From a3be5cfaf7d04a79ba84bb15f2f4e337e64e1fb4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ma=C3=ABl=20Madon?= <mael.madon@irit.fr>
Date: Thu, 30 Nov 2023 16:32:48 +0100
Subject: [PATCH] add raw stats with the boxplots

---
 analyse_campaign.ipynb | 995 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 991 insertions(+), 4 deletions(-)

diff --git a/analyse_campaign.ipynb b/analyse_campaign.ipynb
index 03a5e90..1fd5d3d 100644
--- a/analyse_campaign.ipynb
+++ b/analyse_campaign.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 19,
    "id": "yellow-parking",
    "metadata": {},
    "outputs": [],
@@ -34,6 +34,7 @@
     "plt.rc('ytick', labelsize=BIG_SIZE)    # fontsize of the tick labels\n",
     "plt.rc('legend', fontsize=BIG_SIZE)    # legend fontsize\n",
     "plt.rc('figure', titlesize=BIG_SIZE)  # fontsize of the figure title\n",
+    "pd.set_option('display.max_columns', None)\n",
     "\n",
     "OUT_DIR = f\"out/extension_europar\"\n",
     "nb_exp = 105\n",
@@ -1671,10 +1672,284 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 22,
    "id": "sublime-warning",
    "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"8\" halign=\"left\">energy_in</th>\n",
+       "      <th colspan=\"8\" halign=\"left\">energy_after</th>\n",
+       "      <th colspan=\"8\" halign=\"left\">energy_overall</th>\n",
+       "    </tr>\n",
+       "    <tr>\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>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>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>behavior</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>dm_user_degrad_space</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-1.867970</td>\n",
+       "      <td>3.400831</td>\n",
+       "      <td>-16.724929</td>\n",
+       "      <td>-3.080981</td>\n",
+       "      <td>-0.084491</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.133967</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-1.402243</td>\n",
+       "      <td>2.215109</td>\n",
+       "      <td>-13.631552</td>\n",
+       "      <td>-1.737569</td>\n",
+       "      <td>-0.800495</td>\n",
+       "      <td>-0.272700</td>\n",
+       "      <td>1.149849</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-1.418852</td>\n",
+       "      <td>2.174189</td>\n",
+       "      <td>-13.449533</td>\n",
+       "      <td>-1.712625</td>\n",
+       "      <td>-0.820765</td>\n",
+       "      <td>-0.257426</td>\n",
+       "      <td>1.002755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_degrad_temp</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-0.901719</td>\n",
+       "      <td>2.028123</td>\n",
+       "      <td>-12.241851</td>\n",
+       "      <td>-0.862192</td>\n",
+       "      <td>-0.147734</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.692876</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-2.202945</td>\n",
+       "      <td>2.657532</td>\n",
+       "      <td>-14.598825</td>\n",
+       "      <td>-2.960997</td>\n",
+       "      <td>-1.270645</td>\n",
+       "      <td>-0.561423</td>\n",
+       "      <td>0.514927</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-2.152497</td>\n",
+       "      <td>2.560200</td>\n",
+       "      <td>-14.282203</td>\n",
+       "      <td>-2.975609</td>\n",
+       "      <td>-1.267626</td>\n",
+       "      <td>-0.550581</td>\n",
+       "      <td>0.498136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_delay</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-6.125491</td>\n",
+       "      <td>8.009540</td>\n",
+       "      <td>-32.917464</td>\n",
+       "      <td>-7.960059</td>\n",
+       "      <td>-2.486224</td>\n",
+       "      <td>-0.344612</td>\n",
+       "      <td>0.005170</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>0.377253</td>\n",
+       "      <td>0.585202</td>\n",
+       "      <td>-0.751858</td>\n",
+       "      <td>0.000404</td>\n",
+       "      <td>0.230909</td>\n",
+       "      <td>0.558333</td>\n",
+       "      <td>2.285435</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>0.129725</td>\n",
+       "      <td>0.423847</td>\n",
+       "      <td>-0.722699</td>\n",
+       "      <td>-0.099101</td>\n",
+       "      <td>0.026972</td>\n",
+       "      <td>0.299894</td>\n",
+       "      <td>1.905722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_reconfig</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-1.491425</td>\n",
