viz_note_node.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import nx_altair as nxa\n",
    "g = nx.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[Python Interactive Network Visualization Using NetworkX, Plotly, and Dash](https://towardsdatascience.com/python-interactive-network-visualization-using-networkx-plotly-and-dash-e44749161ed7)\n",
    "\n",
    "NetworkX graph is exported to GEXF format, 标准文件，所以可以使用其他来可视化：  Gephi\n",
    "\n",
    "grave 在命令行下弹出\n",
    "\n",
    "https://docs.bokeh.org/en/latest/docs/user_guide/graph.html\n",
    "\n",
    "http://holoviews.org/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fabfile import get_all_links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_links = get_all_links()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'readme.md': ['20200223_2136-Zettelkasten笔记原则.md',\n",
       "  '20200223_2134-Zettelkasten是绝佳的笔记系统.md',\n",
       "  '20200223_2136-Zettelkasten笔记原则.md'],\n",
       " '20200223_2134-Zettelkasten是绝佳的笔记系统.md': ['20200223_2136-Zettelkasten笔记原则.md'],\n",
       " '20200223_2136-Zettelkasten笔记原则.md': []}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dg = nx.DiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''dg.add_edges_from([\n",
    "    ('Variable', 'Select'),\n",
    "    ('Select', 'GroupBy'),\n",
    "    ('GroupBy', 'Mean'),\n",
    "    ('Mean', 'Which1'),\n",
    "    ('Mean', 'Which2'),\n",
    "    ('Which1', 'Decimal1'),\n",
    "    ('Which2', 'Decimal2'),\n",
    "    ('Decimal1', 'Add'),\n",
    "    ('Decimal2', 'Add'),\n",
    "])\n",
    "'''\n",
    "node_edge_tuples = []\n",
    "for i in all_links:\n",
    "    for j in all_links[i]:\n",
    "        node_edge_tuples.append((i,j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('readme.md', '20200223_2136-Zettelkasten笔记原则.md'),\n",
       " ('readme.md', '20200223_2134-Zettelkasten是绝佳的笔记系统.md'),\n",
       " ('readme.md', '20200223_2136-Zettelkasten笔记原则.md'),\n",
       " ('20200223_2134-Zettelkasten是绝佳的笔记系统.md',\n",
       "  '20200223_2136-Zettelkasten笔记原则.md')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_edge_tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dg.add_edges_from(node_edge_tuples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(dg, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-dd46322acce54f779df9a9c5de725d4f\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-dd46322acce54f779df9a9c5de725d4f\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"domain\": false, \"grid\": false, \"labels\": false, \"ticks\": false}}, \"layer\": [{\"data\": {\"name\": \"data-d9de2a0e4e26a1446b5581ecb18dc7fb\"}, \"mark\": {\"type\": \"line\", \"color\": \"black\", \"opacity\": 1, \"strokeWidth\": 1}, \"encoding\": {\"detail\": {\"type\": \"quantitative\", \"field\": \"edge\"}, \"x\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"\"}, \"field\": \"x\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"\"}, \"field\": \"y\"}}}, {\"data\": {\"name\": \"data-8671481a82f82440258b74902d097613\"}, \"mark\": {\"type\": \"line\", \"color\": \"black\", \"opacity\": 1, \"strokeWidth\": 2}, \"encoding\": {\"detail\": {\"type\": \"quantitative\", \"field\": \"edge\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"x\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"y\"}}}, {\"data\": {\"name\": \"data-b81b03c8b20a1d758baa348a32e59d6d\"}, \"mark\": {\"type\": \"point\", \"fill\": \"red\", \"opacity\": 1, \"size\": 300}, \"encoding\": {\"tooltip\": [{\"type\": \"nominal\", \"field\": \"name\"}], \"x\": {\"type\": \"quantitative\", \"field\": \"x\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"y\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.2.json\", \"datasets\": {\"data-d9de2a0e4e26a1446b5581ecb18dc7fb\": [{\"x\": -0.2420110392078406, \"source\": \"readme.md\", \"pair\": [\"readme.md\", \"20200223_2136-Zettelkasten\\u7b14\\u8bb0\\u539f\\u5219.md\"], \"edge\": 0, \"y\": -0.46127106272424223, \"target\": \"20200223_2136-Zettelkasten\\u7b14\\u8bb0\\u539f\\u5219.md\"}, {\"x\": 1.0, \"source\": \"readme.md\", \"pair\": [\"readme.md\", \"20200223_2136-Zettelkasten\\u7b14\\u8bb0\\u539f\\u5219.md\"], \"edge\": 0, \"y\": -0.17272572121169494, \"target\": \"20200223_2136-Zettelkasten\\u7b14\\u8bb0\\u539f\\u5219.md\"}, {\"x\": -0.2420110392078406, \"source\": 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       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = nx.spring_layout(dg)\n",
    "for n in dg.nodes:\n",
    "    dg.nodes[n]['name'] = n # type(n) -> str\n",
    "nxa.draw_networkx( \n",
    "    G=dg,\n",
    "    pos=pos,\n",
    "    node_tooltip=['name'] # 无效\n",
    ") # .interactive() tip会失效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"500px\"\n",
       "            height=\"500px\"\n",
       "            src=\"example.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1212cc128>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://github.com/WestHealth/pyvis/blob/master/notebooks/example.ipynb\n",
    "from pyvis.network import Network\n",
    "g = Network(notebook=True) #,width=1400)\n",
    "g.from_nx(dg)\n",
    "g.show(\"example.html\")"
   ]
  }
 ],
 "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}