ - 527.1328125, - 612.3938293457031 + 525.9375, + 612.17578125 ], [ - 527.1328125, + 525.9375, 634.7009887695312 ], [ - 128.3466796875, + 128.49609375, 634.7009887695312 ] ], + "bbox": [ + 128.49609375, + 612.17578125, + 525.9375, + 634.7009887695312 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/102/Code/14", + "id": "/page/102/Code/11", "block_type": "Code", - "html": ">>> fin.readline()", + "html": "
>>> fin.readline()\n'aah\\r\\n'", "polygon": [ [ - 129.2431640625, - 639.3648376464844 + 128.27197265625, + 638.47265625 ], [ - 223.75665283203125, - 639.3648376464844 + 225.31640625, + 638.47265625 ], [ - 223.75665283203125, - 649.3274383544922 + 225.31640625, + 661.5214385986328 ], [ - 129.2431640625, - 649.3274383544922 + 128.27197265625, + 661.5214385986328 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" - }, - "images": {} - }, - { - "id": "/page/102/Text/15", - "block_type": "Text", - "html": "
'aah\\r\\n'
", - "polygon": [ - [ - 131.6424560546875, - 651.558837890625 - ], - [ - 176.6634979248047, - 651.558837890625 - ], - [ - 176.6634979248047, - 662.0625 - ], - [ - 131.6424560546875, - 662.0625 - ] + "bbox": [ + 128.27197265625, + 638.47265625, + 225.31640625, + 661.5214385986328 ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/102/Text/16", + "id": "/page/102/Text/12", "block_type": "Text", "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:
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aahed
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", + "polygon": [ + [ + 85.46484375, + 138.1552734375 + ], + [ + 482.607421875, + 138.1552734375 + ], + [ + 482.607421875, + 162.33392333984375 + ], + [ + 85.46484375, + 162.33392333984375 + ] + ], + "bbox": [ + 85.46484375, + 138.1552734375, + 482.607421875, + 162.33392333984375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" + }, + "images": {} + }, + { + "id": "/page/103/Code/4", "block_type": "Code", - "html": ">>> line = fin.readline()\n>>> word = line.strip()\n>>> print word\naahed\nYou can also use a file object as part of a for loop. This program reads words.txt and\nprints each word, one per line:\nfin = open('words.txt')\nfor line in fin:\n word = line.strip()\n print word\nExercise 9.1. Write a program that reads words.txt and prints only the words with more than 20", + "html": "
fin = open('words.txt')\nfor line in fin:\n word = line.strip()\n print word", "polygon": [ [ - 85.9130859375, - 88.68572998046875 + 85.166015625, + 166.095703125 ], [ - 482.90625, - 88.365234375 + 211.1220703125, + 166.095703125 ], [ - 482.90625, - 225.77178955078125 + 211.1220703125, + 216.17578125 ], [ - 85.9130859375, - 225.77178955078125 + 85.166015625, + 216.17578125 ] ], + "bbox": [ + 85.166015625, + 166.095703125, + 211.1220703125, + 216.17578125 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/103/SectionHeader/2", - "block_type": "SectionHeader", - "html": "
Exercise 9.1. Write a program that reads words.txt and prints only the words with more than 20 characters (not counting whitespace).
", "polygon": [ [ - 85.0166015625, - 225.45703125 + 85.6142578125, + 215.733642578125 ], [ - 236.54637145996094, - 225.45703125 + 482.4020690917969, + 215.733642578125 ], [ - 236.54637145996094, + 482.4020690917969, 237.8912353515625 ], [ - 85.0166015625, + 85.6142578125, 237.8912353515625 ] ], + "bbox": [ + 85.6142578125, + 215.733642578125, + 482.4020690917969, + 237.8912353515625 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/102/SectionHeader/2", - "4": "/page/103/SectionHeader/2" + "1": "/page/102/SectionHeader/1", + "4": "/page/102/SectionHeader/2" }, "images": {} }, { - "id": "/page/103/SectionHeader/3", + "id": "/page/103/SectionHeader/6", "block_type": "SectionHeader", - "html": "There are solutions to these exercises in the next section. You should at least attempt each one before you read the solutions.
", "polygon": [ [ - 86.2119140625, - 288.87890625 + 85.9130859375, + 290.0390625 ], [ 482.90625, - 288.87890625 + 290.0390625 ], [ 482.90625, 312.8759460449219 ], [ - 86.2119140625, + 85.9130859375, 312.8759460449219 ] ], + "bbox": [ + 85.9130859375, + 290.0390625, + 482.90625, + 312.8759460449219 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/5", + "id": "/page/103/Text/8", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ 85.763671875, - 314.015625 + 314.40234375 ], [ - 483.205078125, - 314.015625 + 482.90625, + 314.40234375 ], [ - 483.205078125, + 482.90625, 349.2862548828125 ], [ @@ -49634,356 +96699,454 @@ 349.2862548828125 ] ], + "bbox": [ + 85.763671875, + 314.40234375, + 482.90625, + 349.2862548828125 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/6", + "id": "/page/103/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 85.3154296875, - 356.5546875 + 85.6142578125, + 357.134765625 ], [ - 483.50390625, - 356.5546875 + 482.90625, + 357.134765625 ], [ - 483.50390625, + 482.90625, 380.21026611328125 ], [ - 85.3154296875, + 85.6142578125, 380.21026611328125 ] ], + "bbox": [ + 85.6142578125, + 357.134765625, + 482.90625, + 380.21026611328125 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/7", + "id": "/page/103/Text/10", "block_type": "Text", "html": "All right, I'll stop now.
", "polygon": [ [ - 85.9130859375, - 387.685546875 + 85.763671875, + 387.4921875 ], [ - 179.89453125, - 387.685546875 + 178.8330535888672, + 387.4921875 ], [ - 179.89453125, + 178.8330535888672, 398.9402770996094 ], [ - 85.9130859375, + 85.763671875, 398.9402770996094 ] ], + "bbox": [ + 85.763671875, + 387.4921875, + 178.8330535888672, + 398.9402770996094 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/8", + "id": "/page/103/Text/11", "block_type": "Text", - "html": "Write a function called has_no_e that returns True if the given word doesn't have the letter \"e\" in it.
", + "html": "Write a function called has_no_e that returns True if the given word doesn't have the letter \"e\" in it.
", "polygon": [ [ - 85.9130859375, - 405.66796875 + 85.6142578125, + 406.44140625 ], [ - 483.50390625, - 405.66796875 + 482.3984069824219, + 406.44140625 ], [ - 483.50390625, + 482.3984069824219, 429.8642883300781 ], [ - 85.9130859375, + 85.6142578125, 429.8642883300781 ] ], + "bbox": [ + 85.6142578125, + 406.44140625, + 482.3984069824219, + 429.8642883300781 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/9", + "id": "/page/103/Text/12", "block_type": "Text", "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.\"
", "polygon": [ [ - 85.166015625, - 436.60546875 + 85.46484375, + 437.765625 ], [ - 483.50390625, - 436.60546875 + 482.4034729003906, + 437.765625 ], [ - 483.50390625, + 482.4034729003906, 460.7882995605469 ], [ - 85.166015625, + 85.46484375, 460.7882995605469 ] ], + "bbox": [ + 85.46484375, + 437.765625, + 482.4034729003906, + 460.7882995605469 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/10", + "id": "/page/103/Text/13", "block_type": "Text", "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.
", "polygon": [ [ - 85.166015625, + 85.3154296875, 462.515625 ], [ - 482.607421875, + 482.4031066894531, 462.515625 ], [ - 482.607421875, + 482.4031066894531, 485.200439453125 ], [ - 85.166015625, + 85.3154296875, 485.200439453125 ] ], + "bbox": [ + 85.3154296875, + 462.515625, + 482.4031066894531, + 485.200439453125 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/11", + "id": "/page/103/Text/14", "block_type": "Text", "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?
", "polygon": [ [ - 85.763671875, - 492.29296875 + 85.46484375, + 493.06640625 ], [ - 484.1015625, - 492.29296875 + 482.607421875, + 493.06640625 ], [ - 484.1015625, + 482.607421875, 528.2953186035156 ], [ - 85.763671875, + 85.46484375, 528.2953186035156 ] ], + "bbox": [ + 85.46484375, + 493.06640625, + 482.607421875, + 528.2953186035156 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/12", + "id": "/page/103/Text/15", "block_type": "Text", "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?\"
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", + "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?
", "polygon": [ [ - 85.46484375, - 566.54296875 + 85.3154296875, + 566.15625 ], [ - 483.50390625, - 566.54296875 + 482.90625, + 566.15625 ], [ - 483.50390625, + 482.90625, 601.4844818115234 ], [ - 85.46484375, + 85.3154296875, 601.4844818115234 ] ], + "bbox": [ + 85.3154296875, + 566.15625, + 482.90625, + 601.4844818115234 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/Text/14", + "id": "/page/103/Text/17", "block_type": "Text", "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?
", "polygon": [ [ - 85.3154296875, - 602.12109375 + 85.166015625, + 602.89453125 ], [ - 484.1015625, - 602.12109375 + 482.607421875, + 602.89453125 ], [ - 484.1015625, + 482.607421875, 625.850341796875 ], [ - 85.3154296875, + 85.166015625, 625.850341796875 ] ], + "bbox": [ + 85.166015625, + 602.89453125, + 482.607421875, + 625.850341796875 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/103/SectionHeader/3" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6" }, "images": {} }, { - "id": "/page/103/SectionHeader/15", + "id": "/page/103/SectionHeader/18", "block_type": "SectionHeader", - "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:
", + "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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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.
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We can return False as soon as we find a forbidden letter; if we get to the end of the loop, we return True.
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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.
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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", "polygon": [ [ - 86.40000915527344, - 114.5654296875 + 85.68896484375, + 115.99267578125 + ], + [ + 218.7421875, + 115.99267578125 + ], + [ + 218.7421875, + 199.121337890625 + ], + [ + 85.68896484375, + 199.121337890625 + ] + ], + "bbox": [ + 85.68896484375, + 115.99267578125, + 218.7421875, + 199.121337890625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6", + "3": "/page/103/SectionHeader/18", + "4": "/page/104/SectionHeader/15" + }, + "images": {} + }, + { + "id": "/page/105/Text/3", + "block_type": "Text", + "html": "
An alternative is to use recursion:
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Another option is to use a while loop:
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The loop starts at i=0 and ends when i=len(word)-1. Each time through the loop, it compares the ith character (which you can think of as the current character) to the i + 1th character (which you can think of as the next).
", + "html": "The loop starts at i=0 and ends when i=len(word)-1. Each time through the loop, it compares the ith character (which you can think of as the current character) to the i + 1th character (which you can think of as the next).
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", + "html": "Here is a version of is_palindrome (see Exercise 6.6) that uses two indices; one starts at the beginning and goes up; the other starts at the end and goes down.
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9.5. Debugging 85
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Assuming you did Exercise 8.9.
", + "html": "Assuming you did Exercise 8.9.
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", "polygon": [ [ - 128.3466796875, - 616.3031158447266 + 129.31787109375, + 615.65625 ], [ - 340.6640625, - 614.8828125 + 340.962890625, + 615.65625 ], [ - 340.6640625, + 340.962890625, 626.3628234863281 ], [ - 128.3466796875, - 627.2578125 + 129.31787109375, + 626.3628234863281 ] ], + "bbox": [ + 129.31787109375, + 615.65625, + 340.962890625, + 626.3628234863281 + ], "children": null, "section_hierarchy": { - "1": "/page/92/SectionHeader/1", - "2": "/page/102/SectionHeader/1", - "3": "/page/106/SectionHeader/13" + "1": "/page/102/SectionHeader/1", + "2": "/page/103/SectionHeader/6", + "3": "/page/103/SectionHeader/18", + "4": "/page/106/SectionHeader/13" }, "images": {} }, { - "id": "/page/106/ListGroup/139", + "id": "/page/106/ListGroup/140", "block_type": "ListGroup", "html": "86 Chapter 9. Case study: word play
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", + "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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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 ):
", + "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 ):
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\"The question is, what was on the odometer when I first looked?\"
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", + "html": "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 . Exercise 9.9. Here's another Car Talk Puzzler you can solve with a search (http: // www. cartalk. com/ content/ puzzlers ):
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Write a Python program that searches for solutions to this Puzzler. Hint: you might find the string method zfill useful.
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", + "html": "Solution: http: // thinkpython. com/ code/ cartalk3. py .
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", "polygon": [ [ - 128.794921875, - 316.916015625 + 128.9443359375, + 317.94720458984375 ], [ - 527.431640625, - 316.916015625 + 525.9375, + 317.94720458984375 ], [ - 527.431640625, + 525.9375, 340.200927734375 ], [ - 128.794921875, + 128.9443359375, 340.200927734375 ] ], + "bbox": [ + 128.9443359375, + 317.94720458984375, + 525.9375, + 340.200927734375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/2" + "4": "/page/108/SectionHeader/2" }, "images": {} }, @@ -52078,160 +99859,225 @@ "html": "There are several ways to create a new list; the simplest is to enclose the elements in square brackets ([ and ]):
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[10, 20, 30, 40]
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", + "polygon": [ + [ + 128.794921875, + 389.88079833984375 ], [ 364.9114685058594, - 375.890625 + 389.88079833984375 ], [ 364.9114685058594, - 399.8433837890625 + 399.8671875 ], [ - 129.60003662109375, - 399.8433837890625 + 128.794921875, + 399.8671875 ] ], + "bbox": [ + 128.794921875, + 389.88079833984375, + 364.9114685058594, + 399.8671875 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/2" + "4": "/page/108/SectionHeader/2" }, "images": {} }, { - "id": "/page/108/Text/6", + "id": "/page/108/Text/7", "block_type": "Text", "html": "The 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:
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", "polygon": [ [ - 129.392578125, - 444.33984375 + 128.9443359375, + 445.7008056640625 ], [ - 267.15234375, - 444.33984375 + 265.5810241699219, + 445.7008056640625 ], [ - 267.15234375, - 455.94140625 + 265.5810241699219, + 455.66339111328125 ], [ - 129.392578125, - 455.94140625 + 128.9443359375, + 455.66339111328125 ] ], + "bbox": [ + 128.9443359375, + 445.7008056640625, + 265.5810241699219, + 455.66339111328125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/2" + "4": "/page/108/SectionHeader/2" }, "images": {} }, { - "id": "/page/108/Text/8", + "id": "/page/108/Text/9", "block_type": "Text", "html": "A list within another list is nested.
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", "polygon": [ [ - 128.3466796875, - 512.40234375 + 128.57080078125, + 513.1353759765625 ], [ 393.85546875, - 512.40234375 + 513.1353759765625 ], [ 393.85546875, - 523.23046875 + 523.0979614257812 ], [ - 128.3466796875, - 523.23046875 + 128.57080078125, + 523.0979614257812 ] ], + "bbox": [ + 128.57080078125, + 513.1353759765625, + 393.85546875, + 523.0979614257812 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/2" + "4": "/page/108/SectionHeader/2" }, "images": {} }, { - "id": "/page/108/Code/11", + "id": "/page/108/Code/12", "block_type": "Code", "html": ">>> cheeses = ['Cheddar', 'Edam', 'Gouda']\n>>> numbers = [17, 123]\n>>> empty = []\n>>> print cheeses, numbers, empty\n['Cheddar', 'Edam', 'Gouda'] [17, 123] []", "polygon": [ [ - 129.392578125, - 525.9375 + 128.794921875, + 528.7018127441406 ], [ - 356.80078125, - 525.9375 + 349.62890625, + 528.7018127441406 ], [ - 356.80078125, - 587.8125 + 349.62890625, + 587.4424285888672 ], [ - 129.392578125, - 587.8125 + 128.794921875, + 587.4424285888672 ] ], + "bbox": [ + 128.794921875, + 528.7018127441406, + 349.62890625, + 587.4424285888672 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/2" + "4": "/page/108/SectionHeader/2" }, "images": {} }, { - "id": "/page/108/SectionHeader/12", + "id": "/page/108/SectionHeader/13", "block_type": "SectionHeader", - "html": "
The 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:
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88 Chapter 10. Lists
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+ "/page/109/Figure/1": 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} }, { "id": "/page/109/Caption/2", "block_type": "Caption", - "html": "Figure 10.1: State diagram.
", + "html": "Figure 10.1: State diagram.
", "polygon": [ [ - 225.1669921875, - 263.35546875 + 225.55101013183594, + 263.548828125 ], [ - 343.951171875, - 263.35546875 + 343.353515625, + 263.548828125 ], [ - 343.951171875, + 343.353515625, 273.95294189453125 ], [ - 225.1669921875, + 225.55101013183594, 273.95294189453125 ] ], + "bbox": [ + 225.55101013183594, + 263.548828125, + 343.353515625, + 273.95294189453125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} } ], "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": null }, @@ -52575,26 +100493,32 @@ "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.
", "polygon": [ [ - 85.9130859375, - 298.353515625 + 85.46484375, + 298.16015625 ], [ - 482.4033203125, - 298.353515625 + 482.607421875, + 298.16015625 ], [ - 482.4033203125, + 482.607421875, 321.1739196777344 ], [ - 85.9130859375, + 85.46484375, 321.1739196777344 ] ], + "bbox": [ + 85.46484375, + 298.16015625, + 482.607421875, + 321.1739196777344 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, @@ -52604,26 +100528,32 @@ "html": ">>> numbers = [17, 123]\n>>> numbers[1] = 5\n>>> print numbers\n[17, 5]", "polygon": [ [ - 84.7177734375, + 85.46484375, 329.957763671875 ], [ - 227.2587890625, + 206.6983642578125, 329.957763671875 ], [ - 227.2587890625, - 376.857421875 + 206.6983642578125, + 376.50335693359375 ], [ - 84.7177734375, - 376.857421875 + 85.46484375, + 376.50335693359375 ] ], + "bbox": [ + 85.46484375, + 329.957763671875, + 206.6983642578125, + 376.50335693359375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, @@ -52633,55 +100563,67 @@ "html": "
The one-eth element of numbers, which used to be 123, is now 5.
", "polygon": [ [ - 85.763671875, - 384.978515625 + 85.83837890625, + 385.4377746582031 ], [ 367.88360595703125, - 384.978515625 + 385.4377746582031 ], [ 367.88360595703125, 395.5499267578125 ], [ - 85.763671875, + 85.83837890625, 395.5499267578125 ] ], + "bbox": [ + 85.83837890625, + 385.4377746582031, + 367.88360595703125, + 395.5499267578125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, { "id": "/page/109/Text/6", "block_type": "Text", - "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:
", + "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:
", "polygon": [ [ - 86.0625, + 85.46484375, 407.21484375 ], [ - 482.607421875, + 482.4034729003906, 407.21484375 ], [ - 482.607421875, + 482.4034729003906, 442.80694580078125 ], [ - 86.0625, + 85.46484375, 442.80694580078125 ] ], + "bbox": [ + 85.46484375, + 407.21484375, + 482.4034729003906, + 442.80694580078125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, @@ -52691,26 +100633,32 @@ "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.
", "polygon": [ [ - 85.9130859375, + 85.46484375, 454.78125 ], [ - 483.50390625, + 482.90625, 454.78125 ], [ - 483.50390625, + 482.90625, 502.2579650878906 ], [ - 85.9130859375, + 85.46484375, 502.2579650878906 ] ], + "bbox": [ + 85.46484375, + 454.78125, + 482.90625, + 502.2579650878906 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, @@ -52720,51 +100668,63 @@ "html": "List indices work the same way as string indices:
", "polygon": [ [ - 86.361328125, - 513.5625 + 85.53955078125, + 514.3359375 ], [ 300.97442626953125, - 513.5625 + 514.3359375 ], [ 300.97442626953125, 525.1259765625 ], [ - 86.361328125, + 85.53955078125, 525.1259765625 ] ], + "bbox": [ + 85.53955078125, + 514.3359375, + 300.97442626953125, + 525.1259765625 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, { - "id": "/page/109/ListGroup/188", + "id": "/page/109/ListGroup/190", "block_type": "ListGroup", "html": "The in operator also works on lists.
", "polygon": [ [ - 85.09130859375, - 621.84375 + 85.83837890625, + 622.6171875 ], [ - 242.947265625, - 621.84375 + 243.84375, + 622.6171875 ], [ - 242.947265625, + 243.84375, 633.1619873046875 ], [ - 85.09130859375, + 85.83837890625, 633.1619873046875 ] ], + "bbox": [ + 85.83837890625, + 622.6171875, + 243.84375, + 633.1619873046875 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, @@ -52895,40 +100879,46 @@ "html": ">>> cheeses = ['Cheddar', 'Edam', 'Gouda']\n>>> 'Edam' in cheeses\nTrue\n>>> 'Brie' in cheeses\nFalse", "polygon": [ [ - 84.34423828125, - 641.56640625 + 86.2119140625, + 641.9458312988281 ], [ 306.0244445800781, - 641.56640625 + 641.9458312988281 ], [ 306.0244445800781, 700.6854248046875 ], [ - 84.34423828125, + 86.2119140625, 700.6854248046875 ] ], + "bbox": [ + 86.2119140625, + 641.9458312988281, + 306.0244445800781, + 700.6854248046875 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} } ], "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": null }, { - "id": "/page/110/Page/190", + "id": "/page/110/Page/198", "block_type": "Page", - "html": "
10.3. Traversing a list 89
", + "html": "", "polygon": [ [ - 128.6455078125, + 128.72021484375, 61.171142578125 ], [ @@ -52966,72 +100962,90 @@ 71.13372802734375 ], [ - 128.6455078125, + 128.72021484375, 71.13372802734375 ] ], + "bbox": [ + 128.72021484375, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, { "id": "/page/110/PageHeader/19", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 516.076171875, + 514.58203125, 60.71484375 ], [ - 526.236328125, + 525.9375, 60.71484375 ], [ - 526.236328125, - 69.99609375 + 525.9375, + 69.609375 ], [ - 516.076171875, - 69.99609375 + 514.58203125, + 69.609375 ] ], + "bbox": [ + 514.58203125, + 60.71484375, + 525.9375, + 69.609375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/108/SectionHeader/12" + "4": "/page/108/SectionHeader/13" }, "images": {} }, { "id": "/page/110/SectionHeader/1", "block_type": "SectionHeader", - "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:
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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.
", + "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.
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Although a list can contain another list, the nested list still counts as a single element. The length of this list is four:
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The + operator concatenates lists:
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Similarly, the * operator repeats a list a given number of times:
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>>> [0] * 4 [0, 0, 0, 0] >>> [1, 2, 3] * 3 [1, 2, 3, 1, 2, 3, 1, 2, 3]
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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.
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Since lists are mutable, it is often useful to make a copy before performing operations that fold, spindle or mutilate lists.
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Python provides methods that operate on lists. For example, append adds a new element to the end of a list:
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extend takes a list as an argument and appends all of the elements:
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This example leaves t2 unmodified.
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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.
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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:
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", + "id": "/page/112/Text/6", + "block_type": "Text", + "html": "is equivalent to:
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An operation like this that combines a sequence of elements into a single value is sometimes called reduce.
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res is initialized with an empty list; each time through the loop, we append the next element. So res is another kind of accumulator.
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", + "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.
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", + "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.
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92 Chapter 10. Lists
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isupper is a string method that returns True if the string contains only upper case letters.
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", + "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
", "polygon": [ [ - 85.3154296875, - 174.41015625 + 85.46484375, + 174.5068359375 ], [ - 482.90625, - 174.41015625 + 482.4034423828125, + 174.5068359375 ], [ - 482.90625, - 209.804931640625 + 482.4034423828125, + 221.90179443359375 ], [ - 85.3154296875, - 209.804931640625 + 85.46484375, + 221.90179443359375 ] ], + "bbox": [ + 85.46484375, + 174.5068359375, + 482.4034423828125, + 221.90179443359375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/112/SectionHeader/1" + "4": "/page/112/SectionHeader/1" }, "images": {} }, { - "id": "/page/113/TextInlineMath/5", - "block_type": "TextInlineMath", - "html": "Exercise 10.3. Write a function that takes a list of numbers and returns the cumulative sum; that 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].
", + "id": "/page/113/Text/5", + "block_type": "Text", + "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].
", "polygon": [ [ - 85.46484375, - 211.728515625 + 85.6142578125, + 219.65625 ], [ - 483.205078125, - 211.728515625 + 482.3965759277344, + 219.65625 ], [ - 483.205078125, - 246.919921875 + 482.3965759277344, + 246.7265625 ], [ - 85.46484375, - 246.919921875 + 85.6142578125, + 246.7265625 ] ], + "bbox": [ + 85.6142578125, + 219.65625, + 482.3965759277344, + 246.7265625 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/112/SectionHeader/1" + "4": "/page/112/SectionHeader/1" }, "images": {} }, { "id": "/page/113/SectionHeader/6", "block_type": "SectionHeader", - "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:
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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.
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", "polygon": [ [ - 85.3154296875, - 427.32421875 + 85.39013671875, + 428.09765625 ], [ - 381.90234375, - 427.32421875 + 381.49713134765625, + 428.09765625 ], [ - 381.90234375, + 381.49713134765625, 438.6829528808594 ], [ - 85.3154296875, + 85.39013671875, 438.6829528808594 ] ], + "bbox": [ + 85.39013671875, + 428.09765625, + 381.49713134765625, + 438.6829528808594 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/113/SectionHeader/6" + "4": "/page/113/SectionHeader/6" }, "images": {} }, { - "id": "/page/113/Code/11", - "block_type": "Code", - "html": ">>> t = ['a', 'b', 'c']\n>>> del t[1]\n>>> print t\n['a', 'c']\nIf you know the element you want to remove (but not the index), you can use remove:\n>>> t = ['a', 'b', 'c']\n>>> t.remove('b')\n>>> print t\n['a', 'c']", + "id": "/page/113/TextInlineMath/11", + "block_type": "TextInlineMath", + "html": "
>>> t = ['a', 'b', 'c'] >>> del t[1] >>> print t ['a', 'c']
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", + "polygon": [ + [ + 85.763671875, + 493.06640625 ], [ 462.9958190917969, - 438.15234375 + 493.06640625 ], [ 462.9958190917969, - 560.35546875 + 503.3279724121094 ], [ - 85.166015625, - 560.35546875 + 85.763671875, + 503.3279724121094 ] ], + "bbox": [ + 85.763671875, + 493.06640625, + 462.9958190917969, + 503.3279724121094 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/113/SectionHeader/6" + "4": "/page/113/SectionHeader/6" }, "images": {} }, { - "id": "/page/113/Text/12", + "id": "/page/113/Code/262", + "block_type": "Code", + "html": ">>> t = ['a', 'b', 'c']\n>>> t.remove('b')\n>>> print t\n['a', 'c']", + "polygon": [ + [ + 85.6142578125, + 506.98828125 + ], + [ + 206.67054748535156, + 506.98828125 + ], + [ + 206.67054748535156, + 561.12890625 + ], + [ + 85.6142578125, + 561.12890625 + ] + ], + "bbox": [ + 85.6142578125, + 506.98828125, + 206.67054748535156, + 561.12890625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/108/SectionHeader/1", + "4": "/page/113/SectionHeader/6" + }, + "images": {} + }, + { + "id": "/page/113/Text/14", "block_type": "Text", "html": "
The return value from remove is None.
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", + "html": "To remove more than one element, you can use del with a slice index:
", "polygon": [ [ - 86.40017700195312, - 575.05078125 + 86.2119140625, + 575.8618316650391 ], [ - 394.751953125, - 575.05078125 + 393.24322509765625, + 575.8618316650391 ], [ - 393.556640625, - 599.8554382324219 + 393.24322509765625, + 586.265625 ], [ - 85.46484375, - 599.8554382324219 + 86.2119140625, + 586.265625 ] ], + "bbox": [ + 86.2119140625, + 575.8618316650391, + 393.24322509765625, + 586.265625 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/113/SectionHeader/6" + "4": "/page/113/SectionHeader/6" }, "images": {} }, { - "id": "/page/113/Code/14", + "id": "/page/113/Code/16", "block_type": "Code", - "html": ">>> del t[1:5]\n>>> print t\n['a', 'f']", + "html": "
>>> t = ['a', 'b', 'c', 'd', 'e', 'f']\n>>> del t[1:5]\n>>> print t\n['a', 'f']", "polygon": [ [ - 85.166015625, - 595.93359375 + 85.68896484375, + 589.74609375 ], [ - 161.8154296875, - 595.93359375 + 286.27734375, + 589.74609375 ], [ - 161.8154296875, - 639.24609375 + 286.27734375, + 638.47265625 ], [ - 85.166015625, - 639.24609375 + 85.68896484375, + 638.47265625 ] ], + "bbox": [ + 85.68896484375, + 589.74609375, + 286.27734375, + 638.47265625 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/113/SectionHeader/6" + "4": "/page/113/SectionHeader/6" }, "images": {} }, { - "id": "/page/113/Text/15", + "id": "/page/113/Text/17", "block_type": "Text", - "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].
", + "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].
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", + "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.
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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.
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", "polygon": [ [ - 129.392578125, + 129.2431640625, 239.765625 ], [ - 526.236328125, + 525.6027221679688, 239.765625 ], [ - 526.236328125, - 262.96875 + 525.6027221679688, + 262.70599365234375 ], [ - 129.392578125, - 262.96875 + 129.2431640625, + 262.70599365234375 ] ], + "bbox": [ + 129.2431640625, + 239.765625, + 525.6027221679688, + 262.70599365234375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/114/SectionHeader/1" + "4": "/page/114/SectionHeader/1" }, "images": {} }, @@ -55481,7 +104069,7 @@ "html": ">>> s = 'pining for the fjords'\n>>> t = s.split()\n>>> print t\n['pining', 'for', 'the', 'fjords']", "polygon": [ [ - 129.60003662109375, + 129.46728515625, 269.27484130859375 ], [ @@ -55493,14 +104081,20 @@ 315.8204345703125 ], [ - 128.49609375, + 129.46728515625, 315.8204345703125 ] ], + "bbox": [ + 129.46728515625, + 269.27484130859375, + 307.3914489746094, + 315.8204345703125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/114/SectionHeader/1" + "4": "/page/114/SectionHeader/1" }, "images": {} }, @@ -55510,26 +104104,32 @@ "html": "
An optional argument called a delimiter specifies which characters to use as word boundaries. The following example uses a hyphen as a delimiter:
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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:
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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.
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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.
", + "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.
", "polygon": [ [ - 127.8984375, + 129.5419921875, 624.55078125 ], [ - 526.53515625, + 525.5989379882812, 624.55078125 ], [ - 526.53515625, + 525.5989379882812, 647.3350524902344 ], [ - 127.8984375, + 129.5419921875, 647.3350524902344 ] ], + "bbox": [ + 129.5419921875, + 624.55078125, + 525.5989379882812, + 647.3350524902344 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/114/SectionHeader/12" + "4": "/page/114/SectionHeader/12" }, "images": {} }, @@ -55771,26 +104419,32 @@ "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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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.18310546875 + 60.0380859375 ], [ - 484.1015625, - 60.18310546875 + 482.4033508300781, + 60.0380859375 ], [ - 484.1015625, + 482.4033508300781, 71.13372802734375 ], [ @@ -55875,244 +104541,440 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.0380859375, + 482.4033508300781, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/114/SectionHeader/12" + "4": "/page/114/SectionHeader/12" }, "images": {} }, { "id": "/page/115/PageHeader/19", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.68896484375, - 60.37646484375 + 84.64306640625, + 60.85986328125 ], [ - 96.14794921875, - 60.37646484375 + 95.84912109375, + 60.85986328125 ], [ - 96.14794921875, - 69.65771484375 + 95.84912109375, + 70.33447265625 ], [ - 85.68896484375, - 69.65771484375 + 84.64306640625, + 70.33447265625 ] ], + "bbox": [ + 84.64306640625, + 60.85986328125, + 95.84912109375, + 70.33447265625 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/114/SectionHeader/12" + "4": "/page/114/SectionHeader/12" }, "images": {} }, { - "id": "/page/115/TableGroup/184", - "block_type": "TableGroup", - "html": "Figure 10.2: State diagram.
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---|---|---|---|
b | 'banana' | b |
Figure 10.2: State diagram.
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---|---|
b | [ 1, 2, 3 ] |
a | [1, 2, 3] |
b | [1, 2, 3] |
Figure 10.3: State diagram.
", + "html": "Figure 10.3: State diagram.
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", + "id": "/page/115/Code/5", + "block_type": "Code", + "html": ">>> a = 'banana'\n>>> b = 'banana'\n>>> a is b\nTrue", "polygon": [ [ - 85.763671875, - 235.125 + 85.166015625, + 235.73675537109375 ], [ - 171.0791015625, - 235.125 + 170.06735229492188, + 235.73675537109375 ], [ - 171.0791015625, - 288.4921875 + 170.06735229492188, + 282.28228759765625 ], [ - 85.763671875, - 288.4921875 + 85.166015625, + 282.28228759765625 ] ], + "bbox": [ + 85.166015625, + 235.73675537109375, + 170.06735229492188, + 282.28228759765625 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/114/SectionHeader/12" + "4": "/page/114/SectionHeader/12" }, "images": {} }, @@ -56122,55 +104984,67 @@ "html": "
In this example, Python only created one string object, and both a and b refer to it.
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", + "id": "/page/115/Text/7", + "block_type": "Text", + "html": "But when you create two lists, you get two objects:
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", + "html": "So the state diagram looks like Figure 10.3.
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The state diagram looks like Figure 10.4.
", + "html": "The state diagram looks like Figure 10.4.
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+ "/page/116/Figure/3": 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", + "html": "Figure 10.5: Stack diagram.
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", "polygon": [ [ - 128.12255859375, - 351.140625 + 129.2431640625, + 352.270751953125 ], [ 192.350341796875, - 351.140625 + 352.270751953125 ], [ 192.350341796875, - 362.23333740234375 + 374.4273376464844 ], [ - 128.12255859375, - 362.23333740234375 + 129.2431640625, + 374.4273376464844 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/108/SectionHeader/1", - "3": "/page/115/SectionHeader/12" - }, - "images": {} - }, - { - "id": "/page/116/Text/9", - "block_type": "Text", - "html": "b = 'banana'
", - "polygon": [ - [ - 127.1513671875, - 364.4647521972656 - ], - [ - 192.35032653808594, - 364.4647521972656 - ], - [ - 192.35032653808594, - 374.537109375 - ], - [ - 127.1513671875, - 374.537109375 - ] + "bbox": [ + 129.2431640625, + 352.270751953125, + 192.350341796875, + 374.4273376464844 ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/115/SectionHeader/12" + "4": "/page/115/SectionHeader/12" }, "images": {} }, { - "id": "/page/116/Text/10", + "id": "/page/116/Text/9", "block_type": "Text", "html": "It almost never makes a difference whether a and b refer to the same string or not.
", "polygon": [ [ - 128.49609375, - 379.177734375 + 129.5419921875, + 380.337890625 ], [ - 489.48046875, - 379.177734375 + 489.36553955078125, + 380.337890625 ], [ - 489.48046875, + 489.36553955078125, 390.8279113769531 ], [ - 128.49609375, + 129.5419921875, 390.8279113769531 ] ], + "bbox": [ + 129.5419921875, + 380.337890625, + 489.36553955078125, + 390.8279113769531 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/115/SectionHeader/12" + "4": "/page/115/SectionHeader/12" }, "images": {} }, { - "id": "/page/116/SectionHeader/11", + "id": "/page/116/SectionHeader/10", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 129.09375, - 446.66015625 + 128.197265625, + 447.23333740234375 ], [ - 526.53515625, - 446.66015625 + 525.638671875, + 447.23333740234375 ], [ - 526.53515625, + 525.638671875, 481.5849304199219 ], [ - 129.09375, + 128.197265625, 481.5849304199219 ] ], + "bbox": [ + 128.197265625, + 447.23333740234375, + 525.638671875, + 481.5849304199219 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/116/Code/13", + "id": "/page/116/Code/12", "block_type": "Code", "html": "def delete_head(t):\n del t[0]", "polygon": [ [ - 128.6455078125, - 486.87890625 + 127.8984375, + 487.7227783203125 ], [ - 229.6494140625, - 486.87890625 + 228.98683166503906, + 487.7227783203125 ], [ - 229.6494140625, - 510.08203125 + 228.98683166503906, + 509.8793640136719 ], [ - 128.6455078125, - 510.08203125 + 127.8984375, + 509.8793640136719 ] ], + "bbox": [ + 127.8984375, + 487.7227783203125, + 228.98683166503906, + 509.8793640136719 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/116/Text/14", + "id": "/page/116/Text/13", "block_type": "Text", "html": "
Here's how it is used:
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The parameter t and the variable letters are aliases for the same object. The stack diagram looks like Figure 10.5.
", + "html": "The parameter t and the variable letters are aliases for the same object. The stack diagram looks like Figure 10.5.
", "polygon": [ [ - 128.49609375, - 584.71875 + 128.6455078125, + 584.33203125 ], [ - 525.9375, - 584.71875 + 525.6002807617188, + 584.33203125 ], [ - 525.9375, + 525.6002807617188, 607.5579528808594 ], [ - 128.49609375, + 128.6455078125, 607.5579528808594 ] ], + "bbox": [ + 128.6455078125, + 584.33203125, + 525.6002807617188, + 607.5579528808594 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/116/Text/17", + "id": "/page/116/Text/16", "block_type": "Text", "html": "Since the list is shared by two frames, I drew it between them.
", "polygon": [ [ - 128.794921875, + 127.7490234375, 617.58984375 ], [ - 403.119140625, + 401.7183837890625, 617.58984375 ], [ - 401.923828125, + 401.7183837890625, 627.7799530029297 ], [ - 127.599609375, + 127.7490234375, 627.7799530029297 ] ], + "bbox": [ + 127.7490234375, + 617.58984375, + 401.7183837890625, + 627.7799530029297 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/116/Text/18", + "id": "/page/116/Text/17", "block_type": "Text", "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:
", "polygon": [ [ - 128.3466796875, + 128.197265625, 637.3125 ], [ - 526.53515625, + 525.6033325195312, 637.3125 ], [ - 526.53515625, + 525.6033325195312, 672.3909606933594 ], [ - 128.3466796875, + 128.197265625, 672.3909606933594 ] ], + "bbox": [ + 128.197265625, + 637.3125, + 525.6033325195312, + 672.3909606933594 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/116/TextInlineMath/19", + "id": "/page/116/TextInlineMath/18", "block_type": "TextInlineMath", "html": ">>> t1 = [1, 2] >>> t2 = t1.append(3)
", "polygon": [ [ - 128.9443359375, + 128.3466796875, 678.5287933349609 ], [ @@ -57182,31 +106187,37 @@ ], [ 239.4376220703125, - 701.89453125 + 700.6853942871094 ], [ - 128.9443359375, - 701.89453125 + 128.3466796875, + 700.6853942871094 ] ], + "bbox": [ + 128.3466796875, + 678.5287933349609, + 239.4376220703125, + 700.6853942871094 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} } ], "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": null }, { - "id": "/page/117/Page/174", + "id": "/page/117/Page/176", "block_type": "Page", - "html": "96 Chapter 10. Lists
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.37646484375 + 60.4248046875 ], [ - 483.50390625, - 60.37646484375 + 482.4033508300781, + 60.4248046875 ], [ - 483.50390625, + 482.4033508300781, 71.13372802734375 ], [ @@ -57248,489 +106265,486 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.4248046875, + 482.4033508300781, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/PageHeader/21", + "id": "/page/117/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.3154296875, - 60.0380859375 + 85.83837890625, + 61.05322265625 ], [ - 97.2685546875, - 60.0380859375 + 96.74560546875, + 61.05322265625 ], [ - 97.2685546875, - 70.6728515625 + 96.74560546875, + 70.14111328125 ], [ - 85.3154296875, - 70.6728515625 + 85.83837890625, + 70.14111328125 ] ], + "bbox": [ + 85.83837890625, + 61.05322265625, + 96.74560546875, + 70.14111328125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { "id": "/page/117/Code/1", "block_type": "Code", - "html": ">>> print t1\n[1, 2, 3]\n>>> print t2\nNone\n>>> t3 = t1 + [4]\n>>> print t3", + "html": "
>>> print t1\n[1, 2, 3]\n>>> print t2\nNone\n>>> t3 = t1 + [4]\n>>> print t3\n[1, 2, 3, 4]", "polygon": [ [ - 86.4000015258789, - 88.68572998046875 + 86.2119140625, + 87.8818359375 ], [ 175.3162078857422, - 88.68572998046875 + 87.8818359375 ], [ 175.3162078857422, - 171.8143310546875 - ], - [ - 86.4000015258789, - 171.8143310546875 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" - }, - "images": {} - }, - { - "id": "/page/117/Code/2", - "block_type": "Code", - "html": "
[1, 2, 3, 4]", - "polygon": [ - [ - 86.4000015258789, - 165.8056640625 - ], - [ - 149.1643829345703, - 165.8056640625 - ], - [ - 149.1643829345703, 184.00830078125 ], [ - 86.4000015258789, + 86.2119140625, 184.00830078125 ] ], + "bbox": [ + 86.2119140625, + 87.8818359375, + 175.3162078857422, + 184.00830078125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/Text/3", + "id": "/page/117/Text/2", "block_type": "Text", "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:
", "polygon": [ [ - 85.9130859375, - 187.4619140625 + 86.2119140625, + 190.3623046875 ], [ - 482.90625, - 187.4619140625 + 482.4034118652344, + 190.3623046875 ], [ - 482.90625, + 482.4034118652344, 212.57489013671875 ], [ - 85.9130859375, + 86.2119140625, 212.57489013671875 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" - }, - "images": {} - }, - { - "id": "/page/117/Code/4", - "block_type": "Code", - "html": "def bad_delete_head(t):", - "polygon": [ - [ - 85.9130859375, - 216.94921875 - ], - [ - 207.2373046875, - 216.94921875 - ], - [ - 207.2373046875, - 230.87109375 - ], - [ - 85.9130859375, - 230.87109375 - ] + "bbox": [ + 86.2119140625, + 190.3623046875, + 482.4034118652344, + 212.57489013671875 ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/TextInlineMath/5", + "id": "/page/117/TextInlineMath/3", "block_type": "TextInlineMath", - "html": "
t = t[1:] # WRONG!
", + "html": "def bad_delete_head(t): t = t[1:] # WRONG!
", "polygon": [ [ - 107.31600952148438, - 230.09765625 + 86.40000915527344, + 217.529296875 ], [ 269.4473571777344, - 230.09765625 + 217.529296875 ], [ 269.4473571777344, - 241.3125 + 240.84228515625 ], [ - 107.31600952148438, - 241.3125 + 86.40000915527344, + 240.84228515625 ] ], + "bbox": [ + 86.40000915527344, + 217.529296875, + 269.4473571777344, + 240.84228515625 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/Text/6", + "id": "/page/117/Text/4", "block_type": "Text", "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.
", "polygon": [ [ - 85.46484375, - 245.1796875 + 85.763671875, + 246.33984375 ], [ - 483.802734375, - 245.1796875 + 482.3985595703125, + 246.33984375 ], [ - 483.802734375, + 482.3985595703125, 269.40887451171875 ], [ - 85.46484375, + 85.763671875, 269.40887451171875 ] ], + "bbox": [ + 85.763671875, + 246.33984375, + 482.3985595703125, + 269.40887451171875 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/Text/7", + "id": "/page/117/Text/5", "block_type": "Text", "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:
", "polygon": [ [ - 85.9130859375, - 277.6640625 + 85.6142578125, + 279.404296875 ], [ - 482.90625, - 277.6640625 + 482.4054870605469, + 279.404296875 ], [ - 482.90625, + 482.4054870605469, 301.7968444824219 ], [ - 85.9130859375, + 85.6142578125, 301.7968444824219 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" - }, - "images": {} - }, - { - "id": "/page/117/TextInlineMath/8", - "block_type": "TextInlineMath", - "html": "def tail(t):
", - "polygon": [ - [ - 86.13720703125, - 306.66796875 - ], - [ - 163.16015625, - 306.66796875 - ], - [ - 163.16015625, - 319.04296875 - ], - [ - 86.13720703125, - 319.04296875 - ] + "bbox": [ + 85.6142578125, + 279.404296875, + 482.4054870605469, + 301.7968444824219 ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/TextInlineMath/9", - "block_type": "TextInlineMath", - "html": "return t[1:]
", + "id": "/page/117/Code/6", + "block_type": "Code", + "html": "def tail(t):\n return t[1:]", "polygon": [ [ - 107.31602478027344, - 320.009765625 + 86.4000244140625, + 306.28125 ], [ - 171.826171875, - 320.009765625 + 170.0803985595703, + 306.28125 ], [ - 171.826171875, - 330.837890625 + 170.0803985595703, + 330.2578125 ], [ - 107.31602478027344, - 330.837890625 + 86.4000244140625, + 330.2578125 ] ], + "bbox": [ + 86.4000244140625, + 306.28125, + 170.0803985595703, + 330.2578125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/Text/10", + "id": "/page/117/Text/7", "block_type": "Text", "html": "
This function leaves the original list unmodified. Here's how it is used:
", "polygon": [ [ - 86.0625, - 334.8984375 + 85.763671875, + 335.865234375 ], [ - 398.935546875, - 334.8984375 + 398.4983825683594, + 335.865234375 ], [ - 398.935546875, + 398.4983825683594, 346.4358825683594 ], [ - 86.0625, + 85.763671875, 346.4358825683594 ] ], + "bbox": [ + 85.763671875, + 335.865234375, + 398.4983825683594, + 346.4358825683594 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/Code/11", + "id": "/page/117/Code/8", "block_type": "Code", "html": ">>> letters = ['a', 'b', 'c']\n>>> rest = tail(letters)\n>>> print rest\n['b', 'c']", "polygon": [ [ - 85.6142578125, - 350.75390625 + 85.763671875, + 352.5467224121094 ], [ 238.04542541503906, - 350.75390625 + 352.5467224121094 ], [ 238.04542541503906, - 399.0923156738281 + 399.48046875 ], [ - 85.6142578125, - 399.0923156738281 + 85.763671875, + 399.48046875 ] ], + "bbox": [ + 85.763671875, + 352.5467224121094, + 238.04542541503906, + 399.48046875 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/116/SectionHeader/11" + "4": "/page/116/SectionHeader/10" }, "images": {} }, { - "id": "/page/117/SectionHeader/12", + "id": "/page/117/SectionHeader/9", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 86.40005493164062, - 454.78125 + 86.0625, + 455.5242919921875 ], [ - 482.90625, - 453.234375 + 482.40350341796875, + 455.5242919921875 ], [ - 482.90625, + 482.40350341796875, 477.6808776855469 ], [ - 86.40005493164062, - 477.984375 + 86.0625, + 477.6808776855469 ] ], + "bbox": [ + 86.0625, + 455.5242919921875, + 482.40350341796875, + 477.6808776855469 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/117/SectionHeader/12" + "4": "/page/117/SectionHeader/9" }, "images": {} }, { - "id": "/page/117/ListItem/14", + "id": "/page/117/ListItem/11", "block_type": "ListItem", "html": "If you are used to writing string code like this:
", "polygon": [ [ - 111.3070068359375, - 531.73828125 + 109.74462890625, + 532.125 ], [ - 314.96484375, - 531.73828125 + 315.263671875, + 532.125 ], [ - 314.96484375, - 542.56640625 + 315.263671875, + 542.5378875732422 ], [ - 111.3070068359375, - 542.56640625 + 109.74462890625, + 542.5378875732422 ] ], + "bbox": [ + 109.74462890625, + 532.125, + 315.263671875, + 542.5378875732422 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/117/SectionHeader/12" + "4": "/page/117/SectionHeader/9" }, "images": {} }, { - "id": "/page/117/Text/16", - "block_type": "Text", - "html": "word = word.strip()
", + "id": "/page/117/TextInlineMath/13", + "block_type": "TextInlineMath", + "html": "word = word.strip()
", "polygon": [ [ 110.19287109375, - 551.4609375 + 552.62109375 ], [ 210.6938934326172, - 551.4609375 + 552.62109375 ], [ 210.6938934326172, @@ -57741,140 +106755,170 @@ 562.663330078125 ] ], + "bbox": [ + 110.19287109375, + 552.62109375, + 210.6938934326172, + 562.663330078125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/117/SectionHeader/12" + "4": "/page/117/SectionHeader/9" }, "images": {} }, { - "id": "/page/117/Text/17", + "id": "/page/117/Text/14", "block_type": "Text", "html": "It is tempting to write list code like this:
", "polygon": [ [ - 110.267578125, - 571.5703125 + 109.44580078125, + 572.73046875 ], [ - 285.8616943359375, - 571.5703125 + 286.27734375, + 572.73046875 ], [ - 285.8616943359375, - 583.171875 + 286.27734375, + 583.0868835449219 ], [ - 110.267578125, - 583.171875 + 109.44580078125, + 583.0868835449219 ] ], + "bbox": [ + 109.44580078125, + 572.73046875, + 286.27734375, + 583.0868835449219 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/117/SectionHeader/12" + "4": "/page/117/SectionHeader/9" }, "images": {} }, { - "id": "/page/117/Text/18", - "block_type": "Text", - "html": "t = t.sort() # WRONG!
", + "id": "/page/117/TextInlineMath/15", + "block_type": "TextInlineMath", + "html": "t = t.sort() # WRONG!
", "polygon": [ [ - 109.74462890625, + 111.3070068359375, 593.2497253417969 ], [ 273.4383544921875, - 592.453125 + 593.2497253417969 ], [ 273.4383544921875, 603.28125 ], [ - 109.74462890625, - 604.828125 + 111.3070068359375, + 603.28125 ] ], + "bbox": [ + 111.3070068359375, + 593.2497253417969, + 273.4383544921875, + 603.28125 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/117/SectionHeader/12" + "4": "/page/117/SectionHeader/9" }, "images": {} }, { - "id": "/page/117/Text/19", + "id": "/page/117/Text/16", "block_type": "Text", "html": "Because sort returns None, the next operation you perform with t is likely to fail.
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", + "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.
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And these are wrong:
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---|---|
t = t.append(x) | # WRONG! |
t + [x] | # WRONG! |
t = t + x | # WRONG! |
t.append([x]) | # WRONG! | |
t = t.append(x) | # WRONG! | |
t + [x] | # WRONG! | |
t = t + x | # WRONG! |
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.
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", + "html": "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.
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", + "html": "For example, is_sorted([1,2,2]) should return True and is_sorted(['b','a']) should return False.
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", + "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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", + "html": "You can read about this problem at http: // en. wikipedia. org/ wiki/ Birthday_ paradox , and you can download my solution from 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?
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", + "html": "Hint: use the time module to measure elapsed time. Solution: http: // thinkpython. com/ code/ wordlist. py .
", "polygon": [ [ - 85.763671875, + 85.6142578125, 621.8310241699219 ], [ - 482.009765625, + 481.11328125, 621.8310241699219 ], [ - 482.009765625, - 644.66015625 + 481.11328125, + 644.0693206787109 ], [ - 85.763671875, - 644.66015625 + 85.6142578125, + 644.0693206787109 ] ], + "bbox": [ + 85.6142578125, + 621.8310241699219, + 481.11328125, + 644.0693206787109 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/119/SectionHeader/9" + "4": "/page/119/SectionHeader/9" }, "images": {} }, { "id": "/page/119/Text/17", "block_type": "Text", - "html": "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.
", + "html": "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.
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", + "html": "Or you could read the documentation of the bisect module and use that! Solution: http: // thinkpython. com/ code/ inlist. py .
", "polygon": [ [ - 128.6455078125, + 128.9443359375, 197.7099609375 ], [ - 525.9375, + 524.7421875, 197.7099609375 ], [ - 525.9375, + 524.7421875, 220.00921630859375 ], [ - 128.6455078125, + 128.9443359375, 220.00921630859375 ] ], + "bbox": [ + 128.9443359375, + 197.7099609375, + 524.7421875, + 220.00921630859375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/119/SectionHeader/9" + "4": "/page/119/SectionHeader/9" }, "images": {} }, { "id": "/page/120/Text/5", "block_type": "Text", - "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 .
", + "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 .
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", + "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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", + "html": "", "polygon": [ [ 86.4000015258789, - 59.6513671875 + 59.69970703125 ], [ - 484.1015625, - 59.6513671875 + 482.4033508300781, + 59.69970703125 ], [ - 484.1015625, + 482.4033508300781, 71.13372802734375 ], [ @@ -59663,53 +109494,65 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 59.69970703125, + 482.4033508300781, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/119/SectionHeader/9" + "4": "/page/119/SectionHeader/9" }, "images": {} }, { "id": "/page/121/PageHeader/1", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ 85.53955078125, - 59.8447265625 + 60.37646484375 ], [ - 99.28564453125, - 59.8447265625 + 100.92919921875, + 60.37646484375 ], [ - 99.28564453125, - 69.029296875 + 100.92919921875, + 70.33447265625 ], [ 85.53955078125, - 69.029296875 + 70.33447265625 ] ], + "bbox": [ + 85.53955078125, + 60.37646484375, + 100.92919921875, + 70.33447265625 + ], "children": null, "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/119/SectionHeader/9" + "4": "/page/119/SectionHeader/9" }, "images": {} } ], "section_hierarchy": { "1": "/page/108/SectionHeader/1", - "3": "/page/119/SectionHeader/9" + "4": "/page/119/SectionHeader/9" }, "images": null }, { - "id": "/page/122/Page/171", + "id": "/page/122/Page/170", "block_type": "Page", - "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.
", "polygon": [ [ - 128.6455078125, - 285.90216064453125 + 128.9443359375, + 285.591796875 ], [ - 526.53515625, - 285.90216064453125 + 525.9375, + 285.591796875 ], [ - 526.53515625, + 525.9375, 308.1558837890625 ], [ - 128.6455078125, + 128.9443359375, 308.1558837890625 ] ], + "bbox": [ + 128.9443359375, + 285.591796875, + 525.9375, + 308.1558837890625 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -59820,22 +109687,28 @@ "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.
", "polygon": [ [ - 129.09375, - 316.78717041015625 + 128.9443359375, + 316.72265625 ], [ - 527.73046875, - 315.369140625 + 525.9375, + 316.72265625 ], [ - 527.73046875, + 525.9375, 351.23590087890625 ], [ - 129.09375, - 351.720703125 + 128.9443359375, + 351.23590087890625 ] ], + "bbox": [ + 128.9443359375, + 316.72265625, + 525.9375, + 351.23590087890625 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -59848,22 +109721,28 @@ "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.
", "polygon": [ [ - 128.3466796875, - 358.681640625 + 129.09375, + 359.455078125 ], [ - 527.1328125, - 358.681640625 + 525.9375, + 359.455078125 ], [ - 527.1328125, + 525.9375, 382.12091064453125 ], [ - 128.3466796875, + 129.09375, 382.12091064453125 ] ], + "bbox": [ + 129.09375, + 359.455078125, + 525.9375, + 382.12091064453125 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -59876,22 +109755,28 @@ "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.
", "polygon": [ [ - 128.9443359375, - 390.19921875 + 129.5419921875, + 390.69976806640625 ], [ - 527.1328125, - 390.19921875 + 525.9375, + 390.69976806640625 ], [ - 527.1328125, + 525.9375, 413.0069274902344 ], [ - 128.9443359375, + 129.5419921875, 413.0069274902344 ] ], + "bbox": [ + 129.5419921875, + 390.69976806640625, + 525.9375, + 413.0069274902344 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -59904,7 +109789,7 @@ "html": ">>> eng2sp = dict()\n>>> print eng2sp\n{}", "polygon": [ [ - 129.09375, + 129.5419921875, 417.61376953125 ], [ @@ -59913,13 +109798,19 @@ ], [ 228.97686767578125, - 453.62109375 + 451.9653625488281 ], [ - 129.09375, - 453.62109375 + 129.5419921875, + 451.9653625488281 ] ], + "bbox": [ + 129.5419921875, + 417.61376953125, + 228.97686767578125, + 451.9653625488281 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -59932,22 +109823,28 @@ "html": "
The squiggly-brackets, {}, represent an empty dictionary. To add items to the dictionary, you can use square brackets:
", "polygon": [ [ - 128.794921875, - 455.5546875 + 129.59994506835938, + 456.7217712402344 ], [ - 526.236328125, - 455.5546875 + 525.60009765625, + 456.7217712402344 ], [ - 526.236328125, + 525.60009765625, 479.0279235839844 ], [ - 128.794921875, + 129.59994506835938, 479.0279235839844 ] ], + "bbox": [ + 129.59994506835938, + 456.7217712402344, + 525.60009765625, + 479.0279235839844 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -59960,22 +109857,28 @@ "html": ">>> eng2sp['one'] = 'uno'", "polygon": [ [ - 129.59994506835938, - 482.625 + 129.2431640625, + 483.6357727050781 ], [ - 260.578125, - 482.625 + 260.329345703125, + 483.6357727050781 ], [ - 260.578125, + 260.329345703125, 493.5983581542969 ], [ - 129.59994506835938, + 129.2431640625, 493.5983581542969 ] ], + "bbox": [ + 129.2431640625, + 483.6357727050781, + 260.329345703125, + 493.5983581542969 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -59988,22 +109891,28 @@ "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:
", "polygon": [ [ - 128.9443359375, - 497.70703125 + 129.392578125, + 498.09375 ], [ - 526.53515625, - 497.70703125 + 525.638671875, + 498.09375 ], [ - 526.53515625, + 525.638671875, 520.6619262695312 ], [ - 128.9443359375, + 129.392578125, 520.6619262695312 ] ], + "bbox": [ + 129.392578125, + 498.09375, + 525.638671875, + 520.6619262695312 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -60013,52 +109922,30 @@ { "id": "/page/122/Code/10", "block_type": "Code", - "html": ">>> print eng2sp", + "html": "
>>> print eng2sp\n{'one': 'uno'}", "polygon": [ [ 128.9443359375, - 524.00390625 + 525.2687683105469 ], [ 213.2858428955078, - 524.00390625 + 525.2687683105469 ], [ 213.2858428955078, - 535.2313537597656 + 547.4253692626953 ], [ 128.9443359375, - 535.2313537597656 + 547.4253692626953 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/122/SectionHeader/1" - }, - "images": {} - }, - { - "id": "/page/122/Text/11", - "block_type": "Text", - "html": "
{'one': 'uno'}
", - "polygon": [ - [ - 129.60000610351562, - 537.4627685546875 - ], - [ - 204.99609375, - 537.4627685546875 - ], - [ - 204.99609375, - 547.59375 - ], - [ - 129.60000610351562, - 547.59375 - ] + "bbox": [ + 128.9443359375, + 525.2687683105469, + 213.2858428955078, + 547.4253692626953 ], "children": null, "section_hierarchy": { @@ -60067,27 +109954,33 @@ "images": {} }, { - "id": "/page/122/Text/12", + "id": "/page/122/Text/11", "block_type": "Text", "html": "This output format is also an input format. For example, you can create a new dictionary with three items:
", "polygon": [ [ - 128.6455078125, - 551.07421875 + 128.0478515625, + 552.3323211669922 ], [ - 527.1328125, - 551.07421875 + 525.6033325195312, + 552.3323211669922 ], [ - 527.1328125, + 525.6033325195312, 574.4889221191406 ], [ - 128.6455078125, + 128.0478515625, 574.4889221191406 ] ], + "bbox": [ + 128.0478515625, + 552.3323211669922, + 525.6033325195312, + 574.4889221191406 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -60095,27 +109988,33 @@ "images": {} }, { - "id": "/page/122/Text/13", - "block_type": "Text", - "html": ">>> eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'}
", + "id": "/page/122/Code/12", + "block_type": "Code", + "html": ">>> eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'}", "polygon": [ [ 129.09375, - 578.53125 + 579.0967712402344 ], [ - 432.8894348144531, - 578.53125 + 434.49609375, + 579.0967712402344 ], [ - 432.8894348144531, - 589.359375 + 434.49609375, + 595.93359375 ], [ 129.09375, - 589.359375 + 595.93359375 ] ], + "bbox": [ + 129.09375, + 579.0967712402344, + 434.49609375, + 595.93359375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1" @@ -60123,54 +110022,32 @@ "images": {} }, { - "id": "/page/122/Text/14", + "id": "/page/122/Text/13", "block_type": "Text", "html": "
But if you print eng2sp, you might be surprised:
", "polygon": [ [ - 128.6455078125, - 593.61328125 + 129.2431640625, + 593.8157653808594 ], [ - 343.65234375, - 593.61328125 + 341.9150390625, + 593.8157653808594 ], [ - 343.65234375, - 604.44140625 + 341.9150390625, + 605.21484375 ], [ - 128.6455078125, - 604.44140625 + 129.2431640625, + 605.21484375 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/122/SectionHeader/1" - }, - "images": {} - }, - { - "id": "/page/122/Code/15", - "block_type": "Code", - "html": ">>> print eng2sp", - "polygon": [ - [ - 128.794921875, - 607.1484375 - ], - [ - 213.9609375, - 607.1484375 - ], - [ - 213.9609375, - 618.4983673095703 - ], - [ - 128.794921875, - 618.4983673095703 - ] + "bbox": [ + 129.2431640625, + 593.8157653808594, + 341.9150390625, + 605.21484375 ], "children": null, "section_hierarchy": { @@ -60179,27 +110056,33 @@ "images": {} }, { - "id": "/page/122/Text/16", - "block_type": "Text", - "html": "
{'one': 'uno', 'three': 'tres', 'two': 'dos'}
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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.
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The key 'two' always maps to the value 'dos' so the order of the items doesn't matter.
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The len function works on dictionaries; it returns the number of key-value pairs:
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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).
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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:
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", + "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.
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", + "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.
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", + "html": "If you did Exercise 10.11, you can compare the speed of this implementation with the list in operator and the bisection search.
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", "polygon": [ [ - 129.09375, - 329.677734375 + 129.6000213623047, + 330.0373840332031 ], [ - 525.5972290039062, - 329.677734375 + 525.9375, + 330.0373840332031 ], [ - 525.5972290039062, + 525.9375, 352.2911071777344 ], [ - 129.09375, + 129.6000213623047, 352.2911071777344 ] ], + "bbox": [ + 129.6000213623047, + 330.0373840332031, + 525.9375, + 352.2911071777344 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/123/SectionHeader/14" + "4": "/page/123/SectionHeader/14" }, "images": {} }, @@ -61125,26 +111200,32 @@ "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].
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", "polygon": [ [ - 128.57080078125, - 414.17578125 + 128.6455078125, + 414.94921875 ], [ - 221.28578186035156, - 414.17578125 + 223.3740234375, + 414.94921875 ], [ - 221.28578186035156, + 223.3740234375, 424.9820861816406 ], [ - 128.57080078125, + 128.6455078125, 424.9820861816406 ] ], + "bbox": [ + 128.6455078125, + 414.94921875, + 223.3740234375, + 424.9820861816406 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/123/SectionHeader/14" + "4": "/page/123/SectionHeader/14" }, "images": {} }, { "id": "/page/124/Code/9", "block_type": "Code", - "html": ">>> h = histogram('brontosaurus')\n>>> print h\n{'a': 1, 'b': 1, 'o': 2, 'n': 1, 's': 2, 'r': 2, 'u': 2, 't': 1}\nThe histogram indicates that the letters 'a' and 'b' appear once; 'o' appears twice, and\nso on.\nExercise 11.2. Dictionaries have a method called get that takes a key and a default value. If the\nkey appears in the dictionary, get returns the corresponding value; otherwise it returns the default\nvalue. For example:\n>>> h = histogram('a')\n>>> print h\n{'a': 1}", + "html": "
>>> h = histogram('brontosaurus')\n>>> print h\n{'a': 1, 'b': 1, 'o': 2, 'n': 1, 's': 2, 'r': 2, 'u': 2, 't': 1}", "polygon": [ [ - 127.8984375, - 428.95294189453125 + 129.59996032714844, + 427.7109375 ], [ - 525.6051635742188, - 428.95294189453125 + 476.9296875, + 427.7109375 ], [ - 525.6051635742188, - 567.703125 + 476.9296875, + 468.703125 ], [ - 127.8984375, - 567.703125 + 129.59996032714844, + 468.703125 ] ], + "bbox": [ + 129.59996032714844, + 427.7109375, + 476.9296875, + 468.703125 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/123/SectionHeader/14" + "4": "/page/123/SectionHeader/14" }, "images": {} }, { "id": "/page/124/Text/10", "block_type": "Text", - "html": "
>>> h.get('a', 0) 1 >>> h.get('b', 0)
", + "html": "The histogram indicates that the letters 'a' and 'b' appear once; 'o' appears twice, and so on.
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", + "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:
", + "polygon": [ + [ + 129.2431640625, + 491.51953125 + ], + [ + 525.6024169921875, + 491.51953125 + ], + [ + 525.6024169921875, + 526.1414184570312 + ], + [ + 129.2431640625, + 526.1414184570312 + ] + ], + "bbox": [ + 129.2431640625, + 491.51953125, + 525.6024169921875, + 526.1414184570312 + ], + "children": null, + "section_hierarchy": { + "1": "/page/122/SectionHeader/1", + "4": "/page/123/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/124/Code/12", + "block_type": "Code", + "html": ">>> h = histogram('a')\n>>> print h\n{'a': 1}\n>>> h.get('a', 0)\n1\n>>> h.get('b', 0)\n0", "polygon": [ [ 129.60009765625, - 599.4140625 + 530.2839660644531 ], [ - 141.4951171875, - 599.4140625 + 244.64146423339844, + 530.2839660644531 ], [ - 141.4951171875, + 244.64146423339844, 613.4125823974609 ], [ @@ -61257,111 +111391,135 @@ 613.4125823974609 ] ], + "bbox": [ + 129.60009765625, + 530.2839660644531, + 244.64146423339844, + 613.4125823974609 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/123/SectionHeader/14" + "4": "/page/123/SectionHeader/14" }, "images": {} }, { - "id": "/page/124/Text/12", + "id": "/page/124/Text/13", "block_type": "Text", - "html": "
Use get to write histogram more concisely. You should be able to eliminate the if statement.
", + "html": "Use get to write histogram more concisely. You should be able to eliminate the if statement.
", "polygon": [ [ - 128.49609375, - 616.04296875 + 128.794921875, + 616.4296875 ], [ - 508.60546875, - 616.04296875 + 508.27825927734375, + 616.4296875 ], [ - 508.60546875, + 508.27825927734375, 627.4955902099609 ], [ - 128.49609375, + 128.794921875, 627.4955902099609 ] ], + "bbox": [ + 128.794921875, + 616.4296875, + 508.27825927734375, + 627.4955902099609 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/123/SectionHeader/14" + "4": "/page/123/SectionHeader/14" }, "images": {} }, { - "id": "/page/124/SectionHeader/13", + "id": "/page/124/SectionHeader/14", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 128.3466796875, - 677.91796875 + 128.197265625, + 678.3046875 ], [ 525.5980834960938, - 677.91796875 + 678.3046875 ], [ 525.5980834960938, - 701.12109375 + 700.8351440429688 ], [ - 128.3466796875, - 701.12109375 + 128.197265625, + 700.8351440429688 ] ], + "bbox": [ + 128.197265625, + 678.3046875, + 525.5980834960938, + 700.8351440429688 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/124/SectionHeader/13" + "4": "/page/124/SectionHeader/14" }, "images": {} } ], "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/124/SectionHeader/13" + "4": "/page/124/SectionHeader/14" }, "images": null }, { - "id": "/page/125/Page/185", + "id": "/page/125/Page/192", "block_type": "Page", - "html": "104 Chapter 11. Dictionaries
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.0380859375 + 60.521484375 ], [ - 483.802734375, - 60.0380859375 + 482.40338134765625, + 60.521484375 ], [ - 483.802734375, + 482.40338134765625, 71.13372802734375 ], [ @@ -61403,141 +111567,206 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.521484375, + 482.40338134765625, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/124/SectionHeader/13" + "4": "/page/124/SectionHeader/14" }, "images": {} }, { - "id": "/page/125/PageHeader/15", - "block_type": "PageHeader", - "html": "", + "id": "/page/125/TextInlineMath/1", + "block_type": "TextInlineMath", + "html": "def print_hist(h): for c in h: print c, h[c]
", "polygon": [ [ - 84.94189453125, - 60.0380859375 + 86.4000015258789, + 88.68572998046875 ], [ - 99.28564453125, - 60.0380859375 + 196.22775268554688, + 88.68572998046875 ], [ - 99.28564453125, - 69.5126953125 + 196.22775268554688, + 123.03729248046875 ], [ - 84.94189453125, - 69.5126953125 + 86.4000015258789, + 123.03729248046875 + ] + ], + "bbox": [ + 86.4000015258789, + 88.68572998046875, + 196.22775268554688, + 123.03729248046875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/122/SectionHeader/1", + "4": "/page/124/SectionHeader/14" + }, + "images": {} + }, + { + "id": "/page/125/Text/2", + "block_type": "Text", + "html": "Here's what the output looks like:
", + "polygon": [ + [ + 85.9130859375, + 129.744140625 + ], + [ + 237.1201171875, + 129.744140625 + ], + [ + 237.1201171875, + 140.40887451171875 + ], + [ + 85.9130859375, + 140.40887451171875 ] ], + "bbox": [ + 85.9130859375, + 129.744140625, + 237.1201171875, + 140.40887451171875 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/124/SectionHeader/13" + "4": "/page/124/SectionHeader/14" }, "images": {} }, { - "id": "/page/125/Code/1", + "id": "/page/125/Code/3", "block_type": "Code", - "html": "def print_hist(h):\n for c in h:\n print c, h[c]\nHere's what the output looks like:\n>>> h = histogram('parrot')\n>>> print_hist(h)\na 1\np 1\nr 2\nt 1\no 1", + "html": "
>>> h = histogram('parrot')\n>>> print_hist(h)\na 1\np 1\nr 2\nt 1\no 1", "polygon": [ [ - 86.2119140625, - 88.68572998046875 + 85.763671875, + 147.51971435546875 ], [ - 235.66961669921875, - 88.68572998046875 + 227.58737182617188, + 147.51971435546875 ], [ - 235.66961669921875, - 230.6483154296875 + 227.58737182617188, + 231.064453125 ], [ - 86.2119140625, - 230.6483154296875 + 85.763671875, + 231.064453125 ] ], + "bbox": [ + 85.763671875, + 147.51971435546875, + 227.58737182617188, + 231.064453125 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/124/SectionHeader/13" + "4": "/page/124/SectionHeader/14" }, "images": {} }, { - "id": "/page/125/Text/2", + "id": "/page/125/Text/4", "block_type": "Text", "html": "
Again, the keys are in no particular order.
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", "polygon": [ [ - 85.6142578125, - 248.2734375 + 85.9130859375, + 249.046875 ], [ - 482.90625, - 248.2734375 + 482.39501953125, + 249.046875 ], [ - 482.90625, + 482.39501953125, 272.2371826171875 ], [ - 85.6142578125, + 85.9130859375, 272.2371826171875 ] ], + "bbox": [ + 85.9130859375, + 249.046875, + 482.39501953125, + 272.2371826171875 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/124/SectionHeader/13" + "4": "/page/124/SectionHeader/14" }, "images": {} }, { - "id": "/page/125/Text/4", + "id": "/page/125/Text/6", "block_type": "Text", "html": "Modify print_hist to print the keys and their values in alphabetical order.
", "polygon": [ [ 85.6142578125, - 281.91796875 + 283.078125 ], [ 390.69482421875, - 281.91796875 + 283.078125 ], [ 390.69482421875, @@ -61548,314 +111777,415 @@ 293.4543151855469 ] ], + "bbox": [ + 85.6142578125, + 283.078125, + 390.69482421875, + 293.4543151855469 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/124/SectionHeader/13" + "4": "/page/124/SectionHeader/14" }, "images": {} }, { - "id": "/page/125/SectionHeader/5", + "id": "/page/125/SectionHeader/7", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 86.361328125, - 352.6875 + 85.6142578125, + 354.041015625 ], [ - 482.3953552246094, - 352.6875 + 482.90625, + 354.041015625 ], [ - 482.3953552246094, + 482.90625, 376.4458923339844 ], [ - 86.361328125, + 85.6142578125, 376.4458923339844 ] ], + "bbox": [ + 85.6142578125, + 354.041015625, + 482.90625, + 376.4458923339844 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/125/SectionHeader/5" + "4": "/page/125/SectionHeader/7" }, "images": {} }, { - "id": "/page/125/Text/7", + "id": "/page/125/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 85.3154296875, - 385.365234375 + 85.46484375, + 387.10546875 ], [ - 483.50390625, - 385.365234375 + 482.607421875, + 387.10546875 ], [ - 483.50390625, + 482.607421875, 434.222900390625 ], [ - 85.3154296875, + 85.46484375, 434.222900390625 ] ], + "bbox": [ + 85.46484375, + 387.10546875, + 482.607421875, + 434.222900390625 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/125/SectionHeader/5" + "4": "/page/125/SectionHeader/7" }, "images": {} }, { - "id": "/page/125/Text/8", + "id": "/page/125/Text/10", "block_type": "Text", "html": "Here is a function that takes a value and returns the first key that maps to that value:
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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.
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", "polygon": [ [ - 85.763671875, - 585.10546875 + 85.3154296875, + 586.265625 ], [ - 483.205078125, - 585.10546875 + 482.90625, + 586.265625 ], [ - 483.205078125, + 482.90625, 608.6119384765625 ], [ - 85.763671875, + 85.3154296875, 608.6119384765625 ] ], + "bbox": [ + 85.3154296875, + 586.265625, + 482.90625, + 608.6119384765625 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/125/SectionHeader/5" + "4": "/page/125/SectionHeader/7" }, "images": {} }, { - "id": "/page/125/Text/12", + "id": "/page/125/Text/14", "block_type": "Text", "html": "Here is an example of a successful reverse lookup:
", "polygon": [ [ - 86.40007019042969, - 618.36328125 + 85.39013671875, + 618.75 ], [ - 308.091796875, - 618.36328125 + 307.494140625, + 618.75 ], [ - 307.0915222167969, + 307.494140625, 629.8069458007812 ], [ - 85.46484375, + 85.39013671875, 629.8069458007812 ] ], + "bbox": [ + 85.39013671875, + 618.75, + 307.494140625, + 629.8069458007812 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/125/SectionHeader/5" + "4": "/page/125/SectionHeader/7" }, "images": {} }, { - "id": "/page/125/Code/13", + "id": "/page/125/Code/15", "block_type": "Code", "html": ">>> h = histogram('parrot')\n>>> k = reverse_lookup(h, 2)\n>>> print k\nr", "polygon": [ [ - 84.7177734375, - 635.37890625 + 85.6142578125, + 636.9167938232422 ], [ - 233.8330078125, - 635.37890625 + 232.86021423339844, + 636.9167938232422 ], [ - 233.8330078125, + 232.86021423339844, 683.4623947143555 ], [ - 84.7177734375, + 85.6142578125, 683.4623947143555 ] ], + "bbox": [ + 85.6142578125, + 636.9167938232422, + 232.86021423339844, + 683.4623947143555 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/125/SectionHeader/5" + "4": "/page/125/SectionHeader/7" }, "images": {} }, { - "id": "/page/125/Text/14", + "id": "/page/125/Text/16", "block_type": "Text", "html": "
And an unsuccessful one:
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>>> 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
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+ "/page/127/Figure/1": 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S30TI+CMj7Jb9jzXS6d4e0bSHL6fplpbyt96VIgHb6t1P4msHTf8AkZfFX/YQi/8ASS3oA16KKKAMDxF4f/tvUdEuAqg6feC5MhcgqAOgUDkngc4xVDVvHD22uz6Lo+hXusXtsivciFljSINyAWY9cdq66vNl1fVde8V6/aW+v2nh6102UROFt42nn4++zPwB6cUAblnrNt488LataJYTQXCh7S5srpvLZHx0LDPHPUVvaHpx0jQbDTi4c2tukRYdCVAFcH8Jpbd73xWINRbUFOo5FzIwLTDb97gAc+wxXpdABRVHUdMTUhGHuryDZn/j2uGizn1x16VQ/wCEXh/6Cutf+DCT/GgDdorC/wCEXh/6Cutf+DCT/Gj/AIReH/oK61/4MJP8aALN/wCILDS7nyb37TCu0N55tpGh/GQAqPxIq1Y6lYanD5the291H/fglVx+YNcxf6LfxXPkaemtXHyg+fPrLRRZ9ONz5/4DWQ/wvl1K8F5qeqtDKOjWhdpl/wC2zsf/AEEUAekUhzg469s1Q0bSE0WxFql5fXeDnzb24aZz+JrQoA8w+2eKW+Lfh6DXRYw2zQ3LQQ2UjsDhDkuWAyelen1wmvf8lh8Jf9et3/6BXd0AFFFFABWPrP8AyE/Dv/YUX/0VLWxWPrP/ACE/Dv8A2FF/9FS0AdjRRRQAUUUUAcj8RZtIg8NwPrVrcXNr9ugCpA+1hIX+U59AaTXfH1toviUeH49Lvr7UpLYTwRWwU+bliNuSflxgkk8AVnfGH/kTbb/sKWv/AKMFNCqfj6WIBK6AMH0/e0AW9N+In2o6za3ugX9hq2l2xupLGRlZpY8dUYHBrXh8X6bN4HHiwFxYfZTclTjcAByvpnPH1rnrdVf476grAFToMYIPf97XBCK4F5N8J1DiN9Z87OOBYH96R+fH40Ae36Jqf9s6JZ6n9mlthdRLKsUuNyg8jOPaoNQ8S6RpV19mvbwRTbQ23y2PB+gNaiIsUaxooVFAVQOgAp1AHP8A/Cb+Hf8AoIj/AL8yf/E0f8Jv4d/6CI/78yf/ABNdBRQBz/8Awm/h3/oIj/vzJ/8AE1hXHjia2uZXt9Q0nULYuSkTxT20iLngbtsgYj1wufau9rCuPDQvrmWS/wBY1WeF3JW2S4+zxoM8L+6Csw/3ic0Ac4PjB4cteNYW60zH8ckfmo30Me5vzUVZ0G8t9Q1nxJd2snmQS30TI+CMj7Jb9jzXS6d4e0bSHL6fplpbyt96VIgHb6t1P4msHTf+Rl8Vf9hCL/0kt6ANeiiigApshcRsYwC+DtBOAT2p1FAHl9nd+KH+MGlweIPsUUZsJ3ggspHZAOhLFgMnivUK4LU/+S26H/2Cp/8A0Ku9oAKKo6jpiakIw91eQbM/8e1w0Wc+u081Q/4RW176jrR/7ic4/k1AG7RWF/widj3vtaP/AHF7kfyko/4RLTu91rJ/7jV3/wDHaANTwL/yJmnf7r/+htXRVwXgzwnpdz4SsJZJNU3Mr5C6tdKv327CQAVu/wDCF6N3/tE/XVLo/wDtSgDXv7qWztGnhsZ711I/cwFA5GeSN7KOPrWQPGmjxHbqL3Olt3/tG3eBB/20YbD+DGq1/wCDtPis3axsJbm542Rz6rcRoeectliMDnoax/8AhXM99/x+X0djGf8AlnYGV3H/AG0mdgf++BQBt+Mru2vfhzr89rcRTxNp0+JInDKfkPcVdri9e+G3hjw94R1/UrOydtQXTLhftM0zMxGw9s7e3YV2lABRRRQAVwXjWe71bxVofhK3vJ7S2vFkub2S3fY7RIOEDdsnrXe1594pkTR/il4Z1i6YR2U0E1i0zHCo55XJ7ZoAgs7V/A3xD0vSbO7updG1iGQC3uJjJ5MyDOVJ5AIr0ivPNani1v4teG7SykSb+zIprm6aNtwjDAKoJHcntXodABRWNP4cinnkmOp6uhkYsVjvpFUZOcAA8D2qP/hF4f8AoK61/wCDCT/GgDdpsj+XGz7WbaCcKMk/QVif8IvD/wBBXWv/AAYSf402TwzEkbMNS1xyoJCLqD5b2GSBQBJF4t0R5VhmvRZTNwIr6NrZyfQCQDP4ZraVldQykMpGQQcg1wUvhzW9TiaJVu7OB+D/AGhq0kzfjHH8p/77pdG+F9tpVwtwNb1NHDBmisZjbQuc55UEk/i1AHe1gar4f/tHxXomrBVH9neaS5c5IZcbQuMdTnOe3vW8BgADPHqc0tAHGXvj2b+173TtD8O3+sNYtsupYWSNEb+6Cx+Y+1LNdWvxJ8AXsVpbuhnJgeG5YxtDKrDO7GehwffpWDp+san4jutbuT4ntfDlhZ3kkDW8NvF5pC/xu79CfYVb+DkkL+GtTENy1yv9qTkSucs4OMMfc9aAPQLWD7NaQwA5EUapn1wMVLRRQAUUUUAZlr/yUGH/ALBUv/o2Ousrk7X/AJKDD/2Cpf8A0bHXWUAV7yws9Rg8i+tILqHcH8ueMOu4HIOD3BovLCz1CJYr20guY0cSKk0YcBh0YA9x61YooArrYWaX0l8lpAt3KgjknEYEjqOilsZIHpWF/wAK98HnUv7QPhzTvtO7fv8AIGN3rt6Z/CulooAy9Z8N6L4hihj1fTbe8WBt0XmrnYfY9q0wAoAAwBwBS0UAFFFFABRRRQAVx+m/8jL4q/7CEX/pJb12Fcfpv/Iy+Kv+whF/6SW9AGvRRRQAVian4Q8O6zfLe6jo1nc3K4HmSRAk46Z9fxrbooAzbXw/pFjqkupWmnW8F5LGI3ljTaWUYwOOOw/KtKiigAooooAKKKKACiiigAooooAgksbSW8hvJLWB7qEFYpmjBeMHqFbqM98VPRRQAUUUUAFY+s/8hPw7/wBhRf8A0VLWxWPrP/IT8O/9hRf/AEVLQB2NFFFABRRRQBXvLCz1GEQ31pBdRBg4SeMOoYcg4PcetH2Cz+3/AG/7JB9t8vyvtHljzNmc7d3XGecVYooAriws1v2vxaQC8aPymuBGPMKZztLdcZ7U3+zLD+0v7S+w232/Z5f2ryl83Z/d34zj2zVqigAooooAKKKKACiiigArj9N/5GXxV/2EIv8A0kt67CuP03/kZfFX/YQi/wDSS3oA16KKKACiiigCu1jaPfR3z2sDXcaFEnMYMiqeoDdQParFFFABRRRQAUUUUAVvAv8AyJmnf7r/APobV0Vc74F/5EzTv91//Q2roqACiiigDn/Hf/IgeIf+wdP/AOgGp6g8d/8AIgeIf+wdP/6AanoAKKKKACquoadZarZvZ6haw3Vs/wB6KVAyn8DVqigDN0fw/pHh+F4tJ063s0c5fykwW+p6mtKiigAooooAKKKKACiiigAooooAw7zwb4b1DU/7Su9EsZrzIJleIEsfU9ifrVux0LStMvbq9sbCC3uLsgzyRrgyEev51o0UAFFFFABRRRQBmWv/ACUGH/sFS/8Ao2Ousrk7X/koMP8A2Cpf/RsddZQAUUUUAFFFFABRRRQAUUUUAFFFFABXH6b/AMjL4q/7CEX/AKSW9dhXH6b/AMjL4q/7CEX/AKSW9AGvRRRQBnalrun6RdWFtezmOW/m8i3UIzb39OBx+NaNUNR0mHU57KSd222kwnVABhmA4ycZwOvGK4aPXvGOv+K9e0TSJrCyt9OnC/bJoS5ClRhAucE5yST2oA7/AFC/ttK064v7yTy7a3QySOFJwo6nA5NLYXtvqVhb31q++3uIxJG2CMqRkHB5Fcj4c1nVNYm1rwt4g8mLVbNADcWqjZNE44cK2QD9RiuusbKHTtPt7K3BENvGsaAnJwBgUAWKKjlnhhx5sqR56b2AzUf26z/5+oP+/goAsUVX+3Wf/P1B/wB/BR9us/8An6g/7+CgCxRWHf67cWdz+4sYr212g7re9jEue/yPtGP+BfhUMXjjQi2y7uX09+4vYmiUf8DPyH8GNAHRUVFb3MF3Cs1tPHNE3R43DKfxFSEZBGcZ7igCtNqNnb39rYyzol1dbjDEer7RlsfQVarys6DJo3xl8OyTaxqGpPcW90c3kgbywEPCgAADmvVKACiiigArH1n/AJCfh3/sKL/6KlrYrH1n/kJ+Hf8AsKL/AOipaAOxooooAKKKKACiiigAooooAKKKKACiiigAooooAK4/Tf8AkZfFX/YQi/8ASS3rsK4/Tf8AkZfFX/YQi/8ASS3oA16KKKACiiigAooooAKKKKACiiigCt4F/wCRM07/AHX/APQ2roq53wL/AMiZp3+6/wD6G1dFQAUUUUAc/wCO/wDkQPEP/YOn/wDQDU9QeO/+RA8Q/wDYOn/9ANT0AFFFFABRRWR4g0WXXLFbaLV7/TQGy0lk4R2GMYJIPH0oAu2GpWeqQyS2Vwk8ccrQsy9A6nDD8DVqvP8A4ORCDwO8QZmCX9wu5jknDYya9AoAKKga9tVYq1zCGBwQZBkGk+3Wf/P1B/38FAFiiq/26z/5+oP+/gpsl/bCNjHc2zOAdoaYAE9snnH5GgC1RXM/8Ja9rxqWkzxgdZbOVLpPwCkSf+OVesfFeg6jIIrfVbbzz0gkby5f++Gw36UAbFZ13run2OsWOk3E5S9vg5t49jHftGTyBgfjWjVC60mG81Sxv5XYtZl2iQAY3MNuScZ4BPGcc0AX6o6xrFjoOlTalqMxitIQDI4QtjJwOACeprgNK13xx4rvNYt9MudO0+3069lgFzLAZGlwflULnAwOp9619A1K78Y6NrOga6iW2pWUv2W8NsAVdTyGXcDjcM/TrQB2cMqTwxzRnKSKGU4xkEZFPpsUawxJEgwiKFUegFOoAKKKKAMy1/5KDD/2Cpf/AEbHXWVydr/yUGH/ALBUv/o2OusoAKKKKACiiigAooooAKKKKACiiigArj9N/wCRl8Vf9hCL/wBJLeuwrj9N/wCRl8Vf9hCL/wBJLegDXooooAK8i0jxR/wjXj/xk93p97Np0t4mbi1hMvlyBeAwHIBB6+1eu1g6F4cOja1ruoG6Eo1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", + "html": "Figure 11.1: State diagram.
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", + "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.
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", + "id": "/page/127/Code/241", + "block_type": "Code", + "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", "polygon": [ [ - 86.2119140625, - 320.203125 + 85.763671875, + 319.81640625 ], [ 285.163818359375, - 320.203125 + 319.81640625 ], [ 285.163818359375, 391.9113464355469 ], [ - 86.2119140625, + 85.763671875, 391.9113464355469 ] ], + "bbox": [ + 85.763671875, + 319.81640625, + 285.163818359375, + 391.9113464355469 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/126/SectionHeader/7" + "4": "/page/126/SectionHeader/7" }, "images": {} }, @@ -62581,26 +113061,32 @@ "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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", + "html": "Since lists and dictionaries are mutable, they can't be used as keys, but they can be used as values.
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} }, { "id": "/page/128/Caption/2", "block_type": "Caption", - "html": "Figure 11.2: Call graph.
", + "html": "Figure 11.2: Call graph.
", "polygon": [ [ - 274.623046875, - 253.3892822265625 + 275.51953125, + 252.52734375 ], [ - 379.212890625, - 253.3892822265625 + 379.14202880859375, + 252.52734375 ], [ - 379.212890625, - 263.548828125 + 379.14202880859375, + 263.3519287109375 ], [ - 274.623046875, - 263.548828125 + 275.51953125, + 263.3519287109375 ] ], + "bbox": [ + 275.51953125, + 252.52734375, + 379.14202880859375, + 263.3519287109375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} } ], "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": null }, @@ -62991,55 +113555,67 @@ "html": "the run time increases very quickly.
", "polygon": [ [ - 129.2431640625, - 285.205078125 + 128.57080078125, + 285.591796875 ], [ - 286.1279296875, - 285.205078125 + 285.2457275390625, + 285.591796875 ], [ - 286.1279296875, + 285.2457275390625, 296.1878967285156 ], [ - 129.2431640625, + 128.57080078125, 296.1878967285156 ] ], + "bbox": [ + 128.57080078125, + 285.591796875, + 285.2457275390625, + 296.1878967285156 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, { "id": "/page/128/Text/4", "block_type": "Text", - "html": "To understand why, consider Figure 11.2, which shows the call graph for fibonacci with n=4:
", + "html": "To understand why, consider Figure 11.2, which shows the call graph for fibonacci with n=4:
", "polygon": [ [ - 128.197265625, + 127.7490234375, 306.087890625 ], [ - 526.833984375, + 525.5999145507812, 306.087890625 ], [ - 526.833984375, + 525.5999145507812, 328.6828918457031 ], [ - 128.197265625, + 127.7490234375, 328.6828918457031 ] ], + "bbox": [ + 127.7490234375, + 306.087890625, + 525.5999145507812, + 328.6828918457031 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, @@ -63049,26 +113625,32 @@ "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.
", "polygon": [ [ - 128.794921875, - 338.572265625 + 128.6455078125, + 338.958984375 ], [ - 527.431640625, - 338.572265625 + 525.9375, + 338.958984375 ], [ - 527.431640625, + 525.9375, 373.37188720703125 ], [ - 128.794921875, + 128.6455078125, 373.37188720703125 ] ], + "bbox": [ + 128.6455078125, + 338.958984375, + 525.9375, + 373.37188720703125 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, @@ -63078,26 +113660,32 @@ "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.
", "polygon": [ [ - 128.9443359375, - 382.46484375 + 128.197265625, + 383.5597229003906 ], [ - 527.1328125, - 382.46484375 + 526.236328125, + 383.5597229003906 ], [ - 527.1328125, + 526.236328125, 405.8658752441406 ], [ - 128.9443359375, + 128.197265625, 405.8658752441406 ] ], + "bbox": [ + 128.197265625, + 383.5597229003906, + 526.236328125, + 405.8658752441406 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, @@ -63107,214 +113695,221 @@ "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:
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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", "polygon": [ [ - 127.82373046875, + 127.52490234375, 456.771728515625 ], [ - 224.71875, + 344.548828125, 456.771728515625 ], [ - 224.71875, - 469.4765625 - ], - [ - 127.82373046875, - 469.4765625 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" - }, - "images": {} - }, - { - "id": "/page/128/Code/9", - "block_type": "Code", - "html": "
def 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", - "polygon": [ - [ - 128.794921875, - 481.15972900390625 - ], - [ - 344.0494079589844, - 481.15972900390625 - ], - [ - 344.0494079589844, + 344.548828125, 564.2883453369141 ], [ - 128.794921875, + 127.52490234375, 564.2883453369141 ] ], + "bbox": [ + 127.52490234375, + 456.771728515625, + 344.548828125, + 564.2883453369141 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, { - "id": "/page/128/Text/10", + "id": "/page/128/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 129.2431640625, + 128.0478515625, 570.6547393798828 ], [ - 526.53515625, + 525.9375, 570.6547393798828 ], [ - 526.53515625, + 525.9375, 592.9608917236328 ], [ - 129.2431640625, + 128.0478515625, 592.9608917236328 ] ], + "bbox": [ + 128.0478515625, + 570.6547393798828, + 525.9375, + 592.9608917236328 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, { - "id": "/page/128/Text/11", + "id": "/page/128/Text/10", "block_type": "Text", "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.
", "polygon": [ [ 128.3466796875, - 602.89453125 + 603.1497344970703 ], [ - 527.1328125, - 602.89453125 + 525.6033935546875, + 603.1497344970703 ], [ - 527.1328125, - 638.47265625 + 525.6033935546875, + 637.6498870849609 ], [ 128.3466796875, - 638.47265625 + 637.6498870849609 ] ], + "bbox": [ + 128.3466796875, + 603.1497344970703, + 525.6033935546875, + 637.6498870849609 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, { - "id": "/page/128/Text/12", + "id": "/page/128/Text/11", "block_type": "Text", "html": "Exercise 11.6. Run this version of fibonacci and the original with a range of parameters and compare their run times.
", "polygon": [ [ - 128.6455078125, + 128.49609375, 639.6328125 ], [ - 527.1328125, + 525.6038208007812, 639.6328125 ], [ - 527.1328125, + 525.6038208007812, 661.8661956787109 ], [ - 128.6455078125, + 128.49609375, 661.8661956787109 ] ], + "bbox": [ + 128.49609375, + 639.6328125, + 525.6038208007812, + 661.8661956787109 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, { - "id": "/page/128/Text/13", + "id": "/page/128/Text/12", "block_type": "Text", - "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 .
", + "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 .
", "polygon": [ [ - 128.49609375, - 663.22265625 + 128.794921875, + 663.99609375 ], [ - 525.9375, - 663.22265625 + 525.6021728515625, + 663.99609375 ], [ - 525.9375, + 525.6021728515625, 698.4492034912109 ], [ - 128.49609375, + 128.794921875, 698.4492034912109 ] ], + "bbox": [ + 128.794921875, + 663.99609375, + 525.6021728515625, + 698.4492034912109 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} } ], "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": null }, { - "id": "/page/129/Page/192", + "id": "/page/129/Page/197", "block_type": "Page", - "html": "108 Chapter 11. Dictionaries
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.37646484375 + 60.134765625 ], [ 482.90625, - 60.37646484375 + 60.134765625 ], [ 482.90625, @@ -63356,68 +113957,86 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.134765625, + 482.90625, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, { - "id": "/page/129/PageHeader/14", + "id": "/page/129/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.53955078125, - 58.974609375 + 85.763671875, + 60.0380859375 ], [ - 101.67626953125, - 58.974609375 + 101.900390625, + 60.0380859375 ], [ - 101.67626953125, - 69.8994140625 + 101.900390625, + 69.99609375 ], [ - 85.53955078125, - 69.8994140625 + 85.763671875, + 69.99609375 ] ], + "bbox": [ + 85.763671875, + 60.0380859375, + 101.900390625, + 69.99609375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/127/SectionHeader/12" + "4": "/page/127/SectionHeader/12" }, "images": {} }, { "id": "/page/129/SectionHeader/1", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 86.2119140625, - 110.21484375 + 85.166015625, + 111.375 ], [ - 482.90625, - 110.21484375 + 483.50390625, + 111.375 ], [ - 482.90625, + 483.50390625, 158.8529052734375 ], [ - 86.2119140625, + 85.166015625, 158.8529052734375 ] ], + "bbox": [ + 85.166015625, + 111.375, + 483.50390625, + 158.8529052734375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, @@ -63456,330 +114081,536 @@ "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:
", "polygon": [ [ - 85.166015625, - 166.1923828125 + 85.46484375, + 166.7724609375 ], [ 483.802734375, - 166.1923828125 + 166.7724609375 ], [ 483.802734375, 203.0399169921875 ], [ - 85.166015625, + 85.46484375, 203.0399169921875 ] ], + "bbox": [ + 85.46484375, + 166.7724609375, + 483.802734375, + 203.0399169921875 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { "id": "/page/129/Code/4", "block_type": "Code", - "html": "verbose = True\ndef example1():\n if verbose:\n print 'Running example1'\nIf you try to reassign a global variable, you might be surprised. The following example is\nsupposed to keep track of whether the function has been called:\nbeen_called = False", + "html": "
verbose = True\ndef example1():\n if verbose:\n print 'Running example1'", "polygon": [ [ - 86.40005493164062, + 85.83837890625, 208.7547607421875 ], [ - 482.4034729003906, + 254.302734375, 208.7547607421875 ], [ - 482.4034729003906, - 328.32421875 + 254.302734375, + 267.494384765625 ], [ - 86.40005493164062, - 328.32421875 + 85.83837890625, + 267.494384765625 ] ], + "bbox": [ + 85.83837890625, + 208.7547607421875, + 254.302734375, + 267.494384765625 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { "id": "/page/129/Text/5", "block_type": "Text", - "html": "
def example2(): been_called = True # WRONG
", + "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:
", "polygon": [ [ - 86.28662109375, - 325.768798828125 + 85.763671875, + 271.86328125 + ], + [ + 482.90625, + 271.86328125 + ], + [ + 482.90625, + 295.6649475097656 + ], + [ + 85.763671875, + 295.6649475097656 + ] + ], + "bbox": [ + 85.763671875, + 271.86328125, + 482.90625, + 295.6649475097656 + ], + "children": null, + "section_hierarchy": { + "1": "/page/122/SectionHeader/1", + "4": "/page/129/SectionHeader/1" + }, + "images": {} + }, + { + "id": "/page/129/Code/6", + "block_type": "Code", + "html": "been_called = False\ndef example2():\n been_called = True # WRONG", + "polygon": [ + [ + 85.53955078125, + 301.3797912597656 ], [ 285.1484680175781, - 325.768798828125 + 301.3797912597656 ], [ 285.1484680175781, 347.9253845214844 ], [ - 86.28662109375, + 85.53955078125, 347.9253845214844 ] ], + "bbox": [ + 85.53955078125, + 301.3797912597656, + 285.1484680175781, + 347.9253845214844 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/129/Text/6", + "id": "/page/129/Text/7", "block_type": "Text", "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.
", "polygon": [ [ - 85.166015625, - 349.787109375 + 85.763671875, + 352.107421875 ], [ - 482.4032897949219, - 349.787109375 + 483.50390625, + 352.107421875 ], [ - 482.4032897949219, + 483.50390625, 388.29095458984375 ], [ - 85.166015625, + 85.763671875, 388.29095458984375 ] ], + "bbox": [ + 85.763671875, + 352.107421875, + 483.50390625, + 388.29095458984375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/129/Text/7", + "id": "/page/129/Text/8", "block_type": "Text", "html": "To reassign a global variable inside a function you have to declare the global variable before you use it:
", "polygon": [ [ - 85.46484375, - 395.2265625 + 86.0625, + 396.38671875 ], [ - 482.90625, - 395.2265625 + 482.607421875, + 396.38671875 ], [ - 482.90625, + 482.607421875, 420.2839660644531 ], [ - 85.46484375, + 86.0625, 420.2839660644531 ] ], + "bbox": [ + 86.0625, + 396.38671875, + 482.607421875, + 420.2839660644531 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/129/Code/8", + "id": "/page/129/Code/9", "block_type": "Code", "html": "been_called = False\ndef example2():\n global been_called\n been_called = True", "polygon": [ [ - 84.8671875, + 86.40008544921875, 425.9988098144531 ], [ - 201.4726104736328, + 223.822265625, 425.9988098144531 ], [ - 201.4726104736328, + 223.822265625, 484.7384033203125 ], [ - 84.8671875, + 86.40008544921875, 484.7384033203125 ] ], + "bbox": [ + 86.40008544921875, + 425.9988098144531, + 223.822265625, + 484.7384033203125 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/129/Text/9", + "id": "/page/129/Text/10", "block_type": "Text", "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.\"
", "polygon": [ [ - 85.763671875, - 488.8125 + 86.40008544921875, + 489.5859375 ], [ - 482.3973693847656, - 488.8125 + 483.205078125, + 489.5859375 ], [ - 482.3973693847656, - 513.5625 + 483.205078125, + 512.9089660644531 ], [ - 85.763671875, - 513.5625 + 86.40008544921875, + 512.9089660644531 ] ], + "bbox": [ + 86.40008544921875, + 489.5859375, + 483.205078125, + 512.9089660644531 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/129/Text/10", + "id": "/page/129/Text/11", "block_type": "Text", "html": "Here's an example that tries to update a global variable:
", "polygon": [ [ - 86.0625, - 520.5234375 + 86.13720703125, + 521.68359375 ], [ - 333.193359375, - 520.5234375 + 332.296875, + 521.68359375 ], [ - 333.193359375, + 332.296875, 532.7079772949219 ], [ - 86.0625, + 86.13720703125, 532.7079772949219 ] ], + "bbox": [ + 86.13720703125, + 521.68359375, + 332.296875, + 532.7079772949219 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/129/Code/11", + "id": "/page/129/Code/12", "block_type": "Code", - "html": "count = 0\ndef example3():\n count = count + 1 # WRONG\nIf you run it you get:\nUnboundLocalError: local variable 'count' referenced before assignment\nPython assumes that count is local, which means that you are reading it before writing it.\nThe solution, again, is to declare count global.\ndef example3():", + "html": "
count = 0\ndef example3():\n count = count + 1 # WRONG", "polygon": [ [ - 84.94189453125, - 537.5390625 + 86.361328125, + 536.765625 ], [ - 482.40155029296875, - 537.5390625 + 290.162109375, + 536.765625 ], [ - 482.40155029296875, - 666.703125 + 290.162109375, + 587.8125 ], [ - 84.94189453125, - 666.703125 + 86.361328125, + 587.8125 + ] + ], + "bbox": [ + 86.361328125, + 536.765625, + 290.162109375, + 587.8125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/122/SectionHeader/1", + "4": "/page/129/SectionHeader/1" + }, + "images": {} + }, + { + "id": "/page/129/Code/13", + "block_type": "Code", + "html": "
If you run it you get:", + "polygon": [ + [ + 85.763671875, + 590.9823760986328 + ], + [ + 177.35862731933594, + 590.9823760986328 + ], + [ + 177.35862731933594, + 601.734375 + ], + [ + 85.763671875, + 601.734375 + ] + ], + "bbox": [ + 85.763671875, + 590.9823760986328, + 177.35862731933594, + 601.734375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/122/SectionHeader/1", + "4": "/page/129/SectionHeader/1" + }, + "images": {} + }, + { + "id": "/page/129/Text/14", + "block_type": "Text", + "html": "
UnboundLocalError: local variable 'count' referenced before assignment
", + "polygon": [ + [ + 85.166015625, + 606.375 + ], + [ + 452.4832458496094, + 606.375 + ], + [ + 452.4832458496094, + 617.203125 + ], + [ + 85.166015625, + 617.203125 ] ], + "bbox": [ + 85.166015625, + 606.375, + 452.4832458496094, + 617.203125 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/129/Text/12", + "id": "/page/129/Text/15", "block_type": "Text", - "html": "global count count += 1
", + "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.
", "polygon": [ [ - 107.279296875, - 662.7018127441406 + 85.0166015625, + 621.0703125 ], [ - 170.08045959472656, - 662.7018127441406 + 482.90625, + 621.0703125 ], [ - 170.08045959472656, - 684.8584060668945 + 482.90625, + 644.79296875 ], [ - 107.279296875, - 684.8584060668945 + 85.0166015625, + 644.79296875 ] ], + "bbox": [ + 85.0166015625, + 621.0703125, + 482.90625, + 644.79296875 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/129/Text/13", + "id": "/page/129/Code/16", + "block_type": "Code", + "html": "def example3():\n global count\n count += 1", + "polygon": [ + [ + 86.40007019042969, + 650.5078125 + ], + [ + 173.1708984375, + 650.5078125 + ], + [ + 173.1708984375, + 685.65234375 + ], + [ + 86.40007019042969, + 685.65234375 + ] + ], + "bbox": [ + 86.40007019042969, + 650.5078125, + 173.1708984375, + 685.65234375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/122/SectionHeader/1", + "4": "/page/129/SectionHeader/1" + }, + "images": {} + }, + { + "id": "/page/129/Text/17", "block_type": "Text", "html": "
If the global value is mutable, you can modify it without declaring it:
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", + "html": "", "polygon": [ [ - 128.0478515625, - 61.1015625 + 128.27197265625, + 60.8115234375 ], [ 525.6033935546875, - 61.1015625 + 60.8115234375 ], [ 525.6033935546875, 71.13372802734375 ], [ - 128.0478515625, + 128.27197265625, 71.13372802734375 ] ], + "bbox": [ + 128.27197265625, + 60.8115234375, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/130/PageHeader/17", + "id": "/page/130/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.697265625, - 60.2314453125 + 510.99609375, + 60.8115234375 ], [ - 525.638671875, - 60.2314453125 + 525.9375, + 60.8115234375 ], [ - 525.638671875, - 69.802734375 + 525.9375, + 70.189453125 ], [ - 510.697265625, - 69.802734375 + 510.99609375, + 70.189453125 ] ], + "bbox": [ + 510.99609375, + 60.8115234375, + 525.9375, + 70.189453125 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { - "id": "/page/130/TextInlineMath/1", - "block_type": "TextInlineMath", - "html": "known = {0:0, 1:1} def example4(): known[2] = 1
", + "id": "/page/130/Code/1", + "block_type": "Code", + "html": "known = {0:0, 1:1}\ndef example4():\n known[2] = 1", "polygon": [ [ - 128.0478515625, + 127.8984375, 88.68572998046875 ], [ - 223.9716796875, + 223.74656677246094, 88.68572998046875 ], [ - 223.9716796875, + 223.74656677246094, 135.2313232421875 ], [ - 128.0478515625, + 127.8984375, 135.2313232421875 ] ], + "bbox": [ + 127.8984375, + 88.68572998046875, + 223.74656677246094, + 135.2313232421875 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, @@ -63892,26 +114747,32 @@ "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:
", "polygon": [ [ - 129.2431640625, - 140.4755859375 + 128.794921875, + 140.958984375 ], [ - 526.53515625, - 140.4755859375 + 525.638671875, + 140.958984375 ], [ - 526.53515625, - 164.4521484375 + 525.638671875, + 163.93792724609375 ], [ - 129.2431640625, - 164.4521484375 + 128.794921875, + 163.93792724609375 ] ], + "bbox": [ + 128.794921875, + 140.958984375, + 525.638671875, + 163.93792724609375 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, @@ -63921,7 +114782,7 @@ "html": "def example5():\n global known\n known = dict()", "polygon": [ [ - 129.31787109375, + 129.60000610351562, 170.18975830078125 ], [ @@ -63930,46 +114791,58 @@ ], [ 223.74111938476562, - 206.89453125 + 204.540283203125 ], [ - 129.31787109375, - 206.89453125 + 129.60000610351562, + 204.540283203125 ] ], + "bbox": [ + 129.60000610351562, + 170.18975830078125, + 223.74111938476562, + 204.540283203125 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/129/SectionHeader/1" + "4": "/page/129/SectionHeader/1" }, "images": {} }, { "id": "/page/130/SectionHeader/4", "block_type": "SectionHeader", - "html": "
If you compute fibonacci(50), you get:
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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.
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In the first case the result has type int; in the second case it is long. 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.
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", "polygon": [ [ - 128.49609375, + 128.9443359375, 140.958984375 ], [ @@ -65241,188 +116407,230 @@ 151.32086181640625 ], [ - 128.49609375, + 128.9443359375, 151.32086181640625 ] ], + "bbox": [ + 128.9443359375, + 140.958984375, + 522.5784301757812, + 151.32086181640625 + ], "children": null, "section_hierarchy": { "1": "/page/122/SectionHeader/1", - "3": "/page/131/SectionHeader/7" + "4": "/page/131/SectionHeader/7" }, "images": {} }, { "id": "/page/132/SectionHeader/4", "block_type": "SectionHeader", - "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.
", + "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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", + "html": "Use a dictionary to write a faster, simpler version of has_duplicates. Solution: http: // thinkpython. com/ code/ has_ duplicates. py .
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", + "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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", + "html": "Write a program that reads a wordlist and finds all the rotate pairs. Solution: http: // thinkpython. com/ code/ rotate_ pairs. py .
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", + "html": "Exercise 11.11. Here's another Puzzler from Car Talk (http: // www. cartalk. com/ content/ puzzlers ):
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You can use the dictionary from Exercise 11.1 to check whether a string is in the word list.
", + "html": "You can use the dictionary from Exercise 11.1 to check whether a string is in the word list.
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", + "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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", + "html": "Write a program that lists all the words that solve the Puzzler. Solution: http: // thinkpython. com/ code/ homophone. py .
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", + "id": "/page/134/Code/7", + "block_type": "Code", + "html": ">>> t = ('a', 'b', 'c', 'd', 'e')", "polygon": [ [ - 129.46728515625, + 128.86962890625, 402.9609375 ], [ - 302.4140625, + 302.1624450683594, 402.9609375 ], [ - 302.4140625, - 413.015625 + 302.1624450683594, + 413.0083923339844 ], [ - 129.46728515625, - 413.015625 + 128.86962890625, + 413.0083923339844 ] ], + "bbox": [ + 128.86962890625, + 402.9609375, + 302.1624450683594, + 413.0083923339844 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/134/SectionHeader/2" + "4": "/page/134/SectionHeader/2" }, "images": {} }, @@ -65955,176 +117271,282 @@ "html": "
To create a tuple with a single element, you have to include a final comma:
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>>> t1 = 'a',\n>>> type(t1)\n<type 'tuple'>\nA value in parentheses is not a tuple:\n>>> t2 = ('a')", "polygon": [ [ 129.60006713867188, - 429.64453125 + 432.9598083496094 ], [ - 525.6012573242188, - 429.64453125 + 292.10003662109375, + 432.9598083496094 ], [ - 525.6012573242188, - 578.14453125 + 292.10003662109375, + 497.2243957519531 ], [ 129.60006713867188, - 578.14453125 + 497.2243957519531 ] ], + "bbox": [ + 129.60006713867188, + 432.9598083496094, + 292.10003662109375, + 497.2243957519531 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/134/SectionHeader/2" + "4": "/page/134/SectionHeader/2" }, "images": {} }, { - "id": "/page/134/Text/10", + "id": "/page/134/Code/10", + "block_type": "Code", + "html": "
>>> type(t2)\n<type 'str'>", + "polygon": [ + [ + 127.599609375, + 496.546875 + ], + [ + 193.640625, + 496.546875 + ], + [ + 193.640625, + 521.6123962402344 + ], + [ + 127.599609375, + 521.6123962402344 + ] + ], + "bbox": [ + 127.599609375, + 496.546875, + 193.640625, + 521.6123962402344 + ], + "children": null, + "section_hierarchy": { + "1": "/page/134/SectionHeader/1", + "4": "/page/134/SectionHeader/2" + }, + "images": {} + }, + { + "id": "/page/134/Text/11", "block_type": "Text", - "html": "
If the argument is a sequence (string, list or tuple), the result is a tuple with the elements of the sequence:
", + "html": "Another way to create a tuple is the built-in function tuple. With no argument, it creates an empty tuple:
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>>> t = tuple()\n>>> print t\n()", "polygon": [ [ - 129.60009765625, - 620.2548217773438 + 128.49609375, + 553.7578125 ], [ - 286.4745178222656, - 620.2548217773438 + 208.05557250976562, + 553.7578125 ], [ - 286.4745178222656, - 655.875 + 208.05557250976562, + 588.1094207763672 ], [ - 129.60009765625, - 655.875 + 128.49609375, + 588.1094207763672 ] ], + "bbox": [ + 128.49609375, + 553.7578125, + 208.05557250976562, + 588.1094207763672 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/134/SectionHeader/2" + "4": "/page/134/SectionHeader/2" }, "images": {} }, { - "id": "/page/134/Text/14", + "id": "/page/134/Text/13", "block_type": "Text", - "html": "
()
", + "html": "If the argument is a sequence (string, list or tuple), the result is a tuple with the elements of the sequence:
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Because tuple is the name of a built-in function, you should avoid using it as a variable name.
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", "polygon": [ @@ -66133,11 +117555,11 @@ 690.29296875 ], [ - 488.1342468261719, + 488.583984375, 690.29296875 ], [ - 488.1342468261719, + 488.583984375, 700.8349914550781 ], [ @@ -66145,24 +117567,30 @@ 700.8349914550781 ] ], + "bbox": [ + 128.197265625, + 690.29296875, + 488.583984375, + 700.8349914550781 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/134/SectionHeader/2" + "4": "/page/134/SectionHeader/2" }, "images": {} } ], "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/134/SectionHeader/2" + "4": "/page/134/SectionHeader/2" }, "images": null }, { - "id": "/page/135/Page/176", + "id": "/page/135/Page/181", "block_type": "Page", - "html": "114 Chapter 12. Tuples
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.47314453125 + 61.1015625 ], [ - 483.205078125, - 60.47314453125 + 482.4034118652344, + 61.1015625 ], [ - 483.205078125, + 482.4034118652344, 71.13372802734375 ], [ @@ -66204,39 +117638,51 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 61.1015625, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/134/SectionHeader/2" + "4": "/page/134/SectionHeader/2" }, "images": {} }, { "id": "/page/135/PageHeader/20", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.39013671875, - 59.4580078125 + 85.6142578125, + 60.76318359375 ], [ - 101.07861328125, - 59.4580078125 + 101.6015625, + 60.76318359375 ], [ - 101.07861328125, - 69.8994140625 + 101.6015625, + 70.23779296875 ], [ - 85.39013671875, - 69.8994140625 + 85.6142578125, + 70.23779296875 ] ], + "bbox": [ + 85.6142578125, + 60.76318359375, + 101.6015625, + 70.23779296875 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/134/SectionHeader/2" + "4": "/page/134/SectionHeader/2" }, "images": {} }, @@ -66246,26 +117692,32 @@ "html": ">>> t = ('a', 'b', 'c', 'd', 'e')\n>>> print t[0]\n'a'", "polygon": [ [ - 86.4000015258789, - 88.68572998046875 + 85.98779296875, + 86.3349609375 ], [ - 270.73828125, - 87.78515625 + 260.578125, + 86.3349609375 ], [ - 270.73828125, + 260.578125, 123.03729248046875 ], [ - 86.4000015258789, - 123.169921875 + 85.98779296875, + 123.03729248046875 ] ], + "bbox": [ + 85.98779296875, + 86.3349609375, + 260.578125, + 123.03729248046875 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/134/SectionHeader/2" + "4": "/page/134/SectionHeader/2" }, "images": {} }, @@ -66275,55 +117727,67 @@ "html": "
And the slice operator selects a range of elements.
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>>> print t[1:3] ('b', 'c')
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>>> t[0] = 'A' TypeError: object doesn't support item assignment
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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:
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This solution is cumbersome; tuple assignment is more elegant:
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>>> a, b = b, a
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>>> addr = 'monty@python.org'\n>>> uname, domain = addr.split('@')", "polygon": [ [ - 85.0166015625, - 590.90625 + 85.46484375, + 591.29296875 ], [ - 270.5888671875, - 590.90625 + 269.42047119140625, + 591.29296875 ], [ 269.42047119140625, 613.4893646240234 ], [ - 83.8212890625, + 85.46484375, 613.4893646240234 ] ], + "bbox": [ + 85.46484375, + 591.29296875, + 269.42047119140625, + 613.4893646240234 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/135/SectionHeader/8" + "4": "/page/135/SectionHeader/8" }, "images": {} }, @@ -66740,14 +118288,14 @@ "polygon": [ [ 85.46484375, - 620.296875 + 621.6297607421875 ], [ - 482.90625, - 620.296875 + 482.40069580078125, + 621.6297607421875 ], [ - 482.90625, + 482.40069580078125, 643.9359130859375 ], [ @@ -66755,10 +118303,16 @@ 643.9359130859375 ] ], + "bbox": [ + 85.46484375, + 621.6297607421875, + 482.40069580078125, + 643.9359130859375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/135/SectionHeader/8" + "4": "/page/135/SectionHeader/8" }, "images": {} }, @@ -66768,40 +118322,46 @@ "html": "
>>> print uname\nmonty\n>>> print domain\npython.org", "polygon": [ [ - 84.04541015625, - 651.62109375 + 85.53955078125, + 651.9267578125 ], [ - 171.228515625, - 651.62109375 + 173.3203125, + 651.9267578125 ], [ - 171.228515625, + 173.3203125, 698.80078125 ], [ - 84.04541015625, + 85.53955078125, 698.80078125 ] ], + "bbox": [ + 85.53955078125, + 651.9267578125, + 173.3203125, + 698.80078125 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/135/SectionHeader/8" + "4": "/page/135/SectionHeader/8" }, "images": {} } ], "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/135/SectionHeader/8" + "4": "/page/135/SectionHeader/8" }, "images": null }, { - "id": "/page/136/Page/200", + "id": "/page/136/Page/203", "block_type": "Page", - "html": "
12.3. Tuples as return values 115
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", "polygon": [ [ - 129.09375, - 111.568359375 + 128.3466796875, + 110.98828125 ], [ - 525.9375, - 111.568359375 + 526.53515625, + 110.98828125 ], [ - 525.9375, + 526.53515625, 158.57501220703125 ], [ - 129.09375, + 128.3466796875, 158.57501220703125 ] ], + "bbox": [ + 128.3466796875, + 110.98828125, + 526.53515625, + 158.57501220703125 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/1" + "4": "/page/136/SectionHeader/1" }, "images": {} }, @@ -66943,26 +118533,32 @@ "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:
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Here is an example of a function that returns a tuple:
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def min_max(t):\n return min(t), max(t)", "polygon": [ [ - 145.23046875, - 342.052734375 + 129.5999755859375, + 330.451171875 ], [ 260.3636169433594, - 342.052734375 + 330.451171875 ], [ 260.3636169433594, 353.2473449707031 ], [ - 145.23046875, + 129.5999755859375, 353.2473449707031 ] ], + "bbox": [ + 129.5999755859375, + 330.451171875, + 260.3636169433594, + 353.2473449707031 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/1" + "4": "/page/136/SectionHeader/1" }, "images": {} }, { - "id": "/page/136/Text/8", + "id": "/page/136/Text/7", "block_type": "Text", "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.
", "polygon": [ [ - 128.49609375, - 358.1015625 + 129.59996032714844, + 357.71484375 ], [ 525.595458984375, - 358.1015625 + 357.71484375 ], [ 525.595458984375, 381.1408996582031 ], [ - 128.49609375, + 129.59996032714844, 381.1408996582031 ] ], + "bbox": [ + 129.59996032714844, + 357.71484375, + 525.595458984375, + 381.1408996582031 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/1" + "4": "/page/136/SectionHeader/1" }, "images": {} }, { - "id": "/page/136/SectionHeader/9", + "id": "/page/136/SectionHeader/8", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 129.2431640625, - 435.6953125 + 128.794921875, + 435.05859375 ], [ - 525.9375, - 435.6953125 + 525.603271484375, + 435.05859375 ], [ - 525.9375, - 471.41015625 + 525.603271484375, + 470.0458984375 ], [ - 129.2431640625, - 471.41015625 + 128.794921875, + 470.0458984375 ] ], + "bbox": [ + 128.794921875, + 435.05859375, + 525.603271484375, + 470.0458984375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} }, { - "id": "/page/136/Text/11", - "block_type": "Text", - "html": "def printall(*args):
", + "id": "/page/136/Code/10", + "block_type": "Code", + "html": "def printall(*args):\n print args", "polygon": [ [ - 129.09375, + 128.27197265625, 475.4837341308594 ], [ - 234.2172088623047, + 234.28125, 475.4837341308594 ], [ - 234.2172088623047, - 486.4921875 + 234.28125, + 497.64031982421875 ], [ - 129.09375, - 486.4921875 + 128.27197265625, + 497.64031982421875 ] ], + "bbox": [ + 128.27197265625, + 475.4837341308594, + 234.28125, + 497.64031982421875 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} }, { - "id": "/page/136/Text/12", + "id": "/page/136/Text/11", "block_type": "Text", - "html": "
print args
", + "html": "The gather parameter can have any name you like, but args is conventional. Here's how the function works:
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", + "id": "/page/136/Code/12", + "block_type": "Code", + "html": ">>> printall(1, 2.0, '3')\n(1, 2.0, '3')", "polygon": [ [ - 128.49609375, - 503.22674560546875 + 127.7490234375, + 530.578125 ], [ - 526.53515625, - 503.22674560546875 + 260.329345703125, + 530.578125 ], [ - 526.53515625, - 525.5339050292969 + 260.329345703125, + 553.1283569335938 ], [ - 128.49609375, - 525.5339050292969 + 127.7490234375, + 553.1283569335938 ] ], + "bbox": [ + 127.7490234375, + 530.578125, + 260.329345703125, + 553.1283569335938 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} }, { - "id": "/page/136/Code/14", - "block_type": "Code", - "html": "
>>> printall(1, 2.0, '3')\n(1, 2.0, '3')", + "id": "/page/136/Text/13", + "block_type": "Text", + "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:
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>>> t = (7, 3)\n>>> divmod(t)\nTypeError: divmod expected 2 arguments, got 1", + "id": "/page/136/Text/15", + "block_type": "Text", + "html": "
TypeError: init() expected 2 arguments, got 3
\n", "polygon": [ [ - 128.794921875, - 598.6527557373047 + 128.27197265625, + 628.03125 ], [ - 365.466796875, - 598.6527557373047 + 343.353515625, + 628.03125 ], [ - 365.466796875, - 639.6328125 + 343.353515625, + 638.0859375 ], [ - 128.794921875, - 639.6328125 + 128.27197265625, + 638.0859375 ] ], + "bbox": [ + 128.27197265625, + 628.03125, + 343.353515625, + 638.0859375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} }, { - "id": "/page/136/Text/17", + "id": "/page/136/Text/16", "block_type": "Text", "html": "But if you scatter the tuple, it works:
", "polygon": [ [ - 129.5999755859375, + 128.12255859375, 638.7403106689453 ], [ @@ -67358,27 +118997,33 @@ ], [ 289.7188720703125, - 654.71484375 + 649.6875 ], [ - 129.5999755859375, - 654.71484375 + 128.12255859375, + 649.6875 ] ], + "bbox": [ + 128.12255859375, + 638.7403106689453, + 289.7188720703125, + 649.6875 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} }, { - "id": "/page/136/Text/18", - "block_type": "Text", - "html": ">>> divmod(*t) (2, 1)
", + "id": "/page/136/TextInlineMath/17", + "block_type": "TextInlineMath", + "html": ">>> divmod(*t)
", "polygon": [ [ - 128.57080078125, + 129.01904296875, 654.1397552490234 ], [ @@ -67387,60 +119032,107 @@ ], [ 202.82508850097656, - 676.2973480224609 + 664.76953125 ], [ - 128.57080078125, - 676.2973480224609 + 129.01904296875, + 664.76953125 ] ], + "bbox": [ + 129.01904296875, + 654.1397552490234, + 202.82508850097656, + 664.76953125 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/136/Text/18", + "block_type": "Text", + "html": "(2, 1)
", + "polygon": [ + [ + 129.5999755859375, + 666.3347473144531 + ], + [ + 160.98216247558594, + 666.3347473144531 + ], + [ + 160.98216247558594, + 676.7578125 + ], + [ + 129.5999755859375, + 676.7578125 + ] + ], + "bbox": [ + 129.5999755859375, + 666.3347473144531, + 160.98216247558594, + 676.7578125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/134/SectionHeader/1", + "4": "/page/136/SectionHeader/8" }, "images": {} }, { "id": "/page/136/Text/19", "block_type": "Text", - "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:
", + "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:
", "polygon": [ [ - 128.794921875, - 676.37109375 + 128.3466796875, + 678.3046875 ], [ 525.5968017578125, - 676.37109375 + 678.3046875 ], [ 525.5968017578125, - 700.6853485107422 + 700.734375 ], [ - 128.794921875, - 700.6853485107422 + 128.3466796875, + 700.734375 ] ], + "bbox": [ + 128.3466796875, + 678.3046875, + 525.5968017578125, + 700.734375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} } ], "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": null }, { - "id": "/page/137/Page/205", + "id": "/page/137/Page/210", "block_type": "Page", - "html": "116 Chapter 12. Tuples
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.27978515625 + 60.521484375 ], [ - 483.205078125, - 60.27978515625 + 482.4034118652344, + 60.521484375 ], [ - 483.205078125, + 482.4034118652344, 71.13372802734375 ], [ @@ -67482,39 +119180,51 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.521484375, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} }, { - "id": "/page/137/PageHeader/16", + "id": "/page/137/PageHeader/17", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.24072265625, + 85.763671875, 60.2314453125 ], [ - 100.77978515625, + 102.6474609375, 60.2314453125 ], [ - 100.77978515625, + 102.6474609375, 70.4794921875 ], [ - 85.24072265625, + 85.763671875, 70.4794921875 ] ], + "bbox": [ + 85.763671875, + 60.2314453125, + 102.6474609375, + 70.4794921875 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} }, @@ -67524,55 +119234,67 @@ "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.", "polygon": [ [ - 85.9130859375, - 88.68572998046875 + 86.0625, + 84.64306640625 ], [ 452.357177734375, - 88.68572998046875 + 84.64306640625 ], [ 452.357177734375, - 171.706298828125 + 172.08984375 ], [ - 85.9130859375, - 171.706298828125 + 86.0625, + 172.08984375 ] ], + "bbox": [ + 86.0625, + 84.64306640625, + 452.357177734375, + 172.08984375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/136/SectionHeader/9" + "4": "/page/136/SectionHeader/8" }, "images": {} }, { "id": "/page/137/SectionHeader/2", "block_type": "SectionHeader", - "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.
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", "polygon": [ [ - 85.68896484375, - 270.509765625 + 86.13720703125, + 270.896484375 ], [ - 246.53878784179688, - 270.509765625 + 247.130859375, + 270.896484375 ], [ - 246.53878784179688, + 247.130859375, 282.6878356933594 ], [ - 85.68896484375, + 86.13720703125, 282.6878356933594 ] ], + "bbox": [ + 86.13720703125, + 270.896484375, + 247.130859375, + 282.6878356933594 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/137/SectionHeader/2" + "2": "/page/137/SectionHeader/2" }, "images": {} }, { "id": "/page/137/Code/5", "block_type": "Code", - "html": ">>> s = 'abc'\n>>> t = [0, 1, 2]\n>>> zip(s, t)\n[('a', 0), ('b', 1), ('c', 2)]\nThe result is a list of tuples where each tuple contains a character from the string and the\ncorresponding element from the list.", + "html": "
>>> s = 'abc'\n>>> t = [0, 1, 2]\n>>> zip(s, t)\n[('a', 0), ('b', 1), ('c', 2)]", "polygon": [ [ - 85.83837890625, - 285.591796875 + 86.13720703125, + 288.7986755371094 + ], + [ + 243.27981567382812, + 288.7986755371094 + ], + [ + 243.27981567382812, + 336.638671875 + ], + [ + 86.13720703125, + 336.638671875 + ] + ], + "bbox": [ + 86.13720703125, + 288.7986755371094, + 243.27981567382812, + 336.638671875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/134/SectionHeader/1", + "2": "/page/137/SectionHeader/2" + }, + "images": {} + }, + { + "id": "/page/137/Text/6", + "block_type": "Text", + "html": "
The result is a list of tuples where each tuple contains a character from the string and the corresponding element from the list.
", + "polygon": [ + [ + 86.0625, + 340.119140625 ], [ 482.4034118652344, - 285.591796875 + 340.119140625 ], [ 482.4034118652344, 363.91082763671875 ], [ - 85.83837890625, + 86.0625, 363.91082763671875 ] ], + "bbox": [ + 86.0625, + 340.119140625, + 482.4034118652344, + 363.91082763671875 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/137/SectionHeader/2" + "2": "/page/137/SectionHeader/2" }, "images": {} }, { - "id": "/page/137/Text/6", + "id": "/page/137/Text/7", "block_type": "Text", "html": "If the sequences are not the same length, the result has the length of the shorter one.
", "polygon": [ [ 85.6142578125, - 367.576171875 + 372.0234375 ], [ 454.4782409667969, - 367.576171875 + 372.0234375 ], [ 454.4782409667969, @@ -67685,285 +119460,345 @@ 384.1058349609375 ] ], + "bbox": [ + 85.6142578125, + 372.0234375, + 454.4782409667969, + 384.1058349609375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/137/SectionHeader/2" + "2": "/page/137/SectionHeader/2" }, "images": {} }, { - "id": "/page/137/Code/7", - "block_type": "Code", - "html": ">>> zip('Anne', 'Elk')\n[('A', 'E'), ('n', 'l'), ('n', 'k')]", + "id": "/page/137/TextInlineMath/8", + "block_type": "TextInlineMath", + "html": "
>>> zip('Anne', 'Elk') [('A', 'E'), ('n', 'l'), ('n', 'k')]
", "polygon": [ [ - 85.46484375, - 388.845703125 + 85.68896484375, + 388.458984375 ], [ 274.6507568359375, - 388.845703125 + 388.458984375 ], [ 274.6507568359375, - 414.5625 + 412.374267578125 ], [ - 85.46484375, - 414.5625 + 85.68896484375, + 412.374267578125 ] ], + "bbox": [ + 85.68896484375, + 388.458984375, + 274.6507568359375, + 412.374267578125 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/137/SectionHeader/2" + "2": "/page/137/SectionHeader/2" }, "images": {} }, { - "id": "/page/137/Text/8", + "id": "/page/137/Text/9", "block_type": "Text", "html": "You can use tuple assignment in a for loop to traverse a list of tuples:
", "polygon": [ [ - 85.6142578125, - 417.26953125 + 85.68896484375, + 416.8828125 ], [ 391.7991943359375, - 417.26953125 + 416.8828125 ], [ 391.7991943359375, - 428.87109375 + 428.746826171875 ], [ - 85.6142578125, - 428.87109375 + 85.68896484375, + 428.746826171875 ] ], + "bbox": [ + 85.68896484375, + 416.8828125, + 391.7991943359375, + 428.746826171875 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/137/SectionHeader/2" + "2": "/page/137/SectionHeader/2" }, "images": {} }, { - "id": "/page/137/Code/9", + "id": "/page/137/Code/10", "block_type": "Code", "html": "t = [('a', 0), ('b', 1), ('c', 2)]\nfor letter, number in t:\n print number, letter", "polygon": [ [ - 85.24072265625, - 433.8984375 + 85.39013671875, + 434.857666015625 ], [ 264.19586181640625, - 433.8984375 + 434.857666015625 ], [ 264.19586181640625, - 469.2092590332031 + 469.86328125 ], [ - 85.24072265625, - 469.2092590332031 + 85.39013671875, + 469.86328125 ] ], + "bbox": [ + 85.39013671875, + 434.857666015625, + 264.19586181640625, + 469.86328125 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/137/SectionHeader/2" + "2": "/page/137/SectionHeader/2" }, "images": {} }, { - "id": "/page/137/Text/10", + "id": "/page/137/Text/11", "block_type": "Text", "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:
", "polygon": [ [ - 84.8671875, - 474.1171875 + 85.6142578125, + 474.890625 ], [ - 482.607421875, - 474.1171875 + 482.4034729003906, + 474.890625 ], [ - 482.607421875, + 482.4034729003906, 497.77581787109375 ], [ - 84.8671875, + 85.6142578125, 497.77581787109375 ] ], + "bbox": [ + 85.6142578125, + 474.890625, + 482.4034729003906, + 497.77581787109375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/137/SectionHeader/2" + "2": "/page/137/SectionHeader/2" }, "images": {} }, { - "id": "/page/137/Text/11", - "block_type": "Text", - "html": "0 a 1 b 2 c
", + "id": "/page/137/ListItem/207", + "block_type": "ListItem", + "html": "", + "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]:
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]:
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If you need to traverse the elements of a sequence and their indices, you can use the built-in function enumerate:
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12.6. Dictionaries and tuples 117
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", + "html": "The output of this loop is:
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", + "html": "0 a 1 b 2 c Again.
", "polygon": [ [ - 128.72021484375, - 145.6962890625 + 128.6455078125, + 104.80078125 ], [ - 161.068359375, - 144.1494140625 + 165.7001953125, + 104.80078125 ], [ - 161.068359375, + 165.7001953125, 156.02496337890625 ], [ - 128.72021484375, - 156.3310546875 + 128.6455078125, + 156.02496337890625 ] ], + "bbox": [ + 128.6455078125, + 104.80078125, + 165.7001953125, + 156.02496337890625 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/137/SectionHeader/2" + "2": "/page/137/SectionHeader/2" }, "images": {} }, { "id": "/page/138/SectionHeader/3", "block_type": "SectionHeader", - "html": ">>> d = {'a':0, 'b':1, 'c':2}\n>>> t = d.items()\n>>> print t\n[('a', 0), ('c', 2), ('b', 1)]", "polygon": [ [ - 129.6000213623047, - 239.572265625 + 129.16845703125, + 241.6368408203125 ], [ 286.4798583984375, - 239.572265625 + 241.6368408203125 ], [ 286.4798583984375, - 292.166015625 + 288.1824645996094 ], [ - 129.6000213623047, - 292.166015625 + 129.16845703125, + 288.1824645996094 ] ], + "bbox": [ + 129.16845703125, + 241.6368408203125, + 286.4798583984375, + 288.1824645996094 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, @@ -68193,25 +120079,32 @@ "polygon": [ [ 129.60003662109375, - 294.7884521484375 + 294.099609375 ], [ - 526.53515625, - 294.7884521484375 + 525.6033935546875, + 294.099609375 ], [ - 526.53515625, - 317.302734375 + 525.6033935546875, + 316.9450378417969 ], [ 129.60003662109375, - 317.302734375 + 316.9450378417969 ] ], + "bbox": [ + 129.60003662109375, + 294.099609375, + 525.6033935546875, + 316.9450378417969 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, @@ -68221,273 +120114,444 @@ "html": "
Going in the other direction, you can use a list of tuples to initialize a new dictionary:
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>>> t = [('a', 0), ('c', 2), ('b', 1)]\n>>> d = dict(t)\n>>> print d\n{'a': 0, 'c': 2, 'b': 1}", "polygon": [ [ - 129.60003662109375, - 342.052734375 + 129.5419921875, + 343.40625 ], [ - 433.1710205078125, - 342.052734375 + 328.3128967285156, + 343.40625 + ], + [ + 328.3128967285156, + 393.6796875 + ], + [ + 129.5419921875, + 393.6796875 + ] + ], + "bbox": [ + 129.5419921875, + 343.40625, + 328.3128967285156, + 393.6796875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/134/SectionHeader/1", + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/138/Text/9", + "block_type": "Text", + "html": "
Combining dict with zip yields a concise way to create a dictionary:
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The dictionary method update also takes a list of tuples and adds them, as key-value pairs, to an existing dictionary.
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The output of this loop is:
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", + "id": "/page/138/Code/15", + "block_type": "Code", + "html": "0 a\n2 c\n1 b", "polygon": [ [ - 127.7490234375, - 559.1953125 + 127.8984375, + 560.1039276123047 ], [ - 159.0595245361328, - 559.1953125 + 149.2646484375, + 560.1039276123047 ], [ - 159.0595245361328, - 611.0230865478516 + 149.2646484375, + 596.70703125 ], [ - 127.7490234375, - 611.0230865478516 + 127.8984375, + 596.70703125 ] ], + "bbox": [ + 127.8984375, + 560.1039276123047, + 149.2646484375, + 596.70703125 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, { - "id": "/page/138/Text/14", + "id": "/page/138/Text/16", "block_type": "Text", - "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:
", + "html": "Again.
", "polygon": [ [ 128.6455078125, - 619.91015625 + 601.0604858398438 ], [ - 525.9375, - 619.91015625 + 160.0224609375, + 601.0604858398438 ], [ - 525.9375, - 656.26171875 + 160.0224609375, + 611.40234375 ], [ 128.6455078125, - 656.26171875 + 611.40234375 ] ], + "bbox": [ + 128.6455078125, + 601.0604858398438, + 160.0224609375, + 611.40234375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, { - "id": "/page/138/Text/15", + "id": "/page/138/Text/17", "block_type": "Text", - "html": "directory[last,first] = number
", + "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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The expression in brackets is a tuple. We could use tuple assignment to traverse this dictionary.
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.521484375 + 60.47314453125 ], [ - 484.400390625, - 60.521484375 + 482.4034118652344, + 60.47314453125 ], [ - 484.400390625, + 482.4034118652344, 71.13372802734375 ], [ @@ -68557,265 +120635,829 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.47314453125, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, { - "id": "/page/139/PageHeader/18", + "id": "/page/139/PageHeader/17", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.46484375, - 60.37646484375 + 84.94189453125, + 60.47314453125 ], [ - 101.6015625, - 60.37646484375 + 101.67626953125, + 60.47314453125 ], [ - 101.6015625, - 70.14111328125 + 101.67626953125, + 70.52783203125 ], [ - 85.46484375, - 70.14111328125 + 84.94189453125, + 70.52783203125 ] ], + "bbox": [ + 84.94189453125, + 60.47314453125, + 101.67626953125, + 70.52783203125 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, { - "id": "/page/139/Code/1", - "block_type": "Code", - "html": "0\n 1\n 'Cleese'\n 'John'\ntuple", + "id": "/page/139/Text/1", + "block_type": "Text", + "html": "
tuple
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---|---|
('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' |
Figure 12.2: State diagram.
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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' |
Figure 12.2: State diagram.
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", + "id": "/page/139/Code/6", + "block_type": "Code", + "html": "for last, first in directory:\n print first, last, directory[last,first]", "polygon": [ [ - 85.46484375, + 85.763671875, 316.49273681640625 ], [ - 240.556640625, + 321.240234375, 316.49273681640625 ], [ - 240.556640625, - 326.970703125 - ], - [ - 85.46484375, - 326.970703125 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3", - "4": "/page/139/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/139/TextInlineMath/7", - "block_type": "TextInlineMath", - "html": "
print first, last, directory[last,first]
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", "polygon": [ [ 85.46484375, - 343.79296875 + 344.1796875 ], [ - 482.90625, - 343.79296875 + 482.4014892578125, + 344.1796875 ], [ - 482.90625, + 482.4014892578125, 367.201904296875 ], [ @@ -68823,89 +121465,107 @@ 367.201904296875 ] ], + "bbox": [ + 85.46484375, + 344.1796875, + 482.4014892578125, + 367.201904296875 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3", - "4": "/page/139/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, { - "id": "/page/139/Text/9", + "id": "/page/139/Text/8", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 85.6142578125, - 375.697265625 + 85.763671875, + 376.6640625 ], [ - 484.1015625, - 375.697265625 + 482.607421875, + 376.6640625 ], [ - 484.1015625, + 482.607421875, 411.7698974609375 ], [ - 85.6142578125, + 85.763671875, 411.7698974609375 ] ], + "bbox": [ + 85.763671875, + 376.6640625, + 482.607421875, + 411.7698974609375 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3", - "4": "/page/139/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, { - "id": "/page/139/Text/10", + "id": "/page/139/Text/9", "block_type": "Text", - "html": "But 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.
", + "html": "But 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.
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", "polygon": [ [ - 85.0166015625, - 473.34375 + 85.46484375, + 472.95703125 ], [ - 483.50390625, - 473.34375 + 483.205078125, + 472.95703125 ], [ - 483.50390625, + 483.205078125, 496.6968994140625 ], [ - 85.0166015625, + 85.46484375, 496.6968994140625 ] ], + "bbox": [ + 85.46484375, + 472.95703125, + 483.205078125, + 496.6968994140625 + ], "children": null, "section_hierarchy": { "1": "/page/134/SectionHeader/1", - "3": "/page/138/SectionHeader/3", - "4": "/page/139/SectionHeader/3" + "2": "/page/137/SectionHeader/2", + "4": "/page/138/SectionHeader/3" }, "images": {} }, { - "id": "/page/139/SectionHeader/13", + "id": "/page/139/SectionHeader/12", "block_type": "SectionHeader", - "html": "The 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).
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The 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.
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", + "html": "This feature lends itself to a pattern called DSU for
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", + "id": "/page/140/ListItem/1", + "block_type": "ListItem", + "html": "Sort the list of tuples, and
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The first loop builds a list of tuples, where each tuple is a word preceded by its length.
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", + "html": "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/ unstable_ sort. py .
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", + "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
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>>> 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", "polygon": [ [ - 85.0166015625, - 387.4921875 + 86.40007019042969, + 393.6796875 ], [ - 485.89453125, - 387.4921875 + 482.40252685546875, + 393.6796875 ], [ - 485.89453125, - 700.8348159790039 + 482.40252685546875, + 693.0 ], [ - 85.0166015625, - 700.8348159790039 + 86.40007019042969, + 693.0 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/134/SectionHeader/1", - "3": "/page/141/SectionHeader/4" - }, - "images": {} - } - ], - "section_hierarchy": { - "1": "/page/134/SectionHeader/1", - "3": "/page/141/SectionHeader/4" - }, - "images": null - }, - { - "id": "/page/142/Page/179", - "block_type": "Page", - "html": "
12.10. Glossary 121
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", + "html": "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: // en. wikipedia. org/ wiki/ Letter_ frequencies . Solution: http: // thinkpython. com/ code/ most_ frequent. py .
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['deltas', 'desalt', 'lasted', 'salted', 'slated', 'staled'] ['retainers', 'ternaries'] ['generating', 'greatening'] ['resmelts', 'smelters', 'termless']
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", + "html": "Solution: http: // thinkpython. com/ code/ anagram_ sets. py .
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", + "html": "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 . Credit: This exercise is inspired by an example at http: // puzzlers. org .
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", + "html": "Exercise 12.6. Here's another Car Talk Puzzler (http: // www. cartalk. com/ content/ puzzlers ):
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Write a program to find all words that can be reduced in this way, and then find the longest one.
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", + "html": "Solution: http: // thinkpython. com/ code/ reducible. py .
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>>> import string >>> print string.punctuation !\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~
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", + "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.
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", + "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?
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", "polygon": [ [ - 85.6142578125, - 280.37109375 + 86.0625, + 281.337890625 ], [ - 483.50390625, - 280.37109375 + 482.607421875, + 281.337890625 ], [ - 483.50390625, + 482.607421875, 316.3548889160156 ], [ - 85.6142578125, + 86.0625, 316.3548889160156 ] ], + "bbox": [ + 86.0625, + 281.337890625, + 482.607421875, + 316.3548889160156 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, { - "id": "/page/145/Text/6", - "block_type": "Text", - "html": "import random
", + "id": "/page/145/Code/6", + "block_type": "Code", + "html": "import random", "polygon": [ [ - 85.763671875, + 85.98779296875, 324.45703125 ], [ - 155.390625, + 156.884765625, 324.45703125 ], [ - 155.390625, + 156.884765625, 334.9603271484375 ], [ - 85.763671875, + 85.98779296875, 334.9603271484375 ] ], + "bbox": [ + 85.98779296875, + 324.45703125, + 156.884765625, + 334.9603271484375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, @@ -71721,26 +125165,33 @@ "html": "
for i in range(10):\n x = random.random()\n print x", "polygon": [ [ - 85.3154296875, - 347.66015625 + 86.0625, + 349.3867492675781 ], [ - 209.0302734375, - 347.66015625 + 206.70286560058594, + 349.3867492675781 ], [ - 209.0302734375, - 384.3984375 + 206.70286560058594, + 383.7373352050781 ], [ - 85.3154296875, - 384.3984375 + 86.0625, + 383.7373352050781 ] ], + "bbox": [ + 86.0625, + 349.3867492675781, + 206.70286560058594, + 383.7373352050781 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, @@ -71750,7 +125201,7 @@ "html": "
The function randint takes parameters low and high and returns an integer between low and high (including both).
", "polygon": [ [ - 85.46484375, + 85.9130859375, 391.166015625 ], [ @@ -71762,14 +125213,21 @@ 414.8359069824219 ], [ - 85.46484375, + 85.9130859375, 414.8359069824219 ] ], + "bbox": [ + 85.9130859375, + 391.166015625, + 482.4040832519531, + 414.8359069824219 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, @@ -71779,26 +125237,33 @@ "html": ">>> random.randint(5, 10)\n5\n>>> random.randint(5, 10)\n9", "polygon": [ [ - 84.34423828125, - 421.13671875 + 85.6142578125, + 421.91015625 ], [ - 217.16903686523438, - 421.13671875 + 218.2939453125, + 421.91015625 ], [ - 217.16903686523438, + 218.2939453125, 470.02435302734375 ], [ - 84.34423828125, + 85.6142578125, 470.02435302734375 ] ], + "bbox": [ + 85.6142578125, + 421.91015625, + 218.2939453125, + 470.02435302734375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, @@ -71808,26 +125273,33 @@ "html": "
To choose an element from a sequence at random, you can use choice:
", "polygon": [ [ - 86.39997863769531, - 477.984375 + 86.361328125, + 477.59765625 ], [ 396.30767822265625, - 477.984375 + 477.59765625 ], [ 396.30767822265625, - 489.5859375 + 488.9289245605469 ], [ - 85.763671875, - 489.5859375 + 86.361328125, + 488.9289245605469 ] ], + "bbox": [ + 86.361328125, + 477.59765625, + 396.30767822265625, + 488.9289245605469 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, @@ -71837,26 +125309,33 @@ "html": ">>> t = [1, 2, 3]\n>>> random.choice(t)\n2\n>>> random.choice(t)\n3", "polygon": [ [ - 83.89599609375, - 495.7734375 + 85.763671875, + 497.57177734375 ], [ - 193.04296875, - 495.7734375 + 192.8935546875, + 497.57177734375 ], [ - 193.04296875, + 192.8935546875, 556.3113861083984 ], [ - 83.89599609375, + 85.763671875, 556.3113861083984 ] ], + "bbox": [ + 85.763671875, + 497.57177734375, + 192.8935546875, + 556.3113861083984 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, @@ -71866,55 +125345,69 @@ "html": "
The random module also provides functions to generate random values from continuous distributions including Gaussian, exponential, gamma, and a few more.