+       "      <td>2.749601</td>\n",
+       "      <td>-14.269141</td>\n",
+       "      <td>-2.503631</td>\n",
+       "      <td>-0.044122</td>\n",
+       "      <td>0.003535</td>\n",
+       "      <td>1.130387</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-0.229089</td>\n",
+       "      <td>0.992935</td>\n",
+       "      <td>-7.495744</td>\n",
+       "      <td>-0.448563</td>\n",
+       "      <td>-0.147697</td>\n",
+       "      <td>0.203412</td>\n",
+       "      <td>2.056672</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-0.277377</td>\n",
+       "      <td>0.965541</td>\n",
+       "      <td>-7.459471</td>\n",
+       "      <td>-0.541178</td>\n",
+       "      <td>-0.148295</td>\n",
+       "      <td>0.152857</td>\n",
+       "      <td>1.594309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_renounce</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-6.125862</td>\n",
+       "      <td>8.010628</td>\n",
+       "      <td>-32.918394</td>\n",
+       "      <td>-7.963521</td>\n",
+       "      <td>-2.486224</td>\n",
+       "      <td>-0.340315</td>\n",
+       "      <td>0.005170</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-4.335748</td>\n",
+       "      <td>5.281581</td>\n",
+       "      <td>-28.400396</td>\n",
+       "      <td>-5.828892</td>\n",
+       "      <td>-2.473707</td>\n",
+       "      <td>-1.354533</td>\n",
+       "      <td>0.517660</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-4.398738</td>\n",
+       "      <td>5.226126</td>\n",
+       "      <td>-28.204972</td>\n",
+       "      <td>-5.671619</td>\n",
+       "      <td>-2.569655</td>\n",
+       "      <td>-1.583340</td>\n",
+       "      <td>0.388256</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     energy_in                                           \\\n",
+       "                         count      mean       std        min       25%   \n",
+       "behavior                                                                  \n",
+       "dm_user_degrad_space     105.0 -1.867970  3.400831 -16.724929 -3.080981   \n",
+       "dm_user_degrad_temp      105.0 -0.901719  2.028123 -12.241851 -0.862192   \n",
+       "dm_user_delay            105.0 -6.125491  8.009540 -32.917464 -7.960059   \n",
+       "dm_user_reconfig         105.0 -1.491425  2.749601 -14.269141 -2.503631   \n",
+       "dm_user_renounce         105.0 -6.125862  8.010628 -32.918394 -7.963521   \n",
+       "\n",
+       "                                                   energy_after            \\\n",
+       "                           50%       75%       max        count      mean   \n",
+       "behavior                                                                    \n",
+       "dm_user_degrad_space -0.084491  0.000000  1.133967        105.0 -1.402243   \n",
+       "dm_user_degrad_temp  -0.147734  0.000000  0.692876        105.0 -2.202945   \n",
+       "dm_user_delay        -2.486224 -0.344612  0.005170        105.0  0.377253   \n",
+       "dm_user_reconfig     -0.044122  0.003535  1.130387        105.0 -0.229089   \n",
+       "dm_user_renounce     -2.486224 -0.340315  0.005170        105.0 -4.335748   \n",
+       "\n",
+       "                                                                         \\\n",
+       "                           std        min       25%       50%       75%   \n",
+       "behavior                                                                  \n",
+       "dm_user_degrad_space  2.215109 -13.631552 -1.737569 -0.800495 -0.272700   \n",
+       "dm_user_degrad_temp   2.657532 -14.598825 -2.960997 -1.270645 -0.561423   \n",
+       "dm_user_delay         0.585202  -0.751858  0.000404  0.230909  0.558333   \n",
+       "dm_user_reconfig      0.992935  -7.495744 -0.448563 -0.147697  0.203412   \n",
+       "dm_user_renounce      5.281581 -28.400396 -5.828892 -2.473707 -1.354533   \n",
+       "\n",
+       "                               energy_overall                                 \\\n",
+       "                           max          count      mean       std        min   \n",
+       "behavior                                                                       \n",
+       "dm_user_degrad_space  1.149849          105.0 -1.418852  2.174189 -13.449533   \n",
