ST260_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b591a292",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ST260_Day4 cascade experiment 0\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  0.986973  1.235436  1.210178  0.965851\n",
      "1 corr_coef_kl_mean    0.081500  0.151044  0.119446  0.200760\n",
      "2 van_rossum_kl_mean   0.124069  0.129226  0.113795  0.154426\n",
      "3 firing_rate_kl_std   1.549785  1.547256  2.036799  1.115377\n",
      "4 corr_coef_kl_std     0.042769  0.040406  0.031048  0.099105\n",
      "5 van_rossum_kl_std    0.061109  0.078410  0.055801  0.099554\n",
      "ST260_Day4 cascade experiment 1\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  0.923214  1.441769  1.095596  0.922295\n",
      "1 corr_coef_kl_mean    0.062414  0.139256  0.113025  0.274220\n",
      "2 van_rossum_kl_mean   0.131346  0.113706  0.105919  0.135229\n",
      "3 firing_rate_kl_std   1.186032  2.010772  2.177840  0.934474\n",
      "4 corr_coef_kl_std     0.035675  0.034979  0.029688  0.128156\n",
      "5 van_rossum_kl_std    0.065229  0.059170  0.056882  0.078023\n",
      "ST260_Day4 cascade experiment 2\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  0.851991  1.453260  1.616111  0.974710\n",
      "1 corr_coef_kl_mean    0.080947  0.120533  0.094346  0.190881\n",
      "2 van_rossum_kl_mean   0.150002  0.104414  0.105997  0.143570\n",
      "3 firing_rate_kl_std   1.147363  2.207095  2.495029  1.176579\n",
      "4 corr_coef_kl_std     0.049018  0.033892  0.027880  0.104335\n",
      "5 van_rossum_kl_std    0.088176  0.057249  0.052047  0.085868\n",
      "ST260_Day4 cascade experiment 3\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  0.928654  1.418990  1.189287  1.074703\n",
      "1 corr_coef_kl_mean    0.083819  0.140556  0.112785  0.426893\n",
      "2 van_rossum_kl_mean   0.158436  0.106396  0.107691  0.149708\n",
      "3 firing_rate_kl_std   1.562440  1.842360  2.108390  1.312984\n",
      "4 corr_coef_kl_std     0.045479  0.037448  0.030092  0.180342\n",
      "5 van_rossum_kl_std    0.094440  0.054876  0.057812  0.099828\n",
      "ST260_Day4 cascade experiment 4\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  0.921897  1.340551  1.037873  0.936626\n",
      "1 corr_coef_kl_mean    0.082214  0.125619  0.111903  0.210001\n",
      "2 van_rossum_kl_mean   0.150065  0.106294  0.106493  0.142580\n",
      "3 firing_rate_kl_std   1.441818  1.995968  1.507321  0.977195\n",
      "4 corr_coef_kl_std     0.047428  0.032215  0.030307  0.116683\n",
      "5 van_rossum_kl_std    0.089598  0.059311  0.057712  0.094217\n",
      "ST260_Day3 cascade experiment 0\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.026493  1.520515  1.923248  1.500678\n",
      "1 corr_coef_kl_mean    0.104701  0.089768  0.091541  0.145583\n",
      "2 van_rossum_kl_mean   0.123612  0.096957  0.111447  0.145967\n",
      "3 firing_rate_kl_std   1.769099  3.036886  2.967232  2.608647\n",
      "4 corr_coef_kl_std     0.064552  0.037992  0.037054  0.077172\n",
      "5 van_rossum_kl_std    0.062460  0.046104  0.042853  0.075938\n",
      "ST260_Day3 cascade experiment 1\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.530169  1.546635  1.984468  1.106432\n",
      "1 corr_coef_kl_mean    0.080521  0.100612  0.093339  0.131219\n",
      "2 van_rossum_kl_mean   0.143961  0.094955  0.108045  0.135051\n",
      "3 firing_rate_kl_std   2.926234  2.781388  3.499155  1.894637\n",
      "4 corr_coef_kl_std     0.058107  0.039802  0.036897  0.061850\n",
      "5 van_rossum_kl_std    0.073499  0.044087  0.046046  0.079037\n",
      "ST260_Day3 cascade experiment 2\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.424557  2.216687  1.335934  1.206504\n",
      "1 corr_coef_kl_mean    0.087189  0.122369  0.087015  0.132705\n",
      "2 van_rossum_kl_mean   0.157588  0.108414  0.097739  0.148930\n",
      "3 firing_rate_kl_std   2.403610  4.210639  2.333513  2.343499\n",
      "4 corr_coef_kl_std     0.061905  0.047841  0.034496  0.073506\n",
      "5 van_rossum_kl_std    0.073820  0.044837  0.044668  0.075745\n",
      "ST260_Day3 cascade experiment 3\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.109067  1.700136  1.591815  1.377064\n",
      "1 corr_coef_kl_mean    0.095300  0.119047  0.087292  0.133235\n",
      "2 van_rossum_kl_mean   0.123373  0.101060  0.101255  0.127133\n",
      "3 firing_rate_kl_std   1.982566  3.037716  2.880410  2.313191\n",
      "4 corr_coef_kl_std     0.059444  0.045358  0.035869  0.070630\n",
      "5 van_rossum_kl_std    0.061943  0.041976  0.043891  0.059910\n",
      "ST260_Day3 cascade experiment 4\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.351177  1.531216  1.584247  1.211135\n",
      "1 corr_coef_kl_mean    0.106691  0.100482  0.093891  0.117238\n",
      "2 van_rossum_kl_mean   0.154139  0.095736  0.103698  0.130373\n",
      "3 firing_rate_kl_std   2.631366  2.937180  2.801994  1.854813\n",
      "4 corr_coef_kl_std     0.076544  0.042342  0.036620  0.058221\n",
      "5 van_rossum_kl_std    0.062676  0.045699  0.043158  0.085544\n",
      "ST260_Day2 cascade experiment 0\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  0.668862  1.208378  0.919266  1.259637\n",
      "1 corr_coef_kl_mean    0.029905  0.183540  0.144490  0.458035\n",
      "2 van_rossum_kl_mean   0.096500  0.095528  0.101556  0.111119\n",
      "3 firing_rate_kl_std   0.720432  2.361774  0.905329  2.226841\n",
      "4 corr_coef_kl_std     0.021600  0.067548  0.045972  0.187964\n",
      "5 van_rossum_kl_std    0.042706  0.043965  0.044772  0.056551\n",
      "ST260_Day2 cascade experiment 1\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.107279  1.623445  1.337109  1.823644\n",
      "1 corr_coef_kl_mean    0.078758  0.126468  0.124685  0.353503\n",
      "2 van_rossum_kl_mean   0.123534  0.083699  0.095203  0.187807\n",
      "3 firing_rate_kl_std   1.442646  2.370519  2.372831  2.132355\n",
      "4 corr_coef_kl_std     0.045290  0.045093  0.042208  0.168861\n",
      "5 van_rossum_kl_std    0.044461  0.040178  0.051001  0.075651\n",
      "ST260_Day2 cascade experiment 2\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.493321  1.897593  1.254220  1.198272\n",
      "1 corr_coef_kl_mean    0.107974  0.126323  0.128298  0.325627\n",
      "2 van_rossum_kl_mean   0.102298  0.085343  0.089591  0.115119\n",
      "3 firing_rate_kl_std   2.427525  2.775450  1.509845  1.425806\n",
      "4 corr_coef_kl_std     0.060748  0.050961  0.042904  0.162163\n",
      "5 van_rossum_kl_std    0.062005  0.044165  0.041689  0.060160\n",
      "ST260_Day2 cascade experiment 3\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.275394  2.078108  1.284835  1.476382\n",
      "1 corr_coef_kl_mean    0.118781  0.128059  0.119054  0.463423\n",
      "2 van_rossum_kl_mean   0.099763  0.085396  0.096248  0.116569\n",
      "3 firing_rate_kl_std   2.186232  3.306298  1.520574  1.570105\n",
      "4 corr_coef_kl_std     0.059980  0.048514  0.037953  0.196375\n",
      "5 van_rossum_kl_std    0.069561  0.038365  0.040348  0.057696\n",
      "ST260_Day2 cascade experiment 4\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.050876  1.534493  1.612890  2.502646\n",
      "1 corr_coef_kl_mean    0.124070  0.125437  0.118346  0.449936\n",
      "2 van_rossum_kl_mean   0.108653  0.090914  0.087245  0.152646\n",
      "3 firing_rate_kl_std   1.182774  2.579127  2.313011  3.744201\n",
      "4 corr_coef_kl_std     0.069580  0.050538  0.039512  0.195047\n",
      "5 van_rossum_kl_std    0.053323  0.043064  0.044488  0.051442\n",
      "ST260_Day1 cascade experiment 0\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.334031  1.477387  1.664228  1.286520\n",
      "1 corr_coef_kl_mean    0.053460  0.182853  0.179448  0.242619\n",
      "2 van_rossum_kl_mean   0.109441  0.081114  0.086835  0.120541\n",
      "3 firing_rate_kl_std   1.800030  2.354454  1.832858  1.743880\n",
      "4 corr_coef_kl_std     0.034149  0.052004  0.061409  0.125833\n",
      "5 van_rossum_kl_std    0.045051  0.037619  0.041703  0.078561\n",
      "ST260_Day1 cascade experiment 1\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.231254  1.732683  1.846781  1.766643\n",
      "1 corr_coef_kl_mean    0.065123  0.184268  0.175672  0.313314\n",
      "2 van_rossum_kl_mean   0.112153  0.084780  0.091009  0.106556\n",
      "3 firing_rate_kl_std   1.838311  2.338567  2.265707  2.410212\n",
      "4 corr_coef_kl_std     0.041217  0.050045  0.050399  0.141031\n",
      "5 van_rossum_kl_std    0.047719  0.038467  0.047014  0.055152\n",
      "ST260_Day1 cascade experiment 2\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.053591  1.409348  1.545226  1.331617\n",
      "1 corr_coef_kl_mean    0.063900  0.192210  0.168602  0.193364\n",
      "2 van_rossum_kl_mean   0.111535  0.082722  0.090322  0.101182\n",
      "3 firing_rate_kl_std   1.522782  1.639411  1.747386  1.995050\n",
      "4 corr_coef_kl_std     0.044371  0.054097  0.047793  0.087357\n",
      "5 van_rossum_kl_std    0.052603  0.044040  0.055872  0.053264\n",
      "ST260_Day1 cascade experiment 3\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.338145  1.474558  1.874360  1.451393\n",
      "1 corr_coef_kl_mean    0.050481  0.184838  0.177093  0.305414\n",
      "2 van_rossum_kl_mean   0.119719  0.085211  0.092758  0.091403\n",
      "3 firing_rate_kl_std   1.898474  1.872587  2.066865  1.970450\n",
      "4 corr_coef_kl_std     0.033038  0.051182  0.046807  0.148123\n",
      "5 van_rossum_kl_std    0.054834  0.041563  0.061737  0.048628\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ST260_Day1 cascade experiment 4\n",
      "                          whole    random   max_val    kmeans\n",
      "0 firing_rate_kl_mean  1.039347  1.367393  1.544705  1.374146\n",
      "1 corr_coef_kl_mean    0.068209  0.211257  0.181223  0.205381\n",
      "2 van_rossum_kl_mean   0.113590  0.078084  0.087484  0.110152\n",
      "3 firing_rate_kl_std   1.425331  1.902258  2.291986  1.733906\n",
      "4 corr_coef_kl_std     0.040625  0.057214  0.049876  0.102284\n",
      "5 van_rossum_kl_std    0.054427  0.038978  0.050942  0.063164\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import csv\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "datanames = ['ST260_Day4','ST260_Day3','ST260_Day2','ST260_Day1']\n",
    "serials = [0,1,2,3,4]\n",
    "spike_metrics = ['cascade']\n",
    "filename = '80epochs'\n",
    "\n",
    "df = pd.DataFrame(columns=['dataname','serial','grouping method','firing_r_KL_mean', 'corr_c_KL_mean', 'van_r_KL_mean','firing_r_KL_std','corr_c_KL_std','van_r_KL_std'])\n",
    "\n",
    "for filename in ['80epochs']:\n",
    "    for dataname in datanames:\n",
    "        for serial in serials:\n",
    "            run_as_a_whole_datapath = os.path.join(filename, dataname+'_'+str(serial))\n",
    "            max_val_datapath = os.path.join(filename, dataname+'_cascade_GPFA_'+str(serial)+'_merged')\n",
    "            random_datapath = os.path.join(filename, dataname+'_cascade_random_'+str(serial)+'_merged')\n",
    "            kmeans_datapath = os.path.join(filename, dataname+'_cascade_GPFA_kmeans_'+str(serial)+'_merged')\n",
    "            # dbscan_datapath = os.path.join( dataname+'_cascade_GPFA_dbscan_'+str(serial)+'_merged')\n",
    "            datapaths = [run_as_a_whole_datapath, random_datapath,max_val_datapath,kmeans_datapath]\n",
    "            grouping_type = ['whole','random','max_val','kmeans','dbscan']\n",
    "            for spike_metric in spike_metrics:\n",
    "                all_stats = {}\n",
    "                for i in range(len(datapaths)):\n",
    "                    txt_location = os.path.join( datapaths[i], 'metrics','plots_'+spike_metric,'stats.txt')\n",
    "                    if os.path.exists(txt_location):\n",
    "                        with open(txt_location,'r') as f:\n",
    "                            stats = f.read()\n",
    "                        firing_rate_KL_mean = re.findall('firing rate KL mean: ([\\d\\.]+),', stats)[0]\n",
    "                        firing_rate_KL_std = re.findall('kL std ([\\d\\.]+)', stats)[0]\n",
    "                        corr_coef_KL_mean = re.findall('correlation coefficient KL mean: ([\\d\\.]+),', stats)[0]\n",
    "                        corr_coef_KL_std = re.findall('kl std ([\\d\\.]+)', stats)[0]\n",
    "                        van_rossum_KL_mean = re.findall('van rossum KL mean: ([\\d\\.]+),', stats)[0]\n",
    "                        van_rossum_KL_std = re.findall('kl std ([\\d\\.]+)', stats)[1]\n",
    "                        all_stats[grouping_type[i]] = [firing_rate_KL_mean,corr_coef_KL_mean,van_rossum_KL_mean,\n",
    "                        firing_rate_KL_std,corr_coef_KL_std,van_rossum_KL_std]\n",
    "                        if spike_metric == 'cascade':\n",
    "                            df.loc[len(df)] = [dataname,serial,grouping_type[i],float(firing_rate_KL_mean),float(corr_coef_KL_mean),float(van_rossum_KL_mean),float(firing_rate_KL_std),float(corr_coef_KL_std),float(van_rossum_KL_std)]\n",
    "\n",
    "                all_stats_df = pd.DataFrame.from_dict(all_stats)\n",
    "                if len(all_stats.keys()) >0:\n",
    "                    metric_index = pd.Series(list(range(len(all_stats_df))))\n",
    "                    metric_str_index = pd.Series(['firing_rate_kl_mean','corr_coef_kl_mean','van_rossum_kl_mean',\n",
    "                                                    'firing_rate_kl_std','corr_coef_kl_std','van_rossum_kl_std'])\n",
    "                    all_stats_df = all_stats_df.set_index([metric_index,metric_str_index])\n",
    "                print(dataname,spike_metric,'experiment '+str(serial))\n",
    "                print(all_stats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b1f5de6d",
   "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>dataname</th>\n",
       "      <th>serial</th>\n",
       "      <th>grouping method</th>\n",
       "      <th>firing_r_KL_mean</th>\n",
       "      <th>corr_c_KL_mean</th>\n",
       "      <th>van_r_KL_mean</th>\n",
       "      <th>firing_r_KL_std</th>\n",
       "      <th>corr_c_KL_std</th>\n",
       "      <th>van_r_KL_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>0</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.986973</td>\n",
       "      <td>0.081500</td>\n",
       "      <td>0.124069</td>\n",
       "      <td>1.549785</td>\n",
       "      <td>0.042769</td>\n",
       "      <td>0.061109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>1.235436</td>\n",
       "      <td>0.151044</td>\n",
       "      <td>0.129226</td>\n",
       "      <td>1.547256</td>\n",
       "      <td>0.040406</td>\n",
       "      <td>0.078410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>0</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.210178</td>\n",
       "      <td>0.119446</td>\n",
       "      <td>0.113795</td>\n",
       "      <td>2.036799</td>\n",
       "      <td>0.031048</td>\n",
       "      <td>0.055801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>0</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>0.965851</td>\n",
       "      <td>0.200760</td>\n",
       "      <td>0.154426</td>\n",
       "      <td>1.115377</td>\n",
       "      <td>0.099105</td>\n",
       "      <td>0.099554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>1</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.923214</td>\n",
       "      <td>0.062414</td>\n",
       "      <td>0.131346</td>\n",
       "      <td>1.186032</td>\n",
       "      <td>0.035675</td>\n",
       "      <td>0.065229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>1</td>\n",
       "      <td>random</td>\n",
       "      <td>1.441769</td>\n",
       "      <td>0.139256</td>\n",
       "      <td>0.113706</td>\n",
       "      <td>2.010772</td>\n",
       "      <td>0.034979</td>\n",
       "      <td>0.059170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.095596</td>\n",
       "      <td>0.113025</td>\n",
       "      <td>0.105919</td>\n",
       "      <td>2.177840</td>\n",
       "      <td>0.029688</td>\n",
       "      <td>0.056882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>0.922295</td>\n",
       "      <td>0.274220</td>\n",
       "      <td>0.135229</td>\n",
       "      <td>0.934474</td>\n",
       "      <td>0.128156</td>\n",
       "      <td>0.078023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>2</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.851991</td>\n",
       "      <td>0.080947</td>\n",
       "      <td>0.150002</td>\n",
       "      <td>1.147363</td>\n",
       "      <td>0.049018</td>\n",
       "      <td>0.088176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>1.453260</td>\n",
       "      <td>0.120533</td>\n",
       "      <td>0.104414</td>\n",
       "      <td>2.207095</td>\n",
       "      <td>0.033892</td>\n",
       "      <td>0.057249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.616111</td>\n",
       "      <td>0.094346</td>\n",
       "      <td>0.105997</td>\n",
       "      <td>2.495029</td>\n",
       "      <td>0.027880</td>\n",
       "      <td>0.052047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>0.974710</td>\n",
       "      <td>0.190881</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>1.176579</td>\n",
       "      <td>0.104335</td>\n",
       "      <td>0.085868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>3</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.928654</td>\n",
       "      <td>0.083819</td>\n",
       "      <td>0.158436</td>\n",
       "      <td>1.562440</td>\n",
       "      <td>0.045479</td>\n",
       "      <td>0.094440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>1.418990</td>\n",
       "      <td>0.140556</td>\n",
       "      <td>0.106396</td>\n",
       "      <td>1.842360</td>\n",
       "      <td>0.037448</td>\n",
       "      <td>0.054876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.189287</td>\n",
       "      <td>0.112785</td>\n",
       "      <td>0.107691</td>\n",
       "      <td>2.108390</td>\n",
       "      <td>0.030092</td>\n",
       "      <td>0.057812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.074703</td>\n",
       "      <td>0.426893</td>\n",
       "      <td>0.149708</td>\n",
       "      <td>1.312984</td>\n",
       "      <td>0.180342</td>\n",
       "      <td>0.099828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>4</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.921897</td>\n",
       "      <td>0.082214</td>\n",
       "      <td>0.150065</td>\n",
       "      <td>1.441818</td>\n",
       "      <td>0.047428</td>\n",
       "      <td>0.089598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>1.340551</td>\n",
       "      <td>0.125619</td>\n",
       "      <td>0.106294</td>\n",
       "      <td>1.995968</td>\n",
       "      <td>0.032215</td>\n",
       "      <td>0.059311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.037873</td>\n",
       "      <td>0.111903</td>\n",
       "      <td>0.106493</td>\n",
       "      <td>1.507321</td>\n",
       "      <td>0.030307</td>\n",
       "      <td>0.057712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>0.936626</td>\n",
       "      <td>0.210001</td>\n",
       "      <td>0.142580</td>\n",
       "      <td>0.977195</td>\n",
       "      <td>0.116683</td>\n",
       "      <td>0.094217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>0</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.026493</td>\n",
       "      <td>0.104701</td>\n",
       "      <td>0.123612</td>\n",
       "      <td>1.769099</td>\n",
       "      <td>0.064552</td>\n",
       "      <td>0.062460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>1.520515</td>\n",
       "      <td>0.089768</td>\n",
       "      <td>0.096957</td>\n",
       "      <td>3.036886</td>\n",
       "      <td>0.037992</td>\n",
       "      <td>0.046104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>0</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.923248</td>\n",
       "      <td>0.091541</td>\n",
       "      <td>0.111447</td>\n",
       "      <td>2.967232</td>\n",
       "      <td>0.037054</td>\n",
       "      <td>0.042853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>0</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.500678</td>\n",
       "      <td>0.145583</td>\n",
       "      <td>0.145967</td>\n",
       "      <td>2.608647</td>\n",
       "      <td>0.077172</td>\n",
       "      <td>0.075938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>1</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.530169</td>\n",
       "      <td>0.080521</td>\n",
       "      <td>0.143961</td>\n",
       "      <td>2.926234</td>\n",
       "      <td>0.058107</td>\n",
       "      <td>0.073499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>1</td>\n",
       "      <td>random</td>\n",
       "      <td>1.546635</td>\n",
       "      <td>0.100612</td>\n",
       "      <td>0.094955</td>\n",
       "      <td>2.781388</td>\n",
       "      <td>0.039802</td>\n",
       "      <td>0.044087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.984468</td>\n",
       "      <td>0.093339</td>\n",
       "      <td>0.108045</td>\n",
       "      <td>3.499155</td>\n",
       "      <td>0.036897</td>\n",
       "      <td>0.046046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.106432</td>\n",
       "      <td>0.131219</td>\n",
       "      <td>0.135051</td>\n",
       "      <td>1.894637</td>\n",
       "      <td>0.061850</td>\n",
       "      <td>0.079037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>2</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.424557</td>\n",
       "      <td>0.087189</td>\n",
       "      <td>0.157588</td>\n",
       "      <td>2.403610</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.073820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>2.216687</td>\n",