", "polygon": [ [ - 85.763671875, - 562.67578125 + 86.2119140625, + 563.8359375 ], [ - 482.607421875, - 562.67578125 + 482.399169921875, + 563.8359375 ], [ - 482.607421875, - 588.97265625 + 482.399169921875, + 587.4099426269531 ], [ - 85.763671875, - 588.97265625 + 86.2119140625, + 587.4099426269531 ] ], + "bbox": [ + 86.2119140625, + 563.8359375, + 482.399169921875, + 587.4099426269531 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, { "id": "/page/145/Text/13", "block_type": "Text", - "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:
", + "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:
", "polygon": [ [ - 85.9130859375, - 589.359375 + 85.763671875, + 588.19921875 ], [ - 482.90625, - 589.359375 + 483.205078125, + 588.19921875 ], [ - 482.90625, - 624.1640625 + 483.205078125, + 623.8202514648438 ], [ - 85.9130859375, - 624.1640625 + 85.763671875, + 623.8202514648438 ] ], + "bbox": [ + 85.763671875, + 588.19921875, + 483.205078125, + 623.8202514648438 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, @@ -71924,69 +125417,84 @@ "html": ">>> t = ['a', 'a', 'b']\n>>> hist = histogram(t)\n>>> print hist\n{'a': 2, 'b': 1}", "polygon": [ [ - 84.64306640625, - 630.73828125 + 85.763671875, + 631.8984375 ], [ - 207.087890625, - 630.73828125 + 206.70835876464844, + 631.8984375 ], [ - 207.087890625, + 206.70835876464844, 679.181396484375 ], [ - 84.64306640625, + 85.763671875, 679.181396484375 ] ], + "bbox": [ + 85.763671875, + 631.8984375, + 206.70835876464844, + 679.181396484375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, { "id": "/page/145/Text/15", "block_type": "Text", - "html": "
your function should return 'a' with probability 2/3 and 'b' with probability 1/3.
", + "html": "your function should return 'a' with probability 2/3 and 'b' with probability 1/3.
", "polygon": [ [ - 85.6142578125, - 687.19921875 + 86.2119140625, + 686.8125 ], [ 426.0146789550781, - 687.19921875 + 686.8125 ], [ 426.0146789550781, 698.0859603881836 ], [ - 85.6142578125, + 86.2119140625, 698.0859603881836 ] ], + "bbox": [ + 86.2119140625, + 686.8125, + 426.0146789550781, + 698.0859603881836 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} } ], "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": null }, { "id": "/page/146/Page/185", "block_type": "Page", - "html": "13.3. Word histogram 125
", + "html": "", "polygon": [ [ - 128.72021484375, - 61.14990234375 + 128.6455078125, + 61.05322265625 ], [ 525.6033935546875, - 61.14990234375 + 61.05322265625 ], [ 525.6033935546875, 71.13372802734375 ], [ - 128.72021484375, + 128.6455078125, 71.13372802734375 ] ], + "bbox": [ + 128.6455078125, + 61.05322265625, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, { - "id": "/page/146/PageHeader/18", + "id": "/page/146/PageHeader/17", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.3984375, + 509.80078125, 60.8115234375 ], [ @@ -72050,75 +125571,96 @@ ], [ 525.9375, - 70.189453125 + 69.8994140625 ], [ - 510.3984375, - 70.189453125 + 509.80078125, + 69.8994140625 ] ], + "bbox": [ + 509.80078125, + 60.8115234375, + 525.9375, + 69.8994140625 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/145/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/145/SectionHeader/1" }, "images": {} }, { "id": "/page/146/SectionHeader/1", "block_type": "SectionHeader", - "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.
", + "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.
", "polygon": [ [ - 128.49609375, - 112.5343017578125 + 128.6455078125, + 112.4384765625 ], [ 525.6058349609375, - 112.5343017578125 + 112.4384765625 ], [ 525.6058349609375, 146.88494873046875 ], [ - 128.49609375, + 128.6455078125, 146.88494873046875 ] ], + "bbox": [ + 128.6455078125, + 112.4384765625, + 525.6058349609375, + 146.88494873046875 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, @@ -72128,84 +125670,105 @@ "html": "Here is a program that reads a file and builds a histogram of the words in the file:
", "polygon": [ [ - 128.9443359375, - 156.7177734375 + 128.794921875, + 156.427734375 ], [ - 487.27728271484375, - 156.7177734375 + 487.986328125, + 156.427734375 ], [ - 487.27728271484375, - 171.9931640625 + 487.986328125, + 166.91094970703125 ], [ - 128.9443359375, - 171.9931640625 + 128.794921875, + 166.91094970703125 ] ], + "bbox": [ + 128.794921875, + 156.427734375, + 487.986328125, + 166.91094970703125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { - "id": "/page/146/Text/4", - "block_type": "Text", - "html": "import string
", + "id": "/page/146/Code/4", + "block_type": "Code", + "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", "polygon": [ [ - 128.0478515625, + 128.3466796875, 172.853759765625 ], [ - 197.5947723388672, + 473.94140625, 172.853759765625 ], [ - 197.5947723388672, - 184.3681640625 + 473.94140625, + 382.8515625 ], [ - 128.0478515625, - 184.3681640625 + 128.3466796875, + 382.8515625 ] ], + "bbox": [ + 128.3466796875, + 172.853759765625, + 473.94140625, + 382.8515625 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { - "id": "/page/146/Code/5", - "block_type": "Code", - "html": "
def 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\nhist = process_file('emma.txt')", + "id": "/page/146/Text/5", + "block_type": "Text", + "html": "
hist = process_file('emma.txt')
", "polygon": [ [ - 129.5419921875, - 197.24176025390625 + 129.46728515625, + 392.35076904296875 ], [ - 469.5837707519531, - 197.24176025390625 + 291.70343017578125, + 392.35076904296875 ], [ - 469.5837707519531, - 406.0546875 + 291.70343017578125, + 402.3133544921875 ], [ - 129.5419921875, - 406.0546875 + 129.46728515625, + 402.3133544921875 ] ], + "bbox": [ + 129.46728515625, + 392.35076904296875, + 291.70343017578125, + 402.3133544921875 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, @@ -72215,26 +125778,33 @@ "html": "This program reads emma.txt, which contains the text of Emma by Jane Austen.
", "polygon": [ [ - 127.8984375, - 407.6015625 + 129.2431640625, + 408.375 ], [ 478.33929443359375, - 407.6015625 + 408.375 ], [ 478.33929443359375, 418.5179138183594 ], [ - 127.8984375, + 129.2431640625, 418.5179138183594 ] ], + "bbox": [ + 129.2431640625, + 408.375, + 478.33929443359375, + 418.5179138183594 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, @@ -72244,26 +125814,33 @@ "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.
", "polygon": [ [ - 128.3466796875, - 428.09765625 + 128.0478515625, + 428.4307556152344 ], [ - 525.9375, - 428.09765625 + 525.6028442382812, + 428.4307556152344 ], [ - 525.9375, + 525.6028442382812, 450.7379150390625 ], [ - 128.3466796875, + 128.0478515625, 450.7379150390625 ] ], + "bbox": [ + 128.0478515625, + 428.4307556152344, + 525.6028442382812, + 450.7379150390625 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, @@ -72273,26 +125850,33 @@ "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.)
", "polygon": [ [ - 128.6455078125, + 129.09375, 460.6517639160156 ], [ - 526.53515625, + 525.6058349609375, 460.6517639160156 ], [ - 526.53515625, + 525.6058349609375, 519.5409240722656 ], [ - 128.6455078125, + 129.09375, 519.5409240722656 ] ], + "bbox": [ + 129.09375, + 460.6517639160156, + 525.6058349609375, + 519.5409240722656 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, @@ -72302,26 +125886,33 @@ "html": "Finally, process_line updates the histogram by creating a new item or incrementing an existing one.
", "polygon": [ [ - 129.392578125, + 128.197265625, 529.4547729492188 ], [ - 526.833984375, + 525.599609375, 529.4547729492188 ], [ - 526.833984375, - 552.234375 + 525.599609375, + 551.7609252929688 ], [ - 129.392578125, - 552.234375 + 128.197265625, + 551.7609252929688 ] ], + "bbox": [ + 128.197265625, + 529.4547729492188, + 525.599609375, + 551.7609252929688 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, @@ -72331,501 +125922,764 @@ "html": "To count the total number of words in the file, we can add up the frequencies in the histogram:
", "polygon": [ [ - 128.9443359375, - 561.515625 + 128.197265625, + 561.8243255615234 ], [ 525.6035766601562, - 561.515625 + 561.8243255615234 ], [ 525.6035766601562, - 583.9809265136719 + 584.33203125 ], [ - 128.9443359375, - 583.9809265136719 + 128.197265625, + 584.33203125 ] ], + "bbox": [ + 128.197265625, + 561.8243255615234, + 525.6035766601562, + 584.33203125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { "id": "/page/146/Code/11", "block_type": "Code", - "html": "def total_words(hist):", + "html": "
def total_words(hist):\n return sum(hist.values())", "polygon": [ [ - 128.86962890625, - 589.9227752685547 + 128.6455078125, + 589.359375 ], [ - 244.67807006835938, - 589.9227752685547 + 281.2851867675781, + 589.359375 ], [ - 244.67807006835938, - 600.1875 + 281.2851867675781, + 612.0803833007812 ], [ - 128.86962890625, - 600.1875 + 128.6455078125, + 612.0803833007812 ] ], + "bbox": [ + 128.6455078125, + 589.359375, + 281.2851867675781, + 612.0803833007812 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { "id": "/page/146/Text/12", "block_type": "Text", - "html": "
return sum(hist.values())
", + "html": "The number of different words is just the number of items in the dictionary:
", "polygon": [ [ - 150.4599609375, - 602.1177825927734 + 128.794921875, + 616.81640625 ], [ - 281.2851867675781, - 602.1177825927734 + 463.78125, + 616.81640625 ], [ - 281.2851867675781, - 613.3359375 + 463.78125, + 628.283935546875 ], [ - 150.4599609375, - 613.3359375 + 128.794921875, + 628.283935546875 ] ], + "bbox": [ + 128.794921875, + 616.81640625, + 463.78125, + 628.283935546875 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { "id": "/page/146/Text/13", "block_type": "Text", - "html": "The number of different words is just the number of items in the dictionary:
", + "html": "def different_words(hist):
", "polygon": [ [ - 128.6455078125, - 618.3213348388672 + 129.01904296875, + 633.83203125 ], [ - 462.36083984375, - 618.3213348388672 + 265.5995178222656, + 633.83203125 ], [ - 462.36083984375, - 628.283935546875 + 265.5995178222656, + 646.20703125 ], [ - 128.6455078125, - 628.283935546875 + 129.01904296875, + 646.20703125 ] ], + "bbox": [ + 129.01904296875, + 633.83203125, + 265.5995178222656, + 646.20703125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { "id": "/page/146/Text/14", "block_type": "Text", - "html": "def different_words(hist):
", + "html": "return len(hist)
", "polygon": [ [ - 129.392578125, - 633.83203125 + 145.3798828125, + 646.4197845458984 ], [ - 265.5995178222656, - 633.83203125 + 234.21189880371094, + 646.4197845458984 ], [ - 265.5995178222656, - 644.1883850097656 + 234.21189880371094, + 658.96875 ], [ - 129.392578125, - 644.1883850097656 + 145.3798828125, + 658.96875 ] ], + "bbox": [ + 145.3798828125, + 646.4197845458984, + 234.21189880371094, + 658.96875 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { "id": "/page/146/Text/15", "block_type": "Text", - "html": "return len(hist)
", + "html": "Here is some code to print the results:
", + "polygon": [ + [ + 128.197265625, + 662.6243438720703 + ], + [ + 295.696533203125, + 662.6243438720703 + ], + [ + 295.696533203125, + 672.890625 + ], + [ + 128.197265625, + 672.890625 + ] + ], + "bbox": [ + 128.197265625, + 662.6243438720703, + 295.696533203125, + 672.890625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/144/SectionHeader/1", + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" + }, + "images": {} + }, + { + "id": "/page/146/Code/16", + "block_type": "Code", + "html": "print 'Total number of words:', total_words(hist)\nprint 'Number of different words:', different_words(hist)", + "polygon": [ + [ + 128.3466796875, + 678.5287780761719 + ], + [ + 427.6984558105469, + 678.5287780761719 + ], + [ + 427.6984558105469, + 700.6853790283203 + ], + [ + 128.3466796875, + 700.6853790283203 + ] + ], + "bbox": [ + 128.3466796875, + 678.5287780761719, + 427.6984558105469, + 700.6853790283203 + ], + "children": null, + "section_hierarchy": { + "1": "/page/144/SectionHeader/1", + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" + }, + "images": {} + } + ], + "section_hierarchy": { + "1": "/page/144/SectionHeader/1", + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" + }, + "images": null + }, + { + "id": "/page/147/Page/162", + "block_type": "Page", + "html": "
And the results:
", "polygon": [ [ - 147.919921875, - 646.4197845458984 + 86.0625, + 88.41357421875 ], [ - 234.21189880371094, - 646.4197845458984 + 156.45700073242188, + 88.41357421875 ], [ - 234.21189880371094, - 657.421875 + 156.45700073242188, + 98.79791259765625 ], [ - 147.919921875, - 657.421875 + 86.0625, + 98.79791259765625 ] ], + "bbox": [ + 86.0625, + 88.41357421875, + 156.45700073242188, + 98.79791259765625 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { - "id": "/page/146/Text/16", + "id": "/page/147/Text/2", "block_type": "Text", - "html": "Here is some code to print the results:
", + "html": "Total number of words: 161080 Number of different words: 7214
", "polygon": [ [ - 129.60009765625, - 662.6243438720703 + 85.6142578125, + 104.2237548828125 ], [ - 295.83984375, - 662.6243438720703 + 249.6708984375, + 104.2237548828125 ], [ - 295.83984375, - 672.5869445800781 + 249.6708984375, + 126.38037109375 ], [ - 129.60009765625, - 672.5869445800781 + 85.6142578125, + 126.38037109375 ] ], + "bbox": [ + 85.6142578125, + 104.2237548828125, + 249.6708984375, + 126.38037109375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/146/SectionHeader/1" }, "images": {} }, { - "id": "/page/146/Text/17", - "block_type": "Text", - "html": "print 'Total number of words:', total_words(hist) print 'Number of different words:', different_words(hist)
", + "id": "/page/147/SectionHeader/3", + "block_type": "SectionHeader", + "html": "126 Chapter 13. Case study: data structure selection
", + "id": "/page/147/Text/4", + "block_type": "Text", + "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:
", "polygon": [ [ - 86.4000015258789, - 60.47314453125 + 85.3154296875, + 180.2109375 ], [ - 482.40338134765625, - 60.47314453125 + 482.40350341796875, + 180.2109375 ], [ - 482.40338134765625, - 71.13372802734375 + 482.40350341796875, + 203.4140625 ], [ - 86.4000015258789, - 71.13372802734375 + 85.3154296875, + 203.4140625 ] ], + "bbox": [ + 85.3154296875, + 180.2109375, + 482.40350341796875, + 203.4140625 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/3" }, "images": {} }, { - "id": "/page/147/PageHeader/10", - "block_type": "PageHeader", - "html": "", + "id": "/page/147/Code/5", + "block_type": "Code", + "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", "polygon": [ [ - 85.46484375, - 59.79638671875 + 86.39999389648438, + 208.6387939453125 ], [ - 100.5556640625, - 59.79638671875 + 269.4672546386719, + 208.6387939453125 ], [ - 100.5556640625, - 69.75439453125 + 269.4672546386719, + 291.7673034667969 ], [ - 85.46484375, - 69.75439453125 + 86.39999389648438, + 291.7673034667969 ] ], + "bbox": [ + 86.39999389648438, + 208.6387939453125, + 269.4672546386719, + 291.7673034667969 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/3" }, "images": {} }, { - "id": "/page/147/Text/1", + "id": "/page/147/Text/6", "block_type": "Text", - "html": "
And the results: Total number of words: 161080 Number of different words: 7214
", + "html": "Here is a loop that prints the ten most common words:
", "polygon": [ [ - 86.4000015258789, - 88.83526611328125 + 85.98779296875, + 296.2265625 ], [ - 248.55125427246094, - 88.83526611328125 + 326.4787292480469, + 296.2265625 ], [ - 248.55125427246094, - 126.38037109375 + 326.4787292480469, + 307.4548645019531 ], [ - 86.4000015258789, - 126.38037109375 + 85.98779296875, + 307.4548645019531 ] ], + "bbox": [ + 85.98779296875, + 296.2265625, + 326.4787292480469, + 307.4548645019531 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/146/SectionHeader/1" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/3" }, "images": {} }, { - "id": "/page/147/SectionHeader/2", - "block_type": "SectionHeader", - "html": "t = most_common(hist)\nprint 'The most common words are:'\nfor freq, word in t[0:10]:\n print word, '\\t', freq", "polygon": [ [ - 86.2119140625, - 154.0107421875 + 84.7177734375, + 312.8807067871094 ], [ - 263.7333679199219, - 152.4638671875 + 264.1913757324219, + 312.8807067871094 ], [ - 263.7333679199219, - 169.3389892578125 + 264.1913757324219, + 359.4263000488281 ], [ - 86.2119140625, - 169.3389892578125 + 84.7177734375, + 359.4263000488281 ] ], + "bbox": [ + 84.7177734375, + 312.8807067871094, + 264.1913757324219, + 359.4263000488281 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/2" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/3" }, "images": {} }, { - "id": "/page/147/Text/3", + "id": "/page/147/Text/8", "block_type": "Text", - "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:
", + "html": "And here are the results from Emma:
", "polygon": [ [ - 85.6142578125, - 180.5009765625 + 86.39999389648438, + 363.708984375 ], [ - 482.90625, - 180.5009765625 + 248.625, + 363.708984375 ], [ - 482.90625, - 203.607421875 + 248.625, + 375.1138610839844 ], [ - 85.6142578125, - 203.607421875 + 86.39999389648438, + 375.1138610839844 ] ], + "bbox": [ + 86.39999389648438, + 363.708984375, + 248.625, + 375.1138610839844 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/2" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/3" }, "images": {} }, { - "id": "/page/147/Code/4", - "block_type": "Code", - "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\nHere is a loop that prints the ten most common words:\nt = most_common(hist)\nprint 'The most common words are:'\nfor freq, word in t[0:10]:\n print word, '\\t', freq\nAnd here are the results from Emma:\nThe most common words are:\nto 5242\nthe 5205\nand 4897\nof 4295\ni 3191\na 3130\nit 2529\nher 2483\nwas 2400\nshe 2364", + "id": "/page/147/Text/9", + "block_type": "Text", + "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
", "polygon": [ [ - 84.34423828125, - 208.6387939453125 + 85.53955078125, + 379.7578125 ], [ - 326.4787292480469, - 208.6387939453125 + 230.6953125, + 379.7578125 ], [ - 326.4787292480469, - 519.75 + 230.6953125, + 512.4453125 ], [ - 84.34423828125, - 519.75 + 85.53955078125, + 512.4453125 ] ], + "bbox": [ + 85.53955078125, + 379.7578125, + 230.6953125, + 512.4453125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/2" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/3" }, "images": {} }, { - "id": "/page/147/SectionHeader/5", + "id": "/page/147/SectionHeader/10", "block_type": "SectionHeader", - "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
", "polygon": [ [ - 85.9130859375, - 566.15625 + 85.166015625, + 566.54296875 ], [ - 482.90625, - 566.15625 + 483.205078125, + 566.54296875 ], [ - 482.90625, - 601.734375 + 483.205078125, + 601.4728851318359 ], [ - 85.9130859375, - 601.734375 + 85.166015625, + 601.4728851318359 ] ], + "bbox": [ + 85.166015625, + 566.54296875, + 483.205078125, + 601.4728851318359 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/5" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" }, "images": {} }, { - "id": "/page/147/Code/7", + "id": "/page/147/Code/12", "block_type": "Code", "html": "def 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", "polygon": [ [ - 85.3154296875, + 86.28662109375, 606.8987274169922 ], [ @@ -72834,27 +126688,34 @@ ], [ 285.10736083984375, - 667.4765625 + 665.9296875 ], [ - 85.3154296875, - 667.4765625 + 86.28662109375, + 665.9296875 ] ], + "bbox": [ + 86.28662109375, + 606.8987274169922, + 285.10736083984375, + 665.9296875 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/5" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" }, "images": {} }, { - "id": "/page/147/Text/8", + "id": "/page/147/Text/13", "block_type": "Text", "html": "
The first parameter is required; the second is optional. The default value of num is 10.
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", "polygon": [ [ - 85.0166015625, - 689.90625 + 85.46484375, + 690.6796875 ], [ - 238.1602325439453, - 689.90625 + 238.763671875, + 690.6796875 ], [ - 238.1602325439453, + 238.763671875, 700.8348999023438 ], [ - 85.0166015625, + 85.46484375, 700.8348999023438 ] ], + "bbox": [ + 85.46484375, + 690.6796875, + 238.763671875, + 700.8348999023438 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/5" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" }, "images": {} } ], "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/5" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" }, "images": null }, { - "id": "/page/148/Page/197", + "id": "/page/148/Page/231", "block_type": "Page", - "html": "13.6. Dictionary subtraction 127
", + "html": "", "polygon": [ [ - 127.599609375, - 61.171142578125 + 128.6455078125, + 61.14990234375 ], [ 525.6033935546875, - 61.171142578125 + 61.14990234375 ], [ 525.6033935546875, 71.13372802734375 ], [ - 127.599609375, + 128.6455078125, 71.13372802734375 ] ], + "bbox": [ + 128.6455078125, + 61.14990234375, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/5" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" }, "images": {} }, { - "id": "/page/148/PageHeader/18", + "id": "/page/148/PageHeader/20", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.099609375, - 60.37646484375 + 510.697265625, + 60.521484375 ], [ - 525.638671875, - 60.37646484375 + 526.236328125, + 60.521484375 ], [ - 525.638671875, - 70.14111328125 + 526.236328125, + 70.189453125 ], [ - 510.099609375, - 70.14111328125 + 510.697265625, + 70.189453125 ] ], + "bbox": [ + 510.697265625, + 60.521484375, + 526.236328125, + 70.189453125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/5" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" }, "images": {} }, { - "id": "/page/148/Text/1", - "block_type": "Text", - "html": "print_most_common(hist) num gets the default value. If you provide two arguments: print_most_common(hist, 20) num gets the value of the argument instead. In other words, the optional argument overrides the default value.
", + "id": "/page/148/Code/1", + "block_type": "Code", + "html": "print_most_common(hist)", "polygon": [ [ - 129.2431640625, + 129.09375, 88.68572998046875 ], [ - 525.6004028320312, + 251.912109375, 88.68572998046875 ], [ - 525.6004028320312, + 251.912109375, + 98.66162109375 + ], + [ + 129.09375, + 98.66162109375 + ] + ], + "bbox": [ + 129.09375, + 88.68572998046875, + 251.912109375, + 98.66162109375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/144/SectionHeader/1", + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/148/Text/2", + "block_type": "Text", + "html": "
num gets the default value. If you provide two arguments:
", + "polygon": [ + [ + 127.30078125, + 102.7767333984375 + ], + [ + 382.8355712890625, + 102.7767333984375 + ], + [ + 382.8355712890625, + 113.115234375 + ], + [ + 127.30078125, + 113.115234375 + ] + ], + "bbox": [ + 127.30078125, + 102.7767333984375, + 382.8355712890625, + 113.115234375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/144/SectionHeader/1", + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/148/Code/3", + "block_type": "Code", + "html": "print_most_common(hist, 20)", + "polygon": [ + [ + 127.7490234375, + 116.86871337890625 + ], + [ + 270.8298034667969, + 116.86871337890625 + ], + [ + 270.8298034667969, + 126.831298828125 + ], + [ + 127.7490234375, + 126.831298828125 + ] + ], + "bbox": [ + 127.7490234375, + 116.86871337890625, + 270.8298034667969, + 126.831298828125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/144/SectionHeader/1", + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/148/Text/4", + "block_type": "Text", + "html": "
num gets the value of the argument instead. In other words, the optional argument overrides the default value.
", + "polygon": [ + [ + 129.09375, + 130.0341796875 + ], + [ + 525.9375, + 130.0341796875 + ], + [ + 525.9375, 153.26690673828125 ], [ - 129.2431640625, + 129.09375, 153.26690673828125 ] ], + "bbox": [ + 129.09375, + 130.0341796875, + 525.9375, + 153.26690673828125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/5" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" }, "images": {} }, { - "id": "/page/148/Text/2", + "id": "/page/148/Text/5", "block_type": "Text", "html": "If a function has both required and optional parameters, all the required parameters have to come first, followed by the optional ones.
", "polygon": [ [ 128.6455078125, - 161.26171875 + 160.681640625 ], [ 525.9375, - 161.26171875 + 160.681640625 ], [ 525.9375, - 183.884765625 + 183.52386474609375 ], [ 128.6455078125, - 183.884765625 + 183.52386474609375 ] ], + "bbox": [ + 128.6455078125, + 160.681640625, + 525.9375, + 183.52386474609375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/147/SectionHeader/5" + "3": "/page/144/SectionHeader/2", + "4": "/page/147/SectionHeader/10" }, "images": {} }, { - "id": "/page/148/SectionHeader/3", + "id": "/page/148/SectionHeader/6", "block_type": "SectionHeader", - "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).
", "polygon": [ [ - 127.7490234375, + 128.6455078125, 234.158203125 ], [ - 525.6033935546875, + 525.9375, 234.158203125 ], [ - 525.6033935546875, - 268.962890625 + 525.9375, + 268.92987060546875 ], [ - 127.7490234375, - 268.962890625 + 128.6455078125, + 268.92987060546875 ] ], + "bbox": [ + 128.6455078125, + 234.158203125, + 525.9375, + 268.92987060546875 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/5", + "id": "/page/148/Text/8", "block_type": "Text", "html": "subtract 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.
", "polygon": [ [ - 129.392578125, - 276.88067626953125 + 129.09375, + 276.697265625 ], [ - 526.236328125, - 276.88067626953125 + 525.598876953125, + 276.697265625 ], [ - 526.236328125, - 311.3818359375 + 525.598876953125, + 311.501953125 ], [ - 129.392578125, - 311.3818359375 + 129.09375, + 311.501953125 ] ], + "bbox": [ + 129.09375, + 276.697265625, + 525.598876953125, + 311.501953125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Code/6", + "id": "/page/148/Code/9", "block_type": "Code", - "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\nTo find the words in the book that are not in words.txt, we can use process_file to build", + "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", "polygon": [ [ 129.60003662109375, - 314.40234375 + 315.3616943359375 ], [ - 525.5958862304688, - 314.40234375 + 273.7265625, + 315.3616943359375 ], [ - 525.5958862304688, - 405.66796875 + 273.7265625, + 386.2952880859375 ], [ 129.60003662109375, - 405.66796875 + 386.2952880859375 ] ], + "bbox": [ + 129.60003662109375, + 315.3616943359375, + 273.7265625, + 386.2952880859375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/7", + "id": "/page/148/Text/10", "block_type": "Text", - "html": "
a histogram for words.txt, and then subtract: words = process_file('words.txt')
", + "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:
", "polygon": [ [ - 129.60006713867188, - 402.61871337890625 + 128.49609375, + 389.42578125 ], [ - 331.18182373046875, - 402.61871337890625 + 525.9375, + 389.42578125 ], [ - 331.18182373046875, - 426.6733093261719 + 525.9375, + 412.7308654785156 ], [ - 129.60006713867188, - 426.6733093261719 + 128.49609375, + 412.7308654785156 ] ], + "bbox": [ + 128.49609375, + 389.42578125, + 525.9375, + 412.7308654785156 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/8", - "block_type": "Text", - "html": "diff = subtract(hist, words)
", + "id": "/page/148/Code/11", + "block_type": "Code", + "html": "words = process_file('words.txt')\ndiff = subtract(hist, words)", "polygon": [ [ - 129.60008239746094, - 428.09765625 + 127.599609375, + 415.72265625 ], [ - 276.06024169921875, - 428.09765625 + 325.72265625, + 415.72265625 ], [ - 276.06024169921875, - 438.8673095703125 + 325.72265625, + 443.56640625 ], [ - 129.60008239746094, - 438.8673095703125 + 127.599609375, + 443.56640625 ] ], + "bbox": [ + 127.599609375, + 415.72265625, + 325.72265625, + 443.56640625 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/9", - "block_type": "Text", - "html": "
print \"The words in the book that aren't in the word list are:\" for word in diff.keys():
", + "id": "/page/148/Code/12", + "block_type": "Code", + "html": "print \"The words in the book that aren't in the word list are:\"\nfor word in diff.keys():", "polygon": [ [ - 129.09375, + 128.6455078125, 452.84765625 ], [ - 459.0638427734375, + 459.59765625, 452.84765625 ], [ - 459.0638427734375, + 459.59765625, 475.4503173828125 ], [ - 129.09375, + 128.6455078125, 475.4503173828125 ] ], + "bbox": [ + 128.6455078125, + 452.84765625, + 459.59765625, + 475.4503173828125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/10", + "id": "/page/148/Text/13", "block_type": "Text", "html": "
print word,
", "polygon": [ [ - 143.81103515625, + 148.3681640625, 477.59765625 ], [ @@ -73270,56 +127337,70 @@ ], [ 208.05010986328125, - 487.65234375 + 487.6443176269531 ], [ - 143.81103515625, - 487.65234375 + 148.3681640625, + 487.6443176269531 ] ], + "bbox": [ + 148.3681640625, + 477.59765625, + 208.05010986328125, + 487.6443176269531 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/11", + "id": "/page/148/Text/14", "block_type": "Text", "html": "Here are some of the results from Emma:
", "polygon": [ [ - 129.09375, + 127.67431640625, 491.1328125 ], [ - 306.8837585449219, + 308.091796875, 491.1328125 ], [ - 306.8837585449219, - 501.9609375 + 308.091796875, + 501.8858947753906 ], [ - 129.09375, - 501.9609375 + 127.67431640625, + 501.8858947753906 ] ], + "bbox": [ + 127.67431640625, + 491.1328125, + 308.091796875, + 501.8858947753906 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/12", - "block_type": "Text", - "html": "The words in the book that aren't in the word list are: rencontre jane's blanche woodhouses disingenuousness
", + "id": "/page/148/Code/15", + "block_type": "Code", + "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 ...", "polygon": [ [ - 128.86962890625, + 129.46728515625, 505.86474609375 ], [ @@ -73328,176 +127409,183 @@ ], [ 417.2294616699219, - 534.05859375 + 540.2163543701172 ], [ - 128.86962890625, - 534.05859375 + 129.46728515625, + 540.2163543701172 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/148/Text/13", - "block_type": "Text", - "html": "
friend's venice apartment ...
", - "polygon": [ - [ - 129.60009765625, - 530.2537536621094 - ], - [ - 281.2820739746094, - 530.2537536621094 - ], - [ - 281.2820739746094, - 543.33984375 - ], - [ - 129.60009765625, - 543.33984375 - ] + "bbox": [ + 129.46728515625, + 505.86474609375, + 417.2294616699219, + 540.2163543701172 ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/14", + "id": "/page/148/Text/16", "block_type": "Text", "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!
", "polygon": [ [ - 128.49609375, - 543.7265625 + 128.794921875, + 544.4943084716797 ], [ - 525.9375, - 543.7265625 + 526.236328125, + 544.4943084716797 ], [ - 525.9375, - 568.4765625 + 526.236328125, + 566.6519165039062 ], [ - 128.49609375, - 568.4765625 + 128.794921875, + 566.6519165039062 ] ], + "bbox": [ + 128.794921875, + 544.4943084716797, + 526.236328125, + 566.6519165039062 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/Text/15", - "block_type": "Text", - "html": "Exercise 13.6. Python provides a data structure called set that provides many common set operations. Read the documentation at http: // docs. python. org/ 2/ library/ stdtypes. html# types-set and write a program that uses set subtraction to find words in the book that are not in the word list. Solution: http: // thinkpython. com/ code/ analyze_ book2. py .
", + "id": "/page/148/Code/17", + "block_type": "Code", + "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 .", "polygon": [ [ - 128.3466796875, - 568.7106170654297 + 129.392578125, + 568.08984375 ], [ - 525.6002197265625, - 568.7106170654297 + 526.833984375, + 568.08984375 ], [ - 525.6002197265625, - 615.2562255859375 + 526.833984375, + 615.26953125 ], [ - 128.3466796875, - 615.2562255859375 + 129.392578125, + 615.26953125 ] ], + "bbox": [ + 129.392578125, + 568.08984375, + 526.833984375, + 615.26953125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/3" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/6" }, "images": {} }, { - "id": "/page/148/SectionHeader/16", + "id": "/page/148/SectionHeader/18", "block_type": "SectionHeader", - "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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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.08642578125 + 60.76318359375 ], [ - 482.90625, - 60.08642578125 + 483.802734375, + 60.76318359375 ], [ - 482.90625, + 483.802734375, 71.13372802734375 ], [ @@ -73539,39 +127633,53 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.76318359375, + 483.802734375, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/16" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/18" }, "images": {} }, { "id": "/page/149/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 84.568359375, - 60.27978515625 + 85.53955078125, + 60.66650390625 ], [ - 100.107421875, - 60.27978515625 + 101.52685546875, + 60.66650390625 ], [ - 100.107421875, - 70.04443359375 + 101.52685546875, + 69.65771484375 ], [ - 84.568359375, - 70.04443359375 + 85.53955078125, + 69.65771484375 ] ], + "bbox": [ + 85.53955078125, + 60.66650390625, + 101.52685546875, + 69.65771484375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/16" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/18" }, "images": {} }, @@ -73590,104 +127698,132 @@ ], [ 253.77613830566406, - 139.798828125 + 147.2431640625 ], [ 86.4000015258789, - 139.798828125 + 147.2431640625 ] ], + "bbox": [ + 86.4000015258789, + 88.68572998046875, + 253.77613830566406, + 147.2431640625 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/16" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/18" }, "images": {} }, { - "id": "/page/149/Code/2", - "block_type": "Code", - "html": "return random.choice(t)", + "id": "/page/149/Text/2", + "block_type": "Text", + "html": "
return random.choice(t)
", "polygon": [ [ - 106.60693359375, - 148.693359375 + 104.7392578125, + 149.65771484375 ], [ 227.6243438720703, - 148.693359375 + 149.65771484375 ], [ 227.6243438720703, - 159.62030029296875 + 160.5849609375 ], [ - 106.60693359375, - 159.62030029296875 + 104.7392578125, + 160.5849609375 ] ], + "bbox": [ + 104.7392578125, + 149.65771484375, + 227.6243438720703, + 160.5849609375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/16" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/18" }, "images": {} }, { "id": "/page/149/Text/3", "block_type": "Text", - "html": "The 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. Exercise 13.7. This algorithm works, but it is not very efficient; each time you choose a random
", + "html": "The 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.
", "polygon": [ [ - 85.763671875, - 164.6455078125 + 85.3154296875, + 165.67474365234375 ], [ - 483.205078125, - 164.6455078125 + 482.399169921875, + 165.67474365234375 ], [ - 483.205078125, - 200.0787353515625 + 482.399169921875, + 187.98089599609375 ], [ - 85.763671875, - 200.0787353515625 + 85.3154296875, + 187.98089599609375 ] ], + "bbox": [ + 85.3154296875, + 165.67474365234375, + 482.399169921875, + 187.98089599609375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/16" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/18" }, "images": {} }, { "id": "/page/149/Text/4", "block_type": "Text", - "html": "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.
", + "html": "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.
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", "polygon": [ [ - 86.361328125, - 233.19140625 + 85.39013671875, + 233.578125 ], [ - 156.4171600341797, - 233.19140625 + 156.4365234375, + 233.578125 ], [ - 156.4171600341797, + 156.4365234375, 244.38018798828125 ], [ - 86.361328125, + 85.39013671875, 244.38018798828125 ] ], + "bbox": [ + 85.39013671875, + 233.578125, + 156.4365234375, + 244.38018798828125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/16" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/18" }, "images": {} }, { - "id": "/page/149/ListGroup/163", + "id": "/page/149/ListGroup/179", "block_type": "ListGroup", "html": "Write a program that uses this algorithm to choose a random word from the book. Solution: http: // thinkpython. com/ code/ analyze_ book3. py .
", + "html": "Write a program that uses this algorithm to choose a random word from the book. Solution: http: // thinkpython. com/ code/ analyze_ book3. py .
", "polygon": [ [ - 86.40000915527344, - 365.255859375 + 85.3154296875, + 366.22265625 ], [ - 481.7109375, - 365.255859375 + 480.6768493652344, + 366.22265625 ], [ - 481.7109375, - 388.845703125 + 480.6768493652344, + 388.65234375 ], [ - 86.40000915527344, - 388.845703125 + 85.3154296875, + 388.65234375 ] ], + "bbox": [ + 85.3154296875, + 366.22265625, + 480.6768493652344, + 388.65234375 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/148/SectionHeader/16" + "3": "/page/144/SectionHeader/2", + "4": "/page/148/SectionHeader/18" }, "images": {} }, { "id": "/page/149/SectionHeader/11", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 85.46484375, - 443.56640625 + 85.3154296875, + 444.33984375 ], [ - 482.90625, - 443.56640625 + 482.4033203125, + 444.33984375 ], [ - 482.90625, + 482.4033203125, 466.5509033203125 ], [ - 85.46484375, + 85.3154296875, 466.5509033203125 ] ], + "bbox": [ + 85.3154296875, + 444.33984375, + 482.4033203125, + 466.5509033203125 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/149/SectionHeader/11" + "3": "/page/144/SectionHeader/2", + "4": "/page/149/SectionHeader/11" }, "images": {} }, @@ -73959,26 +128158,33 @@ "html": "this the small regard harriet which knightley's it most things
", "polygon": [ [ - 84.8671875, - 472.18359375 + 85.166015625, + 472.4567565917969 ], [ - 412.681640625, - 472.18359375 + 410.888671875, + 472.4567565917969 ], [ - 412.681640625, - 483.01171875 + 410.888671875, + 482.625 ], [ - 84.8671875, - 483.01171875 + 85.166015625, + 482.625 ] ], + "bbox": [ + 85.166015625, + 472.4567565917969, + 410.888671875, + 482.625 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/149/SectionHeader/11" + "3": "/page/144/SectionHeader/2", + "4": "/page/149/SectionHeader/11" }, "images": {} }, @@ -73988,26 +128194,33 @@ "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.
", "polygon": [ [ - 85.166015625, - 487.265625 + 85.3154296875, + 488.0390625 ], [ - 483.802734375, - 487.265625 + 482.90625, + 488.0390625 ], [ - 483.802734375, + 482.90625, 522.9749145507812 ], [ - 85.166015625, + 85.3154296875, 522.9749145507812 ] ], + "bbox": [ + 85.3154296875, + 488.0390625, + 482.90625, + 522.9749145507812 + ], "children": null, "section_hierarchy": { "1": "/page/144/SectionHeader/1", - "3": "/page/149/SectionHeader/11" + "3": "/page/144/SectionHeader/2", + "4": "/page/149/SectionHeader/11" }, "images": {} }, @@ -74017,26 +128230,33 @@ "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:
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13.9. Data structures 129
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", + "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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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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", + "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).
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", + "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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", + "id": "/page/153/Text/13", + "block_type": "Text", + "html": "So if you plot log f versus log r, you should get a straight line with slope −s and intercept log c.
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", + "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?
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", + "html": "Solution: http: // thinkpython. com/ code/ zipf. py . To make the plots, you might have to install matplotlib (see http: // matplotlib. sourceforge. net/ ).
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", + "html": "A 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.
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", "polygon": [ [ - 128.0478515625, + 128.197265625, 602.5078125 ], [ @@ -76587,14 +131444,20 @@ 613.1689300537109 ], [ - 128.0478515625, + 128.197265625, 613.1689300537109 ] ], + "bbox": [ + 128.197265625, + 602.5078125, + 451.828125, + 613.1689300537109 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/154/SectionHeader/8" + "4": "/page/154/SectionHeader/8" }, "images": {} }, @@ -76604,26 +131467,32 @@ "html": ">>> fout = open('output.txt', 'w')\n>>> print fout\n<open file 'output.txt', mode 'w' at 0xb7eb2410>", "polygon": [ [ - 129.31787109375, - 618.7607727050781 + 129.60000610351562, + 617.9765625 ], [ - 381.603515625, - 618.7607727050781 + 380.61517333984375, + 617.9765625 ], [ - 381.603515625, - 660.12890625 + 380.61517333984375, + 658.1953125 ], [ - 129.31787109375, - 660.12890625 + 129.60000610351562, + 658.1953125 ] ], + "bbox": [ + 129.60000610351562, + 617.9765625, + 380.61517333984375, + 658.1953125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/154/SectionHeader/8" + "4": "/page/154/SectionHeader/8" }, "images": {} }, @@ -76633,26 +131502,32 @@ "html": "
If 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.
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", "polygon": [ [ - 128.6455078125, - 689.90625 + 128.49609375, + 690.29296875 ], [ - 310.18359375, - 689.90625 + 310.78125, + 690.29296875 ], [ - 310.18359375, + 310.78125, 700.8349380493164 ], [ - 128.6455078125, + 128.49609375, 700.8349380493164 ] ], + "bbox": [ + 128.49609375, + 690.29296875, + 310.78125, + 700.8349380493164 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/154/SectionHeader/8" + "4": "/page/154/SectionHeader/8" }, "images": {} } ], "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/154/SectionHeader/8" + "4": "/page/154/SectionHeader/8" }, "images": null }, { - "id": "/page/155/Page/218", + "id": "/page/155/Page/224", "block_type": "Page", - "html": "134 Chapter 14. Files
", + "html": "", "polygon": [ [ 86.4000015258789, - 59.94140625 + 60.71484375 ], [ - 483.205078125, - 59.94140625 + 482.4034118652344, + 60.71484375 ], [ - 483.205078125, + 482.4034118652344, 71.13372802734375 ], [ @@ -76737,450 +131624,651 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.71484375, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/154/SectionHeader/8" + "4": "/page/154/SectionHeader/8" }, "images": {} }, { - "id": "/page/155/PageHeader/18", + "id": "/page/155/PageHeader/21", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ 85.46484375, - 59.69970703125 + 61.1015625 ], [ - 100.40625, - 59.69970703125 + 101.6015625, + 61.1015625 ], [ - 100.40625, - 69.65771484375 + 101.6015625, + 70.3828125 ], [ 85.46484375, - 69.65771484375 + 70.3828125 ] ], + "bbox": [ + 85.46484375, + 61.1015625, + 101.6015625, + 70.3828125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/154/SectionHeader/8" + "4": "/page/154/SectionHeader/8" }, "images": {} }, { - "id": "/page/155/Code/1", - "block_type": "Code", - "html": ">>> line1 = \"This here's the wattle,\\n\"\n>>> fout.write(line1)\nAgain, the file object keeps track of where it is, so if you call write again, it adds the new\ndata to the end.\n>>> line2 = \"the emblem of our land.\\n\"\n>>> fout.write(line2)\nWhen you are done writing, you have to close the file.", + "id": "/page/155/TextInlineMath/1", + "block_type": "TextInlineMath", + "html": "
>>> line1 = \"This here's the wattle,\\n\" >>> fout.write(line1)
", "polygon": [ [ - 86.361328125, - 88.68572998046875 + 86.4000015258789, + 88.0751953125 + ], + [ + 290.3558654785156, + 88.0751953125 + ], + [ + 290.3558654785156, + 110.84228515625 + ], + [ + 86.4000015258789, + 110.84228515625 + ] + ], + "bbox": [ + 86.4000015258789, + 88.0751953125, + 290.3558654785156, + 110.84228515625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/154/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/155/Text/2", + "block_type": "Text", + "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.
", + "polygon": [ + [ + 85.6142578125, + 116.4990234375 + ], + [ + 482.4018859863281, + 116.4990234375 + ], + [ + 482.4018859863281, + 139.60589599609375 + ], + [ + 85.6142578125, + 139.60589599609375 + ] + ], + "bbox": [ + 85.6142578125, + 116.4990234375, + 482.4018859863281, + 139.60589599609375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/154/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/155/Text/3", + "block_type": "Text", + "html": ">>> line2 = \"the emblem of our land.\\n\" >>> fout.write(line2)
", + "polygon": [ + [ + 85.53955078125, + 145.9127197265625 + ], + [ + 290.4609375, + 145.9127197265625 + ], + [ + 290.4609375, + 168.0693359375 + ], + [ + 85.53955078125, + 168.0693359375 + ] + ], + "bbox": [ + 85.53955078125, + 145.9127197265625, + 290.4609375, + 168.0693359375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/154/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/155/Text/4", + "block_type": "Text", + "html": "When you are done writing, you have to close the file.
", + "polygon": [ + [ + 85.98779296875, + 174.1201171875 ], [ - 482.607421875, - 88.68572998046875 + 323.96807861328125, + 174.1201171875 ], [ - 482.607421875, - 185.23828125 + 323.96807861328125, + 184.637939453125 ], [ - 86.361328125, - 186.78515625 + 85.98779296875, + 184.637939453125 ] ], + "bbox": [ + 85.98779296875, + 174.1201171875, + 323.96807861328125, + 184.637939453125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/154/SectionHeader/8" + "4": "/page/154/SectionHeader/8" }, "images": {} }, { - "id": "/page/155/Code/2", - "block_type": "Code", - "html": ">>> fout.close()", + "id": "/page/155/Text/5", + "block_type": "Text", + "html": "
>>> fout.close()
", "polygon": [ [ - 86.39999389648438, - 189.7822265625 + 85.68896484375, + 190.94573974609375 ], [ - 170.1826171875, - 189.7822265625 + 172.423828125, + 190.94573974609375 ], [ - 170.1826171875, - 201.48046875 + 172.423828125, + 200.9083251953125 ], [ - 86.39999389648438, - 201.48046875 + 85.68896484375, + 200.9083251953125 ] ], + "bbox": [ + 85.68896484375, + 190.94573974609375, + 172.423828125, + 200.9083251953125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/154/SectionHeader/8" + "4": "/page/154/SectionHeader/8" }, "images": {} }, { - "id": "/page/155/SectionHeader/3", + "id": "/page/155/SectionHeader/6", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 86.39998626708984, - 252.720703125 + 85.6142578125, + 254.07421875 ], [ - 482.90625, - 252.720703125 + 482.40325927734375, + 254.07421875 ], [ - 482.90625, + 482.40325927734375, 276.39093017578125 ], [ - 86.39998626708984, + 85.6142578125, 276.39093017578125 ] ], + "bbox": [ + 85.6142578125, + 254.07421875, + 482.40325927734375, + 276.39093017578125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Code/5", + "id": "/page/155/Code/8", "block_type": "Code", "html": ">>> x = 52\n>>> fout.write(str(x))", "polygon": [ [ - 86.361328125, - 281.53125 + 85.9130859375, + 282.69775390625 ], [ - 201.708984375, - 281.53125 + 201.47799682617188, + 282.69775390625 ], [ 201.47799682617188, - 305.12109375 + 304.8543395996094 ], [ - 85.166015625, - 305.12109375 + 85.9130859375, + 304.8543395996094 ] ], + "bbox": [ + 85.9130859375, + 282.69775390625, + 201.47799682617188, + 304.8543395996094 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Text/6", + "id": "/page/155/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 85.46484375, - 310.341796875 + 86.0625, + 311.311767578125 ], [ - 482.90625, - 310.341796875 + 482.39886474609375, + 311.311767578125 ], [ - 482.90625, + 482.39886474609375, 333.617919921875 ], [ - 85.46484375, + 86.0625, 333.617919921875 ] ], + "bbox": [ + 86.0625, + 311.311767578125, + 482.39886474609375, + 333.617919921875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Text/7", + "id": "/page/155/Text/10", "block_type": "Text", "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.
", "polygon": [ [ - 85.6142578125, - 342.24609375 + 85.763671875, + 343.79296875 ], [ - 483.50390625, - 342.24609375 + 482.3996887207031, + 343.79296875 ], [ - 483.50390625, - 366.22265625 + 482.3996887207031, + 366.2029113769531 ], [ - 85.6142578125, - 366.22265625 + 85.763671875, + 366.2029113769531 ] ], + "bbox": [ + 85.763671875, + 343.79296875, + 482.3996887207031, + 366.2029113769531 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Text/8", + "id": "/page/155/Text/11", "block_type": "Text", "html": "For example, the format sequence '%d' means that the second operand should be formatted as an integer (d stands for \"decimal\"):
", "polygon": [ [ - 85.9130859375, - 375.50390625 + 85.6142578125, + 376.470703125 ], [ - 483.50390625, - 375.50390625 + 482.40252685546875, + 376.470703125 ], [ - 483.50390625, + 482.40252685546875, 398.78790283203125 ], [ - 85.9130859375, + 85.6142578125, 398.78790283203125 ] ], + "bbox": [ + 85.6142578125, + 376.470703125, + 482.40252685546875, + 398.78790283203125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Code/9", + "id": "/page/155/Code/12", "block_type": "Code", "html": ">>> camels = 42\n>>> '%d' % camels\n'42'", "polygon": [ [ - 86.39998626708984, - 404.12109375 + 85.09130859375, + 404.89453125 ], [ 176.90625, - 404.12109375 + 404.89453125 ], [ - 175.7109375, + 176.90625, 439.44635009765625 ], [ - 85.3154296875, + 85.09130859375, 439.44635009765625 ] ], + "bbox": [ + 85.09130859375, + 404.89453125, + 176.90625, + 439.44635009765625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Text/10", + "id": "/page/155/Text/13", "block_type": "Text", "html": "
The result is the string '42', which is not to be confused with the integer value 42.
", "polygon": [ [ - 86.0625, - 444.7265625 + 85.46484375, + 445.5 ], [ 447.9716796875, - 444.7265625 + 445.5 ], [ 447.9716796875, - 456.328125 + 456.0149230957031 ], [ - 86.0625, - 456.328125 + 85.46484375, + 456.0149230957031 ] ], + "bbox": [ + 85.46484375, + 445.5, + 447.9716796875, + 456.0149230957031 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Text/11", + "id": "/page/155/Text/14", "block_type": "Text", "html": "A format sequence can appear anywhere in the string, so you can embed a value in a sentence:
", "polygon": [ [ - 85.3154296875, - 465.22265625 + 85.46484375, + 465.99609375 ], [ - 482.90625, - 465.22265625 + 482.4034118652344, + 465.99609375 ], [ - 482.90625, + 482.4034118652344, 488.59991455078125 ], [ - 85.3154296875, + 85.46484375, 488.59991455078125 ] ], + "bbox": [ + 85.46484375, + 465.99609375, + 482.4034118652344, + 488.59991455078125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Code/12", + "id": "/page/155/Code/15", "block_type": "Code", "html": ">>> camels = 42\n>>> 'I have spotted %d camels.' % camels\n'I have spotted 42 camels.'", "polygon": [ [ - 85.763671875, - 494.61328125 + 85.46484375, + 494.9067687988281 ], [ - 296.4375, - 494.61328125 + 295.573974609375, + 494.9067687988281 ], [ 295.573974609375, 529.2583618164062 ], [ - 84.568359375, + 85.46484375, 529.2583618164062 ] ], + "bbox": [ + 85.46484375, + 494.9067687988281, + 295.573974609375, + 529.2583618164062 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Text/13", + "id": "/page/155/Text/16", "block_type": "Text", "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.
", "polygon": [ [ - 86.2119140625, - 534.4453125 + 85.6142578125, + 535.60546875 ], [ - 482.4034118652344, - 534.4453125 + 482.90625, + 535.60546875 ], [ - 482.4034118652344, + 482.90625, 558.0209350585938 ], [ - 86.2119140625, + 85.6142578125, 558.0209350585938 ] ], + "bbox": [ + 85.6142578125, + 535.60546875, + 482.90625, + 558.0209350585938 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Text/14", + "id": "/page/155/Text/17", "block_type": "Text", "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:
", "polygon": [ [ - 85.46484375, - 566.54296875 + 85.6142578125, + 567.703125 ], [ 482.90625, - 566.54296875 + 567.703125 ], [ 482.90625, 590.6059265136719 ], [ - 85.46484375, + 85.6142578125, 590.6059265136719 ] ], + "bbox": [ + 85.6142578125, + 567.703125, + 482.90625, + 590.6059265136719 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Code/15", + "id": "/page/155/Code/18", "block_type": "Code", "html": ">>> 'In %d years I have spotted %g %s.' % (3, 0.1, 'camels')\n'In 3 years I have spotted 0.1 camels.'", "polygon": [ @@ -77189,11 +132277,11 @@ 596.9127807617188 ], [ - 401.02734375, + 400.1474304199219, 596.9127807617188 ], [ - 401.02734375, + 400.1474304199219, 619.0703887939453 ], [ @@ -77201,49 +132289,61 @@ 619.0703887939453 ] ], + "bbox": [ + 85.3154296875, + 596.9127807617188, + 400.1474304199219, + 619.0703887939453 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Text/16", + "id": "/page/155/Text/19", "block_type": "Text", "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:
", "polygon": [ [ - 85.46484375, - 624.55078125 + 85.3154296875, + 625.6763458251953 ], [ - 483.205078125, - 624.55078125 + 482.4034118652344, + 625.6763458251953 ], [ - 483.205078125, + 482.4034118652344, 647.8329467773438 ], [ - 85.46484375, + 85.3154296875, 647.8329467773438 ] ], + "bbox": [ + 85.3154296875, + 625.6763458251953, + 482.4034118652344, + 647.8329467773438 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/155/Code/17", + "id": "/page/155/Code/20", "block_type": "Code", "html": ">>> '%d %d %d' % (1, 2)\nTypeError: not enough arguments for format string\n>>> '%d' % 'dollars'\nTypeError: illegal argument type for built-in operation", "polygon": [ [ - 84.7177734375, + 85.3154296875, 653.5546875 ], [ @@ -77255,28 +132355,34 @@ 700.6853942871094 ], [ - 84.7177734375, + 85.3154296875, 700.6853942871094 ] ], + "bbox": [ + 85.3154296875, + 653.5546875, + 376.5234375, + 700.6853942871094 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} } ], "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": null }, { - "id": "/page/156/Page/188", + "id": "/page/156/Page/198", "block_type": "Page", - "html": "
14.4. Filenames and paths 135
", + "html": "", "polygon": [ [ - 127.82373046875, - 61.1015625 + 128.0478515625, + 60.8115234375 ], [ 525.6033935546875, - 61.1015625 + 60.8115234375 ], [ 525.6033935546875, 71.13372802734375 ], [ - 127.82373046875, + 128.0478515625, 71.13372802734375 ] ], + "bbox": [ + 128.0478515625, + 60.8115234375, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { - "id": "/page/156/PageHeader/12", + "id": "/page/156/PageHeader/21", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.3984375, - 61.1982421875 + 509.203125, + 60.56982421875 ], [ 525.9375, - 61.1982421875 + 60.56982421875 ], [ 525.9375, - 70.76953125 + 69.94775390625 ], [ - 510.3984375, - 70.76953125 + 509.203125, + 69.94775390625 ] ], + "bbox": [ + 509.203125, + 60.56982421875, + 525.9375, + 69.94775390625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, @@ -77360,84 +132484,102 @@ "html": "In the first example, there aren't enough elements; in the second, the element is the wrong type.
", "polygon": [ [ - 128.3466796875, - 88.83526611328125 + 127.1513671875, + 88.70361328125 ], [ - 527.1328125, - 88.83526611328125 + 525.6033935546875, + 88.70361328125 ], [ - 527.1328125, - 111.375 + 525.6033935546875, + 110.99188232421875 ], [ - 128.3466796875, - 111.375 + 127.1513671875, + 110.99188232421875 ] ], + "bbox": [ + 127.1513671875, + 88.70361328125, + 525.6033935546875, + 110.99188232421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { "id": "/page/156/Text/2", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 128.197265625, - 120.3662109375 + 128.3466796875, + 120.65625 ], [ - 526.236328125, - 120.3662109375 + 526.53515625, + 120.65625 ], [ - 526.236328125, + 526.53515625, 143.39483642578125 ], [ - 128.197265625, + 128.3466796875, 143.39483642578125 ] ], + "bbox": [ + 128.3466796875, + 120.65625, + 526.53515625, + 143.39483642578125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/155/SectionHeader/3" + "4": "/page/155/SectionHeader/6" }, "images": {} }, { "id": "/page/156/SectionHeader/3", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 128.3466796875, - 198.966796875 + 129.392578125, + 199.353515625 ], [ - 525.9375, - 198.966796875 + 525.6033935546875, + 199.353515625 ], [ - 525.9375, + 525.6033935546875, 234.09185791015625 ], [ - 128.3466796875, + 129.392578125, 234.09185791015625 ] ], + "bbox": [ + 129.392578125, + 199.353515625, + 525.6033935546875, + 234.09185791015625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, @@ -77476,26 +132624,32 @@ "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:
", "polygon": [ [ - 128.9443359375, - 243.826171875 + 129.2431640625, + 243.6328125 ], [ - 525.9375, - 243.826171875 + 525.6028442382812, + 243.6328125 ], [ - 525.9375, + 525.6028442382812, 266.49481201171875 ], [ - 128.9443359375, + 129.2431640625, 266.49481201171875 ] ], + "bbox": [ + 129.2431640625, + 243.6328125, + 525.6028442382812, + 266.49481201171875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, @@ -77505,26 +132659,32 @@ "html": ">>> import os\n>>> cwd = os.getcwd()\n>>> print cwd\n/home/dinsdale", "polygon": [ [ - 128.49609375, + 128.72021484375, 272.61962890625 ], [ - 239.66015625, + 239.43768310546875, 272.61962890625 ], [ - 239.66015625, - 322.13671875 + 239.43768310546875, + 320.009765625 ], [ - 128.49609375, - 322.13671875 + 128.72021484375, + 320.009765625 ] ], + "bbox": [ + 128.72021484375, + 272.61962890625, + 239.43768310546875, + 320.009765625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, @@ -77534,26 +132694,32 @@ "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.
", "polygon": [ [ - 129.09375, - 325.23046875 + 129.60000610351562, + 325.43963623046875 ], [ - 526.53515625, - 325.23046875 + 525.5996704101562, + 325.43963623046875 ], [ - 526.53515625, + 525.5996704101562, 347.74578857421875 ], [ - 129.09375, + 129.60000610351562, 347.74578857421875 ] ], + "bbox": [ + 129.60000610351562, + 325.43963623046875, + 525.5996704101562, + 347.74578857421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, @@ -77564,14 +132730,14 @@ "polygon": [ [ 128.6455078125, - 357.134765625 + 357.71484375 ], [ - 527.1328125, - 357.134765625 + 525.6044311523438, + 357.71484375 ], [ - 527.1328125, + 525.6044311523438, 380.1488037109375 ], [ @@ -77579,10 +132745,16 @@ 380.1488037109375 ] ], + "bbox": [ + 128.6455078125, + 357.71484375, + 525.6044311523438, + 380.1488037109375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, @@ -77592,98 +132764,431 @@ "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:
", "polygon": [ [ - 128.6455078125, - 389.0390625 + 128.3466796875, + 389.619140625 ], [ - 527.1328125, - 389.0390625 + 525.603515625, + 389.619140625 ], [ - 527.1328125, + 525.603515625, 412.55181884765625 ], [ - 128.6455078125, + 128.3466796875, 412.55181884765625 ] ], + "bbox": [ + 128.3466796875, + 389.619140625, + 525.603515625, + 412.55181884765625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, { "id": "/page/156/Code/10", "block_type": "Code", - "html": ">>> os.path.abspath('memo.txt')\n'/home/dinsdale/memo.txt'\nos.path.exists checks whether a file or directory exists:\n>>> os.path.exists('memo.txt')\nTrue\nIf it exists, os.path.isdir checks whether it's a directory:\n>>> os.path.isdir('memo.txt')\nFalse\n>>> os.path.isdir('music')\nTrue\nSimilarly, os.path.isfile checks whether it's a file.\nos.listdir returns a list of the files (and other directories) in the given directory:\n>>> os.listdir(cwd)\n['music', 'photos', 'memo.txt']\nTo demonstrate these functions, the following example \"walks\" through a directory, prints\nthe names of all the files, and calls itself recursively on all the directories.\ndef walk(dirname):", + "html": "
>>> os.path.abspath('memo.txt')\n'/home/dinsdale/memo.txt'", "polygon": [ [ - 129.60000610351562, + 129.2431640625, 418.6766662597656 ], [ - 525.603515625, + 291.7033996582031, 418.6766662597656 ], [ - 525.603515625, - 670.5703125 + 291.7033996582031, + 440.833251953125 ], [ - 129.60000610351562, - 670.5703125 + 129.2431640625, + 440.833251953125 ] ], + "bbox": [ + 129.2431640625, + 418.6766662597656, + 291.7033996582031, + 440.833251953125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, { "id": "/page/156/Text/11", "block_type": "Text", - "html": "
for name in os.listdir(dirname): path = os.path.join(dirname, name)
", + "html": "os.path.exists checks whether a file or directory exists:
", "polygon": [ [ - 150.5160675048828, - 666.3346862792969 + 128.794921875, + 447.046875 + ], + [ + 381.2076110839844, + 447.046875 + ], + [ + 381.2076110839844, + 457.2198181152344 + ], + [ + 128.794921875, + 457.2198181152344 + ] + ], + "bbox": [ + 128.794921875, + 447.046875, + 381.2076110839844, + 457.2198181152344 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Code/12", + "block_type": "Code", + "html": ">>> os.path.exists('memo.txt')", + "polygon": [ + [ + 128.794921875, + 463.2890625 + ], + [ + 286.875, + 463.2890625 + ], + [ + 286.875, + 477.984375 + ], + [ + 128.794921875, + 477.984375 + ] + ], + "bbox": [ + 128.794921875, + 463.2890625, + 286.875, + 477.984375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Text/13", + "block_type": "Text", + "html": "
True
", + "polygon": [ + [ + 128.6455078125, + 475.5386657714844 + ], + [ + 152.103515625, + 475.5386657714844 + ], + [ + 152.103515625, + 486.4921875 + ], + [ + 128.6455078125, + 486.4921875 + ] + ], + "bbox": [ + 128.6455078125, + 475.5386657714844, + 152.103515625, + 486.4921875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Text/14", + "block_type": "Text", + "html": "If it exists, os.path.isdir checks whether it's a directory:
", + "polygon": [ + [ + 127.30078125, + 491.51953125 + ], + [ + 384.890625, + 491.51953125 + ], + [ + 384.890625, + 501.8878173828125 + ], + [ + 127.30078125, + 501.8878173828125 + ] + ], + "bbox": [ + 127.30078125, + 491.51953125, + 384.890625, + 501.8878173828125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Code/15", + "block_type": "Code", + "html": ">>> os.path.isdir('memo.txt')\nFalse\n>>> os.path.isdir('music')\nTrue", + "polygon": [ + [ + 128.197265625, + 507.76171875 + ], + [ + 281.24542236328125, + 507.76171875 + ], + [ + 281.24542236328125, + 556.48828125 + ], + [ + 128.197265625, + 556.48828125 + ] + ], + "bbox": [ + 128.197265625, + 507.76171875, + 281.24542236328125, + 556.48828125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Text/16", + "block_type": "Text", + "html": "
Similarly, os.path.isfile checks whether it's a file.
", + "polygon": [ + [ + 129.16845703125, + 560.8326721191406 + ], + [ + 359.490234375, + 560.8326721191406 + ], + [ + 359.490234375, + 570.94482421875 + ], + [ + 129.16845703125, + 570.94482421875 + ] + ], + "bbox": [ + 129.16845703125, + 560.8326721191406, + 359.490234375, + 570.94482421875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Text/17", + "block_type": "Text", + "html": "os.listdir returns a list of the files (and other directories) in the given directory:
", + "polygon": [ + [ + 128.3466796875, + 580.46484375 + ], + [ + 487.653564453125, + 580.46484375 + ], + [ + 487.653564453125, + 591.15283203125 + ], + [ + 128.3466796875, + 591.15283203125 + ] + ], + "bbox": [ + 128.3466796875, + 580.46484375, + 487.653564453125, + 591.15283203125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Code/18", + "block_type": "Code", + "html": ">>> os.listdir(cwd)\n['music', 'photos', 'memo.txt']", + "polygon": [ + [ + 127.8984375, + 597.2776794433594 + ], + [ + 291.7034606933594, + 597.2776794433594 + ], + [ + 291.7034606933594, + 619.4342803955078 + ], + [ + 127.8984375, + 619.4342803955078 + ] + ], + "bbox": [ + 127.8984375, + 597.2776794433594, + 291.7034606933594, + 619.4342803955078 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Text/19", + "block_type": "Text", + "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.
", + "polygon": [ + [ + 128.794921875, + 624.9375 + ], + [ + 525.638671875, + 624.9375 + ], + [ + 525.638671875, + 648.0158386230469 + ], + [ + 128.794921875, + 648.0158386230469 + ] + ], + "bbox": [ + 128.794921875, + 624.9375, + 525.638671875, + 648.0158386230469 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "4": "/page/156/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/156/Code/20", + "block_type": "Code", + "html": "def walk(dirname):\n for name in os.listdir(dirname):\n path = os.path.join(dirname, name)", + "polygon": [ + [ + 127.97314453125, + 653.94140625 ], [ 349.2754211425781, - 666.3346862792969 + 653.94140625 ], [ 349.2754211425781, - 689.51953125 + 688.4912872314453 ], [ - 150.5160675048828, - 689.51953125 + 127.97314453125, + 688.4912872314453 ] ], + "bbox": [ + 127.97314453125, + 653.94140625, + 349.2754211425781, + 688.4912872314453 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} } ], "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": null }, { - "id": "/page/157/Page/220", + "id": "/page/157/Page/235", "block_type": "Page", - "html": "
136 Chapter 14. Files
", + "html": "", "polygon": [ [ 86.4000015258789, - 59.4580078125 + 60.2314453125 ], [ - 482.90625, - 59.4580078125 + 482.4034118652344, + 60.2314453125 ], [ - 482.90625, + 482.4034118652344, 71.13372802734375 ], [ @@ -77725,39 +133236,51 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.2314453125, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, { - "id": "/page/157/PageHeader/13", + "id": "/page/157/PageHeader/16", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.9130859375, - 60.134765625 + 85.166015625, + 60.18310546875 ], [ - 102.0498046875, - 60.134765625 + 102.6474609375, + 60.18310546875 ], [ - 102.0498046875, - 71.0595703125 + 102.6474609375, + 70.52783203125 ], [ - 85.9130859375, - 71.0595703125 + 85.166015625, + 70.52783203125 ] ], + "bbox": [ + 85.166015625, + 60.18310546875, + 102.6474609375, + 70.52783203125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, @@ -77767,113 +133290,137 @@ "html": "if os.path.isfile(path):\n print path\nelse:\n walk(path)", "polygon": [ [ - 127.1513671875, - 88.68572998046875 + 123.416015625, + 87.8818359375 ], [ - 254.4521484375, - 87.591796875 + 253.77169799804688, + 87.8818359375 ], [ - 254.4521484375, - 137.091796875 + 253.77169799804688, + 135.2313232421875 ], [ - 127.1513671875, - 138.638671875 + 123.416015625, + 135.2313232421875 ] ], + "bbox": [ + 123.416015625, + 87.8818359375, + 253.77169799804688, + 135.2313232421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, { "id": "/page/157/Text/2", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 84.8671875, - 139.8955078125 + 85.763671875, + 139.60546875 ], [ - 482.40338134765625, - 139.8955078125 + 483.50390625, + 139.60546875 ], [ - 482.40338134765625, - 188.4287109375 + 483.50390625, + 187.172119140625 ], [ - 84.8671875, - 188.4287109375 + 85.763671875, + 187.172119140625 ] ], + "bbox": [ + 85.763671875, + 139.60546875, + 483.50390625, + 187.172119140625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, { "id": "/page/157/Text/3", "block_type": "Text", - "html": "Solution: http: // thinkpython. com/ code/ walk. py .
", + "html": "Solution: http: // thinkpython. com/ code/ walk. py .
", "polygon": [ [ - 85.3154296875, - 195.9697265625 + 85.46484375, + 195.6796875 ], [ - 324.52734375, - 195.9697265625 + 323.9296875, + 195.6796875 ], [ - 324.52734375, + 323.9296875, 206.52410888671875 ], [ - 85.3154296875, + 85.46484375, 206.52410888671875 ] ], + "bbox": [ + 85.46484375, + 195.6796875, + 323.9296875, + 206.52410888671875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/156/SectionHeader/3" + "4": "/page/156/SectionHeader/3" }, "images": {} }, { "id": "/page/157/SectionHeader/4", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 86.39999389648438, - 258.908203125 + 85.763671875, + 258.328125 ], [ - 482.90625, - 258.908203125 + 483.205078125, + 258.328125 ], [ - 482.90625, + 483.205078125, 283.0028076171875 ], [ - 86.39999389648438, + 85.763671875, 283.0028076171875 ] ], + "bbox": [ + 85.763671875, + 258.328125, + 483.205078125, + 283.0028076171875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { "id": "/page/157/Code/6", "block_type": "Code", - "html": ">>> fin = open('bad_file')\nIOError: [Errno 2] No such file or directory: 'bad_file'\nIf you don't have permission to access a file:\n>>> fout = open('/etc/passwd', 'w')\nIOError: [Errno 13] Permission denied: '/etc/passwd'\nAnd if you try to open a directory for reading, you get\n>>> fin = open('/home')\nIOError: [Errno 21] Is a directory\nTo avoid these errors, you could use functions like os.path.exists and os.path.isfile,\nbut it would take a lot of time and code to check all the possibilities (if \"Errno 21\" is any", + "html": "
>>> fin = open('bad_file')\nIOError: [Errno 2] No such file or directory: 'bad_file'", + "polygon": [ + [ + 85.763671875, + 286.171875 + ], + [ + 379.2314147949219, + 286.171875 + ], + [ + 379.2314147949219, + 310.4282531738281 + ], + [ + 85.763671875, + 310.4282531738281 + ] + ], + "bbox": [ + 85.763671875, + 286.171875, + 379.2314147949219, + 310.4282531738281 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4" + }, + "images": {} + }, + { + "id": "/page/157/Text/7", + "block_type": "Text", + "html": "
If you don't have permission to access a file:
", + "polygon": [ + [ + 86.2119140625, + 313.435546875 + ], + [ + 284.3349609375, + 313.435546875 + ], + [ + 284.3349609375, + 325.9588317871094 + ], + [ + 86.2119140625, + 325.9588317871094 + ] + ], + "bbox": [ + 86.2119140625, + 313.435546875, + 284.3349609375, + 325.9588317871094 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4" + }, + "images": {} + }, + { + "id": "/page/157/Text/8", + "block_type": "Text", + "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
", "polygon": [ [ 86.0625, - 282.884765625 + 328.130859375 ], [ - 482.3996887207031, - 282.884765625 + 358.3153991699219, + 328.130859375 ], [ - 482.3996887207031, - 424.6171875 + 358.3153991699219, + 368.91485595703125 ], [ 86.0625, - 424.6171875 + 368.91485595703125 ] ], + "bbox": [ + 86.0625, + 328.130859375, + 358.3153991699219, + 368.91485595703125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { - "id": "/page/157/Text/14", + "id": "/page/157/Text/9", "block_type": "Text", - "html": "indication, there are at least 21 things that can go wrong).
