results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregate results across all networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>net</th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>tstt</th>\n",
       "      <th>wvc</th>\n",
       "      <th>vmt</th>\n",
       "      <th>costs_rmse</th>\n",
       "      <th>cost_err25</th>\n",
       "      <th>cost_err75</th>\n",
       "      <th>flows_rmse</th>\n",
       "      <th>flow_err25</th>\n",
       "      <th>flow_err75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>friedrichshain-center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>7.265680e+05</td>\n",
       "      <td>0.457995</td>\n",
       "      <td>1.723705e+07</td>\n",
       "      <td>0.425308</td>\n",
       "      <td>-3.565000e-04</td>\n",
       "      <td>3.510000e-04</td>\n",
       "      <td>53.603236</td>\n",
       "      <td>-16.447255</td>\n",
       "      <td>13.078757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10832</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>7.479308e+06</td>\n",
       "      <td>1.473962</td>\n",
       "      <td>3.418976e+06</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>-2.068000e-03</td>\n",
       "      <td>4.462500e-04</td>\n",
       "      <td>3.783068</td>\n",
       "      <td>-1.432877</td>\n",
       "      <td>0.757699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5529</th>\n",
       "      <td>berlin-prenzlauerberg-center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.399862e+06</td>\n",
       "      <td>0.430578</td>\n",
       "      <td>2.896959e+07</td>\n",
       "      <td>0.000694</td>\n",
       "      <td>-4.000000e-06</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.084753</td>\n",
       "      <td>-0.052444</td>\n",
       "      <td>0.019100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8110</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>7.765108e+06</td>\n",
       "      <td>1.484358</td>\n",
       "      <td>3.481516e+06</td>\n",
       "      <td>1.166177</td>\n",
       "      <td>-2.560140e-01</td>\n",
       "      <td>5.580735e-01</td>\n",
       "      <td>636.153594</td>\n",
       "      <td>-161.413190</td>\n",
       "      <td>433.823943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>friedrichshain-center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>7.282254e+05</td>\n",
       "      <td>0.473145</td>\n",
       "      <td>1.730154e+07</td>\n",
       "      <td>0.004241</td>\n",
       "      <td>-3.000000e-06</td>\n",
       "      <td>1.050000e-05</td>\n",
       "      <td>0.919008</td>\n",
       "      <td>-0.214723</td>\n",
       "      <td>0.210487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5579</th>\n",
       "      <td>berlin-prenzlauerberg-center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.399989e+06</td>\n",
       "      <td>0.430615</td>\n",
       "      <td>2.897182e+07</td>\n",
       "      <td>0.000993</td>\n",
       "      <td>-1.000000e-06</td>\n",
       "      <td>4.000000e-06</td>\n",
       "      <td>0.119736</td>\n",
       "      <td>-0.034864</td>\n",
       "      <td>0.055626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11237</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>7.479396e+06</td>\n",
       "      <td>1.473980</td>\n",
       "      <td>3.419014e+06</td>\n",
       "      <td>0.004449</td>\n",
       "      <td>-2.771000e-03</td>\n",
       "      <td>3.570000e-04</td>\n",
       "      <td>2.810595</td>\n",
       "      <td>-1.538646</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10395</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>7.483259e+06</td>\n",
       "      <td>1.474172</td>\n",
       "      <td>3.420307e+06</td>\n",
       "      <td>0.023578</td>\n",
       "      <td>-9.325750e-03</td>\n",
       "      <td>9.904000e-03</td>\n",
       "      <td>21.662715</td>\n",
       "      <td>-5.817831</td>\n",
       "      <td>9.530961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>friedrichshain-center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>7.286761e+05</td>\n",
       "      <td>0.473392</td>\n",
       "      <td>1.730634e+07</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>-7.000000e-06</td>\n",
       "      <td>9.500000e-06</td>\n",
       "      <td>0.945552</td>\n",
       "      <td>-0.405574</td>\n",
       "      <td>0.210028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>friedrichshain-center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>7.273352e+05</td>\n",
       "      <td>0.473767</td>\n",
       "      <td>1.724891e+07</td>\n",
       "      <td>0.019879</td>\n",
       "      <td>-3.900000e-05</td>\n",
       "      <td>4.850000e-05</td>\n",
       "      <td>5.910799</td>\n",
       "      <td>-2.001352</td>\n",
       "      <td>0.879345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                net  mean     sd          tstt       wvc  \\\n",
       "159           friedrichshain-center   1.0  0.500  7.265680e+05  0.457995   \n",
       "10832                    SiouxFalls   1.0  0.001  7.479308e+06  1.473962   \n",
       "5529   berlin-prenzlauerberg-center   1.0  0.001  1.399862e+06  0.430578   \n",
       "8110                     SiouxFalls   1.0  0.200  7.765108e+06  1.484358   \n",
       "1008          friedrichshain-center   1.0  0.010  7.282254e+05  0.473145   \n",
       "5579   berlin-prenzlauerberg-center   1.0  0.001  1.399989e+06  0.430615   \n",
       "11237                    SiouxFalls   1.0  0.001  7.479396e+06  1.473980   \n",
       "10395                    SiouxFalls   1.0  0.005  7.483259e+06  1.474172   \n",
       "1106          friedrichshain-center   1.0  0.010  7.286761e+05  0.473392   \n",
       "650           friedrichshain-center   1.0  0.050  7.273352e+05  0.473767   \n",
       "\n",
       "                vmt  costs_rmse    cost_err25    cost_err75  flows_rmse  \\\n",
       "159    1.723705e+07    0.425308 -3.565000e-04  3.510000e-04   53.603236   \n",
       "10832  3.418976e+06    0.004413 -2.068000e-03  4.462500e-04    3.783068   \n",
       "5529   2.896959e+07    0.000694 -4.000000e-06  1.000000e-06    0.084753   \n",
       "8110   3.481516e+06    1.166177 -2.560140e-01  5.580735e-01  636.153594   \n",
       "1008   1.730154e+07    0.004241 -3.000000e-06  1.050000e-05    0.919008   \n",
       "5579   2.897182e+07    0.000993 -1.000000e-06  4.000000e-06    0.119736   \n",
       "11237  3.419014e+06    0.004449 -2.771000e-03  3.570000e-04    2.810595   \n",
       "10395  3.420307e+06    0.023578 -9.325750e-03  9.904000e-03   21.662715   \n",
       "1106   1.730634e+07    0.002535 -7.000000e-06  9.500000e-06    0.945552   \n",
       "650    1.724891e+07    0.019879 -3.900000e-05  4.850000e-05    5.910799   \n",
       "\n",
       "       flow_err25  flow_err75  \n",
       "159    -16.447255   13.078757  \n",
       "10832   -1.432877    0.757699  \n",
       "5529    -0.052444    0.019100  \n",
       "8110  -161.413190  433.823943  \n",
       "1008    -0.214723    0.210487  \n",
       "5579    -0.034864    0.055626  \n",
       "11237   -1.538646    0.937008  \n",
       "10395   -5.817831    9.530961  \n",
       "1106    -0.405574    0.210028  \n",
       "650     -2.001352    0.879345  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nets = [\n",
    "    'friedrichshain-center',\n",
    "    'berlin-mitte-center',\n",
    "    'berlin-mitte-prenzlauerberg-friedrichshain-center',\n",
    "    'berlin-prenzlauerberg-center',\n",
    "    'berlin-tiergarten',\n",
    "    'SiouxFalls'\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(columns=['net', 'mean', 'sd', 'tstt', 'wvc', 'vmt', 'costs_rmse', 'cost_err25', 'cost_err75', 'flows_rmse', 'flow_err25', 'flow_err75'])\n",
    "for net in nets:\n",
    "    # read 0 SD trials\n",
    "    dft = pd.read_csv(f'output/data/{net}_trials_means_only.csv')\n",
    "    dft['net'] = net\n",
    "    dft['sd'] = 0.0\n",
    "    df = df.append(dft, ignore_index=True, sort=False)\n",
    "    \n",
    "    # read variable SD trials\n",
    "    dft = pd.read_csv(f'output/data/{net}_trials.csv')\n",
    "    dft['net'] = net\n",
    "    df = df.append(dft, ignore_index=True, sort=False)\n",
    "df.loc[df['net']=='berlin-mitte-prenzlauerberg-friedrichshain-center','net'] = 'bmpfc'\n",
    "nets[2] = 'bmpfc'\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>tstt</th>\n",
       "      <th>wvc</th>\n",
       "      <th>vmt</th>\n",
       "      <th>costs_rmse</th>\n",
       "      <th>cost_err25</th>\n",
       "      <th>cost_err75</th>\n",
       "      <th>flows_rmse</th>\n",
       "      <th>flow_err25</th>\n",
       "      <th>flow_err75</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net</th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">SiouxFalls</th>\n",
       "      <th>0.00</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <th>0.000</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <th>0.000</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">1.00</th>\n",
       "      <th>0.000</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001</th>\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>0.005</th>\n",
       "      <td>500</td>\n",
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       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
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       "    <tr>\n",
       "      <th>0.010</th>\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",
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       "    <tr>\n",
       "      <th>0.020</th>\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",
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       "    <tr>\n",
       "      <th>0.050</th>\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>0.100</th>\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",
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       "    <tr>\n",
       "      <th>0.200</th>\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",
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       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
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       "      <td>500</td>\n",
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       "    <tr>\n",
       "      <th>1.25</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.75</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">berlin-mitte-center</th>\n",
       "      <th>0.00</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">1.00</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.005</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.020</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.050</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.200</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.25</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">bmpfc</th>\n",
       "      <th>0.50</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">1.00</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.005</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.020</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.050</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.200</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.25</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.75</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">friedrichshain-center</th>\n",
       "      <th>0.00</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">1.00</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.005</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.020</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.050</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.200</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.25</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.75</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  tstt  wvc  vmt  costs_rmse  cost_err25  \\\n",
       "net                   mean sd                                              \n",
       "SiouxFalls            0.00 0.000     1    1    1           1           1   \n",
       "                      0.25 0.000     1    1    1           1           1   \n",
       "                      0.50 0.000     1    1    1           1           1   \n",
       "                      0.75 0.000     1    1    1           1           1   \n",
       "                      1.00 0.000     1    1    1           1           1   \n",
       "                           0.001   500  500  500         500         500   \n",
       "                           0.005   500  500  500         500         500   \n",
       "                           0.010   500  500  500         500         500   \n",
       "                           0.020   500  500  500         500         500   \n",
       "                           0.050   500  500  500         500         500   \n",
       "                           0.100   500  500  500         500         500   \n",
       "                           0.200   500  500  500         500         500   \n",
       "                           0.500   500  500  500         500         500   \n",
       "                      1.25 0.000     1    1    1           1           1   \n",
       "                      1.50 0.000     1    1    1           1           1   \n",
       "                      1.75 0.000     1    1    1           1           1   \n",
       "berlin-mitte-center   0.00 0.000     1    1    1           1           1   \n",
       "                      0.25 0.000     1    1    1           1           1   \n",
       "                      0.50 0.000     1    1    1           1           1   \n",
       "                      0.75 0.000     1    1    1           1           1   \n",
       "                      1.00 0.000     1    1    1           1           1   \n",
       "                           0.001   200  200  200         200         200   \n",
       "                           0.005   200  200  200         200         200   \n",
       "                           0.010   200  200  200         200         200   \n",
       "                           0.020   200  200  200         200         200   \n",
       "                           0.050   200  200  200         200         200   \n",
       "                           0.100   200  200  200         200         200   \n",
       "                           0.200   200  200  200         200         200   \n",
       "                           0.500   200  200  200         200         200   \n",
       "                      1.25 0.000     1    1    1           1           1   \n",
       "...                                ...  ...  ...         ...         ...   \n",
       "bmpfc                 0.50 0.000     1    1    1           1           1   \n",
       "                      0.75 0.000     1    1    1           1           1   \n",
       "                      1.00 0.000     1    1    1           1           1   \n",
       "                           0.001   100  100  100         100         100   \n",
       "                           0.005   100  100  100         100         100   \n",
       "                           0.010   100  100  100         100         100   \n",
       "                           0.020   100  100  100         100         100   \n",
       "                           0.050   100  100  100         100         100   \n",
       "                           0.100   100  100  100         100         100   \n",
       "                           0.200   100  100  100         100         100   \n",
       "                           0.500   100  100  100         100         100   \n",
       "                      1.25 0.000     1    1    1           1           1   \n",
       "                      1.50 0.000     1    1    1           1           1   \n",
       "                      1.75 0.000     1    1    1           1           1   \n",
       "friedrichshain-center 0.00 0.000     1    1    1           1           1   \n",
       "                      0.25 0.000     1    1    1           1           1   \n",
       "                      0.50 0.000     1    1    1           1           1   \n",
       "                      0.75 0.000     1    1    1           1           1   \n",
       "                      1.00 0.000     1    1    1           1           1   \n",
       "                           0.001   200  200  200         200         200   \n",
       "                           0.005   200  200  200         200         200   \n",
       "                           0.010   200  200  200         200         200   \n",
       "                           0.020   200  200  200         200         200   \n",
       "                           0.050   200  200  200         200         200   \n",
       "                           0.100   200  200  200         200         200   \n",
       "                           0.200   200  200  200         200         200   \n",
       "                           0.500   200  200  200         200         200   \n",
       "                      1.25 0.000     1    1    1           1           1   \n",
       "                      1.50 0.000     1    1    1           1           1   \n",
       "                      1.75 0.000     1    1    1           1           1   \n",
       "\n",
       "                                  cost_err75  flows_rmse  flow_err25  \\\n",
       "net                   mean sd                                          \n",
       "SiouxFalls            0.00 0.000           1           1           1   \n",
       "                      0.25 0.000           1           1           1   \n",
       "                      0.50 0.000           1           1           1   \n",
       "                      0.75 0.000           1           1           1   \n",
       "                      1.00 0.000           1           1           1   \n",
       "                           0.001         500         500         500   \n",
       "                           0.005         500         500         500   \n",
       "                           0.010         500         500         500   \n",
       "                           0.020         500         500         500   \n",
       "                           0.050         500         500         500   \n",
       "                           0.100         500         500         500   \n",
       "                           0.200         500         500         500   \n",
       "                           0.500         500         500         500   \n",
       "                      1.25 0.000           1           1           1   \n",
       "                      1.50 0.000           1           1           1   \n",
       "                      1.75 0.000           1           1           1   \n",
       "berlin-mitte-center   0.00 0.000           1           1           1   \n",
       "                      0.25 0.000           1           1           1   \n",
       "                      0.50 0.000           1           1           1   \n",
       "                      0.75 0.000           1           1           1   \n",
       "                      1.00 0.000           1           1           1   \n",
       "                           0.001         200         200         200   \n",
       "                           0.005         200         200         200   \n",
       "                           0.010         200         200         200   \n",
       "                           0.020         200         200         200   \n",
       "                           0.050         200         200         200   \n",
       "                           0.100         200         200         200   \n",
       "                           0.200         200         200         200   \n",
       "                           0.500         200         200         200   \n",
       "                      1.25 0.000           1           1           1   \n",
       "...                                      ...         ...         ...   \n",
       "bmpfc                 0.50 0.000           1           1           1   \n",
       "                      0.75 0.000           1           1           1   \n",
       "                      1.00 0.000           1           1           1   \n",
       "                           0.001         100         100         100   \n",
       "                           0.005         100         100         100   \n",
       "                           0.010         100         100         100   \n",
       "                           0.020         100         100         100   \n",
       "                           0.050         100         100         100   \n",
       "                           0.100         100         100         100   \n",
       "                           0.200         100         100         100   \n",
       "                           0.500         100         100         100   \n",
       "                      1.25 0.000           1           1           1   \n",
       "                      1.50 0.000           1           1           1   \n",
       "                      1.75 0.000           1           1           1   \n",
       "friedrichshain-center 0.00 0.000           1           1           1   \n",
       "                      0.25 0.000           1           1           1   \n",
       "                      0.50 0.000           1           1           1   \n",
       "                      0.75 0.000           1           1           1   \n",
       "                      1.00 0.000           1           1           1   \n",
       "                           0.001         200         200         200   \n",
       "                           0.005         200         200         200   \n",
       "                           0.010         200         200         200   \n",
       "                           0.020         200         200         200   \n",
       "                           0.050         200         200         200   \n",
       "                           0.100         200         200         200   \n",
       "                           0.200         200         200         200   \n",
       "                           0.500         200         200         200   \n",
       "                      1.25 0.000           1           1           1   \n",
       "                      1.50 0.000           1           1           1   \n",
       "                      1.75 0.000           1           1           1   \n",
       "\n",
       "                                  flow_err75  \n",
       "net                   mean sd                 \n",
       "SiouxFalls            0.00 0.000           1  \n",
       "                      0.25 0.000           1  \n",
       "                      0.50 0.000           1  \n",
       "                      0.75 0.000           1  \n",
       "                      1.00 0.000           1  \n",
       "                           0.001         500  \n",
       "                           0.005         500  \n",
       "                           0.010         500  \n",
       "                           0.020         500  \n",
       "                           0.050         500  \n",
       "                           0.100         500  \n",
       "                           0.200         500  \n",
       "                           0.500         500  \n",
