Example ETMM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import construction as cs\n",
    "from ETN import *\n",
    "from ETMM import *\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters \n",
    "k = 3    # number of static snapshot used for the constructions of ETN\n",
    "gap = 299   # temporal gap\n",
    "label = False # if true, the loaded dataset is labeled\n",
    "file_name = \"InVS13\" # name of the file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the temporal graph as a sequence of static NetworkX graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = cs.load_data(\"Datasets/\"+file_name+\".dat\")\n",
    "if label:\n",
    "    meta_data = cs.load_metadata(\"Datasets/metadata/metadata_\"+file_name+\".dat\")\n",
    "else:\n",
    "    meta_data = None\n",
    "    \n",
    "if label:\n",
    "    graphs = cs.build_graphs(data,gap=gap,with_labels=label,meta_path=\"Datasets/metadata/metadata_\"+file_name+\".dat\")\n",
    "else:\n",
    "    graphs = cs.build_graphs(data,gap=gap,with_labels=label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### count ETN or LETN and store the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file stored in: \tres/InVS13/ETNS/gap_299_k_3.json\n"
     ]
    }
   ],
   "source": [
    "S = count_ETN(graphs,k,meta=meta_data)\n",
    "S = {k: v for k, v in sorted(S.items(), key=lambda item: item[1], reverse=1)}\n",
    "\n",
    "store_etns(S,file_name,gap,k,label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0b1000': 2221, '0b1100': 501, '0b1111': 435, '0b10001000': 270, '0b1110': 217, '0b01001000': 164, '0b00011000': 122, '0b1010': 122, '0b1001': 97, '0b00101000': 90, '0b10001100': 87, '0b1101': 85, '0b1011': 62, '0b01001100': 60, '0b100010001000': 43, '0b10001111': 34, '0b00011111': 27, '0b10001110': 26, '0b00011100': 26, '0b01101000': 25, '0b00101100': 24, '0b00101110': 23, '0b00111000': 23, '0b010010001000': 22, '0b010001001000': 21, '0b01001110': 21, '0b11001100': 19, '0b01001111': 18, '0b00011110': 17, '0b000100011000': 17, '0b1000100010001000': 16, '0b00101010': 16, '0b01111000': 16, '0b001010001000': 15, '0b10001010': 15, '0b00101111': 14, '0b00011001': 14, '0b000110001000': 13, '0b10001001': 13, '0b00111100': 12, '0b010010001100': 12, '0b001000101000': 12, '0b00011010': 11, '0b00011101': 11, '0b01101110': 10, '0b010001001100': 10, '0b001001101000': 10, '0b01101100': 10, '0b00111111': 9, '0b000100101000': 9, '0b11001110': 9, '0b01001010': 8, '0b01001001': 8, '0b11101111': 8, '0b000101001100': 8, '0b100010001100': 7, '0b10101100': 7, '0b0100100010001000': 7, '0b01011000': 7, '0b10001101': 6, '0b00101101': 6, '0b01001101': 6, '0b01111111': 6, '0b00111010': 6, '0b01111100': 6, '0b00011011': 6, '0b001000101100': 6, '0b001001001000': 6, '0b00101011': 5, '0b000110001100': 5, '0b000100111000': 5, '0b11001111': 5, '0b001000101110': 5, '0b001110001000': 5, '0b01111110': 5, '0b10001000100010001000': 4, '0b11001101': 4, '0b100011001100': 4, '0b000110001111': 4, '0b010011001100': 4, '0b11011111': 4, '0b0010001000111000': 4, '0b100010101100': 4, '0b100010001010': 4, '0b000100111100': 4, '0b001001001100': 4, '0b011010001100': 4, '0b11011110': 4, '0b00111110': 4, '0b01001000100010001000': 3, '0b0100100010001100': 3, '0b100010001111': 3, '0b100010001110': 3, '0b0001000100011100': 3, '0b000101101000': 3, '0b00111011': 3, '0b010001001001': 3, '0b010010001111': 3, '0b001101101000': 3, '0b0001000100111000': 3, '0b001101001000': 3, '0b0001100010001000': 3, '0b0010001010001000': 3, '0b00010100010010001000': 3, '0b000100101100': 3, '0b001001101100': 3, '0b0001000110001000': 3, '0b001010001100': 2, '0b0010001000101000': 2, '0b011010001110': 2, '0b000101111111': 