", + "html": ">>> fin = open('/home') IOError: [Errno 21] Is a directory
", "polygon": [ [ - 86.4000244140625, - 426.29730224609375 + 85.46484375, + 370.08984375 ], [ - 338.05523681640625, - 426.29730224609375 + 264.2424011230469, + 370.08984375 ], [ - 338.05523681640625, - 436.2598876953125 + 264.2424011230469, + 396.3403015136719 ], [ - 86.4000244140625, - 436.2598876953125 + 85.46484375, + 396.3403015136719 ] ], + "bbox": [ + 85.46484375, + 370.08984375, + 264.2424011230469, + 396.3403015136719 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { - "id": "/page/157/Text/7", + "id": "/page/157/Text/10", "block_type": "Text", - "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:
", + "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).
", "polygon": [ [ - 85.166015625, - 440.47265625 + 85.763671875, + 398.70703125 ], [ - 482.90625, - 440.47265625 + 482.607421875, + 398.70703125 ], [ - 482.90625, - 467.8058776855469 + 482.607421875, + 436.2598876953125 ], [ - 85.166015625, - 467.8058776855469 + 85.763671875, + 436.2598876953125 ] ], + "bbox": [ + 85.763671875, + 398.70703125, + 482.607421875, + 436.2598876953125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { - "id": "/page/157/Code/8", - "block_type": "Code", - "html": "try:\n fin = open('bad_file')\n for line in fin:\n print line\n fin.close()\nexcept:", + "id": "/page/157/Text/11", + "block_type": "Text", + "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:
", "polygon": [ [ - 84.8671875, - 471.796875 + 85.6142578125, + 443.953125 ], [ - 244.44140625, - 471.796875 + 482.40338134765625, + 443.953125 ], [ - 244.44140625, - 544.0093536376953 + 482.40338134765625, + 467.9296875 ], [ - 84.8671875, - 544.0093536376953 + 85.6142578125, + 467.9296875 ] ], + "bbox": [ + 85.6142578125, + 443.953125, + 482.40338134765625, + 467.9296875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { - "id": "/page/157/Text/9", - "block_type": "Text", - "html": "print 'Something went wrong.'
", + "id": "/page/157/Code/12", + "block_type": "Code", + "html": "try:\n fin = open('bad_file')\n for line in fin:\n print line\n fin.close()\nexcept:\n print 'Something went wrong.'", "polygon": [ [ - 105.9345703125, - 544.88671875 + 86.39998626708984, + 473.0747375488281 ], [ 258.96136474609375, - 544.88671875 + 473.0747375488281 ], [ 258.96136474609375, 556.2033538818359 ], [ - 105.9345703125, + 86.39998626708984, 556.2033538818359 ] ], + "bbox": [ + 86.39998626708984, + 473.0747375488281, + 258.96136474609375, + 556.2033538818359 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { - "id": "/page/157/Text/10", + "id": "/page/157/Text/13", "block_type": "Text", "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.
", "polygon": [ [ - 85.6142578125, - 559.58203125 + 85.9130859375, + 559.96875 ], [ - 482.4071960449219, - 559.58203125 + 482.90625, + 559.96875 ], [ - 482.4071960449219, - 596.1229095458984 + 482.90625, + 596.3203125 ], [ - 85.6142578125, - 596.1229095458984 + 85.9130859375, + 596.3203125 ] ], + "bbox": [ + 85.9130859375, + 559.96875, + 482.90625, + 596.3203125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { - "id": "/page/157/Text/11", + "id": "/page/157/Text/14", "block_type": "Text", "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.
", "polygon": [ [ - 85.6142578125, - 604.0546875 + 85.9130859375, + 603.66796875 ], [ - 482.4033508300781, - 604.0546875 + 482.90625, + 603.66796875 ], [ - 482.4033508300781, + 482.90625, 652.0579223632812 ], [ - 85.6142578125, + 85.9130859375, 652.0579223632812 ] ], + "bbox": [ + 85.9130859375, + 603.66796875, + 482.90625, + 652.0579223632812 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { - "id": "/page/157/Text/12", + "id": "/page/157/Text/15", "block_type": "Text", "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.
", "polygon": [ [ 85.763671875, - 652.78125 + 652.39453125 ], [ - 483.50390625, - 652.78125 + 482.607421875, + 652.39453125 ], [ - 483.50390625, - 700.734375 + 482.607421875, + 700.6622314453125 ], [ 85.763671875, - 700.734375 + 700.6622314453125 ] ], + "bbox": [ + 85.763671875, + 652.39453125, + 482.607421875, + 700.6622314453125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} } ], "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": null }, { - "id": "/page/158/Page/189", + "id": "/page/158/Page/203", "block_type": "Page", - "html": "14.6. Databases 137
", + "html": "", "polygon": [ [ - 127.7490234375, - 61.171142578125 + 128.12255859375, + 61.05322265625 ], [ 525.6033935546875, - 61.171142578125 + 61.05322265625 ], [ 525.6033935546875, 71.13372802734375 ], [ - 127.7490234375, + 128.12255859375, 71.13372802734375 ] ], + "bbox": [ + 128.12255859375, + 61.05322265625, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { "id": "/page/158/PageHeader/21", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 509.501953125, - 61.0048828125 + 509.80078125, + 60.76318359375 ], [ - 525.638671875, - 61.0048828125 + 525.33984375, + 60.76318359375 ], [ - 525.638671875, - 70.6728515625 + 525.33984375, + 70.43115234375 ], [ - 509.501953125, - 70.6728515625 + 509.80078125, + 70.43115234375 ] ], + "bbox": [ + 509.80078125, + 60.76318359375, + 525.33984375, + 70.43115234375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { "id": "/page/158/Text/1", "block_type": "Text", - "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 .
", + "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 .
", "polygon": [ [ - 128.197265625, - 88.12353515625 + 127.7490234375, + 88.51025390625 ], [ - 525.638671875, - 88.12353515625 + 525.6034545898438, + 88.51025390625 ], [ - 525.638671875, - 123.2666015625 + 525.6034545898438, + 123.01416015625 ], [ - 128.197265625, - 123.2666015625 + 127.7490234375, + 123.01416015625 ] ], + "bbox": [ + 127.7490234375, + 88.51025390625, + 525.6034545898438, + 123.01416015625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/157/SectionHeader/4" + "2": "/page/157/SectionHeader/4" }, "images": {} }, { "id": "/page/158/SectionHeader/2", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 129.2431640625, - 182.919189453125 + 128.6455078125, + 182.6279296875 ], [ - 525.6033935546875, - 182.919189453125 + 525.9375, + 182.6279296875 ], [ - 525.6033935546875, + 525.9375, 217.36688232421875 ], [ - 129.2431640625, + 128.6455078125, 217.36688232421875 ] ], + "bbox": [ + 128.6455078125, + 182.6279296875, + 525.9375, + 217.36688232421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78319,26 +134028,33 @@ "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.
", "polygon": [ [ - 129.5419921875, + 128.794921875, 227.77734375 ], [ - 526.53515625, + 525.604736328125, 227.77734375 ], [ - 526.53515625, + 525.604736328125, 250.56182861328125 ], [ - 129.5419921875, + 128.794921875, 250.56182861328125 ] ], + "bbox": [ + 128.794921875, + 227.77734375, + 525.604736328125, + 250.56182861328125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78348,26 +134064,33 @@ "html": "Opening a database is similar to opening other files:
", "polygon": [ [ - 128.42138671875, - 260.841796875 + 127.8984375, + 260.6484375 ], [ 358.2017822265625, - 260.841796875 + 260.6484375 ], [ 358.2017822265625, 271.56182861328125 ], [ - 128.42138671875, + 127.8984375, 271.56182861328125 ] ], + "bbox": [ + 127.8984375, + 260.6484375, + 358.2017822265625, + 271.56182861328125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78377,26 +134100,33 @@ "html": ">>> import anydbm\n>>> db = anydbm.open('captions.db', 'c')", "polygon": [ [ - 129.60000610351562, - 277.083984375 + 129.5419921875, + 278.4786376953125 ], [ - 338.7654113769531, - 277.083984375 + 339.46875, + 278.4786376953125 ], [ - 338.7654113769531, + 339.46875, 300.6352233886719 ], [ - 129.60000610351562, + 129.5419921875, 300.6352233886719 ] ], + "bbox": [ + 129.5419921875, + 278.4786376953125, + 339.46875, + 300.6352233886719 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78406,55 +134136,69 @@ "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.
", "polygon": [ [ - 129.2431640625, - 307.634765625 + 129.392578125, + 307.0546875 ], [ - 525.9375, - 307.634765625 + 525.638671875, + 307.0546875 ], [ - 525.9375, + 525.638671875, 342.2017822265625 ], [ - 129.2431640625, + 129.392578125, 342.2017822265625 ] ], + "bbox": [ + 129.392578125, + 307.0546875, + 525.638671875, + 342.2017822265625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, { - "id": "/page/158/Text/8", - "block_type": "Text", - "html": ">>> db['cleese.png'] = 'Photo of John Cleese.'
", + "id": "/page/158/TextInlineMath/8", + "block_type": "TextInlineMath", + "html": ">>> db['cleese.png'] = 'Photo of John Cleese.'
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", "polygon": [ [ - 129.31787109375, + 128.27197265625, 366.029296875 ], [ - 376.5234375, + 376.224609375, 366.029296875 ], [ - 376.5234375, + 376.224609375, 376.259765625 ], [ - 129.31787109375, + 128.27197265625, 376.259765625 ] ], + "bbox": [ + 128.27197265625, + 366.029296875, + 376.224609375, + 376.259765625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78493,26 +134244,33 @@ "html": ">>> print db['cleese.png']\nPhoto of John Cleese.", "polygon": [ [ - 128.794921875, - 382.078125 + 127.599609375, + 383.17559814453125 ], [ 265.5584411621094, - 382.078125 + 383.17559814453125 ], [ 265.5584411621094, 405.3321838378906 ], [ - 128.794921875, + 127.599609375, 405.3321838378906 ] ], + "bbox": [ + 127.599609375, + 383.17559814453125, + 265.5584411621094, + 405.3321838378906 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78523,14 +134281,14 @@ "polygon": [ [ 128.49609375, - 411.85546875 + 412.3985900878906 ], [ - 487.6875, - 411.85546875 + 487.41802978515625, + 412.3985900878906 ], [ - 487.6875, + 487.41802978515625, 422.5107421875 ], [ @@ -78538,10 +134296,17 @@ 422.5107421875 ] ], + "bbox": [ + 128.49609375, + 412.3985900878906, + 487.41802978515625, + 422.5107421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78560,17 +134325,24 @@ ], [ 469.4934997558594, - 463.7781677246094 + 464.0625 ], [ 129.60009765625, - 463.7781677246094 + 464.0625 ] ], + "bbox": [ + 129.60009765625, + 428.484375, + 469.4934997558594, + 464.0625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78580,36 +134352,43 @@ "html": "
Many dictionary methods, like keys and items, also work with database objects. So does iteration with a for statement.
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for key in db: print key
", "polygon": [ [ - 128.6455078125, + 127.30078125, 500.06756591796875 ], [ @@ -78621,14 +134400,21 @@ 522.2241516113281 ], [ - 128.6455078125, + 127.30078125, 522.2241516113281 ] ], + "bbox": [ + 127.30078125, + 500.06756591796875, + 202.82525634765625, + 522.2241516113281 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, @@ -78638,84 +134424,105 @@ "html": "As with other files, you should close the database when you are done:
", "polygon": [ [ - 129.392578125, - 528.2578125 + 129.2431640625, + 529.03125 ], [ - 436.88653564453125, - 528.2578125 + 437.484375, + 529.03125 ], [ - 436.88653564453125, + 437.484375, 539.4027252197266 ], [ - 129.392578125, + 129.2431640625, 539.4027252197266 ] ], + "bbox": [ + 129.2431640625, + 529.03125, + 437.484375, + 539.4027252197266 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, { - "id": "/page/158/Code/16", + "id": "/page/158/Code/200", "block_type": "Code", "html": ">>> db.close()", "polygon": [ - [ - 129.16845703125, - 545.2734375 + [ + 129.6001434326172, + 546.3185729980469 ], [ 202.82525634765625, - 545.2734375 + 546.3185729980469 ], [ 202.82525634765625, 556.2811737060547 ], [ - 129.16845703125, + 129.6001434326172, 556.2811737060547 ] ], + "bbox": [ + 129.6001434326172, + 546.3185729980469, + 202.82525634765625, + 556.2811737060547 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/2" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/2" }, "images": {} }, { "id": "/page/158/SectionHeader/17", "block_type": "SectionHeader", - "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.
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", + "html": "", "polygon": [ [ 86.4000015258789, - 59.79638671875 + 60.18310546875 ], [ - 483.50390625, - 59.79638671875 + 482.4034118652344, + 60.18310546875 ], [ - 483.50390625, + 482.4034118652344, 71.13372802734375 ], [ @@ -78858,401 +134693,644 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.18310546875, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/17" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/17" }, "images": {} }, { - "id": "/page/159/PageHeader/13", + "id": "/page/159/PageHeader/17", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 84.94189453125, - 59.79638671875 + 85.763671875, + 60.08642578125 ], [ - 100.92919921875, - 59.79638671875 + 102.19921875, + 60.08642578125 ], [ - 100.92919921875, - 70.04443359375 + 102.19921875, + 69.94775390625 ], [ - 84.94189453125, - 70.04443359375 + 85.763671875, + 69.94775390625 ] ], + "bbox": [ + 85.763671875, + 60.08642578125, + 102.19921875, + 69.94775390625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/17" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/17" }, "images": {} }, { "id": "/page/159/Code/1", "block_type": "Code", - "html": ">>> import pickle\n>>> t = [1, 2, 3]\n>>> pickle.dumps(t)\n'(lp0\\nI1\\naI2\\naI3\\na.'\nThe format isn't obvious to human readers; it is meant to be easy for pickle to interpret.\npickle.loads (\"load string\") reconstitutes the object:\n>>> t1 = [1, 2, 3]\n>>> s = pickle.dumps(t1)\n>>> t2 = pickle.loads(s)\n>>> print t2\n[1, 2, 3]\nAlthough the new object has the same value as the old, it is not (in general) the same object:\n>>> t1 == t2\nTrue\n>>> t1 is t2\nFalse", + "html": "
>>> import pickle\n>>> t = [1, 2, 3]\n>>> pickle.dumps(t)\n'(lp0\\nI1\\naI2\\naI3\\na.'", "polygon": [ [ - 86.361328125, + 86.13720703125, 88.68572998046875 ], [ - 482.90625, + 211.90036010742188, 88.68572998046875 ], [ - 482.90625, - 296.61328125 + 211.90036010742188, + 135.2313232421875 ], [ - 86.361328125, - 298.16015625 + 86.13720703125, + 135.2313232421875 ] ], + "bbox": [ + 86.13720703125, + 88.68572998046875, + 211.90036010742188, + 135.2313232421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/17" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/17" }, "images": {} }, { "id": "/page/159/Text/2", "block_type": "Text", + "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:
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Although the new object has the same value as the old, it is not (in general) the same object:
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In other words, pickling and then unpickling has the same effect as copying the object.
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", "polygon": [ [ - 85.3154296875, - 316.529296875 + 85.46484375, + 317.49609375 ], [ - 484.1015625, - 316.529296875 + 482.4024658203125, + 317.49609375 ], [ - 484.1015625, + 482.4024658203125, 340.84991455078125 ], [ - 85.3154296875, + 85.46484375, 340.84991455078125 ] ], + "bbox": [ + 85.46484375, + 317.49609375, + 482.4024658203125, + 340.84991455078125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/17" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/17" }, "images": {} }, { - "id": "/page/159/Text/4", + "id": "/page/159/Text/8", "block_type": "Text", - "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'].
", + "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'].
", "polygon": [ [ - 85.0166015625, - 341.279296875 + 85.9130859375, + 342.82891845703125 ], [ - 483.50390625, - 341.279296875 + 482.90625, + 342.82891845703125 ], [ - 483.50390625, + 482.90625, 389.4783630371094 ], [ - 85.0166015625, + 85.9130859375, 389.4783630371094 ] ], + "bbox": [ + 85.9130859375, + 342.82891845703125, + 482.90625, + 389.4783630371094 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/17" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/17" }, "images": {} }, { - "id": "/page/159/Text/5", + "id": "/page/159/Text/9", "block_type": "Text", - "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
", + "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
", "polygon": [ [ - 85.46484375, - 397.16015625 + 85.6142578125, + 398.3203125 ], [ - 484.1015625, - 397.16015625 + 482.4270324707031, + 398.3203125 ], [ - 484.1015625, - 433.1062316894531 + 482.4270324707031, + 433.125 ], [ - 85.46484375, - 433.1062316894531 + 85.6142578125, + 433.125 ] ], + "bbox": [ + 85.6142578125, + 398.3203125, + 482.4270324707031, + 433.125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/158/SectionHeader/17" + "2": "/page/157/SectionHeader/4", + "4": "/page/158/SectionHeader/17" }, "images": {} }, { - "id": "/page/159/SectionHeader/6", + "id": "/page/159/SectionHeader/10", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 86.0625, - 485.71875 + 85.763671875, + 486.4921875 ], [ - 482.607421875, - 485.71875 + 482.4032287597656, + 486.4921875 ], [ - 482.607421875, - 534.4453125 + 482.4032287597656, + 533.7599487304688 ], [ - 86.0625, - 534.4453125 + 85.763671875, + 533.7599487304688 ] ], + "bbox": [ + 85.763671875, + 486.4921875, + 482.4032287597656, + 533.7599487304688 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/159/Text/8", + "id": "/page/159/Text/12", "block_type": "Text", "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.
", "polygon": [ [ - 85.9130859375, + 85.6142578125, 542.1796875 ], [ - 483.50390625, + 482.4032897949219, 542.1796875 ], [ - 483.50390625, - 566.15625 + 482.4032897949219, + 565.3828125 ], [ - 85.9130859375, - 566.15625 + 85.6142578125, + 565.3828125 ] ], + "bbox": [ + 85.6142578125, + 542.1796875, + 482.4032897949219, + 565.3828125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/159/Text/9", + "id": "/page/159/Text/13", "block_type": "Text", - "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 :
", + "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 :
", "polygon": [ [ - 86.0625, - 573.1171875 + 85.763671875, + 573.50390625 ], [ 482.90625, - 573.1171875 + 573.50390625 ], [ 482.90625, 596.6749572753906 ], [ - 86.0625, + 85.763671875, 596.6749572753906 ] ], + "bbox": [ + 85.763671875, + 573.50390625, + 482.90625, + 596.6749572753906 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/159/TextInlineMath/10", - "block_type": "TextInlineMath", - "html": ">>> cmd = 'ls -l' >>> fp = os.popen(cmd)
", + "id": "/page/159/Code/14", + "block_type": "Code", + "html": ">>> cmd = 'ls -l'\n>>> fp = os.popen(cmd)", "polygon": [ [ - 85.53955078125, - 600.57421875 + 86.13720703125, + 600.9609375 ], [ 201.4779510498047, - 600.57421875 + 600.9609375 ], [ 201.4779510498047, 624.0114135742188 ], [ - 85.53955078125, + 86.13720703125, 624.0114135742188 ] ], + "bbox": [ + 86.13720703125, + 600.9609375, + 201.4779510498047, + 624.0114135742188 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/159/Text/11", + "id": "/page/159/Text/15", "block_type": "Text", "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:
", "polygon": [ [ - 85.763671875, + 86.0625, 628.41796875 ], [ - 483.802734375, + 482.90625, 628.41796875 ], [ - 483.802734375, + 482.90625, 663.8409729003906 ], [ - 85.763671875, + 86.0625, 663.8409729003906 ] ], + "bbox": [ + 86.0625, + 628.41796875, + 482.90625, + 663.8409729003906 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/159/Footnote/12", + "id": "/page/159/Footnote/16", "block_type": "Footnote", - "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.
", + "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.
", "polygon": [ [ - 85.9130859375, - 671.34375 + 86.361328125, + 671.9081573486328 ], [ - 482.90625, - 671.34375 + 482.607421875, + 671.9081573486328 ], [ - 482.90625, - 700.2713241577148 + 482.607421875, + 700.734375 ], [ - 85.9130859375, - 700.2713241577148 + 86.361328125, + 700.734375 ] ], + "bbox": [ + 86.361328125, + 671.9081573486328, + 482.607421875, + 700.734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} } ], "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": null }, { - "id": "/page/160/Page/191", + "id": "/page/160/Page/210", "block_type": "Page", - "html": "14.9. Writing modules 139
", + "html": "", "polygon": [ [ - 128.57080078125, + 129.01904296875, 61.171142578125 ], [ @@ -79290,402 +135374,610 @@ 71.13372802734375 ], [ - 128.57080078125, + 129.01904296875, 71.13372802734375 ] ], + "bbox": [ + 129.01904296875, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/160/PageHeader/14", + "id": "/page/160/PageHeader/17", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.697265625, - 61.05322265625 + 509.80078125, + 61.1015625 ], [ - 525.638671875, - 61.05322265625 + 525.9375, + 61.1015625 ], [ - 525.638671875, - 70.14111328125 + 525.9375, + 70.3828125 ], [ - 510.697265625, - 70.14111328125 + 509.80078125, + 70.3828125 ] ], + "bbox": [ + 509.80078125, + 61.1015625, + 525.9375, + 70.3828125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/160/TextInlineMath/1", - "block_type": "TextInlineMath", - "html": ">>> res = fp.read() When you are done, you close the pipe like a file: >>> stat = fp.close() >>> print stat None The return value is the final status of the ls process; None means that it ended normally (with no errors).
", + "id": "/page/160/Text/1", + "block_type": "Text", + "html": ">>> res = fp.read()
", "polygon": [ [ - 129.60000610351562, + 127.7490234375, 88.68572998046875 ], [ - 525.595947265625, + 249.9697265625, 88.68572998046875 ], + [ + 249.9697265625, + 100.2568359375 + ], + [ + 127.7490234375, + 100.2568359375 + ] + ], + "bbox": [ + 127.7490234375, + 88.68572998046875, + 249.9697265625, + 100.2568359375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/160/Text/2", + "block_type": "Text", + "html": "When you are done, you close the pipe like a file:
", + "polygon": [ + [ + 127.7490234375, + 103.833984375 + ], + [ + 345.4097900390625, + 103.833984375 + ], + [ + 345.4097900390625, + 114.04888916015625 + ], + [ + 127.7490234375, + 114.04888916015625 + ] + ], + "bbox": [ + 127.7490234375, + 103.833984375, + 345.4097900390625, + 114.04888916015625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/160/Code/3", + "block_type": "Code", + "html": ">>> stat = fp.close()\n>>> print stat\nNone", + "polygon": [ + [ + 128.197265625, + 119.188720703125 + ], + [ + 239.43765258789062, + 119.188720703125 + ], + [ + 239.43765258789062, + 156.041015625 + ], + [ + 128.197265625, + 156.041015625 + ] + ], + "bbox": [ + 128.197265625, + 119.188720703125, + 239.43765258789062, + 156.041015625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/160/Text/4", + "block_type": "Text", + "html": "
The return value is the final status of the ls process; None means that it ended normally (with no errors).
", + "polygon": [ + [ + 129.09375, + 158.8287353515625 + ], + [ + 525.595947265625, + 158.8287353515625 + ], [ 525.595947265625, 181.13494873046875 ], [ - 129.60000610351562, + 129.09375, 181.13494873046875 ] ], + "bbox": [ + 129.09375, + 158.8287353515625, + 525.595947265625, + 181.13494873046875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/160/Text/2", + "id": "/page/160/Text/5", "block_type": "Text", - "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).
", + "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).
", "polygon": [ [ - 129.60003662109375, - 189.5888671875 + 128.794921875, + 189.9755859375 ], [ - 525.63525390625, - 189.5888671875 + 525.638671875, + 189.9755859375 ], [ - 525.63525390625, - 249.626953125 + 525.638671875, + 249.13494873046875 ], [ - 129.60003662109375, - 249.626953125 + 128.794921875, + 249.13494873046875 ] ], + "bbox": [ + 128.794921875, + 189.9755859375, + 525.638671875, + 249.13494873046875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/160/Text/3", + "id": "/page/160/Text/6", "block_type": "Text", "html": "You can use a pipe to run md5sum from Python and get the result:
", "polygon": [ [ - 129.60003662109375, - 257.361328125 + 128.794921875, + 258.24578857421875 ], [ 414.5018005371094, - 257.361328125 + 258.24578857421875 ], [ 414.5018005371094, 268.35797119140625 ], [ - 129.60003662109375, + 128.794921875, 268.35797119140625 ] ], + "bbox": [ + 128.794921875, + 258.24578857421875, + 414.5018005371094, + 268.35797119140625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/160/Code/4", + "id": "/page/160/Code/7", "block_type": "Code", "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", "polygon": [ [ - 129.60000610351562, - 271.283203125 + 128.72021484375, + 273.497802734375 ], [ - 350.2265625, - 271.283203125 + 350.82421875, + 273.497802734375 ], [ - 350.2265625, - 381.014404296875 + 350.82421875, + 382.658203125 ], [ - 129.60000610351562, - 381.014404296875 + 128.72021484375, + 382.658203125 ] ], + "bbox": [ + 128.72021484375, + 273.497802734375, + 350.82421875, + 382.658203125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/160/Text/5", + "id": "/page/160/Text/8", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 129.60000610351562, + 128.49609375, 383.22369384765625 ], [ - 525.9375, + 525.6034545898438, 383.22369384765625 ], [ - 525.9375, + 525.6034545898438, 417.57427978515625 ], [ - 129.60000610351562, + 128.49609375, 417.57427978515625 ] ], + "bbox": [ + 128.49609375, + 383.22369384765625, + 525.6034545898438, + 417.57427978515625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/160/ListGroup/191", + "id": "/page/160/ListGroup/208", "block_type": "ListGroup", - "html": "Solution: http: // thinkpython. com/ code/ find_ duplicates. py .
", + "html": "Solution: http: // thinkpython. com/ code/ find_ duplicates. py .
", "polygon": [ [ - 128.3466796875, - 525.1640625 + 127.4501953125, + 526.2059936523438 ], [ 426.3626708984375, - 525.1640625 + 526.2059936523438 ], [ 426.3626708984375, 536.2492980957031 ], [ - 128.3466796875, + 127.4501953125, 536.2492980957031 ] ], + "bbox": [ + 127.4501953125, + 526.2059936523438, + 426.3626708984375, + 536.2492980957031 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/159/SectionHeader/6" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10" }, "images": {} }, { - "id": "/page/160/SectionHeader/10", + "id": "/page/160/SectionHeader/13", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 128.49609375, - 588.97265625 + 127.599609375, + 590.1328125 ], [ - 527.1328125, - 588.97265625 + 525.638671875, + 590.1328125 ], [ - 527.1328125, - 612.4179992675781 + 525.638671875, + 612.5625 ], [ - 128.49609375, - 612.4179992675781 + 127.599609375, + 612.5625 ] ], + "bbox": [ + 127.599609375, + 590.1328125, + 525.638671875, + 612.5625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/160/Code/12", + "id": "/page/160/Code/15", "block_type": "Code", "html": "def linecount(filename):\n count = 0\n for line in open(filename):\n count += 1\n return count", "polygon": [ [ - 127.07666015625, + 129.5999298095703, 617.5568542480469 ], [ @@ -79694,60 +135986,78 @@ ], [ 291.7457275390625, - 683.33203125 + 687.19921875 ], [ - 127.07666015625, - 683.33203125 + 129.5999298095703, + 687.19921875 ] ], + "bbox": [ + 129.5999298095703, + 617.5568542480469, + 291.7457275390625, + 687.19921875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/160/Text/13", + "id": "/page/160/Text/16", "block_type": "Text", "html": "
print linecount('wc.py')
", "polygon": [ [ - 128.794921875, - 690.6796875 + 128.12255859375, + 690.7228546142578 ], [ 255.1002960205078, - 690.6796875 + 690.7228546142578 ], [ 255.1002960205078, - 700.734375 + 701.5078125 ], [ - 128.794921875, - 700.734375 + 128.12255859375, + 701.5078125 ] ], + "bbox": [ + 128.12255859375, + 690.7228546142578, + 255.1002960205078, + 701.5078125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} } ], "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": null }, { - "id": "/page/161/Page/179", + "id": "/page/161/Page/184", "block_type": "Page", - "html": "140 Chapter 14. Files
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.56982421875 + 60.47314453125 ], [ 482.4034118652344, - 60.56982421875 + 60.47314453125 ], [ 482.4034118652344, @@ -79789,39 +136105,55 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.47314453125, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/PageHeader/16", + "id": "/page/161/PageHeader/20", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.3154296875, - 59.69970703125 + 85.763671875, + 60.328125 ], [ - 100.5556640625, - 59.69970703125 + 102.796875, + 60.328125 ], [ - 100.5556640625, - 69.65771484375 + 102.796875, + 70.189453125 ], [ - 85.3154296875, - 69.65771484375 + 85.763671875, + 70.189453125 ] ], + "bbox": [ + 85.763671875, + 60.328125, + 102.796875, + 70.189453125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, @@ -79831,102 +136163,274 @@ "html": "If 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:
", "polygon": [ [ - 85.3154296875, - 88.4619140625 + 85.763671875, + 87.93017578125 ], [ - 482.4033203125, - 86.9150390625 + 482.90625, + 87.93017578125 ], [ - 482.4033203125, + 482.90625, 110.99188232421875 ], [ - 85.3154296875, + 85.763671875, 110.99188232421875 ] ], + "bbox": [ + 85.763671875, + 87.93017578125, + 482.90625, + 110.99188232421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { "id": "/page/161/Code/2", "block_type": "Code", - "html": ">>> import wc\n7\nNow you have a module object wc:\n>>> print wc\n<module 'wc' from 'wc.py'>\nThat provides a function called linecount:\n>>> wc.linecount('wc.py')\n7", + "html": "
>>> import wc\n7", "polygon": [ [ - 86.39999389648438, - 118.15869140625 + 85.46484375, + 115.62890625 ], [ - 279.10546875, - 118.15869140625 + 187.8134765625, + 115.62890625 ], [ - 279.10546875, + 187.8134765625, + 143.279296875 + ], + [ + 85.46484375, + 143.279296875 + ] + ], + "bbox": [ + 85.46484375, + 115.62890625, + 187.8134765625, + 143.279296875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/161/Text/3", + "block_type": "Text", + "html": "
Now you have a module object wc:
", + "polygon": [ + [ + 85.6142578125, + 147.6317138671875 + ], + [ + 239.8095703125, + 147.6317138671875 + ], + [ + 239.8095703125, + 158.4580078125 + ], + [ + 85.6142578125, + 158.4580078125 + ] + ], + "bbox": [ + 85.6142578125, + 147.6317138671875, + 239.8095703125, + 158.4580078125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/161/Code/4", + "block_type": "Code", + "html": ">>> print wc\n<module 'wc' from 'wc.py'>", + "polygon": [ + [ + 85.39013671875, + 164.90972900390625 + ], + [ + 227.70703125, + 164.90972900390625 + ], + [ + 227.70703125, + 187.06732177734375 + ], + [ + 85.39013671875, + 187.06732177734375 + ] + ], + "bbox": [ + 85.39013671875, + 164.90972900390625, + 227.70703125, + 187.06732177734375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/161/Text/5", + "block_type": "Text", + "html": "
That provides a function called linecount:
", + "polygon": [ + [ + 85.98779296875, + 193.8427734375 + ], + [ + 277.3125, + 193.8427734375 + ], + [ + 277.3125, + 204.49493408203125 + ], + [ + 85.98779296875, + 204.49493408203125 + ] + ], + "bbox": [ + 85.98779296875, + 193.8427734375, + 277.3125, + 204.49493408203125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/154/SectionHeader/1", + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/161/Code/6", + "block_type": "Code", + "html": ">>> wc.linecount('wc.py')\n7", + "polygon": [ + [ + 85.166015625, + 211.53515625 + ], + [ + 217.12937927246094, + 211.53515625 + ], + [ + 217.12937927246094, 233.818359375 ], [ - 86.39999389648438, + 85.166015625, 233.818359375 ] ], + "bbox": [ + 85.166015625, + 211.53515625, + 217.12937927246094, + 233.818359375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/Text/3", + "id": "/page/161/Text/7", "block_type": "Text", "html": "
So that's how you write modules in Python.
", "polygon": [ [ - 85.9130859375, - 239.765625 + 86.0625, + 240.15234375 ], [ - 279.7242736816406, - 239.765625 + 280.30078125, + 240.15234375 ], [ - 279.7242736816406, + 280.30078125, 251.2469482421875 ], [ - 85.9130859375, + 86.0625, 251.2469482421875 ] ], + "bbox": [ + 86.0625, + 240.15234375, + 280.30078125, + 251.2469482421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/Text/4", + "id": "/page/161/Text/8", "block_type": "Text", "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.
", "polygon": [ [ 85.9130859375, - 261.80859375 + 261.615234375 ], [ - 482.4034423828125, - 261.80859375 + 482.90625, + 261.615234375 ], [ - 482.4034423828125, + 482.90625, 296.8849182128906 ], [ @@ -79934,289 +136438,366 @@ 296.8849182128906 ] ], + "bbox": [ + 85.9130859375, + 261.615234375, + 482.90625, + 296.8849182128906 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/Text/5", + "id": "/page/161/Text/9", "block_type": "Text", "html": "Programs that will be imported as modules often use the following idiom:
", "polygon": [ [ - 85.763671875, - 308.021484375 + 85.9130859375, + 307.0546875 ], [ - 414.474609375, - 308.021484375 + 412.3828125, + 307.0546875 ], [ - 414.474609375, + 412.3828125, 318.1349182128906 ], [ - 85.763671875, + 85.9130859375, 318.1349182128906 ] ], + "bbox": [ + 85.9130859375, + 307.0546875, + 412.3828125, + 318.1349182128906 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/TextInlineMath/6", - "block_type": "TextInlineMath", - "html": "if __name__ == '__main__': print linecount('wc.py')
", + "id": "/page/161/Code/10", + "block_type": "Code", + "html": "if __name__ == '__main__':\n print linecount('wc.py')", "polygon": [ [ - 85.98779296875, - 325.037109375 + 85.9130859375, + 325.23046875 ], [ - 232.81637573242188, - 323.490234375 + 232.9365234375, + 325.23046875 ], [ - 232.81637573242188, - 347.4583435058594 + 232.9365234375, + 347.66015625 ], [ - 85.98779296875, - 347.4583435058594 + 85.9130859375, + 347.66015625 ] ], + "bbox": [ + 85.9130859375, + 325.23046875, + 232.9365234375, + 347.66015625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/Text/7", + "id": "/page/161/Text/11", "block_type": "Text", "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.
", "polygon": [ [ 85.763671875, - 353.267578125 + 354.62109375 ], [ - 482.607421875, - 353.267578125 + 482.39801025390625, + 354.62109375 ], [ - 482.607421875, - 390.779296875 + 482.39801025390625, + 389.2759094238281 ], [ 85.763671875, - 390.779296875 + 389.2759094238281 ] ], + "bbox": [ + 85.763671875, + 354.62109375, + 482.39801025390625, + 389.2759094238281 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/Text/8", + "id": "/page/161/Text/12", "block_type": "Text", "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?
", "polygon": [ [ - 85.9130859375, - 391.3346252441406 + 85.6142578125, + 390.97265625 ], [ - 483.50390625, - 391.3346252441406 + 482.3984069824219, + 390.97265625 ], [ - 483.50390625, + 482.3984069824219, 425.68621826171875 ], [ - 85.9130859375, + 85.6142578125, 425.68621826171875 ] ], + "bbox": [ + 85.6142578125, + 390.97265625, + 482.3984069824219, + 425.68621826171875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/Text/9", + "id": "/page/161/Text/13", "block_type": "Text", "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.
", "polygon": [ [ - 85.0166015625, - 436.21875 + 85.763671875, + 435.4453125 ], [ - 482.4035339355469, - 436.21875 + 482.607421875, + 435.4453125 ], [ - 482.4035339355469, + 482.607421875, 459.1302185058594 ], [ - 85.0166015625, + 85.763671875, 459.1302185058594 ] ], + "bbox": [ + 85.763671875, + 435.4453125, + 482.607421875, + 459.1302185058594 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/Text/10", + "id": "/page/161/Text/14", "block_type": "Text", "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.
", "polygon": [ [ - 85.166015625, - 470.25 + 85.6142578125, + 469.86328125 ], [ - 483.205078125, - 470.25 + 482.4019775390625, + 469.86328125 ], [ - 483.205078125, + 482.4019775390625, 492.57421875 ], [ - 85.166015625, + 85.6142578125, 492.57421875 ] ], + "bbox": [ + 85.6142578125, + 469.86328125, + 482.4019775390625, + 492.57421875 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/160/SectionHeader/10" + "2": "/page/157/SectionHeader/4", + "3": "/page/159/SectionHeader/10", + "4": "/page/160/SectionHeader/13" }, "images": {} }, { - "id": "/page/161/SectionHeader/11", + "id": "/page/161/SectionHeader/15", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 85.9130859375, - 553.39453125 + 86.0625, + 553.0078125 ], [ - 482.90625, - 553.39453125 + 482.607421875, + 553.0078125 ], [ - 482.90625, + 482.607421875, 575.8339233398438 ], [ - 85.9130859375, + 86.0625, 575.8339233398438 ] ], + "bbox": [ + 86.0625, + 553.0078125, + 482.607421875, + 575.8339233398438 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} }, { - "id": "/page/161/Code/13", + "id": "/page/161/Code/17", "block_type": "Code", "html": ">>> s = '1 2\\t 3\\n 4'\n>>> print s\n1 2 3\n 4", "polygon": [ [ - 86.0625, - 581.625 + 85.9130859375, + 582.78515625 ], [ - 197.6748046875, - 581.625 + 196.21238708496094, + 582.78515625 ], [ - 197.6748046875, - 630.3515625 + 196.21238708496094, + 629.5453796386719 ], [ - 86.0625, - 630.3515625 + 85.9130859375, + 629.5453796386719 ] ], + "bbox": [ + 85.9130859375, + 582.78515625, + 196.21238708496094, + 629.5453796386719 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} }, { - "id": "/page/161/Text/14", + "id": "/page/161/Text/18", "block_type": "Text", "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:
", "polygon": [ [ 86.2119140625, - 635.765625 + 636.15234375 ], [ - 483.50390625, - 635.765625 + 482.90625, + 636.15234375 ], [ - 483.50390625, + 482.90625, 671.361946105957 ], [ @@ -80224,53 +136805,68 @@ 671.361946105957 ] ], + "bbox": [ + 86.2119140625, + 636.15234375, + 482.90625, + 671.361946105957 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} }, { - "id": "/page/161/Code/15", + "id": "/page/161/Code/19", "block_type": "Code", "html": ">>> print repr(s)\n'1 2\\t 3\\n 4'", "polygon": [ [ - 86.40000915527344, - 675.59765625 + 86.0625, + 678.3046875 ], [ - 176.607421875, - 675.59765625 + 175.3162078857422, + 678.3046875 ], [ - 175.412109375, - 701.12109375 + 175.3162078857422, + 700.734375 ], [ - 85.6142578125, - 701.12109375 + 86.0625, + 700.734375 ] ], + "bbox": [ + 86.0625, + 678.3046875, + 175.3162078857422, + 700.734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} } ], "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": null }, { - "id": "/page/162/Page/187", + "id": "/page/162/Page/202", "block_type": "Page", - "html": "
14.11. Glossary 141
", + "html": "", "polygon": [ [ - 128.197265625, + 129.2431640625, 61.171142578125 ], [ @@ -80308,43 +136910,57 @@ 71.13372802734375 ], [ - 128.197265625, + 129.2431640625, 71.13372802734375 ] ], + "bbox": [ + 129.2431640625, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} }, { "id": "/page/162/PageHeader/21", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 511.294921875, - 61.53662109375 + 510.3984375, + 60.8115234375 ], [ - 525.041015625, - 61.53662109375 + 525.9375, + 60.8115234375 ], [ - 525.041015625, - 70.62451171875 + 525.9375, + 69.8994140625 ], [ - 511.294921875, - 70.62451171875 + 510.3984375, + 69.8994140625 ] ], + "bbox": [ + 510.3984375, + 60.8115234375, + 525.9375, + 69.8994140625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} }, @@ -80354,26 +136970,33 @@ "html": "This can be helpful for debugging.
", "polygon": [ [ - 129.392578125, - 88.83526611328125 + 129.09375, + 88.70361328125 ], [ 281.1211853027344, - 88.83526611328125 + 88.70361328125 ], [ 281.1211853027344, 98.79791259765625 ], [ - 129.392578125, + 129.09375, 98.79791259765625 ] ], + "bbox": [ + 129.09375, + 88.70361328125, + 281.1211853027344, + 98.79791259765625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} }, @@ -80383,102 +137006,124 @@ "html": "One 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.
", "polygon": [ [ - 128.0478515625, - 107.665283203125 + 128.6455078125, + 107.12109375 ], [ - 525.6033935546875, - 107.665283203125 + 526.53515625, + 107.12109375 ], [ - 525.6033935546875, + 526.53515625, 154.21087646484375 ], [ - 128.0478515625, + 128.6455078125, 154.21087646484375 ] ], + "bbox": [ + 128.6455078125, + 107.12109375, + 526.53515625, + 154.21087646484375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} }, { "id": "/page/162/Text/3", "block_type": "Text", - "html": "For most systems, there are applications to convert from one format to another. You can find them (and read more about this issue) at http://en.wikipedia.org/wiki/Newline. Or, of course, you could write one yourself.
", + "html": "For most systems, there are applications to convert from one format to another. You can find them (and read more about this issue) at http://en.wikipedia.org/wiki/Newline. Or, of course, you could write one yourself.
", "polygon": [ [ - 128.9443359375, - 163.0792236328125 + 129.392578125, + 162.7119140625 ], [ - 525.6033935546875, - 163.0792236328125 + 526.236328125, + 162.7119140625 ], [ - 525.6033935546875, + 526.236328125, 197.430908203125 ], [ - 128.9443359375, + 129.392578125, 197.430908203125 ] ], + "bbox": [ + 129.392578125, + 162.7119140625, + 526.236328125, + 197.430908203125 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/161/SectionHeader/11" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15" }, "images": {} }, { "id": "/page/162/SectionHeader/4", "block_type": "SectionHeader", - "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:
", + "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:
", "polygon": [ [ - 129.392578125, + 128.6455078125, 573.4085083007812 ], [ - 525.638671875, + 525.9375, 573.4085083007812 ], [ - 525.638671875, - 611.015625 + 525.9375, + 608.30859375 ], [ - 129.392578125, - 611.015625 + 128.6455078125, + 608.30859375 ] ], + "bbox": [ + 128.6455078125, + 573.4085083007812, + 525.9375, + 608.30859375 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/162/SectionHeader/16" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15", + "4": "/page/162/SectionHeader/16" }, "images": {} }, @@ -80877,91 +137634,117 @@ "html": "import urllib", "polygon": [ [ - 129.59994506835938, + 128.3466796875, 612.6786499023438 ], [ - 197.59469604492188, + 197.6748046875, 612.6786499023438 ], [ - 197.59469604492188, - 622.6412506103516 + 197.6748046875, + 623.390625 ], [ - 129.59994506835938, - 622.6412506103516 + 128.3466796875, + 623.390625 ] ], + "bbox": [ + 128.3466796875, + 612.6786499023438, + 197.6748046875, + 623.390625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/162/SectionHeader/16" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15", + "4": "/page/162/SectionHeader/16" }, "images": {} }, { "id": "/page/162/Code/19", "block_type": "Code", - "html": "
conn = urllib.urlopen('http://thinkpython.com/secret.html')\nfor line in conn:\n print line.strip()\nRun this code and follow the instructions you see there. Solution: http: // thinkpython. com/", + "html": "
conn = urllib.urlopen('http://thinkpython.com/secret.html')\nfor line in conn:\n print line.strip()", "polygon": [ [ - 129.392578125, + 129.09375, 637.0676574707031 ], [ - 523.9127807617188, + 439.27734375, 637.0676574707031 ], [ - 523.9127807617188, - 686.2551193237305 + 439.27734375, + 672.890625 ], [ - 129.392578125, - 686.2551193237305 + 129.09375, + 672.890625 ] ], + "bbox": [ + 129.09375, + 637.0676574707031, + 439.27734375, + 672.890625 + ], "children": null, "section_hierarchy": { "1": "/page/154/SectionHeader/1", - "3": "/page/162/SectionHeader/16" + "2": "/page/157/SectionHeader/4", + "3": "/page/161/SectionHeader/15", + "4": "/page/162/SectionHeader/16" }, "images": {} }, { "id": "/page/162/Text/20", "block_type": "Text", - "html": "
code/ zip_ code. py .
", + "html": "Run this code and follow the instructions you see there. Solution: http: // thinkpython. com/ code/ zip_ code. py .
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- } - }, - { - "id": "/page/165/Caption/2", - "block_type": "Caption", - "html": "Figure 15.1: Object diagram.
", - "polygon": [ - [ - 219.4892578125, - 143.9560546875 - ], - [ - 346.939453125, - 143.9560546875 - ], - [ - 346.939453125, - 154.7841796875 - ], - [ - 219.4892578125, - 154.7841796875 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/164/SectionHeader/1", - "3": "/page/164/SectionHeader/3" - }, - "images": {} - } + "bbox": [ + 84.49365234375, + 59.94140625, + 101.52685546875, + 70.0927734375 ], + "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/164/SectionHeader/3" + "4": "/page/164/SectionHeader/3" }, - "images": null + "images": {} }, { - "id": "/page/165/TextInlineMath/3", - "block_type": "TextInlineMath", - "html": ">>> print Point <class '__main__.Point'>
", + "id": "/page/165/Code/1", + "block_type": "Code", + "html": "x\n y\n 3.0\n 4.0\nblank\n Point", "polygon": [ [ - 86.0625, - 174.86676025390625 + 236.64968872070312, + 85.31982421875 ], [ - 224.12109375, - 174.86676025390625 + 336.48046875, + 85.31982421875 ], [ - 224.12109375, - 208.44140625 + 336.48046875, + 129.55078125 ], [ - 86.0625, - 208.44140625 + 236.64968872070312, + 129.55078125 + ] + ], + "bbox": [ + 236.64968872070312, + 85.31982421875, + 336.48046875, + 129.55078125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/164/SectionHeader/1", + "4": "/page/164/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/165/Caption/2", + "block_type": "Caption", + "html": "
Figure 15.1: Object diagram.
", + "polygon": [ + [ + 222.1787109375, + 143.9560546875 + ], + [ + 346.5672607421875, + 143.9560546875 + ], + [ + 346.5672607421875, + 154.7529296875 + ], + [ + 222.1787109375, + 154.7529296875 + ] + ], + "bbox": [ + 222.1787109375, + 143.9560546875, + 346.5672607421875, + 154.7529296875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/164/SectionHeader/1", + "4": "/page/164/SectionHeader/3" + }, + "images": {} + }, + { + "id": "/page/165/Code/3", + "block_type": "Code", + "html": ">>> print Point\n<class '__main__.Point'>", + "polygon": [ + [ + 85.46484375, + 174.603515625 + ], + [ + 215.9033203125, + 174.603515625 + ], + [ + 215.9033203125, + 198.0 + ], + [ + 85.46484375, + 198.0 ] ], + "bbox": [ + 85.46484375, + 174.603515625, + 215.9033203125, + 198.0 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/164/SectionHeader/3" + "4": "/page/164/SectionHeader/3" }, "images": {} }, @@ -81752,26 +138700,32 @@ "html": "
Because Point is defined at the top level, its \"full name\" is __main__.Point.
", "polygon": [ [ - 86.2119140625, - 201.7777099609375 + 85.46484375, + 201.09375 ], [ - 426.12890625, - 201.7777099609375 + 421.17767333984375, + 201.09375 ], [ - 426.12890625, - 216.94921875 + 421.17767333984375, + 211.889892578125 ], [ - 86.2119140625, - 216.94921875 + 85.46484375, + 211.889892578125 ] ], + "bbox": [ + 85.46484375, + 201.09375, + 421.17767333984375, + 211.889892578125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/164/SectionHeader/3" + "4": "/page/164/SectionHeader/3" }, "images": {} }, @@ -81781,26 +138735,32 @@ "html": "The class object is like a factory for creating objects. To create a Point, you call Point as if it were a function.
", "polygon": [ [ - 85.763671875, - 220.46673583984375 + 85.6142578125, + 219.462890625 ], [ 482.40020751953125, - 220.46673583984375 + 219.462890625 ], [ 482.40020751953125, - 243.826171875 + 242.77288818359375 ], [ - 85.763671875, - 243.826171875 + 85.6142578125, + 242.77288818359375 ] ], + "bbox": [ + 85.6142578125, + 219.462890625, + 482.40020751953125, + 242.77288818359375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/164/SectionHeader/3" + "4": "/page/164/SectionHeader/3" }, "images": {} }, @@ -81810,26 +138770,32 @@ "html": ">>> blank = Point()\n>>> print blank\n<__main__.Point instance at 0xb7e9d3ac>", "polygon": [ [ - 85.6142578125, - 246.7265625 + 84.8671875, + 247.377685546875 ], [ 290.3941345214844, - 246.7265625 + 247.377685546875 ], [ 290.3941345214844, - 281.729248046875 + 281.91796875 ], [ - 85.6142578125, - 281.729248046875 + 84.8671875, + 281.91796875 ] ], + "bbox": [ + 84.8671875, + 247.377685546875, + 290.3941345214844, + 281.91796875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/164/SectionHeader/3" + "4": "/page/164/SectionHeader/3" }, "images": {} }, @@ -81840,11 +138806,11 @@ "polygon": [ [ 85.9130859375, - 285.3984375 + 286.171875 ], [ 482.4039611816406, - 285.3984375 + 286.171875 ], [ 482.4039611816406, @@ -81855,10 +138821,16 @@ 308.7898254394531 ] ], + "bbox": [ + 85.9130859375, + 286.171875, + 482.4039611816406, + 308.7898254394531 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/164/SectionHeader/3" + "4": "/page/164/SectionHeader/3" }, "images": {} }, @@ -81868,51 +138840,63 @@ "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).
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", "polygon": [ [ - 86.13720703125, - 390.392578125 + 85.763671875, + 391.166015625 ], [ - 332.595703125, - 390.392578125 + 331.69921875, + 391.166015625 ], [ - 332.595703125, + 331.69921875, 402.1918029785156 ], [ - 86.13720703125, + 85.763671875, 402.1918029785156 ] ], + "bbox": [ + 85.763671875, + 391.166015625, + 331.69921875, + 402.1918029785156 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -81955,22 +138945,28 @@ "html": ">>> blank.x = 3.0\n>>> blank.y = 4.0", "polygon": [ [ - 85.6142578125, - 405.28125 + 85.3154296875, + 406.0546875 ], [ - 175.316162109375, - 405.28125 + 175.8603515625, + 406.0546875 ], [ - 175.316162109375, - 430.8046875 + 175.8603515625, + 429.2578125 ], [ - 85.6142578125, - 430.8046875 + 85.3154296875, + 429.2578125 ] ], + "bbox": [ + 85.3154296875, + 406.0546875, + 175.8603515625, + 429.2578125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -81984,22 +138980,28 @@ "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.
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", "polygon": [ [ - 85.763671875, - 474.890625 + 85.6142578125, + 475.27734375 ], [ - 482.90625, - 474.890625 + 482.4033203125, + 475.27734375 ], [ - 482.90625, + 482.4033203125, 499.0918273925781 ], [ - 85.763671875, + 85.6142578125, 499.0918273925781 ] ], + "bbox": [ + 85.6142578125, + 475.27734375, + 482.4033203125, + 499.0918273925781 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82039,25 +139047,31 @@ { "id": "/page/165/Text/14", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 85.763671875, - 506.98828125 + 85.46484375, + 506.6015625 ], [ - 483.802734375, - 506.98828125 + 482.40325927734375, + 506.6015625 ], [ - 483.802734375, - 530.19140625 + 482.40325927734375, + 529.9748229980469 ], [ - 85.763671875, - 530.19140625 + 85.46484375, + 529.9748229980469 ] ], + "bbox": [ + 85.46484375, + 506.6015625, + 482.40325927734375, + 529.9748229980469 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82071,22 +139085,28 @@ "html": "The variable blank refers to a Point object, which contains two attributes. Each attribute refers to a floating-point number.
", "polygon": [ [ - 86.361328125, + 85.46484375, 537.5390625 ], [ - 483.50390625, + 482.90625, 537.5390625 ], [ - 483.50390625, + 482.90625, 560.8578186035156 ], [ - 86.361328125, + 85.46484375, 560.8578186035156 ] ], + "bbox": [ + 85.46484375, + 537.5390625, + 482.90625, + 560.8578186035156 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82100,22 +139120,28 @@ "html": "You can read the value of an attribute using the same syntax:
", "polygon": [ [ - 85.39013671875, - 568.08984375 + 85.53955078125, + 569.25 ], [ - 354.111328125, - 568.08984375 + 353.74627685546875, + 569.25 ], [ - 354.111328125, + 353.74627685546875, 579.5468139648438 ], [ - 85.39013671875, + 85.53955078125, 579.5468139648438 ] ], + "bbox": [ + 85.53955078125, + 569.25, + 353.74627685546875, + 579.5468139648438 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82129,22 +139155,28 @@ "html": ">>> print blank.y\n4.0\n>>> x = blank.x\n>>> print x\n3.0", "polygon": [ [ - 84.64306640625, - 583.9453125 + 84.8671875, + 584.1516571044922 ], [ 175.31614685058594, - 583.9453125 + 584.1516571044922 ], [ 175.31614685058594, - 645.046875 + 642.8912658691406 ], [ - 84.64306640625, - 645.046875 + 84.8671875, + 642.8912658691406 ] ], + "bbox": [ + 84.8671875, + 584.1516571044922, + 175.31614685058594, + 642.8912658691406 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82158,22 +139190,28 @@ "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.
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", + "html": "", "polygon": [ [ - 128.197265625, - 60.71484375 + 128.6455078125, + 61.1015625 ], [ 525.6033935546875, - 60.71484375 + 61.1015625 ], [ 525.6033935546875, 71.13372802734375 ], [ - 128.197265625, + 128.6455078125, 71.13372802734375 ] ], + "bbox": [ + 128.6455078125, + 61.1015625, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82301,25 +139328,31 @@ { "id": "/page/166/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ 510.3984375, - 60.37646484375 + 60.908203125 ], [ 525.9375, - 60.37646484375 + 60.908203125 ], [ 525.9375, - 69.94775390625 + 70.2861328125 ], [ 510.3984375, - 69.94775390625 + 70.2861328125 ] ], + "bbox": [ + 510.3984375, + 60.908203125, + 525.9375, + 70.2861328125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82333,22 +139366,28 @@ "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", "polygon": [ [ - 129.60000610351562, - 88.51025390625 + 128.6455078125, + 88.31689453125 ], [ - 388.177734375, - 88.51025390625 + 385.9077453613281, + 88.31689453125 ], [ - 388.177734375, - 154.6875 + 385.9077453613281, + 147.42529296875 ], [ - 129.60000610351562, - 154.6875 + 128.6455078125, + 147.42529296875 ] ], + "bbox": [ + 128.6455078125, + 88.31689453125, + 385.9077453613281, + 147.42529296875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82362,22 +139401,28 @@ "html": "
You can pass an instance as an argument in the usual way. For example:
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", + "id": "/page/166/TextInlineMath/3", + "block_type": "TextInlineMath", + "html": "def print_point(p): print '(%g, %g)' % (p.x, p.y)
", "polygon": [ [ - 129.60000610351562, + 128.57080078125, 169.52972412109375 ], [ @@ -82403,10 +139448,16 @@ 191.686279296875 ], [ - 129.60000610351562, + 128.57080078125, 191.686279296875 ] ], + "bbox": [ + 128.57080078125, + 169.52972412109375, + 302.1754150390625, + 191.686279296875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82420,22 +139471,28 @@ "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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>>> print_point(blank) (3.0, 4.0)
", "polygon": [ [ - 128.72021484375, - 225.650390625 + 128.197265625, + 225.84375 ], [ 244.67799377441406, - 225.650390625 + 225.84375 ], [ 244.67799377441406, 248.142333984375 ], [ - 128.72021484375, + 128.197265625, 248.142333984375 ] ], + "bbox": [ + 128.197265625, + 225.84375, + 244.67799377441406, + 248.142333984375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82494,6 +139557,12 @@ 288.5412292480469 ] ], + "bbox": [ + 128.794921875, + 254.2137451171875, + 525.6033325195312, + 288.5412292480469 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", @@ -82504,29 +139573,36 @@ { "id": "/page/166/SectionHeader/7", "block_type": "SectionHeader", - "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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", "polygon": [ [ - 128.9443359375, - 605.6015625 + 128.3466796875, + 605.9077758789062 ], [ 525.9375, - 605.6015625 + 605.9077758789062 ], [ 525.9375, 628.2139282226562 ], [ - 128.9443359375, + 128.3466796875, 628.2139282226562 ] ], + "bbox": [ + 128.3466796875, + 605.9077758789062, + 525.9375, + 628.2139282226562 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/166/SectionHeader/7" + "3": "/page/165/SectionHeader/9", + "4": "/page/166/SectionHeader/7" }, "images": {} }, @@ -82798,69 +139937,84 @@ "html": "To represent a rectangle, you have to instantiate a Rectangle object and assign values to the attributes:
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", + "id": "/page/166/TextInlineMath/17", + "block_type": "TextInlineMath", + "html": "box = Rectangle() box.width = 100.0 box.height = 200.0
", "polygon": [ [ - 128.86962890625, - 666.31640625 + 128.6455078125, + 666.3347778320312 ], [ - 224.12109375, - 666.31640625 + 223.75653076171875, + 666.3347778320312 ], [ - 224.12109375, - 701.12109375 + 223.75653076171875, + 700.6853713989258 ], [ - 128.86962890625, - 701.12109375 + 128.6455078125, + 700.6853713989258 ] ], + "bbox": [ + 128.6455078125, + 666.3347778320312, + 223.75653076171875, + 700.6853713989258 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/166/SectionHeader/7" + "3": "/page/165/SectionHeader/9", + "4": "/page/166/SectionHeader/7" }, "images": {} } ], "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/166/SectionHeader/7" + "3": "/page/165/SectionHeader/9", + "4": "/page/166/SectionHeader/7" }, "images": null }, { - "id": "/page/167/Page/202", + "id": "/page/167/Page/207", "block_type": "Page", - "html": "146 Chapter 15. Classes and objects
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" + "/page/167/Figure/1": 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} }, { "id": "/page/167/Caption/2", "block_type": "Caption", - "html": "Figure 15.2: Object diagram.
", + "html": "Figure 15.2: Object diagram.
", "polygon": [ [ - 221.8798828125, - 159.99029541015625 + 221.5810546875, + 159.8115234375 ], [ - 347.23828125, - 159.99029541015625 + 346.939453125, + 159.8115234375 ], [ - 347.23828125, - 170.15625 + 346.939453125, + 169.95294189453125 ], [ - 221.8798828125, - 170.15625 + 221.5810546875, + 169.95294189453125 ] ], + "bbox": [ + 221.5810546875, + 159.8115234375, + 346.939453125, + 169.95294189453125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/166/SectionHeader/7" + "3": "/page/165/SectionHeader/9", + "4": "/page/166/SectionHeader/7" }, "images": {} } ], "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/166/SectionHeader/7" + "3": "/page/165/SectionHeader/9", + "4": "/page/166/SectionHeader/7" }, "images": null }, @@ -83034,26 +140229,33 @@ "html": "box.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0", "polygon": [ [ - 85.46484375, - 190.5556640625 + 86.2119140625, + 192.59674072265625 ], [ 191.01724243164062, - 190.5556640625 + 192.59674072265625 ], [ 191.01724243164062, 226.947265625 ], [ - 85.46484375, + 86.2119140625, 226.947265625 ] ], + "bbox": [ + 86.2119140625, + 192.59674072265625, + 191.01724243164062, + 226.947265625 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/166/SectionHeader/7" + "3": "/page/165/SectionHeader/9", + "4": "/page/166/SectionHeader/7" }, "images": {} }, @@ -83063,84 +140265,105 @@ "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.\"
", "polygon": [ [ - 85.6142578125, - 232.611328125 + 86.2119140625, + 233.21868896484375 ], [ - 482.90625, - 232.611328125 + 482.4002990722656, + 233.21868896484375 ], [ - 482.90625, + 482.4002990722656, 255.52484130859375 ], [ - 85.6142578125, + 86.2119140625, 255.52484130859375 ] ], + "bbox": [ + 86.2119140625, + 233.21868896484375, + 482.4002990722656, + 255.52484130859375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/166/SectionHeader/7" + "3": "/page/165/SectionHeader/9", + "4": "/page/166/SectionHeader/7" }, "images": {} }, { "id": "/page/167/Text/5", "block_type": "Text", - "html": "Figure 15.2 shows the state of this object. An object that is an attribute of another object is embedded.
", + "html": "Figure 15.2 shows the state of this object. An object that is an attribute of another object is embedded.
", "polygon": [ [ - 85.9130859375, - 264.322265625 + 86.2119140625, + 265.482421875 ], [ 482.4033508300781, - 264.322265625 + 265.482421875 ], [ 482.4033508300781, 287.9248046875 ], [ - 85.9130859375, + 86.2119140625, 287.9248046875 ] ], + "bbox": [ + 86.2119140625, + 265.482421875, + 482.4033508300781, + 287.9248046875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/166/SectionHeader/7" + "3": "/page/165/SectionHeader/9", + "4": "/page/166/SectionHeader/7" }, "images": {} }, { "id": "/page/167/SectionHeader/6", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 85.763671875, - 343.986328125 + 85.6142578125, + 344.108642578125 ], [ - 482.90625, - 343.986328125 + 482.4006042480469, + 344.108642578125 ], [ - 482.90625, - 366.802734375 + 482.4006042480469, + 366.416015625 ], [ - 85.763671875, - 366.802734375 + 85.6142578125, + 366.416015625 ] ], + "bbox": [ + 85.6142578125, + 344.108642578125, + 482.4006042480469, + 366.416015625 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/6" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/6" }, "images": {} }, @@ -83180,25 +140410,32 @@ "polygon": [ [ 86.40005493164062, - 371.63671875 + 372.5366516113281 ], [ - 301.21875, - 371.63671875 + 300.8494873046875, + 372.5366516113281 ], [ - 301.21875, - 431.96484375 + 300.8494873046875, + 431.2772521972656 ], [ 86.40005493164062, - 431.96484375 + 431.2772521972656 ] ], + "bbox": [ + 86.40005493164062, + 372.5366516113281, + 300.8494873046875, + 431.2772521972656 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/6" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/6" }, "images": {} }, @@ -83208,26 +140445,33 @@ "html": "Here is an example that passes box as an argument and assigns the resulting Point to center:
", "polygon": [ [ - 85.3154296875, - 437.5486755371094 + 86.2119140625, + 436.9921875 ], [ - 482.90625, - 437.5486755371094 + 482.3963317871094, + 436.9921875 ], [ - 482.90625, + 482.3963317871094, 459.8548278808594 ], [ - 85.3154296875, + 86.2119140625, 459.8548278808594 ] ], + "bbox": [ + 86.2119140625, + 436.9921875, + 482.3963317871094, + 459.8548278808594 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/6" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/6" }, "images": {} }, @@ -83237,7 +140481,7 @@ "html": ">>> center = find_center(box)\n>>> print_point(center)\n(50.0, 100.0)", "polygon": [ [ - 84.64306640625, + 86.2119140625, 465.9766845703125 ], [ @@ -83246,46 +140490,60 @@ ], [ 238.09054565429688, - 500.3272705078125 + 500.4140625 ], [ - 84.64306640625, - 500.3272705078125 + 86.2119140625, + 500.4140625 ] ], + "bbox": [ + 86.2119140625, + 465.9766845703125, + 238.09054565429688, + 500.4140625 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/6" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/6" }, "images": {} }, { "id": "/page/167/SectionHeader/11", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 86.40003967285156, - 556.1015625 + 86.0625, + 556.48828125 ], [ - 482.4034118652344, - 556.1015625 + 482.607421875, + 556.48828125 ], [ - 482.4034118652344, + 482.607421875, 591.1618499755859 ], [ - 86.40003967285156, + 86.0625, 591.1618499755859 ] ], + "bbox": [ + 86.0625, + 556.48828125, + 482.607421875, + 591.1618499755859 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} }, @@ -83324,26 +140589,33 @@ "html": "box.width = box.width + 50\nbox.height = box.width + 100", "polygon": [ [ - 84.8671875, - 597.09375 + 86.361328125, + 597.28369140625 ], [ - 233.982421875, - 595.546875 + 232.8601837158203, + 597.28369140625 ], [ - 233.982421875, + 232.8601837158203, 619.4402923583984 ], [ - 84.8671875, + 86.361328125, 619.4402923583984 ] ], + "bbox": [ + 86.361328125, + 597.28369140625, + 232.8601837158203, + 619.4402923583984 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} }, @@ -83353,26 +140625,33 @@ "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:
", "polygon": [ [ - 85.9130859375, - 623.77734375 + 85.6142578125, + 625.7109375 ], [ 482.90625, - 623.77734375 + 625.7109375 ], [ 482.90625, - 660.2128601074219 + 660.515625 ], [ - 85.9130859375, - 660.2128601074219 + 85.6142578125, + 660.515625 ] ], + "bbox": [ + 85.6142578125, + 625.7109375, + 482.90625, + 660.515625 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} }, @@ -83382,38 +140661,46 @@ "html": "def grow_rectangle(rect, dwidth, dheight):\n rect.width += dwidth\n rect.height += dheight", "polygon": [ [ - 85.83837890625, - 664.76953125 + 86.28662109375, + 666.3347015380859 ], [ 306.09521484375, - 664.76953125 + 666.3347015380859 ], [ 306.09521484375, - 701.89453125 + 700.685302734375 ], [ - 85.83837890625, - 701.89453125 + 86.28662109375, + 700.685302734375 ] ], + "bbox": [ + 86.28662109375, + 666.3347015380859, + 306.09521484375, + 700.685302734375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} } ], "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": null }, { - "id": "/page/168/Page/229", + "id": "/page/168/Page/230", "block_type": "Page", "html": "
15.6. Copying 147
", + "html": "", "polygon": [ [ - 127.82373046875, + 128.86962890625, 61.171142578125 ], [ @@ -83453,43 +140746,57 @@ 71.13372802734375 ], [ - 127.82373046875, + 128.86962890625, 71.13372802734375 ] ], + "bbox": [ + 128.86962890625, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} }, { "id": "/page/168/PageHeader/12", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 509.501953125, - 60.08642578125 + 510.3984375, + 61.1015625 ], [ - 525.638671875, - 60.08642578125 + 525.33984375, + 61.1015625 ], [ - 525.638671875, - 69.94775390625 + 525.33984375, + 70.3828125 ], [ - 509.501953125, - 69.94775390625 + 510.3984375, + 70.3828125 ] ], + "bbox": [ + 510.3984375, + 61.1015625, + 525.33984375, + 70.3828125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} }, @@ -83499,26 +140806,33 @@ "html": "Here is an example that demonstrates the effect:
", "polygon": [ [ - 128.42138671875, - 88.83526611328125 + 127.52490234375, + 88.365234375 ], [ - 340.65765380859375, - 88.02685546875 + 340.962890625, + 88.365234375 ], [ - 340.65765380859375, + 340.962890625, 98.79791259765625 ], [ - 128.42138671875, - 99.580078125 + 127.52490234375, + 98.79791259765625 ] ], + "bbox": [ + 127.52490234375, + 88.365234375, + 340.962890625, + 98.79791259765625 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} }, @@ -83528,7 +140842,7 @@ "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", "polygon": [ [ - 128.72021484375, + 128.6455078125, 106.52471923828125 ], [ @@ -83537,75 +140851,96 @@ ], [ 296.9815979003906, - 215.015625 + 214.04229736328125 ], [ - 128.72021484375, - 215.015625 + 128.6455078125, + 214.04229736328125 ] ], + "bbox": [ + 128.6455078125, + 106.52471923828125, + 296.9815979003906, + 214.04229736328125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} }, { "id": "/page/168/Text/3", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 128.6455078125, + 128.9443359375, 221.918701171875 ], [ - 526.53515625, + 525.9375, 221.918701171875 ], [ - 526.53515625, + 525.9375, 268.46429443359375 ], [ - 128.6455078125, + 128.9443359375, 268.46429443359375 ] ], + "bbox": [ + 128.9443359375, + 221.918701171875, + 525.9375, + 268.46429443359375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/167/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/167/SectionHeader/11" }, "images": {} }, { "id": "/page/168/SectionHeader/4", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 129.2431640625, - 330.64453125 + 129.09375, + 331.41796875 ], [ - 525.9375, - 330.64453125 + 525.6033935546875, + 331.41796875 ], [ - 525.9375, + 525.6033935546875, 366.35986328125 ], [ - 129.2431640625, + 129.09375, 366.35986328125 ] ], + "bbox": [ + 129.09375, + 331.41796875, + 525.6033935546875, + 366.35986328125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, @@ -83644,26 +140986,33 @@ "html": "Copying an object is often an alternative to aliasing. The copy module contains a function called copy that can duplicate any object:
", "polygon": [ [ - 129.2431640625, - 377.630859375 + 128.794921875, + 377.82421875 ], [ - 526.53515625, - 377.630859375 + 525.638671875, + 377.82421875 ], [ - 526.53515625, + 525.638671875, 400.3648681640625 ], [ - 129.2431640625, + 128.794921875, 400.3648681640625 ] ], + "bbox": [ + 128.794921875, + 377.82421875, + 525.638671875, + 400.3648681640625 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, @@ -83673,7 +141022,7 @@ "html": ">>> p1 = Point()\n>>> p1.x = 3.0\n>>> p1.y = 4.0\n>>> import copy\n>>> p2 = copy.copy(p1)", "polygon": [ [ - 129.16845703125, + 129.6000518798828, 408.0917053222656 ], [ @@ -83682,17 +141031,24 @@ ], [ 244.6780242919922, - 479.02630615234375 + 483.3984375 ], [ - 127.97314453125, - 479.02630615234375 + 129.6000518798828, + 483.3984375 ] ], + "bbox": [ + 129.6000518798828, + 408.0917053222656, + 244.6780242919922, + 483.3984375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, @@ -83702,26 +141058,33 @@ "html": "
p1 and p2 contain the same data, but they are not the same Point.
", "polygon": [ [ - 129.6000518798828, - 485.33203125 + 128.57080078125, + 486.9027099609375 ], [ - 415.33221435546875, - 485.33203125 + 416.267578125, + 486.9027099609375 ], [ - 415.33221435546875, + 416.267578125, 497.0148620605469 ], [ - 129.6000518798828, + 128.57080078125, 497.0148620605469 ] ], + "bbox": [ + 128.57080078125, + 486.9027099609375, + 416.267578125, + 497.0148620605469 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, @@ -83731,26 +141094,33 @@ "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", "polygon": [ [ - 128.27197265625, - 504.28125 + 129.6000518798828, + 504.74169921875 ], [ - 228.9869384765625, - 504.28125 + 231.4423828125, + 504.74169921875 ], [ - 228.9869384765625, - 600.0643157958984 + 231.4423828125, + 600.1875 ], [ - 128.27197265625, - 600.0643157958984 + 129.6000518798828, + 600.1875 ] ], + "bbox": [ + 129.6000518798828, + 504.74169921875, + 231.4423828125, + 600.1875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, @@ -83761,14 +141131,14 @@ "polygon": [ [ 128.794921875, - 607.921875 + 607.53515625 ], [ - 525.638671875, - 607.921875 + 525.6033325195312, + 607.53515625 ], [ - 525.638671875, + 525.6033325195312, 666.8308868408203 ], [ @@ -83776,10 +141146,17 @@ 666.8308868408203 ] ], + "bbox": [ + 128.794921875, + 607.53515625, + 525.6033325195312, + 666.8308868408203 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, @@ -83790,14 +141167,14 @@ "polygon": [ [ 128.197265625, - 677.14453125 + 678.3046875 ], [ - 525.638671875, - 677.14453125 + 525.602783203125, + 678.3046875 ], [ - 525.638671875, + 525.602783203125, 700.8348846435547 ], [ @@ -83805,24 +141182,32 @@ 700.8348846435547 ] ], + "bbox": [ + 128.197265625, + 678.3046875, + 525.602783203125, + 700.8348846435547 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} } ], "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": null }, { - "id": "/page/169/Page/224", + "id": "/page/169/Page/233", "block_type": "Page", - "html": "
148 Chapter 15. Classes and objects
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+ "/page/169/Figure/1": 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" } }, { "id": "/page/169/Caption/2", "block_type": "Caption", - "html": "Figure 15.3: Object diagram.
