IndividualNetworkAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A notebook for analyzing the networks from individual runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I stole this from http://neuroplausible.com/gini\n",
    "\n",
    "def gini(array):\n",
    "    \"\"\"Calculate the Gini coefficient of a numpy array.\"\"\"\n",
    "    # based on bottom eq:\n",
    "    # http://www.statsdirect.com/help/generatedimages/equations/equation154.svg\n",
    "    # from:\n",
    "    # http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm\n",
    "    # All values are treated equally, arrays must be 1d:\n",
    "    array = array.flatten()\n",
    "    if np.amin(array) < 0:\n",
    "        # Values cannot be negative:\n",
    "        array -= np.amin(array)\n",
    "    # Values cannot be 0:\n",
    "    array += 0.0000001\n",
    "    # Values must be sorted:\n",
    "    array = np.sort(array)\n",
    "    # Index per array element:\n",
    "    index = np.arange(1,array.shape[0]+1)\n",
    "    # Number of array elements:\n",
    "    n = array.shape[0]\n",
    "    # Gini coefficient:\n",
    "    return ((np.sum((2 * index - n  - 1) * array)) / (n * np.sum(array)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the EI index as described in \n",
    "# Krackhardt, D., & Stern, R. N. (1988). \n",
    "# Informal networks and organizational crises: An experimental simulation.\n",
    "# Social psychology quarterly, 123-140.\n",
    "\n",
    "def calculate_EI_index(a_graph, first_subgraph, second_subgraph):\n",
    "    \"\"\"\n",
    "    \n",
    "    = (EL - IL) / (EL + IL)\n",
    "    \n",
    "     The possible scores for this index range\n",
    "     from -1.0 to +1.0. As the E-I index\n",
    "     approaches +1.0, all the links would be\n",
    "     external to the subunits. A score of -1.0\n",
    "     would indicate that all the links are internal.\n",
    "     If the links are divided equally, the index will\n",
    "     equal zero\n",
    "    \n",
    "    \"\"\"\n",
    "    IL = len(first_subgraph.edges) + len(second_subgraph)\n",
    "    \n",
    "    # all of the links, minus the internal links I think.\n",
    "    EL = len(a_graph.edges) - IL\n",
    "    \n",
    "    return (EL - IL) / (EL + IL)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is for turning my gross text thing I exported in netlogo into an actual graph so that I can do something useful "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_graph_from_file(file_name):\n",
    "    \"\"\"\n",
    "    Make a networkx object\n",
    "    \"\"\"\n",
    "\n",
    "    g = nx.Graph()\n",
    "\n",
    "    try:\n",
    "        with open(file_name, \"r\") as f:\n",
    "            lines = f.readlines()\n",
    "\n",
    "            node_names = []\n",
    "            node_colors = []\n",
    "            node_is_adversarial = []\n",
    "\n",
    "            edges = 0\n",
    "\n",
    "            for line in lines:\n",
    "                if line == \"\\n\":\n",
    "                    continue\n",
    "\n",
    "                node_info = eval(line)[0]\n",
    "\n",
    "                node_names.append(node_info[0])\n",
    "                node_colors.append(node_info[1])\n",
    "                node_is_adversarial.append(node_info[2])\n",
    "\n",
    "                edges += len(node_info[-1])\n",
    "\n",
    "                g.add_node(node_info[0], color = node_info[1], is_adversarial=node_info[2] )\n",
    "\n",
    "                for other_node in node_info[-1]:\n",
    "                    # should check if the edge exists first\n",
    "                    g.add_edge(node_info[0], other_node, color=\"blue\")\n",
    "                    \n",
    "    except FileNotFoundError:\n",
    "        print(\"Did not find {}\".format(file_name))\n",
    "                \n",
    "    return g\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_subgraphs(a_graph, expected_matching_prop, run_number, field = \"is_adversarial\", sanity_check=True):\n",
    "    \"\"\"\n",
    "    \n",
    "    Args:\n",
    "    - a_graph: (networkx graph object)\n",
    "    - expected_matching_prop: float\n",
    "    - field\n",
    "    \n",
    "    Returns coop, adver\n",
    "    or \n",
    "    matching, not matching\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    bad_or_nah = np.array(list(g.nodes.data(\"is_adversarial\")))\n",
    "\n",
    "    cooperator_nodes = bad_or_nah[np.where(bad_or_nah[:, 1] == 0)][:, 0]\n",
    "    adversary_nodes = bad_or_nah[np.where(bad_or_nah[:, 1] != 0)][:, 0]\n",
    "\n",
    "    adversary_graph = g.subgraph(adversary_nodes)\n",
    "    cooperator_graph = g.subgraph(cooperator_nodes)\n",
    "    \n",
    "    # make sure these are the correct graphs\n",
    "    if sanity_check:\n",
    "        g_colors = np.array(list(cooperator_graph.nodes.data(\"color\")))\n",
    "\n",
    "        num_same_color = stats.mode(g_colors[:,1])[1][0]\n",
    "        \n",
    "        err_string = \"Run number: {}, Got: {}, Expected: {}\".format(run_number, num_same_color/100, expected_matching_prop)\n",
    "        \n",
    "        # this is gonna be a little off because it actually writes at 501, and I don't have that data\n",
    "        # I hate myself\n",
    "        # I think it's ok, but this should still catch the cases where it isn't right at all\n",
    "        if not abs(num_same_color/100 - expected_matching_prop) < 0.05:\n",
    "            print(err_string)\n",
    "    \n",
    "    if field == \"is_adversarial\":\n",
    "        return cooperator_graph, cooperator_nodes, adversary_graph, adversary_nodes\n",
    "    \n",
    "    elif field == \"color\":\n",
    "        # this should probably be only the cooperator nodes\n",
    "\n",
    "        g_colors = np.array(list(cooperator_graph.nodes.data(\"color\")))\n",
    "        \n",
    "        most_popular_color = stats.mode(g_colors[:,1])[0]\n",
    "        \n",
    "        matching_nodes = g_colors[np.where(g_colors[:, 1] == most_popular_color)][:, 0]\n",
    "        other_nodes = g_colors[np.where(g_colors[:, 1] != most_popular_color)][:, 0]\n",
    "\n",
    "        subgraph1 = g.subgraph(matching_nodes)\n",
    "        subgraph2 = g.subgraph(other_nodes)\n",
    "    \n",
    "    return subgraph1, matching_nodes, subgraph2, other_nodes\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_subset_of_dict(a_dict, keys):\n",
    "    \n",
    "    a_list = []\n",
    "    \n",
    "    for k in list(keys):\n",
    "        a_list.append(a_dict[k])\n",
    "    \n",
    "    return np.array(a_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Begin actual batches for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3020: DtypeWarning: Columns 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have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"./BehaviorSpace_output/cleaned_\" + \"random_blend\" + \"-spreadsheet.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>[run number]</th>\n",
       "      <th>1</th>\n",
       "      <th>1.1</th>\n",
       "      <th>2</th>\n",
       "      <th>2.1</th>\n",
       "      <th>3</th>\n",
       "      <th>3.1</th>\n",
       "      <th>4</th>\n",
       "      <th>4.1</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>is-network-fixed</td>\n",
       "      <td>false</td>\n",
       "      <td>NaN</td>\n",
       "      <td>false</td>\n",
       "      <td>NaN</td>\n",
       "      <td>false</td>\n",
       "      <td>NaN</td>\n",
       "      <td>false</td>\n",
       "      <td>NaN</td>\n",
       "      <td>false</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>enforce-max-links</td>\n",
       "      <td>false</td>\n",
       "      <td>NaN</td>\n",
       "      <td>false</td>\n",
       "      <td>NaN</td>\n",
       "      <td>false</td>\n",
       "      <td>NaN</td>\n",
       "      <td>false</td>\n",
       "      <td>NaN</td>\n",
       "      <td>false</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>number-of-adversarial</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>number-of-nodes</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>memory-duration</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>color-mismatch-tolerance</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>connection-strategy</td>\n",
       "      <td>\"reputation\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"reputation\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"reputation\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"reputation\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"reputation\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>number-of-colors</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>initial-network-structure</td>\n",
       "      <td>\"from-file\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"from-file\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"from-file\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"from-file\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"from-file\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cooperator-color-change-strategy</td>\n",
       "      <td>\"naive-majority-vote\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"naive-majority-vote\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"naive-majority-vote\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"naive-majority-vote\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"naive-majority-vote\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>write-networks-to-file</td>\n",