2, '0b10001000100010001100': 2, '0b001001111000': 2, '0b100010001001': 2, '0b0100010001001000': 2, '0b10011001': 2, '0b0010010010001000': 2, '0b0110100010001000': 2, '0b001010001110': 2, '0b0100100010001110': 2, '0b0011010010001000': 2, '0b0001100010001110': 2, '0b000101001000': 2, '0b001010001111': 2, '0b001000111000': 2, '0b00100010001000101100': 2, '0b01000100010001001000': 2, '0b0001010001001101': 2, '0b001001001111': 2, '0b000100011111': 2, '0b010001101000': 2, '0b000100011110': 2, '0b0100010010001000': 2, '0b01101111': 2, '0b000100011001': 2, '0b0001000100011001': 2, '0b100010011110': 2, '0b01111101': 2, '0b10101101': 2, '0b10011010': 2, '0b0001000101001000': 2, '0b01101011': 2, '0b0100010010001100': 2, '0b00111001': 2, '0b11111111': 2, '0b11101110': 2, '0b011110001000': 2, 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'0b0100100010111110': 1, '0b010001111000': 1, '0b10111111': 1, '0b000111001100': 1, '0b10011110': 1, '0b0001001010001100': 1, '0b01011011': 1, '0b001001001001': 1, '0b000100011011': 1, '0b0010001000101100': 1, '0b0100010001011001': 1, '0b0100010001001010': 1, '0b010001011100': 1, '0b0011100010001011': 1, '0b0001000110001110': 1, '0b0001001000101100': 1, '0b0011010001011000': 1, '0b010001011000': 1, '0b011010001010': 1, '0b01001011': 1, '0b0011011001101110': 1, '0b0110110011001101': 1, '0b00011000100010101100': 1, '0b001001011000': 1, '0b0001000101011011': 1, '0b0001000100011010': 1, '0b0010001001101010': 1, '0b00110100010001001000': 1, '0b000100010001001010101010': 1, '0b010001010110100010001010': 1, '0b0100100010101100': 1, '0b01000100100010001000': 1, '0b0010010001001000': 1, '0b010110001000': 1, '0b010101101111': 1, '0b101011001111': 1, '0b001010001001': 1, '0b001110001001': 1, '0b0001000100010001000100100010001101111011': 1, '0b00100100010001101011': 1, '0b100010011111': 1, '0b0010001000110011001101000100011001111111': 1, '0b01000111100010001100': 1, '0b0001000100111110': 1, '0b0100010001100110011010001000110011101110': 1, '0b0010001101101100': 1, '0b10001000100011001100110011001100': 1, '0b0100011010001100': 1, '0b0001001101101000': 1, '0b010001101111': 1, '0b0001100010001111': 1, '0b100011001111': 1, '0b000101011110': 1, '0b00010001001010101100': 1, '0b00100010010001001000': 1, '0b0001000100101000': 1, '0b00100010001000101000': 1, '0b0001000100011111': 1, '0b0010001000101110': 1, '0b0001000110001001': 1, '0b0001010010001001': 1, '0b000110001110': 1, '0b0111100010001000': 1, '0b0010010010101100': 1, '0b0010001101001100': 1, '0b0100011010001000': 1, '0b010010101100': 1, '0b0100011110001000': 1, '0b001001001110': 1, '0b0010001001001110': 1, '0b00010001100010001001': 1, '0b0001001000101011': 1, '0b0111100010001111': 1, '0b001011111111': 1, '0b010011101110': 1, '0b00010001100011001100': 1, '0b0010100010001110': 1, '0b0100010110001111': 1, '0b100010101110': 1, '0b10011111': 1, '0b0001010001101011': 1, '0b0010011010001100': 1, '0b000101111000': 1}\n"
     ]
    }
   ],
   "source": [
    "print(S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load etns \n",
    "SS = load_etns(file_name,gap,k,label=label)\n",
    "assert(SS == S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S_array = list(S.keys())\n",
    "draw_ETN(from_ETNS_to_ETN(S_array[10],k=3,meta=None),multiple=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count \t = \t 2221\n",
      "count \t = \t 501\n",
      "count \t = \t 435\n",
      "count \t = \t 270\n",
      "count \t = \t 217\n",
      "count \t = \t 164\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot 6 most frequent ETN\n",
    "fig_per_row = 6\n",
    "S_array = list(S.keys())\n",
    "for i in range(0,6,fig_per_row):\n",
    "    plt.figure(figsize=(12,3))\n",