       "                      1.25 0.000           1  \n",
       "                      1.50 0.000           1  \n",
       "                      1.75 0.000           1  \n",
       "berlin-mitte-center   0.00 0.000           1  \n",
       "                      0.25 0.000           1  \n",
       "                      0.50 0.000           1  \n",
       "                      0.75 0.000           1  \n",
       "                      1.00 0.000           1  \n",
       "                           0.001         200  \n",
       "                           0.005         200  \n",
       "                           0.010         200  \n",
       "                           0.020         200  \n",
       "                           0.050         200  \n",
       "                           0.100         200  \n",
       "                           0.200         200  \n",
       "                           0.500         200  \n",
       "                      1.25 0.000           1  \n",
       "...                                      ...  \n",
       "bmpfc                 0.50 0.000           1  \n",
       "                      0.75 0.000           1  \n",
       "                      1.00 0.000           1  \n",
       "                           0.001         100  \n",
       "                           0.005         100  \n",
       "                           0.010         100  \n",
       "                           0.020         100  \n",
       "                           0.050         100  \n",
       "                           0.100         100  \n",
       "                           0.200         100  \n",
       "                           0.500         100  \n",
       "                      1.25 0.000           1  \n",
       "                      1.50 0.000           1  \n",
       "                      1.75 0.000           1  \n",
       "friedrichshain-center 0.00 0.000           1  \n",
       "                      0.25 0.000           1  \n",
       "                      0.50 0.000           1  \n",
       "                      0.75 0.000           1  \n",
       "                      1.00 0.000           1  \n",
       "                           0.001         200  \n",
       "                           0.005         200  \n",
       "                           0.010         200  \n",
       "                           0.020         200  \n",
       "                           0.050         200  \n",
       "                           0.100         200  \n",
       "                           0.200         200  \n",
       "                           0.500         200  \n",
       "                      1.25 0.000           1  \n",
       "                      1.50 0.000           1  \n",
       "                      1.75 0.000           1  \n",
       "\n",
       "[96 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = df.groupby(['net', 'mean', 'sd'])\n",
    "summary.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">tstt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">wvc</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
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       "      <th>count</th>\n",
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       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net</th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">SiouxFalls</th>\n",
       "      <th>0.00</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-15292.871544</td>\n",
       "      <td>-15292.871544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-8382.829875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8382.829875</td>\n",
       "      <td>-8382.829875</td>\n",
       "      <td>-8382.829875</td>\n",
       "      <td>-8382.829875</td>\n",
       "      <td>-8382.829875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.277958e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.277958e+05</td>\n",
       "      <td>8.277958e+05</td>\n",
       "      <td>8.277958e+05</td>\n",
       "      <td>8.277958e+05</td>\n",
       "      <td>8.277958e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.514646</td>\n",
       "      <td>...</td>\n",
       "      <td>-11911.621544</td>\n",
       "      <td>-11911.621544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5650.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5650.000000</td>\n",
       "      <td>-5650.000000</td>\n",
       "      <td>-5650.000000</td>\n",
       "      <td>-5650.000000</td>\n",
       "      <td>-5650.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <th>0.0</th>\n",
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       "      <td>1.870592e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.829003</td>\n",
       "      <td>...</td>\n",
       "      <td>-8262.457785</td>\n",
       "      <td>-8262.457785</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-3475.187360</td>\n",
       "      <td>-3475.187360</td>\n",
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       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.654464e+06</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3.654464e+06</td>\n",
       "      <td>3.654464e+06</td>\n",
       "      <td>3.654464e+06</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-1768.643446</td>\n",
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       "      <td>7.480171e+06</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.474030</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tstt                                                \\\n",
       "                    count          mean std           min           25%   \n",
       "net        mean sd                                                        \n",
       "SiouxFalls 0.00 0.0   1.0  0.000000e+00 NaN  0.000000e+00  0.000000e+00   \n",
       "           0.25 0.0   1.0  8.277958e+05 NaN  8.277958e+05  8.277958e+05   \n",
       "           0.50 0.0   1.0  1.870592e+06 NaN  1.870592e+06  1.870592e+06   \n",
       "           0.75 0.0   1.0  3.654464e+06 NaN  3.654464e+06  3.654464e+06   \n",
       "           1.00 0.0   1.0  7.480171e+06 NaN  7.480171e+06  7.480171e+06   \n",
       "\n",
       "                                                                wvc            \\\n",
       "                              50%           75%           max count      mean   \n",
       "net        mean sd                                                              \n",
       "SiouxFalls 0.00 0.0  0.000000e+00  0.000000e+00  0.000000e+00   1.0  0.000000   \n",
       "           0.25 0.0  8.277958e+05  8.277958e+05  8.277958e+05   1.0  0.514646   \n",
       "           0.50 0.0  1.870592e+06  1.870592e+06  1.870592e+06   1.0  0.829003   \n",
       "           0.75 0.0  3.654464e+06  3.654464e+06  3.654464e+06   1.0  1.145810   \n",
       "           1.00 0.0  7.480171e+06  7.480171e+06  7.480171e+06   1.0  1.474030   \n",
       "\n",
       "                     ...    flow_err25               flow_err75               \\\n",
       "                     ...           75%           max      count         mean   \n",
       "net        mean sd   ...                                                       \n",
       "SiouxFalls 0.00 0.0  ... -15292.871544 -15292.871544        1.0 -8382.829875   \n",
       "           0.25 0.0  ... -11911.621544 -11911.621544        1.0 -5650.000000   \n",
       "           0.50 0.0  ...  -8262.457785  -8262.457785        1.0 -3475.187360   \n",
       "           0.75 0.0  ...  -3942.319025  -3942.319025        1.0 -1768.643446   \n",
       "           1.00 0.0  ...      0.000000      0.000000        1.0     0.000000   \n",
       "\n",
       "                                                                             \\\n",
       "                    std          min          25%          50%          75%   \n",
       "net        mean sd                                                            \n",
       "SiouxFalls 0.00 0.0 NaN -8382.829875 -8382.829875 -8382.829875 -8382.829875   \n",
       "           0.25 0.0 NaN -5650.000000 -5650.000000 -5650.000000 -5650.000000   \n",
       "           0.50 0.0 NaN -3475.187360 -3475.187360 -3475.187360 -3475.187360   \n",
       "           0.75 0.0 NaN -1768.643446 -1768.643446 -1768.643446 -1768.643446   \n",
       "           1.00 0.0 NaN     0.000000     0.000000     0.000000     0.000000   \n",
       "\n",
       "                                  \n",
       "                             max  \n",
       "net        mean sd                \n",
       "SiouxFalls 0.00 0.0 -8382.829875  \n",
       "           0.25 0.0 -5650.000000  \n",
       "           0.50 0.0 -3475.187360  \n",
       "           0.75 0.0 -1768.643446  \n",
       "           1.00 0.0     0.000000  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary = summary.describe()\n",
    "df_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>net</th>\n",
       "      <th>mean</th>\n",
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       "      <th>tstt</th>\n",
       "      <th>wvc</th>\n",
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       "      <th>6803</th>\n",
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       "      <th>8176</th>\n",
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       "      <td>1.009526</td>\n",
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       "      <td>1.023558</td>\n",
       "      <td>1.044981</td>\n",
       "      <td>2.367249</td>\n",
       "      <td>-0.297515</td>\n",
       "      <td>1.300617</td>\n",
       "      <td>1489.505010</td>\n",
       "      <td>-259.736099</td>\n",
       "      <td>1336.163241</td>\n",
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       "    <tr>\n",
       "      <th>2714</th>\n",
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       "      <th>3949</th>\n",
       "      <td>bmpfc</td>\n",
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       "      <td>0.001</td>\n",
       "      <td>0.999987</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999993</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.041966</td>\n",
       "      <td>-0.016530</td>\n",
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      "text/plain": [
       "                               net  mean     sd      tstt       wvc       vmt  \\\n",
       "6803             berlin-tiergarten   1.0  0.010  1.001287  1.001257  1.001044   \n",
       "5001  berlin-prenzlauerberg-center   1.0  0.020  0.999416  0.999376  0.999836   \n",
       "8176                    SiouxFalls   1.0  0.200  1.041705  1.009526  1.021219   \n",
       "8988                    SiouxFalls   1.0  0.050  0.996501  0.999074  1.000513   \n",
       "7270                    SiouxFalls   1.0  0.500  1.111435  1.023558  1.044981   \n",
       "2714           berlin-mitte-center   1.0  0.010  1.000503  1.000221  1.000414   \n",
       "5990             berlin-tiergarten   1.0  0.200  0.975427  0.974275  0.980545   \n",
       "3949                         bmpfc   1.0  0.001  0.999987  0.999998  0.999993   \n",
       "6586             berlin-tiergarten   1.0  0.020  0.998057  0.997852  0.998362   \n",
       "1847           berlin-mitte-center   1.0  0.200  0.994081  0.991223  0.999643   \n",
       "\n",
       "      costs_rmse  cost_err25  cost_err75   flows_rmse  flow_err25   flow_err75  \n",
       "6803    0.006264    0.000000    0.000002     0.888839    0.000000     0.304860  \n",
       "5001    0.011646   -0.000047    0.000009     1.755156   -0.593764     0.481607  \n",
       "8176    0.678484   -0.005501    0.454417   680.192357   -9.051711   425.436518  \n",
       "8988    0.177938   -0.082509    0.063924   122.814783  -60.336929    80.179289  \n",