       "      <td>true</td>\n",
       "      <td>NaN</td>\n",
       "      <td>true</td>\n",
       "      <td>NaN</td>\n",
       "      <td>true</td>\n",
       "      <td>NaN</td>\n",
       "      <td>true</td>\n",
       "      <td>NaN</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>input-file-name</td>\n",
       "      <td>\"Barabasi1\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Barabasi1\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Barabasi1\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Barabasi1\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Barabasi1\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>number-of-links</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>max-links</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>adversarial-placement</td>\n",
       "      <td>\"low\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"low\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"low\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"low\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"low\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>adversary-color-change-strategy</td>\n",
       "      <td>\"disengaged\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"disengaged\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"disengaged\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"disengaged\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"disengaged\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>output-file-name</td>\n",
       "      <td>\"new_rep_dis\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"new_rep_dis\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"new_rep_dis\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"new_rep_dis\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"new_rep_dis\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>write-network-freq</td>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>adversary-act-bad-proportion</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[reporter]</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0]) / (num...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0]) / (num...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0]) / (num...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0]) / (num...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[final]</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[min]</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[max]</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[mean]</td>\n",
       "      <td>0.6168862275449097</td>\n",
       "      <td>0.8999999999999925</td>\n",
       "      <td>0.6407385229540923</td>\n",
       "      <td>0.8999999999999925</td>\n",
       "      <td>0.6381836327345309</td>\n",
       "      <td>0.8999999999999925</td>\n",
       "      <td>0.6257884231536924</td>\n",
       "      <td>0.8999999999999925</td>\n",
       "      <td>0.6582035928143712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[steps]</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[all run data]</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0]) / (num...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0]) / (num...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0]) / (num...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0]) / (num...</td>\n",
       "      <td>(count nodes with [is-adversarial = 0 and colo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>527 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         [run number]  \\\n",
       "0                    is-network-fixed   \n",
       "1                   enforce-max-links   \n",
       "2               number-of-adversarial   \n",
       "3                     number-of-nodes   \n",
       "4                     memory-duration   \n",
       "5            color-mismatch-tolerance   \n",
       "6                 connection-strategy   \n",
       "7                    number-of-colors   \n",
       "8           initial-network-structure   \n",
       "9    cooperator-color-change-strategy   \n",
       "10             write-networks-to-file   \n",
       "11                    input-file-name   \n",
       "12                    number-of-links   \n",
       "13                          max-links   \n",
       "14              adversarial-placement   \n",
       "15    adversary-color-change-strategy   \n",
       "16                   output-file-name   \n",
       "17                 write-network-freq   \n",
       "18       adversary-act-bad-proportion   \n",
       "19                         [reporter]   \n",
       "20                            [final]   \n",
       "21                              [min]   \n",
       "22                              [max]   \n",
       "23                             [mean]   \n",
       "24                            [steps]   \n",
       "25                     [all run data]   \n",
       "26                                NaN   \n",
       "27                                NaN   \n",
       "28                                NaN   \n",
       "29                                NaN   \n",
       "..                                ...   \n",
       "497                               NaN   \n",
       "498                               NaN   \n",
       "499                               NaN   \n",
       "500                               NaN   \n",
       "501                               NaN   \n",
       "502                               NaN   \n",
       "503                               NaN   \n",
       "504                               NaN   \n",
       "505                               NaN   \n",
       "506                               NaN   \n",
       "507                               NaN   \n",
       "508                               NaN   \n",
       "509                               NaN   \n",
       "510                               NaN   \n",
       "511                               NaN   \n",
       "512                               NaN   \n",
       "513                               NaN   \n",
       "514                               NaN   \n",
       "515                               NaN   \n",
       "516                               NaN   \n",
       "517                               NaN   \n",
       "518                               NaN   \n",
       "519                               NaN   \n",
       "520                               NaN   \n",
       "521                               NaN   \n",
       "522                               NaN   \n",
       "523                               NaN   \n",
       "524                               NaN   \n",
       "525                               NaN   \n",
       "526                               NaN   \n",
       "\n",
       "                                                     1  \\\n",
       "0                                                false   \n",
       "1                                                false   \n",
       "2                                                   10   \n",
       "3                                                  100   \n",
       "4                                                  100   \n",
       "5                                                 0.25   \n",
       "6                                         \"reputation\"   \n",
       "7                                                   10   \n",
       "8                                          \"from-file\"   \n",
       "9                                \"naive-majority-vote\"   \n",
       "10                                                true   \n",
       "11                                         \"Barabasi1\"   \n",
       "12                                                   2   \n",
       "13                                                   2   \n",
       "14                                               \"low\"   \n",
       "15                                        \"disengaged\"   \n",
       "16                                       \"new_rep_dis\"   \n",
       "17                                                 250   \n",
       "18                                                   1   \n",
       "19   (count nodes with [is-adversarial = 0 and colo...   \n",
       "20                                                0.26   \n",
       "21                                                0.16   \n",
       "22                                                 0.9   \n",
       "23                                  0.6168862275449097   \n",
       "24                                                 500   \n",
       "25   (count nodes with [is-adversarial = 0 and colo...   \n",
       "26                                                0.16   \n",
       "27                                                0.25   \n",
       "28                                                0.24   \n",
       "29                                                0.25   \n",
       "..                                                 ...   \n",
       "497                                               0.33   \n",
       "498                                               0.34   \n",
       "499                                               0.34   \n",
       "500                                               0.28   \n",
       "501                                               0.31   \n",