+       "dm_user_degrad_temp   0.514927          105.0 -2.152497  2.560200 -14.282203   \n",
+       "dm_user_delay         2.285435          105.0  0.129725  0.423847  -0.722699   \n",
+       "dm_user_reconfig      2.056672          105.0 -0.277377  0.965541  -7.459471   \n",
+       "dm_user_renounce      0.517660          105.0 -4.398738  5.226126 -28.204972   \n",
+       "\n",
+       "                                                              \n",
+       "                           25%       50%       75%       max  \n",
+       "behavior                                                      \n",
+       "dm_user_degrad_space -1.712625 -0.820765 -0.257426  1.002755  \n",
+       "dm_user_degrad_temp  -2.975609 -1.267626 -0.550581  0.498136  \n",
+       "dm_user_delay        -0.099101  0.026972  0.299894  1.905722  \n",
+       "dm_user_reconfig     -0.541178 -0.148295  0.152857  1.594309  \n",
+       "dm_user_renounce     -5.671619 -2.569655 -1.583340  0.388256  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "image/png": 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",
@@ -1685,6 +1960,214 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"8\" halign=\"left\">mean_slowdown</th>\n",
+       "      <th colspan=\"8\" halign=\"left\">mean_waiting_time</th>\n",
+       "    </tr>\n",
+       "    <tr>\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>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>behavior</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>dm_user_degrad_space</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-8.084269</td>\n",
+       "      <td>18.870561</td>\n",
+       "      <td>-76.902493</td>\n",
+       "      <td>-10.165553</td>\n",
+       "      <td>-1.959308</td>\n",
+       "      <td>0.034359</td>\n",
+       "      <td>40.228819</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-10.013798</td>\n",
+       "      <td>19.768716</td>\n",
+       "      <td>-84.632801</td>\n",
+       "      <td>-12.295679</td>\n",
+       "      <td>-3.273741</td>\n",
+       "      <td>-0.573356</td>\n",
+       "      <td>25.887822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_degrad_temp</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-1.102648</td>\n",
+       "      <td>23.445859</td>\n",
+       "      <td>-71.196388</td>\n",
+       "      <td>-4.226978</td>\n",
+       "      <td>0.002649</td>\n",
+       "      <td>3.078827</td>\n",
+       "      <td>110.719415</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-8.378221</td>\n",
+       "      <td>16.597800</td>\n",
+       "      <td>-73.094136</td>\n",
+       "      <td>-9.580328</td>\n",
+       "      <td>-4.609944</td>\n",
+       "      <td>-0.209193</td>\n",
+       "      <td>29.503259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_delay</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>11.050614</td>\n",
+       "      <td>65.887604</td>\n",
+       "      <td>-45.600815</td>\n",
+       "      <td>-1.730871</td>\n",
+       "      <td>0.282191</td>\n",
+       "      <td>5.920235</td>\n",
+       "      <td>497.927532</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>6.051661</td>\n",
+       "      <td>39.609161</td>\n",
+       "      <td>-37.269642</td>\n",
+       "      <td>-2.571508</td>\n",
+       "      <td>-0.046843</td>\n",
+       "      <td>4.482085</td>\n",
+       "      <td>378.556454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_reconfig</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-4.260873</td>\n",
+       "      <td>20.557314</td>\n",
+       "      <td>-78.080885</td>\n",
+       "      <td>-10.207327</td>\n",
+       "      <td>-0.951352</td>\n",
+       "      <td>1.854275</td>\n",
+       "      <td>74.885110</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-5.111189</td>\n",
+       "      <td>19.196173</td>\n",
+       "      <td>-85.486778</td>\n",
+       "      <td>-8.199676</td>\n",
+       "      <td>-0.663959</td>\n",
+       "      <td>2.391386</td>\n",
+       "      <td>49.869801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_renounce</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-17.712851</td>\n",
+       "      <td>28.490081</td>\n",
+       "      <td>-94.309652</td>\n",
+       "      <td>-31.117187</td>\n",
+       "      <td>-6.425693</td>\n",
+       "      <td>0.778363</td>\n",
+       "      <td>54.303344</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-20.615220</td>\n",
+       "      <td>31.760702</td>\n",
+       "      <td>-98.105393</td>\n",
+       "      <td>-38.987444</td>\n",
+       "      <td>-10.169572</td>\n",
+       "      <td>0.722228</td>\n",
+       "      <td>41.912748</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     mean_slowdown                                   \\\n",
+       "                             count       mean        std        min   \n",
+       "behavior                                                              \n",
+       "dm_user_degrad_space         105.0  -8.084269  18.870561 -76.902493   \n",
+       "dm_user_degrad_temp          105.0  -1.102648  23.445859 -71.196388   \n",
+       "dm_user_delay                105.0  11.050614  65.887604 -45.600815   \n",
+       "dm_user_reconfig             105.0  -4.260873  20.557314 -78.080885   \n",
+       "dm_user_renounce             105.0 -17.712851  28.490081 -94.309652   \n",
+       "\n",
+       "                                                                 \\\n",
+       "                            25%       50%       75%         max   \n",
+       "behavior                                                          \n",
+       "dm_user_degrad_space -10.165553 -1.959308  0.034359   40.228819   \n",
+       "dm_user_degrad_temp   -4.226978  0.002649  3.078827  110.719415   \n",
+       "dm_user_delay         -1.730871  0.282191  5.920235  497.927532   \n",
+       "dm_user_reconfig     -10.207327 -0.951352  1.854275   74.885110   \n",
+       "dm_user_renounce     -31.117187 -6.425693  0.778363   54.303344   \n",
+       "\n",
+       "                     mean_waiting_time                                   \\\n",
+       "                                 count       mean        std        min   \n",
+       "behavior                                                                  \n",
+       "dm_user_degrad_space             105.0 -10.013798  19.768716 -84.632801   \n",
+       "dm_user_degrad_temp              105.0  -8.378221  16.597800 -73.094136   \n",
+       "dm_user_delay                    105.0   6.051661  39.609161 -37.269642   \n",
+       "dm_user_reconfig                 105.0  -5.111189  19.196173 -85.486778   \n",
+       "dm_user_renounce                 105.0 -20.615220  31.760702 -98.105393   \n",
+       "\n",
+       "                                                                  \n",
+       "                            25%        50%       75%         max  \n",
+       "behavior                                                          \n",
+       "dm_user_degrad_space -12.295679  -3.273741 -0.573356   25.887822  \n",
+       "dm_user_degrad_temp   -9.580328  -4.609944 -0.209193   29.503259  \n",
+       "dm_user_delay         -2.571508  -0.046843  4.482085  378.556454  \n",
+       "dm_user_reconfig      -8.199676  -0.663959  2.391386   49.869801  \n",
+       "dm_user_renounce     -38.987444 -10.169572  0.722228   41.912748  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
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",
@@ -1719,6 +2202,10 @@
     "data_window1 = metrics_relative[metrics_relative.window==1]\n",
     "\n",
     "# Energy metrics\n",
+    "## Stats\n",
+    "display(data_window1.groupby(\"behavior\").describe()[[\"energy_in\", \"energy_after\", \"energy_overall\"]])\n",
+    "\n",
+    "## Boxplots\n",
     "fig, axs = plt.subplots(1,3, sharey=True, constrained_layout=True)\n",
     "for ax in axs:\n",
     "      ax.yaxis.set_major_formatter(\"{x:.0f}%\")\n",
@@ -1727,15 +2214,21 @@
     "data_window1.boxplot(column=\"energy_in\", by=\"bvr_ord\", showmeans=True, ax=axs[0])\n",
     "data_window1.boxplot(column=\"energy_after\", by=\"bvr_ord\", showmeans=True, ax=axs[1])\n",
     "data_window1.boxplot(column=\"energy_overall\", by=\"bvr_ord\", showmeans=True, ax=axs[2])\n",
+    "\n",
+    "\n",
     "for ax in axs:\n",
     "      ax.set_xticklabels(bvr_short_names)\n",
     "      ax.set_xlabel(\"\")\n",
     "# plt.suptitle(\"Energy metrics (relative to baseline) - 1-hour DR window\")\n",
     "plt.suptitle(\"\")\n",
     "plt.savefig(f\"{OUT_DIR}/energy_metrics_window1.pdf\", bbox_inches='tight', dpi=1000)\n",
-    "\n",
+    "plt.show()\n",
     "\n",
     "# Scheduling metrics\n",
+    "## Stats\n",
+    "display(data_window1.groupby(\"behavior\").describe()[[\"mean_slowdown\", \"mean_waiting_time\"]])\n",
