notebooks/multiscale/motifs/proxy-th100-common-neighbor-rule.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common Neighbor Rule in PyC Subgraph (Th=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "from time import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "\n",
    "from utils.utils import *\n",
    "from utils.proximity import *\n",
    "from utils.graph_creation import *\n",
    "from utils.visualization import *\n",
    "from utils.common_neighbor_rule import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Create PyC Subgraph and Load Proximity Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyc_synapses = pd.read_csv('data/synapses.csv')\n",
    "pyc_soma = pd.read_csv('data/soma.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the simple directed graph (no self-loops, no multi-edges)\n",
    "# treat multiple synapses as one connection\n",
    "pyc_subgraph = synapses_to_connections(pyc_synapses[['pre','post']])\n",
    "# remove self-loops\n",
    "g_actual = remove_autapses(edges_to_graph(pyc_subgraph))\n",
    "g_actual.add_nodes_from(pyc_soma[\"segment_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "locs = {n:string2array(np.array(pyc_soma[pyc_soma[\"segment_id\"] == n][\"loc\"])[0]) for n in g_actual.nodes()}\n",
    "axls = {n:np.array(pyc_soma[pyc_soma[\"segment_id\"] == n][\"axon_len\"])[0] for n in g_actual.nodes()}\n",
    "nx.set_node_attributes(g_actual, locs, \"loc\")\n",
    "nx.set_node_attributes(g_actual, axls, \"axl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 111\n",
      "Number of edges: 659\n",
      "Average in degree:   5.9369\n",
      "Average out degree:   5.9369\n"
     ]
    }
   ],
   "source": [
    "g_th100 = get_thresholded_graph(g_actual, axls, 100)\n",
    "print(nx.info(g_th100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load precomputed proximity data and plot\n",
    "prox_th100 = pd.read_csv('saved/proximity_th100_avg10um.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Compare 2-Cell Motif Statistics (OBS vs ER vs PM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 268 ms, sys: 8 ms, total: 276 ms\n",
      "Wall time: 271 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# calculate common neighbor stats in the observation\n",
    "obs_cnr_stats = get_cnr_stats(g_th100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- \"undir_pair\" - number of pairs with [bin] common neighbor\n",
    "- \"dir_spair\" - number of pairs with [bin] common successor (strict, not a predecessor to any)\n",
    "- \"dir_ppair\" - number of pairs with [bin] common predecessor (strict, not a successor to any)\n",
    "- \"undir_conn\" - number of connected pairs with [bin] common neighbor\n",
    "- \"dir_sconn\" - number of connected pairs with [bin] common successor (strict, not a predecessor to any)\n",
    "- \"dir_pconn\" - number of connected pairs with [bin] common predecessor (strict, not a successor to any)\n",
    "- \"dir_uni_sconn\" - number of unidirectionally connected pairs with [bin] common successor (strict, not a predecessor to any)\n",
    "- \"dir_uni_pconn\" - number of unidirectionally connected pairs with [bin] common predecessor (strict, not a successor to any)\n",
    "- \"dir_bi_sconn\" - number of bidirectionally connected pairs with [bin] common successor (strict, not a predecessor to any)\n",
    "- \"dir_bi_pconn\" - number of bidirectionally connected pairs with [bin] common predecessor (strict, not a successor to any)\n",
    "- \"undir_perc\" - percentage of pairs with [bin] common neighbor to be connected\n",
    "- \"dir_sperc\" - percentage of pairs with [bin] strict common successor to be connected\n",
    "- \"dir_pperc\" - percentage of pairs with [bin] strict common predcessor to be connected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>undir_pair</th>\n",
       "      <th>dir_spair</th>\n",
       "      <th>dir_ppair</th>\n",
       "      <th>undir_conn</th>\n",
       "      <th>dir_sconn</th>\n",
       "      <th>dir_pconn</th>\n",
       "      <th>dir_uni_sconn</th>\n",
       "      <th>dir_uni_pconn</th>\n",
       "      <th>dir_bi_sconn</th>\n",
       "      <th>dir_bi_pconn</th>\n",
       "      <th>undir_perc</th>\n",
       "      <th>dir_sperc</th>\n",
       "      <th>dir_pperc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2027.0</td>\n",
       "      <td>4676.0</td>\n",
       "      <td>3927.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.049827</td>\n",
       "      <td>0.085543</td>\n",
       "      <td>0.089381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1741.0</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.135790</td>\n",
       "      <td>0.123666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1106.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.122061</td>\n",
       "      <td>0.191882</td>\n",
       "      <td>0.132609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>637.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.160126</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.179245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.168790</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>145.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.213793</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223684</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   undir_pair  dir_spair  dir_ppair  undir_conn  dir_sconn  dir_pconn  \\\n",