       "502                                               0.32   \n",
       "503                                                0.3   \n",
       "504                                               0.27   \n",
       "505                                               0.28   \n",
       "506                                               0.23   \n",
       "507                                               0.22   \n",
       "508                                               0.22   \n",
       "509                                               0.27   \n",
       "510                                               0.27   \n",
       "511                                               0.29   \n",
       "512                                               0.31   \n",
       "513                                               0.28   \n",
       "514                                               0.28   \n",
       "515                                               0.29   \n",
       "516                                               0.28   \n",
       "517                                               0.28   \n",
       "518                                               0.26   \n",
       "519                                               0.24   \n",
       "520                                               0.25   \n",
       "521                                               0.24   \n",
       "522                                               0.27   \n",
       "523                                               0.25   \n",
       "524                                               0.25   \n",
       "525                                               0.27   \n",
       "526                                               0.26   \n",
       "\n",
       "                                                   1.1  \\\n",
       "0                                                  NaN   \n",
       "1                                                  NaN   \n",
       "2                                                  NaN   \n",
       "3                                                  NaN   \n",
       "4                                                  NaN   \n",
       "5                                                  NaN   \n",
       "6                                                  NaN   \n",
       "7                                                  NaN   \n",
       "8                                                  NaN   \n",
       "9                                                  NaN   \n",
       "10                                                 NaN   \n",
       "11                                                 NaN   \n",
       "12                                                 NaN   \n",
       "13                                                 NaN   \n",
       "14                                                 NaN   \n",
       "15                                                 NaN   \n",
       "16                                                 NaN   \n",
       "17                                                 NaN   \n",
       "18                                                 NaN   \n",
       "19   (count nodes with [is-adversarial = 0]) / (num...   \n",
       "20                                                 0.9   \n",
       "21                                                 0.9   \n",
       "22                                                 0.9   \n",
       "23                                  0.8999999999999925   \n",
       "24                                                 500   \n",
       "25   (count nodes with [is-adversarial = 0]) / (num...   \n",
       "26                                                 0.9   \n",
       "27                                                 0.9   \n",
       "28                                                 0.9   \n",
       "29                                                 0.9   \n",
       "..                                                 ...   \n",
       "497                                                0.9   \n",
       "498                                                0.9   \n",
       "499                                                0.9   \n",
       "500                                                0.9   \n",
       "501                                                0.9   \n",
       "502                                                0.9   \n",
       "503                                                0.9   \n",
       "504                                                0.9   \n",
       "505                                                0.9   \n",
       "506                                                0.9   \n",
       "507                                                0.9   \n",
       "508                                                0.9   \n",
       "509                                                0.9   \n",
       "510                                                0.9   \n",
       "511                                                0.9   \n",
       "512                                                0.9   \n",
       "513                                                0.9   \n",
       "514                                                0.9   \n",
       "515                                                0.9   \n",
       "516                                                0.9   \n",
       "517                                                0.9   \n",
       "518                                                0.9   \n",
       "519                                                0.9   \n",
       "520                                                0.9   \n",
       "521                                                0.9   \n",
       "522                                                0.9   \n",
       "523                                                0.9   \n",
       "524                                                0.9   \n",
       "525                                                0.9   \n",
       "526                                                0.9   \n",
       "\n",
       "                                                     2  \\\n",
       "0                                                false   \n",
       "1                                                false   \n",
       "2                                                   10   \n",
       "3                                                  100   \n",
       "4                                                  100   \n",
       "5                                                 0.25   \n",
       "6                                         \"reputation\"   \n",
       "7                                                   10   \n",
       "8                                          \"from-file\"   \n",
       "9                                \"naive-majority-vote\"   \n",
       "10                                                true   \n",
       "11                                         \"Barabasi1\"   \n",
       "12                                                   2   \n",
       "13                                                   2   \n",
       "14                                               \"low\"   \n",
       "15                                        \"disengaged\"   \n",
       "16                                       \"new_rep_dis\"   \n",
       "17                                                 250   \n",
       "18                                                   1   \n",
       "19   (count nodes with [is-adversarial = 0 and colo...   \n",
       "20                                                 0.3   \n",
       "21                                                0.13   \n",
       "22                                                0.88   \n",
       "23                                  0.6407385229540923   \n",
       "24                                                 500   \n",
       "25   (count nodes with [is-adversarial = 0 and colo...   \n",
       "26                                                0.13   \n",
       "27                                                0.16   \n",
       "28                                                 0.2   \n",
       "29                                                 0.2   \n",
       "..                                                 ...   \n",
       "497                                               0.44   \n",
       "498                                               0.48   \n",
       "499                                               0.46   \n",
       "500                                               0.47   \n",
       "501                                               0.51   \n",
       "502                                               0.43   \n",
       "503                                               0.42   \n",
       "504                                               0.44   \n",
       "505                                               0.43   \n",
       "506                                               0.41   \n",
       "507                                               0.41   \n",
       "508                                               0.36   \n",
       "509                                                0.4   \n",
       "510                                               0.36   \n",
       "511                                               0.38   \n",
       "512                                               0.38   \n",
       "513                                               0.37   \n",