    "    for j in range(fig_per_row):\n",
    "        plt.subplot(1,fig_per_row,j+1)\n",
    "        print(\"count \\t = \\t\",S[S_array[i+j]])\n",
    "        draw_ETN(from_ETNS_to_ETN(S_array[i+j],k,meta_data),multiple=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Null models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def buil_nm(graphs,n,file):\n",
    "    t = 0\n",
    "    to_save = []\n",
    "    f = open(\"null_models/\"+file+\"/\"+file+\"_\"+str(n)+\".txt\", \"a\")\n",
    "    for g in graphs:\n",
    "        for e in g.edges():\n",
    "            s = str(t)+\" \"+str(e[0])+\" \"+str(e[1])+\"\\n\"\n",
    "            f.write(s)\n",
    "        t = t + gap + 1\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "seed = 10\n",
    "n = 5\n",
    "null_models = shuffle_graphs(graphs,n,seed)\n",
    "print(len(null_models))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store null models\n",
    "c = 0\n",
    "for graphs in null_models:\n",
    "    buil_nm(graphs,c,file_name)\n",
    "    c = c + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Egocentric temporal motifs miner ETMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load precoputed etns\n",
    "S = load_etns(file_name,gap,k,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done 1\n",
      "done 2\n",
      "done 3\n",
      "done 4\n",
      "done 5\n",
      "file stored in: \tres/InVS13/ETM_counts/gap_299_k_3.json\n"
     ]
    }
   ],
   "source": [
    "# count etn in null models and store the results\n",
    "\n",
    "counts = counts_ETN_null_models(null_models,S,k,label,meta_data,verbose=True)\n",
    "store_etm_counts(counts,file_name,gap,k,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = load_etm_count(file_name,gap,k,label)\n",
    "assert(tmp == counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '0b010001101101': [1, 0, 0, 0, 0, 0],\n",
       " '0b00100100100010001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b001000101111': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010100010001010': [1, 0, 0, 0, 1, 0],\n",
       " '0b010001001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b000101001011': [1, 0, 0, 1, 0, 0],\n",
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       " '0b10011101': [1, 0, 0, 0, 0, 0],\n",
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       " '0b001001001110111011101110': [1, 0, 0, 0, 0, 0],\n",
       " '0b10001010101011001110': [1, 0, 0, 0, 0, 0],\n",
       " '0b001001001000110011001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010100011001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b010010001100110011001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001001010001000': [1, 3, 6, 1, 1, 1],\n",
       " '0b0001001010001010': [1, 0, 0, 0, 0, 1],\n",
       " '0b0001010010001010': [1, 0, 0, 0, 0, 1],\n",
       " '0b0001000101001110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010001010001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b010010001001': [1, 1, 1, 0, 4, 1],\n",
       " '0b0001001110001000': [1, 0, 0, 0, 0, 0],\n",
       " '0b000100010001000110001001': [1, 0, 0, 0, 0, 0],\n",
       " '0b0110011101111010': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100110011101111': [1, 0, 0, 0, 0, 0],\n",
       " '0b000110001000100011001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b1000100111001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b001010011110': [1, 0, 0, 0, 0, 0],\n",
       " '0b001110001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b001101001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b000110111110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001001001111101': [1, 0, 0, 0, 0, 0],\n",