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       "5990    0.051261   -0.000011    0.000000    18.267463   -6.388354     0.851997  \n",
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       "6586    0.004303   -0.000002    0.000000     1.837549   -0.576753     0.032543  \n",
       "1847    0.159554   -0.000093    0.000059    16.508111   -3.557148     5.568953  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trel = df.copy()\n",
    "to_change = ['tstt', 'wvc', 'vmt']\n",
    "for net in nets:\n",
    "    true_row_values = df_trel.loc[(df_trel['net'] == net) & (df_trel['mean'] == 1) & (df_trel['sd'] == 0), to_change]\n",
    "    df_trel.loc[(df_trel['net'] == net), to_change] /= true_row_values.squeeze()\n",
    "df_trel.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tstt                                              \\\n",
       "                    count      mean std       min       25%       50%   \n",
       "net        mean sd                                                      \n",
       "SiouxFalls 0.00 0.0   1.0  0.000000 NaN  0.000000  0.000000  0.000000   \n",
       "           0.25 0.0   1.0  0.110665 NaN  0.110665  0.110665  0.110665   \n",
       "           0.50 0.0   1.0  0.250073 NaN  0.250073  0.250073  0.250073   \n",
       "           0.75 0.0   1.0  0.488553 NaN  0.488553  0.488553  0.488553   \n",
       "           1.00 0.0   1.0  1.000000 NaN  1.000000  1.000000  1.000000   \n",
       "\n",
       "                                          wvc            ...    flow_err25  \\\n",
       "                          75%       max count      mean  ...           75%   \n",
       "net        mean sd                                       ...                 \n",
       "SiouxFalls 0.00 0.0  0.000000  0.000000   1.0  0.000000  ... -15292.871544   \n",
       "           0.25 0.0  0.110665  0.110665   1.0  0.349142  ... -11911.621544   \n",
       "           0.50 0.0  0.250073  0.250073   1.0  0.562406  ...  -8262.457785   \n",
       "           0.75 0.0  0.488553  0.488553   1.0  0.777331  ...  -3942.319025   \n",
       "           1.00 0.0  1.000000  1.000000   1.0  1.000000  ...      0.000000   \n",
       "\n",
       "                                  flow_err75                                \\\n",
       "                              max      count         mean std          min   \n",
       "net        mean sd                                                           \n",
       "SiouxFalls 0.00 0.0 -15292.871544        1.0 -8382.829875 NaN -8382.829875   \n",
       "           0.25 0.0 -11911.621544        1.0 -5650.000000 NaN -5650.000000   \n",
       "           0.50 0.0  -8262.457785        1.0 -3475.187360 NaN -3475.187360   \n",
       "           0.75 0.0  -3942.319025        1.0 -1768.643446 NaN -1768.643446   \n",
       "           1.00 0.0      0.000000        1.0     0.000000 NaN     0.000000   \n",
       "\n",
       "                                                                         \n",
       "                             25%          50%          75%          max  \n",
       "net        mean sd                                                       \n",
       "SiouxFalls 0.00 0.0 -8382.829875 -8382.829875 -8382.829875 -8382.829875  \n",
       "           0.25 0.0 -5650.000000 -5650.000000 -5650.000000 -5650.000000  \n",
       "           0.50 0.0 -3475.187360 -3475.187360 -3475.187360 -3475.187360  \n",
       "           0.75 0.0 -1768.643446 -1768.643446 -1768.643446 -1768.643446  \n",
       "           1.00 0.0     0.000000     0.000000     0.000000     0.000000  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_trel = df_trel.groupby(['net', 'mean', 'sd'])\n",
    "df_summary_trel = summary_trel.describe()\n",
    "df_summary_trel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>net</th>\n",
       "      <th>mean</th>\n",
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       "      <td>-8382.829875</td>\n",
       "      <td>-8382.829875</td>\n",
       "      <td>-8382.829875</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SiouxFalls</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>8.277958e+05</td>\n",
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       "      <td>-5650.000000</td>\n",
       "      <td>-5650.000000</td>\n",
       "      <td>-5650.000000</td>\n",
       "      <td>-5650.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>1.870592e+06</td>\n",
       "      <td>...</td>\n",
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       "      <td>-3475.187360</td>\n",
       "      <td>-3475.187360</td>\n",
       "      <td>-3475.187360</td>\n",
       "      <td>-3475.187360</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.654464e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.654464e+06</td>\n",
       "      <td>3.654464e+06</td>\n",
       "      <td>3.654464e+06</td>\n",
       "      <td>3.654464e+06</td>\n",
       "      <td>...</td>\n",
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       "      <td>-3942.319025</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-1768.643446</td>\n",
       "      <td>-1768.643446</td>\n",
       "      <td>-1768.643446</td>\n",
       "      <td>-1768.643446</td>\n",
       "      <td>-1768.643446</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.480171e+06</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          net  mean   sd  tstt                                                \\\n",
       "                         count          mean std           min           25%   \n",
       "0  SiouxFalls  0.00  0.0   1.0  0.000000e+00 NaN  0.000000e+00  0.000000e+00   \n",
       "1  SiouxFalls  0.25  0.0   1.0  8.277958e+05 NaN  8.277958e+05  8.277958e+05   \n",
       "2  SiouxFalls  0.50  0.0   1.0  1.870592e+06 NaN  1.870592e+06  1.870592e+06   \n",
       "3  SiouxFalls  0.75  0.0   1.0  3.654464e+06 NaN  3.654464e+06  3.654464e+06   \n",
       "4  SiouxFalls  1.00  0.0   1.0  7.480171e+06 NaN  7.480171e+06  7.480171e+06   \n",
       "\n",
       "                               ...    flow_err25               flow_err75  \\\n",
       "            50%           75%  ...           75%           max      count   \n",
       "0  0.000000e+00  0.000000e+00  ... -15292.871544 -15292.871544        1.0   \n",
       "1  8.277958e+05  8.277958e+05  ... -11911.621544 -11911.621544        1.0   \n",
       "2  1.870592e+06  1.870592e+06  ...  -8262.457785  -8262.457785        1.0   \n",
       "3  3.654464e+06  3.654464e+06  ...  -3942.319025  -3942.319025        1.0   \n",
       "4  7.480171e+06  7.480171e+06  ...      0.000000      0.000000        1.0   \n",
       "\n",
       "                                                                        \\\n",
       "          mean std          min          25%          50%          75%   \n",
       "0 -8382.829875 NaN -8382.829875 -8382.829875 -8382.829875 -8382.829875   \n",
       "1 -5650.000000 NaN -5650.000000 -5650.000000 -5650.000000 -5650.000000   \n",
       "2 -3475.187360 NaN -3475.187360 -3475.187360 -3475.187360 -3475.187360   \n",
       "3 -1768.643446 NaN -1768.643446 -1768.643446 -1768.643446 -1768.643446   \n",
       "4     0.000000 NaN     0.000000     0.000000     0.000000     0.000000   \n",
       "\n",
       "                \n",
       "           max  \n",
       "0 -8382.829875  \n",
       "1 -5650.000000  \n",
       "2 -3475.187360  \n",
       "3 -1768.643446  \n",
       "4     0.000000  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum4plot = df_summary.reset_index()\n",
    "df_sum4plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "          net  mean   sd  tstt                                              \\\n",
       "                         count      mean std       min       25%       50%   \n",
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       "3  SiouxFalls  0.75  0.0   1.0  0.488553 NaN  0.488553  0.488553  0.488553   \n",
       "4  SiouxFalls  1.00  0.0   1.0  1.000000 NaN  1.000000  1.000000  1.000000   \n",
       "\n",
       "             ...    flow_err25               flow_err75                   \\\n",
       "        75%  ...           75%           max      count         mean std   \n",
       "0  0.000000  ... -15292.871544 -15292.871544        1.0 -8382.829875 NaN   \n",
       "1  0.110665  ... -11911.621544 -11911.621544        1.0 -5650.000000 NaN   \n",
       "2  0.250073  ...  -8262.457785  -8262.457785        1.0 -3475.187360 NaN   \n",
       "3  0.488553  ...  -3942.319025  -3942.319025        1.0 -1768.643446 NaN   \n",
       "4  1.000000  ...      0.000000      0.000000        1.0     0.000000 NaN   \n",
       "\n",
       "                                                                    \n",
       "           min          25%          50%          75%          max  \n",
       "0 -8382.829875 -8382.829875 -8382.829875 -8382.829875 -8382.829875  \n",
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       "2 -3475.187360 -3475.187360 -3475.187360 -3475.187360 -3475.187360  \n",
       "3 -1768.643446 -1768.643446 -1768.643446 -1768.643446 -1768.643446  \n",
       "4     0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trel_sum4plot = df_summary_trel.reset_index()\n",
    "df_trel_sum4plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "\n",
    "def plot_metric_dist_over_networks(metric, which, rel=False):\n",
    "    plt.figure(figsize=(10,4))\n",
    "    sns.swarmplot('mean', (metric.lower(), which), 'net', data=(df_trel_sum4plot if rel else df_sum4plot), size=3)\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    plt.ylabel(f'Sample {which} {metric}' + (' relative to true' if rel else ''))\n",
    "    plt.xlabel('Mean perturbation')\n",
    "    plt.title(f'Sample {which} {metric} for all networks')\n",
    "    plt.tight_layout()\n",
    "    relstring = '_rel' if rel else ''\n",
    "    plt.savefig(f'output/plots/all_{metric.lower()}_sample_{which}{relstring}.png', dpi='figure')\n",
    "    plt.show()\n",
    "\n",
    "plot_metric_dist_over_networks('TSTT', 'mean', rel=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All 1-mean trial plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "def plot_trial_outputs(sd=0.02):\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(15,5), sharey=True)\n",
    "    for i,col in enumerate(['tstt', 'wvc', 'vmt']):\n",
    "        sns.swarmplot(x=col, y=\"net\", data=df_trel[(df_trel['mean']==1) & (df_trel['sd']==sd)], size=2, ax=axes[i], color='k')\n",
    "        axes[i].set_ylabel('')\n",
    "        axes[i].set_xlabel(f'{col.upper()} relative to true')\n",
    "        axes[i].grid()\n",
    "#     fig.suptitle(f'{sd} OD demand standard deviation vs. output metrics for all trials')\n",
    "    plt.tight_layout()\n",
    "    plt.subplots_adjust(top=.9)\n",
    "    plt.savefig(f'output/trialoutputs_sd{sd}.png', dpi='figure')\n",
    "    plt.show()\n",
    "\n",
    "plot_trial_outputs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_trial_outputs(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_trial_outputs(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Format DataFrames for $\\LaTeX$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>net</th>\n",
       "      <th>sd</th>\n",
       "      <th>tstt</th>\n",
       "      <th>wvc</th>\n",
       "      <th>vmt</th>\n",
       "      <th>costs_rmse</th>\n",
       "      <th>cost_err25</th>\n",
       "      <th>cost_err75</th>\n",
       "      <th>flows_rmse</th>\n",
       "      <th>flow_err25</th>\n",
       "      <th>flow_err75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10749</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>0.001</td>\n",
       "      <td>7.481684e+06</td>\n",
       "      <td>1.474094</td>\n",
       "      <td>3.419449e+06</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>-0.000206</td>\n",