       "514                                               0.37   \n",
       "515                                               0.38   \n",
       "516                                               0.39   \n",
       "517                                               0.29   \n",
       "518                                               0.33   \n",
       "519                                               0.34   \n",
       "520                                               0.34   \n",
       "521                                               0.29   \n",
       "522                                               0.29   \n",
       "523                                               0.27   \n",
       "524                                               0.29   \n",
       "525                                               0.27   \n",
       "526                                                0.3   \n",
       "\n",
       "                                                   2.1  \\\n",
       "0                                                  NaN   \n",
       "1                                                  NaN   \n",
       "2                                                  NaN   \n",
       "3                                                  NaN   \n",
       "4                                                  NaN   \n",
       "5                                                  NaN   \n",
       "6                                                  NaN   \n",
       "7                                                  NaN   \n",
       "8                                                  NaN   \n",
       "9                                                  NaN   \n",
       "10                                                 NaN   \n",
       "11                                                 NaN   \n",
       "12                                                 NaN   \n",
       "13                                                 NaN   \n",
       "14                                                 NaN   \n",
       "15                                                 NaN   \n",
       "16                                                 NaN   \n",
       "17                                                 NaN   \n",
       "18                                                 NaN   \n",
       "19   (count nodes with [is-adversarial = 0]) / (num...   \n",
       "20                                                 0.9   \n",
       "21                                                 0.9   \n",
       "22                                                 0.9   \n",
       "23                                  0.8999999999999925   \n",
       "24                                                 500   \n",
       "25   (count nodes with [is-adversarial = 0]) / (num...   \n",
       "26                                                 0.9   \n",
       "27                                                 0.9   \n",
       "28                                                 0.9   \n",
       "29                                                 0.9   \n",
       "..                                                 ...   \n",
       "497                                                0.9   \n",
       "498                                                0.9   \n",
       "499                                                0.9   \n",
       "500                                                0.9   \n",
       "501                                                0.9   \n",
       "502                                                0.9   \n",
       "503                                                0.9   \n",
       "504                                                0.9   \n",
       "505                                                0.9   \n",
       "506                                                0.9   \n",
       "507                                                0.9   \n",
       "508                                                0.9   \n",
       "509                                                0.9   \n",
       "510                                                0.9   \n",
       "511                                                0.9   \n",
       "512                                                0.9   \n",
       "513                                                0.9   \n",
       "514                                                0.9   \n",
       "515                                                0.9   \n",
       "516                                                0.9   \n",
       "517                                                0.9   \n",
       "518                                                0.9   \n",
       "519                                                0.9   \n",
       "520                                                0.9   \n",
       "521                                                0.9   \n",
       "522                                                0.9   \n",
       "523                                                0.9   \n",
       "524                                                0.9   \n",
       "525                                                0.9   \n",
       "526                                                0.9   \n",
       "\n",
       "                                                     3  \\\n",
       "0                                                false   \n",
       "1                                                false   \n",
       "2                                                   10   \n",
       "3                                                  100   \n",
       "4                                                  100   \n",
       "5                                                 0.25   \n",
       "6                                         \"reputation\"   \n",
       "7                                                   10   \n",
       "8                                          \"from-file\"   \n",
       "9                                \"naive-majority-vote\"   \n",
       "10                                                true   \n",
       "11                                         \"Barabasi1\"   \n",
       "12                                                   2   \n",
       "13                                                   2   \n",
       "14                                               \"low\"   \n",
       "15                                        \"disengaged\"   \n",
       "16                                       \"new_rep_dis\"   \n",
       "17                                                 250   \n",
       "18                                                   1   \n",
       "19   (count nodes with [is-adversarial = 0 and colo...   \n",
       "20                                                0.31   \n",
       "21                                                0.08   \n",
       "22                                                0.89   \n",
       "23                                  0.6381836327345309   \n",
       "24                                                 500   \n",
       "25   (count nodes with [is-adversarial = 0 and colo...   \n",
       "26                                                0.08   \n",
       "27                                                0.16   \n",
       "28                                                0.26   \n",
       "29                                                0.23   \n",
       "..                                                 ...   \n",
       "497                                               0.26   \n",
       "498                                               0.27   \n",
       "499                                               0.29   \n",
       "500                                               0.34   \n",
       "501                                                0.3   \n",
       "502                                               0.28   \n",
       "503                                               0.31   \n",
       "504                                               0.29   \n",
       "505                                               0.29   \n",
       "506                                               0.32   \n",
       "507                                               0.32   \n",
       "508                                                0.3   \n",
       "509                                               0.29   \n",
       "510                                               0.31   \n",
       "511                                               0.29   \n",
       "512                                               0.27   \n",
       "513                                                0.3   \n",
       "514                                               0.31   \n",
       "515                                               0.33   \n",
       "516                                               0.33   \n",
       "517                                               0.33   \n",
       "518                                               0.34   \n",
       "519                                                0.3   \n",
       "520                                                0.3   \n",
       "521                                               0.35   \n",
       "522                                               0.33   \n",
       "523                                               0.32   \n",
       "524                                               0.35   \n",
       "525                                               0.35   \n",
       "526                                               0.31   \n",