", + "html": "Figure 15.3: Object diagram.
", "polygon": [ [ - 221.5810546875, - 147.533203125 + 222.23399353027344, + 148.5966796875 ], [ - 347.23828125, - 147.533203125 + 346.5672607421875, + 148.5966796875 ], [ - 347.23828125, + 346.5672607421875, 158.7529296875 ], [ - 221.5810546875, + 222.23399353027344, 158.7529296875 ] ], + "bbox": [ + 222.23399353027344, + 148.5966796875, + 346.5672607421875, + 158.7529296875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} } ], "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": null }, { "id": "/page/169/Code/3", "block_type": "Code", - "html": ">>> box2 = copy.copy(box)\n>>> box2 is box\nFalse\n>>> box2.corner is box.corner\nTrue\nFigure 15.3 shows what the object diagram looks like. This operation is called a shallow", + "html": "
>>> box2 = copy.copy(box)\n>>> box2 is box\nFalse\n>>> box2.corner is box.corner\nTrue", "polygon": [ [ - 85.24072265625, - 179.05572509765625 + 85.68896484375, + 179.05078125 ], [ - 482.3999938964844, - 179.05572509765625 + 239.0625, + 179.05078125 ], [ - 482.3999938964844, - 252.77288818359375 + 239.0625, + 241.3125 ], [ - 85.24072265625, - 252.77288818359375 + 85.68896484375, + 241.3125 ] ], + "bbox": [ + 85.68896484375, + 179.05078125, + 239.0625, + 241.3125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, { "id": "/page/169/Text/4", "block_type": "Text", - "html": "
copy because it copies the object and any references it contains, but not the embedded objects.
", + "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.
", "polygon": [ [ 85.763671875, - 252.720703125 + 242.713134765625 ], [ - 482.90625, - 252.720703125 + 482.4046325683594, + 242.713134765625 ], [ - 482.90625, - 277.470703125 + 482.4046325683594, + 277.16082763671875 ], [ 85.763671875, - 277.470703125 + 277.16082763671875 ] ], + "bbox": [ + 85.763671875, + 242.713134765625, + 482.4046325683594, + 277.16082763671875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, @@ -84054,26 +141494,33 @@ "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.
", "polygon": [ [ - 85.3154296875, - 285.591796875 + 85.763671875, + 285.205078125 ], [ - 482.90625, - 285.591796875 + 482.40338134765625, + 285.205078125 ], [ - 482.90625, - 320.783203125 + 482.40338134765625, + 320.34881591796875 ], [ - 85.3154296875, - 320.783203125 + 85.763671875, + 320.34881591796875 ] ], + "bbox": [ + 85.763671875, + 285.205078125, + 482.40338134765625, + 320.34881591796875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, @@ -84083,185 +141530,408 @@ "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.
", "polygon": [ [ - 85.166015625, - 327.55078125 + 85.46484375, + 328.7109375 ], [ - 483.205078125, - 327.55078125 + 482.607421875, + 328.7109375 ], [ - 483.205078125, - 363.90234375 + 482.607421875, + 363.5368347167969 ], [ - 85.166015625, - 363.90234375 + 85.46484375, + 363.5368347167969 ] ], + "bbox": [ + 85.46484375, + 328.7109375, + 482.607421875, + 363.5368347167969 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, { "id": "/page/169/Code/7", "block_type": "Code", - "html": ">>> box3 = copy.deepcopy(box)\n>>> box3 is box\nFalse\n>>> box3.corner is box.corner\nFalse\nbox3 and box are completely separate objects.\nExercise 15.3. Write a version of move_rectangle that creates and returns a new Rectangle", + "html": "
>>> box3 = copy.deepcopy(box)\n>>> box3 is box\nFalse\n>>> box3.corner is box.corner\nFalse", "polygon": [ [ 85.39013671875, - 365.8359375 + 367.76953125 ], [ - 482.3989562988281, - 365.8359375 + 238.09048461914062, + 367.76953125 ], [ - 482.3989562988281, - 454.0667419433594 + 238.09048461914062, + 426.9922790527344 ], [ 85.39013671875, - 454.0667419433594 + 426.9922790527344 ] ], + "bbox": [ + 85.39013671875, + 367.76953125, + 238.09048461914062, + 426.9922790527344 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, { "id": "/page/169/Text/8", "block_type": "Text", - "html": "
instead of modifying the old one.
", + "html": "box3 and box are completely separate objects.
", "polygon": [ [ - 86.39994812011719, - 452.4609375 + 85.9130859375, + 431.19140625 ], [ - 218.2939453125, - 452.4609375 + 286.43975830078125, + 431.19140625 ], [ - 218.2939453125, + 286.43975830078125, + 441.9698486328125 + ], + [ + 85.9130859375, + 441.9698486328125 + ] + ], + "bbox": [ + 85.9130859375, + 431.19140625, + 286.43975830078125, + 441.9698486328125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/164/SectionHeader/1", + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" + }, + "images": {} + }, + { + "id": "/page/169/Text/9", + "block_type": "Text", + "html": "Exercise 15.3. Write a version of move_rectangle that creates and returns a new Rectangle instead of modifying the old one.
", + "polygon": [ + [ + 85.46484375, + 443.953125 + ], + [ + 482.607421875, + 443.953125 + ], + [ + 482.607421875, 466.1851501464844 ], [ - 86.39994812011719, + 85.46484375, 466.1851501464844 ] ], + "bbox": [ + 85.46484375, + 443.953125, + 482.607421875, + 466.1851501464844 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/168/SectionHeader/4" + "3": "/page/165/SectionHeader/9", + "4": "/page/168/SectionHeader/4" }, "images": {} }, { - "id": "/page/169/SectionHeader/9", + "id": "/page/169/SectionHeader/10", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 85.6142578125, + 85.9130859375, 518.58984375 ], [ - 484.1015625, + 483.50390625, 518.58984375 ], [ - 484.1015625, + 483.50390625, 541.3368530273438 ], [ - 85.6142578125, + 85.9130859375, 541.3368530273438 ] ], + "bbox": [ + 85.9130859375, + 518.58984375, + 483.50390625, + 541.3368530273438 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/169/SectionHeader/9" + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" }, "images": {} }, { - "id": "/page/169/Code/11", + "id": "/page/169/Code/12", "block_type": "Code", - "html": ">>> p = Point()\n>>> print p.z\nAttributeError: Point instance has no attribute 'z'\nIf you are not sure what type an object is, you can ask:\n>>> type(p)\n<type '__main__.Point'>\nIf you are not sure whether an object has a particular attribute, you can use the built-in\nfunction hasattr:\n>>> hasattr(p, 'x')\nTrue\n>>> hasattr(p, 'z')\nFalse", + "html": "
>>> p = Point()\n>>> print p.z\nAttributeError: Point instance has no attribute 'z'", "polygon": [ [ - 85.46484375, - 543.33984375 + 86.28662109375, + 546.0527038574219 + ], + [ + 354.41015625, + 546.0527038574219 + ], + [ + 354.41015625, + 582.01171875 + ], + [ + 86.28662109375, + 582.01171875 + ] + ], + "bbox": [ + 86.28662109375, + 546.0527038574219, + 354.41015625, + 582.01171875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/164/SectionHeader/1", + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/169/Text/13", + "block_type": "Text", + "html": "
If you are not sure what type an object is, you can ask:
", + "polygon": [ + [ + 85.763671875, + 585.10546875 + ], + [ + 325.72265625, + 585.10546875 + ], + [ + 325.72265625, + 595.380859375 + ], + [ + 85.763671875, + 595.380859375 + ] + ], + "bbox": [ + 85.763671875, + 585.10546875, + 325.72265625, + 595.380859375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/164/SectionHeader/1", + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/169/Code/14", + "block_type": "Code", + "html": ">>> type(p)\n<type '__main__.Point'>", + "polygon": [ + [ + 86.0625, + 600.0967102050781 + ], + [ + 206.6703643798828, + 600.0967102050781 + ], + [ + 206.6703643798828, + 622.2533111572266 + ], + [ + 86.0625, + 622.2533111572266 + ] + ], + "bbox": [ + 86.0625, + 600.0967102050781, + 206.6703643798828, + 622.2533111572266 + ], + "children": null, + "section_hierarchy": { + "1": "/page/164/SectionHeader/1", + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/169/Text/15", + "block_type": "Text", + "html": "
If you are not sure whether an object has a particular attribute, you can use the built-in function hasattr:
", + "polygon": [ + [ + 85.763671875, + 626.87109375 ], [ 482.40338134765625, - 543.33984375 + 626.87109375 ], [ 482.40338134765625, + 649.4248657226562 + ], + [ + 85.763671875, + 649.4248657226562 + ] + ], + "bbox": [ + 85.763671875, + 626.87109375, + 482.40338134765625, + 649.4248657226562 + ], + "children": null, + "section_hierarchy": { + "1": "/page/164/SectionHeader/1", + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/169/Code/16", + "block_type": "Code", + "html": ">>> hasattr(p, 'x')\nTrue\n>>> hasattr(p, 'z')\nFalse", + "polygon": [ + [ + 85.9130859375, + 654.1407165527344 + ], + [ + 186.9169921875, + 654.1407165527344 + ], + [ + 186.9169921875, 700.6863098144531 ], [ - 85.46484375, + 85.9130859375, 700.6863098144531 ] ], + "bbox": [ + 85.9130859375, + 654.1407165527344, + 186.9169921875, + 700.6863098144531 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/169/SectionHeader/9" + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" }, "images": {} } ], "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/169/SectionHeader/9" + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" }, "images": null }, { - "id": "/page/170/Page/192", + "id": "/page/170/Page/197", "block_type": "Page", - "html": "
15.8. Glossary 149
", + "html": "", "polygon": [ [ - 128.27197265625, + 128.42138671875, 61.171142578125 ], [ @@ -84299,43 +141975,57 @@ 71.13372802734375 ], [ - 128.27197265625, + 128.42138671875, 71.13372802734375 ] ], + "bbox": [ + 128.42138671875, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/169/SectionHeader/9" + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" }, "images": {} }, { - "id": "/page/170/PageHeader/25", + "id": "/page/170/PageHeader/23", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 511.294921875, - 61.53662109375 + 510.3984375, + 60.908203125 ], [ - 526.236328125, - 61.53662109375 + 525.33984375, + 60.908203125 ], [ - 526.236328125, - 70.62451171875 + 525.33984375, + 69.802734375 ], [ - 511.294921875, - 70.62451171875 + 510.3984375, + 69.802734375 ] ], + "bbox": [ + 510.3984375, + 60.908203125, + 525.33984375, + 69.802734375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/169/SectionHeader/9" + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" }, "images": {} }, @@ -84345,55 +142035,69 @@ "html": "The first argument can be any object; the second argument is a string that contains the name of the attribute.
", "polygon": [ [ - 129.2431640625, - 88.02685546875 + 127.599609375, + 88.365234375 ], [ - 525.5966796875, - 88.02685546875 + 526.236328125, + 88.365234375 ], [ - 525.5966796875, + 526.236328125, 110.99188232421875 ], [ - 129.2431640625, + 127.599609375, 110.99188232421875 ] ], + "bbox": [ + 127.599609375, + 88.365234375, + 526.236328125, + 110.99188232421875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/169/SectionHeader/9" + "3": "/page/165/SectionHeader/9", + "4": "/page/169/SectionHeader/10" }, "images": {} }, { "id": "/page/170/SectionHeader/2", "block_type": "SectionHeader", - "html": "class: A user-defined type. A class definition creates a new class object.
", "polygon": [ [ - 128.3466796875, + 128.49609375, 161.30615234375 ], [ - 445.25390625, + 444.357421875, 161.30615234375 ], [ - 445.25390625, + 444.357421875, 171.36590576171875 ], [ - 128.3466796875, + 128.49609375, 171.36590576171875 ] ], + "bbox": [ + 128.49609375, + 161.30615234375, + 444.357421875, + 171.36590576171875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/2" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/2" }, "images": {} }, @@ -84432,26 +142143,33 @@ "html": "instance: An object that belongs to a class.
", "polygon": [ [ - 128.6455078125, + 128.49609375, 212.1580810546875 ], [ - 317.953125, + 317.27569580078125, 212.1580810546875 ], [ - 317.953125, + 317.27569580078125, 222.21783447265625 ], [ - 128.6455078125, + 128.49609375, 222.21783447265625 ] ], + "bbox": [ + 128.49609375, + 212.1580810546875, + 317.27569580078125, + 222.21783447265625 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/2" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/2" }, "images": {} }, @@ -84490,80 +142215,100 @@ "html": "attribute: One of the named values associated with an object.
", "polygon": [ [ - 128.42138671875, - 230.291015625 + 129.16845703125, + 231.2578125 ], [ - 401.923828125, - 230.291015625 + 400.73162841796875, + 231.2578125 ], [ - 401.923828125, + 400.73162841796875, 241.54681396484375 ], [ - 128.42138671875, + 129.16845703125, 241.54681396484375 ] ], + "bbox": [ + 129.16845703125, + 231.2578125, + 400.73162841796875, + 241.54681396484375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/2" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/2" }, "images": {} }, { - "id": "/page/170/Text/7", + "id": "/page/170/Text/193", "block_type": "Text", "html": "embedded (object): An object that is stored as an attribute of another object.
", "polygon": [ [ - 128.0478515625, - 250.013671875 + 129.09375, + 250.787109375 ], [ - 467.3671875, - 250.013671875 + 467.068359375, + 250.787109375 ], [ - 467.3671875, + 467.068359375, 260.87481689453125 ], [ - 128.0478515625, + 129.09375, 260.87481689453125 ] ], + "bbox": [ + 129.09375, + 250.787109375, + 467.068359375, + 260.87481689453125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/2" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/2" }, "images": {} }, { - "id": "/page/170/ListGroup/192", + "id": "/page/170/ListGroup/194", "block_type": "ListGroup", "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:
", + "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:
", "polygon": [ [ - 129.09375, + 129.59994506835938, 421.13671875 ], [ - 525.9375, + 525.6023559570312, 421.13671875 ], [ - 525.9375, + 525.6023559570312, 443.3272399902344 ], [ - 129.09375, + 129.59994506835938, 443.3272399902344 ] ], + "bbox": [ + 129.59994506835938, + 421.13671875, + 525.6023559570312, + 443.3272399902344 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, @@ -84723,7 +142504,7 @@ "html": "from swampy.World import World
", "polygon": [ [ - 128.6455078125, + 129.2431640625, 448.5516662597656 ], [ @@ -84735,14 +142516,21 @@ 458.5142517089844 ], [ - 128.6455078125, + 129.2431640625, 458.5142517089844 ] ], + "bbox": [ + 129.2431640625, + 448.5516662597656, + 286.5208435058594, + 458.5142517089844 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, @@ -84752,26 +142540,33 @@ "html": "Or, depending on how you installed Swampy, like this:
", "polygon": [ [ - 128.72021484375, - 463.2890625 + 129.2431640625, + 463.71453857421875 ], [ - 350.2265625, - 463.2890625 + 349.62890625, + 463.71453857421875 ], [ - 350.2265625, + 349.62890625, 473.6771240234375 ], [ - 128.72021484375, + 129.2431640625, 473.6771240234375 ] ], + "bbox": [ + 129.2431640625, + 463.71453857421875, + 349.62890625, + 473.6771240234375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, @@ -84781,84 +142576,105 @@ "html": "from World import World
", "polygon": [ [ - 128.794921875, - 478.7578125 + 128.86962890625, + 478.9236755371094 ], [ - 249.9082794189453, - 478.7578125 + 251.015625, + 478.9236755371094 ], [ - 249.9082794189453, + 251.015625, 488.8862609863281 ], [ - 128.794921875, + 128.86962890625, 488.8862609863281 ] ], + "bbox": [ + 128.86962890625, + 478.9236755371094, + 251.015625, + 488.8862609863281 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { "id": "/page/170/Text/16", "block_type": "Text", - "html": "The following code creates a World object and calls the mainloop method, which waits for the user. world = World()
", + "html": "The following code creates a World object and calls the mainloop method, which waits for the user.
", "polygon": [ [ - 128.6455078125, - 494.0865478515625 + 129.59994506835938, + 493.83984375 ], [ - 526.53515625, - 494.0865478515625 + 525.6014404296875, + 493.83984375 ], [ - 526.53515625, - 519.2582702636719 + 525.6014404296875, + 509.30859375 ], [ - 128.6455078125, - 519.2582702636719 + 129.59994506835938, + 509.30859375 ] ], + "bbox": [ + 129.59994506835938, + 493.83984375, + 525.6014404296875, + 509.30859375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { - "id": "/page/170/Text/17", - "block_type": "Text", - "html": "world.mainloop()
", + "id": "/page/170/Code/192", + "block_type": "Code", + "html": "world = World()\nworld.mainloop()", "polygon": [ [ - 129.2431640625, - 520.91015625 + 128.86962890625, + 509.2956848144531 ], [ 213.2957763671875, - 520.91015625 + 509.2956848144531 ], [ 213.2957763671875, 531.4522705078125 ], [ - 129.2431640625, + 128.86962890625, 531.4522705078125 ] ], + "bbox": [ + 128.86962890625, + 509.2956848144531, + 213.2957763671875, + 531.4522705078125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, @@ -84868,214 +142684,192 @@ "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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", + "id": "/page/170/Code/19", + "block_type": "Code", + "html": "canvas = world.ca(width=500, height=500, background='white')\nbbox = [[-150,-100], [150, 100]]\ncanvas.rectangle(bbox, outline='black', width=2, fill='green4')", "polygon": [ [ 129.60000610351562, - 588.4447021484375 - ], - [ - 297.333984375, - 588.4447021484375 - ], - [ - 296.9815979003906, - 601.734375 - ], - [ - 129.60000610351562, - 601.734375 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" - }, - "images": {} - }, - { - "id": "/page/170/Text/21", - "block_type": "Text", - "html": "
canvas.rectangle(bbox, outline='black', width=2, fill='green4')
", - "polygon": [ - [ - 127.7490234375, - 600.6387023925781 + 576.2109375 ], [ 459.035400390625, - 600.6387023925781 + 576.2109375 ], [ 459.035400390625, - 611.7890625 + 610.6013031005859 ], [ - 127.7490234375, - 611.7890625 + 129.60000610351562, + 610.6013031005859 ] ], + "bbox": [ + 129.60000610351562, + 576.2109375, + 459.035400390625, + 610.6013031005859 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { - "id": "/page/170/Text/22", + "id": "/page/170/Text/20", "block_type": "Text", "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.
", "polygon": [ [ - 128.49609375, - 615.8015594482422 + 129.2431640625, + 614.8828125 ], [ - 526.53515625, - 615.8015594482422 + 525.9375, + 614.8828125 ], [ - 526.53515625, + 525.9375, 650.1531677246094 ], [ - 128.49609375, + 129.2431640625, 650.1531677246094 ] ], + "bbox": [ + 129.2431640625, + 614.8828125, + 525.9375, + 650.1531677246094 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { - "id": "/page/170/Text/23", + "id": "/page/170/Text/21", "block_type": "Text", "html": "bbox 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.
", "polygon": [ [ - 128.3466796875, - 658.58203125 + 128.49609375, + 658.96875 ], [ - 525.9375, - 658.58203125 + 525.5986328125, + 658.96875 ], [ - 525.9375, + 525.5986328125, 681.5051727294922 ], [ - 128.3466796875, + 128.49609375, 681.5051727294922 ] ], + "bbox": [ + 128.49609375, + 658.96875, + 525.5986328125, + 681.5051727294922 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { - "id": "/page/170/Text/24", + "id": "/page/170/Text/22", "block_type": "Text", "html": "You can draw a circle like this:
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.85986328125 + 60.521484375 ], [ - 483.205078125, - 60.85986328125 + 482.607421875, + 60.521484375 ], [ - 483.205078125, + 482.607421875, 71.13372802734375 ], [ @@ -85117,39 +142917,53 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.521484375, + 482.607421875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { "id": "/page/171/PageHeader/11", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.166015625, - 60.328125 + 85.3154296875, + 60.908203125 ], [ - 99.9580078125, - 60.328125 + 101.900390625, + 60.908203125 ], [ - 99.9580078125, - 69.22265625 + 101.900390625, + 70.2861328125 ], [ - 85.166015625, - 69.22265625 + 85.3154296875, + 70.2861328125 ] ], + "bbox": [ + 85.3154296875, + 60.908203125, + 101.900390625, + 70.2861328125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, @@ -85159,26 +142973,33 @@ "html": "canvas.circle([-25,0], 70, outline=None, fill='red')
", "polygon": [ [ - 86.4000015258789, - 88.68572998046875 + 84.64306640625, + 88.365234375 ], [ 358.3153991699219, - 88.68572998046875 + 88.365234375 ], [ 358.3153991699219, - 98.7099609375 + 98.6483154296875 ], [ - 86.4000015258789, - 98.7099609375 + 84.64306640625, + 98.6483154296875 ] ], + "bbox": [ + 84.64306640625, + 88.365234375, + 358.3153991699219, + 98.6483154296875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, @@ -85188,7 +143009,7 @@ "html": "The first parameter is the coordinate pair for the center of the circle; the second parameter is the radius.
", "polygon": [ [ - 85.6142578125, + 85.166015625, 104.82861328125 ], [ @@ -85197,71 +143018,91 @@ ], [ 483.50390625, - 127.037109375 + 126.98516845703125 ], [ - 85.6142578125, - 127.037109375 + 85.166015625, + 126.98516845703125 ] ], + "bbox": [ + 85.166015625, + 104.82861328125, + 483.50390625, + 126.98516845703125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { "id": "/page/171/Text/3", "block_type": "Text", - "html": "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 ).
", + "html": "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 ).
", "polygon": [ [ - 85.6142578125, - 136.6083984375 + 85.3154296875, + 136.8017578125 ], [ - 483.50390625, - 136.6083984375 + 482.90625, + 136.8017578125 ], [ - 483.50390625, + 482.90625, 159.31719970703125 ], [ - 85.6142578125, + 85.3154296875, 159.31719970703125 ] ], + "bbox": [ + 85.3154296875, + 136.8017578125, + 482.90625, + 159.31719970703125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { - "id": "/page/171/ListGroup/96", + "id": "/page/171/ListGroup/126", "block_type": "ListGroup", "html": "points = [[-150,-100], [150, 100], [150, -100]] canvas.polygon(points, fill='blue')
", "polygon": [ [ - 110.865234375, + 111.30699920654297, 335.32073974609375 ], [ - 357.14404296875, + 357.3984375, 335.32073974609375 ], [ - 357.14404296875, + 357.3984375, 357.47833251953125 ], [ - 110.865234375, + 111.30699920654297, 357.47833251953125 ] ], + "bbox": [ + 111.30699920654297, + 335.32073974609375, + 357.3984375, + 357.47833251953125 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} }, { "id": "/page/171/Text/10", "block_type": "Text", - "html": "I have written a small program that lists the available colors; you can download it from http: // thinkpython. com/ code/ color_ list. py .
", + "html": "I have written a small program that lists the available colors; you can download it from http: // thinkpython. com/ code/ color_ list. py .
", "polygon": [ [ - 85.763671875, + 84.1201171875, 371.443359375 ], [ - 481.412109375, + 480.6768493652344, 371.443359375 ], [ - 481.412109375, - 393.7851867675781 + 480.6768493652344, + 393.873046875 ], [ - 85.763671875, - 393.7851867675781 + 84.1201171875, + 393.873046875 ] ], + "bbox": [ + 84.1201171875, + 371.443359375, + 480.6768493652344, + 393.873046875 + ], "children": null, "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": {} } ], "section_hierarchy": { "1": "/page/164/SectionHeader/1", - "3": "/page/170/SectionHeader/11" + "3": "/page/165/SectionHeader/9", + "4": "/page/170/SectionHeader/11" }, "images": null }, { - "id": "/page/172/Page/158", + "id": "/page/172/Page/163", "block_type": "Page", - "html": "Code examples from this chapter are available from http://thinkpython.com/code/ Time1.py.
", + "html": "Code examples from this chapter are available from http://thinkpython.com/code/ Time1.py.
", "polygon": [ [ - 128.49609375, - 286.2177734375 + 128.3466796875, + 285.591796875 ], [ 525.6451416015625, - 286.2177734375 + 285.591796875 ], [ 525.6451416015625, - 308.794921875 + 308.52392578125 ], [ - 128.49609375, - 308.794921875 + 128.3466796875, + 308.52392578125 ] ], + "bbox": [ + 128.3466796875, + 285.591796875, + 525.6451416015625, + 308.52392578125 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1" @@ -85591,29 +143507,35 @@ { "id": "/page/172/SectionHeader/3", "block_type": "SectionHeader", - "html": "class Time(object):
", + "id": "/page/172/Code/5", + "block_type": "Code", + "html": "class Time(object):\n \"\"\"Represents the time of day.\n attributes: hour, minute, second", "polygon": [ [ 129.5999755859375, 388.2677917480469 ], [ - 228.9868621826172, + 317.8975830078125, 388.2677917480469 ], [ - 228.9868621826172, - 398.3203125 + 317.8975830078125, + 436.21875 ], [ 129.5999755859375, - 398.3203125 + 436.21875 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/172/SectionHeader/1", - "3": "/page/172/SectionHeader/3" - }, - "images": {} - }, - { - "id": "/page/172/Code/6", - "block_type": "Code", - "html": "
\"\"\"Represents the time of day.", - "polygon": [ - [ - 150.51597595214844, - 400.4627990722656 - ], - [ - 307.4368591308594, - 400.4627990722656 - ], - [ - 307.4368591308594, - 410.4253845214844 - ], - [ - 150.51597595214844, - 410.4253845214844 - ] + "bbox": [ + 129.5999755859375, + 388.2677917480469, + 317.8975830078125, + 436.21875 ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/172/SectionHeader/3" + "4": "/page/172/SectionHeader/3" }, "images": {} }, { - "id": "/page/172/Text/7", + "id": "/page/172/Text/6", "block_type": "Text", - "html": "
attributes: hour, minute, second \"\"\"
", + "html": "We can create a new Time object and assign attributes for hours, minutes, and seconds:
", "polygon": [ [ - 148.517578125, - 424.6171875 + 129.5999755859375, + 451.6875 ], [ - 318.251953125, - 424.6171875 + 509.69757080078125, + 451.6875 ], [ - 318.251953125, - 447.0083923339844 + 509.69757080078125, + 461.9999694824219 ], [ - 148.517578125, - 447.0083923339844 + 129.5999755859375, + 461.9999694824219 ] ], + "bbox": [ + 129.5999755859375, + 451.6875, + 509.69757080078125, + 461.9999694824219 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/172/SectionHeader/3" + "4": "/page/172/SectionHeader/3" }, "images": {} }, { - "id": "/page/172/Text/8", - "block_type": "Text", - "html": "We can create a new Time object and assign attributes for hours, minutes, and seconds:
", + "id": "/page/172/Code/7", + "block_type": "Code", + "html": "time = Time()\ntime.hour = 11\ntime.minute = 59\ntime.second = 30", "polygon": [ [ - 128.197265625, - 450.9140625 + 128.49609375, + 466.7298278808594 ], [ - 510.099609375, - 450.9140625 + 213.36328125, + 466.7298278808594 ], [ - 510.099609375, - 461.9999694824219 + 213.36328125, + 513.2754211425781 ], [ - 128.197265625, - 461.9999694824219 + 128.49609375, + 513.2754211425781 ] ], + "bbox": [ + 128.49609375, + 466.7298278808594, + 213.36328125, + 513.2754211425781 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/172/SectionHeader/3" + "4": "/page/172/SectionHeader/3" }, "images": {} }, { - "id": "/page/172/Code/9", - "block_type": "Code", - "html": "
time = Time()\ntime.hour = 11\ntime.minute = 59\ntime.second = 30", + "id": "/page/172/Text/13", + "block_type": "Text", + "html": "
\"\"\"
", "polygon": [ [ - 128.197265625, - 465.99609375 + 150.51597595214844, + 437.0458068847656 ], [ - 213.2858123779297, - 465.99609375 + 166.20706176757812, + 437.0458068847656 ], [ - 213.2858123779297, - 513.2754211425781 + 166.20706176757812, + 447.0083923339844 ], [ - 128.197265625, - 513.2754211425781 + 150.51597595214844, + 447.0083923339844 ] ], + "bbox": [ + 150.51597595214844, + 437.0458068847656, + 166.20706176757812, + 447.0083923339844 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/172/SectionHeader/3" + "4": "/page/172/SectionHeader/3" }, "images": {} }, { - "id": "/page/172/Text/10", + "id": "/page/172/Text/8", "block_type": "Text", - "html": "The state diagram for the Time object looks like Figure 16.1.
", + "html": "The state diagram for the Time object looks like Figure 16.1.
", "polygon": [ [ - 128.794921875, - 517.81640625 + 128.86962890625, + 518.1548461914062 ], [ - 390.1163330078125, - 517.81640625 + 391.166015625, + 518.1548461914062 ], [ - 390.1163330078125, + 391.166015625, 528.2669982910156 ], [ - 128.794921875, + 128.86962890625, 528.2669982910156 ] ], + "bbox": [ + 128.86962890625, + 518.1548461914062, + 391.166015625, + 528.2669982910156 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/172/SectionHeader/3" + "4": "/page/172/SectionHeader/3" }, "images": {} }, { - "id": "/page/172/Text/11", + "id": "/page/172/Text/9", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 128.794921875, - 530.3257141113281 + 129.2431640625, + 530.19140625 ], [ - 526.833984375, - 530.3257141113281 + 525.601806640625, + 530.19140625 ], [ - 526.833984375, + 525.601806640625, 564.6773071289062 ], [ - 128.794921875, + 129.2431640625, 564.6773071289062 ] ], + "bbox": [ + 129.2431640625, + 530.19140625, + 525.601806640625, + 564.6773071289062 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/172/SectionHeader/3" + "4": "/page/172/SectionHeader/3" }, "images": {} }, { - "id": "/page/172/Text/12", + "id": "/page/172/Text/10", "block_type": "Text", - "html": "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.
", + "html": "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.
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Figure 16.1: Object diagram.
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The 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.
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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.
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16.3. Modifiers 153
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return sum
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", "polygon": [ [ - 128.49609375, + 128.197265625, 212.990234375 ], [ - 525.9375, + 525.6033935546875, 212.990234375 ], [ - 525.9375, - 236.28515625 + 525.6033935546875, + 235.51171875 ], [ - 128.49609375, - 236.28515625 + 128.197265625, + 235.51171875 ] ], + "bbox": [ + 128.197265625, + 212.990234375, + 525.6033935546875, + 235.51171875 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/172/SectionHeader/13" + "4": "/page/172/SectionHeader/11" }, "images": {} }, { "id": "/page/174/SectionHeader/4", "block_type": "SectionHeader", - "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:
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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", "polygon": [ [ 129.5999755859375, @@ -86620,143 +144749,68 @@ ], [ 281.2904968261719, - 366.1353454589844 + 467.15625 ], [ 129.5999755859375, - 366.1353454589844 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/172/SectionHeader/1", - "3": "/page/174/SectionHeader/4" - }, - "images": {} - }, - { - "id": "/page/174/Code/8", - "block_type": "Code", - "html": "", - "polygon": [ - [ - 151.20703125, - 366.22265625 - ], - [ - 264.76171875, - 366.22265625 - ], - [ - 264.76171875, - 376.27734375 - ], - [ - 151.20703125, - 376.27734375 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/172/SectionHeader/1", - "3": "/page/174/SectionHeader/4" - }, - "images": {} - }, - { - "id": "/page/174/Text/9", - "block_type": "Text", - "html": "
if time.second >= 60: time.second -= 60 time.minute += 1
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", - "polygon": [ - [ - 150.310546875, - 427.7109375 - ], - [ - 261.17578125, - 427.7109375 - ], - [ - 261.17578125, - 463.6893615722656 - ], - [ - 150.310546875, - 463.6893615722656 - ] + "bbox": [ + 129.5999755859375, + 343.9777526855469, + 281.2904968261719, + 467.15625 ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/174/SectionHeader/4" + "4": "/page/174/SectionHeader/4" }, "images": {} }, { - "id": "/page/174/Text/11", + "id": "/page/174/Text/8", "block_type": "Text", "html": "The first line performs the basic operation; the remainder deals with the special cases we saw before.
", "polygon": [ [ - 129.5419921875, + 129.2431640625, 468.2823486328125 ], [ - 525.9375, + 525.6033325195312, 468.2823486328125 ], [ - 525.9375, - 490.74609375 + 525.6033325195312, + 490.4389343261719 ], [ - 129.5419921875, - 490.74609375 + 129.2431640625, + 490.4389343261719 ] ], + "bbox": [ + 129.2431640625, + 468.2823486328125, + 525.6033325195312, + 490.4389343261719 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/174/SectionHeader/4" + "4": "/page/174/SectionHeader/4" }, "images": {} }, { - "id": "/page/174/Text/12", + "id": "/page/174/Text/9", "block_type": "Text", "html": "Is this function correct? What happens if the parameter seconds is much greater than sixty?
", "polygon": [ [ - 128.9443359375, + 128.49609375, 498.70379638671875 ], [ @@ -86765,174 +144819,210 @@ ], [ 525.6008911132812, - 508.921875 + 508.8159484863281 ], [ - 128.9443359375, - 508.921875 + 128.49609375, + 508.8159484863281 ] ], + "bbox": [ + 128.49609375, + 498.70379638671875, + 525.6008911132812, + 508.8159484863281 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/174/SectionHeader/4" + "4": "/page/174/SectionHeader/4" }, "images": {} }, { - "id": "/page/174/Text/13", + "id": "/page/174/Text/10", "block_type": "Text", "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.
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", "polygon": [ [ - 128.49609375, - 570.796875 + 127.599609375, + 571.95703125 ], [ - 525.9375, - 570.796875 + 525.638671875, + 571.95703125 ], [ - 525.9375, + 525.638671875, 630.9309539794922 ], [ - 128.49609375, + 127.599609375, 630.9309539794922 ] ], + "bbox": [ + 127.599609375, + 571.95703125, + 525.638671875, + 630.9309539794922 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/174/SectionHeader/4" + "4": "/page/174/SectionHeader/4" }, "images": {} }, { - "id": "/page/174/Text/16", + "id": "/page/174/Text/13", "block_type": "Text", "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.
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.76318359375 + 59.69970703125 ], [ 482.90625, - 60.76318359375 + 59.69970703125 ], [ 482.90625, @@ -86976,64 +145072,82 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 59.69970703125, + 482.90625, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/174/SectionHeader/4" + "4": "/page/174/SectionHeader/4" }, "images": {} }, { "id": "/page/175/PageHeader/17", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.83837890625, - 59.6513671875 + 85.6142578125, + 59.8447265625 ], [ - 101.52685546875, - 59.6513671875 + 102.0498046875, + 59.8447265625 ], [ - 101.52685546875, - 70.189453125 + 102.0498046875, + 70.4794921875 ], [ - 85.83837890625, - 70.189453125 + 85.6142578125, + 70.4794921875 ] ], + "bbox": [ + 85.6142578125, + 59.8447265625, + 102.0498046875, + 70.4794921875 + ], "children": null, "section_hierarchy": { "1": "/page/172/SectionHeader/1", - "3": "/page/174/SectionHeader/4" + "4": "/page/174/SectionHeader/4" }, "images": {} }, { "id": "/page/175/SectionHeader/1", "block_type": "SectionHeader", - "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.
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", + "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.\"
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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).
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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.
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This version is shorter than the original, and easier to verify. Exercise 16.5. Rewrite increment using time_to_int and int_to_time.
", + "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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Then at the beginning of each function you could check the arguments to make sure they are valid:
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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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", + "id": "/page/176/ListItem/15", + "block_type": "ListItem", + "html": "156 Chapter 16. Classes and functions
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", + "html": "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 .
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def print_time(time):\n print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)\nTo call this function, you have to pass a Time object as an argument:", + "html": "
def print_time(time):\n print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)", "polygon": [ [ - 85.46484375, - 306.87066650390625 + 85.9130859375, + 305.314453125 ], [ - 431.58013916015625, - 306.87066650390625 + 435.69140625, + 305.314453125 ], [ - 431.58013916015625, + 435.69140625, + 329.0272521972656 + ], + [ + 85.9130859375, + 329.0272521972656 + ] + ], + "bbox": [ + 85.9130859375, + 305.314453125, + 435.69140625, + 329.0272521972656 + ], + "children": null, + "section_hierarchy": { + "1": "/page/178/SectionHeader/1", + "3": "/page/179/SectionHeader/4" + }, + "images": {} + }, + { + "id": "/page/179/Text/8", + "block_type": "Text", + "html": "
To call this function, you have to pass a Time object as an argument:
", + "polygon": [ + [ + 85.763671875, + 334.3926696777344 + ], + [ + 383.4679870605469, + 334.3926696777344 + ], + [ + 383.4679870605469, 344.50482177734375 ], [ - 85.46484375, + 85.763671875, 344.50482177734375 ] ], + "bbox": [ + 85.763671875, + 334.3926696777344, + 383.4679870605469, + 344.50482177734375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89281,27 +147924,33 @@ "images": {} }, { - "id": "/page/179/TextInlineMath/8", - "block_type": "TextInlineMath", - "html": ">>> start = Time() >>> start.hour = 9 >>> start.minute = 45 >>> start.second = 00 >>> print_time(start) 09:45:00
", + "id": "/page/179/Code/9", + "block_type": "Code", + "html": ">>> start = Time()\n>>> start.hour = 9\n>>> start.minute = 45\n>>> start.second = 00\n>>> print_time(start)\n09:45:00", "polygon": [ [ - 84.8671875, - 345.919921875 + 86.0625, + 349.7206726074219 ], [ 197.3759765625, - 345.919921875 + 349.7206726074219 ], [ 197.3759765625, 420.6552734375 ], [ - 84.8671875, + 86.0625, 420.6552734375 ] ], + "bbox": [ + 86.0625, + 349.7206726074219, + 197.3759765625, + 420.6552734375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89310,27 +147959,33 @@ "images": {} }, { - "id": "/page/179/Text/9", + "id": "/page/179/Text/10", "block_type": "Text", "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.
", "polygon": [ [ - 86.0625, - 424.6171875 + 86.40001678466797, + 425.77734375 ], [ - 482.90625, - 424.6171875 + 482.4044189453125, + 425.77734375 ], [ - 482.90625, + 482.4044189453125, 448.32684326171875 ], [ - 86.0625, + 86.40001678466797, 448.32684326171875 ] ], + "bbox": [ + 86.40001678466797, + 425.77734375, + 482.4044189453125, + 448.32684326171875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89339,27 +147994,33 @@ "images": {} }, { - "id": "/page/179/Code/10", + "id": "/page/179/Code/11", "block_type": "Code", "html": "class Time(object):\n def print_time(time):\n print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)", "polygon": [ [ 86.40001678466797, - 453.234375 + 453.54168701171875 ], [ - 452.49615478515625, - 453.234375 + 459.59765625, + 453.54168701171875 ], [ - 452.49615478515625, - 487.8932800292969 + 459.59765625, + 489.19921875 ], [ 86.40001678466797, - 487.8932800292969 + 489.19921875 ] ], + "bbox": [ + 86.40001678466797, + 453.54168701171875, + 459.59765625, + 489.19921875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89368,12 +148029,12 @@ "images": {} }, { - "id": "/page/179/Text/11", + "id": "/page/179/Text/12", "block_type": "Text", "html": "
Now there are two ways to call print_time. The first (and less common) way is to use function syntax:
", "polygon": [ [ - 85.46484375, + 86.2119140625, 492.29296875 ], [ @@ -89385,10 +148046,16 @@ 515.5648498535156 ], [ - 85.46484375, + 86.2119140625, 515.5648498535156 ] ], + "bbox": [ + 86.2119140625, + 492.29296875, + 482.39813232421875, + 515.5648498535156 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89397,27 +148064,33 @@ "images": {} }, { - "id": "/page/179/Code/12", + "id": "/page/179/Code/13", "block_type": "Code", "html": ">>> Time.print_time(start)\n09:45:00", "polygon": [ [ - 85.46484375, - 520.13671875 + 85.98779296875, + 519.75 ], [ - 222.39942932128906, - 520.13671875 + 226.51171875, + 519.75 ], [ - 222.39942932128906, + 226.51171875, 542.9373016357422 ], [ - 85.46484375, + 85.98779296875, 542.9373016357422 ] ], + "bbox": [ + 85.98779296875, + 519.75, + 226.51171875, + 542.9373016357422 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89426,12 +148099,12 @@ "images": {} }, { - "id": "/page/179/Text/13", + "id": "/page/179/Text/14", "block_type": "Text", "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.
", "polygon": [ [ - 85.3154296875, + 85.9130859375, 548.3027038574219 ], [ @@ -89440,13 +148113,19 @@ ], [ 482.4007263183594, - 570.796875 + 570.6088562011719 ], [ - 85.3154296875, - 570.796875 + 85.9130859375, + 570.6088562011719 ] ], + "bbox": [ + 85.9130859375, + 548.3027038574219, + 482.4007263183594, + 570.6088562011719 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89455,27 +148134,33 @@ "images": {} }, { - "id": "/page/179/Text/14", + "id": "/page/179/Text/15", "block_type": "Text", "html": "The second (and more concise) way is to use method syntax:
", "polygon": [ [ - 84.49365234375, - 578.14453125 + 85.9130859375, + 579.69140625 ], [ - 352.318359375, - 578.14453125 + 352.6171875, + 579.69140625 ], [ - 352.318359375, + 352.6171875, 589.9088592529297 ], [ - 84.49365234375, + 85.9130859375, 589.9088592529297 ] ], + "bbox": [ + 85.9130859375, + 579.69140625, + 352.6171875, + 589.9088592529297 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89484,27 +148169,33 @@ "images": {} }, { - "id": "/page/179/Code/15", + "id": "/page/179/Code/16", "block_type": "Code", "html": ">>> start.print_time()\n09:45:00", "polygon": [ [ - 85.0166015625, - 593.2265625 + 85.763671875, + 594.38671875 ], [ - 202.306640625, - 593.2265625 + 201.4779510498047, + 594.38671875 ], [ - 202.306640625, + 201.4779510498047, 617.2813110351562 ], [ - 85.0166015625, + 85.763671875, 617.2813110351562 ] ], + "bbox": [ + 85.763671875, + 594.38671875, + 201.4779510498047, + 617.2813110351562 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89513,20 +148204,20 @@ "images": {} }, { - "id": "/page/179/Text/16", + "id": "/page/179/Text/17", "block_type": "Text", "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.
", "polygon": [ [ 85.6142578125, - 621.84375 + 622.6171875 ], [ - 483.50390625, - 621.84375 + 482.90625, + 622.6171875 ], [ - 483.50390625, + 482.90625, 669.3418731689453 ], [ @@ -89534,6 +148225,12 @@ 669.3418731689453 ] ], + "bbox": [ + 85.6142578125, + 622.6171875, + 482.90625, + 669.3418731689453 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89542,27 +148239,33 @@ "images": {} }, { - "id": "/page/179/Text/17", + "id": "/page/179/Text/18", "block_type": "Text", "html": "Inside the method, the subject is assigned to the first parameter, so in this case start is assigned to time.
", "polygon": [ [ - 85.6142578125, - 676.7578125 + 85.9130859375, + 678.3046875 ], [ - 482.90625, - 676.7578125 + 482.4002685546875, + 678.3046875 ], [ - 482.90625, + 482.4002685546875, 700.8348770141602 ], [ - 85.6142578125, + 85.9130859375, 700.8348770141602 ] ], + "bbox": [ + 85.9130859375, + 678.3046875, + 482.4002685546875, + 700.8348770141602 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89578,9 +148281,9 @@ "images": null }, { - "id": "/page/180/Page/198", + "id": "/page/180/Page/205", "block_type": "Page", - "html": "17.3. Another example 159
", + "html": "", "polygon": [ [ - 128.12255859375, - 61.171142578125 + 128.49609375, + 60.95654296875 ], [ 525.6033935546875, - 61.171142578125 + 60.95654296875 ], [ 525.6033935546875, 71.13372802734375 ], [ - 128.12255859375, + 128.49609375, 71.13372802734375 ] ], + "bbox": [ + 128.49609375, + 60.95654296875, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89630,27 +148345,33 @@ "images": {} }, { - "id": "/page/180/PageHeader/20", + "id": "/page/180/PageHeader/19", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.99609375, - 61.48828125 + 509.80078125, + 60.71484375 ], [ - 525.9375, - 61.48828125 + 525.33984375, + 60.71484375 ], [ - 525.9375, - 70.4794921875 + 525.33984375, + 69.99609375 ], [ - 510.99609375, - 70.4794921875 + 509.80078125, + 69.99609375 ] ], + "bbox": [ + 509.80078125, + 60.71484375, + 525.33984375, + 69.99609375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89664,22 +148385,28 @@ "html": "By convention, the first parameter of a method is called self, so it would be more common to write print_time like this:
", "polygon": [ [ - 128.9443359375, - 88.68572998046875 + 128.0478515625, + 88.22021484375 ], [ 525.6015014648438, - 88.68572998046875 + 88.22021484375 ], [ 525.6015014648438, 110.99188232421875 ], [ - 128.9443359375, + 128.0478515625, 110.99188232421875 ] ], + "bbox": [ + 128.0478515625, + 88.22021484375, + 525.6015014648438, + 110.99188232421875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89693,22 +148420,28 @@ "html": "class Time(object):\n def print_time(self):\n print '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)", "polygon": [ [ - 129.392578125, - 117.2724609375 + 129.60006713867188, + 117.315673828125 ], [ 495.69622802734375, - 117.2724609375 + 117.315673828125 ], [ 495.69622802734375, - 153.0439453125 + 151.787109375 ], [ - 129.392578125, - 153.0439453125 + 129.60006713867188, + 151.787109375 ] ], + "bbox": [ + 129.60006713867188, + 117.315673828125, + 495.69622802734375, + 151.787109375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89722,22 +148455,28 @@ "html": "
The reason for this convention is an implicit metaphor:
", "polygon": [ [ - 128.794921875, - 158.2646484375 + 128.6455078125, + 157.6845703125 ], [ - 370.68499755859375, - 158.2646484375 + 371.14453125, + 157.6845703125 ], [ - 370.68499755859375, + 371.14453125, 168.2518310546875 ], [ - 128.794921875, + 128.6455078125, 168.2518310546875 ] ], + "bbox": [ + 128.6455078125, + 157.6845703125, + 371.14453125, + 168.2518310546875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89746,27 +148485,33 @@ "images": {} }, { - "id": "/page/180/ListGroup/198", + "id": "/page/180/ListGroup/203", "block_type": "ListGroup", "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.
", "polygon": [ [ - 129.09375, - 264.12890625 + 128.6455078125, + 264.708984375 ], [ 525.603515625, - 264.12890625 + 264.708984375 ], [ 525.603515625, 311.3119201660156 ], [ - 129.09375, + 128.6455078125, 311.3119201660156 ] ], + "bbox": [ + 128.6455078125, + 264.708984375, + 525.603515625, + 311.3119201660156 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89865,25 +148628,31 @@ { "id": "/page/180/Text/7", "block_type": "Text", - "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?
", + "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?
", "polygon": [ [ - 128.794921875, - 312.85546875 + 128.6455078125, + 313.37164306640625 ], [ - 525.638671875, - 312.85546875 + 525.6038818359375, + 313.37164306640625 ], [ - 525.638671875, + 525.6038818359375, 335.5513610839844 ], [ - 128.794921875, + 128.6455078125, 335.5513610839844 ] ], + "bbox": [ + 128.6455078125, + 313.37164306640625, + 525.6038818359375, + 335.5513610839844 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", @@ -89894,58 +148663,72 @@ { "id": "/page/180/SectionHeader/8", "block_type": "SectionHeader", - "html": "Here's a version of increment (from Section 16.3) rewritten as a method:
", + "html": "Here's a version of increment (from Section 16.3) rewritten as a method:
", "polygon": [ [ - 128.6455078125, - 392.326171875 + 127.4501953125, + 391.939453125 ], [ 449.4375, - 392.326171875 + 391.939453125 ], [ 449.4375, 402.8829345703125 ], [ - 128.6455078125, + 127.4501953125, 402.8829345703125 ] ], + "bbox": [ + 127.4501953125, + 391.939453125, + 449.4375, + 402.8829345703125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, @@ -89955,26 +148738,33 @@ "html": "# inside class Time:", "polygon": [ [ - 129.31787109375, - 408.375 + 129.2431640625, + 409.2057800292969 ], [ 234.2073974609375, - 408.375 + 409.2057800292969 ], [ 234.2073974609375, 419.1683654785156 ], [ - 129.31787109375, + 129.2431640625, 419.1683654785156 ] ], + "bbox": [ + 129.2431640625, + 409.2057800292969, + 234.2073974609375, + 419.1683654785156 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, @@ -89984,55 +148774,69 @@ "html": "
def increment(self, seconds):\n seconds += self.time_to_int()\n return int_to_time(seconds)", "polygon": [ [ - 150.51611328125, - 432.3515625 + 148.0693359375, + 433.51171875 ], [ - 323.630859375, - 432.3515625 + 323.9296875, + 433.51171875 ], [ - 323.630859375, + 323.9296875, 467.9463806152344 ], [ - 150.51611328125, + 148.0693359375, 467.9463806152344 ] ], + "bbox": [ + 148.0693359375, + 433.51171875, + 323.9296875, + 467.9463806152344 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, { "id": "/page/180/Text/12", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 129.5419921875, - 474.1171875 + 128.794921875, + 474.4187927246094 ], [ - 525.9375, - 474.1171875 + 525.638671875, + 474.4187927246094 ], [ - 525.9375, + 525.638671875, 496.7249450683594 ], [ - 129.5419921875, + 128.794921875, 496.7249450683594 ] ], + "bbox": [ + 128.794921875, + 474.4187927246094, + 525.638671875, + 496.7249450683594 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, @@ -90042,26 +148846,33 @@ "html": "Here's how you would invoke increment:
", "polygon": [ [ - 129.09375, + 127.599609375, 507.0198059082031 ], [ - 315.5625, + 315.539794921875, 507.0198059082031 ], [ - 315.5625, - 517.4296875 + 315.539794921875, + 517.1319580078125 ], [ - 129.09375, - 517.4296875 + 127.599609375, + 517.1319580078125 ] ], + "bbox": [ + 127.599609375, + 507.0198059082031, + 315.539794921875, + 517.1319580078125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, @@ -90071,26 +148882,33 @@ "html": ">>> start.print_time()\n09:45:00\n>>> end = start.increment(1337)\n>>> end.print_time()\n10:07:17", "polygon": [ [ - 129.6001434326172, - 523.23046875 + 128.42138671875, + 523.455810546875 ], [ 291.7513732910156, - 523.23046875 + 523.455810546875 ], [ 291.7513732910156, - 583.55859375 + 582.1954193115234 ], [ - 129.6001434326172, - 583.55859375 + 128.42138671875, + 582.1954193115234 ] ], + "bbox": [ + 128.42138671875, + 523.455810546875, + 291.7513732910156, + 582.1954193115234 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, @@ -90100,7 +148918,7 @@ "html": "
The subject, start, gets assigned to the first parameter, self. The argument, 1337, gets assigned to the second parameter, seconds.
", "polygon": [ [ - 129.2431640625, + 128.3466796875, 588.6678161621094 ], [ @@ -90109,17 +148927,24 @@ ], [ 525.6004638671875, - 610.9749755859375 + 611.015625 ], [ - 129.2431640625, - 612.17578125 + 128.3466796875, + 611.015625 ] ], + "bbox": [ + 128.3466796875, + 588.6678161621094, + 525.6004638671875, + 611.015625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, @@ -90129,127 +148954,120 @@ "html": "This mechanism can be confusing, especially if you make an error. For example, if you invoke increment with two arguments, you get:
", "polygon": [ [ - 128.9443359375, - 620.296875 + 127.30078125, + 620.68359375 ], [ - 525.9375, - 620.296875 + 525.6035766601562, + 620.68359375 ], [ - 525.9375, + 525.6035766601562, 643.5759735107422 ], [ - 128.9443359375, + 127.30078125, 643.5759735107422 ] ], + "bbox": [ + 127.30078125, + 620.68359375, + 525.6035766601562, + 643.5759735107422 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, { - "id": "/page/180/Text/17", - "block_type": "Text", - "html": ">>> end = start.increment(1337, 460)
", + "id": "/page/180/Code/17", + "block_type": "Code", + "html": ">>> end = start.increment(1337, 460)\nTypeError: increment() takes exactly 2 arguments (3 given)", "polygon": [ [ - 129.60015869140625, + 127.97314453125, 649.8988189697266 ], [ - 428.220703125, + 432.98126220703125, 649.8988189697266 ], [ - 428.220703125, - 661.67578125 + 432.98126220703125, + 672.0564117431641 ], [ - 129.60015869140625, - 663.22265625 + 127.97314453125, + 672.0564117431641 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" - }, - "images": {} - }, - { - "id": "/page/180/Text/18", - "block_type": "Text", - "html": "
TypeError: increment() takes exactly 2 arguments (3 given)
", - "polygon": [ - [ - 129.2431640625, - 662.0938110351562 - ], - [ - 433.8984375, - 662.0938110351562 - ], - [ - 433.8984375, - 677.14453125 - ], - [ - 129.2431640625, - 677.14453125 - ] + "bbox": [ + 127.97314453125, + 649.8988189697266, + 432.98126220703125, + 672.0564117431641 ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, { - "id": "/page/180/Text/19", + "id": "/page/180/Text/18", "block_type": "Text", "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.
", "polygon": [ [ - 128.49609375, - 678.3046875 + 127.8984375, + 677.91796875 ], [ 525.6036376953125, - 678.3046875 + 677.91796875 ], [ 525.6036376953125, 700.8349761962891 ], [ - 128.49609375, + 127.8984375, 700.8349761962891 ] ], + "bbox": [ + 127.8984375, + 677.91796875, + 525.6036376953125, + 700.8349761962891 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} } ], "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": null }, { - "id": "/page/181/Page/203", + "id": "/page/181/Page/208", "block_type": "Page", - "html": "160 Chapter 17. Classes and methods
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.521484375 + 60.18310546875 ], [ - 483.50390625, - 60.521484375 + 482.4034423828125, + 60.18310546875 ], [ - 483.50390625, + 482.4034423828125, 71.13372802734375 ], [ @@ -90291,542 +149115,664 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.18310546875, + 482.4034423828125, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, { "id": "/page/181/PageHeader/22", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.09130859375, - 58.92626953125 + 85.39013671875, + 60.134765625 ], [ - 101.67626953125, - 58.92626953125 + 101.97509765625, + 60.134765625 ], [ - 101.67626953125, - 69.94775390625 + 101.97509765625, + 70.576171875 ], [ - 85.09130859375, - 69.94775390625 + 85.39013671875, + 70.576171875 ] ], + "bbox": [ + 85.39013671875, + 60.134765625, + 101.97509765625, + 70.576171875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/180/SectionHeader/8" + "3": "/page/179/SectionHeader/4", + "4": "/page/180/SectionHeader/8" }, "images": {} }, { "id": "/page/181/SectionHeader/1", "block_type": "SectionHeader", - "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:
", + "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:
", "polygon": [ [ - 85.763671875, - 110.1181640625 + 84.8671875, + 110.021484375 ], [ - 483.50390625, - 108.5712890625 + 483.205078125, + 110.021484375 ], [ - 483.50390625, - 150.9169921875 + 483.205078125, + 145.38592529296875 ], [ - 85.763671875, - 152.4638671875 + 84.8671875, + 145.38592529296875 ] ], + "bbox": [ + 84.8671875, + 110.021484375, + 483.205078125, + 145.38592529296875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/1" + "3": "/page/179/SectionHeader/4", + "4": "/page/181/SectionHeader/1" }, "images": {} }, { - "id": "/page/181/Text/3", - "block_type": "Text", - "html": "# inside class Time:
", + "id": "/page/181/Code/3", + "block_type": "Code", + "html": "def is_after(self, other):\n return self.time_to_int() > other.time_to_int()", "polygon": [ [ - 86.4000244140625, - 149.8287353515625 + 100.705078125, + 161.1650390625 ], [ - 191.00730895996094, - 149.5634765625 + 375.328125, + 161.1650390625 ], [ - 191.00730895996094, - 160.1982421875 + 375.328125, + 196.374267578125 ], [ - 86.4000244140625, - 161.7451171875 + 100.705078125, + 196.374267578125 ] ], + "bbox": [ + 100.705078125, + 161.1650390625, + 375.328125, + 196.374267578125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/1" + "3": "/page/179/SectionHeader/4", + "4": "/page/181/SectionHeader/1" }, "images": {} }, { - "id": "/page/181/Code/4", - "block_type": "Code", - "html": "
def is_after(self, other):\n return self.time_to_int() > other.time_to_int()", + "id": "/page/181/Text/4", + "block_type": "Text", + "html": "
To use this method, you have to invoke it on one object and pass the other as an argument: >>> end.is_after(start)
", "polygon": [ [ - 104.21630859375, - 174.21771240234375 + 85.9130859375, + 199.546875 ], [ - 376.5234375, - 174.21771240234375 + 482.4034423828125, + 199.546875 ], [ - 376.5234375, - 197.9033203125 + 482.4034423828125, + 226.6171875 ], [ - 104.21630859375, - 197.9033203125 + 85.9130859375, + 226.6171875 ] ], + "bbox": [ + 85.9130859375, + 199.546875, + 482.4034423828125, + 226.6171875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/1" + "3": "/page/179/SectionHeader/4", + "4": "/page/181/SectionHeader/1" }, "images": {} }, { "id": "/page/181/Text/5", "block_type": "Text", - "html": "To use this method, you have to invoke it on one object and pass the other as an argument: >>> end.is_after(start)
", + "html": "True
", "polygon": [ [ - 85.3154296875, - 199.353515625 + 85.763671875, + 226.6171875 ], [ - 482.90625, - 199.353515625 + 107.32149505615234, + 226.6171875 ], [ - 482.90625, - 225.4842529296875 + 107.32149505615234, + 237.67822265625 ], [ - 85.3154296875, - 225.4842529296875 + 85.763671875, + 237.67822265625 ] ], + "bbox": [ + 85.763671875, + 226.6171875, + 107.32149505615234, + 237.67822265625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/1" + "3": "/page/179/SectionHeader/4", + "4": "/page/181/SectionHeader/1" }, "images": {} }, { "id": "/page/181/Text/6", "block_type": "Text", - "html": "True
", + "html": "One nice thing about this syntax is that it almost reads like English: \"end is after start?\"
", "polygon": [ [ - 84.26953125, - 225.45703125 + 86.2119140625, + 241.505859375 ], [ - 107.4287109375, - 225.45703125 + 470.5677795410156, + 241.505859375 ], [ - 107.4287109375, - 237.67822265625 + 470.5677795410156, + 252.3828125 ], [ - 84.26953125, - 237.67822265625 + 86.2119140625, + 252.3828125 ] ], + "bbox": [ + 86.2119140625, + 241.505859375, + 470.5677795410156, + 252.3828125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/1" + "3": "/page/179/SectionHeader/4", + "4": "/page/181/SectionHeader/1" }, "images": {} }, { - "id": "/page/181/Text/7", - "block_type": "Text", - "html": "One nice thing about this syntax is that it almost reads like English: \"end is after start?\"
", + "id": "/page/181/SectionHeader/7", + "block_type": "SectionHeader", + "html": "# inside class Time:
", "polygon": [ [ - 86.0625, - 275.923828125 + 86.4000244140625, + 149.8287353515625 ], [ - 231.890625, - 275.923828125 + 191.00730895996094, + 149.8287353515625 ], [ - 231.890625, - 293.81585693359375 + 191.00730895996094, + 159.79132080078125 ], [ - 86.0625, - 293.81585693359375 + 86.4000244140625, + 159.79132080078125 ] ], + "bbox": [ + 86.4000244140625, + 149.8287353515625, + 191.00730895996094, + 159.79132080078125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/9", + "id": "/page/181/Text/8", "block_type": "Text", "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:
", "polygon": [ [ - 85.9130859375, - 302.80078125 + 86.2119140625, + 303.57421875 ], [ - 482.90625, - 302.80078125 + 482.4036865234375, + 303.57421875 ], [ - 482.90625, - 351.52734375 + 482.4036865234375, + 351.0957946777344 ], [ - 85.9130859375, - 351.52734375 + 86.2119140625, + 351.0957946777344 ] ], + "bbox": [ + 86.2119140625, + 303.57421875, + 482.4036865234375, + 351.0957946777344 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/10", - "block_type": "Text", - "html": "# inside class Time:
", + "id": "/page/181/Code/9", + "block_type": "Code", + "html": "# inside class Time:", "polygon": [ [ - 85.9130859375, - 354.814453125 + 86.4000244140625, + 354.234375 ], [ - 191.00730895996094, - 354.814453125 + 200.8125, + 354.234375 ], [ - 191.00730895996094, - 365.642578125 + 200.8125, + 365.501220703125 ], [ - 85.9130859375, - 365.642578125 + 86.4000244140625, + 365.501220703125 ] ], + "bbox": [ + 86.4000244140625, + 354.234375, + 200.8125, + 365.501220703125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Code/11", + "id": "/page/181/Code/10", "block_type": "Code", - "html": "
def __init__(self, hour=0, minute=0, second=0):\n self.hour = hour\n self.minute = minute\n self.second = second\nIt is common for the parameters of __init__ to have the same names as the attributes. The", + "html": "
def __init__(self, hour=0, minute=0, second=0):\n self.hour = hour\n self.minute = minute\n self.second = second", "polygon": [ [ - 86.40003967285156, - 379.564453125 + 104.51513671875, + 375.890625 ], [ - 482.404052734375, - 379.564453125 + 353.8125, + 375.890625 ], [ - 482.404052734375, - 441.1767883300781 + 353.8125, + 426.47222900390625 ], [ - 86.40003967285156, - 441.1767883300781 + 104.51513671875, + 426.47222900390625 ] ], + "bbox": [ + 104.51513671875, + 375.890625, + 353.8125, + 426.47222900390625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/12", + "id": "/page/181/Text/11", "block_type": "Text", - "html": "
statement
", + "html": "It is common for the parameters of __init__ to have the same names as the attributes. The statement
", "polygon": [ [ - 86.4000244140625, - 438.92578125 + 85.9130859375, + 430.03125 ], [ - 129.48826599121094, - 438.92578125 + 482.404052734375, + 430.03125 ], [ - 129.48826599121094, + 482.404052734375, 453.37078857421875 ], [ - 86.4000244140625, + 85.9130859375, 453.37078857421875 ] ], + "bbox": [ + 85.9130859375, + 430.03125, + 482.404052734375, + 453.37078857421875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/13", - "block_type": "Text", - "html": "self.hour = hour
", + "id": "/page/181/Code/12", + "block_type": "Code", + "html": "self.hour = hour", "polygon": [ [ - 125.806640625, - 455.5546875 + 127.4501953125, + 457.48828125 ], [ - 211.91885375976562, - 455.5546875 + 212.6162109375, + 457.48828125 ], [ - 211.91885375976562, + 212.6162109375, 467.7762145996094 ], [ - 125.806640625, + 127.4501953125, 467.7762145996094 ] ], + "bbox": [ + 127.4501953125, + 457.48828125, + 212.6162109375, + 467.7762145996094 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/14", + "id": "/page/181/Text/13", "block_type": "Text", "html": "
stores the value of the parameter hour as an attribute of self.
", "polygon": [ [ - 85.53955078125, - 471.41015625 + 85.9130859375, + 472.3686218261719 ], [ 356.46368408203125, - 471.41015625 + 472.3686218261719 ], [ 356.46368408203125, - 483.01171875 + 482.625 ], [ - 85.53955078125, - 483.01171875 + 85.9130859375, + 482.625 ] ], + "bbox": [ + 85.9130859375, + 472.3686218261719, + 356.46368408203125, + 482.625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/15", + "id": "/page/181/Text/14", "block_type": "Text", "html": "The parameters are optional, so if you call Time with no arguments, you get the default values.
", "polygon": [ [ - 85.763671875, - 488.42578125 + 86.4000244140625, + 489.19921875 ], [ - 483.50390625, - 488.42578125 + 482.90625, + 489.19921875 ], [ - 483.50390625, + 482.90625, 513.2017822265625 ], [ - 85.763671875, + 86.4000244140625, 513.2017822265625 ] ], + "bbox": [ + 86.4000244140625, + 489.19921875, + 482.90625, + 513.2017822265625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/16", - "block_type": "Text", - "html": ">>> time = Time() >>> time.print_time() 00:00:00
", + "id": "/page/181/Code/15", + "block_type": "Code", + "html": ">>> time = Time()\n>>> time.print_time()\n00:00:00", "polygon": [ [ - 84.7177734375, - 512.40234375 + 86.13720703125, + 515.49609375 ], [ - 196.62890625, - 512.40234375 + 196.2476348876953, + 515.49609375 ], [ - 196.62890625, - 553.39453125 + 196.2476348876953, + 551.9952239990234 ], [ - 84.7177734375, - 553.39453125 + 86.13720703125, + 551.9952239990234 ] ], + "bbox": [ + 86.13720703125, + 515.49609375, + 196.2476348876953, + 551.9952239990234 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/17", + "id": "/page/181/Text/16", "block_type": "Text", "html": "
If you provide one argument, it overrides hour:
", "polygon": [ [ - 86.4000244140625, - 555.328125 + 85.68896484375, + 554.94140625 ], [ - 296.4375, - 555.328125 + 295.541015625, + 554.94140625 ], [ - 295.2421875, + 295.541015625, 566.6997680664062 ], [ - 85.39013671875, + 85.68896484375, 566.6997680664062 ] ], + "bbox": [ + 85.68896484375, + 554.94140625, + 295.541015625, + 566.6997680664062 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Code/18", + "id": "/page/181/Code/17", "block_type": "Code", "html": ">>> time = Time (9)\n>>> time.print_time()\n09:00:00", "polygon": [ [ - 84.4189453125, + 86.0625, 567.703125 ], [ @@ -90838,115 +149784,174 @@ 607.921875 ], [ - 84.4189453125, + 86.0625, 607.921875 ] ], + "bbox": [ + 86.0625, + 567.703125, + 196.2476043701172, + 607.921875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Text/19", + "id": "/page/181/Text/18", "block_type": "Text", "html": "
If you provide two arguments, they override hour and minute.
", "polygon": [ [ - 86.39999389648438, - 608.6953125 + 86.2119140625, + 609.08203125 ], [ - 362.77734375, - 608.6953125 + 362.13067626953125, + 609.08203125 ], [ 362.13067626953125, - 621.0703125 + 620.1987609863281 ], [ - 85.46484375, - 621.0703125 + 86.2119140625, + 620.1987609863281 ] ], + "bbox": [ + 86.2119140625, + 609.08203125, + 362.13067626953125, + 620.1987609863281 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} }, { - "id": "/page/181/Code/20", + "id": "/page/181/Code/19", "block_type": "Code", "html": ">>> time = Time(9, 45)\n>>> time.print_time()\n09:45:00", "polygon": [ [ - 84.8671875, - 623.00390625 + 85.3154296875, + 624.55078125 ], [ 201.46800231933594, - 623.00390625 + 624.55078125 ], [ 201.46800231933594, - 659.35546875 + 658.9922027587891 ], [ - 84.8671875, - 659.35546875 + 85.3154296875, + 658.9922027587891 ] ], + "bbox": [ + 85.3154296875, + 624.55078125, + 201.46800231933594, + 658.9922027587891 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" + }, + "images": {} + }, + { + "id": "/page/181/Text/20", + "block_type": "Text", + "html": "
And if you provide three arguments, they override all three default values.
", + "polygon": [ + [ + 86.39999389648438, + 662.0625 + ], + [ + 416.56640625, + 662.0625 + ], + [ + 416.56640625, + 673.6967620849609 + ], + [ + 86.39999389648438, + 673.6967620849609 + ] + ], + "bbox": [ + 86.39999389648438, + 662.0625, + 416.56640625, + 673.6967620849609 + ], + "children": null, + "section_hierarchy": { + "1": "/page/178/SectionHeader/1", + "3": "/page/181/SectionHeader/7" }, "images": {} }, { "id": "/page/181/Text/21", "block_type": "Text", - "html": "And if you provide three arguments, they override all three default values. 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.
", + "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.
", "polygon": [ [ - 85.763671875, - 662.8359375 + 85.6142578125, + 675.2109375 ], [ 482.90625, - 662.8359375 + 675.2109375 ], [ 482.90625, - 698.4140625 + 697.9130630493164 ], [ - 85.763671875, - 698.4140625 + 85.6142578125, + 697.9130630493164 ] ], + "bbox": [ + 85.6142578125, + 675.2109375, + 482.90625, + 697.9130630493164 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": {} } ], "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/181/SectionHeader/8" + "3": "/page/181/SectionHeader/7" }, "images": null }, { - "id": "/page/182/Page/205", + "id": "/page/182/Page/206", "block_type": "Page", - "html": "17.6. The __str__ method 161
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# inside class Time:\n def __str__(self):\n return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)", "polygon": [ [ - 128.9443359375, - 170.2529296875 + 129.60003662109375, + 171.4097900390625 ], [ - 234.2073211669922, - 170.2529296875 + 501.134765625, + 171.4097900390625 ], [ - 234.2073211669922, - 181.37237548828125 + 501.134765625, + 217.955322265625 ], [ - 128.9443359375, - 181.37237548828125 + 129.60003662109375, + 217.955322265625 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/178/SectionHeader/1", - "3": "/page/182/SectionHeader/1" - }, - "images": {} - }, - { - "id": "/page/182/Code/5", - "block_type": "Code", - "html": "
def __str__(self):\n return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)", - "polygon": [ - [ - 147.1728515625, - 195.79876708984375 - ], - [ - 500.9252014160156, - 195.79876708984375 - ], - [ - 500.9252014160156, - 218.302734375 - ], - [ - 147.1728515625, - 218.302734375 - ] + "bbox": [ + 129.60003662109375, + 171.4097900390625, + 501.134765625, + 217.955322265625 ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/182/SectionHeader/1" + "2": "/page/182/SectionHeader/1" }, "images": {} }, { - "id": "/page/182/Text/6", + "id": "/page/182/Text/5", "block_type": "Text", "html": "
When you print an object, Python invokes the str method:
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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.
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", "polygon": [ [ - 129.5419921875, - 313.62890625 + 129.60008239746094, + 314.4345397949219 ], [ - 492.46875, - 313.62890625 + 492.3472900390625, + 314.4345397949219 ], [ - 492.46875, + 492.3472900390625, 324.47271728515625 ], [ - 129.5419921875, + 129.60008239746094, 324.47271728515625 ] ], + "bbox": [ + 129.60008239746094, + 314.4345397949219, + 492.3472900390625, + 324.47271728515625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/182/SectionHeader/1" + "2": "/page/182/SectionHeader/1" }, "images": {} }, { - "id": "/page/182/SectionHeader/10", + "id": "/page/182/SectionHeader/9", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 128.794921875, - 393.29296875 + 128.3466796875, + 393.873046875 ], [ - 525.638671875, - 393.29296875 + 525.603515625, + 393.873046875 ], [ - 525.638671875, + 525.603515625, 428.434814453125 ], [ - 128.794921875, + 128.3466796875, 428.434814453125 ] ], + "bbox": [ + 128.3466796875, + 393.873046875, + 525.603515625, + 428.434814453125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/182/SectionHeader/10" + "2": "/page/182/SectionHeader/1", + "4": "/page/182/SectionHeader/9" }, "images": {} }, { - "id": "/page/182/Text/12", + "id": "/page/182/Text/11", "block_type": "Text", "html": "Here is what the definition might look like:
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And here is how you could use it:
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", + "id": "/page/182/Code/14", + "block_type": "Code", + "html": ">>> start = Time(9, 45)\n>>> duration = Time(1, 35)\n>>> print start + duration\n11:20:00", "polygon": [ [ - 129.6001434326172, + 128.0478515625, 547.9886932373047 ], [ - 265.5995788574219, + 265.8076171875, 547.9886932373047 ], [ - 265.5995788574219, + 265.8076171875, 594.5343017578125 ], [ - 129.6001434326172, + 128.0478515625, 594.5343017578125 ] ], + "bbox": [ + 128.0478515625, + 547.9886932373047, + 265.8076171875, + 594.5343017578125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/182/SectionHeader/10" + "2": "/page/182/SectionHeader/1", + "4": "/page/182/SectionHeader/9" }, "images": {} }, { - "id": "/page/182/Text/16", + "id": "/page/182/Text/15", "block_type": "Text", "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!
