Jupyter Notebook
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Glossary Making Tool"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This is a Work-in-progress experiment tool to make living glossaries Webpages using simple Python, Javascript, and CSS from an markdown file written in a Etherpad instance.\n",
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"\n",
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"**What makes it living?** \n",
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"\n",
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"* The glossary structure uses the idea of 'gloss' that means annottation and layer. It makes the glossary a layering of anotations rather than a list of definitions. The pad template uses each word as a card with multiple annotations. See the example.\n",
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"\n",
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"* This tool also offers the option of add properties to each word in order to find connections and multiple types of organization. \n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Words\n",
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"Communicating via **API** with the glossary **Pad** and translating the text written in **Markdown** format using **Pandoc** library."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import json, pypandoc\n",
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"from urllib.request import urlopen\n",
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"from urllib.parse import urlencode\n",
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"\n",
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"\n",
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"with open(\"/opt/etherpad/APIKEY.txt\") as f:\n",
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" api_key = f.read().strip()\n",
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"\n",
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"api_url = \"https://hub.xpub.nl/soupboat/pad/api/1.2.15/\"\n",
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"\n",
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"# wrap in a convenient function (APICALL)\n",
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"def ep (api_fn, api_url, api_key, **data):\n",
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" data['apikey'] = api_key\n",
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" return json.load(urlopen(f\"{api_url}{api_fn}\", data=urlencode(data).encode()))\n",
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"\n",
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"\n",
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"glossary = ep(\"getText\", api_url, api_key, padID=\"binding_glossary\")\n",
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"\n",
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"text = glossary[\"data\"][\"text\"]\n",
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"\n",
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"words = pypandoc.convert_text(text, 'html', format='md')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"From a local markdown text file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pypandoc\n",
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"\n",
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"text = open('glossary.txt', 'r')\n",
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"\n",
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"with text as f:\n",
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" text = f.read()\n",
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" \n",
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"words = pypandoc.convert_text(text, 'html', format='md')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Properties"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The following is a word property dictionary "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"properties = [\n",
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"\t{\n",
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"\t\t'title':'fact',\n",
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"\t\t'symbol':'A',\n",
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" 'color': 'green'\n",
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"\t},\n",
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"\t{\n",
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"\t\t'title':'process',\n",
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"\t\t'symbol':'S',\n",
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"\t\t'color': 'aqua'\n",
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"\t},\n",
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"\t{\n",
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"\t\t'title':'love',\n",
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"\t\t'symbol':'L',\n",
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"\t\t'color': 'orange'\n",
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"\t},\n",
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" \t{\n",
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"\t\t'title':'tool',\n",
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"\t\t'symbol':'T',\n",
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"\t\t'color': 'blue'\n",
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"\t}\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Writing the Website"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Write properties legend"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"description = ''\n",
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"\n",
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"i = 0\n",
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"\n",
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"for title, symbol, color in properties:\n",
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" title = properties[i]['title']\n",
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" description += (f''' {title}s,''')\n",
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" i += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"legend = ''\n",
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"\n",
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"i = 0\n",
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"\n",
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"for title, symbol, color in properties:\n",
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" title = properties[i]['title']\n",
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" legend += f'''<button id=\"{title}\" class=\"btn {title}-s\" onclick=\"filterSelection('{title}')\">{title}</button>\\n'''\n",
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" i += 1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Write Script that will add properties to each word"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"script = ''\n",
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"\n",
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"i = 0 \n",
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"\n",
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"for title, symbol, color in properties:\n",
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" title = properties[i]['title']\n",
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" symbol = properties[i]['symbol']\n",
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" script += (f\"\"\"const {title} = document.getElementsByClassName(\"{title}\");\n",
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" for (let i = 0; i < {title}.length; i++)\"\"\"'{\\n' \n",
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" f\"\"\"{title}[i].innerHTML += \"<span class='symbol {title}-s'>{symbol}</span>\";\"\"\" \n",
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" '\\n}\\n\\n')\n",
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" i += 1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Assign a different color for each property "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"style = ''\n",
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"\n",
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"i = 0 \n",
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"\n",
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"for title, symbol, color in properties:\n",
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" title = properties[i]['title']\n",
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" color = properties[i]['color']\n",
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" style += f'''.{title}-s''' + '{' + f'''color:{color};''''}'\n",
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" i += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"body = f'''<!DOCTYPE html>\n",
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" <html>\n",
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" <head>\n",
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" <meta charset=\"utf-8\">\n",
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" <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n",
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" <link rel=\"stylesheet\" href=\"style.css\"> \n",
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" <title>Binding GLossary</title>\n",
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" </head>\n",
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" <style> {style} </style>\n",
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" <body id=\"glo_body\">\n",
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"\n",
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" <div class=\"head\">\n",
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" <h1> Glossary for a Diffractive Publishing Practice </h1>\n",
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" </div>\n",
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" <hr>\n",
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" <div class=\"description\">\n",
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" <p>This glossary gathers words around bookbiding. It was made as an example to use the tool living-glossary.</p> \n",
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" <p>{description} are the properties that organize and make connections between words.</p>\n",
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" </div>\n",
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" <div id=\"legend\" class=\"legend\"> \n",
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" <button class=\"btn active\" onclick=\"filterSelection('all')\">properties</button> {legend}\n",
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" </div>\n",
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" <hr>\n",
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" <div class=\"words\">\n",
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" {words} \n",
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" </div>\n",
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" </body>\n",
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" <script>\n",
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" {script}\n",
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" </script>\n",
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" <script src=\"main.js\"></script>\n",
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" </html>'''\n",
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"\n",
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"website = open('index.html','w')\n",
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"\n",
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"website.write(body)\n",
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"website.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(words)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from time import sleep\n",
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"\n",
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"while True:\n",
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" print('hey')\n",
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" sleep(5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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