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| # Project specific | ||
| data/models/** | ||
| write-up/resources/** | ||
| write-up/** | ||
|
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| *.bak | ||
| .gitattributes | ||
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#default_exp utils" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Utilities\n", | ||
| "\n", | ||
| "This notebook documents the general utility functions developed in this research\n", | ||
| "\n", | ||
| "<br>\n", | ||
| "\n", | ||
| "### Imports" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#exports\n", | ||
| "import json\n", | ||
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "\n", | ||
| "import junix\n", | ||
| "from html.parser import HTMLParser\n", | ||
| "from nbdev.export2html import convert_md\n", | ||
| "\n", | ||
| "import os\n", | ||
| "import codecs\n", | ||
| "from ipypb import track\n", | ||
| "from warnings import warn" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<br>\n", | ||
| "\n", | ||
| "### User Inputs" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "dev_nbs_dir = '../nbs'\n", | ||
| "docs_dir = '.'\n", | ||
| "ug_docs_dir = 'user-guide'\n", | ||
| "docs_nb_img_dir = f'{docs_dir}/img/nbs'" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<br>\n", | ||
| "\n", | ||
| "### Converting the Notebooks to Documentation\n", | ||
| "\n", | ||
| "We'll first convert the notebooks to markdown" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#exports\n", | ||
| "def convert_file_to_json(filepath):\n", | ||
| "\n", | ||
| " with open(filepath, 'r', encoding='utf8') as f:\n", | ||
| " contents = f.read()\n", | ||
| " f.close()\n", | ||
| "\n", | ||
| " return json.loads(contents)\n", | ||
| "\n", | ||
| "junix.exporter.convert_file_to_json = convert_file_to_json\n", | ||
| "\n", | ||
| "def encode_file_as_utf8(fp):\n", | ||
| " with codecs.open(fp, 'r') as file:\n", | ||
| " contents = file.read(1048576)\n", | ||
| " file.close()\n", | ||
| "\n", | ||
| " if not contents:\n", | ||
| " pass\n", | ||
| " else:\n", | ||
| " with codecs.open(fp, 'w', 'utf-8') as file:\n", | ||
| " file.write(contents)\n", | ||
| " \n", | ||
| "def convert_nbs_to_md(nbs_dir, docs_nb_img_dir, docs_dir):\n", | ||
| " nb_files = [f for f in os.listdir(nbs_dir) if f[-6:]=='.ipynb']\n", | ||
| "\n", | ||
| " for nb_file in track(nb_files):\n", | ||
| " nb_fp = f'{nbs_dir}/{nb_file}'\n", | ||
| " junix.export_images(nb_fp, docs_nb_img_dir)\n", | ||
| " convert_md(nb_fp, docs_dir, img_path=f'{docs_nb_img_dir}/', jekyll=False)\n", | ||
| "\n", | ||
| " md_fp = docs_dir + '/'+ nb_file.replace('.ipynb', '') + '.md'\n", | ||
| " encode_file_as_utf8(md_fp)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<div><span class=\"Text-label\" style=\"display:inline-block; overflow:hidden; white-space:nowrap; text-overflow:ellipsis; min-width:0; max-width:15ex; vertical-align:middle; text-align:right\"></span>\n", | ||
| "<progress style=\"width:60ex\" max=\"14\" value=\"14\" class=\"Progress-main\"/></progress>\n", | ||
| "<span class=\"Progress-label\"><strong>100%</strong></span>\n", | ||
| "<span class=\"Iteration-label\">14/14</span>\n", | ||
| "<span class=\"Time-label\">[00:15<00:00, 1.05s/it]</span></div>" | ||
| ], | ||
| "text/plain": [ | ||
| "\u001b[A\u001b[2K\r", | ||
| " [████████████████████████████████████████████████████████████] 14/14 [00:15<00:00, 1.05s/it]" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "convert_nbs_to_md(dev_nbs_dir, docs_nb_img_dir, docs_dir)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<br>\n", | ||
| "\n", | ||
| "We'll then parse the HTML tables into markdown" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#exports\n", | ||
| "class MyHTMLParser(HTMLParser):\n", | ||
| " def __init__(self):\n", | ||
| " super().__init__()\n", | ||
| " self.tags = []\n", | ||
| " \n", | ||
| " def handle_starttag(self, tag, attrs):\n", | ||
| " self.tags.append(self.get_starttag_text())\n", | ||
| "\n", | ||
| " def handle_endtag(self, tag):\n", | ||
| " self.tags.append(f\"</{tag}>\")\n", | ||
| " \n", | ||
