{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import igraph as ig\n",
    "import dionysus as d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "plt.style.use(\"seaborn\")\n",
    "plt.rcParams[\"figure.figsize\"] = 10, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "celegans = ig.read(\"data/celegansneural.gml\", format=\"gml\")\n",
    "celegans.es[\"weight\"] = celegans.es[\"value\"]\n",
    "celegans.to_undirected(combine_edges=\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_weights = np.unique(celegans.es[\"weight\"])\n",
    "fig, ax = plt.subplots(6,6)\n",
    "for i, w in enumerate(unique_weights):\n",
    "    subg = celegans.subgraph_edges(celegans.es(lambda e: e[\"weight\"] < w), delete_vertices=True)\n",
    "    cl = subg.maximal_cliques()\n",
    "    cl = np.array(list(map(len,cl)))\n",
    "    if len(cl) > 0:\n",
    "        ax[i//6,i%6].hist(cl,bins=10)\n",
    "        # print(f\"Number of cliques: {len(cl)}\\tMax size: {max(map(len, cl))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "subg = celegans.subgraph_edges(celegans.es(lambda e: e[\"weight\"] < 3), delete_vertices=True)\n",
    "cl = subg.maximal_cliques()\n",
    "cl = np.array(list(map(len,cl)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(cl);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "filt = d.Filtration()\n",
    "for v in celegans.vs:\n",
    "    filt.append(d.Simplex([v.index], 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "distinct_weights = np.unique(celegans.es[\"weight\"])[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t, w in enumerate(distinct_weights):\n",
    "    subg = celegans.subgraph_edges(celegans.es(lambda e: e[\"weight\"] >= w))\n",
    "    for clique in subg.maximal_cliques():\n",
    "        for s in d.closure([d.Simplex(clique)], len(clique)):\n",
    "            filt.append(d.Simplex(s, t+1))\n",
    "filt.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Diagram with 297 points, Diagram with 2678 points, Diagram with 171 points, Diagram with 4 points, Diagram with 0 points, Diagram with 0 points, Diagram with 0 points, Diagram with 0 points]\n"
     ]
    }
   ],
   "source": [
    "pers = d.homology_persistence(filt)\n",
    "dgms = d.init_diagrams(pers, filt)\n",
    "print(dgms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.plot.plot_bars(dgms[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.plot.plot_diagram(dgms[1])"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}