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", + "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.
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", + "id": "/page/183/Code/3", + "block_type": "Code", + "html": "# inside class Time:", "polygon": [ [ - 84.94189453125, - 150.43359375 + 85.53955078125, + 151.013671875 ], [ 191.00726318359375, - 150.43359375 + 151.013671875 ], [ 191.00726318359375, 161.92535400390625 ], [ - 84.94189453125, + 85.53955078125, 161.92535400390625 ] ], + "bbox": [ + 85.53955078125, + 151.013671875, + 191.00726318359375, + 161.92535400390625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, @@ -91698,26 +150874,33 @@ "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)", "polygon": [ [ - 105.41162109375, - 176.35174560546875 + 105.26220703125, + 174.41015625 ], [ - 392.361328125, - 176.35174560546875 + 395.6484375, + 174.41015625 ], [ - 392.361328125, - 333.931640625 + 395.6484375, + 332.96484375 ], [ - 105.41162109375, - 333.931640625 + 105.26220703125, + 332.96484375 ] ], + "bbox": [ + 105.26220703125, + 174.41015625, + 395.6484375, + 332.96484375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, @@ -91727,26 +150910,33 @@ "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.
", "polygon": [ [ - 85.9130859375, - 337.21875 + 86.0625, + 337.9921875 ], [ - 483.50390625, - 337.21875 + 482.3995056152344, + 337.9921875 ], [ - 483.50390625, + 482.3995056152344, 360.6119079589844 ], [ - 85.9130859375, + 86.0625, 360.6119079589844 ] ], + "bbox": [ + 86.0625, + 337.9921875, + 482.3995056152344, + 360.6119079589844 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, @@ -91756,26 +150946,33 @@ "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.
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", "polygon": [ [ - 85.166015625, + 85.53955078125, 425.390625 ], [ @@ -91797,14 +150994,21 @@ 436.38189697265625 ], [ - 85.166015625, + 85.53955078125, 436.38189697265625 ] ], + "bbox": [ + 85.53955078125, + 425.390625, + 361.880859375, + 436.38189697265625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, @@ -91814,26 +151018,33 @@ "html": ">>> start = Time(9, 45)\n>>> duration = Time(1, 35)\n>>> print start + duration\n11:20:00\n>>> print start + 1337\n10:07:17", "polygon": [ [ - 84.7177734375, - 439.3125 + 85.53955078125, + 441.89276123046875 ], [ 222.3993377685547, - 439.3125 + 441.89276123046875 ], [ 222.3993377685547, - 513.5625 + 512.8263549804688 ], [ - 84.7177734375, - 513.5625 + 85.53955078125, + 512.8263549804688 ] ], + "bbox": [ + 85.53955078125, + 441.89276123046875, + 222.3993377685547, + 512.8263549804688 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, @@ -91843,185 +151054,264 @@ "html": "
Unfortunately, this implementation of addition is not commutative. If the integer is the first operand, you get
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", + "id": "/page/183/Code/10", + "block_type": "Code", + "html": ">>> print 1337 + start", "polygon": [ [ - 85.9130859375, + 85.3154296875, 546.3027801513672 ], [ - 431.5223083496094, + 201.46791076660156, 546.3027801513672 ], [ - 431.5223083496094, - 568.4593811035156 + 201.46791076660156, + 556.48828125 ], [ - 84.7177734375, - 568.4593811035156 + 85.3154296875, + 556.48828125 ] ], + "bbox": [ + 85.3154296875, + 546.3027801513672, + 201.46791076660156, + 556.48828125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, { "id": "/page/183/Text/11", "block_type": "Text", + "html": "
TypeError: unsupported operand type(s) for +: 'int' and 'instance'
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", + "id": "/page/183/Code/13", + "block_type": "Code", + "html": "# inside class Time:", "polygon": [ [ 85.3154296875, - 638.0859375 + 638.5177764892578 ], [ 191.00721740722656, - 638.0859375 + 638.5177764892578 ], [ 191.00721740722656, - 648.9140625 + 648.4803771972656 ], [ 85.3154296875, - 648.9140625 + 648.4803771972656 ] ], + "bbox": [ + 85.3154296875, + 638.5177764892578, + 191.00721740722656, + 648.4803771972656 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, { - "id": "/page/183/Code/13", + "id": "/page/183/Code/14", "block_type": "Code", - "html": "
def __radd__(self, other):\n return self.__add__(other)\nAnd here's how it's used:", + "html": "
def __radd__(self, other):\n return self.__add__(other)", "polygon": [ [ - 86.39994812011719, + 90.6943359375, 662.9067687988281 ], [ 264.2323913574219, - 662.44921875 + 662.9067687988281 ], [ 264.2323913574219, - 700.8349380493164 + 689.51953125 ], [ - 86.39994812011719, - 700.8349380493164 + 90.6943359375, + 689.51953125 ] ], + "bbox": [ + 90.6943359375, + 662.9067687988281, + 264.2323913574219, + 689.51953125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, { - "id": "/page/183/Text/14", + "id": "/page/183/Text/15", "block_type": "Text", - "html": "", + "html": "
And here's how it's used:
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", + "id": "/page/184/Code/182", + "block_type": "Code", + "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:", "polygon": [ [ - 129.60000610351562, + 129.5419921875, 88.68572998046875 ], [ @@ -92117,39 +151427,52 @@ 123.0897216796875 ], [ - 129.60000610351562, + 129.5419921875, 123.0897216796875 ] ], + "bbox": [ + 129.5419921875, + 88.68572998046875, + 512.6279907226562, + 123.0897216796875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/183/SectionHeader/1" + "2": "/page/182/SectionHeader/1", + "4": "/page/183/SectionHeader/1" }, "images": {} }, { - "id": "/page/184/ListGroup/180", + "id": "/page/184/ListGroup/183", "block_type": "ListGroup", "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.
", + "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.
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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.
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", "polygon": [ [ - 128.9443359375, - 551.07421875 + 128.57080078125, + 551.84765625 ], [ - 408.28472900390625, - 551.07421875 + 409.095703125, + 551.84765625 ], [ - 408.28472900390625, + 409.095703125, 562.1059722900391 ], [ - 128.9443359375, + 128.57080078125, 562.1059722900391 ] ], + "bbox": [ + 128.57080078125, + 551.84765625, + 409.095703125, + 562.1059722900391 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/184/SectionHeader/4" + "2": "/page/182/SectionHeader/1", + "4": "/page/184/SectionHeader/4" }, "images": {} }, @@ -92454,7 +151848,7 @@ "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", "polygon": [ [ - 129.392578125, + 129.09375, 568.4598236083984 ], [ @@ -92466,14 +151860,21 @@ 639.6328125 ], [ - 129.392578125, + 129.09375, 639.6328125 ] ], + "bbox": [ + 129.09375, + 568.4598236083984, + 281.29058837890625, + 639.6328125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/184/SectionHeader/4" + "2": "/page/182/SectionHeader/1", + "4": "/page/184/SectionHeader/4" }, "images": {} }, @@ -92483,26 +151884,33 @@ "html": "
In general, if all of the operations inside a function work with a given type, then the function works with that type.
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", + "html": "", "polygon": [ [ 86.4000015258789, - 60.134765625 + 59.60302734375 ], [ - 483.802734375, - 60.134765625 + 483.205078125, + 59.60302734375 ], [ - 483.802734375, + 483.205078125, 71.13372802734375 ], [ @@ -92587,68 +152009,88 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 59.60302734375, + 483.205078125, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/184/SectionHeader/4" + "2": "/page/182/SectionHeader/1", + "4": "/page/184/SectionHeader/4" }, "images": {} }, { "id": "/page/185/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.24072265625, - 59.50634765625 + 85.83837890625, + 59.5546875 ], [ - 100.77978515625, - 59.50634765625 + 102.57275390625, + 59.5546875 ], [ - 100.77978515625, - 69.85107421875 + 102.57275390625, + 70.4794921875 ], [ - 85.24072265625, - 69.85107421875 + 85.83837890625, + 70.4794921875 ] ], + "bbox": [ + 85.83837890625, + 59.5546875, + 102.57275390625, + 70.4794921875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/184/SectionHeader/4" + "2": "/page/182/SectionHeader/1", + "4": "/page/184/SectionHeader/4" }, "images": {} }, { "id": "/page/185/SectionHeader/1", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 85.9130859375, - 110.6015625 + 85.6142578125, + 110.1181640625 ], [ - 482.90625, - 109.0546875 + 484.1015625, + 110.1181640625 ], [ - 482.90625, + 484.1015625, 158.32891845703125 ], [ - 85.9130859375, - 158.361328125 + 85.6142578125, + 158.32891845703125 ] ], + "bbox": [ + 85.6142578125, + 110.1181640625, + 484.1015625, + 158.32891845703125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/185/SectionHeader/1" + "2": "/page/185/SectionHeader/1" }, "images": {} }, { "id": "/page/185/Text/3", "block_type": "Text", - "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).
", + "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).
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For purposes of debugging, you might find it useful to keep this function handy: def print_attributes(obj): for attr in obj.__dict__:
", + "html": "For purposes of debugging, you might find it useful to keep this function handy:
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", + "id": "/page/185/Code/7", + "block_type": "Code", + "html": "def print_attributes(obj):\n for attr in obj.__dict__:\n print attr, getattr(obj, attr)", "polygon": [ [ - 126.77783203125, - 302.994140625 + 86.4000473022461, + 281.4557189941406 ], [ 285.1539306640625, - 302.994140625 + 281.4557189941406 ], [ 285.1539306640625, 315.80731201171875 ], [ - 126.77783203125, + 86.4000473022461, 315.80731201171875 ] ], + "bbox": [ + 86.4000473022461, + 281.4557189941406, + 285.1539306640625, + 315.80731201171875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/185/SectionHeader/1" + "2": "/page/185/SectionHeader/1" }, "images": {} }, @@ -92832,26 +152310,32 @@ "html": "
print_attributes traverses the items in the object's dictionary and prints each attribute name and its corresponding value.
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", + "html": "", "polygon": [ [ - 127.8984375, + 129.09375, 61.171142578125 ], [ @@ -93164,79 +152708,97 @@ 71.13372802734375 ], [ - 127.8984375, + 129.09375, 71.13372802734375 ] ], + "bbox": [ + 129.09375, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/185/SectionHeader/10" + "2": "/page/185/SectionHeader/10" }, "images": {} }, { "id": "/page/186/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.697265625, - 61.1982421875 + 509.501953125, + 60.8115234375 ], [ 525.638671875, - 61.1982421875 + 60.8115234375 ], [ 525.638671875, - 70.2861328125 + 69.8994140625 ], [ - 510.697265625, - 70.2861328125 + 509.501953125, + 69.8994140625 ] ], + "bbox": [ + 509.501953125, + 60.8115234375, + 525.638671875, + 69.8994140625 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/185/SectionHeader/10" + "2": "/page/185/SectionHeader/10" }, "images": {} }, { "id": "/page/186/Text/1", "block_type": "Text", - "html": "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/ Information_hiding.
", + "html": "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/ Information_hiding.
", "polygon": [ [ - 128.6455078125, - 88.6552734375 + 129.60000610351562, + 88.171875 ], [ 525.9375, - 88.6552734375 + 88.171875 ], [ 525.9375, - 147.6298828125 + 147.57489013671875 ], [ - 128.6455078125, - 147.6298828125 + 129.60000610351562, + 147.57489013671875 ] ], + "bbox": [ + 129.60000610351562, + 88.171875, + 525.9375, + 147.57489013671875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/185/SectionHeader/10" + "2": "/page/185/SectionHeader/10" }, "images": {} }, { - "id": "/page/186/Text/2", - "block_type": "Text", - "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 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
", + "id": "/page/186/TextInlineMath/2", + "block_type": "TextInlineMath", + "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
", "polygon": [ [ 129.2431640625, @@ -93248,71 +152810,90 @@ ], [ 525.6044921875, - 208.828125 + 208.37420654296875 ], [ 129.2431640625, - 208.828125 + 208.37420654296875 ] ], + "bbox": [ + 129.2431640625, + 149.55389404296875, + 525.6044921875, + 208.37420654296875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/185/SectionHeader/10" + "2": "/page/185/SectionHeader/10" }, "images": {} }, { "id": "/page/186/SectionHeader/3", "block_type": "SectionHeader", - "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:
", "polygon": [ [ - 128.6455078125, - 549.52734375 + 127.1513671875, + 549.9140625 ], [ - 525.9375, - 549.52734375 + 525.6008911132812, + 549.9140625 ], [ - 525.9375, + 525.6008911132812, 572.4244232177734 ], [ - 128.6455078125, + 127.1513671875, 572.4244232177734 ] ], + "bbox": [ + 127.1513671875, + 549.9140625, + 525.6008911132812, + 572.4244232177734 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { - "id": "/page/186/ListGroup/202", + "id": "/page/186/ListGroup/226", "block_type": "ListGroup", "html": "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.
", + "html": "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.
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", + "html": "", "polygon": [ [ 86.4000015258789, - 61.1015625 + 60.85986328125 ], - [ - 482.90625, - 61.1015625 + [ + 482.4034423828125, + 60.85986328125 ], [ - 482.90625, + 482.4034423828125, 71.13372802734375 ], [ @@ -93809,57 +153503,71 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.85986328125, + 482.4034423828125, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { "id": "/page/187/PageHeader/16", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.46484375, - 60.08642578125 + 84.568359375, + 60.66650390625 ], [ - 100.705078125, - 60.08642578125 + 101.00390625, + 60.66650390625 ], [ - 100.705078125, - 69.46435546875 + 101.00390625, + 69.65771484375 ], [ - 85.46484375, - 69.46435546875 + 84.568359375, + 69.65771484375 ] ], + "bbox": [ + 84.568359375, + 60.66650390625, + 101.00390625, + 69.65771484375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { "id": "/page/187/Text/1", "block_type": "Text", - "html": "Download http: // thinkpython. com/ code/ BadKangaroo. py . It contains a solution to the previous problem with one big, nasty bug. Find and fix the bug.
", + "html": "Download http: // thinkpython. com/ code/ BadKangaroo. py . It contains a solution to the previous problem with one big, nasty bug. Find and fix the bug.
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", + "html": "If you get stuck, you can download http: // thinkpython. com/ code/ GoodKangaroo. py , which explains the problem and demonstrates a solution.
", "polygon": [ [ 85.3154296875, - 120.65625 + 120.91387939453125 ], [ 482.3996276855469, - 120.65625 + 120.91387939453125 ], [ 482.3996276855469, @@ -93896,39 +153611,53 @@ 143.15118408203125 ] ], + "bbox": [ + 85.3154296875, + 120.91387939453125, + 482.3996276855469, + 143.15118408203125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { "id": "/page/187/Text/3", "block_type": "Text", - "html": "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 .
", + "html": "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 .
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", "polygon": [ [ - 85.0166015625, - 189.3955078125 + 85.3154296875, + 189.90863037109375 ], [ - 482.90625, - 189.3955078125 + 482.4032897949219, + 189.90863037109375 ], [ - 482.90625, + 482.4032897949219, 212.23895263671875 ], [ - 85.0166015625, + 85.3154296875, 212.23895263671875 ] ], + "bbox": [ + 85.3154296875, + 189.90863037109375, + 482.4032897949219, + 212.23895263671875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { - "id": "/page/187/Code/5", - "block_type": "Code", - "html": "from visual import *", + "id": "/page/187/Text/5", + "block_type": "Text", + "html": "
from visual import *
", "polygon": [ [ - 85.53955078125, - 218.109375 + 85.68896484375, + 218.29278564453125 ], [ 191.0072784423828, - 218.109375 + 218.29278564453125 ], [ 191.0072784423828, 228.25537109375 ], [ - 85.53955078125, + 85.68896484375, 228.25537109375 ] ], + "bbox": [ + 85.68896484375, + 218.29278564453125, + 191.0072784423828, + 228.25537109375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { - "id": "/page/187/Code/6", - "block_type": "Code", - "html": "scene.range = (256, 256, 256)\nscene.center = (128, 128, 128)", + "id": "/page/187/Text/6", + "block_type": "Text", + "html": "
scene.range = (256, 256, 256) scene.center = (128, 128, 128)
", "polygon": [ [ - 84.79248046875, - 241.892578125 + 85.6142578125, + 242.666015625 ], [ - 243.3208465576172, - 241.892578125 + 262.8193359375, + 242.666015625 ], [ - 243.3208465576172, - 264.83837890625 + 262.8193359375, + 265.095703125 ], [ - 84.79248046875, - 264.83837890625 + 85.6142578125, + 265.095703125 ] ], + "bbox": [ + 85.6142578125, + 242.666015625, + 262.8193359375, + 265.095703125 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { - "id": "/page/187/Code/7", - "block_type": "Code", - "html": "color = (0.1, 0.1, 0.9) # mostly blue\nsphere(pos=scene.center, radius=128, color=color)", + "id": "/page/187/Text/7", + "block_type": "Text", + "html": "
color = (0.1, 0.1, 0.9) # mostly blue sphere(pos=scene.center, radius=128, color=color)
", "polygon": [ [ - 85.3154296875, - 277.6640625 + 85.763671875, + 278.630859375 ], [ 342.70770263671875, - 277.6640625 + 278.630859375 ], [ 342.70770263671875, 301.42138671875 ], [ - 85.3154296875, + 85.763671875, 301.42138671875 ] ], + "bbox": [ + 85.763671875, + 278.630859375, + 342.70770263671875, + 301.42138671875 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { - "id": "/page/187/Text/8", - "block_type": "Text", - "html": "color 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 ).
", + "id": "/page/187/TextInlineMath/8", + "block_type": "TextInlineMath", + "html": "color 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).
", "polygon": [ [ 85.6142578125, 307.6006774902344 ], [ - 483.50390625, + 482.90625, 307.6006774902344 ], [ - 483.50390625, + 482.90625, 329.7582702636719 ], [ @@ -94070,10 +153827,17 @@ 329.7582702636719 ] ], + "bbox": [ + 85.6142578125, + 307.6006774902344, + 482.90625, + 329.7582702636719 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, @@ -94084,14 +153848,14 @@ "polygon": [ [ 85.46484375, - 339.345703125 + 339.93267822265625 ], [ - 482.90625, - 339.345703125 + 482.607421875, + 339.93267822265625 ], [ - 482.90625, + 482.607421875, 374.2842712402344 ], [ @@ -94099,39 +153863,53 @@ 374.2842712402344 ] ], + "bbox": [ + 85.46484375, + 339.93267822265625, + 482.607421875, + 374.2842712402344 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { - "id": "/page/187/Text/10", - "block_type": "Text", - "html": "The following loop creates a cube of spheres:
", + "id": "/page/187/Caption/10", + "block_type": "Caption", + "html": "The following loop creates a cube of spheres:
", "polygon": [ [ - 84.94189453125, - 383.23828125 + 85.24072265625, + 384.01171875 ], [ - 261.48272705078125, - 383.23828125 + 262.072265625, + 384.01171875 ], [ - 261.48272705078125, + 262.072265625, 394.4212646484375 ], [ - 84.94189453125, + 85.24072265625, 394.4212646484375 ] ], + "bbox": [ + 85.24072265625, + 384.01171875, + 262.072265625, + 394.4212646484375 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, @@ -94141,73 +153919,51 @@ "html": "t = 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)", "polygon": [ [ - 84.7177734375, - 398.70703125 + 84.94189453125, + 400.6478271484375 ], [ 353.153076171875, - 398.70703125 + 400.6478271484375 ], [ 353.153076171875, - 471.5824279785156 + 472.5703125 ], [ - 84.7177734375, - 471.5824279785156 + 84.94189453125, + 472.5703125 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" - }, - "images": {} - }, - { - "id": "/page/187/Text/12", - "block_type": "Text", - "html": "
1. Put this code in a script and make sure it works for you.
", - "polygon": [ - [ - 98.015625, - 484.55859375 - ], - [ - 335.583984375, - 484.55859375 - ], - [ - 335.583984375, - 495.6953125 - ], - [ - 98.015625, - 495.6953125 - ] + "bbox": [ + 84.94189453125, + 400.6478271484375, + 353.153076171875, + 472.5703125 ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} }, { - "id": "/page/187/ListGroup/154", + "id": "/page/187/ListGroup/228", "block_type": "ListGroup", - "html": "You can see my solution at http: // thinkpython. com/ code/ color_ space. py .
", + "html": "You can see my solution at http: // thinkpython. com/ code/ color_ space. py .
", "polygon": [ [ - 86.361328125, + 85.9130859375, 598.89404296875 ], [ - 432.10546875, + 431.5656433105469, 598.89404296875 ], [ - 432.10546875, + 431.5656433105469, 608.9373321533203 ], [ - 86.361328125, + 85.9130859375, 608.9373321533203 ] ], + "bbox": [ + 85.9130859375, + 598.89404296875, + 431.5656433105469, + 608.9373321533203 + ], "children": null, "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": {} } ], "section_hierarchy": { "1": "/page/178/SectionHeader/1", - "3": "/page/186/SectionHeader/12" + "2": "/page/185/SectionHeader/10", + "4": "/page/186/SectionHeader/12" }, "images": null }, { - "id": "/page/188/Page/137", + "id": "/page/188/Page/163", "block_type": "Page", - "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.
", + "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.
", "polygon": [ [ - 128.3466796875, + 128.9443359375, 288.8653259277344 ], [ - 525.9375, + 526.53515625, 288.8653259277344 ], [ - 525.9375, + 526.53515625, 335.4109191894531 ], [ - 128.3466796875, + 128.9443359375, 335.4109191894531 ] ], + "bbox": [ + 128.9443359375, + 288.8653259277344, + 526.53515625, + 335.4109191894531 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1" @@ -94428,18 +154273,18 @@ { "id": "/page/188/Text/3", "block_type": "Text", - "html": "If you are not familiar with Anglo-American playing cards, you can read about them at http://en.wikipedia.org/wiki/Playing_cards.
", + "html": "If you are not familiar with Anglo-American playing cards, you can read about them at http://en.wikipedia.org/wiki/Playing_cards.
", "polygon": [ [ 128.6455078125, - 344.759765625 + 345.0943298339844 ], [ - 527.73046875, - 344.759765625 + 525.9375, + 345.0943298339844 ], [ - 527.73046875, + 525.9375, 367.576171875 ], [ @@ -94447,6 +154292,12 @@ 367.576171875 ] ], + "bbox": [ + 128.6455078125, + 345.0943298339844, + 525.9375, + 367.576171875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1" @@ -94456,29 +154307,35 @@ { "id": "/page/188/SectionHeader/4", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 128.794921875, - 419.9765625 + 129.2431640625, + 421.91015625 ], [ - 528.029296875, - 419.9765625 + 526.53515625, + 421.91015625 ], [ - 528.029296875, - 468.703125 + 526.53515625, + 468.6509094238281 ], [ - 128.794921875, - 468.703125 + 129.2431640625, + 468.6509094238281 ] ], + "bbox": [ + 129.2431640625, + 421.91015625, + 526.53515625, + 468.6509094238281 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, @@ -94517,26 +154380,32 @@ "html": "If 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.
", "polygon": [ [ - 128.6455078125, - 476.82421875 + 129.392578125, + 477.984375 ], [ - 527.73046875, - 476.82421875 + 526.236328125, + 477.984375 ], [ - 527.73046875, - 537.15234375 + 526.236328125, + 537.0749206542969 ], [ - 128.6455078125, - 537.15234375 + 129.392578125, + 537.0749206542969 ] ], + "bbox": [ + 129.392578125, + 477.984375, + 526.236328125, + 537.0749206542969 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, @@ -94546,26 +154415,32 @@ "html": "An 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\").
", "polygon": [ [ - 128.49609375, + 129.5419921875, 546.6612091064453 ], [ - 527.73046875, + 525.9375, 546.6612091064453 ], [ - 527.73046875, + 525.9375, 593.3039245605469 ], [ - 128.49609375, + 129.5419921875, 593.3039245605469 ] ], + "bbox": [ + 129.5419921875, + 546.6612091064453, + 525.9375, + 593.3039245605469 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, @@ -94575,57 +154450,490 @@ "html": "For example, this table shows the suits and the corresponding integer codes:
", "polygon": [ [ - 128.794921875, - 602.5078125 + 128.6455078125, + 602.89453125 ], [ - 465.57421875, - 602.5078125 + 464.9765625, + 602.89453125 ], [ - 465.57421875, - 613.3359375 + 464.9765625, + 612.9499206542969 ], [ - 128.794921875, - 613.3359375 + 128.6455078125, + 612.9499206542969 ] ], + "bbox": [ + 128.6455078125, + 602.89453125, + 464.9765625, + 612.9499206542969 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, { - "id": "/page/188/Text/9", - "block_type": "Text", - "html": "Spades 7→ 3 Hearts 7→ 2 Diamonds 7→ 1 Clubs 7→ 0
", + "id": "/page/188/Table/9", + "block_type": "Table", + "html": "Spades | → | 3 |
---|---|---|
Hearts | → | 2 |
Diamonds | → | 1 |
Clubs | → | 0 |
168 Chapter 18. Inheritance
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.908203125 + 61.171142578125 ], [ - 482.90625, - 60.908203125 + 482.40338134765625, + 61.171142578125 ], [ - 482.90625, + 482.40338134765625, 71.13372802734375 ], [ @@ -94708,39 +155028,51 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 61.171142578125, + 482.40338134765625, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, { - "id": "/page/189/PageHeader/16", + "id": "/page/189/PageHeader/17", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.39013671875, - 59.94140625 + 85.763671875, + 60.908203125 ], [ - 101.07861328125, - 59.94140625 + 102.19921875, + 60.908203125 ], [ - 101.07861328125, - 70.3828125 + 102.19921875, + 70.2861328125 ], [ - 85.39013671875, - 70.3828125 + 85.763671875, + 70.2861328125 ] ], + "bbox": [ + 85.763671875, + 60.908203125, + 102.19921875, + 70.2861328125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, @@ -94750,26 +155082,32 @@ "html": "The mapping for ranks is fairly obvious; each of the numerical ranks maps to the corresponding integer, and for face cards:
", "polygon": [ [ - 85.6142578125, - 87.54345703125 + 85.9130859375, + 88.80029296875 ], [ - 482.90625, - 87.54345703125 + 483.50390625, + 88.80029296875 ], [ - 482.90625, + 483.50390625, 110.99188232421875 ], [ - 85.6142578125, + 85.9130859375, 110.99188232421875 ] ], + "bbox": [ + 85.9130859375, + 88.80029296875, + 483.50390625, + 110.99188232421875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, @@ -94779,446 +155117,536 @@ "html": "Jack 7→ 11 Queen 7→ 12 King 7→ 13
", "polygon": [ [ - 91.51611328125, - 117.5625 + 89.2001953125, + 119.1822509765625 ], [ - 166.5966796875, - 117.5625 + 171.52734375, + 119.1822509765625 ], [ - 166.5966796875, + 171.52734375, 154.63690185546875 ], [ - 91.51611328125, + 89.2001953125, 154.63690185546875 ] ], + "bbox": [ + 89.2001953125, + 119.1822509765625, + 171.52734375, + 154.63690185546875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, { - "id": "/page/189/Text/17", + "id": "/page/189/Text/3", "block_type": "Text", - "html": "is the 2 of Clubs.
", + "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.
", "polygon": [ [ - 86.39999389648438, - 310.3892517089844 + 85.46484375, + 162.3482666015625 ], [ - 159.23655700683594, - 310.3892517089844 + 483.50390625, + 162.3482666015625 ], [ - 159.23655700683594, - 320.3518371582031 + 483.50390625, + 185.818359375 ], [ - 86.39999389648438, - 320.3518371582031 + 85.46484375, + 185.818359375 ] ], + "bbox": [ + 85.46484375, + 162.3482666015625, + 483.50390625, + 185.818359375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, { - "id": "/page/189/Text/3", + "id": "/page/189/Text/4", "block_type": "Text", - "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.
", + "html": "The class definition for Card looks like this:
", "polygon": [ [ - 85.166015625, - 162.3482666015625 + 85.6142578125, + 205.541015625 ], [ - 482.607421875, - 162.3482666015625 + 276.1513977050781, + 205.541015625 ], [ - 482.607421875, - 186.01171875 + 276.1513977050781, + 216.91082763671875 ], [ - 85.166015625, - 186.01171875 + 85.6142578125, + 216.91082763671875 ] ], + "bbox": [ + 85.6142578125, + 205.541015625, + 276.1513977050781, + 216.91082763671875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, { - "id": "/page/189/Text/4", - "block_type": "Text", - "html": "The class definition for Card looks like this:
", + "id": "/page/189/Code/5", + "block_type": "Code", + "html": "class Card(object):\n \"\"\"Represents a standard playing card.\"\"\"\n def __init__(self, suit=0, rank=2):\n self.suit = suit\n self.rank = rank", "polygon": [ [ - 85.46484375, - 205.154296875 + 86.39998626708984, + 221.9366455078125 ], [ - 277.013671875, - 205.154296875 + 321.837890625, + 221.9366455078125 ], [ - 277.013671875, - 216.91082763671875 + 321.837890625, + 294.486328125 ], [ - 85.46484375, - 216.91082763671875 + 86.39998626708984, + 294.486328125 ] ], + "bbox": [ + 86.39998626708984, + 221.9366455078125, + 321.837890625, + 294.486328125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, { - "id": "/page/189/Code/5", - "block_type": "Code", - "html": "
class Card(object):\n \"\"\"Represents a standard playing card.\"\"\"\n def __init__(self, suit=0, rank=2):\n self.suit = suit\n self.rank = rank\nAs usual, the init method takes an optional parameter for each attribute. The default card", + "id": "/page/189/Text/6", + "block_type": "Text", + "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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", "polygon": [ [ - 85.166015625, - 327.357421875 + 85.9130859375, + 328.7109375 ], [ - 416.50506591796875, - 327.357421875 + 417.1640625, + 328.7109375 ], [ - 416.50506591796875, + 417.1640625, 339.4608459472656 ], [ - 85.166015625, + 85.9130859375, 339.4608459472656 ] ], + "bbox": [ + 85.9130859375, + 328.7109375, + 417.1640625, + 339.4608459472656 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/188/SectionHeader/4" + "4": "/page/188/SectionHeader/4" }, "images": {} }, { - "id": "/page/189/Text/7", - "block_type": "Text", - "html": "queen_of_diamonds = Card(1, 12)
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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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suit_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])", "polygon": [ [ - 105.26220703125, - 471.9277038574219 + 107.31596374511719, + 471.796875 ], [ - 402.521484375, - 471.9277038574219 + 401.326171875, + 471.796875 ], [ - 402.521484375, + 401.326171875, 555.0563201904297 ], [ - 105.26220703125, + 107.31596374511719, 555.0563201904297 ] ], + "bbox": [ + 107.31596374511719, + 471.796875, + 401.326171875, + 555.0563201904297 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/189/SectionHeader/8" + "4": "/page/189/SectionHeader/9" }, "images": {} }, { - "id": "/page/189/Text/12", + "id": "/page/189/Text/13", "block_type": "Text", "html": "
Variables 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.
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+ "/page/190/Figure/1": 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} }, { "id": "/page/190/Caption/2", "block_type": "Caption", - "html": "Figure 18.1: Object diagram.
", + "html": "Figure 18.1: Object diagram.
", "polygon": [ [ - 264.462890625, - 223.716796875 + 262.8193359375, + 224.296875 ], [ - 390.568359375, - 223.716796875 + 389.7672424316406, + 224.296875 ], [ - 390.568359375, + 389.7672424316406, 234.7529296875 ], [ - 264.462890625, + 262.8193359375, 234.7529296875 ] ], + "bbox": [ + 262.8193359375, + 224.296875, + 389.7672424316406, + 234.7529296875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/189/SectionHeader/8" + "4": "/page/189/SectionHeader/9" }, "images": {} } ], "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/189/SectionHeader/8" + "4": "/page/189/SectionHeader/9" }, "images": null }, @@ -95392,26 +155856,32 @@ "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.\"
", "polygon": [ [ - 129.2431640625, - 257.16796875 + 128.9443359375, + 256.974609375 ], [ 525.9375, - 257.16796875 + 256.974609375 ], [ 525.9375, 292.284912109375 ], [ - 129.2431640625, + 128.9443359375, 292.284912109375 ] ], + "bbox": [ + 128.9443359375, + 256.974609375, + 525.9375, + 292.284912109375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/189/SectionHeader/8" + "4": "/page/189/SectionHeader/9" }, "images": {} }, @@ -95421,26 +155891,32 @@ "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.
", "polygon": [ [ - 128.49609375, - 302.832763671875 + 128.3466796875, + 302.607421875 ], [ 525.9375, - 302.832763671875 + 302.607421875 ], [ 525.9375, 349.5279235839844 ], [ - 128.49609375, + 128.3466796875, 349.5279235839844 ] ], + "bbox": [ + 128.3466796875, + 302.607421875, + 525.9375, + 349.5279235839844 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/189/SectionHeader/8" + "4": "/page/189/SectionHeader/9" }, "images": {} }, @@ -95450,26 +155926,32 @@ "html": "With the methods we have so far, we can create and print cards:
", "polygon": [ [ - 128.57080078125, - 360.03515625 + 127.7490234375, + 359.6484375 ], [ 409.8777770996094, - 360.03515625 + 359.6484375 ], [ 409.8777770996094, 370.1889343261719 ], [ - 128.57080078125, + 127.7490234375, 370.1889343261719 ] ], + "bbox": [ + 127.7490234375, + 359.6484375, + 409.8777770996094, + 370.1889343261719 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/189/SectionHeader/8" + "4": "/page/189/SectionHeader/9" }, "images": {} }, @@ -95479,84 +155961,102 @@ "html": ">>> card1 = Card(2, 11)\n>>> print card1\nJack of Hearts", "polygon": [ [ - 128.49609375, - 375.697265625 + 129.16845703125, + 376.6640625 ], [ 249.89834594726562, - 375.697265625 + 376.6640625 ], [ 249.89834594726562, 411.1163635253906 ], [ - 128.49609375, + 129.16845703125, 411.1163635253906 ] ], + "bbox": [ + 129.16845703125, + 376.6640625, + 249.89834594726562, + 411.1163635253906 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/189/SectionHeader/8" + "4": "/page/189/SectionHeader/9" }, "images": {} }, { "id": "/page/190/Text/7", "block_type": "Text", - "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).
", + "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).
", "polygon": [ [ - 128.794921875, + 129.5419921875, 417.65625 ], [ - 526.236328125, + 525.9375, 417.65625 ], [ - 526.236328125, + 525.9375, 452.3429260253906 ], [ - 128.794921875, + 129.5419921875, 452.3429260253906 ] ], + "bbox": [ + 129.5419921875, + 417.65625, + 525.9375, + 452.3429260253906 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/189/SectionHeader/8" + "4": "/page/189/SectionHeader/9" }, "images": {} }, { "id": "/page/190/SectionHeader/8", "block_type": "SectionHeader", - "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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", "polygon": [ [ - 128.6455078125, - 554.5546875 + 128.794921875, + 554.94140625 ], [ - 526.53515625, - 554.5546875 + 525.638671875, + 554.94140625 ], [ - 526.53515625, + 525.638671875, 590.0769195556641 ], [ - 128.6455078125, + 128.794921875, 590.0769195556641 ] ], + "bbox": [ + 128.794921875, + 554.94140625, + 525.638671875, + 590.0769195556641 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": {} }, @@ -95624,26 +156136,32 @@ "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.
", "polygon": [ [ - 128.794921875, - 599.4140625 + 128.0478515625, + 600.1875 ], [ - 527.431640625, - 599.4140625 + 525.9375, + 600.1875 ], [ - 527.431640625, + 525.9375, 635.1259155273438 ], [ - 128.794921875, + 128.0478515625, 635.1259155273438 ] ], + "bbox": [ + 128.0478515625, + 600.1875, + 525.9375, + 635.1259155273438 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": {} }, @@ -95653,26 +156171,32 @@ "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.
", "polygon": [ [ - 128.49609375, - 645.046875 + 128.0478515625, + 645.43359375 ], [ - 526.53515625, - 645.046875 + 525.9375, + 645.43359375 ], [ - 526.53515625, + 525.9375, 680.1749267578125 ], [ - 128.49609375, + 128.0478515625, 680.1749267578125 ] ], + "bbox": [ + 128.0478515625, + 645.43359375, + 525.9375, + 680.1749267578125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": {} }, @@ -95682,40 +156206,46 @@ "html": "With that decided, we can write __cmp__:
", "polygon": [ [ - 129.31787109375, - 690.7227630615234 + 128.794921875, + 690.6796875 ], [ - 311.3375549316406, - 690.7227630615234 + 311.9765625, + 690.6796875 ], [ - 311.3375549316406, - 701.12109375 + 311.9765625, + 700.8349227905273 ], [ - 129.31787109375, - 701.12109375 + 128.794921875, + 700.8349227905273 ] ], + "bbox": [ + 128.794921875, + 690.6796875, + 311.9765625, + 700.8349227905273 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": {} } ], "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": null }, { - "id": "/page/191/Page/179", + "id": "/page/191/Page/186", "block_type": "Page", - "html": "170 Chapter 18. Inheritance
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.66650390625 + 60.71484375 ], [ - 483.205078125, - 60.66650390625 + 482.40338134765625, + 60.71484375 ], [ - 483.205078125, + 482.40338134765625, 71.13372802734375 ], [ @@ -95757,285 +156293,520 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.71484375, + 482.40338134765625, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": {} }, { - "id": "/page/191/PageHeader/9", + "id": "/page/191/PageHeader/14", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.166015625, - 60.2314453125 + 85.763671875, + 60.85986328125 ], [ - 100.8544921875, - 60.2314453125 + 101.4521484375, + 60.85986328125 ], [ - 100.8544921875, - 70.2861328125 + 101.4521484375, + 70.33447265625 ], [ - 85.166015625, - 70.2861328125 + 85.763671875, + 70.33447265625 ] ], + "bbox": [ + 85.763671875, + 60.85986328125, + 101.4521484375, + 70.33447265625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": {} }, { "id": "/page/191/Code/1", "block_type": "Code", - "html": "# 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\nYou can write this more concisely using tuple comparison:\n# inside class Card:\n def __cmp__(self, other):\n t1 = self.suit, self.rank\n t2 = other.suit, other.rank\n return cmp(t1, t2)\nThe built-in function cmp has the same interface as the method __cmp__: it takes two values\nand returns a positive number if the first is larger, a negative number if the second is larger,\nand 0 if they are equal.\nIn Python 3, cmp no longer exists, and the __cmp__ method is not supported. Instead you", + "html": "
# 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", "polygon": [ [ - 86.39999389648438, + 86.4000015258789, 88.68572998046875 ], [ - 482.4034423828125, + 321.240234375, 88.68572998046875 ], [ - 482.4034423828125, - 404.5078125 + 321.240234375, + 247.88671875 ], [ - 86.39999389648438, - 406.0546875 + 86.4000015258789, + 247.88671875 + ] + ], + "bbox": [ + 86.4000015258789, + 88.68572998046875, + 321.240234375, + 247.88671875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "4": "/page/190/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/191/Text/2", + "block_type": "Text", + "html": "
You can write this more concisely using tuple comparison:
", + "polygon": [ + [ + 86.28662109375, + 251.173828125 + ], + [ + 343.65234375, + 251.173828125 + ], + [ + 343.65234375, + 261.324951171875 + ], + [ + 86.28662109375, + 261.324951171875 ] ], + "bbox": [ + 86.28662109375, + 251.173828125, + 343.65234375, + 261.324951171875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": {} }, { - "id": "/page/191/Code/2", + "id": "/page/191/Code/3", "block_type": "Code", - "html": "should provide __lt__, which returns True if self is less than other. You can implement\n__lt__ using tuples and the < operator.\nExercise 18.1. Write a __cmp__ method for Time objects. Hint: you can use tuple comparison, but\nyou also might consider using integer subtraction.", + "html": "
# inside class Card:", "polygon": [ [ - 86.39999389648438, - 401.3227844238281 + 85.83837890625, + 267.40777587890625 ], [ - 482.90625, - 401.3227844238281 + 191.00729370117188, + 267.40777587890625 ], [ - 482.90625, + 191.00729370117188, + 277.370361328125 + ], + [ + 85.83837890625, + 277.370361328125 + ] + ], + "bbox": [ + 85.83837890625, + 267.40777587890625, + 191.00729370117188, + 277.370361328125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "4": "/page/190/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/191/Code/4", + "block_type": "Code", + "html": "
def __cmp__(self, other):\n t1 = self.suit, self.rank\n t2 = other.suit, other.rank\n return cmp(t1, t2)", + "polygon": [ + [ + 107.31600952148438, + 291.779296875 + ], + [ + 270.140625, + 291.779296875 + ], + [ + 270.140625, + 338.34136962890625 + ], + [ + 107.31600952148438, + 338.34136962890625 + ] + ], + "bbox": [ + 107.31600952148438, + 291.779296875, + 270.140625, + 338.34136962890625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "4": "/page/190/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/191/Text/5", + "block_type": "Text", + "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.
", + "polygon": [ + [ + 85.763671875, + 343.79296875 + ], + [ + 482.607421875, + 343.79296875 + ], + [ + 482.607421875, + 379.074951171875 + ], + [ + 85.763671875, + 379.074951171875 + ] + ], + "bbox": [ + 85.763671875, + 343.79296875, + 482.607421875, + 379.074951171875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "4": "/page/190/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/191/Text/6", + "block_type": "Text", + "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.
", + "polygon": [ + [ + 85.763671875, + 388.458984375 + ], + [ + 482.4022521972656, + 388.458984375 + ], + [ + 482.4022521972656, + 423.62994384765625 + ], + [ + 85.763671875, + 423.62994384765625 + ] + ], + "bbox": [ + 85.763671875, + 388.458984375, + 482.4022521972656, + 423.62994384765625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "4": "/page/190/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/191/Text/7", + "block_type": "Text", + "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.
", + "polygon": [ + [ + 85.6142578125, + 424.6171875 + ], + [ + 482.4032287597656, + 424.6171875 + ], + [ + 482.4032287597656, 447.8452453613281 ], [ - 86.39999389648438, + 85.6142578125, 447.8452453613281 ] ], + "bbox": [ + 85.6142578125, + 424.6171875, + 482.4032287597656, + 447.8452453613281 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/190/SectionHeader/8" + "4": "/page/190/SectionHeader/8" }, "images": {} }, { - "id": "/page/191/SectionHeader/3", + "id": "/page/191/SectionHeader/8", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 86.2119140625, - 502.734375 + 85.763671875, + 504.1793518066406 ], [ - 482.90625, - 502.734375 + 482.40338134765625, + 504.1793518066406 ], [ - 482.90625, + 482.40338134765625, 526.3359375 ], [ - 86.2119140625, + 85.763671875, 526.3359375 ] ], + "bbox": [ + 85.763671875, + 504.1793518066406, + 482.40338134765625, + 526.3359375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/191/SectionHeader/3" + "4": "/page/191/SectionHeader/8" }, "images": {} }, { - "id": "/page/191/Text/5", + "id": "/page/191/Text/10", "block_type": "Text", "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:
", "polygon": [ [ - 85.763671875, - 534.83203125 + 85.9130859375, + 535.9921875 ], [ 482.3996887207031, - 534.83203125 + 535.9921875 ], [ 482.3996887207031, - 558.80859375 + 558.6969299316406 ], [ - 85.763671875, - 558.80859375 + 85.9130859375, + 558.6969299316406 ] ], + "bbox": [ + 85.9130859375, + 535.9921875, + 482.3996887207031, + 558.6969299316406 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/191/SectionHeader/3" + "4": "/page/191/SectionHeader/8" }, "images": {} }, { - "id": "/page/191/Code/6", + "id": "/page/191/Code/185", "block_type": "Code", "html": "class Deck(object):", "polygon": [ [ - 85.0166015625, - 563.0625 + 85.98779296875, + 564.77978515625 ], [ - 187.2158203125, - 563.0625 + 185.87109375, + 564.77978515625 ], [ - 187.2158203125, - 580.8515625 + 185.87109375, + 575.05078125 ], [ - 85.0166015625, - 580.8515625 + 85.98779296875, + 575.05078125 ] ], + "bbox": [ + 85.98779296875, + 564.77978515625, + 185.87109375, + 575.05078125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/191/SectionHeader/3" + "4": "/page/191/SectionHeader/8" }, "images": {} }, { - "id": "/page/191/Code/7", + "id": "/page/191/Code/12", "block_type": "Code", "html": "
def __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)", "polygon": [ [ - 106.45751953125, - 589.1677856445312 + 105.71044921875, + 587.8125 ], [ - 290.3743591308594, - 589.1677856445312 + 291.05859375, + 587.8125 ], [ - 290.3743591308594, - 662.0625 + 291.05859375, + 660.1024017333984 ], [ - 106.45751953125, - 662.0625 + 105.71044921875, + 660.1024017333984 ] ], + "bbox": [ + 105.71044921875, + 587.8125, + 291.05859375, + 660.1024017333984 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/191/SectionHeader/3" + "4": "/page/191/SectionHeader/8" }, "images": {} }, { - "id": "/page/191/Text/8", + "id": "/page/191/Text/13", "block_type": "Text", "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.
", "polygon": [ [ - 85.6142578125, - 665.9296875 + 85.763671875, + 665.54296875 ], [ - 484.1015625, - 665.9296875 + 482.4033508300781, + 665.54296875 ], [ - 484.1015625, - 701.5078125 + 482.4033508300781, + 700.8349609375 ], [ - 85.6142578125, - 701.5078125 + 85.763671875, + 700.8349609375 ] ], + "bbox": [ + 85.763671875, + 665.54296875, + 482.4033508300781, + 700.8349609375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/191/SectionHeader/3" + "4": "/page/191/SectionHeader/8" }, "images": {} } ], "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/191/SectionHeader/3" + "4": "/page/191/SectionHeader/8" }, "images": null }, { "id": "/page/192/Page/178", "block_type": "Page", - "html": "18.5. Printing the deck 171
", + "html": "", "polygon": [ [ - 128.197265625, - 61.171142578125 + 128.72021484375, + 60.76318359375 ], [ 525.6033935546875, - 61.171142578125 + 60.76318359375 ], [ 525.6033935546875, 71.13372802734375 ], [ - 128.197265625, + 128.72021484375, 71.13372802734375 ] ], + "bbox": [ + 128.72021484375, + 60.76318359375, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/191/SectionHeader/3" + "4": "/page/191/SectionHeader/8" }, "images": {} }, { - "id": "/page/192/PageHeader/17", + "id": "/page/192/PageHeader/15", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.697265625, - 60.66650390625 + 510.3984375, + 60.71484375 ], [ - 525.638671875, - 60.66650390625 + 525.9375, + 60.71484375 ], [ - 525.638671875, - 70.33447265625 + 525.9375, + 70.4794921875 ], [ - 510.697265625, - 70.33447265625 + 510.3984375, + 70.4794921875 ] ], + "bbox": [ + 510.3984375, + 60.71484375, + 525.9375, + 70.4794921875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/191/SectionHeader/3" + "4": "/page/191/SectionHeader/8" }, "images": {} }, { "id": "/page/192/SectionHeader/1", "block_type": "SectionHeader", - "html": "Here is a __str__ method for Deck: #inside class Deck:
", + "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)
", "polygon": [ [ - 128.12255859375, - 111.181640625 + 129.16845703125, + 111.955078125 ], [ - 285.978515625, - 111.181640625 + 302.1966247558594, + 111.955078125 ], [ - 285.978515625, - 138.80535888671875 + 302.1966247558594, + 211.97137451171875 ], [ - 128.12255859375, - 138.80535888671875 + 129.16845703125, + 211.97137451171875 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/1" - }, - "images": {} - }, - { - "id": "/page/192/Code/3", - "block_type": "Code", - "html": "def __str__(self):\n res = []\n for card in self.cards:\n res.append(str(card))\n return '\\n'.join(res)", - "polygon": [ - [ - 150.51600646972656, - 153.140625 - ], - [ - 302.8623046875, - 153.140625 - ], - [ - 302.8623046875, - 213.662109375 - ], - [ - 150.51600646972656, - 213.662109375 - ] + "bbox": [ + 129.16845703125, + 111.955078125, + 302.1966247558594, + 211.97137451171875 ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/1" + "4": "/page/192/SectionHeader/1" }, "images": {} }, { - "id": "/page/192/Text/4", + "id": "/page/192/Text/3", "block_type": "Text", "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.
", "polygon": [ [ - 129.392578125, - 218.302734375 + 128.9443359375, + 218.109375 ], [ - 526.833984375, - 218.302734375 + 525.9375, + 218.109375 ], [ - 526.833984375, + 525.9375, 252.74993896484375 ], [ - 129.392578125, + 128.9443359375, 252.74993896484375 ] ], + "bbox": [ + 128.9443359375, + 218.109375, + 525.9375, + 252.74993896484375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/1" + "4": "/page/192/SectionHeader/1" }, "images": {} }, { - "id": "/page/192/Text/5", + "id": "/page/192/Text/4", "block_type": "Text", "html": "Since we invoke join on a newline character, the cards are separated by newlines. Here's what the result looks like:
", "polygon": [ [ - 128.6455078125, + 128.197265625, 262.1953125 ], [ - 526.53515625, + 525.5950927734375, 262.1953125 ], [ - 526.53515625, + 525.5950927734375, 285.15692138671875 ], [ - 128.6455078125, + 128.197265625, 285.15692138671875 ] ], + "bbox": [ + 128.197265625, + 262.1953125, + 525.5950927734375, + 285.15692138671875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/1" + "4": "/page/192/SectionHeader/1" }, "images": {} }, { - "id": "/page/192/Code/6", + "id": "/page/192/Code/5", "block_type": "Code", "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", "polygon": [ [ - 128.72021484375, + 129.60000610351562, 291.2857666015625 ], [ @@ -96273,114 +157057,138 @@ ], [ 218.51620483398438, - 412.62890625 + 410.99737548828125 ], [ - 128.72021484375, - 412.62890625 + 129.60000610351562, + 410.99737548828125 ] ], + "bbox": [ + 129.60000610351562, + 291.2857666015625, + 218.51620483398438, + 410.99737548828125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/1" + "4": "/page/192/SectionHeader/1" }, "images": {} }, { - "id": "/page/192/Text/7", + "id": "/page/192/Text/6", "block_type": "Text", "html": "
Even though the result appears on 52 lines, it is one long string that contains newlines.
", "polygon": [ [ - 128.6455078125, - 417.42535400390625 + 129.60000610351562, + 416.49609375 ], [ - 509.80078125, - 417.42535400390625 + 509.60345458984375, + 416.49609375 ], [ - 509.80078125, - 428.484375 + 509.60345458984375, + 427.387939453125 ], [ - 128.6455078125, - 428.484375 + 129.60000610351562, + 427.387939453125 ] ], + "bbox": [ + 129.60000610351562, + 416.49609375, + 509.60345458984375, + 427.387939453125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/1" + "4": "/page/192/SectionHeader/1" }, "images": {} }, { - "id": "/page/192/SectionHeader/8", + "id": "/page/192/SectionHeader/7", "block_type": "SectionHeader", - "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:
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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.
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", + "html": "#inside class Deck: def add_card(self, card): self.cards.append(card)
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", + "id": "/page/193/Code/3", + "block_type": "Code", + "html": "def shuffle(self):\n random.shuffle(self.cards)", "polygon": [ [ - 85.166015625, - 149.2734375 + 94.8779296875, + 162.615234375 ], [ - 191.0072784423828, - 149.2734375 + 264.2324523925781, + 162.615234375 ], [ - 191.0072784423828, - 159.50634765625 + 264.2324523925781, + 196.08935546875 ], [ - 85.166015625, - 159.50634765625 + 94.8779296875, + 196.08935546875 ] ], + "bbox": [ + 94.8779296875, + 162.615234375, + 264.2324523925781, + 196.08935546875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/8" + "4": "/page/192/SectionHeader/7" }, "images": {} }, { - "id": "/page/193/Code/4", - "block_type": "Code", - "html": "
def shuffle(self):\n random.shuffle(self.cards)", + "id": "/page/193/Text/4", + "block_type": "Text", + "html": "
Don't forget to import random.
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", + "html": "# inside class Deck:
", "polygon": [ [ - 86.0625, - 202.37579345703125 + 86.39999389648438, + 149.54376220703125 ], [ - 220.25865173339844, - 202.37579345703125 + 191.0072784423828, + 149.54376220703125 ], [ - 220.25865173339844, - 212.48797607421875 + 191.0072784423828, + 159.50634765625 ], [ - 86.0625, - 212.48797607421875 + 86.39999389648438, + 159.50634765625 ] ], + "bbox": [ + 86.39999389648438, + 149.54376220703125, + 191.0072784423828, + 159.50634765625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/8" + "4": "/page/192/SectionHeader/7" }, "images": {} }, { - "id": "/page/193/Text/6", + "id": "/page/193/Text/5", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 85.46484375, - 214.435546875 + 85.763671875, + 213.85546875 ], [ - 482.90625, - 214.435546875 + 482.40118408203125, + 213.85546875 ], [ - 482.90625, + 482.40118408203125, 236.7274169921875 ], [ - 85.46484375, + 85.763671875, 236.7274169921875 ] ], + "bbox": [ + 85.763671875, + 213.85546875, + 482.40118408203125, + 236.7274169921875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/192/SectionHeader/8" + "4": "/page/192/SectionHeader/7" }, "images": {} }, { - "id": "/page/193/SectionHeader/7", + "id": "/page/193/SectionHeader/6", "block_type": "SectionHeader", - "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.
", "polygon": [ [ - 86.0625, - 292.74609375 + 86.361328125, + 292.939453125 ], [ - 482.40313720703125, - 292.74609375 + 483.205078125, + 292.939453125 ], [ - 482.40313720703125, + 483.205078125, 327.62799072265625 ], [ - 86.0625, + 86.361328125, 327.62799072265625 ] ], + "bbox": [ + 86.361328125, + 292.939453125, + 483.205078125, + 327.62799072265625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/193/Text/9", + "id": "/page/193/Text/8", "block_type": "Text", "html": "It 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.
", "polygon": [ [ - 85.46484375, - 336.83203125 + 86.0625, + 337.21875 ], [ - 483.50390625, - 336.83203125 + 482.4034118652344, + 337.21875 ], [ - 483.50390625, + 482.4034118652344, 372.2380065917969 ], [ - 85.46484375, + 86.0625, 372.2380065917969 ] ], + "bbox": [ + 86.0625, + 337.21875, + 482.4034118652344, + 372.2380065917969 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/193/Text/10", + "id": "/page/193/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 85.46484375, - 381.69140625 + 85.9130859375, + 382.271484375 ], [ - 483.50390625, - 381.69140625 + 482.90625, + 382.271484375 ], [ - 483.50390625, - 416.8470153808594 + 482.90625, + 416.8828125 ], [ - 85.46484375, - 416.8470153808594 + 85.9130859375, + 416.8828125 ] ], + "bbox": [ + 85.9130859375, + 382.271484375, + 482.90625, + 416.8828125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/193/Text/11", + "id": "/page/193/Text/10", "block_type": "Text", "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.
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", "polygon": [ [ - 85.763671875, - 471.0234375 + 86.0625, + 470.25 ], [ - 455.712890625, - 471.0234375 + 455.49444580078125, + 470.25 ], [ - 455.712890625, + 455.49444580078125, 481.6770324707031 ], [ - 85.763671875, + 86.0625, 481.6770324707031 ] ], + "bbox": [ + 86.0625, + 470.25, + 455.49444580078125, + 481.6770324707031 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/193/Text/13", + "id": "/page/193/Text/12", "block_type": "Text", "html": "The definition of a child class is like other class definitions, but the name of the parent class appears in parentheses:
", "polygon": [ [ - 85.6142578125, + 85.763671875, 490.74609375 ], [ - 483.50390625, + 483.205078125, 490.74609375 ], [ - 483.50390625, + 483.205078125, 514.092041015625 ], [ - 85.6142578125, + 85.763671875, 514.092041015625 ] ], + "bbox": [ + 85.763671875, + 490.74609375, + 483.205078125, + 514.092041015625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/193/Text/14", - "block_type": "Text", - "html": "class Hand(Deck):
", + "id": "/page/193/Code/13", + "block_type": "Code", + "html": "class Hand(Deck):", "polygon": [ [ - 86.4000244140625, - 518.9765625 + 85.98779296875, + 519.75 ], [ 175.32618713378906, - 518.9765625 + 519.75 ], [ 175.32618713378906, 530.1914672851562 ], [ - 86.4000244140625, + 85.98779296875, 530.1914672851562 ] ], + "bbox": [ + 85.98779296875, + 519.75, + 175.32618713378906, + 530.1914672851562 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/193/Text/15", + "id": "/page/193/Text/14", "block_type": "Text", "html": "
\"\"\"Represents a hand of playing cards.\"\"\"
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", "polygon": [ [ 85.3154296875, - 547.20703125 + 547.98046875 ], [ - 483.50390625, - 547.20703125 + 484.1015625, + 547.98046875 ], [ - 483.50390625, + 484.1015625, 570.9790344238281 ], [ @@ -97122,25 +158045,31 @@ 570.9790344238281 ] ], + "bbox": [ + 85.3154296875, + 547.98046875, + 484.1015625, + 570.9790344238281 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/193/Text/17", + "id": "/page/193/Text/16", "block_type": "Text", "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.
", "polygon": [ [ 85.166015625, - 580.078125 + 580.8515625 ], [ 483.205078125, - 580.078125 + 580.8515625 ], [ 483.205078125, @@ -97151,111 +158080,135 @@ 615.5880432128906 ] ], + "bbox": [ + 85.166015625, + 580.8515625, + 483.205078125, + 615.5880432128906 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/193/Text/18", + "id": "/page/193/Text/17", "block_type": "Text", "html": "If we provide an init method in the Hand class, it overrides the one in the Deck class:
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18.8. Class diagrams 173
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>>> hand = Hand('new hand')\n>>> print hand.cards\n[]\n>>> print hand.label\nnew hand", "polygon": [ [ - 129.09375, - 104.25970458984375 + 128.3466796875, + 104.220703125 + ], + [ + 270.7873840332031, + 104.220703125 + ], + [ + 270.7873840332031, + 163.00030517578125 + ], + [ + 128.3466796875, + 163.00030517578125 + ] + ], + "bbox": [ + 128.3466796875, + 104.220703125, + 270.7873840332031, + 163.00030517578125 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "3": "/page/193/SectionHeader/6" + }, + "images": {} + }, + { + "id": "/page/194/Text/3", + "block_type": "Text", + "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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A natural next step is to encapsulate this code in a method called move_cards:
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def move_cards(self, hand, num):\n for i in range(num):\n hand.add_card(self.pop_card())", "polygon": [ [ - 167.94140625, - 311.501953125 + 149.63818359375, + 300.69580078125 ], [ 349.2699279785156, - 311.501953125 + 300.69580078125 ], [ 349.2699279785156, 335.04638671875 ], [ - 167.94140625, + 149.63818359375, 335.04638671875 ] ], + "bbox": [ + 149.63818359375, + 300.69580078125, + 349.2699279785156, + 335.04638671875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/193/SectionHeader/7" + "3": "/page/193/SectionHeader/6" }, "images": {} }, { - "id": "/page/194/Text/5", + "id": "/page/194/Text/8", "block_type": "Text", "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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} }, { "id": "/page/195/Caption/2", "block_type": "Caption", - "html": "Figure 18.2: Class diagram.
", + "html": "Figure 18.2: Class diagram.
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", "polygon": [ [ - 86.39999389648438, - 200.70703125 + 86.361328125, + 200.513671875 ], [ - 329.90625, - 200.70703125 + 330.205078125, + 200.513671875 ], [ - 329.90625, + 330.205078125, 212.34393310546875 ], [ - 86.39999389648438, + 86.361328125, 212.34393310546875 ] ], + "bbox": [ + 86.361328125, + 200.513671875, + 330.205078125, + 212.34393310546875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/194/SectionHeader/10" + "3": "/page/193/SectionHeader/6", + "4": "/page/194/SectionHeader/13" }, "images": {} }, { - "id": "/page/195/ListGroup/177", + "id": "/page/195/ListGroup/180", "block_type": "ListGroup", "html": "A class diagram is a graphical representation of these relationships. For example, Figure 18.2 shows the relationships between Card, Deck and Hand.
", + "html": "A class diagram is a graphical representation of these relationships. For example, Figure 18.2 shows the relationships between Card, Deck and Hand.
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", "polygon": [ [ - 85.763671875, - 411.46875 + 85.9130859375, + 411.08203125 ], [ - 483.205078125, - 411.46875 + 484.1015625, + 411.08203125 ], [ - 483.205078125, + 484.1015625, 434.9969177246094 ], [ - 85.763671875, + 85.9130859375, 434.9969177246094 ] ], + "bbox": [ + 85.9130859375, + 411.08203125, + 484.1015625, + 434.9969177246094 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/194/SectionHeader/10" + "3": "/page/193/SectionHeader/6", + "4": "/page/194/SectionHeader/13" }, "images": {} }, @@ -98098,160 +159347,228 @@ "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.
", "polygon": [ [ - 85.6142578125, - 443.953125 + 86.0625, + 443.1796875 ], [ - 484.1015625, - 443.953125 + 484.400390625, + 443.1796875 ], [ - 484.1015625, + 484.400390625, 479.1089172363281 ], [ - 85.6142578125, + 86.0625, 479.1089172363281 ] ], + "bbox": [ + 86.0625, + 443.1796875, + 484.400390625, + 479.1089172363281 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/194/SectionHeader/10" + "3": "/page/193/SectionHeader/6", + "4": "/page/194/SectionHeader/13" }, "images": {} }, { "id": "/page/195/Text/11", "block_type": "Text", - "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. 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.
", + "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.
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", + "polygon": [ + [ + 85.6142578125, + 511.62890625 ], [ 484.69921875, - 488.42578125 + 511.62890625 ], [ 484.69921875, 535.2422180175781 ], [ - 85.3154296875, + 85.6142578125, 535.2422180175781 ] ], + "bbox": [ + 85.6142578125, + 511.62890625, + 484.69921875, + 535.2422180175781 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/194/SectionHeader/10" + "3": "/page/193/SectionHeader/6", + "4": "/page/194/SectionHeader/13" }, "images": {} }, { - "id": "/page/195/SectionHeader/12", + "id": "/page/195/SectionHeader/13", "block_type": "SectionHeader", - "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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", + "html": "", "polygon": [ [ - 127.8984375, - 61.171142578125 + 128.794921875, + 60.95654296875 ], [ 525.6033935546875, - 61.171142578125 + 60.95654296875 ], [ 525.6033935546875, 71.13372802734375 ], [ - 127.8984375, + 128.794921875, 71.13372802734375 ] ], + "bbox": [ + 128.794921875, + 60.95654296875, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, { - "id": "/page/196/PageHeader/15", + "id": "/page/196/PageHeader/17", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.099609375, - 61.1015625 + 509.501953125, + 60.908203125 ], [ - 526.236328125, - 61.1015625 + 525.638671875, + 60.908203125 ], [ - 526.236328125, - 70.3828125 + 525.638671875, + 70.2861328125 ], [ - 510.099609375, - 70.3828125 + 509.501953125, + 70.2861328125 ] ], + "bbox": [ + 509.501953125, + 60.908203125, + 525.638671875, + 70.2861328125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, @@ -98360,26 +159701,32 @@ "html": "Deck.shuffle prints a message that says something like Running Deck.shuffle, then as the program runs it traces the flow of execution.
", "polygon": [ [ - 129.09375, - 88.68572998046875 + 127.4501953125, + 88.0751953125 ], [ 525.6044311523438, - 88.68572998046875 + 88.0751953125 ], [ 525.6044311523438, 110.99188232421875 ], [ - 129.09375, + 127.4501953125, 110.99188232421875 ] ], + "bbox": [ + 127.4501953125, + 88.0751953125, + 525.6044311523438, + 110.99188232421875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, @@ -98389,210 +159736,323 @@ "html": "As 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:
", "polygon": [ [ - 128.9443359375, - 120.26953125 + 127.8984375, + 120.5595703125 ], [ - 525.603271484375, - 120.26953125 + 525.9375, + 120.5595703125 ], [ - 525.603271484375, + 525.9375, 143.63482666015625 ], [ - 128.9443359375, + 127.8984375, 143.63482666015625 ] ], + "bbox": [ + 127.8984375, + 120.5595703125, + 525.9375, + 143.63482666015625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, { - "id": "/page/196/Code/3", - "block_type": "Code", - "html": "def find_defining_class(obj, meth_name):\n for ty in type(obj).mro():\n if meth_name in ty.__dict__:\n return ty\nHere's an example:\n>>> hand = Hand()\n>>> print find_defining_class(hand, 'shuffle')\n<class 'Card.Deck'>", + "id": "/page/196/TextInlineMath/3", + "block_type": "TextInlineMath", + "html": "
def find_defining_class(obj, meth_name): for ty in type(obj).mro(): if meth_name in ty.__dict__: return ty
", "polygon": [ [ 128.86962890625, - 150.00067138671875 + 149.853515625 ], [ - 370.140380859375, - 150.00067138671875 + 338.83447265625, + 149.853515625 ], [ - 370.140380859375, - 257.5546875 + 338.83447265625, + 196.83984375 ], [ 128.86962890625, - 257.5546875 + 196.83984375 ] ], + "bbox": [ + 128.86962890625, + 149.853515625, + 338.83447265625, + 196.83984375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, { "id": "/page/196/Text/4", "block_type": "Text", - "html": "So the shuffle method for this Hand is the one in Deck.
", + "html": "Here's an example:
", "polygon": [ [ 128.3466796875, - 260.40472412109375 + 202.640625 + ], + [ + 213.7839813232422, + 202.640625 + ], + [ + 213.7839813232422, + 213.1728515625 + ], + [ + 128.3466796875, + 213.1728515625 + ] + ], + "bbox": [ + 128.3466796875, + 202.640625, + 213.7839813232422, + 213.1728515625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "3": "/page/195/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/196/Code/5", + "block_type": "Code", + "html": ">>> hand = Hand()\n>>> print find_defining_class(hand, 'shuffle')\n<class 'Card.Deck'>", + "polygon": [ + [ + 128.27197265625, + 219.5386962890625 + ], + [ + 370.845703125, + 219.5386962890625 + ], + [ + 370.845703125, + 254.07421875 + ], + [ + 128.27197265625, + 254.07421875 + ] + ], + "bbox": [ + 128.27197265625, + 219.5386962890625, + 370.845703125, + 254.07421875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "3": "/page/195/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/196/Text/6", + "block_type": "Text", + "html": "
So the shuffle method for this Hand is the one in Deck.
", + "polygon": [ + [ + 128.49609375, + 260.26171875 ], [ 375.30267333984375, - 260.40472412109375 + 260.26171875 ], [ 375.30267333984375, - 273.0234375 + 270.51690673828125 ], [ - 128.3466796875, - 273.0234375 + 128.49609375, + 270.51690673828125 ] ], + "bbox": [ + 128.49609375, + 260.26171875, + 375.30267333984375, + 270.51690673828125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, { - "id": "/page/196/Text/5", + "id": "/page/196/Text/7", "block_type": "Text", "html": "find_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.\"
", "polygon": [ [ - 128.3466796875, - 280.177734375 + 128.794921875, + 280.564453125 ], [ - 525.9375, - 280.177734375 + 525.6015014648438, + 280.564453125 ], [ - 525.9375, + 525.6015014648438, 303.1598815917969 ], [ - 128.3466796875, + 128.794921875, 303.1598815917969 ] ], + "bbox": [ + 128.794921875, + 280.564453125, + 525.6015014648438, + 303.1598815917969 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, { - "id": "/page/196/Text/6", + "id": "/page/196/Text/8", "block_type": "Text", "html": "Here'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.
", "polygon": [ [ - 128.197265625, - 313.64630126953125 + 128.9443359375, + 313.62890625 ], [ - 526.236328125, - 313.64630126953125 + 525.6034545898438, + 313.62890625 ], [ - 526.236328125, - 372.603515625 + 525.6034545898438, + 372.41015625 ], [ - 128.197265625, - 372.603515625 + 128.9443359375, + 372.41015625 ] ], + "bbox": [ + 128.9443359375, + 313.62890625, + 525.6034545898438, + 372.41015625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, { - "id": "/page/196/Text/7", + "id": "/page/196/Text/9", "block_type": "Text", "html": "If you violate this rule, your code will collapse like (sorry) a house of cards.
", "polygon": [ [ - 128.6455078125, - 382.872314453125 + 128.49609375, + 382.8515625 ], [ - 460.79296875, - 382.872314453125 + 461.091796875, + 382.8515625 ], [ - 460.79296875, + 461.091796875, 392.83489990234375 ], [ - 128.6455078125, + 128.49609375, 392.83489990234375 ] ], + "bbox": [ + 128.49609375, + 382.8515625, + 461.091796875, + 392.83489990234375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/195/SectionHeader/12" + "3": "/page/195/SectionHeader/13" }, "images": {} }, { - "id": "/page/196/SectionHeader/8", + "id": "/page/196/SectionHeader/10", "block_type": "SectionHeader", - "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).
", + "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).
", "polygon": [ [ - 129.2431640625, + 128.6455078125, 449.75390625 ], [ @@ -98604,29 +160064,36 @@ 496.784912109375 ], [ - 129.2431640625, + 128.6455078125, 496.784912109375 ] ], + "bbox": [ + 128.6455078125, + 449.75390625, + 525.9375, + 496.784912109375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/196/Text/10", + "id": "/page/196/Text/12", "block_type": "Text", "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.
", "polygon": [ [ 128.9443359375, - 507.2713317871094 + 506.6015625 ], [ 525.9375, - 507.2713317871094 + 506.6015625 ], [ 525.9375, @@ -98637,49 +160104,63 @@ 553.8169250488281 ] ], + "bbox": [ + 128.9443359375, + 506.6015625, + 525.9375, + 553.8169250488281 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/196/Text/11", + "id": "/page/196/Text/13", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 128.49609375, + 128.6455078125, 564.3033294677734 ], [ - 525.6033935546875, + 526.53515625, 564.3033294677734 ], [ - 525.6033935546875, + 526.53515625, 598.6539306640625 ], [ - 128.49609375, + 128.6455078125, 598.6539306640625 ] ], + "bbox": [ + 128.6455078125, + 564.3033294677734, + 526.53515625, + 598.6539306640625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/196/Code/12", - "block_type": "Code", - "html": "suffix_map = {}\nprefix = ()", + "id": "/page/196/TextInlineMath/14", + "block_type": "TextInlineMath", + "html": "
suffix_map = {} prefix = ()
", "polygon": [ [ - 128.9443359375, + 128.57080078125, 605.019775390625 ], [ @@ -98691,86 +160172,108 @@ 627.1763763427734 ], [ - 128.9443359375, + 128.57080078125, 627.1763763427734 ] ], + "bbox": [ + 128.57080078125, + 605.019775390625, + 208.05560302734375, + 627.1763763427734 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/196/Text/13", + "id": "/page/196/Text/15", "block_type": "Text", "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).