       "\n",
       "                                                   3.1  \\\n",
       "0                                                  NaN   \n",
       "1                                                  NaN   \n",
       "2                                                  NaN   \n",
       "3                                                  NaN   \n",
       "4                                                  NaN   \n",
       "5                                                  NaN   \n",
       "6                                                  NaN   \n",
       "7                                                  NaN   \n",
       "8                                                  NaN   \n",
       "9                                                  NaN   \n",
       "10                                                 NaN   \n",
       "11                                                 NaN   \n",
       "12                                                 NaN   \n",
       "13                                                 NaN   \n",
       "14                                                 NaN   \n",
       "15                                                 NaN   \n",
       "16                                                 NaN   \n",
       "17                                                 NaN   \n",
       "18                                                 NaN   \n",
       "19   (count nodes with [is-adversarial = 0]) / (num...   \n",
       "20                                                 0.9   \n",
       "21                                                 0.9   \n",
       "22                                                 0.9   \n",
       "23                                  0.8999999999999925   \n",
       "24                                                 500   \n",
       "25   (count nodes with [is-adversarial = 0]) / (num...   \n",
       "26                                                 0.9   \n",
       "27                                                 0.9   \n",
       "28                                                 0.9   \n",
       "29                                                 0.9   \n",
       "..                                                 ...   \n",
       "497                                                0.9   \n",
       "498                                                0.9   \n",
       "499                                                0.9   \n",
       "500                                                0.9   \n",
       "501                                                0.9   \n",
       "502                                                0.9   \n",
       "503                                                0.9   \n",
       "504                                                0.9   \n",
       "505                                                0.9   \n",
       "506                                                0.9   \n",
       "507                                                0.9   \n",
       "508                                                0.9   \n",
       "509                                                0.9   \n",
       "510                                                0.9   \n",
       "511                                                0.9   \n",
       "512                                                0.9   \n",
       "513                                                0.9   \n",
       "514                                                0.9   \n",
       "515                                                0.9   \n",
       "516                                                0.9   \n",
       "517                                                0.9   \n",
       "518                                                0.9   \n",
       "519                                                0.9   \n",
       "520                                                0.9   \n",
       "521                                                0.9   \n",
       "522                                                0.9   \n",
       "523                                                0.9   \n",
       "524                                                0.9   \n",
       "525                                                0.9   \n",
       "526                                                0.9   \n",
       "\n",
       "                                                     4  \\\n",
       "0                                                false   \n",
       "1                                                false   \n",
       "2                                                   10   \n",
       "3                                                  100   \n",
       "4                                                  100   \n",
       "5                                                 0.25   \n",
       "6                                         \"reputation\"   \n",
       "7                                                   10   \n",
       "8                                          \"from-file\"   \n",
       "9                                \"naive-majority-vote\"   \n",
       "10                                                true   \n",
       "11                                         \"Barabasi1\"   \n",
       "12                                                   2   \n",
       "13                                                   2   \n",
       "14                                               \"low\"   \n",
       "15                                        \"disengaged\"   \n",
       "16                                       \"new_rep_dis\"   \n",
       "17                                                 250   \n",
       "18                                                   1   \n",
       "19   (count nodes with [is-adversarial = 0 and colo...   \n",
       "20                                                0.33   \n",
       "21                                                0.16   \n",
       "22                                                 0.9   \n",
       "23                                  0.6257884231536924   \n",
       "24                                                 500   \n",
       "25   (count nodes with [is-adversarial = 0 and colo...   \n",
       "26                                                0.16   \n",
       "27                                                0.16   \n",
       "28                                                0.17   \n",
       "29                                                0.23   \n",
       "..                                                 ...   \n",
       "497                                               0.43   \n",
       "498                                               0.41   \n",
       "499                                               0.44   \n",
       "500                                                0.4   \n",
       "501                                               0.41   \n",
       "502                                               0.38   \n",
       "503                                               0.34   \n",
       "504                                               0.37   \n",
       "505                                               0.36   \n",
       "506                                               0.35   \n",
       "507                                               0.33   \n",
       "508                                               0.35   \n",
       "509                                               0.37   \n",
       "510                                               0.37   \n",
       "511                                               0.36   \n",
       "512                                               0.38   \n",
       "513                                               0.35   \n",
       "514                                               0.33   \n",
       "515                                               0.33   \n",
       "516                                               0.36   \n",
       "517                                               0.34   \n",
       "518                                               0.34   \n",
       "519                                               0.29   \n",
       "520                                               0.27   \n",
       "521                                               0.28   \n",
       "522                                               0.34   \n",
       "523                                               0.33   \n",
       "524                                               0.35   \n",
       "525                                               0.33   \n",
       "526                                               0.33   \n",
       "\n",
       "                                                   4.1  \\\n",
       "0                                                  NaN   \n",
       "1                                                  NaN   \n",
       "2                                                  NaN   \n",
       "3                                                  NaN   \n",
       "4                                                  NaN   \n",
       "5                                                  NaN   \n",
       "6                                                  NaN   \n",
       "7                                                  NaN   \n",
       "8                                                  NaN   \n",
       "9                                                  NaN   \n",