       "      <td>0.002600</td>\n",
       "      <td>2.724160</td>\n",
       "      <td>-0.344420</td>\n",
       "      <td>2.409290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>berlin-mitte-center</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1.086957e+06</td>\n",
       "      <td>0.342367</td>\n",
       "      <td>2.219613e+07</td>\n",
       "      <td>0.324182</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>21.309184</td>\n",
       "      <td>-0.806715</td>\n",
       "      <td>6.868747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5140</th>\n",
       "      <td>berlin-prenzlauerberg-center</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.400370e+06</td>\n",
       "      <td>0.430611</td>\n",
       "      <td>2.897538e+07</td>\n",
       "      <td>0.008165</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>1.183220</td>\n",
       "      <td>-0.171380</td>\n",
       "      <td>0.424600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2595</th>\n",
       "      <td>berlin-mitte-center</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1.048486e+06</td>\n",
       "      <td>0.332959</td>\n",
       "      <td>2.174088e+07</td>\n",
       "      <td>0.019844</td>\n",
       "      <td>-0.000065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.289330</td>\n",
       "      <td>-1.157196</td>\n",
       "      <td>0.044911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>berlin-mitte-center</td>\n",
       "      <td>0.100</td>\n",
       "      <td>1.049891e+06</td>\n",
       "      <td>0.333985</td>\n",
       "      <td>2.173717e+07</td>\n",
       "      <td>0.061774</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>5.806977</td>\n",
       "      <td>-1.640766</td>\n",
       "      <td>1.969993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3791</th>\n",
       "      <td>bmpfc</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2.362585e+06</td>\n",
       "      <td>0.286013</td>\n",
       "      <td>5.714658e+07</td>\n",
       "      <td>0.001261</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.399864</td>\n",
       "      <td>-0.132313</td>\n",
       "      <td>0.175877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8779</th>\n",
       "      <td>SiouxFalls</td>\n",
       "      <td>0.050</td>\n",
       "      <td>7.530126e+06</td>\n",
       "      <td>1.475656</td>\n",
       "      <td>3.430952e+06</td>\n",
       "      <td>0.256450</td>\n",
       "      <td>-0.051175</td>\n",
       "      <td>0.089435</td>\n",
       "      <td>161.665487</td>\n",
       "      <td>-55.615254</td>\n",
       "      <td>83.656966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6658</th>\n",
       "      <td>berlin-tiergarten</td>\n",
       "      <td>0.010</td>\n",
       "      <td>7.168373e+05</td>\n",
       "      <td>0.312802</td>\n",
       "      <td>1.680434e+07</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.954992</td>\n",
       "      <td>-0.154354</td>\n",
       "      <td>0.105830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3789</th>\n",
       "      <td>bmpfc</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2.362511e+06</td>\n",
       "      <td>0.286024</td>\n",
       "      <td>5.714403e+07</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.409370</td>\n",
       "      <td>-0.136217</td>\n",
       "      <td>0.117990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392</th>\n",
       "      <td>friedrichshain-center</td>\n",
       "      <td>0.005</td>\n",
       "      <td>7.291223e+05</td>\n",
       "      <td>0.473484</td>\n",
       "      <td>1.731831e+07</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.472610</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.382608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                net     sd          tstt       wvc  \\\n",
       "10749                    SiouxFalls  0.001  7.481684e+06  1.474094   \n",
       "1949            berlin-mitte-center  0.200  1.086957e+06  0.342367   \n",
       "5140   berlin-prenzlauerberg-center  0.010  1.400370e+06  0.430611   \n",
       "2595            berlin-mitte-center  0.020  1.048486e+06  0.332959   \n",
       "2156            berlin-mitte-center  0.100  1.049891e+06  0.333985   \n",
       "3791                          bmpfc  0.010  2.362585e+06  0.286013   \n",
       "8779                     SiouxFalls  0.050  7.530126e+06  1.475656   \n",
       "6658              berlin-tiergarten  0.010  7.168373e+05  0.312802   \n",
       "3789                          bmpfc  0.010  2.362511e+06  0.286024   \n",
       "1392          friedrichshain-center  0.005  7.291223e+05  0.473484   \n",
       "\n",
       "                vmt  costs_rmse  cost_err25  cost_err75  flows_rmse  \\\n",
       "10749  3.419449e+06    0.004286   -0.000206    0.002600    2.724160   \n",
       "1949   2.219613e+07    0.324182   -0.000003    0.000346   21.309184   \n",
       "5140   2.897538e+07    0.008165   -0.000006    0.000026    1.183220   \n",
       "2595   2.174088e+07    0.019844   -0.000065    0.000000    2.289330   \n",
       "2156   2.173717e+07    0.061774   -0.000012    0.000062    5.806977   \n",
       "3791   5.714658e+07    0.001261   -0.000001    0.000008    0.399864   \n",
       "8779   3.430952e+06    0.256450   -0.051175    0.089435  161.665487   \n",
       "6658   1.680434e+07    0.003133    0.000000    0.000000    0.954992   \n",
       "3789   5.714403e+07    0.001929   -0.000003    0.000003    0.409370   \n",
       "1392   1.731831e+07    0.001675    0.000000    0.000036    0.472610   \n",
       "\n",
       "       flow_err25  flow_err75  \n",
       "10749   -0.344420    2.409290  \n",
       "1949    -0.806715    6.868747  \n",
       "5140    -0.171380    0.424600  \n",
       "2595    -1.157196    0.044911  \n",
       "2156    -1.640766    1.969993  \n",
       "3791    -0.132313    0.175877  \n",
       "8779   -55.615254   83.656966  \n",
       "6658    -0.154354    0.105830  \n",
       "3789    -0.136217    0.117990  \n",
       "1392     0.000000    0.382608  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.loc[df['mean']==1,['net', 'sd', 'tstt', 'wvc', 'vmt', \n",
    "                            'costs_rmse', 'cost_err25', 'cost_err75', 'flows_rmse', 'flow_err25', 'flow_err75']]\n",
    "df1 = df1.reset_index(drop=True)\n",
    "df1.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">tstt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">wvc</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">flow_err25</th>\n",
       "      <th colspan=\"8\" halign=\"left\">flow_err75</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net</th>\n",
       "      <th>sd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">SiouxFalls</th>\n",
       "      <th>0.000</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>7.480171e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.474030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>500.0</td>\n",
       "      <td>7.480052e+06</td>\n",
       "      <td>1328.816366</td>\n",
       "      <td>7.475725e+06</td>\n",
       "      <td>7.479168e+06</td>\n",
       "      <td>7.480045e+06</td>\n",
       "      <td>7.480926e+06</td>\n",
       "      <td>7.483772e+06</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.474024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.924985</td>\n",
       "      <td>0.254655</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.350761</td>\n",
       "      <td>0.754754</td>\n",
       "      <td>-0.388172</td>\n",
       "      <td>0.820333</td>\n",
       "      <td>1.274382</td>\n",
       "      <td>1.814122</td>\n",
       "      <td>3.811896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.005</th>\n",
       "      <td>500.0</td>\n",
       "      <td>7.479971e+06</td>\n",
       "      <td>6667.739101</td>\n",
       "      <td>7.452037e+06</td>\n",
       "      <td>7.475269e+06</td>\n",
       "      <td>7.480063e+06</td>\n",
       "      <td>7.484658e+06</td>\n",
       "      <td>7.503655e+06</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.474010</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.480589</td>\n",
       "      <td>2.295333</td>\n",
       "      <td>500.0</td>\n",
       "      <td>7.060995</td>\n",
       "      <td>3.895448</td>\n",
       "      <td>-4.355011</td>\n",
       "      <td>4.308474</td>\n",
       "      <td>6.780579</td>\n",
       "      <td>9.656296</td>\n",
       "      <td>18.568253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010</th>\n",
       "      <td>500.0</td>\n",
       "      <td>7.480355e+06</td>\n",
       "      <td>13235.637989</td>\n",
       "      <td>7.439085e+06</td>\n",
       "      <td>7.470915e+06</td>\n",
       "      <td>7.480686e+06</td>\n",
       "      <td>7.489097e+06</td>\n",
       "      <td>7.519239e+06</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.474018</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.384356</td>\n",
       "      <td>1.958450</td>\n",
       "      <td>500.0</td>\n",
       "      <td>14.710551</td>\n",
       "      <td>7.466770</td>\n",
       "      <td>-3.360790</td>\n",
       "      <td>9.594757</td>\n",
       "      <td>14.703125</td>\n",
       "      <td>19.280073</td>\n",
       "      <td>42.479815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.020</th>\n",
       "      <td>500.0</td>\n",
       "      <td>7.478847e+06</td>\n",
       "      <td>26331.054793</td>\n",
       "      <td>7.402409e+06</td>\n",
       "      <td>7.460178e+06</td>\n",
       "      <td>7.479238e+06</td>\n",
       "      <td>7.498127e+06</td>\n",
       "      <td>7.559030e+06</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.473865</td>\n",
       "      <td>...</td>\n",
       "      <td>-18.928806</td>\n",
       "      <td>3.040440</td>\n",
       "      <td>500.0</td>\n",
       "      <td>29.282322</td>\n",
       "      <td>15.522379</td>\n",
       "      <td>-14.165706</td>\n",
       "      <td>17.800987</td>\n",
       "      <td>29.296271</td>\n",
       "      <td>40.117881</td>\n",
       "      <td>91.534860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   tstt                                            \\\n",
       "                  count          mean           std           min   \n",
       "net        sd                                                       \n",
       "SiouxFalls 0.000    1.0  7.480171e+06           NaN  7.480171e+06   \n",
       "           0.001  500.0  7.480052e+06   1328.816366  7.475725e+06   \n",
       "           0.005  500.0  7.479971e+06   6667.739101  7.452037e+06   \n",
       "           0.010  500.0  7.480355e+06  13235.637989  7.439085e+06   \n",
       "           0.020  500.0  7.478847e+06  26331.054793  7.402409e+06   \n",
       "\n",
       "                                                                          \\\n",
       "                           25%           50%           75%           max   \n",
       "net        sd                                                              \n",
       "SiouxFalls 0.000  7.480171e+06  7.480171e+06  7.480171e+06  7.480171e+06   \n",
       "           0.001  7.479168e+06  7.480045e+06  7.480926e+06  7.483772e+06   \n",
       "           0.005  7.475269e+06  7.480063e+06  7.484658e+06  7.503655e+06   \n",
       "           0.010  7.470915e+06  7.480686e+06  7.489097e+06  7.519239e+06   \n",
       "           0.020  7.460178e+06  7.479238e+06  7.498127e+06  7.559030e+06   \n",
       "\n",
       "                    wvc            ... flow_err25           flow_err75  \\\n",
       "                  count      mean  ...        75%       max      count   \n",