       " '0b000100011101': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010010010101111': [1, 0, 0, 0, 0, 0],\n",
       " '0b001000111010': [1, 0, 1, 0, 0, 0],\n",
       " '0b0100010010001110': [1, 0, 0, 0, 0, 0],\n",
       " '0b100010011101': [1, 0, 0, 0, 0, 0],\n",
       " '0b100010011100': [1, 0, 0, 0, 0, 0],\n",
       " '0b00010001000110011111': [1, 0, 0, 0, 0, 0],\n",
       " '0b000110001001': [1, 3, 2, 1, 3, 0],\n",
       " '0b000100100010001000111110': [1, 0, 0, 0, 0, 0],\n",
       " '0b001001000100010001101100': [1, 0, 0, 0, 0, 0],\n",
       " '0b010010001000100010001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0110100010011100': [1, 0, 0, 0, 0, 0],\n",
       " '0b00101001': [1, 8, 8, 4, 9, 8],\n",
       " '0b010001101110': [1, 0, 0, 0, 0, 0],\n",
       " '0b001110001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b01101101': [1, 0, 0, 0, 0, 0],\n",
       " '0b000100101110': [1, 0, 0, 0, 0, 0],\n",
       " '0b00010010001000101000': [1, 0, 0, 1, 0, 0],\n",
       " '0b000110001101': [1, 1, 0, 0, 0, 0],\n",
       " '0b01000100010001101000': [1, 0, 0, 0, 0, 0],\n",
       " '0b00100010100010001000': [1, 3, 0, 2, 0, 1],\n",
       " '0b1000100010001100': [1, 0, 1, 2, 0, 0],\n",
       " '0b0001000110001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b101111001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100100010111110': [1, 0, 0, 0, 0, 0],\n",
       " '0b010001111000': [1, 0, 0, 0, 0, 0],\n",
       " '0b10111111': [1, 0, 0, 0, 0, 0],\n",
       " '0b000111001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b10011110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001001010001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b01011011': [1, 0, 0, 0, 0, 0],\n",
       " '0b001001001001': [1, 0, 0, 0, 1, 0],\n",
       " '0b000100011011': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010001000101100': [1, 0, 0, 0, 3, 0],\n",
       " '0b0100010001011001': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100010001001010': [1, 1, 1, 0, 0, 0],\n",
       " '0b010001011100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0011100010001011': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001000110001110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001001000101100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0011010001011000': [1, 0, 0, 0, 0, 0],\n",
       " '0b010001011000': [1, 1, 1, 1, 1, 0],\n",
       " '0b011010001010': [1, 0, 0, 0, 0, 0],\n",
       " '0b01001011': [1, 0, 1, 0, 1, 0],\n",
       " '0b0011011001101110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0110110011001101': [1, 0, 0, 0, 0, 0],\n",
       " '0b00011000100010101100': [1, 0, 0, 0, 0, 0],\n",
       " '0b001001011000': [1, 1, 0, 0, 1, 0],\n",
       " '0b0001000101011011': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001000100011010': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010001001101010': [1, 0, 1, 0, 0, 0],\n",
       " '0b00110100010001001000': [1, 0, 0, 0, 0, 0],\n",
       " '0b000100010001001010101010': [1, 0, 0, 0, 0, 0],\n",
       " '0b010001010110100010001010': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100100010101100': [1, 0, 0, 0, 0, 0],\n",
       " '0b01000100100010001000': [1, 1, 3, 0, 2, 0],\n",
       " '0b0010010001001000': [1, 3, 0, 0, 2, 3],\n",
       " '0b010110001000': [1, 1, 0, 1, 1, 0],\n",
       " '0b010101101111': [1, 0, 0, 0, 0, 0],\n",
       " '0b101011001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b001010001001': [1, 0, 0, 1, 0, 1],\n",
       " '0b001110001001': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001000100010001000100100010001101111011': [1, 0, 0, 0, 0, 0],\n",