       "0      2027.0     4676.0     3927.0       101.0      400.0      351.0   \n",
       "1      1741.0     1031.0     1593.0       164.0      140.0      197.0   \n",
       "2      1106.0      271.0      460.0       135.0       52.0       61.0   \n",
       "3       637.0       85.0      106.0       102.0       20.0       19.0   \n",
       "4       314.0       22.0       17.0        53.0       10.0        2.0   \n",
       "5       145.0       14.0        2.0        31.0        5.0        0.0   \n",
       "6        76.0        5.0        0.0        17.0        3.0        0.0   \n",
       "7        27.0        0.0        0.0        11.0        0.0        0.0   \n",
       "8        19.0        0.0        0.0        10.0        0.0        0.0   \n",
       "9        13.0        1.0        0.0         6.0        0.0        0.0   \n",
       "\n",
       "   dir_uni_sconn  dir_uni_pconn  dir_bi_sconn  dir_bi_pconn  undir_perc  \\\n",
       "0          378.0          334.0          22.0          17.0    0.049827   \n",
       "1          134.0          188.0           6.0           9.0    0.094199   \n",
       "2           51.0           59.0           1.0           2.0    0.122061   \n",
       "3           20.0           18.0           0.0           1.0    0.160126   \n",
       "4           10.0            2.0           0.0           0.0    0.168790   \n",
       "5            5.0            0.0           0.0           0.0    0.213793   \n",
       "6            3.0            0.0           0.0           0.0    0.223684   \n",
       "7            0.0            0.0           0.0           0.0    0.407407   \n",
       "8            0.0            0.0           0.0           0.0    0.526316   \n",
       "9            0.0            0.0           0.0           0.0    0.461538   \n",
       "\n",
       "   dir_sperc  dir_pperc  \n",
       "0   0.085543   0.089381  \n",
       "1   0.135790   0.123666  \n",
       "2   0.191882   0.132609  \n",
       "3   0.235294   0.179245  \n",
       "4   0.454545   0.117647  \n",
       "5   0.357143   0.000000  \n",
       "6   0.600000   0.000000  \n",
       "7   0.000000   0.000000  \n",
       "8   0.000000   0.000000  \n",
       "9   0.000000   0.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_cnr_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0000cf374364cea9fadfce7704286cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 18.7 s, sys: 40 ms, total: 18.8 s\n",
      "Wall time: 19.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# sample graphs from proximity model and compute CNR stats\n",
    "s=100 # total 100 samples (100 continous samples)\n",
    "pm_cnr_samples = proximity_sample_cnr(g_th100, samples=s, \n",
    "                                      ps=prox_th100[\"p_connect\"], \n",
    "                                      edge_order=prox_th100[\"edge_order\"])\n",
    "pm_cnr_stats, pm_cnr_stats_std = cnr_mean_std(pm_cnr_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save sampled CNR stats from proximity model as intermediate data\n",
    "pm_cnr_stats.to_csv(\"saved/th100_cnr_pm100.csv\", index=False)\n",
    "pm_cnr_stats_std.to_csv(\"saved/th100_cnrstd_pm100.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load precomputed CNR stats counts from CFG model\n",
    "pm_cnr_stats = pd.read_csv(\"saved/th100_cnr_pm100.csv\")\n",
    "pm_cnr_stats_std = pd.read_csv(\"saved/th100_cnrstd_pm100.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>undir_pair</th>\n",
       "      <th>dir_spair</th>\n",
       "      <th>dir_ppair</th>\n",
       "      <th>undir_conn</th>\n",
       "      <th>dir_sconn</th>\n",
       "      <th>dir_pconn</th>\n",
       "      <th>dir_uni_sconn</th>\n",
       "      <th>dir_uni_pconn</th>\n",
       "      <th>dir_bi_sconn</th>\n",
       "      <th>dir_bi_pconn</th>\n",
       "      <th>undir_perc</th>\n",
       "      <th>dir_sperc</th>\n",
       "      <th>dir_pperc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1849.92</td>\n",
       "      <td>4512.30</td>\n",
       "      <td>4517.52</td>\n",
       "      <td>178.76</td>\n",
       "      <td>464.47</td>\n",
       "      <td>464.37</td>\n",
       "      <td>449.38</td>\n",
       "      <td>448.97</td>\n",
       "      <td>15.09</td>\n",
       "      <td>15.40</td>\n",
       "      <td>0.096859</td>\n",
       "      <td>0.103024</td>\n",
       "      <td>0.102872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2162.38</td>\n",
       "      <td>1355.90</td>\n",
       "      <td>1351.46</td>\n",
       "      <td>225.84</td>\n",
       "      <td>148.67</td>\n",
       "      <td>148.57</td>\n",
       "      <td>143.70</td>\n",
       "      <td>143.98</td>\n",
       "      <td>4.97</td>\n",
       "      <td>4.59</td>\n",
       "      <td>0.104486</td>\n",
       "      <td>0.109491</td>\n",
       "      <td>0.109753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1317.83</td>\n",
       "      <td>212.15</td>\n",
       "      <td>211.15</td>\n",
       "      <td>145.49</td>\n",
       "      <td>24.65</td>\n",
       "      <td>24.96</td>\n",
       "      <td>23.85</td>\n",
       "      <td>24.12</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.110194</td>\n",