+    "\n",
+    "## Boxplots\n",
     "fig, axs = plt.subplots(1,2, constrained_layout=True)\n",
     "for ax in axs:\n",
     "      ax.xaxis.set_major_formatter(\"{x:.0f}%\")\n",
@@ -1761,6 +2254,7 @@
     "#plt.suptitle(\"Scheduling metrics (relative to baseline) - 1-hour DR window\")\n",
     "plt.suptitle(\"\")\n",
     "plt.savefig(f\"{OUT_DIR}/sched_metrics_window1.pdf\", bbox_inches='tight', dpi=1000)\n",
+    "plt.show()\n",
     "\n",
     "# User_delay outliers\n",
     "fig, ax = plt.subplots(figsize=(15,2))\n",
@@ -1776,10 +2270,284 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 23,
    "id": "quarterly-count",
    "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"8\" halign=\"left\">energy_in</th>\n",
+       "      <th colspan=\"8\" halign=\"left\">energy_after</th>\n",
+       "      <th colspan=\"8\" halign=\"left\">energy_overall</th>\n",
+       "    </tr>\n",
+       "    <tr>\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>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>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>behavior</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>dm_user_degrad_space</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-4.607978</td>\n",
+       "      <td>5.971688</td>\n",
+       "      <td>-23.164035</td>\n",
+       "      <td>-8.036166</td>\n",
+       "      <td>-1.872889</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.135421</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-3.805844</td>\n",
+       "      <td>3.158385</td>\n",
+       "      <td>-15.210102</td>\n",
+       "      <td>-5.135932</td>\n",
+       "      <td>-3.179491</td>\n",
+       "      <td>-1.661223</td>\n",
+       "      <td>0.888404</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-3.923280</td>\n",
+       "      <td>3.016705</td>\n",
+       "      <td>-14.741997</td>\n",
+       "      <td>-5.287614</td>\n",
+       "      <td>-3.479632</td>\n",
+       "      <td>-1.694622</td>\n",
+       "      <td>0.477400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_degrad_temp</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-3.082417</td>\n",
+       "      <td>3.686401</td>\n",
+       "      <td>-20.805412</td>\n",
+       "      <td>-4.774799</td>\n",
+       "      <td>-1.788961</td>\n",
+       "      <td>-0.141586</td>\n",
+       "      <td>0.002555</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-6.438589</td>\n",
+       "      <td>4.267301</td>\n",
+       "      <td>-18.632508</td>\n",
+       "      <td>-8.961855</td>\n",
+       "      <td>-6.061871</td>\n",
+       "      <td>-3.351114</td>\n",
+       "      <td>0.969748</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-5.896070</td>\n",
+       "      <td>3.678149</td>\n",
+       "      <td>-16.516273</td>\n",
+       "      <td>-8.198406</td>\n",
+       "      <td>-5.526574</td>\n",
+       "      <td>-3.225889</td>\n",
+       "      <td>0.762681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_delay</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-15.511465</td>\n",
+       "      <td>13.509674</td>\n",
+       "      <td>-53.034859</td>\n",
+       "      <td>-23.875976</td>\n",
+       "      <td>-12.059613</td>\n",
+       "      <td>-4.943147</td>\n",
+       "      <td>0.002555</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>3.299942</td>\n",
+       "      <td>3.274570</td>\n",
+       "      <td>-1.496679</td>\n",
+       "      <td>0.763093</td>\n",
+       "      <td>2.721466</td>\n",
+       "      <td>5.313718</td>\n",
+       "      <td>15.105251</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>0.438618</td>\n",
+       "      <td>1.247569</td>\n",
+       "      <td>-2.632291</td>\n",
+       "      <td>-0.361052</td>\n",
+       "      <td>0.245837</td>\n",
+       "      <td>0.946996</td>\n",
+       "      <td>5.298520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_reconfig</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-3.261107</td>\n",
+       "      <td>4.983435</td>\n",
+       "      <td>-22.257417</td>\n",
+       "      <td>-5.871021</td>\n",
+       "      <td>-0.806768</td>\n",
+       "      <td>0.001617</td>\n",
+       "      <td>4.452347</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-0.393648</td>\n",
+       "      <td>2.002479</td>\n",