       " '0b00100100010001101011': [1, 0, 0, 0, 0, 0],\n",
       " '0b100010011111': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010001000110011001101000100011001111111': [1, 0, 0, 0, 0, 0],\n",
       " '0b01000111100010001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001000100111110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100010001100110011010001000110011101110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010001101101100': [1, 0, 0, 0, 0, 0],\n",
       " '0b10001000100011001100110011001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100011010001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001001101101000': [1, 0, 0, 0, 0, 0],\n",
       " '0b010001101111': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001100010001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b100011001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b000101011110': [1, 0, 0, 0, 0, 0],\n",
       " '0b00010001001010101100': [1, 0, 0, 0, 0, 0],\n",
       " '0b00100010010001001000': [1, 0, 1, 0, 0, 1],\n",
       " '0b0001000100101000': [1, 2, 2, 3, 1, 1],\n",
       " '0b00100010001000101000': [1, 4, 1, 6, 1, 4],\n",
       " '0b0001000100011111': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010001000101110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001000110001001': [1, 0, 0, 0, 3, 0],\n",
       " '0b0001010010001001': [1, 0, 0, 1, 0, 0],\n",
       " '0b000110001110': [1, 0, 1, 0, 0, 0],\n",
       " '0b0111100010001000': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010010010101100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010001101001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100011010001000': [1, 0, 1, 1, 0, 0],\n",
       " '0b010010101100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100011110001000': [1, 0, 0, 0, 0, 0],\n",
       " '0b001001001110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010001001001110': [1, 0, 0, 0, 0, 0],\n",
       " '0b00010001100010001001': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001001000101011': [1, 0, 0, 0, 0, 0],\n",
       " '0b0111100010001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b001011111111': [1, 0, 0, 0, 0, 0],\n",
       " '0b010011101110': [1, 0, 0, 0, 0, 0],\n",
       " '0b00010001100011001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010100010001110': [1, 0, 0, 0, 0, 0],\n",
       " '0b0100010110001111': [1, 0, 0, 0, 0, 0],\n",
       " '0b100010101110': [1, 0, 0, 0, 0, 0],\n",
       " '0b10011111': [1, 0, 0, 0, 0, 0],\n",
       " '0b0001010001101011': [1, 0, 0, 0, 0, 0],\n",
       " '0b0010011010001100': [1, 0, 0, 0, 0, 0],\n",
       " '0b000101111000': [1, 0, 0, 0, 0, 0]}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply statistical test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of etns:\t 392 \n",
      "number of etm: \t 31\n"
     ]
    }
   ],
   "source": [
    "alpha=0.01\n",
    "beta=0.1\n",
    "gamma=10\n",
    "\n",
    "ETM = get_ETM(counts,alpha,beta,gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count \t = \t 501\n",
      "count \t = \t 435\n",
      "count \t = \t 217\n",
      "count \t = \t 122\n",
      "count \t = \t 97\n",
      "count \t = \t 87\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_per_row = 6\n",
    "for i in range(0,fig_per_row,fig_per_row):\n",
    "    plt.figure(figsize=(12,3))\n",
    "    for j in range(fig_per_row):\n",
    "        plt.subplot(1,fig_per_row,j+1)\n",
    "        print(\"count \\t = \\t\",ETM[i+j][1])\n",
    "        draw_ETN(from_ETNS_to_ETN(ETM[i+j][0],k,meta_data),multiple=True)\n",
    "    plt.show()"
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}