       "      <td>0.115653</td>\n",
       "      <td>0.116973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>544.40</td>\n",
       "      <td>22.90</td>\n",
       "      <td>22.84</td>\n",
       "      <td>63.02</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.59</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.115353</td>\n",
       "      <td>0.120557</td>\n",
       "      <td>0.117970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>173.75</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.83</td>\n",
       "      <td>20.39</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.115432</td>\n",
       "      <td>0.086500</td>\n",
       "      <td>0.088500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44.59</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>5.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.122740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.116907</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.152690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   undir_pair  dir_spair  dir_ppair  undir_conn  dir_sconn  dir_pconn  \\\n",
       "0     1849.92    4512.30    4517.52      178.76     464.47     464.37   \n",
       "1     2162.38    1355.90    1351.46      225.84     148.67     148.57   \n",
       "2     1317.83     212.15     211.15      145.49      24.65      24.96   \n",
       "3      544.40      22.90      22.84       63.02       2.81       2.70   \n",
       "4      173.75       1.68       1.83       20.39       0.22       0.20   \n",
       "5       44.59       0.07       0.20        5.59       0.00       0.02   \n",
       "6       10.02       0.00       0.00        1.30       0.00       0.00   \n",
       "7        1.75       0.00       0.00        0.39       0.00       0.00   \n",
       "8        0.28       0.00       0.00        0.02       0.00       0.00   \n",
       "9        0.08       0.00       0.00        0.02       0.00       0.00   \n",
       "\n",
       "   dir_uni_sconn  dir_uni_pconn  dir_bi_sconn  dir_bi_pconn  undir_perc  \\\n",
       "0         449.38         448.97         15.09         15.40    0.096859   \n",
       "1         143.70         143.98          4.97          4.59    0.104486   \n",
       "2          23.85          24.12          0.80          0.84    0.110194   \n",
       "3           2.72           2.59          0.09          0.11    0.115353   \n",
       "4           0.22           0.19          0.00          0.01    0.115432   \n",
       "5           0.00           0.02          0.00          0.00    0.122740   \n",
       "6           0.00           0.00          0.00          0.00    0.116907   \n",
       "7           0.00           0.00          0.00          0.00    0.152690   \n",
       "8           0.00           0.00          0.00          0.00    0.015000   \n",
       "9           0.00           0.00          0.00          0.00    0.020000   \n",
       "\n",
       "   dir_sperc  dir_pperc  \n",
       "0   0.103024   0.102872  \n",
       "1   0.109491   0.109753  \n",
       "2   0.115653   0.116973  \n",
       "3   0.120557   0.117970  \n",
       "4   0.086500   0.088500  \n",
       "5   0.000000   0.020000  \n",
       "6   0.000000   0.000000  \n",
       "7   0.000000   0.000000  \n",
       "8   0.000000   0.000000  \n",
       "9   0.000000   0.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm_cnr_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CNR curves\n",
    "E, N = len(g_th100.edges), len(g_th100.nodes)\n",
    "ER_p_null = (1 - E / (N*(N-1)))**2\n",
    "\n",
    "plot_cnr(obs_cnr_stats, pm_cnr_stats, pm_cnr_stats_std, ER_p_null, figsize=(10,4), \n",
    "         cfg_name=\"Proximity Model\", fname=\"th100/proximity_th100_cnr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8793027463711647, 6.106235706259406e-05)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson r^2 and p-value for OBS\n",
    "pearson_r2(np.arange(10), obs_cnr_stats[\"undir_perc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2045501930071698, 0.18939789342558672)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson r^2 and p-value for CFG\n",
    "pearson_r2(np.arange(10), pm_cnr_stats[\"undir_perc\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Common Neighbor Rule (OBS vs gER vs PM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CNR curves\n",
    "_, two_pr_GE = compute_three_neuron_motif_probabilities_GE(g_th100)\n",
    "ER_p_null_GE = 1 - 2*two_pr_GE[\"pr_uni\"] - two_pr_GE[\"pr_bi\"]\n",
    "\n",
    "plot_cnr(obs_cnr_stats, pm_cnr_stats, pm_cnr_stats_std, ER_p_null_GE, \n",
    "         figsize=(6,4), fontsizes=(16, 16, 16, 14), cfg_name=\"Proximity Model\",  \n",
    "         fname=\"th100/proximity_th100_cnr_GE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8793027463711647, 6.106235706259406e-05)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson r^2 and p-value for OBS\n",
    "pearson_r2(np.arange(10), obs_cnr_stats[\"undir_perc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2045501930071698, 0.18939789342558672)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson r^2 and p-value for CFG\n",
    "pearson_r2(np.arange(10), pm_cnr_stats[\"undir_perc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "playground",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}