       "10                                                 NaN   \n",
       "11                                                 NaN   \n",
       "12                                                 NaN   \n",
       "13                                                 NaN   \n",
       "14                                                 NaN   \n",
       "15                                                 NaN   \n",
       "16                                                 NaN   \n",
       "17                                                 NaN   \n",
       "18                                                 NaN   \n",
       "19   (count nodes with [is-adversarial = 0]) / (num...   \n",
       "20                                                 0.9   \n",
       "21                                                 0.9   \n",
       "22                                                 0.9   \n",
       "23                                  0.8999999999999925   \n",
       "24                                                 500   \n",
       "25   (count nodes with [is-adversarial = 0]) / (num...   \n",
       "26                                                 0.9   \n",
       "27                                                 0.9   \n",
       "28                                                 0.9   \n",
       "29                                                 0.9   \n",
       "..                                                 ...   \n",
       "497                                                0.9   \n",
       "498                                                0.9   \n",
       "499                                                0.9   \n",
       "500                                                0.9   \n",
       "501                                                0.9   \n",
       "502                                                0.9   \n",
       "503                                                0.9   \n",
       "504                                                0.9   \n",
       "505                                                0.9   \n",
       "506                                                0.9   \n",
       "507                                                0.9   \n",
       "508                                                0.9   \n",
       "509                                                0.9   \n",
       "510                                                0.9   \n",
       "511                                                0.9   \n",
       "512                                                0.9   \n",
       "513                                                0.9   \n",
       "514                                                0.9   \n",
       "515                                                0.9   \n",
       "516                                                0.9   \n",
       "517                                                0.9   \n",
       "518                                                0.9   \n",
       "519                                                0.9   \n",
       "520                                                0.9   \n",
       "521                                                0.9   \n",
       "522                                                0.9   \n",
       "523                                                0.9   \n",
       "524                                                0.9   \n",
       "525                                                0.9   \n",
       "526                                                0.9   \n",
       "\n",
       "                                                     5  \n",
       "0                                                false  \n",
       "1                                                false  \n",
       "2                                                   10  \n",
       "3                                                  100  \n",
       "4                                                  100  \n",
       "5                                                 0.25  \n",
       "6                                         \"reputation\"  \n",
       "7                                                   10  \n",
       "8                                          \"from-file\"  \n",
       "9                                \"naive-majority-vote\"  \n",
       "10                                                true  \n",
       "11                                         \"Barabasi1\"  \n",
       "12                                                   2  \n",
       "13                                                   2  \n",
       "14                                               \"low\"  \n",
       "15                                        \"disengaged\"  \n",
       "16                                       \"new_rep_dis\"  \n",
       "17                                                 250  \n",
       "18                                                   1  \n",
       "19   (count nodes with [is-adversarial = 0 and colo...  \n",
       "20                                                 0.3  \n",
       "21                                                0.15  \n",
       "22                                                 0.9  \n",
       "23                                  0.6582035928143712  \n",
       "24                                                 500  \n",
       "25   (count nodes with [is-adversarial = 0 and colo...  \n",
       "26                                                0.16  \n",
       "27                                                0.15  \n",
       "28                                                0.19  \n",
       "29                                                0.18  \n",
       "..                                                 ...  \n",
       "497                                               0.48  \n",
       "498                                               0.46  \n",
       "499                                               0.38  \n",
       "500                                               0.33  \n",
       "501                                               0.33  \n",
       "502                                               0.34  \n",
       "503                                                0.4  \n",
       "504                                               0.37  \n",
       "505                                               0.35  \n",
       "506                                               0.34  \n",
       "507                                               0.36  \n",
       "508                                               0.35  \n",
       "509                                               0.34  \n",
       "510                                               0.25  \n",
       "511                                               0.29  \n",
       "512                                                0.3  \n",
       "513                                                0.3  \n",
       "514                                               0.29  \n",
       "515                                               0.32  \n",
       "516                                               0.31  \n",
       "517                                               0.29  \n",
       "518                                                0.3  \n",
       "519                                               0.33  \n",
       "520                                               0.23  \n",
       "521                                               0.29  \n",
       "522                                               0.29  \n",
       "523                                               0.31  \n",
       "524                                                0.3  \n",
       "525                                               0.35  \n",
       "526                                                0.3  \n",
       "\n",
       "[527 rows x 10 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, :10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  I'm about to repeat the same thing like 6 times, but I think it is going to be easier than doing it once and slicing and all of that nonsesne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed_blend\n",
      "Did not find ./networks/Barabasi1/fixed_blend_1_final.txt\n"
     ]
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-167-13b1c5bbd03c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[1;31m# whole graph stuff here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mavg_clustering\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mavg_clustering\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malgorithms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcluster\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage_clustering\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         \u001b[0mdegree\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malgorithms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcentrality\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdegree_centrality\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\networkx\\algorithms\\cluster.py\u001b[0m in \u001b[0;36maverage_clustering\u001b[1;34m(G, nodes, weight, count_zeros)\u001b[0m\n\u001b[0;32m    261\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcount_zeros\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m         \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 263\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "# all of the data together.\n",
    "\n",
    "# all of the numpy arrays to keep track of the things I care about go here...\n",