       "      <td>0.122369</td>\n",
       "      <td>0.108414</td>\n",
       "      <td>4.210639</td>\n",
       "      <td>0.047841</td>\n",
       "      <td>0.044837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.335934</td>\n",
       "      <td>0.087015</td>\n",
       "      <td>0.097739</td>\n",
       "      <td>2.333513</td>\n",
       "      <td>0.034496</td>\n",
       "      <td>0.044668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.206504</td>\n",
       "      <td>0.132705</td>\n",
       "      <td>0.148930</td>\n",
       "      <td>2.343499</td>\n",
       "      <td>0.073506</td>\n",
       "      <td>0.075745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>3</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.109067</td>\n",
       "      <td>0.095300</td>\n",
       "      <td>0.123373</td>\n",
       "      <td>1.982566</td>\n",
       "      <td>0.059444</td>\n",
       "      <td>0.061943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>1.700136</td>\n",
       "      <td>0.119047</td>\n",
       "      <td>0.101060</td>\n",
       "      <td>3.037716</td>\n",
       "      <td>0.045358</td>\n",
       "      <td>0.041976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.591815</td>\n",
       "      <td>0.087292</td>\n",
       "      <td>0.101255</td>\n",
       "      <td>2.880410</td>\n",
       "      <td>0.035869</td>\n",
       "      <td>0.043891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.377064</td>\n",
       "      <td>0.133235</td>\n",
       "      <td>0.127133</td>\n",
       "      <td>2.313191</td>\n",
       "      <td>0.070630</td>\n",
       "      <td>0.059910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>4</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.351177</td>\n",
       "      <td>0.106691</td>\n",
       "      <td>0.154139</td>\n",
       "      <td>2.631366</td>\n",
       "      <td>0.076544</td>\n",
       "      <td>0.062676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>1.531216</td>\n",
       "      <td>0.100482</td>\n",
       "      <td>0.095736</td>\n",
       "      <td>2.937180</td>\n",
       "      <td>0.042342</td>\n",
       "      <td>0.045699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.584247</td>\n",
       "      <td>0.093891</td>\n",
       "      <td>0.103698</td>\n",
       "      <td>2.801994</td>\n",
       "      <td>0.036620</td>\n",
       "      <td>0.043158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.211135</td>\n",
       "      <td>0.117238</td>\n",
       "      <td>0.130373</td>\n",
       "      <td>1.854813</td>\n",
       "      <td>0.058221</td>\n",
       "      <td>0.085544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>0</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.668862</td>\n",
       "      <td>0.029905</td>\n",
       "      <td>0.096500</td>\n",
       "      <td>0.720432</td>\n",
       "      <td>0.021600</td>\n",
       "      <td>0.042706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>1.208378</td>\n",
       "      <td>0.183540</td>\n",
       "      <td>0.095528</td>\n",
       "      <td>2.361774</td>\n",
       "      <td>0.067548</td>\n",
       "      <td>0.043965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>0</td>\n",
       "      <td>max_val</td>\n",
       "      <td>0.919266</td>\n",
       "      <td>0.144490</td>\n",
       "      <td>0.101556</td>\n",
       "      <td>0.905329</td>\n",
       "      <td>0.045972</td>\n",
       "      <td>0.044772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>0</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.259637</td>\n",
       "      <td>0.458035</td>\n",
       "      <td>0.111119</td>\n",
       "      <td>2.226841</td>\n",
       "      <td>0.187964</td>\n",
       "      <td>0.056551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>1</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.107279</td>\n",
       "      <td>0.078758</td>\n",
       "      <td>0.123534</td>\n",
       "      <td>1.442646</td>\n",
       "      <td>0.045290</td>\n",
       "      <td>0.044461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>1</td>\n",
       "      <td>random</td>\n",
       "      <td>1.623445</td>\n",
       "      <td>0.126468</td>\n",
       "      <td>0.083699</td>\n",
       "      <td>2.370519</td>\n",
       "      <td>0.045093</td>\n",
       "      <td>0.040178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.337109</td>\n",
       "      <td>0.124685</td>\n",
       "      <td>0.095203</td>\n",
       "      <td>2.372831</td>\n",
       "      <td>0.042208</td>\n",
       "      <td>0.051001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.823644</td>\n",
       "      <td>0.353503</td>\n",
       "      <td>0.187807</td>\n",
       "      <td>2.132355</td>\n",
       "      <td>0.168861</td>\n",
       "      <td>0.075651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>2</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.493321</td>\n",
       "      <td>0.107974</td>\n",
       "      <td>0.102298</td>\n",
       "      <td>2.427525</td>\n",
       "      <td>0.060748</td>\n",
       "      <td>0.062005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>1.897593</td>\n",
       "      <td>0.126323</td>\n",
       "      <td>0.085343</td>\n",
       "      <td>2.775450</td>\n",
       "      <td>0.050961</td>\n",
       "      <td>0.044165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.254220</td>\n",
       "      <td>0.128298</td>\n",
       "      <td>0.089591</td>\n",
       "      <td>1.509845</td>\n",
       "      <td>0.042904</td>\n",
       "      <td>0.041689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.198272</td>\n",
       "      <td>0.325627</td>\n",
       "      <td>0.115119</td>\n",
       "      <td>1.425806</td>\n",
       "      <td>0.162163</td>\n",
       "      <td>0.060160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>3</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.275394</td>\n",
       "      <td>0.118781</td>\n",
       "      <td>0.099763</td>\n",
       "      <td>2.186232</td>\n",
       "      <td>0.059980</td>\n",
       "      <td>0.069561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>2.078108</td>\n",
       "      <td>0.128059</td>\n",
       "      <td>0.085396</td>\n",
       "      <td>3.306298</td>\n",
       "      <td>0.048514</td>\n",
       "      <td>0.038365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.284835</td>\n",
       "      <td>0.119054</td>\n",
       "      <td>0.096248</td>\n",
       "      <td>1.520574</td>\n",
       "      <td>0.037953</td>\n",
       "      <td>0.040348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.476382</td>\n",
       "      <td>0.463423</td>\n",
       "      <td>0.116569</td>\n",
       "      <td>1.570105</td>\n",
       "      <td>0.196375</td>\n",
       "      <td>0.057696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>4</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.050876</td>\n",
       "      <td>0.124070</td>\n",
       "      <td>0.108653</td>\n",
       "      <td>1.182774</td>\n",
       "      <td>0.069580</td>\n",
       "      <td>0.053323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>1.534493</td>\n",
       "      <td>0.125437</td>\n",
       "      <td>0.090914</td>\n",
       "      <td>2.579127</td>\n",
       "      <td>0.050538</td>\n",
       "      <td>0.043064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.612890</td>\n",
       "      <td>0.118346</td>\n",
       "      <td>0.087245</td>\n",
       "      <td>2.313011</td>\n",
       "      <td>0.039512</td>\n",
       "      <td>0.044488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>2.502646</td>\n",
       "      <td>0.449936</td>\n",
       "      <td>0.152646</td>\n",
       "      <td>3.744201</td>\n",
       "      <td>0.195047</td>\n",
       "      <td>0.051442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>0</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.334031</td>\n",
       "      <td>0.053460</td>\n",
       "      <td>0.109441</td>\n",
       "      <td>1.800030</td>\n",
       "      <td>0.034149</td>\n",
       "      <td>0.045051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>1.477387</td>\n",
       "      <td>0.182853</td>\n",
       "      <td>0.081114</td>\n",
       "      <td>2.354454</td>\n",
       "      <td>0.052004</td>\n",
       "      <td>0.037619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>0</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.664228</td>\n",
       "      <td>0.179448</td>\n",
       "      <td>0.086835</td>\n",
       "      <td>1.832858</td>\n",
       "      <td>0.061409</td>\n",
       "      <td>0.041703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>0</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.286520</td>\n",
       "      <td>0.242619</td>\n",
       "      <td>0.120541</td>\n",
       "      <td>1.743880</td>\n",
       "      <td>0.125833</td>\n",
       "      <td>0.078561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>1</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.231254</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.112153</td>\n",
       "      <td>1.838311</td>\n",
       "      <td>0.041217</td>\n",
       "      <td>0.047719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>1</td>\n",
       "      <td>random</td>\n",
       "      <td>1.732683</td>\n",
       "      <td>0.184268</td>\n",
       "      <td>0.084780</td>\n",
       "      <td>2.338567</td>\n",
       "      <td>0.050045</td>\n",
       "      <td>0.038467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.846781</td>\n",
       "      <td>0.175672</td>\n",
       "      <td>0.091009</td>\n",
       "      <td>2.265707</td>\n",
       "      <td>0.050399</td>\n",
       "      <td>0.047014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.766643</td>\n",
       "      <td>0.313314</td>\n",
       "      <td>0.106556</td>\n",
       "      <td>2.410212</td>\n",
       "      <td>0.141031</td>\n",
       "      <td>0.055152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>2</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.053591</td>\n",
       "      <td>0.063900</td>\n",
       "      <td>0.111535</td>\n",
       "      <td>1.522782</td>\n",
       "      <td>0.044371</td>\n",
       "      <td>0.052603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>1.409348</td>\n",
       "      <td>0.192210</td>\n",
       "      <td>0.082722</td>\n",
       "      <td>1.639411</td>\n",
       "      <td>0.054097</td>\n",
       "      <td>0.044040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.545226</td>\n",
       "      <td>0.168602</td>\n",
       "      <td>0.090322</td>\n",
       "      <td>1.747386</td>\n",
       "      <td>0.047793</td>\n",
       "      <td>0.055872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.331617</td>\n",
       "      <td>0.193364</td>\n",
       "      <td>0.101182</td>\n",
       "      <td>1.995050</td>\n",
       "      <td>0.087357</td>\n",
       "      <td>0.053264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>3</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.338145</td>\n",
       "      <td>0.050481</td>\n",
       "      <td>0.119719</td>\n",
       "      <td>1.898474</td>\n",
       "      <td>0.033038</td>\n",
       "      <td>0.054834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>1.474558</td>\n",
       "      <td>0.184838</td>\n",
       "      <td>0.085211</td>\n",
       "      <td>1.872587</td>\n",
       "      <td>0.051182</td>\n",
       "      <td>0.041563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.874360</td>\n",
       "      <td>0.177093</td>\n",
       "      <td>0.092758</td>\n",
       "      <td>2.066865</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>0.061737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.451393</td>\n",
       "      <td>0.305414</td>\n",
       "      <td>0.091403</td>\n",
       "      <td>1.970450</td>\n",
       "      <td>0.148123</td>\n",
       "      <td>0.048628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>4</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.039347</td>\n",
       "      <td>0.068209</td>\n",
       "      <td>0.113590</td>\n",
       "      <td>1.425331</td>\n",
       "      <td>0.040625</td>\n",
       "      <td>0.054427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>1.367393</td>\n",
       "      <td>0.211257</td>\n",
       "      <td>0.078084</td>\n",
       "      <td>1.902258</td>\n",
       "      <td>0.057214</td>\n",
       "      <td>0.038978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.544705</td>\n",
       "      <td>0.181223</td>\n",
       "      <td>0.087484</td>\n",
       "      <td>2.291986</td>\n",
       "      <td>0.049876</td>\n",
       "      <td>0.050942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.374146</td>\n",
       "      <td>0.205381</td>\n",
       "      <td>0.110152</td>\n",
       "      <td>1.733906</td>\n",
       "      <td>0.102284</td>\n",
       "      <td>0.063164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dataname  serial grouping method  firing_r_KL_mean  corr_c_KL_mean  \\\n",
       "0   ST260_Day4       0           whole          0.986973        0.081500   \n",
       "1   ST260_Day4       0          random          1.235436        0.151044   \n",
       "2   ST260_Day4       0         max_val          1.210178        0.119446   \n",
       "3   ST260_Day4       0          kmeans          0.965851        0.200760   \n",
       "4   ST260_Day4       1           whole          0.923214        0.062414   \n",
       "5   ST260_Day4       1          random          1.441769        0.139256   \n",
       "6   ST260_Day4       1         max_val          1.095596        0.113025   \n",
       "7   ST260_Day4       1          kmeans          0.922295        0.274220   \n",
       "8   ST260_Day4       2           whole          0.851991        0.080947   \n",
       "9   ST260_Day4       2          random          1.453260        0.120533   \n",
       "10  ST260_Day4       2         max_val          1.616111        0.094346   \n",
       "11  ST260_Day4       2          kmeans          0.974710        0.190881   \n",
       "12  ST260_Day4       3           whole          0.928654        0.083819   \n",
       "13  ST260_Day4       3          random          1.418990        0.140556   \n",
       "14  ST260_Day4       3         max_val          1.189287        0.112785   \n",
       "15  ST260_Day4       3          kmeans          1.074703        0.426893   \n",
       "16  ST260_Day4       4           whole          0.921897        0.082214   \n",
       "17  ST260_Day4       4          random          1.340551        0.125619   \n",
       "18  ST260_Day4       4         max_val          1.037873        0.111903   \n",
       "19  ST260_Day4       4          kmeans          0.936626        0.210001   \n",
       "20  ST260_Day3       0           whole          1.026493        0.104701   \n",
       "21  ST260_Day3       0          random          1.520515        0.089768   \n",
       "22  ST260_Day3       0         max_val          1.923248        0.091541   \n",
       "23  ST260_Day3       0          kmeans          1.500678        0.145583   \n",
       "24  ST260_Day3       1           whole          1.530169        0.080521   \n",
       "25  ST260_Day3       1          random          1.546635        0.100612   \n",
       "26  ST260_Day3       1         max_val          1.984468        0.093339   \n",
       "27  ST260_Day3       1          kmeans          1.106432        0.131219   \n",
       "28  ST260_Day3       2           whole          1.424557        0.087189   \n",
       "29  ST260_Day3       2          random          2.216687        0.122369   \n",
       "30  ST260_Day3       2         max_val          1.335934        0.087015   \n",
       "31  ST260_Day3       2          kmeans          1.206504        0.132705   \n",
       "32  ST260_Day3       3           whole          1.109067        0.095300   \n",
       "33  ST260_Day3       3          random          1.700136        0.119047   \n",
       "34  ST260_Day3       3         max_val          1.591815        0.087292   \n",
       "35  ST260_Day3       3          kmeans          1.377064        0.133235   \n",
       "36  ST260_Day3       4           whole          1.351177        0.106691   \n",
       "37  ST260_Day3       4          random          1.531216        0.100482   \n",
       "38  ST260_Day3       4         max_val          1.584247        0.093891   \n",
       "39  ST260_Day3       4          kmeans          1.211135        0.117238   \n",
       "40  ST260_Day2       0           whole          0.668862        0.029905   \n",
       "41  ST260_Day2       0          random          1.208378        0.183540   \n",
       "42  ST260_Day2       0         max_val          0.919266        0.144490   \n",
       "43  ST260_Day2       0          kmeans          1.259637        0.458035   \n",
       "44  ST260_Day2       1           whole          1.107279        0.078758   \n",
       "45  ST260_Day2       1          random          1.623445        0.126468   \n",
       "46  ST260_Day2       1         max_val          1.337109        0.124685   \n",
       "47  ST260_Day2       1          kmeans          1.823644        0.353503   \n",
       "48  ST260_Day2       2           whole          1.493321        0.107974   \n",
       "49  ST260_Day2       2          random          1.897593        0.126323   \n",
       "50  ST260_Day2       2         max_val          1.254220        0.128298   \n",
       "51  ST260_Day2       2          kmeans          1.198272        0.325627   \n",
       "52  ST260_Day2       3           whole          1.275394        0.118781   \n",
       "53  ST260_Day2       3          random          2.078108        0.128059   \n",
       "54  ST260_Day2       3         max_val          1.284835        0.119054   \n",
       "55  ST260_Day2       3          kmeans          1.476382        0.463423   \n",
       "56  ST260_Day2       4           whole          1.050876        0.124070   \n",
       "57  ST260_Day2       4          random          1.534493        0.125437   \n",
       "58  ST260_Day2       4         max_val          1.612890        0.118346   \n",
       "59  ST260_Day2       4          kmeans          2.502646        0.449936   \n",
       "60  ST260_Day1       0           whole          1.334031        0.053460   \n",
       "61  ST260_Day1       0          random          1.477387        0.182853   \n",
       "62  ST260_Day1       0         max_val          1.664228        0.179448   \n",
       "63  ST260_Day1       0          kmeans          1.286520        0.242619   \n",
       "64  ST260_Day1       1           whole          1.231254        0.065123   \n",
       "65  ST260_Day1       1          random          1.732683        0.184268   \n",
       "66  ST260_Day1       1         max_val          1.846781        0.175672   \n",
       "67  ST260_Day1       1          kmeans          1.766643        0.313314   \n",
       "68  ST260_Day1       2           whole          1.053591        0.063900   \n",
       "69  ST260_Day1       2          random          1.409348        0.192210   \n",
       "70  ST260_Day1       2         max_val          1.545226        0.168602   \n",
       "71  ST260_Day1       2          kmeans          1.331617        0.193364   \n",
       "72  ST260_Day1       3           whole          1.338145        0.050481   \n",
       "73  ST260_Day1       3          random          1.474558        0.184838   \n",
       "74  ST260_Day1       3         max_val          1.874360        0.177093   \n",
       "75  ST260_Day1       3          kmeans          1.451393        0.305414   \n",
       "76  ST260_Day1       4           whole          1.039347        0.068209   \n",
       "77  ST260_Day1       4          random          1.367393        0.211257   \n",
       "78  ST260_Day1       4         max_val          1.544705        0.181223   \n",
       "79  ST260_Day1       4          kmeans          1.374146        0.205381   \n",
       "\n",
       "    van_r_KL_mean  firing_r_KL_std  corr_c_KL_std  van_r_KL_std  \n",
       "0        0.124069         1.549785       0.042769      0.061109  \n",
       "1        0.129226         1.547256       0.040406      0.078410  \n",
       "2        0.113795         2.036799       0.031048      0.055801  \n",
       "3        0.154426         1.115377       0.099105      0.099554  \n",
       "4        0.131346         1.186032       0.035675      0.065229  \n",
       "5        0.113706         2.010772       0.034979      0.059170  \n",
       "6        0.105919         2.177840       0.029688      0.056882  \n",
       "7        0.135229         0.934474       0.128156      0.078023  \n",
       "8        0.150002         1.147363       0.049018      0.088176  \n",
       "9        0.104414         2.207095       0.033892      0.057249  \n",
       "10       0.105997         2.495029       0.027880      0.052047  \n",
       "11       0.143570         1.176579       0.104335      0.085868  \n",
       "12       0.158436         1.562440       0.045479      0.094440  \n",
       "13       0.106396         1.842360       0.037448      0.054876  \n",
       "14       0.107691         2.108390       0.030092      0.057812  \n",
       "15       0.149708         1.312984       0.180342      0.099828  \n",
       "16       0.150065         1.441818       0.047428      0.089598  \n",
       "17       0.106294         1.995968       0.032215      0.059311  \n",
       "18       0.106493         1.507321       0.030307      0.057712  \n",
       "19       0.142580         0.977195       0.116683      0.094217  \n",
       "20       0.123612         1.769099       0.064552      0.062460  \n",
