FIGURES.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bdf0dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f268e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "#import mne\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e4964c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "patient_information = 'DATA/data_2states.txt'\n",
    "data_file = 'RESULTS/wPLI_Distance/DimRed_wPLI.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bc66ec2b",
   "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>Patient</th>\n",
       "      <th>Diag</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Injury</th>\n",
       "      <th>TSI</th>\n",
       "      <th>State</th>\n",
       "      <th>CRSR</th>\n",
       "      <th>Outcome_Diag</th>\n",
       "      <th>Group_Diag</th>\n",
       "      <th>Outcome_Prog</th>\n",
       "      <th>Group_Prog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WSAS02</td>\n",
       "      <td>UWS</td>\n",
       "      <td>29</td>\n",
       "      <td>M</td>\n",
       "      <td>TBI</td>\n",
       "      <td>-</td>\n",
       "      <td>Acute</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>1</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WSAS05</td>\n",
       "      <td>MCS</td>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>TBI</td>\n",
       "      <td>9 Y</td>\n",
       "      <td>Chronic</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>MCS</td>\n",
       "      <td>3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WSAS09</td>\n",
       "      <td>UWS</td>\n",
       "      <td>50</td>\n",
       "      <td>F</td>\n",
       "      <td>Stroke</td>\n",
       "      <td>25 D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>1</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WSAS10</td>\n",
       "      <td>UWS</td>\n",
       "      <td>75</td>\n",
       "      <td>F</td>\n",
       "      <td>Stroke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acute</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WSAS11</td>\n",
       "      <td>UWS</td>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>Anoxic</td>\n",
       "      <td>1Y</td>\n",
       "      <td>Chronic</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WSAS12</td>\n",
       "      <td>MCS</td>\n",
       "      <td>36</td>\n",
       "      <td>F</td>\n",
       "      <td>TBI</td>\n",
       "      <td>2Y</td>\n",
       "      <td>Chronic</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>MCS</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WSAS13</td>\n",
       "      <td>UWS</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>Anoxic</td>\n",
       "      <td>8Y</td>\n",
       "      <td>Chronic</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WSAS17</td>\n",
       "      <td>Coma</td>\n",
       "      <td>74</td>\n",
       "      <td>F</td>\n",
       "      <td>Anoxic</td>\n",
       "      <td>10D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Coma</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WSAS18</td>\n",
       "      <td>UWS</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>Anoxic</td>\n",
       "      <td>9M</td>\n",
       "      <td>Chronic</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WSAS19</td>\n",
       "      <td>Coma</td>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>Stroke</td>\n",
       "      <td>5D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Coma</td>\n",
       "      <td>3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WSAS20</td>\n",
       "      <td>UWS</td>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>Stroke</td>\n",
       "      <td>20D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>1</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>WSAS22</td>\n",
       "      <td>UWS</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>TBI</td>\n",
       "      <td>20D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WSAS25</td>\n",
       "      <td>UWS</td>\n",
       "      <td>66</td>\n",
       "      <td>M</td>\n",
       "      <td>ICH</td>\n",
       "      <td>3D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WSAS27</td>\n",
       "      <td>UWS</td>\n",
       "      <td>38</td>\n",
       "      <td>F</td>\n",
       "      <td>ICH</td>\n",
       "      <td>24D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>UWS</td>\n",
       "      <td>1</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>WSAS28</td>\n",
       "      <td>MCS</td>\n",
       "      <td>38</td>\n",
       "      <td>F</td>\n",
       "      <td>ICH</td>\n",
       "      <td>23D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>MCS</td>\n",
       "      <td>1</td>\n",
       "      <td>Recovered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>WSAS29</td>\n",
       "      <td>Coma</td>\n",
       "      <td>74</td>\n",
       "      <td>F</td>\n",
       "      <td>ICH</td>\n",
       "      <td>33D</td>\n",
       "      <td>Acute</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Coma</td>\n",
       "      <td>3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Patient  Diag  Age Sex  Injury   TSI    State  CRSR  Outcome_Diag  \\\n",
       "0   WSAS02   UWS   29   M     TBI     -    Acute     4             1   \n",
       "1   WSAS05   MCS   28   M     TBI   9 Y  Chronic    10             2   \n",
       "2   WSAS09   UWS   50   F  Stroke  25 D    Acute     4             1   \n",
       "3   WSAS10   UWS   75   F  Stroke   NaN    Acute     5             1   \n",
       "4   WSAS11   UWS   28   F  Anoxic    1Y  Chronic     6             1   \n",
       "5   WSAS12   MCS   36   F     TBI    2Y  Chronic    11             2   \n",
       "6   WSAS13   UWS   24   M  Anoxic    8Y  Chronic     5             1   \n",
       "7   WSAS17  Coma   74   F  Anoxic   10D    Acute     0             0   \n",
       "8   WSAS18   UWS   53   F  Anoxic    9M  Chronic     5             1   \n",