+       "      <td>-7.474046</td>\n",
+       "      <td>-1.087825</td>\n",
+       "      <td>-0.294584</td>\n",
+       "      <td>0.653253</td>\n",
+       "      <td>7.519880</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-0.851527</td>\n",
+       "      <td>1.645623</td>\n",
+       "      <td>-8.590960</td>\n",
+       "      <td>-1.217988</td>\n",
+       "      <td>-0.548860</td>\n",
+       "      <td>0.023652</td>\n",
+       "      <td>2.290652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_renounce</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-15.511361</td>\n",
+       "      <td>13.509526</td>\n",
+       "      <td>-53.036665</td>\n",
+       "      <td>-23.876321</td>\n",
+       "      <td>-12.059815</td>\n",
+       "      <td>-4.944343</td>\n",
+       "      <td>0.002555</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-11.602787</td>\n",
+       "      <td>7.224251</td>\n",
+       "      <td>-34.537269</td>\n",
+       "      <td>-16.120842</td>\n",
+       "      <td>-10.456832</td>\n",
+       "      <td>-6.337416</td>\n",
+       "      <td>1.501772</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-12.145758</td>\n",
+       "      <td>7.078937</td>\n",
+       "      <td>-34.748314</td>\n",
+       "      <td>-16.564231</td>\n",
+       "      <td>-11.512799</td>\n",
+       "      <td>-6.652916</td>\n",
+       "      <td>0.629497</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     energy_in                                              \\\n",
+       "                         count       mean        std        min        25%   \n",
+       "behavior                                                                     \n",
+       "dm_user_degrad_space     105.0  -4.607978   5.971688 -23.164035  -8.036166   \n",
+       "dm_user_degrad_temp      105.0  -3.082417   3.686401 -20.805412  -4.774799   \n",
+       "dm_user_delay            105.0 -15.511465  13.509674 -53.034859 -23.875976   \n",
+       "dm_user_reconfig         105.0  -3.261107   4.983435 -22.257417  -5.871021   \n",
+       "dm_user_renounce         105.0 -15.511361  13.509526 -53.036665 -23.876321   \n",
+       "\n",
+       "                                                    energy_after             \\\n",
+       "                            50%       75%       max        count       mean   \n",
+       "behavior                                                                      \n",
+       "dm_user_degrad_space  -1.872889  0.000000  3.135421        105.0  -3.805844   \n",
+       "dm_user_degrad_temp   -1.788961 -0.141586  0.002555        105.0  -6.438589   \n",
+       "dm_user_delay        -12.059613 -4.943147  0.002555        105.0   3.299942   \n",
+       "dm_user_reconfig      -0.806768  0.001617  4.452347        105.0  -0.393648   \n",
+       "dm_user_renounce     -12.059815 -4.944343  0.002555        105.0 -11.602787   \n",
+       "\n",
+       "                                                                           \\\n",
+       "                           std        min        25%        50%       75%   \n",
+       "behavior                                                                    \n",
+       "dm_user_degrad_space  3.158385 -15.210102  -5.135932  -3.179491 -1.661223   \n",
+       "dm_user_degrad_temp   4.267301 -18.632508  -8.961855  -6.061871 -3.351114   \n",
+       "dm_user_delay         3.274570  -1.496679   0.763093   2.721466  5.313718   \n",
+       "dm_user_reconfig      2.002479  -7.474046  -1.087825  -0.294584  0.653253   \n",
+       "dm_user_renounce      7.224251 -34.537269 -16.120842 -10.456832 -6.337416   \n",
+       "\n",
+       "                                energy_overall                       \\\n",
+       "                            max          count       mean       std   \n",
+       "behavior                                                              \n",
+       "dm_user_degrad_space   0.888404          105.0  -3.923280  3.016705   \n",
+       "dm_user_degrad_temp    0.969748          105.0  -5.896070  3.678149   \n",
+       "dm_user_delay         15.105251          105.0   0.438618  1.247569   \n",
+       "dm_user_reconfig       7.519880          105.0  -0.851527  1.645623   \n",
+       "dm_user_renounce       1.501772          105.0 -12.145758  7.078937   \n",
+       "\n",
+       "                                                                           \n",