       "21       0.096957         3.036886       0.037992      0.046104  \n",
       "22       0.111447         2.967232       0.037054      0.042853  \n",
       "23       0.145967         2.608647       0.077172      0.075938  \n",
       "24       0.143961         2.926234       0.058107      0.073499  \n",
       "25       0.094955         2.781388       0.039802      0.044087  \n",
       "26       0.108045         3.499155       0.036897      0.046046  \n",
       "27       0.135051         1.894637       0.061850      0.079037  \n",
       "28       0.157588         2.403610       0.061905      0.073820  \n",
       "29       0.108414         4.210639       0.047841      0.044837  \n",
       "30       0.097739         2.333513       0.034496      0.044668  \n",
       "31       0.148930         2.343499       0.073506      0.075745  \n",
       "32       0.123373         1.982566       0.059444      0.061943  \n",
       "33       0.101060         3.037716       0.045358      0.041976  \n",
       "34       0.101255         2.880410       0.035869      0.043891  \n",
       "35       0.127133         2.313191       0.070630      0.059910  \n",
       "36       0.154139         2.631366       0.076544      0.062676  \n",
       "37       0.095736         2.937180       0.042342      0.045699  \n",
       "38       0.103698         2.801994       0.036620      0.043158  \n",
       "39       0.130373         1.854813       0.058221      0.085544  \n",
       "40       0.096500         0.720432       0.021600      0.042706  \n",
       "41       0.095528         2.361774       0.067548      0.043965  \n",
       "42       0.101556         0.905329       0.045972      0.044772  \n",
       "43       0.111119         2.226841       0.187964      0.056551  \n",
       "44       0.123534         1.442646       0.045290      0.044461  \n",
       "45       0.083699         2.370519       0.045093      0.040178  \n",
       "46       0.095203         2.372831       0.042208      0.051001  \n",
       "47       0.187807         2.132355       0.168861      0.075651  \n",
       "48       0.102298         2.427525       0.060748      0.062005  \n",
       "49       0.085343         2.775450       0.050961      0.044165  \n",
       "50       0.089591         1.509845       0.042904      0.041689  \n",
       "51       0.115119         1.425806       0.162163      0.060160  \n",
       "52       0.099763         2.186232       0.059980      0.069561  \n",
       "53       0.085396         3.306298       0.048514      0.038365  \n",
       "54       0.096248         1.520574       0.037953      0.040348  \n",
       "55       0.116569         1.570105       0.196375      0.057696  \n",
       "56       0.108653         1.182774       0.069580      0.053323  \n",
       "57       0.090914         2.579127       0.050538      0.043064  \n",
       "58       0.087245         2.313011       0.039512      0.044488  \n",
       "59       0.152646         3.744201       0.195047      0.051442  \n",
       "60       0.109441         1.800030       0.034149      0.045051  \n",
       "61       0.081114         2.354454       0.052004      0.037619  \n",
       "62       0.086835         1.832858       0.061409      0.041703  \n",
       "63       0.120541         1.743880       0.125833      0.078561  \n",
       "64       0.112153         1.838311       0.041217      0.047719  \n",
       "65       0.084780         2.338567       0.050045      0.038467  \n",
       "66       0.091009         2.265707       0.050399      0.047014  \n",
       "67       0.106556         2.410212       0.141031      0.055152  \n",
       "68       0.111535         1.522782       0.044371      0.052603  \n",
       "69       0.082722         1.639411       0.054097      0.044040  \n",
       "70       0.090322         1.747386       0.047793      0.055872  \n",
       "71       0.101182         1.995050       0.087357      0.053264  \n",
       "72       0.119719         1.898474       0.033038      0.054834  \n",
       "73       0.085211         1.872587       0.051182      0.041563  \n",
       "74       0.092758         2.066865       0.046807      0.061737  \n",
       "75       0.091403         1.970450       0.148123      0.048628  \n",
       "76       0.113590         1.425331       0.040625      0.054427  \n",
       "77       0.078084         1.902258       0.057214      0.038978  \n",
       "78       0.087484         2.291986       0.049876      0.050942  \n",
       "79       0.110152         1.733906       0.102284      0.063164  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouping_methods_makes_the_data_valid = ['whole','random','max_val','kmeans']\n",
    "dataname_filter = df.groupby('dataname').filter(lambda x: set(grouping_methods_makes_the_data_valid).issubset(set(x['grouping method'])))\n",
    "df_filtered = df.loc[df['dataname'].isin(dataname_filter['dataname'])]\n",
    "df_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c044e4eb",
   "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>dataname</th>\n",
       "      <th>serial</th>\n",
       "      <th>grouping method</th>\n",
       "      <th>firing_r_KL_mean</th>\n",
       "      <th>corr_c_KL_mean</th>\n",
       "      <th>van_r_KL_mean</th>\n",
       "      <th>firing_r_KL_std</th>\n",
       "      <th>corr_c_KL_std</th>\n",
       "      <th>van_r_KL_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>0</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.986973</td>\n",
       "      <td>0.081500</td>\n",
       "      <td>0.124069</td>\n",
       "      <td>1.549785</td>\n",
       "      <td>0.042769</td>\n",
       "      <td>0.061109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>1.235436</td>\n",
       "      <td>0.151044</td>\n",
       "      <td>0.129226</td>\n",
       "      <td>1.547256</td>\n",
       "      <td>0.040406</td>\n",
       "      <td>0.078410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>0</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.210178</td>\n",
       "      <td>0.119446</td>\n",
       "      <td>0.113795</td>\n",
       "      <td>2.036799</td>\n",
       "      <td>0.031048</td>\n",
       "      <td>0.055801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>0</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>0.965851</td>\n",
       "      <td>0.200760</td>\n",
       "      <td>0.154426</td>\n",
       "      <td>1.115377</td>\n",
       "      <td>0.099105</td>\n",
       "      <td>0.099554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>1</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.923214</td>\n",
       "      <td>0.062414</td>\n",
       "      <td>0.131346</td>\n",
       "      <td>1.186032</td>\n",
       "      <td>0.035675</td>\n",
       "      <td>0.065229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>1</td>\n",
       "      <td>random</td>\n",
       "      <td>1.441769</td>\n",
       "      <td>0.139256</td>\n",
       "      <td>0.113706</td>\n",
       "      <td>2.010772</td>\n",
       "      <td>0.034979</td>\n",
       "      <td>0.059170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.095596</td>\n",
       "      <td>0.113025</td>\n",
       "      <td>0.105919</td>\n",
       "      <td>2.177840</td>\n",
       "      <td>0.029688</td>\n",
       "      <td>0.056882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>0.922295</td>\n",
       "      <td>0.274220</td>\n",
       "      <td>0.135229</td>\n",
       "      <td>0.934474</td>\n",
       "      <td>0.128156</td>\n",
       "      <td>0.078023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>2</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.851991</td>\n",
       "      <td>0.080947</td>\n",
       "      <td>0.150002</td>\n",
       "      <td>1.147363</td>\n",
       "      <td>0.049018</td>\n",
       "      <td>0.088176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>1.453260</td>\n",
       "      <td>0.120533</td>\n",
       "      <td>0.104414</td>\n",
       "      <td>2.207095</td>\n",
       "      <td>0.033892</td>\n",
       "      <td>0.057249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.616111</td>\n",
       "      <td>0.094346</td>\n",
       "      <td>0.105997</td>\n",
       "      <td>2.495029</td>\n",
       "      <td>0.027880</td>\n",
       "      <td>0.052047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>0.974710</td>\n",
       "      <td>0.190881</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>1.176579</td>\n",
       "      <td>0.104335</td>\n",
       "      <td>0.085868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>3</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.928654</td>\n",
       "      <td>0.083819</td>\n",
       "      <td>0.158436</td>\n",
       "      <td>1.562440</td>\n",
       "      <td>0.045479</td>\n",
       "      <td>0.094440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>1.418990</td>\n",
       "      <td>0.140556</td>\n",
       "      <td>0.106396</td>\n",
       "      <td>1.842360</td>\n",
       "      <td>0.037448</td>\n",
       "      <td>0.054876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.189287</td>\n",
       "      <td>0.112785</td>\n",
       "      <td>0.107691</td>\n",
       "      <td>2.108390</td>\n",
       "      <td>0.030092</td>\n",
       "      <td>0.057812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.074703</td>\n",
       "      <td>0.426893</td>\n",
       "      <td>0.149708</td>\n",
       "      <td>1.312984</td>\n",
       "      <td>0.180342</td>\n",
       "      <td>0.099828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>4</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.921897</td>\n",
       "      <td>0.082214</td>\n",
       "      <td>0.150065</td>\n",
       "      <td>1.441818</td>\n",
       "      <td>0.047428</td>\n",
       "      <td>0.089598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>1.340551</td>\n",
       "      <td>0.125619</td>\n",
       "      <td>0.106294</td>\n",
       "      <td>1.995968</td>\n",
       "      <td>0.032215</td>\n",
       "      <td>0.059311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.037873</td>\n",
       "      <td>0.111903</td>\n",
       "      <td>0.106493</td>\n",
       "      <td>1.507321</td>\n",
       "      <td>0.030307</td>\n",
       "      <td>0.057712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>0.936626</td>\n",
       "      <td>0.210001</td>\n",
       "      <td>0.142580</td>\n",
       "      <td>0.977195</td>\n",
       "      <td>0.116683</td>\n",
       "      <td>0.094217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>0</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.026493</td>\n",
       "      <td>0.104701</td>\n",
       "      <td>0.123612</td>\n",
       "      <td>1.769099</td>\n",
       "      <td>0.064552</td>\n",
       "      <td>0.062460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>1.520515</td>\n",
       "      <td>0.089768</td>\n",
       "      <td>0.096957</td>\n",
       "      <td>3.036886</td>\n",
       "      <td>0.037992</td>\n",
       "      <td>0.046104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>0</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.923248</td>\n",
       "      <td>0.091541</td>\n",
       "      <td>0.111447</td>\n",
       "      <td>2.967232</td>\n",
       "      <td>0.037054</td>\n",
       "      <td>0.042853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>0</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.500678</td>\n",
       "      <td>0.145583</td>\n",
       "      <td>0.145967</td>\n",
       "      <td>2.608647</td>\n",
       "      <td>0.077172</td>\n",
       "      <td>0.075938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>1</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.530169</td>\n",
       "      <td>0.080521</td>\n",
       "      <td>0.143961</td>\n",
       "      <td>2.926234</td>\n",
       "      <td>0.058107</td>\n",
       "      <td>0.073499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>1</td>\n",
       "      <td>random</td>\n",
       "      <td>1.546635</td>\n",
       "      <td>0.100612</td>\n",
       "      <td>0.094955</td>\n",
       "      <td>2.781388</td>\n",
       "      <td>0.039802</td>\n",
       "      <td>0.044087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.984468</td>\n",
       "      <td>0.093339</td>\n",
       "      <td>0.108045</td>\n",
       "      <td>3.499155</td>\n",
       "      <td>0.036897</td>\n",
       "      <td>0.046046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.106432</td>\n",
       "      <td>0.131219</td>\n",
       "      <td>0.135051</td>\n",
       "      <td>1.894637</td>\n",
       "      <td>0.061850</td>\n",
       "      <td>0.079037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>2</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.424557</td>\n",
       "      <td>0.087189</td>\n",
       "      <td>0.157588</td>\n",
       "      <td>2.403610</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.073820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>2.216687</td>\n",
       "      <td>0.122369</td>\n",
       "      <td>0.108414</td>\n",
       "      <td>4.210639</td>\n",
       "      <td>0.047841</td>\n",
       "      <td>0.044837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.335934</td>\n",
       "      <td>0.087015</td>\n",
       "      <td>0.097739</td>\n",
       "      <td>2.333513</td>\n",
       "      <td>0.034496</td>\n",
       "      <td>0.044668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.206504</td>\n",
       "      <td>0.132705</td>\n",
       "      <td>0.148930</td>\n",
       "      <td>2.343499</td>\n",
       "      <td>0.073506</td>\n",
       "      <td>0.075745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>3</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.109067</td>\n",
       "      <td>0.095300</td>\n",
       "      <td>0.123373</td>\n",
       "      <td>1.982566</td>\n",
       "      <td>0.059444</td>\n",
       "      <td>0.061943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>1.700136</td>\n",
       "      <td>0.119047</td>\n",
       "      <td>0.101060</td>\n",
       "      <td>3.037716</td>\n",
       "      <td>0.045358</td>\n",
       "      <td>0.041976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.591815</td>\n",
       "      <td>0.087292</td>\n",
       "      <td>0.101255</td>\n",
       "      <td>2.880410</td>\n",
       "      <td>0.035869</td>\n",
       "      <td>0.043891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.377064</td>\n",
       "      <td>0.133235</td>\n",
       "      <td>0.127133</td>\n",
       "      <td>2.313191</td>\n",
       "      <td>0.070630</td>\n",
       "      <td>0.059910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>4</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.351177</td>\n",
       "      <td>0.106691</td>\n",
       "      <td>0.154139</td>\n",
       "      <td>2.631366</td>\n",
       "      <td>0.076544</td>\n",
       "      <td>0.062676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>1.531216</td>\n",
       "      <td>0.100482</td>\n",
       "      <td>0.095736</td>\n",
       "      <td>2.937180</td>\n",
       "      <td>0.042342</td>\n",
       "      <td>0.045699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.584247</td>\n",
       "      <td>0.093891</td>\n",
       "      <td>0.103698</td>\n",
       "      <td>2.801994</td>\n",
       "      <td>0.036620</td>\n",
       "      <td>0.043158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.211135</td>\n",
       "      <td>0.117238</td>\n",
       "      <td>0.130373</td>\n",
       "      <td>1.854813</td>\n",
       "      <td>0.058221</td>\n",
       "      <td>0.085544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>0</td>\n",
       "      <td>whole</td>\n",
       "      <td>0.668862</td>\n",
       "      <td>0.029905</td>\n",
       "      <td>0.096500</td>\n",
       "      <td>0.720432</td>\n",
       "      <td>0.021600</td>\n",
       "      <td>0.042706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>1.208378</td>\n",
       "      <td>0.183540</td>\n",
       "      <td>0.095528</td>\n",
       "      <td>2.361774</td>\n",
       "      <td>0.067548</td>\n",
       "      <td>0.043965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>0</td>\n",
       "      <td>max_val</td>\n",
       "      <td>0.919266</td>\n",
       "      <td>0.144490</td>\n",
       "      <td>0.101556</td>\n",
       "      <td>0.905329</td>\n",
       "      <td>0.045972</td>\n",
       "      <td>0.044772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>0</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.259637</td>\n",
       "      <td>0.458035</td>\n",
       "      <td>0.111119</td>\n",
       "      <td>2.226841</td>\n",
       "      <td>0.187964</td>\n",
       "      <td>0.056551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>1</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.107279</td>\n",
       "      <td>0.078758</td>\n",
       "      <td>0.123534</td>\n",
       "      <td>1.442646</td>\n",
       "      <td>0.045290</td>\n",
       "      <td>0.044461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>1</td>\n",
       "      <td>random</td>\n",
       "      <td>1.623445</td>\n",
       "      <td>0.126468</td>\n",
       "      <td>0.083699</td>\n",
       "      <td>2.370519</td>\n",
       "      <td>0.045093</td>\n",
       "      <td>0.040178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.337109</td>\n",
       "      <td>0.124685</td>\n",
       "      <td>0.095203</td>\n",
       "      <td>2.372831</td>\n",
       "      <td>0.042208</td>\n",
       "      <td>0.051001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.823644</td>\n",
       "      <td>0.353503</td>\n",
       "      <td>0.187807</td>\n",
       "      <td>2.132355</td>\n",
       "      <td>0.168861</td>\n",
       "      <td>0.075651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>2</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.493321</td>\n",
       "      <td>0.107974</td>\n",
       "      <td>0.102298</td>\n",
       "      <td>2.427525</td>\n",
       "      <td>0.060748</td>\n",
       "      <td>0.062005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>1.897593</td>\n",
       "      <td>0.126323</td>\n",
       "      <td>0.085343</td>\n",
       "      <td>2.775450</td>\n",
       "      <td>0.050961</td>\n",
       "      <td>0.044165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.254220</td>\n",
       "      <td>0.128298</td>\n",
       "      <td>0.089591</td>\n",
       "      <td>1.509845</td>\n",
       "      <td>0.042904</td>\n",
       "      <td>0.041689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.198272</td>\n",
       "      <td>0.325627</td>\n",
       "      <td>0.115119</td>\n",
       "      <td>1.425806</td>\n",
       "      <td>0.162163</td>\n",
       "      <td>0.060160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>3</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.275394</td>\n",
       "      <td>0.118781</td>\n",
       "      <td>0.099763</td>\n",
       "      <td>2.186232</td>\n",
       "      <td>0.059980</td>\n",
       "      <td>0.069561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>2.078108</td>\n",
       "      <td>0.128059</td>\n",
       "      <td>0.085396</td>\n",
       "      <td>3.306298</td>\n",
       "      <td>0.048514</td>\n",
       "      <td>0.038365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.284835</td>\n",
       "      <td>0.119054</td>\n",
       "      <td>0.096248</td>\n",
       "      <td>1.520574</td>\n",
       "      <td>0.037953</td>\n",
       "      <td>0.040348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.476382</td>\n",
       "      <td>0.463423</td>\n",
       "      <td>0.116569</td>\n",
       "      <td>1.570105</td>\n",
       "      <td>0.196375</td>\n",
       "      <td>0.057696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>4</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.050876</td>\n",
       "      <td>0.124070</td>\n",
       "      <td>0.108653</td>\n",
       "      <td>1.182774</td>\n",
       "      <td>0.069580</td>\n",
       "      <td>0.053323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>1.534493</td>\n",
       "      <td>0.125437</td>\n",
       "      <td>0.090914</td>\n",
       "      <td>2.579127</td>\n",
       "      <td>0.050538</td>\n",
       "      <td>0.043064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.612890</td>\n",
       "      <td>0.118346</td>\n",
       "      <td>0.087245</td>\n",
       "      <td>2.313011</td>\n",
       "      <td>0.039512</td>\n",
       "      <td>0.044488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>2.502646</td>\n",
       "      <td>0.449936</td>\n",
       "      <td>0.152646</td>\n",
       "      <td>3.744201</td>\n",
       "      <td>0.195047</td>\n",
       "      <td>0.051442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>0</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.334031</td>\n",
       "      <td>0.053460</td>\n",
       "      <td>0.109441</td>\n",
       "      <td>1.800030</td>\n",
       "      <td>0.034149</td>\n",
       "      <td>0.045051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>1.477387</td>\n",
       "      <td>0.182853</td>\n",
       "      <td>0.081114</td>\n",
       "      <td>2.354454</td>\n",
       "      <td>0.052004</td>\n",
       "      <td>0.037619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>0</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.664228</td>\n",
       "      <td>0.179448</td>\n",
       "      <td>0.086835</td>\n",
       "      <td>1.832858</td>\n",
       "      <td>0.061409</td>\n",
       "      <td>0.041703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>0</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.286520</td>\n",
       "      <td>0.242619</td>\n",
       "      <td>0.120541</td>\n",
       "      <td>1.743880</td>\n",
       "      <td>0.125833</td>\n",
       "      <td>0.078561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>1</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.231254</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.112153</td>\n",
       "      <td>1.838311</td>\n",
       "      <td>0.041217</td>\n",
       "      <td>0.047719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>1</td>\n",
       "      <td>random</td>\n",
       "      <td>1.732683</td>\n",
       "      <td>0.184268</td>\n",
       "      <td>0.084780</td>\n",
       "      <td>2.338567</td>\n",
       "      <td>0.050045</td>\n",
       "      <td>0.038467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.846781</td>\n",
       "      <td>0.175672</td>\n",
       "      <td>0.091009</td>\n",
       "      <td>2.265707</td>\n",
       "      <td>0.050399</td>\n",
       "      <td>0.047014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.766643</td>\n",
       "      <td>0.313314</td>\n",
       "      <td>0.106556</td>\n",
       "      <td>2.410212</td>\n",
       "      <td>0.141031</td>\n",
       "      <td>0.055152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>2</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.053591</td>\n",
       "      <td>0.063900</td>\n",
       "      <td>0.111535</td>\n",
       "      <td>1.522782</td>\n",
       "      <td>0.044371</td>\n",
       "      <td>0.052603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>1.409348</td>\n",
       "      <td>0.192210</td>\n",
       "      <td>0.082722</td>\n",
       "      <td>1.639411</td>\n",
       "      <td>0.054097</td>\n",
       "      <td>0.044040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.545226</td>\n",
       "      <td>0.168602</td>\n",
       "      <td>0.090322</td>\n",
       "      <td>1.747386</td>\n",
       "      <td>0.047793</td>\n",
       "      <td>0.055872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.331617</td>\n",
       "      <td>0.193364</td>\n",
       "      <td>0.101182</td>\n",
       "      <td>1.995050</td>\n",
       "      <td>0.087357</td>\n",
       "      <td>0.053264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>3</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.338145</td>\n",
       "      <td>0.050481</td>\n",
       "      <td>0.119719</td>\n",
       "      <td>1.898474</td>\n",
       "      <td>0.033038</td>\n",
       "      <td>0.054834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>1.474558</td>\n",
       "      <td>0.184838</td>\n",
       "      <td>0.085211</td>\n",
       "      <td>1.872587</td>\n",
       "      <td>0.051182</td>\n",
       "      <td>0.041563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.874360</td>\n",
       "      <td>0.177093</td>\n",
       "      <td>0.092758</td>\n",
       "      <td>2.066865</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>0.061737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.451393</td>\n",
       "      <td>0.305414</td>\n",
       "      <td>0.091403</td>\n",
       "      <td>1.970450</td>\n",
       "      <td>0.148123</td>\n",
       "      <td>0.048628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>4</td>\n",
       "      <td>whole</td>\n",
       "      <td>1.039347</td>\n",
       "      <td>0.068209</td>\n",
       "      <td>0.113590</td>\n",
       "      <td>1.425331</td>\n",
       "      <td>0.040625</td>\n",
       "      <td>0.054427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>1.367393</td>\n",
       "      <td>0.211257</td>\n",
       "      <td>0.078084</td>\n",
       "      <td>1.902258</td>\n",
       "      <td>0.057214</td>\n",
       "      <td>0.038978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>1.544705</td>\n",
       "      <td>0.181223</td>\n",
       "      <td>0.087484</td>\n",
       "      <td>2.291986</td>\n",
       "      <td>0.049876</td>\n",
       "      <td>0.050942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.374146</td>\n",
       "      <td>0.205381</td>\n",
       "      <td>0.110152</td>\n",
       "      <td>1.733906</td>\n",
       "      <td>0.102284</td>\n",
       "      <td>0.063164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dataname  serial grouping method  firing_r_KL_mean  corr_c_KL_mean  \\\n",
       "0   ST260_Day4       0           whole          0.986973        0.081500   \n",
       "1   ST260_Day4       0          random          1.235436        0.151044   \n",
       "2   ST260_Day4       0         max_val          1.210178        0.119446   \n",
       "3   ST260_Day4       0          kmeans          0.965851        0.200760   \n",
       "4   ST260_Day4       1           whole          0.923214        0.062414   \n",
       "5   ST260_Day4       1          random          1.441769        0.139256   \n",
       "6   ST260_Day4       1         max_val          1.095596        0.113025   \n",
       "7   ST260_Day4       1          kmeans          0.922295        0.274220   \n",
       "8   ST260_Day4       2           whole          0.851991        0.080947   \n",
       "9   ST260_Day4       2          random          1.453260        0.120533   \n",
       "10  ST260_Day4       2         max_val          1.616111        0.094346   \n",
       "11  ST260_Day4       2          kmeans          0.974710        0.190881   \n",
       "12  ST260_Day4       3           whole          0.928654        0.083819   \n",
       "13  ST260_Day4       3          random          1.418990        0.140556   \n",
       "14  ST260_Day4       3         max_val          1.189287        0.112785   \n",
       "15  ST260_Day4       3          kmeans          1.074703        0.426893   \n",
       "16  ST260_Day4       4           whole          0.921897        0.082214   \n",
       "17  ST260_Day4       4          random          1.340551        0.125619   \n",
       "18  ST260_Day4       4         max_val          1.037873        0.111903   \n",
       "19  ST260_Day4       4          kmeans          0.936626        0.210001   \n",
       "20  ST260_Day3       0           whole          1.026493        0.104701   \n",
       "21  ST260_Day3       0          random          1.520515        0.089768   \n",
       "22  ST260_Day3       0         max_val          1.923248        0.091541   \n",
       "23  ST260_Day3       0          kmeans          1.500678        0.145583   \n",
       "24  ST260_Day3       1           whole          1.530169        0.080521   \n",
       "25  ST260_Day3       1          random          1.546635        0.100612   \n",
       "26  ST260_Day3       1         max_val          1.984468        0.093339   \n",
       "27  ST260_Day3       1          kmeans          1.106432        0.131219   \n",
       "28  ST260_Day3       2           whole          1.424557        0.087189   \n",
       "29  ST260_Day3       2          random          2.216687        0.122369   \n",
       "30  ST260_Day3       2         max_val          1.335934        0.087015   \n",
       "31  ST260_Day3       2          kmeans          1.206504        0.132705   \n",
       "32  ST260_Day3       3           whole          1.109067        0.095300   \n",
       "33  ST260_Day3       3          random          1.700136        0.119047   \n",
       "34  ST260_Day3       3         max_val          1.591815        0.087292   \n",
       "35  ST260_Day3       3          kmeans          1.377064        0.133235   \n",
       "36  ST260_Day3       4           whole          1.351177        0.106691   \n",
       "37  ST260_Day3       4          random          1.531216        0.100482   \n",
       "38  ST260_Day3       4         max_val          1.584247        0.093891   \n",
       "39  ST260_Day3       4          kmeans          1.211135        0.117238   \n",
       "40  ST260_Day2       0           whole          0.668862        0.029905   \n",
       "41  ST260_Day2       0          random          1.208378        0.183540   \n",
       "42  ST260_Day2       0         max_val          0.919266        0.144490   \n",
       "43  ST260_Day2       0          kmeans          1.259637        0.458035   \n",
       "44  ST260_Day2       1           whole          1.107279        0.078758   \n",
       "45  ST260_Day2       1          random          1.623445        0.126468   \n",
       "46  ST260_Day2       1         max_val          1.337109        0.124685   \n",
       "47  ST260_Day2       1          kmeans          1.823644        0.353503   \n",
       "48  ST260_Day2       2           whole          1.493321        0.107974   \n",
       "49  ST260_Day2       2          random          1.897593        0.126323   \n",
       "50  ST260_Day2       2         max_val          1.254220        0.128298   \n",
       "51  ST260_Day2       2          kmeans          1.198272        0.325627   \n",
       "52  ST260_Day2       3           whole          1.275394        0.118781   \n",
       "53  ST260_Day2       3          random          2.078108        0.128059   \n",
       "54  ST260_Day2       3         max_val          1.284835        0.119054   \n",
       "55  ST260_Day2       3          kmeans          1.476382        0.463423   \n",
       "56  ST260_Day2       4           whole          1.050876        0.124070   \n",
       "57  ST260_Day2       4          random          1.534493        0.125437   \n",
       "58  ST260_Day2       4         max_val          1.612890        0.118346   \n",
       "59  ST260_Day2       4          kmeans          2.502646        0.449936   \n",
       "60  ST260_Day1       0           whole          1.334031        0.053460   \n",
       "61  ST260_Day1       0          random          1.477387        0.182853   \n",
       "62  ST260_Day1       0         max_val          1.664228        0.179448   \n",
       "63  ST260_Day1       0          kmeans          1.286520        0.242619   \n",
       "64  ST260_Day1       1           whole          1.231254        0.065123   \n",
       "65  ST260_Day1       1          random          1.732683        0.184268   \n",
       "66  ST260_Day1       1         max_val          1.846781        0.175672   \n",
       "67  ST260_Day1       1          kmeans          1.766643        0.313314   \n",
       "68  ST260_Day1       2           whole          1.053591        0.063900   \n",
       "69  ST260_Day1       2          random          1.409348        0.192210   \n",
       "70  ST260_Day1       2         max_val          1.545226        0.168602   \n",
       "71  ST260_Day1       2          kmeans          1.331617        0.193364   \n",
       "72  ST260_Day1       3           whole          1.338145        0.050481   \n",
       "73  ST260_Day1       3          random          1.474558        0.184838   \n",
       "74  ST260_Day1       3         max_val          1.874360        0.177093   \n",
       "75  ST260_Day1       3          kmeans          1.451393        0.305414   \n",
       "76  ST260_Day1       4           whole          1.039347        0.068209   \n",
       "77  ST260_Day1       4          random          1.367393        0.211257   \n",
       "78  ST260_Day1       4         max_val          1.544705        0.181223   \n",
       "79  ST260_Day1       4          kmeans          1.374146        0.205381   \n",
       "\n",
       "    van_r_KL_mean  firing_r_KL_std  corr_c_KL_std  van_r_KL_std  \n",
       "0        0.124069         1.549785       0.042769      0.061109  \n",
       "1        0.129226         1.547256       0.040406      0.078410  \n",
       "2        0.113795         2.036799       0.031048      0.055801  \n",
       "3        0.154426         1.115377       0.099105      0.099554  \n",
       "4        0.131346         1.186032       0.035675      0.065229  \n",
       "5        0.113706         2.010772       0.034979      0.059170  \n",
       "6        0.105919         2.177840       0.029688      0.056882  \n",
       "7        0.135229         0.934474       0.128156      0.078023  \n",
       "8        0.150002         1.147363       0.049018      0.088176  \n",
       "9        0.104414         2.207095       0.033892      0.057249  \n",
       "10       0.105997         2.495029       0.027880      0.052047  \n",
       "11       0.143570         1.176579       0.104335      0.085868  \n",
       "12       0.158436         1.562440       0.045479      0.094440  \n",
       "13       0.106396         1.842360       0.037448      0.054876  \n",
       "14       0.107691         2.108390       0.030092      0.057812  \n",
       "15       0.149708         1.312984       0.180342      0.099828  \n",
       "16       0.150065         1.441818       0.047428      0.089598  \n",
       "17       0.106294         1.995968       0.032215      0.059311  \n",
       "18       0.106493         1.507321       0.030307      0.057712  \n",
       "19       0.142580         0.977195       0.116683      0.094217  \n",
       "20       0.123612         1.769099       0.064552      0.062460  \n",
       "21       0.096957         3.036886       0.037992      0.046104  \n",
       "22       0.111447         2.967232       0.037054      0.042853  \n",
       "23       0.145967         2.608647       0.077172      0.075938  \n",
       "24       0.143961         2.926234       0.058107      0.073499  \n",
       "25       0.094955         2.781388       0.039802      0.044087  \n",
       "26       0.108045         3.499155       0.036897      0.046046  \n",
       "27       0.135051         1.894637       0.061850      0.079037  \n",
       "28       0.157588         2.403610       0.061905      0.073820  \n",
       "29       0.108414         4.210639       0.047841      0.044837  \n",
       "30       0.097739         2.333513       0.034496      0.044668  \n",
       "31       0.148930         2.343499       0.073506      0.075745  \n",
       "32       0.123373         1.982566       0.059444      0.061943  \n",
       "33       0.101060         3.037716       0.045358      0.041976  \n",
       "34       0.101255         2.880410       0.035869      0.043891  \n",
       "35       0.127133         2.313191       0.070630      0.059910  \n",
       "36       0.154139         2.631366       0.076544      0.062676  \n",
       "37       0.095736         2.937180       0.042342      0.045699  \n",
       "38       0.103698         2.801994       0.036620      0.043158  \n",
       "39       0.130373         1.854813       0.058221      0.085544  \n",
       "40       0.096500         0.720432       0.021600      0.042706  \n",
       "41       0.095528         2.361774       0.067548      0.043965  \n",
       "42       0.101556         0.905329       0.045972      0.044772  \n",
       "43       0.111119         2.226841       0.187964      0.056551  \n",
       "44       0.123534         1.442646       0.045290      0.044461  \n",
       "45       0.083699         2.370519       0.045093      0.040178  \n",
       "46       0.095203         2.372831       0.042208      0.051001  \n",
       "47       0.187807         2.132355       0.168861      0.075651  \n",
       "48       0.102298         2.427525       0.060748      0.062005  \n",
       "49       0.085343         2.775450       0.050961      0.044165  \n",
       "50       0.089591         1.509845       0.042904      0.041689  \n",
       "51       0.115119         1.425806       0.162163      0.060160  \n",
       "52       0.099763         2.186232       0.059980      0.069561  \n",
       "53       0.085396         3.306298       0.048514      0.038365  \n",
       "54       0.096248         1.520574       0.037953      0.040348  \n",
       "55       0.116569         1.570105       0.196375      0.057696  \n",
       "56       0.108653         1.182774       0.069580      0.053323  \n",
       "57       0.090914         2.579127       0.050538      0.043064  \n",
       "58       0.087245         2.313011       0.039512      0.044488  \n",
       "59       0.152646         3.744201       0.195047      0.051442  \n",
       "60       0.109441         1.800030       0.034149      0.045051  \n",
       "61       0.081114         2.354454       0.052004      0.037619  \n",
       "62       0.086835         1.832858       0.061409      0.041703  \n",
       "63       0.120541         1.743880       0.125833      0.078561  \n",
       "64       0.112153         1.838311       0.041217      0.047719  \n",
       "65       0.084780         2.338567       0.050045      0.038467  \n",
       "66       0.091009         2.265707       0.050399      0.047014  \n",
       "67       0.106556         2.410212       0.141031      0.055152  \n",
       "68       0.111535         1.522782       0.044371      0.052603  \n",
       "69       0.082722         1.639411       0.054097      0.044040  \n",
       "70       0.090322         1.747386       0.047793      0.055872  \n",
       "71       0.101182         1.995050       0.087357      0.053264  \n",
       "72       0.119719         1.898474       0.033038      0.054834  \n",
       "73       0.085211         1.872587       0.051182      0.041563  \n",
       "74       0.092758         2.066865       0.046807      0.061737  \n",
       "75       0.091403         1.970450       0.148123      0.048628  \n",
       "76       0.113590         1.425331       0.040625      0.054427  \n",
       "77       0.078084         1.902258       0.057214      0.038978  \n",
       "78       0.087484         2.291986       0.049876      0.050942  \n",
       "79       0.110152         1.733906       0.102284      0.063164  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e0433116",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataname = 'ST260_Day2'\n",
    "whole = df_filtered.loc[(df['grouping method'] == 'whole') & (df['dataname']==dataname),['firing_r_KL_mean', 'corr_c_KL_mean', 'van_r_KL_mean','firing_r_KL_std','corr_c_KL_std','van_r_KL_std']].mean(numeric_only=True)\n",
    "random = df_filtered.loc[(df['grouping method'] == 'random')& (df['dataname']==dataname),['firing_r_KL_mean', 'corr_c_KL_mean', 'van_r_KL_mean','firing_r_KL_std','corr_c_KL_std','van_r_KL_std']].mean(numeric_only=True)\n",
    "max_val = df_filtered.loc[(df['grouping method'] == 'max_val')& (df['dataname']==dataname),['firing_r_KL_mean', 'corr_c_KL_mean', 'van_r_KL_mean','firing_r_KL_std','corr_c_KL_std','van_r_KL_std']].mean(numeric_only=True)\n",
    "kmeans = df_filtered.loc[(df['grouping method'] == 'kmeans')& (df['dataname']==dataname),['firing_r_KL_mean', 'corr_c_KL_mean', 'van_r_KL_mean','firing_r_KL_std','corr_c_KL_std','van_r_KL_std']].mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f002b931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05143960000000001"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whole[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9b1c73a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "labels = ['Firing Rate', 'Corr Coef', 'Van Rossum Dist']\n",
    "bar_width = 0.2\n",
    "r1 = np.arange(3)\n",
    "r2 = [x + bar_width for x in r1]\n",
    "r3 = [x + bar_width for x in r2]\n",
    "r4 = [x + bar_width for x in r3]\n",
    "fig = plt.figure(figsize=(7, 6))\n",
    "plt.bar(r1, list(whole)[0:3], color='tab:blue', width=bar_width, label='whole')\n",
    "plt.bar(r2, list(random)[0:3], color='tab:orange', width=bar_width, label='random')\n",
    "plt.bar(r3,  list(max_val)[0:3], color='tab:green', width=bar_width, label='max_val')\n",
    "plt.bar(r4,  list(kmeans)[0:3], color='tab:red', width=bar_width, label='kmeans')\n",
    "\n",
    "txt_x_offset = -0.08\n",
    "txt_y_offset = 0.05\n",
    "max_y = 0\n",
    "for i in range(3):\n",
    "    plt.text(x=r1[i]+txt_x_offset, y=whole[i]+txt_y_offset, s=f'{whole[i]:.2f}', color='black', fontweight='bold')\n",
    "    plt.text(x=r2[i]+txt_x_offset, y=random[i]+txt_y_offset, s=f'{random[i]:.2f}', color='black', fontweight='bold')\n",
    "    plt.text(x=r3[i]+txt_x_offset, y=max_val[i]+txt_y_offset, s=f'{max_val[i]:.2f}', color='black', fontweight='bold')\n",
    "    plt.text(x=r4[i]+txt_x_offset, y=kmeans[i]+txt_y_offset, s=f'{kmeans[i]:.2f}', color='black', fontweight='bold')\n",
    "    max_y = max(max_y, whole[i], random[i], max_val[i], kmeans[i])\n",
    "    \n",
    "plt.xticks([r + bar_width*1.5 for r in range(3)], labels,fontsize=15)\n",
    "plt.legend(fontsize=15)\n",
    "plt.ylim(0, max_y + txt_y_offset*4)\n",
    "plt.xlabel('Spike Metrics',fontsize=20)\n",
    "plt.ylabel('mean KL Divergence',fontsize=20)\n",
    "plt.title('Grouping Methods Performance measured by Spike Metrics',fontsize=15)\n",
    "plt.tight_layout()\n",
    "if not os.path.exists('../figs/'):\n",
    "    os.makedirs('../figs/')\n",
    "plt.savefig('../figs/mean_kl_ST260_Day2.pdf',format = 'pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1db21a75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 6))\n",
    "plt.bar(r1, list(whole)[3:6], color='tab:blue', width=bar_width, label='whole')\n",
    "plt.bar(r2, list(random)[3:6], color='tab:orange', width=bar_width, label='random')\n",
    "plt.bar(r3,  list(max_val)[3:6], color='tab:green', width=bar_width, label='max_val')\n",
    "plt.bar(r4,  list(kmeans)[3:6], color='tab:red', width=bar_width, label='kmeans')\n",
    "\n",
    "txt_x_offset = -0.08\n",
    "txt_y_offset = 0.05\n",
    "max_y = 0\n",
    "for i in range(0,3):\n",
    "    plt.text(x=r1[i]+txt_x_offset, y=whole[i+3]+txt_y_offset, s=f'{whole[i+3]:.2f}', color='black', fontweight='bold')\n",
    "    plt.text(x=r2[i]+txt_x_offset, y=random[i+3]+txt_y_offset, s=f'{random[i+3]:.2f}', color='black', fontweight='bold')\n",
    "    plt.text(x=r3[i]+txt_x_offset, y=max_val[i+3]+txt_y_offset, s=f'{max_val[i+3]:.2f}', color='black', fontweight='bold')\n",