       "net        sd                      ...                                   \n",
       "SiouxFalls 0.000    1.0  1.474030  ...   0.000000  0.000000        1.0   \n",
       "           0.001  500.0  1.474024  ...  -0.924985  0.254655      500.0   \n",
       "           0.005  500.0  1.474010  ...  -4.480589  2.295333      500.0   \n",
       "           0.010  500.0  1.474018  ...  -8.384356  1.958450      500.0   \n",
       "           0.020  500.0  1.473865  ... -18.928806  3.040440      500.0   \n",
       "\n",
       "                                                                         \\\n",
       "                       mean        std        min        25%        50%   \n",
       "net        sd                                                             \n",
       "SiouxFalls 0.000   0.000000        NaN   0.000000   0.000000   0.000000   \n",
       "           0.001   1.350761   0.754754  -0.388172   0.820333   1.274382   \n",
       "           0.005   7.060995   3.895448  -4.355011   4.308474   6.780579   \n",
       "           0.010  14.710551   7.466770  -3.360790   9.594757  14.703125   \n",
       "           0.020  29.282322  15.522379 -14.165706  17.800987  29.296271   \n",
       "\n",
       "                                        \n",
       "                        75%        max  \n",
       "net        sd                           \n",
       "SiouxFalls 0.000   0.000000   0.000000  \n",
       "           0.001   1.814122   3.811896  \n",
       "           0.005   9.656296  18.568253  \n",
       "           0.010  19.280073  42.479815  \n",
       "           0.020  40.117881  91.534860  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_summary = df1.groupby(['net', 'sd']).describe()\n",
    "df1_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_summary.to_latex(f'output/latex/summary.tex',\n",
    "                     columns=[p for p in df1_summary.columns.values if (\n",
    "                         (\n",
    "                             p[1] not in {'count', 'min', '25%', '50%', '75%', 'max'}\n",
    "                         ) and (\n",
    "                             p[0] not in {'cost_err25', 'cost_err75', 'flow_err25', 'flow_err75'}\n",
    "                         )\n",
    "                     )],\n",
    "                     formatters = {\n",
    "                         ('tstt', 'mean'): '{:.3e}'.format,\n",
    "                         ('tstt', 'std'): '{:.3e}'.format,\n",
    "                         ('wvc', 'mean'): \"{0:.3f}\".format,\n",
    "                         ('wvc', 'std'): \"{0:.3f}\".format,\n",
    "                         ('vmt', 'mean'): '{:.3e}'.format,\n",
    "                         ('vmt', 'std'): '{:.3e}'.format,\n",
    "                         ('costs_rmse', 'mean'): '{:.3e}'.format,\n",
    "                         ('costs_rmse', 'std'): '{:.3e}'.format,\n",
    "#                          ('cost_err25', 'mean'): '{:.3e}'.format,\n",
    "#                          ('cost_err25', 'std'): '{:.3e}'.format,\n",
    "#                          ('cost_err75', 'mean'): '{:.3e}'.format,\n",
    "#                          ('cost_err75', 'std'): '{:.3e}'.format,\n",
    "                         ('flows_rmse', 'mean'): '{:.3e}'.format,\n",
    "                         ('flows_rmse', 'std'): '{:.3e}'.format,\n",
    "#                          ('flow_err25', 'mean'): '{:.3e}'.format,\n",
    "#                          ('flow_err25', 'std'): '{:.3e}'.format,\n",
    "#                          ('flow_err75', 'mean'): '{:.3e}'.format,\n",
    "#                          ('flow_err75', 'std'): '{:.3e}'.format\n",
    "                     },\n",
    "                     multicolumn=True, multirow=True, longtable=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parallel Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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></th>\n",
       "      <th>tstt</th>\n",
       "      <th>wvc</th>\n",
       "      <th>vmt</th>\n",
       "      <th>costs_rmse</th>\n",
       "      <th>cost_err25</th>\n",
       "      <th>cost_err75</th>\n",
       "      <th>flows_rmse</th>\n",
       "      <th>flow_err25</th>\n",
       "      <th>flow_err75</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.001</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.466678e-04</td>\n",
       "      <td>-2.975000e-07</td>\n",
       "      <td>3.875000e-07</td>\n",
       "      <td>3.984485e-02</td>\n",
       "      <td>-1.357283e-02</td>\n",
       "      <td>1.419052e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.492002e-216</td>\n",
       "      <td>-1.328624e-217</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.295935e-213</td>\n",
       "      <td>-2.166026e-213</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.005</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.519274e-139</td>\n",
       "      <td>-3.598343e-140</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.672035e-136</td>\n",
       "      <td>-2.891512e-136</td>\n",
       "      <td>-1.482517e-137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>0.999989</td>\n",
       "      <td>0.999989</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>7.011562e-04</td>\n",
       "      <td>-1.932500e-06</td>\n",
       "      <td>1.700000e-06</td>\n",
       "      <td>1.970050e-01</td>\n",
       "      <td>-7.301015e-02</td>\n",
       "      <td>6.793717e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.996484e-11</td>\n",
       "      <td>-2.442707e-13</td>\n",
       "      <td>2.815706e-13</td>\n",
       "      <td>1.221744e-08</td>\n",
       "      <td>-3.982289e-09</td>\n",
       "      <td>3.922338e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.010</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.181864e-37</td>\n",
       "      <td>-4.393975e-38</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.262850e-34</td>\n",
       "      <td>-3.530856e-34</td>\n",
       "      <td>-1.810317e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>1.000011</td>\n",
       "      <td>1.000018</td>\n",
       "      <td>1.000010</td>\n",
       "      <td>1.393574e-03</td>\n",
       "      <td>-3.245000e-06</td>\n",
       "      <td>3.665000e-06</td>\n",
       "      <td>3.959655e-01</td>\n",
       "      <td>-1.393173e-01</td>\n",
       "      <td>1.390598e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000001</td>\n",
       "      <td>1.000003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.648622e-05</td>\n",
       "      <td>-1.058162e-06</td>\n",
       "      <td>1.219743e-06</td>\n",
       "      <td>5.292506e-02</td>\n",
       "      <td>-1.725098e-02</td>\n",
       "      <td>1.699127e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.020</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.104289e-10</td>\n",
       "      <td>-2.415925e-12</td>\n",
       "      <td>5.491653e-12</td>\n",
       "      <td>3.655522e-08</td>\n",
       "      <td>-1.859877e-08</td>\n",
       "      <td>1.793694e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>0.999981</td>\n",
       "      <td>1.000020</td>\n",
       "      <td>0.999982</td>\n",
       "      <td>2.684176e-03</td>\n",
       "      <td>-7.120000e-06</td>\n",
       "      <td>6.572500e-06</td>\n",
       "      <td>7.792112e-01</td>\n",
       "      <td>-2.786411e-01</td>\n",
       "      <td>2.789411e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000065</td>\n",
       "      <td>1.000224</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>5.748089e-03</td>\n",
       "      <td>-7.032722e-05</td>\n",
       "      <td>8.106751e-05</td>\n",
       "      <td>3.517509e+00</td>\n",
       "      <td>-1.146535e+00</td>\n",
       "      <td>1.129279e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.050</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000083</td>\n",
       "      <td>0.999944</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.341318e-03</td>\n",
       "      <td>-7.310018e-05</td>\n",
       "      <td>1.661644e-04</td>\n",
       "      <td>1.106075e+00</td>\n",
       "      <td>-5.627550e-01</td>\n",
       "      <td>5.427294e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>0.999973</td>\n",
       "      <td>0.999956</td>\n",
       "      <td>0.999978</td>\n",
       "      <td>6.770576e-03</td>\n",
       "      <td>-1.707000e-05</td>\n",
       "      <td>1.689750e-05</td>\n",
       "      <td>1.947856e+00</td>\n",
       "      <td>-7.025064e-01</td>\n",
       "      <td>6.759352e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000362</td>\n",
       "      <td>1.001314</td>\n",
       "      <td>0.999991</td>\n",
       "      <td>2.953974e-02</td>\n",
       "      <td>-3.418790e-04</td>\n",
       "      <td>4.246990e-04</td>\n",
       "      <td>1.759479e+01</td>\n",
       "      <td>-5.719495e+00</td>\n",
       "      <td>5.723919e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.100</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.001411</td>\n",
       "      <td>0.999051</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>5.689358e-02</td>\n",
       "      <td>-1.244235e-03</td>\n",
       "      <td>2.831201e-03</td>\n",
       "      <td>1.883392e+01</td>\n",
       "      <td>-9.582574e+00</td>\n",
       "      <td>9.241446e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>0.999846</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>0.999833</td>\n",
       "      <td>1.412813e-02</td>\n",
       "      <td>-3.307250e-05</td>\n",
       "      <td>3.263250e-05</td>\n",
       "      <td>3.883883e+00</td>\n",
       "      <td>-1.416221e+00</td>\n",
       "      <td>1.347453e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000815</td>\n",
       "      <td>1.003180</td>\n",
       "      <td>1.000007</td>\n",
       "      <td>5.771249e-02</td>\n",
       "      <td>-5.940940e-04</td>\n",
       "      <td>8.603176e-04</td>\n",
       "      <td>3.255392e+01</td>\n",
       "      <td>-1.052187e+01</td>\n",
       "      <td>1.088244e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.200</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.004887</td>\n",
       "      <td>0.996108</td>\n",
       "      <td>0.999937</td>\n",
       "      <td>1.590940e-01</td>\n",
       "      <td>-3.314182e-03</td>\n",
       "      <td>8.602043e-03</td>\n",
       "      <td>5.283512e+01</td>\n",
       "      <td>-2.692782e+01</td>\n",
       "      <td>2.592671e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>0.999804</td>\n",
       "      <td>1.000196</td>\n",
       "      <td>0.999818</td>\n",
       "      <td>2.825479e-02</td>\n",
       "      <td>-7.398250e-05</td>\n",
       "      <td>6.645000e-05</td>\n",
       "      <td>7.691917e+00</td>\n",
       "      <td>-2.849102e+00</td>\n",
       "      <td>2.718692e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.001216</td>\n",
       "      <td>1.004869</td>\n",
       "      <td>1.000027</td>\n",
       "      <td>8.113060e-02</td>\n",
       "      <td>-7.871592e-04</td>\n",
       "      <td>1.229285e-03</td>\n",
       "      <td>4.457938e+01</td>\n",
       "      <td>-1.436724e+01</td>\n",
       "      <td>1.510288e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.500</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.012997</td>\n",
       "      <td>0.963211</td>\n",
       "      <td>0.989474</td>\n",
       "      <td>3.840748e-01</td>\n",
       "      <td>-6.351080e-03</td>\n",
       "      <td>2.944710e-02</td>\n",
       "      <td>1.328704e+02</td>\n",
       "      <td>-6.948601e+01</td>\n",
       "      <td>6.313742e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>1.005234</td>\n",
       "      <td>1.006710</td>\n",
       "      <td>1.004521</td>\n",
