{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Glossary Making Tool" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "\n", "**What makes it living?** \n", "\n", "* 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", "\n", "* This tool also offers the option of add properties to each word in order to find connections and multiple types of organization. \n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Words\n", "Communicating via **API** with the glossary **Pad** and translating the text written in **Markdown** format using **Pandoc** library." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [], "source": [ "import json, pypandoc\n", "from urllib.request import urlopen\n", "from urllib.parse import urlencode\n", "\n", "\n", "with open(\"/opt/etherpad/APIKEY.txt\") as f:\n", " api_key = f.read().strip()\n", "\n", "api_url = \"https://hub.xpub.nl/soupboat/pad/api/1.2.15/\"\n", "\n", "# wrap in a convenient function (APICALL)\n", "def ep (api_fn, api_url, api_key, **data):\n", " data['apikey'] = api_key\n", " return json.load(urlopen(f\"{api_url}{api_fn}\", data=urlencode(data).encode()))\n", "\n", "\n", "glossary = ep(\"getText\", api_url, api_key, padID=\"binding_glossary\")\n", "\n", "text = glossary[\"data\"][\"text\"]\n", "\n", "words = pypandoc.convert_text(text, 'html', format='md')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From a local markdown text file" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import pypandoc\n", "\n", "text = open('glossary.txt', 'r')\n", "\n", "with text as f:\n", " text = f.read()\n", " \n", "words = pypandoc.convert_text(text, 'html', format='md')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following is a word property dictionary " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [], "source": [ "properties = [\n", "\t{\n", "\t\t'title':'fact',\n", "\t\t'symbol':'A',\n", " 'color': 'green'\n", "\t},\n", "\t{\n", "\t\t'title':'process',\n", "\t\t'symbol':'S',\n", "\t\t'color': 'aqua'\n", "\t},\n", "\t{\n", "\t\t'title':'love',\n", "\t\t'symbol':'L',\n", "\t\t'color': 'orange'\n", "\t},\n", " \t{\n", "\t\t'title':'tool',\n", "\t\t'symbol':'T',\n", "\t\t'color': 'blue'\n", "\t}\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing the Website" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write properties legend" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "description = ''\n", "\n", "i = 0\n", "\n", "for title, symbol, color in properties:\n", " title = properties[i]['title']\n", " description += (f''' {title}s,''')\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "legend = ''\n", "\n", "i = 0\n", "\n", "for title, symbol, color in properties:\n", " title = properties[i]['title']\n", " legend += f'''\\n'''\n", " i += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write Script that will add properties to each word" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "script = ''\n", "\n", "i = 0 \n", "\n", "for title, symbol, color in properties:\n", " title = properties[i]['title']\n", " symbol = properties[i]['symbol']\n", " script += (f\"\"\"const {title} = document.getElementsByClassName(\"{title}\");\n", " for (let i = 0; i < {title}.length; i++)\"\"\"'{\\n' \n", " f\"\"\"{title}[i].innerHTML += \"{symbol}\";\"\"\" \n", " '\\n}\\n\\n')\n", " i += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assign a different color for each property " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "style = ''\n", "\n", "i = 0 \n", "\n", "for title, symbol, color in properties:\n", " title = properties[i]['title']\n", " color = properties[i]['color']\n", " style += f'''.{title}-s''' + '{' + f'''color:{color};''''}'\n", " i += 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [] }, "outputs": [], "source": [ "body = f'''\n", " \n", " \n", " \n", " \n", " \n", " Binding GLossary\n", " \n", " \n", " \n", "\n", "
\n", "

Glossary for a Diffractive Publishing Practice

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\n", "
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This glossary gathers words around bookbiding. It was made as an example to use the tool living-glossary.

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{description} are the properties that organize and make connections between words.

\n", "
\n", "
\n", " {legend}\n", "
\n", "
\n", "
\n", " {words} \n", "
\n", " \n", " \n", " \n", " '''\n", "\n", "website = open('index.html','w')\n", "\n", "website.write(body)\n", "website.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(words)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "from time import sleep\n", "\n", "while True:\n", " print('hey')\n", " sleep(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }