diff --git a/Behavioral_exercise.ipynb b/Behavioral_exercise.ipynb
index 2d0638e7d44bd5659f15ba64b20fb0f381c09913..c3e1405183c6fe31c26314ee729e5a6749a37a9f 100644
--- a/Behavioral_exercise.ipynb
+++ b/Behavioral_exercise.ipynb
@@ -274,10 +274,6 @@
"execution_count": 6,
"id": "7ba3475a-1141-4b56-bc97-8efd0789d6d2",
"metadata": {
- "collapsed": true,
- "jupyter": {
- "outputs_hidden": true
- },
"tags": []
},
"outputs": [
@@ -340,7 +336,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded trials_dfNone file. Skipping processing\u001b[0m\n"
+ "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded trials_df file. Skipping processing\u001b[0m\n"
]
},
{
diff --git a/Center-surround-analysis.ipynb b/Center-surround-analysis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..586a3e7a67177a12c5b01ce70c96d12dc666eeee
--- /dev/null
+++ b/Center-surround-analysis.ipynb
@@ -0,0 +1,3943 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "8656d06d-6d4d-4b99-a8c4-c6026695cb9c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#you must import 6 libraries!\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt, seaborn as sns\n",
+ "import Inflow\n",
+ "Inflow.logging.enable_logging()\n",
+ "import ResearchProjects\n",
+ "import pandas as pd\n",
+ "import one"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "a2afc3de-e477-419b-9d7b-1873f8339767",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#adaptation is a sublibrary within the ResearchProject\n",
+ "from ResearchProjects import adaptation\n",
+ "#inside adaptation experiment there are different file, we import the \"aliases\"\n",
+ "from ResearchProjects.adaptation import aliases"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "c0ad12fc-2794-4e8a-b24d-1f9033d89689",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<function ResearchProjects.adaptation.select.cells_labelled(rois_df, iscell=True, **kwargs)>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\u001b[1;31mType:\u001b[0m module\n",
+ "\u001b[1;31mString form:\u001b[0m <module 'ResearchProjects.core' from 'C:\\\\Users\\\\mohay\\\\anaconda3\\\\envs\\\\Analysis\\\\lib\\\\site-packages\\\\ResearchProjects\\\\core.py'>\n",
+ "\u001b[1;31mFile:\u001b[0m c:\\users\\mohay\\anaconda3\\envs\\analysis\\lib\\site-packages\\researchprojects\\core.py\n",
+ "\u001b[1;31mDocstring:\u001b[0m <no docstring>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "connector = one.ONE()\n",
+ "connector.set_data_access_mode('remote')\n",
+ "ResearchProjects.core?\n",
+ "ResearchProjects.adaptation.select.cells_labelled\n",
+ "display(ResearchProjects.adaptation.select.cells_labelled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "27a7c735-6206-4eef-abfc-8aebc07f06bd",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "subject wm24\n",
+ "start_time 2022-08-22T15:27:00\n",
+ "number 1\n",
+ "lab HaissLab\n",
+ "projects [Adaptation]\n",
+ "url http://157.99.138.172/sessions/04f92e4a-da64-4...\n",
+ "task_protocol \n",
+ "date 2022-08-22\n",
+ "json {'channels': ['R', 'G'], 'whisker_stims': {'St...\n",
+ "extended_qc {'exclude_whisker': ['C1']}\n",
+ "rel_path wm24\\2022-08-22\\001\n",
+ "alias_name wm24_2022_08_22_001\n",
+ "short_path wm24\\2022-08-22\\001\n",
+ "path \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202...\n",
+ "Name: 04f92e4a-da64-4018-aa6c-d9a79a91c831, dtype: object"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#sessions = connector.search(subject = \"wm24\",date_range = \"2022-08-09\",number = 1, details= True) ###Did not work(OSError: No result exists for trials_df in session wm24_2022_08_09_001)\n",
+ "sessions = connector.search(subject = 'wm24', date_range = \"2022-08-22\", number = 1, details = True)\n",
+ "session = sessions.iloc[0]\n",
+ "session"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "f8230f0c-32a7-475a-ab3a-70a692dd9b4d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "trials_df = adaptation.pipelines.get_trials_df(session)\n",
+ "rois_df = adaptation.pipelines.get_rois_df(session) # load from file only. Faster but will not generate the data if none exists\n",
+ "trials_roi_df = adaptation.pipelines.get_trials_roi_df(session)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "ac1e8009-427b-4f56-9831-1cfaeb2bcfe2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32mSAVE_INFO : preprocessed_data : Saving processed trials_df data at wm24\\2022-08-22\\001\\preprocessing_saves\\preproc_data.trials_df.pickle (overwriting)\u001b[0m\n",
+ "\u001b[94;20mINFO : roi_signal_corrections : Correcting signal F of 35 ROIs with rough neuropil correction and slow trend correction \u001b[0m\n",
+ "\u001b[94;20mINFO : slow_trend_correction : Pooling processes for slow_trend_correction\u001b[0m\n",
+ "\u001b[94;20mINFO : roi_signal_corrections : All ROIs corrected successfully\u001b[0m\n",
+ "\u001b[94;20mINFO : roi_signal_corrections : Correcting signal Fneu of 35 ROIs with rough neuropil correction and slow trend correction \u001b[0m\n",
+ "\u001b[94;20mINFO : slow_trend_correction : Pooling processes for slow_trend_correction\u001b[0m\n",
+ "\u001b[94;20mINFO : roi_signal_corrections : All ROIs corrected successfully\u001b[0m\n",
+ "\u001b[94;20mINFO : rois_df : Generating signal variation measurement using method 'delta_over_F'\u001b[0m\n",
+ "\u001b[94;20mINFO : rois_df : Generating is_neuron label based on iscell column values at 0 positionnal indices\u001b[0m\n",
+ "\u001b[94;20mINFO : rois_df : Generating in_D1 label based on the refined_mask variable inside neuropil_mask.\u001b[0m\n",
+ "\u001b[94;20mINFO : rois_df : Generating in_C1 label based on the refined_mask variable inside neuropil_mask.\u001b[0m\n",
+ "\u001b[94;20mINFO : rois_df : Generating in_any_barrel label based on the refined_mask variables inside neuropil_masks of existing whiskers.\u001b[0m\n",
+ "\u001b[94;20mINFO : VGAT_values : Generating VGAT_values columns in the ROI dataframe supplied\u001b[0m\n",
+ "\u001b[94;20mINFO : VGAT_labels : Generating is_VGAT label based on VGAT_value for every ROI\u001b[0m\n",
+ "\u001b[94;20mINFO : VGAT_labels : Threshold absolute values are : up=26.26 and low=18.08 obtained from relative values : up=70 and low=30 percentiles criterion\u001b[0m\n",
+ "\u001b[32mSAVE_INFO : preprocessed_data : Saving processed rois_df data at wm24\\2022-08-22\\001\\preprocessing_saves\\preproc_data.rois_df.pickle (overwriting)\u001b[0m\n",
+ "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded timelines_dict file. Skipping processing\u001b[0m\n",
+ "\u001b[94;20mINFO : suite2p_tiff_verification : Suite2p output length and total lenth of the fiff files provided in the trials_df are valid.\u001b[0m\n",
+ "\u001b[94;20mINFO : trials_roi_df : Splitting and aligning ['F', 'F_var', 'Fneu', 'Fneu_var', 'spks'] by #trial and #roi multi index.\u001b[0m\n",
+ "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded timelines_dict file. Skipping processing\u001b[0m\n",
+ "\u001b[32mSAVE_INFO : preprocessed_data : Saving processed trials_roi_df data at wm24\\2022-08-22\\001\\preprocessing_saves\\preproc_data.trials_roi_df.pickle (overwriting)\u001b[0m\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "trials_df = adaptation.pipelines.generate_trials_df(session_details = session, refresh = True)\n",
+ "rois_df = adaptation.pipelines.generate_rois_df(session_details = session, refresh = True, sigma = 50, F0_index = 50, upper_perc = 70, lower_perc = 30)\n",
+ "trials_roi_df = adaptation.pipelines.generate_trials_roi_df(rois_df, trials_df, session_details = session, refresh_main_only = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "8ccb05a9-ddd0-400c-a18d-6ea12465e2fc",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "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>ypix</th>\n",
+ " <th>xpix</th>\n",
+ " <th>lam</th>\n",
+ " <th>med</th>\n",
+ " <th>footprint</th>\n",
+ " <th>mrs</th>\n",
+ " <th>mrs0</th>\n",
+ " <th>compact</th>\n",
+ " <th>solidity</th>\n",
+ " <th>npix</th>\n",
+ " <th>npix_soma</th>\n",
+ " <th>soma_crop</th>\n",
+ " <th>overlap</th>\n",
+ " <th>radius</th>\n",
+ " <th>aspect_ratio</th>\n",
+ " <th>npix_norm_no_crop</th>\n",
+ " <th>npix_norm</th>\n",
+ " <th>skew</th>\n",
+ " <th>std</th>\n",
+ " <th>neuropil_mask</th>\n",
+ " <th>F</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>F_chan2</th>\n",
+ " <th>Fneu_chan2</th>\n",
+ " <th>iscell</th>\n",
+ " <th>redcell</th>\n",
+ " <th>spks</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>in_D1</th>\n",
+ " <th>in_C1</th>\n",
+ " <th>in_any_barrel</th>\n",
+ " <th>VGAT_value</th>\n",
+ " <th>is_VGAT</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>[31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 3...</td>\n",
+ " <td>[336, 337, 338, 339, 340, 341, 342, 343, 344, ...</td>\n",
+ " <td>[101.35066, 104.43834, 143.1773, 191.57831, 19...</td>\n",
+ " <td>[34, 342]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.149791</td>\n",
+ " <td>3.412601</td>\n",
+ " <td>1.027713</td>\n",
+ " <td>1.233083</td>\n",
+ " <td>87</td>\n",
+ " <td>82</td>\n",
+ " <td>[False, True, True, True, True, True, True, Tr...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>5.784065</td>\n",
+ " <td>1.137769</td>\n",
+ " <td>1.104061</td>\n",
+ " <td>1.162414</td>\n",
+ " <td>1.321311</td>\n",
+ " <td>23.987156</td>\n",
+ " <td>[12617, 12618, 12619, 12620, 12621, 12622, 126...</td>\n",
+ " <td>[110.48663330078125, 131.6634063720703, 86.054...</td>\n",
+ " <td>[81.11736297607422, 93.11980438232422, 73.6161...</td>\n",
+ " <td>[6.409209251403809, 13.033258438110352, 15.877...</td>\n",
+ " <td>[15.295843124389648, 13.344743728637695, 17.07...</td>\n",
+ " <td>[1.0, 0.9649560020447292]</td>\n",
+ " <td>[0.0, 0.4927331507205963]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>14.059807</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>[41, 41, 41, 42, 42, 42, 42, 42, 42, 43, 43, 4...</td>\n",
+ " <td>[313, 314, 315, 311, 312, 313, 314, 315, 316, ...</td>\n",
+ " <td>[102.81334, 102.63232, 98.31734, 93.77065, 122...</td>\n",
+ " <td>[46, 314]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.982629</td>\n",
+ " <td>2.982562</td>\n",
+ " <td>1.004936</td>\n",
+ " <td>1.200000</td>\n",
+ " <td>71</td>\n",
+ " <td>63</td>\n",
+ " <td>[False, False, False, False, True, True, True,...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.598673</td>\n",
+ " <td>1.054807</td>\n",
+ " <td>0.901015</td>\n",
+ " <td>0.893074</td>\n",
+ " <td>0.456027</td>\n",
+ " <td>32.054146</td>\n",
+ " <td>[17711, 17712, 17713, 17714, 17715, 17716, 177...</td>\n",
+ " <td>[209.49720764160156, 196.16419982910156, 163.7...</td>\n",
+ " <td>[94.13381958007812, 108.01216888427734, 89.793...</td>\n",
+ " <td>[20.04505729675293, 25.216228485107422, 19.065...</td>\n",
+ " <td>[12.793187141418457, 14.85401439666748, 15.637...</td>\n",
+ " <td>[0.0, 0.4354095974463412]</td>\n",
+ " <td>[0.0, 0.5837860107421875]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 27.431922912597656, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2883108210421972, 0.5932362809663284, 0.192...</td>\n",
+ " <td>[0.2883108210421972, 0.5932362809663284, 0.192...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>20.949542</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>[50, 50, 50, 51, 51, 51, 51, 51, 51, 52, 52, 5...</td>\n",
+ " <td>[181, 182, 183, 179, 180, 181, 182, 183, 184, ...</td>\n",
+ " <td>[131.20735, 157.59299, 161.65533, 212.736, 164...</td>\n",
+ " <td>[54, 181]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.815101</td>\n",
+ " <td>2.486277</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.313433</td>\n",
+ " <td>49</td>\n",
+ " <td>44</td>\n",
+ " <td>[False, False, False, True, True, True, True, ...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>3.462338</td>\n",
+ " <td>1.012388</td>\n",
+ " <td>0.621827</td>\n",
+ " <td>0.623734</td>\n",
+ " <td>0.862335</td>\n",
+ " <td>46.924488</td>\n",
+ " <td>[22186, 22187, 22188, 22189, 22190, 22191, 221...</td>\n",
+ " <td>[257.7426452636719, 208.847412109375, 217.7744...</td>\n",
+ " <td>[170.16526794433594, 182.0167999267578, 160.24...</td>\n",
+ " <td>[15.707565307617188, 25.36271858215332, 18.577...</td>\n",
+ " <td>[17.577030181884766, 16.453781127929688, 16.13...</td>\n",
+ " <td>[1.0, 0.3117388293427187]</td>\n",
+ " <td>[0.0, 0.5908480286598206]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 19.099365234375, 0.0, 0.0, 0.0...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.5608495401495428, 0.7443431223037524, 0.407...</td>\n",
+ " <td>[0.5608495401495428, 0.7443431223037524, 0.407...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>24.833536</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>[53, 53, 53, 53, 53, 53, 54, 54, 54, 54, 54, 5...</td>\n",
+ " <td>[300, 301, 302, 303, 304, 305, 299, 300, 301, ...</td>\n",
+ " <td>[104.48533, 110.259026, 98.51868, 126.16097, 9...</td>\n",
+ " <td>[58, 302]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.265083</td>\n",
+ " <td>3.852942</td>\n",
+ " <td>1.001532</td>\n",
+ " <td>1.179775</td>\n",
+ " <td>108</td>\n",
+ " <td>105</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>5.667794</td>\n",
+ " <td>1.027905</td>\n",
+ " <td>1.370558</td>\n",
+ " <td>1.488457</td>\n",
+ " <td>1.165069</td>\n",
+ " <td>26.665464</td>\n",
+ " <td>[23842, 23843, 23844, 23845, 23846, 23847, 238...</td>\n",
+ " <td>[152.53948974609375, 188.36045837402344, 137.2...</td>\n",
+ " <td>[97.6956558227539, 107.47368621826172, 91.6819...</td>\n",
+ " <td>[2.0891013145446777, 13.146905899047852, 10.95...</td>\n",
+ " <td>[14.183066368103027, 11.311212539672852, 17.01...</td>\n",
+ " <td>[0.0, 0.3132977713925523]</td>\n",
+ " <td>[0.0, 0.49197638034820557]</td>\n",
+ " <td>[0.0, 1.0597600936889648, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.24668316397308548, 0.4711971469352052, 0.10...</td>\n",
+ " <td>[0.24668316397308548, 0.4711971469352052, 0.10...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>15.331578</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>[56, 57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 5...</td>\n",
+ " <td>[339, 336, 337, 338, 339, 340, 341, 335, 336, ...</td>\n",
+ " <td>[71.31633, 82.23265, 73.58466, 117.8463, 98.42...</td>\n",
+ " <td>[60, 339]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.939076</td>\n",
+ " <td>2.841870</td>\n",
+ " <td>1.007940</td>\n",
+ " <td>1.175258</td>\n",
+ " <td>61</td>\n",
+ " <td>57</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.485627</td>\n",
+ " <td>1.059862</td>\n",
+ " <td>0.774112</td>\n",
+ " <td>0.808019</td>\n",
+ " <td>0.531406</td>\n",
+ " <td>25.071770</td>\n",
+ " <td>[25416, 25417, 25418, 25419, 25420, 25421, 254...</td>\n",
+ " <td>[140.59927368164062, 117.13068389892578, 135.9...</td>\n",
+ " <td>[73.86310577392578, 82.62413024902344, 78.5197...</td>\n",
+ " <td>[25.282196044921875, 14.495699882507324, 18.03...</td>\n",
+ " <td>[13.366589546203613, 12.821345329284668, 13.30...</td>\n",
+ " <td>[0.0, 0.4433111479521616]</td>\n",
+ " <td>[0.0, 0.5073467493057251]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 1.5610218048095703, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.03368734128291486, 0.2728357744731917, 0.16...</td>\n",
+ " <td>[0.03368734128291486, 0.2728357744731917, 0.16...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>15.483268</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>[61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 63, 6...</td>\n",
+ " <td>[187, 188, 189, 190, 186, 187, 188, 189, 190, ...</td>\n",
+ " <td>[92.21834, 110.258316, 141.48834, 102.923706, ...</td>\n",
+ " <td>[65, 189]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.815101</td>\n",
+ " <td>2.486277</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.313433</td>\n",
+ " <td>60</td>\n",
+ " <td>44</td>\n",
+ " <td>[False, False, False, False, False, True, True...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>3.662520</td>\n",
+ " <td>0.996759</td>\n",
+ " <td>0.761421</td>\n",
+ " <td>0.623734</td>\n",
+ " <td>0.615781</td>\n",
+ " <td>29.122208</td>\n",
+ " <td>[27833, 27834, 27835, 27836, 27837, 27838, 278...</td>\n",
+ " <td>[153.00405883789062, 154.6991424560547, 130.38...</td>\n",
+ " <td>[143.9366455078125, 140.1763153076172, 143.484...</td>\n",
+ " <td>[9.330554962158203, 21.728002548217773, 8.4426...</td>\n",
+ " <td>[22.809917449951172, 11.958677291870117, 16.94...</td>\n",
+ " <td>[0.0, 0.1853835544837747]</td>\n",
+ " <td>[0.0, 0.5240342617034912]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 21.451704025268555, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.4880555886878001, 0.42163433406968576, 0.48...</td>\n",
+ " <td>[0.4880555886878001, 0.42163433406968576, 0.48...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>19.285923</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>[102, 102, 102, 102, 103, 103, 103, 103, 103, ...</td>\n",
+ " <td>[191, 192, 193, 194, 189, 190, 191, 192, 193, ...</td>\n",
+ " <td>[104.25302, 112.82933, 82.81866, 89.48833, 79....</td>\n",
+ " <td>[109, 192]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.394949</td>\n",
+ " <td>4.223553</td>\n",
+ " <td>1.007439</td>\n",
+ " <td>1.150685</td>\n",
+ " <td>135</td>\n",
+ " <td>126</td>\n",
+ " <td>[False, False, False, False, True, True, True,...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>6.696339</td>\n",
+ " <td>1.101419</td>\n",
+ " <td>1.713198</td>\n",
+ " <td>1.786148</td>\n",
+ " <td>1.632249</td>\n",
+ " <td>27.695444</td>\n",
+ " <td>[48820, 48821, 48822, 48823, 48824, 48825, 488...</td>\n",
+ " <td>[154.900146484375, 132.25148010253906, 113.308...</td>\n",
+ " <td>[121.14143371582031, 106.08963775634766, 108.8...</td>\n",
+ " <td>[13.373459815979004, 8.250761032104492, 15.124...</td>\n",
+ " <td>[12.934263229370117, 14.001992225646973, 16.03...</td>\n",
+ " <td>[1.0, 0.2705239287399592]</td>\n",
+ " <td>[0.0, 0.5010843873023987]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.5580097968938957, 0.23501575931973562, 0.29...</td>\n",
+ " <td>[0.5580097968938957, 0.23501575931973562, 0.29...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>15.558809</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>[106, 106, 106, 106, 106, 107, 107, 107, 107, ...</td>\n",
+ " <td>[308, 309, 310, 311, 312, 307, 308, 309, 310, ...</td>\n",
+ " <td>[97.78301, 122.948326, 116.52102, 103.80499, 9...</td>\n",
+ " <td>[109, 311]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.815101</td>\n",
+ " <td>2.486277</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.313433</td>\n",
+ " <td>58</td>\n",
+ " <td>44</td>\n",
+ " <td>[False, True, True, True, True, False, True, T...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>3.814316</td>\n",
+ " <td>1.015916</td>\n",
+ " <td>0.736041</td>\n",
+ " <td>0.623734</td>\n",
+ " <td>0.424445</td>\n",
+ " <td>31.225199</td>\n",
+ " <td>[50988, 50989, 50990, 50991, 50992, 50993, 509...</td>\n",
+ " <td>[185.602783203125, 199.90809631347656, 202.467...</td>\n",
+ " <td>[108.78717803955078, 122.18974304199219, 86.58...</td>\n",
+ " <td>[32.40492630004883, 51.75419998168945, 17.0571...</td>\n",
+ " <td>[12.028204917907715, 13.838461875915527, 17.09...</td>\n",
+ " <td>[0.0, 0.11519655841470867]</td>\n",
+ " <td>[0.0, 0.6411923766136169]</td>\n",
+ " <td>[0.0, 6.787431716918945, 11.328902244567871, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.3395221176386508, 0.6257711399229017, -0.13...</td>\n",
+ " <td>[0.3395221176386508, 0.6257711399229017, -0.13...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>33.543051</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>[113, 113, 113, 114, 114, 114, 114, 114, 114, ...</td>\n",
+ " <td>[252, 253, 254, 250, 251, 252, 253, 254, 255, ...</td>\n",
+ " <td>[85.01199, 99.38868, 96.98102, 114.620285, 93....</td>\n",
+ " <td>[118, 251]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.990815</td>\n",
+ " <td>3.005837</td>\n",
+ " <td>1.005462</td>\n",
+ " <td>1.219048</td>\n",
+ " <td>82</td>\n",
+ " <td>64</td>\n",
+ " <td>[False, False, False, True, True, True, True, ...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.605765</td>\n",
+ " <td>1.055293</td>\n",
+ " <td>1.040609</td>\n",
+ " <td>0.907250</td>\n",
+ " <td>0.739351</td>\n",
+ " <td>32.761204</td>\n",
+ " <td>[54512, 54513, 54514, 54515, 54516, 54517, 545...</td>\n",
+ " <td>[146.71072387695312, 180.5610809326172, 159.84...</td>\n",
+ " <td>[115.2066650390625, 112.91555786132812, 107.03...</td>\n",
+ " <td>[20.994434356689453, 32.77158737182617, 13.523...</td>\n",
+ " <td>[17.48444366455078, 14.48888874053955, 13.1622...</td>\n",
+ " <td>[1.0, 0.6139257550332846]</td>\n",
+ " <td>[0.0, 0.571911633014679]</td>\n",
+ " <td>[0.0, 22.53038215637207, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.3185552548066303, 0.26840759667618413, 0.13...</td>\n",
+ " <td>[0.3185552548066303, 0.26840759667618413, 0.13...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>23.304562</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>[113, 113, 113, 113, 114, 114, 114, 114, 114, ...</td>\n",
+ " <td>[278, 279, 280, 281, 277, 278, 279, 280, 281, ...</td>\n",
+ " <td>[133.37334, 123.26165, 147.12932, 128.92233, 1...</td>\n",
+ " <td>[115, 280]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.587293</td>\n",
+ " <td>1.791402</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.533333</td>\n",
+ " <td>25</td>\n",
+ " <td>23</td>\n",
+ " <td>[True, True, True, True, False, True, True, Tr...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>2.606331</td>\n",
+ " <td>0.992922</td>\n",
+ " <td>0.317259</td>\n",
+ " <td>0.326043</td>\n",
+ " <td>0.546683</td>\n",
+ " <td>55.231182</td>\n",
+ " <td>[51977, 51978, 51979, 51980, 51981, 51982, 519...</td>\n",
+ " <td>[160.2012176513672, 246.04684448242188, 122.63...</td>\n",
+ " <td>[122.00780487060547, 124.5162582397461, 107.42...</td>\n",
+ " <td>[32.472190856933594, 41.31606674194336, 22.256...</td>\n",
+ " <td>[18.660598754882812, 14.868660926818848, 13.72...</td>\n",
+ " <td>[0.0, 0.022543458027667683]</td>\n",
+ " <td>[0.0, 0.5727353096008301]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.3427790067639393, 0.39612290259181304, 0.03...</td>\n",
+ " <td>[0.3427790067639393, 0.39612290259181304, 0.03...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>25.789192</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>[121, 121, 121, 121, 121, 121, 122, 122, 122, ...</td>\n",
+ " <td>[213, 214, 215, 216, 217, 218, 212, 213, 214, ...</td>\n",
+ " <td>[93.08266, 101.85034, 101.82765, 102.509346, 1...</td>\n",
+ " <td>[125, 215]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.008665</td>\n",
+ " <td>3.071492</td>\n",
+ " <td>1.001696</td>\n",
+ " <td>1.218182</td>\n",
+ " <td>74</td>\n",
+ " <td>67</td>\n",
+ " <td>[True, True, True, True, True, False, True, Tr...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.781948</td>\n",
+ " <td>1.029498</td>\n",
+ " <td>0.939086</td>\n",
+ " <td>0.949777</td>\n",
+ " <td>0.797232</td>\n",
+ " <td>29.699617</td>\n",
+ " <td>[58572, 58573, 58574, 58575, 58576, 58577, 585...</td>\n",
+ " <td>[217.0276641845703, 191.68612670898438, 115.39...</td>\n",
+ " <td>[156.29226684570312, 150.33575439453125, 149.9...</td>\n",
+ " <td>[14.511828422546387, 15.23607063293457, 8.8653...</td>\n",
+ " <td>[18.992753982543945, 20.224637985229492, 15.88...</td>\n",
+ " <td>[1.0, 0.5222473979185825]</td>\n",
+ " <td>[0.0, 0.46735233068466187]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 18.23613739013672, 0.0, 0...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.48875698009025026, 0.4032714989805199, 0.39...</td>\n",
+ " <td>[0.48875698009025026, 0.4032714989805199, 0.39...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>17.051609</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>[124, 125, 125, 125, 125, 126, 126, 126, 126, ...</td>\n",
+ " <td>[267, 266, 267, 268, 269, 265, 266, 267, 268, ...</td>\n",
+ " <td>[145.6607, 162.42667, 156.56, 135.24237, 122.1...</td>\n",
+ " <td>[129, 268]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>3.050270</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.211009</td>\n",
+ " <td>67</td>\n",
+ " <td>66</td>\n",
+ " <td>[False, True, True, True, True, True, True, Tr...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.541828</td>\n",
+ " <td>1.012344</td>\n",
+ " <td>0.850254</td>\n",
+ " <td>0.935601</td>\n",
+ " <td>0.601344</td>\n",
+ " <td>34.122639</td>\n",
+ " <td>[60161, 60162, 60163, 60164, 60165, 60166, 601...</td>\n",
+ " <td>[243.09519958496094, 177.4669952392578, 174.49...</td>\n",
+ " <td>[154.95980834960938, 137.29550170898438, 130.4...</td>\n",
+ " <td>[45.87001037597656, 30.122678756713867, 43.648...</td>\n",
+ " <td>[27.323877334594727, 22.794326782226562, 19.12...</td>\n",
+ " <td>[0.0, 0.4216052811500901]</td>\n",
+ " <td>[0.0, 0.6233232617378235]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 7.39235258102417, 0....</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.6323846377753256, 0.3075497911254068, 0.181...</td>\n",
+ " <td>[0.6323846377753256, 0.3075497911254068, 0.181...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>34.336797</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>[128, 128, 128, 129, 129, 129, 129, 129, 130, ...</td>\n",
+ " <td>[175, 176, 177, 174, 175, 176, 177, 178, 174, ...</td>\n",
+ " <td>[102.88199, 110.60998, 131.19432, 100.02533, 1...</td>\n",
+ " <td>[136, 177]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.472473</td>\n",
+ " <td>4.336495</td>\n",
+ " <td>1.035730</td>\n",
+ " <td>1.151515</td>\n",
+ " <td>136</td>\n",
+ " <td>133</td>\n",
+ " <td>[False, False, False, True, True, True, True, ...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>7.620947</td>\n",
+ " <td>1.177686</td>\n",
+ " <td>1.725888</td>\n",
+ " <td>1.885379</td>\n",
+ " <td>0.780675</td>\n",
+ " <td>30.666241</td>\n",
+ " <td>[62118, 62119, 62120, 62121, 62122, 62123, 621...</td>\n",
+ " <td>[221.507568359375, 167.84658813476562, 216.181...</td>\n",
+ " <td>[133.40582275390625, 151.7203826904297, 133.34...</td>\n",
+ " <td>[18.057809829711914, 15.891983032226562, 12.62...</td>\n",
+ " <td>[16.887378692626953, 16.757282257080078, 20.00...</td>\n",
+ " <td>[1.0, 0.43407906008740177]</td>\n",
+ " <td>[0.0, 0.5041268467903137]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.350585996819315, 0.6918782055416933, 0.3494...</td>\n",
+ " <td>[0.350585996819315, 0.6918782055416933, 0.3494...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>17.418802</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>[133, 134, 134, 134, 134, 134, 134, 134, 134, ...</td>\n",
+ " <td>[196, 191, 192, 193, 194, 195, 196, 197, 198, ...</td>\n",
+ " <td>[86.511665, 97.57231, 121.71201, 136.07935, 13...</td>\n",
+ " <td>[139, 195]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.432848</td>\n",
+ " <td>4.351917</td>\n",
+ " <td>1.004287</td>\n",
+ " <td>1.116667</td>\n",
+ " <td>158</td>\n",
+ " <td>134</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>6.317062</td>\n",
+ " <td>1.008854</td>\n",
+ " <td>2.005076</td>\n",
+ " <td>1.899555</td>\n",
+ " <td>1.451811</td>\n",
+ " <td>32.600288</td>\n",
+ " <td>[64693, 64694, 64695, 64696, 64697, 64698, 646...</td>\n",
+ " <td>[241.3565216064453, 229.2249755859375, 202.061...</td>\n",
+ " <td>[155.1845703125, 162.07550048828125, 137.78019...</td>\n",
+ " <td>[20.861509323120117, 16.645105361938477, 21.99...</td>\n",
+ " <td>[19.875839233398438, 22.432886123657227, 19.91...</td>\n",
+ " <td>[1.0, 0.12854916895398194]</td>\n",
+ " <td>[0.0, 0.4756465256214142]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 2.7272820472717285, 0.0, 0.0, ...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.5691486631506817, 0.6781690150630921, 0.293...</td>\n",
+ " <td>[0.5691486631506817, 0.6781690150630921, 0.293...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>17.863403</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>[135, 135, 135, 135, 136, 136, 136, 136, 136, ...</td>\n",
+ " <td>[317, 318, 320, 321, 317, 318, 319, 320, 321, ...</td>\n",
+ " <td>[119.56799, 122.02602, 134.26067, 96.63066, 14...</td>\n",
+ " <td>[137, 319]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.564749</td>\n",
+ " <td>1.635435</td>\n",
+ " <td>1.053321</td>\n",
+ " <td>1.583333</td>\n",
+ " <td>22</td>\n",
+ " <td>19</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>2.898275</td>\n",
+ " <td>1.142062</td>\n",
+ " <td>0.279188</td>\n",
+ " <td>0.269340</td>\n",
+ " <td>0.451953</td>\n",
+ " <td>56.391544</td>\n",
+ " <td>[63280, 63281, 63282, 63283, 63284, 63285, 632...</td>\n",
+ " <td>[317.5191955566406, 261.3891906738281, 193.888...</td>\n",
+ " <td>[129.79551696777344, 123.28179931640625, 119.7...</td>\n",
+ " <td>[35.44173812866211, 53.170982360839844, 21.110...</td>\n",
+ " <td>[19.70448875427246, 21.073566436767578, 18.584...</td>\n",
+ " <td>[0.0, 0.04225060687144257]</td>\n",
+ " <td>[1.0, 0.6516932249069214]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 6.874102592468262, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.4469539885137614, 0.3397471620427446, 0.281...</td>\n",
+ " <td>[0.4469539885137614, 0.3397471620427446, 0.281...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>34.789767</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>[141, 141, 141, 141, 141, 142, 142, 142, 142, ...</td>\n",
+ " <td>[276, 277, 278, 279, 280, 275, 276, 277, 278, ...</td>\n",
+ " <td>[124.626335, 108.03297, 181.62836, 120.1023, 1...</td>\n",
+ " <td>[148, 278]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.367490</td>\n",
+ " <td>4.104028</td>\n",
+ " <td>1.016371</td>\n",
+ " <td>1.133333</td>\n",
+ " <td>125</td>\n",
+ " <td>119</td>\n",
+ " <td>[False, False, False, False, False, True, True...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>6.512914</td>\n",
+ " <td>1.114381</td>\n",
+ " <td>1.586294</td>\n",
+ " <td>1.686918</td>\n",
+ " <td>1.618299</td>\n",
+ " <td>31.104467</td>\n",
+ " <td>[68874, 68875, 68876, 68877, 68878, 68879, 688...</td>\n",
+ " <td>[200.85630798339844, 228.59388732910156, 200.2...</td>\n",
+ " <td>[125.1160888671875, 123.97760009765625, 130.18...</td>\n",
+ " <td>[21.816181182861328, 21.834075927734375, 24.80...</td>\n",
+ " <td>[18.0386962890625, 21.598777770996094, 21.8289...</td>\n",
+ " <td>[1.0, 0.59403637207295]</td>\n",
+ " <td>[0.0, 0.49555009603500366]</td>\n",
+ " <td>[0.0, 4.527439117431641, 0.0, 0.0, 0.0, 13.525...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.3447188232432147, 0.32276301024105575, 0.44...</td>\n",
+ " <td>[0.3447188232432147, 0.32276301024105575, 0.44...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>18.963004</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>[177, 177, 177, 178, 178, 178, 178, 178, 178, ...</td>\n",
+ " <td>[315, 316, 317, 314, 315, 316, 317, 318, 319, ...</td>\n",
+ " <td>[136.54901, 98.068665, 111.988014, 102.38901, ...</td>\n",
+ " <td>[182, 316]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>3.050270</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.211009</td>\n",
+ " <td>71</td>\n",
+ " <td>66</td>\n",
+ " <td>[False, False, False, True, True, True, True, ...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.390512</td>\n",
+ " <td>1.002464</td>\n",
+ " <td>0.901015</td>\n",
+ " <td>0.935601</td>\n",
+ " <td>0.963421</td>\n",
+ " <td>21.909998</td>\n",
+ " <td>[87345, 87346, 87347, 87348, 87349, 87350, 873...</td>\n",
+ " <td>[196.985107421875, 129.81231689453125, 109.104...</td>\n",
+ " <td>[83.2863998413086, 88.12649536132812, 80.53460...</td>\n",
+ " <td>[19.832273483276367, 3.934216022491455, 7.2792...</td>\n",
+ " <td>[15.634844779968262, 12.028639793395996, 16.15...</td>\n",
+ " <td>[1.0, 0.6350150704091108]</td>\n",
+ " <td>[0.0, 0.4965686798095703]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 1.1192702054977417, 0.0, 6.603...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.42712841434253623, 0.5596610374297426, 0.35...</td>\n",
+ " <td>[0.42712841434253623, 0.5596610374297426, 0.35...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>13.393158</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>[185, 185, 185, 185, 185, 186, 186, 186, 186, ...</td>\n",
+ " <td>[246, 247, 248, 249, 250, 245, 246, 247, 248, ...</td>\n",
+ " <td>[94.352005, 97.25834, 112.49667, 98.12432, 103...</td>\n",
+ " <td>[189, 248]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.823255</td>\n",
+ " <td>2.511150</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.323529</td>\n",
+ " <td>67</td>\n",
+ " <td>45</td>\n",
+ " <td>[False, False, False, False, False, False, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>3.757463</td>\n",
+ " <td>0.998899</td>\n",
+ " <td>0.850254</td>\n",
+ " <td>0.637910</td>\n",
+ " <td>0.720139</td>\n",
+ " <td>26.368763</td>\n",
+ " <td>[91373, 91374, 91375, 91376, 91377, 91378, 913...</td>\n",
+ " <td>[167.29823303222656, 121.73543548583984, 190.3...</td>\n",
+ " <td>[97.98750305175781, 92.71749877929688, 105.447...</td>\n",
+ " <td>[15.398932456970215, 24.674497604370117, 20.11...</td>\n",
+ " <td>[18.387500762939453, 18.625, 18.82500076293945...</td>\n",
+ " <td>[0.0, 0.23703065094056855]</td>\n",
+ " <td>[0.0, 0.531902551651001]</td>\n",
+ " <td>[0.0, 0.0, 20.364572525024414, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.5979055526302492, 0.46991087817612653, 0.77...</td>\n",
+ " <td>[0.5979055526302492, 0.46991087817612653, 0.77...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>16.534836</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>[226, 226, 226, 226, 226, 227, 227, 227, 227, ...</td>\n",
+ " <td>[461, 462, 463, 464, 465, 460, 461, 462, 463, ...</td>\n",
+ " <td>[77.01567, 77.573, 79.867325, 78.52466, 51.992...</td>\n",
+ " <td>[231, 463]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.008665</td>\n",
+ " <td>3.071492</td>\n",
+ " <td>1.001696</td>\n",
+ " <td>1.218182</td>\n",
+ " <td>86</td>\n",
+ " <td>67</td>\n",
+ " <td>[False, False, False, False, False, False, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.585445</td>\n",
+ " <td>1.016950</td>\n",
+ " <td>1.091370</td>\n",
+ " <td>0.949777</td>\n",
+ " <td>0.821173</td>\n",
+ " <td>15.786131</td>\n",
+ " <td>[112579, 112580, 112581, 112582, 112583, 11258...</td>\n",
+ " <td>[85.48933410644531, 91.53898620605469, 120.094...</td>\n",
+ " <td>[50.592342376708984, 46.48423385620117, 39.837...</td>\n",
+ " <td>[17.71015739440918, 11.310561180114746, 17.023...</td>\n",
+ " <td>[9.5, 12.175675392150879, 13.53153133392334, 1...</td>\n",
+ " <td>[1.0, 0.5569769694576615]</td>\n",
+ " <td>[0.0, 0.5071209073066711]</td>\n",
+ " <td>[0.0, 9.91696548461914, 2.587313652038574, 0.0...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.31598268340817015, 0.1559547081931097, -0.1...</td>\n",
+ " <td>[0.31598268340817015, 0.1559547081931097, -0.1...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>12.698708</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>[232, 233, 233, 234, 234, 234, 234, 235, 235, ...</td>\n",
+ " <td>[195, 195, 196, 195, 196, 197, 199, 195, 196, ...</td>\n",
+ " <td>[89.65165, 80.839005, 107.42232, 83.21235, 114...</td>\n",
+ " <td>[235, 196]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.457185</td>\n",
+ " <td>1.358220</td>\n",
+ " <td>1.026740</td>\n",
+ " <td>1.857143</td>\n",
+ " <td>16</td>\n",
+ " <td>13</td>\n",
+ " <td>[False, True, True, True, True, True, False, T...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>2.329186</td>\n",
+ " <td>1.123641</td>\n",
+ " <td>0.203046</td>\n",
+ " <td>0.184285</td>\n",
+ " <td>0.646814</td>\n",
+ " <td>49.204193</td>\n",
+ " <td>[112823, 112824, 112825, 112826, 112827, 11282...</td>\n",
+ " <td>[77.82234954833984, 79.00138854980469, 129.929...</td>\n",
+ " <td>[98.61880493164062, 105.93647003173828, 106.55...</td>\n",
+ " <td>[14.710390090942383, 9.134910583496094, 25.785...</td>\n",
+ " <td>[18.48411750793457, 15.575603485107422, 20.789...</td>\n",
+ " <td>[0.0, 0.12247908321998226]</td>\n",
+ " <td>[0.0, 0.5557902455329895]</td>\n",
+ " <td>[0.0, 0.0, 11.1720609664917, 0.0, 15.451468467...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.17878273718770565, 0.3562295478287571, 0.37...</td>\n",
+ " <td>[0.17878273718770565, 0.3562295478287571, 0.37...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>24.034887</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>[245, 245, 245, 245, 245, 246, 246, 246, 246, ...</td>\n",
+ " <td>[325, 326, 327, 328, 329, 324, 325, 326, 327, ...</td>\n",
+ " <td>[97.38135, 106.39031, 121.905, 89.459656, 83.7...</td>\n",
+ " <td>[249, 327]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.013710</td>\n",
+ " <td>3.092090</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.225225</td>\n",
+ " <td>76</td>\n",
+ " <td>68</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.303190</td>\n",
+ " <td>1.005409</td>\n",
+ " <td>0.964467</td>\n",
+ " <td>0.963953</td>\n",
+ " <td>0.465690</td>\n",
+ " <td>38.691765</td>\n",
+ " <td>[122172, 122173, 122174, 122175, 122176, 12217...</td>\n",
+ " <td>[263.3775634765625, 170.52735900878906, 195.68...</td>\n",
+ " <td>[100.90291595458984, 115.1917495727539, 108.84...</td>\n",
+ " <td>[37.93521499633789, 38.935028076171875, 23.091...</td>\n",
+ " <td>[24.135921478271484, 25.541261672973633, 25.81...</td>\n",
+ " <td>[0.0, 0.31518866035777654]</td>\n",
+ " <td>[0.0, 0.6001868844032288]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 9.435110092163086, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.1253581893085868, 0.43417906458645533, 0.29...</td>\n",
+ " <td>[0.1253581893085868, 0.43417906458645533, 0.29...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>28.536033</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>[248, 248, 248, 248, 248, 249, 249, 249, 249, ...</td>\n",
+ " <td>[236, 237, 238, 239, 240, 234, 235, 236, 237, ...</td>\n",
+ " <td>[97.9397, 103.28203, 152.8473, 131.3603, 104.0...</td>\n",
+ " <td>[252, 237]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.001734</td>\n",
+ " <td>3.050270</td>\n",
+ " <td>1.001734</td>\n",
+ " <td>1.211009</td>\n",
+ " <td>74</td>\n",
+ " <td>66</td>\n",
+ " <td>[True, True, True, True, False, True, True, Tr...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.698575</td>\n",
+ " <td>1.022971</td>\n",
+ " <td>0.939086</td>\n",
+ " <td>0.935601</td>\n",
+ " <td>0.304923</td>\n",
+ " <td>27.952183</td>\n",
+ " <td>[123618, 123619, 123620, 123621, 123622, 12362...</td>\n",
+ " <td>[128.16749572753906, 180.881591796875, 156.231...</td>\n",
+ " <td>[143.03883361816406, 116.93932342529297, 139.7...</td>\n",
+ " <td>[8.547542572021484, 45.001068115234375, 22.510...</td>\n",
+ " <td>[19.483009338378906, 21.65048599243164, 25.837...</td>\n",
+ " <td>[0.0, 0.27568337357308575]</td>\n",
+ " <td>[0.0, 0.5360695123672485]</td>\n",
+ " <td>[0.0, 25.326162338256836, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.8711807509724349, 0.2996534638086047, 0.799...</td>\n",
+ " <td>[0.8711807509724349, 0.2996534638086047, 0.799...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>23.928035</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>[266, 266, 266, 267, 267, 267, 267, 267, 268, ...</td>\n",
+ " <td>[157, 158, 159, 156, 157, 158, 159, 160, 155, ...</td>\n",
+ " <td>[101.42234, 90.23266, 136.36002, 95.29129, 124...</td>\n",
+ " <td>[268, 158]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.564162</td>\n",
+ " <td>1.692638</td>\n",
+ " <td>1.016665</td>\n",
+ " <td>1.500000</td>\n",
+ " <td>23</td>\n",
+ " <td>21</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>2.737268</td>\n",
+ " <td>1.077109</td>\n",
+ " <td>0.291878</td>\n",
+ " <td>0.297691</td>\n",
+ " <td>0.572762</td>\n",
+ " <td>40.474602</td>\n",
+ " <td>[130191, 130192, 130193, 130194, 130195, 13019...</td>\n",
+ " <td>[173.8922882080078, 104.74407196044922, 214.33...</td>\n",
+ " <td>[104.98516845703125, 115.12113952636719, 112.6...</td>\n",
+ " <td>[27.064977645874023, 14.11889362335205, -3.308...</td>\n",
+ " <td>[17.255870819091797, 16.194067001342773, 22.89...</td>\n",
+ " <td>[0.0, 0.0812641652292859]</td>\n",
+ " <td>[0.0, 0.4896278977394104]</td>\n",
+ " <td>[0.0, 0.0, 5.292405605316162, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2811353291712975, 0.5383316295043573, 0.476...</td>\n",
+ " <td>[0.2811353291712975, 0.5383316295043573, 0.476...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>19.343984</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>[272, 272, 272, 273, 273, 273, 273, 273, 273, ...</td>\n",
+ " <td>[305, 306, 307, 303, 304, 305, 306, 307, 308, ...</td>\n",
+ " <td>[92.97467, 110.365326, 76.94368, 109.505005, 8...</td>\n",
+ " <td>[278, 306]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.225295</td>\n",
+ " <td>3.716985</td>\n",
+ " <td>1.005514</td>\n",
+ " <td>1.166667</td>\n",
+ " <td>106</td>\n",
+ " <td>98</td>\n",
+ " <td>[False, False, False, True, True, True, True, ...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>5.693487</td>\n",
+ " <td>1.068414</td>\n",
+ " <td>1.345178</td>\n",
+ " <td>1.389226</td>\n",
+ " <td>0.838399</td>\n",
+ " <td>29.337097</td>\n",
+ " <td>[135974, 135975, 135976, 135977, 135978, 13597...</td>\n",
+ " <td>[172.20387268066406, 196.96142578125, 150.4193...</td>\n",
+ " <td>[76.48686981201172, 85.49090576171875, 94.5494...</td>\n",
+ " <td>[21.20001220703125, 9.3588285446167, 10.436163...</td>\n",
+ " <td>[10.618182182312012, 14.323232650756836, 16.13...</td>\n",
+ " <td>[0.0, 0.3551817466873422]</td>\n",
+ " <td>[0.0, 0.4980297386646271]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 11.847755432128906, 3.36206650...</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.1251134853395897, 0.09527923850691102, 0.3...</td>\n",
+ " <td>[-0.1251134853395897, 0.09527923850691102, 0.3...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>16.356201</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>[296, 296, 296, 296, 296, 296, 297, 297, 297, ...</td>\n",
+ " <td>[240, 241, 242, 243, 244, 245, 239, 240, 241, ...</td>\n",
+ " <td>[84.87599, 149.15967, 100.07534, 127.11835, 11...</td>\n",
+ " <td>[299, 243]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.954412</td>\n",
+ " <td>2.889354</td>\n",
+ " <td>1.007566</td>\n",
+ " <td>1.229167</td>\n",
+ " <td>62</td>\n",
+ " <td>59</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.354333</td>\n",
+ " <td>1.040510</td>\n",
+ " <td>0.786802</td>\n",
+ " <td>0.836371</td>\n",
+ " <td>0.282072</td>\n",
+ " <td>43.232220</td>\n",
+ " <td>[148199, 148200, 148201, 148202, 148203, 14820...</td>\n",
+ " <td>[244.74801635742188, 309.0820007324219, 281.69...</td>\n",
+ " <td>[132.90200805664062, 135.54522705078125, 126.9...</td>\n",
+ " <td>[32.580955505371094, 10.701029777526855, 33.93...</td>\n",
+ " <td>[22.472362518310547, 34.243717193603516, 30.47...</td>\n",
+ " <td>[0.0, 0.3620637409560754]</td>\n",
+ " <td>[0.0, 0.5504989624023438]</td>\n",
+ " <td>[0.0, 41.399085998535156, 0.0, 0.0, 43.0873756...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.25744066175173336, 0.3001019756083122, 0.16...</td>\n",
+ " <td>[0.25744066175173336, 0.3001019756083122, 0.16...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>28.372598</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>[303, 303, 304, 304, 304, 304, 304, 304, 305, ...</td>\n",
+ " <td>[226, 227, 223, 224, 225, 226, 227, 228, 223, ...</td>\n",
+ " <td>[83.44468, 100.25865, 149.66637, 125.811356, 1...</td>\n",
+ " <td>[310, 227]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.348367</td>\n",
+ " <td>4.052854</td>\n",
+ " <td>1.014812</td>\n",
+ " <td>1.142857</td>\n",
+ " <td>119</td>\n",
+ " <td>116</td>\n",
+ " <td>[False, False, False, True, True, True, True, ...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>6.921710</td>\n",
+ " <td>1.128460</td>\n",
+ " <td>1.510152</td>\n",
+ " <td>1.644390</td>\n",
+ " <td>1.224571</td>\n",
+ " <td>27.631636</td>\n",
+ " <td>[151767, 151768, 151769, 151770, 151771, 15177...</td>\n",
+ " <td>[178.19068908691406, 192.14321899414062, 166.6...</td>\n",
+ " <td>[126.8442611694336, 163.27049255371094, 155.64...</td>\n",
+ " <td>[38.20425796508789, 19.896989822387695, 32.991...</td>\n",
+ " <td>[21.94672203063965, 27.06352424621582, 29.3196...</td>\n",
+ " <td>[1.0, 0.5729795959203344]</td>\n",
+ " <td>[0.0, 0.5129371881484985]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 17.434009552001953, 0.0, 0.0, ...</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.0711396902237596, 0.5727540243990961, 0.43...</td>\n",
+ " <td>[-0.0711396902237596, 0.5727540243990961, 0.43...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>27.890615</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>[324, 324, 324, 324, 324, 324, 324, 325, 325, ...</td>\n",
+ " <td>[304, 305, 306, 307, 308, 309, 310, 302, 303, ...</td>\n",
+ " <td>[145.95636, 193.80801, 164.54097, 169.56934, 1...</td>\n",
+ " <td>[330, 304]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.564387</td>\n",
+ " <td>4.686828</td>\n",
+ " <td>1.018131</td>\n",
+ " <td>1.123188</td>\n",
+ " <td>158</td>\n",
+ " <td>155</td>\n",
+ " <td>[True, True, True, True, True, True, False, Tr...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>7.754662</td>\n",
+ " <td>1.142035</td>\n",
+ " <td>2.005076</td>\n",
+ " <td>2.197246</td>\n",
+ " <td>3.318635</td>\n",
+ " <td>33.220047</td>\n",
+ " <td>[162594, 162595, 162596, 162597, 162598, 16259...</td>\n",
+ " <td>[245.7649688720703, 218.7645721435547, 181.974...</td>\n",
+ " <td>[167.29151916503906, 172.51589965820312, 141.5...</td>\n",
+ " <td>[24.093252182006836, 27.343393325805664, 25.61...</td>\n",
+ " <td>[32.468196868896484, 38.936397552490234, 30.38...</td>\n",
+ " <td>[1.0, 0.5185813445220834]</td>\n",
+ " <td>[0.0, 0.43891873955726624]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 8.143514633178711, 22.779...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.17702394161205348, 0.2297014840258989, -0.0...</td>\n",
+ " <td>[0.17702394161205348, 0.2297014840258989, -0.0...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>27.928532</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>[329, 330, 330, 330, 330, 330, 330, 330, 330, ...</td>\n",
+ " <td>[223, 219, 220, 221, 222, 223, 224, 225, 226, ...</td>\n",
+ " <td>[138.75534, 163.43802, 178.58865, 156.22168, 2...</td>\n",
+ " <td>[336, 224]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.502636</td>\n",
+ " <td>4.413583</td>\n",
+ " <td>1.038486</td>\n",
+ " <td>1.078125</td>\n",
+ " <td>147</td>\n",
+ " <td>138</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>7.613810</td>\n",
+ " <td>1.158409</td>\n",
+ " <td>1.865482</td>\n",
+ " <td>1.956258</td>\n",
+ " <td>0.976461</td>\n",
+ " <td>46.384895</td>\n",
+ " <td>[165073, 165074, 165075, 165076, 165077, 16507...</td>\n",
+ " <td>[558.0877075195312, 543.9400634765625, 568.182...</td>\n",
+ " <td>[189.59619140625, 175.2384796142578, 174.62957...</td>\n",
+ " <td>[47.43489074707031, 52.882080078125, 48.221935...</td>\n",
+ " <td>[35.400634765625, 30.90460968017578, 28.821939...</td>\n",
+ " <td>[0.0, 0.4863324314103594]</td>\n",
+ " <td>[0.0, 0.5507894158363342]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.5328021266560912, 0.30939625155891576, 0.29...</td>\n",
+ " <td>[0.5328021266560912, 0.30939625155891576, 0.29...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>36.552379</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>[353, 353, 353, 353, 353, 353, 354, 354, 354, ...</td>\n",
+ " <td>[340, 341, 342, 343, 344, 345, 338, 339, 340, ...</td>\n",
+ " <td>[93.87101, 126.815994, 110.26731, 85.311005, 1...</td>\n",
+ " <td>[356, 342]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.985086</td>\n",
+ " <td>2.982562</td>\n",
+ " <td>1.007449</td>\n",
+ " <td>1.247525</td>\n",
+ " <td>72</td>\n",
+ " <td>63</td>\n",
+ " <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+ " <td>[False, False, False, False, False, False, Fal...</td>\n",
+ " <td>4.712706</td>\n",
+ " <td>1.075821</td>\n",
+ " <td>0.913706</td>\n",
+ " <td>0.893074</td>\n",
+ " <td>0.250606</td>\n",
+ " <td>34.907230</td>\n",
+ " <td>[177482, 177483, 177484, 177485, 177486, 17748...</td>\n",
+ " <td>[318.28240966796875, 196.26220703125, 275.3386...</td>\n",
+ " <td>[122.53140258789062, 118.51207733154297, 139.7...</td>\n",
+ " <td>[56.325172424316406, 127.82173919677734, 115.7...</td>\n",
+ " <td>[26.671497344970703, 29.33333396911621, 25.060...</td>\n",
+ " <td>[0.0, 0.41391623892189955]</td>\n",
+ " <td>[1.0, 0.7544840574264526]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2301474713828739, 0.16546045480804852, 0.50...</td>\n",
+ " <td>[0.2301474713828739, 0.16546045480804852, 0.50...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>80.087524</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>[382, 382, 382, 382, 383, 383, 383, 383, 383, ...</td>\n",
+ " <td>[347, 348, 349, 350, 345, 346, 347, 348, 349, ...</td>\n",
+ " <td>[95.458, 94.66168, 80.534676, 76.242676, 66.90...</td>\n",
+ " <td>[386, 349]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.006957</td>\n",
+ " <td>3.071492</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.218182</td>\n",
+ " <td>73</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
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+ " <td>[55, 56, 55, 56, 57, 58, 59, 54, 55, 56, 57, 5...</td>\n",
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+ " <td>[0.0, 18.88400650024414, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
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+ " <td>[0.5870835397580942, -0.23022304812531788, -0....</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>23.604597</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
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+ "\n",
+ " lam med \n",
+ "roi# \n",
+ "0 [101.35066, 104.43834, 143.1773, 191.57831, 19... [34, 342] \\\n",
+ "1 [102.81334, 102.63232, 98.31734, 93.77065, 122... [46, 314] \n",
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+ "\n",
+ " footprint mrs mrs0 compact solidity npix npix_soma \n",
+ "roi# \n",
+ "0 1 1.149791 3.412601 1.027713 1.233083 87 82 \\\n",
+ "1 1 0.982629 2.982562 1.004936 1.200000 71 63 \n",
+ "2 1 0.815101 2.486277 1.000000 1.313433 49 44 \n",
+ "3 1 1.265083 3.852942 1.001532 1.179775 108 105 \n",
+ "4 1 0.939076 2.841870 1.007940 1.175258 61 57 \n",
+ "5 1 0.815101 2.486277 1.000000 1.313433 60 44 \n",
+ "6 1 1.394949 4.223553 1.007439 1.150685 135 126 \n",
+ "7 1 0.815101 2.486277 1.000000 1.313433 58 44 \n",
+ "8 1 0.990815 3.005837 1.005462 1.219048 82 64 \n",
+ "9 1 0.587293 1.791402 1.000000 1.533333 25 23 \n",
+ "10 1 1.008665 3.071492 1.001696 1.218182 74 67 \n",
+ "11 1 1.000000 3.050270 1.000000 1.211009 67 66 \n",
+ "12 1 1.472473 4.336495 1.035730 1.151515 136 133 \n",
+ "13 1 1.432848 4.351917 1.004287 1.116667 158 134 \n",
+ "14 1 0.564749 1.635435 1.053321 1.583333 22 19 \n",
+ "15 1 1.367490 4.104028 1.016371 1.133333 125 119 \n",
+ "16 1 1.000000 3.050270 1.000000 1.211009 71 66 \n",
+ "17 1 0.823255 2.511150 1.000000 1.323529 67 45 \n",
+ "18 1 1.008665 3.071492 1.001696 1.218182 86 67 \n",
+ "19 1 0.457185 1.358220 1.026740 1.857143 16 13 \n",
+ "20 1 1.013710 3.092090 1.000000 1.225225 76 68 \n",
+ "21 1 1.001734 3.050270 1.001734 1.211009 74 66 \n",
+ "22 1 0.564162 1.692638 1.016665 1.500000 23 21 \n",
+ "23 1 1.225295 3.716985 1.005514 1.166667 106 98 \n",
+ "24 1 0.954412 2.889354 1.007566 1.229167 62 59 \n",
+ "25 1 1.348367 4.052854 1.014812 1.142857 119 116 \n",
+ "26 1 1.564387 4.686828 1.018131 1.123188 158 155 \n",
+ "27 1 1.502636 4.413583 1.038486 1.078125 147 138 \n",
+ "28 1 0.985086 2.982562 1.007449 1.247525 72 63 \n",
+ "29 1 1.006957 3.071492 1.000000 1.218182 73 67 \n",
+ "30 1 0.601459 1.834612 1.000000 1.548387 36 24 \n",
+ "31 1 0.614491 1.874364 1.000000 1.562500 43 25 \n",
+ "32 1 0.801087 2.432978 1.004338 1.312500 46 42 \n",
+ "33 1 1.240925 3.774605 1.002795 1.160920 107 101 \n",
+ "34 1 0.823255 2.511150 1.000000 1.323529 58 45 \n",
+ "\n",
+ " soma_crop \n",
+ "roi# \n",
+ "0 [False, True, True, True, True, True, True, Tr... \\\n",
+ "1 [False, False, False, False, True, True, True,... \n",
+ "2 [False, False, False, True, True, True, True, ... \n",
+ "3 [True, True, True, True, True, True, True, Tru... \n",
+ "4 [True, True, True, True, True, True, True, Tru... \n",
+ "5 [False, False, False, False, False, True, True... \n",
+ "6 [False, False, False, False, True, True, True,... \n",
+ "7 [False, True, True, True, True, False, True, T... \n",
+ "8 [False, False, False, True, True, True, True, ... \n",
+ "9 [True, True, True, True, False, True, True, Tr... \n",
+ "10 [True, True, True, True, True, False, True, Tr... \n",
+ "11 [False, True, True, True, True, True, True, Tr... \n",
+ "12 [False, False, False, True, True, True, True, ... \n",
+ "13 [True, True, True, True, True, True, True, Tru... \n",
+ "14 [True, True, True, True, True, True, True, Tru... \n",
+ "15 [False, False, False, False, False, True, True... \n",
+ "16 [False, False, False, True, True, True, True, ... \n",
+ "17 [False, False, False, False, False, False, Tru... \n",
+ "18 [False, False, False, False, False, False, Tru... \n",
+ "19 [False, True, True, True, True, True, False, T... \n",
+ "20 [True, True, True, True, True, True, True, Tru... \n",
+ "21 [True, True, True, True, False, True, True, Tr... \n",
+ "22 [True, True, True, True, True, True, True, Tru... \n",
+ "23 [False, False, False, True, True, True, True, ... \n",
+ "24 [True, True, True, True, True, True, True, Tru... \n",
+ "25 [False, False, False, True, True, True, True, ... \n",
+ "26 [True, True, True, True, True, True, False, Tr... \n",
+ "27 [True, True, True, True, True, True, True, Tru... \n",
+ "28 [True, True, True, True, True, True, True, Tru... \n",
+ "29 [True, True, True, True, False, True, True, Tr... \n",
+ "30 [False, False, True, True, True, True, False, ... \n",
+ "31 [False, False, False, False, False, False, Tru... \n",
+ "32 [True, True, True, True, True, True, True, Tru... \n",
+ "33 [False, False, False, False, False, True, True... \n",
+ "34 [False, False, True, True, True, True, True, T... \n",
+ "\n",
+ " overlap radius \n",
+ "roi# \n",
+ "0 [False, False, False, False, False, False, Fal... 5.784065 \\\n",
+ "1 [False, False, False, False, False, False, Fal... 4.598673 \n",
+ "2 [False, False, False, False, False, False, Fal... 3.462338 \n",
+ "3 [False, False, False, False, False, False, Fal... 5.667794 \n",
+ "4 [False, False, False, False, False, False, Fal... 4.485627 \n",
+ "5 [False, False, False, False, False, False, Fal... 3.662520 \n",
+ "6 [False, False, False, False, False, False, Fal... 6.696339 \n",
+ "7 [False, False, False, False, False, False, Fal... 3.814316 \n",
+ "8 [False, False, False, False, False, False, Fal... 4.605765 \n",
+ "9 [False, False, False, False, False, False, Fal... 2.606331 \n",
+ "10 [False, False, False, False, False, False, Fal... 4.781948 \n",
+ "11 [False, False, False, False, False, False, Fal... 4.541828 \n",
+ "12 [False, False, False, False, False, False, Fal... 7.620947 \n",
+ "13 [False, False, False, False, False, False, Fal... 6.317062 \n",
+ "14 [False, False, False, False, False, False, Fal... 2.898275 \n",
+ "15 [False, False, False, False, False, False, Fal... 6.512914 \n",
+ "16 [False, False, False, False, False, False, Fal... 4.390512 \n",
+ "17 [False, False, False, False, False, False, Fal... 3.757463 \n",
+ "18 [False, False, False, False, False, False, Fal... 4.585445 \n",
+ "19 [False, False, False, False, False, False, Fal... 2.329186 \n",
+ "20 [False, False, False, False, False, False, Fal... 4.303190 \n",
+ "21 [False, False, False, False, False, False, Fal... 4.698575 \n",
+ "22 [False, False, False, False, False, False, Fal... 2.737268 \n",
+ "23 [False, False, False, False, False, False, Fal... 5.693487 \n",
+ "24 [False, False, False, False, False, False, Fal... 4.354333 \n",
+ "25 [False, False, False, False, False, False, Fal... 6.921710 \n",
+ "26 [False, False, False, False, False, False, Fal... 7.754662 \n",
+ "27 [False, False, False, False, False, False, Fal... 7.613810 \n",
+ "28 [False, False, False, False, False, False, Fal... 4.712706 \n",
+ "29 [False, False, False, False, False, False, Fal... 4.427410 \n",
+ "30 [False, False, False, False, False, False, Fal... 2.887147 \n",
+ "31 [False, False, False, False, False, False, Fal... 2.892409 \n",
+ "32 [False, False, False, False, False, False, Fal... 3.832190 \n",
+ "33 [False, False, False, False, False, False, Fal... 5.674657 \n",
+ "34 [False, False, False, False, False, False, Fal... 3.672857 \n",
+ "\n",
+ " aspect_ratio npix_norm_no_crop npix_norm skew std \n",
+ "roi# \n",
+ "0 1.137769 1.104061 1.162414 1.321311 23.987156 \\\n",
+ "1 1.054807 0.901015 0.893074 0.456027 32.054146 \n",
+ "2 1.012388 0.621827 0.623734 0.862335 46.924488 \n",
+ "3 1.027905 1.370558 1.488457 1.165069 26.665464 \n",
+ "4 1.059862 0.774112 0.808019 0.531406 25.071770 \n",
+ "5 0.996759 0.761421 0.623734 0.615781 29.122208 \n",
+ "6 1.101419 1.713198 1.786148 1.632249 27.695444 \n",
+ "7 1.015916 0.736041 0.623734 0.424445 31.225199 \n",
+ "8 1.055293 1.040609 0.907250 0.739351 32.761204 \n",
+ "9 0.992922 0.317259 0.326043 0.546683 55.231182 \n",
+ "10 1.029498 0.939086 0.949777 0.797232 29.699617 \n",
+ "11 1.012344 0.850254 0.935601 0.601344 34.122639 \n",
+ "12 1.177686 1.725888 1.885379 0.780675 30.666241 \n",
+ "13 1.008854 2.005076 1.899555 1.451811 32.600288 \n",
+ "14 1.142062 0.279188 0.269340 0.451953 56.391544 \n",
+ "15 1.114381 1.586294 1.686918 1.618299 31.104467 \n",
+ "16 1.002464 0.901015 0.935601 0.963421 21.909998 \n",
+ "17 0.998899 0.850254 0.637910 0.720139 26.368763 \n",
+ "18 1.016950 1.091370 0.949777 0.821173 15.786131 \n",
+ "19 1.123641 0.203046 0.184285 0.646814 49.204193 \n",
+ "20 1.005409 0.964467 0.963953 0.465690 38.691765 \n",
+ "21 1.022971 0.939086 0.935601 0.304923 27.952183 \n",
+ "22 1.077109 0.291878 0.297691 0.572762 40.474602 \n",
+ "23 1.068414 1.345178 1.389226 0.838399 29.337097 \n",
+ "24 1.040510 0.786802 0.836371 0.282072 43.232220 \n",
+ "25 1.128460 1.510152 1.644390 1.224571 27.631636 \n",
+ "26 1.142035 2.005076 2.197246 3.318635 33.220047 \n",
+ "27 1.158409 1.865482 1.956258 0.976461 46.384895 \n",
+ "28 1.075821 0.913706 0.893074 0.250606 34.907230 \n",
+ "29 1.003615 0.926396 0.949777 0.494216 33.261879 \n",
+ "30 1.071801 0.456853 0.340219 0.782892 33.009380 \n",
+ "31 1.001218 0.545685 0.354394 0.474616 27.281857 \n",
+ "32 1.057816 0.583756 0.595383 0.549953 28.595476 \n",
+ "33 1.023936 1.357868 1.431754 0.796461 24.056343 \n",
+ "34 1.001364 0.736041 0.637910 0.593422 20.755442 \n",
+ "\n",
+ " neuropil_mask \n",
+ "roi# \n",
+ "0 [12617, 12618, 12619, 12620, 12621, 12622, 126... \\\n",
+ "1 [17711, 17712, 17713, 17714, 17715, 17716, 177... \n",
+ "2 [22186, 22187, 22188, 22189, 22190, 22191, 221... \n",
+ "3 [23842, 23843, 23844, 23845, 23846, 23847, 238... \n",
+ "4 [25416, 25417, 25418, 25419, 25420, 25421, 254... \n",
+ "5 [27833, 27834, 27835, 27836, 27837, 27838, 278... \n",
+ "6 [48820, 48821, 48822, 48823, 48824, 48825, 488... \n",
+ "7 [50988, 50989, 50990, 50991, 50992, 50993, 509... \n",
+ "8 [54512, 54513, 54514, 54515, 54516, 54517, 545... \n",
+ "9 [51977, 51978, 51979, 51980, 51981, 51982, 519... \n",
+ "10 [58572, 58573, 58574, 58575, 58576, 58577, 585... \n",
+ "11 [60161, 60162, 60163, 60164, 60165, 60166, 601... \n",
+ "12 [62118, 62119, 62120, 62121, 62122, 62123, 621... \n",
+ "13 [64693, 64694, 64695, 64696, 64697, 64698, 646... \n",
+ "14 [63280, 63281, 63282, 63283, 63284, 63285, 632... \n",
+ "15 [68874, 68875, 68876, 68877, 68878, 68879, 688... \n",
+ "16 [87345, 87346, 87347, 87348, 87349, 87350, 873... \n",
+ "17 [91373, 91374, 91375, 91376, 91377, 91378, 913... \n",
+ "18 [112579, 112580, 112581, 112582, 112583, 11258... \n",
+ "19 [112823, 112824, 112825, 112826, 112827, 11282... \n",
+ "20 [122172, 122173, 122174, 122175, 122176, 12217... \n",
+ "21 [123618, 123619, 123620, 123621, 123622, 12362... \n",
+ "22 [130191, 130192, 130193, 130194, 130195, 13019... \n",
+ "23 [135974, 135975, 135976, 135977, 135978, 13597... \n",
+ "24 [148199, 148200, 148201, 148202, 148203, 14820... \n",
+ "25 [151767, 151768, 151769, 151770, 151771, 15177... \n",
+ "26 [162594, 162595, 162596, 162597, 162598, 16259... \n",
+ "27 [165073, 165074, 165075, 165076, 165077, 16507... \n",
+ "28 [177482, 177483, 177484, 177485, 177486, 17748... \n",
+ "29 [192338, 192339, 192340, 192341, 192342, 19234... \n",
+ "30 [191146, 191147, 191148, 191149, 191150, 19115... \n",
+ "31 [211771, 211772, 211773, 211774, 211775, 21177... \n",
+ "32 [215322, 215323, 215324, 215325, 215326, 21532... \n",
+ "33 [231606, 231607, 231608, 231609, 231610, 23161... \n",
+ "34 [236079, 236080, 236081, 236082, 236083, 23608... \n",
+ "\n",
+ " F \n",
+ "roi# \n",
+ "0 [110.48663330078125, 131.6634063720703, 86.054... \\\n",
+ "1 [209.49720764160156, 196.16419982910156, 163.7... \n",
+ "2 [257.7426452636719, 208.847412109375, 217.7744... \n",
+ "3 [152.53948974609375, 188.36045837402344, 137.2... \n",
+ "4 [140.59927368164062, 117.13068389892578, 135.9... \n",
+ "5 [153.00405883789062, 154.6991424560547, 130.38... \n",
+ "6 [154.900146484375, 132.25148010253906, 113.308... \n",
+ "7 [185.602783203125, 199.90809631347656, 202.467... \n",
+ "8 [146.71072387695312, 180.5610809326172, 159.84... \n",
+ "9 [160.2012176513672, 246.04684448242188, 122.63... \n",
+ "10 [217.0276641845703, 191.68612670898438, 115.39... \n",
+ "11 [243.09519958496094, 177.4669952392578, 174.49... \n",
+ "12 [221.507568359375, 167.84658813476562, 216.181... \n",
+ "13 [241.3565216064453, 229.2249755859375, 202.061... \n",
+ "14 [317.5191955566406, 261.3891906738281, 193.888... \n",
+ "15 [200.85630798339844, 228.59388732910156, 200.2... \n",
+ "16 [196.985107421875, 129.81231689453125, 109.104... \n",
+ "17 [167.29823303222656, 121.73543548583984, 190.3... \n",
+ "18 [85.48933410644531, 91.53898620605469, 120.094... \n",
+ "19 [77.82234954833984, 79.00138854980469, 129.929... \n",
+ "20 [263.3775634765625, 170.52735900878906, 195.68... \n",
+ "21 [128.16749572753906, 180.881591796875, 156.231... \n",
+ "22 [173.8922882080078, 104.74407196044922, 214.33... \n",
+ "23 [172.20387268066406, 196.96142578125, 150.4193... \n",
+ "24 [244.74801635742188, 309.0820007324219, 281.69... \n",
+ "25 [178.19068908691406, 192.14321899414062, 166.6... \n",
+ "26 [245.7649688720703, 218.7645721435547, 181.974... \n",
+ "27 [558.0877075195312, 543.9400634765625, 568.182... \n",
+ "28 [318.28240966796875, 196.26220703125, 275.3386... \n",
+ "29 [216.0693817138672, 103.41817474365234, 162.59... \n",
+ "30 [150.27069091796875, 99.25187683105469, 83.786... \n",
+ "31 [113.871826171875, 137.41046142578125, 138.220... \n",
+ "32 [111.15068817138672, 107.00513458251953, 161.0... \n",
+ "33 [137.907958984375, 109.36089324951172, 131.285... \n",
+ "34 [75.97315979003906, 93.12750244140625, 75.7813... \n",
+ "\n",
+ " Fneu \n",
+ "roi# \n",
+ "0 [81.11736297607422, 93.11980438232422, 73.6161... \\\n",
+ "1 [94.13381958007812, 108.01216888427734, 89.793... \n",
+ "2 [170.16526794433594, 182.0167999267578, 160.24... \n",
+ "3 [97.6956558227539, 107.47368621826172, 91.6819... \n",
+ "4 [73.86310577392578, 82.62413024902344, 78.5197... \n",
+ "5 [143.9366455078125, 140.1763153076172, 143.484... \n",
+ "6 [121.14143371582031, 106.08963775634766, 108.8... \n",
+ "7 [108.78717803955078, 122.18974304199219, 86.58... \n",
+ "8 [115.2066650390625, 112.91555786132812, 107.03... \n",
+ "9 [122.00780487060547, 124.5162582397461, 107.42... \n",
+ "10 [156.29226684570312, 150.33575439453125, 149.9... \n",
+ "11 [154.95980834960938, 137.29550170898438, 130.4... \n",
+ "12 [133.40582275390625, 151.7203826904297, 133.34... \n",
+ "13 [155.1845703125, 162.07550048828125, 137.78019... \n",
+ "14 [129.79551696777344, 123.28179931640625, 119.7... \n",
+ "15 [125.1160888671875, 123.97760009765625, 130.18... \n",
+ "16 [83.2863998413086, 88.12649536132812, 80.53460... \n",
+ "17 [97.98750305175781, 92.71749877929688, 105.447... \n",
+ "18 [50.592342376708984, 46.48423385620117, 39.837... \n",
+ "19 [98.61880493164062, 105.93647003173828, 106.55... \n",
+ "20 [100.90291595458984, 115.1917495727539, 108.84... \n",
+ "21 [143.03883361816406, 116.93932342529297, 139.7... \n",
+ "22 [104.98516845703125, 115.12113952636719, 112.6... \n",
+ "23 [76.48686981201172, 85.49090576171875, 94.5494... \n",
+ "24 [132.90200805664062, 135.54522705078125, 126.9... \n",
+ "25 [126.8442611694336, 163.27049255371094, 155.64... \n",
+ "26 [167.29151916503906, 172.51589965820312, 141.5... \n",
+ "27 [189.59619140625, 175.2384796142578, 174.62957... \n",
+ "28 [122.53140258789062, 118.51207733154297, 139.7... \n",
+ "29 [98.648193359375, 89.99758911132812, 103.96626... \n",
+ "30 [86.23758697509766, 87.51654815673828, 73.2742... \n",
+ "31 [78.9422607421875, 75.00231170654297, 68.89260... \n",
+ "32 [92.90933990478516, 81.20604705810547, 75.7087... \n",
+ "33 [106.62879943847656, 111.67545318603516, 90.75... \n",
+ "34 [87.24615478515625, 60.087181091308594, 67.389... \n",
+ "\n",
+ " F_chan2 \n",
+ "roi# \n",
+ "0 [6.409209251403809, 13.033258438110352, 15.877... \\\n",
+ "1 [20.04505729675293, 25.216228485107422, 19.065... \n",
+ "2 [15.707565307617188, 25.36271858215332, 18.577... \n",
+ "3 [2.0891013145446777, 13.146905899047852, 10.95... \n",
+ "4 [25.282196044921875, 14.495699882507324, 18.03... \n",
+ "5 [9.330554962158203, 21.728002548217773, 8.4426... \n",
+ "6 [13.373459815979004, 8.250761032104492, 15.124... \n",
+ "7 [32.40492630004883, 51.75419998168945, 17.0571... \n",
+ "8 [20.994434356689453, 32.77158737182617, 13.523... \n",
+ "9 [32.472190856933594, 41.31606674194336, 22.256... \n",
+ "10 [14.511828422546387, 15.23607063293457, 8.8653... \n",
+ "11 [45.87001037597656, 30.122678756713867, 43.648... \n",
+ "12 [18.057809829711914, 15.891983032226562, 12.62... \n",
+ "13 [20.861509323120117, 16.645105361938477, 21.99... \n",
+ "14 [35.44173812866211, 53.170982360839844, 21.110... \n",
+ "15 [21.816181182861328, 21.834075927734375, 24.80... \n",
+ "16 [19.832273483276367, 3.934216022491455, 7.2792... \n",
+ "17 [15.398932456970215, 24.674497604370117, 20.11... \n",
+ "18 [17.71015739440918, 11.310561180114746, 17.023... \n",
+ "19 [14.710390090942383, 9.134910583496094, 25.785... \n",
+ "20 [37.93521499633789, 38.935028076171875, 23.091... \n",
+ "21 [8.547542572021484, 45.001068115234375, 22.510... \n",
+ "22 [27.064977645874023, 14.11889362335205, -3.308... \n",
+ "23 [21.20001220703125, 9.3588285446167, 10.436163... \n",
+ "24 [32.580955505371094, 10.701029777526855, 33.93... \n",
+ "25 [38.20425796508789, 19.896989822387695, 32.991... \n",
+ "26 [24.093252182006836, 27.343393325805664, 25.61... \n",
+ "27 [47.43489074707031, 52.882080078125, 48.221935... \n",
+ "28 [56.325172424316406, 127.82173919677734, 115.7... \n",
+ "29 [29.582870483398438, 25.465221405029297, 23.08... \n",
+ "30 [21.179241180419922, 64.40867614746094, 16.719... \n",
+ "31 [28.406667709350586, 30.808317184448242, 13.59... \n",
+ "32 [27.076824188232422, 11.97206974029541, 28.079... \n",
+ "33 [25.454360961914062, 27.795305252075195, 17.49... \n",
+ "34 [36.25589370727539, 25.334177017211914, 17.600... \n",
+ "\n",
+ " Fneu_chan2 \n",
+ "roi# \n",
+ "0 [15.295843124389648, 13.344743728637695, 17.07... \\\n",
+ "1 [12.793187141418457, 14.85401439666748, 15.637... \n",
+ "2 [17.577030181884766, 16.453781127929688, 16.13... \n",
+ "3 [14.183066368103027, 11.311212539672852, 17.01... \n",
+ "4 [13.366589546203613, 12.821345329284668, 13.30... \n",
+ "5 [22.809917449951172, 11.958677291870117, 16.94... \n",
+ "6 [12.934263229370117, 14.001992225646973, 16.03... \n",
+ "7 [12.028204917907715, 13.838461875915527, 17.09... \n",
+ "8 [17.48444366455078, 14.48888874053955, 13.1622... \n",
+ "9 [18.660598754882812, 14.868660926818848, 13.72... \n",
+ "10 [18.992753982543945, 20.224637985229492, 15.88... \n",
+ "11 [27.323877334594727, 22.794326782226562, 19.12... \n",
+ "12 [16.887378692626953, 16.757282257080078, 20.00... \n",
+ "13 [19.875839233398438, 22.432886123657227, 19.91... \n",
+ "14 [19.70448875427246, 21.073566436767578, 18.584... \n",
+ "15 [18.0386962890625, 21.598777770996094, 21.8289... \n",
+ "16 [15.634844779968262, 12.028639793395996, 16.15... \n",
+ "17 [18.387500762939453, 18.625, 18.82500076293945... \n",
+ "18 [9.5, 12.175675392150879, 13.53153133392334, 1... \n",
+ "19 [18.48411750793457, 15.575603485107422, 20.789... \n",
+ "20 [24.135921478271484, 25.541261672973633, 25.81... \n",
+ "21 [19.483009338378906, 21.65048599243164, 25.837... \n",
+ "22 [17.255870819091797, 16.194067001342773, 22.89... \n",
+ "23 [10.618182182312012, 14.323232650756836, 16.13... \n",
+ "24 [22.472362518310547, 34.243717193603516, 30.47... \n",
+ "25 [21.94672203063965, 27.06352424621582, 29.3196... \n",
+ "26 [32.468196868896484, 38.936397552490234, 30.38... \n",
+ "27 [35.400634765625, 30.90460968017578, 28.821939... \n",
+ "28 [26.671497344970703, 29.33333396911621, 25.060... \n",
+ "29 [17.01686668395996, 19.73253059387207, 11.9662... \n",
+ "30 [19.921985626220703, 15.634751319885254, 20.07... \n",
+ "31 [16.353347778320312, 16.806005477905273, 16.27... \n",
+ "32 [17.461538314819336, 12.442307472229004, 13.71... \n",
+ "33 [21.64908790588379, 22.69371223449707, 24.4279... \n",
+ "34 [23.923076629638672, 21.512821197509766, 22.12... \n",
+ "\n",
+ " iscell redcell \n",
+ "roi# \n",
+ "0 [1.0, 0.9649560020447292] [0.0, 0.4927331507205963] \\\n",
+ "1 [0.0, 0.4354095974463412] [0.0, 0.5837860107421875] \n",
+ "2 [1.0, 0.3117388293427187] [0.0, 0.5908480286598206] \n",
+ "3 [0.0, 0.3132977713925523] [0.0, 0.49197638034820557] \n",
+ "4 [0.0, 0.4433111479521616] [0.0, 0.5073467493057251] \n",
+ "5 [0.0, 0.1853835544837747] [0.0, 0.5240342617034912] \n",
+ "6 [1.0, 0.2705239287399592] [0.0, 0.5010843873023987] \n",
+ "7 [0.0, 0.11519655841470867] [0.0, 0.6411923766136169] \n",
+ "8 [1.0, 0.6139257550332846] [0.0, 0.571911633014679] \n",
+ "9 [0.0, 0.022543458027667683] [0.0, 0.5727353096008301] \n",
+ "10 [1.0, 0.5222473979185825] [0.0, 0.46735233068466187] \n",
+ "11 [0.0, 0.4216052811500901] [0.0, 0.6233232617378235] \n",
+ "12 [1.0, 0.43407906008740177] [0.0, 0.5041268467903137] \n",
+ "13 [1.0, 0.12854916895398194] [0.0, 0.4756465256214142] \n",
+ "14 [0.0, 0.04225060687144257] [1.0, 0.6516932249069214] \n",
+ "15 [1.0, 0.59403637207295] [0.0, 0.49555009603500366] \n",
+ "16 [1.0, 0.6350150704091108] [0.0, 0.4965686798095703] \n",
+ "17 [0.0, 0.23703065094056855] [0.0, 0.531902551651001] \n",
+ "18 [1.0, 0.5569769694576615] [0.0, 0.5071209073066711] \n",
+ "19 [0.0, 0.12247908321998226] [0.0, 0.5557902455329895] \n",
+ "20 [0.0, 0.31518866035777654] [0.0, 0.6001868844032288] \n",
+ "21 [0.0, 0.27568337357308575] [0.0, 0.5360695123672485] \n",
+ "22 [0.0, 0.0812641652292859] [0.0, 0.4896278977394104] \n",
+ "23 [0.0, 0.3551817466873422] [0.0, 0.4980297386646271] \n",
+ "24 [0.0, 0.3620637409560754] [0.0, 0.5504989624023438] \n",
+ "25 [1.0, 0.5729795959203344] [0.0, 0.5129371881484985] \n",
+ "26 [1.0, 0.5185813445220834] [0.0, 0.43891873955726624] \n",
+ "27 [0.0, 0.4863324314103594] [0.0, 0.5507894158363342] \n",
+ "28 [0.0, 0.41391623892189955] [1.0, 0.7544840574264526] \n",
+ "29 [0.0, 0.32998067402752457] [0.0, 0.554786205291748] \n",
+ "30 [0.0, 0.04981382444036565] [0.0, 0.5327021479606628] \n",
+ "31 [0.0, 0.02072980819769298] [0.0, 0.60174161195755] \n",
+ "32 [0.0, 0.12061438316332257] [0.0, 0.5817732810974121] \n",
+ "33 [1.0, 0.20595270888845352] [0.0, 0.5029698014259338] \n",
+ "34 [0.0, 0.18570381110579157] [0.0, 0.5029839277267456] \n",
+ "\n",
+ " spks is_neuron \n",
+ "roi# \n",
+ "0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... True \\\n",
+ "1 [0.0, 0.0, 0.0, 27.431922912597656, 0.0, 0.0, ... False \n",
+ "2 [0.0, 0.0, 0.0, 19.099365234375, 0.0, 0.0, 0.0... True \n",
+ "3 [0.0, 1.0597600936889648, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "4 [0.0, 0.0, 0.0, 0.0, 0.0, 1.5610218048095703, ... False \n",
+ "5 [0.0, 0.0, 0.0, 21.451704025268555, 0.0, 0.0, ... False \n",
+ "6 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... True \n",
+ "7 [0.0, 6.787431716918945, 11.328902244567871, 0... False \n",
+ "8 [0.0, 22.53038215637207, 0.0, 0.0, 0.0, 0.0, 0... True \n",
+ "9 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "10 [0.0, 0.0, 0.0, 0.0, 18.23613739013672, 0.0, 0... True \n",
+ "11 [0.0, 0.0, 0.0, 0.0, 0.0, 7.39235258102417, 0.... False \n",
+ "12 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... True \n",
+ "13 [0.0, 0.0, 0.0, 2.7272820472717285, 0.0, 0.0, ... True \n",
+ "14 [0.0, 0.0, 0.0, 0.0, 0.0, 6.874102592468262, 0... False \n",
+ "15 [0.0, 4.527439117431641, 0.0, 0.0, 0.0, 13.525... True \n",
+ "16 [0.0, 0.0, 0.0, 1.1192702054977417, 0.0, 6.603... True \n",
+ "17 [0.0, 0.0, 20.364572525024414, 0.0, 0.0, 0.0, ... False \n",
+ "18 [0.0, 9.91696548461914, 2.587313652038574, 0.0... True \n",
+ "19 [0.0, 0.0, 11.1720609664917, 0.0, 15.451468467... False \n",
+ "20 [0.0, 0.0, 0.0, 9.435110092163086, 0.0, 0.0, 0... False \n",
+ "21 [0.0, 25.326162338256836, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "22 [0.0, 0.0, 5.292405605316162, 0.0, 0.0, 0.0, 0... False \n",
+ "23 [0.0, 0.0, 0.0, 11.847755432128906, 3.36206650... False \n",
+ "24 [0.0, 41.399085998535156, 0.0, 0.0, 43.0873756... False \n",
+ "25 [0.0, 0.0, 0.0, 17.434009552001953, 0.0, 0.0, ... True \n",
+ "26 [0.0, 0.0, 0.0, 0.0, 8.143514633178711, 22.779... True \n",
+ "27 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "28 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "29 [0.0, 0.0, 0.0, 21.49405860900879, 0.0, 0.0, 0... False \n",
+ "30 [0.0, 0.0, 0.0, 33.52047348022461, 0.0, 0.0, 0... False \n",
+ "31 [0.0, 5.386137008666992, 0.0, 0.0, 0.0, 0.0, 0... False \n",
+ "32 [0.0, 4.279571056365967, 23.245716094970703, 0... False \n",
+ "33 [0.0, 0.0, 8.385239601135254, 0.0, 0.0, 0.0, 0... True \n",
+ "34 [0.0, 18.88400650024414, 0.0, 0.0, 0.0, 0.0, 0... False \n",
+ "\n",
+ " F_var \n",
+ "roi# \n",
+ "0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ "1 [0.2883108210421972, 0.5932362809663284, 0.192... \n",
+ "2 [0.5608495401495428, 0.7443431223037524, 0.407... \n",
+ "3 [0.24668316397308548, 0.4711971469352052, 0.10... \n",
+ "4 [0.03368734128291486, 0.2728357744731917, 0.16... \n",
+ "5 [0.4880555886878001, 0.42163433406968576, 0.48... \n",
+ "6 [0.5580097968938957, 0.23501575931973562, 0.29... \n",
+ "7 [0.3395221176386508, 0.6257711399229017, -0.13... \n",
+ "8 [0.3185552548066303, 0.26840759667618413, 0.13... \n",
+ "9 [0.3427790067639393, 0.39612290259181304, 0.03... \n",
+ "10 [0.48875698009025026, 0.4032714989805199, 0.39... \n",
+ "11 [0.6323846377753256, 0.3075497911254068, 0.181... \n",
+ "12 [0.350585996819315, 0.6918782055416933, 0.3494... \n",
+ "13 [0.5691486631506817, 0.6781690150630921, 0.293... \n",
+ "14 [0.4469539885137614, 0.3397471620427446, 0.281... \n",
+ "15 [0.3447188232432147, 0.32276301024105575, 0.44... \n",
+ "16 [0.42712841434253623, 0.5596610374297426, 0.35... \n",
+ "17 [0.5979055526302492, 0.46991087817612653, 0.77... \n",
+ "18 [0.31598268340817015, 0.1559547081931097, -0.1... \n",
+ "19 [0.17878273718770565, 0.3562295478287571, 0.37... \n",
+ "20 [0.1253581893085868, 0.43417906458645533, 0.29... \n",
+ "21 [0.8711807509724349, 0.2996534638086047, 0.799... \n",
+ "22 [0.2811353291712975, 0.5383316295043573, 0.476... \n",
+ "23 [-0.1251134853395897, 0.09527923850691102, 0.3... \n",
+ "24 [0.25744066175173336, 0.3001019756083122, 0.16... \n",
+ "25 [-0.0711396902237596, 0.5727540243990961, 0.43... \n",
+ "26 [0.17702394161205348, 0.2297014840258989, -0.0... \n",
+ "27 [0.5328021266560912, 0.30939625155891576, 0.29... \n",
+ "28 [0.2301474713828739, 0.16546045480804852, 0.50... \n",
+ "29 [0.5679862561165399, 0.3468873788565367, 0.703... \n",
+ "30 [0.4788335155797292, 0.516987579730329, 0.0919... \n",
+ "31 [0.7384792119745599, 0.5925478691694359, 0.366... \n",
+ "32 [0.6194280584484223, 0.3238257032467561, 0.185... \n",
+ "33 [0.3347138584366429, 0.4375312905535715, 0.011... \n",
+ "34 [0.5870835397580942, -0.23022304812531788, -0.... \n",
+ "\n",
+ " Fneu_var in_D1 in_C1 \n",
+ "roi# \n",
+ "0 [0.44800236099089397, 0.7714516639019757, 0.24... True False \\\n",
+ "1 [0.2883108210421972, 0.5932362809663284, 0.192... True False \n",
+ "2 [0.5608495401495428, 0.7443431223037524, 0.407... True False \n",
+ "3 [0.24668316397308548, 0.4711971469352052, 0.10... True False \n",
+ "4 [0.03368734128291486, 0.2728357744731917, 0.16... True False \n",
+ "5 [0.4880555886878001, 0.42163433406968576, 0.48... True False \n",
+ "6 [0.5580097968938957, 0.23501575931973562, 0.29... True False \n",
+ "7 [0.3395221176386508, 0.6257711399229017, -0.13... True False \n",
+ "8 [0.3185552548066303, 0.26840759667618413, 0.13... True False \n",
+ "9 [0.3427790067639393, 0.39612290259181304, 0.03... True False \n",
+ "10 [0.48875698009025026, 0.4032714989805199, 0.39... True False \n",
+ "11 [0.6323846377753256, 0.3075497911254068, 0.181... True False \n",
+ "12 [0.350585996819315, 0.6918782055416933, 0.3494... True False \n",
+ "13 [0.5691486631506817, 0.6781690150630921, 0.293... True False \n",
+ "14 [0.4469539885137614, 0.3397471620427446, 0.281... True False \n",
+ "15 [0.3447188232432147, 0.32276301024105575, 0.44... True False \n",
+ "16 [0.42712841434253623, 0.5596610374297426, 0.35... True False \n",
+ "17 [0.5979055526302492, 0.46991087817612653, 0.77... True False \n",
+ "18 [0.31598268340817015, 0.1559547081931097, -0.1... False False \n",
+ "19 [0.17878273718770565, 0.3562295478287571, 0.37... False False \n",
+ "20 [0.1253581893085868, 0.43417906458645533, 0.29... False True \n",
+ "21 [0.8711807509724349, 0.2996534638086047, 0.799... False False \n",
+ "22 [0.2811353291712975, 0.5383316295043573, 0.476... False False \n",
+ "23 [-0.1251134853395897, 0.09527923850691102, 0.3... False True \n",
+ "24 [0.25744066175173336, 0.3001019756083122, 0.16... False True \n",
+ "25 [-0.0711396902237596, 0.5727540243990961, 0.43... False False \n",
+ "26 [0.17702394161205348, 0.2297014840258989, -0.0... False True \n",
+ "27 [0.5328021266560912, 0.30939625155891576, 0.29... False False \n",
+ "28 [0.2301474713828739, 0.16546045480804852, 0.50... False True \n",
+ "29 [0.5679862561165399, 0.3468873788565367, 0.703... False True \n",
+ "30 [0.4788335155797292, 0.516987579730329, 0.0919... False False \n",
+ "31 [0.7384792119745599, 0.5925478691694359, 0.366... False True \n",
+ "32 [0.6194280584484223, 0.3238257032467561, 0.185... False True \n",
+ "33 [0.3347138584366429, 0.4375312905535715, 0.011... False False \n",
+ "34 [0.5870835397580942, -0.23022304812531788, -0.... False False \n",
+ "\n",
+ " in_any_barrel VGAT_value is_VGAT \n",
+ "roi# \n",
+ "0 True 14.059807 False \n",
+ "1 True 20.949542 None \n",
+ "2 True 24.833536 None \n",
+ "3 True 15.331578 False \n",
+ "4 True 15.483268 False \n",
+ "5 True 19.285923 None \n",
+ "6 True 15.558809 False \n",
+ "7 True 33.543051 True \n",
+ "8 True 23.304562 None \n",
+ "9 True 25.789192 None \n",
+ "10 True 17.051609 False \n",
+ "11 True 34.336797 True \n",
+ "12 True 17.418802 False \n",
+ "13 True 17.863403 False \n",
+ "14 True 34.789767 True \n",
+ "15 True 18.963004 None \n",
+ "16 True 13.393158 False \n",
+ "17 True 16.534836 False \n",
+ "18 False 12.698708 False \n",
+ "19 False 24.034887 None \n",
+ "20 True 28.536033 True \n",
+ "21 False 23.928035 None \n",
+ "22 False 19.343984 None \n",
+ "23 True 16.356201 False \n",
+ "24 True 28.372598 True \n",
+ "25 False 27.890615 True \n",
+ "26 True 27.928532 True \n",
+ "27 False 36.552379 True \n",
+ "28 True 80.087524 True \n",
+ "29 True 22.438936 None \n",
+ "30 False 22.286501 None \n",
+ "31 True 26.382097 True \n",
+ "32 True 28.628524 True \n",
+ "33 False 21.779781 None \n",
+ "34 False 23.604597 None "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+ " display(rois_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "ed37df75-f6c4-41aa-832c-03a3c9b4cfce",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>frequency_change</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>...</th>\n",
+ " <th>nontarget_stim_info</th>\n",
+ " <th>complete_stim</th>\n",
+ " <th>target_whisker</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_D1</th>\n",
+ " <th>in_any_barrel</th>\n",
+ " <th>in_C1</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">0</th>\n",
+ " <th>0</th>\n",
+ " <td>[110.48663330078125, 131.6634063720703, 86.054...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[81.11736297607422, 93.11980438232422, 73.6161...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>[67.9014892578125, 84.18050384521484, 79.32645...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[52.151588439941406, 65.11491394042969, 51.899...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>[68.5937728881836, 60.977806091308594, 68.2375...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[57.168704986572266, 70.49388885498047, 47.332...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[0.0, 0.0, 9.140109062194824, 9.33862686157226...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>[88.68118286132812, 103.54595947265625, 58.293...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[57.833740234375, 44.317848205566406, 55.43520...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>[67.2108154296875, 93.54744720458984, 47.41284...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[55.2567253112793, 59.00489044189453, 56.47432...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0....</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">34</th>\n",
+ " <th>145</th>\n",
+ " <td>[79.01573944091797, 149.42901611328125, 109.14...</td>\n",
+ " <td>[0.28543534597878606, 0.14659601694516844, 0.4...</td>\n",
+ " <td>[81.04872131347656, 76.43589782714844, 87.0641...</td>\n",
+ " <td>[0.28543534597878606, 0.14659601694516844, 0.4...</td>\n",
+ " <td>[0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>[70.81188201904297, 130.8088836669922, 74.7765...</td>\n",
+ " <td>[-0.09959991718158123, 0.10129027462650247, 0....</td>\n",
+ " <td>[68.32051086425781, 74.994873046875, 89.817947...</td>\n",
+ " <td>[-0.09959991718158123, 0.10129027462650247, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>[67.38467407226562, 104.03815460205078, 66.875...</td>\n",
+ " <td>[-0.6525859803693786, -0.3806188309835738, -0....</td>\n",
+ " <td>[50.0487174987793, 59.089744567871094, 65.0923...</td>\n",
+ " <td>[-0.6525859803693786, -0.3806188309835738, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>[101.09800720214844, 79.50180053710938, 98.070...</td>\n",
+ " <td>[0.08084930191997584, -0.10169227498872888, -0...</td>\n",
+ " <td>[74.46154022216797, 68.3974380493164, 61.12307...</td>\n",
+ " <td>[0.08084930191997584, -0.10169227498872888, -0...</td>\n",
+ " <td>[1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>[94.76374053955078, 106.85689544677734, 66.061...</td>\n",
+ " <td>[0.1908002463908141, -0.09864775120552391, -0....</td>\n",
+ " <td>[78.14871978759766, 68.52820587158203, 57.7435...</td>\n",
+ " <td>[0.1908002463908141, -0.09864775120552391, -0....</td>\n",
+ " <td>[3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5250 rows × 24 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "0 0 [110.48663330078125, 131.6634063720703, 86.054... \\\n",
+ " 1 [67.9014892578125, 84.18050384521484, 79.32645... \n",
+ " 2 [68.5937728881836, 60.977806091308594, 68.2375... \n",
+ " 3 [88.68118286132812, 103.54595947265625, 58.293... \n",
+ " 4 [67.2108154296875, 93.54744720458984, 47.41284... \n",
+ "... ... \n",
+ "34 145 [79.01573944091797, 149.42901611328125, 109.14... \n",
+ " 146 [70.81188201904297, 130.8088836669922, 74.7765... \n",
+ " 147 [67.38467407226562, 104.03815460205078, 66.875... \n",
+ " 148 [101.09800720214844, 79.50180053710938, 98.070... \n",
+ " 149 [94.76374053955078, 106.85689544677734, 66.061... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ "... ... \n",
+ "34 145 [0.28543534597878606, 0.14659601694516844, 0.4... \n",
+ " 146 [-0.09959991718158123, 0.10129027462650247, 0.... \n",
+ " 147 [-0.6525859803693786, -0.3806188309835738, -0.... \n",
+ " 148 [0.08084930191997584, -0.10169227498872888, -0... \n",
+ " 149 [0.1908002463908141, -0.09864775120552391, -0.... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "0 0 [81.11736297607422, 93.11980438232422, 73.6161... \\\n",
+ " 1 [52.151588439941406, 65.11491394042969, 51.899... \n",
+ " 2 [57.168704986572266, 70.49388885498047, 47.332... \n",
+ " 3 [57.833740234375, 44.317848205566406, 55.43520... \n",
+ " 4 [55.2567253112793, 59.00489044189453, 56.47432... \n",
+ "... ... \n",
+ "34 145 [81.04872131347656, 76.43589782714844, 87.0641... \n",
+ " 146 [68.32051086425781, 74.994873046875, 89.817947... \n",
+ " 147 [50.0487174987793, 59.089744567871094, 65.0923... \n",
+ " 148 [74.46154022216797, 68.3974380493164, 61.12307... \n",
+ " 149 [78.14871978759766, 68.52820587158203, 57.7435... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ "... ... \n",
+ "34 145 [0.28543534597878606, 0.14659601694516844, 0.4... \n",
+ " 146 [-0.09959991718158123, 0.10129027462650247, 0.... \n",
+ " 147 [-0.6525859803693786, -0.3806188309835738, -0.... \n",
+ " 148 [0.08084930191997584, -0.10169227498872888, -0... \n",
+ " 149 [0.1908002463908141, -0.09864775120552391, -0.... \n",
+ "\n",
+ " spks target_stim \n",
+ "roi# trial# \n",
+ "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... C1_10_90 \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... C1_10_20 \n",
+ " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... D1_10_20 \n",
+ " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... D1_10_90 \n",
+ "... ... ... \n",
+ "34 145 [0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 148 [1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 149 [3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ "\n",
+ " target_amplitude frequency_change \n",
+ "roi# trial# \n",
+ "0 0 10_90 80.0 \\\n",
+ " 1 10_90 80.0 \n",
+ " 2 10_20 10.0 \n",
+ " 3 10_20 10.0 \n",
+ " 4 10_90 80.0 \n",
+ "... ... ... \n",
+ "34 145 10_90 80.0 \n",
+ " 146 10_20 10.0 \n",
+ " 147 10_20 10.0 \n",
+ " 148 10_20 10.0 \n",
+ " 149 10_20 10.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 4 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_amplitude ... \n",
+ "roi# trial# ... \n",
+ "0 0 10 ... \\\n",
+ " 1 0 ... \n",
+ " 2 10 ... \n",
+ " 3 0 ... \n",
+ " 4 0 ... \n",
+ "... ... ... \n",
+ "34 145 10 ... \n",
+ " 146 10 ... \n",
+ " 147 0 ... \n",
+ " 148 0 ... \n",
+ " 149 10 ... \n",
+ "\n",
+ " nontarget_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [] \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [] \n",
+ " 4 [] \n",
+ "... ... \n",
+ "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [] \n",
+ " 148 [] \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " complete_stim target_whisker nontarget_stim in_D1 \n",
+ "roi# trial# \n",
+ "0 0 C1_10_90&D1_10 C1 D1_10 True \\\n",
+ " 1 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 2 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 3 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 4 D1_10_90&C1_0 D1 C1_0 True \n",
+ "... ... ... ... ... \n",
+ "34 145 C1_10_90&D1_10 C1 D1_10 False \n",
+ " 146 C1_10_20&D1_10 C1 D1_10 False \n",
+ " 147 C1_10_20&D1_0 C1 D1_0 False \n",
+ " 148 C1_10_20&D1_0 C1 D1_0 False \n",
+ " 149 D1_10_20&C1_10 D1 C1_10 False \n",
+ "\n",
+ " in_any_barrel in_C1 is_neuron is_VGAT in_target_barrel \n",
+ "roi# trial# \n",
+ "0 0 True False True False False \n",
+ " 1 True False True False True \n",
+ " 2 True False True False False \n",
+ " 3 True False True False True \n",
+ " 4 True False True False True \n",
+ "... ... ... ... ... ... \n",
+ "34 145 False False False None False \n",
+ " 146 False False False None False \n",
+ " 147 False False False None False \n",
+ " 148 False False False None False \n",
+ " 149 False False False None False \n",
+ "\n",
+ "[5250 rows x 24 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trials_roi_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "d1aef421-a797-42de-b7d7-fdb34a256664",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "trials_roi_df = adaptation.classifiers.extract_features_from_timeseries(trials_roi_df,features_key = \"neuronal_features\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a7433d9b-ce61-49bf-92e1-1db4b4556658",
+ "metadata": {},
+ "source": [
+ "Bootstrap is a statistical resampling technique that is used to estimate the accuracy of a model on new, unseen data\n",
+ "In machine learning, bootstrap involves randomly selecting data points from the original dataset with replacement to create multiple \"bootstrap samples\".\n",
+ "These samples are the same size as the original dataset, but some data points may appear multiple times in a sample while others may not appear at all"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "ad27afed-fb2c-4414-8445-bce9d2497915",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ROI 32 of 35\r"
+ ]
+ }
+ ],
+ "source": [
+ "rois_classifiers = []\n",
+ "condition_keys = [\"nontarget_amplitude\",\"in_target_barrel\"]\n",
+ "\n",
+ "for roi in rois_df.index : \n",
+ " if not rois_df.loc[roi,\"in_any_barrel\"] :\n",
+ " continue\n",
+ " print(f\"ROI {roi} of {len(rois_df)}\",end = \"\\r\")\n",
+ " \n",
+ " for condition_values , group_df in trials_roi_df.loc[roi:roi].groupby(condition_keys ) : \n",
+ " conditions = {key : value for key, value in zip(condition_keys , condition_values)}\n",
+ " if conditions[\"nontarget_amplitude\"] == '10' :\n",
+ " continue\n",
+ " \n",
+ " results = adaptation.classifiers.get_bootstraped_classifiers_results(group_df, n_estimators = 2000,features_key = \"neuronal_features\", classes_key = \"target_amplitude\", n_jobs = 10)\n",
+ " conditions.update(results)\n",
+ " conditions[\"roi#\"] = roi \n",
+ " rois_classifiers.append(conditions)\n",
+ "rois_classifiers = pd.DataFrame(rois_classifiers).set_index([\"roi#\"]+condition_keys)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "f57c200c-208f-4354-aa99-87fecace53ef",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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+ " <td>(LinearSVC(random_state=1076451085), LinearSVC...</td>\n",
+ " <td>0.756757</td>\n",
+ " <td>bagging_classifier</td>\n",
+ " <td>0.513514</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>True</th>\n",
+ " <td>(LinearSVC(random_state=1854665432), LinearSVC...</td>\n",
+ " <td>0.815789</td>\n",
+ " <td>bagging_classifier</td>\n",
+ " <td>0.578947</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">31</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">0</th>\n",
+ " <th>False</th>\n",
+ " <td>(LinearSVC(random_state=1372234), LinearSVC(ra...</td>\n",
+ " <td>0.864865</td>\n",
+ " <td>bagging_classifier</td>\n",
+ " <td>0.675676</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>True</th>\n",
+ " <td>(LinearSVC(random_state=513234225), LinearSVC(...</td>\n",
+ " <td>0.789474</td>\n",
+ " <td>bagging_classifier</td>\n",
+ " <td>0.421053</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">32</th>\n",
+ " <th rowspan=\"2\" valign=\"top\">0</th>\n",
+ " <th>False</th>\n",
+ " <td>(LinearSVC(random_state=1554834370), LinearSVC...</td>\n",
+ " <td>0.810811</td>\n",
+ " <td>bagging_classifier</td>\n",
+ " <td>0.486486</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>True</th>\n",
+ " <td>(LinearSVC(random_state=1063854735), LinearSVC...</td>\n",
+ " <td>0.763158</td>\n",
+ " <td>bagging_classifier</td>\n",
+ " <td>0.578947</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " classifier \n",
+ "roi# nontarget_amplitude in_target_barrel \n",
+ "0 0 False (LinearSVC(random_state=155186883), LinearSVC(... \\\n",
+ " True (LinearSVC(random_state=1643483216), LinearSVC... \n",
+ "1 0 False (LinearSVC(random_state=695452644), LinearSVC(... \n",
+ " True (LinearSVC(random_state=1457293751), LinearSVC... \n",
+ "2 0 False (LinearSVC(random_state=382593544), LinearSVC(... \n",
+ " True (LinearSVC(random_state=44507617), LinearSVC(r... \n",
+ "3 0 False (LinearSVC(random_state=925239981), LinearSVC(... \n",
+ " True (LinearSVC(random_state=1645765139), LinearSVC... \n",
+ "4 0 False (LinearSVC(random_state=1475813009), LinearSVC... \n",
+ " True (LinearSVC(random_state=994319169), LinearSVC(... \n",
+ "5 0 False (LinearSVC(random_state=486323477), LinearSVC(... \n",
+ " True (LinearSVC(random_state=2113052636), LinearSVC... \n",
+ "6 0 False (LinearSVC(random_state=877822360), LinearSVC(... \n",
+ " True (LinearSVC(random_state=1043475983), LinearSVC... \n",
+ "7 0 False (LinearSVC(random_state=25178392), LinearSVC(r... \n",
+ " True (LinearSVC(random_state=388282772), LinearSVC(... \n",
+ "8 0 False (LinearSVC(random_state=1403762852), LinearSVC... \n",
+ " True (LinearSVC(random_state=1873911440), LinearSVC... \n",
+ "9 0 False (LinearSVC(random_state=1131313478), LinearSVC... \n",
+ " True (LinearSVC(random_state=393821270), LinearSVC(... \n",
+ "10 0 False (LinearSVC(random_state=1800512000), LinearSVC... \n",
+ " True (LinearSVC(random_state=1584281568), LinearSVC... \n",
+ "11 0 False (LinearSVC(random_state=1210718577), LinearSVC... \n",
+ " True (LinearSVC(random_state=267216411), LinearSVC(... \n",
+ "12 0 False (LinearSVC(random_state=1566877312), LinearSVC... \n",
+ " True (LinearSVC(random_state=654514737), LinearSVC(... \n",
+ "13 0 False (LinearSVC(random_state=149381833), LinearSVC(... \n",
+ " True (LinearSVC(random_state=1411234825), LinearSVC... \n",
+ "14 0 False (LinearSVC(random_state=769415877), LinearSVC(... \n",
+ " True (LinearSVC(random_state=1009868822), LinearSVC... \n",
+ "15 0 False (LinearSVC(random_state=1008776582), LinearSVC... \n",
+ " True (LinearSVC(random_state=823201129), LinearSVC(... \n",
+ "16 0 False (LinearSVC(random_state=1824702927), LinearSVC... \n",
+ " True (LinearSVC(random_state=1199276966), LinearSVC... \n",
+ "17 0 False (LinearSVC(random_state=776246997), LinearSVC(... \n",
+ " True (LinearSVC(random_state=728947459), LinearSVC(... \n",
+ "20 0 False (LinearSVC(random_state=2038762765), LinearSVC... \n",
+ " True (LinearSVC(random_state=1794934037), LinearSVC... \n",
+ "23 0 False (LinearSVC(random_state=752484605), LinearSVC(... \n",
+ " True (LinearSVC(random_state=51728583), LinearSVC(r... \n",
+ "24 0 False (LinearSVC(random_state=1536905426), LinearSVC... \n",
+ " True (LinearSVC(random_state=2120384398), LinearSVC... \n",
+ "26 0 False (LinearSVC(random_state=533024125), LinearSVC(... \n",
+ " True (LinearSVC(random_state=2097416533), LinearSVC... \n",
+ "28 0 False (LinearSVC(random_state=1721379888), LinearSVC... \n",
+ " True (LinearSVC(random_state=1269980301), LinearSVC... \n",
+ "29 0 False (LinearSVC(random_state=1076451085), LinearSVC... \n",
+ " True (LinearSVC(random_state=1854665432), LinearSVC... \n",
+ "31 0 False (LinearSVC(random_state=1372234), LinearSVC(ra... \n",
+ " True (LinearSVC(random_state=513234225), LinearSVC(... \n",
+ "32 0 False (LinearSVC(random_state=1554834370), LinearSVC... \n",
+ " True (LinearSVC(random_state=1063854735), LinearSVC... \n",
+ "\n",
+ " score method \n",
+ "roi# nontarget_amplitude in_target_barrel \n",
+ "0 0 False 0.789474 bagging_classifier \\\n",
+ " True 0.783784 bagging_classifier \n",
+ "1 0 False 0.710526 bagging_classifier \n",
+ " True 0.918919 bagging_classifier \n",
+ "2 0 False 0.684211 bagging_classifier \n",
+ " True 0.783784 bagging_classifier \n",
+ "3 0 False 0.736842 bagging_classifier \n",
+ " True 0.864865 bagging_classifier \n",
+ "4 0 False 0.710526 bagging_classifier \n",
+ " True 0.837838 bagging_classifier \n",
+ "5 0 False 0.763158 bagging_classifier \n",
+ " True 0.783784 bagging_classifier \n",
+ "6 0 False 0.789474 bagging_classifier \n",
+ " True 0.918919 bagging_classifier \n",
+ "7 0 False 0.789474 bagging_classifier \n",
+ " True 0.810811 bagging_classifier \n",
+ "8 0 False 0.710526 bagging_classifier \n",
+ " True 0.918919 bagging_classifier \n",
+ "9 0 False 0.842105 bagging_classifier \n",
+ " True 0.756757 bagging_classifier \n",
+ "10 0 False 0.736842 bagging_classifier \n",
+ " True 0.891892 bagging_classifier \n",
+ "11 0 False 0.631579 bagging_classifier \n",
+ " True 0.810811 bagging_classifier \n",
+ "12 0 False 0.736842 bagging_classifier \n",
+ " True 0.891892 bagging_classifier \n",
+ "13 0 False 0.736842 bagging_classifier \n",
+ " True 0.756757 bagging_classifier \n",
+ "14 0 False 0.763158 bagging_classifier \n",
+ " True 0.783784 bagging_classifier \n",
+ "15 0 False 0.736842 bagging_classifier \n",
+ " True 0.810811 bagging_classifier \n",
+ "16 0 False 0.736842 bagging_classifier \n",
+ " True 0.864865 bagging_classifier \n",
+ "17 0 False 0.763158 bagging_classifier \n",
+ " True 0.891892 bagging_classifier \n",
+ "20 0 False 0.837838 bagging_classifier \n",
+ " True 0.815789 bagging_classifier \n",
+ "23 0 False 0.837838 bagging_classifier \n",
+ " True 0.763158 bagging_classifier \n",
+ "24 0 False 0.648649 bagging_classifier \n",
+ " True 0.684211 bagging_classifier \n",
+ "26 0 False 0.837838 bagging_classifier \n",
+ " True 0.842105 bagging_classifier \n",
+ "28 0 False 0.756757 bagging_classifier \n",
+ " True 0.736842 bagging_classifier \n",
+ "29 0 False 0.756757 bagging_classifier \n",
+ " True 0.815789 bagging_classifier \n",
+ "31 0 False 0.864865 bagging_classifier \n",
+ " True 0.789474 bagging_classifier \n",
+ "32 0 False 0.810811 bagging_classifier \n",
+ " True 0.763158 bagging_classifier \n",
+ "\n",
+ " oob_score \n",
+ "roi# nontarget_amplitude in_target_barrel \n",
+ "0 0 False 0.447368 \n",
+ " True 0.621622 \n",
+ "1 0 False 0.473684 \n",
+ " True 0.810811 \n",
+ "2 0 False 0.421053 \n",
+ " True 0.648649 \n",
+ "3 0 False 0.473684 \n",
+ " True 0.675676 \n",
+ "4 0 False 0.473684 \n",
+ " True 0.567568 \n",
+ "5 0 False 0.394737 \n",
+ " True 0.675676 \n",
+ "6 0 False 0.447368 \n",
+ " True 0.702703 \n",
+ "7 0 False 0.421053 \n",
+ " True 0.729730 \n",
+ "8 0 False 0.500000 \n",
+ " True 0.783784 \n",
+ "9 0 False 0.605263 \n",
+ " True 0.675676 \n",
+ "10 0 False 0.526316 \n",
+ " True 0.783784 \n",
+ "11 0 False 0.473684 \n",
+ " True 0.729730 \n",
+ "12 0 False 0.500000 \n",
+ " True 0.729730 \n",
+ "13 0 False 0.500000 \n",
+ " True 0.675676 \n",
+ "14 0 False 0.473684 \n",
+ " True 0.648649 \n",
+ "15 0 False 0.552632 \n",
+ " True 0.756757 \n",
+ "16 0 False 0.500000 \n",
+ " True 0.567568 \n",
+ "17 0 False 0.263158 \n",
+ " True 0.729730 \n",
+ "20 0 False 0.567568 \n",
+ " True 0.578947 \n",
+ "23 0 False 0.702703 \n",
+ " True 0.552632 \n",
+ "24 0 False 0.486486 \n",
+ " True 0.473684 \n",
+ "26 0 False 0.513514 \n",
+ " True 0.552632 \n",
+ "28 0 False 0.594595 \n",
+ " True 0.473684 \n",
+ "29 0 False 0.513514 \n",
+ " True 0.578947 \n",
+ "31 0 False 0.675676 \n",
+ " True 0.421053 \n",
+ "32 0 False 0.486486 \n",
+ " True 0.578947 "
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rois_classifiers.rename(columns = {\"in_target_barrel\" : 'receptive field'}, inplace=True) #location of the freq change stim in the receptive field of the neuron\n",
+ "rois_classifiers.replace({False:\"surround\",True:\"center\"}, inplace=True)\n",
+ "rois_classifiers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "31c84192-bfe8-4ffc-a9e9-da5db9538c26",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th>receptive field</th>\n",
+ " <th>False</th>\n",
+ " <th>True</th>\n",
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+ " <td>0.702703</td>\n",
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+ ],
+ "text/plain": [
+ "receptive field False True \n",
+ "roi# \n",
+ "0 0.447368 0.621622\n",
+ "1 0.473684 0.810811\n",
+ "2 0.421053 0.648649\n",
+ "3 0.473684 0.675676\n",
+ "4 0.473684 0.567568\n",
+ "5 0.394737 0.675676\n",
+ "6 0.447368 0.702703\n",
+ "7 0.421053 0.729730\n",
+ "8 0.500000 0.783784\n",
+ "9 0.605263 0.675676\n",
+ "10 0.526316 0.783784\n",
+ "11 0.473684 0.729730\n",
+ "12 0.500000 0.729730\n",
+ "13 0.500000 0.675676\n",
+ "14 0.473684 0.648649\n",
+ "15 0.552632 0.756757\n",
+ "16 0.500000 0.567568\n",
+ "17 0.263158 0.729730\n",
+ "20 0.567568 0.578947\n",
+ "23 0.702703 0.552632\n",
+ "24 0.486486 0.473684\n",
+ "26 0.513514 0.552632\n",
+ "28 0.594595 0.473684\n",
+ "29 0.513514 0.578947\n",
+ "31 0.675676 0.421053\n",
+ "32 0.486486 0.578947"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scores = rois_classifiers.unstack().unstack()[\"oob_score\"]\n",
+ "scores.columns = scores.columns.rename({\"in_target_barrel\" : 'receptive field'}).droplevel(\"nontarget_amplitude\")\n",
+ "scores"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "b59f8e4e-bbc7-461c-abdf-c473ed4b3713",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='receptive field', ylabel='roi#'>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.heatmap(scores,vmin = 0, vmax = 1, cmap = \"coolwarm\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "1225b0df-2bc9-47f8-b1f0-139f007e5e64",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "VGAT_label_column = rois_df['is_VGAT']\n",
+ "VGAT_label_column = VGAT_label_column[~VGAT_label_column.isna()] #remove nans\n",
+ "\n",
+ "inhib_rois = VGAT_label_column[VGAT_label_column==True] #pick only inhibitory rois\n",
+ "exit_rois = VGAT_label_column[VGAT_label_column==False] #pick only exitatory rois\n",
+ "\n",
+ "score_inhib = scores[ scores.index.isin( inhib_rois.index ) ] #select inhibitory rois in scores\n",
+ "score_exit = scores[ scores.index.isin( exit_rois.index ) ] #select exitatory rois in scores"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "id": "8c5ed453-ea4e-4238-be0e-bebb7a430208",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.matrix.ClusterGrid at 0x1cd8ce39a90>"
+ ]
+ },
+ "execution_count": 85,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x1000 with 4 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.clustermap(score_inhib,vmin = 0, vmax = 1, cmap = \"coolwarm\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "id": "513f278d-057c-404f-b755-e44b3026999a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='receptive field', ylabel='roi#'>"
+ ]
+ },
+ "execution_count": 86,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.heatmap(score_inhib,vmin = 0, vmax = 1, cmap = \"coolwarm\")\n",
+ "# adjust spacing between subplots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "id": "5b4f6c80-f9b3-4874-946b-d009c65be007",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.matrix.ClusterGrid at 0x1cd8e3dabe0>"
+ ]
+ },
+ "execution_count": 87,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x1000 with 4 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.clustermap(score_exit,vmin = 0, vmax = 1, cmap = \"coolwarm\") #exactly same input as heatmap"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "cab87fba-48ac-4993-b362-5f4c27603b6a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\u001b[1;31mType:\u001b[0m module\n",
+ "\u001b[1;31mString form:\u001b[0m <module 'ResearchProjects.adaptation.classifiers' from 'C:\\\\Users\\\\mohay\\\\anaconda3\\\\envs\\\\Analysis\\\\lib\\\\site-packages\\\\ResearchProjects\\\\adaptation\\\\classifiers.py'>\n",
+ "\u001b[1;31mFile:\u001b[0m c:\\users\\mohay\\anaconda3\\envs\\analysis\\lib\\site-packages\\researchprojects\\adaptation\\classifiers.py\n",
+ "\u001b[1;31mDocstring:\u001b[0m <no docstring>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "adaptation.classifiers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f88ce043-511c-44e6-bf2d-557566e3690e",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4687b088-c502-4766-bce1-d9cb5df90553",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0b8b5d9c-5814-4380-913f-f6768bb56bb3",
+ "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": "3.8.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Chance_level.ipynb b/Chance_level.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0c8b6e2998174a375dadb21aa3d339ce675f2aab
--- /dev/null
+++ b/Chance_level.ipynb
@@ -0,0 +1,37254 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "id": "f1794525-10f8-4af4-8129-089a692bcc3e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#you must import 6 libraries!\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt, seaborn as sns\n",
+ "import Inflow\n",
+ "Inflow.logging.enable_logging()\n",
+ "import ResearchProjects\n",
+ "import pandas as pd\n",
+ "import one"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "id": "d9d440e7-44d4-4d3e-9444-486993b1312c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#adaptation is a sublibrary within the ResearchProject\n",
+ "from ResearchProjects import adaptation\n",
+ "#inside adaptation experiment there are different file, we import the \"aliases\"\n",
+ "from ResearchProjects.adaptation import aliases"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "id": "36c1c316-546a-428f-80de-d79d999ff644",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<function ResearchProjects.adaptation.select.cells_labelled(rois_df, iscell=True, **kwargs)>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\u001b[1;31mType:\u001b[0m module\n",
+ "\u001b[1;31mString form:\u001b[0m <module 'ResearchProjects.core' from 'C:\\\\Users\\\\mohay\\\\anaconda3\\\\envs\\\\Analysis\\\\lib\\\\site-packages\\\\ResearchProjects\\\\core.py'>\n",
+ "\u001b[1;31mFile:\u001b[0m c:\\users\\mohay\\anaconda3\\envs\\analysis\\lib\\site-packages\\researchprojects\\core.py\n",
+ "\u001b[1;31mDocstring:\u001b[0m <no docstring>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "connector = one.ONE()\n",
+ "connector.set_data_access_mode('remote')\n",
+ "ResearchProjects.core?\n",
+ "ResearchProjects.adaptation.select.cells_labelled\n",
+ "display(ResearchProjects.adaptation.select.cells_labelled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "id": "3b5e240a-dab1-48f1-b238-9c509f08606f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "subject wm24\n",
+ "start_time 2022-08-22T15:27:00\n",
+ "number 1\n",
+ "lab HaissLab\n",
+ "projects [Adaptation]\n",
+ "url http://157.99.138.172/sessions/04f92e4a-da64-4...\n",
+ "task_protocol \n",
+ "date 2022-08-22\n",
+ "json {'channels': ['R', 'G'], 'whisker_stims': {'St...\n",
+ "extended_qc {'exclude_whisker': ['C1']}\n",
+ "rel_path wm24\\2022-08-22\\001\n",
+ "alias_name wm24_2022_08_22_001\n",
+ "short_path wm24\\2022-08-22\\001\n",
+ "path \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202...\n",
+ "Name: 04f92e4a-da64-4018-aa6c-d9a79a91c831, dtype: object"
+ ]
+ },
+ "execution_count": 120,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#sessions = connector.search(subject = \"wm24\",date_range = \"2022-08-09\",number = 1, details= True) ###Did not work(OSError: No result exists for trials_df in session wm24_2022_08_09_001)\n",
+ "sessions = connector.search(subject = 'wm24', date_range = \"2022-08-22\", number = 1, details = True)\n",
+ "session = sessions.iloc[0]\n",
+ "session"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "id": "aeb95966-3caf-40af-99b5-978f04724128",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "trials_df = adaptation.pipelines.get_trials_df(session)\n",
+ "rois_df = adaptation.pipelines.get_rois_df(session) # load from file only. Faster but will not generate the data if none exists\n",
+ "trials_roi_df = adaptation.pipelines.get_trials_roi_df(session)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "id": "d40db624-1bc8-4085-a2de-b8a7ba2e69bb",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "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",
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+ " .dataframe thead th {\n",
+ " text-align: right;\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>frequency_change</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>nontarget_onset</th>\n",
+ " <th>nontarget_whisker</th>\n",
+ " <th>behavioural_result</th>\n",
+ " <th>complete_amplitude</th>\n",
+ " <th>nontarget_stim_info</th>\n",
+ " <th>complete_stim</th>\n",
+ " <th>target_whisker</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_D1</th>\n",
+ " <th>in_any_barrel</th>\n",
+ " <th>in_C1</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
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+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">0</th>\n",
+ " <th>0</th>\n",
+ " <td>[110.48663330078125, 131.6634063720703, 86.054...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[81.11736297607422, 93.11980438232422, 73.6161...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>[67.9014892578125, 84.18050384521484, 79.32645...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[52.151588439941406, 65.11491394042969, 51.899...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>[68.5937728881836, 60.977806091308594, 68.2375...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[57.168704986572266, 70.49388885498047, 47.332...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[0.0, 0.0, 9.140109062194824, 9.33862686157226...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>[88.68118286132812, 103.54595947265625, 58.293...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[57.833740234375, 44.317848205566406, 55.43520...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>[67.2108154296875, 93.54744720458984, 47.41284...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[55.2567253112793, 59.00489044189453, 56.47432...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0....</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
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+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">34</th>\n",
+ " <th>145</th>\n",
+ " <td>[79.01573944091797, 149.42901611328125, 109.14...</td>\n",
+ " <td>[0.28543534597878606, 0.14659601694516844, 0.4...</td>\n",
+ " <td>[81.04872131347656, 76.43589782714844, 87.0641...</td>\n",
+ " <td>[0.28543534597878606, 0.14659601694516844, 0.4...</td>\n",
+ " <td>[0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>[70.81188201904297, 130.8088836669922, 74.7765...</td>\n",
+ " <td>[-0.09959991718158123, 0.10129027462650247, 0....</td>\n",
+ " <td>[68.32051086425781, 74.994873046875, 89.817947...</td>\n",
+ " <td>[-0.09959991718158123, 0.10129027462650247, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>[67.38467407226562, 104.03815460205078, 66.875...</td>\n",
+ " <td>[-0.6525859803693786, -0.3806188309835738, -0....</td>\n",
+ " <td>[50.0487174987793, 59.089744567871094, 65.0923...</td>\n",
+ " <td>[-0.6525859803693786, -0.3806188309835738, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>[101.09800720214844, 79.50180053710938, 98.070...</td>\n",
+ " <td>[0.08084930191997584, -0.10169227498872888, -0...</td>\n",
+ " <td>[74.46154022216797, 68.3974380493164, 61.12307...</td>\n",
+ " <td>[0.08084930191997584, -0.10169227498872888, -0...</td>\n",
+ " <td>[1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>[94.76374053955078, 106.85689544677734, 66.061...</td>\n",
+ " <td>[0.1908002463908141, -0.09864775120552391, -0....</td>\n",
+ " <td>[78.14871978759766, 68.52820587158203, 57.7435...</td>\n",
+ " <td>[0.1908002463908141, -0.09864775120552391, -0....</td>\n",
+ " <td>[3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5250 rows × 24 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "0 0 [110.48663330078125, 131.6634063720703, 86.054... \\\n",
+ " 1 [67.9014892578125, 84.18050384521484, 79.32645... \n",
+ " 2 [68.5937728881836, 60.977806091308594, 68.2375... \n",
+ " 3 [88.68118286132812, 103.54595947265625, 58.293... \n",
+ " 4 [67.2108154296875, 93.54744720458984, 47.41284... \n",
+ "... ... \n",
+ "34 145 [79.01573944091797, 149.42901611328125, 109.14... \n",
+ " 146 [70.81188201904297, 130.8088836669922, 74.7765... \n",
+ " 147 [67.38467407226562, 104.03815460205078, 66.875... \n",
+ " 148 [101.09800720214844, 79.50180053710938, 98.070... \n",
+ " 149 [94.76374053955078, 106.85689544677734, 66.061... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ "... ... \n",
+ "34 145 [0.28543534597878606, 0.14659601694516844, 0.4... \n",
+ " 146 [-0.09959991718158123, 0.10129027462650247, 0.... \n",
+ " 147 [-0.6525859803693786, -0.3806188309835738, -0.... \n",
+ " 148 [0.08084930191997584, -0.10169227498872888, -0... \n",
+ " 149 [0.1908002463908141, -0.09864775120552391, -0.... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "0 0 [81.11736297607422, 93.11980438232422, 73.6161... \\\n",
+ " 1 [52.151588439941406, 65.11491394042969, 51.899... \n",
+ " 2 [57.168704986572266, 70.49388885498047, 47.332... \n",
+ " 3 [57.833740234375, 44.317848205566406, 55.43520... \n",
+ " 4 [55.2567253112793, 59.00489044189453, 56.47432... \n",
+ "... ... \n",
+ "34 145 [81.04872131347656, 76.43589782714844, 87.0641... \n",
+ " 146 [68.32051086425781, 74.994873046875, 89.817947... \n",
+ " 147 [50.0487174987793, 59.089744567871094, 65.0923... \n",
+ " 148 [74.46154022216797, 68.3974380493164, 61.12307... \n",
+ " 149 [78.14871978759766, 68.52820587158203, 57.7435... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ "... ... \n",
+ "34 145 [0.28543534597878606, 0.14659601694516844, 0.4... \n",
+ " 146 [-0.09959991718158123, 0.10129027462650247, 0.... \n",
+ " 147 [-0.6525859803693786, -0.3806188309835738, -0.... \n",
+ " 148 [0.08084930191997584, -0.10169227498872888, -0... \n",
+ " 149 [0.1908002463908141, -0.09864775120552391, -0.... \n",
+ "\n",
+ " spks target_stim \n",
+ "roi# trial# \n",
+ "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... C1_10_90 \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... C1_10_20 \n",
+ " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... D1_10_20 \n",
+ " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... D1_10_90 \n",
+ "... ... ... \n",
+ "34 145 [0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 148 [1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 149 [3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ "\n",
+ " target_amplitude frequency_change \n",
+ "roi# trial# \n",
+ "0 0 10_90 80.0 \\\n",
+ " 1 10_90 80.0 \n",
+ " 2 10_20 10.0 \n",
+ " 3 10_20 10.0 \n",
+ " 4 10_90 80.0 \n",
+ "... ... ... \n",
+ "34 145 10_90 80.0 \n",
+ " 146 10_20 10.0 \n",
+ " 147 10_20 10.0 \n",
+ " 148 10_20 10.0 \n",
+ " 149 10_20 10.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 4 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_amplitude nontarget_onset nontarget_whisker \n",
+ "roi# trial# \n",
+ "0 0 10 10.0 D1 \\\n",
+ " 1 0 0.0 C1 \n",
+ " 2 10 10.0 D1 \n",
+ " 3 0 0.0 C1 \n",
+ " 4 0 0.0 C1 \n",
+ "... ... ... ... \n",
+ "34 145 10 10.0 D1 \n",
+ " 146 10 10.0 D1 \n",
+ " 147 0 0.0 D1 \n",
+ " 148 0 0.0 D1 \n",
+ " 149 10 10.0 C1 \n",
+ "\n",
+ " behavioural_result complete_amplitude \n",
+ "roi# trial# \n",
+ "0 0 no_answer 10_90&10 \\\n",
+ " 1 no_answer 10_90&0 \n",
+ " 2 no_answer 10_20&10 \n",
+ " 3 no_answer 10_20&0 \n",
+ " 4 no_answer 10_90&0 \n",
+ "... ... ... \n",
+ "34 145 no_answer 10_90&10 \n",
+ " 146 no_answer 10_20&10 \n",
+ " 147 no_answer 10_20&0 \n",
+ " 148 no_answer 10_20&0 \n",
+ " 149 no_answer 10_20&10 \n",
+ "\n",
+ " nontarget_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [] \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [] \n",
+ " 4 [] \n",
+ "... ... \n",
+ "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [] \n",
+ " 148 [] \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " complete_stim target_whisker nontarget_stim in_D1 \n",
+ "roi# trial# \n",
+ "0 0 C1_10_90&D1_10 C1 D1_10 True \\\n",
+ " 1 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 2 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 3 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 4 D1_10_90&C1_0 D1 C1_0 True \n",
+ "... ... ... ... ... \n",
+ "34 145 C1_10_90&D1_10 C1 D1_10 False \n",
+ " 146 C1_10_20&D1_10 C1 D1_10 False \n",
+ " 147 C1_10_20&D1_0 C1 D1_0 False \n",
+ " 148 C1_10_20&D1_0 C1 D1_0 False \n",
+ " 149 D1_10_20&C1_10 D1 C1_10 False \n",
+ "\n",
+ " in_any_barrel in_C1 is_neuron is_VGAT in_target_barrel \n",
+ "roi# trial# \n",
+ "0 0 True False True False False \n",
+ " 1 True False True False True \n",
+ " 2 True False True False False \n",
+ " 3 True False True False True \n",
+ " 4 True False True False True \n",
+ "... ... ... ... ... ... \n",
+ "34 145 False False False None False \n",
+ " 146 False False False None False \n",
+ " 147 False False False None False \n",
+ " 148 False False False None False \n",
+ " 149 False False False None False \n",
+ "\n",
+ "[5250 rows x 24 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "with pd.option_context('display.max_rows', 100, 'display.max_columns', None):\n",
+ " display(trials_roi_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "id": "8490bdd4-330e-49f9-8012-9700b1c9fa06",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "trials_roi_df = adaptation.classifiers.extract_features_from_timeseries(trials_roi_df,features_key = \"neuronal_features\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "id": "c48573c9-273f-467f-afc0-49e7f4000e16",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\u001b[1;31mSignature:\u001b[0m\n",
+ "\u001b[0madaptation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextract_features_from_timeseries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+ "\u001b[0m \u001b[0minput_object\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+ "\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+ "\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'F_var'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+ "\u001b[0m \u001b[0mfeatures_key\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'features'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+ "\u001b[0m \u001b[0mtimepoints\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.8\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+ "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mDocstring:\u001b[0m\n",
+ "Transform a Pandas DataFrame or Series or a dictionary of values indexed by time values\n",
+ "into a TimelinedArray of features extracted from the specified timepoints.\n",
+ "\n",
+ "Args:\n",
+ " input_object (Union[pd.DataFrame, pd.Series, dict]): The pandas DataFrame or Series or\n",
+ " a dictionary of values indexed by time values.\n",
+ " key (str, optional): The key of the column containing the timepoint values.\n",
+ " If input_object is a dictionary, this argument is ignored. Defaults to \"F_var\".\n",
+ " features_key (str, optional): The key in the DataFrame to place the features in.\n",
+ " Only applicable if input_object is a DataFrame. Defaults to \"features\".\n",
+ " timepoints (List[float], optional): A list of timepoints from which to extract features\n",
+ " (must correspond to the values in the column specified by key). Defaults to\n",
+ " [-0.2, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8].\n",
+ "\n",
+ "Raises:\n",
+ " NotImplementedError: If input_object is not a Pandas DataFrame, Series, or dictionary.\n",
+ "\n",
+ "Returns:\n",
+ " TimelinedArray: A TimelinedArray object containing the extracted features and their timeline.\n",
+ "\u001b[1;31mFile:\u001b[0m c:\\users\\mohay\\anaconda3\\envs\\analysis\\lib\\site-packages\\researchprojects\\adaptation\\classifiers.py\n",
+ "\u001b[1;31mType:\u001b[0m function"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "adaptation.classifiers.extract_features_from_timeseries?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "id": "1c69ba62-0dc7-4246-8db1-18afd7f73660",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "TimelinedArray([-0.2937302 , 0.0730396 , 0.01149875, 0.0241783 ,\n",
+ " 0.11542535, 0.2339597 , -0.00069701, 0.01844483,\n",
+ " 0.25725105, -0.20286623])"
+ ]
+ },
+ "execution_count": 125,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#monir\n",
+ "trials_roi_df.iloc[0]['neuronal_features'] \n",
+ "#we are not in target barrel and freq is 10 in this roi and 10 to 90 in the target barrel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "id": "0ff63c86-a651-4fc7-972c-cfb06f7557da",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "def get_single_classifier_chance(\n",
+ " training_data, test_data, *, features_key=\"features\", classes_key=\"target_amplitude\"\n",
+ "):\n",
+ " training_data = training_data.copy()\n",
+ " randomized_training_data = training_data.sample(frac = 1)\n",
+ " training_data.loc[:,classes_key] = list(randomized_training_data.loc[:,classes_key])\n",
+ "\n",
+ " training_set = adaptation.classifiers.get_features_and_classes(\n",
+ " training_data, features_key=features_key, classes_key=classes_key\n",
+ " )\n",
+ " test_set = adaptation.classifiers.get_features_and_classes(\n",
+ " test_data, features_key=features_key, classes_key=classes_key\n",
+ " )\n",
+ "\n",
+ " classifier = adaptation.classifiers.LinearSVC()\n",
+ " classifier.fit(training_set[\"samples_features\"], training_set[\"samples_classes\"])\n",
+ "\n",
+ " score = classifier.score(test_set[\"samples_features\"], test_set[\"samples_classes\"])\n",
+ " return {\n",
+ " \"score\": score,\n",
+ " \"training_trials\": training_data.index,\n",
+ " \"test_trials\": test_data.index,\n",
+ " \"classifier\": classifier,\n",
+ " }\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "id": "11ee8712-aaf0-4c2c-91a3-35b9e52d58ec",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "codition found\n"
+ ]
+ }
+ ],
+ "source": [
+ "subset = trials_roi_df.loc[0:0]\n",
+ "for cond, group in subset.groupby(['in_target_barrel','nontarget_amplitude']):\n",
+ " if cond[0] == True and cond[1] == '0':\n",
+ " print('codition found')\n",
+ " break\n",
+ "\n",
+ " #adaptation.classifiers.get_sample_and_training(subset, max_samples = 0.75)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "id": "1d9915a7-97cd-4a6b-bbc7-71f0211cd9eb",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>frequency_change</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>...</th>\n",
+ " <th>complete_stim</th>\n",
+ " <th>target_whisker</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_D1</th>\n",
+ " <th>in_any_barrel</th>\n",
+ " <th>in_C1</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " <th>neuronal_features</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"38\" valign=\"top\">0</th>\n",
+ " <th>6</th>\n",
+ " <td>[100.87199401855469, 87.00389862060547, 98.347...</td>\n",
+ " <td>[-0.019380325737913043, 0.18458343755734835, 0...</td>\n",
+ " <td>[62.105133056640625, 69.67237091064453, 70.848...</td>\n",
+ " <td>[-0.019380325737913043, 0.18458343755734835, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.1586406918515107, -0.07239579586479972, 0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>[62.95381546020508, 57.82320022583008, 65.7714...</td>\n",
+ " <td>[0.19976095470042038, -0.05398622620535903, 0....</td>\n",
+ " <td>[67.40586853027344, 57.99021911621094, 62.3960...</td>\n",
+ " <td>[0.19976095470042038, -0.05398622620535903, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.30381872957173545, -0.2875727317407065, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>[70.03380584716797, 63.43825912475586, 73.4520...</td>\n",
+ " <td>[-0.3403347810837183, 0.0880496300975233, -0.2...</td>\n",
+ " <td>[47.38875198364258, 63.28361892700195, 50.0978...</td>\n",
+ " <td>[-0.3403347810837183, 0.0880496300975233, -0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.1158566759531461, -0.24487215771161036, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>[130.3365478515625, 119.0087890625, 77.0390396...</td>\n",
+ " <td>[-0.12726088658820112, 0.07642245356457941, 0....</td>\n",
+ " <td>[55.51100158691406, 63.068458557128906, 68.633...</td>\n",
+ " <td>[-0.12726088658820112, 0.07642245356457941, 0....</td>\n",
+ " <td>[9.151693344116211, 0.0, 0.0, 0.0, 0.0, 5.0895...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2559409678391272, 0.11771191691850143, -0.5...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>[84.06704711914062, 64.12518310546875, 82.7639...</td>\n",
+ " <td>[0.13275637081627137, -0.5002529915514548, -0....</td>\n",
+ " <td>[65.09046173095703, 41.603912353515625, 56.581...</td>\n",
+ " <td>[0.13275637081627137, -0.5002529915514548, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.12941679310412216, -0.1998458966893841, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>[109.7398910522461, 77.05498504638672, 79.3592...</td>\n",
+ " <td>[0.1845884188792514, -0.02157296613558047, 0.0...</td>\n",
+ " <td>[66.94621276855469, 59.29829025268555, 63.6894...</td>\n",
+ " <td>[0.1845884188792514, -0.02157296613558047, 0.0...</td>\n",
+ " <td>[2.113368511199951, 0.0, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.16490452819679824, -0.285950893498107, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>[82.01639556884766, 56.73377227783203, 84.5247...</td>\n",
+ " <td>[-0.09417749524349582, 0.27506349218015863, -0...</td>\n",
+ " <td>[56.51344680786133, 70.21515655517578, 55.4987...</td>\n",
+ " <td>[-0.09417749524349582, 0.27506349218015863, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 23.926652908325195, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.06742932492396372, -0.42743440044955994, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36</th>\n",
+ " <td>[58.71508026123047, 43.665916442871094, 48.893...</td>\n",
+ " <td>[0.26346521509705234, -0.09484229177232319, 0....</td>\n",
+ " <td>[69.77995300292969, 56.484107971191406, 67.701...</td>\n",
+ " <td>[0.26346521509705234, -0.09484229177232319, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.22095575502509116, 0.3264835900107608, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>[38.30034255981445, 107.37052154541016, 57.480...</td>\n",
+ " <td>[0.3741567391216821, -0.354969383296631, -0.20...</td>\n",
+ " <td>[73.31051635742188, 46.25428009033203, 51.8606...</td>\n",
+ " <td>[0.3741567391216821, -0.354969383296631, -0.20...</td>\n",
+ " <td>[0.0, 6.112779140472412, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.22173434971107372, 0.15077624019543714, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td>[73.36776733398438, 87.6191635131836, 54.31721...</td>\n",
+ " <td>[0.19683796406183698, 0.08509014106620992, 0.0...</td>\n",
+ " <td>[66.78239440917969, 62.640586853027344, 59.699...</td>\n",
+ " <td>[0.19683796406183698, 0.08509014106620992, 0.0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 17.804487228393...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.39907746867152893, -0.34447767683243824, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>[77.88352966308594, 79.556640625, 84.604385375...</td>\n",
+ " <td>[-0.3631467110904207, -0.07414773281929238, -0...</td>\n",
+ " <td>[46.2420539855957, 56.96577072143555, 49.34474...</td>\n",
+ " <td>[-0.3631467110904207, -0.07414773281929238, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.3195126924143639, -0.357946587662354, -0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52</th>\n",
+ " <td>[67.33567810058594, 48.28118896484375, 71.0715...</td>\n",
+ " <td>[-0.1442309428776943, -0.3727207795621237, -0....</td>\n",
+ " <td>[54.02933883666992, 45.55012130737305, 57.8606...</td>\n",
+ " <td>[-0.1442309428776943, -0.3727207795621237, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.0749953255950385, -0.11083596483827954, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55</th>\n",
+ " <td>[62.857688903808594, 69.17253112792969, 98.870...</td>\n",
+ " <td>[-0.03835850195374511, -0.09370537921914046, -...</td>\n",
+ " <td>[58.22004699707031, 56.166259765625, 46.251834...</td>\n",
+ " <td>[-0.03835850195374511, -0.09370537921914046, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.34675582872189553, -0.06147218810392419, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59</th>\n",
+ " <td>[79.47415161132812, 58.472782135009766, 51.905...</td>\n",
+ " <td>[-0.301195984509098, -0.3407307181051589, -0.1...</td>\n",
+ " <td>[49.151588439941406, 47.687042236328125, 55.01...</td>\n",
+ " <td>[-0.301195984509098, -0.3407307181051589, -0.1...</td>\n",
+ " <td>[0.21878375113010406, 0.0, 0.0, 2.707182645797...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2898785447083861, -0.3538369410897002, -0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>61</th>\n",
+ " <td>[45.047698974609375, 80.32709503173828, 86.065...</td>\n",
+ " <td>[0.024156074799225682, -0.29131259425713013, 0...</td>\n",
+ " <td>[61.78728485107422, 50.0831298828125, 66.60635...</td>\n",
+ " <td>[0.024156074799225682, -0.29131259425713013, 0...</td>\n",
+ " <td>[0.0, 1.9810150861740112, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.14430546202944736, -0.19186351520268025, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64</th>\n",
+ " <td>[100.40974426269531, 118.11618041992188, 144.0...</td>\n",
+ " <td>[-0.030046736575592203, -0.13501141369423728, ...</td>\n",
+ " <td>[59.63080596923828, 55.73594284057617, 55.6063...</td>\n",
+ " <td>[-0.030046736575592203, -0.13501141369423728, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.36986341769212033, 0.2856213246909113, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>67</th>\n",
+ " <td>[24.20662498474121, 103.0528564453125, 102.228...</td>\n",
+ " <td>[-0.03693458966634329, -0.22616292516821382, -...</td>\n",
+ " <td>[59.54278564453125, 52.52322769165039, 46.1564...</td>\n",
+ " <td>[-0.03693458966634329, -0.22616292516821382, -...</td>\n",
+ " <td>[0.0, 6.1036810874938965, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.034808481432044326, -0.3988412414450435, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>77</th>\n",
+ " <td>[69.60657501220703, 59.89699172973633, 33.0299...</td>\n",
+ " <td>[-0.3262067717026943, -0.20628291626061518, -0...</td>\n",
+ " <td>[47.980438232421875, 52.42787170410156, 46.547...</td>\n",
+ " <td>[-0.3262067717026943, -0.20628291626061518, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.27503738004609635, -0.38200986066603304, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>79</th>\n",
+ " <td>[83.3751449584961, 59.43309020996094, 89.95671...</td>\n",
+ " <td>[-0.13134106005640503, 0.1728664054324369, -0....</td>\n",
+ " <td>[54.86307907104492, 66.1515884399414, 59.00489...</td>\n",
+ " <td>[-0.13134106005640503, 0.1728664054324369, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.3120616635082773, -0.09372354484998649, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>80</th>\n",
+ " <td>[42.08283615112305, 48.792240142822266, 53.315...</td>\n",
+ " <td>[-0.16097356093022197, 0.006317768559207317, -...</td>\n",
+ " <td>[53.89731216430664, 60.105133056640625, 44.838...</td>\n",
+ " <td>[-0.16097356093022197, 0.006317768559207317, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.71974372...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.10694041577938958, -0.45814948334946415, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>82</th>\n",
+ " <td>[45.3671760559082, 61.1575813293457, 45.102962...</td>\n",
+ " <td>[-0.3804126544092202, -0.4111308949860192, -0....</td>\n",
+ " <td>[45.985328674316406, 44.84352111816406, 41.322...</td>\n",
+ " <td>[-0.3804126544092202, -0.4111308949860192, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.0922527143204685, -0.13774746044514272, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>87</th>\n",
+ " <td>[67.09834289550781, 54.83423614501953, 79.9757...</td>\n",
+ " <td>[-0.24679074133471796, -0.2260667379097021, 0....</td>\n",
+ " <td>[51.246944427490234, 52.014671325683594, 61.50...</td>\n",
+ " <td>[-0.24679074133471796, -0.2260667379097021, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 4.056626796722412, 4.9645...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.013602904575762508, -0.015408833029702288,...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>90</th>\n",
+ " <td>[74.96868896484375, 63.006832122802734, 73.002...</td>\n",
+ " <td>[-0.26735093752214306, -0.2862214039136709, 0....</td>\n",
+ " <td>[50.98777389526367, 50.288509368896484, 64.132...</td>\n",
+ " <td>[-0.26735093752214306, -0.2862214039136709, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.46502922599479735, -0.16776150114350868, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93</th>\n",
+ " <td>[81.14598083496094, 52.66810989379883, 53.7429...</td>\n",
+ " <td>[-0.061336973892297586, -0.4387157206402077, -...</td>\n",
+ " <td>[58.92176055908203, 44.919315338134766, 56.347...</td>\n",
+ " <td>[-0.061336973892297586, -0.4387157206402077, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 14.115710258483887, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.07788084503784974, 0.22043836401472844, 0.5...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>94</th>\n",
+ " <td>[57.24399185180664, 40.83292770385742, 74.5320...</td>\n",
+ " <td>[-0.46692145595952694, -0.012430241109140526, ...</td>\n",
+ " <td>[44.07823944091797, 60.943763732910156, 46.579...</td>\n",
+ " <td>[-0.46692145595952694, -0.012430241109140526, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.4004910839027843, -0.0615911497822542, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>102</th>\n",
+ " <td>[69.81690979003906, 67.22591400146484, 104.189...</td>\n",
+ " <td>[-0.03953536553132661, -0.15291717335810007, -...</td>\n",
+ " <td>[57.80440139770508, 53.59413146972656, 42.5501...</td>\n",
+ " <td>[-0.03953536553132661, -0.15291717335810007, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.42859246262123, -0.35655641351371625, -0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106</th>\n",
+ " <td>[75.32643127441406, 78.62049865722656, 50.8739...</td>\n",
+ " <td>[-0.09032415395453972, -0.17852165212005172, 0...</td>\n",
+ " <td>[54.88753128051758, 51.61124801635742, 61.2567...</td>\n",
+ " <td>[-0.09032415395453972, -0.17852165212005172, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.051771125230875154, -0.10644662279087692, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108</th>\n",
+ " <td>[71.5877685546875, 63.427188873291016, 83.6428...</td>\n",
+ " <td>[-0.08231641622910321, -0.04060588775294994, -...</td>\n",
+ " <td>[55.056236267089844, 56.603912353515625, 44.83...</td>\n",
+ " <td>[-0.08231641622910321, -0.04060588775294994, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 7.658877849578857, 0...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.24978384964776593, 0.1591797183608242, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>123</th>\n",
+ " <td>[51.110469818115234, 119.89360046386719, 55.19...</td>\n",
+ " <td>[-0.008368330938786342, -0.03842130753140318, ...</td>\n",
+ " <td>[58.65525817871094, 57.540340423583984, 61.882...</td>\n",
+ " <td>[-0.008368330938786342, -0.03842130753140318, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1523734331130...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.30771735952058593, -0.10981481716853166, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>124</th>\n",
+ " <td>[80.1699447631836, 78.87667846679688, 34.03127...</td>\n",
+ " <td>[-0.15543081147981871, -0.2855905214523166, -0...</td>\n",
+ " <td>[53.14425277709961, 48.31051254272461, 48.6112...</td>\n",
+ " <td>[-0.15543081147981871, -0.2855905214523166, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.4342767282191186, -0.1917257202881671, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>127</th>\n",
+ " <td>[66.37210083007812, 55.8890380859375, 58.49185...</td>\n",
+ " <td>[-0.4275613453466342, -0.0795234142405778, 0.2...</td>\n",
+ " <td>[42.5085563659668, 55.42298126220703, 68.47677...</td>\n",
+ " <td>[-0.4275613453466342, -0.0795234142405778, 0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.08982622249594478, -0.2626424843578636, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>129</th>\n",
+ " <td>[88.6202392578125, 114.49066925048828, 51.5933...</td>\n",
+ " <td>[0.40536618024817944, -0.04413966444660514, 0....</td>\n",
+ " <td>[73.11002349853516, 56.42787170410156, 58.0880...</td>\n",
+ " <td>[0.40536618024817944, -0.04413966444660514, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.13272339577414766, -0.3490732114687793, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>131</th>\n",
+ " <td>[81.70460510253906, 76.52861785888672, 139.540...</td>\n",
+ " <td>[-0.2051156853627862, 0.35946644899691343, -0....</td>\n",
+ " <td>[50.515892028808594, 71.46454620361328, 57.970...</td>\n",
+ " <td>[-0.2051156853627862, 0.35946644899691343, -0....</td>\n",
+ " <td>[0.026706727221608162, 0.0, 9.38946533203125, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.028250690057140954, -0.16798914665657105, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>133</th>\n",
+ " <td>[58.30579376220703, 47.300148010253906, 78.277...</td>\n",
+ " <td>[-0.1682515176082425, -0.15456395556673033, -0...</td>\n",
+ " <td>[51.733497619628906, 52.2420539855957, 49.4303...</td>\n",
+ " <td>[-0.1682515176082425, -0.15456395556673033, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.672...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.18034109172975457, -0.009614545972992442, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>134</th>\n",
+ " <td>[63.71532440185547, 81.97270965576172, 73.7440...</td>\n",
+ " <td>[0.08786407787388432, -0.17868437417727567, 0....</td>\n",
+ " <td>[61.210269927978516, 51.317848205566406, 64.30...</td>\n",
+ " <td>[0.08786407787388432, -0.17868437417727567, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.4088370983251731, -0.30791523522131586, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>140</th>\n",
+ " <td>[88.5430908203125, 60.645084381103516, 50.4617...</td>\n",
+ " <td>[0.3098404670758121, -0.25769622047774604, -0....</td>\n",
+ " <td>[69.07579803466797, 48.014671325683594, 44.946...</td>\n",
+ " <td>[0.3098404670758121, -0.25769622047774604, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.13552526822406252, -0.4820798800603281, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>[113.19007110595703, 66.31562805175781, 59.043...</td>\n",
+ " <td>[-0.27854251373066036, 0.2195061242315473, 0.2...</td>\n",
+ " <td>[47.371639251708984, 65.85330200195312, 67.334...</td>\n",
+ " <td>[-0.27854251373066036, 0.2195061242315473, 0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.2590662407604328, 0.06000996370129285, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>[70.78560638427734, 42.672210693359375, 73.129...</td>\n",
+ " <td>[-0.1387883226963254, -0.6539329094909159, -0....</td>\n",
+ " <td>[52.67970657348633, 33.567237854003906, 52.039...</td>\n",
+ " <td>[-0.1387883226963254, -0.6539329094909159, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.07282859954108618, 0.01083607695839526, 0....</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>38 rows × 25 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "0 6 [100.87199401855469, 87.00389862060547, 98.347... \\\n",
+ " 17 [62.95381546020508, 57.82320022583008, 65.7714... \n",
+ " 20 [70.03380584716797, 63.43825912475586, 73.4520... \n",
+ " 24 [130.3365478515625, 119.0087890625, 77.0390396... \n",
+ " 27 [84.06704711914062, 64.12518310546875, 82.7639... \n",
+ " 30 [109.7398910522461, 77.05498504638672, 79.3592... \n",
+ " 34 [82.01639556884766, 56.73377227783203, 84.5247... \n",
+ " 36 [58.71508026123047, 43.665916442871094, 48.893... \n",
+ " 44 [38.30034255981445, 107.37052154541016, 57.480... \n",
+ " 45 [73.36776733398438, 87.6191635131836, 54.31721... \n",
+ " 50 [77.88352966308594, 79.556640625, 84.604385375... \n",
+ " 52 [67.33567810058594, 48.28118896484375, 71.0715... \n",
+ " 55 [62.857688903808594, 69.17253112792969, 98.870... \n",
+ " 59 [79.47415161132812, 58.472782135009766, 51.905... \n",
+ " 61 [45.047698974609375, 80.32709503173828, 86.065... \n",
+ " 64 [100.40974426269531, 118.11618041992188, 144.0... \n",
+ " 67 [24.20662498474121, 103.0528564453125, 102.228... \n",
+ " 77 [69.60657501220703, 59.89699172973633, 33.0299... \n",
+ " 79 [83.3751449584961, 59.43309020996094, 89.95671... \n",
+ " 80 [42.08283615112305, 48.792240142822266, 53.315... \n",
+ " 82 [45.3671760559082, 61.1575813293457, 45.102962... \n",
+ " 87 [67.09834289550781, 54.83423614501953, 79.9757... \n",
+ " 90 [74.96868896484375, 63.006832122802734, 73.002... \n",
+ " 93 [81.14598083496094, 52.66810989379883, 53.7429... \n",
+ " 94 [57.24399185180664, 40.83292770385742, 74.5320... \n",
+ " 102 [69.81690979003906, 67.22591400146484, 104.189... \n",
+ " 106 [75.32643127441406, 78.62049865722656, 50.8739... \n",
+ " 108 [71.5877685546875, 63.427188873291016, 83.6428... \n",
+ " 123 [51.110469818115234, 119.89360046386719, 55.19... \n",
+ " 124 [80.1699447631836, 78.87667846679688, 34.03127... \n",
+ " 127 [66.37210083007812, 55.8890380859375, 58.49185... \n",
+ " 129 [88.6202392578125, 114.49066925048828, 51.5933... \n",
+ " 131 [81.70460510253906, 76.52861785888672, 139.540... \n",
+ " 133 [58.30579376220703, 47.300148010253906, 78.277... \n",
+ " 134 [63.71532440185547, 81.97270965576172, 73.7440... \n",
+ " 140 [88.5430908203125, 60.645084381103516, 50.4617... \n",
+ " 147 [113.19007110595703, 66.31562805175781, 59.043... \n",
+ " 148 [70.78560638427734, 42.672210693359375, 73.129... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "0 6 [-0.019380325737913043, 0.18458343755734835, 0... \\\n",
+ " 17 [0.19976095470042038, -0.05398622620535903, 0.... \n",
+ " 20 [-0.3403347810837183, 0.0880496300975233, -0.2... \n",
+ " 24 [-0.12726088658820112, 0.07642245356457941, 0.... \n",
+ " 27 [0.13275637081627137, -0.5002529915514548, -0.... \n",
+ " 30 [0.1845884188792514, -0.02157296613558047, 0.0... \n",
+ " 34 [-0.09417749524349582, 0.27506349218015863, -0... \n",
+ " 36 [0.26346521509705234, -0.09484229177232319, 0.... \n",
+ " 44 [0.3741567391216821, -0.354969383296631, -0.20... \n",
+ " 45 [0.19683796406183698, 0.08509014106620992, 0.0... \n",
+ " 50 [-0.3631467110904207, -0.07414773281929238, -0... \n",
+ " 52 [-0.1442309428776943, -0.3727207795621237, -0.... \n",
+ " 55 [-0.03835850195374511, -0.09370537921914046, -... \n",
+ " 59 [-0.301195984509098, -0.3407307181051589, -0.1... \n",
+ " 61 [0.024156074799225682, -0.29131259425713013, 0... \n",
+ " 64 [-0.030046736575592203, -0.13501141369423728, ... \n",
+ " 67 [-0.03693458966634329, -0.22616292516821382, -... \n",
+ " 77 [-0.3262067717026943, -0.20628291626061518, -0... \n",
+ " 79 [-0.13134106005640503, 0.1728664054324369, -0.... \n",
+ " 80 [-0.16097356093022197, 0.006317768559207317, -... \n",
+ " 82 [-0.3804126544092202, -0.4111308949860192, -0.... \n",
+ " 87 [-0.24679074133471796, -0.2260667379097021, 0.... \n",
+ " 90 [-0.26735093752214306, -0.2862214039136709, 0.... \n",
+ " 93 [-0.061336973892297586, -0.4387157206402077, -... \n",
+ " 94 [-0.46692145595952694, -0.012430241109140526, ... \n",
+ " 102 [-0.03953536553132661, -0.15291717335810007, -... \n",
+ " 106 [-0.09032415395453972, -0.17852165212005172, 0... \n",
+ " 108 [-0.08231641622910321, -0.04060588775294994, -... \n",
+ " 123 [-0.008368330938786342, -0.03842130753140318, ... \n",
+ " 124 [-0.15543081147981871, -0.2855905214523166, -0... \n",
+ " 127 [-0.4275613453466342, -0.0795234142405778, 0.2... \n",
+ " 129 [0.40536618024817944, -0.04413966444660514, 0.... \n",
+ " 131 [-0.2051156853627862, 0.35946644899691343, -0.... \n",
+ " 133 [-0.1682515176082425, -0.15456395556673033, -0... \n",
+ " 134 [0.08786407787388432, -0.17868437417727567, 0.... \n",
+ " 140 [0.3098404670758121, -0.25769622047774604, -0.... \n",
+ " 147 [-0.27854251373066036, 0.2195061242315473, 0.2... \n",
+ " 148 [-0.1387883226963254, -0.6539329094909159, -0.... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "0 6 [62.105133056640625, 69.67237091064453, 70.848... \\\n",
+ " 17 [67.40586853027344, 57.99021911621094, 62.3960... \n",
+ " 20 [47.38875198364258, 63.28361892700195, 50.0978... \n",
+ " 24 [55.51100158691406, 63.068458557128906, 68.633... \n",
+ " 27 [65.09046173095703, 41.603912353515625, 56.581... \n",
+ " 30 [66.94621276855469, 59.29829025268555, 63.6894... \n",
+ " 34 [56.51344680786133, 70.21515655517578, 55.4987... \n",
+ " 36 [69.77995300292969, 56.484107971191406, 67.701... \n",
+ " 44 [73.31051635742188, 46.25428009033203, 51.8606... \n",
+ " 45 [66.78239440917969, 62.640586853027344, 59.699... \n",
+ " 50 [46.2420539855957, 56.96577072143555, 49.34474... \n",
+ " 52 [54.02933883666992, 45.55012130737305, 57.8606... \n",
+ " 55 [58.22004699707031, 56.166259765625, 46.251834... \n",
+ " 59 [49.151588439941406, 47.687042236328125, 55.01... \n",
+ " 61 [61.78728485107422, 50.0831298828125, 66.60635... \n",
+ " 64 [59.63080596923828, 55.73594284057617, 55.6063... \n",
+ " 67 [59.54278564453125, 52.52322769165039, 46.1564... \n",
+ " 77 [47.980438232421875, 52.42787170410156, 46.547... \n",
+ " 79 [54.86307907104492, 66.1515884399414, 59.00489... \n",
+ " 80 [53.89731216430664, 60.105133056640625, 44.838... \n",
+ " 82 [45.985328674316406, 44.84352111816406, 41.322... \n",
+ " 87 [51.246944427490234, 52.014671325683594, 61.50... \n",
+ " 90 [50.98777389526367, 50.288509368896484, 64.132... \n",
+ " 93 [58.92176055908203, 44.919315338134766, 56.347... \n",
+ " 94 [44.07823944091797, 60.943763732910156, 46.579... \n",
+ " 102 [57.80440139770508, 53.59413146972656, 42.5501... \n",
+ " 106 [54.88753128051758, 51.61124801635742, 61.2567... \n",
+ " 108 [55.056236267089844, 56.603912353515625, 44.83... \n",
+ " 123 [58.65525817871094, 57.540340423583984, 61.882... \n",
+ " 124 [53.14425277709961, 48.31051254272461, 48.6112... \n",
+ " 127 [42.5085563659668, 55.42298126220703, 68.47677... \n",
+ " 129 [73.11002349853516, 56.42787170410156, 58.0880... \n",
+ " 131 [50.515892028808594, 71.46454620361328, 57.970... \n",
+ " 133 [51.733497619628906, 52.2420539855957, 49.4303... \n",
+ " 134 [61.210269927978516, 51.317848205566406, 64.30... \n",
+ " 140 [69.07579803466797, 48.014671325683594, 44.946... \n",
+ " 147 [47.371639251708984, 65.85330200195312, 67.334... \n",
+ " 148 [52.67970657348633, 33.567237854003906, 52.039... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "0 6 [-0.019380325737913043, 0.18458343755734835, 0... \\\n",
+ " 17 [0.19976095470042038, -0.05398622620535903, 0.... \n",
+ " 20 [-0.3403347810837183, 0.0880496300975233, -0.2... \n",
+ " 24 [-0.12726088658820112, 0.07642245356457941, 0.... \n",
+ " 27 [0.13275637081627137, -0.5002529915514548, -0.... \n",
+ " 30 [0.1845884188792514, -0.02157296613558047, 0.0... \n",
+ " 34 [-0.09417749524349582, 0.27506349218015863, -0... \n",
+ " 36 [0.26346521509705234, -0.09484229177232319, 0.... \n",
+ " 44 [0.3741567391216821, -0.354969383296631, -0.20... \n",
+ " 45 [0.19683796406183698, 0.08509014106620992, 0.0... \n",
+ " 50 [-0.3631467110904207, -0.07414773281929238, -0... \n",
+ " 52 [-0.1442309428776943, -0.3727207795621237, -0.... \n",
+ " 55 [-0.03835850195374511, -0.09370537921914046, -... \n",
+ " 59 [-0.301195984509098, -0.3407307181051589, -0.1... \n",
+ " 61 [0.024156074799225682, -0.29131259425713013, 0... \n",
+ " 64 [-0.030046736575592203, -0.13501141369423728, ... \n",
+ " 67 [-0.03693458966634329, -0.22616292516821382, -... \n",
+ " 77 [-0.3262067717026943, -0.20628291626061518, -0... \n",
+ " 79 [-0.13134106005640503, 0.1728664054324369, -0.... \n",
+ " 80 [-0.16097356093022197, 0.006317768559207317, -... \n",
+ " 82 [-0.3804126544092202, -0.4111308949860192, -0.... \n",
+ " 87 [-0.24679074133471796, -0.2260667379097021, 0.... \n",
+ " 90 [-0.26735093752214306, -0.2862214039136709, 0.... \n",
+ " 93 [-0.061336973892297586, -0.4387157206402077, -... \n",
+ " 94 [-0.46692145595952694, -0.012430241109140526, ... \n",
+ " 102 [-0.03953536553132661, -0.15291717335810007, -... \n",
+ " 106 [-0.09032415395453972, -0.17852165212005172, 0... \n",
+ " 108 [-0.08231641622910321, -0.04060588775294994, -... \n",
+ " 123 [-0.008368330938786342, -0.03842130753140318, ... \n",
+ " 124 [-0.15543081147981871, -0.2855905214523166, -0... \n",
+ " 127 [-0.4275613453466342, -0.0795234142405778, 0.2... \n",
+ " 129 [0.40536618024817944, -0.04413966444660514, 0.... \n",
+ " 131 [-0.2051156853627862, 0.35946644899691343, -0.... \n",
+ " 133 [-0.1682515176082425, -0.15456395556673033, -0... \n",
+ " 134 [0.08786407787388432, -0.17868437417727567, 0.... \n",
+ " 140 [0.3098404670758121, -0.25769622047774604, -0.... \n",
+ " 147 [-0.27854251373066036, 0.2195061242315473, 0.2... \n",
+ " 148 [-0.1387883226963254, -0.6539329094909159, -0.... \n",
+ "\n",
+ " spks target_stim \n",
+ "roi# trial# \n",
+ "0 6 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \\\n",
+ " 17 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 20 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 24 [9.151693344116211, 0.0, 0.0, 0.0, 0.0, 5.0895... C1_10_90 \n",
+ " 27 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 30 [2.113368511199951, 0.0, 0.0, 0.0, 0.0, 0.0, 0... C1_10_90 \n",
+ " 34 [0.0, 0.0, 0.0, 0.0, 23.926652908325195, 0.0, ... C1_10_90 \n",
+ " 36 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 44 [0.0, 6.112779140472412, 0.0, 0.0, 0.0, 0.0, 0... C1_10_20 \n",
+ " 45 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 17.804487228393... C1_10_90 \n",
+ " 50 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 52 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 55 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 59 [0.21878375113010406, 0.0, 0.0, 2.707182645797... C1_10_90 \n",
+ " 61 [0.0, 1.9810150861740112, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 64 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 67 [0.0, 6.1036810874938965, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 77 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 79 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 80 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.71974372... C1_10_90 \n",
+ " 82 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 87 [0.0, 0.0, 0.0, 0.0, 4.056626796722412, 4.9645... C1_10_90 \n",
+ " 90 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004... C1_10_20 \n",
+ " 93 [0.0, 0.0, 0.0, 0.0, 0.0, 14.115710258483887, ... C1_10_20 \n",
+ " 94 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 102 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 106 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 108 [0.0, 0.0, 0.0, 0.0, 0.0, 7.658877849578857, 0... C1_10_20 \n",
+ " 123 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1523734331130... C1_10_90 \n",
+ " 124 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 127 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 129 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 131 [0.026706727221608162, 0.0, 9.38946533203125, ... C1_10_20 \n",
+ " 133 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.672... C1_10_20 \n",
+ " 134 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 140 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 148 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ "\n",
+ " target_amplitude frequency_change \n",
+ "roi# trial# \n",
+ "0 6 10_20 10.0 \\\n",
+ " 17 10_90 80.0 \n",
+ " 20 10_90 80.0 \n",
+ " 24 10_90 80.0 \n",
+ " 27 10_20 10.0 \n",
+ " 30 10_90 80.0 \n",
+ " 34 10_90 80.0 \n",
+ " 36 10_20 10.0 \n",
+ " 44 10_20 10.0 \n",
+ " 45 10_90 80.0 \n",
+ " 50 10_20 10.0 \n",
+ " 52 10_90 80.0 \n",
+ " 55 10_20 10.0 \n",
+ " 59 10_90 80.0 \n",
+ " 61 10_90 80.0 \n",
+ " 64 10_90 80.0 \n",
+ " 67 10_20 10.0 \n",
+ " 77 10_20 10.0 \n",
+ " 79 10_90 80.0 \n",
+ " 80 10_90 80.0 \n",
+ " 82 10_20 10.0 \n",
+ " 87 10_90 80.0 \n",
+ " 90 10_20 10.0 \n",
+ " 93 10_20 10.0 \n",
+ " 94 10_90 80.0 \n",
+ " 102 10_90 80.0 \n",
+ " 106 10_20 10.0 \n",
+ " 108 10_20 10.0 \n",
+ " 123 10_90 80.0 \n",
+ " 124 10_20 10.0 \n",
+ " 127 10_90 80.0 \n",
+ " 129 10_90 80.0 \n",
+ " 131 10_20 10.0 \n",
+ " 133 10_20 10.0 \n",
+ " 134 10_90 80.0 \n",
+ " 140 10_20 10.0 \n",
+ " 147 10_20 10.0 \n",
+ " 148 10_20 10.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "0 6 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 17 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 20 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 24 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 27 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 30 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 34 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 36 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 44 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 45 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 50 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 52 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 55 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 59 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 61 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 64 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 67 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 77 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 79 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 80 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 82 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 87 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 90 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 93 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 94 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 102 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 106 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 108 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 123 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 124 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 127 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 129 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 131 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 133 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 134 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 140 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_amplitude ... complete_stim target_whisker \n",
+ "roi# trial# ... \n",
+ "0 6 0 ... C1_10_20&D1_0 C1 \\\n",
+ " 17 0 ... C1_10_90&D1_0 C1 \n",
+ " 20 0 ... C1_10_90&D1_0 C1 \n",
+ " 24 0 ... C1_10_90&D1_0 C1 \n",
+ " 27 0 ... C1_10_20&D1_0 C1 \n",
+ " 30 0 ... C1_10_90&D1_0 C1 \n",
+ " 34 0 ... C1_10_90&D1_0 C1 \n",
+ " 36 0 ... C1_10_20&D1_0 C1 \n",
+ " 44 0 ... C1_10_20&D1_0 C1 \n",
+ " 45 0 ... C1_10_90&D1_0 C1 \n",
+ " 50 0 ... C1_10_20&D1_0 C1 \n",
+ " 52 0 ... C1_10_90&D1_0 C1 \n",
+ " 55 0 ... C1_10_20&D1_0 C1 \n",
+ " 59 0 ... C1_10_90&D1_0 C1 \n",
+ " 61 0 ... C1_10_90&D1_0 C1 \n",
+ " 64 0 ... C1_10_90&D1_0 C1 \n",
+ " 67 0 ... C1_10_20&D1_0 C1 \n",
+ " 77 0 ... C1_10_20&D1_0 C1 \n",
+ " 79 0 ... C1_10_90&D1_0 C1 \n",
+ " 80 0 ... C1_10_90&D1_0 C1 \n",
+ " 82 0 ... C1_10_20&D1_0 C1 \n",
+ " 87 0 ... C1_10_90&D1_0 C1 \n",
+ " 90 0 ... C1_10_20&D1_0 C1 \n",
+ " 93 0 ... C1_10_20&D1_0 C1 \n",
+ " 94 0 ... C1_10_90&D1_0 C1 \n",
+ " 102 0 ... C1_10_90&D1_0 C1 \n",
+ " 106 0 ... C1_10_20&D1_0 C1 \n",
+ " 108 0 ... C1_10_20&D1_0 C1 \n",
+ " 123 0 ... C1_10_90&D1_0 C1 \n",
+ " 124 0 ... C1_10_20&D1_0 C1 \n",
+ " 127 0 ... C1_10_90&D1_0 C1 \n",
+ " 129 0 ... C1_10_90&D1_0 C1 \n",
+ " 131 0 ... C1_10_20&D1_0 C1 \n",
+ " 133 0 ... C1_10_20&D1_0 C1 \n",
+ " 134 0 ... C1_10_90&D1_0 C1 \n",
+ " 140 0 ... C1_10_20&D1_0 C1 \n",
+ " 147 0 ... C1_10_20&D1_0 C1 \n",
+ " 148 0 ... C1_10_20&D1_0 C1 \n",
+ "\n",
+ " nontarget_stim in_D1 in_any_barrel in_C1 is_neuron is_VGAT \n",
+ "roi# trial# \n",
+ "0 6 D1_0 True True False True False \\\n",
+ " 17 D1_0 True True False True False \n",
+ " 20 D1_0 True True False True False \n",
+ " 24 D1_0 True True False True False \n",
+ " 27 D1_0 True True False True False \n",
+ " 30 D1_0 True True False True False \n",
+ " 34 D1_0 True True False True False \n",
+ " 36 D1_0 True True False True False \n",
+ " 44 D1_0 True True False True False \n",
+ " 45 D1_0 True True False True False \n",
+ " 50 D1_0 True True False True False \n",
+ " 52 D1_0 True True False True False \n",
+ " 55 D1_0 True True False True False \n",
+ " 59 D1_0 True True False True False \n",
+ " 61 D1_0 True True False True False \n",
+ " 64 D1_0 True True False True False \n",
+ " 67 D1_0 True True False True False \n",
+ " 77 D1_0 True True False True False \n",
+ " 79 D1_0 True True False True False \n",
+ " 80 D1_0 True True False True False \n",
+ " 82 D1_0 True True False True False \n",
+ " 87 D1_0 True True False True False \n",
+ " 90 D1_0 True True False True False \n",
+ " 93 D1_0 True True False True False \n",
+ " 94 D1_0 True True False True False \n",
+ " 102 D1_0 True True False True False \n",
+ " 106 D1_0 True True False True False \n",
+ " 108 D1_0 True True False True False \n",
+ " 123 D1_0 True True False True False \n",
+ " 124 D1_0 True True False True False \n",
+ " 127 D1_0 True True False True False \n",
+ " 129 D1_0 True True False True False \n",
+ " 131 D1_0 True True False True False \n",
+ " 133 D1_0 True True False True False \n",
+ " 134 D1_0 True True False True False \n",
+ " 140 D1_0 True True False True False \n",
+ " 147 D1_0 True True False True False \n",
+ " 148 D1_0 True True False True False \n",
+ "\n",
+ " in_target_barrel \n",
+ "roi# trial# \n",
+ "0 6 False \\\n",
+ " 17 False \n",
+ " 20 False \n",
+ " 24 False \n",
+ " 27 False \n",
+ " 30 False \n",
+ " 34 False \n",
+ " 36 False \n",
+ " 44 False \n",
+ " 45 False \n",
+ " 50 False \n",
+ " 52 False \n",
+ " 55 False \n",
+ " 59 False \n",
+ " 61 False \n",
+ " 64 False \n",
+ " 67 False \n",
+ " 77 False \n",
+ " 79 False \n",
+ " 80 False \n",
+ " 82 False \n",
+ " 87 False \n",
+ " 90 False \n",
+ " 93 False \n",
+ " 94 False \n",
+ " 102 False \n",
+ " 106 False \n",
+ " 108 False \n",
+ " 123 False \n",
+ " 124 False \n",
+ " 127 False \n",
+ " 129 False \n",
+ " 131 False \n",
+ " 133 False \n",
+ " 134 False \n",
+ " 140 False \n",
+ " 147 False \n",
+ " 148 False \n",
+ "\n",
+ " neuronal_features \n",
+ "roi# trial# \n",
+ "0 6 [0.1586406918515107, -0.07239579586479972, 0.0... \n",
+ " 17 [-0.30381872957173545, -0.2875727317407065, -0... \n",
+ " 20 [-0.1158566759531461, -0.24487215771161036, -0... \n",
+ " 24 [0.2559409678391272, 0.11771191691850143, -0.5... \n",
+ " 27 [0.12941679310412216, -0.1998458966893841, -0.... \n",
+ " 30 [-0.16490452819679824, -0.285950893498107, 0.1... \n",
+ " 34 [-0.06742932492396372, -0.42743440044955994, 0... \n",
+ " 36 [-0.22095575502509116, 0.3264835900107608, -0.... \n",
+ " 44 [-0.22173434971107372, 0.15077624019543714, -0... \n",
+ " 45 [-0.39907746867152893, -0.34447767683243824, -... \n",
+ " 50 [-0.3195126924143639, -0.357946587662354, -0.4... \n",
+ " 52 [-0.0749953255950385, -0.11083596483827954, 0.... \n",
+ " 55 [-0.34675582872189553, -0.06147218810392419, -... \n",
+ " 59 [0.2898785447083861, -0.3538369410897002, -0.2... \n",
+ " 61 [-0.14430546202944736, -0.19186351520268025, -... \n",
+ " 64 [-0.36986341769212033, 0.2856213246909113, 0.1... \n",
+ " 67 [-0.034808481432044326, -0.3988412414450435, -... \n",
+ " 77 [-0.27503738004609635, -0.38200986066603304, -... \n",
+ " 79 [-0.3120616635082773, -0.09372354484998649, -0... \n",
+ " 80 [0.10694041577938958, -0.45814948334946415, -0... \n",
+ " 82 [-0.0922527143204685, -0.13774746044514272, -0... \n",
+ " 87 [-0.013602904575762508, -0.015408833029702288,... \n",
+ " 90 [-0.46502922599479735, -0.16776150114350868, -... \n",
+ " 93 [0.07788084503784974, 0.22043836401472844, 0.5... \n",
+ " 94 [-0.4004910839027843, -0.0615911497822542, -0.... \n",
+ " 102 [-0.42859246262123, -0.35655641351371625, -0.1... \n",
+ " 106 [0.051771125230875154, -0.10644662279087692, -... \n",
+ " 108 [-0.24978384964776593, 0.1591797183608242, -0.... \n",
+ " 123 [-0.30771735952058593, -0.10981481716853166, -... \n",
+ " 124 [-0.4342767282191186, -0.1917257202881671, -0.... \n",
+ " 127 [0.08982622249594478, -0.2626424843578636, -0.... \n",
+ " 129 [0.13272339577414766, -0.3490732114687793, 0.1... \n",
+ " 131 [0.028250690057140954, -0.16798914665657105, -... \n",
+ " 133 [-0.18034109172975457, -0.009614545972992442, ... \n",
+ " 134 [-0.4088370983251731, -0.30791523522131586, -0... \n",
+ " 140 [0.13552526822406252, -0.4820798800603281, -0.... \n",
+ " 147 [-0.2590662407604328, 0.06000996370129285, -0.... \n",
+ " 148 [-0.07282859954108618, 0.01083607695839526, 0.... \n",
+ "\n",
+ "[38 rows x 25 columns]"
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "group #25 rows"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "id": "873417fc-8b5e-40f7-8e15-86eac1a1c683",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\mohay\\AppData\\Local\\Temp\\ipykernel_12312\\2905049508.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " np.array(list(trials_roi_df[\"F_var\"][:]))\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([TimelinedArray([ 4.48002361e-01, 7.71451664e-01, 2.45827776e-01,\n",
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+ " 0.1113253 , -0.04208605, -0.47017075, -0.1140374 ,\n",
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+ " -0.51063657, -0.44302277, -0.34285736, -0.24705356,\n",
+ " -0.21337055, -0.22552823, -0.25335852, -0.52856158,\n",
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+ " -0.14510145, -0.07825688, 0.27150542, -0.62913959,\n",
+ " 0.18168172, -0.3225598 , 0.03587055, -0.0943404 ,\n",
+ " -0.27062424, -0.40625671, -0.19670145, -0.0197669 ,\n",
+ " -0.01197201, 0.15706668, -0.0319113 , -0.12414627,\n",
+ " 0.05836192, 0.02644372, -0.01373844, 0.04475439,\n",
+ " 0.09135854, 0.20561993, 0.09289252, -0.17151892,\n",
+ " -0.11094248, -0.10151372, -0.14415776, -0.20376782,\n",
+ " -0.09956615, -0.25035598, 0.06427117, 0.40038636,\n",
+ " -0.14368298, -0.29925637, -0.20891845, 0.04492845,\n",
+ " -0.14562905, -0.18826272, -0.53726755, -0.20172601,\n",
+ " -0.03830324, -0.19117308, 0.43062161, 0.06907579,\n",
+ " -0.22378496, 0.10555769, -0.07593512, -0.32584976,\n",
+ " -0.25632188, -0.19349076, -0.01406068, 0.27466031,\n",
+ " -0.04792202, -0.32621172, -0.10012672, -0.20503748,\n",
+ " 0.01719771, -0.38120835, -0.4423615 , -0.4932504 ,\n",
+ " -0.20873895, -0.25288817, -0.31185086, -0.5357737 ,\n",
+ " -0.36203457, -0.26290733, -0.07011521, -0.68106295,\n",
+ " -0.22633346, -0.42219049, -0.2889823 , 0.08389703,\n",
+ " -0.18354529, -0.46526391, -0.20727792, -0.25923233,\n",
+ " 0.17466168, -0.30142982, -0.758315 , -0.03811116,\n",
+ " 0.19052389, -0.08288894, -0.04451112, -0.07156015,\n",
+ " -0.13286523, 0.60570874, -0.20339378, -0.36052635,\n",
+ " 0.10691949, 0.10224513, -0.07618682, 0.03148221,\n",
+ " -0.48928229, 0.21527443, 0.08592975, 0.11383149,\n",
+ " -0.03356351, 0.24695101, -0.23506853, 0.19142701,\n",
+ " 0.21640126, -0.31438119, -0.45421408, -0.2131284 ,\n",
+ " 0.05094793]) ,\n",
+ " TimelinedArray([ 0.19080025, -0.09864775, -0.42312811, -0.17565344,\n",
+ " -0.15888025, -0.25244556, -0.21301599, -0.11379768,\n",
+ " -0.0887335 , 0.36716378, -0.18314039, -0.04838181,\n",
+ " -0.13043927, -0.09607538, -0.02360341, 0.19854439,\n",
+ " -0.02193664, -0.06389055, 0.24512737, 0.11341713,\n",
+ " 0.34232338, -0.37980908, -0.0861911 , 0.23596354,\n",
+ " 0.06515666, -0.29384497, 0.14104345, 0.04246446,\n",
+ " -0.09107556, 0.17427348, -0.05974054, -0.17401545,\n",
+ " -0.36136445, 0.36727895, 0.37126503, -0.14771911,\n",
+ " -0.29690686, -0.33153015, -0.06024067, -0.11764691,\n",
+ " 0.37436722, -0.58269795, -0.25689363, 0.46510213,\n",
+ " 0.10991489, -0.20947557, 0.37584031, -0.14703482,\n",
+ " 0.4968512 , 0.14727928, 0.00642169, -0.06128455,\n",
+ " 0.05758115, 0.19196457, 0.35343994, -0.13002259,\n",
+ " -0.0341732 , 0.22595324, 0.12214553, -0.23487693,\n",
+ " 0.05182751, -0.00634356, 0.01165198, -0.33937735,\n",
+ " -0.08602802, 0.13955937, -0.0030568 , 0.17823858,\n",
+ " 0.23570793, -0.22298792, -0.12730615, -0.23754409,\n",
+ " -0.11445125, 0.24436423, -0.40809605, -0.18235336,\n",
+ " 0.00871813, -0.37732798, -0.09920273, -0.00685885,\n",
+ " -0.0486024 , -0.44139633, -0.12943537, -0.1095779 ,\n",
+ " -0.23854505, -0.40121978, -0.01207454, 0.39960258,\n",
+ " -0.22361547, -0.19686888, -0.54787966, -0.37476941,\n",
+ " -0.26948146, -0.06250461, -0.12133435, -0.67504875,\n",
+ " -0.1934918 , -0.46946371, -0.02573184, -0.21345607,\n",
+ " -0.06494324, -0.1447458 , -0.03051066, -0.09035074,\n",
+ " -0.38336763, -0.46336727, -0.02377452, -0.12412626,\n",
+ " 0.18359606, -0.06498301, -0.67465851, -0.46609741,\n",
+ " -0.0055494 , -0.38269158, -0.40515186, -0.12678521,\n",
+ " -0.47030281, -0.21401335, -0.50569227, -0.45387708,\n",
+ " -0.05278086, -0.15274225, -0.04091506, -0.24219301,\n",
+ " -0.18693169, -0.38035018, -0.23403339, 0.21882369,\n",
+ " 0.0663894 , 0.18006608, -0.36941557, -0.1295897 ,\n",
+ " -0.07748536, 0.72164542, 0.15333651, 0.52833962,\n",
+ " 0.06420285, 0.50943905, 0.29677386, 0.42283439,\n",
+ " 0.61726558, -0.020037 , 0.62855669, 0.33786112,\n",
+ " -0.00120007, 0.23865293, 0.31819023, 0.12892263,\n",
+ " 0.15384755, 0.2107985 , -0.00437438, 0.52223839,\n",
+ " 0.22097037, 0.00673943, -0.40176623, -0.03538774,\n",
+ " 0.21975878, 0.08310471, -0.09475243, -0.20602571,\n",
+ " -0.23080855, 0.03543968, 0.19775636, 0.14179551,\n",
+ " -0.13946391, 0.00772231, -0.44143459, -0.11355429,\n",
+ " -0.51156278, -0.10506681, 0.07065152, -0.09080656,\n",
+ " -0.32456373, 0.40157573, -0.35615744, -0.03363059,\n",
+ " 0.11318366, -0.25555947, -0.10642994, -0.1589872 ,\n",
+ " -0.11756884, 0.42536382, 0.1230489 , -0.61998429,\n",
+ " 0.18612308, -0.32984398, -0.08319854, 0.24161065,\n",
+ " -0.36523708, -0.47349724, -0.02383711, -0.34936239,\n",
+ " -0.32613863, -0.46656058, -0.20785135, 0.02316068,\n",
+ " -0.28751116, -0.25730039, -0.1013959 , -0.02048109,\n",
+ " 0.05426731, -0.11224633, 0.09036135, -0.46246221,\n",
+ " 0.04762353, -0.10528897, -0.10175217, -0.35067113,\n",
+ " -0.31287863, -0.27809651, 0.07942843, 0.2174416 ,\n",
+ " 0.09057519, -0.27771581, -0.31709309, -0.17546545,\n",
+ " -0.67349108, -0.55849487, -0.16882544, -0.25922467,\n",
+ " -0.07973422, -0.64326103, -0.30313696, -0.23128767,\n",
+ " 0.28690741, 0.0276736 , 0.09102714, -0.30855922,\n",
+ " -0.14405944, -0.07222683, -0.02902207, -0.16381966,\n",
+ " 0.16686367, -0.21105859, -0.38999766, -0.30846022,\n",
+ " -0.0915164 , -0.55725347]) ],\n",
+ " dtype=object)"
+ ]
+ },
+ "execution_count": 102,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.array(list(trials_roi_df[\"F_var\"][:]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "id": "ad5a3e1f-4446-402d-8b95-eae76ab58e9f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.44800236, 0.77145166, 0.24582778, ..., -0.15825711,\n",
+ " -0.14590299, -0.32151549],\n",
+ " [-0.32539554, 0.02385838, -0.33238546, ..., -0.07801545,\n",
+ " 0.30293524, -0.06316736],\n",
+ " [-0.17911552, 0.18014861, -0.44388942, ..., -0.39626362,\n",
+ " -0.16571452, -0.18205857],\n",
+ " ...,\n",
+ " [-0.65258598, -0.38061883, -0.20007785, ..., -0.06044568,\n",
+ " -0.04015488, 0.00952519],\n",
+ " [ 0.0808493 , -0.10169227, -0.32065112, ..., 0.04475439,\n",
+ " 0.09135854, 0.20561993],\n",
+ " [ 0.19080025, -0.09864775, -0.42312811, ..., 0.12892263,\n",
+ " 0.15384755, 0.2107985 ]])"
+ ]
+ },
+ "execution_count": 112,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# will this work ? bet 1 beer !!! \n",
+ "#You won\n",
+ "\n",
+ "\n",
+ "np.array([ item[:150] for item in trials_roi_df[\"F_var\"] ])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "id": "8ae4c55a-a7db-45bf-8558-0aa322841c64",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.5"
+ ]
+ },
+ "execution_count": 129,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.median(chance_results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "id": "9e4cdd8c-b5ff-4dec-bc39-3709c6402e28",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0\n",
+ "336\r"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[130], line 24\u001b[0m\n\u001b[0;32m 22\u001b[0m sub_chance \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m50\u001b[39m):\n\u001b[1;32m---> 24\u001b[0m chance_results \u001b[38;5;241m=\u001b[39m \u001b[43mget_single_classifier_chance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures_key\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mneuronal_features\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscore\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m 25\u001b[0m sub_chance\u001b[38;5;241m.\u001b[39mappend(chance_results)\n\u001b[0;32m 26\u001b[0m chance\u001b[38;5;241m.\u001b[39mappend(sub_chance)\n",
+ "Cell \u001b[1;32mIn[127], line 8\u001b[0m, in \u001b[0;36mget_single_classifier_chance\u001b[1;34m(training_data, test_data, features_key, classes_key)\u001b[0m\n\u001b[0;32m 5\u001b[0m randomized_training_data \u001b[38;5;241m=\u001b[39m training_data\u001b[38;5;241m.\u001b[39msample(frac \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 6\u001b[0m training_data\u001b[38;5;241m.\u001b[39mloc[:,classes_key] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(randomized_training_data\u001b[38;5;241m.\u001b[39mloc[:,classes_key])\n\u001b[1;32m----> 8\u001b[0m training_set \u001b[38;5;241m=\u001b[39m \u001b[43madaptation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclassifiers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_features_and_classes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mtraining_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures_key\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclasses_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclasses_key\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 11\u001b[0m test_set \u001b[38;5;241m=\u001b[39m adaptation\u001b[38;5;241m.\u001b[39mclassifiers\u001b[38;5;241m.\u001b[39mget_features_and_classes(\n\u001b[0;32m 12\u001b[0m test_data, features_key\u001b[38;5;241m=\u001b[39mfeatures_key, classes_key\u001b[38;5;241m=\u001b[39mclasses_key\n\u001b[0;32m 13\u001b[0m )\n\u001b[0;32m 15\u001b[0m classifier \u001b[38;5;241m=\u001b[39m adaptation\u001b[38;5;241m.\u001b[39mclassifiers\u001b[38;5;241m.\u001b[39mLinearSVC()\n",
+ "File \u001b[1;32m~\\anaconda3\\envs\\Analysis\\lib\\site-packages\\ResearchProjects\\adaptation\\classifiers.py:46\u001b[0m, in \u001b[0;36mget_features_and_classes\u001b[1;34m(dataframe, features_key, classes_key)\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;124;03mPrepare data for use with scikit-learn classifiers.\u001b[39;00m\n\u001b[0;32m 31\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 42\u001b[0m \n\u001b[0;32m 43\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 44\u001b[0m \u001b[38;5;66;03m# format features and classes data for using it with sklearn classifiers classes fit, score, predict...\u001b[39;00m\n\u001b[0;32m 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m---> 46\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples_features\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdataframe\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfeatures_key\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[0;32m 47\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples_classes\u001b[39m\u001b[38;5;124m\"\u001b[39m: np\u001b[38;5;241m.\u001b[39marray(dataframe[classes_key]),\n\u001b[0;32m 48\u001b[0m }\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "condition_keys = ['in_target_barrel','nontarget_amplitude']\n",
+ "grand_results = []\n",
+ "for roi in rois_df.index : \n",
+ " print(roi)\n",
+ " for condition_values, group in trials_roi_df.loc[roi:roi].groupby(condition_keys):\n",
+ " if cond[1] == '10' :\n",
+ " continue\n",
+ " conditions = {key : value for key, value in zip(condition_keys , condition_values)}\n",
+ " \n",
+ " data = []\n",
+ " chance = []\n",
+ " for i in range(500):\n",
+ " print(i, end= \"\\r\")\n",
+ " training, test = adaptation.classifiers.get_sample_and_training(\n",
+ " group, max_samples = 0.5\n",
+ " )\n",
+ " score = adaptation.classifiers.get_single_classifier_results(\n",
+ " training, test, features_key=\"neuronal_features\")\n",
+ "\n",
+ "\n",
+ " data.append(score[\"score\"])\n",
+ " sub_chance = []\n",
+ " for _ in range(50):\n",
+ " chance_results = get_single_classifier_chance(training, test, features_key = \"neuronal_features\")['score']\n",
+ " sub_chance.append(chance_results)\n",
+ " chance.append(sub_chance)\n",
+ " print()\n",
+ " s = np.median(data)\n",
+ " ch = np.median(chance)\n",
+ " cs = (s - ch) / (1 - ch)\n",
+ " dico = {\"index\":roi, \"score\":s , \"score_values\":data, \"chance_values\":chance ,\"chance\":ch , \"corrected\" : cs}\n",
+ " dico.update(conditions)\n",
+ " \n",
+ " grand_results.append( dico )\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14b6cf76-8ee3-4867-816b-43158f023bf2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "classifier_data = adaptation.classifiers.get_features_and_classes(group,features_key='neuronal_features')\n",
+ "X = classifier_data[\"samples_features\"]\n",
+ "y = classifier_data[\"samples_classes\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "id": "320e73c7-6384-474e-b72c-1664fa1f4713",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "trial#\n",
+ "0 [0.44800236099089397, 0.7714516639019757, 0.24...\n",
+ "1 [-0.32539554129285003, 0.023858377160785173, -...\n",
+ "2 [-0.17911552203251063, 0.18014861315202807, -0...\n",
+ "3 [-0.15754997708637508, -0.5217095653778792, -0...\n",
+ "4 [-0.2178897487180029, -0.11685176608670593, -0...\n",
+ " ... \n",
+ "145 [-0.01820918607240805, -0.015286885737646978, ...\n",
+ "146 [-0.07075263048698627, -0.14321302983827025, -...\n",
+ "147 [-0.27854251373066036, 0.2195061242315473, 0.2...\n",
+ "148 [-0.1387883226963254, -0.6539329094909159, -0....\n",
+ "149 [-0.25454560318475217, 0.3316156094474721, -0....\n",
+ "Name: F_var, Length: 150, dtype: object"
+ ]
+ },
+ "execution_count": 147,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "one_roi_all_trials = trials_roi_df['F_var'][0]\n",
+ "one_roi_all_trials"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "id": "32127b4d-2cae-4008-8b05-07ba02d5f938",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "239"
+ ]
+ },
+ "execution_count": 155,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(trials_roi_df['F_var'][0][7])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "id": "79be1159-3de8-4407-8c02-e4cab4eca109",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "212"
+ ]
+ },
+ "execution_count": 157,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "min([232,260,212,245])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "id": "774de75e-7c3a-4d4a-ad21-0f1434549766",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "pandas.core.series.Series"
+ ]
+ },
+ "execution_count": 158,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(one_roi_all_trials)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 203,
+ "id": "8c7be2e4-c933-4d20-899e-f47627a30071",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "trial#\n",
+ "0 [0.44800236099089397, 0.7714516639019757, 0.24...\n",
+ "1 [-0.32539554129285003, 0.023858377160785173, -...\n",
+ "2 [-0.17911552203251063, 0.18014861315202807, -0...\n",
+ "3 [-0.15754997708637508, -0.5217095653778792, -0...\n",
+ "4 [-0.2178897487180029, -0.11685176608670593, -0...\n",
+ " ... \n",
+ "145 [-0.01820918607240805, -0.015286885737646978, ...\n",
+ "146 [-0.07075263048698627, -0.14321302983827025, -...\n",
+ "147 [-0.27854251373066036, 0.2195061242315473, 0.2...\n",
+ "148 [-0.1387883226963254, -0.6539329094909159, -0....\n",
+ "149 [-0.25454560318475217, 0.3316156094474721, -0....\n",
+ "Name: F_var, Length: 150, dtype: object"
+ ]
+ },
+ "execution_count": 203,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "one_roi_all_trials"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 224,
+ "id": "e609f678-2b21-403c-9521-119ecc730a89",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "def normalization_one_roi(one_roi_all_trials):\n",
+ " lenght = []\n",
+ " one_roi_all_trials\n",
+ " for trial in one_roi_all_trials:\n",
+ " t = len(trial)\n",
+ " lenght.append(t)\n",
+ " minimum_lenght = min(lenght)\n",
+ " #print(lenght)\n",
+ "\n",
+ " two_D_trials = []\n",
+ " for trial in one_roi_all_trials:\n",
+ " new_trial = trial[:minimum_lenght]\n",
+ " two_D_trials.append(new_trial)\n",
+ " two_D_trials = np.array(two_D_trials)\n",
+ " \n",
+ " minimum_value = two_D_trials.min()\n",
+ " maximum_value = two_D_trials.max()\n",
+ " \n",
+ " temporary_normalized_trials = []\n",
+ " for trial in one_roi_all_trials:\n",
+ " temporary_normalized_trials.append( (trial - minimum_value) / (maximum_value - minimum_value) )\n",
+ " \n",
+ " normalized_output = pd.Series(temporary_normalized_trials)\n",
+ " normalized_output.index = one_roi_all_trials.index \n",
+ "\n",
+ " return normalized_output\n",
+ "\n",
+ "rois_numbers = pd.unique( trials_roi_df.index.get_level_values('roi#') )\n",
+ "\n",
+ "new_column = pd.Series()\n",
+ "for roi_nb in rois_numbers :\n",
+ " one_roi_all_trials = trials_roi_df.loc[roi_nb:roi_nb]['F_var']\n",
+ " new_column = pd.concat([new_column, normalization_one_roi(one_roi_all_trials)])\n",
+ "\n",
+ "trials_roi_df.loc[:,'F_var_norm'] = new_column #for creating a new column for all the rows\n",
+ "\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "de1701f5-c330-48a3-a057-d66db0c392a4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def extract_features_from_timeseries(\n",
+ " input_object,\n",
+ " *,\n",
+ " key=\"F_var\",\n",
+ " features_key=\"features\",\n",
+ " timepoints=[-0.2, 0 ,0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6],\n",
+ " time_window = 0.2\n",
+ "):\n",
+ "\n",
+ " if isinstance(input_object, pd.core.frame.DataFrame):\n",
+ " input_object.loc[:, features_key] = input_object.apply(\n",
+ " lambda series: extract_features_from_timeseries(\n",
+ " series,\n",
+ " key=key,\n",
+ " timepoints=timepoints,\n",
+ " ),\n",
+ " axis=1,\n",
+ " )\n",
+ " return input_object\n",
+ " \n",
+ " elif isinstance(input_object, (pd.core.series.Series, dict)):\n",
+ " \n",
+ " features = []\n",
+ " for point in timepoints:\n",
+ " features.append( input_object[key].isec[point] )\n",
+ " return TimelinedArray(features, timeline=timepoints)\n",
+ " \n",
+ " else:\n",
+ " raise NotImplementedError(\n",
+ " \"input_object can obly be a dataframe, a pandas series or a mappable like a dict\"\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 261,
+ "id": "b643c1cf-ff39-42e2-872f-b19dcae2d1f5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "time_window = 0.2\n",
+ "timepoints=[-0.2, 0 ,0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6]\n",
+ "#trial = one_roi_all_trials.iloc[5]\n",
+ "trial = trials_roi_df['F_var_norm'][0][0]\n",
+ "\n",
+ "values = []\n",
+ "old_values = []\n",
+ "for point in timepoints :\n",
+ " \n",
+ " value = trial.isec[point - (time_window / 2) : point + (time_window / 2)]\n",
+ " old_value = trial.isec[point]\n",
+ " #print(value,value.mean())\n",
+ " \n",
+ " values.append(value)\n",
+ " old_values.append(old_value)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 279,
+ "id": "9a1b4a78-b861-45dd-999e-099484aea92c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x23a806e8a30>]"
+ ]
+ },
+ "execution_count": 279,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import scipy\n",
+ "trial = trials_roi_df['F_var_norm'][0][1]\n",
+ "b,a = scipy.signal.butter(3,0.01)\n",
+ "filtered_trial = scipy.signal.filtfilt(b,a,trial)\n",
+ "plt.plot(trial)\n",
+ "plt.plot(filtered_trial)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 262,
+ "id": "754a8cb0-a9a9-4f2e-a52d-cdcdf2b4242a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x23a51d39dc0>]"
+ ]
+ },
+ "execution_count": 262,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(*trial.pack)\n",
+ "[ plt.plot(*item.pack) for item in values ]\n",
+ "plt.plot( timepoints , [ item.mean().item() for item in values ] , 'o', color = \"pink\")\n",
+ "plt.plot( timepoints , old_values , 'ro')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 225,
+ "id": "e4b3c6da-7033-4746-81a1-801105bdc1be",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>frequency_change</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>...</th>\n",
+ " <th>target_whisker</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_D1</th>\n",
+ " <th>in_any_barrel</th>\n",
+ " <th>in_C1</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " <th>neuronal_features</th>\n",
+ " <th>F_var_norm</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">0</th>\n",
+ " <th>0</th>\n",
+ " <td>[110.48663330078125, 131.6634063720703, 86.054...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[81.11736297607422, 93.11980438232422, 73.6161...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.29373020232037816, 0.07303959775594149, 0....</td>\n",
+ " <td>[0.6322541180489223, 0.7761630194668011, 0.542...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>[67.9014892578125, 84.18050384521484, 79.32645...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[52.151588439941406, 65.11491394042969, 51.899...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.13887659963863402, 0.4147024933008791, 0.4...</td>\n",
+ " <td>[0.2881543117582828, 0.44354418976450954, 0.28...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>[68.5937728881836, 60.977806091308594, 68.2375...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[57.168704986572266, 70.49388885498047, 47.332...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[0.0, 0.0, 9.140109062194824, 9.33862686157226...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.15659750000313963, 0.19433377025185258, 0.1...</td>\n",
+ " <td>[0.353237144467088, 0.5130807630741878, 0.2354...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>[88.68118286132812, 103.54595947265625, 58.293...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[57.833740234375, 44.317848205566406, 55.43520...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.06945498179116452, -0.19292336493298068, -...</td>\n",
+ " <td>[0.36283207589450067, 0.20081037474950209, 0.3...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>[67.2108154296875, 93.54744720458984, 47.41284...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[55.2567253112793, 59.00489044189453, 56.47432...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0....</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.29605039572842456, -0.21797866295520857, -...</td>\n",
+ " <td>[0.3359857350022453, 0.38093950349419936, 0.35...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">34</th>\n",
+ " <th>145</th>\n",
+ " <td>[79.01573944091797, 149.42901611328125, 109.14...</td>\n",
+ " <td>[0.28543534597878606, 0.14659601694516844, 0.4...</td>\n",
+ " <td>[81.04872131347656, 76.43589782714844, 87.0641...</td>\n",
+ " <td>[0.28543534597878606, 0.14659601694516844, 0.4...</td>\n",
+ " <td>[0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.12998113130297562, -0.04041878154425823, -0...</td>\n",
+ " <td>[0.5507430001328958, 0.4898315981599429, 0.630...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>[70.81188201904297, 130.8088836669922, 74.7765...</td>\n",
+ " <td>[-0.09959991718158123, 0.10129027462650247, 0....</td>\n",
+ " <td>[68.32051086425781, 74.994873046875, 89.817947...</td>\n",
+ " <td>[-0.09959991718158123, 0.10129027462650247, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.17850266845055784, 0.13588496412603587, 0....</td>\n",
+ " <td>[0.38182085212651884, 0.46995512438796805, 0.6...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>[67.38467407226562, 104.03815460205078, 66.875...</td>\n",
+ " <td>[-0.6525859803693786, -0.3806188309835738, -0....</td>\n",
+ " <td>[50.0487174987793, 59.089744567871094, 65.0923...</td>\n",
+ " <td>[-0.6525859803693786, -0.3806188309835738, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.8367421288529879, 0.5799961483465563, -0.25...</td>\n",
+ " <td>[0.13921555710873, 0.25853261571872754, 0.3377...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>[101.09800720214844, 79.50180053710938, 98.070...</td>\n",
+ " <td>[0.08084930191997584, -0.10169227498872888, -0...</td>\n",
+ " <td>[74.46154022216797, 68.3974380493164, 61.12307...</td>\n",
+ " <td>[0.08084930191997584, -0.10169227498872888, -0...</td>\n",
+ " <td>[1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.03735654613817374, -0.16723968654290852, -...</td>\n",
+ " <td>[0.46098728858876215, 0.38090289574953606, 0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>[94.76374053955078, 106.85689544677734, 66.061...</td>\n",
+ " <td>[0.1908002463908141, -0.09864775120552391, -0....</td>\n",
+ " <td>[78.14871978759766, 68.52820587158203, 57.7435...</td>\n",
+ " <td>[0.1908002463908141, -0.09864775120552391, -0....</td>\n",
+ " <td>[3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.3758403148406946, 0.1221455348751175, -0.08...</td>\n",
+ " <td>[0.5092248176994613, 0.3822385850910634, 0.239...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5250 rows × 26 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "0 0 [110.48663330078125, 131.6634063720703, 86.054... \\\n",
+ " 1 [67.9014892578125, 84.18050384521484, 79.32645... \n",
+ " 2 [68.5937728881836, 60.977806091308594, 68.2375... \n",
+ " 3 [88.68118286132812, 103.54595947265625, 58.293... \n",
+ " 4 [67.2108154296875, 93.54744720458984, 47.41284... \n",
+ "... ... \n",
+ "34 145 [79.01573944091797, 149.42901611328125, 109.14... \n",
+ " 146 [70.81188201904297, 130.8088836669922, 74.7765... \n",
+ " 147 [67.38467407226562, 104.03815460205078, 66.875... \n",
+ " 148 [101.09800720214844, 79.50180053710938, 98.070... \n",
+ " 149 [94.76374053955078, 106.85689544677734, 66.061... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ "... ... \n",
+ "34 145 [0.28543534597878606, 0.14659601694516844, 0.4... \n",
+ " 146 [-0.09959991718158123, 0.10129027462650247, 0.... \n",
+ " 147 [-0.6525859803693786, -0.3806188309835738, -0.... \n",
+ " 148 [0.08084930191997584, -0.10169227498872888, -0... \n",
+ " 149 [0.1908002463908141, -0.09864775120552391, -0.... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "0 0 [81.11736297607422, 93.11980438232422, 73.6161... \\\n",
+ " 1 [52.151588439941406, 65.11491394042969, 51.899... \n",
+ " 2 [57.168704986572266, 70.49388885498047, 47.332... \n",
+ " 3 [57.833740234375, 44.317848205566406, 55.43520... \n",
+ " 4 [55.2567253112793, 59.00489044189453, 56.47432... \n",
+ "... ... \n",
+ "34 145 [81.04872131347656, 76.43589782714844, 87.0641... \n",
+ " 146 [68.32051086425781, 74.994873046875, 89.817947... \n",
+ " 147 [50.0487174987793, 59.089744567871094, 65.0923... \n",
+ " 148 [74.46154022216797, 68.3974380493164, 61.12307... \n",
+ " 149 [78.14871978759766, 68.52820587158203, 57.7435... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ "... ... \n",
+ "34 145 [0.28543534597878606, 0.14659601694516844, 0.4... \n",
+ " 146 [-0.09959991718158123, 0.10129027462650247, 0.... \n",
+ " 147 [-0.6525859803693786, -0.3806188309835738, -0.... \n",
+ " 148 [0.08084930191997584, -0.10169227498872888, -0... \n",
+ " 149 [0.1908002463908141, -0.09864775120552391, -0.... \n",
+ "\n",
+ " spks target_stim \n",
+ "roi# trial# \n",
+ "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... C1_10_90 \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... C1_10_20 \n",
+ " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... D1_10_20 \n",
+ " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... D1_10_90 \n",
+ "... ... ... \n",
+ "34 145 [0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 148 [1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 149 [3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ "\n",
+ " target_amplitude frequency_change \n",
+ "roi# trial# \n",
+ "0 0 10_90 80.0 \\\n",
+ " 1 10_90 80.0 \n",
+ " 2 10_20 10.0 \n",
+ " 3 10_20 10.0 \n",
+ " 4 10_90 80.0 \n",
+ "... ... ... \n",
+ "34 145 10_90 80.0 \n",
+ " 146 10_20 10.0 \n",
+ " 147 10_20 10.0 \n",
+ " 148 10_20 10.0 \n",
+ " 149 10_20 10.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 4 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_amplitude ... target_whisker nontarget_stim in_D1 \n",
+ "roi# trial# ... \n",
+ "0 0 10 ... C1 D1_10 True \\\n",
+ " 1 0 ... D1 C1_0 True \n",
+ " 2 10 ... C1 D1_10 True \n",
+ " 3 0 ... D1 C1_0 True \n",
+ " 4 0 ... D1 C1_0 True \n",
+ "... ... ... ... ... ... \n",
+ "34 145 10 ... C1 D1_10 False \n",
+ " 146 10 ... C1 D1_10 False \n",
+ " 147 0 ... C1 D1_0 False \n",
+ " 148 0 ... C1 D1_0 False \n",
+ " 149 10 ... D1 C1_10 False \n",
+ "\n",
+ " in_any_barrel in_C1 is_neuron is_VGAT in_target_barrel \n",
+ "roi# trial# \n",
+ "0 0 True False True False False \\\n",
+ " 1 True False True False True \n",
+ " 2 True False True False False \n",
+ " 3 True False True False True \n",
+ " 4 True False True False True \n",
+ "... ... ... ... ... ... \n",
+ "34 145 False False False None False \n",
+ " 146 False False False None False \n",
+ " 147 False False False None False \n",
+ " 148 False False False None False \n",
+ " 149 False False False None False \n",
+ "\n",
+ " neuronal_features \n",
+ "roi# trial# \n",
+ "0 0 [-0.29373020232037816, 0.07303959775594149, 0.... \\\n",
+ " 1 [-0.13887659963863402, 0.4147024933008791, 0.4... \n",
+ " 2 [0.15659750000313963, 0.19433377025185258, 0.1... \n",
+ " 3 [-0.06945498179116452, -0.19292336493298068, -... \n",
+ " 4 [-0.29605039572842456, -0.21797866295520857, -... \n",
+ "... ... \n",
+ "34 145 [0.12998113130297562, -0.04041878154425823, -0... \n",
+ " 146 [-0.17850266845055784, 0.13588496412603587, 0.... \n",
+ " 147 [0.8367421288529879, 0.5799961483465563, -0.25... \n",
+ " 148 [-0.03735654613817374, -0.16723968654290852, -... \n",
+ " 149 [0.3758403148406946, 0.1221455348751175, -0.08... \n",
+ "\n",
+ " F_var_norm \n",
+ "roi# trial# \n",
+ "0 0 [0.6322541180489223, 0.7761630194668011, 0.542... \n",
+ " 1 [0.2881543117582828, 0.44354418976450954, 0.28... \n",
+ " 2 [0.353237144467088, 0.5130807630741878, 0.2354... \n",
+ " 3 [0.36283207589450067, 0.20081037474950209, 0.3... \n",
+ " 4 [0.3359857350022453, 0.38093950349419936, 0.35... \n",
+ "... ... \n",
+ "34 145 [0.5507430001328958, 0.4898315981599429, 0.630... \n",
+ " 146 [0.38182085212651884, 0.46995512438796805, 0.6... \n",
+ " 147 [0.13921555710873, 0.25853261571872754, 0.3377... \n",
+ " 148 [0.46098728858876215, 0.38090289574953606, 0.2... \n",
+ " 149 [0.5092248176994613, 0.3822385850910634, 0.239... \n",
+ "\n",
+ "[5250 rows x 26 columns]"
+ ]
+ },
+ "execution_count": 225,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trials_roi_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 217,
+ "id": "314bf227-c7fe-4706-a9c6-49d4bec9adad",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Series([], dtype: object)"
+ ]
+ },
+ "execution_count": 217,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.concat([new_column,new_column])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 202,
+ "id": "5e8d46fa-40dd-4bdc-90d6-e8d98e218479",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "trial#\n",
+ "0 [0.6322541180489223, 0.7761630194668011, 0.542...\n",
+ "1 [0.2881543117582828, 0.44354418976450954, 0.28...\n",
+ "2 [0.353237144467088, 0.5130807630741878, 0.2354...\n",
+ "3 [0.36283207589450067, 0.20081037474950209, 0.3...\n",
+ "4 [0.3359857350022453, 0.38093950349419936, 0.35...\n",
+ " ... \n",
+ "145 [0.4248275106567477, 0.4261276990541367, 0.429...\n",
+ "146 [0.4014499077546569, 0.369210863264242, 0.3628...\n",
+ "147 [0.3090001372520848, 0.5305916871387009, 0.548...\n",
+ "148 [0.37117950174378816, 0.14198163088809942, 0.3...\n",
+ "149 [0.31967683064113844, 0.5804713830005989, 0.22...\n",
+ "Length: 150, dtype: object"
+ ]
+ },
+ "execution_count": 202,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "normalization_per_roi(one_roi_all_trials)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 184,
+ "id": "f4e4762a-2831-41e4-a54d-180b163969a1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-0.9730504837650866\n",
+ "1.2745471264192774\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(two_D_trials.min())\n",
+ "print(two_D_trials.max())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 185,
+ "id": "c74d3320-3853-4903-84e5-e4181bfe4185",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))\n",
+ "two_D_trials = (two_D_trials - two_D_trials.min()) / (two_D_trials.max() - two_D_trials.min())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 189,
+ "id": "30516f05-a679-409f-8d2f-85b1408f8ce2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.0"
+ ]
+ },
+ "execution_count": 189,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "two_D_trials.max()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "id": "fd3577f7-5898-4038-8176-772cf3a3b541",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x23a25ceba60>]"
+ ]
+ },
+ "execution_count": 149,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(trials_roi_df['F_var'][0][0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 131,
+ "id": "be54267a-3241-4c0c-b761-da4e7fcc09e7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#we created function get_loocv_result which contains the function needed to get the chance and accuracy and then \n",
+ "#the function for getting the accuracy correction\n",
+ "#In scikit-learn, normalization can be done using the StandardScaler or \n",
+ "#MinMaxScaler classes from the sklearn.preprocessing module.\n",
+ "from sklearn.datasets import make_blobs\n",
+ "from sklearn.model_selection import LeaveOneOut\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.svm import LinearSVC\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from numpy import random\n",
+ "from sklearn.pipeline import make_pipeline\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.svm import SVC\n",
+ "#X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))\n",
+ "#X_scaled = X_std * (max - min) + min\n",
+ "# create dataset\n",
+ "#X, y = make_blobs(n_samples=100, random_state=1)\n",
+ "# create loocv procedure\n",
+ "def accuracy_correction(accuracy, chance):\n",
+ " if isinstance(chance,(list,np.ndarray)):\n",
+ " chance = np.median(chance)\n",
+ " corrected_accuracy = (accuracy - chance) / (1 - chance)\n",
+ " return corrected_accuracy\n",
+ "\n",
+ "\n",
+ "\n",
+ "def get_loocv_classifier_accuracy(training_data, *, features_key = 'features', classes_key = 'target_amplitude'):\n",
+ " classifier_data = adaptation.classifiers.get_features_and_classes(training_data,features_key=features_key,classes_key=classes_key)\n",
+ " X = classifier_data[\"samples_features\"]\n",
+ " y = classifier_data[\"samples_classes\"]\n",
+ " cv = LeaveOneOut()\n",
+ " # enumerate splits\n",
+ " y_true, y_pred = list(), list()\n",
+ " for train_ix, test_ix in cv.split(X):\n",
+ " # split data\n",
+ " X_train, X_test = X[train_ix, :], X[test_ix, :]\n",
+ " y_train, y_test = y[train_ix], y[test_ix]\n",
+ " # fit model\n",
+ "\n",
+ "\n",
+ " model = LinearSVC()\n",
+ " #model = RandomForestClassifier(random_state=1)\n",
+ " model.fit(X_train, y_train)\n",
+ " # evaluate model\n",
+ " yhat = model.predict(X_test)\n",
+ " # store\n",
+ "\n",
+ " y_true.append(y_test[0])\n",
+ " y_pred.append(yhat[0])\n",
+ " # calculate accuracy\n",
+ " acc = accuracy_score(y_true, y_pred)\n",
+ " #print('Accuracy: %.3f' % acc)\n",
+ " return acc\n",
+ "\n",
+ "\n",
+ "def get_loocv_classifier_chance(training_data,*, features_key = 'features', classes_key = 'target_amplitude',number_of_estimator =200):\n",
+ " classifier_data = adaptation.classifiers.get_features_and_classes(training_data,features_key=features_key,classes_key=classes_key)\n",
+ " X = classifier_data[\"samples_features\"]\n",
+ " y = classifier_data[\"samples_classes\"]\n",
+ " cv = LeaveOneOut()\n",
+ " chance_accumulation = []\n",
+ "\n",
+ " for i in range(number_of_estimator):\n",
+ " print(i, end = '\\r')\n",
+ " random.shuffle(y)\n",
+ "\n",
+ " cv = LeaveOneOut()\n",
+ " # enumerate splits\n",
+ " y_true, y_pred = list(), list()\n",
+ " for train_ix, test_ix in cv.split(X):\n",
+ " # split data\n",
+ " X_train, X_test = X[train_ix, :], X[test_ix, :]\n",
+ " y_train, y_test = y[train_ix], y[test_ix]\n",
+ " # fit model\n",
+ " model = LinearSVC()\n",
+ " #model = RandomForestClassifier(random_state=1)\n",
+ " model.fit(X_train, y_train)\n",
+ " # evaluate model\n",
+ " yhat = model.predict(X_test)\n",
+ " # store\n",
+ "\n",
+ " y_true.append(y_test[0])\n",
+ " y_pred.append(yhat[0])\n",
+ "\n",
+ " # calculate accuracy\n",
+ " acc = accuracy_score(y_true, y_pred)\n",
+ " chance_accumulation.append(acc)\n",
+ " #print('Chance level: %.3f' % np.median(chance_accumulation))\n",
+ " return chance_accumulation\n",
+ "\n",
+ "\n",
+ "def get_loocv_results(training_data, *, features_key = 'features', classes_key = 'target_amplitude', number_of_estimator = 201):\n",
+ " accuracy = get_loocv_classifier_accuracy(training_data, features_key = features_key, classes_key = classes_key)\n",
+ " StandardScaler(accuracy)\n",
+ " chance = get_loocv_classifier_chance(training_data, features_key = features_key, classes_key = classes_key, number_of_estimator = number_of_estimator)\n",
+ " StandardScaler(chance)\n",
+ " acc = accuracy_correction(accuracy, chance)\n",
+ " return acc\n",
+ " \n",
+ "\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dabb546c-392f-4410-8c1f-ec0632032e33",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "get_loocv_results(group,features_key='neuronal_features')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 463,
+ "id": "ffa5630e-4b07-4721-8348-5a31694123ec",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('10_20',)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('10_90',)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+5nEjgfuJPTxudnii6OExI1y7du16F8HjBsNoNMKoP9j5b5+G8kFkKA1Tv24GjEYjtBp9tBqzJ9hR7wzbo8jjLbhHkMSOyWRI0Pr9viGK29vbGA6HRlFMp9OhewptotjpdMbeT1K3sbGBTqdjsqUqXEdk2ERx2tBTH57qcaPBnzHrcavAE0UPjxmh0Whc7yJ43GgYDLH+ls9i/S2fBQZ7J3kkGQdBFF0ZOm8GtJsDPOO2d+AZt70D7ebhGHysRxdRpNHJRDYkZOl02pypSEWRaka/3x/bx6jEXtvNJoq6hxHYzXjabrfRbreRzWYjk+7Y0OcfJFFURdXDw0aj0TiU/kEHjYfHzQ5PFD08ZgS/aHhcL1CpmnUfZMijP0txNtAQUdffRqNRQFEcjUYm8QzbgSSs0+kY8sj2SafTRhVMpVKB8FIlcq59h1Qtr169ilOnToXuSw0jikr27NDTOEQxbh9rNpuoVCqxrvW49dBoNMbU8oOAJ4oetwo8UfTwmBH8ouFxvaBKzqyfe7MqitcDbB8XAdOENASJIhVFOgQSiQTa7TaKxaJRFu2QVqqDmlTG7h/arv1+H41GAwsLC84kN/pc194sVTKnPQ5jGkXxIJRzj5sH6jg5SHii6HGrwBNFD48ZgYqAh8dhgyRj1gYSFUXfr/cPJTiuBC8kX/o3JrWxQ+nS6TSazSYKhULAMFaCl0qljOoIjBNFKpVEq9VCt9vF4uKi83p9dxxFUYnipIQ20+yD9UTRIwqH1T/8HkWPWwWeKHp4zAD09HuD2uN6gMofDaRarTbT5/p+vTfoERUXLlzAcDg05M+Ghmuyvufm5syRGUQikcDc3Bza7TZyuZwhiiT1xCRFURPajEYjrK6u4uTJk0gkEuaIDr2eZXKFnlLlVEVxmuNUplUUfX/0CMNhEUWfgMnjVoEnih4eM4AaSB4eh43hcGhIBQCsrq7O7LlhypLHZFy9etX83Ol0xpQ2Rb/fHwsvpepHoqgEkmROk9nYx210u93QvYJ6RMbm5iay2Szy+TwAmPMbVQENI4r8vSraGnpKchyFaYxuJm7y8HDhMBVnTxQ9bgV4oujhMQN4ouhxPcFjFNj/XGfk7QX7CT1dX1+fWTluRNhK4HA4RK/XCyVNSiJJtLg/UZ/BcxLZNv1+35AxPps/6zPt9ypRrNVqyOfzhlTaRFHPUnQRRS1T2LdEIYoottvtwPt86KlHFHz/8PCYLdLXuwAeHjcDPFH02BMSCWRuXzA/7xWqKFJh0nPx9vvcvRBF7qHLZDL7KsNBIZlK4Fv/0Rnz86xhG6xKFF31SbLHvU8kinYGRyp3yWTSkDbuUbWfG9UH0ul0YF+knuHY7XaRzWYD5NClLrLcyWQy8HtNrBOlotrf5MLW1hYWFhZQKBTM+262ebZer6NQKEyVAMjDjcPsH/udXz08wtDr9dDtdlEsFq93UTxR9PCYBTxR9NgLEqkkFu55+L6fo4oijf9p94m5sJ/jMbrdbqxkDyRQPD/wsJDNpfCff/upB/Z83UvHcEmGl2pIr+7v0+Mu2HbNZjNA2ngN9zrSMLaV30lGLNu12WwaNZBEpdfrYX5+HvV63bwzrA+4iKK+f7+hpxpay+fdbPPs2toazpw5c2SdKjcaDpMoTpvh18MjDjqdDprN5pEgir53e3jMADSGbjYDxuPw0Ol09hyqSaKoh5HPYv+MnSQnLkajEbrdbqz7ut0uNjY29lrE64I4ias0GynnB1UUGR6qSh0VQg095V5EtmsikUCn0wmcmegiijyTMYyssxyXL18ecyi4Qk/jEEXXNSS1UYjKetrv98dCXW+mvWGj0QitVsuvHTPCYe6pDjtT1MNjvzisY17iwBNFD48ZwB9M7rFf1Ot1tFqtPd07GAwCiuKs+iJVpmkNcypncYyoo7QgxsXm5iYeeOABbG9vh9aNKl8M4e12u6Y+qQja4ZpKFEn+7KMser2eCRPlvdrmui/RdeahgmVSuJ6nbamkkKGlUSG1+9mjyD2Y0zzvRkK73b7pyO/1xGEQRT0P1RNFj4PAUVoXb57Z1sPjOsITRY+9YNQfYO3/+zTW/r9PY9Dt7bn/6B7Ffr+PTCYzM8NzL0Sx2+0in8/H+p7rtSC2Gn08aekP8KSlP0CrEU2mbKytrWFhYQGDwQDnzp1DpVIZqyPuE2XYKRPTcJ5QcqX/ZqilnbSm1+sF3qEGsZ3MiGolnxcFknqC79Dn23Ob7m+0E+zYmHXoqYbI3gxotVooFAp+7ZgBZrEvO+57dI+wh8es4Ymih8dNBk8UPfaLwWDve6809NAOG9wv9nJeWK/XM2f8TcKNuOeMYaArKyu444470Ov1cO7cOTSbTXONhqMPh0NkMhmjBGro6Wg0Miqwhp6mUimjOiaTSUPMqB6TeJLQafioqpb2GYxa1wyH1TMYGepqH7Wh96nCaZNeV0KdvSqKqrLq724mcB/SzfZd1wOHRRTtZFIeHrPGUVoXPVH08JgBPFH02C/2szAoUZi1orgXokhFMW7o6Y1mbOn+y2QyiWPHjuG2224L7LXUcGASRRJCe48iFUT+TVU6TTBDR0A2mzX7FnlExtzcnHm3qpN2ghmbKPb7/QBRpKNB4SKKJMtKavUMyGkQtr+RSuz16h+tVuvACdxgMAicgeqxd8TJsDsLeKLocdDwiqKHx00GTxQ99ov9LAy6b8uV2GQ/2EuSpm63i1wuF+u+o+Q5jQMSObvMJIWaAZnqoSYFcoWeahZT3kuSyOtJ6EgUVVEk2SD0ufZeQy037yWpBXbaTp+l1+m3KlHUZDZhxvr6+nponYaFrdLpEUYiDxqXL18+UCJAx8FenDEe4zis/aueKHocNI5Sv/JE0cNjBtjPweQeHgDG1J9pYKs4s0zosJc9itOoJIPBAFtbWzfM2GFopuvb9BB73TdKw1LnCU1mw32CVPV0jyIVQ97DMw7tPY8kdxrqORwOkcvl0G63AYz3C3UCRCmKthPMFXrKb1F1W9u0Wq2G1mkYUQrrR3vpk9NiNBrtKxNxHPCs0cPM1Hkz4zCJomYp9vCYNY5S0q6jUQoPjxscXlH02C/2o6yRJNJ4nqVCsddnxb2PBPlGMbh6vV5oOGShUDD7FO3QUz2r0N6jqGckAkHyxX2E/DvfT7LGZ1ARpJLJn/P5fCRR5Pt5D5+vsO8LUxQ1bJZ1oN8U1h/C+goVN/vawyBWrNuDJIqtVgv5fN4rijOC9r2DrE+fzMbjVoInih4eM4Anih77xSyMX6o5szSkpzVip90nxHKSeBx1UMVz1YkSRTv0VI+ssMmVKorcW6jJbBhiymclEgmTeZT7Akmo7D2DuVzOHLviIorcp2gns1HY9+lc5wqjVaKopHVaoqj3EzTSD3qu7XQ6KJVKB9ov2+02crmcVxRnBLsvHvR7PFH0uBWQnnyJh4fHJHiieGNgNBphbW0NJ06cuN5F2UEigczDygCAZGo2RJEZK/f7rL2qk67QxSgoUczn89MVch9IphJ4+redMj/HBYmiK2GLHZLJOcHeQ0pjVvf1pdNpY3y22+1AaCrPUmy1WgHCaR+XYT+f/YFlskM2lSiSkNlhzKqGusC/kwjaiZWUwIaFU4X1136/j2w2a/6tIbsHPdd2u12USqVAJttZQseXVxRnA9tRdlChe6r4+zXf42aHJ4oeHjOA36N4Y2A0GqFSqRwZophIJbHwrEdgOByicvGi+T3D3uywuzDooe00lParhGh45DTodrtTEUW97zCRzaXwS295xtT3dbvdUFIFwJx5COwqcVQBNSxO9/VRleARGXo925bPtcNCSdBscmaHuCqhUzC0VfdO6reEEUVbQdQkPy5FkX93IUpRdGVUPSxFcXl5OXJvZRQGgwFqtRoWFxedf6eaCBzOIfG3Ag4ioiLsPRyHh3Ech8ethaNmR/rQUw+PGYAGzFEb4B5BDIfDsYPLjwJUhQF2En/U6/VY9+q3zDL0aq8eec2aGbccB70XbJbQMw9dxmihUAiEeipBUuJonz2o+554Pf+uiWY0FJOJbnQ/I4mb1n02m0W32w0NPeU99vOjDG6+m9cp6WW/sRXFsL4QlfWUSqvW22EQq2mVcRv9fj+SZHJ/IjDbPcW3Mg6rf9CR4+FxELAjUK43PFH08JgBDuugX4/9gUdQ6CHkRwFUTogo9cWGncBhVh71vfZpNbDjlmNubu6GIYqsF03ootB9ikpw9F47mQ2w6yywCWgikTDJc2wiNzc3ZxLVsN2VqPHZ3Kdot4eqgVQWlRzp9XZf0DBb/t0OPaWiSCI8raJo7wWzw2oPEvtVLweDQeQ8w4ynwN6OoPEYx2ERRc0s7Am+x6yhynic/nXp0qUDLY8nih4eM4D3MN4YYDsdlcQpo/4Aa//nM6i+4++QQnBfWFwDxPY+Ru15ajQaqNVqUz93Gmh45KRkDyQP2Wz20Nuk1ejj6WfejqefeTtajcmOg2azicuXL6NaraJWq6HVamF7e3uMDGSzWXQ6nQApVEURCIb12klhXEolSZkSL/6+0+kYQqOJb3Q+4hEZLqKo77PPUIwyuG2i7Mp6SkVRQ6NdCOuvStZIFA8j9FS/Ya9OjElEUcNyD+O4j1sBh6ko2smpPDxmBc7LcfsxI1gOCp4oenjMAF5RvDFAUnKk1KvBEBiMkEjsTseqQk2CK1FK2ALTbrenIop76dM6FmjgT3pHOp2+LtkD280B2s147221WshmsygUCuZ4imazic3NzcB1rj2BzGzKxDKudqK6Z5/ROBqNDJG2k2al02mT+MbOmKrg+Y52v6BCSZISpSiyLIRLUQR29vaxLW1iO62iSNDhYO/tPCh0Oh1TD3o25jQgUYxz7IcnG7PB9SCK12vu8rh5MS1RDJtnZgWfzMbDYwbQUBSPo4vhcOcA8qOiKCrs0NNpFEXeq2GNYQZqXO/jXvp0WDKUqLLfKAdXs54LhQIGgwGWlpZQrVad5c5ms6hWq4GFvt1um3s16yn/zedTOVTk83k0Gg0kEglcvXoVhUIBy8vLRu3SozRICLUd+LNNSGzFL0pR1L4FwBBlG/V6HZlMBsVicUyVjlIUbfDaTqeDVquFer2OwWCAVquFVCqF5eVl57NmgW63a7KtZjIZdDqdqZ9ht6tCw04BryjOCteDKHLuspNMeXjsFZw74vZjOpbjJr+bFt6y9fCYAdSA8gv+0QWJ4pFSFP8enOQ1e2QcuMKgwhQKZreMu/hM2oezuro69nw7GUoUARwMBoYoThNuez3ARC309FL5c9VlLpczmWv5Tc1mE/l83nh/lSjyWXxuKpXC2tpaIPSy1+vh1KlTGAwGuP/++3Hp0iWMRiN0u13zHnuPoiKTyZjwTYJlHA6HaDabY2qkGip2W+qxG4p2uz0Wkmo/Kw7Yjy9evIhEIoFMJoPl5WWcPn0aS0tLaDQasZ81LWalKGazWWcdaSIbwGfOnBWuRzKbG8HJ5XFjgaQvTqQB16CD7O+eKHp4zABq0B1lY/dWx3A4HAuZOyqwjwGYdo8i+6D+28ZgMECxWIylKsYJp65Wq4H32EdjTCKlShSPevjd5uYmLl++bIgZQ85cRmImkzF7BW1FUffx2YfdAzv1vry8bNRDPRYCAMrlMh71qEeh0+lgc3MTjUbDJLsJex6wQ147nc4YUUwmk+j3+9jY2AAQJC02UdR79RgQQvdbKvYyN7Juy+UyFhcXkclkDAkol8sHShRVUZybm9tTBEIUUdSjMTxmh8Paw6rRFp4oeswa6pCcNGfq1oWDgieKHh4zwGGe7+WxdxxkeMZ+oXsS9qIoMvyJxlLYApPP52MRRVWmwgijTZTsPW6TjCgNPT1KSYaIwWCAq1evAtghDwsLC+h0OlhbW0OtVgttIxqSSn77/b6pG1UU9bB7vjOVSuHYsWPI5/PY2toyddjpdJDP55HJZFAqlVAqlcbOM9TQVjU08vm8SbJDcDyMRiO0Wq2xPqPzmSsbqz2etP3tupl2buz3++j3+ygUCmNZT9nPD8o4UmV1r0dXhBFFPsuriLMHx9RhrMNeUfQ4KEyzRzHMOTdLeKLo4TEDuLIYehw9qJJz1NpJQ6amOR5Dz8BTohiGOERxOBziypUrgeeEKZRqJNmK4qSFjioYz8o7akpvr9czyX/4HSdOnDAhoOvr686wRE2aFJX1lMROyZaSr8XFRZRKJVy4cAGDwSCwz/H48eOoVCqm7akO2qHIbB9XCKUm3Jmfnx8j6lGKon4H0el0kM1mzd5JDYGNG/JM8DiQfD4fCE3mOMlms7HPGp0Grn6+F7LIPmDXqVcTDw5so0kh77OEJ4oes8Y0exR1Dgd29ojP2rbxRNHDYwbwiuKNAVUkjgYpSWDuRBGDcnpMUZw29JSKVVj/43VxDPZms4l2ux0glHZ5WE6bKGpSh0lGlIaeHjZRTCQTeNLTj+FJTz+GRNJNrHu9HrrdrmkP1kE6ncbKygoWFxedCV0Gg4Eh5MPh0BBoOin43a4spXYCGE1cMxqNzH7GRCKB06dPo9frIZFImOyndtZZJXp2SLOqz8VicYxITiKKtkOC35nJZNBsNgP7bqcNPWXoLvusfY5iNpuNncF3GvT7/bHEJGFnZkaBbWX3aXt/osfscZjrsCeKHrMGnYVxiaJm3q1UKjOPzPFE0cNjBohDFK9du3Ykk6jcSlCieBTaIpFOYvEfPBrdrykjlUmb/jONgqHZMpmwxAUNcQxLRELU63X0ej1Uq1UA7qyMrr0RdihinOMxWB6W8bCQy6fwG+9+Fn7j3c9CLu8OR2ad8lB7zVIKhCc6GQ6HZi+oHo2hSXDsrKckXTZRTCaTyOVyZp9er9czdZpOp03dbW9vj80/qijy+rD5SUNX9d1hyWz4Oz36gvNgNps1aqfrWXGghEodKJxnlTzOEprIhthrQhvXOLMznnrMDhoO6omix40KOg/jJrNhJBGwm3RtlvBE0cNjBohDFF1p7z0OFzTCaXAfNWjW0mkVRe4Pi+p/JBWTwk95HIB9cLyCeyKj+nScZDa8jt9wlNDr9VAqlczRFHZoJ5Un+xu5eJPIMBusEkUAZo8inw0E94YCuySp3++jVCqh3W6bemObptNp1Go1VCoVQ2r5fG2ffD5vCA/bk+3b7XZRLpcD4ZxRexT5Oz6n3W4jk8kYori1tTVGWqdRFBuNBkqlUuBem0SXSqWZh58yfFax1/nCVWfMcjsrDIfDA03scyNB938eVlI5TxQ9Zo1pItR0reG/feiph8cRxaTMfgcxgD2mw1FTFAndW1Ov1wPG/iTYoadh/U+Prsjn886QSWAnfJBKCIlFHKLoOuh9kkdUiSJV0aMEkjMSRRJxkhUeZWGTbtaFHeLM7+R3q2OAdRemyvZ6PczPz5uD3Fm+ZDJplLdKpWKOudAsuISeIarn/JEoLi4uGhWZ744KPdVvYfZVHV9hGVTjoNVqGaJI2Gd7zs/Pzzz8VDOeEntVFF17hWedxKbf72Nra2umz7wRoSHXh5koyGc69zgoxM16qs7HafIbxC7HTJ/m4XGLI8oYOogB7DEdaEwcFUVx1B9g/Q8/i/zHtjHq7xju3W53KsKkRDFKqdC/Z7PZ0EPE6/U6SqWSOZqg1Wo5Cd9wOAyEutqJbIDJBpuGYB7UvqJKpeJ8bqvRx7c+6p341ke9E62Gu76ZdZN1wBAfErm5uTmk0+kxVYttwnupvDFxjyqKer39M69hBtB8Ph84B1STAaVSKTQaDeTzeayurgZCWwkSxdFoZM7UBGDU3Hw+HwhdmkT0tF+0Wi2k0+mx925ubqLdbk9lULMMdn+y6yaTyaDf78+03zAplGJax5KWU7950vfvNbuqV7TG+8ZBwdVGnih6HASm2aPI6+y8ATMpx0yf5uExBW7GxS1qYE+ToMTj4ED15qj0v1FngEQ/mK0vzhmGBA0kW22xoUk6XIlNCA35y2azaLVaTiOfhrwSRdvAjlP2tbW1AzPwRqMRrly5EtrW2xtdbG+EE4DRaGSUztFoZIiiKorpdHpMnaVaVygU0Gw2jUIV1veUfLr2KHJfYjabRalUMiSfiiKfyeczgY5N8LXdSYjYb1jmMJUubI+iJl/SEFr+juGwcfbbsI91u12zR8euJ7uvFIvFUHV8L3CNvb1kbNXjNdSIc/X1MNU+7ru8A/JwiaI/2sTjMOBDTz1ueZw7d+6mI05RA9sv6B6ToERxGvUljuFiK45hxyXQwOdezqjQ00mKYhwc5P4qZh3dr1OA+4uVKPIcxWazaYgkQeJHJYpKHbBDLl11GbVHkYQwkUigWCwGiKJew7+dPHkS1Wp1TFHk+3u9nlEU2dacm8KIooss8b52u23CX9kXeTYjVb9JiqL2sWazOdaXODbsvj4/Px8Il90P7Lrfz3NIFDUjoSs8GwAuXry454gTryju4LCIous9njh6zAqMsgEmb93g9XboqVcUPW4a8OyvmwmTFMVbkSiORqMjtR+QOMyztuKCBv80DhT7SASFPqff7weMVFdCm1arhUKhgE6ng1Qqhe3t7YDhr7Czrelh63HBMcH+MWult1qtYmFhYU/zjC7Y/X4f9Xo9QBTX19fRbrdRr9dNyLB9L4manpunxEGVpqg9iu1226iSJHfMpqrnbZVKJUMAqWbac042m0Wz2QyEnipJI5ENUw9dddRoNFAsFsf2idVqNZTL5VhqmV7TaDTG9gnqfkpiNBoFzqvcL6KcHdPsobWJou4pdYWHMxx4L99wq64rNq4nUeTvPTz2C53/Z71HcXV1dU+2mCeKHtcNg8HgSBKI/cATxXF0Oh2sr69f72KMYW5u7kjsU1SoojgNXAaoyxupRjb3zym4P5Ghf5ubm+bQcFf2Rs16GWYET1LZ5+bmTGjiLBPajEYjtNttFIvFPZFPDdVNp9OmTmgodrtdk/ik1+sFwh9JEoHdhZxQZY51aO9ps/cG2mfvpdNptFotQzBoUHAfZbPZRD6fR7/fH0uMxHBiJYrMOku4VEUX2SNxY/k0/DmdThsCyTaOgj7ftU9Q+5p9PUnxfuFKZENMk9AmjCi6FMXhcGjG114VxVtxXbHhInAHEbFkOysAn/nUY3aww9YnwbVVIeqIrL30U08UPa4LdJ/MzQSfzGYcR/VYkFkntOl2u/smxFQUibiGjk3S4hxw7krQ0W63kcvljKLY6/UCCVAUdphemKoZtnBxPOTzeZPsxP7+/YDHHOyVfDJLLPcpal9h2TudjskM6yIqDJUMIzj22YWa3l+vb7VaKBaL5nf5fB71et3cTxWM9bm1tYVkMonFxUVsbm4G3p/L5QxR1D6ibVkul51E0QaNFDVW+H36+7m5udDkSfp8kiZXf7FDX3WuLZfLMwk/dZ2hSEwzX+jYYKgvMK7qA7ukeK972Pmum20bx7RwJYFy1cl+ndMuQuqJosesMG34u90fo/ZT79UG9UTR47qAiRluJUXxVl3Mj8oeGrvuZ31ERq/Xm2gMT4LuKZumv+jRFxqip6GNtqFP1YvX0IjlUQlccNLptDlPUcEQGT3iwYWwhYvPo5FFojgr8s6wRw31nAZUFHnu3XA4NI6AXq+HQqGAdruNubm50H1yzASq9aOEmqGjw+EQm5ubzv5I9VLDV/P5PBqNhnG4kdywzAxfzmaz5oxFgkp6p9MZ80QTVO9IdKOiJPhM/TbuC9XssK5v0/FCw77RaDjnjCiiyCy+YeMl7jhynaFIzEpRtFX3breLQqGwL0VRQ89uVbiUFbtORqMRLl26tK/3uJKG7XWO8fCwYW89iHO97SCZdVSbJ4oe1wWDwSBw+PPNgjCiyMF/Ky7mR4Uo2hPqrBXFvX1nAihnkFjIAggeETFNlkg7oym/1fV3RS6XMyGBDDvlc0kk6YWMUhRdoYJ8Lw1/G0qINanKrBTFZrOJQqEQ6u1PJBO4+4lLuPuJS0gkx0kuyXev1zPEbmNjw/ytUCiYMycZxmu/p91uG6Kg2UD5PFUUoxZxV/iqtks2m8VoNEKn0zEJb6gY2mdmJpNJHDt2zBBTDZNVUKWbFCWhe1PZb6gK8lxP1z7Xfr+PCxcuBMo1Go3QbDbNeZMKuxw6PhKJhFHCXTh//nwsshhlpM2CKLoURSWKe1UUeb7nrYw4RFHPIJ3VewCvKHrMDmEJr6LgckK64BVFjxsKTIRxs02uYeEu9objWwlHJfTUXuBnrSjuhSgm0kngKceRf9YdSKSTATUw7kZ2kiuXojhp/6AmtKnX6ygWiwEik8lkjPLkMrBYTma3tNHv9537GwGYvZipVAr5fN4Qm1kQRRK4qH2PuXwKv/f+b8Xvvf9bkcuPL8wk191u1yiHrCsqfMPhENlsFv1+H6VSaSzMs9VqGYVP9yCSHOoexbBFnERVjYdUKhU4ciObzRrlkf/WIya0n/PcxWKxiGq1asiV3ddIftXgdvVHNWxoqDSbTXO2I+teQzABmNBZLReJZ9gRFfp+mwhEOX4ajcbYWZc2Jo21aUKYlXBOUhTpdNirt380GnlFC/H2ag2HQ08UPY409kIUgV3HmSeKHjcN9joYjjrCBumtTBRnlXJ+v3AZErMMBd6rcqpGpWYwjZsam+GaNlFUQ3oSUWSYIYkRSYge7xCWjCSVSqHdbjuJIgmQq15YNqpeJESzIIq1Wg3z8/Pm+XsZd6ooNhoNlMtlQ7hUQWUo58mTJ7G6uhroZ2xbmyjSsOTvoxRF7he1Q0NzuRzm5uZMAhvNgqrvtLNpqiNheXkZw+EQjUZj7N0aEqx/cx0DYit9lUoFxWLRhC0DO+q1ElZmkdX7qLi55gw9isO1L08JmWI4HCKXy2F7e3vsb4owVVzLFxe6vmk5Xesex9tes54C05/zeDMirqK43+Q/PpmNx0Fir/aSHh8UZYd6ouhxw4AL5s0wwdqLe5iiaHvEbxUclT00Lk8wMLvMeHs1QFzG47SKoioVLkVRiaSCSgST2AC7+7RGo5EhQRqiaiOVSqHT6TiNbC5MUYpiMpk0CXP4+/2i0WiYMNqohTMKHLObm5uYm5tDPp83bUy1jgSu2+1ifn4enU4H7XbbzGv8u7axkkclPDRi7bJS0bUzoabTaSwuLpr2Jqlnu1NR1X2DvFfb5dixY8hkMtjY2BgjlJqURn9HdLtdcyQHvw3YIeqlUimQ0ZYJbejVtucFKpGFQgH9fn+sHvjura0tk/xI+1UYUeR+eACRToiojKdahjhj3LVvyPUzsKswx3EM7bdcNzNsA9tFnvU8y73CK4oeBwlX+HscG4XROFF2w16dUZ4oelwXcFKfdfjf9UDcvS9HQVW7HqARe70NmYNe4PeSfXDUHyL9kXVU/+/9GPV3w/tIFOMqihqqR2ISR1EEduqgUqkYYsXMj7rvIZ/Ph+7/4nmL9lEbLB/gNsw09HS/hrJCScbW1pb5BhutZh/P/5r34Plf8x60mm4C0e/3UavVkMlkUCwWTRgnSVYulwss0Aw/ZTguybetKJKgs11InPQ6LYOr37IMLNOxY8cMEeMecBI4Hu8BBIlir9dDNptFoVAYU3O1P9kkkyBB5n1MxsQxz9BldXyMRiNzXqca8zyPkUQxzKnT7/cDSYCIKEUxlUphcXExUlWMynhKxF2vXKGzrj4Ylg13WniiEk9RZL/cT9SCK5mNr3+PWcF2HMedF7gOaW4BG15R9LihwMV7mgQBRxWuhcPGrUwUgaOxkLraYJYJbRi6OZ2xN0KiM8Sw2QOwc980oadRIXr2HsWwsLp8Po/t7W1zTh8NZj2qwd7vo+FXJCausk4KPeWzgemShUSBSXkGg0FgX9pYu4yAKxeauHKhyaofu3ZtbQ3z8/NmLyLVQ2CnTm2lqlwuo1KpANhJZJPP5zEcDtFsNseIYr/fD4RukmRpe/L3YaFuvJ6EDdghb4PBAIVCwZB7DQG1HQmaOMbVTjZR1Gu63a7Z1wrs9BWGlGYyGZONVFXObrdrFEedF0h4s9lsaDkAmEQ5NhEI20NIw6tUKqFer4eOz6iMp8Re54swp48dCTDtHKlHm1xvR9z1RtzQU+4pntV7gKOxvnncHLCJYtyxbWcPd93j9yh63FDgYLhZiOKk0LY4ZPJmxiwPUt8rXAv8rBRtKiX7TdCkBnccTyL3bDUaDfM7PcBdFb2wPcH5fB6FQiGwr07JCb+JeyF5DZ+3vb1tknG4yhe2OGnWU5aDyVP2U4fcn6jkddo9XFQ7aVhyjzHDJ3muIokgn819ea1WyyiKw+EwcBQFn8XkPXQIsJ4bjQYuXbpkQoJd5IV1xGdwH2k2m8Xa2hr6/b7JygoE+7kqoAx1pZIWhyjyZyUpBFXBVCqFubk5Y7ywL+dyOTQaDRPqrG2tjomwtopSFMPamGMhkUiMZYBVTNqjCMR3ZtjrAQmy/ftut2vqnmHe00CdLbc6UYmrKGYymZkTRU/UPWYFew9sVN/Sa3U+D5sP9tpPb13L1eO64lYjil5RvP4e14NSFFutFtbW1gDs/zuV2Oi/o66397W5FMWoPprP53HmzBlznf4f2K2jTCZjwks5flutlsmsGaYaRik9LC+wm2UzKoQwDnEmEVMCN227UPU6duxY4AB5Gvw06nXfIgCjylLJZFgtCR2wqygyzFKJYr/fR7vdRrlcxoULF1CpVJDJZEJDT5n8Rfc78oxETYDDfaSsb5aHhJhk1FVHStyUjHU6HaOGsY40aUsmkwlkQ+V7qtWqCW3WjK/r6+tYWFgw17uMfHrLXYoiy2FDnSRLS0uR4aeT5vE4jiVXH6Wi60pko0mRpiUwqggfFaLSaDRmeuxQXNjO2ChFcb9ztKuf7GUftIeHnnFLTEMUdY7XZGauezxR9LihwIU+zCi8kWAb4S6P+K1KFLmoHlWiOAtFsdfrGcNov8qpHY4ZhxiFharEXRT06AUSQiWa9MDbRDGRSGBtbQ0nTpwwYZSu8oUZwOr9BGDOVQ2bEy5cuBC6T5LQJDa2ojhNu9RqNeTzeVM3/D+NTCpbuVwu8FzuPWw2mzh+/Lj5vWuPIo8j0YyzvG5+fh5nzpzB1atXDRlXcDyR8G1tbaFSqeDYsWNIpVLY2tpCMplEoVAw2WSVmHMvC/tXGFFUFZT/5s+NRsPswWSbdTod5PN5E+qsCcuUKLKN+LfNzU3kcjnzPJeaTuLP0MG4/VvHRyaTcWYnjtrDq4ij+rnmmTCiqGdQ7kVRtBMmHQXU6/XQxFcHCVfyIxdRPAhF0ePGBY/kuV7vvnLlSuQ1UREW2he9ouhx00Ezvd3osImiK6HJrbq4HKXstmFEcb+Oil6vZ54x7Xe6+okm/oirKLo86TS2wzzgLmjGU4afUlFUVWo4HJqzBHO5nNP45veFefD5TJaNWYFdRLHVajmPcLBRrVbNXj1VIKc9Z65er6NcLhuSRSWLezeHw2FoqCLfS9KjewH5dyqTehYiz2mkKsmspr1eD/V6PfDtfGa9Xke9XjeqMOfUbreL0WiEUqlk9iwCu3OV/pvheMB4VlC2iYsoarIehtLqUQ8c99qeyWQSnU4HhULBPL/RaGB7exulUsmQwTCiyP5kZ3Il4jjpFhYWzD5SQr8lCq653YYrzJvtbZNRO/R02rlI59ejoigyKdNhw26XsNDT/e5RDNtGcjNmNO92uxPPH73RMRgM8OCDD8ZqP51LZ/VunhsbhriKom4pCJsP9pow69azXD2ODGggzirb4fWCiyhGGSuHsaBcvnz5wN8RB/TUH1WiuJ9Mg0S/3zeq5LTfORy6HQrTKophoacMT4yjlgC7iWxolJPA0LBiCGG/30e1WsXKyopZoMIUxbCjUXTjPcutIZWKjY0NLC0tTZwnqHzy+XsNPW21WigWi2YRZzm5X5EeaBIzTdhj7yvk37RNdd8c/02iWCwWAezutVxaWkIul8PVq1fNMxOJBNrtNtrtNpaWllAulwEA9957L1760pfi//yf/4OtrS2jKFKx3NraQqfTCdQ9VRa2o8IOgVZPdavVCpy3yaQ1bEMSmEQiYeqLjgA6HBhyyv2UrCuXoWOHZbvmWZeTwSZu8/PzqFargWuYeCgOJs0ZUUTR/j1Dh4G9E0Vtx6MA1/i9HghTFKd1GtkIC+O/0e0YF+igu5nR6/WM020Stre3Z1ofmqUacK/3Uc4pO/Q0SlHcj53jiaLHdceNvk9xGqI4C2ISB41G40gs1kdRUXRl6NxPmzCMj0Rnmu8cDAYYFVJILeQAJAzBY39qtVqRYVxRiiIXGDvjaavVCi0jFUUthxKcbDaLbreLra0tlMtlQwTCjCcSoDCiqPvoqF72+/1AKBAJCJWkKGgZdI+iM5w1ATzisWU84rFlwCo6QwJtQsiEM1qOdDpt9v01m01DtFlnLIvWI88W1OM7GAZdKBQwGu2cF7i0tITBYIBisWgIHvGoRz0KuVzOlAsA/st/+S84d+4cfvEXfzGgmpEMbm9vG68425aOBH6DwlYUdU8hnQBAkCja2XJ1Pux0OiiXy2g2mxiNRlhbW8PS0pJ5HsPA0un0WFvTSeE6yF7bwkUU7fGRzWYDx7lsbW1NzHiq74gKVbPfx3tciiKw22f3s0fxKEWrXC+iGCf0lL+f9buAo7EPf9Zg4qibGb1eDysrK5F7l4nhcDhx+8M0YEQB+43LmT2Nosh/uxxtGiU0LY7O7OJxy2KWRxRcD0xDFKMG/Sz3dTCk4XrjKBLFSqUS2EC+3/BThnvshSgOEyOMnnoCy897LBLpXSWQk/8kjy5DPsNCT12K4ubmJjY3N0O/hUa6a99tPp834Y5LS0uBvzu/L4Io2t8K7DqNtD3W19eNcjmNx15Dbl3tki+k8daPPBtv/cizkS+kA/cBu4SAdaGh8loWqnHdbhfr6+tYXFw0BF7LwP8Ph0O0Wi2TIZQEST3D3P/HvZXpdBrz8/MB8qde40wmg1qthvvvvx/ATjteunQJm5ubhsRls1nzPnVucFy4lJaw0FOGDbPMVAl1nwzvV8LebrexsLBgEkDR2UBFWoliWHuqYyGOoqhhv8TS0pIh6cBOyHLcvjVpvXIpivxG/b0dEt7pdKZeA6KyGe8Xe3WeXS+iaJf3sJyyxFFY42aNsC0FNxN6vZ5xuE0SLLjlYlagY5B17BrP04ae6rwedu208ETR47rjVlIUowb9pE3N02DWE9peoWrF9Q7LYRuo4QrMJqFNFCGJgr0wqJJHNTCqbPa3aFlUUVTDm0cwhIWm0ABX7yO9nvl8HteuXcP8/HzgmWEGGUMNNSOm/a26eA0Gg8A+EB5uz4U8qg/Zz1cCPU0yG2bzTCQSgWyifL/tEGAGT2YoTSQSKJVKqFQqY+3J7yaZIjEYDneOOeFY2dzcxMrKivkOngNYrVYD30l1M5PJ4BOf+ETgb/fdd58JD221WiYZEUOdarVaoA/EURQ5fvg81i/JqO6T0Xq31e12u41ut4uVlRWzp43fEkUUlbQPh8MA2QPCiaJtIOVyORPqSuMqrmNtUtIZl7HHvqPfZe9xZYjwNDgoolitVrGxsbGne69HCKZNulmOMBwEibwZieJhKYrXM1yZc5LtPHJBtx3MAjzWSBXFvRJFPRbL1RfjZOcPgyeKHocOe1K/lYhi1CI6q4mS7zoKdUpD5igkLaJqY5OrWSja9j6BuLAXBhdRjCobCZjL6GFfcxney8vLY4si9+/wufYY5YJ64sQJ55ENYYaX66gPm3wQrVYL/X7fPGtjYwPHjh0z1/G+MIVSn0XDnHUct13a7XbAgFfiTeNe95Xw2vX1dVPW+fl5bG9vB+Y6jm+GtbI/6p6/VCpljkxgW2i78KxJguRqbm4Of/3Xfx34jk9+8pMAdkgRFUzWX7/fN2dWauijK+upTSYHgwFarRbm5uYC3mwSaiXoHG/pdNqcnUgyWC6XTZl4XRxFMZFImKyx9hwXljHXNf9QQeWRLHHny0lE0WXsAeOkThPZ8L5p4QpznQWq1eqhJDGJk7Ar7nOmqYeDIHU3K1E8KMVaUavV9uyY2C84p/N4pqj+6HJI7AecGzlnRTmZwu7XCAvdUuCyQX0yG48bBvZg2Eta8KOEOERRE2KEGbmzJIr0mF9vxPV4b2xsHFpKdds42Y+iqA4AtvE0E3G/28Pgg5ex+X//DqP+MGAMA7tEIOpb9P+KsD2KwA6RqdfrAcOG4Yn8LpaBihWVl2PHjhnFmklBqDy5ysH213fxWk1mw/dyPuB/TDLCsVOtVrG+vj72HpsokmCFtUur2ceLn/qnePFT/xSt5u7Yo1qm45o/k+Sp6pnJZMzRB7pnj3XKb2QfazQaKBaLSKVSgb6nR11QTdR6455FJfhahx/72McC3/eRj3zElIVnK1IZbjabJvMoyxoWeqq/o9eaqqsaKQwx1zMjaeQwNLZUKhlyo0d0KIkNyyarhJLhuXH2KIaB39tutw1Bj4O9KIou6NEYwC5RnIY4HYSiSCfIpL2Ys0C73TZn0O4HcQizva941grWfpPk3MrodDrXzQZUZ/b8/LzzXEPFfo/AIjjv6RwbFnoaJ5mN2iBhyWz2mifDE0WPQ4c9qR/2XoJZIw5R1L+5vjVMJdkLhsP9pwCfFeJ6JPWIiYPGLBVFqh977cOD/gCjeg+DShvAyBjBfF7cZ4Zl4dMskvbfbNJBIgHsnnHIZ9hnj5FsMJSR49nVh+3wRd7vKjuTlfR6PbM30f4ekkUbrhDDyGMDRsBX/q6Kr/xdFZBq5lEJ2nc5xrmnhOSC386jQohkMmlURXp4SUTa7TaKxaLZF8jwWu6VZVIfrRMN92SoJr+ZZbQVxS9/+ctoNBqmHRuNBrLZLLa2tgwR03DIOERRvdt2fbO/qgJKcIyx7kqlUiATqvb5sCy9fB+f32g0nO0ddx7ht1HpjDsHTCIZYeTNrksqx3ofw53jwhWRsF+wPuhM2gvibjWY1R64OPuv7DC9Wa83N6OiCIxHFRwEOp3OkbBXFhcXJya1YUK3/YIOMZ13XQ6PuKGnQJB8usQKextEXHii6HHocIXm3Mhk0Q5HmEQUXX+jcjSLOjiocKS9QEnEpBCKg9rXMhqNAgqU/a79GA2q1u2l7YbDgfXvYHZMOzzRhu4rtGGrYXw+fy6Xy4Hz+WxFkclKqAjZdcTFXcvsKqfrb+zv9h5Flo8ZRKl68Tlsu9FoNLZYu4xFOwvnJJDokNho25LsMUQpn8+bIyq0fngtk8fwG5kYh+XPZDImJLPX66FUKqHdbuP48ePOcvH5aszwOJBLly7h0qVLY/XxhS98wWRFrdVqyOVyqNVqJoMsCSqwSxS1H9sGB4kMM7tqfVNltRVBOq4KhQJSqRTq9ToWFhbMc3Ufc5gCDgSPNrD3QhLTKPo07JWkx8GktSqMKLLMhEtRtK+ZBB3bszpLkcpvqVSamiiyPHEVu7BjdaZFHKKo6+Je1b+odr9ZieJhKKWzDumMC9vJzyiPqOgmRpDsF3TMar/Zzx5FIPp8cl67l3wRR8Oa9Lil4FpIDyPM5aBgTzZRhCiKKPJZ+4WqUoedVMCFOIbMcDg8sMVoOBwGskXSULfLtxfsW1EchGcmY11FKZ4a+mrDVR5VaxKJBBYXF42qqIYr1TT217CFh4Y2n+lqX3ox9W8st723iKR4OBxifn7eOa6GwyEWFxfHVEWXsWiPt0ltROOW85G9V5EZ8jKZDNrtNprNJqrVKorFojNEk3suSRSZsKbf7xsFkolcGO7qOvh9fX3d9AEqPSS1uVwuoCZynyQAfO5znzOktNlsGmeAkm4liprohvVn1xkTMLjULBo9tiLIZBHAjoHkSuDA8oTt8ev1euboFrbHJES1NzO17nUPddizXfvlqJoqeXKFSk+rKMZ1UE6DVqtlSP20ewj5TXGJYpSiOM1ZwFFEke2k/WqvoadR77nZiCL740GE6Sq4vsyq/04Dly06KanNrBTFbreLbDYbK/R0WkWRPyvsRGDTwBNFj0OHazDcyAltXMZu2EAMI29qNO0XnDyO2t7PqIX0IBVFDdULe9deVUVVP/jMaSZi+zo1ZrjXK6odOfnbz1EioMakHda3sLCAWq1myLMqj0oU9X38uyaXYR2Eta+titK7by909F6fOnVq7Fw7fd/8/PzYsSGTiGIcQ44hpaxzJq7RfY7ZbBZzc3Mol8u4evUqWq2WCaVUry0VtmazacJp6/W6CQtneCvrdHt7e+ybmdyFpBTYmUMKhYJRg7PZbIAo/uN//I/Nz5/5zGfMz1RBVVlhIhwA5ls7nQ7Onz8/5jVnOZUoan3Pzc2ZceRSBIFdxwqTDPG5yWQS1Wo1UIeuexl62+l0xsYdoU6bqD18qVTKnHs5rQG1l+zGOo7Dth/sRVHca5lcoFOD80CxWJzqgHH2rWmIYlida9KmSQhzLmibal/YD1EMcyrsJZHZUQYdPgcRpqtgUqdZZB+fFq790Pl8PrAH3casbFV+tzrj9pPMRq93wUUUuaZMgieKHocOV2jkjU4UZxF6Sg/ufrG6umoUiqNUp9ebKGrd2u+ys0nGBYmXHcoa12BwEUUNwYsiihqqfPXq1UBbK1G0j8bQhSiRSGBhYQGbm5uBMek61kQTfmiSGRpptvqgP9v9Xr/TDpEdDocolUpjxol+ExOnRCk0/D7WURyDh8lnqCi6ssDyXMgTJ06g2+2a/Yl6lEYymUSz2Qwka+n1emafIetPQz1Jfj7wgQ/gLW95Cz74wQ/iy1/+MtbW1jAajbC1tYXz58+j3+9jaWnJZFXNZrOBRDbf+Z3faYyfz3zmM0Yl7Ha7OHnyJLrdrjEYRqNRIAHPaDRCvV7H8vJyIAuh1iNDT8O81+o4YLsR9XrdHHVity3rSB1v+lyqp/yWsGN3bHIQpQBxPyrH2SwS2oQp+Rqe7DII96IoKmahyNTrdZRKJfPvOMk9FHtRFMPKPO1eTVc7a53oNXslP1GK4jRhzwBw7ty5qd9/mOAacNChp51Ox0RpHLZjOyxx1sLCwljECtt2VsqnvbUBCN+jGAZXfwzbauEKPY2badYTRY9Dh8v7d9RIzTRwEUVdMPTnsMWE3rtZTEDNZvNIEEXb+zqJKB7UYmQbIy6P216JopIJNULifourL+iePjszpoLfxHBC3YTPxcAOA3QlCllYWMD29vbYfilbUdTEK3pIMdUSllmfoftT1TBj0hb+Td9Jlcs25DSZTTKZRKlUChixrrBdu/xxFEXu6eOh8i5SQ6NeiTzrhSSWZxhyP58aQf1+H9ls1vTNTqeDU6dO4X/9r/+Fb/7mb8Y/+Sf/BM985jNx11134SUveQle97rX4R3veAe2t7eNgaFhv5/4xCcAAKdPn8aZM2fwiEc8AsBOQhuel3js2DET6keDQZ0NShRXVlYCR7OwHm0jhP/vdruB/mI7I9g2JMoKTbJAhw6Jo00UWZfsGy4FTklKmNIE7IxT7i9NJpNTGarTRmsMBoNAgjF7fyKwG6Id97n2/Gqr9nuBTRS5hsQlQKzvafYohrWPvV920nsnEUVbUdzrHsUowz1uefv9PprN5pFWFZUoHqSi2G63TZTG9VAUXYmzyuWyc2uDznt7depTobftRr5j2izGUbanwqUoxu1/nih67Bmbm5t72tQbFnp6lMIkp8EkRVH/HuaNohd+v4oiQ8eGw2FkncYNOdgPbFISRaCm3QszDWhMh5F3YOesub0cz6Hkhpg2BClZnEOyOAdgdxGichalKLK+er0elpeXAyFiShTVY+oiislkEsePH0exWAxcZxNFLuR8bi6XMyGVrF+732vyCDuRh+3JVVLr8vjbRNHOymgTRbv8Y+2SAE7fXsDp2wus+gABtPexqlHBsvR6PZw9exadTseoixp6ypCq4XBo9n6xfHwWwx9/7/d+D2984xsD3zwcDnHu3Dnce++9eNOb3oRf+IVfMH1hYWEBAPDAAw8YwvyEJzwBqVQKj3nMY0wbfPazn0U+n8epU6fM3ka2gSbJIQljNs6VlRVsbm6a7yWh5XUK7r1UZ4nWHevWNcfp/NjpdEz9u/a1MvSUBH4SUZwUesr+QKK4X0XRZfwBuyHqLJd9hiKAAFmOA9uo3G8yGyrPrlC8uE40VRTjrOdhoZyc/+J+z7REca+GfpSiOA1oNx1lm4drwGGEntKhdtj1ERYm72pjbfv9bOtZW1tzKofTJF0LAx23/NmeP+18AXHf5Ymix57RarX25AGadsPuUcckoqgTTFTo6SwURRp5JJ5h7XP16lVj+B0U7HaOWnCmMXKYlGN1dTV2OVgnYbDVlb2Ak/RURDGVwMo/ehxW/tHjkEjvhmKSjCWTydC6GQwGJnQulUqZBCv8HlfoaZgHf2FhIaAk8DtsRa7T6RhPP73N7HM0NAnt97bh6PLkKolVAqP1qz+ToOv+XiWKtipl9798IY13f+a5ePdnnot8Ie00WnVxpVpIUI1KJBKGLDabTacalslk0Gq1AmQcgDni4UMf+hBe+cpXmt+/5CUvwT/+x/8YX/M1XxNIbvOOd7wDV65cAbDj3LDDTr/2a78Wc3NzhigCwGc/+1l0Oh2Uy2WjmLKOlZgoOeY+SL2eZFcTymhdsL/Yqgv7M/dy2tB66vV6hgDaZJ39g3skAfecYiuKYYY9/6YOmf0SxTBiqsmKgPGjMcJU1CjY79qvoshjMWxME36qexTjliWMKALxFbppQ0/3iknPiGt4M3vyUSeKh6Eo7mX8zQphoaeAO3ST420/mU+5bqujaFb2rz7Hng84x9jRIbGeu++Sedyy2OvZd1GT7Sz26B02bKJoL/Z2CF4UUdzv99OQ44Id9rxut4t2ux07Rn0vcBHFMONhmgytGxsbqNfrzr5HZccuh61kuBb0KOXOVY/282gcxSWKYc9ku00ySlSlosLG7K5hoaeujJRhZWM2Vzv0VEme7uVz7VFUD2wcRXFubm6qRbNUKhklVd+n4ZVxQ09V5dFkOySK3BfHNq5Wq+YA+VQqhXw+j/X1daRSKXMgPbBjgC8uLppwXRoc7XYbrVYL9913H1772teacv7Ij/wIfvVXfxVvfvOb8ba3vQ0PPPAAnv/85wPYSXjzjne8w4SyZjIZfOQjHzHfcPfddyOdTuOuu+4yv/vsZz9r2l33FvJ4DrYLSSPrIJHYOWuT0Qfsb0xko2D2UF5nK4qMdNDjTuy/8/1sN3tOGI1GZp8sn+PqK3EVxXa7bRxIUcq9C1FE0TVm7XLYoac6LvdKFPerKPJYDBs8BiYOXA6aSXDN/dOGxk2rKBLTrrdhCqh9zSR0Oh3Mz88H1qrNzc3Y9dxsNiMzc84C7JMH6cTX+px2j+csEDU/AAhdz/azrUcjM4hZZczVsWTPB3bo6TSOE08UPfaMbre7J6IYFud/GOf1HATCwo2IaRTF/U6UjPePCrOkWsMwwoOCKkRMKBNFFKd5brvddpZ9a2trzPsdR1EEwkOstre3A8dr6HM5GWsa8bihOq5xQGOYz2efcPWbarVqzmFKJBLIZrNotVrmeu5Hs48wiLM48Do7dHM0GhkVE9gliixDlKKof1NCovVJchpVf9qf5+fnzV4SV+ipvXBGzS8Mn2Q70rlAokhiwWy0JOl6pAaz5dFjTLJeLpdRLBbNt6VSKVy9ehUXLlzAT/zETxjS8Z3f+Z342Z/9WUMkqPL8wA/8gCnn29/+dly7dg2XLl1Cu93GRz/6UQA7Y+iuu+5CKpXC2bNnTf1+9rOfNXVGYss+OxwOA3tbGd6p9UsVWcmpXY+2Y8PuY6wze5yzv7O+1RseZjiScPMauyxxiaK2kYYbx5kTo1T+sNBTWy0PSzIVN7LBJqX7VRQ1NFrBuS2OYWw7CeKAc4zuhTwIRdGur70Q60nzZ1yDv9vtolgsBpwNrVYrtqOi2+0e+PYR9tH9hEJOgp7dSxwVscDuH9r2+zkig2uozrOzsn11DgiLbOM8F7Y/0/ncfZfM45aEHbY0C1zv5Ct7xbREMWwi3K9HGIDZI0W4wkb4jjCyNSuo8dNut1GtVp2T4bRl4ETrqsdmszm22GpYb9QiFEYU19fXnQdP07jldypRjDPpDwYDpJDE1nu/iK33fhGjvvuICiZ30e8ajUbY3t42aiL7VaFQMOGP/F5Xuu1J4H262PC+9fX1MaLoCpG1iaL+zbVXUhVFJruZ1Dc0qYsr9FTHmz0W2q0Bvu9b7sX3fcu9aLcGRgXUMxT1/VQcmbgmn88HlNJ0Oo1sNotKpWIMASr8VKxItqrVKiqVCl72speZPvec5zwHP/uzPwtgN/sqcc899+DOO+8EAHzsYx/DX/3VXyGbzSKbzeILX/gCAOCrvuqrzPl36XQaX/3VXw0AeOihh0z/ZTZZPehcVQy7vhOJBMrlskmQxZBfm/Tr3i8guEcxmdzJAFssFgPPp2MhmUwGzkgkoVRVykUcwpwKYaGnvV4vMIZI/G0CceHChT0bq/1+3xmlwTmCY8ReM2q1GlqtlqnfOOuqTcr2s37Yx2LYiBt+akfPRNWj7YxaXV01fZF9bZaKoqu+prVfJiWzmSaaxLZ36JCJg8FgcKDbRoDJ3zoL2ERxVspaHEyy26K2EO1njyIVRfu7p32ea2xFOUZtRdG1BofBE0WPPYHngM1yUN8KRDFq8dtPJi2Ww94Q7apTvn+ahWkvUKKoxryNaUIggN0F3q4rvs8VeqpEUYmYIpvNOs+O297edp4lxolW9+VQXYtNFFNJ9Ddb6G+2AmoKwT5hL0yNRsOEbiqhW1hYQKVSCRiN+0naYBPFdDqNjY0NDAYD45EkUXBtnue3uEikTWBVZWTdugw5e6yRHNuhpzbRtRf+0XCE+z61hfs+tYXRcGQSqegZihp6qmXqdrsolUqBMlKdGo1Gpg+SXPIZg8HOYfbFYhG/9Eu/ZJLFfOM3fiPe+MY3GrLE64hMJoPv+q7vMv9+73vfi1qthi9/+cvm/U984hMxHA5x7NgxpFIpPOlJTzLX33///aYOGEbLf/P+MCfHwsICGo1GgPSSWAC76e3ZNq7x3Gw2USgUAn2RBJF1yLBYllHbi2WjgaX9xy6vfoPOQY1GYyz5Ed+tDg0m+ZkE11xOJ4INe46w+7DOxXEJjGuP4l6Jop3t1IaGeE8qk475qHlQk6VwfdAw52kibOIQRZv47EXFmYWiyGfYc2Kv14tdHjp7Dkt9269tEgadO4DDTWo4KezUVuincYJEgc5C22lh/851n8Jld0Ypii6iGLY/04Ynih57Aj3AnihOTxTDMI0H1QUat+r1d028LI+tWswartBTF6YhiqxrO3EKAJMsxBVqq0libAJEaJIPotFohCrnNHL3oyjqdysp0fIMh8Mxori5uYn5+Xmj/LFOstmsuY7P4/dMs7Dpxnd1PjChje5VVDViGqLIRUvrg/2X5N4V4mijXC6jVquNOQHsMk1SUtmnmFTBJooa0ss2sYliu902SWOuXbtmEg2xLNvb2+h2uzh+/Dje+973AthJSvPmN7/Z1O9wOES73cbFixfx8Ic/3DgKXvjCF5qF/Y/+6I/Q7/fx+c9/3pT/cY97XMAY+Pqv/3rzt0996lPG0GdfUgIM7PR115xO5W5jYwP5fN7UAduO4e78mf1R65V7XrX9aBzxfEG+x2WMsT2azaY5f5Jzi2sc8z4tp0YUcJyqo4dtGEb2bLiOcIk6SoLt22q1xgw0PRZEyXsUDpMosv/aCkWz2cT29nZA+dVw/KjvsPfA8Xl8zrTJ3VzjO6pO9pKkZRZE0d6fxufq/yeBa/thqW8HpfTZdXGYCW2iEtkA42uWK9Q7zCnEhGM2OBfaNuMkouiKRHP1Rdsx6iK6XlH0ODRMGmRhiDJWr0fWq1lgGqIIuBe0Xq+HSqWyL68d9zPRKKMR5lIUaeRMs/9g2raxFcU4RHHS99PAm5ubG1P/qFq4nhOHKALjqmK1WkWxWHQab2Ghp1HhxYqdhSe4f1D3SinR0bHRbDZNiKYrRLRUKqHVagUSsvB9cfcPqdGqCw/rgca0qkv2d08iirxH64NzStheT9dYY5vp3zSZTZy24PtIfjm/2SQ7mUyarJs0dJUoahhqpVIJqInAbtjul770JVy+fBkA8PVf//Uol8umnjY2NkwCHO6BnJubw7Fjx/Dt3/7tAHZSrH/6058OEMWv/uqvDhgAT3ziE83feJ7i+fPnDVG0w/vCiCKwMz8xkQ2VQCWKuVzOfDvDpQkqkbYXnnXJetPwfP6fZbt27ZohEqx79uUoY1+VR41oYJnZ5nslirYTLkyNZPun02kzfu37NGQsjqpiG6573UumDpIoFItF1Go11Go1XL58GefOnTMhs9zDzXFdrVbRbrcnEkWdu7QvTqsohl13EEQxqp7jEkU6Vni9zilxMBgMUCgU9hx+WqlUIu+16+ygMp/aKu9hKoqTbNio0FMgXNhgxIkLYf3HnocmlcVVHrVx+MxJiqInih4Hir0SxSiP3I2azMY1+O39NVFeSIaa7XfPoKY31/A5F1F0nYUWheFwiAsXLkxVHl0EqMKEPVtVsShwcnN9F7/f1Y/YBkogXOXRDH8MIWS4nZ2RzqUoTtN/7fBLJWSsO3of1Sjd2NjAysoKBoPdYyp0gSiXy6jX64FspKy7/RBFKgpMxkGiwWvDVENglzyoIWgrikp6VVFUAyVs30w+nw8YGKxbV/iNC51ux5AdJf9UOlT9aLVaWFhYGAuP5YHq7XbbJI3R5zQaDSwuLiKZTOJ973ufeffTnvY0U0fr6+vodDpYWVkxY5nvGY1G+NZv/VZz3+/+7u/iM5/5jPn+r/3arzWZqFOpFO666y7zjM997nM4fvw4Tp48icXFRVMXGq4elqiGbX7y5EnjoFCSQpJXr9dx7NixACEDdhxMuVzOSRQ1xDeZTAZULb1e966xjBqtYEPvVZWZvyPppTOC30IHWhyCEkYU+S7C3lfJczNd90Upiv1+P3Ak0DSOnyiEHYthY35+Htvb26Z/3nnnnTh58iROnTpliAfbkXNzXKLINtSzWmdxXFQUUdzLHsVJa3mcvWtKFPm9TNAUd/0YjUbI5XJ7JoqTjjbT8QXsjShOKpvt6AAON6psUjIXlyKn4y3siIxJTnEAY++NoyhOIoosn4ae2uVXouiT2XgcODTMcRoVbFaL20GDewDiwhUrrkZx1OKixvd+FEXG+3Ofle6hU9gTXhw0m0202+09l48eazVMtTxR5M1+jiYc0WeQRESpqHpOm+tbcrmcSS7SaDRQLBYNie10OoE9TqxfJbrT1olNFHUvm4bW6QKgRN8VIkoHDpOzqKIYd2Eg7NBN1rmSPiIqmY2t6ijBdL2TpHEwGOChhx6aGD47Pz8fSEREI8QOhw3rY+12x6hpmphFr+X3DQYD5PN5QzDYLhxrw+EQ9Xod2WzWZFK9du0aFhYWzLV/+qd/ap77zGc+E8BOQpN0Om0MRp65yG9pt9t44hOfiNtuuw0A8Jd/+ZfGefOEJzwBS0tLxuhkH3jc4x4HYCdBy5e+9CXUajWj7r3rXe/CO97xDrPflEo95yOCiW/Yd5Rc0Utdr9eRTqfN+YZ6P/udHQZNQsE+yrGrx3Ww/rmHTxPChO1RZBld854STzqVlCjy/XY/dr0jjCjqN/B+Pp+h2zZRVCdOGFHs9XoBw3Qv87gL9Xp97HxPF+bm5nDnnXfi2LFjgUQciUQCp0+fxpUrV0x/iEMUdY8i65sJxdTBEgdhc6+GtdrYi2M6bM4icrncxCMu9BgeKmjTKorA/jJvRmUg5991rZiWVPf7fTz00EO4fPly6HtcGU8PSrl0YdIevaj1DAivfzoIXdDIFIVuW3EhrqKo671LUeSaSDss7vzhiaLHnsBBNu0E4vIiKQ4z61UUqtUqrl69Gvv6aYiiiygB0aobr4taGPTeKM8myxOW1MWF1dXV0LML42AwGBivdZjaF6ftuYDZ/a7dbhsjVdVGVXW5KEeRUiWZ1WoV5XLZ7C3ivjNC96ztxWBzEUVgV1Hk31x77+zr7e9ZWFgwxEMVxThEUfunvWeSRpCGKNKgp2pokwF9DhdR3jMpjIvGsZIH1/xBNU/rkmqFvS/O1ce6f+9ksUNbWadsD3r+XaHbXJj5++XlZdTrdTQaDeTzeeRyOZN05wMf+AAA4NSpU3jsYx9ryAP7br/fDxjviUQCjUYD5XIZ3/u93ztW/ic/+cmmjjudjikfiSIAfPSjH0WtVkO1WsVrX/ta/MzP/Axe97rX4c///M+xvb2NYrFoDA3tS3Su2J7qZDJp3rW6uoqlpSXT3+w5jmXTEMNEIoHV1VVzj63Ka9sx3JlZXaP2KALh2Z51TiZJUaLIZ2v5a7WaSTqkcM2x/X5/zODXcc4y2wYqv5/9O4wougy//cI+03EvyGQyWFpaMkfVaIKkMNh7FIFgYqpZhp66QtYPYo+iRoFEPYP9gesNyeM0jtj9bNWJIjPA+FoxLalut9s4duwYFhYWcOHCBWfWcBdRPKikOS7MIvQ0TFEMqyt17trviiKKrvwVLkXR3ipi36Pr+TT17Imix57ATrnfcDsbewkHOQh0u110Op09h3ZMqygCO97IqLCVZrPpNFiAXe8s61cVNzsEQUOy4npt+SzXhO+CiwxzUg4jinEURX4nF36+R/cn6j4HNUz1DLywdymh0YyVDJMLO2Rb+zQXgjjqaDKZRCKbQiK7mwCC32UbwlT09Pe83l5gmVBFj3iISxTDCBzrAEAgdJnGtSu81CaKNNZZP/y7a9FKpXYOpVclMwz2vjj73YRtHC6uZLC4kkGvv1uv2p5c2DWkjgfV93o9vPrVr8bLXvYyPPjgg6YMVKtKpZIJGybBabfb+NznPmfUz3vuuQeZTAb1eh3lctmEO5PEEFQdSqUSvvd7v3esHZ/ylKdgOBwil8uhWq0aZfDuu+821/zN3/wN1tbW8MIXvhAf+9jHzO//7M/+DKurq4Yosg6IVquFTqdjEo2oc4ThhplMJrAP0aUAq4rM31NR1PBl2wHB5/T7/QBRtPubgu1s9wk+h3XL9UadIfYzWfdh71AMh8Mxp4WOO6q29hpoEyPXeLCVzjBDb1pDe5q9SlFYWFjAcDhErVYbS5Tkgiv0lMSJ9RFnbYr6XvY1lzMvzhxtY9JaDrizZ2tZdT7S0NO4W3lUldorqbL7kuvv+wk95Vm7xWIRt99+O2q1Gq5cuRKYV1xEETg8sjitaGG3fZjtS0eT6xvCiKI6W11wtbVLgFABYJaiiyeKHvvCtBPIJPVlmnCTg0Sv18Pp06exvr6+p/vtjfk6oO2JkBNLsViMJKY8zNuFra2twDlkOonZG8QZSuXaw+WC7tEKy+Zlg+XghMlwWH6v69ppQk9J/Fhf3HPE76Vhx8nXVhQTiUTgoHFFNpvF1tYWisXi2J5GGp+64GufViN20iQ9GAwwSgKZbzuLYy96PEZ/30V0jyKwaxjwe1VpdO0lZDl4zh/vj6t8hhFF+32q/KjxrSGy/AYSPu7fsL/RHiNUV5jJlnOMSxnQOlKEJQ1g+fLFNO594P/B+770AuTyKVMeGklU9pTc9vt9QxTf+c534ud//udx77334iUveYmpHw0ry2QyOHHiRIBYfeQjHzHledrTnma+ieSw0+kYpwf739bWlsmyeubMGTz3uc8NfNfTnvY0DIdDFAoF1Go1oyg+5jGPMdf86Z/+KZ73vOfhgQceCNz7kY98BPV6HXNzc8axwDlpa2sLW1tbKJVKqFar2NzcNPWQTO7s16TSSeKmRo2GAGv7aRSFhrC6FEUl65lMxvQ9OnBc42x7e9tk5CT4HJ0r2B/ssabP7Pf7znnZZdAOBoOxhEB2OPPJkycD92i/sdVcxaRwQWBv5GdWyiSwQxZXV1cj24bQ0FN1DpBgxlUU45Q/jBRMu13Aju5wfR9VURc07BQI7omN2wban+K2t0uNitM2xLQOfI3wSaVSOH36NObn5wN5Duy6IOxtJQeJqPafpCgSrjkgLCNtVBRN2PNbrZZzXnApivbe7LDxE8fhofBE0WNqaCfbS+hplLG6l4XuIECjix7+aRGlKNrfSKPMTshhI4ooXrlyJZBYRScJe8+eTRTjhHvqvXFURT5/a2sL9XrdKGQknQrWzzShp/Tcc9+keuO0T2p/U0VxNBphbW3NZOtT5PN5bGxsoFwuB/ZE8nkkjKrs6R4kLrKTvqXVauHq1avGmNWQEYaiqboxNzeHVqs1Nn5UrVMsLS0hn8/PTFHkd6nqwnKqYWH3+9FohAsXLph+T1VU682eF0jue70eSqVSYFxEEcVJhqVrvqLBohlPmbWUobb8ZhJFAPiv//W/mmd87GMfw+/8zu8gkUjg2LFjAGBUPRIAKnMf/vCHzX3f+I3fiEajgfn5eaMItlotk9SFbba2tobFxUXTzzX8dGlpCWfPnjUqDOu03+/jtttuMyGsX/ziF43j6+zZs3jGM54BYGcv7ic+8QlD1DjnXbx40RzlkclkTAKfq1evGqK4tbWFlZUV0w/s8Ccqhuo00p9J+JhcQxVwHWsa/aDORA0tVJDA2oricDgMRB/wWUpkqRwTrnMPFfoOEr4wRRGASSak5WJ/USeU/U12ORhmrPPM9V4/k8kkFhcXzXE1UbCdXErAeMxKXBI0yeiNUmumgfaTixcvOtePKKJo709lO09DWHWujZv85cqVKwFnx6T1SUO7gelDT11tUiqVnAqYjcPIfBrHAeGyjVzhy/Y1dBa57GLOd2FlcvXR7e1tZ6LDMEUxrKyKaaMIPFH0mBoa/rYXRXFSjP9RIIrEsWPH9qQqRhFF26hnnUwKPY06o6vVapnn2vVr72UgUbT3h0S9l0ZoJpPB+vr6xIlWVTVOpJOIYlxFkQQqm80a49tOrmBfz/fT2Or1elhcXMTW1tbYQsu9ZJrchImbqDDZEy3f6SKKLjVic3MT9Xodd9xxh6lLfjvbkfWlhiQT1ADjCp/dJlRy+Pu4WU/Dxqga/ao66Tfb7cs663a75mcXUdSy6T4t7tXTOca1ALK+JhkzrmuYBIrhX5r5VL39bPdsNot3vvOd+Lu/+7vAc/79v//3Zo+WkiN6l+v1OtbX182RFl/3dV9nkiVRbee19MbzuzudjiGPqVQK99xzD77qq74KAPAt3/ItRm1iMhwaFqlUKnBMBgB8wzd8A37jN34DL3vZy8zvPvShD5lyd7tdXL58GceOHUO5XDYhpQBw4sQJlMtlVCoV9Pt9NBoNLC0tBca79kUmyaHRr3XJvs165bVsn8uXLxvSOhwOkc/nTViuqo7D4RDnzp0z38I+yDNQFRoazPttMmkfvcPfufqWbXBzPIQpii6wzkgUwyIS1Hhnmbe2tgLJU+I4/hTTkpRJz2IZwhKXud7NkHa2RaPRQLfbjR1aGYcoRrXBXuyN9fX10PUqah5yhVvainOctZX9ICrMVcHoCCDeXOla3waDAba2tia+S+1DOpX0OeqoduEwjkmzibALcRwVrn2KUYpiVIhxWD/mHta4imIccE6LC08UPaaGdvaD2KM4q7jqvUIHHDMQTjtx2YuIDmCXophKpQKHpbsQtoDQYKexqGovJy2bKNKgcSlRNrgPiQpruVyeuGCo8kdFgP/eD1FUw4YGnSoEhGtB0gyHXMw0Wx9BZQPYbRsaPnxmWGppmyiORiNcunQp8K3MBLe8vIzkKIHsZ2vYft+XMOwFjV8NMeX30lDW30fVnd2+ccJNXIv4aDQyJDWVShmnBY0OW2FVsO+x3u0jEfhO3cfFa0ej4NEgYdByREHnl3ZrgB96/gfwL1/8MWCYNiGJmi1YFR4asOl0Gq9//evNM7/6q78awM7ZZG9605vGzg2kwTEYDPCJT3zC3Pdt3/ZtqNVqOHHihKnjdDodIGb9fh/VahXLy8umXtimv/3bv423ve1teO1rXxsghnSIkJx/8zd/s3nn933f9+G//bf/hlwuh2//9m83df6Xf/mXpqwLCws4ceKEIWbZbDagxM7Pz6NQKODy5csol8uGnKg6x77J+qITQdUyXkvny2AwCITicd5h2HKhUDAEQhXFXq8XUHD4XDrPCE2iY/+eYH+z5/uwxBXaN/lsew2b5KDhnMT5UZ0vNlR1ozNF3zXt1o1Z7U8EYI5m6Xa7Zq1gWaOgYeisC7udoxBXUXRds5fMp7VaDd1uF8vLy6HlCwufDNuXx7LFjfBRRXFaohhnC4KrvgaDAWq12sR3aWg3nX0E2zSsHoDDURQZnh+FOKTdlfk0yrFERdlF6KJsMdee0jBFMU65uV7GhSeKHlPDJopHLfR0L8ROYadN3ouqGOUNtf+m+3jC7oka+PSQ0+tkK2g0vgg1aFwKnw2GvDI8rlwuo1qtxkpUQKLI0FL+TcEJb1onAevRRRS52ChxZt+iIUKjvFwuB9q3Wq2iUCgYNZTGN41cKk2uhcYmikqMB4MBLly4gFKphOPHj//9HSOkqn30VhsYDgeB5DR2Yg17j6IqkGF9Zy+JAWzDls9gfajjQY17tofdhjxHj88OI4q674ZtxFBXfmvUngsa7VHfm0qlTEKo0XCET354HZ/96wqSyZQxcPR8THV08ADxP/mTP8GnP/1pAMCTnvQk/NEf/RGWl5cBAPfeey/+7M/+LEAUNTT44x//uCnLt3zLt2Aw2DkGQwkAnwXszLXb29s4depUYK9fv99HuVzG05/+dCwsLBiiyGu4VzGbzeLf/Jt/gx/5kR/BH/7hH+IXfuEXTJnm5+fxpCc9CcCOeve5z30OyWQSxWLR1CENG9ugTiaTOH36tCkrx5mthmliHqqHqkirQ8WlSjOpGLBz4Ls9r6dSOwmCuD+bZWHUgN2PtS8StqKoDruw8H1CiSLrwA5vnqRe6Byj14cZy7bqaBvi08yhezkPOSypV6PRMCHEJIqTEtoQShRViTxoRXFa+6XX62FjYwOnT5+OXOPDwk9dfUEdF3H3tquiOMnWUdWe9ytRCQsTtsmMRgdFQYki301w7HPMunDQiuJwOMTFixdn4iAJmxfCHBB0vLngGru01cL+5lIU40Zm+T2KHgcKXVymNe4PI/TUTmIwLezFk+nOp/FyRX2H/TeSlrC9ZkCQvNqTeq1WQ7lcNunTWb+awt8mpkp+4oSejkYjzM/PGwP++PHjWFtbC73HNmRo8EdNeJPa3v5uqpQajklwsbENBN0jxYVwcXHRKJOj0Qi9Xg/z8/Not9sBRZFkaDgcBrI1KmyiqJkX6/U65ufnUS6Xnd83HO6GktoqB5+tSqaGbrnqTsnHNLAXILaZGtpsQ1XywhapbreLYrFo1B/Nxsoyuogiw3/tELaw0FNVo1whh3yPa27gGKGxqucJ0jCjofSzP/uz5r6f/MmfxKlTp/C6173O/O61r32tqR8+d2trC4PBAJ/85CcB7Mwpd911FzKZTGDfp6rZwE6IWy6XQy6XCziTNGyzUCgE9rAkk0lzPEo2m0WhUMA/+2f/DC984QvNeZPZbBbr6+v4B//gH5h3vfOd7wwQd2BXAbE95MPh0BwVou1nK7uu0FPOcbYTi2Hh/DYq1zTESqXSmFHG/pbL5cz36/iz1xqSGBtKTFTRYNuHGeQuouhyBMZVFG2VXutbvzeMKB6Goliv153KEh0rJNuqGNsIU0O4hvH7Z6Uoso5tTEMUh8Mhtra28LCHPWzieuUiinFIb5z2U0de3H39mv+g2+0GQkI3NzfH3ukqK8fsJKhaaEcQxVEUDzqqjEcuxXGQTArlDCOKYd/AbQ7A+Lrsso14dE0ulxtTjsPG0HA4xMbGRuD3Nhi1EBeeKHpMDSVS0yoWhxF66ornngYuL+vKyspUquI0RFETZHCPkA0m3HAtJI1Gw+xfUuNIjRhtJ04+cYki7+MER8N/MBiEHi5sh54qCbAXZiWKUW1v9x16713lp7Fn38Nr6/V6QJE7deoUVldXUavVUCgUxg5+VnW22+3iypUrY6oYME4U9ewzPa9MSZJdNhIuW1Gkga1hfKwH1zhkuTTMLw5s4zGZTBoPMBc6Po8ETcvoUhSpUq2trWFjYyOgLtnv5O+YYp39JWrPkyqKNkHQumHqfrvP9Pq9gHHDMcr/2M8+8pGP4G/+5m8A7ISc/sN/+A+RTqfxohe9CE9+8pMBABcvXsQb3vAGbG5uGmWr1+vhS1/6klnAv+mbvgnAzhjV9lNHQL1ex9bWFu644w7zHXb9DgYDQ6Z1HOXzeRPOToK7traG1dVVNBoNdDodrK2t4Z577jHPete73jWm+vPbNSySUGOPRgqJlbYZiSLbj0SKbX7x4kVDeLTPM/S10+kgmUyOZRNlGRjaqso969Du8zzYXKHzDkmKjlnuzZ5EFOk8sjFpH6Ar4sImMPwdy8o+7jLEp1UUpyWKJISu7+C7mcDJNd/zWnte5lhh9t1WqzUTRVGVbhuu5FYujEYjXLlyBfPz82YOt+fcbreL1dVVAG4CERaFovPatIqiPiMM7PP8TjqfuDbZRDGsv3LMRkHHOBCuKIZlPAWmz0Q7LehAnuY9UdFhrrEQ1o69Xs+E8tv3ue5hREehUHDuhbQVRToz6AwNK18c50rgO2Nf6eHx95gUShOFSYvmLBRFGov7ud+e0AuFQmjIjQtR3+FKZqOhvJOIov1tzJpIMsMFUY0Y/ZnEVPdOhUGNQ+6R4oRVLpfNeXA27NBTJYqu9zEUNqrtwzbYKyFSQ8W1IDCEUA9xB3bq/fjx47h06RIWFhaQz+cDiiINCnqKmZXPNkDs8DtVFNVQcBku9oLKRdelpPK79D8bNM7tNpiEra0tVCoVXL16FWtra4aw0CudzWbNd7rUR7sNh8OdYw22t7cxNzeHUqlkMopGhZ4yxbr2pSiiyGyTtnOA441hyq5EA+12x0kglND0ej385m/+pvn7j//4jwfCBP/Df/gPpn++4Q1vwKc//WkzXjKZTCDs9GlPe5rJFsoy6h6+breL9fV1zM3NGQWahFJJDd/PeUNV5oWFBUPuOX+cOnUKpVIJuVwOx48fx7Fjx/DIRz4SwE7mVhq6VFN1PzB/R7gMX1vZ1fLov4fDnSyw7AMkCawLjrV+v2+Ioj2OR6ORyQJsE8Ww8G0SV4L/5r30tLOs/MYww0/n1b2EcfI+Jbaj0WhsuwCv0RBeEvj9KorTljlszeB+NB7jovOejbBIDzrTSBTjlmcSUbTDkIm4exQrlQrS6XRgjrDnuu3tbVNmV/QMHSuuCAleF4fo23P5pD3cvV7PzKM8G3plZcWMLe4FDns+YY8dF2wCaG9F0Drbqx24tra2r9BUOpfiOCF4TVQfU4fBJMKvzjxV5dXRpmCoKqNG7LLZRHFtbQ0rKyuBCBl73iba7XZ8ezbWVR63FJi5z4YarmGhXfvFrIjirBVFYEdVjJP1C5hOUVTizVBKG0oU7YWXIWDc2M7JQ/e5aNy/vZhH1RXDwVgf+pyozdc0BGj06b7JsIVwUtvrczRMkH2x0+mYcx5VXeKiDexM4NVq1al8FYtFnD17NkDI1aAZDocoFotoNpumnLbBo3tN7I386miIUtbpOFCjmuBzbVIZVp+sq7jOHSZsWV5extLSEorFIubm5pDJZMwxFbpnjWoIv5ttogvnaDTC6uoqstmsURruuOMOc9adXY9KcEnqJhlDSlBc5HU02s126VrE261WIAGHhvGQKH7kIx8xGUsf//jH4/nPf34gOdKjH/1ovPSlLwWw09b//J//c3z84x9Hq9VCJpPBpz71KfO+b/zGb0ShUDD1poRxOByarKMkB/wODQNmHc3NzRmFgH1G26PZbBojN5lMmnliYWEBnU4HT3/600253vOe95j61DBYqmwaAuky9AaD3Yx/JDeq/LNe2+02Njc3sbS0ZFRVdTywnzHcjdew/7daLTSbTacDiv/XuYLvdjlndE7iuOS8ZStu9jqnxCws0maScqH3sc/ZBJBztqqIrvDHadfPvSqKYWoJ+6POuWFE0a5XtjvXpmlCQicRxf2Gnrbb7bHjHWyCYIeaMiqFoMOIar6WT/vsJKJorx2TMp9yHI9GI1y7dg0LCwtmDz6jZsLsEUWcrQy6P5HX2v0zjLQrwub7ZrNp1vk42dddIPmK4yDgfBzVx+LsUwbGj7+6cOFCgIhGKYqMkOr1eoG+o+vH6uqqcXKnUilTz5z7bEfbpDwTCk8UPcYQtvesVqs5idI04aKTFs1pQ2dciFIe4iBs34Z9PhYwOSTB9feoPSzTKopqGHLBcJEyVwIS2/hzgd5GTSev3s+we7nwa7gksKtMuDApjJkLmH4z06oDCGQ/1NBB/pvvqFQqKBQKzrLr/iXukVLCpYs/Q63sRU8VFPYlkjVmQI3at0SjkN+pdcIFQENJw8YUy6BkJwr0SJZKJWQyGePJLJVKKJVKxqjK5XLmm3T/qRrdGkpWrVZRLpfNmY7D4c4epuXlZWxvb6NarQa8o+oA4IJrhzMSg8EA1Wo10MdcKu9wOESlUsHCwoJzEe8Pdvclclxo+NRoNMKb3/xmc/1P/dRPBZRJGqIvfelLcfbsWQDAxsYGfuzHfgw/+ZM/iU6nYxLgnDp1Co997GMDbcj3zM3NYWtrC4uLi1hbWxtTMFRF13bVUDJey//X63VzxIgSMEYIPO1pTzPvePe7322e0Wq10Ov1cOnSJVQqlcB44nzj6kPsAzRU2I70bDcaDWxsbBhlk23N59PBxW/QY1k4jzGpiJ5JqESR/UkJqJ1IotFooF6vjymKVCiZxTNu4jYNPeUz7TZ2Qd/BdrXnVrazHW5qzyHTbt2Imoei7nG9g89Sp1xYuKLrvZyrJkWf2IhDFMOuiUsU2Y5hRJFOKG1re58iFUWtPzt8cFpFGJic0IZjnmXkOGK5bYU4bK3Q+TAMevQM4FYUoxLZEGEJbba2tnD8+HHccccdSKVSOHfuXGzlmWBEVRw1TRX8sD6mmWftMWC/V6NlGDnC++woAt7DcQ/AREPZWF1dRSaTQblcNvMXRQNX5I+GeceBJ4oeYwg7r28wGDj3YewlxTQA5wCfJm76oBAWHutahMOuVaJif5NtBNA44juiNv/bZWAoHbDrWVRSxHa0EzTEDT1l+CYnf+5HU8VlEnTxjxvy4YLueyQJSiaTxqNHNUkXAHvPRCqVMme/TSo7Q2tVJUyn00YhiiKKfLaSPhq4rHNzXyqBkXQRLho0TuywOd2DGxZ2CgTDV+MQxfX1dSwtLY0pL3wOSYp6/DURhxI6lv3ChQuYn5/HwsLCWNhRIpHAbbfdhmq1ikajEXgfw4M4t7CNXfuCSGLUkLEXxcFgYIwYVdpzhRSyuSRSyZRpL76TddDtdvH+978f9913HwDg7rvvxgtf+MKAkclzBVOpFH71V38Vz3rWs8z7f+u3fgvPf/7zzQL/lKc8xYxHVb0Gg4FJ4NPr9bC+vh6Yi/lNShRpvLqIIv9eq9WwsLAQUPnS6TQajQYWFhZw55134tixYwCA973vfVhdXcX6+jouXbqEVCqF48ePG2NIn69JGdgHObf0+32jZHDeIklsNps4ceKECfPk9yhRYLZUGjpUqdThValUAomhdMypE4XzsO4RBnaMrmq1GmhHJdAcr5MyTPId+nzO43GImO0oDFPrbKLoUkjVgRAX0+4JC3MuqpoCwBw6HmePopaD/+dxKJNgky0bOhbC/jYJtgNGywnAOKEUPM+UqNfrZl5nG7PPsBxxiL79HZOOyGAEQqPRwPLysqn7VCqFZrM5pt651gr2NZv42bCT1LgUb5tMuhC2x3MwGJiM0ktLS7jtttuwubk5dsRVFNQZNQlRdhyh80LUeK9UKma+ZkQH+wedhK665Vo5NzeHZrM5ds3m5iZGoxFKpZJ5PgUHl6LIfjbNdpTrb5V7HClwEnNNnqPR7llqimm8corLly/vr7Ah79gP2YyabFyK136JIgdxWPIFu0y2IcHkK8DuYd2uZ9iKou31unbtmnOS4l4+JYr0hNnKqAs0/vguln8vZNEmilyUuCD3ej2Uy2XjxVXFVMMauWdjkoFQKpUCBIbPYH2EGYKchElQ+M1UNDR0LJFO4fg/eQJaT10CUrtHH1D1sRUnTdIDRDtWlChOMli5V6VcLjuT2bAs3D8FIBB6qkSRfZr7x3iEgssISiaTOHPmjMk4y9/R66x1SJVJ200dBKosa70kk0lzhEoikTCLaL6YxocvfSd+68/vxvKxeXMv24te12aziS9+8YvmeT/6oz8a+Ga2TbvdRrfbxcrKCt7xjnfg3/7bf2vq6tq1a+b+r/3arzXGmXp02Z94TikJGo0gGgxsS+0f+m+dkxKJBDqdDubn5w15S6V2ktyQQAIwSW1arRY+8YlPYGVlBYuLizhx4gQymQyOHz+Ofr9vxkMul0OxWDR9R50ZNHhUUWR5Wq2WIYk0PjUskI4Vzi8kiuyPnMc4vkjM1IC1Q+c4B9hjgMRc51Q6ldhHXO+2QSWA4xbYmXu4l2qSMaaOLHst1X1+GqLL8qbT6cC6xL9funRpamUqLsLWPDu8U8Ohbdht5Oq3cYliXEXRhbgkOWq+1bB2e7+h7kfc3t42/ZTPY6SQnpEbRcRcdRnnnNm1tTUsLi4G1sNsNmvm6ElEsd/vm/2VYeVzhVrb38O53d4PboPk98KFC+b+ra0tLC0tBa5Lp9M4c+YM0ul0rDMeuTa5osNcCFPkFKp+Rq2zJHScp7PZrCGKdIZpXdnPymQyaDQagWva7TYajQZOnjwZiKJQRZFtznYhQYwT7WDqIdZVHrcM2AldEyvDd2y5OkwFc92vHb/T6cxsbyOxlz0XRLVaRbVaDR3oYSTO9Xv13tgTjKpErhAie3LVidv+m2Y8dRFZTnT2YmkrUQwzszdM9/t9NJtNcy2Jok5Crjrht9uhpxqGOS3sCY5kgoln+v1+gCi69qyxbHFDfNrttqkTGpT0aoYRRRp7aryR0JRKJfOMSaGndvk0ZEaVuTAokY9SFLl35eTJk4HQRn2OesHT6TSazab5JiVNGqrDuUIVb50ntOyZTMZEGNheZ9aHK/SUWTFZZhpNtiKqHv9MJoNms2myGNNxoO8aDAao1+vo9XooFAp44IEHzPOe8pSnAAiORTrRWB+5XA4vf/nL8Uu/9Et4+MMfHqjvxz/+8ea9bJPhcCdlPJNmbW9vY3l5GcvLyygWi7h48aK5X0MTXe3En9Vo5TeRgPFMzl6vh263i6c+9anmOX/5l39p+qzunc7n81hdXTVGI9eBSqUSiA5hX9CQUbY3vfn2OGR52cc1MkKVM5K1drttDq5WpZX1qmuUTVoJzg3ajpxb6JjQ+T1MudGIBj02iv0nTsi3zo9sQ52j1Lmnih6VIZuchIWHKiYpcdOC5I/jNMoYd81/OkeNRqPAeZZRiEMUZ4Gwtb7RaJizUG1HLp2YXA8YDaJEkXMkHVxR7RZmT0Rdz7FIZxG/g1mFgd0EgNeuXXMqTUxiFrV222oix62tKMYZE8lkElevXjXzKqMRaOvYWFpainUkGus4k8nEsj016iqKKLKfa79WJwHfPRqNUKvVzFFjGrJqR1txf6K+R/Mj8Jr5+XkztzLMn05ODfm3nXHT2N6eKHoEoB3exmg0csY1x1UU7UFkhwnOAnZ40TTodrsmFCMuwhYP/m2SokjlQp9BI07LpSFNduipTp72QmN7G8MUR5LESqXi/BvLyzAZGpxhDgUNpbENIZaD18YFJ1M+v16vm8WJi7F6oamwahtEHTMS9r7t7e2xcBX2+SiiqCGbdiil7TRRkq/7Gu3ykGzZ4SQuaF+IIorcs6lHd7gURX47FyKGtIYRRS6wfK7uu1QMhztJgphEi6GHuVzOGCZ8vjpZAJg9kK1Wy5TDVhR5HY0YLrgM69RD2GnEbGxsmHCe0WiE+++/37Ttox/9aNNmdvswTJuL9t133403velN+P7v/37k83l87/d+L5aXl03SIJ1r+O719XWcOnXKqHMLCwtYWlrClStXzDxLEqiKmobn8vt5liKdeRrSyYQWuVwO99xzj6mDe++91zyT38jnLS8v49KlS4Fvr9frgXrgGGXfs+uJcxjLyT7EMjKUS51CfIaSUI4f9kUlityLxTm42WwaZwDHFb9d99GpV95e0yYdkeHazzhJUbSVKiUaOt+wLezxxXB4u6xxiKKWdxqEzTkMn2OZdQ+vay5zkREtj63IhWE/iiIRx2gOe486oey24D5F5g6wiaJmeY1aUwmdx3VdD1Mie72eUZzssnEtYT/r9XomuY29VrCc6nyyYSeyCZvrJxH3wWCAy5cvY35+HouLi2i324Zc8XkktkQ6nTYqWhRYx7YSH4Y4oae2Pad7bO21ajQaoV6vo1wum76t66hdVrVl2X/UNlDSrXOXOqltWyHONpSxb5zqao+bHmqM26DX20UU4yhE6pXRiXKWmGaDro3BYGA81XExyVgPI4pqrNj7HmyDJIooargTMJ4MR71d6XQaly5dQqvVwuXLl3Hp0iVsbGyYUC5VwfhsfoNmTwUQqSgqYVA1gWVQo86uH5sIuOqSEyLLrHH4VCZIdmyiyLP59J4wcFImCaXXl4YrFyu7fW2imEwmUa1WUSwWTftxQRkNhqh84CvI3lfHsL9L6rQPa1+x689+t36PvYC5Frp+v2/UK4JhMPZz2K+4V4ULnBqrtgdW1SCqpLaBSIO93W6b8UTio4ucKtTaRoPBTtp37uOwjXM71IkhV+1WH//yuz6EX/7Ja+h2dve3UTFdXFw0izMVxTvuuMM5P/BbqRrrPrn5+Xn863/9r/Enf/IneM1rXmOUPXuR7/V6qFQqKBaL5hn8e6lUwsmTJ805jOz/GgZs1xedKfl83owFliuZTKJYLGJpaclksWVSm7W1Nfzt3/5tYF7TfTL5fN6QQ5bb7nf2v3ktn0UyrxEPW1tbgT2aSiJtz7yqVbaiSLKpRh5Dj3VMULVSUqXE1KXYRoX+qYHNcL5J6gkJs2uPoiubpRp+nENsAsCyT1qT9xN9A4yTq36/Hzivj2uPyynHuqXay/vt8mikQVQ5osjHJEIQZ19g2Huo2IWtz0x+Rkchw7TV/lFFcVIooBKR8+fPm99nMhlcu3YNa2trWF9fx+bmJra2tnD58mWTEd3uJywr24nOjjASr3spXaCzTu0Gu74m2VeDwQAXL140YfdcF7a3twOJq5gETbG8vIzNzU3nc+kw6na7Zh6LEwnHOnKtW2Hl120utq02Go1QrVZx7do1XLlyBZubm3jwwQcDjizCTr6lfYNzvNq7nLs4rnRPqYaZcm6Nuz8R8ETRwwInvTBF0eWFnGaPIjsnfw4jinE8fK5jPDhwoghHGGjAzUpRBMKJYpgBT29XGFFUlYjGui6uXIwI9fCeOnUKxWIR5XIZx48fx8mTJ1EqlcwGaV3A+F4SLyosnHzUELZBg4nfpm2hE5mrfuIofapYst/pfVycXd7AdruNQqEQULbDwHqj2qXGKesgSlEEdkMA6/U65ufnx4giRiN0L1eR3OxiONg1YFnesPBRV+ipbRDZdakLBcu3urqKEydOBNogLKRR+wN/z4XXJoqtVmsss51mFVXwu3K5nAkjdRnASjqJTqeDUqlkzuNzKYokCfqcbreLfm+Av3rfNXz2Y00MB8E6ZhlSqRQuXbpkxtSjHvUo2NA5YDAYmDPL+DcmLWASoMFggMXFRXOuJLDTL5vNJu68804Mh8OAt5/PyeVyJoyIY0qNBN1byWsajUbgSBAlkfw3idkzn/lM802vetWr8Dd/8zeB7+Q95XIZg8HAqKL0zhM2qbcNHPYb7rljn02ndxLscL8xr7P7UavVMllcWS7tJySKqjR2OjvnZNpjxM5+qGPPRQxcpINzrF6v+5KjDDKb7Oo+Jc5jWof22kElRdfruKGne1EUbSJtP0/3fnOtcK3z7IfVahXb29tjRq/2iTjO5LC1SI9yCsM09ouL+MzPz4c+i/2g3W6jWCya+Y3fx3oIy/JpQ+cEOniBnX68tbWFYrGIfD5vxtalS5ewsrICYLef6lxFxzTXBI1YULBtokJjuZ+b+5hd9eXKcaHfduHCBRw/ftys0cnk7vE+Oo76/f7YNhmG0rrKV6/XTXIw1kWczKdxFEVeF1jXMS6gJJNJk2X27NmzOHbsGO644w4cP34ct912m1Fs2Tfs0FM+k3aH7SzkO7gGa0SVHXrKa+PCE0UPg+FwiHq9bhJpuP4OuFNyTxt6SmPINTnGCRUB3Md4qAHCZ8RNn0yDImrxtBfI/RJFO4ssF42w0FN9l20Qc3LQe3Wh5cKdy+WM0ar7FGyiyOcw3MRl9LmIFtuZzgA7XEX/HUZKoqDKIr9N97Mw3EdDz/RnNVCj3sXQxIWFBWPUa+guv99uX1VxaZTTuB0jigJXXbrCWnT/VFTd6b+13a5evYr19XXUajWzB88uh73HT4kir+HiSEWERHE02j3YXvs2FS97fwQN3vn5eVSrVWNMuOYVe6y1220sLCyYsUBjSCMX7D62vr6Oer2Obm987uF3sa+nUil85StfMX+3iWIyuXsuGOeeRCKB1dVVU/fZbBaLi4sm2cVoNMLi4qKZa3iu2vz8vLmffZv1x6OJbCeL7U1mf7PJhM5b+o0s32AwwD333IPjx48DAL7yla/gRS96EX74h3/YhKPTMBmNRjh27BjW1tZMO2ud8WfuA7L7qJICLUcmkwmc75ZI7CTNUvLBMatOG3WGUUnjN+vcpSHTWh6dm2xSZhudrsQhLqLBvjspzIsGX5SiqMZg2HxgO1QmzW1APEXRThCiDhSXSmiTerZ3mH3AvfGAm7hGOZOJKGff+fPnJ67Rce0X11rearUCmXddz5qbm0O9Xjd9W9cj7WtxnOO2UsS1m+1YKBRQLBZNmKbuJaYDmn1N52sSxTAHu2bdddUVxw2dcIDbNopynFy+fBknT54MJOfr9Xqo1+tjSWz6/b4zzHRhYcG5fYbEkuvm3NxcaBKgq1evmp85B7jaXklpWFSBPX9wPuP4sTPj6vi2VczRaDcJDyMtXOViO9AmUgch64LRHHHhiaKHQbfbxfb2dihRVC+YYtpJDoAZJK6BGpco6t45+x36DE0EEQVOmFFE0S6bTZ5sRA1kAGMKpq3A8Rmc0HXhtsM4XOTbPpvHfp+m6efeN71WwzyVdEQtrnboqV1neq+LKLo8glrPahTzWtYPEPTE62TKcFm9NqqfsV50cSQxigpn4vdxcuZErR5Z1yLq6ku2ophKpQLeZ5eSo/fSm0gwu+n29jZqtZq53/bO6nPVq6okmSRJy8m6ZTY9Oyya12i7c8zm83k0m82A+kbjRZ+t9cV9qapA6H4P7m3RuSudTiObzaJeC+6tI9QwSKVSePDBB83fHvWoRwXGFxX8ubk5E9Zcr9fNcRm8RsvG+uI5gltbW8hmsybJju7N5HzJMqmhwD7FuuC3sc/ZIcRqZNpEMZFIoFQq4c///M/xdV/3deaeX/u1X8Pdd9+Nt7/97cZop0E/NzdnlHbbMUSCZPcZjh0+Q8OY6Ug4ffo0gB3DsdfroVgsmnHearWMIcnyawSGhjyzv7E/ueYiltkm0cBOyHCr1cK1a9cCZNY2UFXBVTDqIEpRtEPI1RGjIe7sQ/ZcFLZHMU445SRFcTQamYRPWl6OC5eKqUousDvmwxw+SlC49qoDMkrt29zcHJu37PIzxDsKk7bOqOGubcwxqO3rehYJoo5l3W9ml1n/b8NeW9WZq/eMRiNsbm6avbosmxI12gKNRsMQdmbltsFxEabwMvmYkled49Vh67KXdO0m5ubmTPu6nKouO7FcLptzeRW9Xs8kxWE5XCpuv98PnBWue4JtO46h8sCuHaXX6RhR5w7HiIsoauSHDa4zDBMOI3qsX81nYK+3k5wnNjxR9DBgKENY6KmGFO4FGmZJb7jrWXH3DNB7bINEZjAYGM9THOLZ7XZRrVYjPS2uBTJswNFgi3qenXxHJ3xbsRsMBmaPEoCxuHZV8gh7obU3SNPw1AlRr9UJjt/NpCxhoTLaT2yFAwimk7YnYC2HS3XiPVwoeI2+g78nYWAbVKtV42FVj1wYdCwUCgW0221TxzYBV9jGnipkUX3bNhrt77p69erYAmdfa6sldtjJxsYGTp48Gdg3MxqNIp0pLuNaCYmCRIEGjPY1GgkkBDZRvHjxovG+U43k39iGWo5Wq2UIixrlOiar1Srm5+cDCj7J/9Lyrpe60WyY++ggAHb6+rlz58x1Z8+exUMPPWTCq1KplClHt9tFuVw25JRhXnQcKPEdDAYmTKzRaBjvf7PZRLFYHFOCOZdxvNIzrmNiOBwao9QVHaGkRfsGQzL7/T4e97jH4V3vehde/epXm+MvLl++jBe+8IX4/d//feOZz+VygQPFSWDYxkrstY/q91CJZPl1LpqbmzNZGpkpGNgJIWPyLvZ1HYvs09pHVfVk2Wj0D4dDE1Whv2eZbrvtNuRyOZw/f948xzXv2esA3z9p/md98vv03cCOsddoNAJEUec+XesUcdbpOGqn/QwSa/ud7Et08GhCGw1p5LX2OqfztK20hX1Hp9Mxoe9h5VeFKwxhZJRRFOpM0Lbk0VQ657qexURhJFrs41yLGZqq/SVsXeI6yH6rWTO1bNvb2yiXy2OORF1H2V6ao2BSfoaw/s9ENpynWH+cF1qtlnGkueBStzOZDNbW1rCysjLmnGFklO0oSCaTxuGo4NzEttKQWwXHotoSYURR+7Wqk6xzHSMkqel02kSN8BqXE73RaDjLxjmK60hY36f6aCfD4s+uSKgoeKLoYcB06WGKYhSJjAOV0jX8AtjxDqo6MYnYccDY+0vsvRztdttp0LrA+PGod9sL10EQRZJoxqKrMajhIXaWMfXoq6GpC5mtKJIoaOIXfZ4u5vwbJzomELFhE1bbaNEJzA6vGAx2UnR/6UtfCiwO9v48esM56dnlYBIELmzJ5M4RCaVSKbCYT1IU6WFlAhQAhqxN6issFxUNGnWhRsBwEDD++S7WPRdFDa+MUhRtksGMdvRo6n48GsouaD9mZjz2WTWK7LBkkgHF3NwcSqVSIIyVizhJQKPRMOqPEkUNa2WG3vn5eRMSZRsTGv7LtmOftQ26jY0NY5CzblnvJIrJZBJnz57FqVOnsLm5iUqlgnQ6bdRTXs/9NSSlOqZJZLjoA7tqLvuY64xPtiHbq16vBwwFjl0mwRkMBmNzCx03SjTo1WZ9sZzf8z3fg7/8y7/E8573PHP/b/3Wb6HT6RiiSBJA5VK/V4mirSjyXTSeWQ6uLbyvWCyiUCgEHIpUFG3VifMJFVnOm8lkEo1GwyiKLJsqnoVCwXwH60aVvIWFBdx2223Y2trC9va2U8XS8FFgXKkMA8eezi38PbBDMur1emBOsFWsVCoVUEHCyKMNl/Fr/93VD1OpFKrVqjNkl44gji2GNetY4zNcxynY5YpSFLkORKlv3PcchbB3MNx7Y2MD6+vrqFQqgfqoVqsoFArOKA5FJpMx+8A1URP3oFWrVWxsbATGUFjbkWQOBoPAthGqk2yzSqWCxcXFUAVdyQIdAtr37Xpme4TZf3qckd5HO6FerweiAWy49iJzfV9aWnKOOUYO2VhaWgqMB9abZqMG3O3OI7c08oP1E5coEho5xb2p3IeoUJuSbdtoNJxjj2ObDoy40W+2Gs5nxYUnih4GmtUwTFEMmyjY2emFc0HDLzQMgcafLpaTFlj+XQe6kgk+o9PpGMNhEpgd0U73rtDFXydXF9RLakNDb2zVhd6gbrcbUAA5YbF97CQdrFPbU2Z7um0jQxcMfpsdAqNkW4miCy6iqO2pXkmWudVq4eLFi9je3kahUMDx48cD/UjVEDoINIRDww2BHQOLCVW4MNfrdRSLxUB44iSiqAlEuBgw3Msm1jZItLUPcPFwht8Md8MHCTWs+T4aYnbfsxVFLUer1cKFCxewsrJiMkuynRgyE/YtdighFT9b8VaVivfYRJEG+MWLFwNGMUmK9g31ump79Xo9kzK9XC4b5Uj742g0wvb2ttmzwsRXvGZubg7t1q7xUSyW0Gq1jFLOb61Wqya74O233450Oo1MJoPbbrsNzWbTHM1BQ2ZxcdEQDP5fHT78Ha/nHhIa2FTg7f7MZ6gqoAYG265YLAbCrFzZczUsk3M6/6b96vTp03jXu96FF7zgBQB2nHmf+tSnzBxC5wKJmIZDaWiYrbDzPWo0UYFTY5WkmvXBcWQ7RGjYpVIpk7yH40TXMjofhsNhgJCqCqshn+pUS6d3DvZeWFjA1atXjaJMcE5gyHvc6Bv2e/YHvpt9jsawtqNN8LiPSxE38qff70eu12FEsVarBZ6vc5oazblcbkxRZFuRXHBeV6KoxnxUBEZUptBer2ecUlFGcdg2CrZlu93G8vIykskkNjY2cO7cOayurpo21zkwSuFJpVJm3k0mk8aBSPVIt4GEzcXqgNBQ/F5v5yzYXq+Hzc1Ns6fPpfzyeweDAWq12lhIv11uVfv4bhcRs/fRciyzj9mObYUd6QTs9KOzZ88GlFN+A5VDF4HkfOYibmqD2k50YDc5EfsDv9dl56kDhCRfn2crihrloFBnBvs7j1Ox38fr2TfDiCLfwW+0oxB86KnHnsEJPGyi6vf7ZqKzwQ7earWwurrqfL4azBrzbi+Wcb2hUfvvaEQx29ik5zFrYqFQGNvAr9CyXbx4MdIz4zL4bNjGNI0g7oWxiaKGU3a73QBRZP3a+2iU5NkTnh26pgSW30tvOZ/B8mhYg/1NGmvvIoqqKG5sbGB7exsnTpzAwx72MGQymTGyq2oK938xBT73ueiCx7C4XC5nDAUSPxrsYQaCvlPT5/P8Ozs5iAtsIy6gOnGrSmyDiwmfQTCLK/tbq9VyEkVXmWi40gNer9cNySahI2GM8s7zG7g3lnWnyg6NQrYFCcWFCxdMMpZ2ux0IoWZfplHMiAZbkdYwoG63azJZso+yz3PcMYEEvbkAAgQquJc3bYgJDaPRaISvfOUr5rpHPvKR5psSiQROnTqFdDqNzc1NNJtNJBIJnDhxIhBeqkmWaEBzfFJt5vgYDAbmCAc7LNAOGbOT/ajaT8cbSRZB5Y7jmf+maq5Ehw7BRCKBf/SP/pF5xvvf/37zzM3NTXS7XdTr9YATRd9nK4r6PXoGpEvpUIcEsHOwOUmdEkYNWdUQOI5Tfp86JtWZxXpVBdomuUS5XMby8jIqlQquXLkSIKFzc3NYX183/c1O2uQCv0WdI+l02oTOqeOC0MgRYHfLiI79KLKh7dJoNELXO/ZJu91ocOrcyX7MMitRtHMJsG3YVi4ngx2+t7m5OaYeTSKK/X7fqG5Ra3AYGeVY4Bqdy+Vw5swZ3H777caZGcepDYw7cdnmVMtpC01SFIFdG0EdH/1+36x5PKePzlTXs/geklUgeLzMxsYGer0erly5EsjbwLrSYyhsZzjnVpaxXq+jWq2OOant8ricipqxmeDcGKU2Ly0tYXt7e+z3ttPFJoucl3Qu0GsVnKeB3fGm12i/4jFFLmgILb+V+RQUdOJpDoZJe4wzmQw2NzcDzls7OiEOPFH0GIMdXklEKYrqObfjwwldePmzphbnYIkbeqoDGgieociBS2M/ihAAO4fmZrNZFIvF0PLbZev1epHeyihFkQPUJg1chJmUxiaK+lw1coBdA8I+f0vDWGkEEarSKenl/UoU1RjlpOXyytn1ZC/EGkpWq9XQ7/dx+vTpQPZE25BXRWZra8vUCydrkkBVObhQ8u80wnSfyCSiSEVxMBigVCoZEhH23QSNf6p3dnit8WqmUzj+T78WqX94B0bJRGAx1TAghsSQVJHwRIWeEhxby8vL5igUklcqdDz/zDaOL126ZIxC9gUSRV7LtlSiWKvVMBqNcO3aNVy9ehXHjx83ql61WjXknuXjMRfNZtOEGnHh1XFEw5VZbRlSCOweaMzQuGKxiEqlgqWlpcB4Y1lTc0P88Refif/xF49BaT5r9vTRiBoOh/jiF79o6uL2228PtHsikTBZQqlust3UUOYcQSMxl8uZOaZWq5mQrW63i2KxaO63nQlsLxJMmyiqcV2r1Ywh6bpflTW2ZT6fN04YhlYDwHOf+1zzjA9+8IPI5XKo1WqoVCp4xCMeEThMnO+jUWLP+WxvVc41VJVtRANTSV6tVkOpVArMe2rY0VnAMct9rnSm0TGi/VkNeJ0zw9R51u+pU6cwPz+PCxcuGEKr+5jD5n0bfL/2KaqprC97ruHvWV8MYdRsk1RBbFSrVWxubgaceWGkhM+wDWmWR+dnNcBtRdGVMITOnkwmExhz/F6C/XR9fX1MIeI4CatnjiF7TmOkgNa569uVhPE5HDulUikQ0dJoNAL1ZNe99jP2Da5LTGQF7B5JNcmxzTWE/XY43Akhv3btGlZWVowibhNFth3HLOd+lpHt02g0TMZdmyjaxN8OG9XELqwL3a7gqp9Jx5Lp2mZHjrnqqlgsmuN7Njc3sbGxYZJSaQZVnUOVsE+yF1WhI+x+pjaQnqOsexnZbjZRdNmV/E5GS0U5nPnudDptwljVKWwLApPgiaIHgGA2x6hrwkiXhjPYiwihz1eiqAscED/01FaddELTCSSOQlmr1YzXUNUM13fyWfT87IUo6nfYoYaqeNhEkQa+y5PKOnURxV6vF8jiqWXkoqUkxU6WomRP93HxvWHfxm/Ra1Rh3NzcDJAo1pkd88/FgQSViXxoLAwGA2OU9Ho9o2qz3I1GwxBLEt1JCwLLrgRdU1vzGheoSGnGRa0Xe4Jn3evkH0YUqVjYpD/MuKXn/fTp01hbW0M2mw3sZ6HnXcMg+W3cl6PzA8vI79AU7do+VDDvuOMO5HI5nDhxAp1Ox6hddvsypGZ5edkYP1zUtJ2Gw6FRsTR0mgZyOp3GxsYGFhYWUKvVsLCwYP4G7Ko2fK6qOuwv/K6HHnrIvPdRj3oUms1moC93Oh3UajXk83mcPHnS9BnuQVS1hH2bhoESWr6fWQptokhi1Wq1sLS0ZPqzqgAcR/Q422FxHOc6byhhYzQFiSLvPX36NJ70pCcBAO6//35cuHAB6+vrOHnypFEu1PlCI5ZrAd+nihGJoR0Kpd/OMFIqOkwmw3WI362hp2rUs3+oEasEWY3/QqEwFk4aNrZJykqlEm6//XZUq1WjXtOJE5coahQP20ajEfg+DbFjNIESRRqPwM466DoiANhRZRuNhmlj9n8XVMUnNAxPx6+em6qRPnY0CbCzj5RHEDDkWvsvwfdQXQmLlgirZ43m0Lbs9XpYW1vD+fPnTZ25QhDz+fyY2m2/i/POlStXjILlKquq4Nr3G42GyX6skTFhbaLhuVzrOf/S0c1kTy5FUUk+y8AQcCVMjGbi2qEOeK6n6nxTRY3jg+Gf3I9KQupysrrqVqHfqUQxbJ9iIpHA0tKSOUJtZWUFS0tLWFpaCvQztSEZCh2HKKqzN+wb9Dv5bB0bGtGgEVy0xcL6O8tMO4nvVrAP6vM4ZhOJhCn7pO80z4t1lcdNDxpJUZMUF+goRbHf72N+fj5SlQPGFUUguBdpErGjIaDX6X4/LjCaPTEMqmwwK2ZY+e2yTVIU7XAEgt4kWxXSkCcNkQKC4VoaMkLwWpeCR6JoqwzALvFTo832aOuCz/LaIYKELiCu0FMtn52ZUb38OvmpIVgsFk0oh3oEuZg0m83APrHRaIRqtWqyO6q3bZJHTfcoADt70KhyRKmKaijz31o/9iKj5VGlhddr+nE7rFh/di0u3Muaz+cNeSN5pyHgUhS52K+trRnjl44GNfZZh1r+wWCAM2fOBA6jzufzhgDowk8jiqqUetd1Lx3rnIqaevS5+LGPU80tl8umz/Nsvrm5ubHwZg0F4wLbbDZx4cIFU/67777bhBd2u11cvHgRjUYD5XIZ2WzWHETP57J+NAySc4waUOl02owBDQe2xzfrjlkANaJC5z7Wv72HSR089pzDEE2qCGrUAsDzn/98c/173/teLC4uGg+5Egclemwz9icl8zoWtP/SCcbwSzruVG0i2dZrOXepw8W1PlBJYv3wncViMWBwRhmu2nepLtJrn81msbq6ashNlOMV2J3XuE9JnW+6/4llU7VejT0lisPhMHT/uBrwbKMoRdGuQ85pdjSPJjPRuVvnCrY7nasatWKHLeva2Ww2sby8vCei6LJper0eFhYWcObMGWxubuLy5ctjCqzu/9e+4Ao/ZD1Vq1UzZ9kGOPulKpX8tkKhENinFhY2rM4HPqfT6ZhM7el02kQ9ALtKn92+VHPVYcb6YvtynzMjTlRRpKOO36jkVp3/qsa2221TnzaRjhP+mM1mjbPEJophR6SwjZl1ViO1CLU/pyGKLIMq7jrfEkrA+X4dG+qA5Zims9XuA0r+OIcpUbTndLYl24Fl4RoIeEXRY0rQw8SJIEopCSOKXHTK5XJsokijivHo9LTHURTtgWmHJDAMaZKiSEVgNNo9jN72LhMsG0MGJhFFewDr31zfaIe22d9MY5DedvvvumjyOdxvoEcD6D06OWloliqirjqkd9+e9LXcXET1Gl3s7JATGvT2N2g52WdoBHCyYxjOQw89hFKpZAgFsJsNUb89zoJgfze/TVVlF5LJpAlDu3LlyphX3qgrgyEqH3wQ+NsNpJOpsT5MkDgwLIV9a5KiyDqgN1cNG7YzDW96tPmNVC7W19fRaDTM2NT+wkyerE8eQJ1MJp2p0AuFgiEUapToOK3X6xgMdhItaIihPebt6ASqTlTmtre3sbi4aNqR+wjpeU9gDv/pR+/Hm/79VbRbffPtXLBbrVYg4+mdd95pktVcvXoVx44dw8mTJ7GwsIDhcBjYG6ftzQWdv7NDs9iH1Mml/UC/l0SU71GiaB+FoceQcC5Ug9w2LNQ5RQPo6tWraLVagfDTj370o4FjPPi9JDHNZjNAMFinqubxd6pg02DinKCKCPss64t9i32JfZkOA9aPjgc6kWwHHDBucLqcOYR9RAD78mAwMP1bz7cLA8uczWaNIUxHFscFySfLxpAz2xFgKyskhHZoHBUfbaMox7A9d7NvMAJB3+cKLWR7sO8CO21WLpfN3GNHUrCv6NpiZ2JXRx37d1gUk73Wcmyk0zsJihYXF1GpVAJnRmrilSgyqmr4qVOnzBFGLnLGcjAjtyrtJDCqKLvaQ+dDnS9dYZucV+v1emD/NtuQfY91Q5UKgDmqRyMs9HsZkmqXJZVKmbHL9Z0OTjvZjZYzKuwUCO7j07mRUShR0HBL+0gNjWjjOjkNUVRbNSyiRx1oes6o3kvbhddpeCiw29+5BirJBMbtLj5bo2W4rnDdZtniwBNFDwC7RNH2IG5sbAQGTRRRtI1OhT2AOHFquBoHcRiJsu+3F2EdLPTWc38Yy+wayLoXhmQizEvFZ6nhPklRDPubHQYGBFUh3t/v983eMnqkSHwU6nHUEAcmO3CpCbZnjO9n/bKMLlVQwxpcZeC/XftaOXnpAkJcu3bNKJHdbhfnz58PlEfJiu6N4mTbarXMOXZcRGh82kRxknKtIWGqqPE77f7U7/exublpQpH0GAGFaYPRCN0LFSTWOkingueu8f9KJGjY2eG/vJ79RUOHqb4Mh0MTXsg2p1NIw9BoIG9sbCCZTGJhYQHdbhdra2smnE890jZR5LiwkxOwj+k9jEKg2su2Go12wl51wVPVVduSiyeVFzXaeQ2JIrBjBC0sLCCRSOJj91bwyb9ooN/fNWLZxv1+Hw8++CAA4MyZM8jlcshms1heXjbhtBpGpHt31aHEb7ZVRu5fpdEYRhT5faraqPLIcttJphgKqERRn2sbFrxWyQEdBE94whOwvLwMALj33nuNcco5iQd287w0ex3he1lmJaWck9SoIZlW5VsdA5wH6WS0VUJVIdn3ON9wjCjRSqfTRqkDgPX19UiiaCt2VF6KxSL6/b7JQhoVdaDrCNc+tivbjBEXHDs8ikVD56mQ23ut7GgPkh+G2XL+C1u/XE4+Psc+mF3JhP3ddpgbCcnJkydNaKCWmeRyOByiVquZpGU2UVRjmntmXWA98X4dA8CO8+q2224LJGhRBZBj1tWOWi7NceBSFJUosi/q/llVal3rkobnKnkNI1oaGcF1UOduEiP+Wx1EdHgoEVZw7AHBsx05d/L4FA3hVWKs36dOhjB7SZ0z2n5xRAVV0WyiqFElfK69rrvUPV6rpDLM3nMl07Mj53T8c13X+UcdP5wv1ca2nRksC/sG21JDwvncOPBE0QNAUFHUxaPdbgeUBtdg0EWLi7WGBAJupYmLIju5EsVJBrwOGNtzCuwSEZsonj9/PhAKUK/XA6EA/P6wMqhXPGwjsV4bZiTQELCfYYftMESH6cgZQsFsrgo1jnSfIid1Tha2UqXKoO11Yh3YEzINdRdR1G/SBdf+TiYs0UWOSVo4wbVaLTSbTaPwsh9mMhnUajWzwGlyFvatzc1Ns9AryeP3sPwuKFlnf9A9L2EG1sWLF5FKpXDy5EnMz89jYWEhVoirq45ZVhoIGtJDpUXLwO9vNptmv8yxY8eQy+WMc0FDQW2llJ7iVGonGUq1WsXZs2cNEdcwTQ0X1zA8Zj9Ur6XWKfvHYDAwe7tIFNlu3W7XJN7RCAe2M0mPEgcS2O3tbczPz6NSqRhiw7JRTeKcoEOTdcFv7Xa7WF9fNwbWnXfeCWBc2eDcyBB39if7zC4Nq+c3cf8kDQQlcErY+TMTQuhcqsq2GluZTCaQCIZOM1uJ4v815HZxcdGUfTAYmPnnGc94BoAddf7jH/944L3cuzkcDk1YrK3iqHGiDgASA9aPGsTqqFHYe7X1Z60z9lcaTSyjbVgxMoH9WhUfG2GhhQwBHQ53zrFj29hzn7YZ24rPVGN6Y2MDW1tbSKfTZj7nlgPWoe0M5VroIhw2UZyk4nC82evg6upqwHhWZwjR6XTMnM3+pqGKo9EIy8vL6PV6Y4k8qG6TaNApYK8zGgbf7/cDypLt1FTSbBNFYPe8RyCYWIX9JEpR1Hl4eXkZ7XbbmaGV/ZAKMfe5ajm5VkxSFFkufnuYLcK/q+NQkx9R7ePawf7D+nDZL6x7JYrJ5M45xayndrttnIAc34RtQypR1FB/hdpFOj+w3qLWV/Y3zolKjtSJEOXwt/8WlyimUilzDioVXDthGjCumLJv8++1Ws2QRXWsES5FcTAYOIki10ubbEbBE0UPAMHBpAsAF3NN0W0PmnPnzplOzQ7LrFOEHeqjCzXvUZIWRrDs+5Wg2h5N9TDzed1u12Rf7PV6uHDhAkqlUmCBokfNFT5rGyJRYa1R30ECaC/Wqijqt/I/epeZwMBVJ0DQ6633u4giVQreT8OVfcG+BwhmLnUt4Ppv1yLGfQv2s/VIA3r7FhYWApvrqXowNFLfQTLHc57YP0iW+C5VN1zQ/k4DmuGuNDxstYD9eGFhAcDOBnoSap2Qw/qE7bVWosg61zBbHpmh12tYEPeYsM/yQPvFxUXjILH7KFUOnsVFA5WKlYYc6TsBjBnr9jyxsbFh6pQZTnlwvIYu9ft9LC4umr1M/H4aWbaaQKWRnmwmOdJ+x/ajQWvXda+3e7ZbMpk0504Sd955pzGgbALE/qVGg3rYWRd2mv5ms4nFxUXj0LLrTecaEgp+L3/Pb2AdcHwsLCwYA5vv1v1+dviu9q2lpSUzXxSLRUOan/Oc55iy/8Vf/IUpAxWUbreLt7/97fi93/s9vO9978MnP/lJnDt3DrVazRgsLAuNJFVkqebqfmm2CcMA7ZBG3s/51BWKxf/To6/36DV8BkllGFwGparl/EaOPXs+ZzizrrmuyJJ6vW5ISz6fN44OrSvdh6/zDMeqS1FkW4cppgT7lD6D860mdaJiYoc+c/2kI1KVGxq7J06cwNramvk3/5ZM7mS5LRaLZp4NI4rsR6xPvkP3/fJ7+Dd7TVJnqR12CmBs/SPUkch3nTp1Cmtra07nNecw7vnWREua/dilTNnOI5dqrNdyLdds4Ow7un0D2E2+pmNDo4wUdAKqwtdoNLC5uYnt7W3z3UzoZa8zLqLIeTVK4dLxru0RltCGoH3IcabCAv+t0RouxCGKNnljmRkFwLlXj0jh9XayHjo+WU/VatXM2652CVMUgd3MwxrFwflikhprnhfrKo9bAupB1NApmygqhsOhmRAUNtGyCYQa7faCPYkk8r1KFO3wC32+Ym5uzpwb1ev10Gg0MD8/P7bHIowo2t8TFc8e9S1xFEV+B9sACMae6yKj7wR2FUX7fntC0bh5LtIkmGpo2t+he0ttw9kOmXAZVswMaato3PROA4llWVpawtWrVwMTHZOC2Asok7Zw31u328X8/HyANNGgCyP5LqLICVzDevUZ2gcHgwFOnDhhyuVanMbbLvhvJX7s61zw8/m8IVm79ydMW/d6PZN5lM+id75UKqFarRpjW8tVr9fRbDZx6tQpZDIZY6izTpaWlsx5iAT7GPuKGjFErVbDV77yFZRKJTM20+md7KSsM02RryGursgEW9Gg4yCRSJgxrbAdOjsOCgnB/nvjid86GAzw5S9/2fz99ttvN+WyFcVut2vCXG0vL5VSGvUkuFTO6ElWZcFGv983x5toPdvXcLww3b4q6JoYyPbuAzBjSccIlVIart/0Td9kxsSf/dmfmb7J8fdzP/dz+LEf+zH89E//NF75ylfiRS96EZ75zGeiXC7jcY97HH7lV37F7JfSPbKsVzsBFdeFVquFUqkU8IwDu2cM8jvsMcX7+XvODfoMgt/NZD6DwSDS+HQ5CPX97CMuz7+GR6vaouF1tnOKcxrVQyWKzGKsc7euE3yvOi7iRO1wXLmMSZ0DNfScYLvxuwCY9VSdHuogYSQJHbCVSgWLi4tjRA8YzyI6HA4DW15sMqjtZdsi/B4STk1kw3eGkWqd7wgmT9FQVi03bQZ1dgG7UQhhTgr9Js6hLoclsLsWNRoNk6hMlUA6gdPpdCAzLK9Rp5br29WxROf7yZMnTbIvYKe92Q+1v9lrviYRi+qTmvBHEZXQhvWmERe2zca+qkRRy+uqXxdRtJ0Z/FbaOXyuS1FUAqt9jmWo1WqBxDWcxzUyRPsSnUbJZHD/KzDuZImDmRDFVquF97///XjDG96AH/7hH8Z3fud34tnPfjae97zn4Xu+53vw6le/Gv/zf/5PfOlLX5rF6zxmDA211AQIJIr0FusByQQXddt7aW/4tydThS3Bh2VsU3CCpGFhE0UXCQMQMLgZVsuBa3v3Jk0+JIphXrCogcjwNtchs6oS8P/25OEyFhW6oVyfrf/nd/C72UZ6KDnfaRNCPt/VzjRIe72eOVjdLisVMZ0MbUKk2SPz+bzZ/0HjqVwuG3KgkyC99CxHLpfD6dOnA84DGqph7WOHH3FB4MJKI08VL+1DvJbX2OqHO7TI7RXUtmcd2UfD8Hou9vSwax3TIOC4oTLN8TY3N4ft7W2srKwYBUqVMKY459hgfdL44OJn779ot9vmGBQlVKlUyhgyQHB/YDKZNOcgamiUhrYSbJ+trS3j3AhLwqFzhPZJ9jeqp/l8PrBe3X777caAtR0jNFL5PJJADTPVEDEl5mwrJbZ2n2T4J69hvdpGiioI9vepQanOC41AoCKjysvc3JzZS1koFPD4xz8ewI4qdt999xkD5O1vfzve+c53IgwXL17E6173Ojz72c/G61//eqyvrxvDkGoU25+GHYlAo9FAoVAYO/Ynn88HksZwvCls5VgNXBfx4Fo3Go1CE5qxvux5X0Mc1YjXsc5+aG/nAILOvV6vZ/aJcc5hPdjqK50qShTVGQGM72PLZDJoNBrm3bZqSOg4VoV0ZWXFRHRwfrAdwVw/dW815yc9fJz7OmlraHSSqkBRiiIQVJb4vVwn1QnENtF20XBXe12OSkykY51IpVLI5/PmLEKFOh3ttV+VP5cj0Sa4aivYzmquzSRAnH/U4UloNI7t1FLngr6X3z0ajbC9vY3Tp09jOBzi2LFj6HZ3jvVi/2AEh25dsPsb6z4s3BWA2Qvt+n2UrcZ5ht9s23apVMoct6V14oo8IDhf8Lp+v4/19fVAQiQ+m5EOdOa4FEV9J+tHx4HuQafyrY4RWwBQJxKvc0VZxEX0JqsI1Ot1vPWtb8Xv//7v44Mf/GDsTZF33HEHXvjCF+J7vud78HVf93V7fb3HDEEDl8pLvV43ndilKGpHZoev1+vmcGtgdzJRdTLMK6eTyCSCRnByIcmrVqs4duyYGRz28RFcHBKJndDASqVi0vQz5IFl1+9zeR+BXUIUFR8fNRhZd3b4qE6iSuC5SLsWpqh3K8lQI4no9/solUoBL6LWnRq/9qTGUC5tL3ruwpRmIpPJGDWJz+WZUjyQXROXZDIZLC4umjDnUqlklEd1CHBCLBQKxsB+2MMeFnCAaPnDiKJ6b3kfjXUuaNwvRrLNMDC9X/spk3yEORDscDCtc13keR6kvagqsWT/Yihlt9sN7GktFArm2AtVBNTAoMrFd7LPK4Ek8aDqRGOHY77X28kQeubMGVy5csUYdzRWeFi5th0N32w2i0ajgWKxaMY525tjgu2jyieNaTXGBoOdLKpU5UhCiG6vi3Ihb8L9jh07ZohiIpHAmTNnnF5Yrns616gBT8NYDTXWC4kOnRna97QfkCDQYGE/0RA2lrPT6aBUKgWiNYDd/dgacqTOu2w2i+3t7YBDQ/sS58GnPvWp+NSnPgUAePe7342XvOQluHr1Kl7zmteY8v6zf/bPcPz4caytrWFjYwObm5v4q7/6K0O+fvM3fxO/8zu/g5e85CX4t//235rxwQRd7LMsC50dzMTIMufzeWOY8Rmj0SiQ1EQVSn7n5uamGbt8ls6pVACiwk/pVNAQRc5lVMzYL23HAh1hNMbZ1sxQqYoBHZ50SFE9ZB2xjdk3OSYTiYTJCLmwsDAWTsl3kUjr3HjlyhUcP37c9GUdZxw3zHhLBxGJFsH2ZBsMh0MznnW7A59bKpVM9nE6kLTP2/OlrXZyzt/a2sLCwoKpK5bZPouSibK4TYDfynNedf0kOXGt57Qr7LM6R6MRTp06hStXrphoBK1Dl11Bh146nXY6y10hs6qEKzRxE+duzp20d7gWaUgjCRUTFtG+s8H1ZXNz0yT5YtlXVlZw7tw5NJtNLC0tmTbleOG9bDfbyRtmKzKTtV0e1neY81xDXvkc3a+fyWTMXmBC9xSGgfbY5uamCY+nTaFqLAm11reuBfp93JfP8U8bhuXkvE1nC20Ol6JIe96O/FAH7qSMseZ5sa4SXLp0Ca961atw22234eUvfzne9773mcWO/9GDz7AB/du5c+fwi7/4i3jyk5+Mpz71qXjrW986bRE8Zgx60OiZV3LHyUO9bGpMkCxwYlLDVcM3wwgXMB5PHYcocjBy0V1bW8NwOMS1a9cAjJ/Px8kplUqZ/ZPVahVLS0tYX183hzkDu5NEVPgpB7rWhY3Lly8HPH6VSiUQKjVJUeS/qTpwIqDxYJMw+98aukRDyoYSHvWAqzdLQ9h00uVY10VKJyMNcbG9pFy4dc9XvV4P7ElRTzL75crKCiqVivE022EkXIxoKGm7uohiGJm3PfA01Bh6qoqHhmDZ6iy/g6G2ugDY4PPV8KdxoPXKLIB2nWp90fFDY9hOnECVuNvtmkVbw//4LVz4dP8bANx2222Bg797vZ4xRqn69Xo9XL58GadOnTLlViXEfia/h/2Vzg2S0I2NDSwvL48ZClw8qYpR6VdDn4t1KpXC+fPnsb6+Hmj7Xq9vEjvQUfbAAw8AAB72sIeZcxddRJHzENuA9cr5jkoJDSk6rLhPU8kCy6r/rlar5pgPGoWpVCpgAGp5SLTZ9/g+ZuElVF1Uo1CVBVWY5+bm8LSnPc3c/3//7/9FtVrFT/7kT5p58gUveAF+7Md+DP/qX/0rvP71r8cv/uIv4kMf+hDuvfdevPzlLw84Un7v934P/+E//AcAu+ORfZxGGLOHArtzBstcLBYDe/LoOVeiqCGYdDipI4N9iX9Pp9MmI2jYvM73aZioEgCNzLEVxX6/b/Z9cpzYiiLXVHUK8FvUYaT9hGRRlQM9W1HDKemgpFFqz0c81gcYd1zyd4nEzjEzlUolMLYIrT/2xUKhYELh1QDv9XpYWlpCq9Uy69zVq1fNPMc6VnA+rFarJqmZ7svn/M3raFewb9mKIstUq9WMs4jvtYm0wkX6OK9kMhksLCwEVCbtiy5nIb/D1fdcNpSGiSs6nQ4uX75s1m46FzjXMNM71wydw+j4oyPWVor5f27d4bqiju2TJ08a0q0qOOuM3677JDle+v2+M2yX65IrUsxW+PVnm0S5FEW+W9+liqIL1WoVly5dwnA4xB133GHmD323Kq9cc8Oc77lcDpVKJZB0bDAYmPlI7TSOJ36H3T/p2Oa6RaeOOkhrtdqYUBGG2ERxe3sbr3rVq/CoRz0Kv/zLv2wO+XzWs56Fn/iJn8Db3vY2nDt3Dq1WC41GAxcvXjTnt21ubuKjH/0ofvmXfxnf933fhzNnzmA0GuFjH/sYvvu7vxtf8zVfgz/5kz+JWxSPGYOEhUaa7fXipKoLFjs4Fx16tnVQ2UQxTAXTAaObpMPUKCBIFPnudrttFgrdkwPs7mfhQKFqwsPblRjQmLMT8mh5uUiHlXE4HGJ7exvVahXnzp3D1atXUa/XzfNoTLqIIhcTGoOaxIXfxv0MUVBllXvTXOCixQWD/1dPGCc4e3ENI4q1Ws0k+FDSpGXjRKbhWDTGWYf8P40j7jvkosP03uyjzIhJY1kPsWb9ApPPUNRQD20XVS7sRdpFLm2iqGQBAJBK4th3PR6tpy6hP9zxYOqGfXUKsI3sTIQaysif7f1cHNsEwx0bjUZgjKiT6FOf+hRe85rX4PLly2Mh6TRkOeap9pFwpNNpXL16FcvLy2YBLJVKgfBCO4ECy0njhmR/c3MTlUoF5XLZeKTt0FMu9CyT7qECdvc/Hjt2DGfPnt0hHZ0aXv+7Zfz5g89DMrVTnlqthk6ng89//vNm7nr4wx8e8AjruNM9VaxDOtd4rTpLdVzNz8+bfWc6b7r2+eo5mOwTHDs8QkD7gd5vh9ip45ZjQ41qkhBmMy2Xy2Zc3XXXXXjEIx4BAPjwhz+M//f//X/xxS9+EQBw9uxZ/Kt/9a+MuqR19YhHPAJvfvOb8Rd/8Rd46Utfagjr29/+dmxtbRnjWMOv0um0URBY/yTJwO6ZnKx3l1MtjCjqOsc659zD/UCqRNrQMFiOAa6LLCvnLdthQQOc5VD1YTgcBpQXzh+c+1RxUMcUoX1AQzF1raFDiu1vE0XOp+wv/Jnfxnkkn8+bpGG8lmXQMaqOSmbG1DmZ0Q6cC7ie22NaobYJlXJVH9XBlkzu7tPS39vrJxUrEmC+Rx0BNtjHw2wbJuXSxHKtVguFQsG5/qiDwC6fSzFTgq599dKlS2YscH7SEMh6vW7GINck3WpCx56dY4DXjkYj1Go1nDx5MrBec/5g/02lUkZRdBFF7Zcco+12eyyEE9hdg12Cg71P8eLFi6bt7LXPVvJ0HgRg9vdrWL+C925sbOC2224zSfmAXWcWofOLjnU6wmxFkQSeZRoOh8aZqPUEjGe213Yi2WcEAu0r2iJUlaMS+ChiE0USxE6ng6c85Sn4pV/6JVy+fBnvf//78bM/+7P4ju/4Dtx+++1jE1cikcDi4iK+4Ru+Af/8n/9z/M7v/A7Onz+PD3zgA3jFK16BhYUFfO5zn8Nzn/tc/Mqv/Erc4njMEBoyx86pHlclEcA4UdQEAXbHr1QqJkOay+um77R/Pw1RzOVyJqzFJj0ss3pnSbTS6bSZFJQoajihDU6GfL5LIbp27ZqZaO644w7cdttt5mBufrO9V4C/B3YnUIZF6ILABDT6TtdiRcOKpClsUlDCS6NElVJOcHb70kOm5eACvr29jWKxGDD89DoSClVF9Nw3TnRUw2wDkuqcHhS/trZmvJcaruVaxFUxd8EmfWH3sh0JeyGnscO+omGcvD6RTgGpBAZ/rwTqgqpOHJJeJUGDwQCVSgUbGxuBd/Makjj7W1TZ4H2693I4HOK5z30u/vN//s/4p//0n+KnfuqncPHixcAeB3UmacibJhhieE+lUjFhXvYeT02iwvtYX5ubmxgMdhIDsT7t95MI0CDgoqwGOscHy1gul/Gwhz0MiVQfpfJOna+traHf7+P48eP4whe+YO4lMaKBq32GXloaIronkX1D+66OIRoG9rjkM/keGmesO4Zick6oVCpGXVNjj/czzFETg9ghXryeZWZ7MvyP83yxWMQ3fdM3mTp9z3veA2BnPP/8z/+8eRbb1h5fp06dwite8Qr84A/+oPnWt7zlLQEnharC6ixi3emczrJxrKjHnteo4sx/hymKwG7EhkYL2FBjU50CbGeuCbbDgmoh99XbajLbhc45voMhpAwXZTldoXGsKxr6SrhYBhID1jXnI9aVqoEsw9WrVwMKEA1Q2+lAp4zOD9qvbOco64Tv5pjQ/ecuosj5VMuoyqG2Nb9VozZcTs96vY6VlRXjQNOoqqjQU9caw3564sQJc7wVv29packZ2qzkLcwRrGNKx4oSG66d7OOpVMrUC0mVEo98Po9sNmtIymg0Mk5L24nF/a2M6rHtOnV0nTlzxmzRoS0VRRTpyGFIsw17/iXszKeDwcAo1ADG+pzaI7aayLNgbQemPpvji2sdoao2n837uWYBu2ugPpftqAnEGInCe/h+Xh/2DUqqOZdpKDi3fIQ5OGzEJoqbm5t49rOfjQ9/+MP4q7/6K/yLf/EvTBz7XvDMZz4Tv/7rv47z58/jZ37mZ7CysuKUmz0ODpT4VS1UWVw7uS4EqoiQUKmaQHAR52Hx6vnSAWJ7QyiVRylmuvhR1WToIr2u+g02UdQy2kSYk64aowpbKXAlNuh2u39/qHeQENshPWFkhMb93NycSS+vRJFhclof9qBnucLCXGxyroadKh9KFPWdOtlpSIp68NUY0TLQCCQJYtsBu/spabDROFHSR698Mpk0E/v6+jrm5+cD7RblbNB2sKEknt+n9aSKBSdz9ay6Qqa4WKpHT/s5Q6hsRdEmisnkbhh1v983URv6Lg2PdaX+pnFAEkKSwcXpgQceMIdhj0YjvO9978MrX/lK/Mt/+S9x/vx58z0keUpGNdSM76KSwvtUdUskdo+KsdXRQqGAkydPGsNN79GETWwDeuTVQGf7qfOD5UwkdjLe1Wo1LC4uGofKV77yFXPdox71KKc3n4afGksaEaFEkX11MNg924rec9vYt9VW28DguBgOh4FsmNrOGk5FUqIhYvacrnMflRfO6XS+MZvq05/+dNh41atehcc+9rGmXEpIte5Yrle84hWmzt761rcaFYt9kHMjIwg47zDr7uXLl02fazQaSKV2z5nU6/kz61wjA2xFURNRKQkNA5+lYdS6Z5rKoq0o8u+cz1gGRmMxHJRzKf9WKpWM8Tcajcb6jpJrdbbYiTo4t9n9Ethdd9SRA+zMYwwzpdGdyWSM40Lr48KFC4YckDTSYUXnrIJ1z7mP6ikVMHVWE91uF+fPnzfnxLIMLsJAMq5txTVKwXcyjJnESucdG6w7+2/aB5PJneNqrl27ZhTF5eXlsfcDCNhQrr8D4/v60ul0wKnNuXA0GploACqKHMfaDweDgZmbaSeRsNj1xP5RKBQC+9Ht9gQQOS9GKYq9Xi80mkudRwqboI1GI0P2aKPZ9axhynYEhzpLbDSbTZNvwF7D9Tv5TawLBa+z7ZOFhQXzLa1WC9Vq1cynXF/V8a7268bGhpNc8z6G8c/NzZk1Y+ZE8a//+q/xx3/8x3jqU58a95ZYKJVKePWrX42HHnoIL37xi2f6bI9o9Pt903ls+Z1GlZIHXex18qDBaBNFACZhhU0U9V2caPkekqCwiZKww4ZojFK+tw0GndQ6nY6ZjOyFS71zrglLSQ9VLdcApaHAhUvviwpZocFBRVETBgC76cVtJc+lKOrCb09KqlSwTJqghG3gCj1tNpvGGOd9wM7+lmq1akKJlLDoBM++RALQbDYNUaRCw/ZSUsR+UiqVzL3JZBLr6+tmcWPdsH7D+hG/ybUg2IqiHQ7Ie9VTbmc8ZVsRGjo2HA7x0EMPoVFroPbR88h8qYF+t2eMWr6DzxoOdzN+cvEm8ez3+yiXy6hUKoEFiwuJS1EEdg+ZZpIJ1m2v18MnP/lJcx2/ezgc4g/+4A/w3Oc+Fz//8z8fIGoMMWLf0f7JZzMkmIsb/84QR3sP4CMe8Qhks1nMz8+bI210zHBscCHlmKFhynbl2LEzxfZ7wB+8qY3X/v8+hsxcwRDQVCqFBx980Fz3yEc+cqwt2UfUsQbsEi0a4jTKdS4dDoemr7vGLe8hAadHWNuVRFL3fTKyguXheGdiCn4bDQutS7v8w+HQJP7h83n/E57wBJTLZVPeF7/4xXj+85+PRCKBlZWVsfMMde7nvHLnnXfiBS94AYCdbS1ve9vbzPtZ14VCAfPz84ExnE6n8bCHPQzLy8tYXV1Ft9s1GXWZ+EjnfR0vdli4ElpdQ9iH7WgBGzQ2mVlye3vbqG7VahWNRgNbW1uBtYHjmf1TnQCtVst4/e13kxRy/zXXMzWA1fDX+q5UKmNEkYoi21YNd4bz63zHuVZD8tm/bYWLfZZZKvm9HKv2OFJiy/E5Pz8fUKB0Dm+329jY2MDCwgKWl5fN2kDHiX0sF9eTRCIxNocqWA/pdNokvOP6EpbURR0P9rNU7eHee9pCYdE9dO5qXfAZSg5VCU+lUgHi0263jY2gZ+Yy8oYOObXHmHkW2FWEdb7Sus9ms8ap5tozyD6hjoY4RJFlppPdlR9i0phkX+TWIvYLvoPriO5TdBFF26mo7VCr1Uy/ZhQD77Gj0DgX2kTVlbUcAE6ePBlwOmnWb7Ybx5cqsFwr2Oe0j7At9VxlO8pgEmITxa//+q+Pe+meUCgU8FVf9VUH+g6PIDipa7gPwQGpHjPteCQeXLToFbQ7P0M7wxQX/ltVSh6KHKUoKqiw0Qhl6IqtKOo3ttttLC4umr22OmCUKBYKhTGiaJMee3JQ4pvL5czkpPdpGJyNjY0No7Dwm1ivNLptss7FVqFq3GAwGMtCqmFEanxom9qKooYhaSZMfv/29rbZn0hPHvuR1hkJSiqVwsbGRqBt+H3sm6xP9VRyfx37Dh0VJCn0olKBcYFldE3YqvZy0dYFke3CutUwMCWKChoxqVQKW1tbKJfL6HW76D60jdS1DjBCILkMwbHCzGr0unOhGgx2k/zYHnsdxwq28Wg0ChzmDewsbh/5yEfMtb/+67+Ol7/85YHjGV73utcZxYsLMeszl8sFDjanM4b/qRrPRZQhdXa/5gLJ79C5g4YujSU+T5Pw8Hv4nUHjC/j0Bwf4i3dvIoFk4OxN7rsDdoii7VDodru4dOmSaXOOY44FGuIkKOzLLMf8/Hwgu6dCw+Zskk+DV7+XRp8qTGr8a8IJ29CyxxwNilKphJWVlYByxTkpk8ngpS99KQDgrrvuws/8zM+Y+WFxcTFAgNhHdJ7hXPWyl73MlONNb3qT+b0qiuq0Yt285z3vwdOe9jS85jWvMZlPB4OBcTbpN/JenR/VALadbyw7Vb0oRZHGJvco0cBfWlpCLpfDwsKCcQ4R/B5NdsO+o+fOsdx2xAuPl1FHrrYl12uN2KhWqwGiyLlMo0dYvwwxtucMZiRVRZGK+MLCgqknRjhQmW+1WsZ2oFJN4qJ1os5B1q1GvLCuarUarl27hpWVFQyHQ0O4mDWVa4E6t6i0MvrE5cQDYCI20um0aVvOp2FbGDhG7b+pDcHxfPLkSZOILcwRwfYk0SHUqauqshJF1h0zA3N/GtdSRl6oE4H1z/makStsCx5HpXWkRwG5orRIclRk0DGsv1fRQBVFLY9dPy4Cx2O49Ngmru1KFK9evQpgd+zaESz8JkbbsIw6l9L5wzqiis1xreULI4p2pIH9jZwDeeYq+zC/kfOHfaSOrnk656rjsN1uo1wuxyaJwIzOUfS4MUHyoAY/wUlMDTo73E5DdJQUKBj6xp+BcaJIzwgHjoY7xYFtIPLcL5soqleQg0X3MxDqTdIJmLAHmH2NZlylomgbPFEhkZxoSUZYd4PBAGtraya8clLoKduFRsH29vaYd5vlZPtSUdTQTRrqLBuwu0DQCKS3yg4hVNVRy8uJtVwuI5PJmCQiXKhYPq0z9glOvFQ7kskkVlZWjAFNokhnQVgWXT1fygVVW2yDXb3qJIqaLMImihwfDCeZm5tDuVxGvx8M1dYQGtvA5qLERAXcOM/+q1k0WU/6s/YVPp/GJ/cR0ynxsY99DMCOAfb4xz8eL3rRi/D7v//7eMpTngJghyg98MADJuHH6dOnzeHyPGuR9arjQQ1gkgvueWGbaHvw+7kYKynkYqsKiS6ehL2fVetbf67X68ag/ru/+zsAO3vquE+Pqu7m5iZWV1extrYWOIqC5aVxyb5hq+qJxM7xAuwHer+Gyar3WPudhgOy/vls2wikwWErevyb9nGOZx4Dw3s0oQ3L++/+3b/Dvffei7e+9a2BvUokeJxnXESRc9LjHvc4PPnJTwYA3H///fj4xz8ecEbZzqJKpYJ/8S/+BV7ykpfgU5/6FH7nd34Hn/70p7GwsIB2u22clWrYdTodEzXDeuPcqUYq1xAlmLaqY4PGZrPZRLfbRblcDqhHc3NzoWe8KVEjKpUKCoXCmCOFijCwE5rGPmffz2cq4ebcrOsb/876UnWNCS50rdR+qESRzyZRVNVKyRb7F59DZwTLwr+r0av9l3XRbDaxtbVlzjRVNapUKpkENKrSqpObzhSNkFGQYNEZw32/Gs1io9FoBI4qIai28RtYhuXlZRNNQKJtg84YJYpK8Pg8bWsl2dzbSRK9vLyMarVq+retZLO8qvpms1lzjzo66BDlvRyfusZouTieNNGgXqdQO5Cky1ZVw8Yjf99qtUz9sH75rbRPOEf1ej3zPbaCqEnllBRzv7c6c+gQoaPTVmC59tht7IpCY71wblIxh2uCJgHkGKNtZIeQ83m0j+lQow0Z18b2RPEWBjshDRlbURwMBiaRCn/34IMPmqxkdngeO7jCNSnYpEZDqPgemwiFQZ/LkBmWyw5Z0sm2Uqkgl8uZsDcdVBo7HuZ1sT25dhw/FzANR1CvVdgA5STLiVfDDtbX17G4uBjYv0TY7afPY1ltZU0NOC4UnLzUAUCjSScWJRVU7bgXan5+3njBOaG5JlA9OuPs2bO4cuUKNjc3sbGx4fT00RBheWiEqVpMotjtdo2BH3ZWEL3WUcYgsEsU1WCn4ca2cBFF1hEn6GQyiWazidXVVczPz/+99zuo7vDMM7aXGpQ00ujlVQdFIpHA0tJS4AxUGhO2cq0qcDK5c7A9vecA8NBDD5ljZp7ylKeYsbO0tIRnPvOZ5jmf/exnjZda+xQJbafTwfb2tqkD1qUa4zTY1tfXzYJqh7XzWnUe6WLP39PI7na7xgBj21DJ0z5oRxHQSL5y5YqJArjzzjtN/W9ubpp9sGfOnDEGlT0X0IuvjhR+u6plzFaqSpcaziSmLiVbyQ6NZtvw4/2se3UA8d+qKPKdbD+i2+2aUEC+fzAY4K677jKkiM9gGBuJP8eXfoMaQS9/+cvN79/ylreYn0lyaCR94AMfwOMf//jANQDw0Y9+1OxL5zypIersHwqW316HdNywDqPC3DjPbG9v48SJE8YQZ53qPj39dv5f+81oNDIhrPw364lnCrLs6gRSqIplr0kujEY7Ifxcz4Hd6Bz9Do4LILiXUxOqaRg853v7GAKuWRplo2HNejC4HQ1CZfvUqVMBBzDXB1UtNTpJnSS6J9MFTQAD7IS/KkFme+k85HI0s/4vX75syqBjhOtEmBPB5eC0FUXbCa7zGkkLv7NUKpnf0QnN9VxJVDqdNu+cm5szmaY1oooOWJJpdeSoQsk2sbdRKFz2I+s6nU6PbftZW1szkSe2/cT2qVarAQeoJn/iOKRNxbqiHajRKRqGzva7evWqcWjr1go9todl43hmf7aJoh0xoND+zLrkmsI6p+1E29LORaG2A+eaO+64IxBNELbf04UDJYof/vCH8UM/9EP4hm/4Btx111148pOfjFe84hX44Ac/eJCv9YgJdlSbRGnIED0yarhvbm6aQcBJjxOBHWIIjMvstsJBQqHX2AvspG9gKCMHBhcbnUz1G7nngioMJ+Fut+tMzRxGgF3X2LI/91jZxnpYKAv3BvI6Povn3thpknmf63lqGNqbp20j1iaKfK4a9DROafCqZ03PCeRkGbZHkQYsjblSqYQ77rgDW1tb2N7exqVLl0zSikajYcqlhK1YLJqJXg0kGos0dhmaZreZbg4PA+vVVhTL5bJZMIFgCA7rlYotQx4ZdkWPrG3MURXkYq0kQQkEFxd7A7+SE1Vj7YWViwWfzz22rIdPfOIT5tqnPOUpgTp//OMfb/72t3/7twCAra0tcz4a+8a1a9fwtKc9DXfffTfOnz9vFkr2C/YNAPiu7/ouPOtZz8Kv/MqvBEJ+2EbqFNHv4ffxe0kU6/W6CXkjIbD3qGmdAkDq7xfPZDKJ+++/3/z+9ttvN89fWVnBiRMnTJQAF1rbwba0tGTmVZaXxIjvILlToqkkgONFFRxgxyhhCDANeIYX2olNBoOdM7j0bE81DlRR0/Lqflv2l3K5bMYAnWkck0y2xOdoqnpbUdR66vf7+JZv+RacPXsWAPDJT34Sf/u3fxtwUFUqFbzxjW/Ei1/8Yly4cAHAzthjuT/0oQ8ZY4nhhawz9mk7WiaMKLL+6blnnYWBa16n08HS0tIYeWFdj0YjM6eHRdXQcUYDkdenUjt7L5W4s0wuFcvey8660DZX5y3PYtO/s+9o9I0mQrKjA6gEsb45HuzM3GyDhYWF/z97/x0mZ3Wk/8N3d09PzkGjgAJRgIiyRBAimBxEMMFgko2RMSzJxmQwwYCxMcEYDEIgAyYaTA7CSOQggjA5SSTFkSbP9OTp8P7R+6mp55mWF2zv7vf9Lee6uBjNdD/hnDpVdVfdVWcYUOTzXo/7rBpHhqA7sPfMQzqdVlVVldrb23PSPrn/moKCHnzzGfQMMsV8NDc366uvvjI9sybb65vSrF69Wu3t7TZXvO+agCJ7jOHn1etAdInXh+HmRQSrPfCRgnX2fg3Zz4lEQiNGjAjYGAIJHnx5PYeu5bn8fLPnGOEaW56V73ugCA2WdQ/PG74I52JHo1ErqUBOkDUP8KBl86zhswe9v8MB9cwB4NCXurB/k8mkli5damyGsP5jD7a1tQ2TSS9r3m/iZ4LEPhgMXZbPejDJ79hPMAC8Xvivxn8bUDzllFO0ww47aM6cOVq4cKEWL16st956S3/605+000476aSTTvrvuvW342sOBA3D6aP3KGOf3cI5HTt2rB10SxQDZRNOqeeKuudSrOHvhaORaxpQZHDK4/F4gCriNxnKrLm5Wel02pofEK3hncMbN+xoh0Ejz8/3PKggshouzF4TlYXfh6kzdAX19CpvhNcEPL1iJLrl7xVec+/MSkHaDP/2zg+DAALnQ/lIcziTIQ05RmGjV1hYqFGjRqm6ulpjxoxRaWmpSkpK1NbWZhlMjBRODkARZxdZ4/pQQ8NAESMZDkj4QAYyHXZ2fUZRGt6y3NeHEilvaGhQSUlJ4Py18FpxPZ7ZdyIlQksmFJnz71VYWGhRVdY+bBA8XZjr+/3+0Ucf2We32mqrgGFaZ511DKi88847FlzwTbEymYzuvPNOJRIJNTQ06Jprrgm0ffcOysKFCzV37lxlMhndfPPNgfMA/fz6YEM48EQG0dMOWTe/7tyX6w+6dY/Hh+TGA8Vx48ZZMILjRKLRqNrb2y3qHHYCRo8ebbKI7Hu6nQ8UhEGmp/OzHv4zXkZw4PPysofE+6g1GQk+7+lgPquVCyiG2R8E8tD/4WARZ6ZxTbKO0Aw9xd0DYdb0Rz/6kd3vj3/8ozKZbAe/X//619p5550DWcRp06bp/fff11ZbbSVJWrx4sZ2ZBsghaNDX1xegQobn2cu9f34P8P6rQT28Dwzxjj4AyxFMPEdY/3OcEEwY31QsHKTiXcP6LPwM2DKyhnwXfdve3h5odMY1kT3sL1nA8DFXfN7XwXv59fVa6HZJw6iquYAlAT8PFAkEcR/f4Zc1J7PsgSLz7PeBX28pC/6KiooCWTUfWPHrNTg4qLFjx6qtrU1LlizJuRbePvIMiURC3d3dtpbhQB/DN/PzAYtwRpF590CPMxs9UCR445vqhXWsB4rY08HBQVVVVQ3rv+CzW+GAJDqL9ff3CbOgADOpVMqC895n8/po9erVGjlypGXncvmGAEnAO+wbjmWCKcEzINORSERNTU0W/KKbr3/enp6eAGimARyfBSgCFrkXGedcIxLJNlcK+x8+S4sO8OwP7LH3belf4AM+AGrvf/X19Zkf+b8OFG+88UaLDp944om65557NHfuXM2ePVs77LCDMpmMbrzxRt14443/Hbf/dvyDAaVMCkap/M8IKU5o2PmKxWKqrq5WS0uLpet9Nz42cTqdtroBr7hyRXLDzl+4zfWaBkaUDc5GhVYX3giRSEQNDQ3Ky8uzmiSyQ964emATzvzkciA8DdBvVj8vXiEMDAzkfD9PrZSCZ+ag+Ig4+ncLO5QMn8kIj3BGEePmqSPMmQ8oQFPxYDwej1uDFpwwn40Mg20aD3R1dZkDwP05+gLDV1JSoo6ODnOaPLjFwHFPnxX3a7UmylEuo+OdAtYu7Gxyb4yZdxTCGUXWDdCIAxV2yrlGcXGxEomE7UnWmqw7Cn5NGa28vDwzgL42Jfx8ZI4AUzjW77//vj3P+uuvr/z8fHPiCgsLLavY3NysxsZGc9x8FuSFF16w+z3yyCOWDWLv8+x33HGHfW7p0qVqamoKzImfK74fzg5xPIoPZuC8LVu2zGQZEICe6R/oD1wHGfdnKAIUORoCOeIohjAlmTWoq6sLgAbv2DGXNCbxEX7v/CGHufa1pIBDmEunsrc82PZRfxxg5NAHHpAXD/w8wGONvMPOZ3BMvdPunyHMTjjssMPMmXvggQd0xRVXaI899tBVV11lmeqCggJdeeWVuuuuuzRu3Djtueee9q7PPPOMyQmggX0NIAmvEXtqxYoVNud+rcIObq7BsRzoOPYi1+M90XMeMOMMsmasJUCRdcils9bkeHJvrw/T6SwlG1vjdRNAHhnkmWB1+KYdqVRKiUQiAIpYS97L0069PPI7Otjm5+dboOq/AorYTO8co2ewg6w5ssq6hO0Y64R9570zmYyWLFmi0tLSADjlHlBqvX6Jx+NWF9/X16f29vZhcsIe6u3tVVFRkcaMGWNzKAWPGfPDZ5O4hvfPPMsF0EZAnk6f4YAb8klGjnXjXbyPQimCpEAW11/L+40+6ONpuT44FJ4TaQgoJpNJ2+fex5CyYLqtrc2SAGQKc/lOzEl1dbXa29tNbtCR4SY1hYWFKigoUFdXl8kFNtED4UwmW75QXl4eyOZjA/wxIewnmHjUueaqY2Vu1gQU/XuFmSae+gog9H5UJBKxzvDIiz8SAz3xdYJh0n8TUJw1a5ai0ajmzZunP/zhDzr00EO1xx57aObMmXruuef0ox/9yMDit+N/dninGMMWpuf4FLeP6nhDF41mzwaiyyXOqz/TyJ//V1VVZff1ypsRNs7FxcVr7FbpP+dr5HwWD6AYVsTpdNoAIpkCDLZ3nv5RvUeuzQWoYZPyGQxoeXm50W2koU6M4YHixRhjpOLxuGWLfDTJv1cuReTfKxzhk4JHnvhMAwaYv3ujj6LCIKFMMTI4Z/6Zwlx8DCZAMJUaOhIE8IIjThSQLIWnxSE33N8HO7zypltseJB58MNfhyCInyvu6wGzV+zhSDvZpKqqKnMs6UKXCwSEGzOwF2mNz729gfb3rqys1MqVK815CddpeaBILSHNC3p7ey2juOmmmxoQJ5BSUFCgTTbZxK714YcfGoUtkUgonU6rs7NT7733nn0mnU7ryiuvtPmMxbK1mK+//rreeeedwLu/8sorgX8D7NAZXgfxd0A1+85TcqCYA9p8QKqvL3gwMvqPZ4/FYlpvvfWsLoc9jgzi7IZBCPLhM4qsWUlJiRn5XPVWHij6upfwCGf6fYCONWaf+gwlDnt3d7d6e3vV0NCglStX6osvvrAsgs8EkTGUgk3GiN5zD78ekUjEnFbWxOsPDxTT6bQqKyt1+OGHS8rq87lz55rzlJeXp/3220933XWXDjnkELtOGCiSqffZJd8OPtcAcHR2dgYCWV7fr8mZz2QyampqUmVlZWDOkVFfesDa+N/zrF7PI1MezORiivi97AfP7Wt3CTD6owAAYLwvQPLzzz9XW1ub0um0ysrKVFpaqpaWFstAhFkk/n294xqeP2SaYA5USMAz8u1rrLzcezsC+AXwILONjY1atmyZurq6jE4Y9h9YC1/Dl0qlrGMmwaBw9tWvhx/4D/gfgBP/nJnM0NFPAIfW1lYLOIUDvgyvA3iWcMCHIDnzjQ5HL3i5JzDV3t5uNZCsKf4EugibyFqFASDr47PQPJMPPPEO6O+w/PiMom/847NsxcXFWrlyperq6kxWwp1FeYZYLGblFPF4XKtXrzagz/08UIxGo6qvr1dXV5dd0/caIPDCc/rAjZd73+OD5AlAUcpS5RsbG4etMfY0DBR9IINreBuBHeN3AFXmkj2Oj4sv19TUZMca+bKxrzO+EVC89dZbv9bnFi1apPXXX3+NZy7SWnvx4sXf5Pbfjn/z8FmKXBlFImp8FqdRGooejx07Vo2NjfYdgCKGtKamJhAZ41rhjE94sPnW9Nw+EgugwACS8g9nsSTZJiZr5cEx78wG/UdAMWw02PDhomIUUElJSYCeRzQ3PDC+zEtjY6PVvxQWFqqrqytnJCiXQ8Gz08ks198ZGAm/9t6RCRsdjDQHfqNsi4uLTbnSyMRnI3l/jIHPVkEFpqbPA0WMNUqSgeHHOcI5RxZwvBKJRM73z+VQ+CJ75iHXfKGAwxlFhgc1PlKN85lKpZSXn6fE5BL1bV0lRbPXoCOmpxVxDyK8OD1+bZLJbA1ka2urIpHsUQW5Oi/isPqIOefAff755ya7U6ZMsUwLh33n5+cHjjH64IMPFIlEzAlJp9N64403hs3FbbfdZt1aY7HsmYs333zzsM9Rv458+z1MxDjczY33AZTgHHd2dqqlpUVNTU3q7u5WY2Ojurq6hrrvRQd1yZ9rdfUD4xXPz65RT0+PUU/XW289awXPNQcHB9Xb26vi4uKctEAGwDOcHcVh5FgBH1RCxj39MBdQJJrtj+bgiJHu7m6tWLFCK1eutBboYQezrKxMtbW1Kisr06hRozR27FittdZaFpTy9eKsvTR0DFBtba0ikYhaWlrU3d1tsulpUjg04WyElz3+Fo/HddRRRwXes6SkRKeccoqeeOIJXXjhhVpvvfUCTvyUKVNUXV0tKVunCOXdZ/r9vHIv9CBObSwWCxyrQObCA52Ojo5hOgL2hNfVPkCErvFBJh9UIEPGgFLuqanIV1hvpVIpo4GGRyYz1EAMIMMz+fmH2kqQhzrj0aNHq6amRlJWN7a1tSmZHOpV4O/pKXK+47nX9dyLeSZwAcsi7HcA7LEb6E3vhxAUamho0NKlSy17Mn78eKPfhbuG8lxcj33V09NjwNJT7Jkr5qG7u9vkCH1LvVc6nQ40IWM9kXMo4szd2LFjtXLlykAGPDwIznj6bxg44/v4LFNLS4tKS0uHAVCeNRKJWHAce+pBPu/mZZiMuG9KFX5un3RAd+FXYoc9ldMHQWCkACS9/JAA4No+cM37eRtNvW1tba2WL1+uyspKWxfAK9emdwHXY864FvoGoM/fwmwrX6OJnPijNwoKClRRURFoDMlz5zpSjgA8AcnwWno9y/WRj3Q6bXLBHvJ6Dl0ebiT0X41vBBSPPfZYTZ8+3ZoYrGmUlZVp1apVa4x8LV26VJLsob8d/zsDig6b1EdL2LwIoY82S0NKnQgPGxgnA/oHCm9NQNH/7DOA/u/h4aPEvsgdQ1ZeXm7dH/33yUgBurwjgXLy1AwfIQ1nZMLGG6DiNzdG21PI2PTe8HZ2dgYMOe+G4007bd43V13cmrJTnl7De4a/yzsiA8wDv/fX4V4Yl56eHvX09Gj8+PHm7BDV8scZEGHlHZmDtdZay/7e09NjzXp84Tl0CZ9d9bJDwbr/jg8eUMMVXrPVq1cbsPXDG8Q1gUA/vDx6R4L9ghxQp0qUdmBgQPH8fHUN9KqgrEiR6NDhxMlk0uo2kJlUKmUGy6+Zf5+8vDyttdZaqq6utqwPhy4zPLACKBYXF6ujoyNQn7fpppsaUwDnIy8vTxMnTrS5/OCDD8zpYr19VnDy5MmSsk7lVVddZc/93nvv2edGjBhh13vhhRfU1dUViKB62WVO2ZPh9vueKjlhwgQVFBSopqZGNTU1qqystAj1qlWr1NfXp+q6QhWWpA3Y0clVkjbZZJMAWGO/skeR01wGF2PuZQidE2ZneJ0XzkKTmfSD4AhOMYGn1tZWdXR0qK6uTuPGjbPGN8yt3x8Eb7yzAQ3KOy8FBQUGyACK0Wi2o2BxcbFKSkq0cuXKYRRPAjrhTIR3KP2/6+vrddlll2natGk6+eST9dJLL+mSSy7RiBEjzA5VV1cbKyMWi2m33XaTlK0T/Pzzz21ekRdkgnVjDwFAqMciewJQ8IHBdDpLTw0f4t7Z2amqqqpAYA8Z5d0BJmQOurq6AnrFO+hkAr0Oz6XnJQXsca5AK3rB1y15ZgLgDTZEYWGhqqurLQvBPekiTiORsJx7loevdfTzR2Y5FosFzinlub1+DbOK8C18Mx/vCJOVr66uDgCYvLw8NTU1DQOKyCcyLGV1JoEPXzfvQQsdZ/3+lLLgq7Ky0mTY2xHPSkK3sA4FBQWqr6/XZ599ZvokPGBw+KZQYZ/J60Lkuq+vz4C+n0vAan19vekzyoY8OGY9fZC4qKjIDpknURCNZuu0AZDYefwTTx/1bC8fTPAy4GvxCf4lk0kLiLG32R++ZMRn6gG/zHMmk7HEBfLBM6RS2dpIgndhX5MaRu8Hex+KOWT+0cNS1t4hP2Q4i4uLA5lF5DFXRpFro0P9Wnq/ledg33kdLmUxFtRav4fXFABf0/hGQHHrrbfWq6++qilTpujkk082elR47Lzzzurs7NQRRxwxLOX60ksv6Re/+IUikYh23XXXb3L7b8e/YfhILxmhsKLyjn00mm2kwsYPZzH4mX+jJJqbm1VeXm5Oz5qAos+E+Ug2qfdcRcteaXpHDWNN8b4HN3yHg8V9pN07dGRZmB9AT0dHh9FbcmWnUHwYMZSVp0nQYp5rptNpLVmyRG1tbeaIEMnr6+tTIpGwZjsYhvB5RCiN/ypj6CO10vCz/gDGGDRv9FkPDxShWUSjUTvk2tMZkC2i9V4J++f3Tj/399kAqDA8j88o+gwCgNyDSwwUjnU4K0rdYzjrEc4oIiNryn6zn/h8OPJLp1PO6aJpAplQGrHwfMgMMoGhwKnjHuGsPAbNXyOTGWrNjUHyYJbIJVQ938hm2rRptu+Li4ttDxUVFVlWEcoilJ9UKmVZwfz8fF133XV2/5tuuslk5qabbrL7HH300dp4440lZYFna2vrMDYBzg1OKZFSAgA+a+INZyaTsUOuI5HsESI1NTUWTKJTIw6gD4JOmjTJZIs5Zq4AFd5xDMuE13GxWEyVlZW2T/xe9aCYfcnneJ/wXPjsRzSa7bS6zjrrqKioKMD6qK2tDWT00e38zu9L3iscGMvVfAOnqLq6WuPGjVNtbW0g4u6z+uF58UDCP9OMGTN05ZVX6rjjjlNNTY0BIZqmRaPZ+jY6IXr66UsvvWR7zT8310d3oCPIIPIz7wNQbG1tVTqdVltbmyorKwPz4VkOXA+QAcDA0faMgu7ubrsX8+OzIryjz4pD+/SDYCeBoPBgfxMAwqZBf/UgGecXh9VnQP3B3ugqfw8pSMHzLBqei/tSN03jI6iQPsjG9fy1kTmfWeF4IL9fGPyO+sdwkI+1ikajam1ttZIYX+7ggYqU9WnIHnvbQJAacOUH80xtOvPEvid7TOOx8EDPe93A8DRPn1FkVFVVBfQL80YJgQciPgPNu+J3cI2ioiJjYmBf0Emc69vW1mZMB990iIAFc4ZOCwfSCFQRCEomk2psbFRdXV2gTtQDRV93620udqGmpsYCC/gWXvdkMhl1dnbambHhYHtRUZGdbeoZHqwHtpu16erqsoAq8kOgjwQG7+F96fD6+1p2/u4DoWGZZj3xB303WTrHYj9z2aqvM74RUFywYIFmz56tyspK/fGPf9TEiRN12223DfvcpZdeqsrKSv31r3/VuHHjNGnSJE2bNk3jxo3TTjvtpNWrV6uqqkqXXnrpP/XQ345/frBRAIJk43J9BoXuU98+c+IdVf7D4AC2fOE0I5xRZPP5+iGMRC6gyPcxymxGNozPknoHHiWIUvDZUa6F0spkMmptbdXy5cuNzgBlKww6GAA8vo8B5t0BsH19fero6FA6ndbIkSPNKeLdUqmU2tvbVVFRYUaBaJ0ky5b6DGwuBeKfy0fypNxAkTVA2foMgQelyWRSTU1Nys/PV11dXSCixloODGTPXoNC6dcf5QpdztNocg3kB4PmgaKnHCIXPpshDXUD9MYTgOAzVQzvDIQjwmuaX0Z4XqUsg8LXIkBb7O7uVl40pqrVESU/aFFUwWALcorD4zO+np7FfkYmfAaB3xUUFFiG0tPgmCecoA8++ECSVFdXpy233NLkK0x5/c53viMpK6+ffvqpOZKfffaZnR+2+eaba9SoUTriiCMkZTvH/elPf9Lnn3+u+fPnS8pmE3fffXdts802krJy/O677wYcYPYncuCDC9DZMOR8nmerqKiwIyKQ58HBwf9cgwI9dmu/Hrgpob7e7Lr5mskNNtjAsho8DyDBZ1HWRD1FLnBkqA/x7ev9vg0DRQIQYaCIHPh9V11dbcfw+HnzgTj0o89Ie5kOZ5/CI/x53yjCN8Twe9HbCZ4dwM99+A5rM3LkSKv7IjKPA1RZWWk1ZXvssYdd95lnnrGsFYM9i6ygWwlGAdihKSaTSVVWVtp8d3R0qLq6eli2CP3AXFAb5o8hQZehn6Qhqib7Db3n5YD5gjI+cuTIwL0zmYzpg8HBwWFsCOaUIAZgpbe3V1999VWAegrd0suel8XOzk6VlpYGmnHwd+94hzPVHnj4MhCAFZ/1QVBpKJjpM+ze76CGure316iz4f3C+/usuZ87z+KhyywBOCj4njrJNcjMe5YIsuXtmLfH8XhcnZ2dBj59Zo17EbAMD5+V84FI5ol74RsQfC4tLbUMKIMAhTR05h820esv9mAikbDzmv0a0mFcGtLJBQUFGjVqlCRpxYoVgR4KfNafv+r9nHB2PZ1Oq6GhQZFIxGrLYS50d3ebHiXb7ZsOoQ/RLTT9GzFihNVjYze8DkMmoNB7Xct5nyUlJSbnXv59VjMSyZa3lJeXmy8ZZr/4vcFe8AFBnqe7u1sVFRVmZwD52P1wYNuzA6Sg/7Jq1SqTZeTwH/mKaxrfuJnNzJkztWjRIh133HFqbm7OSUddb7319PLLL2uHHXbQwMCAPv74Y7322mtavny5MpmMdtppJ7388stad911v+ntvx3/4mCjoISIgoajetRGhAEXwztCHkSgEMlU+gxTru96RyYcgfZ1XbnuzcbhPXxNWrhGAcNGe2oi8hhmNjdGluxgfX296uvrVVhYqKKiIovi59po3NPTTDwnnixQY2Oj4vG4SktLh2UdBwcHjYLpqZsoeJwhH41rbm62In6irX69cGp4Jm9gGbwPCjtcD+jfFyMLJdY3rvABBoIE3I9nwnARZY7FYvbOXgZRrhg0HBtPMwk7zH6tpWC7ez/8HBFl9n/zGcVw9N8PgAvrx7yyp1KplBlmvs979vX1KaKIKhNxpZd0KhYdqtWJRCJGM+U9/WHcPrLJPfh7OGrrI/O8NxkQ9EFfX5+WLFliGZmpU6cG3stHLPPy8rTZZpvZHLz33nvWkvz111+330+ZMkWpVEonnHCCrcftt9+u3//+9/aZ4447TvF4XFOmTLHfvfvuuwHjydwhCzioAwMDFuX24AMn3GdNkGv2ejqdViqZ1rMPd+iFx7qUSqbtXSQZPYx7ATR89hJwlCvA4TMl6CVkiBpp1k+SNQIK00JzUYTCTSx4T68TpOCRQMiFj6iHnSYcX+prct0bfct3WVfmlbXiP38/D1CxLz4LyfwAIABvFRUVBhqpae7u7taoUaM0adIkSdmjWlpbWwN1QMiA13+8E44VP3OsQFVVVSBIgX0Mlx4AZPPy8qx2HAeZd/H7FL1FeQIOOWydMFgqLCxUZWWlKioqhmUzAW3xeDyQQWVuuf/g4KBWrVqlrq4uVVRUqKysTOPGjTP59Wwi9o0HjgAP9IW/PoE9sq/eHvvACiwh9JTPgBUVFam+vj7gC6DvuFdPT485uL29vers7LTALQFNbAlZHA8mvPwyz4Dt6upqpVKpAFBkv/tOpz4g7vW9t5msv69lZX0IIHhKYF9fn8rLy1VcXKzW1tacgJ8gl89a+b/juzCfzFWYWZRIJKxWFT/C63Yve9jy2tpaW3t0Z5h6yv7HNo8bN87em32M7cYOIRfhLCCMIAIAXV1dFlgrKSkJHC/j78u1JFkzKN6XrHUsNlQf633VwcHs+bB0+Q13G2XNPcMDG0CZjdc12GD0XHd3dyATDQhMpVJWOuCDctTH5+Xlqbq6OvCs6EyGpyQTdAgHnfgcMuuDrd80s/hPdT2tqqrSrFmz9Nprr2nKlCk56agbbbSRnnvuOS1dulSPPvqo7rjjDj366KNasmSJnn32WW244Yb/zK2/Hf/iwHB7BzCcckdRho28Fy6EztNSo9GoFcx6KiGfZ3ydjCLPkYuWgTHFMSSyxTNLWZqnj9QROQPIoQBxwnGM+D3GxEfspCGHyEc+GZ7Hzmf8ZpekkSNHqq6uzpQ2c0Thd2trqyorK804810akgCWmL+BgQEDXqlUSl1dXWpqarKaHWkI/IWzVGFl4Z3bMD2CEYlkue7UF2cyGePyo8y8TFHk7+cKOo6fMwrG/Vi6dKkFAKTgOWE8IyDIAzXksbm5We3t7WaA6MIrBdut45AwwkARwBymF0G1wTh4AI4hJCNCl1f//GRJ/PW4/4gRI1RSUmIH/OI8sk7IJHuVWg7v7JFpx9iVlJSopaXFwACGFcqPPxZi++23t2fxwSTorb6hzbvvvmuZct/IBvA3cuRIHXTQQZKyTsu8efMkZe3ID3/4Q6XTaatllLJOvw/y+JpUniMWi1ktIyAJ2eN9Caz4d/Br7EckElFbW5vVzwNAcBJ8xBh5Ym7DzgVrixPHfsWB9qCe769evdqi6DyfDwb4QeAhVybfZxXDGUV+XlNGMZlMqrS01PZCrkY9BByYl3D2J0zt8vsXIEFGC93ImvkMZF5enlpbW1VYWKiysjKLisdiMdXU1Ki5uVmZTEY77rijPdvbb79tR70Q3ERe0Qs+C+MpXTiTqVTKWsrX1taqpaVlmJ5HPzAPNDvxlF/Wg2sjg93d3SYPNKTxdefoGoAbc8Po6OgIMGfa29utSZTPmEWjUdXU1Gj8+PEqLy+3Wjq/1h4oYof8v+noCOAGgAKqfV2r71oZpqH6wGNxcfEwefby6SmfzJHvpO1rv3IBRQKOkUjEKP4M5J8mTsg/MuJr2snkhJkbfu0JnlFWwDVYL5+1loYa77GHOU8ynU5r5cqVw4LxhYWFKiwsVFNT0zAdg0wia8itb5DEHvDyBV3SB7PDcu33IPYKu+/PauXenrmDnCWTycBRWR7Qennj57DP5BsN+uSGD0KwJgShCVx0dHRY/XA8HjcgH6ZO9/b2qq6uTqtWrVJ5efmwBoOe8s4e4Bl8oNUHW3hWZBca8MDAgBobGy0zClDEb+vt7dWHH34oaeiMZuSSdfJ7zPfgIADhz0zkHbFV2D/2Yi4WzD8a/9LxGFOmTNFrr72mWbNmrZGOutZaa2nGjBk64ogjNGPGDI0dO/ZfueW3418cfnP5yFM4o4hy8dQdHzlnMwAU2Sx0roQX7jMR/rvcz4NGX6PIM+WiQbEZUMLQADxQrKioCNCQAD048ChCvuOdHk/74G/hZwhTdaSg4xVWwv4zPlvH86RSKS1btszOdMQoeqDIURIeDGBIi4qKVFVVpREjRmj06NFWbxTO3rKmuaJK3rDxmbBCAZiQxcHhACj6A86j0ahKS0stqsXvMZiZTMbWiefp6enRsmXL1N/fr9LSUlVXV9saEUkHMDz//PN6++237aw4wD1rV1ZWpvLyclVUVJisQouEesV6tba22juGs0Rcz3+G9/NOP/vAG1gfTUeWMSiAHT+3UjZjSx2dr0MKA0WcUAyPd5i4HvOFAwT1OryGLS0tgUY2W221VUAG/L3y8/NVW1ur2tpaSVmgmExm60LefPNNSVnq6vrrr2/76owzzhi2H4477jiVlZXZ59dZZx1J2ayebzXP2obnnv2NwSZLRVbDy4uX40gkMixKH4vFArTTiRMn2vwB0letWqWamhpzZnGgcg32HfvdAzsP3ngeHAvGmqK+PisapkNJw4FiLvYGMh+e18HBQaujzmQylqHww1OocZh9BNtnlthH6CrvUHo974NvXs54XgIB6AGyeN3d3RbQkLIBBp918JkGnCQfwPEglRrdZDKptrY2oyVKGhYgYj2pg/elDh6Ick/miWemQQw0Nd/Bls974OfXCBlhr/f29lo2xMuGD6j4DDLzDuPDUwEB1LwjGQ++R1DQd6zmvTwN0MuVr3GEbppLniVZzbgHAtgB6rv9XObKEBO447N1dXV2Lz8n6AyfUWRdkU+f3fdA0QehACgACI5kwe/xe9MDxf7+fpWVldkRIeXl5VYawIjFYhoxYoSampqG6RmCBZ594IOWfB5AwiBw44My0lAGGZ2FjOEnIu88P3s8HBjy+8Ufa8U9PFAMMyAIRPAsXv/5ZnVcwx9oz3fRE3RBhsnhs6HInQ8iEYDnWZlP1tH7bewBaj79nvTzOTAwdFxYR0eHRo4caXRS7AcA75NPPtH6668fKAUjI8oeRWfBrPH2xNdx8p7sD79eYaAYzj6vafzL5yhGIhEdd9xx+vTTTzVz5kyjo2633XbDzsj6dvzvD4TLF7yGFZoXRIQq7LigjNi0yWS2oQURFAyLvy4bwCsorzByAbJcB6L79D/XYAPyjGVlZcOopx6cEi0NA8WwcfaKmO+iPNZEQeUdc727N7q+lgPAEY/HA1E9HBAUJbUSKGOUWdiRzs/PV2trq5qbm83QsYYYxzAIBPh4A5rLWcXB4lpk23AcvBEh48caegUNPYiGJEuXLlVnZ6fVbQLuvXGme+j8+fN18MEH65hjjtFnn31mBfQ4mMxZLBazYnWoJ9KQI4Ti9IDNRzulIac9nIUCvPrjO/xnmRcP3vg/9Qk+680+oCgemQ4DC2SDvcM7k61BZsLUHgxXb29vwHFNJBKqr6+3RjYFBQXWyMVHNf17pVIpywJ2dnZqyZIleuedd2wed9xxRwOnmUxGm222mfbee297h9LSUh199NEBZ4PjlAYGBvTWW2/ZZ3E2cYD5HVFVTz31wNAHLfxe9CCSkZeXp7ffftv+ve6665rRjkajRoeE0uRpaeGME3OO7vCsCb+n0BEELbz+85kIP2Bs+Ey438fMN7Lh9at3QJA3r2+RE77HfvODgBXUQd9kwjtZnqYMJT4MWNBL/nsMGhDhuHNOJ3NSU1Oj1atXa5tttjEa7xtvvKFUKmW13ayR3wcEfVasWBHQ0dizpqYmawgE/a+zs9Pkj/nDIfWOIZ8hKABQxJn0xwAB1FhD79x5qqwUtIs08vF7Anlhj5JJYH75DBQ+7hM+CiIM6n3Ai3mjJAL7wzzhxIbZSeyRjo6OgCPtZY5nZM09iykWi1kgEsZEOMjns9TMgS+H8MODHw9ysGX8PpwJ8/4BdhI7jYziV3k769/Xl7uwDwgaVFZW2lEMDGxsPB4P1B5zLfaOZzcxf+zHjo4OOyYHGfd2w/terCHP7ueKfe+zgDwjNo31wodARpFfv0fCw8uB9z8ZJSUlprN94BR6OvuGdfHXIqjvGRoEVltaWkyXIOPSkJ71rAyCB+g9aPCsGXLis/qdnZ3GRPGfRy4Au4WFhWpsbAywo5hfLyfIM8/kgyoEMH25F/rHs+D+V4Aio7q6WrNnz9aCBQs0efJkLViwQFOnTtVJJ520xu6o347/+YFzAPVMCtYYMryhIPsTpj94Y4YAdnR0DLsW3+WzYQMdFlaiJNI/Boo44USuPcccRQXlDyXgHU0ceZRqGKTmGhh4HBFviHyW1kf1wlFSlLzn2XujwkYHoHtQCV0Qg05EOTxH8XjcGs74ozg8UAyvJwrJUzvDYJLn9NQUr5T92nH9qqoqA4OAKU9n5L1jsZg1ssAw+edGOXd1den555+3eb733nuNgrt69WqbCwAc9Rk+cEAE0hsHnB/v7CA7KOLwfAEUmS8PFHFewveRhrKDvqaF+QFge2XuaySZM58lxwhwbwxnOKtBEwCckq6uLjsLk2zrd77znYDhAYRzLzI2W2yxhT37u+++q9dee83+vd122wUoePF43OoRJemkk04adjbV1ltvbd/31/IZfm8okU/WF5n2FDnmzw8izNSESlIsL8/qE6UsUPRNqYqKijR69OiA842TFpYL5MYHS3x2z0fl0WPxeDzQXCQXNVzKAqiysjK7Nueq+lFaWmqd7rxe8cEu5tXrZOSMYEw4Ci8NRfbJVPiAUpiGhX3o7++3zIk0/Lw8BvOTSCRMx5aVlRlQ5J149vLyclVXV2urrbaSJLW0tOiLL74wQIhu4h4EVHD4fOCBoyt8/R+DaL0PCGAzAWU+qENmg73HngPAUF7AvvSUQc808cFKT72MxWI2P+xLf+90Oh0ImjKKi4ut67fPpiKLOKlelyeTSZWVldm9CDRlMhl7D//dcJCNtfLNfhjMDw51+OgMAhWeLomuC9eM5+XlGb2QIKG3icibt1meneJ9IPaFt08ejDFffA7Zp07b+05evvkOQVW/RyRp1KhRBljoNQAzRlLgPEBvJ6ShhjG+FhuWAhlWn7lDh3l6J/uCLJtnYOF/+AA/8xkOLviyBu7FPDE/YX8LGfJ2xuttdGM4o4gsMrec3cz+Zv/4bHosFlNbW5sF/aC2I+9SVs9yDjEBSPagP7fQl914O4Av0dnZaaVGsA+wuQSNksmkMYjCgUf2OoE29odP5Hgd50Eg8sNzsoe9bfkfB4qMqVOn6o033tAf//hHlZeX64YbbtDEiRN16623/rtv9e34J4bPKIb57H54CpCPDA0MDGj16tUBUIDCz2SyRcheOUjDuex+hKNu6XRan3/+ubVbDjel4TPhqJNvZkMBeHl5uYFXrwR4N59RDHP2vUMa/rdvg45SSiQS5vD5jSwNRTGZfyJDYdqGd+ql4BmIfLagoMAK1pPJpCmD8BzhmFRWVqqtrc2eoa+vT83NzeZUffDBB1q0aJFR/QClXD/sKBItZM6oufHzhiHg/aEiE4llvcJZ1nBmxEcqw4fKfvbZZ/bZBQsWWHQ+Go1a7VppaakqKirsWX0BOPfw58J5uiOGlDXJBQhYH9/wAmWOM+DBC8aPdcyVoezs7LQifmSUSKTfozwPhrqlpUVXXHGFHnnkETU2NlokkT3q6VTl5eVqaWmxjnWZTCaQwZs2bVrAmSYi6TNUkUgk0NDm7bffDtQnbrnlloFsi5StW58zZ46uvvpqXXzxxYG1kGSdVCXp73//e0AW2Adc03eD89mYSCRby3HEEUdojz320KeffjrMKcmVUczPjxtQrKio0JgxYwLBDB9U4Xm8HgoP72T6jBYBKmlItwCEvUz4jBvDZxm98xN2wOk27NfLgyLmwjtdvjkJ84Vz7gf3h/IeiWRrO2k209LSYrWWqVS29obnQdf7iLsf/K6pqUm1tbUaGBjQyJEjjWmB/mLU1dWpvLw8EGBYuHBhoCY6nEEBKFZVVZnTytz6TJ5fo+rqajtKxbNi+DtNX3Baw0CRbtd5eXl2ZBAHtRO08LRyDwJZJ6/ryfB4YEaWF73lAScDG5lr7n1G0etZWCyepYHDPDg4aEEkarx8dtnrd398CHLEXCNDpaWlAZtHttwHWaj1ZC4Y0OoJ2oUDzOhKH5zNFSDnWh7EIrO+LIPvMs/RaNQymAAadLRfk3BwkXsgozU1NWpvbzd/ore3V11dXaqrq1NjY6M6OjoMRPJskkyf+YwivoqnzjL3vLcPZnuaqQeK/vM+MeB1kKeL4gd5/Y+cZjIZA7Vh4M21kEE/wskFnpmfwxlgv/bYUX+OMeUunDmJTuI6AFbKR7yv5mvmua6niBKoKS8v14gRI+y9CVp3d3erv79fS5YsUWdnp0aNGqWioqKAP4p8+4Zz3pdg/smgRiKRgA8sZUEqzbA8Vft/LKN4zz33aPfdd9eIESNUUFBgLc7vvvtum/QTTjhBixYt0jHHHKOmpibNnDnzWzrq/wMDheGBYpie4aPlPtLDv72T5Wkikizi6CkkOPC5HCqvdLu6utTd3a3KykqL4uT6nv8Oz++pFIlEQs3NzSooKFBFRYWdU+ijpZJMqQEkvCOWlzf8/Ec2aDij2N/fr0QiEej26rNSHvQQ5SMK6YEinwGo+ft7Zw+aGkaf70BLIiIJwPAgrbe3V01NTcpksvWB1DNybZ8FCWcUM5mMVq1aFZhzzldiPTzN1FM9cFoAtN5pCFND+K6XHx9oKCwsDDReWbRokZYvX26fo27KRzS908S9ALo0zGhtbQ04Dul0Wt3d3TavawKK3ilgABR95BXFj3IPB2uYTxoFMW+AIZ8R53kAoyeeeKIuueQSnXzyyZo2bZoOOeQQzZo1Sx999FEgGINxLCoqUl1dnRlozj6UstlAHFP/7Bh3dMNGG21k8rFgwQJbk3XXXdfOUGUNATSbbLKJDj74YJN/9lwmk9GYMWMCdY++KY8H7JIsw0PgxDsyc+fO1cMPP6ylS5fqsssuC2QD2tvb1dvbm6X/pYccldaWVqsR2mSTTYZlDX3gyx/5498xPADXOGupVEqjR482Q062rbKyMvCu4YAWo7293XSjp/GFgaJfJ9beyw6OUDh7U1hYOKzuLGwbfDOPwsJC1dXVGWMBPQfIxGEqKysL1Cl7Cpp3JnFkaEYCMPFrsHTp0mHOve+Y+/rrrweAonfCc0XYBwcH1d/fr8cff1y77767LrjggoAjhf2jOyUOr1/75uZmq3GUNIx6ik3wdgAZICvigx3ojXAgjWYV3inMz89XZWWlNeLhmSKRSKCelDn21MDwvPPMBA2wcSUlJYFnSSaT1iGW79DAzjvrni3jGRxcBzns7e21Dps+e+eBr7cHeXl5lhn1DCLf4dZ3gZSGgGIYoPhr+yybD+zxTAAc7uMBkM9YYv+oXWUuuBaBAZ/Z9QFU1jqdztbF5+fnq6ysTGVlZVq9erUBeOyOJPOdfN+Dvr4+Wzu/HryvB1+AcN8YzGcUuQZziowxtx5oMteeyeDn1WcGvU7y3/UZde4B/TSccUOOCCzgY/n7Q0XmXQnWFxcXG3DnfvTZkIY68aNDPUBmb9E7gudiPmAN+OfwjJ54PK5x48YFSrt6e3tVUlKi9vZ2e+5MJqPy8nJrWsiexdfhnck8skbt7e2qrq420Mue8nr3vwUoplIpHXzwwTryyCM1f/58NTc3a3BwUM3NzZo/f76OOuooHXjggTZhNTU1mjNnjl555RVtscUWATqqj95/O/5nhleKPhKNQWN4OiYGRBqq8fDnAXl6D9FT31m0q6vLNi3OUJiOQee2trY2rb322sOUSTgj4KkigBmvjMmGoODq6+vV0tISoHGGMyReKUpDxwF4oIbh9Jkafz+uHa4rCwNFH3HzDo2PEnpaCkoBReoPUMeBqqystGYrzc3NBgJ89DmZTFrb/9raWtXV1dlh0rSy5v4VFRXDHFUAIevos1Q8p6eM+u9CeyLIgHz4TnM+w+ZlzgMF5qyhoSEgDx9++KF1RuR5fCTaO+c4ZrFYzGpfULIejLDe3gD44dfFy0oYbDIXOIE44JlMRmUVZWpYN6OmiVFlIkPHWTAikYjVWBKo8E5oQUGBli1bpgULFgTm45133tEVV1yhww47TLvssouuueYaNTQ02L5nPrq7u1VcXGxNaKQsUMxkMgYAcL7CoAjgJ0kNDQ32tx122MGAJbqELCfZprAjy5xBI+zq6rKaQT6PDvBRbZ9tZW3vu+8+e8ZXX31Vy5cvN5BAdr2/v1+KJHXL05P1i2uLtPizocDDJptsEggyhB1fnCkPGnIN1ruqqiqQffDP39/fr/LyctMnxcXF6ujosPuxTplMxhwYsiqS7Iyv8GDPI6PeseN6/llwlvLy8lRRUWG05PCgUQRODDXGkUhE5eXlKigoMDDLMSDFxcVWY8OzMh8+Y8lew3lDB3ugSBdgP8djxozRhAkTJA0dk+GDmbBTeCacZ5z/vr4+XXPNNert7dWTTz6pK664IlCLFY9njzLq6OhQc3OzATeCNwA2plZx6gABAABJREFU9D4BK/+OrGVbW5sFUcjWY+t4H5+xkIaaBgEUfUCssLDQml55Z5sAim+ZL2X1MLW90pBt8oFX3iccmPLBhb6+voBeWpO9lIaCC2GgiMx3dnbasSTMg6fjAmDQRz5Izef9oe7oljBQxM6EM1nMAc/n1wLAE4lEzFZ555zPAAIymYzVcAJ+ARU8OwFC7yMAFD1IIhBMB8+qqip1dHSYnElDgSXAY15enlasWBGYd5/NY37CQBHbQn0uGXCe0QMZ5g0dxTv6QHx43/r5BYTiM4SDRcy99/lSqVRAzhmeheHP+vRnR6LrfFOYgYEByyaSNefviUTC6L4Ad96P+cKfQbao4+f6sVgs0FWd/eUBW0FBgelv5hd7QAkXwLCurk6JRML+zZyzTmQrkVNfT8m6Y4/9+G8BildffbUefPBBSdmOdc8995w+/vhjPffcczruuOMkSY888oiuvvrqwPe22WYbLVy4UNddd53Kysp04403fktH/V8YnlKSK1vE8BEzho/yhevYcIRzKR4yeeFIOYOIbF9fn9Zaay0rFJaGMjZhGkk4KuLpkSib6urqQBSbDmM4Iv5aAEUPEsLOoTdsYeopIIyIIp/LlVH0DmM4a0bmDwXrqQzMMY4+2QiuTxaSDJ+P0GJ0CBAQEcMhTaeHzjzyWQi/nlLWea+urjZl44GiB6W+FoBBDY/vXodCLykpCWREmX8P9Dxdt7GxcViHuOeff94olR5UhoEiGQQMPcbUO6LeEfEOtjdQyL6v8fC1DBgmrouR9LUzmUxGhUVF6o8m1R9Nqf8/6Sp+QO9i//gINfUnzz33nH1++vTpw86nXbZsmX77299qp5120lFHHaWHHnpIK1eu1Lx583TDDTfoiiuusEY266yzjnUK9EAxnBFgjjxd1D9DaWmpCgsLA5Hp7u5uM64+K8J7RiIRTZs2za7zwgsv2Lx7Z6K7u9uoW1wHnbZo0aJAdlSSHnvssYDhrKysVEFBgcrLy7Tl1HU0YnSB3n3vHfv8ZpttFgBXXhalLDiDahSm9/mBE0n2IMyEQKZ99qq4uFhtbW0BJ0NSoO3/wMDQ+ZD+HDo/PDURefF72QdSeBbuWVhYaK37Odye4XWLb7nunVvkhEg7tcJ+b3lnh+EBCmehScMbivmGHz09PSoqKtJ2221nc3jAAQfouOOO07x580yucA77+vqsGVB+fr46Ozv1+eefm3MtSQ888IBuvfVW0/HIf21trQXGGxsbhzFCsEs+m+LXnLrOyspKs1nYHQ9OPJiWglkm5gJbgHPI2mD/CH7htPKcxcXF6uzsNL3l9RTP6rtbSkN+QyqVsrIO1tQHNL3t9veEwuwbdfjrcdYdQCeTyai9vd2yOgQnfEYlfEwUda3MnWc08Q5h6iny4oGi1+k8D/fAbgBK2T8eKEKPRvaxwT4DiF3l+sw588Lvu7u7VVpaau/uwVL4oHfvX2FnCaD5gBP7Cd3Ad3zWF6DI+/nMFffFv/FB2WQyabqJZ/NMDAJTvJ/P0DHfyL33f1i/8DnBfp3xw7xuRa/6QAaZR5IayDzPBpAlSOF1pPcpaAzlgSD7n6AkWUa/7t4PzM/Pt06/2BKv2/k5Go1aYx1JgUZOzc3NWrlypQW/CTy1tbUFjodBV9ArwsvC1xnfCCjedtttikQiOuOMMzRr1iztuOOOmjhxonbccUfNmjVLZ555pjKZTE4AGIlEdOKJJ2rRokU6+uij1dzcrJkzZ36T2387/sWBU+Y3NyMMFFEyCDcbkY3kOyv56BHZKc6igkLBZ8ODZjMVFRWmWMKRLzpDMcJUmVyAt7S01MARCrGmpsbooWHKg6fsSEPOSdiwoXR5Nj7DnHj6lQeKGCcichhHlIune2B8wgDbg5EweIrFsvUNX3zxhUaMGBEw4nRG5V1xDOj855UlytavgSRbx7KyssDz+GgmMkHhtXdMoXt1dXUZoI5EIuYAMzwQ8Q4uhqu/v3+Y8yplQYV3jvge7wwYJDLPuwICcNLb29tNeXpH3jsXDL6P3KGcofHxfdY3DLY8Xcc7sX4MDAyYrCDHyAFzCaCSpJtuukl333235s+fr2uvvVbbbrttAOi+8MILOuSQQ7Tffvtp991317nnnqs///nPtqaTJ082Qw1F2juz3mhGIhHLADLy8vL0ne98R3V1daqurg5Ql6ibQw/5ulN0zvTp0+1ar776qsmEp1m1t7cHDjv27IfnnntuGKB/6qmnArRB5N/rP9/xdPLkyXY9Mrze4cHp/UdA0QdcfHbKX6e7u9si1/5+PKfXIdBOkTucbg+i/fDZWh/wYgCA0D/IKABqcDB7IHlzc7NR1aWsHhgxYoQ5yoCTMWPGqKura9gZbTSmgi7ogWKYbu73JfVmnj2C/vHN8To6OlRSUqI99tjDftfT06NHH31Uxx57rKZNm6arrrpKbW1tVj+PPuYcQp+NZ1xyySV69tlnA9mYVCp7vAMHi1Pr7XU089nW1mYO+1dffSUp22ynqKhI1dXVgQ7MHjB7x5NrksUCbLAWyFYymbSMJ9+DRoy9YP2h4GEzoUr6gBfr4DOUyEZLS4t6e3sDOpvaxNLS0kCQEPCBjvPgjet2dHSourra5NozkTxQpPkHgaEwtdQzVXgvrwe4tg84MPc8C3vKgxjPGPEZRWxueI9yxqTPDKPfmRN+7+s2PfDzGeZodKh+fvHixYpGs+dAdnd3my/R3t5uwRuu29TUFOj+G5YbfBZfVoFuykVZBmh4H9JTpLlm2IZ534cgUBgo8q4+2A5QDq8f7+6v39nZaVRN9IW/Lnq3tLRU7e3t6uzstEwettr7ObkSDz4Dm8lkVFZWpp6eHgtaev8O4Ef9rmeI+SM3aPLlawtZK0AjPnEkku3AzH4bHBxUQ0ODYrGYJkyYoGQyqYaGBtPdnoaL/JWWlgaCRLzf1xnfCCh+8cUXkqT9998/59/3228/SdKXX365xmvU1tbq1ltv1UsvvaTNN9/8m9z+2/EvjoGBATU1NZmCRUgAFf5zvnsWmxSAySakkYkUrEMsLS21KBeby0d8MpmhWjcUjne22Ljcz5/TJMmUMYoNZevpm5FIlgqF0kY5lZWVmZH7R2e1ecoE7wdQxkh4AI2h8dSRpqYmuzbXoX6Gs/9Q5ChnP+9kD6Gg5OXlBZxsX4vHXPBvnpn1IdpJJiR8JmEmk7Fuib5NOoNaCWhnOIL8PxwgYG39/GL4w84iIK60tNTobsgTCtq/f2Nj4zDZXrFihVasWGEGyGdjvQORyQzV4EBvYi3Ly8vV2tpqn8WhohlP2Mlg/dvb2y0LVFtbGwCKXpZ4d+4Xj8fV292jmpY8VayW4rHh7eNxaD1QJPNZXFysjz/+WJ9++qmk7CHxY8aMUXd3t0aPHq2TTjpJt9xyi1544QWdfvrpGjNmzLB586Ourk4zZ84M0IxYG59t91HxMFDccsstVVhYqOLi4kBGIxrN1rdAa5QUAOvI+uTJk01OFixYEMgOo0eg7vqMMc/4+OOP27PwbN3d3XrwwQeH0cAy6Yh+/8v39MSfe/T3v78rSaqvr9eECRMMGPg1C4MtgiNeJhg8d5iG6GXIU5y8PkHO+T7r7h0Y5u4fAUXPzMiVUfS1WJ5KBz26qKjIjuNBl6GvGchIPB7XiBEjhgEnMqp0UuVZeS8PFJlPmA84YNiRTCaj6upqOz/TM2M233xz3X///frhD39ocyplWRAPPfSQbrnlFgswYFvy8rKdMp9++mn7/A9+8ANbvzPPPFPvvfee6YNkMmnPGI/HNWHCBNvj3kYODg4as2PFihXWOIOsEDLDf8yXl3VPxWetwyDD18qWl5crkUjYPvA2wcuu/zvrQ6kI90I/8jnWiawsc897k0n2x21wT38siAcN+A/MJddC36DzuBdsFQ/AsKu+FMZnn1lnZCvMGmKdmf9wt0/0BfLH84UDK/zfsyN8NksaoiP6cw1ZQ+bP7wN6NfhAWmFhodWijhkzRj09Pero6FBXV5eBkuXLl5s88LtwRpH3R6b8PvaBIv952DC8u8/E8p6stweKXvbCGUVpKAjoQd4/AooVFRWB33sd0NnZqb6+Pjsr2PshkUhElZWVxkQiUOgbCxUUFKizszNw7iTP5M/HTKVS1qAKoIhc+SNekHtkCKDIz9Rb+oCxD2yHM66VlZXq6+uz/8aOHWvyWF1drdraWnV2dhoYxF6i/7mmT6z8t2QUoUURIQsPfu8V9ZrGtGnTtHDhwm9y+2/HvziSyeSwOqxcw0fKw0ARwSVKgUMDQEEQfdbSU/mgqRAB4vMeKOIg8Hn/997eXrW0tGjVqlWB7/N/f64YGwtHi0grxqSlpSVgOL0j6JWpFKxRjMVigVo8wAyRc+7lm7p4Gk46nQ5kiYjW4QxihHAqoDJ4oMiBsqwRztz48eOtVtFH/FE6ntZARywcda7p645YN+YBiquP5jIHHuhBwWDguNDan2fyBt6/H2uEHOBsVlVVacmSJXZdapMk6c033zSZ497hrE48Hreif5S8d9R8owcyaiUlJYGCdgYdZJPJpEaOHKmCggItXbpUK1asUGNjo1asWKGmpqaAESCz2tnZmT2TrKdXJc0Z1XQVqPwf6E2f0cbwFRQU6JFHHrHPzJgxY5hMxONxjR49Wj/72c/05ptv6oYbbtAhhxyiPfbYQyeddJJ+97vf6a677tK8efP09NNPa/LkyRY5D9N3BgYGAgZTkkaPHq2RI0faM2y77bYmxwDFxsZGq02k/oy1IcuKo1lUVGTnM65evVpffvml6Sv0CDVv6Btk5bPPPrNg5pQpU/Tzn//cnuuuu+6yvUtGMZWK6I7rF+nFxwbUncgGorbYYosAWwLZCa+9tOYjLHAOkT2cFq93cZigU/l6QWpVeDe61/E9nKkwcyEsL+hlP3d+eIcFvejXA7o4NH6CcD4TCBCQZKCypaXFdAkgEuDjMyXYFSlIRe3p6bHMEeCZd2EvptNpo63z92233Va33HKLnn32Wf32t7/VgQceaPI7d+5coy7iOA0ODqq9vd263W6wwQa66aabtNNOO0nK2poTTjhBy5cvD6y5t43SEDU0nDFJJpMaM2aMNtxwQ6Mh8n0ASjqd1ieffKJRo0Zp++23N0DhM+/YWmQRh9MfyxM+3xNZ8o5ieN2ROzKK3in22TWu6R1+ZBw74GnqPnvW2toaqHf39hQd6FkZyMOoUaNM3gESI0aMCDi8RUVF6urqCgRcAAAeYLEXvb5g8M4eKEajUeuUzv71gSMf5PCgnECvXxvmmiw55QLMmbc/DGwQnWS5TiaTsS7mVVVVqq6uNqDY19en7u5u1dTUqLS01OYBe+Hpj7y3z5R5oOh1EXuLTCPv6YGyTzRAiwXMeSYTP/u9wxx7Bgb6zVPMkUX2GGvh5WncuHGWTfMgmPfGHjIvBFz5HfW7PhCGPqSjMe9KF1FfS42u9s0feXf2D59Hd/Ps4cCRPw6GtS8uLrZzsT31m+cqKiqyQCzBNP/+kqyDtP/b1xnfCCjuuuuuymQyOv300wOt0KVsO/OzzjpLkUhEu+6669e7+ddEs9+Of8/o7++3A0rDc49QS8GmNz774SPYKJpEIhHYrPzfUzRyUXN4Hoycj8oXFxcHgKJ/PloLFxUVWT2aB1W+qB0HhWykd3xReJJyzoePNPJOHvxSi8cmTCaTWrZsmc0XwAOnnvliDnzbYx/tREnzvjgh3jBJQ3WZXJeII4qC58SQ+4gmBo36FJxnT1Xw0f+2tjajBjPfn3/+eYD6R5aUs84wCAx+5x0WQCmKzj8vMuZBON3fli1bZtf44Q9/aD+/+uqrBsLD5xNigDzAx1EHBHGsBnMMUKTQ3TtL3d3dVrNZW1ur/Px81dfXa9y4caqurtaECRNUWVmp6upqm0tkqqioSG1tbVYXyigtDQJF9pqnJCaTSeual5+fr6eeeso+v88++5gT7aPu0pBBnjZtmn7729/q9ttv15lnnqmjjz5au+yyi0aNGmX1Kd4Z5vvIKQEJT1vcdNNN7RmguuLUlpeXq66uztaeGidpqBMqwQsM9tSpU+16L730kq0H60gAyDs0mUxG8+bNs+99//vf17rrrmtHeHz66ad66623zHkk4hoem266aQAY4rR8U6DoAwN+DRh0JUZnElxjT3kno7Oz0xxhAIl3NtZkR72DwPuGM8OSAkARxxe9DFCTstTJ6urqQAbHgz+CIP6MMu9Eed3P83v95J04Ov164MOzQWHnKBkfiU+ns/TUrbfeWpdeeqn22WcfSdn9umDBAmsIk06n1dnZqQ8++MCeaZdddlF+fr5+8YtfmAw2NTVpxowZgW6PzDdgBBCMg9jZ2anu7m4rAfD10GTvuE46ndZNN92k1tZWvfLKK3rppZdMF3q2zuDgoDmt6EW/BswdOiucxfGy6zNtnnra19en9vb2QBDGB1a4vw/0oUt9p08yOqlU9tgojiLxe8Bfl/n0QR9YPwQs/LMwzzQg810qPUDygccwJdHrDQ8UPQCBUuv3fn9/f6B+2AdqfG06wRP0dyaTPe7DB1ZoRsT9GL29vWZTaGrC2sJEamtrUyQSsXNGybZ5dhXzjZz69/DB01QqFfCNvPz4gBRZXlhR6Ky+vj6tXr1aTU1NBn4HBgbU3Nxsds/rH6+zPGvD/94z0Py+I7OcSCQCv/clGhxBw2e83fbgmXsgS7CtvJ7m2Qnws9ZhBpq3RTw/zxf2gbleuA7SB3h7enpUU1NjQTOyxJzt6LPQft6w4cg1g31F99xvOr4RUrv00ktVXV2thoYGbbvttlpnnXW03Xbbad1119XUqVO1fPlyVVZW6pJLLvnGD/Lt+O8fKDlPjfDOUFdXlxobGwNCjqIgne5/5zedV05Ej3AsPecdhxEKAdkiP1AqPipEQxuya/n5+SovL7fzuVBcnmOeyWTbCq9evToQ0cY4+eYj4eEjmTyTj1ARfWSuBgYG1NDQYBQtX5TM8NGnvLyh4zH8/cNA0VMQPBXHZwoAB1CRKisrrdYOmh1rTlSzqqoqsI4YNxxpKBKZTMYOMoYCjNHq7OwMRLl9ZB1D69cUQO2NBJRYf4SAFDyUW8pmrjCYPsJ/8MEHW/eyV155JZAJ8YMofSQy1BUMA4uSBkD7KDSOngdtkoxiw9oxl+F1krJMDBxU9gZRZ29cwpkhn5lgPpj/aDSqlStX6pNPPpEkbbzxxho3bpw5tOyBsMNAtBJnZ2BgwA6+53vIjKc7+gwYRgoZPeKII7TBBhvopz/9qTbbbLOAgcKQx+NxVVVVqby83BgD0AvZ/8i0B4ovvviiyTuyxhE8vjakp6fHgGJhYaEOOOAADQ4O6sADD7Rr3XPPPYGMWC6g6I/8IMLr9yrD76kwiPSy4jMS/jMcQM76IId8pqyszM4mBARIQ4DKO3BrAoo41D6qnevzyIMHigTDACWp1FBzGt8RUQoGCAcGBjRixAiLaKM/WTuyETw/nwN8ohvRRUPZ36HGQlVVVdbFGoYD9wAoplIptbW1GVCUpHnz5qmqqkrt7e0aHMye1/b666/b37fffntztGbNmmW1iO+9957uuecek0PklKN12CuA3+7ubo0bNy5gM6GicyA49uWdd94JHBn25ptvmt5FtyCzADWANPNOkEWS7QvsDuDHy64UBGxkENGLPkAUto/ocWw68ggdvLi42LozArTKysoCgMyvlTTkVOeSTQCcz0j6RkAeAPkMDf6ADxjy3rnqiqEKervFkShe7wPuvJ73Tjpz4+XeB459Ax4C3ryXd/7z8vKM9dLV1WX+Cwe4Q09ta2uzM195TtYBXwmfzesw/IVYLBbopsv8MZ/IB+/F5/FN6EdQWVmpqqoqVVRUqKyszDofwxzzoNQDLp6Xwc/MUZgCK8mAIPKBrLGulD7gX3o/rq+vzyi96GfkHzAV9h14T+TTBw55L3yjXEDdgz8C9L7O18sD102lUqqqqjLAiz/mA5MEpDwoZL9yT9YaO+Tvx9++zvhGQHHttdfWq6++qu9+97vKZDL66quvtGDBAn355ZfKZDLaYYcd9NJLLw3ruvft+H9jUNCK4pCC3cBQPDjtACsMUy5A5jN/bDRv2FH03vlMp9OqrKy0ejafCmcAaBFkzp8h4oPyqaio0PLlyw0Eh4/uGBgYUEdHR6BxhqSAwffdtsIRLEbYUcRgSzK6zujRo9XQ0GBONWdL8RmiQ74+hM+y0X0dhX9Gnh3nyTey8TWMkgxAo/QAbiipnp4ea9Hu6cWsu79fIpFQaWmpZR0aGxuNAkY9ks9+sC6Dg8GOibyXNwwYK1+bxDp4o+edhEgkEuhQuO6669qB211dXfrggw/MkPsB4MNxoFmCB7AFBQVm7L2T5jMiZKdR6ji6zCXzxNolk0nLKjJHtOuHdsQIP7M/zwz56+josCyKzybutddekhRoduRlF2cFeUkmk9aVjUw860AzoEwmEwi6eIMPPTmZTGrChAm67bbbdO2115pMMnCuffYFozo4OKgRI0aYwWTOt9xyS9vDd911lw477DD99a9/VWNjo62DzzRI0jPPPGNrMWPGjP/salqubbbZxpplzJ8/3yjra6LcTJw4MTB3vHMYDLJncMC84+kDPTiyHijiEPI35BNdRBZ7YGBAnZ2d1sSG9aUWGt27JqDoD27HUfAOrn9e/5wAU/Y32XbmkYwT2ZFwvVt9fb1Rs8KOLxQu//ySAuyTsWPHKhqNWuMab08kqbq6Wk1NTYFaKNY0nR7qZtjb26sttthCa6+9tqTs2ZzUMbW1tSkWixlQrKqq0oYbbmj7vbS0VDfeeKNd//rrr1cikTB9TdDNH5zOddAjPlMAFbe3tzcA3sON/958801lMhnL7EnDG6mhq/310VtkdP4RUPTzz9xizwh++Yw6IMYDJro3e6DI79HTUPyxK+h/3ikMFHPJJnrZf4fPAIiqq6vV0tISCEIA/PzZk6yTB4o8l/8e5Ss8N3qOAJ+vPZOGQC/dQseMGROQe7qvk7VneMCPjly1apUdh4EOpHlTOp22411qamrU19enjo4OO6/P22GCpaw9vgPvzfzk5WWbwRDwYp69fgOk+Ewo80iwD53mM7v5+fmaP3++3njjjYAO9esRtnk+kEdAOjyYL4KfvDe+jM+6+yZ7UhYojhw50ur8mCfkNh6PDzsWiCQFvqEPnjJndBsNA0ivW8n2c0/fswHAiu/k7WQmkzEqr7dbNOfyvhd71PvUyEbYhuWa/zWNb8z93GCDDfTMM89o6dKlevTRR3XHHXfo0Ucf1ZIlS/T8889r4403/qaX/Hb8Dw3q0DxQRImgEDwFAUWBEvQOtc/c4YwAOCWZwQakIfxELP05X96QMDCs/C68WQFnFRUVqqur08qVKy36xfPjjHkAGAZgPsrpM1rMEYotDBR910HuNXLkSKs/I4rsqatsbBRZmKLJv/2zoZChB+HoQ73DufTKKRrNdvgi2ofyJXqJ0xgGijwXETcclsrKykCkmvXza8T8+mycb0DkqXKeEsfzA4j8M/FOHryn02nLKNbW1qqoqEg77rij3eeNN95QeXl5IErL95nHeDxu1E8PFGlFnk5nu/t5Rx6QSf0nIA5KmAeKOBIo9YKCgoADlkwmVVtbGwg2SENnafl/I0Me4BH99ECRro8+6MBceznDmenq6jLASfAB44wBJorMdZEZDtsuKipSa2urGUMvB4x0Om3dTtPptLXs7urqsqy7FGz4VFZWpm222cbmee7cubrooou0xx576PDDD9e1114baOGfTqcDtZqHHnqoXTsvL88alCSTSd16660BHeDHhAkTVFZWFshgetqUHxhk5sSvYzij6Pcyz0GNiR84Wx58+Xon1gF9iZMQdq4ZZI09/cln6f2zht+tsrLSHCZqfrwsABQBgHyP/2Mv/BE+AAr2NXPmgQjyyn9lZWXWDIbnps6Gngk+e5PJZCxQxPwcddRR9q533nmnRowYoba2Nn300Uemo7773e+avicq/53vfEe77LKLpGyH01mzZgX0FTXO3Id7S0MUSHQLNHOyRKlUtn7Py62UzV7ScMNn33hP9hFA1OtUbJkHitipsHySlffzB20PPeUdT5+VAjSQ8faAjM7T1Kkh5+iYfzQ8OAoDCr++PnsCwwA97INqdEb1GVLAmZ9bH/DNZDIm1wQr2trazN/Bv8gFFFkPz2iBwRHOfjPv3EfK7vXe3l6Vl5ebvOOTQPEl2JtMZs9VLCwsDOgSvsdRJX5OkGv2VkVFhfLy8qzJk9cjvAt+oZSlYWODfXYbv45EBPJ0xx136Oyzz9ZPf/pTvf/++zntQ5ilwVwCXNEXYV1ZVlZmgRuAUSKRsPlB1/qeFZIM2BKw9Uc4xWIxo077QaID2fPAEzmi7Ke/vz/gX/v3amlp0V133aUVK1YEmBt8Bh+vq6vL2GEEuH3vB/QkWXzPusCOcn8fpA0HLKUhf+2/Gv90keBaa62lGTNm6IgjjtCMGTM0duzYf/ZS347/oeHT4T6jiLPT19cXONqAz3mg5CN1OJm++6a/bmlpqaXjicKSCSPyg3HwjqIkM1j8LkwXIdINYKHJgc8akl0hY0LkxmckPQBE4TFH/iDvXBkFlLB3yKnhCVNcULzMaTiz5je5pyvwHHSII0voDZ6/LqOqqkqdnZ2mmNPptNX2jBgxwhQVhsVTfMrLy03xs6YYOu7lM5LMH/PPmnol5A0yxhOF6+tOPPURueMdMOKrV6+WJOviudN/Np+Qsp0yqVfw9DcULgCCaLen/JFhy2QyBhQBzwBZFLavT/OF577jqc/Q4LSz1hhsDw7D0dPW1lZdf/31Ov7447VkyRLLgkYiES1dulQffvihpGwWbOzYsQGj4EEEc0tUG2NJVg7ZJsIpDWV4AAc+k4Vc1NbWmpPPnsll1FesWGHODOdBcaYpe8E7kvF4XNdee60OO+ww1dbWBq71ySef6Be/+IVOPvlkNTQ0SMo2UeOIkJEjR2q77bYLyO5BBx1kzzRnzhy7Vp8LZEjZ+sRcgaRcQIw5w1n1ugljHs5++O+Wl5cHnCYP2NgXNPfx3/WBvPD3wsOfpThy5MhAxoFr+oY6DN6Zo15aWlosy8Eg2o/t8O/mQZ2vs8Op8dSswsJCO0sMnecdHfSYBy9SNkjkqVV+TdCP6FyAoiTdd999dhakP29z++23tyw5QZl4PK6ZM2fafe644w6je/kaXm9Xw5l8H6BBj+Dg/vnPfw5knljfl156KcBG8fPp5wbd5Qf38JR47BRzlUqlrEO1p98RxGHu/F7AnhOEI3CGLoHpQ4lCIpHQ4OCgNfHxWWGeIQya+D72iDUIAznegznh+bz+81nSMFAkcO39Gq7ls/qMkSNHqq2tLVAW4oEi78TvPVAEAHh9wTMjs/yuq6vL9p1nC9CNHL/JN3kiq86eZp+EdZJnH/mATH5+9ixRf0QE12MeACT4NX5++Sw0ZMoYIpGIdaBOpVJ65513rHmgf3+e0esE9g6Bs7BvIw2xprBzHCHiO88TlPEJDdaqpKRETU1Nxkrp6elRSUmJ+Uh++H3mWTboKvzg+vp684XDsi1Jv/jFL/Tzn/9cu+66qy688EK99NJLARnC5vpzgr1OZB8xN2REYTexJshZf3+/li5daiVC7GmvM/6r4I3Jw9f61Lfj/xPDO/peceJQoZzoYEZkRxoyODgWCKcHV/wbR5jiYhQ4n/WR4TU5Wzgv4YiHV7AYYN4t/Fmeq6qqypQi38sViffUDBSCzzjm+k46nTZ6JgDKOxq5NiIORNgBxICh0DAuZFOJIhNdxBCGnVLuwX2ojQRwVlZWBhS+fzfvvCYSCVVXVw8Drijanp4eVVRUGCDDIPb19VmNlR9cB4AEMMU5w4jzPZ/V4RmXLl1q1yM4NWLECK2//vqSpA8//FCrV69WeXl5gLKDgQXwoCyR60hkqNNaXl6eHe+C4sXpZv/wLjRU8Jk41p31xfmJxWJGO00mk4HPSApkYJubm/WDH/xAf/zjH/XII49o991314cffmgO5xNPPGGf3XXXXQ3g+mysf+90Oh1w6uhk6Wmyfm8xD55u5IG1lN2jRLT9PcNAEX0SjUbteBVv+NBBrHs8HtfYsWN11VVX6bXXXtOcOXN0wgknaMMNN7Rrvv/++9pjjz304IMP6o477jA53m+//UxOeY7x48db1nnlypW69tpr9frrr6utrS3wnJttttmwrD7zER6sdy7dFc4ohkddXd0wcMa6+Yg1Xez88E6Id1BzDfQB+8ozJrhGLqDIO1RVVVm7ebIcPjrNevvoNfoSGhUOJ9dEx3rdBlDknXwNfTQaVXV1daBBCnMTzohxTep2CM7U19dbhvqLL77Q/PnzVVBQoJdfftnmafvttzc2iKd/jR8/3oBmIpHQ7NmztWLFCpNp7st7oke4rgeKfLavr0+9vb1GO41Gozr66KNt7l9//XXLirJveTfovgQxfBANRxA9xHyGmTLUZ0IdBQyQmfMBBWxlMpm02jPPpAAYoRcLCgrU1dWl3/72t5o3b56xTnzQDN3n7RV7jswzcwTDg8/44KoP9IZtNPaN+fNMJ57BZ8P8XvGBFECId+S9rDP4N9f3Mtnf3296zwexeX7WiA7tZJ95bgJr6GzsCwFRT6/0NZDMuc8Ys4fxhQiiewose8cHQHnmTCZjzeJ8dgvfC5ve19cXqP9dvXq17VfkMfx8/DuRSFjTQq4dBorcl7UvLy+3Wk7WnaY3ZCfJLvokAEFpOueGKfHSkI+AvDPXrCX/9/6t1/vpdLbp09///neTlVdeeUU77rij9t9/fz3xxBP2LD4B4r+P/CErUtZm0EU8nNhpbm5WV1eXRo8eHQCHYaD4355R/Hb8/99AoXtQgeFHuSLU0pCAYvwAij4iR4YBRxiDRvaipKTEukn6bIQ0vCV8OAIWBlqeGiQFz/QKUxqkoSwIFBJPo/HvxvfD9Bg2PvxwH0WUhoxAIpFQTU2NKSBP2fBRYd7ZR0l5Dl9bglLyzpSnnPnaIJ+5CjuMXA/Q6jMk4YwmTkckEtHy5cvNkeCAZjIbntIETYb6O7Jj6XTaDlv3SimcUSRQ0NnZGciS+k51YUpjrqMx0um0AYF0Oq0XX3xRFRUVgXPFotFo4GgQ37nOrwWd5XyW1wdMAIbMX3FxsdWeekeGulGfwfF/i0QiampqUmFxkTo3KdLysYPq7c8a0sWLF2vbbbcNNLlobm7WySefbDVMzz33nP1tt912C4BY5gxjjNzkogESkfRyzdoWFhYG6IbMGY6fr0fymXqffWeOfWAp7OSGM9DsDyhXkydP1vHHH697771X9913n9Zaay1J2QzsZZddpssuu8ye/YADDgg8I/L+/e9/3z5z9dVX65hjjtFe++ymjzp/ro86f660BgJA0e+PcBaAgXz6ABPvjDyEsw6SjJ7l9ys6A6CYK3ATvn8YEOcaXr6lYBMT1nVNQBHwXlRUZM41teDeyQ6zU8isI2fsF0kWYPJrQ/MTb0+8Ywily7MSPFD0ews9FIlELAiSSCQC3ZHvvfdeffzxx1brvNVWW6muri7w3Mx/Op3WhRdeaPv/tttu07Jly6zUgf22atUqPfvss3rvvfes+6MHMeGmOw888ICdJbzDDjvogAMOsOd7++23zTYy58jPG2+8occff3xYx0PkABvmZcUHd8JZSgJAvglYPB63LAT2CMAEiPdZMoJXmUyWanzIIYfoD3/4gy644AItXrw4kFEliOqZP7wHz8pnwg2/vKwjf8ilDyoy78gUQQu+54N+2Bsvw755FPeGfujlMtfe9EwED0pLS0sD4IFaR5/pIXPnmULY31Qqperq6kCDMzJ46GZpqHZeGqqbYyAb+GbICzIEwAUMEixkMG/MGTrLgxxk7e9//3vgvMPPPvssYAt9sCiVSqmhocHkuKioSCUlJers7FQikVBzc/MwvyudTgeO4yotLQ3UfQMU0TVk6mAGRSLZDqCrVq2y+aSTeCaTsdpAZNKvsQeInj6K7gonEzKZjL744oth2X8pWypzxBFH6IADDtBbb71la+flccyYMXYMDHqZa8H6AGC2tbVZfwxq28NNFb09+LcDxf32288Q8b979Pb26sorr9SsWbP+W67/7cgODxQ92PKCh7LF6fPgQhpSpIBGn9UCjKBEAIpEeKG3Au4wVvzNC7OkwJlrUjBiwzP6DQXw8FQ4IkQeKIWBoqfb+M2MAeWAdx8953koiuYIAJzmvLxscyAUNQo6nJXBmPjoIL8PU2h8NJL6Kx+lzDUwuhgfn2lk8NzUPkLrwsgCnrzj7KNrOI6+/oispc8qeicV2SG7hLEPA0XWmuf1jWzWWWcd+9zOO+9sv3/xxRctKuuBIs5Xf3+/1V2GnQs6uhFx9FQODPqSJUvs2mTXkUFP1cTQQRvKZDJ2KC6KPZYXU7QsX8lCKZlK6eWXX9Y222yjzz77TFL2PLHNN99cUrbF/w9/+EPddttt+vTTTyVJm2yyidZbb71hTZ68DPn91tnZaZRd/o5Ti04AWFZWVtrv+KwHeBhd5g8nhTnx2RQPKIiu81zoFx9M4bo4IGRRtt56a/35z3/W9773PVtv9szUqVO1zjrrBOhmAOUpU6Zo0qRJod2RUV96ufrSyxWLRTVx4sRhdD4cmvDwUXD0B8PrV/YM8uV1k9+3zBPnyvH84WcJ6x9/31yDjAHP6oE9z/GP3o89yHd94wd0itfB/n7Iiw+slJWVWeDQZzW98xV2yCORoZqrXECRd0NP+MY4yPfhhx9u8vbcc89p/vz59t0999zTwI+nZwHU1157bR1++OGSssGJe+65x+xaX1+f/vjHP2qXXXbRiSeeqEMOOUS77babioqKNH78eP3gBz/QfffdF+jUGYlENHv2bLv/EUccoc0339xo1m+++abZJih9kUhEq1at0rHHHqtf/epXuuGGGwJrE25u5geBXdabrG1RUZE6OjqGgXSO7/GNrTwFjmwTIMjTBI888ki99dZbJjtz584NAGXunSujyPrzHNg5nw31tgegGK4L80CRoKQHijx3Lt3Ez+E9kU6nNXbs2EC9sJdhv2eYc3SXfy8+5zsNIxccnQJgIjgMNbStrc2uSZ059+Fa/l3Qw14H+T3sWQ/4JrwXNaweQFKPSyCCfYqfAPhMp9OWrWdgr7wuQW4SiYRdg7nLz89XUVGRxowZo8bGRqvbRX7JvHo2D9dncI22tjbl5WVr/+jRIWXtEO9GjSbz72nZPgjqEwvofi93PijvA9C8vySdfPLJuuSSSzRx4kT73YcffqijjjpKF1xwgWVGed/S0lLV1tZq2bJlFpjxthKfuL293foheP+NAJAPuDD+7UDx8ccf19SpUzVjxoxANPtfGatWrdLll1+uCRMm6KyzzjKH/Nvx3zN8JtBH5rxxgePd09MTiKCEKaQYMd+YBUWD4PI7TyvxTShQlkT8wkCxvLw8ADRQdD6z5aP9OPfhKBrA0Csnacg4AdTC0XcctZ6enmHzxrWhTfqunRii+vp6o2Wi+DFuXong8PkmBD5yhPPinXbADP/OlVVIpYbasjNHHhRJQzSQvLw8LVmyxAxINJptaLFkyRLLLnENT7vzmUiAcXl5udGOPdD3gI/54108fdk7qZ4CFY1GA0djABTT6exh28jVq6++alQaGv0QEMCQUffh5xqwCg3Lg0sAEAAQGg5/5x18Bq63t1ejR49WS0uLRYsjkezRKu3t7UbLoq7jb3/7m3bZZRdrmLP++uvr9ddf15133qmd/rMOs7+/XxdddJHNwX777WfOEkEgqDa+XsUDbuqfcNS8I4X8sueRI9YmlyON0cTRJsCDM4YRZl3pSkmtLLIdZhd44EGDJdbxkksu0c0332xHo0jSYYcdFsiCeLCQTqd1++23a9asWbrmmmt0xBFHaJ999tEGG2ygkSNH6sc//rHJjA9g+Qi9f0b/3mHdxe/98JkSZC4MFAsLC62zqM+IMMId/FjXfzSgrfP8fm5ysRB4fhyPiooK031kt2jq4TOfPB/UQ/Y/FDmCSmRA0PmeTsU+DNeISUOg078La+CzSTiwAD+cxfLycu2www6Ssm3l77//frv2XnvtZfNM1twHFDOZjE455RSb+4ceekgrV65UQ0OD9tlnH1155ZU5Swyam5v14Ycf6tRTT9UxxxxjDvorr7yid999V1L2WJutt95aBQUFmjx5sj3fRx99ZACQ9Xjsscfs/Z988kl7b4CDpIA+Rla9Y4ltJlDX0tJijaWamprU2Nho9h8ntKurS0VFRWpoaNBtt92mxsZG6z3g7dRpp52mp59+OjAH8+fPt7X3QDGcUfQBJy8XPqiJHqKBFk6675HgA1n825+tjCxJMnn2VGd0JPf1wIDuzN7ehR1t5p09IsmOlEEPkvX2XVAJOocb0xQVFamxsVF1dXXq6upSX1+furq6jK2EH8b8+uwpAMEDRV8j7IGlr30me8zzE1T1DaqgdfqMIj4CmW8/Fi9ebPMDW4Q9y3moUG8JSpOJ9aVQDQ0NFpBmnQcHB7V8+XILTCPr2DASFT09PZZdQ/4467SsrCzgZ4WDWbmyi3l52dIMdDZ+HHIHqA0DxU033VQHHXSQnn76af31r3+1AGYmk9F9992nAw44QPfff3/AHtIkimOVvI/NfdC73m7AGPD9PsJJgq8zvjZQPP/881VQUKAnn3xSu+66q8aOHaszzzxTr7322tcuiJSy1LE//elPdo3zzz9fTU1Nmj59ug466KCvfZ1vxzcfGNIw7QsHhkgS0RccIw9SPFj0ChihxdFGGXsah1cyUjY6W1RUZFGh/wooejqd5+IzeAYUDo5ie3t7AIzkckx94bz/PZE0Drj1jlU8HldHR4cpGYwd1/HnxQEeaYTgHU7+5ikCPqPo6RpS1vkDaHj6U3jgFGIsfZ0ERhdDS9YwHo+rqalJ+fn51nCDDBjP6+mYZG+KiorU3t6uVCplHTGhMDE89RfH2dfpIZPeyeGzgKCVK1fa9XxGsby8XFtttZUkafny5friiy8MQHmHHhmJRCJ68cUXA7To7u5ui+ySDfcZcyiZxcXFtl98VJs5kYYcMqKi0Ieg+rE2ykixJT1qev0LXXTBheZ47Lzzzrrrrrssin377bdbB0Y/DjjgADMcsVjMnj2cIWGPU4eBoyINdQfG+BYXF1tmS1Kg3ow95cEj88PnyPbwvr5JhiSbP57JR7e5Joa9pqbGajE8YMnPz9dOO+2khx9+WDNnztQ555xj3U198Ivnhaa13Xbb6dRTT9UZZ5yh+/7ygI498FZdcPI8HXzQoba/AYfoyVxA0cu0f3b2blg3xWIxa6Lkn5FBhtVnfsIZRRxCRq7MR3j4zFt4jj3YY/gMPHQ379zzDLnAMHogkUiYw4ItCNMPod5Bb8N2eLaBHzwLQRRpKMCHPsfJR/fyrCNGjJCUzRwyAFzrrbeeNthgAw0MDFgABZ3JXmTPk8UeGBjQzJkzdeCBB5oDHIvFtP/+++vwww/X3nvvralTp9o5jJL0l7/8RT/96U/13nvv6fbbb7ffH3PMMRYoAihK0sKFC62zKnLp65I//fRTrVy50tbAA0rkAntBBlgKdkVub2+XlJW9kpIS1dXVWYYKGRkYGFBPT4++/PJL7b777jrzzDO13Xbb6bTTTtOiRYvMP7juuuuseUlBQYHV1r7yyitKJBLGCCCQGw4GeLYJtHh/tifyReAWO8RnAWdhGUfOw/YRe9rZ2Wky6OnU4YAlz4aNb25u1gYbbKDNNttMb7/99jAwRgAcfQ+NFqcd4Iie8awiBnpvYGDA6ppLSkq0cuXKAC2UAFI4o4j+8EyggYHsUTfHHnusLrvsMstK8j32kNc9+I1SVrf5hnA+mIg9aW1tDZRNSFk2DM1s0OHILXNAQIM5wB4hA9gUbC9Z0BEjRigvL88AOeuAXigsLNTKlSvt88lkUq2trWpvb9eqVavs/Otly5aZb+uz3vhinqbvM9N+7dPptIF9b1c571iSJk2aZPpun3320d/+9jedf/759t6tra067rjjNHr0aO29994aP368NttsM+2www768Y9/rC+//DLADotGo0ax9f6ZJOtGnE6nA7aH8W8Hir/61a/06aefaubMmcrLy9OKFSt01VVXabvttlNZWZmmTp2q4447Tueff76uuuoq/elPf9If//hHXXLJJTrttNM0Y8YM1dfXa5111tFPfvITPfvss0qlUtp444316KOP6oUXXshBDfp2/DsHQKW/v1+rVq3S0qVL1dzcrK+++spaAtOlDGcprCxxpPy5hD4aTLQMhwaqpaRAZFnKGio6U+WiUaG4fOQIw4njEX6/0tJSJRKJQJQNWh7P7x0hnhGHBloLGxAl3tHREYju83w9PT2qq6szIwLg9BHttrY2y3auWrUqQKHiWTB0PprJfPqsBcCSSFE0mu2KFo7AMx8DAwN2cGt5ebkpT29Mm5ubVVVVZb/HQHu6LvLhqbAAbByzcDOH0tLSQMMQ3hMZSCaTWrZsmTkHfj24ls8qZTKZNQLFSCQSAFKzZs0yY+0dd9Zsxx131CGHHKIZM2bY3zGaXvZ5ZpwpGvVIQ9E61hrAhFHLz88357iurs7WH8DW1dWlwvwCpRe3a+vajRT/z3mbOXOm5syZo7q6OpPZ/Px8XXTRRTryyCPtHTfaaCNNnDjR5qmiosL2HwEAT30C6Pn6ROaOfcw7eCoqeyMWi2nUqFEB+pVnFHCgMIAHYOej9dJQV09fB+uzywyOv0mlUuYcco4nz15RUaGLLrpIv/71r22/plLZplKtra0WlAlTyzOZjNIp6S+zVuqW332idHqIXg/AZvDcft/kopcy1gQU8/PzrbNtruAOjgd/9wElaXjH068DFGkqhUPm67mYe38P9GVBQYHWWmstxeNxFRcXW2OoXKwEBgDPO67oBoIIUNups/bH/jBy0WGhmklD3YGLiooCDSwI8vFZAA/rvu2229qeYuy5556mW2m57wFGJBJRR0eHBgcHddBBB5nsU3MlZbOCc+bM0TnnnKPzzz9fd911l+bNm6clS5bo4osvNgdw+fLl+sEPfqBnn31WUjZTs+eeexpN3gPFN99802iHsVhMH3/8sb788svAs7/wwgumpwDMmUzGSjAIOrAfoeXyflL2uDPmjZpOPtfS0qK2tja9/vrrOuWUU0wGUqmUHnnkEW277ba66KKLdP7559sxH7FYTL/5zW8CDIjXXnst0EwpnNXmmp6FRODSyxm6yvdH8MECAtA+4EyQIldQ2TfyQX9R6+b1pmcvEeh89tlntXr1anV3d+uyyy4L0Cih53r6LIE5DxRpAoaO9IwU/BR6K5SUlJgcxWIx65DK3KGvw0EW/97I9FVXXaW7775b1113nX7/+98Po5Aj17xvV1eXBTc9k8xnBaWhbNtLL72Uk9K4bNkyW0fmlG6jACwANL4P+zKTyaipqUn19fX2fQKRUFD5Hg2XeF70LjKDfqqtrdXYsWNVV1en8ePHa6211rLzp6UsuPUBF0mmg7wP42WN/cf30D0ff/yxpGzyY9y4cQH5kqQf/ehH+utf/xrwYVavXq3GxkatWrVKzc3Nam1t1ccff6zrrrvOZJT/WltbLTHhwS2lNPjZfn2/LkiUvmEzm7Fjx2r27Nn68ssvdc4552jUqFGmpN566y3NmTNHl19+uc4880z95Cc/0SmnnKKLLrpI1157rebOnaumpiZbrIMOOkh/+9vf9P7772vGjBnf5DG+Hf/kYDMWFBSoqqpK48aNU21trXW3wzmRhjdO8FlIsgBEXHBUAXu+axPODMYnXMflqT7he0oK0BeTyaRF+zyn3huQmpoadXR0BKLvGE1qrzwIAwhyYG80mqU3ZjIZO2h3xIgRRrfxg+cAgOGkY7Q6OzstG8k8EfXCiHvqFPPkM4k4zyg4fiYT62sb/OC6UEFZK5QIxpfvkr3ljDzuk5+fb/RJLyMYdhQ2Tq7v+FZVVRWIYnmaCuAPQOXraABrzKlvIAP1tKamJhB5jEQi2n///e39r7zySl1xxRVmbDBofX19mjlzptXRvPDCC/r4448t6xrO4khSW1ubmpqaLDhC5pWOaihhggxEPJEt3t3X7uE0NzSsDNzr6KOP1uzZszU4OBiILnOtiy++WGeccYamTp2qs846K7DuOCPUs1Bjwl6jwZDPKJJV8iCLOeW7zIsPnPhsBY6Ojw5L2XbvBBt8Eyy+R9CKxkzegQjX6FGw39zcbEEVz5BAxrzjUl5eroGBgUBdM59jTv3AmfUZVJ/JDwPNNY1cBpj3IyPhgSLv4Jt3hYGtlJt6motJEL5vaWmpmpqaAkAXvZoLKLImZNQ5yxCZxrkLszoAAB7A4qjhlA8ODqqsrEzd3d1W2+wdnjXZAfRASUmJBZ+Ki4uN/obtAYgQ8GSQofJZRSlL3QYoorvYU2QnyPTU19fr4IMPtu/m5+fr7LPP1iuvvKINNtjA5s+Di6233lqPPPKIBcGp35dkGUoc5Y022sgc0TfeeMP0SjibyHj++ecD+xtZACh6gIMtAUjg1JKx6uvrs0AsNiUSiejVV1/VhRdeaOB84403VlVVlT3Dyy+/rNtuu83+feutt2r69OmaOnWq/e6FF14w2+vZZ37/hctVoGF6VhOyS2YR+We/4g/w3oDRNe1HZIKfOzo6DFQTUOVZPJ0wlUpZDbkkPfLII0aHRd8gA76PA0HzwcFBffnllzr55JP1+OOPW3DVZ//4NzRMTzOH2h/eJ/gj3iZwP+Z7xYoVeuCBB+zvN998s1577TVjNHV3d6uhocECML29verq6lJFRYX5CmSvaTTkM3+S9Mwzz9j1N9tsM/v5q6++skAEup799uabb+rmm282AOyDdjU1NTavNFijtMGzRjxVHNDsg58w4ACj6ArmKz8/X5WVlQauAIoE2sKNlXy5EfLm1wq/o6WlxY5yWn/99W0/eqCfTqdVW1ur6667Ttddd52mTp2q+vp6jRo1SuPGjdPYsWMtmzl37lxjE+BXsl74j9JQrX8ymT23edWqVYGkSa4AyprGP9X1dPTo0brsssu0bNkyvfDCC7rooou0yy67GLUt13/rrbeefvzjH+vWW2/V8uXLdf/992u33Xb7Z27/7fgnh6f8eSXDvz11DoPN/31G0QM8jE64GxebCuPLhvOOHYLqj0QIDyiNUrCjWHd3tznSOFWpVMo2Otke/s7z8T4oB7J1/pyinp4e9ff3a+XKlQHqYridPs4rDia/IzoLDTCRSJgyiMfjGjVqlFpaWuydcjmN3DOTyaijo8M2PA63zw6EKbtcx1NoAa4+8i4NORjUHBBVRi4qKipUXl6u0aNHW6DH17r6zJsPNEhZoAi45rm9IqcRUEFBgUUsAQFEJAGrPT096u3ttTMUOReOd41Go9piiy10+umn2/1nz56tiy++2LIEn3/+uY455piAkZekRx991Awe8gDoeeutt7Txxhtr0qRJ2muvvXTHHXfojTfeMGojdJ6FCxfqqaee0p133qmVK1daYX+4iVBhYaEd67F69Wodf/wJ9hzf/e53dc4556wxY47RO/DAAzVr1ixNmjQpENnt6upSJBJRVVWVysvLjerDentHwkccPQUqTH2WFABKrDHOB98NOy0FBQVqaWkJNFmoqKiwLnWcg9Xa2qpoNBpouCNlnXQyuNSZYPg97dlnHHwmCioU3/Uy5encfm59XS1gxzsDOK3MT3htPPsiTFPnOdF1fs/zs3fqmGc/r+G6rng8Hsjurml4OrQHD+hxfw/0NN9hHvgu9GsCXn6/U/MF0wFHG/3LfPjjflgv72jmcuw9zZt3INhEsIb7+Hfm3Zqbm1VWVhbwOcrLyzV9+nSzW1DFWUtkjwBZfn6+Dj30UO2zzz7aZZdd9PDDD+tnP/uZioqKVFxcbBkNQCuUyHXWWUd//vOfA51XY7GYfvCDH1hmCabClltuKSl7uHlDQ4P6+/vV29trtX/xeNzOznv11VfNNhNwI0jp54J1xX7gWJMBy8vLU1VVlVavXq2BgQGNGTNG6XRazz77rM4880y7zs4776yHHnpIH3zwgc477zzV19cH1uj000/XUUcdpUwmo2nTptnvX375ZdtHgFUCgZ4t5GUGPeUZHewp1p9/I8fsF/wKgpm5gjsEy4qKipSfn6/29nY7+xEg5D/n5SmVSmnRokUBuX/44YcDgEQK6hnkG935m9/8RrfccosOO+ywQK1fOCgFeOS9CdDwXP7ZcpWheDq7lO34Gy4VO+ecc7RixQoVFBRozJgxBgp7e3u1dOlS9fb2Wudh/Jp0Oq2KiooAMGau/bEYvpzsq6++CgB+KOrt7e06/vjj9bvf/U433HCDsQ2kIdZOf3+/MXnoyIr/g20j0wk4Zx0JsGGvyVZ7Bpifr7KyMrW3t1vQf/Xq1aqqqgp0kcU+5gpGVVdXB0ofPvroI/vMxhtvbPLp14aAe0FBgXbYYQe9+OKLeuWVV/T2229ryZIlevnll3XmmWeaDP7lL38JBEKQMdbfsz4ymYyuueYa7b333po+fbquvPJKNTY25qwFX9P4l47HiEQi2n777XXBBRdo3rx5SiQSamlp0SeffKJXX31Vf//737Vs2TL19fXp008/1S233KIf/vCHw+gf347/meFpR14JMRAuD6i8c8RnETA2C9GzMD3SZ8VwMHykE0XOxs81ODg9HImqqqoyIfcOFArLF1/7Z+Y9eS5PM5KGqFcdHR2qrKxUOp1WS0uL1lprLSsmZ/hGNgyAZTweNyca4yMNRfij0ajRHHwBethZ8pE3TwtLp4eOoAhTPJkTaC8YKAwSRfh+zYhe4wAS3SXiCi3WZwikoeg1Dot3bqn5wTnAGBJY+NnPfqbp06frkUceMefGO9NE51ConnY6atQo+9kHNWbOnKmzzz7b5vHRRx/Vscceq/fff1977rmn0V/GjRtnEbonn3wycJYbEdu8vLxANP29997TXXfdpQMOOEB77723/uM//kMbb7yxxo8fr+9973s6++yzdcIJJ2ifffaxTCrKmgANhr2np0c/+9nPtHLlUBfX66//o0WlJQUMGO9JltDXoRD4aGtrs9qwsrIyFRQUqKGhQZ2dnaqqqrL5ZQ0kBYCiz3jlisazz72Bw7H2QAC54ABk5LaqqsqAIseX9Pb2mjOAbvBZdLLmra2tqqiosM9xTyio7BkPvlKplGpra43i7kGYd1gkKT9/aL+S/SIIxPd8Ri4MZjxgZT94h42/+4POPRDnfrwLezIXIPXXpFPmPxr5+fmqqKiwGmL0Xq415v196QAsECh61Dt7vcVg3XgPHDPACfsAYOCdLV8WEB5+Pj0IYG/xnD6A5Zs4wGaZNGmSZQWPPvpoCyZgqzwgwAHmu1D8f/vb3+qqq67S+PHjzV6yd6BRp1IptbS02M/5+fk699xzdeedd2rGjBk677zztN5661n9HHWQ2223nb3zW2+9pVQqpXnz5llgcaeddrKjgNrb2/Xhhx+azuDe3uZ5GfC1+8hRU1OTSktLzT588skneuKJJ3TTTTfp1FNPNZnec889NWvWLJWWlqqkpETHHXecnnjiCf3617/W9ttvr1/84hc65JBDzE6PHTtWG220kaTsmaft7e0B2fZNVvgd6+6ZBHzmH1G9YV+whzjaoLGxMQDU/DUAH9g3HHt0EOAMefe1vGGgKEn33Xef0Xq988/3vEw2NDRYs5e+vj4tXLgwoNMA0tIQK4Pn8s1dCgsL1dDQEKCkeh3Ie2JPWltbjSJcUFBgYL6trU3nnntuYE/W1dVprbXWMgZBWVmZJQAAYOXl5UqlUibDkUhEjY2NRrOcOHGitt56a3sWGs744EZhYaHefvtt86ueffZZffbZZxb0ILPt9StUU+bB1xcDZJF3H9zMy8tTZ2enBSHRF+GALMGDzs5O6wpcVlZm5TXIQJjNQUCSYBHA1ANFMqxkzplLzyrz2Wj/bEceeaQdefH8889ryZIlFpjwAUMffEun0/rd736n3/3ud8pksrW1v/vd7zRu3Dgdd9xx+uKLL/R1xr/9HMWqqiptsMEG2mabbbTFFltozJgxwxbi2/G/M3xkLpPJWDMZL/SAOh+Z9RnF8M9sYgTdK1nviPnzFLu6ukxhQK8go+frEaSh6BER2mQyqerq6mGK22cIyFZgwD0NwIMKvu+L3VEsUB7CSt4fz0BNls/oeScRyhYGwFM2S0tL1draaooY0JIrg8tcoXxwZqjfQkH6xjG9vb3q6+szWiyAnDltbm7W4sWLDSh2dXWZkvMNIrwTyTx7+YDCEaahSUPZCA52xuAVFBTo3nvv1bPPPqt0Oq0bb7wxUB/hI/xkC6DNMEaPHj1Mtlm/Aw44QLfffrvpnfnz52v69OlG/1hnnXX02GOP6cADD5QkdXR06JlnnrHnLy8vV29vr7766iujfIV1WEtLi9544w0tXbp0WDbtk08+0YEHHqhly5aZ7BFQYd5OOeUUffjhh4HvlZRknW8MgHdakam2tjZVVFQMWx/kyB9tEovFVFJSYpFb5NQbFkCBB1jMPU4y7x7OSErZGjjqwfz6Ixd+rxHgYM+TfccRwzmH6kRWx2cKoellMplhzR+kobNZuVZhYaHpON9xNBqNKpnyZ0ulA06+d/BzUU/DAA6Z9X/z88HfvT7j72G6GOvggeKaANTXGcy7pz6zz8J7PJxRBGxBF0W2eB//3LnWA8edbC3AO5VK2TmntMdnr4SBgH//TCYzrJMrYNEDEPTUwMCAWltbjSlRWFio22+/XQ899JCOO+64ACuB+wMeAcIDAwPq7+83xzg/P9+AqZdvgAHf8cAakLzjjjvqscce08EHH2yyhbxKMhAoSa+//rqi0ag595K077776rvf/a79+4UXXjCQUVRUFGD3eKCIAx22latWrdIZZ5yhzTbbTJtttplOOukkHX/88br77rvtmWbMmKFrrrnG3gEwkkqltO++++r222/XgQceaEEIZG377be3tZs7d67JE5/zGUUvS7nWH7spaZiD7tdEGgLIsFny8vK0bNmygJ/ja9UAXZSE8GwENwjusU/a29sDtoh5fPbZZwMMhHB5DCDgiSeeCOy5BQsWGK0T/UdggOCtz+jX1dVpcHDQKI1k33IBRe/nXH/99TYnP/jBD3TVVVfZUUnvvvuuzjvvvIA+YJ8RHJaGjkRKpVJGDWZ+UqmUdbmVpOnTp2vcuHH23aVLl1pPA0B6JBLRBx98EFjLe+65xwKPUF9HjBhhAVv2je8rQcCadQz3GoC90N7ers8//1wNDQ1asWKFli9frkQiYfqEd+XZVq9ebd3ofSdYH3BBhskosgYEmrydnzx5stlmghPIB+wOvhsONhYUFBgrIZ1O67bbbrPARLgpGfbvggsu0D333GO/x4b09/fr7rvvDpTr/KPxbweK347/d4ePqsHBLikpGUatwjnwvHjvDPsInU/BE9n0tYF83p+n2NvbG4goeuOTTGZbF3/++ee28ZPJpFauXKlUKmWdOD2oxCFFKVMb50FheGPjTHjaDs/Q29uryspKo1UQwaqoqFBHR4c5JDhMno7A74gYARDpSoURikajxr33HSj9fPsoI4oZh4S1odFEfX29WltbzQgvW7bMOpd6J7uvr8/O5clkMrrpppt0xRVXqKWlxSJhvuFBmIMvBeksKEYycB7kIlPQBqFkDQ4O6ve//719ZvHixca5DzuKKPh4PG4HVEsyA+flkc9LWefm7rvvHua4brLJJrrnnntUU1OjY445xn7/+OOPmzNSUVGhvr4+3Xzzzfb3M888Uy+//LJ++ctfavr06XbdsrIyTZo0STNmzNAJJ5xgAHbp0qV2phjOWTQa1dy5czV16lQ7x62kpNTukUxm144AgnfCkQfkABBNBhtnlKwY+7ykpEQdHR2B86N8IxvmzWe3PFD0II796QGTpyaGM4pe3hnFxcWWFWVdPX0qmUyqo6PD6mRpQsBe4rB6dE24jhNdAuAls4/jLw0FPMIBHhxoryN9JjJMPfXAjbnhb2GHLZzB8ywI7yR7toef5zBF6psMnKaamhqru4Zt4B1J7u8dXJ6ntLTUKLM4UT6zjfNHt0sPismEFBcXW32RJHPMcV59UMUPL6NQK71jB7BFtzBv6Kmenh51d3dbK/xkMtv4ya8/jnwqlVJFRUWAgoz+RfYk2fEHNDljHtkPlEagTz09VsoyIghednZ2WrBl6tSpNvevv/66+vv79fzzz0vKNr/ZYYcdAg0vXnjhBVsn5Jy19eUcHih6muOcOXP09NNPW6Oa8DjwwAN1xhlnWMbGM0Z8QxaCkTBB4vG4pk+fbteZN2+ezZuvk2L4wElYZ/B3ZI739WtHZtfrJx/gIZvus5bYJK/nfO0fsu2DnJICRx2MHz/efr7zzjuNGeOfmcE8hetNAYroc+RlYGAgABSRr+rqap144onaa6+9tNNOO2n69Ok65ZRTdN999+nzzz8fNjfRaLYs4frrr7fnOP7441VdXa0//vGPJm+/+93v9Le//c3ek2eg9MPXafIsyBX1+zRqkqRtt91WBQUFmjBhgqRsRhFWGT5PJpMJAEVJuuuuu8zWczRZTU1NoHszzCie0/tvBNnYX+iC/v5+HXzwwTr00EO19dZba9q0adp00001duxYlZaW6le/+pX5bd7PghbKHkB/AvzDdpU9RvCcDKskbbnlloEEC35wOp22LCfzDZj0a3nssceqvLxckvTUU0/p008/tXXwdcrJZFKnnHKKHnzwQZuzX//613rwwQd17LHHmp7+uuNboPh/bHiKB5vVK1SiEwisF2TvjPusIsaxo6MjcPwFG4iNTIS2p6dnWOMLnFEcjry8PDU2NppigrrmgR7PTWQXgEqnP56P7CnDgzWcUR+RGRgYsIPR/WbOz88eiL18+XI1Nzfbe/naJ5xanIL6+nrFYjFTan6QyeFAXUnD3s9HlwoLC20emWMofNDQVq1apS+++EKPPfaY3nzzzWFZiIqKCq1evVrz58/X7rvvrssvv1yPPvqoZs+ebevh18Y7kShiOrrhHIQzwR4oQkFEaWYyGc2ePdsO0GU899xzRk/G8AN0JKm+vt4ygtKQgfaBCmnIuA8ODmr77bfXn//8Z1OK3/nOd/Tggw+qvr5eAwMD+u53v6u11lpLUrZ5RENDg2KxbB1hS0uL7r33XklZgH3QQQdp/PjxOvjgg3XppZfqiy++0OOPP66XX35Z9913n6699lqddNJJmjNnjtZdd11JWarPTjvtpPnz5+uhhx7SEUccoQsuuMAijHl5ebr66qvtnXzTFZ+hYx1wXNlvgCwop+wBroGxgkqJ3LPv2eveeSRanAso4kh7lgFr6pkKXl7836QsHRn6Ke3ieVZpqIEIUdyOjg5bv0wmY8437ASyzT6jzGcJ0kQiEaPeSkNO6eDgEFDg3Xw2EGco/BmuH84YhtkQa8oA0t0vF1D0wNQ7CbkoUl93+LNq8/Pz7dy8XPWlucA910ilUmpra7NsNM45zwe1luBWeXm5HbMUj8dVUVFhIA8niOfyUfhcIME7V+gJSZYxxDEEzKRSqUC3SYKV0WjU9pYHK8huKpWyZlXodU+tI1vIcS2AZx8IkIaO1PBA0Wedq6urDQSsXr3aGo6VlJRY99Nly5bp4YcfNlB8wAEHKB6Pa91117WjN95880319PSourraaJRk0sMZRd8BMZPJaPHixbr77rslZeVu22231QEHHKCzzjpLl156qebNm6djjjlG6XTa9iNynk5nm9NxfAry0NnZaTpoq622svlYsGCB7UeyqF5foKMkqbGxUa+++qp12kYGwuwlP7yOoUEP/3mwH3a8U6lsV8+uri47L1CSzRPP5rO/nkrIMQZS1oasWrXK5sbT5Hnm5ubmYWcMfvbZZ1q2bJmtEY3Huru7rXYVWmw0GtXzzz9vlMHBwUH9/e9/13XXXadTTz1VP/7xj3XUUUfZe+CPzJkzR01NTZKy4H/06NGKx+PaaqutdPbZZ9uzULrxl7/8RatXrzYdyLFe+FrsGXRZaWmpBgcHtWDBAvvb1KlTlZ+fr/XWW8/msaGhwXQZtsHPp5Sl2953333GNqitrQ0cKYUO6OnpUSQS0eLFi7XBBhvoqKOOUmtra8CWoWOKi4s1f/78Ycd2ePm65JJLtHr1atuznmFCgBabhM/l9zT7jn0SBor19fWqrKy048i4L0Ge0tJSC8T5TuPSENOtoqJCP/rRj+y7V1xxhWVAfbOeM844w5oW5eXl6Z577tERRxyhUaNG6dRTT9Xy5ct1/vnnm6/yX41vgeL/oYEB904Nv5OG6lFQit5Y4vQhyGQd+H9/f79WrFhhPG0+Kw057/7aOAQ+2wW9wtM+6RgqyQrOOzo6TElIQ12jfPManA7+lisjyvcxYNBiycQ1NjaqtLRULS0tqqysNAXV2tpqZw36GiYiT9CPkslstylfO+OdoGQyaaDWA0WMi38PsocUleM04gThmDQ1Nen888/Xueeeq9NOO02TJ0/Wb37zG61atcqM4nHHHadjjz02QJ+ZM2eO2tvbDSh6KjKDn4kK+wyTrx/ycw2tmCxPY2OjZs2aNUw2n332WWvqQDZUkhWwFxYWBjKKAEXmyGevkel0Oq2tt95aTz31lO6++2795S9/UU1NjTmLsVhMhx12mL3bk08+aeDl0UcfNWN72GGHmZPtaWaxWEz19fUWDU6n05owYYLuvfdebb755pKyRu/www/X0Ucfrc8//9yef/Lkybr//vu17XbbqnmdiPo2K1MmMnTeo88GRKND5yR5ucjPz1djY6M5iPzdZ8Oo66TRjXe0cKI9ICITJ8mCJPzN07Z8oIbv+4yJf3YfnPK0wYqKCsuCcJ2uri6Vl5dbFgDgAZitqalRPB43WcV551m8bqOeV8rqtvr6etMJkUhE0Vhas5+Yptue3kmFhTFzBHlXDwB8NlDSsL3sn4GMZzgzxiCz7/WPrzkNN5uQhp+h+E0GcxKNRs0JoT4oTD0NB4f4He/H+a3SUGRfylLkuB5rDpWK7JU0lKWIxWIGMrxM5qKwe7okgM2DcmqvyTJ7Kh4MEeoLvT71QIVrejnzDiBAmWZpZEM8AEEmoBGyB9EVnrINxbe8vFwFBQXmhEaj0QD91HcUPfjgg219+Mzg4KAWLlxocwSQDZzVKllAE+CQSqWszb6UrX+6++67dfHFF+vnP/+5dt11VzsexQNF9oU/Q8/vw7a2NtuzJSUlVpP12WefacmSJQG7zPrxHvn5+XryySe144476vzzzw8wPsiyhwMZzL3PDkMRxJ/wwWZPsZeGgszejnk58Hu5q6tLiUQikFHcZJNNrIRBytYqSkONWsLBiIceesh+h22TpNdee82yQp6m2tfXZ74Rv4fGK8nOCPXjvvvu0y677KKmpiazB57B8/Of/9xsVn5+vn784x9bk6eOjg796U9/0mGHHab1119fBx10kM4991ydccYZOu+883TiiSfq2muv1fXXX68vv/zS5qu4uFgNDQ12XuCkSZNUUlISAIqS1NDQoJ6eHlVVVWlwcFBtbW3WN6C+vt7W9dZbb7X69YqKCpsPgjzokXQ6rd///vdqaWnRp59+asditbe3B/zOWCymv/zlL/Yc++23n/bZZx/ttttu2mKLLexzt956q9lATzEn8JRKpQzMe6CIfBJ45XurVq2ybP3GG29sa48/JA0dXcW1wsE0KRjAO/bYYy2Aft9996mxsdECjG+88YYOP/xwy+DH43Hdeuut+v73v6+ysjKlUimr6TzkkEO0cOHCYfKTa3wLFP+PDShBHIIrBYGWd4hQAmGqKZsAo0hGrrq6OtDoxmcUPS0znU4b/YxrE6FNJpMGnOrr69XR0WFt0evr61VaWqrq6mpztFDy4QhgcXGxfvGLX2j33XfX22+/bZtRCjqvnhaIAWPTUuOXSCSsAU00GrVunplMxoq7mUcMjJ8jD8pRQjgZBQUF5ixIQcop1+JZzz77bO2000568sknzWnEgLS0tGj58uVqaWkJKMRVq1bpD3/4gw488EB973vf07Rp0/S3v/3N/l5TUyMpm10mupzLoEpBaq2PLnPkCs8dphoSde/r69Ott95qIOjQQw+1NXvmmWeMxkLXM2/8CUQw1l57bZNnDDpryztAXa6qqtKUKVPs4HfvWPh290888YQBJ+qCotGofvSjHymRSKinp0eJREL9/f36/PPPVVxcrMbGRjMMrNeoUaN0yy23aPfddx+2/zbeeGM98sgjmjt3riZPnqyi4mIlov2KVBYo6gAT8sczdHR0mGPhgUV3d7fRsX19qc/QpNNpK8r3oBCwwGfZD8wNc+mzh1KwiyYyAnD1WfloNBqg0UhDoMPLGNkPMqKcm8W8IncYRw+E/fWkIMDzzl4kEjFHbMixT2nTKbVad1KxCgrzTf9wD09p45o8Szjr5oEia5groxjWGV6Gmds1AcV/lnrK4HkJTrHmHhj6rBgjFotZBJyz4CRZTZwkdXZ2WuaX/ZWXlxdoZc8z+MCBLwdAl4cziswlz+bXN51OG/gFKPrOpegmZMkzRPyacV3kvLa21vYOerq3t1cjR460QB363mfZk8mk2trajKbGdQnk+CCalJWb8ePHmwx3d3dr2223tb8z7+PHj9fkyZMtCObB5MsvvzzsPdiL/vc+mPL222+bHSgvL9eZZ55p80KAMx6Pq7y83PQoc5NMJo0u64+Sicfj6uzsVFlZmdn/rbbayp6TbJPPGnsa6xVXXKF99tnHGrM98sgjgXPt0F1+79E12+9/5tIzJqADd3V1mY8B2PB7u7q62mQJGy4NNXnq7+8PAMWJEydq//33t/17//33a3BwUIlEwuTQj/vvv99+vuiii+znhQsXWrMUZNtnxH1G8cknn7R3ffDBB7VgwQLdd999Ovvssy3bv2DBAm277bZasmSJnn76aX311VeSsnTQzTbbzMpLaDZ2xRVXaI899hj2vA0NDXruuef017/+VQ899JDuuusuYyBNmTJFH330kX3n1Vdfte9tu+22Ftzi6BhJ1rWcmnlfvzdt2jTtuuuukqSVK1da3wB0DLaE+clkMurs7LSOwJJ0xx13qLOz04JH7Befhd1iiy10zz336MYbb9Ts2bM1d+5c86fuuOMOLV++3IIj+C/IA70fWCd0CHsHv4Xf+2zpFltsEZA17FA4OeA7tIYz4DDHvv/970vK7qU///nP+vTTT3Xqqadqv/32MypvYWGhfve732mvvfaSpICPRjNDb7P+0fgWKP4fGhiL2tpaa0zQ3d1ttW2+8FoaOhPKg0SuAy2CVsOeghnORnl+PxEuMhxh55XsGw49TS9QENKQY4nRx2Hw4Onll1/Wddddp08++US/+c1v1NzcHIhc43j5Wh0OpU+lso0WqqqqhnVnk7KRr3HjxhnVwEdHcVA8xYaMZSaTUVtbm1atWmWblSyVv4fPkgFAFi1apCuvvFJffvmlfvazn1l2jcgpgOGXv/ylXWejjTYKKJrXXnvNaH9VVVW6/PLL9dBDD9nc33PPPdYi3CswKdu57je/+Y0ZBsAFkTeu4YGxJKuBTSaTeu2116w2r7KyUj//+c+1ySabSJKWLFmihoYGa1gUdrw5rkSS6urqzCn1YAKZY+5wcHyNma/PkbLHbHDm11dffaVPP/1U999/v1pbWyVlaTprrbWW+vv7NW7cOFVXV6u0tFSxWEyjR49WW1ubOSY4ZlCPH3zwQZ1wwgmKxWLaZpttdNttt+naa6/V1ltvbWAImhTZHp6LLpU4v5IsmJBIJNTV1WXUNebcZ6r8uXccJJ6Xl2d1FMypP8vO0++YW9+1DzlFpnxtrjeS0lC3PqgwnsIJ/RSDST1VJBLR+PHjzRHwAIKoNb8nWi3JMrA+2+TpjAxPT/RgxlPLcEB8zaDPgnl6qt+z4axGmObGtTzoCcssz5gLKKJD/tkR7lQMdTGcQfQgjkEGt7Cw0MC6lK0TZg66u7stkAGYgCbtZZj35XsDAwNWj5or0CQp4FyxBpz5FolEVFdXZ+fYwXhBJ3i7gO1hfqHx+2fGPqCXqQGmZKKsrMzmiGfyWXccMA9esAc8O79Db3jQ2tvbq80222zYHBxyyCEGogcHB7XVVlvZOtHQhvfgXWOxmB1vgO3j/S677DK79syZMzVhwoSAjBPMIBPr14azFj2lmb3X29trmQtJmjJlit3nrbfeUldX17Bs/NKlS/WTn/xkGNMkmUzqmWeeCchKmLXgz/SUhgI2rDO0TSl7REoikVBDQ4Nlsajt9swmHzj3zKeBgQGVlZVp8eLFkrJgZ8yYMaqpqbEGQ21tbZo/f7516/SBmJaWFqvhGzt2rE488UTLzgMUkSX0nPcjMpmMlixZYvffdNNNlU6ntd5662m77bbT6aefrquvvlojR46UJH3++ec6+OCDddVVV9l8HXfccabn0O+RSETV1dW6+eab1dLSojvuuEPnn3++tttuO7M5uUZra6uOPPJILVmyRIODg3rttdfsb9OmTbMAiweKlI9goz2QWm+99XT88cfbvwHdDM5TZt1isZiee+65wGd6e3stq+hZcH/4wx/sMz/+8Y9NB0ejUY0cOVJHHnmkpGxG9cEHH7Sgge8C75MJ6HHkzZ/l6TOK/v0233xzW1d0FYEMfG4f3JSCPSuYt1gspsMPP9xYaH/96181depUvfLKK3av9ddfX3fddZemT59u8kdAJy8vT4lEQgMDA3Yu9X81vgWK/4cGTlQ8HldBQYHa2tpMccNB95SyWGyoS93AwIDeeecdzZ49W9OmTdOkSZP0/e9/XxdeeKGeeuopffLJJ1bPJAVT6z67BigkouWjdmRAeFZJlgWiQx6/I/pPRsdHwhsaGnTxxRfbeycSCV1xxRVmLL3zipPowSYZmLq6OqsfxPiw2UaPHh3IsvjN6IEiHQ5p5tPc3GzNfLh3mOrlI+c4PI8++qj9vbu7Wz/5yU9MeRQUFGittdbS7bffblHbtddeWw888IAWL16sM844w2rxJOnwww/XY489pt1331319fUWcerq6tLdd98dcHLT6bRWrFihnXfeWX/961911llnqbGx0Z7RgzACC95hLisrM+XqnZOf/vSnqq6uNoqmJL3yyiuB7Ku/TkdHh3WCGzlypIGdsGL1WVgPFJFjT7FkLo8++mi7z1/+8hddc8019u/TTjvNosOe/lZbW6uKigqTEQAYThlyesMNN9g5aDvuuKM5UwQnCvILVNmRp7zlfcqP5wdAYSKRsOgoDZH6+/utKyhryr6oqamxvdDa2mqAv7Ky0sAx90bOvAPOdTzlzje9+fLLL412nUgktGzZMq1YscKMvm/kAIWbQI8HOQBF9AGRc+9UoR9wUInw8k7+ecMZSvYxDql3dAHz2b2f0p3XL9bdN3yuvNgQUJSGHBlAAxFk9Jp3LnkGL7u5Moo+a+Xr7Pyg7jQMMnPVZX2TET6rFhkIZ7lyAdy8vGxb+fLy8pzn3fo6RN4TfZnJBLuU+vsgW7A1fMDQD38tDxQ5nLuioiJAb/dAj7M7cXg93dQ3fOGZ/boVFhZaiQOBPbI7UC/Zz+g+jlUiiMkAKHLvMJhEZ2Uy2e6HG264YWAOdt99dwMt1FxtvPHGkrLNVVasWBGoGydASSMxvzeeeeYZO+tu/Pjx+tGPfmTBEE8bhp6ILiKAA82Y7xAQIPhB8DSdTmv99dc3MLRgwQLLFnKf559/XltuuaXef/99m6cDDjjA3nvu3LkmF9CKPdCk5ML7Guh7sss455z/OXbsWBUVFamnp0dLly6VFHTEfV0pcyAN1aDhXK+77rq2Zocffrg987333mtr7ct0OGtRkvbff38VFBRYZ9jm5matXLnS1iqTyai/v1+VlZVqaWmx5yPQKklTp041CidyNHHiRD3wwAPadNNNJWWB65dffikpC1R22GEH060+OYBs0DDpkksu0f33369nn31Wjz32mBYtWqRHHnlEDz74oGbNmmV0zfb2dp133nn68MMP9eabb5osT5kyxYLIEydOtHtgL2AZ+UY2a6+9ttZdd1279uLFi/XOO+/YPvLMEeTONwZCd9x9993GUEmlUvrkk0/08ssvS5ImTJigXXbZJaBPpKydZ9xxxx2ml3xGWpI++OCDwDnMHihmMtnjJwhEZjIZk2spC+y9HxzOHhLYIigRrtf2bL/a2lrtvffekoYYgVI2eHf55Zfrnnvu0RZbbGGAWRrSQcjbwMDA125q8y1Q/D82EEQolCtWrNCIESMsSkjtBAZnyZIluuSSS7Tlllvqxz/+se644w69++67krIA7LnnntNFF12ko48+WjvssINuuummQHdBaWiD4/yj9H1ERsoaD+9U8qw0bPFONH/zBe+M8847zzJCjDfffFO33357IJILmIPm5w+yp0MfxoEMLCAT54VIExFEgF08HldXV5eamprsug888ID23XdfHXDAAWpqajKgGKYj+Qg6WSDoJoz58+cHIrDpdFpXXnml/fuSSy5Rfn6+xowZo5NPPllPPfWU7rnnHr311ls677zzNHLkSFVXV6unp0dHHHGEKcI///nP1jRFyirkH/zgB9Z8pqurS7/85S8DmVkoHsyHV1xQTObOnWuF5OPGjdMBBxygdDptB0xL2cg41/XXkGTGTspmdHH8wtRTHBWcPv6Wn59vnV19BK+vr09HHnmkOc633Xab3nvvPUnZM4+23XZbtbe3G0WXexBFr6mpsQ6KI0aMCESSPVUzTFVEViIZqb6jUJHPOlVcmK3R7Orq0rJly5RIJLRo0SKlUikDbeXl5YrH43bcgM/246z29/cbTQrHZ+TIkdZ1GPDsnbtwRjESiQRqGHHsR40apbFjx6qsrEy1tbXW8c7T75CTgoICWwtfX4ex8/QdAD573Dfgwen3v0dOPOUWuZEUyISGM3hGCRzM6NoL39ec332p2H8CRZ8NwzDjEIfnx4MsD0D4TC4KJ7JXVFSUs8GVz44ywvrhnxlQQMOZqjD1dE0ZRbo+h4EitHcat/D5IXpvxsAV9VfIpO9a6+viw4P18BlF5i8WiwWOhWDN4/G4KisrVV1dHWgWJg3ZJQ+eY7GYZSU9UGTOfOAOJ5414jMwZcrKysyuIRc+A8l3fOMpv5cHBwe1zTbb2PNuscUWmjhxogVLydr5z7z00kv64osvzEbR1IP9hz0fHBwMsE7+4z/+Q7W1tQEQ65vheEq6z8Ayx96Ohx1eaKic17dq1SrLhkWjUc2ePVvHHXec2epx48bpb3/7m/7whz+YvnjyySfV3t6uzs5Otba2muMeBoo8O0DRU9YZ3reAgZNMJjVhwgTzQRKJhB5++GF9/PHHFrTBRhM05Zrrr7++AZ7tttvOunu+88471rXdsyw87RQnH6qllKVuohMJ7NFpF0DqaZY77LBDADyh16qrq/Xyyy8Hri1lwVA8nj0v0weKWM/wQOetu+66WnvttbXZZptpwoQJmjhxoq677jqTv87OTh1xxBFGb918882tHCIajWrUqFEqLc129162bJlisZh14KXRS0FBgcaNG6cxY8bonHPOsWd4+OGH1d7eLmmIpg7V86uvvrLvb7HFFnZ0RH9/v2655RalUtnGiHfccYdd78gjjzTb7ff6pEmTtOeee0rK0l4JWvsAwoUXXqhTTjlF++23n1599VXzA5LJpDWKGhwctABWJpMxnycajWqjjTYy2UVfsacAjwQyfQmIXx++V1JSou9///vWAbWsrEwnnXSS3nrrLR166KEmD57xFolELOg5ZswYY0Z9nfEtUPw/NBBSaKMoY4xAV1eXUWzYlMcff7yefvrpYcBr/fXXNyFlNDU16bbbbtO8efMCThvOF0LpHSkPFHGSUGJEBmOxbCdKwIp3FGmfj+F47LHHLPtWUVGh8847z57vwgsv1Oeff27GD8fG02oxqNXV1VbHmZ+fb0CRTABRf5/K98/W29urRCKh6upqe85bb71VkvTRRx/p6aefDtDv/PCOSX5+vp5//nm7PpFCSTrjjDO0ZMkSRSIRXX755Rbp3GGHHTRlyhQzbrFYtn5sk0020aRJk8wY4cjX19dr3333lZSlxzz66KM2N1dddZVeeumlwPPdcsstWrRokTKZYHdO/p9MJvXJJ5/o4Ycf1m9/+1tdeuml+tWvfmXfnzlzpkXwN9xwQ6OzPf+fbeB9phIFCVVIknWSlYaopx0dHSbfPqLNQJmTHcPgZDIZVVZWauedd7bPMegel0qlTNaRFaJzyWT2XM/Gxkars/WRSoY3Sv5MKk+bicfzTHkPDAxowoQJqqio0Nprr20UMN+llMwahiUajZpz64MhUrbLIo4jAZlMJqPPPvvM5NBTdgkCEMFnkD3A6fQZBA+eqDP1VBw/2Ec+2+ezbL4ODFmF5sdzspY+o8j6+YZaPC+y5WXWnic+1KCJz7GO4XXyDIjw3s213n7uWCuyzz5aLSkAHPz9cmXavsnwtC1/v7D+CT+PNFRKUFFRYWCPQdMO1p7P++tEo1GVlpaqra0tQL2sqqqyLLFvAhEePqjBtbEVBDfpvsi+BoSxH2Ag+Cx1uMNs+AgFnw2ShvSyJLOVmUzGnERJxsohYMN+4HfILECTBl4M5JlMk5Q9847MaDqdtuObAGBSFigiI4D3tdZay6iw/P6xxx6zGrvJkyfrO9/5zjAZJkPig11Qev0cExSEds6e7evrM1AWiURMt0qyxjvXXnutfvWrX5ks7bDDDnr11Ve16aabqry83JrgrFixQl999ZU1IWPfe5Duj8jxVE10GO/hgySJRMKOKEFvdHd3a++999Y111yjU045xeaNYFk6ndaiRYvsGuuvv74FxPLy8gJn0l144YXq6uoywN3e3q5nnnlGUvZoFEouPJh7/vnnDZzgB0UiEdXU1NgRLxyHUlNTowkTJlhX0P7+fstsY6/otC1lqaD77ruv4vG4NRzMRWtkf3oGBnV63q4WFRVp7ty5tk6AOe7lA2h5eXkaO3aspGywYHBw0JgxgMsNNtjA5vHAAw800L1w4ULrEgvDjSN+fPD8sMMO0wknnGAB5FtvvdUCE2Rha2trtccee0gaYlJ4XXf66afbz3fffXcgsPXQQw+ZD5NKpXT22Wfr7bfftvlGVtZaay21t7crk8n2oGCvrb322oHsNhnveDyutrY289VgDGBfGcgfOqS+vl51dXW65557dNlll+nNN9/UT37yE/NNPEPOU7V9KQDP8XXGt0Dx/9BAEOkkijLFkCGM0OcWL15sirG4uFg77bSTzj//fD311FN68skn9fTTT+vOO+/UOeeco+985zt2H+oKvEOBsYfnLQ3VjHm6l+9aB9XHZ4bCgI7oFGDhhBNOsHuedtpp2m233bTffvtJykYfTzvtNNtwbCrv1JHBzMvLU3t7u52pRZQoEomoqalJK1eu1MDAgNra2ow2gjFqbW1VV1eXampq1NXVpb6+Pn3wwQdGnZSy9AYK68PrgwLnOT294vLLL7dOnd3d3Tr33HPV0dGhG2+80Z7/D3/4gzkvGEIi9v68w2g0ajVuFEdL2fOgoCjdcMMNkrIKZp999pGUNSy///3vrd7LU4rfe+897b333po0aZK+973v6YILLtDzzz9vnb8mT56srbfe2jLX8XhcW2+9taRsofuXX35pShIAkMlkAkBxxIgRAaA4ODho51tmMhmjInmHnvf2gL61tdWcDIAyY9SoUdptt93U0NBgtXasCY4qskxLeJ4xV4dKD2yQb183lv1MxrKFnPnm9xBRfNYZoOjBKQCENWZtIpGIRo8ebc4kgY66ujp1dXWpubk5QFvzgNoHUgCo7EWytT4ggVx4UBmOWAOaqMuh+ylOM9lMn4EjMOHXwtPSCEix5hhrX3uNI8scMnDs/VEgfMYHs7xz7B0rv0bI5Zqop2H5CYNATxFakzx904GDEH5v1pcRfh9GYWGh7SsP9lpbW1VdXZ3zPX22YsSIEUZ3Yg2rqqqMOodz489HZfgMkZ9zDywJ0kARRSZZO19zitx6YIXO8UEE38KfewFqoVZjs5A19AkBV4JgPmuYSqVs/3FGr1+PoqIi7bzzzjrrrLN09tln6yc/+YkFTdvb2y1gueWWW9r1Fy5caAGN9vZ2VVdXW9MiZLenpydwNiznI7a3t5seZw8lEgkDE8w5dNRwthS9REaYpl+1tbVKpVJWvydJL774oi6//PJAN9dzzz1Xl156qWpqatTf36/u7m5tt9129vdXXnnF5ppeBdD8zjnnHF100UXWq4A1giFESQ3f8dm5kpISC6D09fXpZz/7mVEFW1tb9frrr1stJj6Lb2Sz3nrrqbOz06j9u+22m6qqqmw9fvrTn9oef/75520ddtttN9tzm222mX3nxRdftCwYAJggSCaT0RtvvGG6a8qUKRo5cqQ9H7YN4IIOu+aaa/Tmm28awER+ARFe7rw9on4d3Yu8etZIaWmpbrjhBk2aNCmwX6dNmxbwqSKRSOA4K3oN0CFVkjbccEOzF3l5efr5z39uf7vppptsv+bn5yuRSCiVSll2NR6P68ADD1RdXZ01p+vr69Ndd92l2267zeb6hBNOsOCF1+OMHXfc0Tq0fvjhh7Yer732mi688MLAO/b09Gi//fZTQ0ODyRXsifLycjU1Nemrr76y+WSO0F1k+qHMd3d3W/KEfgLhjKK3S6z5qFGjtO+++6qsrMwCrdhgKVgSIQ1Rq2EnhM+ZXtP4Fij+HxooQZQnRn/s2LHq7e1VVVWVHT2Rl5cXaMN8xhln6Ne//rW+973vqaamxiJFG220kY4//nhdddVVFrV9+eWXA2l7aagQPRaL6dprr9WPfvQj3XvvvQGHHqXqgaLPoPiuhWE6TDwe11lnnWWdMffYYw/NmDFDAwMDOvbYY62e6/3339fVV19tUURfI4nDQCSyubnZHGIyMHRhI0peW1ur0aNHG8BubW1VY2OjAbDm5maVlpYG6A9SttD87bffVltbmykrFAWAM51Oa+nSpXr77bclZWsipkyZorPPPtu6fi5cuFAnnHCCOjs7JUnHHnusUT8Aol7BEwFFkadSKY0YMUJbbrmldtppJ0nZGs/rr79eJ554oq3hmWeeqcsuu8yaYLzzzjtW9M3zLlq0SCeccIIZAj/y8vK0+eab62c/+5mkIcc8Ly8v4BQsXLgwABSJiPtr1tbWBrKXGH+cF1q5e+ozhpH1i8fjam5utgYcW265pUaNGmX3OOaYYxSNRq3rrq9txOnDgero6NC6666r1tZWRaNRtbe3215g+Eitd0QHB4eAoqf8FhcXq6Ojw2ppvLOKbCArZIh9RjEccJCytZ1kTfhMfn6+qqqqVFFRoZaWFvX19ZlewAn3e9nTdqQh0JFOZw8MrqqqyplhCQMQDBj7l+g5jhD0GupbfVdk35wEEMyaeJod18H5IPtJFzsP0GKuLgn59LIdXkv0nwdZ7Kdwht3u4YBiLBazNc4FFPkdEeZ/FShKWefCZ8VyPaOfO8bg4KDq6+sNOKEv29vbVV5ePmyOcmXTaSrS3d2tRCKhzTffXJFIxLIh/yhjOjg4aNlXDxQ9k8R3mAxT99Hn/n35Lg6Zj8R7B5fPsh+6urqswYU/Bslfg2v39/ebHfCUXoJNUtbZBij6DM7g4KDOOussXXbZZSopKTHKNWfWSQqcudje3q6PPvpIzc3NZo94Fu519913GzPou9/9rjbYYAPb/ytXrrTPd3Z2auTIkYGgGEDR0zdxXLHtgDn0KrKy6aabGnX/hRdesAxPNJo9BPyss84yfUogwXd1ffHFFwMZRH4+/fTTNXfuXP32t7/VD37wAy1fvtx0ALRmWEFeNjo6Ouzog1gspjFjxuj3v/+9HSnAePbZZwN+RzQaDYCbddZZx456SKfTKi0t1W9/+1uzKc8++6yOPfZYDQ4OBq5NTwDWnEBpR0eHPvvsM6tJ93uxsrJSzz33nH1v2223tecfGBjQyJEjTZdgEwiQEFhNpVJWSx8uB5CCDBcAB4Eyz6BiX5Gpv+mmm6wh3OjRozV58uRhpRZkCKVsljg/Pz8wl+uvv77paSnbcIb6uccff1x33XWXyUgikdDChQst8L7zzjursrJS3d3dOvHEE832Pvzww3rsscckZfXD8ccfPyww5n3Unp6eQK3p9ddfrzfeeEPnnHOOfefwww/XRhttJClLUd17770teEMigQCLPw6LDDL2iXKN9vZ2jRgxwoJUUIPpHMzIFazFrhJgBdCzP33gx69xXl625tzr8v9qfAsU/w+NBx54wKJ/KBOMJRuUDomxWExPPfWUpKzA7bTTTrapvCH1dToovLa2Ni1YsCAghICyZcuWafbs2Vq0aJGuuuoqbbnllrr//vuN7uqNv6ckkZkgS0ZmgDqJ119/XTfddJOkrAG94oorAscGnHHGGfaOV199td555x17Jt/lk3tdddVV2n777TVt2jQtWbLEIp2c44bTibLm7LeWlharVWtpaVE6nbZaTkmBVP9dd91lGTEAsM/AZDIZ3Xnnnfb5Qw89VMlk9uxFaKySrCC/tLTU6BG+FgMFG41GA2f+AcgqKyvV1dUVKOi+9tprTRHvvffemjlzpvLz83XuuefaZy6++GKrs/rkk090yimnWFfVzTffXL/85S/1l7/8RU8++aT+/ve/6+GHH9aYMWMss0P22LeDf+ONNwIOCl1dPVAcOXKkyRzZAV/zFgaKON5e5ouKitTR0WFOVzwe18yZMyVlaSI//OEP7QgU77gzACbUP8XjQweKd3V1DQOKPrvrQdTAwBC9xJ+dBPAiAMEewnhjIFhDT3nj3dnXPrpYVlampqamAIUWJ2f06NGBFvlQwL1jzjwy5wQdWDNo2j7L7B11Bnsdyhrt92kDT7bRR2qhuhLp9nVVgDr2NIEd5gYQAFDMZDLKczRHghI+8+T/z/tjXHMBRTLgYaDl/87akE1aE1DEGYYm/O8CiuiBXCPs3DH6+/tVVVVlwQjWgcYtYWqsDyb44FtxcbG++uorFRYWmjMN9RRZydUsp6+vz85U81RZn6kuKiqy+6GDJJmM+Swy8uopk+wt7zh60IiDTSMpgCKMFxq8AKpogkMABN0kDTmL7Beem33tgUk0GjUbxXsTHIvH44Ea73nz5qmoqMgaiPG+qVRKTU1NdvxRLBbTqaeeauUUdCn1gUQcWfSI36v+mcmcknGWsnabTCVsB08/RRYvv/xyO3oIPdHT06PKykqts846Rld86623hpWYtLa26p577rHrvfXWW5o+fbreeecdWzfWizXgfdra2iywEIvFdPvtt1v9oA9yPPPMMxoxYoS9dywWM3BTUFCg6urqgD5Kp9PafPPN9cgjj5id/+tf/6oLLrjAmgeNGTNGU6ZMMdkdHBwMBErfeecddXR0qKKiYlggxDeL2WabbUy/k33kGdC33kak0+nAkTj4Vgz0GXqazLFvcIh+JdCaSCSsNn/evHm6+uqr9fDDDweOXGKQUZSyZSTR6NBB9FI2CI7ekLK+jC8bOuGEE7RkyRLzlzzL6sADD1QymW3Uts4661gHUzLLUtZ3GjFiRCBwwt5gdHV1ad9991Vtba2k7H466qij7Br77ruvLrvsMl111VUaPXq0JOndd9/VaaedZnvZD2i1kqxBD0FGMqPV1dUWqG1vb7ceGNh+P3wAl3nyQSlsMXaRtfMD/4EMZq4eH7nGvwwU4U3PmjVL5513nk499VQdd9xxOvXUU3Xeeedp1qxZeuGFF3IW7n87co9EIqGLLrpIm266qUpLS1VRUaGpU6fqqquuGuZwfZPx7LPPKpFIWEYwDM5SqZQ1qPjyyy8D5874ekQMLcYNZ8yfGzd//vzAJsSZCDdl+eqrr3TSSSdp6tSpeu6550wpSUNZyGRyqENccXGxOjs77d9EeU899VS75sUXX6zKykqLypSUlGjttdfW2Wefbc9/zDHH6JlnnrHMFgq1r69P119/vc4991wlk9kDpm+77TZzlvgcTiXGvKyszGr+ent7NXr0aLW3tys/P18PPPCAPdvMmTO17rrrSsp2gVu2bJm6urrU2dlpip1nHBwcDBj3733vewY2dtxxxwDNVspGWGmNnUwmrWMaz4jT4SPlOOTJZFLTpk0LtDOXshTM2267zRysvfbaS9OnT5eUjajNmTNHixYt0vHHH29KZ/Lkybrxxhv1H//xH9p///01efLkQD0Whobs8YYbbmj0mzfffDNAZyLC7M9QHDVqVICi2NHRYfMVjrZJWccb4Ma74pTiHMZiMR144IF69dVXdf/992vEiBHWXZCoqjSk6AEz1Gsw3zhRYe4/htpnsH2QRZLVJvBM1dXV6ujoUHFxsd2PIA+OJPslXHOC4eCZGfX19XYQM/OA3OXn56uiokKVlZWBd/D0YuaWdyJaj04oKChQZ2enzZenx+YavlYDeWxublZ/f791POb//h6sGcbUZ8hjsWz3STrHAfR9pimcUczLiwVoqjhyvKsHgTjquYBirvpHRjijiGEPA8Xa2trAofY+0/mvjPLycnMUcg3//n74unGohW1tbaqsrMxJn/XZdj+H0CwBALwf615SUhIAeYyuri6Vl5cPA+E+Wu6PN/G+BsEogi3IDHrWn/PonWpPccY5pakZTVAAigAs5DSZTFqpAuCcZ2c9keFoNGpAJFzv5AGxNNRoqLa2Vo2NjUokEpapkLL2HQYG34WOdsEFF9jc7r333lpvvfVsP8Ri2c7IPT09w7oi+8ACa+qDtsgD4LK3t1c1NTWm7wgg+Vq8yspKXX/99dp2220Vi8UCDKeBgQGVl5crLy/P7Mzg4KBefPFF07XpdFp33nnnsCzx8uXLNX36dD322GN2lEt3d7d1rOZZCFRmMhl9/PHH+o//+A97ttNPP92Oc3jnnXcssMq70PFygw02sAaAzAvBtenTp+vKK680+Zk3b56t6Z577mnNl8h88p6S9Pe//129vb12fiX2YcmSJdaDYLPNNlNNTU0AKErZID/gnyY7PvDY2dlpNPFwYCQc0KOOkEAOstvX12eBBWiTNOs76KCDjHEU1mswoKRsRjGVShmNNy9v6CxR/50jjjjC5Karq0s/+tGP7CzMF198UVL2mK+ddtrJAgJFRUX68Y9/HDjGJxqN6thjj7UeFz6Q6oOglEBAX81kMlZ7ueOOO+qyyy5TWVmZampqdPvtt9s9nnvuOV1xxRXmYyD73mehltPrQzri19bWBvqFJJPJQCdfaajkAWZUmDHkWTn4EeHEizSUQOjp6dHAwECgG/4/Gv80UHzxxRe17777qq6uTjvvvLNOPPFE/eY3v9H111+vOXPm6Prrr9dvfvMbnXjiidp5551VV1en/fbbb1hjjG9HcCxZskSbbbaZLr74Yn3wwQfm9C1cuFCnn366ttlmG2sx/U0HmT66nPX09FiBLU4ONVKeKrHHHnsYBYyDhH20GOOyyy67mFC++OKL5lBJQ86qPyzW1zV+9NFH+v73v6/rrrsuQAXFeELHKSwstCwOTtmcOXMszT916lT95Cc/CdB8Ro0apcHBQZ1yyilG1eno6NDxxx+vX//613bmW15ens466yzNnj07MG8PPfRQwGhjJDGyc+bM0Q9/+EN9+umnys/PV1FRkdW2JRIJi3zm5WUL3n/605/atW+99VZFo1Fr9OCdlQULFtgBtXvttZdGjBhhzyBJ55xzjlE6xowZEzB4GFVp6Kwofw6ad0qg4QwMDAQAdywW03XXXWfZUimraM455xxT6H/605/0wx/+0BTq5ptvruuuu04jR45UeXm5mpub1dzcbHTEMFAFjNCYIZFI6LPPPrO5INuKkRw5cqQZREkGBgCKPhPjaaJlZWUGGAcGBgI0TwIRUhbk+no1nFqUrgdXzc3NqqioMPpkJpMxukyuGkUyQ2sCTTh5ULiQfzqckvnzwRTmsaSkJOD80iU13BSI4AmR/rAs8xwAdWkos8JcMpgLDBfr6+uCpOFnQfmfPXjq7u5WR0eHent7NXbsWHsvGvDwPV9XzHW80cR59Q4/z83nsoGeoXfJiwVrQvhskCY8GAC9Hqjyb585Cg//roD8kpKSYeDMg/FcQOyfHbR7D1NDGeEsA8PTpYuKiuzIFmSdAIZ/fl+P57MikixiLw3VRvX19RldXhqSD85VLS8vNyfW1wT56yAviUTCbCRAMZxh81ky6NbISX9/v9ra2lRVVWXXZV/29/fbHorH41ZHyxxABeQePgvLc9IpmFFcXGzMD0nGRPFNbgAE+fn5qqmpsedcd911Ddi8//77euCBB6w5hpSV208//dRsUDQa1aGHHmoNN/icZzuw1r4Ugu/6vwMGfICTQ8q7urqsY2cqldJRRx2l/fffX1OmTNFjjz2mKVOmGHuBoGtLS4tRmePxeCDT9swzz9j6dnd3G9MmLy9Pb7zxhlEfe3p69LOf/UyzZ8+24JMHttyzsLBQn3zyiU488UTb98ccc4z2228/Y0ZlMhkLJmOD+OxGG21kwH3RokXq6ekxnZdOp7X99tvr6quvHqYLdt11V2OgABTXXXddq8199913DZx6mqQ/FmPrrbc2sOmz8DAnsJmsKQEVuuVKspp+zyrzVEQSCfh7BGIGBwdVVVVlDCtAN1lNvu9lQgoCxZUrV6qtrU1LliyRlK31ZK94UJNOp3XWWWeZj/Puu+/q/PPP1/z5820/7bfffqqoqFBbW5tKS0utRtCfxzhjxgyNGzfOmrbxHr5Rm2/ctv/++weA5nrrraf7779fmUzGAscbbbSRrr/+elvfm2++2WiuBJPo1F5QUGC1j4y2trZAvbykQKAMG8cgkeCDedgJ1g35wzdiP3vmBYHZnp4ejRo1ao22Kjy+MVAcGBjQkUceqe9+97t68sknA50E/9F/fX19euKJJ7TTTjvpqKOO+pcyY/9fHclkUvvuu6+++uorjRo1SvPmzVN3d7d6enp07733qqysTG+//bal1v+Z8fTTT5uS8Xx1IuZkFih+lrIghQgV2QYP4vi5trbWIpxffPGFli1bZsIejUa1cOFCOyh+6tSp+s1vfqO//e1vxvmWsi2kofwxfN0Gig/l/cEHH+jee++VlN0EV1xxhXp7e61DnyRr3d7c3Kybb7450LjkT3/6kw4//HAtXrxYJ554oh5++GFJWUWJAe7t7dXjjz8eUIJEpd9++22ddtppmjdvno499litWLFCZWVl1pxjwYIFFsnac889VVNTo4MOOsgytI899pgSiYTROHxG0Z+deNRRR5nzwcjPz9c999yjCy+8UHfddVfAWeP5ysrK1NraavVAGE+UCJ+Lx7NnRu29997aZZddlJ+fr/POO09bb711gPeel5entdde27j8/f39VvcyadIk3XTTTSopKbGo2JgxY8zhQrZQXJ7X7+mnr7/+uslYJpNRR0eHmpqaJGUPKuZ8M6g7BC68Q+0zUBhvHDnonDQyIJKI4wh1zIMLDAxrROe4eDxuB90nk9kOqP6oAL8e0FM8IMnLz1PPRsUa2LRcispqa1DoOEZE/srKygLGlAAMDhbzSuQRR8lnMWtqanKCokgkoo6ODgORHkhz7bAhj8Vi5iwDCgBn6AefQWaEr7Vy5UqVlJTY0QJQRMvLy20v8Yw4Nb6OA5nivXHoodH79YCCXVxSoEtu2VCzHt1BxaVD9DGAuAeKBFI8/WpNGcU1UU+ZQ+9EjR49+h92nmPf/LsGwCXX8Fk0RtjhKy0tVVNTk7FSJA0Dsh4oejoroNgfVSEpsF40K2FeV6xYoYqKikDQg+eSho7iwF7E43GVlpaqtbXVam55Nr830IPIJUAxLy9P3d3dgbNOfYAIGjTPR00wMhaNRo0FAH3MO33Inp8DgJUH19RlMsIlEujSZDIZaPzxhz/8QcuXLze6a09Pj/72t79ZM7G99tpL48ePt2ci897U1KT6+npbT9bOB4d8YKa9vd0oo8xbRUWFsW7I5JEJKSoq0sMPP6zrr79eEydOtKZbZFIjkYhaWv5/7P15mGVZVeaPv3eKuHHjDjFHRuRQWUUVIpMgY8kgU1VBK1oCKuIAoqLSigNdit00yFehQRAFQfvp37dtBKR9KBS1Vb7M1RYgWCiCiEDNOUVkZszzdO/9/XH57HjPjnNv3MjKKgrI9Tz5ZGbEvefss8/ea613rXetPRuCCASSCVx8/OMfV73eOo/4ox/9aAgEfN/3fZ8e9ahH6aMf/aie85znhHn4/d//fb34xS8O+p1xkzn653/+Zz3rWc8K8/LkJz9Zv/Zrv6ZqtRqAotTKBvIMTiWkS+fOzk447zY+2/eaa64J5TBSy3Z9+7d/ewCKALpcLhfuub29rX/7t39LlBFISaD4+Mc/Psy5A0W+QyCUcVAiQXab+2xtbens2bOBZsqeAhiS2SRTSuC/Wq2Gekf2+tra2h6g6DphaGgo1KmeOnVKX/jCF8Ke4MzQWM/V63VVKhW98Y1vDPvlne98p/7gD/4gfOYHf/AHVS6X99Rf/9Iv/ZKe9KQn6aqrrtKv//qvh/fOPeMGYSsrK4G5U6vV9Au/8AuSWl1M//AP/zD4NHTy3tnZ0VOf+lS99a1vDdd4+ctfri984Quq11sNiQhuX3nllYln453zrhBsl2cS+Tn+jteW4svElFRnCrqOk1p7eGlpKVUPd5IDA8XnPve5+t//+38HpXvdddfpda97nf7iL/5Ct9xyi770pS/p9ttv15e+9CXdcsst+ou/+Au97nWv03XXXRcUzXve856Q3r0ku/Inf/InoeHMn//5n4e0O1FAlM7f/d3fhVbLB5VPf/rTmpubCxFJUty+mBYXF0OG7ju+4zt07NixoGydCy+1Fj2F65L09Kc/PdwL+inO9d/8zd+E39EJ7UlPepLe8573hM6kGxsb+tznPpdQNDgwOGq0a97c3NQNN9wQxvLqV79al19+udbX11Uul0O0jKj1uXPnVKvV9D/+x//Qa1/72rDhvvjFL+rZz362PvnJT0pqGeU3v/nNestb3hLG++d//ucJA0pW4FWvelX4zOzsrG644YYQ+Z2YmNB73/ve8PsXv/jFQZkBtLa2tvS3f/u3CapIJpPRuXPnwnhGRkZ0zTXX7KGGUR/wYz/2Y4mzoKRdBy+Xy4UzxzAGvG8H+WRDCoWCXve61+n06dN63vOeF+gmTiXMZrN66UtfGuiiUkvZv+UtbwmOgivA7e3tkBEgIgdoAkg4UOSMIkAekUep5VjHtXM8twNtntUpiYBAir6ppyNTjVPY19enWq2WyLZxfRQvXW2phQGs9vT0hI6lLp5hcHDR09urzX6pMF5WwWir0NE8K4ETyf+Zu7h+l/ECZACdPoceSEFoNrKyspIASTFYQABNgGzegzfXYTxeJ8Y1uf6xY8d07NgxDQ0NKZNpHd/D5ycmJsJYWPvMYa1WS8ylvwOowERocfIlBSe6t7eghzy6osc8aUxgJ8YO4EvLKLrTHwNF1zntxOfTs4dpks1mL0p9ol/vIBlFukci0M6o7ZXSM4rMjd8LGnF8f+qVAYNOD15YWAiBN9dvzHOpVAo1jFAEq9WqMpmMyuWyZmdnA9XTj1ahvsyj7YC9ubm5kHnxqDwZNIANz8e8MQcwXo4cOZJgIBAciZ1UAjmMpV6vq1arhQZlkoLtwwGkc3m5XNZjH/vY0CBlcXFRb3jDG9RotBqhFYtF/emf/ml4lhe/+MVBbw4NDSmXy2lqakqNRuuwdTKXzBX3xU54IGh7ezs0JmGe/NxG9FPs+EqtgEOj0QiBHK8rZKzFYjFkFc+cOaM77rhDxWIxUcoBi6ZcLuvNb36zXvOa14T99IEPfEAvfelLgz8ByHn729+uH/7hHw7Bx4c97GF605veFPb3wx/+8ARtFJvvB60fO3YsgAdsN+90eXk5dJ992tOepte85jV63OMepze84Q1aXFzU9PR06JxOgMDt3z//8z+H+W82W0dtfepTn5LUKru46qqrwpwT1JqbmwvrivpQzwgvLCyExko8J/uGQAe6mmwb/qGkUFJDBhGASkDQ68x9byCFQiFkFWdnZ8ORF1LLd8BXiSWfz+uqq67Sm9/85vAzAu+XX365HvGIR6i/vz90nsXulMtl/cVf/IXe9a536bLLLgvvn3HFDcKwOQRGfvEXf1H/+3//b330ox8NtftcG180k8nopS99qV70ohdJaiUUnvvc52p9fV1f+tKXgm0gEYKeWVpa0ujoqCQFG8wewGZ7wBHfG1/ag+GAQXSw9/Qg0IPtoqyDsbjt2k8OBBT/7M/+LBSRkvn6wAc+oFe84hW6/vrr9ahHPUoPetCDdPnll+tBD3qQHvWoR+n666/XK17xCn3gAx/QnXfeqe/93u9Vs9nU3/7t3yac6EvSAopSC0S54kCe//znh832zne+84Lusb29rXe9611BIZTL5T30HK8jvOaaa4IxhOsv7dZt7OzsBKPaaDR0zTXXhO9+/OMf1/b2duDNQ2ft7+/Xox/96FDP1Gg0EvWNn/rUpxLGFFCIUsQovetd7woF0Q9+8IP1kpe8RPPz8+FcL5QtitEpaT/6oz+qm2++eQ9Hu1Kp6B3veIee97zn6RGPeEQA62fOnNHHPvaxUEfRaDT0f//v/9UnPvGJxPf//d//Xb/xG7+h9fV1TU9P69Of/rSkVmTqyU9+coie//iP/3jY8H/xF3+htbW1RGT+r/7qr4KB/d7v/d4EFXFxcVFnzpwJn0ehuEF2gFMul4PzD91ldXVV58+fD4Ykk8mEmimvW2wHFCuViv7zf/7P6unp0WMe8xi97W1vCxkDr3VAIRUKBS0tLam/vz/8njWXzWZ1/PjxUCD+6U9/OtQ09vT0hGY9Uot6Cr2XWjicDKeeAgrdaQEc001T2o3w09mU5j5ey4E4lRqwmsvlErV8nr1zwVl1x1Tara2ixlVqGVU/DHx4eFgzMzMJY45z7dQw9qSUpHuy5vgMhtQzFlILFND52MGL128h0LcA2WR3+Z1nMnHA/X7+rpyaSL0F2TkMXza7Wyfqz8FcOrhjr9RqtRDc8QyXZ7KZO9ahBx/aZRQ9w5KWUYzrc2JpB7zT5N4Ait1kFJlXjilBarVacDiROKOYzWYDEPPnbFdrSYQegM17IrDHZ5hrHL1cLheAIt0t2b/YnEOHDoVGM5wzB2DwwAEObiaT0crKigYGBiTtOmOAUPYoDjeOGo4ugTi+642m2Av1ej1QPxHPejabzXB2HrK+vh6CQtwDuvna2ppe+cpXBpbK+973Pn3iE5/Q4cOH9c53vjPUjv/QD/2QJicnw3MStBsfH1elUgm6hGM4+vv792SGoM7yM46SmpmZCQCD7Bp73PUhc03AGTogWVZnrtTr9UQTnFtuuUUf+chHwjFEnAPJdSXp537u5/SOd7wjzMXJkyd1/fXX6//8n/+jhYUF/czP/Iz+8A//MKyla665Rh/96EdVqVSCnevr6wvMqBMnTui2225TJpPRrbfeGsYyMTGR8CkIINTrdc3Ozobs3cjIiJ7//Ofr7W9/u5773OeqXC7r8ssv17Fjx1Qul7W6uqqzZ8+GkhipBRTZK5lMRjfddFMIsj3xiU8M9+IdACwI2qF/aKiEznNdR7C90WiErrqADYIu6PP19XUtLi5qamoq6HvOLSWL5fqYeXA9UygUEg1taJQotTqexvrd90Wj0dALX/hC/cAP/EDid9ddd12ippy/0dUEeLCLAEEvjwCAMY+8UyjB/f39yufz4TgcxuRr+81vfnPIit5222161ateFRI+0i5QZN+TlcxkMsEuO2OJDuTsFfaGB5uk5DFEPCvlDOgpD5zOz89rdHQ0MCi6PUNROiBQ5Oybpz71qaGD4UHkyJEj+su//Es95WsdNP/4j//4QN//Zpa1tbWQQfL2yS6ZTEbPfOYzJSmcIXMh8qd/+qfa3NwMjhjRIJSnd5R62tOeFsCWG1doq2TrUEgPechDwrr4whe+oDvuuEOrq6v65Cc/GbjT1113nXp7e1Uul7W4uKhsNqvHP/7x4Z6f/vSng9OCUcRhwXhOTU2F9ZjL5fTKV75Sm5ubGhgYCLV8GGkcPO6HgR8fH9df//VfB2M0MTGhd77znXrsYx8bNp/X7P3Jn/xJokD/9a9/ffjdL/zCLwRe+/vf/369/e1vD5FcSbr++utDrSXNbgCh8/Pz+tCHPqTNzU0tLi7q7//+70MXNv/u8vKyzp8/H6LjGPG4vkDabYQAkBwYGNDp06c1NTUVFM/Q0JBqtVoAijSbcLqUR5W5Hp9/0pOepFtuuUV//ud/HiJ67vBIu40wAJzlclnlcjnUJBGNbzQaIcu8tramf/3Xfw1KDrqy1GrGQq3l6dOnQ9E/Spv7AzocAAA63EGrVCpaWlpSpVIJ3QI9c+WdMAFmIyMjAYhms9mQjeSzaRF0MjMY00CVyxfUe25bOrmi5tfOUSQySHDEj7wAZHgnPkAPz+sGEmoYhpO6Cc/4+RjJ+Hi2kXl1px8nnfnlOw7aAYoxRYprpmWWfB07VdADGg74HDzEGVKcNa//4vOjo6NaW93QB2+c0Xv/f7dpdWU9GF70nTtXbpTbAUW+55H4NPEgzn7imdSLIaOjo6mUI4+2+3PFQDHNmUsDvuxLf07oXbFwzXp9tyvt5uampqamQo2wBwVcJwEAl5eXQ8ACe8Q5htBFqdXGWfTrpT2L/7tYLIajMfgOAJX302g0QmbCyzlwMD2w53WEXN/XL88APZPgKHp5fX09PNvW1pYuu+yyhK161atepbm5ucQ5uC972csS9DbXW9Kuk93T0xMyufzMM4PUo6Hvjhw5okKhoHPnzgXKIowA5oA1hpPrQWCOYvBgFADhSU96UnimW265JZFZ+pEf+ZE9e2N9fV3Pfvaz9b73vS+AvY2NDb3yla/US17yEn3wgx8Mz/orv/Ir+sAHPqDR0dEA/NFVDtw+9KEPKZfLhYxiLpfTkSNHAsji2bAzxWJRAwMDiYCZtJsVZk83m83QdK9UKoUmd7feeqte//rXh/4EfkwZx2J4dhmdiX9BsGRxcTH4DV5zB1Ak4zswMBBKX7zBzdramubm5kJQYnBwUNVqVaVSaY+tRHjHcWlBoVBINLECdBMkZp0h2DD2387Ojn73d383gM2enp6QxIiFICr2j+A4gVoHw9lsNuxr1i22lb3W09OT0F1cEzBdLBb1xje+MTCsPvzhD+tNb3pTGA8gcmurdeSaNyrzkhCCQdVqVbVaLfi/MORiv4IAG/4T9ommeqwNMorOUGo2m4la8f3kQEDx85//vDKZjH7pl36pI12m4w2z2cCp//znP39B1/hmlH//938Pxtk7mcXC76anp0NtWLfCRv3sZz+r22+/PSw8AGOz2dQdd9yhf/u3f5PUomSMjIwEJwLl4lkYWtgTzSgUCsHh39nZ0Uc+8hGtra2F2j9J+v7v/35JLUcOA3/VVVeFzOQ//uM/BuPhlCKU48bGhv7zf/7PwQC98IUv1HXXXadcLpdQiE41AkjUajWdOXMmnBU1MTGhV7/61frHf/xHffrTn9YVV1whafdMq2c+85lBgd9yyy36l3/5F0ktMP3Vr35VUquj1S//8i8n+Oq//du/HYBsoVDQ93//92t7eztELjc2NgJlQZL+x//4H/ru7/5ujY+P68d//McDv/2xj32sxsfHdebMGWWzWU1OTobaF6iVMRVT2nWIUXa1Wk2XX365RkZGVC6XQ2aPZ+XdAYT5HU4j7xjF7NF4HHgMvTtAKysrqlQq4TooZ4AiTlsmk9FTvnaOo9Q6O+nGG2/Uq1/96sRRILQrz2azob00RtqzVBhijLMDRWouqXMDSECh8ag2kUeehQCH15oRDfUMgwvZNgyVZ64KubyqZ5pa+expra+uhYgmGQg+R+YfgOVUOgwXkVN+5m3Tt7a2tL6+Hmgs7kwwRuYRJ481RBYzro/1ZgDsS88Ue0YxnhenxyAOBjwi7RHxeD36/QGiTikk88y8k7kaGhrS+XNz+qPful1v+LV/0drqZoL+E1NPPYqLs58GFKWWY+p6yMWjv91ItVoN2a2LIWlU1/g5/P8xrVTSnvUtKfWaMYBcXV1NnRfWMw7R/Py8br311hAk4XrSLngDhLDv2F9ee+h1hs4skZRYe57J3NjY0PDwsDY2NgKLJpPJaHx8PARHySCur69rZWVF+Xw+OGtkHhindzB0ZgvNrxD2s2dGqtWqpqenNT09rcHBwXBfbOHCwkJwiEulkp71rGeFDNudd96pJz7xiWF/P+c5zwngxpkA0OiZW5p+obcJEDmVlOAfLA1Jgba6tramnp6e4Hh7zVVarXOz2QznxsU0Zanls3gzk5tuuklS67iFxzzmMXsy2QTOJicn9b73vU/Pfe5zw+95zsHBQb3//e/Xr/7qrybsGeMqFAqpQJFynOPHjydq1oaHh8O74Vm9Cy/rD33Nmgdozc3NqVgshmB5s9kMR4e94AUvCFTbfL515jCBVwLE2D7WAeChWCzq7Nmz4T6sNy8XaDabIWtGOQalGZlMJpQpcPwJ8819ACtxgDjWB/l8PgEUEbqk5nK5BHMCAEXDnK2tLY2MjOjNb36zXvrSl+r1r399KFWI9RFBF54bP4ngC0CS9bK0tBTOM+3r6wuBHvY0JSrQ7fENAWD1el0Pf/jD9e53vzs899TUVBjPQx7ykPAs2BbmBFuGfiDLPzExEQCr2/E4S4u9IljAe3Tb7HXY+D74xd3STw8EFOlsCEXsQoXvc71LosQ5cZ0ytf67tIPNpVaUbmlpKfFHUsIRJ0pFFoI0tPP/n/nMZ4bCfmm3TofFTbRC2nXystlsgi7y0Y9+VCdPngztjEdHR/W4xz0uKFVaMG9sbITOZXNzc/rSl74Uzpopl8vBsA4MDOh//s//Gc4UOnr0qH7pl35JxWJRy8vLYTwol0KhEJTa2tqajh8/Hv4m41Cv13XZZZeFIm3fRLlcTj/5kz8Znoesohep/6f/9J9UKpV07bXX6hd/8RfDvGKYnvOc54SUP5HY9fV1PeYxjwnPvLCwECg1SDab1Y//+I+r0WhofHw8dCBzpwnD7wqTsUNBwdGO6xLje3m0Gacc48nP3MC784/hckenXm91sqRIPI6u0k2VsXuXu/e+9736r//1v+oP/uAPErUhKNBz584FR4f3FNd+cR9/NoILZP480+R1F6x3P8MOR5dndGod85aWTWQMDjCgscb0MoCI1NorGxsbuvvuuwNw41pkHMlAovAZI2AcWlGz2QyGCnoxdVAAR6fiVKvVoJ+9FhIha4/09fWF9vpkW3HiuadnF3gH7ZrqeCbLQWMchfV6QQwwv2dv0HCN725tbenuu+9WqX83u0XWxut0HCgyNu4V3x8BFLejnroz2Y10oopeLCEoET9XO0CLkyulg0Y+wzv3DIXXNiL5fD5kBtH5m5uboY6HMTrY8xoj39PYEj7T29sbAo4eAOM9+TPgbA0MDITaLZxyAmjokJ6enkDty+fziTpgB8kEKzyr4c4iwndcf5bLZU1PT4dAG2NfWloKjjU6Gpro61//+rD2YA/k83m95CUvCfsCtgVZPG8gxOHsOMreTZl1y95mDOgXgneADeyv1y67/uGZ+LlT4ZwlAYvK980LXvCCPRTvfD6f0M+FQkG//du/rd/6rd8KOv3Rj360/vRP/1SPf/zjE/fz0oSenh5dddVVIUBz0003aXp6OryzK664ItiPTCaTqKt0Jg/BXH6HfkeYY7pQ/szP/Iye9rSnJWzsJz/5yeDnPfaxjw2MAHQT8876JMjImsJnQocSDCBg42CXI16GhoZCnS3PsrOzE5rhSQrUbq7hdtf9Q383aUcxXHnllWF9xP0XqInEPkmt0pNf+7Vf0xOf+MQEc8Z1JHsRPYxdZm7chlAS4Z1bCXQSDOF3JDI8U0xToIGBAT396U9PNJaSWoE+8M7q6qoqlUoIaKIf8JOy2WziKBcCSk559/WDz4F/71lO9wkYJyyLYrEY2HndNhU9kAWiK9YXv/jFg3xtj8Df5XqXRImDLzvRlvx37Q7L/G//7b+pVquFP0RynvzkJ4cN9Td/8zfBIfO0/Pve975wnWc+85mhDTIbbGtrKwHAHEygGB73uMcFRfSJT3xCf/3Xfx025tOe9rQEVQ2wub6+rsc+9rHh3p/61KcCbaxSqYQFffbs2UTXq1/+5V8OKX+imdLueTT5fD7ULKIIqA+Qkk4RNXqe8clkMnre854XjMYHP/hBvf3tbw+F8N///d+vpz71qSFq/Bu/8RsJuowk/ezP/mwAIUS7cKZ//dd/PWz+3t5ePf7xj9dP//RP601vepP+6q/+So973OMS9GB36FZXV0P9jYsrahSGgzx3AD0j4zQp7nP27Nmg/GPwiCJCqeEU4jxRE+RZFKcPopQBTYcOHUq00XYZGhrSr/3arwXAXSwWE3W1jMczP2S+nb7IGuB5ARNkBhwoFgqFRMdXd3Q9oygpULe9KQVClgHnB8e80WgkIo8YXHfYx8bGdOTIkRBcmJ+fD/un0Wg15XCHhzGyv+guiJGAMoQxYU2tr6+H/ewgEiPrAFZSwlGCbbCxsRHWI2uCIFQM2nD8Y6AImIbCx/pinj2zwXtwEOjru15vNS6iPnZ5eTkcajwxMaFqpZq4t2el04AiQJp3lAYUWQvtBOrP/UlwND3oR1AirT7S27r7u4mv2Wg0gnMlKQQFYwEorq2thb2C0xNnk9OAYr1eDzp1cHAwjA09zrrjZ67H+L6029CCPeMBHmq8cezJgELVrtVqe469cF3EmDnawkGFz2McpPOMEeBjYWFBjUZDo6OjiaBMs9nUQx/6UL3sZS9LzO9znvMcjY+PB/o5e4L7kKF0ai7r/PDhwwmgyH4nWAVV2Kng2Ww2kbVER3tjH37Ge2k2m6lz12g09pTiDAwM6LrrrtsTxBgaGgqUZ9+7P/iDP6h3v/vd+qM/+iP9r//1v3T06NGEr8DYPAhQKBRCEHdpaUl/9Vd/FT57xRVX7AmmuE7mnWEjJIVAtj8jNrbRaB2FcuzYMf3lX/6l3vOe9+glL3lJYDIhHFMGo4Z5cl+K7DzvZHJyUgMDA2HfuJ6HKZbJZAJllbFCPZaStdf4ZJ4Z9/KMNOAmtfZdWoLp277t20Lw2oEiNorg+Pb2dsiUcZwZcxczF9ij0m42TUrSYmOGAjRvB4pDQ0MBSPM3z8jag9LZ29ur1dVVvfKVr0z4f97xlCAuawSguLPTOq+7r69PxWIxAHOeA3/bg1s8Gx1uuQ7P4jWz6A8CIYVCqzM0Ablu5EAW60lPepKazaZe+9rX3qOz/F772tcqk8nscagvycWR3/iN39Di4mL4Q6bqyJEj+u7v/m5JrdT4V77yFa2urgZDc+7cudCN6tu//dt15ZVXqtFohHa7ZMlQjAA0L2QmgvrkJz9ZUqsL2//8n/8zjO1Zz3pWcFydnra+vh5S9FKroFtqGZJarRY2wzve8Y5gXH7wB39Qj3nMY0LhvFNM5+fngyHHOHsHRo8sS0n6rdfMNJutphg/9VM/JamlGLyL3Otf//pgGKXW5v2t3/qtcG7Ogx/8YH33d393eE42L8r7Gc94hm666Sa9//3v14c//GF95CMf0ete9zo961nP0uHDh4PDjRJzKh/K0yPLrvwxzB5Jj5WlZwRd0TBWutK5QXQKkbQbyfJ75fN5zc/Ph4CBU3BQtDgbAMWtrS399m//tq655hr9yI/8iH7xF39R/9//9/9penpa//f//l+9+tWvVqPR6pQ3PDwcjrfgXbgSJWLOWpMUggVQRwGStVottHqHxy/ttu3HgNG1lfG7QeQIBxobOAAChKK8mUMy6i5uYHkfuVyrc+3g4KCGhoYSRtk/02i0GsGQBSAa6rWTRPh5Z9VqNRzBw4Hh6IPh4WHNzs6mZhTZT1yP5ghEZ/kMjpc77A4822UUY6DIvnGAylp3J9vncWtrK2RX77jjjkC5ZjweTe0vlxMOeVyjyHrI5/MaGhpqCxR3dnba0k6ZkzRg9fUUwILX5uK8poFe33dxIxu/Zr1e16FDhyQpoWdiYU5mZ2c1MTERmir5Wokzik6J5V0XCgUdOnQosE9wKgGhnmnxd8czkFFhL2ET8vl8qKX3pjZOPZ+cnEwEenAGuR+dp33s6DzWMddkvubn5zU+Pq6FhYVE1mNlZUVjY2NhPOwPbNev/uqvhoBbPp/XL/zCL4SslaRExo9xbm+3unQyPq7l5QZO+SY4BE3dqY/cA/3D9Rzssveg8vL8vn6411Of+tQEiPjhH/5h9fT0hOyzr0sHa+jbnZ0dHT16VE95ylMC7ZLjjZB6vZ54p/l8PgBFSXrPe94T/n3VVVclygzQPx4QRdBv7BnfTzj37ovU63UdOXJEP/ZjP6avfvWr+ru/+zv91E/9lJ7//OfrJ3/yJxMHqyPoRK5BEENS0NvcZ21tLfg/cUAzn8/r7Nmz4XP4d/l8q1nR8vKyMplMOEqlWq1qc3MzQen2PRrT8nt6evZkFR/84AeH+ff3sbi4GBpnYSs3NjY0MDAQmthhg92/Yby8B57d2SzYJLL8vBPAIMGNc+fOhSx1LMvLyyHgwfVIavzu7/5uAPnf+73fG8aGP4o+Irizvr4e5oxGQYuLi8H/wM+I55RABDRTT7zwfl3H4W8RbIDW2o0cCCi+9KUvVTab1e23367v/M7v1I033rjH0LeTnZ0dvfe979WjHvUo3X777cpkMvqP//E/HuT239TikVeno8Xiv/PvuPT29qparSb+SC0Qw7EMkvSxj31Mq6urgcJx09f4/5J07bXXqlQqJbp6Efkg6oEThoEkm5LL5UKdorSb+XzQgx6kK6+8MlGfAWVtZWVFV199dVA4X/jCF4KjQNOTZrMZjtjI5XL6uZ/7ubDwz58/r/X1dd15552BJlIoFDQ+Pp7IrKXV0rjDDcCQWkGNxcVFFQoFvfjFL97j4H3v936vHvSgBwUFxAYtFov6yEc+oj/4gz/QO9/5zkT9gteQUQNz7NgxPeQhDwlARVJwcHg3ZMIcnJA1RSG648x7cUcCQcG64mF8KGAUC38zd8wVhpt7FgoFDQ8Ph+sWi0UtLi6GNeqF/NyPtQrtb2trS9/1Xd+l3/3d39V//+//XT/7sz+rK6+8MtSA4NByDiDghHHFEXqMsz+71wr29fWFSB7z6obbI8xcM3Yu/DmItrpRQpnHIBnjl8vtji0GEbwPJJ/Ph8iipET3Nq+JcQfasyaAVT6H8SGaStYTB42mQ+6EILy32dnZkBXF8HonPBwAzygS3XQHx+fAx8McxgEKz0Y6rQon0eubz58/H+hW7PPt7e3QLMLvEVNQXYjgxjRsl+3t7W9YoBhnFNsBRbJIUnoNI9dw8QBMLACEUqkU1iA2K80JZS2Q6fAMCTqzXq+HA6xdx/Bzd5JZo+wHaZduByDxzpZkRrFdrDUCeaxr/g1Q9M7d6OW7775bd999t2ZmZtRoNIKdm52dDeeKNpvNkI1aWFgIDUV8jlm/ZD3/9m//Vj/6oz+qt7/97br88stDYIR5ousxOo36Z8+U+bzzrvk/wSfmFp2CbpUUxkLA1SmKNGVzdoXrVsYJGIZt1Nvbqxe84AUhC51WvuTBStcTHjTzbDkBdbchxWIx1HtKSvSDeMhDHhKysLBIvKEROt91FlnqGCjGdhG7xjp/4hOfqDe84Q161ateFUpj/POeUeTfgBafQ+YevepMIiSfbx0Nwxp3vxG2CLWrUqvWE1DJ9fAt4r3O3vOD5zOZjK666qrgb7geWVxc1ODgYDjmDN3u1E2nlXpAg/fifpYfr8Mcb2xsBL3He1xeXg6B05GREZVKpbD3EQIf/M2z46sNDAzoxhtv1Je//GU9+9nPTmQf6XFAYBpW0eDgYBgn7xnaqbTra/AMzWYzUNCxdzQkgi7v65ngMDYQNgDlUftJ+x7eKXL11Vfrla98pf6f/+f/0YkTJ/T85z9f1WpVV199tR70oAfpyJEjKpfLYUOurKzo1KlT+vKXv6x/+Id/CPUwkvRf/+t/TXS6/FYXT8ufPn1aD3/4w1M/d/r06dTvdCO5XE7/4T/8h3CI98c//nG9613vCsckOO306U9/+p6OXisrK8FxplsmimJhYUG1Wi2AMmiu7rx/z/d8j7a3tzUyMhLuWSqVAoXyEY94hB760Ifq85//vO644w7dfvvtwVDk83l97nOfC3z9Rz/60UFZoggHBgY0MDCg8fFxzc7OBseaSKZTQRCnuPB7HPBms6mFhQUNDg7q0KFDuv7660MNZ7FY1Ite9KIAoIgy88z9/f36yZ/8SS0vLwflvLa2ppmZGQ0PD4fjH4hEooigSnqkiEwVBehuoChyBuBhdFES7vj7u+R3HiXmXpISQNEzjHzXsz2MFWUFUHDaJsqccfFzKFl8nlobotO1Wk2nTp0K9BMyvLRgx4AQofeId39/f8gESApOAkaFTEG1Wg21fL5eWTte24jR9EiypES9D3QZSSHjy709Y9uiRpYkrYS1GNdp8H75HfQwSSFyzLrDeeE949B71zay09yv0WiEQn4Hs/x+dHRUt912WyLTLu3WnPT392tzczPQWFgvgKrl5WVdccUVoT4HZ5g9HWdUMYZch2dhPnB4nFLk78GpXhsbG+FMvGq1GmpeMplW18lSfy3cd/1rrAmaLC0uLu5pH55W6+bCXO5HPb2/AUUy7XFGEYplLMViUbOzs5LaZxRjWV5ebltSwXsZHR0N55NCfyYbL+3W2ki7gY+VlRVVq9UQGUf3bG5uhrP6WHcAzXK5nACKzWYznPOK80mt47FjxzQ9PR1YNVJLj9BcCgC3vr6uSqUSsnK+ztHv6C8PnvT29uro0aM6c+aMVldXQ1CVWjR03NTUVFjDOOius2mUAkD99m//dr373e/WyZMngz0B0ACSHEChE8iqeCDEQYzbEmcyrK6uhrHGWSVsFWuFvgcDAwOJ+mEPvLAG8S9e97rX6Y//+I/1rGc9S2NjY0HvEXT27wJM48wxuoX3yHd4tzjvmUyrA/jQ0JAuv/zyUOvJdx7+8Idreno66CDAGUFbabccA3AA6ykGw/gZgLednR2Vy2WdP38+6FyceQdjHnB1FhHziP3knTtdX9o9h5R1yFw1m80wJva+1+IxV7A15ufnA5DjmZw5hbA2H/CAB4SExOWXXx58BgeKNNKJfYy+vr6Q4Wf/b29vh/fm4NnnGPAEKN/a2tLg4GDiuBzW0V133aWxsbHAPHE6PMJ1sAUE8r2mvq+vT5dddpk+//nPh3vgW+ZyuXAsTKPR0NzcnB74wAdqaWkpzMGhQ4d06623JgAl8y61gDR6jLVGFhIafKzj1tfXNT4+HuaOddaNHLhY4jd/8zf1R3/0R6Fj4eLioj74wQ/qLW95i2644Qb9/M//vH7qp35KP//zP68bbrhBb3nLW/TBD35Qi4uLwcn77//9v+vVr371QW/9TS3f/u3fHhZ6pxpQfnfo0KFw1lu3Ql0hnP+1tTW9+tWv1tve9ja95S1vCSD0iiuu0LFjx4LzjRKan5/X5uamjhw5kohILS4uam1tTQMDAyGKOjAwkIjIZbOtJjdes4RShnOez+f16Ec/Onzn5ptvDgXI/f39ibN3vv/7vz+0aS6VShoeHg4Oj6TQ0IDNhfHz2ppYAE3Qi+j0hZF72cteFt7Rz/7sz2p0dDQYSIAic4XigIKJcV9YWNBll10WaBwYNADj+vp6IkMr7Z7lFVNUPProdNm4RiAGit6wxjOQXFtSiFA6UOQeZIcwSESoAJZpRtOjW5lMJkQlMWw4F+7oAb4OHToUznHCeFCvQGOGOKsINcqzyNTPOV2TM8HofhpTcTDWGEgHUp5RxOhC4ePdeWbYs0kAxRhUxA0WnDoD9cwznmTNiCITTGg0GsHZxUjyLN5QBuOFc8U65BmLxWKg6qYBHN4R0VfAAJk86KyuL7xjpTNSnMLl78F/v7a2Fih8DhqYWxwiAltHjhxRPp8PYIJgwurqqqrGyKg3drudeq2yC062A1MXaMxpINLnq1PG8eshOE8xUIwdcIRMvtQ+o+iSyWQ0OzsbqGwu9Xpd58+f18TEhPr6+nT27FmNjIwEijjUd8bFOpV2uxJzbpjT2XECaR6GI+w1zTjm2Bd3+NETQ0NDQV86Bd2b5LC/vM5LUgIosvfISDv9HkZIpVLR4OCgVlZWwvmRgK/t7e3QnZG9yBrt7+8PTibBFAR6IMAVqifzRCaPIA/z7jrMga/bIS81mJubCwCfgBZBYJxkGAe8m1qtFuxPGtOHvbq2tqajR4/qZS97WSjjKBaLwebErDa3B86GYb0SMMMXYL4BbpLCcQTeYE1qBedHR0f3nPXq7BaeKQ4wup3mHQLQyTDzjrC5gChsMbrUwTqf9awa92Gfstb92Cie04N1vb29oS8B9wCYEWyEbgl9mU7u7CuCzJ7txffyusuHPvSh4T7sUanF5KKUg+fA3tfrdQ0ODob53dnZCWDHgaKvJ3wBfJp6vdXQZ3FxMQSmNzY2QkAfWjdBd3w81uP29nY45swTC+j1RqPVfND3qNeQZrPZUPfIfoltRiaT0ZEjR0LHboJL29vbgdlHsy+CwviO7Atpbyd81k8cbN1PLqiq/md/9md1xx136A1veIOuvvrqxKZO+5PNZnX11VfrDW94g+644w695CUvuZDbflNLqVQKSskBkUuz2QznAPkB9d0KSuQFL3hBqgMgtRyZF77whcrlcjp//nyglzabrcNxOcfOnX46nWE4AJhPf/rTw3Uf+9jH6oEPfGBwYskmUAzMJnvc4x4XvvPZz342KIpCoaCPfexj4d9kRre2tsK5ZV7vQQbDnaBMJpOot5SS0Uf+jRHp7e3V0NCQlpeXw1lVf/RHf6QbbrhBv/zLv6xCoRCoiwBLDIg7rysrKyHL5J1ZUQBOHfW27G6YPXsq7YJaPyhYUgC1RBq5j6+huM4rrgNCiBbOz88nKApxPYlTtwCia2trwbEC9KFkUWaMjcwp4/YmMtQQ5PP5RMQNANTf3x+UnUcxMQ48IwEJDDoAbWNjQydOnAjHzVDryvWYQyieHqGOwQTK30G+N7gg6gmYr9fryuaymj+e1cyRppqZZiJy6TTJQqEQIui8TzIZgEgocYwPuhNOiEdhec9E+6vVaqDYxOuFOU/TGTiUBDv8/CpviIBQn0FAwJ083jVBo1gAaKx3oqtOx8JxW11d1ejoaLiWB0WoJVlZXdTr3/Gd+v0/e4L6+nqCk+NHILh4hiQNDLY7/iGeLyhd9xfxQJWk4Li1yxQCnKTuM4orKyupQJH5zuVywTkk4Ej9W/wenBrpzg8OIg4VFEvWC/RLdyR5TtaHB2Y8EyVpD1CklgmHmX0ez41n7hcXFxNrPg6mlctljYyMBH3LuKG204mRYBrrCd0YH7uBfYB5QVdF6JDoBfQAR5R4QxBnmWCPPODLfqvVamHvk9GF0grYZV9ubm4mgGIs7lN6EIv3nMvlgt2Iv+++ibMjvEEKQTRp9zgbglxLS0vhjMqY9Xb8+PGwNrD3hUIh+CLSLijzszEZk9tpWA9uV5hPMm1LS0th3rEDMVjnnq6TnBXA/Qlm8G8CJA7yPBPua5TGaHzX9SRBVj7rFGZfi41GQw972MPC/x/1qEcF28R7Zo8yJ/hQ+BBeMsNceFbax+zXxZ9iHVCnSkCA3hveXdTXhx94D30dvwMmkXd35+fYCtdj2Ww2BC69B0cccKOpEmepsu7Onj0bbBtrnOACf/DnfDzuzzCmNMZImhyIeuoyODioG264QTfccIO2t7f11a9+VadOndLy8nKY2EqloiNHjuiBD3zgvlHHS9I6D/Dmm2/Wxz/+cX3mM59JgCZJuvHGG3XHHXdIkn7iJ37iwNfHoD7ykY/U7/3e7+nkyZPq7+9Xf3+/vuu7vissOOhyHE1Rr9cDwBkZGQnGC6Wxs7MTojzcR2rV8L31rW/V2tqafv7nfz4YUKdccoYQ3/Vn/vznP6/Z2Vk94AEP0Gc/+9lQJ/CkJz0ptDBvNBohm1er1UJU1A8jZ5MRwUM5MRYUkoMnnAac8JmZGR0+fFhPfvKTdfz48RAVWlpa0uTkpGZnZ4NT78DOs05E7oieEbFyBRfTgRgL+4d3iPGtVCpaX18PzidAkWeSki3sMbhxxo8xMweTk5NaWFgINTibm5uam5vTsWPH9tAQpV0nirHibDWbzXDg7+LiYnBk+D6R7bW1tfAuyJwNDAxobW0t/O7IkSOanp4Oz+HzHGctAbPQS2kFDZipVCohWler1TQ+Pq5sNqvp6WmdOnUqrCfeoXfJw1mJMy44GrwDxuLKmXXGdTO5rDKjfVpZWFDJ3jPigQ4cMHcScHS9For7ck6pr0GuR/dW9vDhw4e1vLwcIus+j+2CSozB1xt1FQRvYpBZLBZ17ty5ABTdyQPoEjhhXfo6LZfLOnfuXCI75JnInZ0dra6uamRkJAQDANjlclknT55U+WuNa7a2N/SM77s8sCVWp5cTdVbtMoNxxh5ZX19PHOnwjSLsOf//2tpaW0DrARlnGrQTaPNpWVjXf9lsVg94wAPCtX2fIZ49Yb3gvPG+cbb5GY4va93HQdCE+5Cl8J+hM5kjsgAAFZxObAzf8VrERqMRdA57yueNn7H+AQeSQi0g/2fO/ABt/h13lvXsBRQ7wJwDRfYT+ts7SvN+AFp0boSJADBA35NhQYdC3Z2fn9fExESYI2qpeKZ4jQGOCC7xXLxvAmVxOQnPhy3DlkMnR/8BoKEiOiAg0PyIRzwiobOvuOKKRKCOeQAoMgcAac6xZM3yvAS0HLyx3vL5Vqf2hYWFxFr0deylGw6enQHGHuH+ZBRhRZCZxa8CEHpTJd5NtVpVT0+Pzp49G56lWq2GPQew8owi33U/5JGPfKRe+9rX6itf+Yr+w3/4D+EdwUApFouq1Wph3lhXHuzhHXvfCU82+Lj5mz3gwQP8oEajEY4DIcNK0IV7sS64H76fB+SxP+wL1jD70ynwNLdC53iNOMK1R0ZGND09reHhYa2tranRaIRABj4QQSjXpZ4BlZLnLkoK9O9u5KL06S4UCnrIQx6i6667Ts973vP0Yz/2Y3re856n6667Tg95yEMugcQu5YUvfKEe9rCHqdls6rnPfa4++tGPSmot9BtvvFE/8zM/I6nVOdSzdd0KtLRMJqPHP/7x+vmf/3k95znP0ZOe9CQ98YlPDO8KR7e3tzdkZTAQOKUYmMXFxeAQsolZiMViUe94xzt0880369nPfnbY7NAthoaGAtcdQzY5ORk6Y33xi1/U4uKi6vW6/vZv/zY8x/XXXy9JiU1GjeLOzo5WVlaCovd6CTayO8IeFcKhpjMYUq1WNT8/Hw535w8Rb3dmUS4oUJwkosvQGInI+RlWTsNxiiDGF+WOw4OCwEnAgKKo0/adgxuUCZGypaWlEGl0gAoAcYDNMzJ2d6wKhUJwxre3t0MBPArdM5dkAFDCOGUYxc3NTZ09ezY0oCBq7YYI40+G1tdGrVYL63dkZEQDAwM6dOiQRkdHEw4d4HhkZETHjh0LRo/vxu3404AC75yaDY8y8vkYKPp+iWl8GCDmAsfCMz8YUUmq1WphfbBWyKJDEWXfxs0XyLT29vaqUqkkMrPcK03cGPL+aSDiVFuepb+/v22zLu/oh+PFembcUFmdYoezwXpk/QHaiPxTQw1w9DPXaBTl2fK0Z0aHONhgD7ke+EYSHDUkm83uabwRf94ZGO0AtbR7dEW7xmsexODeOIexwyYpRNhZ195u3te0H46N3oL66OP1umWusbOzE/Y7GRnohVyfAKLbGPQ73+Fn6Ein7ErJTs1xwHB1dVWzs7MJIEG9Ms9FQNe7eLv+RmjzT6CUsiFKSebm5oJDSRZjeHg4QdP1bBMNdtAnsU53SiZZ0ttuu02lUilQetH70m5DNBf2kh8p5R1SYbGkZSRZUw62c7lcoBBCi6XujrFgGwcGBsJ72tzc1Hd8x3eEa1911VWJMWJ30VcekOD8TK7jDAoPFhM0Yx3Q3ZqgAs/HZ92Go+/Zh/ybPeC1p7wjwBGZS88cswa8nnh7ezt023ZWUKFQ0NDQUPANAZweXHSwxnNff/31+pEf+ZHEuykUCjp37lyiAR5zTPCG9efgn3XgAVSEuXPQxHo7c+aMJiYmQk0wgQhYOFyT9c8YeMeAaX5GEMMBrM8FGUMfDwwaqK8xUGQfV6vVUGcvKfyNHuZz7EvmjWf1/UmgLJPJhHrnbuT+daDTt7jk83n99V//tY4fP67Tp0/rGc94Rsj4/dAP/ZCWlpb0yEc+MhzPcFBhcRONwwljUztIxOhS10jGgAXH4kbpOgWNz+bzeY2NjenKK68MEbxCoZBozEJHSzJkuVxO3/md3ymppVxvv/12ra+v6wMf+EB4hh/4gR+QtMvNdsU1Pj6uM2fO7MmqxYIj7FQkjOnS0lJCUS4sLIQuWRh36JM4Qg4ivAkJ40DB+ByPjIxodnY2QdeQFOYOQ00k0JVQHPVG+bgDDQCJqYSxQsVBZj74HEARg+tGxemmTgchKj4xMRGeG6pfNpsNRdye4XJHxKOh9Xpds7OzOnToUGIOvXDcKUCeUeTa5XI5ZAahTTMHAAzWAsEEd7IAu06XbQcUvX6y2Wwmzk1ycM59d3Z2lGlKhfPbKi9ltLOdnp1xBy7OYpbL5XBUEaDea6BoZOMHh3tUEWfcI61OLXRaaSfx97i0tKSRkRFJu2vPM7ClUilEjF0wuG78CfoASvr6+gLQ84wi7wWAms/ngw7zgA77utlsqlGX/vo9d+mv33OXspl8uAfAIe2ZAS4O1huNFp210/m392chs4DgcO7XlMdtR5pks1nNz8+rVqu1vVYMFKXdTo6sTZxdKTn/OHfoJ6+xHB8fD2ucOj30o9/PgWMMFNH5gCLWhwdGCM6gE9BF6E90ilPl+BMzINBFx44dC0cwzMzMaG5uLtDdXDy74QAgrv2F4sn9q9WqarWahoeHdfjw4QQ1HdBZq9WCrvf7ecAYJ5Tr+tpg/gjy8KzoU95x7BwjzI+DOHe0ATppIJOAkgcNoXqyntDV9EfwbE+9vnusy9bWlp7xjGeEdfmIRzxCksK6Q3d6ps8zoehPfB7mlKwe38Xu4PwDMnmnrBlnWjDXHjwAEJIFhHXl+wPQ6h1qWeMEnd0OOUB1enMmkwn9Ktyv8bXgQdG41t87tQKcsBMeSOZnvv+kXRYTgQnmZHl5ObGfvN622Ww1denv79fw8HAIinEdpwMzF/ht9Xo9BBtYPwRGvT7RkyZS8nxe3wOSQubea+NdWP/FYlFDQ0O64oorEtcmgDw3Nxdqkbknc+Ug1QPeANVu5EBA8cSJEzpx4sSeVP8luXhy/PhxfeELX9CrXvUqPfShDw2K5FGPepTe9KY36dOf/nTXUYBY2Dg7Ozvh4G0WKAsZesnQ0FAAImyMpaUlSbuOxJkzZ0KTFhwHFBc1HB75AvD42XZsKqcsPPKRjwxjvvXWW/XBD34wdNl73OMep7GxsQT9A7CWz7ea6KBcVlZWEhQAH4dnFAG3cLa9NT+1HyMjIzp58mQAHSgeop6MgygUmRM673lDBQycdzpjDvxdpVEncEq4p9fTYCgwFihSV9yuiDxz6EDRM40oWcbgdAyMiRtvHCiiydQzQAOlK2rsaHsmTGpleG677bbgBKFc04Ci1wHFuol1gAPkGT0cAwflUDh4xxh51po/r88ra4t1LCVrG5zuIrWc17m5OTUbDfXetqaRs3nVt3eDBQ4uecdO10V4JowE84BRIupOxNOL6hkjAIeCeJ8jjHcneqFHNdlPNPvwg849e0kHbF/nrClqzDY3NxOHCRMYonbVM4o4AGS+HQyw513HjY2NaXlpTa/5j5/Va/7jZ7WzveuE8axpzxw3FOK5qHf5RhTOmkXQUZ2CA57JbieZTEYLCwsaHh5um1GMM2ncH7DB+nDnkPnnj9P1+f7g4GB4n9DznVGCoCtZi2QOoCrH3U49SITj7hRqHM14j+JMO3vEdYhn6MiK9/b2anR0VEePHg2Bt7QMJKUbgDJ3RBEft2eEBwYGgk2hNpH3m0bp9HlqNBqJbufSbnMVp4w2Go3AGsIeALZxxOMxo/MI7LGnvTwEfRuPE3vjsrm5qYGBgUCRxMGen59XqVTa48RD9W80GvqxH/sxvf71r9d/+2//LZxPCeBknzAm5sWDp9lsNgSSHCgyDmyTvyN8J/S1Z6zdNngAg2sgZDk5K5u6V9gyfvYz+wL777qP+QCAxwEExsQZh/5+2Jtcg4ZMnoXHfvT19WllZWUPnd3XB+8bm+Q2hLk7f/58gk6NPWk0GqGDKVk5OqizBpgLbAxnhRKIgWnh9FzsDnskZl6RkPFAhx+345RvD/Cw7h3M0WUciWuW3Q9Db1JC1mw2E30D2gW80+RAQPH48eO64oor9JWvfCX192tra/r7v/97/f3f//1BLntJIqlUKnrNa16jf/3Xf9XKyoqWlpb02c9+Vi9/+cvbRuC6ETYZNBacJ0BNNpvV0tJSADA4kBxUHRfKk+1xSgMGxoGGdwp0KtHY2FgArijwZrMZMoqS9O///u+68cYbw/+f/vSnJyLMRGGI/mDEenpahwp7a2sMl9eP4dgC+AAObLS5uTmNjo6qUqlobm5O1Wo1OEkoVyKUKysr2tzcDBtxcXExcMCd4sL9cGKWlpYSGcqYJoHixpHGSSZi6+Abh0FSagYqpiXxnlB6KBsUFkEEjCC/xxGIaS+eFUV5cZ/e3t7EQerSrhPlzs7q6qrOnDkTGvswzzgA3ozGHSvWmQv1pQRXHATxe7rzSa3aa7q1EXzgnCj2nhvqWBxk856dUso7GhgY+FrGZSHxbpA404Jzxx9/9uHh4ZCZBlTF42B+4+MoPBOGofYMgdcftRPf8549AXjifLDOCoVCOILCv4uMj4+Hs6y8xgbgi3POnsLZh+rrlFefVxr/lEolDQ0Nqd5IOph+FEm3GUWcQWcVfCOKr71cLrdvwx30Xad1QVbJG4XEkpZRlHazUlLy3Eb0H0wXaffYBvbYsWPHgq3BGWXteP0UEmcpCMRxDqM77Z6RZG+5TqGxGXtfSh714KAyDgZ6VklSyLyT6fM94o0oPNDrOgdZXl5OjMGd676+vmCjnHIe2wlpVx+SiaAeGEYRY3B7gg3c3Gydf8vxIR405Tw3t+u8t6GhoZCxYcy+VnkuFxohMa8Ew1kX6DTOYfQGP9g+/ACu/Yu/+It65jOfGf5PQNaZCw7AnWFDENqpx4AWjkDx+/JOAYgxUHRWUFpGkX9Lrb28sLAQwKG0Wx7DHKGnndXje9L9Om8OJe2Cme3t7XDmID+nzwVzhe70DC7+EN2g6T7qVHh0PkFm5sLLGjzYS0AH/wO/hX1Jp1NJIRjU29ubYCQwvlKpFModYNH19fUlzmVkP21vtxr/eUYxk8kkjvtgDy4vL4egJ0He2DchseJ2BT8HobYWfY0v6Pvce1gsLS1pYGBgT2JiPzkw9TRWHi533nmnnvKUp+hpT3vaQS97Se4jwbFn0bL5V1ZWdObMmbA56AQmKUT5UWZOQSFz5EDGo0jQLXAeUWbZbDZQgvg+0ccHPvCBAWx99rOfDV1gi8Winva0pyWKdj2CiULmTCya70BFwOGAfsd88FxE73AMFhYWNDIyEowvBx7jpJJ9WF1dVU9Pj06ePKmRkZGw4T2yyfhQaBgUr2mA2omBwVh7NBXFAhWJOXZDwLPFNBUpSX10WoiDUOpIAbYAa+9CiDF2mqMrHxxtInhOJwTkucPCM5w9e1b5fF61Wk1XXnml6vV6UHxE5TAUjB0DT9DBBWPhRfcxdcPr5liX0Krz+Va31Xw+nwC4PHMsfMbpTU7ldgesWq3uyfAivI9Y37oD6lF3nGWy66wr1hqRR5zdubk53X333eE8U0mhwN6dBKK4nQABhjGXy4VjatAHExMTOnv2bHDoeHcDAwNhL7kjwTMePnxYMzMzCaDImsrlcgnnhuCXOzkx+KA5VC6XC6yDnkIS2BF1RT+6s+LX8YwsUXiCVN8Mks1mA/WundDAJW2OJAXqdbtMItIOKHr2DPuE/sTxJ4OLMx1ngtEV0i6d33VUGjOA/0ODLhQKoTYOto0DxVgP+HEc7aj/fMdBTuycM8dkWBx4QPlEp2Fj0ceMVWrtm1OnTiVsRcwA4b5xRsN1FQFRPwqEjBi2wDt+N5tNnT17VkNDQ0E/sK4IkrGfCdLCGpIUdBiZMGremXMfP/p8amoqBMoAF9gP6iMdnKM3AR28l5hSTfDSm+7xTvw9Q6H0cXlQjyACcwrgZp0R+JKUsK2sNQcwXNczR27buH+5XNaZM2dCkyTmh7FzL4KhnrX15+fZSBjwzOvr6yHQ7RRUfKOpqanQlAdQze88y97b26vDhw9rZGREU1NTCXszMTGhQmG3QRK20TP7+BbOIMPXLJVKmpqaCt3TPVvebDYDTdmbn3ngnKwxzC9nEbiPt7OzE3wF30PxESitAHHryDS6jUMRdt8En9B/FtsYMrP41rxLZ0kQsGOM0Jr3KydxuVdqFDuByUvy9ROPGLHRUPJTU1OhrohNQSQDZ7O/v1+rq6sB9A0PDycULNeFuuOGySldAAOPsuRyudDdUlJoozwzMxMor094whM0ODiYyAJyXQwjra0xXKurqzp27JgmJyf3PIOU7JCGEoe7X6vVgjNChyiiWkSncFzm5uYC6CBi5FQ0xslzUltZr7fOBfJjM6RkpznaxzudAtoQtRcAuEajkXCq5+fnE5kwB5zMI+/HQZhH8zECNI1BOXlUjbmMMznecIJ59dpGlHq5XNbQ0JCq1aqOHz+ugYEBDQwMaHt7OwFOHXDxTKwFwICLd7xlbbPGoEcDhhEOgva24VKSiuiAzQUKijdEcmAbZzOcVhJHcRmr/9zv69et1WohMgndhn/jFNTrdZ09ezY4eEeOHEk0XHD6uIPReGyxOFDkPTB29uLGxkZYmzgHtO5HXzgQ7+npSRxgHkfbWYM8F8CcdxuPl7XrznlflOXydvu+ZuJnxVHlfdDV91tJYGe0CyCg4/aTNOopgm4gU8GabDabGhsbSzhorD0XB4pO8/dsOT8nS8M1AF7VajXU6EOHdruI7eAZcJQBKmnP68FUp5LG6w16IPPs2UYyiuh59DPBIp6Ts2fL5fIeoMi42e8xUPRxEoSlZAX6OOUPAEe+Q8DNDwXPZDLhrFUHWaVSKVDxnelDoBPQ7tdhn7oDPzc3l3DmoTrGwM/1OSwCxuOlMOzznZ2d0BCM+7pz7oEJnH3mFz+E0gt0oNTSUZVKJdR8erabdQXVkzHG1FPWUEz1dKC4tLSkSqUSdCbzg8Rn8PEOfJ8w58wBAhPHGR/+u4GBAa2uroaGVt6F2IFiLteqVx0fHw9lCQj9DQjIx9l5nn1zczMEMdjPOzs76u/vV61WU7VaDeuKeVpZWdHRo0cT/hc2MZvNhveOn8d7k3b7NECPxod0fYa/J+2y33wvkW13Fh6CDkoLfscBHQCrA1MPWjjVl7X2dQeKl+T+Ka5g3DBAzWMhQ/tyqgLd1aASoIid7oICWlpaClE8p4CgVAGK0u6BwqToyS74eTvIU5/61JDZcZCDkTp79qxOnjwZOOqjo6OanZ0NhhIndmdnJ4AANhib3IHr+Pi4JIUIcz6f1/DwcJg3Wr5DkWSj5vN5zc/Ph3Q/TgbZ17W1NQ0PD2tlZUWNxm79BuLGgEgXyoGfEd1jHlCWGKpms6nTp08rk8mE7o9Ob8WAcT13GKDWxsrYgQsBBn7H9dNoXVCavCulR8j5m4yqF6kXCgUtLy+HZ4uNkVM8WZ9O2yXTmgYUKVLnXhgCnJyFhYXQSt2Nq1N9YsHI4ET4vPMdN8ROBY3Hvbm5qZmZmUTWxv/twJl3Aegi8s9+2dra0rFjxzQyMqJarRbAcQyS/bk86LBfRpHPbW9va2JiIqFnOBeOjAxjHhwc1OrqahhzTN2kvndjYyOxLmKKdrPZ1OLiYtjjaVkqHC+n/pXsGAHEr90u08U+5v8Ep76VJN6rsczNzSWOTGon7TKKUrJ2CmGt+nps5/TEQBEn2YOEjD+2jfxuaGgoQfv0dcrYfIxcw3VLLOzHOLvJmFw88871M5lMgurMd9HfsAZmZ2dDiQl6iSMx0EM46Oxxt0NOcwOMAlzQzYuLizp58qSWlpZ0+vTpQHMcHBzUwMDAng7CTjlEAPl9fX2hMRfP54wjB7fYft7x8vJywkbi3+DHuM71QNvk5GTChgDIncqMnvagKu/cdaMHrD0I7xnRRqMRjnyiEzhzHbNLCKB6gCumnjI+f18A3Hq9Ho5g6O/vVy6XC0F3P0IFEAEwYf7cHjkTDRaFpMTZuwQnRkdHQ+AOv8Lrxz0oyNpjfZP182ZnrA8HTvidnvEmqMD6YOxQPD3Ixz6j8RprmjF4OQNgNg46NxoNTUxMhM97gAYBFBIo92AVbKBcLpcIeiHeP8Tfd9wrAp+GgJq/L4Azn2VuPMjcjVwCit9C4psKsEDUaX19XV/+8pcTn0E4hJfMFQXJLFKnXKytrWlpaUnj4+PBuSb9nc1mA73Lax1Y4Gy0ra0tPfrRj06MnbMepWQdCBuEyNbExIQmJiZUq9U0NjaWyDhhSEZGRjQzMxNAFU721tZW4G97pBsl12g0NDw8HKKbRMWWl5c1NjYW5sEdDeYSI03UiDMC2bxQuRzM8nzOkZeSxwF4NsgpLXNzcxoZGQlUQGkXFKPwHSh63QjROVckGCcHjHHWJaYzYHAqlUpwDuje6RlSxh1TQsjquqPklCvmAkPCmvJoYKVSSYzLjb0DvfjYBhw0b+EeK9Y05gSO1NGjR8O7IbDC/Rl3a13sgrStrS3dddddOnHihGZnZwPIJtMvKVC5WJduYKrVagBkdDvt7+/XwMCApqam9tC/6RoZHz/gtUbdUE9ZL6wfP++MZx4eHta5c+f2ZFep0cUoxtdlnXLOlaSwP3Ak2YP9/f0JmluauJMQO/K8JyKy7QCMn5XIfmkHCr5ZBVCRti5gAHh2op3ETm98jwDqv0bpjKP60m5kPxYHiq7vnD6YBhTjrI1T2eKABsGRWO/19vYmnPH4ucj+o4/q9bpmZma0uLioqampsEYpl/Bsp//tYwMQrK+va3V1NZRhsO8Besw598cRxqanzR86Fkd9Y2NDhw8f1tDQkEZHR3X8+PGQtZmamgr9BlgDngna2dlJnN/GniQ4h/3N5XKhrpj786wOOLe3W0cwxQeXF4vFwPBBF/t7xZbwzNhQdB/MiDRKIGOOM4oANXwr7DoZvVwup9nZ2XAMhAPFeC8Anv1oDfcxEIDtysqKzp07F96XH8NFRpOGNnG2HwDjQNT3LsEzbLg3TyN4xrmU1P2NjIwEuwQTy0ENoJBrcj++7wFMp8S6v+LU7Xw+H3pteJDD/RmAKWsMP80/z7vAFnhjPvd5YKh5eU4cbGHtuT8IhZZAfKFQCLYvLaPI+0ccKMLwyufzoTEPR8AAFCuVSgi8eBY+LdDdTi4BxW8hQaG4o8/CPnLkSMK4eate6hXZQDR1iTfs+vq61tfXQ8twJJfLBeXqTQ04PmBoaCg46VtbW9rc3NTjH//4xDWe8IQnBConETBX6GQia7VagtrR09MTrs0GGRoa0vr6eiLSg1NMK3EHiouLi4lOsygcScEZB/QCOP05AdF+niL3xKktFAphc3uGBuXsmTAiTP4zMrPLy8va2NjQ6OjoHkfFaYXuOOEwOf3Gj01wCgMOCz/je4grn1wup3K5nMg88nyu+D0i7hF/wDB1jqxFN3IYTwxoT0+PlpaWlM1mw9mX/i5jJwGBkuzv2B1JKekwthMiw0Typd2W2dyfOW+tlV1jODo6qvHxcV122WUaHBzU8PBwcHT8+kQkfS62t7dD4wfW4tZW64zSvr4+TU5O6syZM4ksCDVHbticokLgxiO+7Z6ZyDKGNAbRHBBMsxyEBhdp2SnWxuDgYMjgcz8CPB5gIjKbBj7QF7Ehd+F9sw7aPXPs5H6rZRMlhQxPvBeazabOnTsXWs/HGbKD3sOdR+rL4/cS71PEKVrOYMABdefNgwtpFFDGA6Wcz2PbWPM4mIVCYY8zLiWbr2CDl5eXtbCwoGq1qkOHDqlSqejkyZNaX18POg3bgMPqQUgc1Hq9rkqlotXVVS0tLWliYiJkRNCRzKnrQ/ZsDPqdbUBGDJrd8vKyBgYGAg2T9VAul0MN8MmTJxMUSPZlvV5PHJ+DPenr61N/f79mZmbC7wB+cQaJ94mDjx9Ah2tJoUmM0/ec4usMIuwS10S38n59HWOP4uy2/4xMJWdYktHLZrOhZo/r891YbzI3+Xw+APw0oAhw55xnxjQzM6NSqRSaBuZyuaDb4rWJbWeO44Cqg2Lm3LOElH1Qp1epVMJ8ELzAPrDvmHeaw3hQh/EjvO8777wzvFf3IaSkDe/t7dXi4mKYVz9zFFCM/8l8uf1nvziV1DOZvk4IqLgvKu36aSQHWCdQtv1a7AFfZx4IaQcUAaHQtyn1IZO8vb0dgrHUizNfaYHudnIJKH4LCREU6FVs1EqlokqlopWVlUSkjk1IDQIACaOCQslms6Ho/vDhw+FeHiVzCgmHW7NRS6VScApRyqOjo+HMIkm69tprE/WOnmXD+ADQfLPRuU7aje7wLGfPng3XYnOjwDCm0E6gZGCUq9VqgpePYeHzDtJQAE63YH7caSV6DIUFh8KpNtJuRNMjftR8QvdJi7B7bQDvGWAQA0W6e/FZAAZz4plRlFia4kEpuULHqfA5dqofc7K5uan5+flQF8PPPPMUA5lisajFxcWE8XcaDeMEJPFO43cBUAbM+3MQ3U4Tz+xyPTcSGJ3t7e1WR9CdbVWfeFyZ7xhWqb+UOCA5LVsD0GHveLYYig31Yd6QpaenJ9RirK2thcBKfA9oKRhhnJJO4Jj5JTqKM+rGLZvNho6yfi3qP9IinLwbmlKxpnkv7H+nyeMAxdfyPRwyir1ZveptD9Or3vYwFXp3u+pC32mX6XLhPK5vNeFdx0CQM2dxhPbLKLYT1hzXh4LloEza1a1pQDEOWlEbTdmBr1EHTzwb4k40DidZe7IVzAXr1dkJ8bwB9La2trS2tqbz58+rVquFOSuXyzp27Ji2t7d18uRJFQqFYMMAir7OnYIJUKxWq8rlcsGmk/VxvYsQPIwp6Gk1ng400R3oEa+LHhoa0vj4uKanp4NO80xurHdgzpBF9Owj+hY940BmaWkp6EKAELYXf8Kb0LhTTpbFGUAEjh1g80wIxynwHM60YdysBe7tQHB+fj5k4py2GgdWGGexWNRll10W1rw7+jMzMyF4QTCuXq8nGgtdccUVoQQDgBHvF2d/+fwShPFACowqKPeARxIF2CF8SAJKvk5ZRzBWHGTDSqGkZ3OzdZ5uJpPR0NBQWPseOJCUoF7yb6dd5nK5cLRavV7X0tJSSHjgQzAGb2CFeAaVbB9A0Zsb8p3z58+Hcgj3b1ZWVkLdM2sIn9MZVVIyKYIwV+wFfFpAam9vbwgyYb8JbjC+ToHfNLkEFL+FhMXIBk6rreDf0PjYVESDenpaZ9DwPRbi4cOHE1k0r0XKZrMhokT0g01Bdq2vr09ra2uamZnRxsaG1tbWdP3110uSHvKQh+jqq68OCqder6tWq4XrOH8eYIvD6y2pcUgxmESBMAx+7iMKDmUD1dSNHEXmRJo9YxjPu1McqDuAYsEcujPiY4jfD2NwpQKFhshtmuMUR95RXhjMXC4XgGZahzzGyHyTOfPfx8/NXPCeJKVmFHHaAKUbGxuamprS+Pi4enp6tLq6moh2IlwDAIxSXl5eDllg1rKLR4ydisg7XVhYUG9vryqVSqLQPabLxsIzsL94dhSzA7t6va56o67eYwNqjhXVXykHpwrDFgtrmPPLEPYbrb8LhUKCIsnYoITPz8+HeXQHhffIe+gWMAEQHfjFQLHZbNXcnD17NjGmUqmUyNIhrGMy3LwD1hR6imymB2NigYLlxjifz+pZP3i5Hv2UsvL5bNgHGOFuajgAIN9qkslkQnMIZHt7W4uLiwE4x8Gsg4hT+7hfqVQKNbueFcSmtRMPYpCl8t9xHddfricBKdgM9Ik3ucBJYw2mnTHpzIJisai5uTn19vaGs4il3bVNYGVoaCiAGXSDMwnYV6zb3t7e8B238ehqntmfFbuDvXeGhwe7/J247kG/UXuJfertbR1JASBg37lu80ws81ar1QJTIE2HuE3a3t5OUDt9btD56AR0Gc/o84Bf0tfXF3wR1ouzLrLZ3eZVPj7mwBkjAA3mFyBJ0x/XMTRF8f3irJb4eJl6vR4ytvhj3BuKtvstAEhn67g4eMAv83fpvuLRo0fV09MTjhbxfbW0tBT8LQ824mM5eOS+MJzQu1ILqJXLZd199906d+6cBgYGArXY14PPF9cAOHEWqr87fD8CpT6egYGBUBvpfiPv1wPOdBIlSMT6dt0Us7KYXwJF2Ww2dNP14Lu/gzSgyPW8MQ86wssx2CeUqDAvcWfWbuRgn/6a3HLLLYm0MOJn1t18881dpTaf/OQnX8gQLskFCEqHKCiRKCKbfX19WlxcDBlDNi6OI4rQlQbKks5UbFwirmxCNi2L3o0mVAOiidBRf+InfkI/+qM/qmKxqLvuuitBm6WD1okTJxKUBZ7Toz7UPToVKZtt1UdNT0+HLAwODpssl2sVfwM4ABI9PT3hbCpJYdxk4uJaD4xSNpsNhdOc00WtJIqir69PMzMzCUooDgBCVpgxolTYb+0KlZ0ixHedQsq8MIe9vb3huASeByXLu2VevOMda8avi5HCmDkdGQXJGpubm9PKyoq+4zu+I3RA48yrGDxxL5prNBqNAO6cVhJH4VHWPBsga3V1Vfl8Xv39/Tp37lw4rNsbRziIigWHIK2onb95txhJzzjTzKEdQONZYufYgzl0I04DX5lMJnShdRBMZ0Fp9xBf1kanbCLCWEZHR4MBjOnnRNgLhYJmZ2dD/Ww2m03NygHuWF8Ott35gtoVZzER3oMf1ePjdqpQvD8uSXs5fPhwInM2PT0dztWVkgGINOnkHzgow+EZHBzU/Pz8nvM12wXGpGQTI3QNa8ADPh7YyOfziY7VrDNpN/i1srISmCicnetZpBgoYo+wv729vRodHQ1lF55BQHCgjx49qq2tLd12221aXV0NTa7IIKELvA5MUqBh0g0yLXvq/4+7uLpjjN3e3NxMdAN3KRaLWl5eTjAoYLksLCzsofnOzc2Fz8UsF6iAceOoOKNI1hA773V2t956a4IqiU+CP+Jz7TYUVgp+Cd3OsRMEhV1HcF+eAZ3iZw6yH5yl44CZWn5fuzCLHNQ2m80QwB4aGgo+jAdyJycntby8HBrqOUCIgzcOUPL5fKifPHv2rKrVanhm3h+ZXwILBNDJ7DLfzEW9Xg9sKWwyAQf+BkieO3cuzEs+n9fRo0fV29urEydOJILo7gvGSQtA//b2dsLn5B1kMhmtrq4Gm4O/CDCtVqvhGBBANgEImGswnTgiyGmp7L2JiYng33mSw3UC4Dk+39gDUWkZQNgC3iRPanWIXVxcVF9fn2ZnZ5XPt5pLHjp0KPhE6Ilu7HpYi11/0uTFL35x299x86c85Sn7XgeH6ZLcN4JixfFikXpU7uTJk4kF6hGx2MGX9tZ2sGH8Mx798KilN6fI5XJhPJVKRaVSScvLyzp+/Ljm5+eDQUzLVjhIYUyAAVL/fp6ftFvrxTmJOzs7qc0HvJYN0MZ3oQp5lnFzc1NjY2OJDFZMhUVRUR8BvZHsCTQOnHenW9K1zME298/n8+G9tnOYcXpwnhwoNhqNBGXFjY8708vLyxodHU3QwjyrE0esfe05PcejdKwjahKHhoYCcGSNxPWJXJff8yx9fX0aHx8P144zivyc9cxz9Pf3a3p6Wjs7O4GiUigUwjlO8Xpv5+j6fMUSZxSLvUWt3H5ejZU1ZQaHEsGMNEHx05HX3y2GqFPNHO+deiDWoQPFUqmUoKF3E32kBtIzNP49p7SOjIzo9OnTWl5e7njO3s7OjgYHB4O+SMuCOIXLHVsX9ixrZ5cC19DH/+aM5uYWNfYjm2FuAM7dZFK/laWnpydE4HGs4+NeOmUUOwUhAFY4mNQlEYSbnp7WyMhIahdUF5xWD2KSicIJlPbSw3Gw+b9T8rBvtVot6C50XybTakCCnvZrkBVHr6KzYTHEwQlfz/l8XpVKJQDdyclJLSwsaHV1VeVyOdgB9oXUogEPDg6qXq+H2mD0cAwUoRcSOPGa6mazGfTE5uZmokGa77fe3t7gGPv7AHR4tuTUqVMqlUo6fPiw/u3f/i1ci6Dr2NiYbr311kTDF/Qb9o1/A9Z5v/gSBJewT9BSATXezZZnZ824TaZZntf4Y2/iLBz+FPYGsMu4fA34PdP2AdkkgKevDUCvvz9sHb0WZmZmgk6vVCrhPM0YlHjNHKVBNFOjP4X7IDwH42k2mzp//nwARuVyWadOnQr+BeuS/7MX/e/e3t5Qu8l+wcdi/AQCYnosa5mafAJB7MWZmZkEdZr5Zo97gPDw4cOampoKbAl6P+B7rK6u6syZM5qcnAylMe7DEAiHkRUH4nn/cSY0Diw5yyt+93wHHeN7jcZ1R48eDZlEkkBzc3MaGhrS+fPn2zbaaicHtoREdC7Wn0ty34mnoz1jQMTRO3OhcOj05NQYwJ6UpNXxHRRmTO0AaBEZ9AwBGwmQ6NEXNguRQ4+OSrsNZTy6DVCEux8fJE4dCcAEw813JSU47nzHgSIdr7y5AMXbcUYxvrdHjKipKxRa3a+gUGBwcSQWFxe1uLgY6jFiqgNnbklKFGbH4t9lHRANdTqrZxn5Oe/BHS+61Xk2xtcc75/MKA65072YIzrW+nrDYJXL5T0KDqCN0XewyO+Zb/8Oz+YUXjLtROolhXdC84XFxcW2gMTnF4MWO8qesW00GioV+7Txj2dU+NKScplWbQb1Be2uvba2lnBk/bO+l9KE/Ymxh47DGsO4urPT6Xr+XP7u4zokHGWc5MnJSc3NzYXamXbPS4Yy/r2vTTKo3lnRBSNPNiFkSTYbesVPflq/8/KvanFhNZGd9oDAJUkXgnM7Ozuan59PdOeVtO8+6ZS1JbDgXXkJ7NRqNfX19enUqVPBYW0HONGPnn3A5rnD5jTCeO0SjPA6Vw/OOcsAXZS277Glo6OjCf3izIJ2QNGp/2S6OD4GXY2TzZgI3vE7r6OMbTMA0QOu/o7JoDpA8XfCM6KP/Tm8RGRpaUlzc3MaGxvTyMjIHoYJNqy/v19Hjx5NAF8CvtgsbEo+32p84kcjzM/PhwzQ+Pi4CoVCyKiQycFmeMLCA07YSA8OYHtZO6wT5hcwjj7lGqwRPzYirVbT17EHWuIsrZf2oPO5F3vCg38cwRXfr9lsBgop3UepO8TnYI3y3tkLZKfOnz8famL9ffGesIWwc/BpHOhhh6FNxkEmxu372AOIBFAAiux55mpiYiLB0hkdHQ0gFLsktXwz6oMZI417+HPs2LFAFY7reqHgsuYd6EE7JjgKe4714eL1s2l6EgAYlxNVq9VwNB31k6zper0eaj89SdCNHCij+OpXv/ogH78k9zPBQEgKVEmPNkK5AzSxWTxNDT3VU/8OFDGGXoDPvT3TyPURp8y4oW00GsGhPHr0aKC3uBO3uLgYzu6RktmVmG7DdQuFghYWFkKnSAeKXMNpp3zHo0x0eDt//nzCAXXAheGMaXhOA15aWkoYpc3NTQ0ODurs2bMB0BIBHRwc1Llz54IC8XkEZFC35U6z/9vpqt56HUDoAJ2//X1A98IQeBMeB0exo+NAkbWHeDbHHRXGsLW1FbqAujAOB5A7OzuJ7JGDae7F7/1dSdLY2FjIqLEmcFSbzVZHx8OHD7elhnJNHJj4AF6vYYEiI7WyMtlsqw4LMJomBFpi2pS/q07AjmeHQsOcc1g13/f33G09QwwU/Z049Y7/Hz58WKdOnQrrzu8Tr900Q0kDrbGxMc3MzCSCEC7Uk2Sz2XB8SSyrXzvTlGCH04guSbqwRs+ePauxsbE987UftSntXSEARZglXC+bzYbgzfj4uO66666OFGHYMzj2MBOgX7lec6DomVH04+bmpiYnJyXt2hgHimS1uK6DMdY/tVfQAAFisb3ieX18rEscTc8Q4vhJuwFbmDwOaly3+v7yLLp/Bt1J0NSPn5B2a7Lja7p48xOOTfBuzQSmuTZ/O5CTFPQw9ZEA5L6+vlDDjh327CJAan19XSMjI6EzOLXv2AAH+dyTuRwfH9eJEycCk4P7tAOKnAmdz7fOyltaWtLAwECCopiWKULwrwjKxyDPgRvBLcaQxgbJZDIql8t77kewnOZ1rPPh4WFtbW3pzJkzesADHhDKTfz+AwMDmpmZ0dGjR3Xy5Mnw3vCNyLaxriqViubn58O7wF64f8keie0ywNX1gq93vks5D74Ga6avr08rKythD3mNbdwgL5PJaGxsLNA45+fndfbs2cByYo27D4NP51Rw7B1rvNlsBpr68vJyYE7RjI39TqCBfZYW/CT4kxaQjYPz7sMidBtPu3aaXAKK30LiYGl9fT04haurq4GigqJDkcXKxouOY444SnVoaChE/1D07iA3m83Q7hwpFouhUyNj3N7e1okTJ9Tb26tSqRTGG9e9cBzEHXfcEZ4z/hyUEzcgUBRQOg6kaMUeU6ni+jxpd3N69pX7AFCghjAP/AzFhbEnwzg2Nhbm89y5c3roQx+qxcVFbW5uBmcf+i60hWKxGBQwHSaHhobCONKicvzff+6ZvlgwphsbGyG6HtP1ePY4YkUWFkqJ/44155RUxkRGMW7Owvc8o8h9PKoZO0iuXFkHCE4Yzls+v3uuHoamU92VpNAqnAhqmiNFFiEGYXQsa9ecg/oDF48q4kh3EkCWO2m8d58XP66lG4kzz3FWJn7efD6vQ4cO6atf/apGRkYSv3NDmQYoent7tby8HMBunK12oXW8JB07dkz/+q//uifTtbm1ew4kAP4SUNxfsANxs41uZD+gCGCbnZ0NP/M11Nvbq8svvzy1XwJCJsl199bWlhYXFzU0NBR0nev/tLVLEDQGEsxBTD0lk+FZmDTnD4CYBhTd6XOHjmt4VoB/s1/pzElTE89uMPeuI51VEANFMioEVV0fEPRFvJwCgcFQrVY1Njam06dPh98BltB73oUyphbOzc0FCvnGxkbIzm1ubmp7e1sbGxvKZnebzXD2qteBEczks3fddVc474/14dkobN3Ro0d15syZUDJAIMCBIucCSgrd2/v6+jQ6Ohrq/Xin7YCiBwY8SMmanJubS9TXsz5HRkaCHazX66nX5ugw131zc3M6dOiQzpw5EwAFRytMTk7qzjvvDGdb8t6xjQMDA+EoFAAm+wBgxs/ZQ/hxy8vLbbNlzE8noOisOKee0isD4EtZEu+MwIbvNb4XC+udEolarRbOqYyFAMfGxoYmJyc1OzsbxsC6IvhKvSisBnxGz/C7LU6bI9dBsZ3q6+sLOm9jY0OLi4va2toKNp+u+J4w2E8uWcJvIWFzQE3wCJpnklBm8UG/ThFwegCL2Gv4MA6SEsABsObdqKTdbBhc+ZmZGS0vL2tycjIYXpzgOCpar9dDMwvGFjul5XI5UQjNs6ytrSWOeOCaZCFcHOB55AcuOlladyIwvihwxKmXGBCij3yHFs3Dw8OJM50AWmxybysNSOLwVc/wuIPhkTCydLxPrwPw50bZ0u3Lo9lkPtNoglzDDVkMRF1xU+tC5hiDEoMnHCMixu2AIlFAnwsHx9vb27r77rsT43ZngbnirLP9gCJZBkCmO1ZuVAkUuHj2Ik2ggPkYnIZUKBT27cKJ0Yrr8NxB5h0fFCimzbH/Pr5WsVjUwMBAohMqz+Q1OPE8kRUFcPvaij8b66g0SrCUdPipjbwknWVkZERjY2MX9N1O0WwCaJ4pI2NDJg6b1Wm9Yy8ASQQiPcvozru0t76W8XiGIO6CjE7FKeTz/n3fp74m3TGO2Qc8O5/xPcZnnMGBnkS/sMc96xTfn4Aj93H76jV3UF19jDGDgQCmXyMOFLteQM8ODg6G5jZep4ZDzF7PZrMhQAkFHx+Gc5EJzuXz+dDBlPt7FpXALuwb1gd+D6CU9z45ORlqMLExHsxyajtAkfEODAxobm4uBFY9QIAAhvBjuDbvJJfLheNAXJrNZjgWjLmg7tDXigeq8e+wnf6ePMs9NjamU6dOJQLN1LzFGXO3H4VCITSSYR/kcq0Ga7xL1gHvJGZlxdRTnxsPejhYl3Zpmd7fgbXhwUCEAFAsl19+eWBMUSub5ttgIwlYcL6oN8DxQIwfY8F68GRMWo+C2Bb5GmcMCKVAjUYjnKd94sQJLSwsaH5+XuVyOXGOeDdyIKD4K7/yK/r4xz++r6N0Se6fAljg8E0WK1QAFihGD8PGRkaJeMQqpp7G9Q0YL4+MOi3N6QN+hMb6+nqgeeAMOhcf8cwTSh8l7A4E9RVS0rg7AHWlt7a2lqAn+P1QGI1GI3RzJYMaOy0YarqyISiOTCYTomwAReoTAXwOyOmyhRH1GjueDWBUq9W0sLAQ5jANKELx8bkkMuhKBPAsKUR0AWrMo0dDkfi9ozhj8XU0NjamoaEhbWxs6MSJEwGwxHPLukL5+7VioIjTxOc9s4zSxABzaD3gk/Uuac+8pAnvxNe0SyaTsVqVvWBkZGREm5ubexpikMWNgyX+br32Im2OWYtk7OMsegwUeZ5uxI17GjW3XbaTugo3dtRoSLsRbBfWEfOAwY2zMnH2h3HGtSWFfCEREb8EErsTar4uRDplFHmf0m4wkGOUKG2g2VqnQIZnpXBk0fVcN84opq1d1gyZRQeKnqXg/3FpRQwE24FkX3cxdT/NyXX7yj19HxKYzWazoW9AfP9GoxEYGTHIY55jOi7S19eX0DeeOYmfy7NOCO+vUqloY2Mj9dkymYzm5+dVrVYToJZ6Lw+uUjuH3iVbiQ/hAGNra0sDAwNB93ggAD3rNbIDAwMhUMu6iamnrCtsO2upVquF/gLYlRh0eIYYoBiDI4Kz/l1nfTE3y8vLifOhmU8yWPV6XXNzcxoeHk4APsZBwL1YLKpcLofvSa2MI+dI+jrxDGsul0sAReYFJhnvOQ1sxoFdxu6BcNfhnk3f2dkJx47hcwH+46AOEtf5+TNJCmdDponrhaWlpdBfwW28tNsTg7pDei5ILRaT76G4y2kai4n6RgJf/nvWWqPRCH00xsfHlc1mdfLkSZXL5cDC6BbLHUjDv+Utb9EznvEMjYyM6PnPf77e/e53a25u7iCXuCRfR2HTsMiIfnqmypWpN76Qkm3CyUrGlBg3jkSBEJSPtFuAvbPTao5CK19qA2nL7DxsjEZ8TQR+PgYm5p0zdoxGo9EIHUrjrA9zFYs7ANA4cF4AcVxDUohaxo4DTXny+XygCeTz+UQjG4wroJJ7cD9vguBjJ+PpWcU06invNDZYzAfrASW9vr4e5hFj5Yqe8yFjBc97wllJU06+jjA4o6OjOnbsWLhG/D4woChfniXOKHJfitw9uwtQnJiYCMdyzM3N6ciRI4nnXFxcVK1WC2uDeUkDjZ5R9PboPieMI81ZrNdbDRgWFxcTYHFhYUEDAwMdgWIngINBxLC6QYIuy//d+bqQGsU0cbqUC/vDO7/F1NNYeIcx/Sim6cT1J9xvD1D8mqPDO78EFC+epEXhpf2pp04xJSvhYG9ra2uP7m4nTnMrlUqqVqvhZzHFMW28OGMxUGyXOedz/jO/LmvNx5c25hjYxToHXcDvoUZ69tBp9IjPPaAERzzO5FBqgs5zAaghXDMGivl8PhzhEWcUAbDUl7m/gY3hkHKejX3vHURxwIeGhoK9lFqOM0FA9DcZoHw+H0oE0CPOSPLAJqyezc3NPXPqAVMChD4vmUwmHH/Bea6xrnSqoWe1sWnoYnQzv/O1C+DHT3DdiR0EtFAPGOs7Av6skYGBgRD0lxRYVGnUUMbM/zliy/0IP9fS1wzPzjzG+5D1STCCQCef5d3SjM4z6QSb04CiZ29jIUDuGXcXap6xqbCzvEYRn8nPTHTGGmd38o7j0pMYCPLOR0ZGgg/h9oyaXV+f6+vr4TgeMo61Wu3eySg+4xnPUKFQ0OLiot773vfqhS98ocbHx/XkJz9Zb3zjG/Xv//7vB7ncJbmPBQcaMOHggQXPRgIousF2ig6bo1NGMQZf0DHY6NRKnTlzRkePHlWpVApdO3EeAYpO93Tn2p1k2he3oypWq9UATFG+FG37Z6ElpQlUHGmXghFz9xE2PWfHxRlF6EFQLHGAeM4YKKJsiBIxjkajocXFRZ05c0a5XC4oHY9ktssopoEdPhtH23198D65RrPZVLVaDTQeFzdmfm+nVfk6iqOMPT09eshDHrIHsDBPMRBIyyjibNKRzIEyWeF6va6pqSkdOXJkT/R+eXk5UK8weLHBQZxGkubIusHLZHbXslNnC4WCDh8+rMXFRS0vL6vZ3D3LKd5nvo87ibd39+ispBB1jfd67NB2kv2A4tGjR1MdYgIk3vxqP/CLHpOS1KEYfKQBRUCGSyaTCbT3dsDmkhxc2gVTpO6op1KyfbwDRTKK+1GtWZfoMcoUfH+nZRHja7C2YqCIbvd1EwPFtGffLxgRszTSMorOlAD84Niivz2b5vePs5tpmXa+j08QB39jwemO9SJ1WPH5qo3G7hFPNH1xPwN2UblcDvd1/wM6srMAPIiHPazX64lgA+snk2nV2hFgBvg7/dRtBfaeevW0NUPQCxCLsI4JbKZlFJlDgKIHP9FzHjiJwRb3Q9f7ewAkUtpB3XYcXIsTBbVaLfhlrIXYn8FvIbvONarV6h6qLE1h/B35ddrZVQAR7wT/lPlxn5YzhAkAsq6decL8xWNwofbQe0y44McBRPk9zDBnvOFDeFbUr0PwIy5Xifc8c0DWOw58ekIAWV9fV19fn0qlks6fPx9YbPdKRvFDH/qQZmZmdOONN+onfuInNDw8rHq9rk984hN6xSteoYc+9KG68sor9Su/8iv62Mc+domiej8TMh3FYjHQM9kAHCEh7TqyGAl3HtlUZB9ioJgWZUIpogC9vu7UqVOamJgIBbbz8/OhyQAbgHESaXOH1Fsbez1CWu0KhyLv7OyEjcMz+nEDdAJLE8+acjBt/Nw+V0TtYufej8jgiAAMT0xLwTCgKFEQKysruvvuu7W4uKidndaZkMPDw6rVamE8DhRdgTAW3osLxpu5xmhTg0iWGUoOTk8a7QhFzDX93u4ItaNlsX7Szgb0qDLXiwEQ65X5pnW0GwqUM8EFp2PiYBC58yxqO0XrP+O5XXBI8vm8svmc+h4zqaWjOW18zfi7k3D48GHNz8/r3LlziY7EPsedMjMuTrHB0POsrEN34nhX3WbXWPPtAEC7MbKe/Hgbpwh3uv/GxobOnz+vnZ0dLS0tBQfSfx/v5ZCh78nq1W9/tF799kdLmXoii3xJLo50ms/9MooOFNE16AmnRO4XJOGz6AIySDGIjTOLLp618CCG14tLSoCZWA/E1+X/DnDi33tGMS2jR+aQ7A11UjwzwNYDvnEWin/Hzj9CAInrdtof6JgYJOMLxAfd8ztsPHbHx0f3cafK80yAFmxtoVAI9psMF1km1hB2l/tgH1zXI/Hz9vX1he6d7dYddZoEOX1unKkRv0uvw4vBS6PRCKUH/q49AMJ6dsDma5AGeT09PVpaWgrUx3gfenkRmWZsFkeXtEsKYBe5BgELHwcUcvctECiuaeuQa8K6Yj2QXWQvNBqNcBwGHdldfF54t+2CRB7k98C5j3dzczOcQ86Z36xjp6ZzHuzKysoem1QsFsMzQ1FlfO4zSbugz2nu8Xyxn9EbJDXI2vPu75WMotSilz33uc/VO97xDp09e1af/OQn9YpXvEIPfvCD1Ww2dccdd+itb32rrrnmGg0PD+uHf/iHL1FU7ycCNYOIFps6k8mE1tc4tg5MXAmwCXDIXKHH9BoUn9MCyABtb2+HBesHuXrRfJxRZON5Ns0zBlwXiTc/xmN7eztEd6SW4UCxeHQqTXxuoHcQyXVhc+NIxFEf5s65/yhrgC5ziWML15+oINTM8fHxUEfgEVrmoFqthmwgPwMkxI64R1VjoOhAgkyclGz+4FlhvxcA1x1Hp/mlgUNpV1GmKTSMYnyul38/rhmC8oGhIHI8MzOj48ePK5/PJ2gqGEcilO6sxc6g1AoenDp1Ktwbhe3iY8zmssofrSp3tKK19da8OZ0tm83qyJEj2t7eDjWzaYahGzAXMwjIUqf9n591yrLEwnrqxnl34f2S0XAd0iljBPWm2dxt5sB5iUjMFpB2gUehkNX3veC4vu8Fx5XJNkN9SZoTd0kuTDplmTsBxZ6entCFkbWJOCOmm3fln4X65k2Qwl7Mtj+ywG2b3xNmQLwfnbbOs/peirNJaZn7mHrKOOJ5AgRD88O5x17QtMTBQzz32BW3Cwj72QFWO9nZ2UnUN/t8eN2ezyNjorkHgM3fB462O7+8A4JqrBHvFQANdGRkJPzeg8g8Jx0yuQe2mznic2NjYyHw6zZqY2Mj2HeOd2BtIA5042Awc+QBdfcHGo1GonfA0NBQaFjCumCOAINp9gmwjg/IfMRA0TOKOzs74agIGE0xeCkUCqpWqwGc83tAuGcVndni677RaGh4eDhRVuPCz516ypw5hRlfQ9rV9Q4O/d/t9jtSKpUC26her2txcTF0QffrU85VLBY1Pz8fus87Gw77tLy8vKdRIkCRQIGvzzgRwVE1vDfuE4+bhkP4ajSM7OnpCfe7VzKKsWQyGV199dV63etep3/9138NIBGK6tLSkm688cYERfV3fud3LlFUv06CMUmL5BDRwygQlUvLKMZt6N1xjoGiZxhY9DjmExMTiTF422cfT5xRjIEimyqO4sSCg+FUT+aFsTqPPE24N89KRBN6goMx6vU6RcmoEfX34RufDNL29nY4KwpaLpE+r1Nh3v1eAwMDgWbia8Ej2tD+nA7iQBFHCgXv0WlfI9VqVStfO5MOJ4DIPxlcp8vU6/VwThqSBvTSFFoMFD2qivB9rhc7ZHD3ySYNDw9rZmYmEY3e3t5ONF8iABID2M3NTS0sLKhUKgXAnQYUvaMqxpxsOGN2oAVYdOeH9X8QUOMAjnXs1Jg4aEBNTrdyoUCRNVEul8PZiMxZGtBDoFI1m00NDQ1pcHBQIyMjexzytIBR7Mj6Gu+GFnhJupM0ZxXZj3qKMxXblXY/aycOFKVkUCXOKLZbu66P/PsOFB284MB55sKf1T8bU+/9Mw4U08Ck016hb1cqlUQQl/WMAx9nFBFAYOx4MjZ30tsJQDEtw0EQMz7yAVCB012r1TQ/Px+Ck7BJCFR6103+ADSbzabW1tYStXIE+sggejmFB2ShvkqtJk3eRI/nYbw9PT3huCZokOhvjvqKdRf+wPj4eGqJhmcUmfM0O1Ov10MDMF93BDl7enqCvVpdXQ3dLvnD/MXvF8HeOq26Wq1qeXk5rIE09hg+CP4JaymmYTuQQ+dKrTU+NjaW6I/gz809PBDC3Dgo93lH1+PL8nxcdz+g2NPTE+ii/PFAOHRi3sHw8HAAk+wZsuFk9uJj16Rd39X9LN5NTOX2gM3Ozo5WV1cTSQh0BTWIjIOgCV32434SneSiHo9x/Phx/cIv/II++MEPBorqC1/4wnDGyyc+8Qn9xm/8RoKi+tGPfjTVgb4kF1+y2Wzi8E0UE1my/v7+EPXzxehA0emqaVQaj5TF9D+pFW3r6enR8ePHgyPgUTQ2nAOEOKPomUNXOjiXnWoM4efjzPvYAS0UQ6eJR2QxGmw6BwRE99j0aY4NmUbu5xFS5swdCqJU+Xw+FCzz/gBj0F1csWSz2dQzCLl2NpvV9PR04ucx7ZD37SDVs6Y8O+/GKRc0SsI4eWBhe3s7AEskpkWl1bzwO67D3xjatGu5AkbW1tY0Pj4e1ie0ISKzdI9Lo6g5gK3XW/WNk5OT4R47OzuhEYALEf5sNquMMtqeWlZ+fkfNxm6DoE6ZvINERF3i95RWJ+PR/nw+vyfA0EnYQwcZkwtA0Q09/46dBmm3jrld58t248CJ29lp6OYPTunmD06pvrMLIi4BxYsnnain+61zF2dBIDil+4nXo/FeoYx51o/1m3ZN9F184DzUdw/s8dzoPkl7xu77zu2dSzdAkc6O6MlsNhu6cNNIAzDkdsgznNyDjpToawRAFzcCSxP0fFqtI++CelP2LaCCcx+Hh4c1Pz8frsE4YQ/B5PHAlKREgM6Dpnyvt7c3NM3j+R3Ij4+Pa35+Xo1G8vguHG3Gwn1hePA5ALDTWAE9MzMzOnfunHp7e1WpVPbYFO7jvhDP7UCa4Gg+n1e5XA5Bbz63tbWlwcHBsL5p2sT6zWazGhkZSbCO4rXHvHhG0WvimFdfI8wJY2PMHmhmnUHBdWaRC2stDjK5X0R2jM9i53lXjJNx+R51+xnv51gcsEEvdaBIgNdZcb6vANZ+BvahQ4f2PLOPD9vMff1deTadd4TNRdbW1pTL5fSABzwgjKnRaIQut9VqNVB375OMYieBovq//tf/0vT0dFuK6rXXXqvR0VG9613vureGckm+JmxgaXcBYmBo8sGCw0FzkMfiJKKUVlMRZ8M8k9jT0xOiGURU2ATz8/OB+uV0IBx/QFMMFN1ZAMB2qjEkS8L4cSbZ3GlO5/b27llAPiaeGQXogAanwrNZsWAQyYQBElCIcbQtptqUSqXQHACg6BnGToJRiuvDULj+HngXTl+Ks4UewZWk6enp8BmaGMXvhDnCwCBpGcW054nr53xtu7iTEncvS6MZk1UkGg31xLNOHshoNpuampoKBtjXVlpnU8/kZZXR9j9Oq/HP51Xs6U3QWtqJP3M7kJQmDprJZDNXOKHuPFAz1K1caEYRwaC58UYPpWWV8/l8ImoaS1ojG2lX52xvNvTLz/+kfvn5n1Q9Wl6XgOLFkU7U04MIAMHXVaGw/5mhfM4z8NIuRZ6xOfU0be2i99KCkDjNsS5yoNgp+ICOTQOKjC8Ovvp9AJGFQkEjIyPBPrgzzvN5MC3NQZ+YmND8/HyCKuj0/3ZBO4SMomfqGDc2jt+hu+K9jQ49depUIvNFJ1Poc6yt7e1tHTp0KIBHgBbPhO3q7+/X9PR0ANduY5rN3XOLV1ZWQld4roFeBoy438G1AIpkNjc2NjQ1NaWpqamQgYS2OTk5mRpo5507E4b15R1KM5lM6D/gQJEA5+zsrKanp1WtVlWr1VSr1VStVgMlMa77jWnI+FoeiBseHoKwi7IAAJyHSURBVA6Bw3a0VgeKrD2AGnthe3s7sLawSW7z0c9xkMn9olKpFNYB8894Y5YNAWIvx+G67WwE4uCdzLRnR/GdeedQlycnJ7WxsRG66Z46dUq1Wk2jo6OpATKeaXV1VceOHQs/jzOKno2MKdrIyspKKPPhvUgKR3JQrxv7XZ3kXgOKLjFF9c4770xQVBcXF3XnnXfeF0P5lhYWFBnD8fHxBFAEQDl9M87I8HlqtuLrs2H9Zx6ljeueOK5jZWVF5XI5RABd3NjFQDG+P3SVdpsfLr3U2minTp1KZE08o4IsLS0ljilw2oqn9d3RBnzFR3T4dYnSQV3AYSAi6UDRlTlO9NDQkObn5xM1Fd0CRZQuUWjORcRRckOAE1OtVkMWB3BNZMrB+pVXXqm5uTnNzMyENtxOEfVrQify8bpTxffSnJO0LG787lyq1aoqlUr4f7uME/W63JOAiYNozyjOz8+rt7c31LxirIhspwFF7uu/K5X6tba21jWljjnoZOjaCY6kA0PvpkdAqNuMD9fEqB4UKKJr+vv7tby8HNaKO5NpEXjPiseS1siGccbXirNEB3nuS9JeOlFPDyLtgGI3gYy0QFN/f2uvEZXfDyjisMdA0R15/s89YZpIezOKLu2AogchPaPoANIZH176kM/ndfjw4UDPcxaH67LYqS6VShocHNTZs2dDg694TJ3eJ3s/9gPcXgDc3W5ub2/r/PnzGhwcDMcvzM7OJgJ7Hqhjj/IsXptMhgUdjH4EdOI/uG3lWtVqVfPz84l3TKMvnv/w4cMJoB3TJL3hzNjYmI4ePRqyvMx7u/OEPUCLrUkDitJuYNyzpxsbG1pcXNTw8HBo7OPC/TsBRYT5Zk9Q2ya1D6RhU/33+DQOFJ1dxP70YDM2Mi2jKClkn93X8dpHF8+6x5JmPzc2NkI5DPNC9o1ggicKvERnbm4uBAR6e3t15swZra2tqVKphDNnPVvsUi6Xgy+IYIeZh/jYKAIc7jPHZ5Xu7Oxoc3MzsMoIdsTsq07ydbGGl112WYKi+r73vU9XX33112Mo31KCwslms+HYBhQSkYlisRhaP0tJJYIhRRHEDm3M55d20/zw1AEhSG9vr86dOxcoeoVCIRHNdAXsdRLUjklJYIBybRdp5ho00zl06NCewmLui1ALgGB8MDZ0oXIjClD0bp1pdYojIyNBKRFJ5Fwdp0ph+PxnDuhQZADF/RQA7wV6kqQwLzgcGKXYqfDsbiaT2ZPVKpVKKhaLGh8fVyaTCbRWd6JQlhsbG3toOO6os3bSjGrstAHQ2gGtUqmUqGeEwpQm1Wp1TxtpV/A4Cqurq1pdXdXIyEhiHOypNKPgEfokUCyFrr37Ce+lUw2fS+wQSq3jKliPrGl3bHt7e1P3Rqcx8d2DAkX2RqVSSQBFX3NpGUVv1BFLu2ixMx38Wtwvba4uyYVJJ+rpQYT6O19XlUqlq/UZO63SLv14cXFRMzMzIWOQ1pVT2s1KuM7xYF68NtOAYjwmB4F8p9NnHDz4/QjOOH0NfUz5g2edfI3Hax3dPzw8rLNnz4ZzD53at9/7dLaJzx9AkQ7r2I1MJqOFhQXlcjkdPnxYa2tr2tnZCdk35o/v8wz8HwABIAAgZjKZwJSSFIJ5lHowtzjfHJMUg36C2f5u01gv/B/dDAWU8QPssNFpNi0OAgDqstndTr+xrXW208LCgkZHR4Otm5+fT1wfmx4DxbSjXJgfDwR3krRAi4NnP9KjWCwmni2mixIUiYMNXI/gLX/jXzK3Lrw/X+f8Ow0orq2tJY5pg6VGls6TGd55lYY23B+G3OTkZGAYeJA5lkKhoP7+fs3OzoafxSDe7T3vxZtHeTdgBGDuupITANISLmnydQ+blstlPec5z9E111zz9R7KN70QucnlcokDYdngRMFio+MZRRxfAII7uh4dQlDiw8PD4d6+iOHd12o15XKtInFXyg4SqKVEEXHejEdVM5lM4mzDWIgOLiwsaGhoKJFxaOfAQ6/0/5dKpaB03Rg5UARA+rPEG5NIHREvsnwYOhQLoMqjapJC9zOMI+9pv6wUz4miRZHHip7rOI3KI8bUCsSKj+LxoaEh9ff3JzLQGACuyRrwe3pGsZ1RjYGA07B87O2en0Oc0wTF6kfJxOuDtT4xMZEwQsyPRx0Rf854nWWze8+VaicAq26BYlr21DPz1I14ownvPHkQuRCgyFxSWxNnO9oBRZyDNGDXiZYbO7L5qLPzJaB4caQd9fQgWXNpt/mSrysP/OwnbiOk1toZHR0NjlYmkwlNUNLWDHRIP46Jde7Xdn0p7TY6mZmZSVzPwV4a64Axp2UUY7AWZzV4Pq9d4vqulxif35sgY7PZ1JEjRzQ1NZWgnabtQ8Tfqdtx7CTfBSgyf+fPn1cul9PIyEjIPG1sbOjIkSNBP5MlIbg9PT0d6sawsa6byaS6DSbTh/PvdWrovlwuFzqgMq+eUfTn8cBYLpcLdorfxx1PfV21C37GQQDeN+ssZtGwL+r1us6ePatSqRRAAPPhvgsA2xsCbm9v69SpU4l1EWe10mxSnCzw4LJ/l4witoV17HslDYy2C8DwO7L5+K0cOxIzl4rFYipzSUoPJnqNLuNkncGoYm2vrq6GuaQxHsJ1AY8OFNP2ez7f6lfh3WzxBxy083wc9eWNejh6w+eg0WgdF+JCQOReAYpPe9rT9PSnP1133333Qb52Se4n4kDRIyksWoAfAArwyObEYUbRpEVf483N5uSQ45gmVigUdMUVVwRDQkYTJQQwQgEwRgy7K02MGXWQadJsNrW8vKzh4eE9nyHqR6RY2s1oea0IyseL2Pv7+4ORYB4AYP6saZRQjq9woOjPub6+rkqlEqKpfo2+vr5A7STq2g1tzjOKgARafMeOEkaRzoHuqMB3T6N7AOqhzcT35lniDKgbm5ij7xJ3D2NcceCg3fOvr6+3pW02Go3Q5AbnyusSmecrr7xyj4FzakgsRPzi50RKpVJXIIt5bWfEY0mrxXTDyRrAcFwIfRRpZwg7idd5Ed33tZjmoGJ4094xhrXT+9/a3o2i5/O7tOVLQPHiSTuq4kHXCNmHC12TcRAJnbO1taWxsTH19/drdHRUhw4dSh3X8PBwCKLy+04BEdfTBPLcqXbAhvOextDxz+A0poGJuEbS69ahpcbXlvYets5+4tloftHT07MvUHQdQwDR7+E2HVs5MzOjbDYbaPtSy3ZQG+42kDk6cuRIoKieOHEivFfAIY4888Q78iyazzdHB3AsEofEM4euF6W94Bq7y/rAx4qBoq8VtwEu6EHGRoCYeXWgwFiKxaLuuusujY+Ph3P/GCPHWiDsOwd5gOK4Pj5OAsQSr4U4KMQ6wneLWVHY0DijyHfj6+Eb+f3ZI5TweIkL38dX9awm1yMQ4AITiGs0m7ud3VlfBA5WV1dVKpU0OzsbqKUI74EutE6XTtMx7EPAorQXpLMvm81myGD29fUFXcPxOD5Pm5ubiZIb7oWd7UYOZM1vuukm3XTTTQlqoMutt96qK664InTbuST3L3Gg6JuOTUnUrlarpbb89sglEcPYSU3LKHp0KwaKfIbv+mb2OjCiRowpn8+H85KazaYWFxd18uTJcAZjmqyvr4czbNKc63ic0t46p62tLZVKpaAEcAC8s55ThGJqZBpQ5LmXl5f3AEWpFSWq1WptKSuDg4OBruNz2ylqj1IDBFNI7YrJDTtjh+aB+AHC8TOx1jBm8e+4fmxwnHraKaMY00ZYa4ylE4jqpLC5L/PDtbyWxc9iiwVF7o4pQi0wAD2+Px3x9hNv0NGNpEVsfT1i0Ng7F9qQRrqwRjBeG10qlbS8vJzIlrbLKLY7yma/Jj89PT3a2rSmBPnWZ3lvBwW6lyRd2lEV29XpdJJOTJH9BGDIGgIEwA7pRuIMioOQ+HceUFpYWFC1Wk3ofvQI9iMt85p2vzT6J3vHA2ToFw8+pknaWneASpCPo2jaAX8peVQVWTnGHTN1ent7NTc3p2x2tyM3z0ojGQ+6+tETSLVaDWULMzMzOnXqVLAH1Ny7/vfsG+MhaO4B14GBgRCEhuUQl574nPFe8E8Acz5W9G+SQbJ3Lj2YzLrwmjPv8s5YarWaDh8+rP7+/tD4i3HjV3hmOmZpbG5uanx8PByRheynx2NfL85Wc318PlhPblcIfMSBTM/I+zxD35V2u8sSuOfdsgYZTwwmfWxpDB4P6ngWF38A4MjzFIvFANpcHGS6r91O9zEvpVJJS0tLieA0ugCgzXOwN9kvTkNnDM54cxkcHOyqeZ50kamnW1tbuuuuu3TXXXddzMtekosk7rx7LRsRJjKE/B6F6NEtV4pQxFzY9EicjUsDivH4yuXyHtBCZMhrMFCAMzMz2tnZ0dGjR1WpVNoCpPn5eY2Ojral93m2jOeO6aN0joIuiRLAccdoo9DToryxZDKtjldQDvxa3BMnKa0IvlKpaGtrK9EkpZ2DhnAoMOMrl8uhkYorSeaK9+gURqdEnDt3LhFAclpJvV7fQxFFScfRTb4bZxTTnsUbFfgzxxnANEnLsLnEz+1GhQhpWl2HtBuV5zouXlPXev6kMe7r6wvNljqJ1/x2I2nAL46KQ2/CAHaTqbxY4lRaWpA7UExzqjKZjI4ePZp6vfPnz4f29WniTgP3l5IR90tyz6Ud9bRbirWLHz1zUIkbIuVyuYS960biRhlxA44Y3LCWoOg7AEBH+XqL17fbXtfFaRlFL3kgmOfUQj/AO6YGxvbY54lg2MDAQOgY3s6uOI2vHVBkrJVKJXEWoV8XH8GPpADUe+Yxm231VDh06JBGRkYC3XN+fl6Li4sh6IRgR9BtvAcca7KSZH+gRMbN1GLdyHx7RpFmJgjz6N+L6e/+Llk/AFnWWWy3sIGA0vHx8eCDEMyELSQlg7D4cNvb2xoYGEjoQ5+bdv5aHPiO7bj7fp6M4D2QoYOt5M8VB3ak1joql8uJgAKBPeY+rdaRf3sGzRMQ/nxpLB+YR+gKPkO9crlcVqVSaQv+lpaWUkuc0j7Lcw4MDGhhYSHsnZg14J1NWctra2sqlUp75qBdjSnHZHQjl8Km30KCAiHbFRsfFBKL2DNC0oVTT1HAnm1LE+5Vq9W0vr4eNgngxTMeGITe3l6NjIxocHAwsZFjcSPQjlbnQIDrkFFkrgCKhw4dkqQAJD36uLm5qZ2dHR05ciRx/XbUU7K2pVIpRFl9LDgoExMTqR1AM5lMANc+72n38vlwxe5nXznIc8DkitWjncViUYuLi4kOa3EmMM4ootQdbPp3PaOY9hnG6evPI+tkp9o5gTGFJxbG4IEKv57XeKSJ7y8XDBX7Qtmstq4oqfzow9IBMizQq7qt0dqvOypOLw7mPaGeXoiwXgmwQIXqlFGUlPpMCwsLod6jnfT29qre3Nav/84j9DOvOKae4i5Q9ADKJbln0g4odkuZdrkn7yTtkPC1tbVwRmc3EmeEYqDo1+aa2D0/egonGdACgOjU8EZSwmmMMzd8l33kutsdxzgQkua0eqYGR7xWq4V+AO0CsQ4UYxo7ewr7WCgUEt3GfX/j8BJU456c9Yxks9kEEISW2t/fr3K5rEKhsKfkgTn3Eg0yMW53OVrEqX6MzwOyPoe8U9hAPk/OhPJ5joONMesrzhbHGcVYT0PXdcrlwMBAaGoTZ3VpngaQi4Ffp4BquzpLfz8eFOnp6Ql1wASAx8fH94yLaxNI8WcdGRkJ80otJiDQ3yP35/vFYlELCwshucC+iH1Rb7DE+ud9s2YApgsLC+rv71d/f3/qvsBnXVxcTLyzdowV1hlZYrKK6AenIHO+M8+ZybQaGZXL5UQdK79rx3zqltVxCSh+C4nTQGNHHEXtUR6UZNwkhRQ8NQGxxFEllDzfb+cgOE2RY1N8A7qBrNVq6u/vD+dGeeQqzZBhfOKMoYtvYD7HnDAP0Guc308HLyKTW1tbgaIaP18aeONaHHjsmdtsdrd+I86qugwODiaoI+3uhfBuef+Mm3fjhoP14krbn7+/v18bGxuJiCRRdH4WU3Dr9fqe+3FNzyi2U6pOaUH4t2cU2ynC/bJxTj3FSfCx0FipnaRRZ3g25qDZbCqTzag+WVTfA0eVyXbvCFMn0S1QdFpYLDxfLrdL/b5Q+qXTdQ8irFeyiNTWdKpRlJRwoqTWcy4uLmpsbKzj/VpBp2390M9cqWc9f1yFQlIfXgKKF0fazeOFUE/vidCMhjXkDmC37zrOPsZAMe15vK0+NVrstXq9Hmqb9gOKnlmMg7EegIwDhFzXM+Y+1rT3AL2PvdWtLohrip0Kx/exM3GWw59/Y2NDQ0NDWlpaSmRm2Zs4zw42Y/1FwxwPUOI044vEFFv3i6i9B0wQFI2fEyDtdox1FdvLGJR7gxfEfTTm3wOl8btIA3KsZ+bCaZluWwE7zBFHxnAf9wHTJF5r7WjM7hvRgJCMZzugmZZRJPhM5p7resDB97L7LH19fTp8+LA2NjZ04sQJbWxspCY6CKhybff3HHARHKenQNo+olzKG0fy83Y1nwBFsvj4GABF/L+dnZ3gXxKAWF5eDg2bfCydmFNxMqOdXAKK30Li0SoHMb6ZXTHFFBZpbzF0GvU0BoqSwqbsZHT4ebFYDErMJQYQAKfYiUwDiktLS6HJTacxOH/fG48AjHlmFINHUVEMm5ube7qT+bVjceA6OTmZ6M5WKpVCFJVoYrvMihu+/aJ9fp4VjgP3ZKw8Z0y58t9Dy2C9+DMRFY5rQnlfgDXWWUzN8nvF909rA82a7Caj2C4qjsT1QPH1OmUUG42GZmdnEwd6M2aOQmmXaelW8vn8vocFI+5kxuIUHCL+ZEEuRC4UKLL+2DecqRoHbmJx1kGj0dDU1JQmJyf3HT/rLl5rzrC4JPeeXAj19J5IPp9P0BZpQpFGP2snBDARpyASTIypqWSm/Pw19B3NU7yuzKXdGo73gut6v4471oyz2Uyet9guo0jZhddWdSN8zgONrjfTGlX5MwCyarWalpeXg43F1jOvUInRf+gN9m6pVNpT+iHtNtlh3zMOwAfX49gJso+FQiGcpexjjwOWPF9auURsj+JuqtLu+8M+OBB0GiXSTeBdatEMGT9zQvkOman+/v4QbPZsWjt9Hq/Zdn6VA0WyYvx/e3s7le7Jeogzip4ZJ8iD7XD/hfvyffbC4OCgJiYmVK/Xdfr06T0+GsAQf2RpaSm17wVdcvP5/J49hrC32d9Iu71EQIL3XKvVQtdxAsOFQkErKyvK5XKJbDl7Or62r+k0OX78eNvfJcbW1acuyTeFuFNI/Q4K0zMgbkQ6RY3SIiMxUMQI49h3E52EYooi8+gnYADOPxlQFGLa9ZvNZnCqPToXO+sOtABBbEbfqH6fOJLkEdNuGy847YX6RyKmw8PDifoEgGJa4Tntmxlvp4yiH1br32EdoLR2dnY0Pj6eoJFwP1fQULgATzhHUHVdnHpKZs0DEu3WiK8rwEycUYzrf9KMaFrdSSyeUfTIOKC1XYZLatFCKpWKNjc3A31EkmZmZrS5uZmo2WnUG8oubGvr7LKaje6PDCDa201GsdMRGvE6SYtyH0QuFCgiOEb5fF7Hjh0LP+8UZMGpOHv2bDhoej/J5/Pa2a7rMzdN6Uv/tKJ6PdkA6VJG8d6VC6Ge3hPJZrOJ1vWLi4uq1WoHAoqDg4Nt63w924ieWl9f18DAQKAlxgGsxcXFcP5qp/UW29lY98SUPQ+4sTc8owig4v/tMooHobanOcqAYQ/mFAoFra+v77GPPNPq6mo46xCfAUo6WRnvQMk8wE4BPPb19WlycnLPOGu1WsiaeaCTLI0fGcb85HKt5mOrq6shmOWg1f0nsmbOcsJHiOco7sLJnHmwE1sV2x+kU8DFy0scKCL4L3zfj0vChu5HPfW15pRqvz/Xoi7cM4p05o33VVqZjo/V7TP/92CCP4N/nnc9MjISSpZceLfsG87CTssOe1IhLaPIeLvN2sWSyWQSNbyAWM459yylj9nXA2yGeyqXgOK3kMRRewTj4RvAN368YZ0DHjvLDuqkXQPidIxujDJ1d/Pz84lromSgaBJB5H5sWAcVAD4UOhvcgSKROY/Mrq2thc2Yz+e1trYWNp1TXeL55U+76FpaZiQNtMYGAKC4ubmpu+++e09NTD6f1+HDh8P4OwFFj6SyFuhqx/UwbmSfWQ8+PgDX0NCQGo1GoosW9Mz4Ofz9sNZYI1J6ACJ2jgCKcaTOjXM7Z5QOZZ2AIq22PXrP3/s5uAsLCxoYGAjnJ0F1mZ+fD84pc9DYqav3i8ta/Ojt0gEzjN06uHFDJpc0oNiJUrufxFneg4q/ezeE7QQneH5+XplMZk8b8HbSAorSf3zOp/RfXvwlbW3sBprSMuiX5OLKfQ0UXXCAyfZ1u4+QNDYCHSZdT3HQ9eDgYNDP0Lo5wJssQKe5iPVhbCvijAr7mXs6QMHW70c9bTQabantrmeRNHYD2bWYmlmtVjU/P78HKO7s7GhlZUXlcjkxdsBEWkaRrJMznMrlcmAlpb0nZ1BAC8ReuM6hbp+gKcFit5kxUAQMOWDC1sb1hPGxG8wDz9NoNAKLyKnSLu38Kb5PABaw67p9e3t7D4DkMx6Q70Q9db+LgLGDRykJSMmW4svxfmMwmhaIdZ3hpTnO+PH16n6G+2uw29L6FDAW7wEQs7Oy2ayWl5cTHbfTMru8x1KptC+DCWEfxwLDzbul+3uv1WpBv/hc9vb2XtBZyLFcEFC8ZES/MYWNExe3E4nBgEq7URMiMIgb1TQwwgJ2BUI0pB2XO5YTJ04kskLcF+Wyvb0dzv4hsugAIY5GQTtlzMxDPE5X7rlcLsEtLxQKCeDI5+MaiDQahUtM5fWf+5jTMjMo/vX19T0KnnFz726opyh17uPZTK7lVGN3ODCOnCc5PDysnZ2dPe2WY+PoPwe0+3uVdh2j2dnZRH2qPw+BgjijyN8YjzQHjChhJ+W9tramtbW1PfQVNwhpoJ+IKU5gT0+PJicndffdd2tnZyccdEuEvdM76iRx7VIniY94cSHq7++1U13KftLX19cVwEuTNOqMS9qzsnaWl5f3rUt0aZc1dErTJbn3pNuA4b0hZPxjdkg3gq2JHbpCoaCxsbGgpzY2NkJnbBqfeZdJMmcemEwDYIwvBoqx4+rAILYjbrObzab6+/tDQCUtI4VudwZN2hy4pDXLoj4LPYy+HBwcTDCFpOS5sOgeunXyrMxBnFEkEIYD3eksWspD/AgufBxANAL9lKBpX19fYIg4YGFOeGYvqZCS5zPvl1F0xhPXxjdLA1TtqKfYf7/H4OBgYs1iB8lSSbv0U+a6U0YRMCntdnV18Ou22zt28mxkT9ud3RwHSNx3jEEj9b8xUIwzilBGAYpp64QgxeLioiqVSirVG0oqgRDu5+L7tlsdw/uPbR1zCK05ni+unXbO5sXIKF6QN3DttdemLh53gK+44op9r5PJZHT77bdfyBAuyQWIR4m8exiRGKJC/vm4FssXPE6+i1MWuSbc9Eqlsu+GyWZb575kMplQ30ara6crQO+AbuId1vgMWdKNjY3QXQslEGeLHDh5Ns1pmRh/5qG/vz9R68lm9rN+YgHEel2j1DIG1K8xD+0U9MbGhh74wAdqamoqRItiAL4f9ZRn297eDk4DESmUFQ4J64aItdOVDx8+rBMnTiifz4euZvH7THO6AU3Uffo7RMGePXs2zH8MFNMaUXRbo0in1k5C1joeu88z4NYdpPn5eQ0ODobPEYQpFosaGhoKXXc9a38hwjrrZMj9edvVKeTzrfNI3WBSb3Uh0m1Gr91YDir1eqvVfq1WOxDwyGazyrSJxOPUXpKLIx5wQr6eGcXFxUVNTk4GEHBQoBiDg7Tfr6ysqFKpJHQ9dnB9fV1DQ0OJg73RuWl0cvShB8JiyWaze2rY0dkxVdFrpjrVlXHma/ycPKPv183NzT2ZC29e4swf2EJzc3MhuIPz7foD20/2NwYvzB8MkaWlpX3LAnCcaW6XyWRC98/YVlHDCgDN5XLhrGPmJI16GrOT8EXW1tYSc+lgMk2gQeKneM8EpN37wxchOEGW1YNpq6urgaY7MzMTOnhSuwf46mZvsJecvsq7xrb7vPj+73SWqa9hxoJv5uuJ68dA0X1X/Fl8nLihndv2fD6v1dXV0OCHuWBuc7lcaOKHL5GmD/gO72m/wC7vLbbrrIHl5eXga8UCldp9+04Z4YPIBV3h9OnTbX/HRuvmLMVLUdv7VtKop2TtiDqwIFnY7sBLyYxiWudH38jSbkH/7OxsACKdFI9HiTwa4plPMoq0MSb6yff8qAc/3gJxAJsGFB0I+3O5UgaQxkCFn7czVDGAA7B5FpRajjTKgBfXe/ZnZ6d1dtPs7KyGh4fbUlwRlK+DYRSlU0Q8c4Wix1DyOb5fq9V06tSphDJsNw6n4zAWj0RiPMiaxtnrNANJPYRno9tF+fbTPdS1xuIGjvXvwQOOYCHYkc3unh01OjqaaKTA9S5EuO9+QNEpvmlCAMTPHIRCd1/LfpTPeA9Ku7Ww3dYDu7R7xv0ym5fkYJL23rphltwb4iDHg0oHBYoEzWJBF6+tralarSb2N4efb21taWJiQrOzs3vo7Gl0uJjJEAt6hjo+d05j/ZK2f9Kevbe3d0/2DIkzmpL2UN4khcYb2Cr/HtlCp/r19vZqZWUljMk7PHpjq7SMIufOMZft9CpAfG1tTfV6PZQHpAUP8WUAFNj2+fn5BFDknv6dmHoKzdnnup2OYV16p3ZJCeYHn2sH5Hy83sTG9TzjKhQKqlQqWlhY0ODgYOiUSpa1G+GdEBxgbjyQz9r2ulUYLWln3qI3Yuoy18N/cN8pZhjF4/dgcmw7vfaUNcS79HeVz7eOXioWi5qbmwvrO5a4bCVND8aCj8xz+bglBTp4nCVknOwF5KC0+rbjOsiHjx07dsmAfgMLDnIczfWoJ1QFFF4npwnn3a/H553SidJZX1/f1wHlnkTvCoWCarVaMHqMzyOaboCIKgEcnXaKDA0NBQDrUT8MuXcDReI5g6biBgnlMjQ01DZyhGPuc4+C5Vpp9FWUlnPjK5WKlpeXNTg4qJ2dHY2NjWlubi40amgnDlRiR4A1EtNfcFY8GwtAx8ASQQck+dhjcdCfRqMkm+yHzsbzEytAX6sxXQnpRMNMG2M8bz5fcbZ9cXExrDWv5ZybmwstvDEUvj8uRDY3NxOHeLeTTo1spJYzt7GxoeHh4fCzrxdQ5EDvduLGG7knmal8Pj0CXKvVum7icUn2F9Z7u0Yw96WQCZCUABsHBYppAIrfcyxDDFh6eno0OzurI0eO7AEajCU+K1Bqr+8QnOe0MhBKNOLPI2n14JISJRbtMoouaQ6pNxpxUI4OHBkZ0blz50JdvSQdOnRIp06d0uHDh4MDzuedPov9WV9fD/6Cg+5OQBEa6dramoaGhpTL5VIzolIrq7i6uhqAeK1W09133x3GEgMUxuJsET+brx3dPQY4i4uLWl5eDucGEuD3PeTNYmJhPVDqEAvAHv9scHBQJ06cULVaDTbeA/7thL1Nls67wzI+wM/Ozo76+vpCnW4ulwvnYbcLuvhn42djjRaLxVCjHjPe0oAuIDpe2x70dZ1ANhz/JGZgMT73XzwLKrVvfpg2NkCsC34G94wZQgRWvPa327rIbuRAQLGbLOEluf8KjrRvHpQBC8+zIRigGFy6cRkcHNT8/Hzo3paWUaRWgzP3upH+/n7NzMyoVCqpWq0GUNfX16fV1dUERSfmkFOz1Gy2OrfFtUsArJh6Wq1WtbCwkKgzQLxVuLS3plHa/4BaxuYdSwGKXlOYFnXic8vLy6ERS6VS0enTpwNQzOfzwdB2GkNahzqESHusZJx6SrQL0Ms7pQZgcXGxIz3ZnxEFTNYNgeZbLpe1tLSUqGHEKMUGEmcqLeLt193vTCNX5jhADpwZJ53wkKWlJR09elRSsgsiEVq+74GMC6Wekrncz5B3amQjJQMziEck70vZj7bKe4334YUCjnZ74GLUdFySXYn189dThoeHE9kJDu3uNjDg9VZpzm0ul9PCwoIuu+yyUFeGFAoFlUqlRBDWKXm5XKsTaiz7ZRS5bxrzx49qSNsn7YLAfmRHGlD099luD5IF9NrDen232zX7jKYh2WxWlUpF2WxWp06dCnTyarWaCkRzuZxWVlbC55wa2KkhF9TXs2fPhq60cQkB4vaHdeJNZXh2t3dxX4dO9pY59vVHvevp06c1OzsbjlaJ12mnfghklxysuECrJHifzWY1NDSk2dnZRKB8v8QQ75RnjHswOBWY5k4E+rPZrAYGBnTy5MnU9eP04piGubGxEfYE9FrekYP2tGcnmx2vKQ/SuD/C3sSniH0rLzNp9zPPKHbax+iU2K4DSPv7+8P54i5kFGm+JCWPcbmncqkQ41tIWFxxHSKKL5fLBaCI009huEfOfENUKhWtrKwkKG4eESFiFdc/7jfOSqUSup1BHUVR+vlTUjKCmMlkgqKDThJvKp7HnwVDwc9jmgcpf48QxRnFdoopvrcrUhSs0yec/uqf42dkHwFLUERQ+IcPH9a5c+cSvHoXb7mNMA/QcduBRea6r69Po6OjibpNqeWIzc3N7VGGaeNAUTcaDc3MzCQAHvMPqHcg3Q4oxg5YmjjFqF3kmcZL1DPEdZK8Bwev7uxIrXXIuncAxHqG1npPqKdk6jtJNxnUuCaxG2ru10PS3td+hreTFPJ7G0PcH5/7G13a0c+/HuIdPyUFoHjQjGK7hk9uR+OGYuVyWQMDA0F3+3omu5JGd28HFN3mOlB0gOcUzjRpZ69KpVKop94vo9iOtQBQxI57FhUHdmxsTOfPnw/ARWoFiUdHRzUzM6Otra3A0Intay6XCw1ZfI210+tOd8SG4vt4hsqlUqlobW0tUVPucx3PXzabDXV6aVnXtDlq19CmXC7r6NGjqtfrgSm0urqq1dXVAKD2Y3vF/0b8HFHeS6VSCQCKMXUDFPE/PPDr2UTeDUCRNY6vRhY+7doAZJ87fCiSEE4djhs5pekdgGL8bBw/Ie02peM6JDtiIegbZxQXFhYSSZGDUE+pVXYhI0ujvFhY1/69NBr7hcoloPgtJF4QLCUddallRIvFYqIwGQ47C5CMpBupSqUS6AYx9RS6gbT/sQI+TloK04gDxetZTTZBbED4fxrtlOf0Ymhp1+F0GoErko2NjUQGhyjXQR39OBqLw+GKJi0Cub29ra2tLZXL5YTiTztIN5drNZlZWlpKdTzm5+fD2UCx8aa+EmUV05RiZRc76t7Rzo1FXF/ooMvpy0TYmQtvgx4DxTRKFVHQdo6p19J0Aoq0u/fjPXx9+P+ZU+ilPl9k/pDYWDaaDRUeMqL+R0xKBwAprP/9gGKnRjYI2eH7u7QDiheaUewtFvScnxnUL73mYcoXsnv2/CW5OJKW4b+YtKh7IgTaDgoU06i0UstxnJiYSG2q4s000jKK7QKN6BgfY5wpcptLNgld2I2eSHsOAl3xc8bP1U7HeCDTr+OZDnyOs2fPJs7bLZVKOnToULAF2B73AaCMcoZvu/H5OHt6ekKGtVAoJLp0p60B5o4x4M944zmXbDYbGD/uJ7XTU9ToxdeYn58PQfqxsTFVq9XQSXt9fV0zMzM6efJk24yRByk8MCklO8j7fGYyrXP7lpaWEjW8nQSb3Wg0EuvEgyH+3qnVRBqNhqrVqmZmZvZcm/fkdGXuSe0qAW/81ThwT42jS7FYDO/d95CvUTKK/L7dERN9fX2B/sx9lpeXtbm5mWDmkP3tFijG65cu77E/4c9K51lkv7KTg8gloPgtJDFQRFmg0EZHRxOfQYG4o++RNa5DIblH/Xxz0qylG2PsvHsMnhdWZ7O7jVxiAxZTMfzcQ5e0rqd+/7RGBc1mMwGY230/LXrXSdIi02nnJVH7R7tmFEm5XE5kdJFCoaCRkRGdOHEiMUYOay2VSol7ezE6ihkgF1MxMQLtaEkoRBwCInj+fA5IiXyR3XYjls1mVavVtLKykqgnlfZ2ycQ5k9JpOR7ckNIdWGnXSBDhR3nHjgpCZDp2lqDlxtF3opPNZlMNNdX3oFGVHjymTK47ddwOgKd9zp+3nQwPD3/DAMW0AMCFgrtiX0Hf84Jh/eh/vFKFnv3rRy7JhUn83u5PgPxCm9m0yyhCn/TPukBdhCYPCHFnPV7jaRnFmEUj7e53SgKwue2A4n7vAQpnrO9ivdkOKAJWGo1GsBWZTGZPmcHw8LD6+vr2OLX9/f0aHBxM1Dh67wAAfvz87YAiuhf7T1ObuMwkngP0LKCXLqntMoSTk5MBPDnltl1gIQ0o0pGUMTQajVCKMTIyosnJSU1MTIQD2dPGzRxwtqPPQ3wwPdLX16fJyclAS+0mo0h3cK/L5z2RUYzLcpg7wGOxWEyU5HBtgF4cIAAokkQolUp7gCJ7qV2No5cOxfWFXmYjKZRWxYJvA1Dc3NzU7OxsCBYhB6lRzOfzmpqaSnSQJwO8srLSFiiy5z2gfrGop/fYO6jX67rlllt0880366tf/arm5+e1vLysarWqoaEhfdu3fZue+MQn6tGPfvQlI3w/EAeKbBacfxRTrNg86sXmizdusVjc0/4ZITXfDTXTs0mlUil0lXKjBsUvVrw+Jn6fpugYh0ec3NigvNiorvTc4MbUImmXjrif483zuMMBCE9zQqCCkP1xypKUXutWLpcDBbevry9QPKmj83EC1lzRECWEBurRYYxPGl2Hug7OKmMucaCcJgKNhDoUMqcDAwMh6ku9Ku+IsaY5MAQ+2mVl41qH/TKKnKXE52MQT/Q3ra5vY2NDhw4d2kMHcUWfBjz3E97bfvup24jiPTnS4r4UMggXS/L5fHA04mzxJbl4ElPB70kW+GILYK3b8Tj42U/Hp1HfvH7NHUcvK/CgE0A2Boqd6LzUspOFakdR71TjxnXSbHoM/NOaa/gYeQava/cMTS6X05EjR/Z8HzDtVEYHG2tra4ljpjwAmKYXNzc3Va1WtbOzExrUNBqN0HyonaAjnGbIv9v5F3GwtJ2ep74OcXprHNB0UCx1pt3752jShzjNN00GBwe1vr6uM2fO7Gs/8A88sUCmFqaU+yj4ZfgzvM+BgQGdPn1a/f39Yez4fWTw44wi72RlZUVjY2MhW4846yne315Gw98egPBGV/w8bU3x/kqlkra3tzU1NaXDhw8n7sc7xX/sZGMymYwOHz6s7e1tLS0t6fz58xoeHg57pt3ZppRLwVbiDNCLlVG8YKC4s7Ojt7/97XrTm96kM2fO7Pv5o0eP6oYbbtDP/dzPXTLGX0fxTYPyZjPyc6I7OLT5fOtMGedZo8SRoaEhTU9Pa3x8fI8B84LpboAiUqlUdP78+T2bfHR0VAsLCx2BItG3NHH6JNFhPy5ka2sr1CZIu7TTOLMaA41ms6m5uTkVi8WODTFiiquD27ieA6G7Kd93hVgul3Xu3Lk99yFzBTg5f/586PImJbOZnK3p1GGUJBFXnCrmrl3Gtlqt6ty5c0GR01IcgT7B+yJby1gB6kRVM5lMOOyYeW4X0aeLZ5phjlugd4o80/2N+1BPQb2K329+fl4PeMADEtfw88NioEhXze3tbdV3dtSY39B2Nqv8YJ8y2f0zLbFj1m4u9mtk840m7d7XhUo+36Opu6SV2Rl959WH71cA5ptJ4gzUhQRH7i05aEbRAdqFZEU9u8e8+F4mwxQDRT6PpO0Fd2zpZMyB6mk129001ohr9dPu3WnfeOaUTrDtnN00cZBEIBFd5wCvmyw1jjMZL6nl68zMzLT1FaTdo0Ko4cvlcqpWq5qbm9sD3hDmiPG2AwgxiOMZ45IZns/nuVt95bX0UmveJiYmOn6nWCx2xTLhfZKp5X7r6+sql8shsJJGjZZ2g7c0N+L8UUkJPzVOTDgde3t7W0NDQ2o0GgkqMZI2TwBxxhTXJ9JZ3hs9Esj3983P8vm8ZmZm9G3f9m2Jtc369cTEfmsf35Ig88zMTOLom3bBiY2NDVWr1QAUL2bg84Ks4uzsrJ72tKfpV3/1V3XmzJkEj7zdn5MnT+plL3uZrr32Ws3NzV2UwV+Sg4svNDZwTKmjThHliaLzxR4be2guAAlplzJz5swZlcvlrpSaRzmLxWICXHoNH+Ny8TGNjo62PcQVRcOzeKaJujIHFNBf44ximgNUr9c7dlxj3GmGmy5fseNPVpCsVWyovebNpbe3Nxie9fX1PQbIAbJHn5hzxkFNDRk15rldRhHQt7q6qp6enj2ZVwCbNxOABuJA0RXdwMBAoPug4NMM2fj4eNu6o7jxTjvgQfTPaz7z+XyY/9hQeEZAaq2hc+fOBbqKrxmCM8FhbEiLH75NCx/8qtRlww9/V35+WCwHOQrkG0EuNlDMqqAbXvBv+rlnf1pbG/VLQPFekjgDdX+i+B60RjGuDTyoxGwQZ9BIu8dT+f3IYHbKKOKsAkygneHopjU267ZeKv6M33u/rGQu1+pK6RkmnqsbcdonNsmD21wPX6aTMIcAp2azqf7+/rZ0PsSbmlCPxve83MEF/e4ZxTR75fRHniPts06/9Z91s26dLeXZrf2+Q41oJ/GMojPQeN/9/f2htIGxsq6kJMuHRnh+T2d9eUDd1x3XaGcf0gJTvm6kpE1NSwxw37TrZ7Ot40x6e3v3+Jzovm6b2cSSz+c1ODgYjotpt06pX+zv77+orJtw/YN+oV6v63u+53v0yU9+MrzQa6+9Vr/zO7+jj3zkI/rc5z6nW2+9Vf/8z/+sD33oQ3rDG96gZzzjGZJaC/Wmm27S933f97WlTVySe1fcMMXgz7NxNLSR9p4dFWcgkaGhIc3Pz4fPbWxsaGFhQdlsVmNjY3uMYprERcu1Wi0oItZMWntyIkvdGCKPckpJZeX1INBHcLhjgBcba79eJ/FaRxeydIAJhK6f3uU1bg4TRw35HJSEs2fPanx8PPF7B6SuTAF6Tj2F1ulAsV1mIJvNBtomz+G1Nzgv9Xo9UUvDuvJ6QNYCzg71Le2yaLSfT4u8dZtR9DXYrrg8bQ6RxcXFcCAvc4vgBDDHFyIe/Yy7Dsef26+RzTeSpGUyLmat2/0JwHwzSawn708UXy9D+HrcO42a6o6eU0/3yyi6XowPicd++37ppl5qv/e0X2dFGqRBYUX3dSt+vjD3g+GE0xz3TEiTGHwAyHp6ejQwMNBRT3Ksyfz8fOJMRI7yaJcpjIFi2ufi97i2thaownH28KAZRbe52O7p6ekQLN5Pfw4PD++rX51W7hnFuA7VgaI/s2eIc7lcyCr69b3ZnT+XB/cBqu0o1vG8EShwoMga8AzpfqVG0m4pVBqd14Fu7NPsJw6me3p61N/f3/boKvxVKKcXm7VxYOrp61//ev3jP/6jMpmMHvnIR+pd73qXHvzgB7f9/DOe8QzdcMMN+uIXv6if+Imf0L/8y7/oH/7hH/TGN75Rv/7rv36PBn9JDi6uPIjCsXG9RtE3tjew8ehwvBDpCsrGvfvuu1WpVEI9WTeOsTv4RCNXVlYS4MjbgCPQMbvJoGQymUBt5HpEgugSJ+2lDaA42lGPALDdZhR9PpaXl7WyshLOy/G6sdnZ2URHzdi47OzsqFqtanl5eU/nzXw+r7m5uRCRcnEl7fPe29sbDrr3jmk4IoDpdgqvXq/vab3NdbwDLiDcHQ0H+7FDWSqVNDMzE7rOtYuKxhFHH1e8ZmLF73uCufB3Hktcj1qv17WwsKBjx45pY2Mj0GARD0KkNa7oRjywgcOytLSkWq2WuA+//2aR2LG+UGNIJntzc0eHjrbmcXNzQ1s7rZojb/xwSe65EOBhXtfX10OQ6OshABb24EH3SFwv2EnY414vhR1JyzDEQUBsUAwS0rIbgAE6WrsdTdPZ3VBP98s87ReMYq5pokZGr1vxbJxnEWdnZ3X48GFNT0/v6TOAuE2LdSYB0Hw+r3K53HFM+Xw+MFxWVlbCnAwPD6ce6yDt1jV2AxQd6C4sLKhWq6V2t0wDPJ1AB/aNwC1nU1LCcjHABOvbGwCS8fW1480M46B/3Njo5MmTKpfL4T0RnHY/If4/100LmPo87ezs6PTp0xoZGdGZM2dSM4rsCwISSLvAcqlUCiAxDlB7kiVOyHQS/yxj6xSQIZDPerqY9YnSAYHi9va23vrWtwaQ+IlPfKJratNDH/pQfepTn9ITnvAEfe5zn9Pv/d7v6eUvf3lXPOhLcnGERewZRbJOcaSjr68v8X8UghudNAMxNDSkO+64Q9lsNnQs6+vr6xooumBY1tbWEsrEO6j5Z/3A1P2kp6cnART9+oBGNilrFEXkm91pHTs7OyqXy/tmTQuF1kHtzPmZM2eUyWQ0NjamTCaj2dlZnTt3TqVSKRTN04CGcbgAFOfn5wNYcKCwtraWGonyDLHPJVFcDDQ1FF5f0wmUNxqtg2GdhkHzG+oRuB+OBo4QdFWu489aLpfDM7bLKDI/aTSr2JFIW5Peup5nBvi3e69+jXPnzml0dDQEFgg0IBjnbDYb6pG6W7G7Ejt8AwMDwQnw57iYhuL+IPH7uxCq6MrKik6dOtViNzSaevmbWk00zkydUFOt95TWqv2S3DPZ2dkJ9UPsl69nCUqpVAqdCQ+6hmI2SydxZ11qPbsfkRE7625PALFpVMS4Tp0xxeDbdc9BgUY3GUXqodpJsVgMgWLqvA/CcvAsKYwksonecMTrGD34zbzF+hD7H9v4dmNoNpsaGhoK9WKSAqU2TbCV2Nh2NYrYQrJGa2trGhsbC30d/HPO8pL2zwhz33w+H+r7h4eHu/7+QcRtEu/Fr00JCp+N17mPub+/PzRjwv+J90mj0Qg9GGI/cHp6WiMjIwm2FFnOM2fO6NChQ+rr69P09HSii328H+OgzX6lKpTm+DqLM4rd2iyva93a2uq4x7iPJzgOUgfcjRwIpf2f//N/QnORd7/73QeufykWi3rXu96lhz3sYTp//rz+5m/+Rtdff/2BrnFJLlxiOgOKIo1GWKvV2tYAtqOeSgp1jRMTE6EujsLa/YCiG7W04m1PxadxzlH83Yhveo9qZTKZADa5pq9zDHd8H+o2aZfcSSEQJVtaWtLc3JyuuOIKlctlTU9Pa3V1VYVCQYcPH9bCwkLIpnYyrjs7OyqVSiqXy5qamgrjlFrR+/2UTAy6HCg6pRXjh0PSji9fr9dVLpcTFKpisai5ublEZCym+VKjyXjTACzNGTrVWQAA/btpke80xb+yspLoTNrb2xsCCvE7JZvINTBorJ+YuoKjk6CJbBysniCNUpvLtbrwefOab7ZGNmly0Ih4vV7XqVOnVCqVNDo6qmZDamwuSpKOH6+p3thpSyW7JPdM3FGHefH1mGei7efPn9edd96pQ4cOHdhZ7ibT5p/1UgKYO7Ar0tYw3wFExKUIUnqdIKCq3b6I9dd++yeXy7U9foGA8+bmZtsz5iQFhgsMmd7e3rbXTJO4C7fUCjDQJTWtLCYNKMYOPAFLbMV+gJms7vDwsJaWlhKZwrSAHGNB2tUoAqgAlYzLg5UONg8K9Ov1uqanp1Uul/cwnbrVn53uw+9iam8csK9Wq4neGO2yf1LL95yZmVG1Wk2cpchY8T/xTXxvHDlyRKurq6FjK2tjY2NDs7OzOnLkSPg8LCcfh9f1x1TTTkARGxyvs7SMYjf6hvftHXf3+zw9EegEG7PL7okcCCh+4hOfkNSikz7oQQ+6oBs++MEP1jXXXKMPf/jDuvnmmy8BxftQYppcHOmIgaILES0Ua7sUeiaT0eDgYIK+SM3jfq2BUQbejUpqnadERIznSAOKaT/vJMViUcvLy6HmQGopNIBKNpvV0tKSxsbG9owzBrAoG48stQN3RPjy+daZS4BrVyq9vb2q1Wq67bbbdOjQoY70KIyQRwuRtbU13XnnnW0NlbT3vB0/YgJjSCR8bW0tFKvHa8Tno1QqaXFxMfyMKLrXAnBfIucA6Dhbi+RyOY2NjYUmPe0UrjsXSFyfmHZ9ajmPHDkSxuAdA2PDCghfXl5Ws9nU2bNnNTk5GX7vEW4PrhAA6e3t1frKmg4i7c5GItp9+PBhSS2D166e4RtZ0rKz3Qp1KaOjo63jYupNZTOtjEBvsVeNxt5I+CW5OJLJZMK+Z49/vea5r69PhUJBd99994Ea2SC5XK7rdRc7l+h2qIVpdhQgCVBsx4bgum57qIuLM5lOYfWxdMo6eNA0FnyGTnbFxfXiQai+7qw3Go3Q1M2zPtgnPgvI8nmnkzVSLBYDVW+/MXnN/qFDh7SxsaEzZ87o6NGjHXWQs7faAQTsgR9jBe2R6wIYONID6YZ6Oj09raGhIQ0NDenuu+9O/L4b/elzmiYAtTgJ4AwoxuJ2lDWedl0PTtPsJ87KS7vnNcZruL+/X/39/QEwUvd5/PjxPaUsXncq7TYu5Ppub3K5XNuMPdTPuE+E13CyxrpZ//G6309HZTKZ4Ef29vZqbm5Ohw4d2vc+3cqBNOQ//dM/KZPJ6OlPf/o9uunTn/50NZtN/dM//dM9us4lOZgAFL0bLcpiP4MZA8X9lIw3QSFKv1/xNEYH5c9GLZfL4XgOKb224qBAESO4sbHRNrKVy+USikNKb5rjIAAnolOdIgCU7Jj/nPeCoevp6WkbGYopxGnizpmLK940uoRHsnnffqDtfg5C3M3NqabcyzueekS80/votAbSPofEHU9j2dra0tTUlGq1WiITx/uN37vUAuGlUkm5XE5zc3Pq7+9PdbycHsU8YFgOKu0opX4UivTN18jGhfVxodSpNB3UTaOtS3Jx5P4wz3Fd4EEEndXtZx2weOA0LmNA/BB2dHHa+N2R9FpL9LlTKgnCpTGKLkTSaiTvDfFg2+bmptbW1hIBUdetbnPieY9t3PDwcGhYst969OA4pTRDQ0Oamprq6Ac5EN3v+nQmx//xQAo6Pw3od7p+X1+fhoeHE/7DQQNtMf0yFgK/8XU6BTbYA7CnYiGQiv/hfS983NSat7Oj/f39ofu9B3+RUqmkjY2NPY1s2jEl0zKKvt5gRLlc6D4h6HMhupJA+sUs6zuQljh58qQk6eEPf/g9uinfjyMcl+TeFT8Tzhva7Bc1ktIzivt9nmMOpF1j3Ok7ftAp9CRJ4SB4dxDjTQAg7dbwkdGJef/xZ2JlzzxwH5QHTVkAi/t1Pl1fX9fGxsaezq3SbgRpZWWl41mQHlFu99xkaGMF5nTbNGXrHHk+Tze2/QC/X8PngdpMp6BRyxfX5LR7lrimoN3n4trAdodC8/xnzpzR5OSkms1mKs2LOfS1ABjr6enR/Px8KqCHagoljDETFc8X8ur99hGVHjoudTGnnZ5jcHBQ8/Pz35SNbBCnLHXbGKCtZKTRQ30aHtudz2/GObs/yv1lng9iM5ChoaGO5+65pGUUATTosLSMIvraA0vxdbHhcc0XbBbO/JP22i3GcqH7h+zKxayDaic82/Lysvr7+xNjjjOK7YBiGigCjO23Ft0ucZ1KpaJisajp6WlNT0/r/PnzWlpaSlAZqc3cT3K5nJaWlhIZZs8oovPTgvmdxt7b25ugBccH0nfz/tt1+kToVbAfKImBYl9fX1ugKCmU8PBO48ZEjKsTUOS+7coJisViyOTik8S+pWcV0+YC8E6NYFrn+QtpWIf/1W3G3gWf5GLq2ANpSKhknTjp3Qjfd2raJbn3hU0VRzm6WczeJcyzXu0EsOSHp6KU20VJHCji6KMg4LjHHbb8fgcxej72dhsql8vtqfNywMxn6vV6UDYopk4ZRTKQMVCktgKDsLy8nOh+Go+j20gVY3bxusQ0OiM0Co/ioVgBjftJX19fotifznEeJfb/M44Y5CEx3Wo/cOxrOq22j5+fPn1aExMT6unpSb0uETp3tDyKXy6XNTk5mToe1uXOzk4CeA8MDCibzapU7lfzior6Hz6hTG5/ddypSU1/f7/W1tYShu+bTWKH454AxWw2o9GJPg2P90q6lFG8r+T+NM8X0vUU/dLtZ9MAizepidewZybaAUW/v+u2bDYbAljYa2k3aBdnpC40owjb5r5gLeCfLC0t7SmvSKOe+nc6CT5FNzrEqbZ8fnh4WMPDw5qcnFR/f792dnY0Ozurs2fPSmrVZfb393eVsaSUg2Cinwu4tLSkarXadYC2nXgHc6l76mmnjCJBiYMCRbrOt/teqVQKGVZ8PoT1DVDsdG+YQO1ov9j1tMY4fKZd8IFn4Xdp2Vf//kGEa7UrNekk2WxWx44dO/A9O17zIB+mTqzbaFo7gfdON8VLct+IA0XfPN0YC68f22/he3TTG6kQKWsHcHCkvXgcBXHo0KFAQ0jrOnchQNGpDWlSLBb3GKYYQHEdMooAxU4ZRZx96v18TK5Y0poY+PN2CxT7+vq0tpashXMFmxa1oogaYwI4J6raTaOUcrmcOAy+WCwmqLDUJ/Le1tfXE/Ug8bqM104ngO/rtFOH1P7+fo2Pj3d0eGie4wECbxZTKpXaNgwisILSj9932rvpJJ0iqARUzp49+03byOZiAkXE9cz9JdPVrbzoRS+66HX+d911lzKZjP7lX/7lol7XnT2f5wu538V67gvJKB5E0jKKAAEyfvEa9qw5QLbTOncHt1AoaG1tLWQUHSimNbO7JxlF7nNvizNROjUkc53vP28HsAYGBrrOqnqGJ+5PwDmLQ0NDmpiYCOUsNKbZ7x65XOu8aMBKLtdqTFIqlTQ1NaWxsbEQxLwn+ok1gXRLPe3kZ5AM2K/bfBzk5Vk7ZRTX19dDts59TspI6vX6nuM1YiEg026PZ7O7HULTaKcOlNNAH/9vl5k+iJ8W3xcgfCFB34sdKD6QhrzYnPQLQdqX5MKFbInT6NiI+ykMHPhugCIbKp/PJxZsLpdLHLEQS1pGMS4ApilOGj3wIEaP5+h0/EG7jKLXqnEdolIY+U5zhKKLI13+79XV1Y4BGa/N2M/RoRbTxcFTmgEii+bXJ0vXKaPoc4hh5wDdYrEYOt7RCAZHqdlshk6qcfF6/Mz7RVa5JvqqU33iyMhIeMftDCe0n83NzfB76hP3E6jeadHBvr4+VatVbS+sa2dhvatMy37OwsDAwJ662m8miYHiPXHym82mNtZ3tL35jZtNfMtb3qJ3vOMdX+9h7JF3vOMdiWZKndbs0aNHNTU1pYc+9KH3wciSksvl7nHge7/rp2UUOVqhXYATG8q8dbJt3jALx5ZjBZx6GtubewI87suMYj7fOs+uv78/NUDM/LbL/nQKrnVL7fOmbp3eBU2b6M/QTT+HXC6n8fHxPb7O4uKienp6gp28kOy3C+NCugGKMbiMhZrN/YBiPO5MJhNKitIEvwqf1f0p7xi83xr2oEuakL3PZDL7AsU08SAFzxV3gHWKeLfi2c77AzvoUnu3byEZGRkJEVQ/tL4bkIWicnDRTrh+3AYbxdBu48XNbOKsUjab1dTUlIrF4h7FdFCDTyYwm812VfeGpAFFIls+dqn9HHn20TOPGAJoNu1op9y3Ww6717wggO12irZYLAaA4/WhPFe7e8bO+8jIiGZmZoLCB5Stra2FehOUIt3d2hnkgwBFV/CdCtRd2tE8iH62yyh2EjKKadHBYrGovp5e9X1uSfN/9xWp3jkA0w31KJvN6ujRo9+0jWziTME9ySg2G9IdX17SHV9ZUqP+jQkUa7XaN0x32zR9yJ46dOjQRW2+0K2Qzbk3r5+WUeQ4mzTqqbRbT9YN68cDuGSx0H/MqZc0uFwo8CDgfF+8s2KxGI5KiPVznFFM+3enrEwntomLA8X9fIRKpaLl5eXgY+zX7T0t67S1taX5+flw5AE+2j0JaMVB/m6eBQZV2n0POpb488PDwx3n3tlbMfW0r6+vI4BD9mMMEASW0v2EGCjG+8UDE3w+9unuSfO1C6Ge3htyQbv8D//wD/ccGXAQOXfu3AV/95JcuJRKpRDpwgjFZ/a0ExTVfhEaPru6uron43LkyBEtLy93zCiy4byWQ2opGZSvt9pGMpnMgc5n8ogVRnS/uktpb2SWueS7KH2oA2mKkAwXUSwUAdcENHWKJAEyuzF0KEMHhXyv3fdRmNBf3dCRJUuT2HnP5XKqVqtaWFhI1DZvbW2pUqmEqDogiLGk1RWgdA8KFGmpvp+0cyjiJkPd1Ogi1IfStr4TuGw2m+rktnUbXdwvuvuNLN02cfpmkve97316zWteo9tuu02lUkmPfOQj9Vd/9Vfq7+/Xi170Ii0sLOgv//IvJUlPecpT9PCHP1zFYlH/7//7/6qnp0c/93M/p9/8zd8M1/vyl7+sn/7pn9ZnP/tZXXHFFXrrW9+qa665Ru9///vb0jm/+MUv6oYbbtDNN9+s/v5+XXvttfq93/u94Mi63HTTTfrJn/xJSbuO1Stf+Uq95jWv0fHjx/XCF75Qd955p/7yL/9Sz3nOc/Sbv/mbuvzyy/W5z31Oj3jEI1Sv1/WSl7xEH/vYxzQ9Pa1jx47ppS99qX7pl37pos7rfSFxNsQzijs7Ozp8+HDqGqbzqQck08R1EUDRDzePqacXi1oNO+i+oGpzDnNfX19HoHshGcX97CyCPe8mC1sqlTQ7O6uhoaFQvhHb2GazqTvvvFOVSmVPz4hms6mpqSlNTEwkntc7tF4oYIz7AnTz/qgXjP25e0r93++cP3ocUC6FwGhaWFjo2m9tJ8ViMQTR2zX18yxsLKwJxpF2liI+5oUENrsNZNzbckEj+KM/+qOLPY5Lch+Jd46iCLlb2iafZeG3E4BiDNwApt7gxCVugQwgQHHSDOVibByUMwZodXU1UEe7+Z4b8JjGyzXjmjSe0SO/6+vriYY/0A32ozViuPaLVvJZKdnplOhWu851+Xw+RO28e1u9Xu84trSo2eDgoO6++25Vq9U9oBDqFdE8xuq0qW7uEY99ZGQk1P51q2z3O/uSdX+QoyeoUeFddXJKFheXNDjS3njeX2goX0+Js/Df7DI1NaUf+ZEf0e/8zu/oB37gB7S8vKybb765o6P4J3/yJ/rVX/1VfeYzn9E//MM/6EUvepGe8IQn6JprrlG9Xtf111+vY8eO6TOf+YyWl5f18pe/vOMYFhYW9LSnPU0//dM/rd/7vd/T+vq6fv3Xf10/9EM/pI997GN7Pv9d3/Vd+v3f/3296lWv0le+8hVJu1R2SeF3r371q1Pv12g0dOTIEd14440aHh7Wpz71Kb3kJS/RxMSEfuiHfqjbqbtfCk46f9qxYKDs0wwlTRd6czeyTT09PapUKiGo6wHNe9K8Jhbsw30hZC/Hx8f3/RyAywFQfIaiy0Gpp3EzoHZHl2Dn2lFPV1ZWVKlU1NPTo1OnTqlWq4UA3+zsrCqVSmo9JnKhtGHYRQcpTSiXy1peXt5j9w9aP+cgtRsplUqam5sLR4NIu4GRfD6vjY2Ntu8V2S8JQrDDu667kERo9wxxRjHtLEUP5Bxk/3Gv+0Pd/IE97m/UWo5L0hIWrAPFdnUSsfT394fi4E4KIpvNhkNOY9mP8833vT7uzJkzoWHI6dOnL0oqnkxgJpNRsVjUyspKV+ceuVKRkm3CvaAZ7nuacs3lWmfu9Pf3Jxo6uRLaTwHGlNf9hPH43GUymbbK3rNyHtFrNBodgWJa1CyTyWh4eFizs7MaGxsLtM3e3l6Njo6q0WgdpIyDI7WAYhr1FjpsJ4VLncjq6uqBDOrW1lZHx21paUm5XKuVeTf1iVKyAc/+QHFR1cFa6hqs1+uam5tLzaZ/K0k3NdIHlaWV8zp1el6ZjO4Tmk8+n+/6PU5NTWlnZ0fPec5zdNlll0mSHvawh3X8zsMf/vAAwq666iq97W1v00c/+lFdc801+vCHP6zbb79dN910UziQ+bWvfa2uueaattd729vepkc+8pF63eteF372x3/8xzp69Ki++tWv6oEPfGDi8z09ParVaspkMuEervOf8pSnJMDpXXfdlfh+oVDQa17zmvD/yy+/XP/wD/+g9773vd/wQFHaBTFp588hHJqNPkrTd74XsOs9PT2BuUH5AHIxM/CFQkETExMX5Vr7yX7OPjannW64GNTTtCZ8nTJEsGUobYl9oYWFhdCgT5Lm5uZ01113aWNjQ5lMRkeOHEm9Js93oaCfmsODAMW+vr5UFuBBaZH4G92OG/o1NG1p90gtp1rvd89O9ysWi6GeN22NxP4qfpcnNHxfFQqF0JMBiTu8dyv3lGp8MeVAQPHjH//4vTWOS3IfCVETeOf5fL6r9saSgmHoJqPYLjvXDVBEMHz9/f0aGBjQyspKiJTeU/FmNhsbG13XH0jJaCUZplKplPh5T0/PHoUhKQEqBwcHQydhScGoVKvVfTNWXqPYjdLv7e0NdYEu29vbqVRFrxv0Ft+d6hOl9gasXC5rfn4+HCzc19enTCYTInCu9AHf7eii3WRRvclQt1HPThQlqMKZTEZra2vBAd5P4uYrneZuaGhIMzMzeyLnjUZDp0+fDt3vvpUlrZ7nnkq1PKqjR8vKZC5+t7h7Kt/xHd+hpz/96XrYwx6m6667Ttdee62e97zndTyiKj7neGJiIjh6X/nKV3T06NHE+n3sYx/bcQyf//zn9fGPfzw1iHL77bfvAYrtBB3yqEc9at/Pvv3tb9cf//Ef68SJE1pfX9fW1pYe8YhHdHWfbxSJqYUuXsLQjvETZ9BikHT06NHE/+PzFu+p3FeZjm5LY2KaL9KJItmNLZGS1NNurlsulzU7O6tSqbTHZnJMCbaGoHG1WtWdd96pQ4cOpc7t0NDQRQGKS0tLBwqg0ngmtqX7lVLEAhukW/ol9tb9D0qnCAzsBxT3Y8vVajVtbGxocXGxbWLD7Q37kvvyHpjLtLMU+cxBaxQ7lfjc13IgoPjd3/3d99Y4Lsl9JCxUatYofD8InXM/xZ3NZttmXDop8/iajUYjUcMBXfNiCBnFnp4eDQ8P68tf/rIqlUrXETKnEEFt8fGnKQxJe6ie1DwCHnd2djo2sfHxS91HREulUqBi+ly3A0coqHq9Hpo9cK9Oyqud8aSG9Ny5c2o0GglHF4UPOKWuL22NMd/7PTPr7CDR004Rdw6x5Rm7XYcxVamT0apUylqcWkpQW6FdDwwMdJ3F/GYWDPXFjrS23tP9I3rrksvl9OEPf1if+tSn9KEPfUh/8Ad/oP/yX/6LPvOZz+jyyy9P/U68n9NAxEFkZWVFz372s/WGN7xhz++6zSp5U4f9amj/7M/+TP/pP/0n/e7v/q6uvvpqVSoVvfGNb9RnPvOZgw/+fiDtaGfd7GdARJreiDOK8Z7oZMsuJg313pb9/A2n2V7IOu9mHtI+0wko8nOAkX9ufn4+EQRFp2UyGQ0ODrZ9b/58F9rxmc6nB33/0E/9uLBOlN408TKdbgXbzVj5PgCwG6C433MWi0VNTU3p+PHje363HxU1piPHv/frHLRGsV2jq6+HfGNoikty0cQBwH6ZwXayX5Smr6+vbSe5diCjXcG3fz4+buOeCJnAQqGgYrGoiYkJ3X333V1/18/WISrZjcLgbCvmzw+lJ9N7kHfSbUS0v78/Qd/wusV2oAfnDmW7tramkZGRjvfrZICIPsbPmM1mNTw8HOoPisVi2+fC2HRzwC9HblyMDqC9vb3q7+/f94DfTuPZjzIrSePj4zp79mxw+s6dOxeO0bgku87SxXZ072nr+XtTMpmMnvCEJ+g1r3mNPve5z6mnp0fvf//7L+ha3/Zt36aTJ0+GQ8El6ZZbbun4ne/8zu/Uv/3bv+n48eO68sorE3/agT6vK0K6Bfef/OQn9V3f9V166Utfqkc+8pG68sordfvtt3f13fujXGgW3NkW7YJmaUdDtBP/zDcbUMT+xmd1xo78PRW/1n7NXKCf+ueazeYeZo+fT9zper6OLvT9eVnPQfyMcrms/397dx7eRJ3/Afw9udM2bXpAW+4K5ZBDQUFRUFw5BGFhZUURBURB12tREdZdQERUFrzXWxHEFcRdEfm5yiFaLpFbOVXA1iLQAi090/RI5vdHd7KTNEmTNMkk6fv1PH2eNHN9J01n5vM9Pt/Kykqn9xqbw9BVIIGilIBROu/q6mpHZbVGo2n0+L4Ek1LuC0/PCfLvlOv/suvfwd13TZ6Ez9/gPFLu+9FxpaCgkW4W0kN5KAJFnU7n90B314DF3UBetVrttu9+IFQqlSNQBOAIgKSpHDyRZ0mVv+faGuVpH66fu9FodMxTJN3Y/AlEfK2lks+lKL/Ae+uC4toSUVlZicTERK8PJI3dgFq2bOm225zUrcZutzeoRZTzJ1AE4PNcX42VW7qZ+zp/orvyuA1GBAHGbi1g7NYC+G8XHymRRXFxMYDGs8M1J/KKriY/6ApAaksDUlsafB6nHW47d+7EM888gz179iA/Px+rV6/GuXPn0K1bt4D2N2TIEHTs2BGTJk3CgQMHsH37dsyePRuA50q8+++/H8XFxRg/fjx2796NEydOYP369bjzzjs9BkAdOnRARUUFNm3ahPPnz3tMYOZOdnY29uzZg/Xr1+Pnn3/GnDlzGg1mI1mgreBS4hFPPR3cjVH0RqrUBIL0/xMmer3e6xQm8uEors8LnpK1BcL1ntPYZ5iYmAiLxeIUIFRUVCAhIaFBtlZfAkXXQD/Q65VKpfK7QlrqXumandWfIDyQQNFoNDpaQIH/VTILgoDWrVv7lPG9sWz48jHVnkj/u+7GqrqbI9K1wiKQMYre5pkMt+i4UlDQyL/EgWbOamyAsC9cb2qugaKnFrlg3dykz0E+Ls5sNsNkMuHMmTMeb+pS1lXXG65rghvpoiA/T6k1Sn5DcJ3Q1t9A0VdS1jbgf591Y39/QRCcLuo1NTWOeQ49aexiqNVq3QaK0mcotSgC7lPC+3qzkQ829+U74+vA/EACRenv6a4cglqFhN6tkdC7NQR1/XKpi25VVVWTpiGKZU2dQxEAVCoB6a3jkN46DipV069poZCYmIgtW7ZgxIgR6Ny5M2bPno3nn38ew4cPD2h/arUaa9asQUVFBfr27Yu7774bf/vb3wDAYxftVq1aYfv27bDZbBg6dCh69uyJ6dOnw2w2e/zMrrrqKtx777245ZZb0KJFCyxevNhxTW3snnPPPffgpptuwi233IIrrrgCRUVFuO+++wI630ggBYr+ZjCUpsjwxHWMYmOBqGuLVCRWjLijVqu9Dh/wlLdAChQ99UIK5O/hT4uidK+U/11KSkoajL2XV341Fig2tUVRKpfFYvF7+/j4eEfehUCeHQMJFF0TFLk+ZwWDFHT6UgZfege4zqUIwCk5YzRSfoIOCqumjlcBGm9RbIwUaMkv4K6BonzOoFCQgiD5MVNSUhAfH4+ysjKcP3/ebU2UlHRFuhBIrVvyC7d8wLM8FbU0Xk7erVIeIEkXQF8DRXkNsa/nbLfbHRPbNza+UR4oSt0mfZlXKJDvhrzGTa/Xu50zC/C9RVH6/viTyKaxdbVarV+JbORl8efGrlKp0KZNG2i12ojtDqm0ps7hFS26deuGdevWeVy+bNkyp99zcnIarCPNsSjp2rUrtm3b5vh9+/btAIBOnToBqG8NdL32ZmdnY/Xq1X6UvH4aLWkqLVEUUVtbi2PHjjX4P3A9nl6vx9KlS7F06VKn9Z599lnHa9fzjmTyQNGf76xer3fKiu1uv9IccFIrkTfy+0U0dT1tjKf5HKXhJZ4yWfs7R53r9diXa5CUBAaAI8u6p2M2lk1eHrA0JdA3GAy4cOGC390aTSYTCgsLkZSUFNAQjEACReB/cxErNVWE9Lwq9Tpx7XrqWsHsOpeiVOZoqpxxFRtXCvKZFJjIMzUFso9gBIpy7loUQ30jU6lUTv/kZrMZWq0WqampjsQvrqQAUD6ZuxQIyGu6pIu+/OYtBYquNxjXuXd8vQD7+xlJGV6li7y3LJ9SOaRzksZVNFaj1tSxE1ImWtfU7hIpc5qvYxR9HZ/Y2GcB/K8riL//N1LQ6q7MoijCVlENW0V1g4flWHmQC4VgBIqiKKKm2oaa6uAnx4lkn376KTZu3Ii8vDx89dVXmDZtGq6++mp07NgxLMdvbpUf0jXT3xYFd3Oyyfk7RlHeSyeWKloSExMdCeCkSleg4fASV76O75fI5/kFfHvwNxgMjh5D7loT5fztetrUFkV///5SrySp0sfffBFSoOvvcaXup0rNIyx/Xm1sjCLg+f82mitnorPUFDApYUpTMio1NU2/PNCSuHa5DMeYIYPB4PEYnlpe3QWKUuucdBGQLujy2kTAc6DoOk7R14uJa4toY6T5IqWgvLHulvIEPVKXy1AFitLnLVVieHpwlwJFX7qJmkwmnz8fX7qexsXF+ZSR1pUUcLutwbXZUbz2KIrXHgVswZ0fMFb5MpemL0Q7cPxIKY4fKYXYjD768vJy3H///ejatSsmT56Mvn374rPPPgvpMaWukc0pIJcE2qLYWEAt7Vc+BsqbWG1RlJPfO6QWRU/PKv4O80hISHCqePQl2JaCc3dJbOSk8X/hCBR1Ol3A+SmMRiMsFovfcygC/+sq6u9x4+LiUFNT4/VvGUqeAkV5d1Q5qSeZHLueUlSRuqj4OyWGXFMDOHd9uJVoUXSdZ0pOSiPtmpRHCgClDGBSLZc0US7wv4uJwWBw6jokPSi49u83GAwoKytzTLHh63n72z03Pj4eFovF0aXU0xyKEnmgKAXEUs24N4G0GMhbFL0N/pZab3357rZo0aJBpjZPfAk+4+LiAkpkIwXl/kxyTJ5JXZg8dSkj7yZOnIiJEycqdvzm2KIozXnr7z3NW88fKfCTKhd9GaMob1GMlEQZwSS1skq9nry1Gvrb9dQ1b4Ivf0+VSgWdTofi4uIGSWxc991Yt0zXbq+BPh8JggC9Xh/Qc5zJZEJpaSlUKpXflabS84S/5ZYq4q1WK+Li4sIebMm7eLtOd+XufNxNjeYu4WE0ic5SU8CkFsVgzknoL3ddT12DgnAEit4eWORdRuSkWj/ppiwFjp4mZZUuGK43LPmxpaBUej9ULYpSoCgdp7HgSOpqI92UpACzqWNcGyOf4NeVP7WScXFxPmdbDOVFXKPRwGq1xkxXL6UFOtaFlMcWRf++s966oEuBi1S52Nh1WZ7wJprHS3njmnjE2/fN30DR9TP25Z6hVqthMplw9uxZr91O/b2mNbVFOCEhIaDnP+m5KJAWRWmoj78VRdIzkTS1WLiDLXfPq4DnFkXXCgX5/120VpIxUGxmpABBydS7nv7xXNdRKpAF6i+I7pK2yG+wUsuga22uvFZTet/bxO+uCW18vVmYTCavNx9XUl9/SWM3SqnrpjzTZzgudFKNp7tAUR6w+rIfT9lzw0lqQY/FBzMlSA9V0Vo729xF68NSoAIdowjAa8uP9ADqS9dT14AplrueyqcMaSxQ9Oea7Boo+vI9VqlUiI+PR8uWLb3ea+XdiH3R1L9fenp6QM9XUiVuVVVVQPezQOc01uv1qKysDErGfX95el6Vjw+Wc/0bxkLlWOxdKcgr6eKZnJys2GSevjy863Q6v7NLBpOn1OTy1jWpVs219dNdSmdvgSJQ36+9urrar66n/iYVkrcOSuXydmOSbl6VlZUBdblsCk8titKNwtcbakJCgiOltyehTuwg/Z2UrPiIJVK3aQbeFA3k3eoDGZ/lrYu1FDA0FhTJA4toHy/ljesUEt7uE/6OUfSl1dbdNlKCPG/kieN8oWSLsMlkcj8nsA+8DffxRsqCrsR5u5s7UfofAtz3bnKdribUvbBCLfauFOSV9A+u5EOW601NqbTH3kjlcVcTKyXasVgsjhpf14l+pYuIlPhGChQ93cyNRiMqKythNptDFlBIqbl9mUMR+F/AW11d7RTkejqHYNaceZtDzJ+bqi+BYiDdaPzVlORR5Ez6/+LnSdEkkOBMq9V6HUcu3c8bC2Kke6x0TW8OXU8bS3jlb6ucPAj1Zxtf7ueBJO9T6pkpPj4eKSkpAW0baOVEXFwc4uPjFWkJdw36pO+BvOXalTwPhy9znEY6BorNUFOntwi2UE0y31Tu0hxLD6harRaVlZXQ6XQNWgHlgaLUMiado6cHXClQ9DQtRDDPSepK4csUE7W1tQ1a8DzdMJt6EZcfw1v3ZH+620qfubcLdSCpvv3FQDF4pIeqSKtcIu+a+98rFMGZdG1u7LOVxtNJ19VAJkyPBvKA2Zd7nD+fgTwI9bWFKDk52ackZr4GivLs4EpRqVQBB4qB0ul0SExMjIgu0/Ku5NLvruQJbWIh4zMDxWbI26Sv4ST94/g7qDxc3I1TlGqFNRoNKisr3bYoymuQtFotrFar4+LmKVDU6/UB9/v3h8lkgl6v9ynLp1qtRnl5eYPMr94S/QTrIu7pBi6Kot9BndFo9JrUxtfpNprCY0uxIMCQnQZDdhoQgw9uoRC0RFcCkJxmQHKaAeBHTyEWiu6e8v8FX7qeyod9xGKgKK+kDXbCK9eMl77sW6fT+bSer4FiIN1fY4FUOa1UTxL5M51U+eytRVHeyCAfSxytGCg2Q/6MgwsV1y4ikRoougZE8ppZqZXQ9cFVfkHQ6XQoLy931Cp6utAJggCtVhvyv4uUXtqX4Ei6OLt2ffIUKAa7xlyr1TYI8AK54CYkJDhNU+IqHF1PzWaz27+toFbB1LcNTH3bQFDzcuyLYPWIUKkEZLaNQ2bbOKhU0XsTjxaBjmuKBdJUFqFqUWyM1PXUl0Ry0UweSEnTgLnT1NadYAcsGo3Gp5ZH6Z7c3P6P5F2mlXh2lX+v5C2KnuajdJ1LMdqHSvDJpBnS6XSKX2jkN6xQ3ECDQa/Xuw0UpRuuXq93jPvwdPGS5lyUB4qe1jUajSH/HKQuEb4ER1Lw6noDC2WLovwG3qJFC5w9e9bpvUCOYTQa3ZYXqH+AslqtMTmnWKwKZDwPkZL8zWrpz36l66G3fbt2PY1V8mERrpnH5Zr64B6KQLFly5aNrheO6akikXyKGSUCRfn/jbwsQOMtikBop98Kh+gtOQWsTZs2ShfB6R8vUscoersoywMolUqFtLQ0j/tQq9WNtigCQGJiYsgnZZdqunwdl9e+ffsGDyBSgh5XTW1RdB30LSVyKC0tbdIxpMDedbwpABQWFiI1NVWxi7goirBb62C31kX1GIZwUqlUSE5ObvJ+RFFEXa0ddbXKjvmJRPIHoWBpzi2K8i6Rwd6vry2KzSFQdL1ne+qq2dReTEq1EDXXrqfyVjwlPnd3gaJU8ePu/8/1/ywSevE1RfSWnAIWCUGZa4tiJJTJHU+JW7RaLVq1agWg/gHI3fQR0sNnSkqKo8XK24UuPj4+LIFibW2tz5+5p3mP3M3b1dQWRXcX3ZSUFJSUlDilPA/kGO6yn0rdUZWaJgYAYLOjaPUhFK0+BNia3wNAIARB8CuhkSeiHfj5UAl+PlQCMcI/+nXr1mHAgAEwm81ITU3FyJEjceLECQDAVVddhVmzZjmtf+7cOWi1WmzZsgUAUF1djRkzZqB169aIj4/HFVdcgZycHMf6y5Ytg9lsxtq1a3HxxRdDr9cjPz8fu3fvxpAhQ5CWloakpCRce+212Ldvn9OxfvzxRwwYMAAGgwEXX3wxvvrqKwiCgDVr1jjWOXnyJMaPH48WLVogJSUFo0ePRl5eXkg+q0gUquRL/nQ9bQ6BohRIybO8urt/N7UXk1LTi0hdmJtbhYs802iktCh6+w64Zs0P2rh6hUTm0znFPI1G40gUE8mBopTQxl0g6O1iLR/ALO9SonRacvmNsyk3G6n7qfxzsdvtTerC6a6bkNRae+7cOWRkZAR8o4iPj8eZM2ccLVG1tbUoKipCu3btAi4vRb9xE36H80VnodGG9yaekZGBPXv2+Lx+ZWUlHnnkEfTq1QsVFRWYO3cu/vCHP+D777/HhAkTsGjRIixcuNDxP71q1Sq0atUKAwcOBAA88MADOHLkCD766CO0atUKn376KW644QYcPHgQ2dnZAACLxYK///3vePfdd5GamoqWLVvil19+waRJk/CPf/wDoiji+eefx4gRI3Ds2DGYTCbYbDaMGTMG7dq1w86dO1FeXo5HH33Uqey1tbUYNmwYrrzySuTk5ECv12PBggW44YYbcODAgZBnHI4EoeourdPpHNdcX5LZSD0rIvV+21RSoCiNwZfO1/U71pReTNIcekp8b6VkRNEcdARCPhepEueuVqsdYw7VajUqKytRXFyM9u3be9xGqkyPhCnpmio2rxYU8aSsoYBytXO+kAdE/iRSkQIy1/NSelCzVP6m1ki6CxRtNpvHFkhfy+auXAkJCbhw4QKqq6sDvlFoNBqnmuYzZ84gMzMzYr93FB7ni86i8OwZpYvRqLFjxzr9/t5776FFixY4cuQIxo0bh+nTp2Pbtm2OwHDFihUYP348BEFAfn4+li5divz8fEcviBkzZmDdunVYunQpnnnmGQD1Ad3rr7+OSy65xHGc3/3ud07Hffvtt2E2m7F582aMHDkSGzduxIkTJ5CTk4OMjAwAwNNPP40hQ4Y4tlm1ahXsdjuWLFni+P9eunQpzGYzcnJyMHTo0CB/WpEnVIFiUlKS47W37IrSGClBEFBXVxezwbl07lJwaDKZUFhY2CAhW11dXUD3KikQVbLrqTRlVXMi/V2VbFG0WCywWq0oLCxEeXk5kpOTPQ45krapqalxdDuN5r8ZA0VShGsXmEjtSqHX6x1dFP25OUiBomsLm9KBIoCg1G4ZjUacO3fO6b2mtpZ6G3+Rnp6OgoICJCUlNWnS3qqqKlgsFsc0IdS8paXWt/Yr0aLoj2PHjmHu3LnYuXMnzp8/7/g/yc/PR48ePTB06FB8+OGHGDhwIHJzc7Fjxw689dZbAICDBw/CZrOhc+fOTvusrq5Gamqq43edTodevXo5rVNYWIjZs2cjJycHZ8+ehc1mg8ViQX5+PgDgp59+Qtu2bZ3Op1+/fk77+OGHH3D8+HGYTCan961Wq6P7bKwLR9cz6frp7hrs+oCt9D0olERRdLQoSvMcW61Wp2EddXV1DYJHX8i7tioVKNbV1TXr5GtKPCtqNBqUlJSgrq4OGRkZDfIpuCOfGo2BIlEAgtUFMtTk2av8qc3yFPREQqAodclpCnfJYYIxRtFb9liDwYCSkpKAE5kkJCSgsLAQGo3Ga00gNR8ff/g1AKBrr2So1JF7HRo1ahTat2+Pd955B61atYLdbkePHj0c3aEmTJiAhx56CP/4xz+wYsUK9OzZEz179gQAVFRUQK1WY+/evQ2uPQkJCY7XRqOxwbV40qRJKCoqwssvv4z27dtDr9ejf//+TqnfG1NRUYHLLrsMH374YYNlLVq08Hk/0UyaRimUvD28Sj0ppLJE80OrL2pqahxjz9PS0nD+/Hm0bt3asTzQMYpKtyiq1WrU1taykjPMNBoNsrKy/GqJ1+l0qKqqcnT5Vvq5rykYKJIipJtapGcblA9KDqRF0ds+leLrJMDeSN1E5cFzKFsUgfob/vHjxwM+hsFggFqtRmZmpuJ/AyJfFRUV4aeffsI777zj6Fq6bds2p3VGjx6NadOmYd26dVixYgUmTpzoWNa7d2/YbDacPXvWsb2vtm/fjtdffx0jRowAUJ+U5vz5847lXbp0wcmTJ1FYWIj09HQAwO7du5320adPH6xatQotW7ZUNnGUgnQ6Xcgrp7xdP+Xp+WM9UBQEwWlcol6vhyiKqKmpcbwX6BhFpSd+l7qeRnPQEY0EQfC7u7ZWq0VJSQni4uKcem5Eo9i9WlBUiIQWtsbodDrU1NT41WIWqnTowZCcnOw0tiVQer3ekZAIaPr4AW8tikD9TbJt27YB16YKgoC2bdtG/PeNSC45ORmpqal4++23cfz4cXz99dd45JFHnNaJj4/HmDFjMGfOHBw9ehTjx493LOvcuTMmTJiAiRMnYvXq1cjNzcWuXbvw7LPP4j//+Y/XY2dnZ+ODDz7A0aNHsXPnTkyYMAFGo9GxfMiQIejYsSMmTZqEAwcOYPv27Zg9ezaA/1WITZgwAWlpaRg9ejS2bt2K3Nxc5OTk4KGHHsJvv/0WrI8pogXyoOkvb4Gi/Noc7a0bvnB9rkhNTXWq4AACq7CVT7+hRGWj1PU0lgN9T9xlWleKL/MiyrueRrvoPwOKWlLNX6RnYJMSt/jTYuZpWo1I4GtK9cZIn4tcU/brS7mMRmPEf1/8IgjQZyVDn5UMsJUzvAQgKUWPpBQ9EMEfvUqlwkcffYS9e/eiR48eePjhh7F48eIG602YMAE//PADBg4c2CCb79KlSzFx4kQ8+uij6NKlC8aMGYPdu3c3mvV3yZIluHDhAvr06YM77rgDDz30kFMWZ7VajTVr1qCiogJ9+/bF3Xffjb/97W8A4BgTFhcXhy1btqBdu3a46aab0K1bN9x1112wWq3NtoUxFLw9SLsGirHw8OqJuy64RqMRdXV1bufS9YfS8xhKgWos//08CdUUM4ForFIbqP8/i+REjf4QxEjv+0dNVlZWhqSkJJSWlkbUjfn06dOIi4uDzWaL6Kb5qqoqlJWVOQbG+9IaZ7FYUFlZ6TQGRxRFnDx5MmamZKipqUFRUREyMzMB1CfWaMq5SYPFOX6QQsFqtSI3NxdZWVkhn6+0Odu+fTsGDBiA48ePo2PHjkoXx61Y/C6cP38eRqPRbZKWkydPolWrVo7pFaI9Xb83p06dgs1ma3AvslgsKCsrQ0ZGRsD3qtLSUoiiiLKyMsXu40eOHEG7du2cxhc3BwUFBaitrUXbtm2VLgpOnz4NlUrVaFKyY8eOoWXLlkHpwaWkGKqap2ij0WgaZCOLRFIXS41G43O2MXddT/2ZXiMaaLVav5JaNCbaM4MRNUeffvopEhISkJ2djePHj+PPf/4zrr766ogNEmOVP2MUY5mnDLNxcXE4d+4campqAr4Pq1SqoN7zAi1Dc7xPRtJwHl9bN6XGhWgX21cMimgajQbl5eURXzMmTfba1DGK0TAe0x/yRD/BCIDj4+MjPrlRsImiCNj++3CnDk6XYPKNKIoQ//vRCyrlk0xFq/LycsyaNQv5+flIS0vD4MGD8fzzzytdrGansTFczeX7rVKpPN5nU1NTcfbs2YCD5UgIVsIx1UokCtVcpIHwNVDUarUx8bdioEiK0Wg0qKqqiooaTrVajZqaGr/GKLretGMtUAT+19oajHTdsfbZ+MRmx/mPDwIA0sb1BDTN8DNQiGgHfjxwAUD99BgCP/qATJw40SnLKilDqtBs7tRqtceeP/Hx8SgsLAx4CE4kZB1tri2KkXTevv79ExMTQ57EKhwi41OnZkmj0aCuri4qAkWDwYDKykoGii7kiX4i5SJORNTcKJ1oJVIkJyfDZDK5XSYIAtLT0wMe7hIJgWJzbVGMpGy9iYmJPlU2JCQk+DxcKZI1v28bRQyNRhM1g+oNBgNqa2ubdIFmoEhERKHAQLFeY9mzExISPAaSvuxb6UAxLi4u5p4jfBEfH4/k5GSliwEgsrrBhkPkN+VQzJL+2aJh7ITBYPC764PrecVioCh1PY3FcyMiihbupoWg4IqEQFE+PU1zIghCVDwrxiI2AZBi1Gp1UMa2hYNGo2lydtZYDKakC3eszBdERBSN2KIYetI9Ltbu40Te8MmOFBVNcwq2b9/e723kNbyxGCgC9SmgrVZrTJ4bEVE0YKAYHpGUVIUoHPhtJ0VFU3Dhb1ldb9yxGihKiX548yQiUoan6THYHTW4vE2/QRSLOEaRKESkOZekm0osB4qFhYURM9A8qggCdG2THK8pjAQg0ayD+mQRLIeqw/bxx/fKDM+BqFnxND0GE40FFwNFam549SAKEZVK5TQ5b7Ampo80er2eYxQDJKhVSBqYhaSBWRDU/PzCSaUS0CYrAYlmXcTH6Fu2bMGoUaPQqlUrCIKANWvWOC0XRRFz585FZmYmjEYjBg8ejGPHjjmt4247AJg8eTLGjBkTusJTWHjqemq322PyvqOU5pbxkohPJkQholarG9y4Y/GGrVKpYDAYePMkCpHKykpccskleO2119wuX7RoEV555RW8+eab2LlzJ+Lj4zFs2DBYrdYwl5SU4uneIooiK/GCSKvV8l5HzQq7nhKFiNT1tDlITEyERsPLCVEoDB8+HMOHD3e7TBRFvPTSS5g9ezZGjx4NAFi+fDnS09OxZs0a3HrrrT4fJy8vD1lZWQ3ev/baa5GTkxNQ2UlZ7HoaXG3atFG6CERhxasHUYi4dj2NZS1atGCgGACxzoZzK77HuRXfQ6xrHt+VSGG3iTiyvxjnCqoQzfk+cnNzUVBQgMGDBzveS0pKwhVXXIEdO3b4ta+2bdvizJkzjp/9+/cjNTUV11xzTbCLTSFgt9sbjFNkoEhETcEnO6IQUavVqKurA8DMc0QUGgUFBQCA9PR0p/fT09MdyyTjx49v0G2uuroaN954I4D6a1ZGRgYAwGq1YsyYMejfvz/mzZsXotJTMCUkJKC8vByJiYmO9zhGkYiagoEiUYjIu57GasZTIooeL774olPLIwDMmjXLbc+HKVOmoLy8HBs3bmSLVJRITk7GqVOnnAJFjlEkoqZgoEgUIgwUiSjUpBbAwsJCZGb+b+qNwsJCXHrppQ3W7dSpk9N7JpMJJSUlTu8tWLAA69evx65du2AymUJSbgo+tVoNjUYDq9UKg8EAgF1PiahpePUgChH5GEVOH0FEoZCVlYWMjAxs2rTJ8V5ZWRl27tyJ/v37+72/Tz75BPPnz8fHH3+Mjh07BrOoFAYpKSkoLi52/M6up0TUFGxRJAoR+fQYbFEkokBVVFTg+PHjjt9zc3Px/fffIyUlBe3atcP06dOxYMECZGdnIysrC3PmzEGrVq38nh/x0KFDmDhxImbNmoXu3bs7xjjqdDqkpKQE85QoRAwGA+rq6lBXVweNRsOup0TUJAwUiUJEEARHEhsGikSRydLCjLgeyVCpI7fVZc+ePbjuuuscvz/yyCMAgEmTJmHZsmWYOXMmKisrMW3aNJSUlGDAgAFYt26do/uhP8exWCxYsGABFixY4Hif02NEF7PZjJKSEqSlpbHrKRE1iSAyHWPMKysrQ1JSEkpLS50GuVPo5efno127diguLoZWq+V4H3Ii2uwo25oHAEgc2AGCmg90oWK1WpGbm4usrCwYDAbY7SJ+y60AALTJSoBKFbmBIgWX63ch1oiiiF9//RXt27fHuXPnYDKZYDQalS4WEUUhtigShZC8RTEWH0ioaQS1CkmDLlK6GM2SSiWgXUdW3FDsEQQBCQkJqKio4BhFImoSVl8ThQG7nhIRUbiYzWZcuHCBYxSJqEl49SAKIWmcIgNFIiIKF41GA7VajaqqKgaKRBQwXj2IQkiaS5GBIrkj1tlwbtUBnFt1AGJdw0nPKXTsNhE//nABP/5wAXYbh+pT7ElJSYHFYmHXUyIKGMcoEoWQfC5F3qzJLZtd6RI0W3Y7A0SKXUajEcnJyWxRJKKAMVAkCiH5XIpERETh1Lp1a6WLQERRjNVMRCEkdT0lIiIiIoomDBSJQkje9ZSIiIiIKFowUCQKIbVajbq6Oo5PJCIiIqKowjGKRCGkVqtRWVnJjKdEEWr1sl+Qlm6EEKZq03v+0j08ByIiImoitigShZBarUZtbS0DRfJAgLZlPLQt4wGw1TmsBCAuQQutTh3xH315eTmmT5+O9u3bw2g04qqrrsLu3bsdy0VRxNy5c5GZmQmj0YjBgwfj2LFjTvsQBAFr1qxpsO/JkydjzJgxIT4DIiKKRgwUiUJIpVKhpqaGgSK5JWhUMA/OhnlwNgQNL8fhpFIJ6JBtgjlVh0jvGX733Xdj48aN+OCDD3Dw4EEMHToUgwcPxqlTpwAAixYtwiuvvII333wTO3fuRHx8PIYNGwar1apwyYmIKJrxyYQohNRqNQNFIgpYVVUVPvnkEyxatAjXXHMNOnXqhHnz5qFTp0544403IIoiXnrpJcyePRujR49Gr169sHz5cpw+fdptC6I3eXl5EAShwc+gQYNCcm5ERBTZGCgShZBKpYIoigwUiSggdXV1sNlsMBgMTu8bjUZs27YNubm5KCgowODBgx3LkpKScMUVV2DHjh1+Hatt27Y4c+aM42f//v1ITU3FNddcE5RzISKi6MJkNkQhJAgCVCoVA0VyS6yzoeizIwCA1NEXQ9DwexIudpuIY0dKcb7QitQWhrAls/GXyWRC//798dRTT6Fbt25IT0/HypUrsWPHDnTq1AkFBQUAgPT0dKft0tPTHcsk48ePb3Atqq6uxo033gigvgdERkYGAMBqtWLMmDHo378/5s2bF6KzIyKiSMZAkSjE1Go1A0XySKzmPJtKsdXZIdpFpYvRqA8++ABTpkxB69atoVar0adPH4wfPx579+71az8vvviiU8sjAMyaNcvtXK9TpkxBeXk5Nm7cCJUqQqNoIiIKKQaKRCHGQJGImqJjx47YvHkzKisrUVZWhszMTNxyyy246KKLHC2AhYWFyMzMdGxTWFiISy+91Gk/GRkZ6NSpk9N7JpMJJSUlTu8tWLAA69evx65du2AymUJyTkREFPlYTUgUYgwUiSgY4uPjkZmZiQsXLmD9+vUYPXo0srKykJGRgU2bNjnWKysrw86dO9G/f3+/j/HJJ59g/vz5+Pjjj9GxY8dgFp+IiKIMWxSJQqxly5YQIj3/PhFFrPXr10MURXTp0gXHjx/HY489hq5du+LOO++EIAiYPn06FixYgOzsbGRlZWHOnDlo1aqV3/MjHjp0CBMnTsSsWbPQvXt3xxhHnU6HlJSUEJwZERFFMgaKRCEWFxendBGIyIObJl+Err2SoVJHbmVOaWkpHn/8cfz2229ISUnB2LFj8fTTT0Or1QIAZs6cicrKSkybNg0lJSUYMGAA1q1b1yBTamP27NkDi8WCBQsWYMGCBY73r732WuTk5ATzlIiIKAoIoihG/kh+apKysjIkJSWhtLQUiYmJSheHiP5LrLPh/McHAQBp43oy62kIWa1W5ObmIisrCwaDAXabiB8PXACAiA8UKbhcvwtEROQeWxSJiBQjQJNidLymMBIAQ5zG8ZqIiIicMVAkIlKIoFEh+YYuShejWVKpBFzUhT0siIiIPGHWUyIiIiIiInLCQJGIiIiIiIicsOspEZFCxDo7iv9zFACQcmM3CBrW3YWL3SbixI9lAICOXROZzIaIiMgFA0UiIsWIsFfWOl5TeNXW2JQuAhERUcRi9TURERERERE5YaBIREREREREThgoEhERERERkRMGikRERDFq8uTJGDNmjOP3QYMGYfr06UHZd15eHgRBwPfffx+U/RERUWRhoEhEREQR4fDhwxg7diw6dOgAQRDw0ksvKV0kIqJmi4EiEZFiBKiTDFAnGQBweoZw0xvU0BvUSheDZCwWCy666CIsXLgQGRkZSheHiKhZY6BIRKQQQaNCyo1dkXJjV86hGGYqtYCO3ZLQsVtSxM+haLfbsWjRInTq1Al6vR7t2rXD008/DQA4efIkxo0bB7PZjJSUFIwePRp5eXlBOW6HDh3wzDPPYMqUKTCZTGjXrh3efvvtBuv9+OOPuOqqq2AwGNCjRw9s3rw54GP27dsXixcvxq233gq9Xt+U4hMRURPxyYSIiCiCPf7441i4cCHmzJmDI0eOYMWKFUhPT0dtbS2GDRsGk8mErVu3Yvv27UhISMANN9yAmpqaoBz7+eefx+WXX479+/fjvvvuw5/+9Cf89NNPTus89thjePTRR7F//370798fo0aNQlFRkWN5QkKC15977703KGUlIqLg0ihdACIiInKvvLwcL7/8Ml599VVMmjQJANCxY0cMGDAA//znP2G32/Huu+9CEOpbRZcuXQqz2YycnBwMHTq0yccfMWIE7rvvPgDArFmz8OKLL+Kbb75Bly5dHOs88MADGDt2LADgjTfewLp167BkyRLMnDkTABpNdpOYmNjkchIRUfAxUCQiUohYZ8eF9T8DAJKHdWb30zCy20Tk/lwGAMjqnBix3U+PHj2K6upqXH/99Q2W/fDDDzh+/DhMJpPT+1arFSdOnAjK8Xv16uV4LQgCMjIycPbsWad1+vfv73it0Whw+eWX4+jRo473OnXqFJSyEBFReDFQJCJSjAhbqdXxmsKr2mpTugiNMhqNHpdVVFTgsssuw4cffthgWYsWLYJyfK1W6/S7IAiw2+1+7SMhIcHr8ttvvx1vvvmm32UjIqLQYqBIREQUobKzs2E0GrFp0ybcfffdTsv69OmDVatWoWXLlop23/zuu+9wzTXXAADq6uqwd+9ePPDAA47l7HpKRBSdGCgSERFFKIPBgFmzZmHmzJnQ6XS4+uqrce7cORw+fBgTJkzA4sWLMXr0aMyfPx9t2rTBr7/+itWrV2PmzJlo06ZNWMr42muvITs7G926dcOLL76ICxcuYMqUKY7l/nQ9rampwZEjRxyvT506he+//x4JCQnswkpEFGYMFImIiCLYnDlzoNFoMHfuXJw+fRqZmZm49957ERcXhy1btmDWrFm46aabUF5ejtatW+P6668PayvdwoULsXDhQnz//ffo1KkT1q5di7S0tID2dfr0afTu3dvx+3PPPYfnnnsO1157LXJycoJUYiIi8oUgiiIHxsS4srIyJCUlobS0lF18iCKIWGfD+Y8PAgDSxvWEoOHk76FitVqRm5uLrKwsGAwG2G0ifjxwAQDQtVdyxCazoeBz/S4QEZF7TLFHRERERERETtj1lIhIMQJU8VrHawovra75tuBu3boVw4cP97i8oqIijKUhIqJIxECRiEghgkaF1NHdlS5Gs6RSC8junqR0MRRz+eWXN5qNlIiImjcGikRERM2M0WhkFlEiIvKKYxSJiIiIiIjICVsUiYgUItbZUfLVMQCAeXA2BA3r7sLFbheRd6wcANAh2wSVimNEiYiI5BgoEhEpRkRdcZXjNYWRCFgtdY7XRERE5IzV10REREREROSEgSIRERERERE5YaBIREQU5ebNm4dLL73Ur20EQcCaNWuCcvxly5bBbDYHZV9ERBQZGCgSERFFqEGDBmH69OmNrjdjxgxs2rQp9AUKo9WrV2Po0KFITU2FIAhu530sKCjAHXfcgYyMDMTHx6NPnz745JNPwl9YIqIYxECRiIgoSomiiLq6OiQkJCA1NVXp4gRVZWUlBgwYgL///e8e15k4cSJ++uknrF27FgcPHsRNN92EcePGYf/+/WEsKRFRbGKgSESkIEGvhqBXK12MZkmtUUEdwVOSTJ48GZs3b8bLL78MQRAgCAKWLVsGQRDw5Zdf4rLLLoNer8e2bdsadD3dvXs3hgwZgrS0NCQlJeHaa6/Fvn37AipHXl4eBEHA6tWrcd111yEuLg6XXHIJduzY0WDdNWvWIDs7GwaDAcOGDcPJkycDPX3ccccdmDt3LgYPHuxxnW+//RYPPvgg+vXrh4suugizZ8+G2WzG3r17Az4uERHVi9w7JBFRjBM0aqSN7Ym0sT0haBgshpNKLaBLTzO69DRDpY7MORRffvll9O/fH1OnTsWZM2dw5swZtG3bFgDwl7/8BQsXLsTRo0fRq1evBtuWl5dj0qRJ2LZtG7777jtkZ2djxIgRKC8vD7g8f/vb3zBjxgx8//336Ny5M8aPH4+6ujrHcovFgqeffhrLly/H9u3bUVJSgltvvdWxfOvWrUhISPD68+GHH/pVpquuugqrVq1CcXEx7HY7PvroI1itVgwaNCjg8yQionqcR5GIiCgCJSUlQafTIS4uDhkZGQCAH3/8EQAwf/58DBkyxOO2v/vd75x+f/vtt2E2m7F582aMHDkyoPLMmDEDN954IwDgySefRPfu3XH8+HF07doVAFBbW4tXX30VV1xxBQDg/fffR7du3bBr1y7069cPl19+udtxhnLp6el+lenjjz/GLbfcgtTUVGg0GsTFxeHTTz9Fp06d/D9BIiJywkCRiEhhlQfOKF2EmFct1sIOG2xVNbDZVVDH6QAA585UKVwy72pr7KiqrHOUs6SoGgCQ1ba7U9kry2tRV2t3vHf2XCEW/v1JbN+xFefPn4PNZkNVlQX5+fkBl0XecpmZmVl/nLNnHYGiRqNB3759Het07doVZrMZR48eRb9+/WA0GoMewM2ZMwclJSX46quvkJaWhjVr1mDcuHHYunUrevbsGdRjERE1NwwUiYgUItbZUZpzAraKGug7JENQRWYXyFhkt4vIP1GB2ho7kpK1EITo+uzj4uK9Ln/wz1Nx4UIxnp6/GG3atINOp8eNo65DTU1NwMfUarWO19LnZbfbfd5+69atGD58uNd13nrrLUyYMMGn/Z04cQKvvvoqDh06hO7duwMALrnkEmzduhWvvfYa3nzzTZ/LRkREDTFQJCJSjIjas5VKF6J5EgFLRe1/f9F6XVVJWq0ONpvN7+127f4Of3/2JQy+/gYAwKlTv6Go+Hywi+ekrq4Oe/bsQb9+/QAAP/30E0pKStCtWzcACHrXU4vFAgBQqZzTLajVar8CWCIico+BIhERUYRq17Yd9u3fjfyTvyI+Lt7nAOiirI74179X4tJL+qC8vAxPPvU3GA3GkJZVq9XiwQcfxCuvvAKNRoMHHngAV155pSNw9LfraXFxMfLz83H69GkA9YEnAGRkZCAjIwNdu3ZFp06dcM899+C5555Damoq1qxZg40bN+Lzzz8P/gkSETUzzHpKREQUoe67dzpUajUGXtsH3Xq2w6lTvk038dLzb6C09AIGD7sK9z90N6bedR/S0lqEtKxxcXGYNWsWbrvtNlx99dVISEjAqlWrAt7f2rVr0bt3b0cCnVtvvRW9e/d2dCnVarX44osv0KJFC4waNQq9evXC8uXL8f7772PEiBFBOSciouZMEEVRVLoQFFplZWVISkpCaWkpEhMTlS4OEf2XWGfD+Y8PAgCMF7fkGMUQqhZrcQZl6NCuHQx6AwS9Fj8euAAASEs3RN0YxUC1yAxtq2I0sFqtyM3NRVZWFgwGg9LFISKKWGxRJCIiIiIiIicMFImIiJqxZ555psHE99JPY1lKiYgodjGZDRGRktQqgBkaFaFSCeDgC+Dee+/FuHHj3C4zGtlVlYiouWKgSESkEEGjRotbeqHywBmli9LsqNQCul6S7DRpfXOVkpKClJQUpYtBREQRhl1PiYgo5gmoT1bDBkRiDj8iIt8wUCQiopinggCIImpra5UuCinMYrEAqJ9eg4iIPGPX0/+yWCzYvHkz9u7di3379mHv3r3Iz88HADzxxBOYN2+eT/spLCzEokWL8PnnnyM/Px9GoxHdu3fHpEmTcNdddzWagv3EiRNYtGgRNmzYgDNnzsBkMqFPnz6YNm0axo4d29TTJKIIItrsKNuah7oyK/TtzJweI4TUUEEvanC+uAgajQZq2FDwmwW1NXYkJGocLY6xzmptHufpjiiKsFgsOHv2LMxmM9RqtdJFIiKKaAwU/2vXrl1NnqB37969GDZsGIqKigAACQkJKC8vx7Zt27Bt2zb8+9//xtq1a6HT6dxu/8UXX+Dmm2921HYmJiaiuLgYGzZswIYNG3DnnXdiyZIlzWa+L6KYJ4qoOV2mdCmaBUEQkKYy4XRVCfJPnoSgUaHgVP34xIRyDdBMrqullWxFM5vNyMjIULoYREQRj4GiTHJyMvr06eP4efjhh1FQUODTtqWlpRg5ciSKiorQtWtXfPDBB7j88stRU1ODd955Bw8//DDWr1+P6dOn4/XXX2+wfW5uLsaNGweLxYKrr74a7733Hjp37oyKigosXrwY8+fPx9KlS9G1a1fMnDkz2KdORBTztIIa7VQpqIUNQmYyHhu/CQDwh0lZ0GqbR6B4y7QspYugKK1Wy5ZEIiIfMVD8r4EDB6K4uNjpvb/85S8+b//cc8+hoKAARqMRX3zxBbKy6m/GOp0O999/P8rKyvDXv/4Vb7/9NqZPn47OnTs7bT937lxUVlYiIyMDn3/+OcxmM4D6Vsknn3wSBQUFePvtt/H0009j6tSpSE5ObtoJExE1Q4IgQAcNVHoDCk7Wj1e0Vgqw6ZrHkH2DwaB0EYiIKEo0jzujD5paw7h8+XIAwK233uoIEuUefPBBJCQkwGaz4cMPP3RaVllZiU8++QQA8Kc//ckRJMo9/vjjAICysjKsWbOmSWUlIiIiIiLyhoFiEPz000+OxDfDhw93u05CQgIGDhwIANiwYYPTsm3btqGqqsrr9h06dEC3bt3cbk9ERERERBRMDBSD4NChQ47XPXr08LietOzIkSNN2v7w4cMBlZOIiIiIiMgXHKMYBKdPn3a8bt26tcf1pGVlZWWoqKhAQkKC0/bJyckwGo2Nbi8/njvV1dWorq52/F5aWuo4LhFFDrHO7nhdXlEOcHqMsFGV6x2vq6orUGdvHvWmvA8QEZHEZDJ5nU2BgWIQlJeXO17HxcV5XE++rLy83BEoStt721a+XH48d5599lk8+eSTDd5v27at1+2IiJqjfc8oXYLwebjhrYGIiJqp0tJSJCYmelwetYHismXLcOeddwa8/ZdffokbbrghiCWKHI8//jgeeeQRx+92ux3FxcVITU3lHIxEEaasrAxt27bFyZMnvV6siYiiCa9tRJHPZDJ5XR61gWIkkX/IFovF4wXRYrG43UZ6LV/ubfvG/qh6vR56vd7pPXeZVIkociQmJvJhiohiDq9tRNEragPF8ePHY+TIkQFvn5SUFLSytGrVyvH61KlTHi+Ip06dAlB/0ZS6ncq3v3DhAqqqqjyOU5S2lx+PiIiIiIgo2KI2UHTXaqYUeabSQ4cOOaaxcCVlN7344ou9bt+3b1+v23fv3r1J5SUiIiIiIvKmeaR5C7HOnTujXbt2AIB169a5XaeyshJbt24FAAwdOtRp2YABAxytiJ62//XXX3H06FG32xNR9NLr9XjiiScipuKLiCgYeG0jin4MFINAEARMnDgRAPDRRx8hLy+vwTqvvfYaKioqoFarMWHCBKdl8fHxGDt2LADgjTfecExnIff3v/8dQP34xDFjxgT3BIhIMXq9HvPmzePDFBHFFF7biKIfA0WZCxcu4Pz5844fu71+jjOLxeL0fkVFRYNtZ8yYgYyMDFgsFtx4443Yu3cvAKCmpgZvvPEG5syZAwCYNm0aOnfu3GD7+fPnIz4+HmfOnMGoUaNw7NgxAPUtkfPnz8ebb74JAJg9ezaSk5NDcv5EREREREQAIIiiKCpdiEjRoUMH/Prrr42uN2nSJCxbtqzB+3v37sWwYcNQVFQEoL71z2q1ora2FkB9l9G1a9d6rF374osvcPPNNzuymyYlJaGiogI2mw0AcOedd2LJkiWc4oKIiIiIiEKKLYpBdNlll+Hw4cN4+OGHkZ2djdraWsTHx2PAgAF455138OWXX3rtgjFixAgcOHAAU6dORYcOHWC1WpGcnIwhQ4bg3//+N9577z0GiUREREREFHJsUSQiIiIiIiInbFEkIgqz8vJyzJs3Dz179kRCQgKSkpLQt29fPP/886ipqVG6eEREfisqKsLSpUtx++234+KLL0Z8fDz0ej3atGmDMWPG4NNPP1W6iETkJ7YoEhGF0a+//opBgwY5siPHxcXBZrOhuroaANC7d29s2rSJSauIKKpotVrU1dU5fjcYDFCr1aisrHS8N3z4cPz73/9GXFycEkUkIj+xRZGIKEzq6uowatQo5OXlITMzExs3bkRlZSUsFgs++ugjmEwm7N+/H7fffrvSRSUi8ktdXR369euH119/HSdOnEBVVRUqKiqQm5uLu+66CwDw5Zdf4p577lG4pETkK7YoEhGFyZIlS3D33XcDAL799lv079/fafnKlStx2223AQC++uorXH/99WEvIxFRIL755htcd911Hpffe++9eOuttwAA+fn5aNu2bbiKRkQBYosiEVGYvP/++wCA6667rkGQCAC33norsrKyAADLly8Pa9mIiJrCW5AIwNGqCAB79uwJdXGIKAgYKBIRhYHFYsH27dsB1I/TcUcQBNxwww0AgA0bNoStbEREoWYwGByvpfmhiSiyMVAkIgqDo0ePwm63AwB69OjhcT1pWUFBAYqLi8NSNiKiUMvJyXG87tmzp3IFISKfMVAkIgqD06dPO163bt3a43ryZfJtiIiiVUlJCZ599lkAwMCBA9GlSxeFS0REvmCgSEQUBuXl5Y7X3lLDy5fJtyEiikZ2ux133HEHzpw5A4PBgFdffVXpIhGRjxgoEhEREVFI/PnPf8bnn38OAHjttdfQq1cvhUtERL5ioEhEFAYmk8nx2mKxeFxPvky+DRFRtJkxY4ajBfHFF1/ElClTFC4REfmDgSIRURi0atXK8frUqVMe15Mvk29DRBRNZs6cieeffx4A8Nxzz2H69OnKFoiI/MZAkYgoDLp16waVqv6Se+jQIY/rScsyMjKQkpISlrIREQXTY489hsWLFwMAFi1ahEcffVThEhFRIBgoEhGFQVxcHK6++moAwLp169yuI4oi1q9fDwAYOnRo2MpGRBQsM2bMwHPPPQegPkh87LHHFC4REQWKgSIRUZhMmjQJAPDNN99g586dDZb/61//wi+//AIAmDhxYljLRkTUVDNmzHDqbsogkSi6MVAkIgqTSZMmoWfPnhBFEWPHjsWmTZsA1KeP/9e//oWpU6cCAIYPH47rr79eyaISEflFPibxhRdeYHdTohggiKIoKl0IIqLmIi8vD9dddx3y8vIA1HdJtdvtsFqtAIDevXtj06ZNSE5OVrCURES+y8/PR/v27QEAKpUKLVq08Lr+jBkzMGPGjHAUjYiaQKN0AYiImpMOHTrgwIEDeO6557B69Wrk5uZCq9Wie/fuGD9+PB588EHodDqli0lE5DO73e70urCw0Ov6FRUVoS4SEQUBWxSJiIiIiIjICccoEhERERERkRMGikREREREROSEgSIRERERERE5YaBIREREREREThgoEhERERERkRMGikREREREROSEgSIRERERERE5YaBIREREREREThgoEhERERERkRMGikREREREROSEgSIRERGAefPmQRAECIKgdFEU8fXXX0MQBKSnp8NisShdnKBZvHgxBEHAoEGDlC4KEVFUYaBIRETUzNntdkyfPh0AMGPGDMTFxSlboCD605/+hNTUVGzevBmrV69WujhERFGDgSIREcWsZcuWOVoJ8/LylC5OxProo49w8OBBpKWl4b777lO6OEGVkJCARx55BAAwd+5c2O12hUtERBQdGCgSERGhvuupKIoQRVHpooTd008/DQC45557EB8fr3Bpgu/++++HwWDA4cOHsWbNGqWLQ0QUFRgoEhERNWMbN27EkSNHAAC33367wqUJjaSkJIwYMQIA8MorryhcGiKi6MBAkYiIqBlbsmQJAKBPnz7o2rWrwqUJnQkTJgAANm/ejBMnTihcGiKiyMdAkYiIYk5OTg4EQcCdd97peC8rK8sxXlH6ycnJcSxvLOtphw4dIAgCJk+eDADYt28fJkyYgLZt28JoNKJTp0545JFHcP78eaftvv32W9x8881o164dDAYDOnbsiFmzZqG8vLzR87DZbHj//fcxcuRItGrVCnq9HqmpqRgwYABeeOEFVFVV+f/hyFitVqxduxYAMHbs2EbLsmzZMgwbNgwZGRnQ6XRISkpCdnY2rr/+ejzzzDOOlklP1qxZ4/RZmM1mXH755XjyySdx4cIFn8r8xRdf4Pbbb8dFF12E+Ph4GAwGZGVlYezYsVi2bJnHjK033ngjDAYDAGDlypU+HYuIqFkTiYiIYsw333wjAmj055tvvnFs88QTTzjed6d9+/YiAHHSpEni8uXLRZ1O53afnTt3Fs+cOSOKoiguXrxYFATB7Xp9+vQRy8vLPZ7Dr7/+Kl5yySVey9+pUyfxp59+CvhzysnJcexr06ZNHtcrLy8XBw4c2OjnOXbsWLfbFxcXi7/73e+8btuyZUtxx44dHstw/vx58frrr2+0DEuXLvW4jyuvvFIEIF511VU+f0ZERM2VJogxJxERUUTo27cvDh48iM8++wyzZ88GAKxfvx6tWrVyWi8rK8vvff/www9YuXIlOnXqhBkzZqBnz54oLy/He++9h3/+85/4+eefMWPGDNx000147LHHcOWVV+LBBx9Ely5dcP78ebzyyiv44osvsG/fPixYsAALFy5scIyioiIMGDAAJ0+ehF6vx9SpU3HttdeiQ4cOqKiowIYNG/Dyyy/j+PHjGD58OPbt24ekpCS/z2Xr1q0AAEEQcNlll3lcb968eY51R44ciQkTJjhaBc+ePYv9+/fj888/d9saW11djcGDB2Pfvn1Qq9W47bbbMGLECGRlZaG2thZbtmzBCy+8gLNnz2LEiBHYv38/2rdv77QPi8WC6667DgcPHgQAXHbZZZg2bRp69OgBvV6PkydPYsuWLVi1apXX8+3Xrx++++477Nq1C1ar1dHCSEREbigdqRIREYXK0qVLHS1Nubm5Xtf1tUUR/22RqqysbLDOH//4RxGAqFarxZSUFHHs2LFiXV2d0zp1dXWOlq3U1FSxtra2wX5uu+02EYDYvn178ZdffnFbnn379onx8fEiAPGvf/2r13PzZPjw4SIAsWPHjl7Xa9u2rQhA/OMf/+h1vaKiogbv/fWvfxUBiGazWdyzZ4/b7fLy8sTMzEwRgHjbbbc1WP7www87Pvv7779ftNvtbvdTXV0tFhQUeCzf+++/79jPd9995/VciIiaO45RJCIi8oMgCHj33XfdTkovzUFos9lgtVrx9ttvQ61WO62jVqsxbdo0APUth67j+vLy8hwtY6+++qrHVs/evXvj/vvvB1A/X2QgfvvtNwBAy5Ytva5XUFAAABg4cKDX9VJSUpx+r6iowGuvvQYAeOqppzy2WrZv3x5z5swBAPzrX/9CZWWlY1lJSQneeustAPUtiS+//LLHcaQ6nQ7p6ekeyyc/z19++cXruRARNXcMFImIiPzQq1cvdOvWze2ySy65xPF6yJAhDQInd+u5Biz/+c9/YLPZEBcXh+HDh3styzXXXAMAOH36NPLz830qv9y5c+cAAMnJyV7Xy8zMBACsWrXKY7IYdzZv3ozS0lIAwB//+Eev60rnUltbi7179zre//rrrx3HfOihhxoE3v6Q/z2k4JeIiNxjoEhEROSHzp07e1xmNpv9Xs81++mePXsA1I/L02g0DTK1yn9Gjhzp2C6QwKe4uBhA44HipEmTANRncM3KysIDDzyATz/91BFoeiKdC1AfbHo7lx49erg9l/379zteN9ai2Rj5ecpbLYmIqCEGikRERH5w1+VUolKp/F7PZrM5LTt79mxA5fKnpU8iJXNpbJqNOXPmYMqUKRAEAWfPnsVrr72Gm266CS1btkSPHj3wxBNPoLCwsMF2wTgX+XQjUstmoOTnqdVqm7QvIqJYx6ynREREEUQKHNPS0vDNN9/4vF0gGVxbtGiBsrIyR8uiJ1qtFkuWLMGjjz6KlStX4uuvv8aePXtQU1ODw4cP4/Dhw3jhhRfwz3/+E6NHj25wLkD9vJO+Bmdt2rTx+1x8IT9PeasuERE1xECRiIgogqSmpgKo75LarVu3Jo3Ja0yLFi1w4sQJnye7v/jii/HUU0/hqaeegtVqxbZt27BixQosX74cFRUVGD9+PE6cOOFo+ZPORTpWIAFgWlqa4/WZM2cCCogl8vNs165dwPshImoO2PWUiIhilqfsmJGsd+/eAOrnH5SP8QuFnj17AgBOnDgBu93u17YGgwGDBw/Ge++9h8WLFwOo79r5+eefO9aRzgUAtm/fHlAZ+/Tp43i9ZcuWgPYh+fnnnx2vu3fv3qR9ERHFOgaKREQUs+QTqldXVytYEt+NGjXKEeC+9NJLIT2WlBymoqICR48eDXg/119/veO1fEzh4MGDHWM1X3nlFYii6Pe+r7vuOsTHxwMA/vGPfzQY0+mP3bt3A6gf68gWRSIi7xgoEhFRzJInPzlx4oSCJfFdly5dcPPNNwMAPvroI7zwwgte18/NzcXKlSsDOpY8i+iuXbvcrlNcXIz/+7//8xrkbdiwwfFa3jXUbDbjgQceAFCfMfXhhx/22nJZWFiId9991+k9s9mMe+65BwCwd+9eTJ8+3WNZamtrvSbQkc5xyJAhHtchIqJ6HKNIREQxq3fv3jAYDLBarZgzZw60Wi3at2/vyDraunVrGI1GhUvZ0BtvvIE9e/bgl19+waOPPorPPvsMEydORPfu3aHX61FUVIQffvgB69atw9dff40//OEPGD9+vN/H6dChA3r16oUDBw5g06ZNuPPOOxusU1ZWht///vfo0KEDbrrpJlxxxRVo3749NBoNzpw5g//7v/9zBHetW7d2mrIDAObPn4/Nmzdj586dePnll5GTk4OpU6fi0ksvRXx8PC5cuIDDhw/jq6++wpdffomePXvi7rvvdtrHU089hY0bN+LgwYN49dVXsWPHDtxzzz3o2bMndDodfvvtN2zduhUrV67EggULMHny5AbncezYMZw8eRIA8Ic//MHvz4qIqLlhoEhERDHLZDLhoYcewqJFi7Bv3z4MHTrUafk333yDQYMGKVM4L1JSUrB9+3aMGzcOW7duxZYtW7yOz0tMTAz4WFOnTsWDDz6Izz77DBaLxeO0Hnl5eV5bNzMzM/HZZ58hISHB6X29Xo+NGzdi8uTJWL16NX744QdHK6M77s4lLi4OX3/9NcaOHYstW7Zg7969mDZtmo9nWG/FihUA6j/bESNG+LUtEVFzxECRiIhi2sKFC5GdnY3ly5fj8OHDKC0tbdI4t3DJyMjAli1b8J///AcrV67Ejh07UFBQgNraWpjNZmRnZ6N///74/e9/j2uuuSbg49x+++2YOXMmKioqsHbtWtx6661Oy9u3b49du3bhiy++wLfffotff/0VhYWFqKiogNlsxsUXX4xRo0Zh2rRpHgNWk8mETz75BNu2bcP777+PrVu34vTp06iqqkJiYiI6duyIfv364cYbb2wQzEvS0tKwefNmfPrpp1ixYgW+++47nDt3DoIgoFWrVrjsssswZswYjB071u32UqB41113QafTBfx5ERE1F4IYyMhyIiIiihn33Xcf3njjDQwePBgbN25UujhBt23bNgwcOBA6nQ7Hjh1jIhsiIh8wmQ0REVEzN3fuXMTHx+Orr77Cd999p3Rxgu6pp54CAEyZMoVBIhGRjxgoEhERNXMZGRl4+OGHAdQnn4klO3fuxIYNG2AymfDEE08oXRwioqjBMYpERESEmTNnQqOpfyzwltQm2hQVFeGJJ55Anz59kJGRoXRxiIiiBscoEhERERERkRN2PSUiIiIiIiInDBSJiIiIiIjICQNFIiIiIiIicsJAkYiIiIiIiJwwUCQiIiIiIiInDBSJiIiIiIjICQNFIiIiIiIicsJAkYiIiIiIiJwwUCQiIiIiIiInDBSJiIiIiIjIyf8D9pOpg7ZGO+sAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for cond, sub_group in group.groupby(['target_amplitude']):\n",