| "get_substring_idxs = lambda string, substring: [num for num in range(len(string)-len(substring)+1) if string[num:num+len(substring)]==substring]\n", | ||
| "\n", | ||
| "def convert_df_to_md(df):\n", | ||
| " idx_col = df.columns[0]\n", | ||
| " df = df.set_index(idx_col)\n", | ||
| " \n", | ||
| " if not isinstance(df.index.name, str):\n", | ||
| " df.index.name = df.index.name[-1]\n", | ||
| " \n", | ||
| " df.columns = [col[0] if not isinstance(col, str) else col for col in df.columns]\n", | ||
| " \n", | ||
| " table_md = df.to_markdown()\n", | ||
| " \n", | ||
| " return table_md\n", | ||
| "\n", | ||
| "def extract_div_to_md_table(start_idx, end_idx, table_and_div_tags, file_txt):\n", | ||
| " n_start_divs_before = table_and_div_tags[:start_idx].count('<div>')\n", | ||
| " n_end_divs_before = table_and_div_tags[:end_idx].count('</div>')\n", | ||
| " \n", | ||
| " div_start_idx = get_substring_idxs(file_txt, '<div>')[n_start_divs_before-1]\n", | ||
| " div_end_idx = get_substring_idxs(file_txt, '</div>')[n_end_divs_before]\n", | ||
| "\n", | ||
| " div_txt = file_txt[div_start_idx:div_end_idx]\n", | ||
| " potential_dfs = pd.read_html(div_txt)\n", | ||
| " \n", | ||
| " assert len(potential_dfs) == 1, 'Multiple tables were found when there should be only one'\n", | ||
| " df = potential_dfs[0]\n", | ||
| " md_table = convert_df_to_md(df)\n", | ||
| "\n", | ||
| " return div_txt, md_table\n", | ||
| "\n", | ||
| "def extract_div_to_md_tables(md_fp):\n", | ||
| " with open(md_fp, 'r') as f:\n", | ||
| " file_txt = f.read()\n", | ||
| " \n", | ||
| " parser = MyHTMLParser()\n", | ||
| " parser.feed(file_txt)\n", | ||
| "\n", | ||
| " table_and_div_tags = [tag for tag in parser.tags if tag in ['<div>', '</div>', '<table border=\"1\" class=\"dataframe\">', '</table>']]\n", | ||
| " \n", | ||
| " table_start_tag_idxs = [i for i, tag in enumerate(table_and_div_tags) if tag=='<table border=\"1\" class=\"dataframe\">']\n", | ||
| " table_end_tag_idxs = [table_start_tag_idx+table_and_div_tags[table_start_tag_idx:].index('</table>') for table_start_tag_idx in table_start_tag_idxs]\n", | ||
| "\n", | ||
| " div_to_md_tables = []\n", | ||
| "\n", | ||
| " for start_idx, end_idx in zip(table_start_tag_idxs, table_end_tag_idxs):\n", | ||
| " div_txt, md_table = extract_div_to_md_table(start_idx, end_idx, table_and_div_tags, file_txt)\n", | ||
| " div_to_md_tables += [(div_txt, md_table)]\n", | ||
| " \n", | ||
| " return div_to_md_tables\n", | ||
| "\n", | ||
| "def clean_md_file_tables(md_fp):\n", | ||
| " div_to_md_tables = extract_div_to_md_tables(md_fp)\n", | ||
| " \n", | ||
| " with open(md_fp, 'r') as f:\n", | ||
| " md_file_text = f.read()\n", | ||
| "\n", | ||
| " for div_txt, md_txt in div_to_md_tables:\n", | ||
| " md_file_text = md_file_text.replace(div_txt, md_txt)\n", | ||
| "\n", | ||
| " with open(md_fp, 'w') as f:\n", | ||
| " f.write(md_file_text)\n", | ||
| " \n", | ||
| " return" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "md_fps = [f'{docs_dir}/{f}' for f in os.listdir(docs_dir) if f[-3:]=='.md' if f!='00-utilities.md']\n", | ||
| "\n", | ||
| "for md_fp in md_fps:\n", | ||
| " div_to_md_tables = clean_md_file_tables(md_fp)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<br>\n", | ||
| "\n", | ||
| "And finally change the filepaths for any images in the notebooks" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "#exports\n", | ||
| "def clean_md_file_img_fps(md_fp):\n", | ||
| " with open(md_fp, 'r') as f:\n", | ||
| " md_file_text = f.read()\n", | ||
| "\n", | ||
| " md_file_text = md_file_text.replace('../docs/img/nbs', 'img/nbs')\n", | ||
| "\n", | ||
| " with open(md_fp, 'w') as f:\n", | ||
| " f.write(md_file_text)\n", | ||
| " \n", | ||
| " return" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "for md_fp in md_fps:\n", | ||
| " clean_md_file_img_fps(md_fp)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "batopt", | ||
| "language": "python", | ||
| "name": "batopt" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.1" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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