{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "URL: https://docs.bokeh.org/en/latest/docs/examples/topics/categorical/les_mis.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import holoviews as hv\n",
    "from holoviews import opts\n",
    "hv.extension('bokeh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.sampledata.les_mis import data\n",
    "\n",
    "nodes = data['nodes']\n",
    "names = [node['name'] for node in sorted(data['nodes'], key=lambda x: x['group'])]\n",
    "\n",
    "N = len(nodes)\n",
    "counts = np.zeros((N, N))\n",
    "for link in data['links']:\n",
    "    counts[link['source'], link['target']] = link['value']\n",
    "    counts[link['target'], link['source']] = link['value']\n",
    "    \n",
    "xname, yname, color, alpha = [], [], [], []\n",
    "for i, node1 in enumerate(nodes):\n",
    "    for j, node2 in enumerate(nodes):\n",
    "        xname.append(node1['name'])\n",
    "        yname.append(node2['name'])\n",
    "\n",
    "        alpha.append(counts[i,j])\n",
    "\n",
    "        if node1['group'] == node2['group']:\n",
    "            color.append(node1['group'])\n",
    "        else:\n",
    "            color.append('lightgrey')\n",
    "\n",
    "ds = hv.Dataset((xname, yname, color, alpha), ['x', 'y', 'Cluster', 'Occurences'])\n",
    "overlaid = ds.to(hv.HeatMap, ['x', 'y'], ['Occurences']).overlay()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmaps = ['Greys', 'Reds', 'Greys', 'Greens', 'Blues',\n",
    "         'Purples', 'Oranges', 'Greys', 'Greys', 'PuRd', 'Reds', 'Greys']\n",
    "\n",
    "combined = hv.Overlay([o.opts(cmap=cm).sort() for o, cm in zip(overlaid, cmaps)], label='LesMis Occurences')\n",
    "styled = combined.opts(\n",
    "    opts.HeatMap(logz=True, clim=(0.1, None), clipping_colors={'NaN':(1,1,1,0.)}, xaxis='top', xrotation=90,\n",
    "                 fontsize={'ticks': '7pt', 'title': '18pt'}, invert_xaxis=True, tools=['hover'],\n",
    "                 labelled=[], axiswise=True),\n",
    "    opts.Overlay(height=800, width=800)\n",
    ")\n",
    "styled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hv.Layout([el.opts(width=300, height=300) for el in styled if len(el)>10][:-1],\n",
    "          label='LesMis Large Clusters').cols(3)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}