    "    plt.text(x=r4[i]+txt_x_offset, y=kmeans[i+3]+txt_y_offset, s=f'{kmeans[i+3]:.2f}', color='black', fontweight='bold')\n",
    "    max_y = max(max_y, whole[i+3], random[i+3], max_val[i+3], kmeans[i+3])\n",
    "    \n",
    "plt.xticks([r + bar_width*1.5 for r in range(3)], labels,fontsize=15)\n",
    "plt.legend(fontsize=15)\n",
    "plt.ylim(0, max_y + txt_y_offset*4)\n",
    "plt.xlabel('Spike Metrics',fontsize=20)\n",
    "plt.ylabel('mean KL Divergence std',fontsize=20)\n",
    "plt.title('std of of Grouping Methods Performance',fontsize=15)\n",
    "plt.tight_layout()\n",
    "if not os.path.exists('../figs/'):\n",
    "    os.makedirs('../figs/')\n",
    "plt.savefig('../figs/var_kl_ST260_Day2.pdf',format = 'pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81227db3",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'firing_r_KL_mean'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.9/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   3628\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-> 3629\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   3630\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~/anaconda3/lib/python3.9/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~/anaconda3/lib/python3.9/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: 'firing_r_KL_mean'",
      "\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/tmp/ipykernel_41509/3499058205.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mwhole_group\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwhole_group_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mfiring_r_t_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiring_r_p_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mttest_ind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'firing_r_KL_mean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhole_group\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'firing_r_KL_mean'\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     18\u001b[0m     \u001b[0mcorr_c_t_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorr_c_p_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mttest_ind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'corr_c_KL_mean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhole_group\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'corr_c_KL_mean'\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     19\u001b[0m     \u001b[0mvan_r_t_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvan_r_p_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mttest_ind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'van_r_KL_mean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhole_group\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'van_r_KL_mean'\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~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3503\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   3504\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-> 3505\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   3506\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   3507\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~/anaconda3/lib/python3.9/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   3629\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   3630\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-> 3631\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   3632\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   3633\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[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'firing_r_KL_mean'"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "# Group the DataFrame by dataname and grouping_method\n",
    "grouped_df = df.groupby(['dataname', 'grouping method'])\n",
    "\n",
    "# Initialize whole_group_data with empty DataFrames for each dataname\n",
    "datanames = df['dataname'].unique()\n",
    "whole_group_data = {dataname: pd.DataFrame() for dataname in datanames}\n",
    "\n",
    "# Iterate over the grouped data and perform a t-test for each combination of dataname and grouping_method\n",
    "for (dataname, grouping_method), group_df in grouped_df:\n",
    "    if grouping_method == \"whole\":\n",
    "        whole_group_data[dataname] = group_df\n",
    "        continue\n",
    "\n",
    "    whole_group = whole_group_data[dataname]\n",
    "\n",
    "    firing_r_t_stat, firing_r_p_value = stats.ttest_ind(group_df['firing_r_KL_mean'], whole_group['firing_r_KL_mean'])\n",
    "    corr_c_t_stat, corr_c_p_value = stats.ttest_ind(group_df['corr_c_KL_mean'], whole_group['corr_c_KL_mean'])\n",
    "    van_r_t_stat, van_r_p_value = stats.ttest_ind(group_df['van_r_KL_mean'], whole_group['van_r_KL_mean'])\n",
    "\n",
    "    print(f\"Dataname: {dataname}, Grouping Method: {grouping_method}\")\n",
    "    print(f\"Firing R KL Mean t-stat: {firing_r_t_stat}, p-value: {firing_r_p_value}\")\n",
    "    print(f\"Corr C KL Mean t-stat: {corr_c_t_stat}, p-value: {corr_c_p_value}\")\n",
    "    print(f\"Van R KL Mean t-stat: {van_r_t_stat}, p-value: {van_r_p_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5d3f623",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in group_df: Index(['dataname', 'serial', 'grouping method', 'firing_r_KL_mean',\n",
      "       'corr_c_KL_mean', 'van_r_KL_mean', 'firing_r_KL_std', 'corr_c_KL_std',\n",
      "       'van_r_KL_std'],\n",
      "      dtype='object')\n",
      "Columns in whole_group: Index([], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"Columns in group_df:\", group_df.columns)\n",
    "print(\"Columns in whole_group:\", whole_group.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f4219a68",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n",
      "/tmp/ipykernel_41509/3200674483.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  t_test_results = t_test_results.append({\n"
     ]
    },
    {
     "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>Dataname</th>\n",
       "      <th>Grouping Method</th>\n",
       "      <th>Firing R KL Mean t-stat</th>\n",
       "      <th>Firing R KL Mean p-value</th>\n",
       "      <th>Corr C KL Mean t-stat</th>\n",
       "      <th>Corr C KL Mean p-value</th>\n",
       "      <th>Van R KL Mean t-stat</th>\n",
       "      <th>Van R KL Mean p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>2.255654</td>\n",
       "      <td>0.054088</td>\n",
       "      <td>7.654858</td>\n",
       "      <td>5.989886e-05</td>\n",
       "      <td>-1.427680</td>\n",
       "      <td>1.912326e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>max_val</td>\n",
       "      <td>5.134958</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>28.329782</td>\n",
       "      <td>2.604736e-09</td>\n",
       "      <td>-11.438586</td>\n",
       "      <td>3.086081e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ST260_Day1</td>\n",
       "      <td>random</td>\n",
       "      <td>3.215403</td>\n",
       "      <td>0.012324</td>\n",
       "      <td>20.645624</td>\n",
       "      <td>3.173345e-08</td>\n",
       "      <td>-14.213362</td>\n",
       "      <td>5.846621e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.936027</td>\n",
       "      <td>0.088890</td>\n",
       "      <td>9.363312</td>\n",
       "      <td>1.384060e-05</td>\n",
       "      <td>1.958989</td>\n",
       "      <td>8.579199e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>max_val</td>\n",
       "      <td>0.925386</td>\n",
       "      <td>0.381832</td>\n",
       "      <td>1.948281</td>\n",
       "      <td>8.722350e-02</td>\n",
       "      <td>-2.250574</td>\n",
       "      <td>5.451846e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ST260_Day2</td>\n",
       "      <td>random</td>\n",
       "      <td>2.706094</td>\n",
       "      <td>0.026820</td>\n",
       "      <td>2.217485</td>\n",
       "      <td>5.740684e-02</td>\n",
       "      <td>-3.413283</td>\n",
       "      <td>9.178526e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>-0.067012</td>\n",
       "      <td>0.948217</td>\n",
       "      <td>5.512973</td>\n",
       "      <td>5.650375e-04</td>\n",
       "      <td>-0.359323</td>\n",
       "      <td>7.286532e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>max_val</td>\n",
       "      <td>2.584217</td>\n",
       "      <td>0.032403</td>\n",
       "      <td>-0.817383</td>\n",
       "      <td>4.373736e-01</td>\n",
       "      <td>-4.687185</td>\n",
       "      <td>1.567227e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ST260_Day3</td>\n",
       "      <td>random</td>\n",
       "      <td>2.540983</td>\n",
       "      <td>0.034658</td>\n",
       "      <td>1.457443</td>\n",
       "      <td>1.830997e-01</td>\n",
       "      <td>-5.325778</td>\n",
       "      <td>7.061073e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>1.527385</td>\n",
       "      <td>0.165184</td>\n",
       "      <td>4.121682</td>\n",
       "      <td>3.336460e-03</td>\n",
       "      <td>0.320739</td>\n",
       "      <td>7.566266e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>max_val</td>\n",
       "      <td>2.961608</td>\n",
       "      <td>0.018102</td>\n",
       "      <td>5.551251</td>\n",
       "      <td>5.401803e-04</td>\n",
       "      <td>-5.260562</td>\n",
       "      <td>7.639826e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ST260_Day4</td>\n",
       "      <td>random</td>\n",
       "      <td>9.902997</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>8.445306</td>\n",
       "      <td>2.950775e-05</td>\n",
       "      <td>-3.889314</td>\n",
       "      <td>4.613695e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Dataname Grouping Method  Firing R KL Mean t-stat  \\\n",
       "0   ST260_Day1          kmeans                 2.255654   \n",
       "1   ST260_Day1         max_val                 5.134958   \n",
       "2   ST260_Day1          random                 3.215403   \n",
       "3   ST260_Day2          kmeans                 1.936027   \n",
       "4   ST260_Day2         max_val                 0.925386   \n",
       "5   ST260_Day2          random                 2.706094   \n",
       "6   ST260_Day3          kmeans                -0.067012   \n",
       "7   ST260_Day3         max_val                 2.584217   \n",
       "8   ST260_Day3          random                 2.540983   \n",
       "9   ST260_Day4          kmeans                 1.527385   \n",
       "10  ST260_Day4         max_val                 2.961608   \n",
       "11  ST260_Day4          random                 9.902997   \n",
       "\n",
       "    Firing R KL Mean p-value  Corr C KL Mean t-stat  Corr C KL Mean p-value  \\\n",
       "0                   0.054088               7.654858            5.989886e-05   \n",
       "1                   0.000891              28.329782            2.604736e-09   \n",
       "2                   0.012324              20.645624            3.173345e-08   \n",
       "3                   0.088890               9.363312            1.384060e-05   \n",
       "4                   0.381832               1.948281            8.722350e-02   \n",
       "5                   0.026820               2.217485            5.740684e-02   \n",
       "6                   0.948217               5.512973            5.650375e-04   \n",
       "7                   0.032403              -0.817383            4.373736e-01   \n",
       "8                   0.034658               1.457443            1.830997e-01   \n",
       "9                   0.165184               4.121682            3.336460e-03   \n",
       "10                  0.018102               5.551251            5.401803e-04   \n",
       "11                  0.000009               8.445306            2.950775e-05   \n",
       "\n",
       "    Van R KL Mean t-stat  Van R KL Mean p-value  \n",
       "0              -1.427680           1.912326e-01  \n",
       "1             -11.438586           3.086081e-06  \n",
       "2             -14.213362           5.846621e-07  \n",
       "3               1.958989           8.579199e-02  \n",
       "4              -2.250574           5.451846e-02  \n",
       "5              -3.413283           9.178526e-03  \n",
       "6              -0.359323           7.286532e-01  \n",
       "7              -4.687185           1.567227e-03  \n",
       "8              -5.325778           7.061073e-04  \n",
       "9               0.320739           7.566266e-01  \n",
       "10             -5.260562           7.639826e-04  \n",
       "11             -3.889314           4.613695e-03  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from scipy import stats\n",
    "\n",
    "# # Group the DataFrame by dataname and grouping_method\n",
    "# grouped_df = df.groupby(['dataname', 'grouping method'])\n",
    "\n",
    "# # Initialize whole_group_data with DataFrames for each dataname containing only the \"whole\" group\n",
    "# datanames = df['dataname'].unique()\n",
    "# whole_group_data = {dataname: df[(df['dataname'] == dataname) & (df['grouping method'] == 'whole')] for dataname in datanames}\n",
    "\n",
    "# # Iterate over the grouped data and perform a t-test for each combination of dataname and grouping_method, excluding the \"whole\" group\n",
    "# for (dataname, grouping_method), group_df in grouped_df:\n",
    "#     if grouping_method == \"whole\":\n",
    "#         continue\n",
    "\n",
    "#     whole_group = whole_group_data[dataname]\n",
    "\n",
    "#     firing_r_t_stat, firing_r_p_value = stats.ttest_ind(group_df['firing_r_KL_mean'], whole_group['firing_r_KL_mean'])\n",
    "#     corr_c_t_stat, corr_c_p_value = stats.ttest_ind(group_df['corr_c_KL_mean'], whole_group['corr_c_KL_mean'])\n",
    "#     van_r_t_stat, van_r_p_value = stats.ttest_ind(group_df['van_r_KL_mean'], whole_group['van_r_KL_mean'])\n",
    "\n",
    "#     print(f\"Dataname: {dataname}, Grouping Method: {grouping_method}\")\n",
    "#     print(f\"Firing R KL Mean t-stat: {firing_r_t_stat}, p-value: {firing_r_p_value}\")\n",
    "#     print(f\"Corr C KL Mean t-stat: {corr_c_t_stat}, p-value: {corr_c_p_value}\")\n",
    "#     print(f\"Van R KL Mean t-stat: {van_r_t_stat}, p-value: {van_r_p_value}\")\n",
    "\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "# Group the DataFrame by dataname and grouping_method\n",
    "grouped_df = df.groupby(['dataname', 'grouping method'])\n",
    "\n",
    "# Initialize whole_group_data with DataFrames for each dataname containing only the \"whole\" group\n",
    "datanames = df['dataname'].unique()\n",
    "whole_group_data = {dataname: df[(df['dataname'] == dataname) & (df['grouping method'] == 'whole')] for dataname in datanames}\n",
    "\n",
    "# Create an empty DataFrame to store the t-test results\n",
    "t_test_results = pd.DataFrame(columns=['Dataname', 'Grouping Method', 'Firing R KL Mean t-stat', 'Firing R KL Mean p-value', \n",
    "                                       'Corr C KL Mean t-stat', 'Corr C KL Mean p-value', 'Van R KL Mean t-stat', 'Van R KL Mean p-value'])\n",
    "\n",
    "# Iterate over the grouped data and perform a t-test for each combination of dataname and grouping_method, excluding the \"whole\" group\n",
    "for (dataname, grouping_method), group_df in grouped_df:\n",
    "    if grouping_method == \"whole\":\n",
    "        continue\n",
    "\n",
    "    whole_group = whole_group_data[dataname]\n",
    "\n",
    "    firing_r_t_stat, firing_r_p_value = stats.ttest_ind(group_df['firing_r_KL_mean'], whole_group['firing_r_KL_mean'])\n",
    "    corr_c_t_stat, corr_c_p_value = stats.ttest_ind(group_df['corr_c_KL_mean'], whole_group['corr_c_KL_mean'])\n",
    "    van_r_t_stat, van_r_p_value = stats.ttest_ind(group_df['van_r_KL_mean'], whole_group['van_r_KL_mean'])\n",
    "\n",
    "    # Append the t-test results to the DataFrame\n",
    "    t_test_results = t_test_results.append({\n",
    "        'Dataname': dataname,\n",
    "        'Grouping Method': grouping_method,\n",
    "        'Firing R KL Mean t-stat': firing_r_t_stat,\n",
    "        'Firing R KL Mean p-value': firing_r_p_value,\n",
    "        'Corr C KL Mean t-stat': corr_c_t_stat,\n",
    "        'Corr C KL Mean p-value': corr_c_p_value,\n",
    "        'Van R KL Mean t-stat': van_r_t_stat,\n",
    "        'Van R KL Mean p-value': van_r_p_value\n",
    "    }, ignore_index=True)\n",
    "\n",
    "# Display the t-test results DataFrame\n",
    "t_test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "520d983b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparison: whole vs random\n",
      "Firing R KL Mean t-stat: -5.527302844162693, p-value: 2.6273599909494553e-06\n",
      "Corr C KL Mean t-stat: -6.49245167084784, p-value: 2.10459467169106e-07\n",
      "Van R KL Mean t-stat: 5.636614645073407, p-value: 2.917201578361048e-06\n",
      "Comparison: whole vs max_val\n",
      "Firing R KL Mean t-stat: -3.9643402076284597, p-value: 0.00034248766655346324\n",
      "Corr C KL Mean t-stat: -4.880482727652051, p-value: 2.3856650989971076e-05\n",
      "Van R KL Mean t-stat: 5.454831110493608, p-value: 1.0362493157038284e-05\n",
      "Comparison: whole vs kmeans\n",
      "Firing R KL Mean t-stat: -2.095052025570281, p-value: 0.044229935078144135\n",
      "Corr C KL Mean t-stat: -6.79031737119329, p-value: 1.1473850014443276e-06\n",
      "Van R KL Mean t-stat: -0.8251157040749317, p-value: 0.41454146027611316\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "# Group the DataFrame by grouping_method\n",
    "grouped_df = df.groupby(['grouping method'])\n",
    "\n",
    "# Initialize a dictionary to store the data for each grouping method\n",
    "data_by_grouping_method = {method: pd.DataFrame() for method in df['grouping method'].unique()}\n",
    "\n",
    "# Organize the data by grouping method\n",
    "for grouping_method, group_df in grouped_df:\n",
    "    data_by_grouping_method[grouping_method] = group_df\n",
    "\n",
    "# Compare 'whole' grouping method with other methods\n",
    "whole_group = data_by_grouping_method['whole']\n",
    "other_methods = ['random', 'max_val', 'kmeans']\n",
    "\n",
    "for method in other_methods:\n",
    "    other_group = data_by_grouping_method[method]\n",
    "\n",
    "    firing_r_t_stat, firing_r_p_value = stats.ttest_ind(whole_group['firing_r_KL_mean'], other_group['firing_r_KL_mean'],equal_var=False)\n",
    "    corr_c_t_stat, corr_c_p_value = stats.ttest_ind(whole_group['corr_c_KL_mean'], other_group['corr_c_KL_mean'],equal_var=False)\n",
    "    van_r_t_stat, van_r_p_value = stats.ttest_ind(whole_group['van_r_KL_mean'], other_group['van_r_KL_mean'],equal_var=False)\n",
    "\n",
    "    print(f\"Comparison: whole vs {method}\")\n",
    "    print(f\"Firing R KL Mean t-stat: {firing_r_t_stat}, p-value: {firing_r_p_value}\")\n",
    "    print(f\"Corr C KL Mean t-stat: {corr_c_t_stat}, p-value: {corr_c_p_value}\")\n",