+       "                            min        25%        50%       75%       max  \n",
+       "behavior                                                                   \n",
+       "dm_user_degrad_space -14.741997  -5.287614  -3.479632 -1.694622  0.477400  \n",
+       "dm_user_degrad_temp  -16.516273  -8.198406  -5.526574 -3.225889  0.762681  \n",
+       "dm_user_delay         -2.632291  -0.361052   0.245837  0.946996  5.298520  \n",
+       "dm_user_reconfig      -8.590960  -1.217988  -0.548860  0.023652  2.290652  \n",
+       "dm_user_renounce     -34.748314 -16.564231 -11.512799 -6.652916  0.629497  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
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",
@@ -1790,6 +2558,214 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"8\" halign=\"left\">mean_slowdown</th>\n",
+       "      <th colspan=\"8\" halign=\"left\">mean_waiting_time</th>\n",
+       "    </tr>\n",
+       "    <tr>\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>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>behavior</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>dm_user_degrad_space</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-13.633441</td>\n",
+       "      <td>27.039215</td>\n",
+       "      <td>-92.582660</td>\n",
+       "      <td>-21.212897</td>\n",
+       "      <td>-6.729771</td>\n",
+       "      <td>-0.749711</td>\n",
+       "      <td>131.478401</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-18.869530</td>\n",
+       "      <td>23.554534</td>\n",
+       "      <td>-86.943046</td>\n",
+       "      <td>-26.599470</td>\n",
+       "      <td>-12.080521</td>\n",
+       "      <td>-3.194531</td>\n",
+       "      <td>24.956349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_degrad_temp</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>1.121780</td>\n",
+       "      <td>33.129993</td>\n",
+       "      <td>-87.882516</td>\n",
+       "      <td>-12.825810</td>\n",
+       "      <td>0.050172</td>\n",
+       "      <td>5.490376</td>\n",
+       "      <td>111.629661</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-16.983520</td>\n",
+       "      <td>23.677696</td>\n",
+       "      <td>-98.151039</td>\n",
+       "      <td>-27.954375</td>\n",
+       "      <td>-11.649045</td>\n",
+       "      <td>-1.814109</td>\n",
+       "      <td>51.461138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_delay</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>174.122567</td>\n",
+       "      <td>1085.504228</td>\n",
+       "      <td>-83.189618</td>\n",
+       "      <td>-5.155342</td>\n",
+       "      <td>8.512618</td>\n",
+       "      <td>58.761415</td>\n",
+       "      <td>11013.255190</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>122.142251</td>\n",
+       "      <td>501.679032</td>\n",
+       "      <td>-85.610973</td>\n",
+       "      <td>-4.262216</td>\n",
+       "      <td>10.211168</td>\n",
+       "      <td>82.891839</td>\n",
+       "      <td>4727.425802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_reconfig</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-7.091520</td>\n",
+       "      <td>27.586357</td>\n",
+       "      <td>-93.357154</td>\n",
+       "      <td>-14.004595</td>\n",
+       "      <td>-2.444722</td>\n",
+       "      <td>2.824920</td>\n",
+       "      <td>98.160246</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-8.541583</td>\n",
+       "      <td>24.533368</td>\n",
+       "      <td>-88.967533</td>\n",
+       "      <td>-14.951783</td>\n",
+       "      <td>-2.205166</td>\n",
+       "      <td>4.317396</td>\n",
+       "      <td>43.413566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dm_user_renounce</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-28.116897</td>\n",
+       "      <td>40.597865</td>\n",
+       "      <td>-99.271473</td>\n",
+       "      <td>-57.538028</td>\n",
+       "      <td>-17.370536</td>\n",
+       "      <td>3.670126</td>\n",
+       "      <td>62.572502</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>-28.819942</td>\n",
+       "      <td>43.914435</td>\n",
+       "      <td>-99.918207</td>\n",
+       "      <td>-68.581117</td>\n",
+       "      <td>-17.944596</td>\n",
+       "      <td>4.668253</td>\n",