    "# holy shit this is dumb, I should use a data frame or a dict or anything else\n",
    "avg_clustering = np.array([])\n",
    "\n",
    "avg_degree_centrality = np.array([])\n",
    "gini_degree_centrality = np.array([])\n",
    "\n",
    "avg_degree_centrality = np.array([])\n",
    "gini_degree_centrality = np.array([])\n",
    "\n",
    "avg_betweenness_centrality = np.array([])\n",
    "gini_betweenness_centrality = np.array([])\n",
    "\n",
    "EI_index_array = np.array([])\n",
    "\n",
    "coop_avg_degree_centrality = np.array([])\n",
    "coop_gini_degree_centrality = np.array([])\n",
    "\n",
    "coop_avg_degree_centrality = np.array([])\n",
    "coop_gini_degree_centrality = np.array([])\n",
    "\n",
    "coop_avg_betweenness_centrality = np.array([])\n",
    "coop_gini_betweenness_centrality = np.array([])\n",
    "\n",
    "adver_avg_degree_centrality = np.array([])\n",
    "adver_gini_degree_centrality = np.array([])\n",
    "\n",
    "adver_avg_degree_centrality = np.array([])\n",
    "adver_gini_degree_centrality = np.array([])\n",
    "\n",
    "adver_avg_betweenness_centrality = np.array([])\n",
    "adver_gini_betweenness_centrality = np.array([])\n",
    "\n",
    "for exp_name in [\"new_rep_dis\", \"rep_blendHighHigh\", \"rep_blendLowHigh\", \"fixed_dis\",\n",
    "                 \"fixed_blend\", \"random_blend\", \"random_dis\"]:  # obvi missing some of the data.\n",
    "\n",
    "    df = pd.read_csv(\"./BehaviorSpace_output/cleaned_\" + exp_name + \"-spreadsheet.csv\")\n",
    "    \n",
    "    \n",
    "    run_number = 1\n",
    "    print(exp_name)\n",
    "    \n",
    "    for ii in range(1, df.shape[1], 2): # the cols with the data in them\n",
    "        if ii % 100 == 0:\n",
    "            print(ii)\n",
    "        \n",
    "        network = df.iloc[11, ii].strip('\"')  # need this all the time to find the file\n",
    "        final_matching_prop = float(df.iloc[20, ii])\n",
    "        best_possible_matching_prop = float(df.iloc[20, ii+1])\n",
    "\n",
    "        \n",
    "        g = construct_graph_from_file(\"./networks/\"+network+\"/\" + exp_name +\"_\" + str(run_number) +\"_final.txt\")\n",
    "        \n",
    "        # whole graph stuff here\n",
    "        avg_clustering = np.append(avg_clustering, nx.algorithms.cluster.average_clustering(g))\n",
    "        \n",
    "        degree = nx.algorithms.centrality.degree_centrality(g)\n",
    "        avg_degree_centrality = np.append(avg_degree_centrality, np.mean(list(degree.values())))\n",
    "        gini_degree_centrality = np.append(gini_degree_centrality, gini(np.array(list(degree.values()))))\n",
    "        \n",
    "        between = nx.algorithms.centrality.betweenness_centrality(g)\n",
    "        avg_betweenness_centrality = np.append(avg_betweenness_centrality, np.mean(list(between.values())))\n",
    "        gini_betweenness_centrality = np.append(gini_betweenness_centrality, gini(np.array(list(between.values()))))\n",
    "\n",
    "        \n",
    "        coop_graph, coop_nodes, adver_graph, adver_nodes = construct_subgraphs(g, final_matching_prop, run_number)\n",
    "        \n",
    "        # subgraph stuff here\n",
    "        coop_degree = select_subset_of_dict(degree, coop_nodes)\n",
    "        coop_avg_degree_centrality = np.append(coop_avg_degree_centrality, np.mean(coop_degree))\n",
    "        coop_gini_degree_centrality = np.append(coop_gini_degree_centrality, gini(coop_degree))\n",
    "        \n",
    "        coop_btw = select_subset_of_dict(between, coop_nodes)\n",
    "        coop_avg_betweenness_centrality = np.append(coop_avg_betweenness_centrality, np.mean(coop_btw))\n",
    "        coop_gini_betweenness_centrality = np.append(coop_gini_betweenness_centrality, gini(coop_btw))\n",
    "        \n",
    "        adver_degree = select_subset_of_dict(degree, adver_nodes)\n",
    "        adver_avg_degree_centrality = np.append(adver_avg_degree_centrality, np.mean(adver_degree))\n",
    "        adver_gini_degree_centrality = np.append(adver_gini_degree_centrality, gini(adver_degree))\n",
    "        \n",
    "        adver_btw = select_subset_of_dict(between, adver_nodes)\n",
    "        adver_avg_betweenness_centrality = np.append(adver_avg_betweenness_centrality, np.mean(adver_btw))\n",
    "        adver_gini_betweenness_centrality = np.append(adver_gini_betweenness_centrality, gini(adver_btw))\n",
    "        \n",
    "        EI_index_array = np.append(EI_index_array, calculate_EI_index(g, coop_graph, adver_graph))\n",
    "        \n",
    "        run_number += 1\n",
    "            \n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Network type RQ:\n",
    "\n",
    "# need some validation that these are actually the correct files from each run\n",
    "\n",
    "all_\n",
    "\n",
    "for exp_name in [\"new_rep_dis\", \"rep_blendHighHigh\", \"rep_blendLowHigh\", \"fixed_dis\",\n",
    "                 \"fixed_blend\", \"random_blend\", \"random_dis\"]:  # obvi missing some of the data.\n",
    "\n",
    "    df = pd.read_csv(\"./BehaviorSpace_output/cleaned_\" + exp_name + \"-spreadsheet.csv\")\n",
    "    \n",
    "    print(exp_name, df.iloc[24].describe())\n",
    "    \n",
    "\n",
    "    for ii in range(1, df.shape[1], 2): # the cols with the data in them\n",
    "        network = df.iloc[11, ii].strip('\"')\n",
    "\n",
    "        if network == \"Barabasi1\":\n",
    "            b_durations = np.append(b_durations, int(df.iloc[24, ii]))\n",
    "        elif network == \"Erdos_Renyi1\":\n",
    "            e_durations = np.append(e_durations, int(df.iloc[24, ii]))\n",
    "        elif network == \"Lattice1\":\n",
    "            l_durations = np.append(l_durations, int(df.iloc[24, ii]))\n",
    "        elif network == \"Watts_Strogatz1\":\n",
    "            w_durations = np.append(w_durations, int(df.iloc[24, ii]))\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over all of the\n",
    "\n",
    "matched_percentage = np.array([])\n",
    "\n",
    "for exp_name in [\"new_rep_dis\", \"rep_blendHighHigh\", \"rep_blendLowHigh\", \"fixed_dis\",\n",
    "                 \"fixed_blend\", \"random_blend\", \"random_dis\"]:  # obvi missing some of the data.\n",
    "        \n",
    "    df = pd.read_csv(\"./BehaviorSpace_output/cleaned_\" + exp_name + \"-spreadsheet.csv\")\n",
    "    \n",
    "    for ii in range(1, df.shape[1], 2):\n",
    "        this_runs_steps = int(df.iloc[24, ii])\n",
    "        \n",
    "        if this_runs_steps >= 500:  # you did not converge (unless you did at the end?)\n",
    "            matched_percentage = np.append(matched_percentage, float(df.iloc[526, ii]) / float(df.iloc[526, ii+1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = construct_graph_from_file(\"./networks/Barabasi1/rep_dis_12017_final.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run number: 1, Got: 0.11, Expected: 0.19\n"
     ]
    }
   ],
   "source": [
    "coop_graph, coop_nodes, adver_graph, adver_nodes = construct_subgraphs(g, final_matching_prop, run_number)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_popular_color_graph, other_colors_graph = construct_subgraphs(g, field=\"color\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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eD2zBYbGQ4RRyc3M1bNzDat6hVKWlpfJpf6MeaPompezEVqxYoREjRqht27aaNm2abr31VtORgKvCQoZTaNy4sV58ta+2H43X3uxfVWoVK68glUJ2Qvv371d4eLj27NmjmJgYztNwGnzKGk7jjkYdlfD5L6rfqIbOnbWkAj7Q40zOnDmjiRMnys/PTx06dNC2bdsoYzgVChlOo3H9jnrnjQXqFviMauY9pJ6dR+qLL74wHQsVYMWKFbr77ru1fft2bd26VVFRUTwrhtPhGTKc1ubNm9WnTx89++yz+vvf/847sB3QgQMHFB4erpycHEVHRys4ONh0JKDS8DcUnJa/v7/S09O1fv16PfLII8rPzzcdCZeouLhYr732mnx9fRUYGKht27ZRxnB6FDKc2k033aR169apadOm8vPz086dO01Hwh/46quv1KpVK3333XfKzMzUmDFjVKtWLdOxgErHyRou48MPP1RkZKTmzJmjXr16mY6DC+TmJ2rR8ve1btl2HdhepJkzZ6pr166mYwFVikKGS8nIyFDv3r01YMAAvfbaa3J3dzcdyeXtOvq1Nhx6RSePnVHujmJFDpmtOxvx6Wm4Hk7WcClt27ZVenq6UlJSFBISol9++cV0JJdVWlqq2bNn658xI1Wterl++bFEdTzcdPzMFtPRACMoZLicG264QWvXrlWLFi3k6+ur7du3m47kUizL0sqVK+Xt7a1FixZpQOgYVXerrbva1JN34A3y9AgwHREwgpM1XFpsbKxGjRqld999V48//rjpOE4vOztbEREROnTokKZOnaqQkBC5ubkpNz+Rd5DD5VHIcHmZmZnq1auXnnjiCb355ps8V64ER44c0YQJE7RixQq98sorev7551WjRg3TsQBb4WQNl9emTRtlZGQoIyNDPXr00MmTJ01HchqFhYWaOHGi7rnnHjVs2FDff/+9hg0bRhkD/wOFDEhq2LChVq9erXvvvVe+vr7Kzs42HcmhlZWVaf78+WrevLl2796tLVu2aPLkybr22mtNRwNsi5M18B8WLlyoESNGaMaMGXrqqadMx3E469at0+jRo3XNNddo2rRp8vf3Nx0JcAgUMvA/ZGdn67HHHlOvXr00adIkVa/Obyr9I7t27VJkZKR2796tyZMnq1evXnJzczMdC3AYnKyB/8Hb21vp6enatm2bunXrphMnTpiOZFs//vij/vrXvyooKEhdunTRjh071Lt3b8oYuEwUMvA7GjRooK+//lq+vr5q27atMjMzTUeylTNnzuitt96Sl5eXatWqpZycHI0aNYr3TgNXiEIGLsLd3V2TJk3SlClT1K1bN8XGxpqOZFx5ebk++eQTtWjRQhkZGUpNTdU777yj66+/3nQ0wKHxDBm4RN99950ee+wxhYaGasqUKS751Z2kpCSNHj1akjRt2jTdf//9hhMBzoNCBi7DL7/8on79+qm4uFiff/65brjhBtORqsSePXsUFRWlLVu26K233tKTTz6patU4sAEViT9RwGW47rrrtGLFCgUGBsrX11cZGRmmI1Wqn3/+WSNHjlS7du3k5+en3bt3q1+/fpQxUAn4UwVcJnd3d73xxht655131KNHD3344YemI1W4s2fP6u2331bz5s1VUlKinTt3asyYMapTp47paIDT4mQNXIWdO3eqZ8+e6tatm95++22Hf65sWZaWLFmiqKgoeXl5acqUKWrZsqXpWIBLoJCBq5Sfn6+BAwfq1KlTiouL04033mg60hXZvHmzRo8erV9//VXTpk1Tly5dTEcCXAona+Aq1a9fX8uWLVPnzp3Vtm1bbd682XSky3Lw4EE99dRT6t27t8LCwrRlyxbKGDCAQgYqQLVq1TRx4kRFR0crJCRE8+bNMx3pD506dUpRUVFq27atWrZsqZycHD377LP8+knAEAoZqECPPvqokpKSNHXqVA0dOlQlJSWmI/2Xc+fOadasWWrevLlOnjyp7777Tq+88or+9Kc/mY4GuDQKGahgLVq0UFpamo4eParOnTvr6NGjpiNJ+tcHtpYvX6577rlHy5Yt05o1a/T+++/r5ptvNh0NgChkoFJ4eHgoPj5e3bt3l6+vrzZt2mQ0T2Zmprp06aKXX35Z77zzzm+/+xmAfVDIQCWpVq2aJkyYoNmzZ+vRRx9VTExMlWfIy8vToEGD9PDDD+uJJ55Qdna2evTowW9iAmyIQgYqWUhIiJKTkzV9+nQ9//zzOnv2bKX/zNOnT2vChAny9vaWp6encnJy9MILL/B7nQEbo5CBKnDXXXcpNTVVJ0+eVKdOnXTkyJFK+TllZWWaO3eumjdvroMHD2rr1q1644035OHhUSk/D0DFoZCBKlKvXj0tXrxYoaGh8vX1VXJycoX+91evXi0fHx/FxsZq+fLl+vjjj3XbbbdV6M8AUHl4UxdgwNdff61nnnlGr776qoYOHXpVz3S3b9+uiIgI7d+/X1OmTNGjjz7KM2LAAbGQAQN69OihjRs36r333lNYWJiKi4sv+79x7NgxPf/88+rSpYseeughbd++XT179qSMAQdFIQOG3HHHHdq0aZMKCwsVFBSkQ4cOKTc/USk/TFZufuLv/ntFRUX6xz/+obvvvlseHh7avXu3RowYoZo1a1ZhegAVjZM1YJhlWZo6daq+3vievLvUUuGvZxXY/c96oOmbaly/42//XHl5uWJjYzVu3DgFBgbqrbfe0u23324wOYCKxHcgAMPc3Nz00ksv6baUH/TdgW+Ut++cSq1i5RWk/lbICQkJGj16tGrWrKnPP/9cgYGBhlMDqGgUMmAT7Vo9rt25Sbrh1nOq7lZbnh4BysnJUWRkpLZv365Jkybp8ccf5xkx4KQ4WQM2kpufqLyCVNVTS8VMWaPPP/9cUVFRGj58uGrVqmU6HoBKxIe6ABu5sba/Uj5zU5f7wlStWjXt2rVLERERlDHgAjhZA4YlJiYq6/u1OnA8TTuzD6ruOS+lpKTorrvuMh0NQBWikAHDyq7Zr7VpH8ijQXX1HfNnBd8xSo3rU8aAq+FkDRhiWZbef/99RUREqIXfNbrxtlqy3M4pryDVdDQABrCQAQOOHz+u5557Tj/88INmLHhH+8rmqNQq/u3T1QBcD4UMVLGlS5dq6NChGjx4sBYvXqyaNWuqcX5j5RWkytMj4N9eBgLAdfC1J6CKFBQUKDw8XBs2bNCCBQvUvn1705EA2AjPkIEqkJiYqHvvvVc1atRQdnY2ZQzgv3CyBipRcXGxxo8fr08//VQxMTEKCQkxHQmATVHIQCXJysrSwIEDdddddyk7O1uNGjUyHQmAjXGyBipYWVmZJk2apODgYEVGRmrx4sWUMYA/xEIGKtD+/fv19NNPq0aNGsrIyFDjxo1NRwLgIFjIQAWwLEtz586Vn5+fevfurXXr1lHGAC4LCxm4SsePH9eQIUOUl5enhIQE3X333aYjAXBALGTgKixdulTe3t669957tXnzZsoYwBVjIQNX4NSpUwoPD1dycrLi4+MVGBhoOhIAB8dCBi5TYmKifHx8VKtWLWVlZVHGACoECxm4ROdf8rFw4ULFxMTo4YcfNh0JgBOhkIFLcP4lH82bN1d2drYaNmxoOhIAJ8PJGriIsrIyvfXWW+ratateeuklxcXFUcYAKgULGfgd+/bt09NPP62aNWsqIyNDt912m+lIAJwYCxn4D+df8uHv768+ffpo3bp1lDGASsdCBi5w4Us+EhMT1apVK9ORALgIFjLw//znSz4oYwBViYUMl8dLPgDYAQsZLi0hIUHe3t6qXbs2L/kAYBQLGS7pwpd8zJ07Vw899JDpSABcHIUMl8NLPgDYESdruAxe8gHAzljIcAnnX/JRq1YtXvIBwJZYyHBq51/yERAQoD59+uibb76hjAHYEgsZTuvYsWN67rnndPjwYSUkJPC9YgC2xkKGU4qPj5ePj4+8vb2VmppKGQOwPRYynMr5l3ykpKRo6dKlateunelIAHBJWMhwGudf8lGnTh1t3bqVMgbgUFjIcHjFxcUaN26cPvvsM17yAcBhUchwaFu3btXAgQPVokULXvIBwKFxsoZDOv+Sj27duikqKoqXfABweCxkOIyPPvpItRvmK+fQRu3cmqsf99TlJR8AnAaFDIdh1T2mjMMf6uy5c3pgyPUKvnOCbruOMgbgHDhZwyGcOHFC69M/U07mKdVvVENu7mU6cjrNdCwAqDAUMmxv7dq18vHxUdM/e6vnC0116111Vd2ttjw9AkxHA4AKw8katlVcXKyxY8dq0aJF+vDDD/Xggw8qNz9ReQWp8vQIUOP6HU1HBIAK42ZZlmU6BPCfduzYoX79+unOO+9UTEyMGjRoYDoSAFQqTtawFcuyFB0drU6dOik8PFyLFy+mjAG4BE7WsI3jx49r8ODB+umnn7Rx40Y1a9bMdCQAqDIsZNjCV199JR8fH7Vu3VopKSmUMQCXw0KGUUVFRYqMjNRXX32lRYsW6f777zcdCQCMYCHDmKysLLVt21a//PKLsrKyKGMALo1CRpUrLy/XP//5TwUHB2vcuHH69NNPVb9+fdOxAMAoTtaoUocPH9agQYNUXFystLQ0NW3a1HQkALAFFjKqzJIlS9SmTRt17NhRCQkJlDEAXICFjEr366+/auTIkUpISNCyZcsUEMArLwHgP7GQUanS0tLUunVrlZeXa+vWrZQxAPwOFjIqRVlZmSZNmqTp06dr1qxZevzxx01HAgBbo5BR4XJzczVw4EBVr15dmZmZ8vT0NB0JAGyPkzUq1MKFC+Xr66vQ0FB98803lDEAXCIWMirEqVOnNGzYMGVkZGjVqlVq06aN6UgA4FBYyLhqycnJ8vHxUb169ZSZmUkZA8AVYCHjip07d06vv/66YmJiNHfuXIWGhpqOBAAOi0LGFdm7d68GDBig+vXrKysrSzfddJPpSADg0DhZ47JYlqX58+erXbt26tevn1auXEkZA0AFYCHjkv3888964YUXlJOTo2+//Vb33HOP6UgA4DRYyLgk69evl7e3tzw9PZWWlkYZA0AFYyHjokpKSjR+/Hh98skn+uCDD9StWzfTkQDAKVHI+F27d+9Wv379dOuttyorK0uNGjUyHQkAnBYna/wXy7I0e/ZsdejQQS+88IK++OILyhgAKhkLGf/mp59+UlhYmA4fPqzk5GS1aNHCdCQAcAksZPxm1apV8vHxkZeXlzZt2kQZA0AVYiFDxcXFioqK0tKlSxUbG6vOnTubjgQALoeF7OK+++47+fr66ujRo8rOzqaMAcAQCtlFlZeXa/r06XrggQcUERGhzz//XNddd53pWADgsjhZu6CjR4/qmWee0alTp5Samqo77rjDdCQAcHksZBezbNkytW7dWgEBAUpKSqKMAcAmWMguorCwUKNHj9aaNWu0ZMkStW/f3nQkAMAFWMguYMuWLWrTpo2Kioq0detWyhgAbMjNsizLdAhUvMTERKVv/1oHT2zSti0HNPSpyXrqqadMxwIA/A5O1k6q3OOgMg58qhtuq6lBf79Fgbf/2XQkAMBFcLJ2MpZl6eOPP1bE6NGSLFVzd1O5SpRXkGo6GgDgIljITuTkyZMaOnSoduzYoeiPp+qg2/sqtYpV3a22PD0CTMcDAFwEhewkVq9erbCwMPXt21cLFixQ7dq1lZt/u/IKUuXpEaDG9TuajggAuAg+1OXgioqKFBUVpWXLlmn+/Pnq0qWL6UgAgCvAM2QHlpGRoTZt2ujnn39WdnY2ZQwADoyTtQMqLS3VpEmTNGPGDM2YMUNPPvmk6UgAgKtEITuYvXv36umnn1bdunWVmZkpT09P05EAABWAk7WDsCxLc+fOVUBAgJ544gmtWbOGMgYAJ8JCdgDHjx/XkCFDlJeXpw0bNsjLy8t0JABABWMh29zy5cvl4+Oje+65R5s3b6aMAcBJsZBt6vTp0xo1apS+/fZbxcXFqUOHDqYjAQAqEQvZhjZu3CgfHx9ZlqWsrCzKGABcAAvZRkpKSjRx4kTNmzdPs2fPVs+ePU1HAgBUEQrZJnbt2qUBAwbo5ptvVlZWlm666SbTkQAAVYiTtWHl5eWaMWOG7r//fr3wwgv68ssvKWMAcEEsZIMOHz73eDzUAAADS0lEQVSsZ599VgUFBdq0aZOaNWtmOhIAwBAWsiGLFi1SmzZtdP/99ys5OZkyBgAXx0KuYvn5+frb3/6m9PR0rVixQr6+vqYjAQBsgIVchdavXy9vb2/Vr19fW7dupYwBAL9hIVeB4uJijRs3Tp999pnmzZun7t27m44EALAZCrmSZWdna8CAAWrevLmys7PVsGFD05EAADbEybqSlJWVaerUqXrwwQcVGRmpuLg4yhgA8LtYyJXg4MGDGjRokCQpPT1dTZo0MRsIAGB7LOQKZFmWFixYIF9fX4WEhOjbb7+ljAEAl4SFXEFOnDihv/zlL8rJydE333wjb29v05EAAA6EhVwBVq1aJW9vbzVp0kTp6emUMQDgsrGQr0JRUZEiIyO1YsUKxcbGqnPnzqYjAQAcFAv5CqWnp6t169YqKChQdnY2ZQwAuCos5MtUWlqqN998U7NmzdLMmTPVt29f05EAAE6AQr4Me/bs0cCBA+Xh4aHMzEzdcsstpiMBAJwEJ+tLYFmW5syZo8DAQPXv31+rVq2ijAEAFYqF/AeOHTumIUOG6OjRo9qwYYNatmxpOhIAwAmxkC/iiy++kI+Pj3x8fLRp0ybKGABQaVjI/8Pp06c1cuRIJSQkKD4+XoGBgaYjAQCcHAv5PyQnJ8vb21vVqlVTVlYWZQwAqBIsZEmz5v9dh3/erqMHCrVqSZbmzJmjRx55xHQsAIALcflCzs1PVOahT3TiSKE87/qTVm2aLe8mlDEAoGq5/Mk6ryBV7jVLVecad9WoJf1abbfpSAAAF+TyC9nTI0Dtui1XqVWs6m615ekRYDoSAMAFuVmWZZkOYVpufqLyClLl6RGgxvU7mo4DAHBBFDIAADbg8s+QAQCwAwoZAAAboJABALABChkAABugkAEAsAEKGQAAG6CQAQCwAQoZAAAboJABALABChkAABugkAEAsAEKGQAAG6CQAQCwAQoZAAAboJABALABChkAABugkAEAsAEKGQAAG6CQAQCwAQoZAAAboJABALABChkAABugkAEAsAEKGQAAG6CQAQCwAQoZAAAboJABALABChkAABugkAEAsAEKGQAAG6CQAQCwAQoZAAAboJABALABChkAABv4PznZFtD5PDx0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.drawing.layout.kamada_kawai_layout(most_popular_color_graph)\n",
    "\n",
    "nx.draw(most_popular_color_graph, pos=pos, with_labels=True, font_size=3,\n",
    "        node_color='yellowgreen', node_size=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "adver_graph, coop_graph = construct_subgraphs(g, field=\"is_adversarial\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.drawing.layout.kamada_kawai_layout(cooperator_graph)\n",
    "\n",
    "nx.draw(cooperator_graph, pos=pos, with_labels=True, font_size=3,\n",
    "        node_color='yellowgreen', node_size=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.drawing.layout.kamada_kawai_layout(adversary_graph)\n",
    "\n",
    "nx.draw(adversary_graph, pos=pos, with_labels=True, font_size=3,\n",
    "        node_color='yellowgreen', node_size=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.drawing.layout.kamada_kawai_layout(g)\n",
    "\n",
    "nx.draw(g, pos=pos, with_labels=True, font_size=3,\n",
    "        node_color='yellowgreen', node_size=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07975460122699386"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EI_index(g, cooperator_graph, adversary_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(adver_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x = nx.algorithms.centrality.betweenness_centrality(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002977622025241072"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[77]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-145-694110abce53>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malgorithms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcentrality\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbetweenness_centrality\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madver_nodes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\networkx\\classes\\graph.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, n)\u001b[0m\n\u001b[0;32m    456\u001b[0m         \u001b[0mAtlasView\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m         \"\"\"\n\u001b[1;32m--> 458\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madj\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    459\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    460\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0madd_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_for_adding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\networkx\\classes\\coreviews.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mAtlasView\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_atlas\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "nx.algorithms.centrality.betweenness_centrality(g[list(adver_nodes)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([12., 22., 36., 14., 12.,  3.,  0.,  0.,  0.,  1.]),\n",
       " array([0.        , 0.01282865, 0.0256573 , 0.03848595, 0.05131459,\n",
       "        0.06414324, 0.07697189, 0.08980054, 0.10262919, 0.11545784,\n",
       "        0.12828648]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist(nx.algorithms.centrality.betweenness_centrality(g).values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1., 12.,  0., 60.,  0., 17.,  8.,  0.,  1.,  1.]),\n",
       " array([0.01010101, 0.01616162, 0.02222222, 0.02828283, 0.03434343,\n",
       "        0.04040404, 0.04646465, 0.05252525, 0.05858586, 0.06464646,\n",
       "        0.07070707]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UBu80uA/4+OofBHCEwV++R7sf0gWbXf+4fQB/zuBI9uCqr7O24j4BXsrgIuLB7hdw51bsY833eD0zDPQx98cLu/1xV7c/fnMr9tGt+92ul7uBN27hPn4Z+Pdp1eadopLUiFmfQ5ckTYiBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSI/4P10zS8Y+J08YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nx.algorithms.centrality.degree_centrality(g).values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13484621626455803"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini(np.array(list(nx.algorithms.centrality.degree_centrality(g).values())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.011333333333333332"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.algorithms.cluster.average_clustering(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.032929292929292926"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(list(nx.algorithms.centrality.degree_centrality(g).values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}