    "    print(f\"Van R KL Mean t-stat: {van_r_t_stat}, p-value: {van_r_p_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e6cd4756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAJOCAYAAABfvHoDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC8w0lEQVR4nOzdd1gUV9sG8HvpvaqAAqJYsJcoNgR7L1Fj7zExsRtjEmvsMZrYNfrGGHs3ttg1CnZFsaCioCgdBVRAFKSc7w+/nbCyC4u7FLP377r2UmbOmXlmdnb32bNnzpEJIQSIiIiIiHSYXlEHQERERERU1JgUExEREZHOY1JMRERERDqPSTERERER6TwmxURERESk85gUExEREZHOY1JMRERERDqPSTERERER6TwmxURERESk85gUk1Jubm6QyWSYOXNmoe97w4YNkMlkkMlkhb7vovT06VOMGTMGFStWhImJiXQO9u/f/0HbS01NxcaNG9G/f39UqlQJtra2MDQ0hL29PWrWrIlBgwZh69atSE5O1u6BfMSK8rrXlpkzZ0rXjkwmw/fff59nnaysLDg7OyvUe/LkScEHq4Kvr2+xiEMdQ4YMgUwmQ7NmzYo6lGLpY3ouIyIisGrVKgwdOhT16tWDi4sLTExMYG5ujkqVKmHIkCE4d+6cRvvIfj5kMhk8PT3Vqte3b1+Fehs2bNAojsL0McWs00mx/MPDzc2tSOMorA/ion5zatasGWQyGYYMGVLo+y7uXr16hcaNG2PlypV4+PAh0tLSNNre9u3b4e7ujiFDhmDbtm0ICQnBy5cvkZGRgefPnyMwMBCbN2/GgAEDUKZMGUyfPh2c8f2/adu2bcjKysq1zOnTpxEVFVXgsRSX91wiZQ4cOIDRo0djw4YNuH79OiIjI5GWlobXr18jJCQEGzduhLe3N7788ktkZmZqZZ/+/v548OBBrmWSk5Nx4MABrewvN0WdIxQHOp0UExUX27ZtQ2hoKGQyGf744w9ER0cjOTkZycnJ6Ny5c762NXXqVPTr1w/R0dEwNzfH2LFjcezYMYSGhuL58+d4+PAhjh07hrFjx8LOzg7JycmYO3eu1t7kqfiwsrJCVFQUTp8+nWu5TZs2SeWJdJWpqSk6dOiAX375BadOnUJQUBDi4+Nx//59bNu2DbVr1wYA/PHHH5g6darG+5O/3jZv3pxrud27d+PNmzd8fRYCJsWk1JMnTyCEKJKfkYcMGQIhhE61XN66dQsAULNmTQwbNgxOTk6wsLCAhYUF9PX11d7Oxo0b8dNPPwEA6tWrh+DgYCxbtgxt27ZFuXLlYGtrC3d3d7Rt2xbLli1DaGgovv/+e+jp8a0AKNrrviB89tlnAHL/0E1JScG+ffsAAD179iyUuIiKo2HDhuHw4cOYOHEiWrZsCQ8PD9jb26Ny5cro27cvLl++jDp16gAAVqxYgdevX2u0P/nrc8uWLbl+3slfvx/r61P+ef4x/ErMT0KiYkD+5mpjY/PB23jx4gXGjBkDAHB2dsbJkydRunTpXOtYW1tjwYIFOHz4MBPj/6CBAwcCAPbu3avyA3zv3r149eoVypUrhyZNmhRmeEQfFWNjYwwYMADAu/fsoKAgjbbXvXt3mJubIywsTGVf5fDwcPj5+UEmk0n7poKjk5+C8n4zs2bNAgCEhYUpdGDX5KaJI0eOoHv37nBxcYGxsTEsLCxQvnx5+Pj4YNasWQovIvkNGmFhYQCAWbNm5Ygje8d0IQSuXr2KadOmoXHjxrC3t4ehoSFsbW1Rv359zJgxAwkJCUrjkslkaN68ufR3uXLlcuwru7z6OV+8eBEDBgxA+fLlYWpqCjMzM5QtWxaNGjXCpEmT4O/vL5WV9yP08/MD8K418/19Z9+PujfahYaGYsKECahVqxZsbW1hamoKd3d3dOjQAX/88QdevnyZo86dO3cwfPhweHh4wNzcHCYmJnB2dka9evUwbtw4/PPPP7nuMzdv377FypUr4ePjgxIlSsDY2BilS5dG9+7dcfjwYaV15OdZ/jzL3/zkj/x8s16zZo1009z8+fPzlWC3a9dOaVKc/TrMzMzEqlWrpGtPJpNh6dKlCuWfPHmCsWPHwsPDAxYWFjA3N0eVKlUwbtw46TpXRt2blXK7Lt/vs37gwAG0atUKJUuWhKmpKapVq4bZs2fn2rqT2/bfj/H27dsYMGAAnJ2dYWxsDGdnZwwZMgSPHj3K9RgAYN++fWjZsiVsbW1hYWGBGjVqYO7cuXjz5o1W+/X5+PigbNmyePXqFfbu3au0jLwVauDAgWrf3PrixQvMnTsXDRo0gL29vXT8ffv2xaVLl3KU1/Q9NyUlBbNnz0b16tVhbm4OGxsbNGvWDHv27Mkz1uTkZMyfPx8NGjSAra0tTExM4Orqiv79++PChQt51n/9+jVmz56NatWqwdTUFCVLlkSbNm3w999/51kXKJj3HHX7Zud2H4c2r+e///4brVu3hp2dHczNzVG9evU8X2vvCw4OxpgxY1C1alVYWlrCzMwMlStXxtixYxEeHq6y3vuv2S1btqBly5ZwcHCAnp4exo8fr3YM6jA0NJT+b2JiotG2zM3N0b17dwD/dmF6n7wVuWnTpmr3xc/IyMCff/6Jdu3awdHREUZGRtJ1u3XrVqWt0vnNEfLz2aDOjXapqan47bff0KZNGzg6OsLY2BhOTk7w9PTE1KlTcefOnRx1UlJSsHDhQjRu3Bh2dnYwNDREiRIlUKVKFfTs2RNr165FSkqKWudMInTQmTNnBIBcHz4+Pvne7ujRo/Pc7qhRo6TygwcPzrP8+vXrpfL79+/Ps7yjo6O4ceNGjtjyqvf+pVC2bFkBQMyYMSPHtn755Zc8t9WxY0ep/IwZM/Isn30/69evVxpTdosWLRIGBga5bnPJkiUKdbZv355nnWrVqqncZ27Cw8NFtWrVct123759RVpamtLzrOoxePBgtWOoX7++ACAsLS1FamrqBx3H++RxrFmzRvj4+OR6jrdv3y6MjY1VHouJiYnYtWuX0v3IXwt5ve5yuy7l8Q0ePFhMmTJFZRxVq1YVsbGx+d5+9hh37Nih8lhtbW3F7du3VR7DyJEjVcZWq1YtsW/fPunvx48f53o+lMn+ehNCSOeiTZs2OcpGRUUJPT09AUAEBwcrvPZU7fv06dPCzs4u1+t2+vTpCnXy+56bvfylS5dElSpVVNabM2eOynNx+/ZtUaZMmVz3O3HiRJGVlaW0fmxsbK77njZtWq7XbkG958if47Jly+ZaLvtr4n3aup4nTJig8thq1Kgh9u7dm+c19euvv+Z6nszMzMTff/+ttK78Nfvjjz+Kvn375qg7bty4XM9RfmRmZgpvb28BQNjY2HzQ+2z2a/vMmTPixIkTAoCwtrYWb968yVHew8NDABBr164Vjx8/lupmzw2yCwsLE7Vq1cr1mmvfvr149eqVQr28Xp/y95P3y6vz2ZBXzDdv3hRubm657rtWrVoKdaKjo0XFihXzjNnf3z/P50ThuPJV+j8iIyNDJCcni8mTJwsAwtXVVSQnJys8Xr9+na9tnjp1SnoSevbsKXx9fUVERIR49uyZCAgIELt27RI9e/YUEydOlOqkpqaK5ORk4erqKgCIyZMn54gjPT1dKv/333+L5s2bi+XLl4vz58+LR48eifj4eHH37l3x+++/Sy+ecuXK5XhxJScniyNHjkgx3r17N8e+slOVHDx48EDo6+sLAKJly5bi2LFjIiwsTMTHx4vbt2+LAwcOiCFDhoh+/fpJddLS0kRycrLw8vISAET//v1z7Dt7sphXUrxkyRJpvYeHh9i4caN4/PixePHihXjw4IHYtGmT6Nixo1i2bJlU58WLF8LCwkIAEHXr1hV79+4VoaGhIiEhQdy9e1ccPXpUjB49WjRv3lz9Jz3b81ijRg0BQOjr64vvv/9e3Lt3T8THx4uLFy+KLl26SPGOHDlSoW5KSopITk4W/fv3FwCEl5eXwnlR9003JSVF+lBp0aJFvo9BFXncZcqUEXp6emLixIni9u3bIiEhQQQEBEhfwM6dOyclV66urmLr1q0iKipKREdHiy1btghnZ2fp/Fy8eDHHfrSZFMvfXNu2bSvOnz8v4uPjxZ07d8T48eOFTCYTAETTpk2VJkLqJMVlypQRxsbGwtvbW5w4cUI8e/ZMREREiKVLlwojIyMBQDRq1Ehp/CtXrpTOaaNGjcQ///wj4uLixMOHD8XcuXOFsbGxKF++fJ5JRG7eT4qDgoKkcx8dHa1QduHChQKAaNiwoRBC5JkUX79+XUqeqlevLrZs2SKePHkinj9/Lq5fvy4+//xzqf7atWulevl9z82eOJQvX16UKFFCrFy5UoSGhor4+Hhx6tQpUb16dem47t27lyPW+Ph44eTkJAAIU1NTMX/+fPHw4UMRFxcnTp06JZo0aSLtY+HChTnqZ2VliaZNmwoAQiaTifHjx4s7d+6I+Ph4cf78edGmTRuF6+39a7cg33O0mRRrcj2vXbtWOof169cXJ0+eFHFxcSIkJETMnDlTGBoainLlyuV6TWV/TXTq1EkcP35cxMbGiri4OHHixAnpOTAxMVGanMtfs/IvP0OHDhVXr14V8fHx4t69e+LChQvqnFKVsrKyRGxsrDhx4oRo1aqVFOvy5cs/aHvvJ8WZmZmidOnSAoDYuXOnQtmrV69Kx/7y5cs8k+LExETh7u4uAAh7e3uxaNEiERQUJF68eCGCg4PF/PnzhampqfQ5nF1+cwR1Pxuyl1UWc2hoqLC1tZVep5MnTxYBAQEiISFBREVFiRMnTohx48bleJ0MGDBAAO++MC1cuFB6bT558kScO3dOLF68WHzyySfi2rVr+Xp+dDIpllP3jUUd8m/LderUUdnqoEpuH8T5kZycLL0g/vzzzxzrs78Y8/qwVRXT8uXLBQBRqlSpHK2eecntDTq73JLisLAwYWhoKACIxo0b5/i2m132LxQHDx6UPkATEhLyFXdesifp//vf/3Ksz8rKEr1795bK3Lp1K0cZdZNCVR48eCBtf8SIER+0DWWyf+P+7bffVJaTt0yULFlSRERE5FgfFhYmSpQoIb1G3qfNpBiAaNeuncjIyMhRZu7cuVKZPXv25Gv72X/ZadeuncL1Jbdo0SKpTFBQkMK6169fCxsbGwFA1KtXT2mr0I4dOxTOuTaSYiGEqFevngAgfv31V4Wy8i9zq1atEkLknRTXrFlTAO9abVQ1HEybNk0AECVKlMhRRt333OzvVRYWFjnOpRBCREZGSh/wP/zwQ47148aNkxLaY8eO5ViflpYmfVE3NjYWT58+VVi/e/duKYZ58+blqJ+RkaGQJL1/7Rbke442k+IPvZ7fvHkj7O3tpeshJSUlR/2NGzfmej1HR0dLX7KGDx+u9BjS09NFs2bNBKD4C6Rc9l/bvv/++1zORv706NFDIXb5o1SpUmLNmjUfvN33k2IhhJg4caL0pSA7+a/PPXv2FEKIPJPiMWPGCODdr4X3799Xuv+TJ09K23i/FTU/OYK6nw3ZyyqLuW3btlLir6zBRO7961P+a9XSpUtz3Xd+6WSf4oKQkZEBAChdunSRTTphYWEh9U86efJkgexDfpwlS5aEkZFRgewjN6tXr0Z6ejr09PSwceNGmJubqyxrYGAg/V8et5mZmUY3synzxx9/AADq1q2L4cOH51gvk8mwbNkyqS+avLw2vXjxQvq/tbW1ynKZmZl49eqV0sfbt29V1qtSpQpGjBihdN21a9ek0TOmTZsGZ2fnHGVcXV0xZcoUAMCNGzcQEBCg1nF9qMWLFysdteOHH35AmTJlAADr16//4O0vW7ZM4fqSy953M3u/egA4ePCg1M/9559/VtofsXfv3mjUqNEHx6XKoEGDACiOQnHz5k0EBgbCyMgIffr0yXMbZ86cwe3btwG8u4ZNTU2VlpsyZQrMzc0RHx+PEydOaBz7mDFj4OHhkWN5mTJl0Lp1awA5z3VmZqbUf/HTTz9F27Ztc9Q3MjLC8uXLAQBpaWnYsmWLwnp5/TJlyiidAEVfXz9Hn/rsCvI9R9s+5Ho+dOiQdA/Lzz//DDMzsxz1Bw0ahHr16qnc75o1a5CWlgZ7e3vpuXifgYEBZs+eDeDdPTvK7hUBAFtbW6nPekExMjLCiBEj8j1MZl7kr89jx44hLi4OAJCeno4dO3YorM9NSkoK1q1bBwD48ccfUblyZaXlWrVqhRYtWgAAtm7dqnHsuX025OXBgwc4fvw4AOD777/P9b3v/etT/vqSv59rC5NiLZEP03L06FEsWbIEr169KpD9ZGRkYOPGjejSpQtcXV1hZmam0BH+l19+AYA8BwP/UPLjvHv3LiZNmqTyxr6CIr8pxcvLCxUqVFC7Xq1atSCTyZCcnIxhw4YhMjJSK/G8ePEC9+7dA/Dv8DrKODg4wNvbGwA0nhFJGaHm8HVnzpyBpaWl0od8KDdlOnTooHLd+fPnpf/ndg6yDydUEOdAzsPDA1WqVFG6zsDAAJ06dQLw7mbRD1G+fHlUqlRJ6To7OzuULFkSABAbG6uwTr4/CwsLhRta3te1a9cPiis3ffr0gYGBAW7duoXAwEAA/97Y06FDB9jZ2eW5jVOnTgGANESVqi9XmZmZUhJ77do1jWNv3769ynXyD/73z3VgYCASExMB5D6MVZ06deDu7g5A8ZoUQkg34XXu3FlpwggA1apVU3ktFNR7jrZ96PUsf92bm5tLX06UkTfUKCO/ppo2bYr09HSV15T8ehJC4Pr160q31aJFC41vfMtuy5YtSE5ORlJSEh4/fozNmzejQoUKmDVrFmrVqvXB7x/K1KhRA7Vq1UJGRoaUCB89ehTx8fEoWbIk2rVrl+c2Ll68KN3Y6OPjo/Jcvnr1CjVr1gSgnddnbp8Necl+k2l+h2uT5yKTJ0+Gn5+f1oZwZVKch4yMDJUXVvZZxwYMGID69esjKysLEyZMQIkSJdCsWTNMmzYNJ06cyLUVTl1xcXFo0KABhgwZgr///hsRERF48+aN0rLyDwRta9asGT799FMAwIIFC+Dg4ICGDRti4sSJOHjwYP7v9Mwn+Z3Q8kHU1VW+fHmMGzcOwLsWIFdXV9SuXRtjxozBrl278Pz58w+KJzw8XHoxVq1aNdey1apVA4BcR2H4UNmTmoJ47suXL69ynfx4bGxsch0CztnZWWrFLohzIKesVVHZ+hcvXiApKSnf289rmDt5i9n7d97LR5KoWLFirsPf5RX/h8j+wbpp0yZkZmZi+/btAP4dti0v8i/aCQkJsLKyUvnlytLSUkpc5K1emsjtfKs619mvrw95XSYmJkotknk9H6q+gBXUe462aeN6zm0sdVXnB/j3mtq/f3+u11OpUqWkOqquqdzeoz6EiYkJLCwsYGlpCTc3NwwYMADXrl1DgwYNEB8fj65du6pstf4Q8teh/Muq/Fcd+RfavGRvCPP09Mz1fMp/4dDG61OT8y7/PLeyskK5cuXyVXfhwoUwMjLCw4cP0axZMzg5OaFXr15YsWIF7t69+8ExMSnOw5YtW1ReWF999ZVUzsDAAKdPn8a0adPg5OSEtLQ0+Pn5Yd68eWjbti0cHBwwY8YMjZLjQYMGISAgAAYGBhg7dixOnjyJx48fIyEhQZr9bNKkSQD+/WmhIOzatQu//PILypcvj8zMTFy5cgWLFi1C165dUapUKYwZM+aDkg11yLdraWmZ77qLFy/GH3/8gRo1akAIgVu3bmHlypXo3bs3HB0dMWDAAMTExORrm/Ih0IB3LYC5kcecvY62ODs7S2+cuf1K0KpVK2kgdflDHcp+GpWTH09ex5+9TEGcg/f3oc76D4lD3clU3j+38i+MuXX5AdQ7jx9C/hPstm3bcOzYMcTGxsLW1lZqOc/Lh3zZSk1NzXed96lzvt8/15q+LrP/0pef6+l9BfGeo20fej3Lz5Em50eb11Ru71HaYmpqip9//hkAEB8fj507d2pt2/3794e+vj6uXbuGS5cuSUP+qdN1Aii616cm512Tz3NPT09cvXoV3bp1g5GREZ4+fYrdu3dj7NixqF69OmrXro1jx47le7tMirXIwsICc+bMQVRUFAIDA/H777+jf//+sLGxwcuXLzF79mz07t37g7YdGhoqPcErVqzAsmXL0KpVK7i5ucHOzk6a/UzTGXbUYWhoiIkTJ+LRo0cICQnBpk2b8MUXX8DR0RGvX7/GypUr0bJlywJJzDVJLGUyGYYNG4bbt28jIiICO3fuxOjRo+Hm5ob09HRs3boVjRo1ytebS/YXc15dZuTrP+QNIC9mZmbSz0n+/v4Kv2IUNPnxqNNlSNU5ULcfvjrXlLrPg7I4CpI8Gc7r15SC6nrVuXNnWFtbIzo6GmPHjgXwrg+zuvcGyJObevXq5fhipeqR27ikBUnT12X2RC4/19P7CuI9R75ddRRk44j8HGlyfuTbmDhxotrXVFHPitagQQPp//I+9trg6OiIVq1aAXj3y3NaWho8PDxy7ZOd3ftf9tU5l5qOg64pTRuKatWqhb179+LFixc4ffo05s6dCx8fH+jp6eHWrVvo0KGDNFunupgU5yH7lMPqvuHLZDJUr14dX375JbZs2YKoqCipr+X+/ftx8+bNfMeRvU7fvn1VlpP3FywsFSpUwMCBA7F27VpERERIH7bXrl3DoUOHCmR/AD7oHGbn7Ows/dQSGhoq9cUOCwvL1w1Yrq6u0geUvG+xKvKfdNQdgD2/unXrBuDdG8zu3bsLZB/KyI/n5cuXubZ6RUVFSR/+758DeV9AVd2BgHc3nsTHx+cZz/3799Vab2trCysrqzy3py1ly5YFADx8+BBZWVkqyxXU/QAmJiZS/9rQ0FAA6nedAP79mTQoKKhQv3R9iOzX14e8Lq2traWb4/K6ntSd1Uxb7zmAeq8XAIiOjs7XdvNDfr5CQkKQmZmpslxu50d+Td24cUOrsRWk7F80tH1Tvfz1qMnrE9D887GwyD/P5f22P5SZmRmaN2+OqVOnwtfXF7dv30bJkiUhhMj3zZc6nRTLRwPI7QWtDWZmZtKd90DONwl14sj+IaSqXHh4OM6ePatyG9ln4imIYzYwMFCYCexDjjMv8hs6zp8/L71xaEomk2HixIlSf9f8TN1pa2sr9Un866+/VJZ79uyZ9Nx4eXlpEK1qX3/9tdRaMHnyZK32d8tN9uPJ7Rxkn33s/XPg5OQE4N0HrKqE8cyZM2olY/fv31eZyGRkZEhf1ho3bpzntrRJPoVycnIyfH19VZY7cOBAgcWQ/adYd3f3fJ2DNm3aAHjX0r1r164P2n9hvedWr15dej3ndk3eunULDx8+BKB4TcpkMun5OnTokMoW17t37yI4ODjf8WnyngP8+3qJi4tTGHkmuwcPHmiUaORFfr5SUlJyHe1I1UyKwL/XlJ+fX4HGqk3ZP2PlN2lqS7du3aT38PxO6+zj4wNjY2MAHz6yTkHnCO+Tt4wD72a61ZZq1apJjYf5fW3pdFJsb28P4N0bi6Y/M+XVupN9qkz5ft//O7dv9dm/BSr70ExPT8eXX36Z64Wcfb8f2oKQW9ICaH6cefn6669hZGSErKwsDB48ONfuItmf08ePH+fan/vp06fSTzjvx52XYcOGAQCuX78uDYnzvvHjx0v7/+KLL/K1fXXZ2tpixYoVAIDIyEi0bt26QFuK5D755BPpxse5c+cq3WdkZCTmzZsH4N3QdXXr1lVYL/9J8sWLF0qnz01JSVE6LJYq33zzjdLrdOHChYiKigIADB06VO3taYO8+wLw7kuLsgR/z549Wr2r/X1eXl4ICgpCUFBQrl+glWnTpg2qV68OAPj222/zTAafPHmS4xi1+Z6bG319feln9r1790qjHGSXnp4u/bJlYmKSo1VOXj8yMhILFy7MUT8zMxPffPONyhgK8j1H/noRQiidHjgjI0Pr0xu/r1OnTlLckyZNUvpevGnTplxHOBg1ahSMjY2RkZGBgQMH5tkVo6B+RZHL61eBFy9e4IcffgDw7hrr0qWLVvdvZmaGO3fuICgoCMHBwXB1dVW7rpWVlfRZtGHDhly/DALvWmff/2VPGzlCflSqVEm6AXjBggW4evWqyrLZ3y9SUlIQERGR67bluUh+X1s6PXmHfLYY4N1sclFRUeLt27ciPT1d6cD/ufHx8RHVqlUTc+bMEb6+viIqKko8f/5c3L9/XyxfvlwatL906dI5Bu2XT/tqZ2cnjh8/Ll6+fCnS09NFenq6NBFIRkaGNNOVpaWlWLZsmXj48KF49uyZOHr0qGjUqJEA3k1hCxWDuqenpwsrKysBQHTo0EE8ePBAvHnzRtpXdqomMRg8eLBwc3MTkyZNEidOnBDh4eHixYsX4uHDh+LPP/8ULi4uAoAwNzdXOXuWkZGR2LVrl0hISJD2nZmZKZXLa0a7pUuXSuurVq0qNm3aJJ48eSJevHghQkJCxLZt20SXLl0UZrSbMWOGcHBwEGPHjhWHDh2SZsB7/Pix2LVrlzRFs56entJpsnOTfUY7AwMDMXnyZBEUFCQSEhLEpUuXxKeffprnxBqaTt6RXfYpjs3MzMSYMWPE0aNHpWN++vSpuH79uli0aJHCdKCzZs3KsS35OlXTc8pln9HOzc1NbN++XcTExIjo6GixdetWadZGVTPapaenS9ecnZ2d2Lx5s3j69KmIjY0V+/btEzVr1hT29vbS60idGe3atWsnLly4IBISEsS9e/fEN998o5UZ7TSZYEQ++Q0A0aRJE3H69GkRHx8vHj16JH766SdhYmKi9Rnt8iOvyTsCAgKkCTOsrKzEzJkzpdmnnj17Jm7duiXWrVsnOnfuLPT19UVcXJxCfXXfc9WdRCC3SSyyz2hnbm4uFixYIM0C+s8//0gzpQEQCxYsyFH//RntvvnmG3Hv3j2RkJAgLly4kOeMdgX5niOEEI0bN5YmPVixYoWIjIyUZoHz9vYWxsbG0ixveU3znJvcrufsM9p5enqKkydPivj4ePHw4UMxa9YstWa0W716tbTe3d1drFmzRjx48EC8ePFCREdHiwsXLohff/1VeHp6iurVq+crvvzS19cXXbt2FRs2bBCBgYEiLi5OJCQkiMDAQLFs2TLp8w0qJoxRh7LJO9SV1+QdL1++FJUrV5au2c8//1z4+vqK2NhY8fz5cxEcHCz27Nkjhg0bJqysrMTu3bsV6ucnR1D3syGvstlntDMzMxPTpk0TN27cEAkJCSI6OlqcPn1afPvttwoztT5+/FgYGBiILl26iD///FPcvn1bxMXFidjYWHHx4kUxZMgQaZ8TJkxQ69xKsear9H+Q/I3l/Ud+kxNlc3+//7C3t1eaEAQGBkrTab7/yH4R+fn5SR9Iyh4TJ07Mc6Yj+TSryh7Z5ZYU53WcpqamYt++fTn2HRMTI1387z+y7yevpFgIIRYsWCBNN63qkX3u9eyJgqqHvr6+NLNXfoWHh0sfcqoeffv2VTkLoDaTYiGE2Lp1qzR1aF4PS0tLMXPmTKUzlOXnjW/btm3S7FTKHiYmJmLXrl0q658+fVqYmJgorWttbS18fX3VmtFu8ODBYtKkSSrjqFq1qoiNjVUaQ0EnxVlZWeKrr75SGVuNGjXE3r17pb+VzQ6Yl4JMioUQ4sKFC1Kyldfr6fnz5znqq/Oeq42kWAghbt++nWes3377rcpZSGNjY0WVKlVU1p0yZYrK66Kg33Pu3Lkjzer1/sPY2Fjs2LFDrRntNLmehfh3Nldlj+rVqytcz6qey//973+5vnfIH3Xr1s13fPmhzvulvr6+mDx5cr5nrpUryKRYiHezBGafwjy3x4EDB3LUVzdHyM9nQ15lb9y4ofCFQ9mjVq1aSs9Dbo9WrVrlOuutMjrdfQJ4N0POd999h6pVq2o0tMjGjRuxdu1a9OnTBzVq1ECJEiWgr68PGxsbNGjQALNmzcKDBw+UzthSvXp1nD17Ft27d0fp0qUV+vVk5+3tDX9/f/Tp0welSpWCoaEhHB0d0alTJxw6dEi6cSM3c+fOxcqVK9GwYUNYW1vnOl6qMgsWLMDmzZsxZMgQ1K5dGw4ODjAwMIClpSXq1KmD7777Dvfv35fGMs7O0dERly5dwsCBA1G2bFmNZsT7/vvvcffuXYwaNQoeHh4wNzeHmZkZKlSogA4dOmDdunUKP4+PHz8eu3fvxtdff4169epJ59nc3BzVqlXDqFGjcPv2bYwcOfKD4nFxcUFAQABWrFiBpk2bws7ODoaGhnBycsKnn36Kv//+G9u2bSu0WQD79euHR48eYf369ejTpw8qVKgAa2trGBgYwM7ODtWrV8fgwYOxadMmxMbGYsaMGSpnKFNX3759ERQUhDFjxqBy5cowMzODmZkZKleujLFjx+L+/fu5TqTQvHlzXL58GZ999hlKlSoFIyMjuLq6Yvjw4bh58yZ8fHzUjmX+/PnYs2cPmjdvDjs7O5iYmKBq1aqYNWsW/P394eDgoNGxfiiZTIY1a9Zgz549aNasGaytrWFmZoaqVatixowZuHz5ssKYpIU5Ooa6GjdujODgYKxYsQKtW7eGg4MDDA0NYWpqinLlyqFr165Yt24dnj59Cltb2xz1tfWeq44aNWogKCgI8+bNQ/369WFtbQ0jIyO4uLigb9++OH/+PH799VeVN0w5ODjg2rVrmDlzJqpUqQITExPY29ujRYsW2Ldvn9QlSJmCfs+pVq0arl27hqFDh6JMmTLS+02/fv1w9erVDx7pKL8WLVqEgwcPomXLlrCxsYGZmRmqVKmC6dOn4/Lly0qvgfcNHz4coaGhmDp1Kjw9PWFnZwd9fX1YWlqiatWqGDJkCA4cOCBNqFJQzp49i1mzZqFly5Zwd3eHpaUlDA0NUaJECTRu3BiTJk3CvXv38NNPPxXZzLV5cXJywrlz57Bv3z707NkTrq6uMDExgZGREZycnNC8eXP8/PPPCAkJUdr9Q9Mc4UPUrl0b9+/fx5IlS+Dt7Q17e3sYGhqidOnSaNCgAaZNmyaNqw68u8H9woULCs+Vubk5jIyM4OzsjC5dumDnzp04ceJEnkNgvk/2/1k8EdFHrVmzZvDz88PgwYOLbCgwbViyZAkmTJgAS0tLJCYmFtsPXyKi/xqdbykmIipO5Dca1q1blwkxEVEhYlJMRFSIcpved9euXThz5gwAoFevXoUVEhERAch7Qm0iItKaKlWqoH///ujatSs8PDxgYGCAx48fY9u2bdKQeu7u7kU+cxcRka5hUkxEVIgSExOxZMkSLFmyROl6Z2dnHDhwoMBvQiMiIkVMiomICtGWLVtw9OhRXL16FU+fPsXLly9haWmJypUro0uXLhg1alSxHHWCiOi/jqNPEBEREZHOY0uxFmRlZSE6OhqWlpa8W5yIiIjoAwkhkJycjNKlSxfKOMnZMSnWgujoaLi4uBR1GERERET/CREREXB2di7UfTIp1gJ5/7+IiAhYWVkVcTREREREH6ekpCS4uLgUyb0VOp0UZ2Zm4s8//8SWLVtw9+5dvHr1Cg4ODqhTpw6GDh2Krl27qrUdeZcJKysrJsVEREREGiqK7qg6mxS/ePECHTp0wOXLlyGTyVCpUiW4ubkhOjoaBw4cgIGBgdpJMRERERF93HQyKc7KykKXLl1w+fJldO/eHcuWLVPotxIZGYnQ0NAijJCIiIiICpNOJsW///47zp8/j+bNm2P37t057m50dnYu9M7dRERERFR0Cnesi2Ji2bJlAIA5c+YU+nAfRERE/xUzZ86ETCZTeDg6OuZaJy0tDVOnTkXZsmVhbGwMd3d3/Pnnn9L6vXv3ol69erCxsYG5uTlq166NzZs3K2zDzc0tx35lMhlGjRpVIMdJukHnWopDQkJw//592NnZoXHjxjhw4AB2796NmJgYlCxZEq1atcLAgQNhbGxc1KESEREVe9WqVcOpU6ekv/X19XMt36tXLzx9+hTr1q1DhQoV8OzZM2RkZEjr7ezsMHXqVHh4eMDIyAiHDh3C0KFDUapUKbRt2xYA4O/vj8zMTKnOnTt30Lp1a/Ts2VPLR0e6ROeS4uvXrwMAPDw8MHDgQGzdulVh/c6dO7Fo0SIcO3YMZcuWLYoQiYiIPhoGBgZ5tg7LHTt2DH5+fggNDYWdnR2Ad62+2TVr1kzh73HjxmHjxo04f/68lBSXLFlSoczPP/8Md3d3+Pj4fNhBEEEHu0/ExMQAePctc+vWrfjiiy/w5MkTpKam4tSpUyhfvjzu37+PHj16ICsrS+k20tLSkJSUpPAgIiLSRSEhIShdujTKlSuHPn365Hqj+sGDB1GvXj0sXLgQZcqUQaVKlTBx4kS8efNGaXkhBP755x88ePAA3t7eSsu8ffsWW7Zsweeff85ZZUkjOtdSnJKSAgBIT09H06ZNsXbtWmldy5YtsXfvXtSpUwfXr1/H4cOH0blz5xzbmD9/PmbNmqWVeIQQSE9PV5mAExEpY2BgAAMDnXsLp2KmQYMG2LRpEypVqoSnT59i7ty5aNy4Me7evQt7e/sc5UNDQ3H+/HmYmJhg3759iI+Px8iRI/H8+XOFfsWJiYkoU6YM0tLSoK+vj99++w2tW7dWGsP+/fvx8uVLDBkypKAOk3SETAghijqIwvTrr7/iu+++AwDs2bMHPXr0yFGmZcuWOH36NEaOHIlVq1blWJ+Wloa0tDTpb/nsK4mJiWpP3pGZmYn4+HgkJycjPT39A4+GiHSZubk5SpQoATMzs6IOhQjAu4Ynd3d3fP/995gwYUKO9W3atMG5c+cQGxsLa2trAO9urPvss8+QkpICU1NTAO+GTg0NDcWrV6/wzz//YM6cOdi/f3+OrhUA0LZtWxgZGeHvv/8u0GOjwpGUlARra+t85VTaonPNDLa2ttL/PTw8lJapUqUKTp8+jSdPnihdb2xsrNGNeJmZmYiIiEBaWhqsra1hYWEBfX19/uxDRGoRQiAtLQ3Pnz9HREQEypUrByMjo6IOiwjm5uaoUaMGQkJClK53cnJCmTJlpIQYePeZK4RAZGQkKlasCADQ09NDhQoVAAC1a9dGUFAQ5s+fnyMpDgsLw6lTp7B3796COSDSKTqXFFeuXFn6v6rEVr48+52t2hQfH4+0tDS4urpK34qJiPLD1NQUlpaWePz4MZ49e8ax1alYSEtLQ1BQEJo2bap0fZMmTbB79268evUKFhYWAIDg4GDo6enleg3Lvwi+b/369ShVqhQ6duyosu7MmTNzdHl0cHBAbGxsnsdz4cIF+Pj4oHr16rh586a0fMOGDRg6dGiO8m/evIGJiYnG+6WioXM32tWpU0e6YFXdDCBfXqZMGa3vXwiB5ORkWFtbMyEmIo3o6+vD2toar1+/ho71hKNiYuLEifDz88Pjx49x5coVfPbZZ0hKSsLgwYMBAJMnT8agQYOk8v369YO9vT2GDh2Ke/fu4ezZs/juu+/w+eefS5+J8+fPx8mTJxEaGor79+9j8eLF2LRpEwYMGKCw76ysLKxfvx6DBw/Os399tWrVEBMTIz0CAwPzPLbExEQMGjQILVu2VLreyspKYZsxMTFSfqHJfqno6FxLsbm5OTp06IC9e/di48aNaNOmjcL62NhYHD9+HADQokULre8/PT0d6enp0jdkIiJNmJqaIj4+Hunp6exCQYUuMjISffv2RXx8PEqWLImGDRvi8uXL0pCmMTExCA8Pl8pbWFjg5MmTGDNmDOrVqwd7e3v06tULc+fOlcqkpKRg5MiRiIyMhKmpKTw8PLBlyxb07t1bYd+nTp1CeHg4Pv/88zzjzM+wcXJfffUV+vXrB319fezfvz/HenUmKvmQ/VLR0bmkGAB+/PFHHDhwADt27ECbNm2kb7Tyu1ffvHmD8uXLF8gg4PJRJvIa3JyISB3y9xKOYENFYceOHbmu37BhQ45lHh4eOHnypMo6c+fOVUiSVWnTpo3av5DIh40zNjZGgwYN8NNPP6F8+fIqy69fvx6PHj3Cli1bVMby6tUrlC1bFpmZmahduzbmzJmDOnXqaLRfKlo6130CAGrVqoWVK1dCCIEhQ4agbNmyqF+/PsqUKYPjx4+jRIkS+Ouvvwq01YU31RGRNvC9hCh38mHjjh8/jrVr1yI2NhaNGzdGQkKC0vIhISGYNGkStm7dqrJbhoeHBzZs2ICDBw9i+/btMDExQZMmTRRuMMzvfqno6dyQbNmdO3cOv/zyCy5duoSkpCSULl0aHTt2xOTJk/PVnzg/w4ekpqbi8ePHKFeuXI6+R0RE+cX3FKL8yW3YuMzMTDRs2BDDhg3D119/DeDdDXP79+9XuNHufVlZWahbty68vb2xfPnyfO+X/sUh2YpI06ZNVd4hS0RERP89uQ0bl5ycjGvXruHGjRsYPXo0gHcJrxACBgYGOHHihNL7jfT09FC/fn2VQ9HltV8qHnSy+wTpDjc3N8hkMpVjTmvTkydPIJPJ4ObmVuD7ovzbsGEDZDIZZ70i0nHyYeOcnJxyrLOyskJgYCBu3rwpPb7++mtUrlwZN2/eRIMGDZRuUwiBmzdvKt2mOvul4kGnW4qJiIjov23ixIno3LkzXF1d8ezZM8ydOzfHsHFRUVHYtGkT9PT0UL16dYX6pUqVgomJicLyWbNmoWHDhqhYsSKSkpKwfPly3Lx5U2EW3Lz2S8UPk2IiIiL6z8rvsHHqePnyJYYPHy5NV12nTh2cPXsWnp6eau+Xih8mxcWU26TDRR1Cvjz5WfVsQkRE9N/yMXxGyT+XPmTYuOxmzpyJmTNnKixbsmQJlixZkmu9vPZLxQ/7FBMRERGRzmNSTMVKVlYW7OzsoK+vjxcvXiisu3z5MmQyGWQyGdatW5ejbl431V2+fBnt27eHra0tzM3N0bRpU5w+fVplLCkpKZg7dy5q1qwJc3NzWFlZoUGDBli1ahUyMjLyfWwZGRlYs2YNvLy8YGNjAxMTE3h4eGDatGlISkpSezsrV66ETCZDp06dVJZ5/vw5jIyMYGhoqDAm5p07d9C/f3+4uLjAyMgINjY2qFixIvr164djx46pHUP2m9ZSUlIwZcoUVKpUCSYmJmjWrJlU7vLly/j+++9Rr149lCpVCsbGxnBxccHAgQNx9+5dpdueOXMmZDIZZs6cicTERIwfPx6urq4wNjZGhQoVMGfOHJXnXwiBP/74A7Vr14apqSlKlSqFPn364OHDh3ke08WLF9G9e3c4ODjAyMgIzs7OGDRoEIKCgpSWb9asGWQyGXx9fXH79m107doVJUqUgJWVFVq1aoVr165JZc+dO4d27drBzs4OlpaW6NixI+7fv59nTEREVHiYFFOxoqenhyZNmiArKwvnz59XWHf27Fnp/35+fgrrwsPDERYWBldXV6WjPxw6dAje3t7w9/eHu7s7DA0Ncf78ebRt2xa+vr45ysfFxaFRo0aYPn067t69iwoVKsDZ2RlXr17F6NGj0aFDB6Smpqp9XElJSWjZsiVGjBiBS5cuScno48ePMW/ePDRs2BDPnj1Ta1u9e/eWhgZ6/vy50jJ79uxBeno62rRpA3t7ewDA1atX4enpiW3btiE5ORlVq1aFi4sL4uLisH37dqxZs0bt45F78+YNvL298fPPP8PAwABVq1aFsbGxtH7AgAH45Zdf8OTJEzg4OKBKlSpITk7Gli1bUL9+faXnXi4xMRGNGjXCqlWrYG9vj9KlS+PRo0f48ccfMWLECKV1Ro0ahS+//BK3bt2Co6MjXFxcsH///jyHSlq9ejW8vLywb98+AO8m+ElJScHmzZtRt25dHD6s+qfiK1euoGHDhjh79izc3Nygp6eHf/75By1atMDdu3exe/dutGjRAjdu3EC5cuWQlZWFI0eOwNvbG0+fPs3jDBMRUWFhUkzFjo+PD4Ccia+fnx/09PTg6OiodF32uu+bMGECZs+ejadPn+LatWuIi4tD//79kZGRgUmTJuUoP2LECAQGBqJatWoIDg7GrVu3cO/ePfj7+8PBwQEnT57EjBkz1D6mr776CmfPnkXLli0REhKCJ0+eIDAwELGxsejevTuCgoIwatQotbZVsmRJtGzZEunp6dizZ4/SMtu3bwcA9OvXT1o2Z84cvHnzBlOmTMGzZ89w8+ZNBAYG4uXLl/D390evXr3UPh65v/76C69evcLdu3dx7949BAQE4MCBA9L6H3/8EY8ePUJ8fLw0zFF8fDz++OMPpKenY9iwYSqnJ161ahVKliyJsLAw3LhxA48fP8bBgwehr6+PP/74I0dL68GDB7F69WoYGxvjr7/+wuPHj3H9+nVERESgdu3a+OWXX5Tu5+bNmxg7diyEEFi4cCFiYmLg7++P2NhYjBw5Eqmpqejfvz9iYmKU1p8+fTpGjBghXVtPnz5F165dkZycjNGjR2P48OFYsGABYmJicP36dURFRcHT0xNxcXFYvHhxvs85EREVDCbFVOx4e3sDUEyKs7KycOHCBdSqVQtt27ZFeHi4QjeJvJLidu3aYdKkSdDX1wcAGBoaYunSpTA2NsaVK1cUumqEhIRg7969AIDNmzfD3d1dWlevXj2sWLECwLukLTk5Oc/juX37Nnbs2IGyZcti3759CvPe29raYvPmzXBxccFff/2FsLCwPLcH/JvsypPf7KKjo3H27FmYmZmha9euCscFAD/88EOOKczr1aunkECrKzMzE9u3b0eVKlWkZdlnVRs0aJDC8QKAgYEBhg0bhj59+iA0NBSXL19Wum0DAwNs3boVpUuXlpZ17txZOqajR48qlJcnvWPHjkX37t2l5SVLlsT27dtVTof866+/IiMjA127dsV3330HPb13b4vGxsZYuXIlqlWrhsTERKxevVpp/erVq+PXX3+VzqmxsTEWLlwIAPD19YWPjw8mTJggbdfGxgazZ88GgHx1WSEiooLFpJiKnbp168LCwgI3btyQks6bN28iMTERPj4+SluS5V0r5An1+7744oscy0qUKCF1tQgNDZWWnzx5EkIIeHl5oU6dOjnq9ejRA87OzkhJScGFCxfyPB75T/K9evWCpaVljvVmZmZo1aoVhBA4d+5cntsDgG7dusHU1BRnz55FdHS0wrqdO3ciKysLnTt3hoWFhbTcxcUFALBr1y619qGOatWqoW7durmWuX//PmbMmIHu3bujWbNm8PLygpeXl/T83bp1S2m9du3awdnZOcfy+vXrA1B8zl69eoWLFy8CgNKuFY6OjgqJcnYnTpwAAIwZMybHOplMhrFjxyqUe9/QoUNzJNyVKlWCmZkZAGDYsGE56sivq+zHQERERYtDslGxY2BggMaNG+PEiRO4cOEC2rVrp9ASXLNmTQDvkuLBgwcjJiYGISEhcHJyQsWKFZVuM3trb3alSpXCgwcP8OrVK2lZcHAwAKBq1apK6+jp6cHDwwORkZEIDg5Gu3btcj2ewMBAAO+SY3ni9j55C3FUVFSu25KztLREp06dsHv3buzcuRPffPONtE7eety3b1+FOuPHj8epU6fw5ZdfYtGiRWjbti28vLzQvHlzqd9xfmVvIVZm/vz5mDZtmsouEgBU9ovO7TkDoPCcPXz4EFlZWTAxMUG5cuXUjvXly5eIi4sDoPr5rlatGoB/rwt14yxRogTCw8OVri9ZsmSOYyAiLZtpXdQRqGdmYlFHQP+PLcVULL3fGuzn5weZTIamTZuifPnycHZ2VliXvY4y5ubmSpfLf9IWQkjL5ImKPPlSxsHBAQDU6j6RmPjuDe/hw4e4cOGC0kdkZCSAdzeuqUtZF4pHjx7B398fNjY2aN++vUL5jh074vDhw2jcuDGCg4OxbNky9OzZE46OjujVq5faCXl2qs4r8K71fsqUKZDJZJg/fz7u3r2LV69eISsrC0IITJ06FQCQnp6er23n9pyVKFFCZTzy5yy77Empquc7r+da3iL8PnnrsbL1qrpyEBFR0WFSTMVS9n7F8m4F1atXl1o0vb29ERoaisjISLWS4vyQdznIbTQI+agByrpDqNre2rVrIYTI9fH+APG5ad++PWxsbODv7y8NOSZPkHv06JGj3zAAdOjQARcuXEBcXBz279+PMWPGwMbGBrt370bnzp1VJqgfYuvWrQCA7777DpMmTULVqlVhbm4uJYQRERFa25f8HMfHx6sso+z5zN69RNXznZ/nmoiIPl5MiqlY8vT0hKmpKa5du4arV6/i+fPnCkmv/P++vr559ifOr0qVKgEA7t27p3R9VlaWNPKBvGxu5D/L37lzRyvxyRkbG0v9ZOXJsLJRJ5Sxs7ND165dsXz5cty5cwfW1ta4ceOGwti6mpLfCNm4cWOl61X1Jf4QFSpUgJ6eHlJTU1WOU61svGEbGxupK4Oq51s+nrI6zzUREX28mBRTsWRkZIQGDRogPT0d8+fPB6DYEixPgPfs2YOgoCCULFlSZZ/Q/GrTpg1kMhnOnz+PGzdu5Fi/d+9eREZGwtzcHE2aNMlze926dQMAbNmyRWEiDW3I3oVCPmyck5OTwgQaeXFwcJD64b5/054mTE1NAUDpWLwnTpzQalJsYWGBRo0aAYDS8ZafPn0qjSjyvrZt2wKANKpIdkIIabm8HBER/TcxKaZiS54EHzx4EIBiS7CHhwccHBxw8OBBCCG01koMvGt1lLfADho0SGGEgICAAGk0gtGjR6v1k3q9evXQq1cvJCQkoHXr1jkS7czMTPj6+qJ///5IS0vLV6zNmzeHk5MTgoKCpPGWe/fuLfW7za5Pnz44fPgw3r59q7B8z549CAwMhEwmUzraxofy8vICAPz88894/PixtNzf3x+ff/65wtBt2jBx4kQAwLJly7B//35peXx8PPr376/yZr9vv/0WBgYGOHDgABYtWiSVe/v2LcaNGye1pKuaMISIiP4bmBRTsSVPdIUQqFKlSo4boby9vaWbrbTVn1hu9erVqFGjBu7cuYNKlSqhdu3aqFatGj755BPExMSgVatW+er/u27dOikhrlu3LsqWLYuGDRuiZs2asLS0RPPmzbFt2zaFm8fUoaenh969ewP4d8zb90edkDt27Bg6deoEKysr1KhRA56enihdujR69uyJzMxMTJs2LceYwpoYPnw4ypcvj0ePHsHDwwM1a9aEh4cHPD09YW1tjZEjR2ptXwDw6aefYvjw4UhNTUW3bt1Qvnx51KtXDy4uLrh+/Tq+++47pfVq166N5cuXQyaTYeLEiShdujQ8PT3h4OCAFStWwNjYGFu3boWjo6NW4yUiouKFSTEVW40aNZJuFlOW9CrrTqEtJUuWxKVLlzB79mxUqVIFwcHBCAsLQ/369bFixQocOXIkXy2dFhYWOHbsGLZu3Yq2bdvi9evXCAgIQHx8PGrWrIkffvgBV69e/aDW0+z9h93d3eHp6am03MaNGzF8+HBUrFgR0dHRuH37NszMzNCtWzf4+flJE0poi5WVFc6fP49BgwbBysoKDx48wNu3bzFhwgRcunSpQG5cW7NmDf73v/+hZs2aiI6ORnh4OLp06QJ/f3+Vw/UB78Y2PnfuHD799FNkZWXh5s2bMDMzw4ABAxAQEICOHTtqPVYiIipeZCK/TVOUQ1JSEqytrZGYmAgrK6tcy6ampuLx48coV66c1n8+JiLdw/cUKgpukw4XdQh5emKS/1k6iwTHKVaQn5xK29hSTEREREQ6j0kxEREREek8JsVEREREpPOYFBMRERGRzmNSTEREREQ6j0kxEREREek8JsVEREREpPOYFBMRERGRzmNSTEREREQ6j0kxEREREek8JsVEREREpPOYFBMRERGRzmNSTEREREQ6j0kxEREREek8JsVEREREpPOYFNN/mpubG2QyGZ48eVLg+3ry5AlkMhnc3NwKfF/038RriIio6DApJiIiIiKdx6SYiIiIiHSeQVEHQCrMtC7qCPJnZmJRR0BERET0wdhSTEREREQ6j0kxFStZWVmws7ODvr4+Xrx4obDu8uXLkMlkkMlkWLduXY66ed1Ud/nyZbRv3x62trYwNzdH06ZNcfr0aZWxpKSkYO7cuahZsybMzc1hZWWFBg0aYNWqVcjIyMj3sWVkZGDNmjXw8vKCjY0NTExM4OHhgWnTpiEpKUnt7axcuRIymQydOnVSWeb58+cwMjKCoaEhEhISpOV37txB//794eLiAiMjI9jY2KBixYro168fjh07pnYMGzZsgEwmw5AhQ/DmzRtMnjwZ5cuXh6mpKSpXrowVK1ZIZRMSEjBu3DiULVsWJiYmqFatGjZs2KB0u7GxsVixYgXatm0LNzc3mJiYwNbWFj4+Pti8eXOO8kIItGnTBjKZDMOGDVO6zUGDBkEmk6Ft27YQQqh9jABw9+5dyGQy2NnZ4e3btyrLffLJJ5DJZDh48KC0LDQ0FAsWLECzZs3g4uICY2NjlCxZEu3atcPhw4fzFQcRERU8JsVUrOjp6aFJkybIysrC+fPnFdadPXtW+r+fn5/CuvDwcISFhcHV1VXpnfuHDh2Ct7c3/P394e7uDkNDQ5w/fx5t27aFr69vjvJxcXFo1KgRpk+fjrt376JChQpwdnbG1atXMXr0aHTo0AGpqalqH1dSUhJatmyJESNG4NKlS1Iy+vjxY8ybNw8NGzbEs2fP1NpW7969YWBggBMnTuD58+dKy+zZswfp6elo06YN7O3tAQBXr16Fp6cntm3bhuTkZFStWhUuLi6Ii4vD9u3bsWbNGrWPR+7t27do2bIlfvnlF1haWsLR0RHBwcEYO3YsZs+ejWfPnqFRo0ZYs2YN7O3tYW9vj3v37mHo0KFYv359ju398ccfGDt2LM6dOwcDAwPUqFEDVlZWOHv2LAYNGoQRI0YolJfJZFi/fj1sbW3x559/Yv/+/Qrr//rrL2zevBl2dnZYv349ZDJZvo6vWrVqqFGjBl68eIHjx48rLRMcHIyAgADY2tqiXbt20vKffvoJkyZNwvXr12FmZoaaNWvC0NAQx48fR6dOnbBgwYJ8xUJERAWLSTEVOz4+PgByJr5+fn7Q09ODo6Oj0nXZ675vwoQJmD17Np4+fYpr164hLi4O/fv3R0ZGBiZNmpSj/IgRIxAYGIhq1aohODgYt27dwr179+Dv7w8HBwecPHkSM2bMUPuYvvrqK5w9exYtW7ZESEgInjx5gsDAQMTGxqJ79+4ICgrCqFGj1NpWyZIl0bJlS6Snp2PPnj1Ky2zfvh0A0K9fP2nZnDlz8ObNG0yZMgXPnj3DzZs3ERgYiJcvX8Lf3x+9evVS+3jk9uzZg1evXiEkJAS3bt3C48ePpX3//PPPGDhwIJydnREREYGAgABERUVh3rx5AICpU6ciMzNTYXvNmjXD6dOnkZycjIcPH8Lf3x9hYWG4desWqlSpgjVr1uR47suUKYPVq1cDAL788kvExsYCAGJiYvDVV18BAFavXo3SpUvn+/iAf8+h/LjeJ1/eo0cPGBkZSct79OiBy5cvIykpCQ8ePIC/vz+io6Nx9uxZODk5YerUqXj06NEHxURERNrHpJiKHW9vbwCKSXFWVhYuXLiAWrVqoW3btggPD1foJpFXUtyuXTtMmjQJ+vr6AABDQ0MsXboUxsbGuHLlikJXjZCQEOzduxcAsHnzZri7u0vr6tWrJ3UNWLVqFZKTk/M8ntu3b2PHjh0oW7Ys9u3bh/Lly0vrbG1tsXnzZri4uOCvv/5CWFhYntsDck/U5ImXmZkZunbtqnBcAPDDDz8oJG/y48qeQKsrIyMDGzduRLly5aRlffr0QaNGjfDmzRucO3cOW7ZsQalSpaT1P/zwA8qUKYOYmBjcvn1bYXteXl5o3ry59DzJ1axZUzrvW7duzRFH79690bdvX8THx2PYsGEQQmDYsGFISEjAgAEDPijhl+vbt6/UNeL169c51u/YsUMql1379u3RoEGDHK3TTZs2xZw5c5CZmYmdO3d+cFxERKRdTIqp2Klbty4sLCxw48YNKem8efMmEhMT4ePjo7QlWd61Qp5Qv++LL77IsaxEiRJSV4vQ0FBp+cmTJyGEgJeXF+rUqZOjXo8ePeDs7IyUlBRcuHAhz+PZt28fAKBXr16wtLTMsd7MzAytWrWCEALnzp3Lc3sA0K1bN5iamuLs2bOIjo5WWLdz505kZWWhc+fOsLCwkJa7uLgAAHbt2qXWPtRRp04dpeeodu3aAN4lhu+30Orr66NmzZoAFM+7XHJyMtauXYvBgwejTZs2aNq0Kby8vKQW/Vu3bimN5bfffoOzszOOHDmCtm3b4ujRo3B1dcXKlSs1OUSULVsWjRs3RkpKikKfYQC4ceMG7t+/DycnJzRr1ixH3bi4OCxbtgz9+vVDq1at4OXlBS8vLyxdujTXYyEiosLHIdmo2DEwMEDjxo1x4sQJXLhwAe3atVNoCZYnVH5+fhg8eDBiYmIQEhICJycnVKxYUek2s7f2ZleqVCk8ePAAr169kpYFBwcDAKpWraq0jp6eHjw8PBAZGYng4GCFfqTKBAYGAniXHF+8eFFpGXkLcVRUVK7bkrO0tESnTp2we/du7Ny5E9988420Tt56/H7L5fjx43Hq1Cl8+eWXWLRoEdq2bSu1zMr7HeeXqvNasmRJtdZnP+/AuySzU6dOORL97FT1o7axscGGDRvQunVrnDx5EjKZDBs2bIC1tebDG/br1w8XLlzA9u3b0adPH2m5/Fz37t0benqKbQwnTpxAr169kJioerhCVcdCRESFjy3FVCy93xrs5+cHmUyGpk2bonz58nB2dlZYl72OMubm5kqXyxOZ7KMSyBO17D/5v8/BwQEA1Oo+IU+KHj58iAsXLih9REZGAgDevHmT5/bklHWhePToEfz9/WFjY4P27dsrlO/YsSMOHz6Mxo0bIzg4GMuWLUPPnj3h6OiIXr16qZ2QZ2dmZqZ0ubzLQF7rs5/3zMxM9OrVC9HR0ejQoQP8/PwQHx+PjIwMCCGk7h/p6ekq46lTpw5sbW0BABUrVlT5y0F+9erVCwYGBjh27JjU1UYIIXV/eL/rycuXL9GnTx8kJiZi0KBBuHz5Ml68eIHMzEwIIXDy5Mk8j4WIiAoXk2IqlrL3K5Z3K6hevbrUount7Y3Q0FBERkaqlRTnh7zLQW6jQTx9+hQAlHaHULW9tWvXQgiR62PmzJlqx9m+fXvY2NjA398fDx8+BKD6pi+5Dh064MKFC4iLi8P+/fsxZswY2NjYYPfu3ejcuXORJmlXr17Fw4cPUbZsWezduxfe3t6wt7eX+hdHRETkuY2vv/4az58/h56eHoKDg/Hzzz9rJbYSJUqgVatWePv2rdTf/MKFCwgPD0eFChVQv359hfJHjx7Fixcv0KhRI2zYsAENGjSAjY2N9CVMnWMhIqLCxaSYiiVPT0+Ympri2rVruHr1Kp4/f66Q9Mr/7+vrm2d/4vyqVKkSAODevXtK12dlZeH+/fsKZXMj74Zx584drcQnZ2xsjO7duwP4NxlWNuqEMnZ2dujatSuWL1+OO3fuwNraGjdu3MC1a9e0GmN+yG+c/OSTT2BsbJxjfV79bzdv3ozdu3fD0dERx44dg6GhIWbNmoWAgACtxCc/p9u2bVP49/1uKsC/x9KoUSOlw8CxLzERUfHDpJiKJSMjIzRo0ADp6emYP38+AMWWYHkCvGfPHgQFBaFkyZIq+wDnl3wyiPPnz+PGjRs51u/duxeRkZEwNzdHkyZN8txet27dAABbtmxRmEhDG7J3oZAPG6fqpi9VHBwcpNEjcuvLW9BMTU0B/NsKn116erp0c5oyERERGDNmDIB3Yx23bt0aM2fORHp6OgYMGJCvMaVVkd/c6Ovri4iICGk4PGVJcW7HkpCQoHTyGSIiKlpMiqnYkifB8jv+s7cEe3h4wMHBAQcPHoQQQmutxABQoUIFqQV20KBBCiMkBAQEYOzYsQCA0aNHq9V9ol69eujVqxcSEhLQunXrHIl2ZmYmfH190b9/f6SlpeUr1ubNm8PJyQlBQUHS6AzKbvoC3g2Vdvjw4Rwzs+3ZsweBgYGQyWRKR5IoLA0bNoSBgQEuXLiATZs2ScsTExPRv39/pQkm8K5v75AhQ5CYmIjhw4ejY8eOAN4N/da4cWOFc6MJCwsLdO7cGVlZWRg+fDji4uJQu3ZtVKlSJUfZpk2bAng30sepU6ek5TExMejRo8cHzYhIREQFi0kxFVvyRFcIgSpVquS48c3b21u6UUtb/YnlVq9ejRo1auDOnTuoVKkSateujWrVquGTTz5BTEwMWrVqla/+v+vWrZMS4rp166Js2bJo2LAhatasCUtLSzRv3hzbtm3L9zTEenp66N27NwBI0zQra7mUr+/UqROsrKxQo0YNeHp6onTp0ujZsycyMzMxbdo0hTGUC5ujoyPGjx8PABg8eDDKli2LevXqwcnJCfv378eSJUuU1luyZAlOnz6N8uXLY9GiRdJyfX19bNq0CRYWFli+fHmuU3qrS94yn9e5/uSTT/DZZ58hPT0drVu3RsWKFVGnTh24uroiICBAa32diYhIe5gUU7HVqFEj6WYxZUmvsu4U2lKyZElcunQJs2fPRpUqVRAcHIywsDDUr18fK1aswJEjR2BiYqL29iwsLHDs2DFs3boVbdu2xevXrxEQEID4+HjUrFkTP/zwA65evZqvbcpl7z/s7u4OT09PpeU2btyI4cOHo2LFioiOjsbt27dhZmaGbt26wc/PD7Nnz873vrVt4cKFWLp0KTw8PBAbG4uwsDC0atUK586dUzr03d27dzF16lTo6elJCXB27u7uWLx4sdSa/PLlS43ia9++vTS6hUwmUxie7X1bt27F9OnT4ebmhrCwMMTGxuKzzz6Dv78/atWqpVEcRESkfTKR36YpyiEpKQnW1tZITEyElZVVrmVTU1Px+PFjlCtX7oMSICKi7PieQkXBbdLhog4hT09M8j9LZ5GYqXosc12Un5xK29hSTEREREQ6j0kxEREREek8TvNMRDrjxo0b0tBt6lixYkWRjshBRESFh0kxgGnTpmHevHkAgDlz5mDatGlFHBERFYTExERcuHAhX+WJiEg36HxSHBQUhF9++aWowyCiQtCsWbN8D3tHRES6Qaf7FAsh8NVXX8HQ0BAtWrQo6nCIiIiIqIjodFK8bt06nDt3Dj/++CNcXFyKOhwiIiIiKiI6mxTHxcXhhx9+QNWqVfHNN98UdThEREREVIR0tk/xN998g+fPn2Pv3r0wNDQs6nCIiIiIqAjpZEvxP//8g61bt2LAgAFKpw8mIiIiIt2icy3Fqamp+Prrr2FtbY1ff/31g7aRlpaGtLQ06e+kpCRthUdERERERUDnWornzp2Lhw8fYt68eXBwcPigbcyfPx/W1tbSgzfpEREREX3cdCoplo9JXLduXYwYMeKDtzN58mQkJiZKj4iICC1GSURERESFTae6T4wcORIZGRlYvXo19PQ+/PuAsbExjI2NtRgZERERERUlnUqKb9y4AZlMhi5duuRYJ5/OdcGCBVi5ciVcXFzg7+9f2CESERERURHQqaQYADIzM/H06VOV61+9eoVXr17BxMSkEKMiIiIioqKkU32KX758CSGE0sfgwYMBAHPmzIEQAk+ePCnaYEkr3NzcIJPJCuX5fPLkCWQyGdzc3Ap8X0Vtw4YNkMlkGDJkSFGHQkREpBU6lRQTERERESmjc90nPhY1NtYo6hDyJXBwYFGHQERERPTB2FJMRERERDqPLcX/b8OGDdiwYUNRh0FERERERYAtxVSsZGVlwc7ODvr6+njx4oXCusuXL0Mmk0Emk2HdunU56uZ1U93ly5fRvn172NrawtzcHE2bNsXp06dVxpKSkoK5c+eiZs2aMDc3h5WVFRo0aIBVq1YhIyMj38eWkZGBNWvWwMvLCzY2NjAxMYGHhwemTZuWr6nCV65cCZlMhk6dOqks8/z5cxgZGcHQ0BAJCQnS8jt37qB///5wcXGBkZERbGxsULFiRfTr1w/Hjh3L9zEpExkZiSpVqkAmk+Grr75CVlYWAGDmzJmQyWSYOXMmEhISMHLkSDg7O8PU1BS1atXCjh07pG2EhYVh6NChKF26NExNTfHJJ5/g8OHDKvcphMCOHTvQunVr2Nvbw9jYGOXLl8fYsWMRGxurtM7JkycxevRo1KpVC3Z2djAxMYG7uztGjBiB8PBwpXWGDBkCmUyGDRs2IDo6Gp9//jmcnJxgYmKCatWqYdWqVUrrZWRkYNmyZfD09ISlpSWMjY1RunRpNG7cGDNmzMDLly/VPLtERFRQmBRTsaKnp4cmTZogKysL58+fV1h39uxZ6f9+fn4K68LDwxEWFgZXV1eloz8cOnQI3t7e8Pf3h7u7OwwNDXH+/Hm0bdsWvr6+OcrHxcWhUaNGmD59Ou7evYsKFSrA2dkZV69exejRo9GhQwekpqaqfVxJSUlo2bIlRowYgUuXLknJ6OPHjzFv3jw0bNgQz549U2tbvXv3hoGBAU6cOIHnz58rLbNnzx6kp6ejTZs2sLe3BwBcvXoVnp6e2LZtG5KTk1G1alW4uLggLi4O27dvx5o1a9Q+HlUePXqEpk2b4v79+5g4cSL+97//5Zgo58WLF2jYsCHWrVsHBwcH2Nvb4/bt2+jbty82bdqEBw8ewNPTE7t27ULp0qVhYWGBgIAAdO3aFadOncqxz/T0dPTu3Rt9+/bFqVOnYGJigipVquDp06dYsWIF6tati+Dg4Bz12rdvj99++w2xsbEoW7YsKlasiKdPn2LNmjWoW7cu7t27p/I4w8LC8Mknn2D79u0oXbo07O3tce/ePYwePRrz5s3LUb5Pnz4YP348/P394eDggFq1asHAwABXr17F7NmzOdoNEVExwKSYih0fHx8AORNfPz8/6OnpwdHRUem67HXfN2HCBMyePRtPnz7FtWvXEBcXh/79+yMjIwOTJk3KUX7EiBEIDAxEtWrVEBwcjFu3buHevXtSUnPy5EnMmDFD7WP66quvcPbsWbRs2RIhISF48uQJAgMDERsbi+7duyMoKAijRo1Sa1slS5ZEy5YtkZ6ejj179igts337dgBAv379pGVz5szBmzdvMGXKFDx79gw3b95EYGAgXr58CX9/f/Tq1Uvt41Hmzp078PLywpMnTzB79mz88ssvSsutXr0aLi4uiIiIwPXr1xEZGYmff/4ZADBp0iQMGjQILVq0QGxsLK5du4anT5/iq6++QmZmJqZOnZpjez/++CN2796NOnXq4MaNG4iKisLNmzcRHx+PkSNHIiYmBv37989R77fffkNkZCSePn2KGzduIDAwEHFxcZg3bx4SEhJyfT7mzZsHLy8vxMTE4Pr164iKisJvv/0GAJg7d65Cy+/169fx119/wcXFBffu3cPDhw9x9epVhIeH4/nz51i7dq30xYWIiIoOk2Iqdry9vQEoJsVZWVm4cOECatWqhbZt2yI8PFyhdS2vpLhdu3aYNGkS9PX1AQCGhoZYunQpjI2NceXKFYWuGiEhIdi7dy8AYPPmzXB3d5fW1atXDytWrAAArFq1CsnJyXkez+3bt7Fjxw6ULVsW+/btQ/ny5aV1tra22Lx5M1xcXPDXX38hLCwsz+0B/ya78uQ3u+joaJw9exZmZmbo2rWrwnEBwA8//AAjIyOFOvXq1VNIoPPL398fPj4+ePr0KZYtW4bp06erLGtgYIAtW7agVKlS0rKJEyfC2dkZMTExiIiIwLp162BpaQng3a8HP//8M0xMTHD16lWF1vG4uDgsWbIEVlZWOHjwIGrXri2tMzU1xYoVK1C/fn1cu3YN586dU4hj+PDhKF26tMIyU1NTTJkyBV5eXvD19UVUVJTSY7C3t8eGDRtgY2MjLRsxYgTq1q2L1NRUnDlzRlouP++fffYZqlSporAdKysrfPHFF3BxcVF5voiIqHAwKaZip27durCwsMCNGzekpPPmzZtITEyEj4+P0pZkedcKeUL9vi+++CLHshIlSkhdLUJDQ6XlJ0+ehBACXl5eqFOnTo56PXr0gLOzM1JSUnDhwoU8j2ffvn0AgF69ekmJXnZmZmZo1aoVhBA5EjdVunXrBlNTU5w9exbR0dEK63bu3ImsrCx07twZFhYW0nJ54rVr1y619qEuPz8/tGzZEomJifjzzz8xduzYXMu3b98+RzKqr6+PGjXeDUPYt29fmJmZKay3sbFBuXLlAACPHz+Wlh85cgRpaWlo27YtnJ2dc+xLT09P6nv9/q8LAHDt2jVMmjQJXbp0gY+PD7y8vODl5SV1t7h9+7bSY+jbty/Mzc1zLK9fvz4AxetJft7/+ecfld1diIio6HH0CSp2DAwM0LhxY5w4cQIXLlxAu3btFFqCa9asCeBdkjN48GDExMQgJCQETk5OqFixotJtZm/tza5UqVJ48OABXr16JS2TJ0RVq1ZVWkdPTw8eHh6IjIxEcHAw2rVrl+vxBAa+G8N53759uHjxotIy8hZiVS2T77O0tESnTp2we/du7Ny5E9988420Tt563LdvX4U648ePx6lTp/Dll19i0aJFaNu2Lby8vNC8efMP/vn+6tWrUhK+c+dO9OjRI886qp6LkiVL5rk+KChI4bmSn9vLly/Dy8tLaT35tO7Zz60QAqNHj5a6PKiiKonN7XoCoBBjo0aN0KBBA1y5cgUuLi5o3bo1vL294ePjg7p160Imk+UaAxERFQ62FFOx9H5rsJ+fH2QyGZo2bYry5cvD2dlZYV32Osooa9UDIN0EJoSQlskTmuw/77/PwcEBANTqPpGYmAgAePjwIS5cuKD0ERkZCQB48+ZNntuTU9aF4tGjR/D394eNjQ3at2+vUL5jx444fPgwGjdujODgYCxbtgw9e/aEo6MjevXqpXZCnl1UVBRSU1NhYWGBypUrq1Xn/VZgOXlymNf67M+V/NxGRESoPLcPHz4EoHhuN2/ejN9++w3m5ub47bffEBISgtevX0vTvsv7IKenpyuNJT/Xk56eHo4ePYpx48bB1NQUBw4cwLfffot69eqhXLlyHAqSiKiYYFJMxVL2fsXybgXVq1eXWjS9vb0RGhqKyMhItZLi/JB3OchtNAh566Oy7hCqtrd27Vop6VL1mDlzptpxtm/fHjY2NvD395cSP3mC3KNHjxz9hgGgQ4cOuHDhAuLi4rB//36MGTMGNjY22L17Nzp37qwyCVSlW7du+Oabb/D8+XO0atUKDx48yFd9TcnP7dSpU/M8t9mTz61btwIAFi1ahBEjRqBChQowNTWV1kdERGg1TltbWyxduhRxcXG4ceMGli1bhubNm0tDz6m6YZKIiAoPk2Iqljw9PWFqaopr165JN1dlT3rl//f19c2zP3F+VapUCQBUDsmVlZWF+/fvK5TNjbwbxp07d7QSn5yxsTG6d+8O4N9kWNmoE8rY2dmha9euWL58Oe7cuQNra2vcuHED165dy3ccixcvxqhRo/D06VO0aNFCStALw4eeW/lNmo0bN86xLj09HUFBQRrHpoxMJkPt2rUxduxYnD59Whr5ZO3atQWyPyIiUh+TYiqWjIyM0KBBA6Snp2P+/PkAFFuC5Qnwnj17EBQUhJIlS6rsA5xfbdq0gUwmw/nz53Hjxo0c6/fu3YvIyEiYm5ujSZMmeW6vW7duAIAtW7YoTKShDdm7UMiHjXNyckKzZs3U3oaDg4N0E9v7N+2pa8WKFfjyyy8RHR2Nli1bqj2KhqY6duwIIyMjHDlyRBrlQR3yVmF5i39269evR1xcnNZizE3Dhg0BfPh5JyIi7WFSTMWWPAk+ePAgAMWWYA8PDzg4OODgwYMQQmitlRgAKlSoILXADho0SGEkgYCAAGl0hdGjR6vVfaJevXro1asXEhIS0Lp16xyJdmZmJnx9fdG/f3+kpaXlK9bmzZvDyckJQUFBUqtj7969c0yYAbybQOLw4cN4+/atwvI9e/YgMDAQMplM6Wgb6pDJZFizZg0GDRqE8PBwtGjRQuonXZBKly6N8ePHIz09XelELEIIXL16FSNGjFB4HuU35U2bNk0hAT527Bi+++47mJiYaC3GrVu3Ys6cOTkm6EhISMDy5csBvBtxhYiIihZHn6BiS57oCiFQpUqVHDe+eXt7Y/fu3QC0159YbvXq1QgODkZgYCAqVaqE6tWrIz09XepS0apVq3z1/123bh1evHiBkydPom7dunB1dYWTkxNev36Nhw8fSjeBKZu+Ojd6enro3bs3li5dKk3T/P6oE3LHjh3Dzp07YWxsjIoVK8LU1BSRkZGIiYkBAEyfPl1hDOX80tPTw59//om3b99ix44daNGiBc6ePQtHR8cP3qY65s2bh+joaGzZsgXNmzeHo6MjXF1dkZaWhtDQUOlmyHHjxkl1vv/+e2zfvh1XrlxB2bJlUblyZbx8+RJPnjxB8+bNUbp0aanfsabi4uLw448/4scff0SZMmVQunRpvHnzBsHBwXj79i3KlCmDOXPmaGVfRET04dhSTMVWo0aNpJvFlCW9yrpTaEvJkiVx6dIlzJ49G1WqVEFwcDDCwsJQv359rFixAkeOHMlXa6KFhQWOHTuGrVu3om3btnj9+jUCAgIQHx+PmjVr4ocffsDVq1c/qIUye/9hd3d3eHp6Ki23ceNGDB8+HBUrVkR0dDRu374NMzMzdOvWDX5+fpg9e3a+9/0+fX19bN68GT169EBISAhatmxZ4F0RDAwMsHnzZhw+fBiffvopAODGjRuIiYlBpUqVMHr0aPj6+ir0/3Z1dcWlS5fQvXt3GBkZ4f79+zAxMcGsWbNw7NgxGBhor72gR48eWLBgAVq3bg19fX0EBgYiJiYG1atXx9y5c3Hnzh24urpqbX9ERPRhZCL72EH0QZKSkmBtbY3ExERYWVnlWjY1NRWPHz9GuXLltPoTLRHpJr6nUFFwm3S4qEPI0xOTD5+ls1DNTCzqCIqV/ORU2saWYiIiIiLSeUyKiYiIiEjnMSkmIiIiIp3HpJiIiIiIdB6TYiIiIiLSeUyKiYiIiEjnMSkmIiIiIp3HpJiIiIiIdB6T4iLCOVOISBv4XkJEpB1MiguZfPrYtLS0Io6EiP4L0tPTAbybYpuIiD4ck+JCZmBgAHNzczx//hyZmZlFHQ4RfcSEEEhMTISxsTEMDQ2LOhwioo+aQVEHoItKlCiBiIgIPH78GNbW1jA1NYW+vj5kMllRh0ZEHwEhBNLT05GYmIhXr16hTJkyRR0SEdFHj0lxETAzM0O5cuXw7NkzvHjxAvHx8UUdEhF9hIyNjVGmTBlYWVkVdShERB89JsVFxMjICM7OzlKLT1ZWVlGHREQfEX19fXaZICLSIibFRUwmk8HIyKiowyAiIiLSabzRjoiIiIh0HpNiIiIiItJ5TIqJiIiISOcxKSYiIiIincekmIiIiIh0HpNiIiIiItJ5TIqJiIiISOcxKSYiIiIincekmIiIiIh0HpNiIiIiItJ5TIqJiIiISOcxKSYiIiIincekmIiIiIh0HpNiIiIiItJ5TIqJiIiISOcxKSYiIiIincekmIiIiIh0HpNiIiIiItJ5TIqJiIiISOcxKSYiIiIincekmIiIiIh0HpNiIiIiItJ5TIqJiIiISOcxKSYiIiIincekmIiIiIh0HpNiIiIiItJ5TIqJiIiISOcxKSYiIiIinadzSbEQAufPn8d3332Hhg0bwsbGBkZGRihdujR69OiBM2fOFHWIRERERFTIDIo6gMJ2+vRptGrVCgCgp6eHChUqwNzcHCEhIdi7dy/27t2LadOmYc6cOUUcKREREREVFp1sKa5QoQJ+++03xMfH48GDBwgICEBCQgImT54MAJg7dy4OHTpUxJESERERUWHRuaTY09MTQUFBGDFiBGxtbaXlRkZG+Omnn9C+fXsAwNq1a4sqRCIiIiIqZDqXFFtZWcHAQHWvkdatWwMAgoODCyskIiIiIipiOpcU5yU1NRUAYGpqWsSREBEREVFhYVKcjRACu3fvBgA0adKkiKMhIiIiosKic6NP5Gbt2rW4ceMGjIyMMH78eJXl0tLSkJaWJv2dlJRUCNERERERUUFhS/H/CwgIwLhx4wC8G33C3d1dZdn58+fD2tpaeri4uBRWmERERERUAJgUA3j8+DE6deqE1NRU9OvXDxMnTsy1/OTJk5GYmCg9IiIiCilSIiIiIioIOt99IjY2Fq1bt0ZMTAw6duyIDRs2QCaT5VrH2NgYxsbGhRQhERERERU0nW4pfv78OVq3bo1Hjx7Bx8cHu3fvhqGhYVGHRURERESFTGeT4levXqFDhw64c+cO6tevj7///pvDsBERERHpKJ1MitPS0tC1a1dcuXIF1apVw7Fjx2BpaVnUYRERERFREdG5pDgzMxN9+vTB6dOn4e7ujpMnT8LOzq6owyIiIiKiIqRzN9rt2rUL+/fvBwDo6emhZ8+eSss5OTlJE3kQERER0X+bziXF2SfdCAkJQUhIiNJyZcuWLayQiIiIiKiI6Vz3iSFDhkAIkefjyZMnRR0qERERERUSnUuKiYiIiIjex6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHSeRknx5cuXUbduXYwaNSrPsl988QXq1q2La9euabJLIiIiIiKt0ygp3rZtG27duoWmTZvmWbZhw4a4efMmtm3bpskuiYiIiIi0TqOk2M/PDwDg4+OTZ9mOHTsCAM6cOaPJLomIiIiItE6jpDgyMhLGxsZwcnLKs6yTkxOMjY0RFRWlyS6JiIiIiLROo6T4zZs3MDIyUru8sbExkpOTNdklEREREZHWaZQUlypVCsnJyYiOjs6zbFRUFJKSklCiRAlNdklEREREpHUaJcUNGzYEAKxatSrPsvIyDRo00GSXRERERERap1FSPGzYMAghsHDhQvz+++8qy/3vf//DwoULIZPJMGzYME12SURERESkdQaaVG7dujU+++wz7NmzByNGjMDKlSvRuXNnlC1bFjKZDE+ePMHff/+Nu3fvQgiBHj16oH379tqKnYiIiIhIKzRKigFg48aNkMlk2L17N+7cuYO7d+8qrBdCAAD69OmDdevWabo7IiIiIiKt03iaZ1NTU+zcuROnTp1Cv379ULZsWRgbG8PExARubm7o378/Tp8+jW3btsHU1FQbMRMRERERaZXGLcVyLVq0QIsWLbS1OSIiIiKiQqNxSzERERER0ceOSTERERER6Ty1u0+cPXsWAGBmZoZ69eopLMsvb2/vD6pHRERERFQQ1E6KmzVrBplMBg8PD2mECfmy/JDJZMjIyMhflEREREREBShfN9oJIZCVlZVjWX63QURERERUnKidFL+fDKtaRkRERET0seGNdkRERESk85gUExEREZHO0ygp1tPTQ5kyZdQuX65cORgYaG2+ECIiIiIirdC4pZg32hERERHRx65Qu0+8ffsWenrssUFERERExUuhZagvX77Es2fPYGNjU1i7JCIiIiJSS746+N6+fRs3b95UWPbmzRts2rRJZR0hBF6+fIk9e/YgKysLdevW/aBAiYiIiIgKSr6S4n379mH27NkKy5KSkjB06NA86wohIJPJMGHChPxFSERERERUwPKVFNvY2MDV1VX6OywsDHp6enB2dlZZR09PD1ZWVqhevTqGDx+Opk2bfni0WnbkyBEsXrwYAQEBSEtLQ+XKlTF06FCMGjWKfZ+JiIiIdIhMaDAchJ6eHhwdHREdHa3NmArFzz//jMmTJwMAypcvDwsLC9y5cwdZWVno0qUL9u3bp3ZinJSUBGtrayQmJsLKyqogwyYiIipybpMOF3UIeXpi0q+oQ1DPzMSijqBYKcqcSqPm0BkzZuDbb7/VViyF5tKlS5gyZQr09PSwbds2PHr0CLdu3UJAQAAcHBxw8OBBLF68uKjDJCIiIqJCopNJ8dy5cyGEwBdffIG+fftKy2vVqiUlwz///DPS09OLKkQiIiIiKkQF2nH20KFDGDduHCZMmIBTp04V5K7UlpSUJMUybNiwHOt79uwJKysrJCQk4MyZM4UdHhEREREVAY2S4r1796J8+fL4+uuvc6ybMGECunbtipUrV2LZsmVo27YtvvvuO012pxU3btzA27dvYWJionR4OENDQ9SvXx8AcOXKlcIOj4iIiIiKgEZJ8cGDBxEWFpZjRImAgAAsXboUQgi4uLjA3d0dQggsXrwYvr6+muxSYyEhIQAAV1dXGBgoH3yjfPnyCmWJiIiI6L9No6TY398fANCyZUuF5X/++ScAoFu3bggNDUVwcDBGjRoFIQTWrl2ryS419uLFCwCAra2tyjLydfKy70tLS0NSUpLCg4iIiIg+Xvkap/h9cXFxMDAwgKOjo8LyEydOQCaT4YcffpCGNZsyZQpWrVqFS5cuabJLjaWmpgIAjIyMVJYxNjYG8G62PmXmz5+PWbNmaT84NX0MQ+EAH89wODXKueZdqIgFDg4s6hD+U/ga0p6P4fUD8DWkbU9+7ljUIajh4xjqrMbGGkUdglp04TWkUUvxy5cvYWFhobAsISEBDx8+hI2NDTw9PaXlTk5OMDc3R0xMjCa71JiJiQkA4O3btyrLpKWlAQBMTU2Vrp88eTISExOlR0REhPYDJSIiIqJCo1FLsYWFBRITE5Geng5DQ0MAwPnz5wEAjRo1ylFeXqYo5dU1Ivs6VV0sjI2NpdZkIiIiIvr4adRS7OHhASEEjhw5Ii3buXMnZDJZjpvvXr9+jcTExBxdLQpbxYoVAQDh4eHIyMhQWiY0NFShLBERERH9t2nUUty9e3dcvnwZX3zxBe7fv4+YmBjs3LkTenp66Nmzp0JZf39/CCFQrlw5jQLWVJ06dWBoaIjU1FQEBAQodPEAgPT0dOkGwgYNGhRFiERERERUyDRqKR49ejRq1qyJhIQETJkyBcuXL4cQAmPGjJGGNZPbu3cvZDIZvL29NQpYU1ZWVmjVqhUAYN26dTnW7969G0lJSbC3t0ezZs0KOToiIiIiKgoatRSbmJjg/PnzWLp0KS5dugQbGxt06tRJYepk4N1NbX5+fnB1dUWbNm00Clgbpk6dimPHjuGPP/5As2bNpHhv3bqFCRMmAAC+//77XEeoICIiIqL/Do2SYuDdzXbTpk3LtYyRkRFu3ryp6a60pkmTJpgzZw6mTZuGfv36Ydq0abCwsMCdO3eQlZWFjh074ttvvy3qMImIiIiokGjUfeJjNnXqVPz9999o0aKFNIxcjRo1sHTpUhw4cAD6+vpFHSIRERERFRKNW4o/Zp06dUKnTp2KOgwiIiIiKmJqJ8WbNm0CAFhbW6Nr164Ky/Jr0KBBH1SPiIiIiKggqJ0UDxkyBDKZDJUrV5aSYvmy/JDJZEyKiYiIiKhYUTspdnV1hUwmQ+nSpXMsIyIiIiL6mKmdFD958kStZUREREREHxudHX2CiIiIiEhOo6Q4PDwcUVFRapePjo5GeHi4JrskIiIiItI6jYZkc3Nzg5OTk9qJcZMmTRAREYGMjAxNdktEREREpFUad58QQhRoeSIiIiKiglaofYpTU1NhYKDT84UQERERUTFUaElxdHQ04uLiYG9vX1i7JCIiIiJSS76abc+ePQtfX1+FZa9evcLs2bNV1hFC4OXLlzhy5AiEEGjQoMEHBUpEREREVFDylRSfOXMGs2bNUpiwIyUlBbNmzcqzrhACJiYmmDx5cv6jJCIiIiIqQPlKit3c3ODj4yP97efnB0NDQzRq1EhlHT09PVhZWaF69eoYPHgwKlSo8OHREhEREREVgHwlxYMHD8bgwYOlv/X09GBnZ4czZ85oPTAiIiIiosKi0VAQ69evh6mpqbZiISIiIiIqEholxdlbjYmIiIiIPlaFOk4xEREREVFxpJWZNNLS0vDXX3/h/PnziIyMREpKisqZ62QyGf755x9t7JaIiIiISCs0ToovXryI3r17Izo6GkIIabg2eVKcffi27OuJiIiIiIoLjZLiiIgIdOzYEYmJiahZsybatWuHhQsXwsLCAuPHj0dsbCxOnz6N0NBQlChRAl9//TX09fW1FTsRERERkVZolBQvXrwYiYmJaN++PQ4dOgSZTCYlxdlnuVu9ejXGjh2LW7du4cCBAxoHTURERESkTRolxSdOnIBMJssxy937RowYgRcvXmD69OlYu3YtvvzyS012S0RERPSfEDg4sKhDoP+n0egT4eHh0NPTQ926dRWWv337NkfZUaNGQSaTYcOGDZrskoiIiIhI6zRKioUQsLS0hJ7ev5sxNzdHUlJSjtEnrK2tYW1tjfv372uySyIiIiIirdMoKS5TpgwSExORlpYmLXN2dkZmZiaCgoIUyr5+/RovX77E69evNdklEREREZHWaZQUV6xYEQDw5MkTaVmDBg0AAGvWrFEou3jxYggh4ObmpskuiYiIiIi0TqOkuH379hBC4O+//5aWffHFFxBCYNWqVejQoQOmTp2KTp06YcaMGZDJZOjbt6/GQRMRERH9FwkhMHPmTJQuXRqmpqZo1qwZ7t69m2e9pUuXonLlyjA1NYWLiwu++eYbpKamSuvnz5+P+vXrw9LSEqVKlcKnn36KBw8eKGxjyJAhkMlkCo+GDRtq/RiLK42S4i5dusDHxwfh4eHSMi8vL3z33XcQQuDYsWP4+eefceTIEQgh0LRpU0yaNEnjoImIiIj+ixYuXIjFixdj5cqV8Pf3h6OjI1q3bo3k5GSVdbZu3YpJkyZhxowZCAoKwrp167Bz505MnjxZKuPn54dRo0bh8uXLOHnyJDIyMtCmTRukpKQobKtdu3aIiYmRHkeOHCmwYy1uNBqSzcXFBWfOnMmxfMGCBWjdujV27NiBiIgIWFtbo127dhg0aBAMDLQyszQRERHRf4oQAkuXLsXUqVPRvXt3AMDGjRvh4OCAbdu24auvvlJa79KlS2jSpAn69esHAHBzc0Pfvn1x9epVqcyxY8cU6qxfvx6lSpXC9evX4e3tLS03NjaGo6Ojtg/to1BgGWqrVq3QqlWrgto8ERER0X/K48ePERsbizZt2kjLjI2N4ePjg4sXL6pMir28vLBlyxZcvXoVnp6eCA0NxZEjRzB48GCV+0pMTAQA2NnZKSz39fVFqVKlYGNjAx8fH8ybNw+lSpXSwtEVfxolxeHh4dDX10eZMmXUKh8dHY2MjAy4urpqslsiIiKi/5zY2FgAgIODg8JyBwcHhIWFqazXp08fxMXFwcvLC0IIZGRkYMSIESq7rAohMGHCBHh5eaF69erS8vbt26Nnz54oW7YsHj9+jOnTp6NFixa4fv06jI2NtXCExZtGfYrd3Nzg6empdvkmTZqgfPnymuySiIiI6D9h69atsLCwkB7p6ekAkGOWYCFErjMH+/r6Yt68efjtt98QEBCAvXv34tChQ5gzZ47S8qNHj8bt27exfft2heW9e/dGx44dUb16dXTu3BlHjx5FcHAwDh8+rOGRfhw07j7x/iQd2i5PRERE9F/UpUsXaShbANK8D7GxsXBycpKWP3v2LEfrcXbTp0/HwIED8cUXXwAAatSogZSUFAwfPhxTp05VmGRtzJgxOHjwIM6ePQtnZ+dc43NyckLZsmUREhLyQcf3sSnUu95SU1N5ox0RERERAEtLS1haWkp/CyHg6OiIkydPok6dOgCAt2/fws/PDwsWLFC5ndevXyskvgCgr68PIYTUGCmEwJgxY7Bv3z74+vqiXLlyecaXkJCAiIgIhQT9v6zQMtTo6GjExcXpTGdtIiIiovyQyWQYP348fvrpJ1SsWBEVK1bETz/9BDMzM2lkCQAYNGgQypQpg/nz5wMAOnfujMWLF6NOnTpo0KABHj58iOnTp6NLly7Q19cHAIwaNQrbtm3DgQMHYGlpKfVftra2hqmpKV69eoWZM2eiR48ecHJywpMnTzBlyhSUKFEC3bp1K/yTUQTylRSfPXsWvr6+CstevXqF2bNnq6wjhMDLly+lsYqz/0xARERERP/6/vvv8ebNG4wcORIvXrxAgwYNcOLECYUW5fDwcIWW4WnTpkEmk2HatGmIiopCyZIl0blzZ8ybN08qs3r1agBAs2bNFPa3fv16DBkyBPr6+ggMDMSmTZvw8uVLODk5oXnz5ti5c6fCvv/LZCIfnXxnzZqFWbNmSZ298+r4nZ0QAiYmJvD19c3XzXkfg6SkJFhbWyMxMRFWVlYFvj+3SR9Hh/cnJv3yLlQM1ChX/EdDCRwcWNQh/KfwNaQ9H8PrB+BriOhjUdg5VXb5ail2c3ODj4+P9Lefnx8MDQ3RqFEjlXX09PRgZWWF6tWrY/DgwahQocKHR0tEREREVADylRQPHjxYYSBoPT092NnZKZ3VjoiIiIjoY6HRjXbr16+HqamptmIhIiIiIioSGiXFuU0fSERERET0sSiwIdkyMzOxevVqnDx5Evr6+ujYsSOGDRtWULsjIiIiIvpgGk3zvH79eujr66NXr1451vXt2xfjxo3DoUOHsH//fgwfPhx9+vTRZHdERERERAVCo6T4+PHjAID+/fsrLPf19cWePXsghEDjxo3RqlUrAMDu3btx4MABTXZJRERERKR1GiXFN2/eBAA0adJEYfmmTZsAAF9++SXOnTuHEydOYNasWRBCYMOGDZrskoiIiIhI6zRKiuPj42FsbIwSJUooLD916hRkMhnGjh0rLRs1ahQA4Nq1a5rskoiIiIhI6zRKipOSkmBiYqKwLCYmBpGRkShVqhSqVasmLbe1tYWVlRXi4uI02SURERERkdZplBTLp+F7/fq1tMzPzw8A0LhxY6V13k+iiYiIiIiKmkZJcfXq1QEAu3btkpZt2rQJMplMYTpoAEhMTERSUhIcHR012SURERERkdZpNE5x37594efnh1GjRuHKlSuIjY3FsWPHYGxsnGOYtkuXLgEAKlasqMkuiYiIiIi0TqOkeNiwYdizZw9OnTqF33//HUIIyGQyzJ07N0eL8O7du5W2IBMRERERFTWNkmJ9fX0cO3YM27dvx8WLF2FjY4MOHTrkGKLt7du3iImJgbe3N9q3b69RwERERERE2qbxNM96enro379/jgk8sjMyMsKRI0c03RURERERUYHQ6EY7IiIiIqL/ggJJir/55hsMGzasIDZNRERERKR1BZIU79ixg9M5ExEREdFHg90niIiIiEjn6VRSnJycjC1btmDAgAGoVKkSTE1NYWZmhmrVquG7775DTExMUYdIREREREVA49EnPiYjR47Eli1bAACWlpbw8PBASkoKHjx4gHv37mH9+vU4evQo6tevX8SREhEREVFh0qmWYgD49NNPcerUKTx//hw3btxAcHAwHjx4AE9PTyQkJKBHjx548+ZNUYdJRERERIWoQFqKhRAFsVmNLVu2DHZ2djmWu7u7Y8+ePahQoQIiIiJw7NgxdOvWrQgiJCIiIqKioFFLcXh4OKKionIsj42NRWZmZo7l0dHRCA8P12SXGlGWEMu5uLjAw8MDABAcHFxYIRERERFRMaBRS7GbmxucnJyUJsbKNGnSBBEREcjIyNBktwUmNTUVAGBqalrEkRARERFRYdK4T3F+u0oU164Vt27dklqImzRpUsTREBEREVFhKtTRJ1JTU2FgUPwGvMjMzMSYMWMAAC1atMAnn3ySa/m0tDSkpaVJfyclJRVofERERERUsApt9Ino6GjExcXB3t6+sHaptilTpuDcuXOwtLTE77//nmf5+fPnw9raWnq4uLgUQpREREREVFDy1Wx79uxZ+Pr6Kix79eoVZs+erbKOEAIvX77EkSNHIIRAgwYNPijQ77//HgcPHsx3vfXr16NRo0Yq169ZswYLFy6EgYEBtm/fDnd39zy3OXnyZEyYMEH6OykpiYkxERER0UcsX0nxmTNnMGvWLMhkMmlZSkoKZs2alWddIQRMTEwwefLk/EeJdy3NDx48yHe9lJQUlet27tyJUaNGQSaTYcOGDejYsaNa2zQ2NoaxsXG+YyEiIiKi4ilfSbGbmxt8fHykv/38/GBoaJhrS6yenh6srKxQvXp1DB48GBUqVPigQLds2SLNRqcNR44cwcCBA5GVlYVVq1ahf//+Wts2fVwCBwcWdQhERERUxPKVFA8ePBiDBw+W/tbT04OdnR3OnDmj9cAK0tmzZ/HZZ58hPT0d8+fPx8iRI4s6JCIiIiIqQhoNBbF+/fqPbkzf69evo3Pnznjz5g0mT56MSZMmFXVIRERERFTENEqKs7cafwwePHiAdu3aISkpCSNHjsRPP/1U1CERERERUTFQ/AYNLkBjx45FfHw8ZDIZbt68CS8vL6XlPv/8c3z++eeFHB0RERERFZVCG6e4OJBPuCGEwMWLF3HhwgWlj/Dw8CKOlIpKeno6fvjhB9SoUQPm5uYoXbo0Bg0ahOjo6Dzr/vXXX6hatSqMjY1RtWpV7Nu3T2F9cnIyxo8fj7Jly8LU1BSNGzeGv7+/QpkhQ4ZAJpMpPBo2bKjVYyQiIqKcdCop9vX1hRAiz8fMmTOLOlQqIq9fv0ZAQACmT5+OgIAA7N27F8HBwejSpUuu9S5duoTevXtj4MCBuHXrFgYOHIhevXrhypUrUpkvvvgCJ0+exObNmxEYGIg2bdqgVatWiIqKUthWu3btEBMTIz2OHDlSIMdKRERE/5IJIURRB/GxS0pKgrW1NRITE2FlZVXg+3ObdLjA96ENT0z6FXUI6pmZmOtqf39/eHp6IiwsDK6urkrL9O7dG0lJSTh69Ki0rF27drC1tcX27dvx5s0bWFpa4sCBAwrjYdeuXRudOnXC3LlzAbxrKX758iX279+v+XGRSnwNaU+NcspfE8UNh14k+jgUdk6VnU61FBN9iMTERMhkMtjY2Kgsc+nSJbRp00ZhWdu2bXHx4kUAQEZGBjIzM2FiYqJQxtTUFOfPn1dY5uvri1KlSqFSpUr48ssv8ezZM+0cCBEREanEpJgoF6mpqZg0aRL69euX6zfW2NhYODg4KCxzcHBAbGwsAMDS0hKNGjXCnDlzEB0djczMTGzZsgVXrlxBTEyMVKd9+/bYunUrTp8+jUWLFsHf3x8tWrSQ+sMTERFRwWBSTDpt6+10WFhYSI9z585J69LT09GnTx9kZWXht99+y3Nb2ac/B97d0Jl92ebNmyGEQJkyZWBsbIzly5ejX79+0NfXl8r07t0bHTt2RPXq1dG5c2ccPXoUwcHBOHz44/i5n4iI6GOlU0OyEb2vS2UDNFgYIP1dpkwZAO8S4l69euHx48c4ffp0nv2aHB0dpVZhuWfPnim0Hru7u8PPzw8pKSlISkqCk5MTevfujXLlyqncrpOTE8qWLYuQkJAPOTwiIiJSE1uKSadZGstQoUIF6WFqaiolxCEhITh16hTs7e3z3E6jRo1w8uRJhWUnTpxA48aNc5Q1NzeHk5MTXrx4gePHj6Nr164qt5uQkICIiAg4OTnl/+CIiIhIbWwpJsomIyMDn332GQICAnDo0CFkZmZKLcB2dnYwMjICAAwaNAhlypTB/PnzAQDjxo2Dt7c3FixYgK5du+LAgQM4deqUwk10x48fhxAClStXxsOHD/Hdd9+hcuXKGDp0KADg1atXmDlzJnr06AEnJyc8efIEU6ZMQYkSJdCtW7dCPhNERES6hUkxUTaRkZE4ePAggHfDpWV35swZNGvWDAAQHh4OPb1/f2hp3LgxduzYgWnTpmH69Olwd3fHzp070aBBA6lMYmIiJk+ejMjISNjZ2aFHjx6YN28eDA0NAQD6+voIDAzEpk2b8PLlSzg5OaF58+bYuXMnLC0tC/bAiYiIdBzHKdYCjlOs3McwxiqAPMcppv8evoa0h+MUE5E2cZxiIiIiIqIixKSYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nlMiomIiIhI5zEpJiIiIiKdx6SYiIiIiHQek2IiIiIi0nk6nxSnpqaiYsWKkMlkkMlkiIyMLOqQiIiIiKiQ6XxSPHfuXDx8+LCowyAiIiKiIqTTSXFQUBB++eUXdOnSpahDISIiIqIipLNJsRACX331FfT19bFs2bKiDoeIiIiIipBBUQdQVNatW4dz585h3rx5cHNzK+pwiIiIiKgI6WRLcVxcHH744QdUrlwZEydOLOpwiIiIiKiI6WRL8TfffIPnz59j165dMDIyKupwiIiIiKiI6VxS/M8//2Dr1q3o06cPWrZs+UHbSEtLQ1pamvR3UlKStsL7b5mZWNQREBEREalFp7pPpKam4uuvv4alpSUWL178wduZP38+rK2tpYeLi4sWoyQiIiKiwvbRtBR///33OHjwYL7rrV+/Ho0aNQLw75jES5YsgZOT0wfHMnnyZEyYMEH6OykpiYkxERER0Ufso0mKo6Oj8eDBg3zXS0lJAQCEhITgl19+Qa1atTBmzBiNYjE2NoaxsbFG2yAiIiKi4uOj6T6xZcsWCCHy/WjVqhUA4O7du3j79i0ePXqEMmXKwNHRUeEhV7duXTg6OuLXX38tqkMlIiIiokL20STF2vLq1Ss8ffo0x0MuLi4OT58+xatXr4owyv+mmTNnwsPDA+bm5rC1tUWrVq1w5cqVXOukp6dj9uzZcHd3h4mJCWrVqoVjx47lKBcVFYUBAwbA3t4eZmZmqF27Nq5fv650m1999RVkMhmWLl2qjcMiIiKi/4CPpvuEpj799FMIIVSul8lkAICIiAg4OzsXVlg6pVKlSli5ciXKly+PN2/eYMmSJWjTpg0ePnyIkiVLKq0zbdo0bNmyBWvXroWHhweOHz+Obt264eLFi6hTpw4A4MWLF2jSpAmaN2+Oo0ePolSpUnj06BFsbGxybG///v24cuUKSpcuXZCHSkRERB8ZnUmKqej169dP4e/Fixdj3bp1uH37tsrh8TZv3oypU6eiQ4cOAIARI0bg+PHjWLRoEbZs2QIAWLBgAVxcXLB+/XqpnrJZCqOiojB69GgcP34cHTt21NJRERER0X+BznWfoOLh7du3+P3332FtbY1atWqpLJeWlgYTExOFZaampjh//rz098GDB1GvXj307NkTpUqVQp06dbB27VqFOllZWRg4cCC+++47VKtWTbsHQ0RERB89JsVUqA4dOgQLCwuYmJhgyZIlOHnyJEqUKKGyfNu2bbF48WKEhIQgKysLJ0+exIEDBxATEyOVCQ0NxerVq1GxYkUcP34cX3/9NcaOHYtNmzZJZRYsWAADAwOMHTu2QI+PiIiIPk5Miv+ffLQK9ifWjld3z8DCwkJ6nDt3DgDQvHlz3Lx5ExcvXkS7du3Qq1cvPHv2TOV2li1bhooVK8LDwwNGRkYYPXo0hg4dCn19falMVlYW6tati59++gl16tTBV199hS+//BKrV68GAFy/fh3Lli3Dhg0bpL7jRERERNkxKaYCYVahAW7evCk96tWrBwAwNzdHhQoV0LBhQ6xbtw4GBgZYt26dyu2ULFkS+/fvR0pKCsLCwnD//n1YWFigXLlyUhknJydUrVpVoV6VKlUQHh4OADh37hyePXsGV1dXGBgYwMDAAGFhYfj222+V9j0mIiIi3cMb7ahA6BmboUKFCnmWE0IgLS0tz3ImJiYoU6YM0tPT8ddff6FXr17SuiZNmuSY2CU4OBhly5YFAAwcOFAar1qubdu2GDhwIIYOHarO4RAREdF/HJNiKhQpKSmYN28eunTpAicnJyQkJOC3335DZGQkevbsKZUbNGgQypQpg/nz5wMArly5gqioKNSuXRtRUVGYOXMmsrKy8P3330t1vvnmGzRu3Bg//fQTevXqhatXr+L333/H77//DgCwt7eHvb29QjyGhoZwdHRE5cqVC+HoiYiIqLhjUkyFQl9fH/fv38fGjRsRHx8Pe3t71K9fH+fOnVMYDSI8PBx6ev/26klNTcW0adMQGhoKCwsLdOjQAZs3b1YYg7h+/frYt28fJk+ejNmzZ6NcuXJYunQp+vfvX5iHSERERB8xJsVUKExMTLB37948y/n6+ir87ePjg3v37uVZr1OnTujUqZPa8Tx58kTtskRERPTfxxvtiIiIiEjnMSkmIiIiIp3HpJiIiIiIdB6TYiIiIiLSeUyKiYiIiEjnMSkmIiIiIp3HpJiIiIj+r737jorq+NsA/iy9N5UqigUhAooRBEvsYscae2/YW2KLJWqiRP3ZjZqoUSPWVHsP2I3G3oJdUFRsNOkw7x++e+PKLiywsOo+n3P2KPfOzJ27u3Pvd2dnZ4h0HoNiIiIiItJ5DIqJiIiISOcxKCYiIiIincegmIiIiIh0HoNiIiIiItJ5DIqJiIiISOcxKCYiIiIincegmIiIiIh0HoNiIiIiItJ5DIqJiIiISOcxKCYiIiIinWeg7QoQEZEK0+O1XYO8rffRdg2IiDSCPcVEREREpPMYFBMRERGRzmNQTEREREQ6j0ExEREREek8BsVERB+gkJAQyGQyLFq0KM+0ixYtgoeHB0xNTeHq6ooxY8YgNTVV2p+ZmYkpU6agXLlyMDU1Rfny5TFz5kxkZ2dLaYQQmD59OpydnWFqaor69evj2rVrRXFqRERawdkniIg+MH/++Sf+/vtvODs755l248aNmDhxIn766SfUqlULN2/eRJ8+fQAACxcuBADMmTMHK1euxPr16+Hl5YV//vkHffv2hbW1NUaNGgUAmDt3LhYsWIB169ahUqVK+Pbbb9GkSRPYTLOBvql+kZ0rEVFxYVBMRPQBefToEYYPH479+/ejZcuWeaY/deoUateujW7dugEA3Nzc0LVrV5w5c0YhTZs2baTy3NzcsHnzZvzzzz8A3vQSL1q0CJMnT0b79u0BAOvXr4eDgwNkp2Wwa2Cn6dMkIip2HD5BRPSByM7ORs+ePTFu3Dh4eXmpladOnTo4d+6cFATfvXsXe/bsUQio69Spg8OHD+PmzZsAgEuXLuH48eNo0aIFAODevXt48uQJgoKCpDzGxsaoV68ekm8na+r0iIi0ij3FREQfiDlz5sDAwAAjR45UO0+XLl3w7Nkz1KlTB0IIZGZmYsiQIZg4caKUZsKECYiPj4enpyf09fWRlZWFWbNmoWvXrgCAJ0+eAAAcHBwUynZwcEDm3UwNnBkRkfYxKCYieg8lXQuHhUVn6e/du3dj8eLFOH/+PGQymdrlREREYNasWVi+fDkCAgJw+/ZtjBo1Ck5OTpg6dSoAYOvWrQgLC8OmTZvg5eWFixcvYvTo0XB2dkbv3r2lst49rhCikGdJRPT+YFBMRPQeMqsYgNMLB0t///LLL4iNjUWZMmWkbVlZWfjiiy+waNEi3L9/X2k5U6dORc+ePTFgwAAAgI+PD16/fo1BgwZh8uTJ0NPTw7hx4zBx4kR06dJFSvPgwQOEhoaid+/ecHR0BPCmx9jJyUkqOzY2FgbWvI0Q0ceBVzMioveQnrEZKlasKP09aNAgtG7dWiFN06ZN0bNnT/Tt21dlOcnJydDTU/z5iL6+PoQQUk+vqjTyKdnKlSsHR0dHHDx4ENWqVQMApKen48iRIzBva17wkyQieo8wKCYi+gCUKFECJUqUUNhmaGgIR0dHeHh4SNt69eoFFxcXhIaGAgBat26NBQsWoFq1atLwialTpyI4OBj6+vpSmlmzZqFMmTLw8vLChQsXsGDBAvTr1w/Am2ETo0ePxuzZs+Hu7g53d3fMnj0bZmZmsA60LqZngIioaDEoJiL6iERFRSn0+k6ZMgUymQxTpkzBo0ePUKpUKSkIllu6dCmmTp2KoUOHIjY2Fs7OzggJCcG0adOkNOPHj0dKSgqGDh2KV69eISAgAAcOHEDXc12L9fyIiIqKTPCXEoWWkJAAa2trxMfHw8rKqsiP5zZxd5EfQxPuf5f3HKpE2sA2pDk+6320XQW1XOl9RdtVICI1FHdM9TbOU0xEREREOo9BMRERERHpPAbFRERERKTzGBQTERERkc5jUExEREREOo9BMRERERHpPAbFRERERKTzGBQTERERkc7jinYfoA9hQn8iIiKiDwl7iomIiIhI5zEoJiIiIiKdx6CYiIiIiHQeg2IiIiIi0nkMij8Cffr0gUwmU3gEBgbmmmfdunU58shkMqSmpkppQkND4e/vD0tLS9jb26Nt27aIjIxUKEdZGTKZDPPmzSuScyUiIiIqCpx94iPRrFkzrF27VvrbyMgozzxWVlY5glwTExPp/0eOHMGwYcPg7++PzMxMTJ48GUFBQbh+/TrMzc0BAI8fP1bIv3fvXvTv3x8dOnQozOkQERERFSsGxR8JY2NjODo65iuPTCbLNc++ffsU/l67di3s7e1x7tw51K1bFwBy5N++fTsaNGiA8uXL56suRERERNrE4RMfiYiICNjb26NSpUoYOHAgYmNj88yTlJSEsmXLonTp0mjVqhUuXLiQa/r4+HgAgJ2dndL9T58+xe7du9G/f//8nwARERGRFjEo/gg0b94cGzduxF9//YX58+fj7NmzaNiwIdLS0lTm8fT0xLp167Bjxw5s3rwZJiYmqF27Nm7duqU0vRACY8eORZ06deDt7a00zfr162FpaYn27dtr5LyIiIiIiovOBsUxMTH44osv4OnpCXNzc1hbW8Pb2xtDhw7FgwcPtF09lTZu3AgLCwvpcezYMXTu3BktW7aEt7c3Wrdujb179+LmzZvYvXu3ynICAwPRo0cPVK1aFZ999hm2bduGSpUqYenSpUrTDx8+HJcvX8bmzZtVlvnTTz+he/fuCuOSiYiIiD4EOjmmeP/+/ejUqRMSEhJgYWEBDw8PpKenIyoqCitWrECLFi1QtmxZbVdTqeDgYAQEBEh/u7i45Ejj5OSEsmXLquz1VUZPTw/+/v5K84wYMQI7duzA0aNHUbp0aaX5jx07hsjISGzdulXtYxIRERG9L3QuKL506RLatm2L7OxsfP/99xgwYIA0U0N2djb+/vtvODk5abmWqllaWsLS0jLXNC9evEB0dHS+zkMIgYsXL8LHx0dh24gRI/DHH38gIiIC5cqVU5l/zZo1qF69OqpWrar2MYmIiIjeFzoXFIeEhCA1NRVr165Fnz59FPbp6emhZs2a2qlYASUlJWH69Ono0KEDnJyccP/+fXz11VcoWbIk2rVrJ6Xr1asXXFxcEBoaCgCYMWMGAgMD4e7ujoSEBCxZsgQXL17E999/L+UZNmwYNm3ahO3bt8PS0hJPnjwBAFhbW8PU1FRKl5CQgF9++QXz588vprMmIiIi0iydCopPnz6Nv//+G+7u7ujdu7e2q6MR+vr6uHLlCn7++WfExcXByckJDRo0wNatWxV6lKOioqCn998Q8ri4OAwaNAhPnjyBtbU1qlWrhqNHj6JGjRpSmhUrVgAA6tevr3DMdz9QbNmyBUIIdO3atWhOkoiIiKiI6VRQvHPnTgBAy5YtER8fjx9++AHHjx9HWloa3N3d0bVrV9SpU0fLtcwfU1NT7N+/P890ERERCn8vXLgQCxcuzDWPEEKtOgwaNAiDBg1SKy0RERHR+0inguJz584BeLPQRZUqVRAdHS3tO3jwIJYvX46hQ4di2bJlkMlk2qomERERERUznZqSTb4k8YIFCxAXF4cNGzYgMTERz58/x5w5c6Cnp4fly5ernJZMLi0tDQkJCQoPIiIiIvpw6VRQ/Pr1awBARkYGFi1ahB49esDCwgIlSpTA+PHjMWrUKADA7NmzkZmZqbKc0NBQWFtbSw9XV9diqT8RERERFQ2ZUHfgqJaNHz8eO3bsyHe+tWvXSjNKeHt749q1a7C1tUVsbCwMDBRHj0RHR6NMmTIAgDNnzsDf319pmWlpaQqrxSUkJMDV1RXx8fGwsrLKdx2JqHi5TVS9sM375P53LbVdhTz5rPfJO9F74ErvK9quAhGpISEhAdbW1lqJqT6YMcUxMTGIjIzMdz557zAA2NraAgAqVKiQIyAGAFdXV1hYWCApKQn3799XGRQbGxvD2Ng433UhIiIiovfTBzN8IiwsDEKIfD8aN24sleHh4QEAuQa08oU8srKyivaEiIiIiOi98cEExZogH0Zx7949pfvj4+Px8uVLAMqXTyYiIiKij5NOBcVt2rSBsbExYmJicPDgwRz7165dC+DNUsqqhk4QERER0cdHp4LikiVLYvjw4QCA4cOH486dO9K+kydPYubMmQCAUaNGwcTERCt1JCIiIqLi98H80E5TZs2ahfPnzyM8PBweHh7w8fFBWloabty4AQBo3rw5pk2bpuVaEhEREVFx0qmeYuDNj+wOHDiABQsWwMfHB7du3UJUVBRq1KiB5cuXY8eOHTA0NNR2NYmIiIioGOlcTzEAGBgYYMyYMRgzZoy2q0JERERE7wGd6ykmIiIiInoXg2IiIiIi0nkMiomIiIhI5zEoJiIiIiKdx6CYiIiIiHQeg2IiIiIi0nkMiomIiIhI5zEoJiIiIiKdp5OLdxCRbrv/XUttV4GIiN4z7CkmIiIiIp3HoJiIiIiIdB6DYiIiIiLSeQyKiYiIiEjnMSgmIgJw48YNBAcHw9raGpaWlggMDERUVJRaebds2QKZTIa2bdsqbE9MTMTo0aNRtmxZmJqaolatWjh79qy0PyMjAxMmTICPjw/Mzc3h7OyMXr16ISYmRpOnRkREamBQTEQ6786dO6hTpw48PT0RERGBS5cuYerUqTAxMckz74MHD/Dll1/is88+y7FvwIABOHjwIDZs2IArV64gKCgIjRs3xqNHjwAAycnJOH/+PKZOnYrz58/j999/x82bNxEcHKzxcyQiotzJhBBC25X40CUkJMDa2hrx8fGwsrLSdnWIKJ+6dOkCQ0NDbNiwIV/5srKyUK9ePfTt2xfHjh1DXFwc/vzzTwBASkoKLC0tsX37drRs+d8UcL6+vmjVqhW+/fZbpWWePXsWNWrUwIMHD1CmTJkCn1Nx8Vnvo+0qqOVK7yvargIRqUGbMRV7iolIp2VnZ2P37t2oVKkSmjZtCnt7ewQEBEjBbW5mzpyJUqVKoX///jn2ZWZmIisrK0dvs6mpKY4fP66yzPj4eMhkMtjY2OT3VIiIqBAYFBORTouNjUVSUhK+++47NGvWDAcOHEC7du3Qvn17HDlyRGW+EydOYM2aNVi1apXS/ZaWlqhZsya++eYbxMTEICsrC2FhYfj777/x+PFjpXlSU1MxceJEdOvWjd86EREVMwbFRKRTNm7cCAsLC+kRGRkJAGjTpg3GjBkDX19fTJw4Ea1atcLKlSuVlpGYmIgePXpg1apVKFmypMpjbdiwAUIIuLi4wNjYGEuWLEG3bt2gr6+fI21GRga6dOmC7OxsLF++XDMnS0REauMyz0SkU4KDgxEQECD9XapUKRgYGKBy5coK6T755BOVwxzu3LmD+/fvo3Xr1tK27OxsAICBgQEiIyNRoUIFVKhQAUeOHMHr16+RkJAAJycndO7cGeXKlVMoLyMjA506dcK9e/fw119/fVC9xByrS0QfCwbFRKRTLC0tYWlpqbDN399f6jGWu3nzJsqWLau0DE9PT1y5ohgMTpkyBYmJiVi8eDFcXV0V9pmbm8Pc3ByvXr3C/v37MXfuXGmfPCC+desWwsPDUaJEicKcHhERFRCDYiLSeePGjUPnzp1Rt25dNGjQAPv27cPOnTsREREhpenVqxdcXFwQGhoKExMTeHt7K5Qh/2Hc29v3798PIQQ8PDxw+/ZtjBs3Dh4eHujbty+ANz/G69ixI86fP49du3YhKysLT548AQDY2dnByMioaE+ciIgkDIqJSOe1a9cOK1euRGhoKEaOHAkPDw/89ttvqFOnjpQmKioKenr5+xlGfHw8Jk2ahIcPH8LOzg4dOnTArFmzYGhoCAB4+PAhduzYAeDNVG1vCw8PR/369Qt1XkREpD7OU6wBnKeYiIiIqPA4TzEREX1U8rtsdv369SGTyXI83l74JK9lswFACIHp06fD2dkZpqamqF+/Pq5du1Zk50lEHw8GxUREpFEFWTb7999/x+PHj6XH1atXoa+vj88//1xKk9ey2QAwd+5cLFiwAMuWLcPZs2fh6OiIJk2aIDExsUjPmYg+fBw+oQEcPkFE9J+CLpv9tkWLFmHatGl4/PgxzM3N1Vo2WwgBZ2dnjB49GhMmTAAApKWlwcHBAXPmzEFISEihz42IihaHTxAR0UehMMtmv23NmjXo0qULzM3NAai3bPa9e/fw5MkTBAUFSfuNjY1Rr149nDx5snAnRkQfPQbFRESkMQVdNvttZ86cwdWrVzFgwABpmzrLZsuns3NwcFAoz8HBQdpHRKQKg2IiIiowTSyb/a41a9bA29sbNWrUUNiu7rLZMplM4W8hRI5tRETvYlBMREQFFhwcjIsXL0oPX19flctm5zb7hFxycjK2bNmi0EssJ182OykpCdHR0Thz5gwyMjKkZbMdHR0BIEevcGxsbI7eYyKidzEoJiKiArO0tETFihWlh7W1db6XzX7btm3bkJaWhh49eqhMY25uDicnJ2nZ7DZt2gAAypUrB0dHRxw8eFBKm56ejiNHjqBWrVoFPEMi0hVc0Y6IiDQqv8tmv23NmjVo27YtSpQokaPcvJbNlslkGD16NGbPng13d3e4u7tj9uzZMDMzQ7du3Yr0nInow8egmIiINKqgy2bfvHkTx48fx4EDB5SWm9ey2QAwfvx4pKSkYOjQoXj16hUCAgJw4MABWFpaFs3JEtFHg/MUawDnKSYiIiIqPM5TTERERESkRQyKiYiIiEjnMSgmIiIiIp3HoJiIiIiIdB6DYiIiIiLSeQyKiYiIiEjncZ5iDZDPapeQkKDlmhARERF9uOSxlDZmDGZQrAGJiYkAAFdXVy3XhIiIiOjDl5iYCGtr62I9Jhfv0IDs7GzExMTA0tISMplM29X5aCUkJMDV1RXR0dFcJIWoANiGiAqO7ad4CCGQmJgIZ2fnHKteFjX2FGuAnp4eSpcure1q6AwrKytekIgKgW2IqODYfopecfcQy/GHdkRERESk8xgUExEREZHOY1BMHwxjY2N8/fXXMDY21nZViD5IbENEBcf28/HjD+2IiIiISOexp5iIiIiIdB6DYiIiIiLSeQyKiYiIiEjnMSguZvXr14dMJkNERES+80ZEREAmk6F+/foarxcRERGRLmNQrAFubm6QyWS5PhYtWqTtahYbeeD/9sPIyAilS5dG+/btcejQoQKVu27dOshkMri5ualMs379eujr60Mmk2Hu3Lk56jR9+vQCHVtZPeTn9eLFC5VpMzMzYW9vL6XXxPF12YkTJzBo0CB4enrC2toaxsbGcHFxQatWrbB69Wq8fv1a21UskGfPnuGbb75B7dq14eDgACMjI9ja2iIgIACTJk3CzZs381We/Jq0bt06pftfvXoFPz8/yGQyVKpUCY8ePQLw3wdvTa3M+fa18Ysvvsg17eLFixWuGaRZ3bp1g0wmQ/fu3dVKv2DBAshkMnh5eRVxzfKnT58+Oe4vhoaGcHBwQPPmzfHLL78UqFx13vsHDhyAqakpZDIZhg8fDvk8BfI69enTp0DHVlUPmUyGy5cv55q+WrVqUlpNHF/XMSjWIHd3d9SuXVvpw8XFBQBQpkwZeHh4wMzMLN/lm5mZwcPDA2XKlNF01YuEq6urdP5eXl6Ij4/HH3/8gSZNmmD27NkaP95PP/2Efv36ITs7G/Pnz8f48eM1fox3ZWRkYOvWrSr379+/H8+ePSvyenzskpOT0blzZ9SpUwerVq3CgwcP4OrqiipVqkAIgd27d2PgwIFwd3fHlStXtF3dfFm3bh3Kly+PadOm4eTJkzAzM4Ovry/s7e1x/vx5fPfdd6hcuTLmzJmjkeO9fPkSjRs3xrlz5+Dh4YEjR45I16eitGnTJmRlZancHxYWVuR10GW9evUCAPz5559ISkrKM7389ejZs2eR1qug7O3tpfuLr68v0tPTsW/fPnTq1AmDBg3S+PH27duHNm3aIDU1FaNGjcKyZcuK5cNbbu3i2rVruHjxYpHXQacIKrSyZcsKAGLt2rXarsp7oV69egKA+PrrrxW2Jycni+HDhwsAQk9PT/z777/5Knft2rUCgChbtmyOfatWrRIymUwAEIsXL1a7TgUhr4e7u7uQyWQiMDBQZdrOnTsLAMLDw0Njx9c16enponbt2gKAcHR0FOvXrxfJyckKaa5duyZCQkKEgYGB+OOPP7RT0QL4/vvvBQAhk8nE8OHDRXR0tML+V69eiRUrVggXFxfRpk0btctVdU16/vy58PX1FQBE5cqVxePHjxX2h4eHCwBCU7cGeT3k7/99+/YpTffvv/8qpOOtSfMyMzOFo6OjACB+/vnnXNNev35del9GRUUVUw3V07t3bwFA9O7dW2F7ZmammD17tvT+OXDgQL7Kze29v3v3bmFsbCwAiLFjx6pdp4KQ18PZ2VlYWloKFxcXkZWVpTTthAkTFNqNJo6v69hTTMXG1NQUCxcuhJubG7Kzs/Hnn39qpNwffvhB6hn4/vvvMXLkSI2Um5cyZcqgbt26OH36NG7fvp1jf2JiInbs2IFy5cqhdu3axVKnj9GMGTNw4sQJODg44NSpU+jVqxdMTU0V0lSuXBkrV65EeHg47O3ttVTT/Ll27RrGjBkD4M37dunSpShdurRCGhsbGwwePBjXrl1D8+bNC3W858+fo2HDhrh48SK8vb0RHh4OR0fHQpWprh49egBQ3eu1YcMGAO9vr+THQF9fH127dgUAbNy4Mde08tejfv36cHV1LfK6aYK+vj4mTZqEOnXqAAB+//13jZS7a9cutGvXDmlpaRg/fjzmz5+vkXLzYmpqivbt2+PRo0cIDw/PsV8IgU2bNsHc3Bzt2rUrljrpAgbFxUzVD+3kY5LWrVuHe/fuoU+fPnBxcYGBgYE0DlXVD+3u37+vMNY2LCwMfn5+MDMzg52dHT7//HPcvXtXZZ0uXLiA1q1bw9bWFhYWFggMDMSvv/4KABof32dgYIBq1apJ9S6sFStWYMiQIdL/hw4dWugy8yO3m/2vv/6KlJQUdO/ePc/n8OHDhxg5ciQqVaoEU1NT2NjYoEGDBtLr8K4nT55g6dKlaNq0Kdzc3GBiYgJbW1vUq1dPuqG9q7DvE22Ij4/HkiVLAACLFi3KdTw5ANSpUwe1atXKsX337t1o1qwZSpYsCWNjY5QrVw5Dhw5FdHS00nLkY2Hv37+P8PBwNG/eHCVLllRou2+3jd9++w1169aFjY2NlC8vc+bMQXp6OoKCgqT3sCrW1tYICQnJs0xVnj17hoYNG+Ly5cuoUqVKsX94qFevHlxdXfHHH3/kGPcthMDGjRulICA3Qghs2bIFTZo0QYkSJWBsbIzy5ctj5MiRePLkidI8Bw8exPDhw1G1alXY2dnBxMQEFSpUwJAhQxAVFaU0z9vX45iYGPTr1w9OTk4wMTGBl5cXvv/++4I9EVom/9Bx6NAhPH36VGkaebD1dvqUlBRs3rwZXbp0gYeHBywsLGBhYQFfX198++23Ksfyv92OTp8+jebNm8PW1hbm5ub47LPP8Ndff2n8HP39/QFo5v6yY8cOdOjQAenp6fjqq680NoRJXfL7i7JrekREBKKjo9GuXTuYm5vnWs7Lly8xefJkeHt7w9zcHJaWlggMDMSqVauQnZ2dI31cXBzWrFmDNm3aoGLFijA1NYW1tTUCAgKwZMkSZGZmKj3O29fEvXv3om7durC0tIS1tTWaN2+OCxcu5PcpKH7a7aj+OORn+IT8a/zw8HCF7fKvXyZOnChsbGyEsbGx+PTTT4Wnp6eYPn26EOK/r1Xq1aunkPfevXvSsIKJEydK/69atar0lY+Tk5N49uxZjvocPHhQSmNlZSX8/PyEk5OTACAWLFhQoK8y8xqq0LRpU5VfQ+Xm3eETS5culb7iW716daHqVJB6NGrUSMTFxQkTExNRsWLFHOkaNmwoAIh///1X9O/fX+XxIyIihLW1tQAgTE1NhY+Pj3B1dZWe+y+++CJHnm+++UZKX6FCBeHn5yfKlCkj5Rk8eHCOPIV5n2jLxo0bBQBRqlQpkZGRUaAy5OcKQJQuXVpUr15dmJmZCQDC1tZWnD17NkceeZuePXu20NPTE7a2tsLf31+ULl1aarvyMr/77jsBQDg4OAh/f39RqlQpce/evVzrlJGRIczNzQUAsX379gKdV27eviY9ffpUeHl5CQCiWrVq4vnz5yrzFdXwiWPHjkmvw4YNGxTSHD16VAAQXbt2FdHR0SqPn56eLj7//HNpv7Ozs6hatar0Wjo5OYnIyMgc+fT19YVMJhP29vbC19dXeHt7S899iRIlxLVr13LkkV+Pp0+fLhwdHYWJiYn49NNPhbOzs3T8b7/9ViPPUXHz9vYWAMTChQuV7j9y5Ih0bYmPjxdCCHHs2DEBQBgYGIjSpUsLPz8/4e7uLgwMDAQA8emnn+YY0iTEf6//0qVLhaGhoShRooSoXr26dL0zMDDIcS/MS15DFUJCQgQA0b59+3yV++57/48//hCGhoYCgJg6dWqh6lSQelSoUEFkZWUJFxcXYWlpKV6/fq2Qrl+/fgKA2L9/v3Q/UHb8q1evChcXFwFAGBkZicqVK4sKFSpIww07duwosrOzFfJs2LBBSl+2bFnh7+8vypcvL/T09AQA0bJlS6VDOuTP34oVK4RMJhNOTk7i008/ldqbhYWFuHHjRqGfo6LEoFgDNBkU6+vri+DgYPHixQtpX0pKihAi76DYwMBAWFlZiT179kj7Hj9+LKpUqSIAiAkTJijkS0hIkMaY9e3bV7qoZWdni2XLlkmBkiaD4hcvXghbW1u1xrW96+2gePHixdLY5Pw875oOioUQ0o365MmTUpro6Gihp6cnatSoIYQQKoPiR48eCTs7OyGTycTs2bNFamqqtO/EiRPSxWznzp0K+Y4dOyb++usvkZmZqbD90qVL4pNPPhEAREREhMK+gr5PtGnYsGECgGjbtm2B8u/cuVM657CwMGl7fHy8aNeunQAg3NzcctzQ5W1aX19fzJgxQwrIs7OzpddI3jaMjIzEjz/+KN1YMjIy8gzgz549K32ge/XqVYHOLTfy+oeGhkrvh+rVq4uXL1/mmq8og+Jr164JACIoKEghzcCBAwUAsWfPnlyDYnlQXa1aNXHhwgVpe3Jyshg6dKgAIPz8/HLk++GHH8SjR48UtiUnJ4tZs2YJAKJ+/fo58sivx4aGhqJjx44Kr9Hy5csFAGFiYlIkr11RmzNnjsrnSoj/Xo8uXbpI2+7fvy+2bdsmEhMTFdI+fvxYdOzYUfoA8S75629oaChCQ0Ol61V6erro3r27ACACAgLyVf/cAtC0tDTh7u4uAIiZM2fmq9y33/u//vqrFBCrU05RBcVCCDFu3DgBQGzatElKk5KSIqysrISTk5PIzMxUGRQnJSWJChUqCABi5MiR0occId78DkP+YXnZsmUK+S5duiR27dqlcD8SQog7d+6IunXrCgBi3bp1Oeouf/7MzMwU7ssJCQmiUaNGAoDo3LlzQZ+aYsGgWAPkDV/V4+0gNq+g2NHRUSQlJSk9Tl5BMQAxf/78HPl27NghAIgqVaoobF+5cqUAIDw9PZXexOV10kRQnJSUJE6ePCn9YKp8+fI5Glxe5MGovHdCJpPl6HXKT50K6t2gePv27QKAGDJkiJRG3nu4ZMkSIYTqoHjs2LECgBgzZozSY8mDuoYNG6pdv0OHDgkAYuDAgQrbC/o+0aa2bdvm+vzkRf5+GzVqVI59r1+/FiVLlhQAxJo1axT2ydt069atVZYtfy5HjBiR73r9+eefAnjTU10U5PWXtxU/Pz+1AriiDIqFEKJatWpCX19fxMTECCGESE1NFTY2NsLe3l5kZGSoDIpjY2OFsbGxsLKyyvFjRCGEyMrKEv7+/gKAOHr0qNr1q1OnjgAgHj58qLA9r+vxp59+KgCI33//Xe1jvS8ePnwo9fi9+2PntLQ0qdNi9+7dapWXnJwsjIyMhLu7e459ubWjZ8+eSR0veX1Ye5uyADQ1NVVcvHhRBAcHCwDCzs5OPHnyRO0yhVB878vbzaxZswpcp4J6Nyi+fPmyACCaN28updmyZYsA/vu2VVVQvGTJEgFAtGvXTumxLl26JGQymShfvrza9bt9+7YAIJo0aZJjX27XRPl5WFtbq30sbTAAaYy7u7vScXo+Pj5ql9GhQ4c8xwflpn///jm2ycdYvTte9ODBgwDejBszMMj5Vujbty/Wr19f4LrMmDEDM2bMyLE9KCgIP/74I4yNjQtUrvj/uSGFEHj48GGB66cp8vGm27Ztw+LFi2FoaIiwsDAYGBigS5cuueaV/xhkwIABSvc3a9YMRkZGOHnyJDIzMxVep8TERGzZsgXHjx/H48ePkZKSAiEE0tLSAACXLl1Sedz8vE+0KTExEQAK1CaSkpJw6tQpAMCIESNy7DczM8PAgQMRGhqKAwcOoF+/fjnSyKexyo06ad5VmPMqiJcvXyI5ORk2NjbFcjxVevbsibFjx2Lz5s0YO3Ysdu3ahbi4OIwaNUrpNUhuz549SEtLQ3BwcI4fIwKAnp4eWrVqhbNnz+LIkSP47LPPFPb/888/+PXXX3H9+nXEx8dLU8PdunULAHD58mWl09J17dpV6Wvk7++P8+fPv1dtRV0uLi5o0KABDh8+jLCwMHzzzTfSvl27duHVq1ewt7dHUFCQQr7s7Gzs3LkTBw4cwN27d5GUlCRdi2UyGW7duoXk5GSl040qu76VLFkSbm5uiIyMxN27d1G9evV8ncf69euV3p8CAwOxYsUKODg45Ks8ZVT95qA4+fj4oEqVKjh48CBiY2Nhb2+v9nR5ed1fqlSpAjc3N9y9excPHz5UaFtpaWn47bffEB4ejqioKCQnJ0uvN5D7/UXZ8Xx8fGBiYoL4+Hi8ePECJUqUyLXu2sKgWIO++uqrQk+e/cknnxQ4b8mSJWFtbZ1juzxQf3duSvkNoUqVKkrLU7VdXa6urtKcys+fP8fNmzchk8ng4+NTqLmWS5cujZCQEHz11VeYNGkSLC0tMWzYsELVtTAMDQ3RqVMnLF++HHv27EHZsmVx9epVtGzZEqVKlVKZLykpSfoxSF7zaqampuLFixfShf7ChQto1aoVYmJiVOZ5+fKl0u35fZ9ok6WlJQAUaFGO27dvIzs7W/oxljLyhQlULY6hTnssSJstzHnlx/Tp07FmzRrcvXsXjRs3xtGjR1GyZMkiPWZuunbtinHjxmHDhg0YO3as9AMi+Q+KVJHPPX369GlpdoF3yX84Jl+IBHjzwXn48OFYvnx5ruWraisVKlRQuv19bCv50bNnTxw+fBibNm1SCIrlwVbXrl0VPqTExcWhRYsW0odMVV69eqU0KM7teYyMjCzQ82hvbw93d3fpuLdu3UJGRgYqVKhQqPso8Gbu8F69emHlypWwsLDAvHnzClVeYfXo0QPjx4/H5s2b0b17d+zbtw9eXl7w9fXNNZ+83UybNk3l2gDPnz8H8KbdyIPiqKgoBAUFITIyUmXZqtoMoPr1LlWqFKKjo5GUlMSgmNRTmJ4jVXn19JRPMiK/Ictv0O9StV1d/fr1U1jB7fr16wgODsb8+fNhY2ODKVOmFLjsSZMmITExEaGhoRgxYgTMzc21uppPz549sXz5coSFhaFs2bLSttzEx8dL/z9x4kSex0hJSQEAZGVloVOnToiJiUGLFi0wYcIEeHl5wcbGBvr6+rh9+zbc3d2RkZGhtJz8vk+0Sd57d+/evXznld9oS5UqpXL2D/mHDHnP7bvUaY8FabPy84qLi0NcXFyR9eC6uLjg8OHD+Oyzz3Djxg0EBQUhPDxc6Yei4uDo6IjGjRtj//79OHr0KPbu3QtPT0/4+fnlmk/eVqKjo/PsvZO3E+DNr/aXL18Oc3NzzJs3D02aNIGLi4s0pV+PHj2wcePGAreVt3vOPiQdOnTA0KFDcffuXZw8eRK1atVCXFwc9uzZAyDntWvs2LE4deoUPDw8MHv2bAQGBqJkyZIwMjIC8Kaj4tGjR8X6PDZv3lxhxcbo6Gh07NgRGzduhImJCVavXp3vMuW6d++O169fIyQkBP/73/9gaWmJadOmFbi8wurevTsmTpyIsLAw6OvrIzMzU63pC+Xt5ty5c3mmfbvd9OnTB5GRkQgICMCMGTPg6+sLOzs7GBoaIjMzU/pXlQ+53bx/d0EqNvI3rqpP6aoChYKqXLkytm7dCj09PcycObNAgc7bZs+ejREjRkAIgQEDBqicvqw4BAYGwt3dHTt37kRYWBisrKwQHBycax4LCwvp/+np6RBvxvirfMinIztz5gxu376NsmXL4vfff0fdunVRokQJ6OvrA3g/vvLTFPn0avLhI/khf36fPXum8iIs710s7AfA/KpatSrMzMwghMDRo0eL9FjlypXDoUOHUKpUKVy4cAEtWrTQ6nLY8pt5z549kZ6ertbNXf5aTp48Oc928nagJJ+Pd/78+RgyZIg0vZTcx9RW8sPCwgJt27YF8F/v8LZt25CWloZPPvlEYShDZmYmtm3bBgDYvn072rdvD2dnZykgzszMVDkdXnFydXXFb7/9BnNzc6xZswbHjx8vVHmDBg2S5iT++uuvsXDhQk1Us0CcnZ3RsGFD/PPPP5g3bx709PTUWq5b3m5u3bqVZ7uRT/UaExOD8PBwmJmZYc+ePWjatCkcHBxgaGgI4ONvMwyKdVilSpUAQOXa6kWxXG716tXRqVMnZGRkKB1vnF+LFy9G3759kZWVhW7dukk9HdrQvXt3pKWl4enTp+jQoUOOBSbeZW1tDWdnZwBvFnJQl3zIRfXq1ZWOy85trNeHpkWLFrCwsEBsbGy+P/RUrFgRenp6SEtLUzn2U/68y9tCcTE0NJTm5M3rq31N8PT0xIEDB2BjY4OTJ09Ky9VqQ7t27WBhYYGoqCjIZDK1bu6VK1cGAFy9ejVfx5K3FWVzV2dkZODGjRv5Ku9jIh8Lv23bNmRkZKgcp/rs2TO8fv0adnZ28PDwyFHO1atXc12+uziVLl1a+v1AYb6JlBs7dqz0befYsWOxatWqQpdZUPIhRlFRUahXr57SsfXvKki7efDgAYA31ww7O7sc+z+m+4syDIp1WJMmTQC86SlQdlF7u8dFkyZOnAjgTS+OvAEWlEwmw+rVq6VAu0OHDkpX/ykOPXv2RKNGjdCoUSMMHDhQrTzywGjRokVqH0cebCubfD8jIyNfZb3vbGxspJvc6NGj85yQ/8SJEzh58iSAN70k8mBo6dKlOdKmpKRIX7E2bdpUg7VWz4QJE2BoaIj9+/dj5cqVuaaNj4/Hjz/+WKjj+fr6Yu/evbCwsMDhw4fx+eefq/y6uyiZmZnhiy++QKNGjRASEiINN8pNy5YtYWRkhD179ki/hVBHbm1l7dq1ePbsmfoV/8g0btwYjo6OePHiBX744QccP35c6YcU+XOYkJCg8BW73Ny5c4ulvuoaPXo0TExMcOTIEbWGpeXl66+/xpdffgkAGDx4sLSwSXHr0KEDgoKC0KhRI7VXbZXfX5YsWaL2kAX56x0bG6s0z/v2emsag2Id1rVrVzg6OuL69esYPHiw1HMkhMCKFSuKrPFXrVoVTZs2RWZmpkYamJ6eHsLCwtCqVSukpqYiODgYp0+f1kBN86d8+fI4dOgQDh06hJo1a6qVZ8KECbCzs8P69esxduxYxMXFKex/+fIlfvrpJ3z77bfStsDAQBgYGODEiRP4+eefpe3x8fHo3r27ypWqPlTTp09HzZo18fTpU9SsWRMbNmzI0ct58+ZNDBs2DPXr10dsbKy0fcKECQDe9Ma+/X5OTExEr1698OzZM7i5ueU5S0hR8Pb2lr6eHTp0KEaOHJljNpX4+HisXr0a3t7eGvkWJDAwEDt27ICJiQl27dqFnj17Kl3RqqhNnz4dhw4dwooVK9RK7+zsjNGjRyMjIwNNmzbNsSKoEAJnzpzBkCFDFL4VkP8ob8qUKQoB8L59+zBu3DiYmJgU/mQ+UPr6+ujWrRsAYNy4cRBCoF69ejl+BG1jYwMvLy9kZmZizJgxSE9PB/Dmtw1z5szB1q1bpaEU7wMHBwf07t0bAFT+uCy/5s2bh8GDByM7Oxu9e/fG9u3bNVJuflhYWGD//v04dOiQNPQlLyEhIShfvjzCw8PRvXt3PH78WGF/UlIStm3bhrFjx0rbvLy8YGtri4cPH2LWrFlSYJyamopRo0Z9GKvSFUZRzfWmSzS5eEduZaizop0qUDH36MGDB4WRkZE0f6C/v7+0atP8+fMF8GaBjPxQZ07gw4cPSxPgP378WK1y313R7l0pKSnSKnK2trbi4sWLOepkamoqSpQoofLx9oIWedVDPk+xOnJb0e748ePSfLmGhobCx8dHBAQEiPLly0urDr074fmXX34pvaZlypQR1atXF6ampsLQ0FCsWLFC6fNUmPeJtiUmJooOHTpI9TM1NRXe3t7C399fWuAEeLNi3ZUrVxTyvr2inaurq/Dz85NWWLK1tRVnzpzJcTx5m85tZTpNPVerV6+W6oP/n8O7Ro0awsPDQ1pAwMDAQMybN0/tMvO6Ju3evVsqu1+/ftLCI2/P1ZpbO1G24EVu9ZDPU5yX3BbvyMjIED169JD2Ozo6iho1aoiqVasKS0tLafvbK2Y9ePBA2NnZSe8ZX19f4ebmJgCIBg0aSAtIvPs85XU9/vrrrzU277k2XbhwQXreoGS+brkdO3ZI1yI7Ozvh5+cnXbOmTp2qsr3k1Y5U3Q9zo86cwLdu3ZLmYn57oZfc5DVHd3Z2tvT+MzY2FgcOHMhRJ2Nj41zbzcqVK9Wuh3yeYnXktqLdjRs3RLly5aR7+SeffCICAgJEpUqVhL6+vgByLqCybNkyhXbm5+cnrKyshEwmE6tWrVL5POV1TVTnuqpt7CnWcY0bN8apU6fQsmVLAG9miHBxccHmzZsREhICoGh+hNSwYUP4+fkhNTUVCxYs0EiZJiYm2LFjB2rWrIlXr14hKCgI//77r0KalJQUvHjxQuVDPsdvcapduzauX7+OyZMno3Llyrh37x4uX74MPT09NGvWDMuXL8fixYsV8sydOxeLFi2Cp6cnnjx5ggcPHqBx48Y4duwYmjVrVuznUNQsLCzw66+/4ujRo+jfvz9cXV1x//59XLp0CUIItGzZEmvWrMHNmzfh7e2tkDc0NBQ7d+5EkyZNkJSUhMuXL6NkyZIYPHgwLl26JM3PrC39+/fHnTt3pB7xhIQEnD9/Hk+fPkW1atUwadIkREZGSl/hakKLFi2wceNG6Ovr46effsLo0aNzpMmtnbx69UpjdVGXgYEBNmzYgN27d0s9ZRcuXMDjx49RqVIlDB8+HBEREQrjw8uUKYNTp06hffv2MDIywr///gsTExPMmDED+/bty3VuZF3g6+srzaNvYmKCjh07Kk3XunVr7N27F7Vq1UJKSgoiIyNRsWJFhIWFYebMmcVZZbVUrFhRGjqgqd5imUyGdevWoX379khLS0Pbtm1z/JgvLS0t13ajbPhJUfP09MSlS5fw3Xffwd/fH48ePcLFixeRnp6OevXq4X//+x+2bNmikGfYsGEICwuDr68vXr58idu3b8PPzw979uxROefxx0ImxHs8NwZp1blz5+Dn54eqVavi4sWL2q4OERERUZFhTzGptHbtWgBvejKJiIiIPmYMinVceHg4tmzZojBsICMjAwsWLMCKFSugp6en9kwKRERERB8q3R5QRXjw4AH69u0LQ0NDlCtXDlZWVrh58yYSEhIAvBmPmddSkkREREQfOo4p1nF37tzBokWLEB4ejpiYGCQmJsLOzg4BAQEYPnw4goKCtF1FIiIioiLHoJiIiIiIdB7HFBMRERGRzmNQTEREREQ6j0ExEREREek8BsVEREREpPMYFBMRERGRzmNQTEREREQ6j0ExEREREek8BsVEREREpPMYFBMRERGRzvs/5mKw+aMtrU0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 750x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Initialize lists to store the t-statistics and p-values for each metric\n",
    "t_stats = []\n",
    "p_values = []\n",
    "\n",
    "# Set up the labels for the comparison groups\n",
    "comparison_labels = [f\"whole vs {method}\" for method in other_methods]\n",
    "\n",
    "# Collect the t-statistics and p-values for each comparison and metric\n",
    "for method in other_methods:\n",
    "    other_group = data_by_grouping_method[method]\n",
    "\n",
    "    firing_r_t_stat, firing_r_p_value = stats.ttest_ind(whole_group['firing_r_KL_mean'], other_group['firing_r_KL_mean'], equal_var=False)\n",
    "    corr_c_t_stat, corr_c_p_value = stats.ttest_ind(whole_group['corr_c_KL_mean'], other_group['corr_c_KL_mean'], equal_var=False)\n",
    "    van_r_t_stat, van_r_p_value = stats.ttest_ind(whole_group['van_r_KL_mean'], other_group['van_r_KL_mean'], equal_var=False)\n",
    "\n",
    "    t_stats.append([firing_r_t_stat, corr_c_t_stat, van_r_t_stat])\n",
    "    p_values.append([firing_r_p_value, corr_c_p_value, van_r_p_value])\n",
    "\n",
    "# Convert the lists to NumPy arrays for easy plotting\n",
    "t_stats = np.array(t_stats)\n",
    "p_values = np.array(p_values)\n",
    "\n",
    "# Set up the x-axis labels for the metrics\n",
    "metrics = ['Firing R KL Mean', 'Corr C KL Mean', 'Van R KL Mean']\n",
    "\n",
    "\n",
    "def autolabel(rects, ax, is_ttest=False):\n",
    "    \"\"\"Attach a text label above or below each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        if is_ttest:\n",
    "            label_format = f'{height:.3f}'\n",
    "        else:\n",
    "            label_format = f'{height:.2e}'\n",
    "\n",
    "        if height >= 0:\n",
    "            ax.annotate(label_format,\n",
    "                        xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                        xytext=(0, 3),  # 3 points vertical offset\n",
    "                        textcoords=\"offset points\",\n",
    "                        ha='center', va='bottom')\n",
    "        else:\n",
    "            ax.annotate(label_format,\n",
    "                        xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                        xytext=(0, -3),  # 3 points vertical offset\n",
    "                        textcoords=\"offset points\",\n",
    "                        ha='center', va='top')\n",
    "\n",
    "title_fontsize = 19\n",
    "ylabel_fontsize = 18\n",
    "xticklabel_fontsize = 16\n",
    "yticklabel_fontsize = 16\n",
    "legend_fontsize = 16\n",
    "\n",
    "# Create a bar plot for the t-statistics\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "x = np.arange(len(metrics))\n",
    "width = 0.2\n",
    "\n",
    "bar_list_ttest = []\n",
    "for i, label in enumerate(comparison_labels):\n",
    "    bars = ax.bar(x + i * width, t_stats[i], width, label=label)\n",
    "    bar_list_ttest.append(bars)\n",
    "\n",
    "ax.set_ylabel('t-statistic', fontsize=ylabel_fontsize)\n",
    "ax.set_title('t-statistics of Grouping Methods under 3 Metrics', fontsize=title_fontsize)\n",
    "ax.set_xticks(x + width * (len(comparison_labels) - 1) / 2)\n",
    "ax.set_xticklabels(metrics, fontsize=xticklabel_fontsize)\n",
    "ax.tick_params(axis='y', labelsize=yticklabel_fontsize)\n",
    "ax.legend(loc='upper left', fontsize=legend_fontsize)\n",
    "\n",
    "# Add text labels to the bars\n",
    "for bars in bar_list_ttest:\n",
    "    autolabel(bars, ax, is_ttest=True)\n",
    "plt.tight_layout()\n",
    "# plt.show()\n",
    "plt.savefig('../figs/t-stats.pdf',format = 'pdf')\n",
    "\n",
    "# Create a bar plot for the p-values\n",
    "fig, ax = plt.subplots(figsize=(7.5, 6))\n",
    "\n",
    "bar_list = []\n",
    "for i, label in enumerate(comparison_labels):\n",
    "    bars = ax.bar(x + i * width, p_values[i], width, label=label)\n",
    "    bar_list.append(bars)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.axhline(0.05, color='red', linestyle='--')\n",
    "ax.set_ylabel('p-value (log scale)', fontsize=ylabel_fontsize)\n",
    "ax.set_title('p-values of Grouping Methods under 3 Metrics', fontsize=title_fontsize)\n",
    "ax.set_xticks(x + width * (len(comparison_labels) - 1) / 2)\n",
    "ax.set_xticklabels(metrics, fontsize=xticklabel_fontsize)\n",
    "\n",
    "yticks = list(ax.get_yticks())\n",
    "yticks.append(0.05)\n",
    "yticks = sorted(yticks)\n",
    "\n",
    "yticklabels = [str(tick) if tick != 0.05 else f\"{tick:.2f}\" for tick in yticks]\n",
    "# yticklabels[-2] = f\"{0.05:.2f}\"  # Red color for the 0.05 tick label\n",
    "ax.set_yticks(yticks)\n",
    "ax.set_yticklabels(yticklabels, fontsize=yticklabel_fontsize)\n",
    "ax.get_yticklabels()[-4].set_color(\"red\")\n",
    "\n",
    "ax.legend(loc='upper left', fontsize=legend_fontsize)\n",
    "\n",
    "# Add text labels to the bars\n",
    "for bars in bar_list:\n",
    "    autolabel(bars, ax)\n",
    "plt.tight_layout()\n",
    "# plt.show()\n",
    "plt.savefig('../figs/p-val.pdf',format = 'pdf')\n",
    "# The rest of the code remains the same as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e14168c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}