       "9   WSAS19  Coma   40   M  Stroke    5D    Acute     0             0   \n",
       "10  WSAS20   UWS   42   F  Stroke   20D    Acute     3             1   \n",
       "11  WSAS22   UWS   18   F     TBI   20D    Acute     5             1   \n",
       "12  WSAS25   UWS   66   M     ICH    3D    Acute     2             1   \n",
       "13  WSAS27   UWS   38   F     ICH   24D    Acute     5             1   \n",
       "14  WSAS28   MCS   38   F     ICH   23D    Acute     7             2   \n",
       "15  WSAS29  Coma   74   F     ICH   33D    Acute     0             0   \n",
       "\n",
       "   Group_Diag  Outcome_Prog     Group_Prog  \n",
       "0         UWS             1      Recovered  \n",
       "1         MCS             3        Unknown  \n",
       "2         UWS             1      Recovered  \n",
       "3         UWS             0  Non-recovered  \n",
       "4         UWS             0  Non-recovered  \n",
       "5         MCS             0  Non-recovered  \n",
       "6         UWS             0  Non-recovered  \n",
       "7        Coma             0  Non-recovered  \n",
       "8         UWS             0  Non-recovered  \n",
       "9        Coma             3        Unknown  \n",
       "10        UWS             1      Recovered  \n",
       "11        UWS             0  Non-recovered  \n",
       "12        UWS             0  Non-recovered  \n",
       "13        UWS             1      Recovered  \n",
       "14        MCS             1      Recovered  \n",
       "15       Coma             3        Unknown  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info = pd.read_csv(patient_information, sep = '\\t')\n",
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a23be96e",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'RESULTS/wPLI_Distance/DimRed_wPLI.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/jt/1njpx4d93pn5y3nd0p7l5hwr0000gn/T/ipykernel_87211/444269070.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m';'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    931\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0;31m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m             \u001b[0;31m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m             self.handles = get_handle(  # type: ignore[call-overload]\n\u001b[0m\u001b[1;32m   1218\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    787\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    790\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'RESULTS/wPLI_Distance/DimRed_wPLI.csv'"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(data_file, sep = ';')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec9857b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Outcome']=info['Group_Prog']\n",
    "data['CRSR']=info['CRSR']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f087c24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_reco=data.query('Outcome ==\"Recovered\"')\n",
    "data_nonreco=data.query('Outcome ==\"Non-recovered\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "81668c7f",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'BaseNorm_B_Dist_Base_Anes'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3620\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3621\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3622\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'BaseNorm_B_Dist_Base_Anes'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/jt/1njpx4d93pn5y3nd0p7l5hwr0000gn/T/ipykernel_87211/2018113829.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"CRSR\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"BaseNorm_B_Dist_Base_Anes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mregplot\u001b[0;34m(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)\u001b[0m\n\u001b[1;32m    849\u001b[0m ):\n\u001b[1;32m    850\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 851\u001b[0;31m     plotter = _RegressionPlotter(x, y, data, x_estimator, x_bins, x_ci,\n\u001b[0m\u001b[1;32m    852\u001b[0m                                  \u001b[0mscatter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_reg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m                                  \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogistic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlowess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, color, label)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;31m# Extract the data vals from the arguments or passed dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         self.establish_variables(data, x=x, y=y, units=units,\n\u001b[0m\u001b[1;32m    110\u001b[0m                                  x_partial=x_partial, y_partial=y_partial)\n\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, data, **kws)\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m                 \u001b[0mvector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mvector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3504\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3505\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3506\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3507\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3508\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3621\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3622\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3623\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3624\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3625\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'BaseNorm_B_Dist_Base_Anes'"
     ]
    }
   ],
   "source": [
    "sns.regplot(x=\"CRSR\", y=\"BaseNorm_B_Dist_Base_Anes\", data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a2b28e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Norm_B_Dist_Base_Anes'}, xlabel='[Outcome]'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Outcome','Norm_B_Dist_Base_Anes']].boxplot(by='Outcome')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}