", "polygon": [ [ - 128.9443359375, - 633.84033203125 + 128.6455078125, + 633.4453125 ], [ 525.9375, - 633.84033203125 + 633.4453125 ], [ 525.9375, 668.1919479370117 ], [ - 128.9443359375, + 128.6455078125, 668.1919479370117 ] ], + "bbox": [ + 128.6455078125, + 633.4453125, + 525.9375, + 668.1919479370117 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/196/Text/14", + "id": "/page/196/Text/16", "block_type": "Text", "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:
", "polygon": [ [ - 128.49609375, - 677.91796875 + 128.794921875, + 678.678352355957 ], [ - 525.9375, - 677.91796875 + 525.6033935546875, + 678.678352355957 ], [ - 525.9375, + 525.6033935546875, 700.8349533081055 ], [ - 128.49609375, + 128.794921875, 700.8349533081055 ] ], + "bbox": [ + 128.794921875, + 678.678352355957, + 525.6033935546875, + 700.8349533081055 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} } ], "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": null }, { - "id": "/page/197/Page/153", + "id": "/page/197/Page/183", "block_type": "Page", - "html": "176 Chapter 18. Inheritance
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.4248046875 + 61.0048828125 ], [ - 483.50390625, - 60.4248046875 + 482.40338134765625, + 61.0048828125 ], [ - 483.50390625, + 482.40338134765625, 71.13372802734375 ], [ @@ -98812,167 +160321,316 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 61.0048828125, + 482.40338134765625, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/197/PageHeader/15", + "id": "/page/197/PageHeader/18", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.09130859375, - 60.37646484375 + 86.73486328125, + 61.63330078125 ], [ - 100.77978515625, - 60.37646484375 + 103.02099609375, + 61.63330078125 ], [ - 100.77978515625, - 70.62451171875 + 103.02099609375, + 71.20458984375 ], [ - 85.09130859375, - 70.62451171875 + 86.73486328125, + 71.20458984375 ] ], + "bbox": [ + 86.73486328125, + 61.63330078125, + 103.02099609375, + 71.20458984375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { "id": "/page/197/Code/1", "block_type": "Code", - "html": "class Markov(object):\n def __init__(self):\n self.suffix_map = {}\n self.prefix = ()\nNext, we transform the functions into methods. For example, here's process_word:\n 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)", + "html": "
class Markov(object):", "polygon": [ [ - 86.0625, - 88.68572998046875 + 86.4000015258789, + 88.22021484375 ], [ - 464.9765625, - 88.68572998046875 + 197.525390625, + 88.22021484375 ], [ - 464.9765625, + 197.525390625, + 98.6483154296875 + ], + [ + 86.4000015258789, + 98.6483154296875 + ] + ], + "bbox": [ + 86.4000015258789, + 88.22021484375, + 197.525390625, + 98.6483154296875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/197/Code/2", + "block_type": "Code", + "html": "
def __init__(self):\n self.suffix_map = {}\n self.prefix = ()", + "polygon": [ + [ + 107.31600189208984, + 110.98828125 + ], + [ + 232.85025024414062, + 110.98828125 + ], + [ + 232.85025024414062, + 147.42529296875 + ], + [ + 107.31600189208984, + 147.42529296875 + ] + ], + "bbox": [ + 107.31600189208984, + 110.98828125, + 232.85025024414062, + 147.42529296875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/197/Text/3", + "block_type": "Text", + "html": "
Next, we transform the functions into methods. For example, here's process_word:
", + "polygon": [ + [ + 84.568359375, + 152.2705078125 + ], + [ + 453.0234375, + 152.2705078125 + ], + [ + 453.0234375, + 163.534912109375 + ], + [ + 84.568359375, + 163.534912109375 + ] + ], + "bbox": [ + 84.568359375, + 152.2705078125, + 453.0234375, + 163.534912109375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/188/SectionHeader/1", + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" + }, + "images": {} + }, + { + "id": "/page/197/Code/4", + "block_type": "Code", + "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)", + "polygon": [ + [ + 107.3160400390625, + 169.3817138671875 + ], + [ + 410.291015625, + 169.3817138671875 + ], + [ + 410.291015625, 313.4813232421875 ], [ - 86.0625, + 107.3160400390625, 313.4813232421875 ] ], + "bbox": [ + 107.3160400390625, + 169.3817138671875, + 410.291015625, + 313.4813232421875 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/197/Text/2", + "id": "/page/197/Text/5", "block_type": "Text", - "html": "
Transforming a program like this—changing the design without changing the function—is another example of refactoring (see Section 4.7).
", + "html": "Transforming a program like this—changing the design without changing the function—is another example of refactoring (see Section 4.7).
", "polygon": [ [ - 85.46484375, - 319.04296875 + 84.568359375, + 318.65625 ], [ - 482.607421875, - 317.49609375 + 482.4033508300781, + 318.65625 ], [ - 482.607421875, + 482.4033508300781, 341.7848815917969 ], [ - 85.46484375, - 342.24609375 + 84.568359375, + 341.7848815917969 ] ], + "bbox": [ + 84.568359375, + 318.65625, + 482.4033508300781, + 341.7848815917969 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/197/Text/3", + "id": "/page/197/Text/6", "block_type": "Text", "html": "This example suggests a development plan for designing objects and methods:
", "polygon": [ [ - 85.166015625, - 350.947265625 + 85.9130859375, + 351.52734375 ], [ - 432.23175048828125, - 350.947265625 + 432.703125, + 351.52734375 ], [ - 432.23175048828125, - 362.162109375 + 432.703125, + 361.71588134765625 ], [ - 85.166015625, - 362.162109375 + 85.9130859375, + 361.71588134765625 ] ], + "bbox": [ + 85.9130859375, + 351.52734375, + 432.703125, + 361.71588134765625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/196/SectionHeader/8" + "3": "/page/195/SectionHeader/13", + "4": "/page/196/SectionHeader/10" }, "images": {} }, { - "id": "/page/197/ListGroup/152", + "id": "/page/197/ListGroup/181", "block_type": "ListGroup", - "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).
", + "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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", + "html": "", "polygon": [ [ - 128.3466796875, + 128.9443359375, 61.171142578125 ], [ @@ -99362,43 +161112,57 @@ 71.13372802734375 ], [ - 128.3466796875, + 128.9443359375, 71.13372802734375 ] ], + "bbox": [ + 128.9443359375, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/197/SectionHeader/9" + "3": "/page/195/SectionHeader/13", + "4": "/page/197/SectionHeader/12" }, "images": {} }, { "id": "/page/198/PageHeader/24", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.3984375, - 61.14990234375 + 510.099609375, + 61.34326171875 ], [ - 525.33984375, - 61.14990234375 + 525.638671875, + 61.34326171875 ], [ - 525.33984375, - 70.04443359375 + 525.638671875, + 70.14111328125 ], [ - 510.3984375, - 70.04443359375 + 510.099609375, + 70.14111328125 ] ], + "bbox": [ + 510.099609375, + 61.34326171875, + 525.638671875, + 70.14111328125 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/197/SectionHeader/9" + "3": "/page/195/SectionHeader/13", + "4": "/page/197/SectionHeader/12" }, "images": {} }, @@ -99408,51 +161172,64 @@ "html": "parent class: The class from which a child class inherits.
", "polygon": [ [ - 128.3466796875, + 129.60000610351562, 88.7381591796875 ], [ - 377.767822265625, + 378.31640625, 88.7381591796875 ], [ - 377.767822265625, - 99.0966796875 + 378.31640625, + 98.79791259765625 ], [ - 128.3466796875, - 99.0966796875 + 129.60000610351562, + 98.79791259765625 ] ], + "bbox": [ + 129.60000610351562, + 88.7381591796875, + 378.31640625, + 98.79791259765625 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/197/SectionHeader/9" + "3": "/page/195/SectionHeader/13", + "4": "/page/197/SectionHeader/12" }, "images": {} }, { - "id": "/page/198/ListGroup/214", + "id": "/page/198/ListGroup/222", "block_type": "ListGroup", "html": "Exercise 18.6. The following are the possible hands in poker, in increasing order of value (and decreasing order of probability):
", + "html": "Exercise 18.6. The following are the possible hands in poker, in increasing order of value (and decreasing order of probability):
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", "polygon": [ [ - 128.9443359375, + 129.60003662109375, 488.42578125 ], [ @@ -99944,97 +161797,124 @@ 498.5602722167969 ], [ - 128.9443359375, + 129.60003662109375, 498.5602722167969 ] ], + "bbox": [ + 129.60003662109375, + 488.42578125, + 478.1915283203125, + 498.5602722167969 + ], "children": null, "section_hierarchy": { "1": "/page/188/SectionHeader/1", - "3": "/page/198/SectionHeader/7" + "3": "/page/195/SectionHeader/13", + "4": "/page/198/SectionHeader/7" }, "images": {} }, { "id": "/page/198/ListItem/18", "block_type": "ListItem", - "html": "Card.py : A complete version of the Card, Deck and Hand classes in this chapter.
", + "html": "Card.py : A complete version of the Card, Deck and Hand classes in this chapter.
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When 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
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Now we can create a button with this function as its command:
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", + "html": "What happens if you press the buttons more than once? Solution: http: // thinkpython. com/ code/ button_ demo. py
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", + "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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width and height are the dimensions of the canvas in pixels.
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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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", + "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.
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oval takes a bounding box and draws an oval within the specified rectangle:
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", + "id": "/page/203/Text/12", + "block_type": "Text", + "html": "canvas.polygon([[0, 100], [100, 200], [200, 100]], fill='red', outline='orange', width=10)
", "polygon": [ [ - 85.46484375, - 411.08203125 + 85.763671875, + 411.85546875 ], [ - 369.3515625, - 411.08203125 + 369.94921875, + 411.85546875 ], [ - 369.3515625, + 369.94921875, 434.264404296875 ], [ - 85.46484375, + 85.763671875, 434.264404296875 ] ], + "bbox": [ + 85.763671875, + 411.85546875, + 369.94921875, + 434.264404296875 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/3" + "4": "/page/203/SectionHeader/3" }, "images": {} }, { "id": "/page/203/SectionHeader/13", "block_type": "SectionHeader", - "html": "en creates a new Entry:", + "id": "/page/203/Text/15", + "block_type": "Text", + "html": "
en creates a new Entry:
", "polygon": [ [ - 85.3154296875, - 524.77734375 + 86.0625, + 526.7109375 ], [ - 187.9564971923828, - 524.77734375 + 190.5029296875, + 526.7109375 ], [ - 187.9564971923828, + 190.5029296875, 536.9349670410156 ], [ - 85.3154296875, + 86.0625, 536.9349670410156 ] ], + "bbox": [ + 86.0625, + 526.7109375, + 190.5029296875, + 536.9349670410156 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, @@ -103261,26 +165742,32 @@ "html": "entry = g.en(text='Default text.')
", "polygon": [ [ - 85.3154296875, - 542.56640625 + 85.39013671875, + 543.7658081054688 ], [ - 264.6123046875, - 542.56640625 + 264.1914978027344, + 543.7658081054688 ], [ - 264.6123046875, - 553.7284088134766 + 264.1914978027344, + 553.78125 ], [ - 85.3154296875, - 553.7284088134766 + 85.39013671875, + 553.78125 ] ], + "bbox": [ + 85.39013671875, + 543.7658081054688, + 264.1914978027344, + 553.78125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, @@ -103291,14 +165778,14 @@ "polygon": [ [ 85.6142578125, - 558.80859375 + 560.35546875 ], [ - 483.50390625, - 558.80859375 + 482.90625, + 560.35546875 ], [ - 483.50390625, + 482.90625, 583.0149688720703 ], [ @@ -103306,53 +165793,240 @@ 583.0149688720703 ] ], + "bbox": [ + 85.6142578125, + 560.35546875, + 482.90625, + 583.0149688720703 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { "id": "/page/203/Code/18", "block_type": "Code", - "html": ">>> entry.get()\n'Default text.'\nte creates a Text widget:\ntext = g.te(width=100, height=5)\nwidth and height are the dimensions of the widget in characters and lines.\ninsert puts text into the Text widget:\ntext.insert(END, 'A line of text.')", + "html": "
>>> entry.get()\n'Default text.'", "polygon": [ [ - 84.568359375, - 589.8448181152344 + 85.9130859375, + 589.359375 ], [ - 415.739990234375, - 589.8448181152344 + 168.6884765625, + 589.359375 ], [ - 415.739990234375, - 700.685417175293 + 168.6884765625, + 612.0014190673828 ], [ - 84.568359375, - 700.685417175293 + 85.9130859375, + 612.0014190673828 + ] + ], + "bbox": [ + 85.9130859375, + 589.359375, + 168.6884765625, + 612.0014190673828 + ], + "children": null, + "section_hierarchy": { + "1": "/page/200/SectionHeader/1", + "4": "/page/203/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/203/Text/19", + "block_type": "Text", + "html": "
te creates a Text widget:
", + "polygon": [ + [ + 85.68896484375, + 618.36328125 + ], + [ + 195.43359375, + 618.36328125 + ], + [ + 195.43359375, + 629.0939636230469 + ], + [ + 85.68896484375, + 629.0939636230469 + ] + ], + "bbox": [ + 85.68896484375, + 618.36328125, + 195.43359375, + 629.0939636230469 + ], + "children": null, + "section_hierarchy": { + "1": "/page/200/SectionHeader/1", + "4": "/page/203/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/203/Text/20", + "block_type": "Text", + "html": "text = g.te(width=100, height=5)
", + "polygon": [ + [ + 85.763671875, + 635.9238128662109 + ], + [ + 253.78172302246094, + 635.9238128662109 + ], + [ + 253.78172302246094, + 645.8864135742188 + ], + [ + 85.763671875, + 645.8864135742188 + ] + ], + "bbox": [ + 85.763671875, + 635.9238128662109, + 253.78172302246094, + 645.8864135742188 + ], + "children": null, + "section_hierarchy": { + "1": "/page/200/SectionHeader/1", + "4": "/page/203/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/203/Text/21", + "block_type": "Text", + "html": "width and height are the dimensions of the widget in characters and lines.
", + "polygon": [ + [ + 85.9130859375, + 652.39453125 + ], + [ + 415.96875, + 652.39453125 + ], + [ + 415.96875, + 662.9789733886719 + ], + [ + 85.9130859375, + 662.9789733886719 + ] + ], + "bbox": [ + 85.9130859375, + 652.39453125, + 415.96875, + 662.9789733886719 + ], + "children": null, + "section_hierarchy": { + "1": "/page/200/SectionHeader/1", + "4": "/page/203/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/203/Text/22", + "block_type": "Text", + "html": "insert puts text into the Text widget:
", + "polygon": [ + [ + 86.2119140625, + 673.27734375 + ], + [ + 253.5556640625, + 673.27734375 + ], + [ + 253.5556640625, + 683.8929824829102 + ], + [ + 86.2119140625, + 683.8929824829102 + ] + ], + "bbox": [ + 86.2119140625, + 673.27734375, + 253.5556640625, + 683.8929824829102 + ], + "children": null, + "section_hierarchy": { + "1": "/page/200/SectionHeader/1", + "4": "/page/203/SectionHeader/13" + }, + "images": {} + }, + { + "id": "/page/203/Code/23", + "block_type": "Code", + "html": "text.insert(END, 'A line of text.')", + "polygon": [ + [ + 86.361328125, + 690.6796875 + ], + [ + 269.4205017089844, + 690.6796875 + ], + [ + 269.4205017089844, + 700.734375 + ], + [ + 86.361328125, + 700.734375 ] ], + "bbox": [ + 86.361328125, + 690.6796875, + 269.4205017089844, + 700.734375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} } ], "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": null }, { - "id": "/page/204/Page/182", + "id": "/page/204/Page/202", "block_type": "Page", - "html": "
19.6. Packing widgets 183
", + "html": "", "polygon": [ [ - 128.3466796875, + 128.794921875, 61.171142578125 ], [ @@ -103390,43 +166070,55 @@ 71.13372802734375 ], [ - 128.3466796875, + 128.794921875, 71.13372802734375 ] ], + "bbox": [ + 128.794921875, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/PageHeader/21", + "id": "/page/204/PageHeader/19", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.697265625, - 60.85986328125 + 509.80078125, + 60.76318359375 ], [ - 526.236328125, - 60.85986328125 + 525.9375, + 60.76318359375 ], [ - 526.236328125, - 69.65771484375 + 525.9375, + 69.94775390625 ], [ - 510.697265625, - 69.65771484375 + 509.80078125, + 69.94775390625 ] ], + "bbox": [ + 509.80078125, + 60.76318359375, + 525.9375, + 69.94775390625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, @@ -103436,26 +166128,32 @@ "html": "END is a special index that indicates the last character in the Text widget.
", "polygon": [ [ - 128.197265625, + 127.599609375, 88.68572998046875 ], [ - 445.8515625, + 445.552734375, 88.68572998046875 ], [ - 445.8515625, + 445.552734375, 98.79791259765625 ], [ - 128.197265625, + 127.599609375, 98.79791259765625 ] ], + "bbox": [ + 127.599609375, + 88.68572998046875, + 445.552734375, + 98.79791259765625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, @@ -103465,55 +166163,67 @@ "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.
", "polygon": [ [ - 128.197265625, - 107.42474365234375 + 127.8984375, + 107.314453125 ], [ 525.6033935546875, - 107.42474365234375 + 107.314453125 ], [ 525.6033935546875, 141.92486572265625 ], [ - 128.197265625, + 127.8984375, 141.92486572265625 ] ], + "bbox": [ + 127.8984375, + 107.314453125, + 525.6033935546875, + 141.92486572265625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/TextInlineMath/3", - "block_type": "TextInlineMath", - "html": ">>> text.insert(1.1, 'nother')
", + "id": "/page/204/Text/3", + "block_type": "Text", + "html": ">>> text.insert(1.1, 'nother')
", "polygon": [ [ - 129.31787109375, - 146.5797119140625 + 127.8984375, + 146.1796875 ], [ 286.4743957519531, - 146.5797119140625 + 146.1796875 ], [ 286.4743957519531, 156.54229736328125 ], [ - 129.31787109375, + 127.8984375, 156.54229736328125 ] ], + "bbox": [ + 127.8984375, + 146.1796875, + 286.4743957519531, + 156.54229736328125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, @@ -103523,99 +166233,82 @@ "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:
", "polygon": [ [ - 128.794921875, - 161.34674072265625 + 127.7490234375, + 160.48828125 ], [ 525.5955200195312, - 161.34674072265625 + 160.48828125 ], [ 525.5955200195312, - 183.7880859375 + 183.6539306640625 ], [ - 128.794921875, - 183.7880859375 + 127.7490234375, + 183.6539306640625 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" - }, - "images": {} - }, - { - "id": "/page/204/Text/5", - "block_type": "Text", - "html": ">>> text.get(0.0, END)
", - "polygon": [ - [ - 128.3466796875, - 188.30877685546875 - ], - [ - 255.9462890625, - 188.30877685546875 - ], - [ - 255.9462890625, - 201.8671875 - ], - [ - 128.3466796875, - 201.8671875 - ] + "bbox": [ + 127.7490234375, + 160.48828125, + 525.5955200195312, + 183.6539306640625 ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/Text/6", - "block_type": "Text", - "html": "'Another line of text.\\n'
", + "id": "/page/204/Code/5", + "block_type": "Code", + "html": ">>> text.get(0.0, END)\n'Another line of text.\\n'", "polygon": [ [ - 128.9443359375, - 200.50274658203125 + 128.794921875, + 188.30877685546875 ], [ 260.32940673828125, - 200.50274658203125 + 188.30877685546875 ], [ 260.32940673828125, - 212.501953125 + 210.46533203125 ], [ - 128.9443359375, - 212.501953125 + 128.794921875, + 210.46533203125 ] ], + "bbox": [ + 128.794921875, + 188.30877685546875, + 260.32940673828125, + 210.46533203125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/Text/7", + "id": "/page/204/Text/6", "block_type": "Text", "html": "
The delete method removes text from the widget; the following example deletes all but the first two characters:
", "polygon": [ [ 128.3466796875, - 215.269775390625 + 214.62890625 ], [ 525.603515625, - 215.269775390625 + 214.62890625 ], [ 525.603515625, @@ -103626,20 +166319,26 @@ 237.57696533203125 ] ], + "bbox": [ + 128.3466796875, + 214.62890625, + 525.603515625, + 237.57696533203125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/Text/8", - "block_type": "Text", - "html": ">>> text.delete(1.2, END) >>> text.get(0.0, END) 'An\\n'
", + "id": "/page/204/Code/7", + "block_type": "Code", + "html": ">>> text.delete(1.2, END)\n>>> text.get(0.0, END)\n'An\\n'", "polygon": [ [ - 128.9443359375, + 128.49609375, 242.2318115234375 ], [ @@ -103651,82 +166350,100 @@ 276.5823974609375 ], [ - 128.9443359375, + 128.49609375, 276.5823974609375 ] ], + "bbox": [ + 128.49609375, + 242.2318115234375, + 260.36907958984375, + 276.5823974609375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/Text/9", + "id": "/page/204/Text/8", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 129.2431640625, + 128.6455078125, 278.79168701171875 ], [ - 525.9375, + 525.6036376953125, 278.79168701171875 ], [ - 525.9375, + 525.6036376953125, 325.3372802734375 ], [ - 129.2431640625, + 128.6455078125, 325.3372802734375 ] ], + "bbox": [ + 128.6455078125, + 278.79168701171875, + 525.6036376953125, + 325.3372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/Text/10", + "id": "/page/204/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 129.09375, - 333.544921875 + 128.794921875, + 333.73828125 ], [ - 525.9375, - 333.544921875 + 525.6034545898438, + 333.73828125 ], [ - 525.9375, + 525.6034545898438, 356.2702941894531 ], [ - 129.09375, + 128.794921875, 356.2702941894531 ] ], + "bbox": [ + 128.794921875, + 333.73828125, + 525.6034545898438, + 356.2702941894531 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/Text/11", + "id": "/page/204/Text/10", "block_type": "Text", - "html": "You can see my solution at http: // thinkpython. com/ code/ circle_ demo. py .
", + "html": "You can see my solution at http: // thinkpython. com/ code/ circle_ demo. py .
", "polygon": [ [ - 128.6455078125, + 127.8984375, 364.9649963378906 ], [ @@ -103738,256 +166455,275 @@ 375.00830078125 ], [ - 128.6455078125, + 127.8984375, 375.00830078125 ] ], + "bbox": [ + 127.8984375, + 364.9649963378906, + 474.76568603515625, + 375.00830078125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/203/SectionHeader/13" + "4": "/page/203/SectionHeader/13" }, "images": {} }, { - "id": "/page/204/SectionHeader/12", + "id": "/page/204/SectionHeader/11", "block_type": "SectionHeader", - "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).
", + "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).
", "polygon": [ [ - 129.09375, - 427.857421875 + 128.9443359375, + 427.32421875 ], [ - 526.53515625, - 427.857421875 + 525.6033935546875, + 427.32421875 ], [ - 526.53515625, + 525.6033935546875, 462.2090148925781 ], [ - 129.09375, + 128.9443359375, 462.2090148925781 ] ], + "bbox": [ + 128.9443359375, + 427.32421875, + 525.6033935546875, + 462.2090148925781 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, { - "id": "/page/204/Text/14", + "id": "/page/204/Text/13", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ 129.09375, 470.9854431152344 ], [ - 527.73046875, + 526.53515625, 470.9854431152344 ], [ - 527.73046875, - 505.44140625 + 526.53515625, + 505.3360290527344 ], [ 129.09375, - 505.44140625 + 505.3360290527344 ] ], + "bbox": [ + 129.09375, + 470.9854431152344, + 526.53515625, + 505.3360290527344 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, { - "id": "/page/204/Text/15", + "id": "/page/204/Text/14", "block_type": "Text", "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.
", "polygon": [ [ - 128.9443359375, - 514.1124572753906 + 128.794921875, + 513.94921875 ], [ - 527.1328125, - 514.1124572753906 + 525.638671875, + 513.94921875 ], [ - 527.1328125, - 536.37890625 + 525.638671875, + 536.26904296875 ], [ - 128.9443359375, - 536.37890625 + 128.794921875, + 536.26904296875 ] ], + "bbox": [ + 128.794921875, + 513.94921875, + 525.638671875, + 536.26904296875 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, { - "id": "/page/204/Text/16", - "block_type": "Text", - "html": "class SimpleTurtleWorld(TurtleWorld): \"\"\"This class is identical to TurtleWorld, but the code that lays out the GUI is simplified for explanatory purposes.\"\"\"
", + "id": "/page/204/Code/15", + "block_type": "Code", + "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 ...", "polygon": [ [ 129.59999084472656, 540.9238891601562 ], [ - 465.873046875, + 467.96484375, 540.9238891601562 ], [ - 465.873046875, - 582.3984375 - ], - [ - 129.59999084472656, - 582.3984375 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" - }, - "images": {} - }, - { - "id": "/page/204/Text/17", - "block_type": "Text", - "html": "
", - "polygon": [ - [ - 150.01171875, - 589.701904296875 - ], - [ - 234.2117919921875, - 588.5859375 - ], - [ - 234.2117919921875, + 467.96484375, 624.0525054931641 ], [ - 150.01171875, + 129.59999084472656, 624.0525054931641 ] ], + "bbox": [ + 129.59999084472656, + 540.9238891601562, + 467.96484375, + 624.0525054931641 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, { - "id": "/page/204/Text/18", + "id": "/page/204/Text/16", "block_type": "Text", "html": "def setup(self): self.row() ...
setup is the function that creates and arranges the widgets. Arranging widgets in a GUI is called packing.
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", "polygon": [ [ 128.197265625, - 659.35546875 + 659.7421875 ], [ - 526.833984375, - 659.35546875 + 525.638671875, + 659.7421875 ], [ - 526.833984375, - 682.0970687866211 + 525.638671875, + 682.171875 ], [ 128.197265625, - 682.0970687866211 + 682.171875 ] ], + "bbox": [ + 128.197265625, + 659.7421875, + 525.638671875, + 682.171875 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, { - "id": "/page/204/Text/20", + "id": "/page/204/Text/18", "block_type": "Text", "html": "Here is the code that creates the Canvas and the column Frame that hold the other widgets:
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- } + "images": {} }, { - "id": "/page/205/Text/2", - "block_type": "Text", - "html": "Figure 19.1: TurtleWorld after running the snowflake code.
", + "id": "/page/205/FigureGroup/122", + "block_type": "FigureGroup", + "html": "Figure 19.1: TurtleWorld after running the snowflake code.
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The first widget in the column is a grid Frame, which contains four buttons arranged twoby-two:
", "polygon": [ [ - 85.3154296875, + 85.9130859375, 374.73046875 ], [ @@ -104205,14 +167019,20 @@ 397.6479187011719 ], [ - 85.3154296875, + 85.9130859375, 397.6479187011719 ] ], + "bbox": [ + 85.9130859375, + 374.73046875, + 482.4033508300781, + 397.6479187011719 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, @@ -104222,26 +167042,32 @@ "html": "self.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()", "polygon": [ [ - 127.8984375, - 402.1875 + 126.703125, + 402.57421875 ], [ 410.6488037109375, - 402.1875 + 402.57421875 ], [ 410.6488037109375, - 474.890625 + 473.9493713378906 ], [ - 127.8984375, - 474.890625 + 126.703125, + 473.9493713378906 ] ], + "bbox": [ + 126.703125, + 402.57421875, + 410.6488037109375, + 473.9493713378906 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, @@ -104251,26 +167077,32 @@ "html": "
gr creates the grid; the argument is the number of columns. Widgets in the grid are laid out left-to-right, top-to-bottom.
", "polygon": [ [ - 85.166015625, - 479.466796875 + 85.46484375, + 478.7578125 ], [ 482.404296875, - 479.466796875 + 478.7578125 ], [ 482.404296875, 501.77294921875 ], [ - 85.166015625, + 85.46484375, 501.77294921875 ] ], + "bbox": [ + 85.46484375, + 478.7578125, + 482.404296875, + 501.77294921875 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, @@ -104280,26 +167112,32 @@ "html": "The 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.
", "polygon": [ [ - 85.46484375, + 85.763671875, 510.08203125 ], [ - 483.50390625, + 482.3975830078125, 510.08203125 ], [ - 483.50390625, - 545.66015625 + 482.3975830078125, + 545.6129455566406 ], [ - 85.46484375, - 545.66015625 + 85.763671875, + 545.6129455566406 ] ], + "bbox": [ + 85.763671875, + 510.08203125, + 482.3975830078125, + 545.6129455566406 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, @@ -104309,26 +167147,32 @@ "html": "The next widget in the column is a row Frame that contains a Button and an Entry:
", "polygon": [ [ - 85.6142578125, - 553.78125 + 86.0625, + 554.94140625 ], [ - 450.6328125, - 553.78125 + 450.333984375, + 554.94140625 ], [ - 450.6328125, + 450.333984375, 565.0639495849609 ], [ - 85.6142578125, + 86.0625, 565.0639495849609 ] ], + "bbox": [ + 86.0625, + 554.94140625, + 450.333984375, + 565.0639495849609 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, @@ -104338,26 +167182,32 @@ "html": "self.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()", "polygon": [ [ - 128.23300170898438, + 124.91015625, 570.4317932128906 ], [ - 402.22265625, + 400.1586608886719, 570.4317932128906 ], [ - 402.22265625, + 400.1586608886719, 616.9774017333984 ], [ - 128.23300170898438, + 124.91015625, 616.9774017333984 ] ], + "bbox": [ + 124.91015625, + 570.4317932128906, + 400.1586608886719, + 616.9774017333984 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, @@ -104367,67 +167217,79 @@ "html": "
The 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.
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", + "html": "The option pady \"pads\" this row in the y direction, adding 30 pixels of space above and below.
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self.bu(text='Run code', command=self.run_text)", "polygon": [ [ - 171.3779296875, - 367.76953125 + 169.734375, + 368.3087463378906 ], [ 417.2407531738281, - 367.76953125 + 368.3087463378906 ], [ 417.2407531738281, 378.2713317871094 ], [ - 171.3779296875, + 169.734375, 378.2713317871094 ] ], + "bbox": [ + 169.734375, + 368.3087463378906, + 417.2407531738281, + 378.2713317871094 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, @@ -104775,26 +167709,32 @@ "html": "
run_text is similar to run_file except that it takes the code from the Text widget instead of from a file:
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", "polygon": [ [ - 128.794921875, - 512.015625 + 128.3466796875, + 512.2393493652344 ], [ - 526.236328125, - 512.015625 + 525.6034545898438, + 512.2393493652344 ], [ - 526.236328125, - 534.4453125 + 525.6034545898438, + 534.83203125 ], [ - 128.794921875, - 534.4453125 + 128.3466796875, + 534.83203125 ] ], + "bbox": [ + 128.3466796875, + 512.2393493652344, + 525.6034545898438, + 534.83203125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/204/SectionHeader/12" + "4": "/page/204/SectionHeader/11" }, "images": {} }, { "id": "/page/206/SectionHeader/13", "block_type": "SectionHeader", - "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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", + "html": "Here is code that creates a color selection Menubutton (you can download it from http: //thinkpython.com/code/menubutton_demo.py):
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186 Chapter 19. Case study: Tkinter
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The first argument of mi is the Menubutton these items are associated with.
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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).
", + "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).
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", "polygon": [ [ - 85.9130859375, - 430.8046875 + 85.6142578125, + 431.96484375 ], [ 482.4049072265625, - 430.8046875 + 431.96484375 ], [ 482.4049072265625, 454.5489196777344 ], [ - 85.9130859375, + 85.6142578125, 454.5489196777344 ] ], + "bbox": [ + 85.6142578125, + 431.96484375, + 482.4049072265625, + 454.5489196777344 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, @@ -105385,44 +168445,50 @@ "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.
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", + "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):
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", + "id": "/page/207/Code/13", + "block_type": "Code", + "html": "ca.bind('<ButtonPress-1>', make_circle)", "polygon": [ [ - 86.0625, - 563.0625 + 86.28662109375, + 563.44921875 ], [ 290.35308837890625, - 563.0625 + 563.44921875 ], [ 290.35308837890625, 573.4313812255859 ], [ - 86.0625, + 86.28662109375, 573.4313812255859 ] ], + "bbox": [ + 86.28662109375, + 563.44921875, + 290.35308837890625, + 573.4313812255859 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, @@ -105472,26 +168550,32 @@ "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.
", "polygon": [ [ - 85.9130859375, - 579.69140625 + 85.763671875, + 580.078125 ], [ - 483.50390625, - 579.69140625 + 482.607421875, + 580.078125 ], [ - 483.50390625, - 614.6749267578125 + 482.607421875, + 614.8828125 ], [ - 85.9130859375, - 614.6749267578125 + 85.763671875, + 614.8828125 ] ], + "bbox": [ + 85.763671875, + 580.078125, + 482.607421875, + 614.8828125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, @@ -105502,25 +168586,31 @@ "polygon": [ [ 85.763671875, - 624.1640625 + 624.55078125 ], [ - 482.90625, - 624.1640625 + 482.607421875, + 624.55078125 ], [ - 482.90625, - 659.7421875 + 482.607421875, + 659.7409362792969 ], [ 85.763671875, - 659.7421875 + 659.7409362792969 ] ], + "bbox": [ + 85.763671875, + 624.55078125, + 482.607421875, + 659.7409362792969 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, @@ -105530,40 +168620,46 @@ "html": "def make_circle(event):\n pos = ca.canvas_coords([event.x, event.y])\n item = ca.circle(pos, 5, fill='red')", "polygon": [ [ - 86.0625, + 86.39995574951172, 666.3347778320312 ], [ - 327.0111389160156, + 328.412109375, 666.3347778320312 ], [ - 327.0111389160156, - 701.5078125 + 328.412109375, + 700.6853790283203 ], [ - 86.0625, - 701.5078125 + 86.39995574951172, + 700.6853790283203 ] ], + "bbox": [ + 86.39995574951172, + 666.3347778320312, + 328.412109375, + 700.6853790283203 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} } ], "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": null }, { - "id": "/page/208/Page/185", + "id": "/page/208/Page/187", "block_type": "Page", - "html": "
19.8. Binding 187
", + "html": "", "polygon": [ [ - 128.3466796875, + 129.31787109375, 61.171142578125 ], [ @@ -105601,43 +168703,55 @@ 71.13372802734375 ], [ - 128.3466796875, + 129.31787109375, 71.13372802734375 ] ], + "bbox": [ + 129.31787109375, + 61.171142578125, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/PageHeader/14", + "id": "/page/208/PageHeader/16", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 510.697265625, - 61.05322265625 + 509.501953125, + 60.76318359375 ], [ 525.638671875, - 61.05322265625 + 60.76318359375 ], [ 525.638671875, - 70.43115234375 + 69.94775390625 ], [ - 510.697265625, - 70.43115234375 + 509.501953125, + 69.94775390625 ] ], + "bbox": [ + 509.501953125, + 60.76318359375, + 525.638671875, + 69.94775390625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, @@ -105647,26 +168761,32 @@ "html": "The 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.
", "polygon": [ [ - 128.9443359375, - 87.78515625 + 129.392578125, + 88.0751953125 ], [ - 525.9375, - 87.78515625 + 525.638671875, + 88.0751953125 ], [ - 525.9375, + 525.638671875, 147.57489013671875 ], [ - 128.9443359375, + 129.392578125, 147.57489013671875 ] ], + "bbox": [ + 129.392578125, + 88.0751953125, + 525.638671875, + 147.57489013671875 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, @@ -105676,102 +168796,190 @@ "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.
", "polygon": [ [ - 129.392578125, - 157.39453125 + 129.09375, + 157.201171875 ], [ - 526.236328125, - 157.39453125 + 525.6021118164062, + 157.201171875 ], [ - 526.236328125, + 525.6021118164062, 191.94488525390625 ], [ - 129.392578125, + 129.09375, 191.94488525390625 ] ], + "bbox": [ + 129.09375, + 157.201171875, + 525.6021118164062, + 191.94488525390625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { "id": "/page/208/Code/3", "block_type": "Code", - "html": "bu = g.bu('Make text item:', make_text)\nen = g.en()\nen.bind('<Return>', make_text)\nmake_text is called when the Button is pressed or when the user hits Return while typing\nin the Entry. To make this work, we need a function that can be called as a command (with\nno arguments) or as an event handler (with an Event as an argument):\ndef make_text(event=None):\n text = en.get()\n item = ca.text([0,0], text)", + "html": "
bu = g.bu('Make text item:', make_text)\nen = g.en()\nen.bind('<Return>', make_text)", "polygon": [ [ 129.59999084472656, - 197.84271240234375 + 197.806640625 + ], + [ + 333.5504150390625, + 197.806640625 + ], + [ + 333.5504150390625, + 232.19427490234375 + ], + [ + 129.59999084472656, + 232.19427490234375 + ] + ], + "bbox": [ + 129.59999084472656, + 197.806640625, + 333.5504150390625, + 232.19427490234375 + ], + "children": null, + "section_hierarchy": { + "1": "/page/200/SectionHeader/1", + "4": "/page/207/SectionHeader/9" + }, + "images": {} + }, + { + "id": "/page/208/Text/4", + "block_type": "Text", + "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):
", + "polygon": [ + [ + 128.9443359375, + 237.4453125 ], [ 525.6033935546875, - 197.84271240234375 + 237.4453125 ], [ 525.6033935546875, + 272.74188232421875 + ], + [ + 128.9443359375, + 272.74188232421875 + ] + ], + "bbox": [ + 128.9443359375, + 237.4453125, + 525.6033935546875, + 272.74188232421875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/200/SectionHeader/1", + "4": "/page/207/SectionHeader/9" + }, + "images": {} + }, + { + "id": "/page/208/Code/5", + "block_type": "Code", + "html": "def make_text(event=None):\n text = en.get()\n item = ca.text([0,0], text)", + "polygon": [ + [ + 129.60003662109375, + 277.6640625 + ], + [ + 291.7458190917969, + 277.6640625 + ], + [ + 291.7458190917969, 312.9913024902344 ], [ - 129.59999084472656, + 129.60003662109375, 312.9913024902344 ] ], + "bbox": [ + 129.60003662109375, + 277.6640625, + 291.7458190917969, + 312.9913024902344 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Text/4", + "id": "/page/208/Text/6", "block_type": "Text", "html": "
make_text gets the contents of the Entry and displays it as a Text item in the Canvas.
", "polygon": [ [ - 128.49609375, - 318.849609375 + 128.9443359375, + 318.462890625 ], [ 502.04083251953125, - 318.849609375 + 318.462890625 ], [ 502.04083251953125, 329.1498718261719 ], [ - 128.49609375, + 128.9443359375, 329.1498718261719 ] ], + "bbox": [ + 128.9443359375, + 318.462890625, + 502.04083251953125, + 329.1498718261719 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Text/5", + "id": "/page/208/Text/7", "block_type": "Text", "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.
", "polygon": [ [ 128.6455078125, - 338.37890625 + 338.765625 ], [ - 526.53515625, - 338.37890625 + 525.6033325195312, + 338.765625 ], [ - 526.53515625, + 525.6033325195312, 373.5198669433594 ], [ @@ -105779,57 +168987,69 @@ 373.5198669433594 ] ], + "bbox": [ + 128.6455078125, + 338.765625, + 525.6033325195312, + 373.5198669433594 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Text/6", - "block_type": "Text", - "html": "class Draggable(Item):
", + "id": "/page/208/Code/8", + "block_type": "Code", + "html": "class Draggable(Item):", "polygon": [ [ - 129.6000213623047, + 129.46728515625, 379.417724609375 ], [ - 244.67799377441406, + 253.705078125, 379.417724609375 ], [ - 244.67799377441406, - 389.619140625 + 253.705078125, + 395.033203125 ], [ - 129.6000213623047, - 389.619140625 + 129.46728515625, + 395.033203125 ] ], + "bbox": [ + 129.46728515625, + 379.417724609375, + 253.705078125, + 395.033203125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Code/7", + "id": "/page/208/Code/9", "block_type": "Code", "html": "
def __init__(self, item):\n self.canvas = item.canvas\n self.tag = item.tag\n self.bind('<Button-3>', self.select)\n self.bind('<B3-Motion>', self.drag)\n self.bind('<Release-3>', self.drop)", "polygon": [ [ 150.51602172851562, - 402.57421875 + 403.8067321777344 ], [ - 362.478515625, - 402.57421875 + 359.7080993652344, + 403.8067321777344 ], [ - 362.478515625, + 359.7080993652344, 474.7403259277344 ], [ @@ -105837,73 +169057,91 @@ 474.7403259277344 ] ], + "bbox": [ + 150.51602172851562, + 403.8067321777344, + 359.7080993652344, + 474.7403259277344 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Text/8", + "id": "/page/208/Text/10", "block_type": "Text", "html": "
The 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.
", "polygon": [ [ - 129.392578125, - 479.91796875 + 128.794921875, + 480.3046875 ], [ - 526.236328125, - 479.91796875 + 525.603271484375, + 480.3046875 ], [ - 526.236328125, + 525.603271484375, 503.0939025878906 ], [ - 129.392578125, + 128.794921875, 503.0939025878906 ] ], + "bbox": [ + 128.794921875, + 480.3046875, + 525.603271484375, + 503.0939025878906 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Text/9", + "id": "/page/208/Text/11", "block_type": "Text", "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:
", "polygon": [ [ - 129.2431640625, - 512.9637451171875 + 128.197265625, + 512.7890625 ], [ - 526.53515625, - 512.9637451171875 + 525.5986938476562, + 512.7890625 ], [ - 526.53515625, + 525.5986938476562, 535.2698974609375 ], [ - 129.2431640625, + 128.197265625, 535.2698974609375 ] ], + "bbox": [ + 128.197265625, + 512.7890625, + 525.5986938476562, + 535.2698974609375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Code/10", + "id": "/page/208/Code/12", "block_type": "Code", "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')", "polygon": [ @@ -105917,80 +169155,98 @@ ], [ 323.07843017578125, - 612.1013488769531 + 612.5625 ], [ 150.5160369873047, - 612.1013488769531 + 612.5625 ] ], + "bbox": [ + 150.5160369873047, + 541.1677398681641, + 323.07843017578125, + 612.5625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Text/11", + "id": "/page/208/Text/13", "block_type": "Text", "html": "
cget stands for \"get configuration;\" it takes the name of an option as a string and returns the current value of that option.
", "polygon": [ [ - 129.2431640625, - 617.58984375 + 128.49609375, + 617.9765625 ], [ - 527.1328125, - 617.58984375 + 525.5963134765625, + 617.9765625 ], [ - 527.1328125, + 525.5963134765625, 640.4559020996094 ], [ - 129.2431640625, + 128.49609375, 640.4559020996094 ] ], + "bbox": [ + 128.49609375, + 617.9765625, + 525.5963134765625, + 640.4559020996094 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Text/12", + "id": "/page/208/Text/14", "block_type": "Text", "html": "drag computes how far the object has moved relative to the starting place, updates the stored coordinates, and then moves the item.
", "polygon": [ [ - 128.794921875, - 649.6875 + 127.4501953125, + 650.07421875 ], [ - 526.833984375, - 649.6875 + 525.6035766601562, + 650.07421875 ], [ - 526.833984375, + 525.6035766601562, 672.6309127807617 ], [ - 128.794921875, + 127.4501953125, 672.6309127807617 ] ], + "bbox": [ + 127.4501953125, + 650.07421875, + 525.6035766601562, + 672.6309127807617 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/208/Code/13", + "id": "/page/208/Code/15", "block_type": "Code", "html": "def drag(self, event):\n dx = event.x - self.dragx", "polygon": [ @@ -106004,31 +169260,37 @@ ], [ 302.1921691894531, - 702.66796875 + 700.734375 ], [ 150.5160675048828, - 702.66796875 + 700.734375 ] ], + "bbox": [ + 150.5160675048828, + 678.5287475585938, + 302.1921691894531, + 700.734375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} } ], "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": null }, { - "id": "/page/209/Page/165", + "id": "/page/209/Page/167", "block_type": "Page", - "html": "
188 Chapter 19. Case study: Tkinter
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.134765625 + 60.56982421875 ], [ - 484.1015625, - 60.134765625 + 482.4034118652344, + 60.56982421875 ], [ - 484.1015625, + 482.4034118652344, 71.13372802734375 ], [ @@ -106070,115 +169338,104 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.56982421875, + 482.4034118652344, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/209/PageHeader/17", + "id": "/page/209/PageHeader/16", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.6142578125, - 59.748046875 + 85.68896484375, + 61.0048828125 ], [ - 101.4521484375, - 59.748046875 + 101.82568359375, + 61.0048828125 ], [ - 101.4521484375, - 70.2861328125 + 101.82568359375, + 70.189453125 ], [ - 85.6142578125, - 70.2861328125 + 85.68896484375, + 70.189453125 ] ], + "bbox": [ + 85.68896484375, + 61.0048828125, + 101.82568359375, + 70.189453125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { "id": "/page/209/Code/1", "block_type": "Code", - "html": "dy = event.y - self.dragy\nself.dragx = event.x\nself.dragy = event.y", + "html": "
dy = event.y - self.dragy\nself.dragx = event.x\nself.dragy = event.y\nself.move(dx, dy)", "polygon": [ [ - 128.23300170898438, - 88.68572998046875 + 125.58251953125, + 86.57666015625 ], [ 258.9920959472656, - 87.8818359375 + 86.57666015625 ], [ 258.9920959472656, - 144.9228515625 - ], - [ - 128.23300170898438, - 146.4697265625 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" - }, - "images": {} - }, - { - "id": "/page/209/Code/2", - "block_type": "Code", - "html": "
self.move(dx, dy)", - "polygon": [ - [ - 128.23300170898438, - 147.919921875 - ], - [ - 217.15916442871094, - 147.919921875 - ], - [ - 217.15916442871094, 159.62030029296875 ], [ - 128.23300170898438, + 125.58251953125, 159.62030029296875 ] ], + "bbox": [ + 125.58251953125, + 86.57666015625, + 258.9920959472656, + 159.62030029296875 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/207/SectionHeader/9" + "4": "/page/207/SectionHeader/9" }, "images": {} }, { - "id": "/page/209/Text/3", + "id": "/page/209/Text/2", "block_type": "Text", "html": "
This computation is done in pixel coordinates; there is no need to convert to Canvas coordinates.
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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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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.
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", "polygon": [ [ - 85.9130859375, - 425.00390625 + 85.6142578125, + 426.55078125 ], [ - 482.90625, - 425.00390625 + 482.40325927734375, + 426.55078125 ], [ - 482.90625, + 482.40325927734375, 449.2760009765625 ], [ - 85.9130859375, + 85.6142578125, 449.2760009765625 ] ], + "bbox": [ + 85.6142578125, + 426.55078125, + 482.40325927734375, + 449.2760009765625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/209/SectionHeader/9" + "4": "/page/209/SectionHeader/8" }, "images": {} }, { - "id": "/page/209/Text/11", + "id": "/page/209/Text/10", "block_type": "Text", "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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g.bu(text='This is wrong!', command=the_callback())
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", + "html": "For example, the GUI in Exercise 19.3 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?
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", "polygon": [ [ - 129.60000610351562, + 128.9443359375, 292.552734375 ], [ @@ -106880,43 +170281,55 @@ 315.1659240722656 ], [ - 129.60000610351562, + 128.9443359375, 315.1659240722656 ] ], + "bbox": [ + 128.9443359375, + 292.552734375, + 525.9375, + 315.1659240722656 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/209/SectionHeader/9" + "4": "/page/209/SectionHeader/8" }, "images": {} }, { "id": "/page/210/SectionHeader/8", "block_type": "SectionHeader", - "html": "GUI: A graphical user interface.
", "polygon": [ [ - 128.42138671875, + 129.60000610351562, 365.1332092285156 ], [ @@ -106938,39 +170351,51 @@ 375.19293212890625 ], [ - 128.42138671875, + 129.60000610351562, 375.19293212890625 ] ], + "bbox": [ + 129.60000610351562, + 365.1332092285156, + 273.2406311035156, + 375.19293212890625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/210/SectionHeader/8" + "4": "/page/210/SectionHeader/8" }, "images": {} }, { "id": "/page/210/ListGroup/161", "block_type": "ListGroup", - "html": "pack: To arrange and display the elements of a GUI.
", - "polygon": [ - [ - 129.2431640625, - 637.3125 - ], - [ - 361.58203125, - 637.3125 - ], - [ - 361.58203125, - 647.4208679199219 - ], - [ - 129.2431640625, - 647.4208679199219 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/200/SectionHeader/1", - "3": "/page/210/SectionHeader/8" - }, - "images": {} - }, - { - "id": "/page/210/Text/21", - "block_type": "Text", - "html": "geometry manager: A system for packing widgets.
", - "polygon": [ - [ - 128.27197265625, - 656.566162109375 - ], - [ - 356.501953125, - 656.566162109375 - ], - [ - 356.501953125, - 666.703125 - ], - [ - 128.27197265625, - 666.703125 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/200/SectionHeader/1", - "3": "/page/210/SectionHeader/8" - }, - "images": {} - }, - { - "id": "/page/210/ListItem/22", - "block_type": "ListItem", - "html": "190 Chapter 19. Case study: Tkinter
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.08642578125 + 59.94140625 ], [ - 484.400390625, - 60.08642578125 + 482.607421875, + 59.94140625 ], [ - 484.400390625, + 482.607421875, 71.13372802734375 ], [ @@ -107408,35 +170917,82 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 59.94140625, + 482.607421875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", - "3": "/page/210/SectionHeader/8" + "4": "/page/210/SectionHeader/8" + }, + "images": {} + }, + { + "id": "/page/211/PageHeader/18", + "block_type": "PageHeader", + "html": "", + "polygon": [ + [ + 85.763671875, + 60.37646484375 + ], + [ + 102.0498046875, + 60.37646484375 + ], + [ + 102.0498046875, + 70.62451171875 + ], + [ + 85.763671875, + 70.62451171875 + ] + ], + "bbox": [ + 85.763671875, + 60.37646484375, + 102.0498046875, + 70.62451171875 + ], + "children": null, + "section_hierarchy": { + "1": "/page/200/SectionHeader/1", + "4": "/page/210/SectionHeader/8" }, "images": {} }, { "id": "/page/211/SectionHeader/1", "block_type": "SectionHeader", - "html": "Exercise 19.4. For this exercise, you will write an image viewer. Here is a simple example:
", "polygon": [ [ - 85.46484375, + 84.8671875, 113.0185546875 ], [ - 451.529296875, - 111.4716796875 + 452.42578125, + 113.0185546875 ], [ - 451.529296875, + 452.42578125, 124.00079345703125 ], [ - 85.46484375, - 124.8134765625 + 84.8671875, + 124.00079345703125 ] ], + "bbox": [ + 84.8671875, + 113.0185546875, + 452.42578125, + 124.00079345703125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", @@ -107474,27 +171036,33 @@ "images": {} }, { - "id": "/page/211/Text/3", - "block_type": "Text", - "html": "g = Gui() canvas = g.ca(width=300) photo = PhotoImage(file='danger.gif') canvas.image([0,0], image=photo) g.mainloop()
", + "id": "/page/211/Code/3", + "block_type": "Code", + "html": "g = Gui()\ncanvas = g.ca(width=300)\nphoto = PhotoImage(file='danger.gif')\ncanvas.image([0,0], image=photo)\ng.mainloop()", "polygon": [ [ - 84.79248046875, - 130.2275390625 + 85.24072265625, + 131.44677734375 ], [ 279.8783874511719, - 128.6806640625 + 131.44677734375 ], [ 279.8783874511719, 190.1864013671875 ], [ - 84.79248046875, - 191.7158203125 + 85.24072265625, + 190.1864013671875 ] ], + "bbox": [ + 85.24072265625, + 131.44677734375, + 279.8783874511719, + 190.1864013671875 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", @@ -107509,14 +171077,14 @@ "polygon": [ [ 85.166015625, - 195.9697265625 + 196.1630859375 ], [ - 484.1015625, - 195.9697265625 + 482.607421875, + 196.1630859375 ], [ - 484.1015625, + 482.607421875, 232.03125 ], [ @@ -107524,6 +171092,12 @@ 232.03125 ] ], + "bbox": [ + 85.166015625, + 196.1630859375, + 482.607421875, + 232.03125 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", @@ -107532,27 +171106,33 @@ "images": {} }, { - "id": "/page/211/Text/5", - "block_type": "Text", - "html": "
g.la(image=photo) g.bu(image=photo)
", + "id": "/page/211/Code/5", + "block_type": "Code", + "html": "g.la(image=photo)\ng.bu(image=photo)", "polygon": [ [ - 85.46484375, - 238.21875 + 84.49365234375, + 238.9921875 ], [ - 176.4580078125, - 238.21875 + 177.802734375, + 238.9921875 ], [ - 176.4580078125, - 261.80859375 + 177.802734375, + 261.69146728515625 ], [ - 85.46484375, - 261.80859375 + 84.49365234375, + 261.69146728515625 ] ], + "bbox": [ + 84.49365234375, + 238.9921875, + 177.802734375, + 261.69146728515625 + ], "children": null, "section_hierarchy": { "1": "/page/200/SectionHeader/1", @@ -107566,22 +171146,28 @@ "html": "
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.
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", + "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:
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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.
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", + "html": "Solution: 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.
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This can be rewritten as:
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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.
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", + "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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return count
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", + "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.
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", + "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 .
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", "polygon": [ [ - 128.197265625, + 128.794921875, 527.484375 ], [ @@ -113170,10 +177762,16 @@ 550.5708923339844 ], [ - 128.197265625, + 128.794921875, 550.5708923339844 ] ], + "bbox": [ + 128.794921875, + 527.484375, + 526.833984375, + 550.5708923339844 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1" @@ -113186,22 +177784,28 @@ "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.\"
", + "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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", + "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.
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", + "id": "/page/223/Text/6", + "block_type": "Text", + "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.
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---|---|---|
size | Algorithm A | Algorithm B |
10 | 1 001 | 111 |
100 | 10 001 | 10 101 |
1 000 | 100 001 | 1 001 001 |
10 000 | 1 000 001 | > 1010 |
Input 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 |
At 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.
", + "html": "At 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.
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", + "html": "The 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.
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", + "html": "For 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 an2 > bn.
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", + "html": "The 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.
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", + "html": "An 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.
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", + "html": "All functions with the leading term n2 belong to O(n2) ; they are quadratic, which is a fancy word for functions with the leading term n2.
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---|---|---|
growth | ||
O(1) | constant | |
O(logb | n) | logarithmic (for any b) |
O(n) | linear | |
O(n logb | n) | \"en log en\" |
O(n | 2 ) | quadratic |
O(n | 3 ) | cubic |
O(c | n ) | exponential (for any c) |
Order of 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) |
For 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.
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", + "id": "/page/224/TextInlineMath/6", + "block_type": "TextInlineMath", + "html": "Exercise B.1. Read the Wikipedia page on Big-Oh notation at http://en.wikipedia.org/ wiki/Big_O_notation and answer the following questions:
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The 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.
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", + "html": "The performance of dictionaries is one of the minor miracles of computer science. We will see how they work in Section B.4.
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", + "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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", + "html": "If 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.
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", "polygon": [ [ - 85.6142578125, - 275.371337890625 + 85.9130859375, + 274.95703125 ], [ - 482.90625, - 275.371337890625 + 482.4033508300781, + 274.95703125 ], [ - 482.90625, - 297.966796875 + 482.4033508300781, + 297.5279541015625 ], [ - 85.6142578125, - 297.966796875 + 85.9130859375, + 297.5279541015625 ] ], + "bbox": [ + 85.9130859375, + 274.95703125, + 482.4033508300781, + 297.5279541015625 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, @@ -115853,114 +182105,142 @@ "html": "I 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:
", "polygon": [ [ - 85.763671875, - 307.0546875 + 85.6142578125, + 307.44140625 ], [ - 483.205078125, - 307.0546875 + 482.4034118652344, + 307.44140625 ], [ - 483.205078125, + 482.4034118652344, 354.09295654296875 ], [ - 85.763671875, + 85.6142578125, 354.09295654296875 ] ], + "bbox": [ + 85.6142578125, + 307.44140625, + 482.4034118652344, + 354.09295654296875 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/227/ListGroup/208", + "id": "/page/227/ListGroup/209", "block_type": "ListGroup", "html": "For 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.
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class LinearMap(object):
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add appends a key-value tuple to the list of items, which takes constant time.
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", + "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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", + "html": "", "polygon": [ [ - 128.12255859375, - 60.66650390625 + 129.01904296875, + 60.76318359375 ], [ 525.6033935546875, - 60.66650390625 + 60.76318359375 ], [ 525.6033935546875, 71.13372802734375 ], [ - 128.12255859375, + 129.01904296875, 71.13372802734375 ] ], + "bbox": [ + 129.01904296875, + 60.76318359375, + 525.6033935546875, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/228/PageHeader/13", + "id": "/page/228/PageHeader/12", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 511.294921875, - 60.66650390625 + 510.099609375, + 60.37646484375 ], [ 525.638671875, - 60.66650390625 + 60.37646484375 ], [ 525.638671875, - 70.04443359375 + 69.65771484375 ], [ - 511.294921875, - 70.04443359375 + 510.099609375, + 69.65771484375 ] ], + "bbox": [ + 510.099609375, + 60.37646484375, + 525.638671875, + 69.65771484375 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { "id": "/page/228/Text/1", "block_type": "Text", - "html": "not be the best option. There are other data structures (see 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.
", + "html": "not be the best option. There are other data structures (see 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.
", "polygon": [ [ - 129.5419921875, - 87.93017578125 + 128.197265625, + 87.8818359375 ], [ 525.6397094726562, - 87.93017578125 + 87.8818359375 ], [ 525.6397094726562, 123.1868896484375 ], [ - 129.5419921875, + 128.197265625, 123.1868896484375 ] ], + "bbox": [ + 128.197265625, + 87.8818359375, + 525.6397094726562, + 123.1868896484375 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, @@ -116232,26 +182611,33 @@ "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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", "polygon": [ [ - 129.392578125, + 129.09375, 182.3216552734375 ], [ @@ -116273,256 +182659,283 @@ 192.28424072265625 ], [ - 129.392578125, + 129.09375, 192.28424072265625 ] ], + "bbox": [ + 129.09375, + 182.3216552734375, + 255.13873291015625, + 192.28424072265625 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { "id": "/page/228/Code/4", "block_type": "Code", - "html": "def __init__(self, n=100):\n self.maps = []\n for i in range(n):\n self.maps.append(LinearMap())\ndef find_map(self, k):\n index = hash(k) % len(self.maps)\n return self.maps[index]\ndef add(self, k, v):\n m = self.find_map(k)\n m.add(k, v)\ndef get(self, k):\n m = self.find_map(k)\n return m.get(k)", + "html": "
def __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.", "polygon": [ [ - 148.0693359375, - 203.80078125 + 129.6000518798828, + 204.9609375 ], [ 346.04296875, - 203.80078125 + 204.9609375 ], [ 346.04296875, - 403.34765625 + 414.0838317871094 ], [ - 148.0693359375, - 403.34765625 + 129.6000518798828, + 414.0838317871094 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" - }, - "images": {} - }, - { - "id": "/page/228/Text/5", - "block_type": "Text", - "html": "
__init__ makes a list of n LinearMaps.
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", "polygon": [ [ - 128.794921875, - 421.91015625 + 129.392578125, + 422.2896728515625 ], [ - 526.833984375, - 421.91015625 + 525.638671875, + 422.2896728515625 ], [ - 526.833984375, - 444.5958251953125 + 525.638671875, + 444.7265625 ], [ - 128.794921875, - 444.5958251953125 + 129.392578125, + 444.7265625 ] ], + "bbox": [ + 129.392578125, + 422.2896728515625, + 525.638671875, + 444.7265625 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/228/Text/7", + "id": "/page/228/Text/6", "block_type": "Text", "html": "find_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.
", "polygon": [ [ - 128.794921875, - 451.6875 + 128.9443359375, + 452.8016662597656 ], [ - 527.431640625, - 451.6875 + 525.6033325195312, + 452.8016662597656 ], [ - 527.431640625, + 525.6033325195312, 487.3028259277344 ], [ - 128.794921875, + 128.9443359375, 487.3028259277344 ] ], + "bbox": [ + 128.9443359375, + 452.8016662597656, + 525.6033325195312, + 487.3028259277344 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/228/Text/8", + "id": "/page/228/Text/7", "block_type": "Text", "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.
", "polygon": [ [ - 128.49609375, - 494.2265625 + 128.3466796875, + 495.0 ], [ - 527.73046875, - 494.2265625 + 525.9375, + 495.0 ], [ - 527.73046875, + 525.9375, 517.8148193359375 ], [ - 128.49609375, + 128.3466796875, 517.8148193359375 ] ], + "bbox": [ + 128.3466796875, + 495.0, + 525.9375, + 517.8148193359375 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/228/Text/9", + "id": "/page/228/Text/8", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 128.3466796875, + 128.794921875, 525.55078125 ], [ - 526.53515625, + 525.638671875, 525.55078125 ], [ - 526.53515625, + 525.638671875, 584.9098205566406 ], [ - 128.3466796875, + 128.794921875, 584.9098205566406 ] ], + "bbox": [ + 128.794921875, + 525.55078125, + 525.638671875, + 584.9098205566406 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/228/Text/10", + "id": "/page/228/Text/9", "block_type": "Text", "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.
", "polygon": [ [ - 128.49609375, - 592.83984375 + 128.3466796875, + 593.1156616210938 ], [ - 526.53515625, - 592.83984375 + 525.6040649414062, + 593.1156616210938 ], [ - 526.53515625, - 627.64453125 + 525.6040649414062, + 627.6168212890625 ], [ - 128.49609375, - 627.64453125 + 128.3466796875, + 627.6168212890625 ] ], + "bbox": [ + 128.3466796875, + 593.1156616210938, + 525.6040649414062, + 627.6168212890625 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/228/Text/11", + "id": "/page/228/Text/10", "block_type": "Text", "html": "Here (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.
", "polygon": [ [ - 128.0478515625, - 635.37890625 + 127.7490234375, + 635.765625 ], [ - 526.53515625, - 635.37890625 + 525.6033935546875, + 635.765625 ], [ - 526.53515625, + 525.6033935546875, 682.5168380737305 ], [ - 128.0478515625, + 127.7490234375, 682.5168380737305 ] ], + "bbox": [ + 127.7490234375, + 635.765625, + 525.6033935546875, + 682.5168380737305 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/228/Text/12", + "id": "/page/228/Text/11", "block_type": "Text", "html": "Here is an implementation of a hashtable:
", "polygon": [ [ - 129.2431640625, + 128.27197265625, 690.6796875 ], [ @@ -116534,28 +182947,36 @@ 700.8348388671875 ], [ - 129.2431640625, + 128.27197265625, 700.8348388671875 ] ], + "bbox": [ + 128.27197265625, + 690.6796875, + 312.812255859375, + 700.8348388671875 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} } ], "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": null }, { - "id": "/page/229/Page/184", + "id": "/page/229/Page/185", "block_type": "Page", - "html": "208 Appendix B. Analysis of Algorithms
", + "html": "", "polygon": [ [ 86.4000015258789, - 59.748046875 + 60.134765625 ], [ - 483.50390625, - 59.748046875 + 482.4034423828125, + 60.134765625 ], [ - 483.50390625, + 482.4034423828125, 71.13372802734375 ], [ @@ -116597,46 +183024,60 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.134765625, + 482.4034423828125, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/229/PageHeader/9", + "id": "/page/229/PageHeader/10", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 84.79248046875, - 60.328125 + 85.6142578125, + 60.47314453125 ], [ - 99.73388671875, - 60.328125 + 102.0498046875, + 60.47314453125 ], [ - 99.73388671875, - 69.99609375 + 102.0498046875, + 70.04443359375 ], [ - 84.79248046875, - 69.99609375 + 85.6142578125, + 70.04443359375 ] ], + "bbox": [ + 85.6142578125, + 60.47314453125, + 102.0498046875, + 70.04443359375 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { "id": "/page/229/Code/1", "block_type": "Code", - "html": "class 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", + "html": "
class 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)", "polygon": [ [ 86.4000015258789, @@ -116648,85 +183089,142 @@ ], [ 311.3056945800781, - 379.1173095703125 + 369.31640625 ], [ 86.4000015258789, + 369.31640625 + ] + ], + "bbox": [ + 86.4000015258789, + 88.68572998046875, + 311.3056945800781, + 369.31640625 + ], + "children": null, + "section_hierarchy": { + "1": "/page/222/SectionHeader/1", + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" + }, + "images": {} + }, + { + "id": "/page/229/Code/2", + "block_type": "Code", + "html": "
self.maps = new_maps", + "polygon": [ + [ + 127.1513671875, + 369.123046875 + ], + [ + 232.8502655029297, + 369.123046875 + ], + [ + 232.8502655029297, + 379.1173095703125 + ], + [ + 127.1513671875, 379.1173095703125 ] ], + "bbox": [ + 127.1513671875, + 369.123046875, + 232.8502655029297, + 379.1173095703125 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/229/Text/2", + "id": "/page/229/Text/3", "block_type": "Text", "html": "
Each HashMap contains a BetterMap; __init__ starts with just 2 LinearMaps and initializes num, which keeps track of the number of items.
", "polygon": [ [ - 85.166015625, - 384.01171875 + 85.3154296875, + 384.978515625 ], [ - 482.90625, - 382.46484375 + 482.4005432128906, + 384.978515625 ], [ - 482.90625, + 482.4005432128906, 407.8648681640625 ], [ - 85.166015625, + 85.3154296875, 407.8648681640625 ] ], + "bbox": [ + 85.3154296875, + 384.978515625, + 482.4005432128906, + 407.8648681640625 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/229/Text/3", + "id": "/page/229/Text/4", "block_type": "Text", "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.
", "polygon": [ [ - 85.46484375, - 416.8828125 + 85.166015625, + 417.26953125 ], [ - 483.802734375, - 416.8828125 + 482.4026794433594, + 417.26953125 ], [ - 483.802734375, + 482.4026794433594, 452.6288757324219 ], [ - 85.46484375, + 85.166015625, 452.6288757324219 ] ], + "bbox": [ + 85.166015625, + 417.26953125, + 482.4026794433594, + 452.6288757324219 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/229/Text/4", + "id": "/page/229/Text/5", "block_type": "Text", "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.
", "polygon": [ [ - 85.166015625, + 85.46484375, 462.515625 ], [ @@ -116738,32 +183236,39 @@ 485.1978759765625 ], [ - 85.166015625, + 85.46484375, 485.1978759765625 ] ], + "bbox": [ + 85.46484375, + 462.515625, + 482.4041748046875, + 485.1978759765625 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/229/Text/5", + "id": "/page/229/Text/6", "block_type": "Text", "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?).
", "polygon": [ [ 85.46484375, - 493.83984375 + 495.0 ], [ - 484.1015625, - 493.83984375 + 482.4034118652344, + 495.0 ], [ - 484.1015625, + 482.4034118652344, 529.9618835449219 ], [ @@ -116771,28 +183276,35 @@ 529.9618835449219 ] ], + "bbox": [ + 85.46484375, + 495.0, + 482.4034118652344, + 529.9618835449219 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/229/Text/6", + "id": "/page/229/Text/7", "block_type": "Text", - "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!
", + "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!
", "polygon": [ [ 85.9130859375, 539.47265625 ], [ - 483.50390625, + 482.40411376953125, 539.47265625 ], [ - 483.50390625, + 482.40411376953125, 586.9188842773438 ], [ @@ -116800,82 +183312,104 @@ 586.9188842773438 ] ], + "bbox": [ + 85.9130859375, + 539.47265625, + 482.40411376953125, + 586.9188842773438 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/229/Text/7", + "id": "/page/229/Text/8", "block_type": "Text", "html": "To 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.
", "polygon": [ [ - 85.6142578125, + 86.0625, 596.3203125 ], [ - 483.50390625, + 482.90625, 596.3203125 ], [ - 483.50390625, + 482.90625, 668.2659072875977 ], [ - 85.6142578125, + 86.0625, 668.2659072875977 ] ], + "bbox": [ + 86.0625, + 596.3203125, + 482.90625, + 668.2659072875977 + ], "children": null, "section_hierarchy": { "1": "/page/222/SectionHeader/1", - "3": "/page/227/SectionHeader/5" + "2": "/page/225/SectionHeader/1", + "4": "/page/227/SectionHeader/5" }, "images": {} }, { - "id": "/page/229/Text/8", + "id": "/page/229/Text/9", "block_type": "Text", "html": "The 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.
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} }, { - "id": "/page/230/Caption/3", + "id": "/page/230/Caption/2", "block_type": "Caption", - "html": "Figure B.1: The cost of a hashtable add.
", + "html": "Figure B.1: The cost of a hashtable add.
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", + "html": "After 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).
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", + "html": "Figure B.1 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.
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} }, { "id": "/page/233/Caption/4", "block_type": "Caption", - "html": "Figure C.2: Stack diagram.
", + "html": "Figure C.2: Stack diagram.
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from swampy.Lumpy import Lumpy
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message = 'And now for something completely different'\nn = 17\npi = 3.1415926535897932", "polygon": [ [ - 84.8671875, - 316.2886962890625 + 86.13720703125, + 313.62890625 ], [ 368.7734069824219, - 316.2886962890625 + 313.62890625 ], [ 368.7734069824219, 350.6402893066406 ], [ - 84.8671875, + 86.13720703125, 350.6402893066406 ] ], + "bbox": [ + 86.13720703125, + 313.62890625, + 368.7734069824219, + 350.6402893066406 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/232/SectionHeader/11" + "4": "/page/232/SectionHeader/11" }, "images": {} }, @@ -118123,26 +184822,32 @@ "html": "
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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:
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from Lumpy import Lumpy
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", + "html": "Figure C.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.
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+ "/page/234/Figure/1": 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at6sHxv8A8iH4h/7Btx/6Lat8L/Hh6r8xS2Zqab/yCrP/AK4J/wCgirVVdN/5BVn/ANcE/wDQRVqsp/ExoKKKKkAooooAKKKKACiiigAooooAK5jxP/yMXhH/ALCUn/pPLXT1zHif/kYvCP8A2EpP/SeWurB/xflL/wBJZMtjp6KKK5Sgqhrf/ICv/wDr3f8Akav1Q1v/AJAV/wD9e7/yNZV/4UvR/kYYn+BP0f5FyH/UR/7o/lT6ZD/qI/8AdH8qq6rftpmny3a2slwI1LMqMowAMknJHp2ya0jsjWPwou0Uincob1GaZPPHbQtLM4RF6k0NqKu9i0m3ZElFU4tShklWNkmhZ/uebGVDfQ1cqYVIVFeDuOUJR0kjN0H/AJBKf9dZv/RjVpVm6D/yCU/66zf+jGrSrPDfwYei/I5cH/u9P/CvyCiiitzoCioL25+x2FxdeWZPJiaTYvVsDOB+VZqeIA+gafqwsLlxerEVhjKb0MmNoO5gOpA60AbNFZ9tq0c0dxJc28titvgu1yyBcEZyGViMfjUdrrlpdT3gWa3NtbIkn2lZlZCrbup6DBU96ANSiqQ1nSzaNdjUrM2ytsM3nrsDemc4zTrXVNPvpDHZ31rcOqhisMyuQD0OAelAFuiszSNYGrTalGLaSH7DdtakuQfMIAO4e3zCtOgAooooAKKKKACsHxv/AMiH4h/7Btx/6Lat6sHxv/yIfiH/ALBtx/6Lat8L/Hh6r8xS2Zqab/yCrP8A64J/6CKtVV03/kFWf/XBP/QRVqsp/ExoKKKKkAooooAKKKKACiiigAooooAK5jxP/wAjF4R/7CUn/pPLXT1zHif/AJGLwj/2EpP/AEnlrqwf8X5S/wDSWTLY6eiiiuUoKoa3/wAgK/8A+vd/5Gr9UNb/AOQFf/8AXu/8jWVf+FL0f5GGJ/gT9H+Rch/1Ef8Auj+VUdct7y70e5tbGOB5Z42j/fymNVBBGchWz9MVeh/1Ef8Auj+VPrSOyNY/CiG0+0fZk+0xxRygYZYpC6j6EqP5VW1NWC282wvHDMHkVRk4wRnHfGQfwq/RUVaftIOJpCXLK5j393DqNutrZSiSd3VlZBny8EHcfTpVr7He/wDQTk/78p/hV0ADoAM0tYrDc0nOo9X2bS09H5mjq2SjFaedn+hg6La3b6YpTUHRfNl+URIf+WjeorQ+x3v/AEE5P+/Kf4VHoP8AyCU/66zf+jGrSrPD4eDowd3svtS7epy4OrJYant8K6Lt6FH7He/9BOT/AL8p/hR9jvf+gnJ/35T/AAq9RW31aHd/+BS/zOn2svL7l/kRxxssISV/NOMMzKBu/AcVma/p09/pkVpaW9tIqzRO0c0hjXYjBsAqremOla9FbpWVjJu+pzaaRqQsUtxb2MMNrJFJa2yzvIrbSSwZigI6jHBwRTW03W3vNSvI0s7Z7uOBR5U53/Ix3AsUxkq2A3b9a6aimByS6Fqu29HkWu24ltpFWa+llI8twWBZkJ5A4/ya0NP0i7tNSjnZbcRBrotsc7iJJA6nG3rwQefzrdooAaqIhbaqruOWwMZPqadRRQAUUUUAFFFFABWD43/5EPxD/wBg24/9FtW9WD43/wCRD8Q/9g24/wDRbVvhf48PVfmKWzNTTf8AkFWf/XBP/QRVqqum/wDIKs/+uCf+girVZT+JjQUUUVIBRRRQAUUUUAFFFFABRRRQAVzHif8A5GLwj/2EpP8A0nlrp65jxP8A8jF4R/7CUn/pPLXVg/4vyl/6SyZbHT0UUVylBVDW/wDkBX//AF7v/I1fqhrf/ICv/wDr3f8Akayr/wAKXo/yMMT/AAJ+j/IuQ/6iP/dH8qfTIf8AUR/7o/lT60jsjWPwoKKKKZQUUUUAZug/8glP+us3/oxq0qzdB/5BKf8AXWb/ANGNWlWGG/gw9F+Rz4P/AHen/hX5BRRRW50BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWD43/wCRD8Q/9g24/wDRbVvVg+N/+RD8Q/8AYNuP/RbVvhf48PVfmKWzNTTf+QVZ/wDXBP8A0EVaqrpv/IKs/wDrgn/oIq1WU/iY0FFFFSAUUUUAFFFFABRRRQAUUUUAFcx4n/5GLwj/ANhKT/0nlrp65jxP/wAjF4R/7CUn/pPLXVg/4vyl/wCksmWx09FFFcpQVQ1v/kBX/wD17v8AyNX6oa3/AMgK/wD+vd/5Gsq/8KXo/wAjDE/wJ+j/ACLkP+oj/wB0fyp9Mh/1Ef8Auj+VPrSOyNY/CgoooplBRRRQBm6D/wAglP8ArrN/6MatKs3Qf+QSn/XWb/0Y1aVYYb+DD0X5HPg/93p/4V+QUUUVudAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVg+N/+RD8Q/8AYNuP/RbVvVg+N/8AkQ/EP/YNuP8A0W1b4X+PD1X5ilszU03/AJBVn/1wT/0EVaqrpv8AyCrP/rgn/oIq1WU/iY0FFFFSAUUUUAFFFFABRRRQAUUUUAFcx4n/AORi8I/9hKT/ANJ5a6euY8T/APIxeEf+wlJ/6Ty11YP+L8pf+ksmWx09FFFcpQVQ1v8A5AV//wBe7/yNX6oa3/yAr/8A693/AJGsq/8ACl6P8jDE/wACfo/yLkP+oj/3R/KoL24uIFjW2tTcSyNgAvsReM5ZsHA+gNTw/wCoj/3R/Ks7W4dUuII4dNMCq7fv2eZo32eiEK2CfXsOnPI0jsjWPwotaderqFmJxG0Z3MjoSDtZWKkZHXkHmpp54raFpZnCovUmoNNilgsI4Zbe3tymVWK3cuirnjkqCePamamrBbeYI0iQTCR1UZOMEZx3xnP4VnWm4U3KP9f8Ma04qUkmLFqcMkqRvHNCz8J5sZUN7A+vtV2se+u4dSt1tbKXzJndSGUZ8rBB3H06Va+xXn/QUm/79p/hXPTxE22kudd1b7t/y7ms6UUk37r7O/8AkR6D/wAglP8ArrN/6MatKsHRbS6fTFKahKg82X5RGh/5aN6itD7Fef8AQUm/79p/hRh6s1Rh+7ey/l7epx4OEfq1P3l8K79vQvUVR+xXn/QUm/79p/hR9ivP+gpN/wB+0/wrX20/+fb/APJf/kjp5I/zL8f8ie9ufsdhcXXlmTyYmk2L1bAzgflVC312KbQbDVGt5gb2ONorZcGQs4yF6gZx3JA4NaUcbLCI5HMpxgswA3fgOKp6lZSyx2slmIhLaSiSONyVRhtKlcgHHDHnB7VundXMmLb6oJBcG6tLiwWBQztdbApBzyGViOMc88U9NV06S2+0pqFq0G8J5qzKV3HoM5xnnpWMdH1Kf7RNIVUmWKaK2mu3mTKMSQSR8oORjAOMA+1S3GlX99HqM0sdtBPcRRxxxJIWUlCWBZto5OccA4A70wNkXtqzzoLmEtb8zASDMfGfm9OPWqdrrlre6i9tbSwTRLD5vnxTB164IOOnY9azptL1XUJb+S4is4RPHAIoxM0mTG5ba/yjg5xxn8aVrDWptVuNQWKxtZJLE26Mspd1cHKk5QAjJ/D3zgAGzaalY35cWd7bXOz73kyq+364PFVtM1galf6rai2kiOn3AgLsQRISivke2GFUNK0rUbbWxeToghNuYm330k8m7cDn5lAA9hj+ldAqIpYqqgscsQOp6c0AOooooAKKKKACsHxv/wAiH4h/7Btx/wCi2rerB8b/APIh+If+wbcf+i2rfC/x4eq/MUtmamm/8gqz/wCuCf8AoIq1VXTf+QVZ/wDXBP8A0EVarKfxMaCiiipAKKKKACiiigAooooAKKKKACuY8T/8jF4R/wCwlJ/6Ty109cx4n/5GLwj/ANhKT/0nlrqwf8X5S/8ASWTLY6eiiiuUoKoa3/yAr/8A693/AJGr9VdSt3u9MurePG+WJkXPTJGKzrJunJLszHERcqMkt7P8ieH/AFEf+6P5U+stJdZSNV+wWhwAM/aT/wDEU77RrP8Az4Wn/gSf/iKhV422f3P/ACIjiYpLR/8AgMv8jSorN+0az/z4Wn/gSf8A4ij7RrP/AD4Wn/gSf/iKPbx7P7n/AJD+sw7P/wABl/kaVFZv2jWf+fC0/wDAk/8AxFH2jWf+fC0/8CT/APEUe3j2f3P/ACD6zDs//AZf5BoP/IJT/rrN/wCjGrSqjpFtNaaakM4US7nZghyBuct1/Gr1PDpqjBPsvyHhYuNCCe9l+QUUUVsbhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWD43/5EPxD/ANg24/8ARbVvVg+N/wDkQ/EP/YNuP/RbVvhf48PVfmKWzNTTf+QVZ/8AXBP/AEEVaqrpv/IKs/8Argn/AKCKtVlP4mNBRRRUgFFFFABRRRQAUUUUAFFFFABXMeJ/+Ri8I/8AYSk/9J5a6euY8T/8jF4R/wCwlJ/6Ty11YP8Ai/KX/pLJlsdPRRRXKUFFFFABRUEF5BczTRRl/MhYLIrRsuM9OoGR7jinxzxyySxoSWiYK4KkYJAP48EdKAJKKRmCKWYgKBkk9AKFZXRXRgysMgg5BFAC0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVg+N/wDkQ/EP/YNuP/RbVvVg+N/+RD8Q/wDYNuP/AEW1b4X+PD1X5ilszU03/kFWf/XBP/QRVqqum/8AIKs/+uCf+girVZT+JjQUUUVIBRRRQAUUUUAFFFFABRRRQAVzHif/AJGLwj/2EpP/AEnlrp65jxP/AMjF4R/7CUn/AKTy11YP+L8pf+ksmWx09FFFcpQUUVQ1v/kBX/8A17v/ACNRUlyQcuxFWfJBz7K5WsXu/wC2715dNuYoZQgSV3iKnaD2Dk9/SotOF3DdamP7Mu4YpW8yEvNGQTsAIGHJGTntirEPh/STDGTYxElR60//AIR/Sf8Anwi/Ks1Ks18K+9//ACJip4hq/Iv/AAJ//ImLpek3Xl3lvNbTrDLamPdcPhmf/a2uwY+rgL+PbV8PwC1sVtxp9xa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} }, { "id": "/page/234/Caption/2", "block_type": "Caption", - "html": "Figure C.3: Object diagram.
", + "html": "Figure C.3: Object diagram.
", "polygon": [ [ - 265.060546875, - 230.677734375 + 264.1640625, + 230.87109375 ], [ - 389.970703125, - 230.677734375 + 388.4765625, + 230.87109375 ], [ - 389.970703125, - 241.505859375 + 388.4765625, + 240.95196533203125 ], [ - 265.060546875, - 241.505859375 + 264.1640625, + 240.95196533203125 ] ], + "bbox": [ + 264.1640625, + 230.87109375, + 388.4765625, + 240.95196533203125 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/233/SectionHeader/16" + "2": "/page/233/SectionHeader/16" }, "images": {} } ], "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/233/SectionHeader/16" + "2": "/page/233/SectionHeader/16" }, "images": null }, { "id": "/page/234/Code/3", "block_type": "Code", - "html": "from swampy.Lumpy import Lumpy", + "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()", "polygon": [ [ - 129.01904296875, + 129.59996032714844, 262.67877197265625 ], [ @@ -118600,90 +185395,73 @@ ], [ 286.5208435058594, - 272.830078125 - ], - [ - 129.01904296875, - 272.830078125 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/232/SectionHeader/1", - "3": "/page/233/SectionHeader/16" - }, - "images": {} - }, - { - "id": "/page/234/Code/4", - "block_type": "Code", - "html": "
def countdown(n):\n if n <= 0:\n print 'Blastoff!'\n lumpy.object_diagram()\n else:\n print n\n countdown(n-1)", - "polygon": [ - [ - 129.59996032714844, - 286.55859375 - ], - [ - 286.875, - 286.55859375 - ], - [ - 286.875, - 380.14453125 + 406.77838134765625 ], [ 129.59996032714844, - 380.14453125 + 406.77838134765625 ] ], + "bbox": [ + 129.59996032714844, + 262.67877197265625, + 286.5208435058594, + 406.77838134765625 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/233/SectionHeader/16" + "2": "/page/233/SectionHeader/16" }, "images": {} }, { - "id": "/page/234/TextInlineMath/5", - "block_type": "TextInlineMath", - "html": "
lumpy = Lumpy() lumpy.make_reference() countdown(3)
", + "id": "/page/234/Text/4", + "block_type": "Text", + "html": "countdown(3)
", "polygon": [ [ - 128.12255859375, - 384.6217956542969 + 129.5419921875, + 405.66796875 ], [ - 244.6779327392578, - 384.6217956542969 + 205.7431640625, + 405.66796875 ], [ - 244.6779327392578, + 205.7431640625, 418.973388671875 ], [ - 128.12255859375, + 129.5419921875, 418.973388671875 ] ], + "bbox": [ + 129.5419921875, + 405.66796875, + 205.7431640625, + 418.973388671875 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/233/SectionHeader/16" + "2": "/page/233/SectionHeader/16" }, "images": {} }, { - "id": "/page/234/Text/6", + "id": "/page/234/Text/5", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ 128.6455078125, - 423.45703125 + 423.84375 ], [ 525.9375, - 423.45703125 + 423.84375 ], [ 525.9375, @@ -118694,198 +185472,245 @@ 459.1739501953125 ] ], + "bbox": [ + 128.6455078125, + 423.84375, + 525.9375, + 459.1739501953125 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/233/SectionHeader/16" + "2": "/page/233/SectionHeader/16" }, "images": {} }, { - "id": "/page/234/Text/7", + "id": "/page/234/Text/6", "block_type": "Text", "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.
", "polygon": [ [ - 128.197265625, + 128.49609375, 468.703125 ], [ - 526.236328125, + 525.9375, 468.703125 ], [ - 526.236328125, - 503.5078125 + 525.9375, + 503.1959533691406 ], [ - 128.197265625, - 503.5078125 + 128.49609375, + 503.1959533691406 ] ], + "bbox": [ + 128.49609375, + 468.703125, + 525.9375, + 503.1959533691406 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/233/SectionHeader/16" + "2": "/page/233/SectionHeader/16" }, "images": {} }, { - "id": "/page/234/Text/8", + "id": "/page/234/Text/7", "block_type": "Text", "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.
", "polygon": [ [ - 128.197265625, - 512.015625 + 128.794921875, + 512.7178039550781 ], [ - 526.833984375, - 512.015625 + 526.236328125, + 512.7178039550781 ], [ - 526.833984375, + 526.236328125, 547.2189636230469 ], [ - 128.197265625, + 128.794921875, 547.2189636230469 ] ], + "bbox": [ + 128.794921875, + 512.7178039550781, + 526.236328125, + 547.2189636230469 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/233/SectionHeader/16" + "2": "/page/233/SectionHeader/16" }, "images": {} }, { - "id": "/page/234/SectionHeader/9", + "id": "/page/234/SectionHeader/8", "block_type": "SectionHeader", - "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.
", + "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.
", "polygon": [ [ - 127.8984375, + 128.197265625, 601.734375 ], [ - 525.9375, + 525.638671875, 601.734375 ], [ - 525.9375, - 626.484375 + 525.638671875, + 624.9375 ], [ - 127.8984375, - 626.484375 + 128.197265625, + 624.9375 ] ], + "bbox": [ + 128.197265625, + 601.734375, + 525.638671875, + 624.9375 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": {} }, { - "id": "/page/234/Code/11", - "block_type": "Code", - "html": "from swampy.Lumpy import Lumpy", + "id": "/page/234/Text/10", + "block_type": "Text", + "html": "
from swampy.Lumpy import Lumpy
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214 Appendix C. Lumpy
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- } + "images": {} }, { - "id": "/page/235/Text/2", - "block_type": "Text", - "html": "Figure C.4: Object diagram.
", + "id": "/page/235/FigureGroup/156", + "block_type": "FigureGroup", + "html": "Figure C.4: Object diagram.
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", + "id": "/page/235/Code/3", + "block_type": "Code", + "html": "numbers = [17, 123]\nempty = []", "polygon": [ [ - 85.6142578125, - 345.533203125 + 85.46484375, + 346.28875732421875 ], [ 185.7769317626953, - 345.533203125 + 346.28875732421875 ], [ 185.7769317626953, 368.4453430175781 ], [ - 85.6142578125, + 85.46484375, 368.4453430175781 ] ], + "bbox": [ + 85.46484375, + 346.28875732421875, + 185.7769317626953, + 368.4453430175781 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": {} }, { - "id": "/page/235/Text/4", - "block_type": "Text", - "html": "
lumpy.object_diagram()
", + "id": "/page/235/Code/4", + "block_type": "Code", + "html": "lumpy.object_diagram()", "polygon": [ [ - 86.2119140625, - 381.884765625 + 85.39013671875, + 382.8515625 ], [ 201.4779815673828, - 381.884765625 + 382.8515625 ], [ 201.4779815673828, 392.8343505859375 ], [ - 86.2119140625, + 85.39013671875, 392.8343505859375 ] ], + "bbox": [ + 85.39013671875, + 382.8515625, + 201.4779815673828, + 392.8343505859375 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": {} }, { "id": "/page/235/Text/5", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 85.6142578125, - 397.546875 + 85.46484375, + 397.93359375 ], [ - 483.50390625, - 397.546875 + 482.90625, + 397.93359375 ], [ - 483.50390625, - 445.5 + 482.90625, + 445.00091552734375 ], [ - 85.6142578125, - 445.5 + 85.46484375, + 445.00091552734375 ] ], + "bbox": [ + 85.46484375, + 397.93359375, + 482.90625, + 445.00091552734375 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": {} }, { "id": "/page/235/Text/6", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 85.763671875, - 454.39453125 + 85.6142578125, + 454.0078125 ], [ - 483.802734375, - 454.39453125 + 482.40325927734375, + 454.0078125 ], [ - 483.802734375, - 478.37109375 + 482.40325927734375, + 476.6009216308594 ], [ - 85.763671875, - 478.37109375 + 85.6142578125, + 476.6009216308594 ] ], + "bbox": [ + 85.6142578125, + 454.0078125, + 482.40325927734375, + 476.6009216308594 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": {} }, @@ -119145,26 +186069,33 @@ "html": "from swampy.Lumpy import Lumpy
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+ "/page/236/Figure/1": 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FwBweT0wamutXs7OQRyPK8hTfthgeUhfUhAcD3PoarXWkXVxPqjreQol7arbopgJMZG75id43ffPGB9aqXnhk3Fyl0r2rzmBIZfPhYqducMoDgjqeMntQBo3Wu6bZx20k1xiO6/1DrGzrIcZABAIyR0HU9quW9wl1As0YkCt0EkbI34qwBH4ismbRbsRaZHZ3VnbrYtu2mzZlY7WXgCQbR8x45+tbSBgihyC2OSBgE/SgCvd3ZtzHHHEZZ5M7EBx06knsKjivJluUgu4BE0mfLZH3KxHOOgwaS8jmju4byCMylFZHjBAJU4ORnuCKglifVpYVltpIrWJt58w7WdsEADByBz1rgqVKqm1Fu91ZW0a0vd29eunbv0wjBxV9ur6/df8Ar8rGsf8AIEv/APr2k/8AQTUXh3/kWdJ/684f/QBVfVtIsl0a+YRvkW8hH71/7p96i0DSbN/Dmlu0b7mtIif3rj+Ae9a81fn+Ffe//kTW1P6tu/i7eXqb9FUf7Hsf+ecn/f5/8aP7Hsf+ecn/AH+f/Gq5q/8AKv8AwJ//ACJzWpd393/BL1YHhj/j41//ALCsn/ouOpL3T7bzEtLZHFxJyW81z5ad26/gKh8KoI5NdQEkLqjgEnJ/1cdRCrOdTlkkrdnfXtsjqhCMaE2nul+a82blzcR2ltLcTFhFEpdyqliAOvABJ/CoI9UtJZ7aFHkL3MRmi/cuAUGOScYHUcHB5qzLIsULyOGKqMkKhYkewGSfwrD8MWnlx3NwROF8xobZJ4WjaOBSSq4YA9zz6YHaus4DfJAGTwKzlv7udPOtrIPb/wAJaTazj1Ax/Or8iCSNkPRgQazYLi6s7ZLV7GWWSNQiPHjY4HAOSeK5cROSkldpa6pX1+5/8H89qUU09E35/wBIv21wl1bpNHnaw7jBHqDWKP8AkoR/7BQ/9GmrdtosAhzcqWncl3KyMBknPABrIGl2n/CeGLY+z+zA2PNbr5p75qFPEOEXKKvp1f8Alp+J1UI0k6lm9n0/4J1dFUf7Hsf+ecn/AH+f/Gj+x7H/AJ5yf9/n/wAa15q/8q/8Cf8A8iclqXd/d/wS9QTgZNUf7Hsf+ecn/f5/8amt7G3tSxhVhuGDl2b+ZqoyrN+9FW9X/khNQto393/BMzT9Xu7iezeeKJba/Vmt9md6YGQG7HK88YxjHPWtusaw0We1ntfOu0ltrJWW2RYirAHgbjk5wvHAHrV2CC/TU7qaa8jksnVBBbiHa0RH3iXz82foMVsZmfBrF1JcwSvFELG4uXto8Z8xSu4BiehBKnjHGRya3KxYNEmiuoQ12jWUFw9xFEIiH3Nu4LZwQCxxx6enO1QAVy+rf8lF8Nf9ed9/7QrqK5fVv+Si+Gv+vO+/9oV1YP8AiP8Awy/9JZMtjqKKKK5SgooooAKKKKACiiigArl4v+Sp3X/YFh/9Hy11FcvF/wAlTuv+wLD/AOj5a6sNtU/wv80S+h1FFFFcpQVgeJ/9boP/AGFYv/QXrfrK13SJdXt7VYLw2k1tcrcJL5Qk5UEYwT71FRNx0OjCTjCsnJ2Wv5GrRWB/ZXiP/oZk/wDBen/xVH9leI/+hmT/AMF6f/FUc7/lf4f5lfV6f/P2P/k3/wAib9FYH9leI/8AoZk/8F6f/FUf2V4j/wChmT/wXp/8VRzv+V/h/mH1en/z9j/5N/8AIm/RWB/ZXiP/AKGZP/Ben/xVH9leI/8AoZk/8F6f/FUc7/lf4f5h9Xp/8/Y/+Tf/ACJv0Vgf2V4j/wChmT/wXp/8VR/ZXiP/AKGZP/Ben/xVHO/5X+H+YfV6f/P2P/k3/wAib9FYH9leI/8AoZk/8F6f/FUf2V4j/wChmT/wXp/8VRzv+V/h/mH1en/z9j/5N/8AImlrH/IEv/8Ar2k/9BNReHf+RZ0n/rzh/wDQBWbcaH4gubaWCTxMuyVCjY09M4Iwf4q29PtBYaba2YfeLeFIgxGN20AZx+FJXcrtWKqezhQ5IzUne+l+3mkWagu7pbSAyEFmJ2og6ux6AVMzKiFmICqMknsKz7RWvbgX8oIjAIt0PYd2Puf5VFabVoQ+J/h5/wBdTnhFfFLZf1YmsbVoEaSYhrmU7pWH6AewrL8Mf8fGv/8AYVk/9Fx1v1geGP8Aj41//sKyf+i46cYKnyxidFOTlSqt9l+aN+iiitjjCiiigArAH/JQj/2Ch/6NNb9YA/5KEf8AsFD/ANGmon09Tqwv2/8ACzfoooqzlCiiigAooooAKKKKACuX1b/kovhr/rzvv/aFdRXL6t/yUXw1/wBed9/7Qrqwf8R/4Zf+ksmWx1FFFFcpQUUUUAFFFFABRRRQAVy8X/JU7r/sCw/+j5a6iuXi/wCSp3X/AGBYf/R8tdWG2qf4X+aJfQ6iiiiuUoKKKgvLuKws5bucsIolLOUQsQB7Dk0AT0VUtNStr0yLGZEeMBmSaJo2AOcHDAccHn2qeOeGWHzY5Y3i5+dWBHHvQBJRVWbUrKC3S4kuohC7qiuGyCzHAGR71KbiBYPPM0YhxnzCw2/nQBLRUT3VvHGJHniWMjcGZwAR65pXnhiRXklRFYgBmYAEnpQBJRVS61CO2WF/LeWOWVYt8ZUhCSFGckHqccZqeSeKJkWSVELnagZgNx9B60ASUUUUAFFFU764kUpa2x/0mbof7i92P+etRUqKnHmZUIuTsiGc/wBpXRtV/wCPWI/vyP427J/U/lWl0GBUVtbx2tukMYO1e56k9yfepaijTcbyn8T3/wAvRf8AB6lTknpHZf1cKwPDH/Hxr/8A2FZP/Rcdb9YHhj/j41//ALCsn/ouOrl8SNqP8Gp6L80b9FFFWcoUUUUAFYA/5KEf+wUP/RprfrAH/JQj/wBgof8Ao01E+nqdWF+3/hZv0UUVZyhRRRQAUUUUAFFFFABXL6t/yUXw1/1533/tCuorl9W/5KL4a/6877/2hXVg/wCI/wDDL/0lky2OoooorlKCiiigAooooAKKKKACuXi/5Kndf9gWH/0fLXUVy8X/ACVO6/7AsP8A6Plrqw21T/C/zRL6HUUUUVylBWN4puoLbw7drPc29v56GBHuJDGm5gerAEjv27Vs1geJ/wDW6D/2FYv/AEF6mbajdG+GpxqVVGWxzE2qaG1rdwQ+IbILdJGxaW7aV0dSCY9zqd0Z569Mng5xSRazon2a4Mmsac0kksTmKS5UpKEPRtkSgZ9cN0GemK9HopWn3X3f8Evnwv8AJL/wJf8AyJwM3iDw5c2Mx+06JDO1xFP5az7hIUKn5jsGCQMdDUl94s0i606CKLU9Lt9suXhjuB93BxhjGQDnB4H0Nd1RRafdfd/wQ58L/JL/AMCX/wAiec2Os6LGumJd6po0q2txPIxNwWIV923b+7AyN3PTpQPEWhWlpaPNqmizNDHJCYHmZo1DNkFTsJzgYxt/H19GootPuvu/4InPDdIS/wDAl/8AInnVt4l0qLwvpVlbalpIeHyXkE1w0WCrBsABG64PWk1XW9Hvb57ka5p7LPbrE8P2hAEIznDNExIOe2OnQ9vRqKLT7r7v+CPnwv8AJL/wJf8AyJzkHjTwzFbxRnXLQlECkmXcTgeuOfrUn/Cb+GP+g3Z/9/K36QkKCSQAOSTRafdfd/wQ58L/ACS/8CX/AMic9N478MxQu41i1cqMhVfk+1aeloHtxfM6yS3SiTepyNpGVA9sGqF+DqGm3164PkR28ot1Pf5Tlz/T2q54d/5FnSf+vOH/ANAFc1JurNTlstv8/wDLy9TWtClGjemmnezu7+dtl8/+AaVFFFdhwBWB4Y/4+Nf/AOwrJ/6LjrfrA8Mf8fGv/wDYVk/9Fx1EviR1Uf4NT0X5o36KZLKsMLyvu2opY7VLHA9AOT9BVKz1vT7+aGK2meR5YBcJiJwNhOASSMDnscH2qzlNCigkAEk4ArOW+vJ08+2s1e36rvk2s49QMfzrKpWjTsn17Jv8i4wctjRrAH/JQj/2Ch/6NNbVtcJdW6TR52t2I5B7g1ij/koR/wCwUP8A0aaHJSUZLZnRhk06if8AKzfooorU5AoooJwMmgAorE0/V7u4ns3niiW2v1ZrfZnemBkBuxyvPGMYxz1rboAKKw4NYupLmCV4ohY3Fy9tHjPmKV3AMT0IJU8Y4yOTW5QAVy+rf8lF8Nf9ed9/7QrqK5fVv+Si+Gv+vO+/9oV1YP8AiP8Awy/9JZMtjqKxPFLNFpcEyeWJY7y38tpGwqkyKOT6cmtuoLqytb6NY7u2huEVtwWWMOAfXB71ylGNcavf2Elxa3BtZ5wsJjlRGjRfMfYN6lmIweevPtU6XmopcXVjNPZNcJb+fHOImVF5Iw6FycZGc7ueemKvQ6Xp9vby28NlbpDL/rI1jG1+3I78etMTRtMjtJrRdPtRbzf62LylKyf7wxz+NAGToetXesm4+0NDbQxQgMAhWRmPWVCTjyj/AAnHPPpUmh28H257mwQw2DRbEyTm5YH/AFuD+W48tnPTGdNtH0xn3tp1oW8n7PkwLnyv7nT7vt0pbTSNMsJTLZ6daW0hXaXhgVCR6ZA6cCgCzNIIYZJSCQiliB7Cs6G1ubq3S5e+mjmkUOojxsTPIGMc/jWmQCCCMg9RWeun3USeRb3xjt+iqYwzIPQNn+YrkxEJSknZtdk7a991/wAA2pSST1s/P+mMtdage3Uz7xKMq4SF2GQccECufj1S1/4Wdcy5l2nRoV/1L5z50nbGa7C3gjtoEhiGEQYFc3F/yVO6/wCwLD/6PlrrwUMQqc1KSvy9n3Xn/kTUlScrpPfv/wAA2v7Ys/Wb/vw/+FH9sWfrN/34f/Cr1Fc/LX/mX/gL/wDkh3p9n9//AACj/bFn6zf9+H/wrG1+8hu5tEERf5dVizujZf4X9QK6esDxP/rdB/7CsX/oL0pRrJe9JNej/wA2dGEcPbKyfXr5PyN+s2617TrOSRJpZf3RCyOkEjohOOGZVKg8jqe4rSrIGm6hbzXC2d7bpbz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+ "/page/236/Figure/3": 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} }, { "id": "/page/236/Caption/4", "block_type": "Caption", - "html": "Figure C.6: Object diagram.
", + "html": "Figure C.6: Object diagram.
", "polygon": [ [ - 263.267578125, - 355.974609375 + 265.95703125, + 356.94140625 ], [ 388.3171081542969, - 355.974609375 + 356.94140625 ], [ 388.3171081542969, 367.3749084472656 ], [ - 263.267578125, + 265.95703125, 367.3749084472656 ] ], + "bbox": [ + 265.95703125, + 356.94140625, + 388.3171081542969, + 367.3749084472656 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": {} } ], "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": null }, { "id": "/page/236/Code/5", "block_type": "Code", - "html": "lumpy = 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", + "html": "
lumpy = 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)", "polygon": [ [ - 127.599609375, - 390.392578125 + 128.57080078125, + 391.5007629394531 ], [ - 245.337890625, - 390.392578125 + 244.6779327392578, + 391.5007629394531 ], [ - 245.337890625, - 510.46875 + 244.6779327392578, + 531.3515625 ], [ - 127.599609375, - 510.46875 + 128.57080078125, + 531.3515625 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" - }, - "images": {} - }, - { - "id": "/page/236/Text/6", - "block_type": "Text", - "html": "
box2 = copy.copy(box)
", - "polygon": [ - [ - 128.794921875, - 512.7890625 - ], - [ - 239.44757080078125, - 512.7890625 - ], - [ - 239.44757080078125, - 523.4073791503906 - ], - [ - 128.794921875, - 523.4073791503906 - ] + "bbox": [ + 128.57080078125, + 391.5007629394531, + 244.6779327392578, + 531.3515625 ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": {} }, { - "id": "/page/236/Text/7", - "block_type": "Text", - "html": "lumpy.object_diagram()
", + "id": "/page/236/Code/6", + "block_type": "Code", + "html": "lumpy.object_diagram()", "polygon": [ [ - 127.8984375, - 537.15234375 + 129.5419921875, + 537.8327941894531 ], [ 244.6779327392578, - 537.15234375 + 537.8327941894531 ], [ 244.6779327392578, - 547.7953948974609 + 548.3671875 ], [ - 127.8984375, - 547.7953948974609 + 129.5419921875, + 548.3671875 ] ], + "bbox": [ + 129.5419921875, + 537.8327941894531, + 244.6779327392578, + 548.3671875 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/234/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/234/SectionHeader/8" }, "images": {} }, { - "id": "/page/236/Text/8", + "id": "/page/236/Text/7", "block_type": "Text", - "html": "
Figure C.5 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.
", + "html": "Figure C.5 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.
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+ "/page/237/Figure/1": 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} }, { "id": "/page/237/Caption/2", "block_type": "Caption", - "html": "Figure C.7: Class diagram.
", + "html": "Figure C.7: Class diagram.
", "polygon": [ [ - 225.1669921875, - 197.7099609375 + 225.615234375, + 197.9033203125 ], [ 343.65234375, - 197.7099609375 + 197.9033203125 ], [ 343.65234375, - 208.828125 + 208.0546875 ], [ - 225.1669921875, - 208.828125 + 225.615234375, + 208.0546875 ] ], + "bbox": [ + 225.615234375, + 197.9033203125, + 343.65234375, + 208.0546875 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/236/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/236/SectionHeader/8" }, "images": {} } ], "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/236/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/236/SectionHeader/8" }, "images": null }, { "id": "/page/237/Text/3", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 85.763671875, - 230.291015625 + 85.0166015625, + 230.677734375 ], [ 482.43389892578125, - 230.291015625 + 230.677734375 ], [ 482.43389892578125, 265.5469970703125 ], [ - 85.763671875, + 85.0166015625, 265.5469970703125 ] ], + "bbox": [ + 85.0166015625, + 230.677734375, + 482.43389892578125, + 265.5469970703125 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/236/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/236/SectionHeader/8" }, "images": {} }, @@ -119997,7 +187088,7 @@ "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", "polygon": [ [ - 86.39998626708984, + 85.0166015625, 272.07879638671875 ], [ @@ -120006,17 +187097,24 @@ ], [ 306.07977294921875, - 477.15142822265625 + 484.9453125 ], [ - 86.39998626708984, - 477.15142822265625 + 85.0166015625, + 484.9453125 ] ], + "bbox": [ + 85.0166015625, + 272.07879638671875, + 306.07977294921875, + 484.9453125 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/236/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/236/SectionHeader/8" }, "images": {} }, @@ -120026,55 +187124,69 @@ "html": "
point = instantiate(Point)", "polygon": [ [ - 85.39013671875, - 490.74609375 + 86.361328125, + 491.1328125 ], [ - 222.39939880371094, - 490.74609375 + 224.12109375, + 491.1328125 ], [ - 222.39939880371094, - 501.57421875 + 224.12109375, + 501.5394287109375 ], [ - 85.39013671875, - 501.57421875 + 86.361328125, + 501.5394287109375 ] ], + "bbox": [ + 86.361328125, + 491.1328125, + 224.12109375, + 501.5394287109375 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/236/SectionHeader/9" + "2": "/page/233/SectionHeader/16", + "4": "/page/236/SectionHeader/8" }, "images": {} }, { "id": "/page/237/Text/6", "block_type": "Text", - "html": "
Figure C.6 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.
", + "html": "Figure C.6 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.
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} }, { "id": "/page/238/Caption/2", "block_type": "Caption", - "html": "Figure C.8: Class diagram.
", + "html": "Figure C.8: Class diagram.
", "polygon": [ [ - 268.646484375, - 322.6893005371094 + 266.853515625, + 322.330078125 ], [ - 385.95587158203125, - 322.6893005371094 + 386.384765625, + 322.330078125 ], [ - 385.95587158203125, - 333.158203125 + 386.384765625, + 332.6518859863281 ], [ - 268.646484375, - 333.158203125 + 266.853515625, + 332.6518859863281 ] ], + "bbox": [ + 266.853515625, + 322.330078125, + 386.384765625, + 332.6518859863281 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} } ], "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": null }, @@ -120378,36 +187552,42 @@ "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.
", "polygon": [ [ - 128.794921875, - 352.6875 + 128.49609375, + 353.267578125 ], [ - 526.236328125, - 352.6875 + 525.6034545898438, + 353.267578125 ], [ - 526.236328125, + 525.6034545898438, 412.51287841796875 ], [ - 128.794921875, + 128.49609375, 412.51287841796875 ] ], + "bbox": [ + 128.49609375, + 353.267578125, + 525.6034545898438, + 412.51287841796875 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { "id": "/page/238/Text/4", "block_type": "Text", - "html": "Here's an example that shows a HAS-A relationship. You can download it from http: //thinkpython.com/code/lumpy_demo7.py.
", + "html": "Here's an example that shows a HAS-A relationship. You can download it from http: //thinkpython.com/code/lumpy_demo7.py.
", "polygon": [ [ - 129.392578125, + 128.49609375, 421.13671875 ], [ @@ -120416,176 +187596,247 @@ ], [ 525.6057739257812, - 446.66015625 + 443.89886474609375 ], [ - 129.392578125, - 446.66015625 + 128.49609375, + 443.89886474609375 ] ], + "bbox": [ + 128.49609375, + 421.13671875, + 525.6057739257812, + 443.89886474609375 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/238/Text/5", - "block_type": "Text", - "html": "from swampy.Lumpy import Lumpy
", + "id": "/page/238/Code/5", + "block_type": "Code", + "html": "from swampy.Lumpy import Lumpy", "polygon": [ [ - 128.9443359375, + 128.49609375, 449.0077209472656 ], [ - 286.5209045410156, + 286.875, 449.0077209472656 ], [ - 286.5209045410156, - 459.421875 + 286.875, + 459.03515625 ], [ - 128.9443359375, - 459.421875 + 128.49609375, + 459.03515625 ] ], + "bbox": [ + 128.49609375, + 449.0077209472656, + 286.875, + 459.03515625 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/238/Code/6", - "block_type": "Code", - "html": "
lumpy = 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", + "id": "/page/238/Text/6", + "block_type": "Text", + "html": "
lumpy = Lumpy() lumpy.make_reference()
", "polygon": [ [ - 128.49609375, - 471.796875 + 128.72021484375, + 473.3957214355469 ], [ 244.67799377441406, - 471.796875 + 473.3957214355469 ], [ 244.67799377441406, + 495.5533142089844 + ], + [ + 128.72021484375, + 495.5533142089844 + ] + ], + "bbox": [ + 128.72021484375, + 473.3957214355469, + 244.67799377441406, + 495.5533142089844 + ], + "children": null, + "section_hierarchy": { + "1": "/page/232/SectionHeader/1", + "2": "/page/237/SectionHeader/9" + }, + "images": {} + }, + { + "id": "/page/238/Code/7", + "block_type": "Code", + "html": "box = Rectangle()\nbox.width = 100.0\nbox.height = 200.0\nbox.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0", + "polygon": [ + [ + 128.57080078125, + 509.9787292480469 + ], + [ + 234.21726989746094, + 509.9787292480469 + ], + [ + 234.21726989746094, 580.9133453369141 ], [ - 128.49609375, + 128.57080078125, 580.9133453369141 ] ], + "bbox": [ + 128.57080078125, + 509.9787292480469, + 234.21726989746094, + 580.9133453369141 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/238/Text/7", - "block_type": "Text", - "html": "
lumpy.class_diagram()
", + "id": "/page/238/Code/8", + "block_type": "Code", + "html": "lumpy.class_diagram()", "polygon": [ [ - 128.794921875, - 595.16015625 + 129.09375, + 595.3397521972656 ], [ 239.4476318359375, - 595.16015625 + 595.3397521972656 ], [ 239.4476318359375, - 605.98828125 + 605.3023529052734 ], [ - 128.794921875, - 605.98828125 + 129.09375, + 605.3023529052734 ] ], + "bbox": [ + 129.09375, + 595.3397521972656, + 239.4476318359375, + 605.3023529052734 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/238/Text/8", + "id": "/page/238/Text/9", "block_type": "Text", - "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.
", + "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.
", "polygon": [ [ - 128.6455078125, - 609.46875 + 129.5419921875, + 610.2421875 ], [ - 526.53515625, - 609.46875 + 525.9375, + 610.2421875 ], [ - 526.53515625, + 525.9375, 657.2549133300781 ], [ - 128.6455078125, + 129.5419921875, 657.2549133300781 ] ], + "bbox": [ + 129.5419921875, + 610.2421875, + 525.9375, + 657.2549133300781 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/238/Text/9", + "id": "/page/238/Text/10", "block_type": "Text", "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.
", "polygon": [ [ - 129.09375, - 665.15625 + 129.392578125, + 665.9296875 ], [ - 526.53515625, - 665.15625 + 525.638671875, + 665.9296875 ], [ - 526.53515625, + 525.638671875, 700.8349227905273 ], [ - 129.09375, + 129.392578125, 700.8349227905273 ] ], + "bbox": [ + 129.392578125, + 665.9296875, + 525.638671875, + 700.8349227905273 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} } ], "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": null }, { - "id": "/page/239/Page/85", + "id": "/page/239/Page/86", "block_type": "Page", - "html": "218 Appendix C. Lumpy
", + "html": "", "polygon": [ [ 86.4000015258789, - 60.521484375 + 60.8115234375 ], [ - 483.50390625, - 60.521484375 + 482.4034729003906, + 60.8115234375 ], [ - 483.50390625, + 482.4034729003906, 71.13372802734375 ], [ @@ -120627,278 +187884,233 @@ 71.13372802734375 ] ], + "bbox": [ + 86.4000015258789, + 60.8115234375, + 482.4034729003906, + 71.13372802734375 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/239/PageHeader/9", + "id": "/page/239/PageHeader/6", "block_type": "PageHeader", - "html": "", + "html": "", "polygon": [ [ - 85.0166015625, - 60.37646484375 + 84.568359375, + 60.95654296875 ], [ - 101.00390625, - 60.37646484375 + 102.3486328125, + 60.95654296875 ], [ - 101.00390625, - 70.33447265625 + 102.3486328125, + 70.52783203125 ], [ - 85.0166015625, - 70.33447265625 + 84.568359375, + 70.52783203125 ] ], + "bbox": [ + 84.568359375, + 60.95654296875, + 102.3486328125, + 70.52783203125 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { "id": "/page/239/Text/1", "block_type": "Text", - "html": "Here's a more complex example using my solution to Exercise 18.6. You can download the code from http://thinkpython.com/code/lumpy_demo8.py; you will also need http: //thinkpython.com/code/PokerHand.py.
", + "html": "Here's a more complex example using my solution to Exercise 18.6. You can download the code from http://thinkpython.com/code/lumpy_demo8.py; you will also need http: //thinkpython.com/code/PokerHand.py.
", "polygon": [ [ - 85.3154296875, - 87.73681640625 + 85.46484375, + 88.365234375 ], [ - 483.50390625, - 87.73681640625 + 482.40582275390625, + 88.365234375 ], [ - 483.50390625, + 482.40582275390625, 123.1868896484375 ], [ - 85.3154296875, + 85.46484375, 123.1868896484375 ] ], + "bbox": [ + 85.46484375, + 88.365234375, + 482.40582275390625, + 123.1868896484375 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { "id": "/page/239/Code/2", "block_type": "Code", - "html": "from swampy.Lumpy import Lumpy", + "html": "
from swampy.Lumpy import Lumpy\nfrom PokerHand import *\nlumpy = Lumpy()\nlumpy.make_reference()\ndeck = Deck()\nhand = PokerHand()\ndeck.move_cards(hand, 7)", "polygon": [ [ - 84.26953125, + 86.2119140625, 129.23968505859375 ], [ - 243.3208770751953, + 243.3955078125, 129.23968505859375 ], [ - 243.3208770751953, - 142.119140625 + 243.3955078125, + 261.615234375 ], [ - 84.26953125, - 142.119140625 + 86.2119140625, + 261.615234375 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" - }, - "images": {} - }, - { - "id": "/page/239/Text/3", - "block_type": "Text", - "html": "
from PokerHand import *
", - "polygon": [ - [ - 85.3154296875, - 151.8837890625 - ], - [ - 206.70834350585938, - 151.8837890625 - ], - [ - 206.70834350585938, - 163.59124755859375 - ], - [ - 85.3154296875, - 163.59124755859375 - ] + "bbox": [ + 86.2119140625, + 129.23968505859375, + 243.3955078125, + 261.615234375 ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/239/Text/4", - "block_type": "Text", - "html": "lumpy = Lumpy() lumpy.make_reference() deck = Deck()
", - "polygon": [ - [ - 84.49365234375, - 178.01763916015625 - ], - [ - 206.490234375, - 178.01763916015625 - ], - [ - 206.490234375, - 224.56317138671875 - ], - [ - 84.49365234375, - 224.56317138671875 - ] - ], - "children": null, - "section_hierarchy": { - "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" - }, - "images": {} - }, - { - "id": "/page/239/Code/5", + "id": "/page/239/Code/3", "block_type": "Code", - "html": "hand = PokerHand()\ndeck.move_cards(hand, 7)", + "html": "
lumpy.class_diagram()", "polygon": [ [ - 84.4189453125, - 226.79461669921875 + 86.40000915527344, + 263.3775634765625 ], [ - 212.765625, - 226.79461669921875 + 196.330078125, + 263.3775634765625 ], [ - 212.765625, - 249.8203125 + 196.330078125, + 273.796875 ], [ - 84.4189453125, - 249.8203125 + 86.40000915527344, + 273.796875 ] ], - "children": null, - "section_hierarchy": { - "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" - }, - "images": {} - }, - { - "id": "/page/239/Text/6", - "block_type": "Text", - "html": "
lumpy.class_diagram()
", - "polygon": [ - [ - 85.763671875, - 261.80859375 - ], - [ - 196.24761962890625, - 261.80859375 - ], - [ - 196.24761962890625, - 273.34014892578125 - ], - [ - 85.763671875, - 273.34014892578125 - ] + "bbox": [ + 86.40000915527344, + 263.3775634765625, + 196.330078125, + 273.796875 ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/239/Text/7", + "id": "/page/239/Text/4", "block_type": "Text", - "html": "Figure C.8 shows the result. PokerHand inherits from Hand, which inherits from Deck. Both Deck and PokerHand have Cards.
", + "html": "Figure C.8 shows the result. PokerHand inherits from Hand, which inherits from Deck. Both Deck and PokerHand have Cards.
", "polygon": [ [ - 85.46484375, - 277.470703125 + 86.0625, + 278.4375 ], [ - 484.69921875, - 277.470703125 + 482.90625, + 278.4375 ], [ - 484.69921875, + 482.90625, 301.8497314453125 ], [ - 85.46484375, + 86.0625, 301.8497314453125 ] ], + "bbox": [ + 86.0625, + 278.4375, + 482.90625, + 301.8497314453125 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} }, { - "id": "/page/239/Text/8", + "id": "/page/239/Text/5", "block_type": "Text", "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.
", "polygon": [ [ - 85.9130859375, - 311.115234375 + 86.361328125, + 311.30859375 ], [ - 484.1015625, - 311.115234375 + 483.50390625, + 311.30859375 ], [ - 484.1015625, + 483.50390625, 346.375732421875 ], [ - 85.9130859375, + 86.361328125, 346.375732421875 ] ], + "bbox": [ + 86.361328125, + 311.30859375, + 483.50390625, + 346.375732421875 + ], "children": null, "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": {} } ], "section_hierarchy": { "1": "/page/232/SectionHeader/1", - "3": "/page/237/SectionHeader/9" + "2": "/page/237/SectionHeader/9" }, "images": null } diff --git a/data/examples/markdown/multicolcnn/_page_1_Figure_0.jpeg b/data/examples/markdown/multicolcnn/_page_1_Figure_0.jpeg index 9a0faead..87f9ab8d 100644 Binary files a/data/examples/markdown/multicolcnn/_page_1_Figure_0.jpeg and b/data/examples/markdown/multicolcnn/_page_1_Figure_0.jpeg differ diff --git a/data/examples/markdown/multicolcnn/_page_2_Picture_0.jpeg b/data/examples/markdown/multicolcnn/_page_2_Picture_0.jpeg index bf048033..e67f0317 100644 Binary files a/data/examples/markdown/multicolcnn/_page_2_Picture_0.jpeg and b/data/examples/markdown/multicolcnn/_page_2_Picture_0.jpeg differ diff --git a/data/examples/markdown/multicolcnn/_page_6_Figure_0.jpeg b/data/examples/markdown/multicolcnn/_page_6_Figure_0.jpeg index ab475233..7976b848 100644 Binary files a/data/examples/markdown/multicolcnn/_page_6_Figure_0.jpeg and b/data/examples/markdown/multicolcnn/_page_6_Figure_0.jpeg differ diff --git a/data/examples/markdown/multicolcnn/_page_7_Figure_0.jpeg b/data/examples/markdown/multicolcnn/_page_7_Figure_0.jpeg index d234c6bd..4fed315e 100644 Binary files a/data/examples/markdown/multicolcnn/_page_7_Figure_0.jpeg and b/data/examples/markdown/multicolcnn/_page_7_Figure_0.jpeg differ diff --git a/data/examples/markdown/multicolcnn/multicolcnn.md b/data/examples/markdown/multicolcnn/multicolcnn.md index 55bd9749..37fa7c33 100644 --- a/data/examples/markdown/multicolcnn/multicolcnn.md +++ b/data/examples/markdown/multicolcnn/multicolcnn.md @@ -2,236 +2,227 @@ Diptodip Deb Georgia Institute of Technology diptodipdeb@gatech.edu -# Jonathan Ventura University of Colorado Colorado Springs - -jventura@uccs.edu - # Abstract *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.* # 1. Introduction -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. +Learning 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. -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]). +Lempitsky 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)). However, in many counting problems, such as those -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. +Jonathan Ventura University of Colorado Colorado Springs jventura@uccs.edu + +> 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 [\[18\]](#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. -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]. +> The 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). -Following on the intuition that multiscale integration is key to achieving good counting performance, we propose to incorporate dilated filters [25] into a multicolumn convolutional neural network design [28]. 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, 26]. 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. +> Following 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. # 2. Related Work -Counting using a supervised regressor to formulate a density map was first shown by [15]. 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 +Counting 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 ![](_page_1_Figure_0.jpeg) -Figure 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]. 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). +Figure 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). -our 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] introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches. +our 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. -These issues are addressed by the work of [18]. 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]. 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]. +These 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). -However, the architectures of both [18] and [27] are not fully convolutional due to requiring multiple image patches and, as discussed in [25], the experiments of [11, 17] and [9, 12, 16] 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] 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], 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]. We build on these approaches with our aggregator module as described in Section 3.1, which should allow for extracting information from more scales. +However, 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. -It 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] and using CNNs (convolutional neural networks) along with boosting in order to improve the results +It 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 ![](_page_2_Picture_0.jpeg) Figure 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. -of regression for production of density maps [24]. In the same spirit, [4] combines deep and shallow convolutions within the same network, providing accurate counting of dense objects (e.g. the UCF50 crowd dataset). +of 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). -In this paper, however, we aim to apply the dilated convolution method of [25], 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, 28] to aggregate multiscale information for the counting problem. +In 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. # 3. Method -### 3.1. Dilated Convolutions for Multicolumn Networks +## 3.1. Dilated Convolutions for Multicolumn Networks -We propose the use of dilated convolutions as an attractive alternative to the architecture of the HydraCNN [18], 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]. 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. +We 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 network[1](#page-2-1) , 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. -Dilated convolutions, as discussed in [25], allow for the exponential increase of the receptive field with a linear increase in the number of parameters with respect to each hidden layer. +Dilated 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. -In a traditional 2D convolution, we define a real valued function F : Z 2 → R, an input Ωr = [−r, r] 2 ∈ Z 2 , and a filter function k : Ωr → R. In this case, a convolution operation as defined in [25] is given by +In a traditional 2D convolution, we define a real valued function F : Z 2 → R, an input Ωr = [−r, r] 2 ∈ Z 2 , and a filter function k : Ωr → R. In this case, a convolution operation as defined in [\[25\]](#page-8-5) is given by -$$(F*k)({\bf p})=\sum_{{\bf s}+{\bf t}={\bf p}}F({\bf s})k({\bf t}).\tag{1}$$ +$$(F*k)(\mathbf{p}) = \sum_{\mathbf{s}+\mathbf{t}=\mathbf{p}} F(\mathbf{s})k(\mathbf{t}). \quad (1)$$ -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 +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 [\[25\]](#page-8-5) the dilated convolution as -$$(F*_{l}k)({\bf p})=\sum_{{\bf s}+l{\bf t}={\bf p}}F({\bf s})k({\bf t})\tag{2}$$ +$$(F*_{l}k)(\mathbf{p})=\sum_{\mathbf{s}+l\mathbf{t}=\mathbf{p}}F(\mathbf{s})k(\mathbf{t})\tag{2}$$ where l is the index of the current layer of the convolution. -Using 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] and [8], 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. +Using 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. -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. +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 [\[28\]](#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.](#page-5-0) -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 +The 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 -1 Implementation available on https://github.com/ diptodip/counting. +1 Implementation available on [https://github.com/](https://github.com/diptodip/counting) [diptodip/counting](https://github.com/diptodip/counting). training, we use a scaled mean absolute error for our loss function: -$$L=\frac{1}{n}\sum_{i=1}^{n}|\hat{y}_{i}-\gamma y_{i}|\tag{3}$$ +$$L = \frac{1}{n} \sum_{i=1}^{n} |\hat{y}_i - \gamma y_i| \qquad\qquad (3)$$ -where γ is the scale factor, yˆi is the prediction, yi 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 γ −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 γ = 255. +where γ is the scale factor, $\hat{y}_i $is the prediction, $y_i $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 $γ^{-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 $γ = 255. $ -### 3.2. Experiments +## 3.2. Experiments -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. +We evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data [\[18\]](#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. -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. +We 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. -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. +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 [\[10\]](#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. -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. +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,](#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) #### 3.2.1 UCF50 Crowd Counting 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. -We take 1600 random patches of size 150 × 150 for the training. For testing, we do not densely scan the image as in [18] 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 × 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 σ = 15. +We take 1600 random patches of size 150 × 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 × 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 σ = 15. -#### 3.2.2 TRANCOS Traffic Counting +#### 3.2.2 TRANCOS Traffic Counting -TRANCOS is a traffic counting dataset that comes with its own metric [14]. 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]. More formally, we define +TRANCOS 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 -$$GAME(L)=\frac{1}{N}\cdot\sum_{n=1}^{N}\left(\sum_{l=1}^{4^{L}}\lvert e_{n}^{l}-t_{n}^{l}\rvert\right)\tag{4}$$ +$$GAME(L) = \frac{1}{N} \cdot \sum_{n=1}^{N} \left( \sum_{l=1}^{4^L} |e_n^l - t_n^l| \right) \tag{4}$$ -where N refers to the number of images, L is the level parameter for GAME, e l n is the predicted or estimated count in region l of image n and t l n is the ground truth count in region l of image n [14]. +where N refers to the number of images, L is the level parameter for GAME, e l n is the predicted or estimated count in region l of image n and t l n is the ground truth count in region l of image n [\[14\]](#page-8-1). 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]. -#### 3.2.3 UCSD Crowd Counting +#### 3.2.3 UCSD Crowd Counting -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]. +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 [\[18\]](#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). -For this dataset, each object is annotated with a 2D Gaussian of covariance Σ = 8 · 12×2. The ground truth map is produced by summing these. When we make use of the perspective maps provided, we divide Σ 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] the best results as well as the split used in [28]. +For this dataset, each object is annotated with a 2D Gaussian of covariance Σ = 8 +· 12x2. The ground truth map is produced by summing these. When we make use of the perspective maps provided, we divide Σ 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] the best results as well as the split used in [28]. -First, we split the dataset into four separate groups of training and testing sets as used in [18] and originally defined by [20]. These groups are "upscale," "maximal," "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. +First, 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. -We 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. +We 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. #### 3.2.4 WorldExpo '10 Crowd Counting -The WorldExpo dataset [27] 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. +The 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. Without perspective maps, we generate label density maps for this dataset in the same manner as previously described: a 2D Gaussian with σ = 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. -When perspective maps are used, however, we follow the procedure as described in [27], 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]. 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: +When 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: -$$D_{\bf x}=\frac{1}{||Z||}({\cal N}_{h}({\bf x},\sigma_{h})+{\cal N}_{b}({\bf x}_{b},\Sigma_{b}))\qquad\qquad(5)$$ +$$D_{\bf x}=\frac{1}{||Z||}\left(\mathcal{N}_{h}(\bf x,\sigma_{h}) + \mathcal{N}_{b}(\bf x_{b},\Sigma_{b})\right)\tag{5}$$ -where xb 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]. We sum these Gaussians for each person to pro- +where $x_b$ 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]. We sum these Gaussians for each person to produce -| Method | MAE | -| --- | --- | -| AMDCN | 290.82 | +| Method | MAE | +|--------------|--------| +| AMDCN | 290.82 | | Hydra2s [18] | 333.73 | -| MCNN [28] | 377.60 | -| [27] | 467.00 | -| [23] | 295.80 | -| [3] | 318.10 | +| MCNN [28] | 377.60 | +| [27] | 467.00 | +| [23] | 295.80 | +| [3] | 318.10 | -Table 1. Mean absolute error of various methods on UCF crowds +Table 1. Mean absolute error of various methods on UCF crowds -duce the final density map. We set σ = 0.2M(x) for Nh and σx = 0.2M(x), σy = 0.5M(x) for Σb in Nb. +duce the final density map. We set σ = 0.2*M*(x) for *Nh* and σx = 0.2*M*(x), σy = 0.5*M*(x) for Σb in *Nb*. # 4. Results -### 4.1. UCF Crowd Counting +## 4.1. UCF Crowd Counting -The 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 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. +The 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. -We report a state of the art result on this dataset in Table 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]. 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. +We 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. -### 4.2. TRANCOS Traffic Counting +## 4.2. TRANCOS Traffic Counting -Our 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] across all levels. +Our 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](#page-5-2) shows that we achieve state of the art results as measured by the GAME metric [\[14\]](#page-8-1) across all levels. -### 4.3. UCSD Crowd Counting +## 4.3. UCSD Crowd Counting -Results are shown in Table 3 and Figure 3. We see that the "original" split as defined by the creators of the dataset in [5] and used in [28] 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 and Figure 3 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 +Results 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 -| Method | | GAME | GAME | GAME | GAME | -| --- | --- | --- | --- | --- | --- | -| | | (L=0) | (L=1) | (L=2) | (L=3) | -| AMDCN | | 9.77 | 13.16 | 15.00 | 15.87 | -| [18] | | 10.99 | 13.75 | 16.69 | 19.32 | -| [15] + | SIFT | 13.76 | 16.72 | 20.72 | 24.36 | -| from [14] | | | | | | -| [13] + | RGB | 17.68 | 19.97 | 23.54 | 25.84 | -| Norm + Filters | | | | | | -| from [14] | | | | | | -| HOG-2 | | 13.29 | 18.05 | 23.65 | 28.41 | -| from [14] | | | | | | +| Method | GAMEproperly if we don't do this + + i = 0 + stack = [] + result = [] + buffer = "" + + while i < len(text): + for delim, class_name in DELIMITERS: + if text[i:].startswith(delim): + if stack and stack[-1] == delim: # Closing + stack.pop() + result.append({"class": class_name, "content": buffer}) + buffer = "" + i += len(delim) + break + elif not stack: # Opening + if buffer: + result.append({"class": "text", "content": buffer}) + stack.append(delim) + buffer = "" + i += len(delim) + break + else: + raise ValueError(f"Nested {class_name} delimiters not supported") + else: # No delimiter match + buffer += text[i] + i += 1 + + if buffer: + result.append({"class": "text", "content": buffer}) + return result diff --git a/marker/processors/footnote.py b/marker/processors/footnote.py index 6dfd033b..41f350a2 100644 --- a/marker/processors/footnote.py +++ b/marker/processors/footnote.py @@ -1,34 +1,21 @@ -from statistics import mean +import re from marker.processors import BaseProcessor from marker.schema import BlockTypes -from marker.schema.blocks import Footnote from marker.schema.document import Document - -from rapidfuzz import fuzz - from marker.schema.groups import PageGroup class FootnoteProcessor(BaseProcessor): """ A processor for pushing footnotes to the bottom, and relabeling mislabeled text blocks. - - Attributes: - page_bottom_threshold (float): - The fraction of page height that is considered the bottom. - Default is .8 - - line_height_scaler (float): - The amount to scale line height by to consider a block a footnote. (from N to 1+(1-N)) - Default is .99 """ block_types = (BlockTypes.Footnote,) def __call__(self, document: Document): for page in document.pages: self.push_footnotes_to_bottom(page, document) - + self.assign_superscripts(page, document) def push_footnotes_to_bottom(self, page: PageGroup, document: Document): footnote_blocks = page.contained_blocks(document, self.block_types) @@ -39,4 +26,13 @@ def push_footnotes_to_bottom(self, page: PageGroup, document: Document): if block.id in page.structure: # Move to bottom if it is page.structure.remove(block.id) - page.add_structure(block) \ No newline at end of file + page.add_structure(block) + + def assign_superscripts(self, page: PageGroup, document: Document): + footnote_blocks = page.contained_blocks(document, self.block_types) + + for block in footnote_blocks: + for span in block.contained_blocks(document, (BlockTypes.Span,)): + if re.match(r"^[0-9\W]+", span.text): + span.has_superscript = True + break diff --git a/marker/processors/ignoretext.py b/marker/processors/ignoretext.py index 7873c313..d52ffcd8 100644 --- a/marker/processors/ignoretext.py +++ b/marker/processors/ignoretext.py @@ -1,7 +1,7 @@ import re from collections import Counter from itertools import groupby -from typing import List +from typing import Annotated, List from rapidfuzz import fuzz @@ -13,29 +13,39 @@ class IgnoreTextProcessor(BaseProcessor): """ - A processor for ignoring text blocks that are common elements in the document. - - Attributes: - common_element_threshold (float): - The minimum fraction of pages that a block must appear in to be considered a common element. - Default is 0.6. + A processor for identifying and ignoring common text blocks in a document. + These blocks often represent repetitive or non-essential elements, such as headers, footers, or page numbers. """ block_types = ( - BlockTypes.Text, BlockTypes.PageHeader, - BlockTypes.PageFooter, BlockTypes.SectionHeader, + BlockTypes.Text, BlockTypes.SectionHeader, BlockTypes.TextInlineMath ) - common_element_threshold = .20 - common_element_min_blocks = 3 - max_streak = 3 # The maximum number of blocks in a row to consider a common element - text_match_threshold = 90 + common_element_threshold: Annotated[ + float, + "The minimum ratio of pages a text block must appear on to be considered a common element.", + "Blocks that meet or exceed this threshold are marked as common elements.", + ] = 0.2 + common_element_min_blocks: Annotated[ + int, + "The minimum number of occurrences of a text block within a document to consider it a common element.", + "This ensures that rare blocks are not mistakenly flagged.", + ] = 3 + max_streak: Annotated[ + int, + "The maximum number of consecutive occurrences of a text block allowed before it is classified as a common element.", + "Helps to identify patterns like repeated headers or footers.", + ] = 3 + text_match_threshold: Annotated[ + int, + "The minimum fuzzy match score (0-100) required to classify a text block as similar to a common element.", + "Higher values enforce stricter matching.", + ] = 90 def __call__(self, document: Document): first_blocks = [] last_blocks = [] for page in document.pages: initial_block = None - block = None last_block = None for block in page.contained_blocks(document, self.block_types): if block.structure is not None: @@ -55,8 +65,8 @@ def __call__(self, document: Document): @staticmethod def clean_text(text): text = text.replace("\n", "").strip() - text = re.sub(r"^\d+\s*", "", text) # remove numbers at the start of the line - text = re.sub(r"\s*\d+$", "", text) # remove numbers at the end of the line + text = re.sub(r"^\d+\s*", "", text) # remove numbers at the start of the line + text = re.sub(r"\s*\d+$", "", text) # remove numbers at the end of the line return text def filter_common_elements(self, document, blocks: List[Block]): @@ -74,7 +84,7 @@ def filter_common_elements(self, document, blocks: List[Block]): common = [ k for k, v in counter.items() if (v >= len(blocks) * self.common_element_threshold or streaks[k] >= self.max_streak) - and v > self.common_element_min_blocks + and v > self.common_element_min_blocks ] if len(common) == 0: return diff --git a/marker/processors/line_numbers.py b/marker/processors/line_numbers.py index eea7be75..d38d9131 100644 --- a/marker/processors/line_numbers.py +++ b/marker/processors/line_numbers.py @@ -1,13 +1,29 @@ +from typing import Annotated + from marker.processors import BaseProcessor from marker.schema import BlockTypes from marker.schema.document import Document class LineNumbersProcessor(BaseProcessor): + """ + A processor for ignoring line numbers. + """ block_types = (BlockTypes.Text, BlockTypes.TextInlineMath) - strip_numbers_threshold: int = .6 - min_lines_in_block: int = 4 - min_line_length: int = 10 + strip_numbers_threshold: Annotated[ + float, + "The fraction of lines or tokens in a block that must be numeric to consider them as line numbers.", + ] = 0.6 + min_lines_in_block: Annotated[ + int, + "The minimum number of lines required in a block for it to be considered during processing.", + "Ensures that small blocks are ignored as they are unlikely to contain meaningful line numbers.", + ] = 4 + min_line_length: Annotated[ + int, + "The minimum length of a line (in characters) to consider it significant when checking for", + "numeric prefixes or suffixes. Prevents false positives for short lines.", + ] = 10 def __init__(self, config): super().__init__(config) @@ -27,11 +43,10 @@ def ignore_line_number_blocks(self, document: Document): tokens_are_numbers = [token.isdigit() for token in tokens] if all([ sum(tokens_are_numbers) / len(tokens) > self.strip_numbers_threshold, - block.polygon.height > block.polygon.width # Ensure block is taller than it is wide, like vertical page numbers + block.polygon.height > block.polygon.width # Ensure block is taller than it is wide, like vertical page numbers ]): block.ignore_for_output = True - def ignore_line_starts_ends(self, document: Document): for page in document.pages: for block in page.contained_blocks(document, self.block_types): @@ -57,7 +72,7 @@ def ignore_line_starts_ends(self, document: Document): len(raw_text) - len(spans[0].text.strip()) > self.min_line_length ]) - ends= all([ + ends = all([ spans[-1].text.strip().isdigit(), len(raw_text) - len(spans[-1].text.strip()) > self.min_line_length ]) @@ -76,4 +91,3 @@ def ignore_line_starts_ends(self, document: Document): if ends: span = page.get_block(line.structure[-1]) span.ignore_for_output = True - diff --git a/marker/processors/list.py b/marker/processors/list.py index ff394a4a..9d7105ee 100644 --- a/marker/processors/list.py +++ b/marker/processors/list.py @@ -1,4 +1,4 @@ -from typing import List +from typing import Annotated, List, Tuple from marker.processors import BaseProcessor from marker.schema import BlockTypes @@ -11,8 +11,14 @@ class ListProcessor(BaseProcessor): A processor for merging lists across pages and columns """ block_types = (BlockTypes.ListGroup,) - ignored_block_types = (BlockTypes.PageHeader, BlockTypes.PageFooter) - min_x_indent = 0.01 # % of page width + ignored_block_types: Annotated[ + Tuple[BlockTypes], + "The list of block types to ignore when merging lists.", + ] = (BlockTypes.PageHeader, BlockTypes.PageFooter) + min_x_indent: Annotated[ + float, "The minimum horizontal indentation required to consider a block as a nested list item.", + "This is expressed as a percentage of the page width and is used to determine hierarchical relationships within a list.", + ] = 0.01 def __init__(self, config): super().__init__(config) diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py index c81b92e5..c41853ac 100644 --- a/marker/processors/llm/__init__.py +++ b/marker/processors/llm/__init__.py @@ -1,5 +1,6 @@ +import traceback from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Optional +from typing import Annotated, Optional from tqdm import tqdm @@ -14,37 +15,35 @@ class BaseLLMProcessor(BaseProcessor): """ A processor for using LLMs to convert blocks. - Attributes: - google_api_key (str): - The Google API key to use for the Gemini model. - Default is None. - model_name (str): - The name of the Gemini model to use. - Default is "gemini-1.5-flash". - max_retries (int): - The maximum number of retries to use for the Gemini model. - Default is 3. - max_concurrency (int): - The maximum number of concurrent requests to make to the Gemini model. - Default is 3. - timeout (int): - The timeout for requests to the Gemini model. - gemini_rewriting_prompt (str): - The prompt to use for rewriting text. - Default is a string containing the Gemini rewriting prompt. - use_llm (bool): - Whether to use the LLM model. - Default is False. """ - - google_api_key: Optional[str] = settings.GOOGLE_API_KEY - model_name: str = "gemini-1.5-flash" - use_llm: bool = False - max_retries: int = 3 - max_concurrency: int = 3 - timeout: int = 60 - image_expansion_ratio: float = 0.01 - gemini_rewriting_prompt = None + google_api_key: Annotated[ + str, + "The Google API key to use for the Gemini model.", + ] = settings.GOOGLE_API_KEY + model_name: Annotated[ + str, + "The name of the Gemini model to use.", + ] = "gemini-1.5-flash" + max_retries: Annotated[ + int, + "The maximum number of retries to use for the Gemini model.", + ] = 3 + max_concurrency: Annotated[ + int, + "The maximum number of concurrent requests to make to the Gemini model.", + ] = 3 + timeout: Annotated[ + int, + "The timeout for requests to the Gemini model.", + ] = 60 + image_expansion_ratio: Annotated[ + float, + "The ratio to expand the image by when cropping.", + ] = 0.01 + use_llm: Annotated[ + bool, + "Whether to use the LLM model.", + ] = False block_types = None def __init__(self, config=None): @@ -69,6 +68,11 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block): raise NotImplementedError() def rewrite_blocks(self, document: Document): + # Don't show progress if there are no blocks to process + total_blocks = sum(len(page.contained_blocks(document, self.block_types)) for page in document.pages) + if total_blocks == 0: + return + pbar = tqdm(desc=f"{self.__class__.__name__} running") with ThreadPoolExecutor(max_workers=self.max_concurrency) as executor: for future in as_completed([ @@ -81,10 +85,5 @@ def rewrite_blocks(self, document: Document): pbar.close() - def extract_image(self, page: PageGroup, image_block: Block): - page_img = page.lowres_image - image_box = image_block.polygon\ - .rescale(page.polygon.size, page_img.size)\ - .expand(self.image_expansion_ratio, self.image_expansion_ratio) - cropped = page_img.crop(image_box.bbox) - return cropped \ No newline at end of file + def extract_image(self, document: Document, image_block: Block): + return image_block.get_image(document, highres=True, expansion=(self.image_expansion_ratio, self.image_expansion_ratio)) diff --git a/marker/processors/llm/llm_complex.py b/marker/processors/llm/llm_complex.py index 2ac5dab0..52c46364 100644 --- a/marker/processors/llm/llm_complex.py +++ b/marker/processors/llm/llm_complex.py @@ -12,9 +12,9 @@ class LLMComplexRegionProcessor(BaseLLMProcessor): block_types = (BlockTypes.ComplexRegion,) - gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images. + complex_region_prompt = """You are a text correction expert specializing in accurately reproducing text from images. You will receive an image of a text block and the text that can be extracted from the image. -Your task is to correct any errors in the text, and format it properly. +Your 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. Formatting should be in markdown, with the following rules: - * for italics, ** for bold, and ` for inline code. @@ -29,27 +29,32 @@ class LLMComplexRegionProcessor(BaseLLMProcessor): **Instructions:** 1. Carefully examine the provided block image. -2. Analyze the text representation -3. If the text representation is largely correct, then write "No corrections needed." -4. If the text representation contains errors, generate the corrected markdown representation. -5. Output only either the corrected markdown representation or "No corrections needed." +2. Analyze the existing text representation. +3. Generate the markdown representation of the content in the image. **Example:** Input: ```text -This is an example text block. +Table 1: Car Sales ``` Output: ```markdown -No corrections needed. +## Table 1: Car Sales + +| Car | Sales | +| --- | --- | +| Honda | 100 | +| Toyota | 200 | ``` **Input:** +```text +{extracted_text} +``` """ def process_rewriting(self, document: Document, page: PageGroup, block: Block): text = block.raw_text(document) - - prompt = self.gemini_rewriting_prompt + '```text\n`' + text + '`\n```\n' - image = self.extract_image(page, block) + prompt = self.complex_region_prompt.replace("{extracted_text}", text) + image = self.extract_image(document, block) response_schema = content.Schema( type=content.Type.OBJECT, enum=[], @@ -79,4 +84,5 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block): return # Convert LLM markdown to html + corrected_markdown = corrected_markdown.strip().lstrip("```markdown").rstrip("```").strip() block.html = markdown2.markdown(corrected_markdown) \ No newline at end of file diff --git a/marker/processors/llm/llm_equation.py b/marker/processors/llm/llm_equation.py new file mode 100644 index 00000000..74cfc4a3 --- /dev/null +++ b/marker/processors/llm/llm_equation.py @@ -0,0 +1,91 @@ +from marker.processors.llm import BaseLLMProcessor + +from google.ai.generativelanguage_v1beta.types import content + +from marker.schema import BlockTypes +from marker.schema.blocks import Equation +from marker.schema.document import Document +from marker.schema.groups.page import PageGroup + +from typing import Annotated + + +class LLMEquationProcessor(BaseLLMProcessor): + block_types = (BlockTypes.Equation,) + min_equation_height: Annotated[ + float, + "The minimum ratio between equation height and page height to consider for processing.", + ] = 0.08 + equation_image_expansion_ratio: Annotated[ + float, + "The ratio to expand the image by when cropping.", + ] = 0.05 # Equations sometimes get bboxes that are too tight + equation_latex_prompt: Annotated[ + str, + "The prompt to use for generating LaTeX from equations.", + "Default is a string containing the Gemini prompt." + ] = """You're an expert mathematician who is good at writing LaTeX code and html for equations. +You'll receive an image of a math block that 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, and fenced by delimiters. + +Some guidelines: +- Output valid html, where all the equations can render properly. +- Use