       "      <td>6.835180e-02</td>\n",
       "      <td>-9.956500e-05</td>\n",
       "      <td>2.876625e-04</td>\n",
       "      <td>1.894833e+01</td>\n",
       "      <td>-5.577093e+00</td>\n",
       "      <td>8.144473e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.001500</td>\n",
       "      <td>1.006074</td>\n",
       "      <td>1.000041</td>\n",
       "      <td>9.752500e-02</td>\n",
       "      <td>-9.195945e-04</td>\n",
       "      <td>1.488716e-03</td>\n",
       "      <td>5.293088e+01</td>\n",
       "      <td>-1.703521e+01</td>\n",
       "      <td>1.804641e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       tstt       wvc       vmt     costs_rmse     cost_err25  \\\n",
       "sigma method                                                                    \n",
       "0.001 uniform      1.000000  1.000000  1.000000   0.000000e+00   0.000000e+00   \n",
       "      od-specific  1.000000  1.000002  1.000000   1.466678e-04  -2.975000e-07   \n",
       "      spatial      1.000000  1.000000  1.000000  4.492002e-216 -1.328624e-217   \n",
       "0.005 uniform      1.000000  1.000000  1.000000  7.519274e-139 -3.598343e-140   \n",
       "      od-specific  0.999989  0.999989  0.999992   7.011562e-04  -1.932500e-06   \n",
       "      spatial      1.000000  1.000000  1.000000   1.996484e-11  -2.442707e-13   \n",
       "0.010 uniform      1.000000  1.000000  1.000000   9.181864e-37  -4.393975e-38   \n",
       "      od-specific  1.000011  1.000018  1.000010   1.393574e-03  -3.245000e-06   \n",
       "      spatial      1.000001  1.000003  1.000000   8.648622e-05  -1.058162e-06   \n",
       "0.020 uniform      1.000000  1.000000  1.000000   1.104289e-10  -2.415925e-12   \n",
       "      od-specific  0.999981  1.000020  0.999982   2.684176e-03  -7.120000e-06   \n",
       "      spatial      1.000065  1.000224  0.999997   5.748089e-03  -7.032722e-05   \n",
       "0.050 uniform      1.000083  0.999944  1.000000   3.341318e-03  -7.310018e-05   \n",
       "      od-specific  0.999973  0.999956  0.999978   6.770576e-03  -1.707000e-05   \n",
       "      spatial      1.000362  1.001314  0.999991   2.953974e-02  -3.418790e-04   \n",
       "0.100 uniform      1.001411  0.999051  0.999997   5.689358e-02  -1.244235e-03   \n",
       "      od-specific  0.999846  0.999986  0.999833   1.412813e-02  -3.307250e-05   \n",
       "      spatial      1.000815  1.003180  1.000007   5.771249e-02  -5.940940e-04   \n",
       "0.200 uniform      1.004887  0.996108  0.999937   1.590940e-01  -3.314182e-03   \n",
       "      od-specific  0.999804  1.000196  0.999818   2.825479e-02  -7.398250e-05   \n",
       "      spatial      1.001216  1.004869  1.000027   8.113060e-02  -7.871592e-04   \n",
       "0.500 uniform      1.012997  0.963211  0.989474   3.840748e-01  -6.351080e-03   \n",
       "      od-specific  1.005234  1.006710  1.004521   6.835180e-02  -9.956500e-05   \n",
       "      spatial      1.001500  1.006074  1.000041   9.752500e-02  -9.195945e-04   \n",
       "\n",
       "                     cost_err75     flows_rmse     flow_err25     flow_err75  \n",
       "sigma method                                                                  \n",
       "0.001 uniform      0.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00  \n",
       "      od-specific  3.875000e-07   3.984485e-02  -1.357283e-02   1.419052e-02  \n",
       "      spatial      0.000000e+00  3.295935e-213 -2.166026e-213   0.000000e+00  \n",
       "0.005 uniform      0.000000e+00  2.672035e-136 -2.891512e-136 -1.482517e-137  \n",
       "      od-specific  1.700000e-06   1.970050e-01  -7.301015e-02   6.793717e-02  \n",
       "      spatial      2.815706e-13   1.221744e-08  -3.982289e-09   3.922338e-09  \n",
       "0.010 uniform      0.000000e+00   3.262850e-34  -3.530856e-34  -1.810317e-35  \n",
       "      od-specific  3.665000e-06   3.959655e-01  -1.393173e-01   1.390598e-01  \n",
       "      spatial      1.219743e-06   5.292506e-02  -1.725098e-02   1.699127e-02  \n",
       "0.020 uniform      5.491653e-12   3.655522e-08  -1.859877e-08   1.793694e-08  \n",
       "      od-specific  6.572500e-06   7.792112e-01  -2.786411e-01   2.789411e-01  \n",
       "      spatial      8.106751e-05   3.517509e+00  -1.146535e+00   1.129279e+00  \n",
       "0.050 uniform      1.661644e-04   1.106075e+00  -5.627550e-01   5.427294e-01  \n",
       "      od-specific  1.689750e-05   1.947856e+00  -7.025064e-01   6.759352e-01  \n",
       "      spatial      4.246990e-04   1.759479e+01  -5.719495e+00   5.723919e+00  \n",
       "0.100 uniform      2.831201e-03   1.883392e+01  -9.582574e+00   9.241446e+00  \n",
       "      od-specific  3.263250e-05   3.883883e+00  -1.416221e+00   1.347453e+00  \n",
       "      spatial      8.603176e-04   3.255392e+01  -1.052187e+01   1.088244e+01  \n",
       "0.200 uniform      8.602043e-03   5.283512e+01  -2.692782e+01   2.592671e+01  \n",
       "      od-specific  6.645000e-05   7.691917e+00  -2.849102e+00   2.718692e+00  \n",
       "      spatial      1.229285e-03   4.457938e+01  -1.436724e+01   1.510288e+01  \n",
       "0.500 uniform      2.944710e-02   1.328704e+02  -6.948601e+01   6.313742e+01  \n",
       "      od-specific  2.876625e-04   1.894833e+01  -5.577093e+00   8.144473e+00  \n",
       "      spatial      1.488716e-03   5.293088e+01  -1.703521e+01   1.804641e+01  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def means_to_normal_areas(means, nmean, nsd):\n",
    "    means.sort()\n",
    "    dist = norm(nmean, nsd)\n",
    "    weights = [None]*len(means)\n",
    "    for i,mean in enumerate(means):\n",
    "        lower = -np.inf if i==0 else means[i-1]+(means[i]-means[i-1])/2\n",
    "        upper = np.inf if i==len(means)-1 else means[i]+(means[i+1]-means[i])/2\n",
    "        weights[i] = dist.cdf(upper) - dist.cdf(lower)\n",
    "    return weights\n",
    "\n",
    "def compute_parallels(net, net_in_filename, p):\n",
    "    metrics = ['tstt', 'wvc', 'vmt', 'costs_rmse', 'cost_err25', 'cost_err75', 'flows_rmse', 'flow_err25', 'flow_err75']\n",
    "    metrics_normalizable = ['tstt', 'wvc', 'vmt']\n",
    "    sigmas = df['sd'].unique().tolist()\n",
    "    sigmas.sort()\n",
    "    df_res = pd.DataFrame(index=pd.MultiIndex(levels=[[],[]],\n",
    "                             codes=[[],[]],\n",
    "                             names=['sigma', 'method']),\n",
    "                          columns=metrics)\n",
    "    \n",
    "    for sigma in sigmas[1:]: # skip sigma=0\n",
    "\n",
    "        # uniform perturbation\n",
    "        df_up = df_trel[(df_trel['net'] == net) & (df_trel['sd']==0)]\n",
    "        up_weights = means_to_normal_areas(df_up['mean'].unique().tolist(), 1, sigma)\n",
    "        df_res.loc[(sigma, 'uniform'),] = df_up[metrics].apply(lambda c: (c*up_weights).sum())\n",
    "        \n",
    "        # od-specific perturbation\n",
    "        df_op = df_trel[(df_trel['net'] == net) & (df_trel['mean']==1) & (df_trel['sd']==sigma)]\n",
    "        df_res.loc[(sigma, 'od-specific'),:] = df_summary_trel.loc[(net, 1, sigma),[(metric,'mean') for metric in metrics]].values\n",
    "        \n",
    "        # spatially-correlated perturbation\n",
    "        df_pp = pd.read_csv(f'output/data/{net_in_filename}_trials_partial_perturb.csv')\n",
    "        df_pp = df_pp[(df_pp['method']=='By origin') & (df_pp['p']==p)]\n",
    "        df_pp.drop(['method', 'p'], axis=1, inplace=True)\n",
    "        df_pp[metrics_normalizable] /= df.loc[(df['net'] == net) & (df['mean']==1) & (df['sd']==0), metrics_normalizable].values\n",
    "        df_pp = df_pp.append({\n",
    "            'mean': 1,\n",
    "            'tstt': 1,\n",
    "            'wvc': 1,\n",
    "            'vmt': 1,\n",
    "            'costs_rmse': 0,\n",
    "            'cost_err25': 0,\n",
    "            'cost_err75': 0,\n",
    "            'flows_rmse': 0,\n",
    "            'flow_err25': 0,\n",
    "            'flow_err75': 0\n",
    "        }, ignore_index=True)\n",
    "        df_pp.sort_values('mean', inplace=True)\n",
    "        pp_weights = means_to_normal_areas(df_pp['mean'].tolist(), 1, sigma/p)\n",
    "        df_res.loc[(sigma, 'spatial'),] = df_pp[metrics].apply(lambda c: (c*pp_weights).sum())\n",
    "    \n",
    "    df_res.to_csv(f'output/data/{net_in_filename}_parallel.csv')\n",
    "    return df_res\n",
    "\n",
    "parallels = {}\n",
    "for net in nets:\n",
    "    parallels[net] = compute_parallels(net=net, net_in_filename=(\n",
    "        net if net is not 'bmpfc' else 'berlin-mitte-prenzlauerberg-friedrichshain-center'\n",
    "    ), p=0.25)\n",
    "parallels['bmpfc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>tstt</th>\n",
       "      <th>wvc</th>\n",
       "      <th>vmt</th>\n",
       "      <th>costs_rmse</th>\n",
       "      <th>cost_err25</th>\n",
       "      <th>cost_err75</th>\n",
       "      <th>flows_rmse</th>\n",
       "      <th>flow_err25</th>\n",
       "      <th>flow_err75</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.001</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>0.999984</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>4.527014e-03</td>\n",
       "      <td>-1.612759e-03</td>\n",
       "      <td>1.425117e-03</td>\n",
       "      <td>3.090349e+00</td>\n",
       "      <td>-1.488966e+00</td>\n",
       "      <td>1.350761e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.506020e-214</td>\n",
       "      <td>-2.882165e-214</td>\n",
       "      <td>-2.854854e-216</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.883252e-211</td>\n",
       "      <td>-4.731311e-212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.005</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.191407e-137</td>\n",
       "      <td>-1.486498e-137</td>\n",
       "      <td>-2.380675e-138</td>\n",
       "      <td>1.039032e-134</td>\n",
       "      <td>-1.205047e-134</td>\n",
       "      <td>-5.406207e-135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>0.999973</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>2.307181e-02</td>\n",
       "      <td>-7.969041e-03</td>\n",
       "      <td>7.384252e-03</td>\n",
       "      <td>1.561637e+01</td>\n",
       "      <td>-7.313843e+00</td>\n",
       "      <td>7.060995e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.181229e-09</td>\n",
       "      <td>-5.130590e-10</td>\n",
       "      <td>6.622782e-10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.561315e-07</td>\n",
       "      <td>2.697227e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.010</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.454839e-35</td>\n",
       "      <td>-1.815179e-35</td>\n",
       "      <td>-2.907067e-36</td>\n",
       "      <td>1.268773e-32</td>\n",
       "      <td>-1.471496e-32</td>\n",
       "      <td>-6.601575e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>1.000025</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>1.000059</td>\n",
       "      <td>4.630291e-02</td>\n",
       "      <td>-1.541171e-02</td>\n",
       "      <td>1.501355e-02</td>\n",
       "      <td>3.058220e+01</td>\n",
       "      <td>-1.395527e+01</td>\n",
       "      <td>1.471055e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.000129</td>\n",
       "      <td>1.000005</td>\n",
       "      <td>1.000007</td>\n",
       "      <td>5.116996e-03</td>\n",
       "      <td>-2.222534e-03</td>\n",
       "      <td>2.868940e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.109543e+00</td>\n",
       "      <td>1.168418e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.020</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.413285e-09</td>\n",
       "      <td>-7.590266e-10</td>\n",