+       "      <td>67.537500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     mean_slowdown                                      \\\n",
+       "                             count        mean          std        min   \n",
+       "behavior                                                                 \n",
+       "dm_user_degrad_space         105.0  -13.633441    27.039215 -92.582660   \n",
+       "dm_user_degrad_temp          105.0    1.121780    33.129993 -87.882516   \n",
+       "dm_user_delay                105.0  174.122567  1085.504228 -83.189618   \n",
+       "dm_user_reconfig             105.0   -7.091520    27.586357 -93.357154   \n",
+       "dm_user_renounce             105.0  -28.116897    40.597865 -99.271473   \n",
+       "\n",
+       "                                                                     \\\n",
+       "                            25%        50%        75%           max   \n",
+       "behavior                                                              \n",
+       "dm_user_degrad_space -21.212897  -6.729771  -0.749711    131.478401   \n",
+       "dm_user_degrad_temp  -12.825810   0.050172   5.490376    111.629661   \n",
+       "dm_user_delay         -5.155342   8.512618  58.761415  11013.255190   \n",
+       "dm_user_reconfig     -14.004595  -2.444722   2.824920     98.160246   \n",
+       "dm_user_renounce     -57.538028 -17.370536   3.670126     62.572502   \n",
+       "\n",
+       "                     mean_waiting_time                                     \\\n",
+       "                                 count        mean         std        min   \n",
+       "behavior                                                                    \n",
+       "dm_user_degrad_space             105.0  -18.869530   23.554534 -86.943046   \n",
+       "dm_user_degrad_temp              105.0  -16.983520   23.677696 -98.151039   \n",
+       "dm_user_delay                    105.0  122.142251  501.679032 -85.610973   \n",
+       "dm_user_reconfig                 105.0   -8.541583   24.533368 -88.967533   \n",
+       "dm_user_renounce                 105.0  -28.819942   43.914435 -99.918207   \n",
+       "\n",
+       "                                                                    \n",
+       "                            25%        50%        75%          max  \n",
+       "behavior                                                            \n",
+       "dm_user_degrad_space -26.599470 -12.080521  -3.194531    24.956349  \n",
+       "dm_user_degrad_temp  -27.954375 -11.649045  -1.814109    51.461138  \n",
+       "dm_user_delay         -4.262216  10.211168  82.891839  4727.425802  \n",
+       "dm_user_reconfig     -14.951783  -2.205166   4.317396    43.413566  \n",
+       "dm_user_renounce     -68.581117 -17.944596   4.668253    67.537500  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
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",
@@ -1816,7 +2792,12 @@
     "# For 4-hour time window \n",
     "#########\n",
     "data_window4 = metrics_relative[metrics_relative.window==4]\n",
+    "\n",
     "# Energy metrics\n",
+    "## Stats\n",
+    "display(data_window4.groupby(\"behavior\").describe()[[\"energy_in\", \"energy_after\", \"energy_overall\"]])\n",
+    "\n",
+    "## Boxplots\n",
     "fig, axs = plt.subplots(1,3, sharey=True, constrained_layout=True)\n",
     "for ax in axs:\n",
     "      ax.yaxis.set_major_formatter(\"{x:.0f}%\")\n",
@@ -1831,8 +2812,13 @@
     "#plt.suptitle(\"Energy metrics (relative to baseline) - 4-hour DR window\")\n",
     "plt.suptitle(\"\")\n",
     "plt.savefig(f\"{OUT_DIR}/energy_metrics_window4.pdf\", bbox_inches='tight', dpi=1000)\n",
+    "plt.show()\n",
     "\n",
     "# Scheduling metrics\n",
+    "## Stats\n",
+    "display(data_window4.groupby(\"behavior\").describe()[[\"mean_slowdown\", \"mean_waiting_time\"]])\n",
+    "\n",
+    "## Boxplots\n",
     "fig, axs = plt.subplots(1,2, constrained_layout=True)\n",
     "for ax in axs:\n",
     "      ax.xaxis.set_major_formatter(\"{x:.0f}%\")\n",
@@ -1857,6 +2843,7 @@
     "#plt.suptitle(\"Scheduling metrics (relative to baseline) - 4-hour DR window\")\n",
     "plt.suptitle(\"\")\n",
     "plt.savefig(f\"{OUT_DIR}/sched_metrics_window4.pdf\", bbox_inches='tight', dpi=1000)\n",
+    "plt.show()\n",
     "\n",
     "# User_delay outliers\n",
     "fig, ax = plt.subplots(figsize=(15,2))\n",
-- 
GitLab