{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Untitled25.ipynb", "provenance": [], "authorship_tag": "ABX9TyNJdlLdL56gLSWYqG177B0A", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "TQUe8ZZed-Bp" }, "source": [ "Run in colab by clicking the link above to view the results as a paginated table with word counts for each chapter near the bottom of the notebook. Total word count at the very bottom." ] }, { "cell_type": "markdown", "metadata": { "id": "X7D8JEIgy9fV" }, "source": [ "This notebook counts through the translatable words of each chapter, including chapter links; ignores code blocks, markdown characters, and tokens containing digits." ] }, { "cell_type": "code", "metadata": { "id": "kT4wyWuy2szs" }, "source": [ "import os\n", "import pandas as pd\n", "import re" ], "execution_count": 1, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9J62Uh4H2Y9g", "outputId": "8d2c4675-ddf9-4cd5-8fb2-ee4cae9b034d" }, "source": [ "!git clone https://github.com/icculp/Learning-Bitcoin-from-the-Command-Line.git" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "fatal: destination path 'Learning-Bitcoin-from-the-Command-Line' already exists and is not an empty directory.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "k_g4-Fvn2yPy" }, "source": [ "def count_words():\n", " \"\"\" Counts words ignoring code blocks and digits\n", "\n", " To test for quality:\n", " lines 14-16 to test a single chapter\n", " uncomment line 45 to view accepted word tokens\n", " uncomment line \n", " \"\"\"\n", " columns=['Chapter', 'Word Count']\n", " counts = []\n", " repo_path = '/content/Learning-Bitcoin-from-the-Command-Line/'\n", " for chapter in os.listdir(repo_path):\n", " ''' uncomment lines 8-10 to test a single chapter, replacing ch_name\n", " with the name you want to test\n", " '''\n", " #ch_name = '03_2_Knowing_Your_Bitcoin_Setup.md'\n", " #if chapter != ch_name:\n", " # continue\n", " ignore_list = ['bitcoin.conf-annotated.txt', 'TODO.md', 'TODO-30.md']\n", " if chapter in ignore_list or\\\n", " not chapter.endswith('md'): # g\n", " continue\n", " count = 0\n", " flag = 0 # ignores words between code markdown ```\n", " with open(repo_path + chapter) as ch:\n", " for line in ch.readlines():\n", " if flag:\n", " if '```' in line[:3].replace(' ', ''):\n", " flag = 0\n", " continue\n", " continue\n", " if '```' in line:\n", " flag = 1\n", " continue\n", " for word in line.split():\n", " if '.md' in word:\n", " ch_link_tokens = word.split('_')\n", " if ']' in word: # indicates trailing link with chapter name; counts last word of trailing link before chapter name\n", " count += 1\n", " link_tokens_count = len(ch_link_tokens[2:]) # ignoring chapter numbers in chapter link tokens\n", " count += link_tokens_count\n", " # print(word, '[TOK]', link_tokens_count, end='[SEPTOK]')\n", " continue\n", " ignore = ['*', '**', '#', '##', '###', '####',\n", " '-', '—', '>', '`', '/', '&', '|', '~']\n", " if any(ch.isdigit() for ch in word) or\\\n", " word in ignore or\\\n", " '`' in word or\\\n", " '~/' in word or\\\n", " '/.' in word or\\\n", " '|-' in word or\\\n", " (word[0] == ':' and word[-1] == ':') or\\\n", " (word[0] == '\"' and word[-1] == '\"'):\n", " # print(word) # , end='[SEP]') # view rejected tokens\n", " continue\n", " # print(word, count) # , end='[SEP]') # view accepted tokens\n", " count += 1\n", " counts.append((chapter, count))\n", " # print(chapter, count)\n", " return pd.DataFrame(counts, columns=columns)" ], "execution_count": 3, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "2-pkEaYF3Uxm" }, "source": [ "chapter_word_counts = count_words()\n", "chapter_word_counts.sort_values(by=['Chapter'], inplace=True)\n", "# view accepted or rejected tokens below if line 53 or 51 uncommented in count_words(), respectively" ], "execution_count": 4, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 631 }, "id": "K73GH7UFmwI5", "outputId": "d1430339-8b1b-43f3-cdcf-1019948c7b9c" }, "source": [ "from google.colab import data_table\n", "data_table.DataTable(chapter_word_counts, include_index=False)" ], "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n window.createDataTable({\n data: [[\"01_0_Introduction.md\",\n{\n 'v': 1144,\n 'f': \"1144\",\n }],\n [\"01_1_Introducing_Bitcoin.md\",\n{\n 'v': 2735,\n 'f': \"2735\",\n }],\n [\"02_0_Setting_Up_a_Bitcoin-Core_VPS.md\",\n{\n 'v': 226,\n 'f': \"226\",\n }],\n [\"02_1_Setting_Up_a_Bitcoin-Core_VPS_with_StackScript.md\",\n{\n 'v': 2723,\n 'f': \"2723\",\n }],\n [\"02_2_Setting_Up_Bitcoin_Core_Other.md\",\n{\n 'v': 254,\n 'f': \"254\",\n }],\n [\"03_0_Understanding_Your_Bitcoin_Setup.md\",\n{\n 'v': 248,\n 'f': \"248\",\n }],\n [\"03_1_Verifying_Your_Bitcoin_Setup.md\",\n{\n 'v': 773,\n 'f': \"773\",\n }],\n [\"03_2_Knowing_Your_Bitcoin_Setup.md\",\n{\n 'v': 517,\n 'f': \"517\",\n }],\n [\"03_3_Setting_Up_Your_Wallet.md\",\n{\n 'v': 1699,\n 'f': \"1699\",\n }],\n [\"03_3__Interlude_Using_Command-Line_Variables.md\",\n{\n 'v': 347,\n 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ChapterWord Count
7201_0_Introduction.md1144
2101_1_Introducing_Bitcoin.md2735
2002_0_Setting_Up_a_Bitcoin-Core_VPS.md226
9702_1_Setting_Up_a_Bitcoin-Core_VPS_with_StackS...2723
9402_2_Setting_Up_Bitcoin_Core_Other.md254
.........
45A3_0_Using_Bitcoin_Regtest.md980
38CLA.md495
74CONTRIBUTING.md529
53LICENSE-CC-BY-4.0.md2716
2README.md1687
\n", "

101 rows × 2 columns

\n", "
" ] }, "metadata": { "tags": [] }, "execution_count": 5 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_gGsbGDckIrC", "outputId": "a9811107-2a07-464a-d96a-c6767e5ea7d4" }, "source": [ "total_count = chapter_word_counts['Word Count'].sum()\n", "total_count" ], "execution_count": 6, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "88215" ] }, "metadata": { "tags": [] }, "execution_count": 6 } ] }, { "cell_type": "markdown", "metadata": { "id": "14kdusmNzcfz" }, "source": [ "To convert the table to a markdown format and save as 'Chapter_word_counts.md', run the cells below" ] }, { "cell_type": "code", "metadata": { "id": "uc7eKo9TkidD" }, "source": [ "from IPython.display import Markdown, display\n", "from tabulate import tabulate\n", "\n", "\n", "# borrowed from https://stackoverflow.com/questions/33181846/programmatically-convert-pandas-dataframe-to-markdown-table\n", "\n", "def pandas_df_to_markdown_table(df):\n", " fmt = ['---' for i in range(len(df.columns))]\n", " df_fmt = pd.DataFrame([fmt], columns=df.columns)\n", " df_formatted = pd.concat([df_fmt, df])\n", " return Markdown(df_formatted.to_csv(sep=\"|\", index=False))\n", "\n", "def df_to_markdown(df, y_index=False):\n", " blob = tabulate(df, headers='keys', tablefmt='pipe')\n", " if not y_index:\n", " return '\\n'.join(['| {}'.format(row.split('|', 2)[-1]) for row in blob.split('\\n')])\n", " return blob" ], "execution_count": 7, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Y0QVAGKqzWX6" }, "source": [ "mkt = pandas_df_to_markdown_table(chapter_word_counts)\n", "\n", "with open('Chapter_word_counts.md', 'w') as m:\n", " m.write(str(mkt.data))" ], "execution_count": 8, "outputs": [] } ] }