       "      <td>1.816724e-09</td>\n",
       "      <td>1.485409e-06</td>\n",
       "      <td>-4.499215e-07</td>\n",
       "      <td>6.601317e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>0.999823</td>\n",
       "      <td>0.999888</td>\n",
       "      <td>1.000061</td>\n",
       "      <td>9.309633e-02</td>\n",
       "      <td>-3.375813e-02</td>\n",
       "      <td>2.926473e-02</td>\n",
       "      <td>6.132611e+01</td>\n",
       "      <td>-2.967423e+01</td>\n",
       "      <td>2.928232e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.008569</td>\n",
       "      <td>1.000323</td>\n",
       "      <td>1.000475</td>\n",
       "      <td>3.400871e-01</td>\n",
       "      <td>-1.477131e-01</td>\n",
       "      <td>1.906796e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.374190e+01</td>\n",
       "      <td>7.765552e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.050</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.003627</td>\n",
       "      <td>0.999949</td>\n",
       "      <td>1.000116</td>\n",
       "      <td>7.302031e-02</td>\n",
       "      <td>-2.296636e-02</td>\n",
       "      <td>5.496976e-02</td>\n",
       "      <td>4.494498e+01</td>\n",
       "      <td>-1.361357e+01</td>\n",
       "      <td>1.997402e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>1.000014</td>\n",
       "      <td>0.999752</td>\n",
       "      <td>1.000385</td>\n",
       "      <td>2.349438e-01</td>\n",
       "      <td>-8.067622e-02</td>\n",
       "      <td>7.461138e-02</td>\n",
       "      <td>1.501757e+02</td>\n",
       "      <td>-7.109309e+01</td>\n",
       "      <td>7.564710e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.052644</td>\n",
       "      <td>1.002277</td>\n",
       "      <td>1.002623</td>\n",
       "      <td>1.722952e+00</td>\n",
       "      <td>-7.136395e-01</td>\n",
       "      <td>1.039932e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.553695e+02</td>\n",
       "      <td>3.884458e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.100</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.061892</td>\n",
       "      <td>0.999133</td>\n",
       "      <td>1.001981</td>\n",
       "      <td>1.243785e+00</td>\n",
       "      <td>-3.906131e-01</td>\n",
       "      <td>9.368042e-01</td>\n",
       "      <td>7.652607e+02</td>\n",
       "      <td>-2.318095e+02</td>\n",
       "      <td>3.400148e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>1.000918</td>\n",
       "      <td>0.999570</td>\n",
       "      <td>1.000782</td>\n",
       "      <td>4.835962e-01</td>\n",
       "      <td>-1.615089e-01</td>\n",
       "      <td>1.552084e-01</td>\n",
       "      <td>2.956346e+02</td>\n",
       "      <td>-1.425474e+02</td>\n",
       "      <td>1.544471e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.135394</td>\n",
       "      <td>1.006770</td>\n",
       "      <td>1.005805</td>\n",
       "      <td>3.272527e+00</td>\n",
       "      <td>-1.222403e+00</td>\n",
       "      <td>2.258598e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-6.051129e+02</td>\n",
       "      <td>7.187360e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.200</th>\n",
       "      <th>uniform</th>\n",
       "      <td>1.221739</td>\n",
       "      <td>0.998302</td>\n",
       "      <td>1.005702</td>\n",
       "      <td>3.642845e+00</td>\n",
       "      <td>-9.350561e-01</td>\n",
       "      <td>2.917651e+00</td>\n",
       "      <td>2.129231e+03</td>\n",
       "      <td>-6.505843e+02</td>\n",
       "      <td>9.198030e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>1.003641</td>\n",
       "      <td>0.999167</td>\n",
       "      <td>1.001212</td>\n",
       "      <td>9.209205e-01</td>\n",
       "      <td>-3.197129e-01</td>\n",
       "      <td>3.011052e-01</td>\n",
       "      <td>5.729854e+02</td>\n",
       "      <td>-2.831589e+02</td>\n",
       "      <td>2.953365e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.211486</td>\n",
       "      <td>1.011026</td>\n",
       "      <td>1.008603</td>\n",
       "      <td>4.539554e+00</td>\n",
       "      <td>-1.606708e+00</td>\n",
       "      <td>3.322544e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.926791e+02</td>\n",
       "      <td>9.842603e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.500</th>\n",
       "      <th>uniform</th>\n",
       "      <td>2.143637</td>\n",
       "      <td>0.988738</td>\n",
       "      <td>1.006279</td>\n",
       "      <td>1.101748e+01</td>\n",
       "      <td>-7.210978e-01</td>\n",
       "      <td>1.066772e+01</td>\n",
       "      <td>5.159242e+03</td>\n",
       "      <td>-1.698119e+03</td>\n",
       "      <td>1.864358e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od-specific</th>\n",
       "      <td>1.053952</td>\n",
       "      <td>1.009137</td>\n",
       "      <td>1.012905</td>\n",
       "      <td>2.105843e+00</td>\n",
       "      <td>-5.595320e-01</td>\n",
       "      <td>9.558546e-01</td>\n",
       "      <td>1.319096e+03</td>\n",
       "      <td>-5.392608e+02</td>\n",
       "      <td>7.960418e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial</th>\n",
       "      <td>1.265957</td>\n",
       "      <td>1.014091</td>\n",
       "      <td>1.010587</td>\n",
       "      <td>5.423111e+00</td>\n",
       "      <td>-1.869410e+00</td>\n",
       "      <td>4.075753e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.206988e+02</td>\n",
       "      <td>1.168664e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       tstt       wvc       vmt     costs_rmse     cost_err25  \\\n",
       "sigma method                                                                    \n",
       "0.001 uniform      1.000000  1.000000  1.000000   0.000000e+00   0.000000e+00   \n",
       "      od-specific  0.999984  0.999996  0.999993   4.527014e-03  -1.612759e-03   \n",
       "      spatial      1.000000  1.000000  1.000000  2.506020e-214 -2.882165e-214   \n",
       "0.005 uniform      1.000000  1.000000  1.000000  1.191407e-137 -1.486498e-137   \n",
       "      od-specific  0.999973  0.999986  0.999999   2.307181e-02  -7.969041e-03   \n",
       "      spatial      1.000000  1.000000  1.000000   1.181229e-09  -5.130590e-10   \n",
       "0.010 uniform      1.000000  1.000000  1.000000   1.454839e-35  -1.815179e-35   \n",
       "      od-specific  1.000025  0.999992  1.000059   4.630291e-02  -1.541171e-02   \n",
       "      spatial      1.000129  1.000005  1.000007   5.116996e-03  -2.222534e-03   \n",
       "0.020 uniform      1.000000  1.000000  1.000000   2.413285e-09  -7.590266e-10   \n",
       "      od-specific  0.999823  0.999888  1.000061   9.309633e-02  -3.375813e-02   \n",
       "      spatial      1.008569  1.000323  1.000475   3.400871e-01  -1.477131e-01   \n",
       "0.050 uniform      1.003627  0.999949  1.000116   7.302031e-02  -2.296636e-02   \n",
       "      od-specific  1.000014  0.999752  1.000385   2.349438e-01  -8.067622e-02   \n",
       "      spatial      1.052644  1.002277  1.002623   1.722952e+00  -7.136395e-01   \n",
       "0.100 uniform      1.061892  0.999133  1.001981   1.243785e+00  -3.906131e-01   \n",
       "      od-specific  1.000918  0.999570  1.000782   4.835962e-01  -1.615089e-01   \n",
       "      spatial      1.135394  1.006770  1.005805   3.272527e+00  -1.222403e+00   \n",
       "0.200 uniform      1.221739  0.998302  1.005702   3.642845e+00  -9.350561e-01   \n",
       "      od-specific  1.003641  0.999167  1.001212   9.209205e-01  -3.197129e-01   \n",
       "      spatial      1.211486  1.011026  1.008603   4.539554e+00  -1.606708e+00   \n",
       "0.500 uniform      2.143637  0.988738  1.006279   1.101748e+01  -7.210978e-01   \n",
       "      od-specific  1.053952  1.009137  1.012905   2.105843e+00  -5.595320e-01   \n",
       "      spatial      1.265957  1.014091  1.010587   5.423111e+00  -1.869410e+00   \n",
       "\n",
       "                      cost_err75     flows_rmse     flow_err25     flow_err75  \n",
       "sigma method                                                                   \n",
       "0.001 uniform       0.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00  \n",
       "      od-specific   1.425117e-03   3.090349e+00  -1.488966e+00   1.350761e+00  \n",
       "      spatial     -2.854854e-216   0.000000e+00 -1.883252e-211 -4.731311e-212  \n",
       "0.005 uniform     -2.380675e-138  1.039032e-134 -1.205047e-134 -5.406207e-135  \n",
       "      od-specific   7.384252e-03   1.561637e+01  -7.313843e+00   7.060995e+00  \n",
       "      spatial       6.622782e-10   0.000000e+00  -2.561315e-07   2.697227e-07  \n",
       "0.010 uniform      -2.907067e-36   1.268773e-32  -1.471496e-32  -6.601575e-33  \n",
       "      od-specific   1.501355e-02   3.058220e+01  -1.395527e+01   1.471055e+01  \n",
       "      spatial       2.868940e-03   0.000000e+00  -1.109543e+00   1.168418e+00  \n",
       "0.020 uniform       1.816724e-09   1.485409e-06  -4.499215e-07   6.601317e-07  \n",
       "      od-specific   2.926473e-02   6.132611e+01  -2.967423e+01   2.928232e+01  \n",
       "      spatial       1.906796e-01   0.000000e+00  -7.374190e+01   7.765552e+01  \n",
       "0.050 uniform       5.496976e-02   4.494498e+01  -1.361357e+01   1.997402e+01  \n",
       "      od-specific   7.461138e-02   1.501757e+02  -7.109309e+01   7.564710e+01  \n",
       "      spatial       1.039932e+00   0.000000e+00  -3.553695e+02   3.884458e+02  \n",
       "0.100 uniform       9.368042e-01   7.652607e+02  -2.318095e+02   3.400148e+02  \n",
       "      od-specific   1.552084e-01   2.956346e+02  -1.425474e+02   1.544471e+02  \n",
       "      spatial       2.258598e+00   0.000000e+00  -6.051129e+02   7.187360e+02  \n",
       "0.200 uniform       2.917651e+00   2.129231e+03  -6.505843e+02   9.198030e+02  \n",
       "      od-specific   3.011052e-01   5.729854e+02  -2.831589e+02   2.953365e+02  \n",
       "      spatial       3.322544e+00   0.000000e+00  -7.926791e+02   9.842603e+02  \n",
       "0.500 uniform       1.066772e+01   5.159242e+03  -1.698119e+03   1.864358e+03  \n",
       "      od-specific   9.558546e-01   1.319096e+03  -5.392608e+02   7.960418e+02  \n",
       "      spatial       4.075753e+00   0.000000e+00  -9.206988e+02   1.168664e+03  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parallels['SiouxFalls']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "fig, axes = plt.subplots(nrows=3, ncols=2, sharex=True, sharey='col', figsize=(9,9))\n",
    "\n",
    "ls = ['-', '-.', ':']\n",
    "\n",
    "sigmas = parallels['bmpfc'].index.get_level_values('sigma').unique()\n",
    "methods = parallels['bmpfc'].index.get_level_values('method').unique()\n",
    "\n",
    "axes[0][0].set_title('bmpfc')\n",
    "axes[0][1].set_title('SiouxFalls')\n",
    "for i,method in enumerate(methods):\n",
    "    for j,netw in enumerate(['bmpfc', 'SiouxFalls']):\n",
    "        for k,metric in enumerate(['tstt', 'wvc', 'vmt']):\n",
    "            axes[i,j].plot(sigmas, parallels[netw].xs(method, level='method')[metric].values, c='k', linestyle=ls[k], label=metric.upper())\n",
    "            axes[i,j].set_xscale('log')\n",
    "            axes[i,j].grid()\n",
    "            if i==2:\n",
    "                axes[i,j].set_xlabel('$\\sigma$')\n",
    "    axes[i][0].set_ylabel(method, rotation=0, size='large', labelpad=50)\n",
    "axes[0][1].legend(title='Metric\\n(normalized)', loc='center left', bbox_to_anchor=(1,0.5))\n",
    "# fig.suptitle('Metrics relative to true values for fixed input errors')\n",
    "plt.tight_layout()\n",
    "# fig.subplots_adjust(top=0.9)\n",
    "plt.savefig('output/plots/parallel_bmpfc_sf.png', dpi='figure')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}