+ " print(cond)\n",
+ " adaptation.plots.show_traces_averages(sub_group)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "10756b4e-4cd3-4b8b-932f-20c86cf4b4df",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x23a3504aeb0>"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(data,bins=20)\n",
+ "plt.xlim(0,1)\n",
+ "plt.axvline(0.5,ls = '--', color = \"black\")\n",
+ "plt.axvline(np.median(data),ls = '-', color = \"orange\", label = str(np.median(data)))\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 335,
+ "id": "d3aa4670-5ffc-40fa-93af-d2d72017deca",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x26e287cdb80>"
+ ]
+ },
+ "execution_count": 335,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(data,bins=10)\n",
+ "plt.xlim(0,1)\n",
+ "plt.axvline(0.5,ls = '--', color = \"black\")\n",
+ "plt.axvline(np.median(data),ls = '-', color = \"orange\", label = str(np.median(data)))\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 336,
+ "id": "d382808e-5ea2-4f58-a01f-332556704f0e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x26e28818c10>"
+ ]
+ },
+ "execution_count": 336,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(data,bins=10)\n",
+ "plt.xlim(0,1)\n",
+ "plt.axvline(0.5,ls = '--', color = \"black\")\n",
+ "plt.axvline(np.median(data),ls = '-', color = \"orange\", label = str(np.median(data)))\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 340,
+ "id": "902be428-f8f6-4c06-b3e6-19958ce75e37",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " 0.5135135135135135,\n",
+ " 0.5135135135135135,\n",
+ " 0.5675675675675675,\n",
+ " 0.5675675675675675,\n",
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+ " 0.5135135135135135,\n",
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+ " 0.4864864864864865,\n",
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+ " 0.5405405405405406,\n",
+ " 0.40540540540540543,\n",
+ " 0.40540540540540543],\n",
+ " [0.5135135135135135,\n",
+ " 0.5405405405405406,\n",
+ " 0.5675675675675675,\n",
+ " 0.43243243243243246,\n",
+ " 0.3783783783783784,\n",
+ " 0.3783783783783784,\n",
+ " 0.5405405405405406,\n",
+ " 0.35135135135135137,\n",
+ " 0.43243243243243246,\n",
+ " 0.4594594594594595,\n",
+ " 0.5135135135135135,\n",
+ " 0.5945945945945946,\n",
+ " 0.5135135135135135,\n",
+ " 0.6216216216216216,\n",
+ " 0.5135135135135135,\n",
+ " 0.3783783783783784,\n",
+ " 0.43243243243243246,\n",
+ " 0.2702702702702703,\n",
+ " 0.43243243243243246,\n",
+ " 0.43243243243243246,\n",
+ " 0.4864864864864865,\n",
+ " 0.4864864864864865,\n",
+ " 0.32432432432432434,\n",
+ " 0.4864864864864865,\n",
+ " 0.43243243243243246,\n",
+ " 0.2972972972972973,\n",
+ " 0.7027027027027027,\n",
+ " 0.6216216216216216,\n",
+ " 0.6216216216216216,\n",
+ " 0.43243243243243246,\n",
+ " 0.43243243243243246,\n",
+ " 0.43243243243243246,\n",
+ " 0.35135135135135137,\n",
+ " 0.40540540540540543,\n",
+ " 0.3783783783783784,\n",
+ " 0.40540540540540543,\n",
+ " 0.40540540540540543,\n",
+ " 0.43243243243243246,\n",
+ " 0.3783783783783784,\n",
+ " 0.32432432432432434,\n",
+ " 0.6756756756756757,\n",
+ " 0.5405405405405406,\n",
+ " 0.3783783783783784,\n",
+ " 0.5135135135135135,\n",
+ " 0.4864864864864865,\n",
+ " 0.5675675675675675,\n",
+ " 0.40540540540540543,\n",
+ " 0.5675675675675675,\n",
+ " 0.2972972972972973,\n",
+ " 0.4864864864864865]]"
+ ]
+ },
+ "execution_count": 340,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "chance\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 337,
+ "id": "13a51765-fb93-4b59-8940-051f12ae9143",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 1.0)"
+ ]
+ },
+ "execution_count": 337,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(np.array(chance)[6,:],bins=20)\n",
+ "plt.xlim(0,1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 342,
+ "id": "8bdf989b-841c-44aa-ace8-557e2ab4f269",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.4864864864864865"
+ ]
+ },
+ "execution_count": 342,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.median(chance)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 338,
+ "id": "0081e6c6-11f6-45e0-ba2c-458f01b0b005",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x26e28534fa0>"
+ ]
+ },
+ "execution_count": 338,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(np.array(chance).T,bins=20)\n",
+ "plt.xlim(0,1)\n",
+ "plt.axvline(0.5,ls = '--', color = \"black\")\n",
+ "plt.axvline(np.median(chance),ls = '-', color = \"orange\", label = str(np.median(chance)))\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 275,
+ "id": "d45f13bd-3bfa-489d-b841-c47c4ff64896",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x26e3fb9dc10>"
+ ]
+ },
+ "execution_count": 275,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(np.array(chance).T,bins=20)\n",
+ "plt.xlim(0,1)\n",
+ "plt.axvline(0.5,ls = '--', color = \"black\")\n",
+ "plt.axvline(np.median(chance),ls = '-', color = \"orange\", label = str(np.median(chance)))\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 339,
+ "id": "d404ae9f-f539-4496-9c30-f93887891b84",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x26e2b3877f0>"
+ ]
+ },
+ "execution_count": 339,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(np.array(chance).flatten(),bins=20)\n",
+ "plt.xlim(0,1)\n",
+ "plt.axvline(0.5,ls = '--', color = \"black\")\n",
+ "plt.axvline(np.median(chance),ls = '-', color = \"orange\", label = str(np.median(chance)))\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 222,
+ "id": "27120d11-1974-48ce-af60-a0bde6fe97e3",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "([0.5454545454545454,\n",
+ " 0.45454545454545453,\n",
+ " 0.36363636363636365,\n",
+ " 0.6363636363636364,\n",
+ " 0.45454545454545453,\n",
+ " 0.7272727272727273,\n",
+ " 0.8181818181818182,\n",
+ " 0.45454545454545453,\n",
+ " 0.45454545454545453,\n",
+ " 0.36363636363636365,\n",
+ " 0.6363636363636364,\n",
+ " 0.6363636363636364,\n",
+ " 0.36363636363636365,\n",
+ " 0.6363636363636364,\n",
+ " 0.45454545454545453,\n",
+ " 0.8181818181818182,\n",
+ " 0.45454545454545453,\n",
+ " 0.45454545454545453,\n",
+ " 0.7272727272727273,\n",
+ " 0.45454545454545453,\n",
+ " 0.18181818181818182,\n",
+ " 0.45454545454545453,\n",
+ " 0.7272727272727273,\n",
+ " 0.6363636363636364,\n",
+ " 0.5454545454545454,\n",
+ " 0.5454545454545454,\n",
+ " 0.45454545454545453,\n",
+ " 0.6363636363636364,\n",
+ " 0.2727272727272727,\n",
+ " 0.45454545454545453,\n",
+ " 0.2727272727272727,\n",
+ " 0.6363636363636364,\n",
+ " 0.45454545454545453,\n",
+ " 0.8181818181818182,\n",
+ " 0.7272727272727273,\n",
+ " 0.8181818181818182,\n",
+ " 0.5454545454545454,\n",
+ " 0.6363636363636364,\n",
+ " 0.6363636363636364,\n",
+ " 0.6363636363636364,\n",
+ " 0.5454545454545454,\n",
+ " 0.45454545454545453,\n",
+ " 0.45454545454545453,\n",
+ " 0.7272727272727273,\n",
+ " 0.45454545454545453,\n",
+ " 0.45454545454545453,\n",
+ " 0.2727272727272727,\n",
+ " 0.6363636363636364,\n",
+ " 0.6363636363636364,\n",
+ " 0.7272727272727273,\n",
+ " 0.7272727272727273,\n",
+ " 0.7272727272727273,\n",
+ " 0.2727272727272727,\n",
+ " 0.8181818181818182,\n",
+ " 0.7272727272727273,\n",
+ " 0.9090909090909091,\n",
+ " 0.7272727272727273,\n",
+ " 0.8181818181818182,\n",
+ " 0.36363636363636365,\n",
+ " 0.36363636363636365,\n",
+ " 0.8181818181818182,\n",
+ " 0.6363636363636364,\n",
+ " 0.5454545454545454,\n",
+ " 0.5454545454545454,\n",
+ " 0.7272727272727273,\n",
+ " 0.7272727272727273,\n",
+ " 0.5454545454545454,\n",
+ " 0.6363636363636364,\n",
+ " 0.5454545454545454,\n",
+ " 0.7272727272727273,\n",
+ " 0.7272727272727273,\n",
+ " 0.6363636363636364,\n",
+ " 0.5454545454545454,\n",
+ " 0.45454545454545453,\n",
+ " 0.6363636363636364,\n",
+ " 0.7272727272727273,\n",
+ " 0.5454545454545454,\n",
+ " 0.5454545454545454,\n",
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+ ]
+ },
+ "execution_count": 222,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "raw_results = adaptation.classifiers.get_single_classifier_results(training, test, features_key=\"neuronal_features\")\n",
+ "chance_results = [ get_single_classifier_chance(training, test, features_key=\"neuronal_features\")['score'] for _ in range(1000)]\n",
+ "chance_results, raw_results['score']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "876783e7-842f-43b4-ad74-eb2ae58d7957",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "subset = trials_roi_df.loc[0:0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "96048cd7-0683-4403-8e3b-a5c070b8cecb",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>frequency_change</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>nontarget_onset</th>\n",
+ " <th>nontarget_whisker</th>\n",
+ " <th>behavioural_result</th>\n",
+ " <th>complete_amplitude</th>\n",
+ " <th>nontarget_stim_info</th>\n",
+ " <th>complete_stim</th>\n",
+ " <th>target_whisker</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_D1</th>\n",
+ " <th>in_any_barrel</th>\n",
+ " <th>in_C1</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " <th>neuronal_features</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
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+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"150\" valign=\"top\">0</th>\n",
+ " <th>0</th>\n",
+ " <td>[110.48663330078125, 131.6634063720703, 86.054...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[81.11736297607422, 93.11980438232422, 73.6161...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.29373020232037816, 0.07303959775594149, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>[67.9014892578125, 84.18050384521484, 79.32645...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[52.151588439941406, 65.11491394042969, 51.899...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.13887659963863402, 0.4147024933008791, 0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>[68.5937728881836, 60.977806091308594, 68.2375...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[57.168704986572266, 70.49388885498047, 47.332...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[0.0, 0.0, 9.140109062194824, 9.33862686157226...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.15659750000313963, 0.19433377025185258, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>[88.68118286132812, 103.54595947265625, 58.293...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[57.833740234375, 44.317848205566406, 55.43520...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.06945498179116452, -0.19292336493298068, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>[67.2108154296875, 93.54744720458984, 47.41284...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[55.2567253112793, 59.00489044189453, 56.47432...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0....</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.29605039572842456, -0.21797866295520857, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>[61.2387809753418, 62.04039001464844, 93.07856...</td>\n",
+ " <td>[0.023354701103315244, -0.0602884150450634, -0...</td>\n",
+ " <td>[63.858192443847656, 60.75550079345703, 62.709...</td>\n",
+ " <td>[0.023354701103315244, -0.0602884150450634, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.09585280544451584, 0.08839419743372925, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>[100.87199401855469, 87.00389862060547, 98.347...</td>\n",
+ " <td>[-0.019380325737913043, 0.18458343755734835, 0...</td>\n",
+ " <td>[62.105133056640625, 69.67237091064453, 70.848...</td>\n",
+ " <td>[-0.019380325737913043, 0.18458343755734835, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.1586406918515107, -0.07239579586479972, 0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>[104.04031372070312, 89.15300750732422, 81.111...</td>\n",
+ " <td>[-0.024577687714442492, -0.13861195509594057, ...</td>\n",
+ " <td>[61.811737060546875, 57.57212829589844, 53.838...</td>\n",
+ " <td>[-0.024577687714442492, -0.13861195509594057, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.17903471929644427, -0.11314780519622668, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>[81.16952514648438, 96.71385192871094, 72.9607...</td>\n",
+ " <td>[-0.05375759988888351, -0.04555461901997415, -...</td>\n",
+ " <td>[60.085575103759766, 60.38875198364258, 61.652...</td>\n",
+ " <td>[-0.05375759988888351, -0.04555461901997415, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 16.464019775390...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.056415259345327345, -0.38576577276236984, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>[101.66789245605469, 145.193115234375, 88.2653...</td>\n",
+ " <td>[0.365474007550674, 0.032538860639681935, 0.51...</td>\n",
+ " <td>[75.31051635742188, 62.955989837646484, 80.853...</td>\n",
+ " <td>[0.365474007550674, 0.032538860639681935, 0.51...</td>\n",
+ " <td>[22.30367088317871, 19.749446868896484, 0.0, 1...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.11893396048355051, 0.19793689371662737, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>[80.41024017333984, 99.21864318847656, 74.6269...</td>\n",
+ " <td>[-0.3700912806005721, -0.18048151937104914, -0...</td>\n",
+ " <td>[47.6577033996582, 54.691932678222656, 43.5452...</td>\n",
+ " <td>[-0.3700912806005721, -0.18048151937104914, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 1.8619872331619263, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.13360868692080818, 0.18285725484724114, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>[94.21552276611328, 86.79850769042969, 85.0309...</td>\n",
+ " <td>[0.10451741410126203, -0.12455170707257092, 0....</td>\n",
+ " <td>[64.93887329101562, 56.437652587890625, 71.378...</td>\n",
+ " <td>[0.10451741410126203, -0.12455170707257092, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 5.640011787414551, 0...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.06606075047913067, 0.03045827186574529, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>[68.03812408447266, 89.03655242919922, 104.884...</td>\n",
+ " <td>[-0.14030594184020395, 0.11681621501625446, 0....</td>\n",
+ " <td>[55.66748046875, 65.20782470703125, 68.4523239...</td>\n",
+ " <td>[-0.14030594184020395, 0.11681621501625446, 0....</td>\n",
+ " <td>[0.0, 7.206940174102783, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.020764577031397124, 0.22413008417466124, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>[54.27216720581055, 64.89025115966797, 89.1744...</td>\n",
+ " <td>[-0.04471774756904711, -0.032109036195919816, ...</td>\n",
+ " <td>[58.980438232421875, 59.44743347167969, 45.748...</td>\n",
+ " <td>[-0.04471774756904711, -0.032109036195919816, ...</td>\n",
+ " <td>[0.0, 0.0, 31.858652114868164, 14.622788429260...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>correct</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.18366586265757104, -0.09561425035674856, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>[73.89498901367188, 88.20506286621094, 75.3498...</td>\n",
+ " <td>[-0.10454668169070291, -0.5576836941783401, -0...</td>\n",
+ " <td>[56.48655319213867, 39.67237091064453, 37.4767...</td>\n",
+ " <td>[-0.10454668169070291, -0.5576836941783401, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.32084606454126974, 0.21170013194548504, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>[49.37717819213867, 40.45200729370117, 64.5281...</td>\n",
+ " <td>[-0.6322334626683969, -0.027267495315954463, -...</td>\n",
+ " <td>[36.83863067626953, 59.28606414794922, 44.2493...</td>\n",
+ " <td>[-0.6322334626683969, -0.027267495315954463, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.2233702461580103, -0.10584663877430595, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>[60.29892349243164, 85.16168212890625, 72.3468...</td>\n",
+ " <td>[-0.39550408606288084, -0.363552456070871, -0....</td>\n",
+ " <td>[45.48655319213867, 46.67237091064453, 50.3520...</td>\n",
+ " <td>[-0.39550408606288084, -0.363552456070871, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.10200682795348072, -0.3634837937046822, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>[62.95381546020508, 57.82320022583008, 65.7714...</td>\n",
+ " <td>[0.19976095470042038, -0.05398622620535903, 0....</td>\n",
+ " <td>[67.40586853027344, 57.99021911621094, 62.3960...</td>\n",
+ " <td>[0.19976095470042038, -0.05398622620535903, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.30381872957173545, -0.2875727317407065, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>[57.56557083129883, 51.068687438964844, 58.784...</td>\n",
+ " <td>[-0.005997255257728517, -0.2022945641621877, 0...</td>\n",
+ " <td>[59.70415496826172, 52.420536041259766, 59.970...</td>\n",
+ " <td>[-0.005997255257728517, -0.2022945641621877, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0834541320800...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.05333520181619443, -0.037444531879346, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>[42.880428314208984, 65.3575439453125, 82.6388...</td>\n",
+ " <td>[-0.08486048023513294, -0.18244962021015382, -...</td>\n",
+ " <td>[56.623470306396484, 53.002445220947266, 42.65...</td>\n",
+ " <td>[-0.08486048023513294, -0.18244962021015382, -...</td>\n",
+ " <td>[0.0, 0.7954126000404358, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.057095191872701845, -0.047725034214181755, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>[70.03380584716797, 63.43825912475586, 73.4520...</td>\n",
+ " <td>[-0.3403347810837183, 0.0880496300975233, -0.2...</td>\n",
+ " <td>[47.38875198364258, 63.28361892700195, 50.0978...</td>\n",
+ " <td>[-0.3403347810837183, 0.0880496300975233, -0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.1158566759531461, -0.24487215771161036, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>[78.99820709228516, 115.13985443115234, 55.532...</td>\n",
+ " <td>[0.1840468480473831, -0.022674418129115716, 0....</td>\n",
+ " <td>[66.75794982910156, 59.08802032470703, 66.2004...</td>\n",
+ " <td>[0.1840468480473831, -0.022674418129115716, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.2484370567745084, 0.11998574722982666, 0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>[94.26325225830078, 184.75173950195312, 104.01...</td>\n",
+ " <td>[-0.22139016448293267, 0.14230573772644806, 0....</td>\n",
+ " <td>[51.78483963012695, 65.28117370605469, 72.7334...</td>\n",
+ " <td>[-0.22139016448293267, 0.14230573772644806, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.07128055945243081, 0.1258235445609875, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>[72.58927154541016, 96.52001190185547, 56.0740...</td>\n",
+ " <td>[-0.2976526146619502, -0.2058549229338863, -0....</td>\n",
+ " <td>[48.95354461669922, 52.36185836791992, 49.6308...</td>\n",
+ " <td>[-0.2976526146619502, -0.2058549229338863, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.31407936104983597, -0.1496493146878156, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>[130.3365478515625, 119.0087890625, 77.0390396...</td>\n",
+ " <td>[-0.12726088658820112, 0.07642245356457941, 0....</td>\n",
+ " <td>[55.51100158691406, 63.068458557128906, 68.633...</td>\n",
+ " <td>[-0.12726088658820112, 0.07642245356457941, 0....</td>\n",
+ " <td>[9.151693344116211, 0.0, 0.0, 0.0, 0.0, 5.0895...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2559409678391272, 0.11771191691850143, -0.5...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>[89.026611328125, 87.17903900146484, 115.55131...</td>\n",
+ " <td>[-0.303403285236063, -0.40443718230751385, -0....</td>\n",
+ " <td>[48.73105239868164, 44.98288345336914, 49.3325...</td>\n",
+ " <td>[-0.303403285236063, -0.40443718230751385, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.25648891043416605, -0.06470452797842258, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>[76.88912200927734, 96.81219482421875, 52.2906...</td>\n",
+ " <td>[-0.12027609235266702, -0.13963080926573726, -...</td>\n",
+ " <td>[55.45232391357422, 54.73838806152344, 46.0782...</td>\n",
+ " <td>[-0.12027609235266702, -0.13963080926573726, -...</td>\n",
+ " <td>[4.5889716148376465, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.44187417553120845, -0.18892344423372182, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>[84.06704711914062, 64.12518310546875, 82.7639...</td>\n",
+ " <td>[0.13275637081627137, -0.5002529915514548, -0....</td>\n",
+ " <td>[65.09046173095703, 41.603912353515625, 56.581...</td>\n",
+ " <td>[0.13275637081627137, -0.5002529915514548, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.12941679310412216, -0.1998458966893841, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>[70.43943786621094, 67.65316009521484, 91.5920...</td>\n",
+ " <td>[-0.12080809091117684, 0.16048087477853296, 0....</td>\n",
+ " <td>[55.70170974731445, 66.13936614990234, 63.3838...</td>\n",
+ " <td>[-0.12080809091117684, 0.16048087477853296, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.00535585894090174, 0.13174976772022057, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>[52.862815856933594, 81.0198974609375, 58.4936...</td>\n",
+ " <td>[-0.08420647129191677, -0.05084344653211932, -...</td>\n",
+ " <td>[57.039119720458984, 58.27383804321289, 45.249...</td>\n",
+ " <td>[-0.08420647129191677, -0.05084344653211932, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.42875129629467834, -0.4122965672741163, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>[109.7398910522461, 77.05498504638672, 79.3592...</td>\n",
+ " <td>[0.1845884188792514, -0.02157296613558047, 0.0...</td>\n",
+ " <td>[66.94621276855469, 59.29829025268555, 63.6894...</td>\n",
+ " <td>[0.1845884188792514, -0.02157296613558047, 0.0...</td>\n",
+ " <td>[2.113368511199951, 0.0, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.16490452819679824, -0.285950893498107, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>[61.807518005371094, 56.86727523803711, 54.347...</td>\n",
+ " <td>[0.06604966925592491, -0.09479743060955638, -0...</td>\n",
+ " <td>[62.55256652832031, 56.579463958740234, 41.591...</td>\n",
+ " <td>[0.06604966925592491, -0.09479743060955638, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.12314718773306868, -0.16273326443163694, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td>[88.6243896484375, 66.70386505126953, 73.59136...</td>\n",
+ " <td>[-0.3233407345214396, -0.12016641033468067, -0...</td>\n",
+ " <td>[47.98288345336914, 55.52322769165039, 52.1246...</td>\n",
+ " <td>[-0.3233407345214396, -0.12016641033468067, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.19784900826085494, 0.062175475457198905, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
+ " <td>[30.058061599731445, 72.42082977294922, 99.798...</td>\n",
+ " <td>[0.015579863124009314, 0.0971588280260063, 0.0...</td>\n",
+ " <td>[60.65525817871094, 63.68459701538086, 63.1026...</td>\n",
+ " <td>[0.015579863124009314, 0.0971588280260063, 0.0...</td>\n",
+ " <td>[0.0, 0.0, 20.99272346496582, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.09310310438392891, -0.046343915663542755, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>[82.01639556884766, 56.73377227783203, 84.5247...</td>\n",
+ " <td>[-0.09417749524349582, 0.27506349218015863, -0...</td>\n",
+ " <td>[56.51344680786133, 70.21515655517578, 55.4987...</td>\n",
+ " <td>[-0.09417749524349582, 0.27506349218015863, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 23.926652908325195, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.06742932492396372, -0.42743440044955994, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td>[79.57770538330078, 64.9237060546875, 85.03238...</td>\n",
+ " <td>[-0.18878235652392203, -0.39369606966162674, -...</td>\n",
+ " <td>[53.05868148803711, 45.454769134521484, 49.953...</td>\n",
+ " <td>[-0.18878235652392203, -0.39369606966162674, -...</td>\n",
+ " <td>[5.771840572357178, 0.0, 5.634783744812012, 0....</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.43925763920793215, -0.12073934696734709, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36</th>\n",
+ " <td>[58.71508026123047, 43.665916442871094, 48.893...</td>\n",
+ " <td>[0.26346521509705234, -0.09484229177232319, 0....</td>\n",
+ " <td>[69.77995300292969, 56.484107971191406, 67.701...</td>\n",
+ " <td>[0.26346521509705234, -0.09484229177232319, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.22095575502509116, 0.3264835900107608, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37</th>\n",
+ " <td>[71.07194519042969, 37.78615951538086, 70.8722...</td>\n",
+ " <td>[-0.099390634297021, -0.1972715242662317, 0.23...</td>\n",
+ " <td>[56.51100158691406, 52.88019561767578, 68.7481...</td>\n",
+ " <td>[-0.099390634297021, -0.1972715242662317, 0.23...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.3216613023238765, -0.05579784277374154, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38</th>\n",
+ " <td>[91.78521728515625, 92.76158905029297, 91.2677...</td>\n",
+ " <td>[-0.05687701978876784, -0.244417231724589, -0....</td>\n",
+ " <td>[57.97555160522461, 51.01711654663086, 57.3251...</td>\n",
+ " <td>[-0.05687701978876784, -0.244417231724589, -0....</td>\n",
+ " <td>[7.951125621795654, 0.0, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.24295755943595615, 0.3099773600145452, 0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>39</th>\n",
+ " <td>[84.99097442626953, 87.59393310546875, 79.3668...</td>\n",
+ " <td>[-0.028277544252167736, -0.240024114679574, 0....</td>\n",
+ " <td>[58.711490631103516, 50.853302001953125, 67.33...</td>\n",
+ " <td>[-0.028277544252167736, -0.240024114679574, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.4415662288665...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.35026443795160744, 0.05911030048999485, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td>[34.91205978393555, 50.3759880065918, 54.32717...</td>\n",
+ " <td>[-0.45521257995033215, -0.2802300644116437, -0...</td>\n",
+ " <td>[42.84596633911133, 49.3398551940918, 47.74816...</td>\n",
+ " <td>[-0.45521257995033215, -0.2802300644116437, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.07131691493638509, 0.017797547110946624, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41</th>\n",
+ " <td>[30.715330123901367, 55.607913970947266, 46.54...</td>\n",
+ " <td>[-0.07007801749950612, -0.328814752282973, -0....</td>\n",
+ " <td>[57.334964752197266, 47.733497619628906, 42.45...</td>\n",
+ " <td>[-0.07007801749950612, -0.328814752282973, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 11.688969612121582, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.09884544639752846, -0.3788618921501363, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42</th>\n",
+ " <td>[64.60285186767578, 106.04995727539062, 57.325...</td>\n",
+ " <td>[0.15862931905914932, -0.06399512306467645, 0....</td>\n",
+ " <td>[65.74082946777344, 57.47677230834961, 59.9437...</td>\n",
+ " <td>[0.15862931905914932, -0.06399512306467645, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.31382009716959736, 0.047844504856988615, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43</th>\n",
+ " <td>[55.19425582885742, 63.48979187011719, 40.7338...</td>\n",
+ " <td>[-0.05467859837029114, -0.10182105954141278, -...</td>\n",
+ " <td>[57.586795806884766, 55.836185455322266, 57.94...</td>\n",
+ " <td>[-0.05467859837029114, -0.10182105954141278, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.02...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.12571834442119553, -0.11715802496472115, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>[38.30034255981445, 107.37052154541016, 57.480...</td>\n",
+ " <td>[0.3741567391216821, -0.354969383296631, -0.20...</td>\n",
+ " <td>[73.31051635742188, 46.25428009033203, 51.8606...</td>\n",
+ " <td>[0.3741567391216821, -0.354969383296631, -0.20...</td>\n",
+ " <td>[0.0, 6.112779140472412, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.22173434971107372, 0.15077624019543714, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td>[73.36776733398438, 87.6191635131836, 54.31721...</td>\n",
+ " <td>[0.19683796406183698, 0.08509014106620992, 0.0...</td>\n",
+ " <td>[66.78239440917969, 62.640586853027344, 59.699...</td>\n",
+ " <td>[0.19683796406183698, 0.08509014106620992, 0.0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 17.804487228393...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.39907746867152893, -0.34447767683243824, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td>[66.25225067138672, 70.46928405761719, 81.0243...</td>\n",
+ " <td>[-0.2951114471823508, -0.26006837876560024, -0...</td>\n",
+ " <td>[48.51833724975586, 49.81418228149414, 55.4621...</td>\n",
+ " <td>[-0.2951114471823508, -0.26006837876560024, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.2597560882568...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.27907480779008736, 0.03636179081996047, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47</th>\n",
+ " <td>[78.35653686523438, 90.84310913085938, 50.7485...</td>\n",
+ " <td>[0.0027415386495680738, -0.3924749697694411, -...</td>\n",
+ " <td>[59.50611114501953, 44.8410758972168, 51.18581...</td>\n",
+ " <td>[0.0027415386495680738, -0.3924749697694411, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.4198751922383796, -0.29187665550714936, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48</th>\n",
+ " <td>[50.477317810058594, 52.974334716796875, 98.83...</td>\n",
+ " <td>[-0.170515648519368, -0.08204723069435876, -0....</td>\n",
+ " <td>[53.371639251708984, 56.6577033996582, 52.0782...</td>\n",
+ " <td>[-0.170515648519368, -0.08204723069435876, -0....</td>\n",
+ " <td>[0.0, 0.0, 12.839415550231934, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>error</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.04819120535954457, 0.09834841579284731, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49</th>\n",
+ " <td>[78.33626556396484, 63.61309051513672, 45.4645...</td>\n",
+ " <td>[-0.05601202025643073, 0.10586576435554138, -0...</td>\n",
+ " <td>[57.44009780883789, 63.44743347167969, 57.8606...</td>\n",
+ " <td>[-0.05601202025643073, 0.10586576435554138, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 2.0834388732910156, 1.31890356...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.15709045775573335, 0.11013780258789338, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>[77.88352966308594, 79.556640625, 84.604385375...</td>\n",
+ " <td>[-0.3631467110904207, -0.07414773281929238, -0...</td>\n",
+ " <td>[46.2420539855957, 56.96577072143555, 49.34474...</td>\n",
+ " <td>[-0.3631467110904207, -0.07414773281929238, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.3195126924143639, -0.357946587662354, -0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51</th>\n",
+ " <td>[82.61448669433594, 44.03900146484375, 72.4047...</td>\n",
+ " <td>[-0.038260489836340096, -0.047035223226256216,...</td>\n",
+ " <td>[58.122249603271484, 57.79706573486328, 59.684...</td>\n",
+ " <td>[-0.038260489836340096, -0.047035223226256216,...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.12023647923594184, -0.06421172611519112, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52</th>\n",
+ " <td>[67.33567810058594, 48.28118896484375, 71.0715...</td>\n",
+ " <td>[-0.1442309428776943, -0.3727207795621237, -0....</td>\n",
+ " <td>[54.02933883666992, 45.55012130737305, 57.8606...</td>\n",
+ " <td>[-0.1442309428776943, -0.3727207795621237, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.0749953255950385, -0.11083596483827954, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>53</th>\n",
+ " <td>[67.91109466552734, 96.16732025146484, 106.792...</td>\n",
+ " <td>[0.009303552100374344, -0.17025002599493916, -...</td>\n",
+ " <td>[59.71638107299805, 53.05379104614258, 51.1980...</td>\n",
+ " <td>[0.009303552100374344, -0.17025002599493916, -...</td>\n",
+ " <td>[0.0, 2.5876357555389404, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.46087358930812883, -0.20997387152137403, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54</th>\n",
+ " <td>[79.631591796875, 99.23580169677734, 67.710121...</td>\n",
+ " <td>[0.22314286333295716, -0.24837688663163768, -0...</td>\n",
+ " <td>[67.65525817871094, 50.1589241027832, 48.75061...</td>\n",
+ " <td>[0.22314286333295716, -0.24837688663163768, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.2976068960015559, -0.2337016056030365, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55</th>\n",
+ " <td>[62.857688903808594, 69.17253112792969, 98.870...</td>\n",
+ " <td>[-0.03835850195374511, -0.09370537921914046, -...</td>\n",
+ " <td>[58.22004699707031, 56.166259765625, 46.251834...</td>\n",
+ " <td>[-0.03835850195374511, -0.09370537921914046, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.34675582872189553, -0.06147218810392419, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>56</th>\n",
+ " <td>[83.6211929321289, 89.99833679199219, 38.67985...</td>\n",
+ " <td>[0.09419315954663361, 0.08293464780154033, 0.0...</td>\n",
+ " <td>[63.21516036987305, 62.79706573486328, 62.0782...</td>\n",
+ " <td>[0.09419315954663361, 0.08293464780154033, 0.0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 12.035335540771484, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.12088726780346111, -0.032241339133646, -0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>57</th>\n",
+ " <td>[48.80213928222656, 67.05059051513672, 69.4643...</td>\n",
+ " <td>[-0.042127125546975806, 0.1309242041239606, -0...</td>\n",
+ " <td>[58.26161193847656, 64.6845932006836, 52.63814...</td>\n",
+ " <td>[-0.042127125546975806, 0.1309242041239606, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 10.164849281311035, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.02221208985969802, -0.028490028173294924, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>58</th>\n",
+ " <td>[43.20699691772461, 86.48943328857422, 69.5520...</td>\n",
+ " <td>[-0.1248084634622857, -0.26186370182479807, -0...</td>\n",
+ " <td>[55.38630676269531, 50.30073165893555, 55.9853...</td>\n",
+ " <td>[-0.1248084634622857, -0.26186370182479807, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.28427237817009676, 0.07280642829347027, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59</th>\n",
+ " <td>[79.47415161132812, 58.472782135009766, 51.905...</td>\n",
+ " <td>[-0.301195984509098, -0.3407307181051589, -0.1...</td>\n",
+ " <td>[49.151588439941406, 47.687042236328125, 55.01...</td>\n",
+ " <td>[-0.301195984509098, -0.3407307181051589, -0.1...</td>\n",
+ " <td>[0.21878375113010406, 0.0, 0.0, 2.707182645797...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2898785447083861, -0.3538369410897002, -0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>60</th>\n",
+ " <td>[43.493316650390625, 67.37313079833984, 41.901...</td>\n",
+ " <td>[-0.15413821610496273, -0.16581109112988884, -...</td>\n",
+ " <td>[54.811737060546875, 54.37897491455078, 53.264...</td>\n",
+ " <td>[-0.15413821610496273, -0.16581109112988884, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 1.8363639116287231, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.17100977900144365, -0.23644636047852982, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>61</th>\n",
+ " <td>[45.047698974609375, 80.32709503173828, 86.065...</td>\n",
+ " <td>[0.024156074799225682, -0.29131259425713013, 0...</td>\n",
+ " <td>[61.78728485107422, 50.0831298828125, 66.60635...</td>\n",
+ " <td>[0.024156074799225682, -0.29131259425713013, 0...</td>\n",
+ " <td>[0.0, 1.9810150861740112, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.14430546202944736, -0.19186351520268025, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>62</th>\n",
+ " <td>[69.98731994628906, 44.415992736816406, 56.436...</td>\n",
+ " <td>[-0.6752727451579381, -0.20290599875685558, -0...</td>\n",
+ " <td>[35.831295013427734, 53.359413146972656, 52.18...</td>\n",
+ " <td>[-0.6752727451579381, -0.20290599875685558, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.21930540692070996, 0.17883559573003496, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>63</th>\n",
+ " <td>[66.64591217041016, 56.006431579589844, 67.560...</td>\n",
+ " <td>[-0.1968960519967103, -0.28216712184208254, -0...</td>\n",
+ " <td>[53.61124801635742, 50.44498825073242, 40.0366...</td>\n",
+ " <td>[-0.1968960519967103, -0.28216712184208254, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.1722745895385...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.3039832100466536, 0.1915875160209097, -0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64</th>\n",
+ " <td>[100.40974426269531, 118.11618041992188, 144.0...</td>\n",
+ " <td>[-0.030046736575592203, -0.13501141369423728, ...</td>\n",
+ " <td>[59.63080596923828, 55.73594284057617, 55.6063...</td>\n",
+ " <td>[-0.030046736575592203, -0.13501141369423728, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.36986341769212033, 0.2856213246909113, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>65</th>\n",
+ " <td>[35.64735412597656, 43.14385986328125, 41.5602...</td>\n",
+ " <td>[0.2110368033649102, 0.13835801207285486, 0.03...</td>\n",
+ " <td>[68.5843505859375, 65.88752746582031, 62.07334...</td>\n",
+ " <td>[0.2110368033649102, 0.13835801207285486, 0.03...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11.23...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.041238023850725, -0.1455926229558748, -0.11...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66</th>\n",
+ " <td>[71.81153106689453, 57.25579071044922, 79.4886...</td>\n",
+ " <td>[-0.30055408617343826, 0.004062966016386816, -...</td>\n",
+ " <td>[49.53545379638672, 60.83863067626953, 57.5305...</td>\n",
+ " <td>[-0.30055408617343826, 0.004062966016386816, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.20142496914011293, 0.3958612359215496, -0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>67</th>\n",
+ " <td>[24.20662498474121, 103.0528564453125, 102.228...</td>\n",
+ " <td>[-0.03693458966634329, -0.22616292516821382, -...</td>\n",
+ " <td>[59.54278564453125, 52.52322769165039, 46.1564...</td>\n",
+ " <td>[-0.03693458966634329, -0.22616292516821382, -...</td>\n",
+ " <td>[0.0, 6.1036810874938965, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.034808481432044326, -0.3988412414450435, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>68</th>\n",
+ " <td>[128.7064208984375, 149.93251037597656, 135.77...</td>\n",
+ " <td>[-0.17306684044691387, 0.1467819244998106, 0.3...</td>\n",
+ " <td>[54.76039123535156, 66.62836456298828, 72.4547...</td>\n",
+ " <td>[-0.17306684044691387, 0.1467819244998106, 0.3...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 5.692770957946777, 0.0, 0.0, 0...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.3635190905722923, 0.12402280399234121, 0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69</th>\n",
+ " <td>[129.32156372070312, 100.10975646972656, 99.19...</td>\n",
+ " <td>[-0.04618558941454977, -0.07786919399351391, 0...</td>\n",
+ " <td>[59.30073165893555, 58.12469482421875, 73.9951...</td>\n",
+ " <td>[-0.04618558941454977, -0.07786919399351391, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 6.817755222320557, 0.0, 0...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.2871885478249861, -0.06660201335551236, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>70</th>\n",
+ " <td>[62.72924041748047, 52.49836349487305, 96.7172...</td>\n",
+ " <td>[0.030821484816920198, 0.0383107592037115, 0.2...</td>\n",
+ " <td>[62.163814544677734, 62.44009780883789, 69.493...</td>\n",
+ " <td>[0.030821484816920198, 0.0383107592037115, 0.2...</td>\n",
+ " <td>[0.0, 0.0, 3.947056770324707, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.10678601420513019, 0.08652861199023938, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71</th>\n",
+ " <td>[94.40939331054688, 65.8897933959961, 43.83576...</td>\n",
+ " <td>[0.20417718744471106, -0.03033553773674708, -0...</td>\n",
+ " <td>[68.54278564453125, 59.8410758972168, 49.48899...</td>\n",
+ " <td>[0.20417718744471106, -0.03033553773674708, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.3237963303789482, -0.08130754748958187, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>72</th>\n",
+ " <td>[66.21219635009766, 97.37837219238281, 68.4393...</td>\n",
+ " <td>[-0.3209433470485387, -0.1581098757030202, -0....</td>\n",
+ " <td>[49.0, 55.04156494140625, 54.69926834106445, 5...</td>\n",
+ " <td>[-0.3209433470485387, -0.1581098757030202, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 4.024373531341553, 2.6950...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.11188509357738591, -0.01005331679667346, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>73</th>\n",
+ " <td>[100.31412506103516, 48.935543060302734, 40.20...</td>\n",
+ " <td>[-0.06146412558950083, 0.0713148699720483, -0....</td>\n",
+ " <td>[58.61858367919922, 63.545230865478516, 58.892...</td>\n",
+ " <td>[-0.06146412558950083, 0.0713148699720483, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.08120320192335687, 0.09236620888870556, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>74</th>\n",
+ " <td>[98.16809844970703, 83.87969970703125, 59.1194...</td>\n",
+ " <td>[-0.08479882168349195, -0.2685656852417914, -0...</td>\n",
+ " <td>[57.581905364990234, 50.76039123535156, 54.638...</td>\n",
+ " <td>[-0.08479882168349195, -0.2685656852417914, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.23791490254987677, 0.16111473391310682, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75</th>\n",
+ " <td>[39.58367919921875, 70.92807006835938, 102.050...</td>\n",
+ " <td>[-0.43264813603015806, -0.1297134236607614, -0...</td>\n",
+ " <td>[44.44743347167969, 55.687042236328125, 58.254...</td>\n",
+ " <td>[-0.43264813603015806, -0.1297134236607614, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.0740241454336928, -0.2526596912926837, 0.07...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>76</th>\n",
+ " <td>[90.72032928466797, 69.23870086669922, 99.6507...</td>\n",
+ " <td>[-0.17347125141821915, -0.07810114251272157, 0...</td>\n",
+ " <td>[54.04645538330078, 57.5843505859375, 61.12958...</td>\n",
+ " <td>[-0.17347125141821915, -0.07810114251272157, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 19.95299530029297, 0...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.22818338488193032, 0.10502950680758155, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>77</th>\n",
+ " <td>[69.60657501220703, 59.89699172973633, 33.0299...</td>\n",
+ " <td>[-0.3262067717026943, -0.20628291626061518, -0...</td>\n",
+ " <td>[47.980438232421875, 52.42787170410156, 46.547...</td>\n",
+ " <td>[-0.3262067717026943, -0.20628291626061518, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.27503738004609635, -0.38200986066603304, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78</th>\n",
+ " <td>[85.4166259765625, 39.24307632446289, 25.82027...</td>\n",
+ " <td>[-0.09094673203521032, 0.03568569712316148, -0...</td>\n",
+ " <td>[56.45232391357422, 61.15403366088867, 57.9779...</td>\n",
+ " <td>[-0.09094673203521032, 0.03568569712316148, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.219...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.20029733825104348, -0.019619538606299676, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>79</th>\n",
+ " <td>[83.3751449584961, 59.43309020996094, 89.95671...</td>\n",
+ " <td>[-0.13134106005640503, 0.1728664054324369, -0....</td>\n",
+ " <td>[54.86307907104492, 66.1515884399414, 59.00489...</td>\n",
+ " <td>[-0.13134106005640503, 0.1728664054324369, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>correct</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.3120616635082773, -0.09372354484998649, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>80</th>\n",
+ " <td>[42.08283615112305, 48.792240142822266, 53.315...</td>\n",
+ " <td>[-0.16097356093022197, 0.006317768559207317, -...</td>\n",
+ " <td>[53.89731216430664, 60.105133056640625, 44.838...</td>\n",
+ " <td>[-0.16097356093022197, 0.006317768559207317, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.71974372...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.10694041577938958, -0.45814948334946415, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>81</th>\n",
+ " <td>[143.31285095214844, 241.0919952392578, 139.50...</td>\n",
+ " <td>[0.28761989712564257, -0.006891816149983842, -...</td>\n",
+ " <td>[70.90708923339844, 59.977996826171875, 53.369...</td>\n",
+ " <td>[0.28761989712564257, -0.006891816149983842, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.2806819954356836, 0.028434918729493223, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>82</th>\n",
+ " <td>[45.3671760559082, 61.1575813293457, 45.102962...</td>\n",
+ " <td>[-0.3804126544092202, -0.4111308949860192, -0....</td>\n",
+ " <td>[45.985328674316406, 44.84352111816406, 41.322...</td>\n",
+ " <td>[-0.3804126544092202, -0.4111308949860192, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.0922527143204685, -0.13774746044514272, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>83</th>\n",
+ " <td>[76.06987762451172, 96.85617065429688, 73.2967...</td>\n",
+ " <td>[-0.34848006107284624, -0.19417891888034708, -...</td>\n",
+ " <td>[46.84352111816406, 52.56968307495117, 36.9021...</td>\n",
+ " <td>[-0.34848006107284624, -0.19417891888034708, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 1.9594131708145142, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.16074487625299158, 0.2769847034155389, 0.54...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>84</th>\n",
+ " <td>[80.28424072265625, 93.1191177368164, 35.11492...</td>\n",
+ " <td>[-0.12207049662461984, -0.059592445035989976, ...</td>\n",
+ " <td>[55.2493896484375, 57.567237854003906, 54.6161...</td>\n",
+ " <td>[-0.12207049662461984, -0.059592445035989976, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.2571475549627524, 0.17345825854257613, 0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>85</th>\n",
+ " <td>[60.58255386352539, 64.3961410522461, 96.63408...</td>\n",
+ " <td>[-0.25627909135683047, -0.39599508484936463, -...</td>\n",
+ " <td>[50.5745735168457, 45.391197204589844, 43.7334...</td>\n",
+ " <td>[-0.25627909135683047, -0.39599508484936463, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.12107095006439643, 0.1493125472712992, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>86</th>\n",
+ " <td>[55.03602600097656, 67.45455932617188, 55.5102...</td>\n",
+ " <td>[-0.41043758449362056, -0.2803641423462246, 0....</td>\n",
+ " <td>[45.085575103759766, 49.91197967529297, 63.486...</td>\n",
+ " <td>[-0.41043758449362056, -0.2803641423462246, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.37360834950963556, -0.044459555646546516, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>87</th>\n",
+ " <td>[67.09834289550781, 54.83423614501953, 79.9757...</td>\n",
+ " <td>[-0.24679074133471796, -0.2260667379097021, 0....</td>\n",
+ " <td>[51.246944427490234, 52.014671325683594, 61.50...</td>\n",
+ " <td>[-0.24679074133471796, -0.2260667379097021, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 4.056626796722412, 4.9645...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.013602904575762508, -0.015408833029702288,...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>88</th>\n",
+ " <td>[75.91631317138672, 117.85779571533203, 112.56...</td>\n",
+ " <td>[0.6176921569054851, -0.02168531267157498, -0....</td>\n",
+ " <td>[83.33985137939453, 59.61369323730469, 57.0342...</td>\n",
+ " <td>[0.6176921569054851, -0.02168531267157498, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 3.646857500076294, 37.41185379...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.03798425597520885, 0.403944756721094, -0.16...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>89</th>\n",
+ " <td>[100.48384094238281, 58.31154251098633, 34.267...</td>\n",
+ " <td>[-0.2118148036565822, -0.17777621414661174, -0...</td>\n",
+ " <td>[52.82884979248047, 54.09291076660156, 58.7799...</td>\n",
+ " <td>[-0.2118148036565822, -0.17777621414661174, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 2.820892810821533, 5.042201519...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.3160092699178422, -0.2899910985571208, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>90</th>\n",
+ " <td>[74.96868896484375, 63.006832122802734, 73.002...</td>\n",
+ " <td>[-0.26735093752214306, -0.2862214039136709, 0....</td>\n",
+ " <td>[50.98777389526367, 50.288509368896484, 64.132...</td>\n",
+ " <td>[-0.26735093752214306, -0.2862214039136709, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.46502922599479735, -0.16776150114350868, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>91</th>\n",
+ " <td>[91.20542907714844, 83.42327117919922, 84.3542...</td>\n",
+ " <td>[-0.09219241885724334, -0.47233935650707887, -...</td>\n",
+ " <td>[57.628360748291016, 43.52322769165039, 46.449...</td>\n",
+ " <td>[-0.09219241885724334, -0.47233935650707887, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.17241031721870484, -0.03714868882900112, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>92</th>\n",
+ " <td>[53.37754440307617, 62.96772003173828, 67.4969...</td>\n",
+ " <td>[-0.3381470058960022, -0.1400723728270602, -0....</td>\n",
+ " <td>[48.35207748413086, 55.70170974731445, 55.4156...</td>\n",
+ " <td>[-0.3381470058960022, -0.1400723728270602, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.0982104121630473, 0.17067831176903206, 0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93</th>\n",
+ " <td>[81.14598083496094, 52.66810989379883, 53.7429...</td>\n",
+ " <td>[-0.061336973892297586, -0.4387157206402077, -...</td>\n",
+ " <td>[58.92176055908203, 44.919315338134766, 56.347...</td>\n",
+ " <td>[-0.061336973892297586, -0.4387157206402077, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 14.115710258483887, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.07788084503784974, 0.22043836401472844, 0.5...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>94</th>\n",
+ " <td>[57.24399185180664, 40.83292770385742, 74.5320...</td>\n",
+ " <td>[-0.46692145595952694, -0.012430241109140526, ...</td>\n",
+ " <td>[44.07823944091797, 60.943763732910156, 46.579...</td>\n",
+ " <td>[-0.46692145595952694, -0.012430241109140526, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.4004910839027843, -0.0615911497822542, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>95</th>\n",
+ " <td>[59.50749588012695, 50.100955963134766, 71.551...</td>\n",
+ " <td>[0.013299991084257022, -0.31546109771100195, -...</td>\n",
+ " <td>[61.60635757446289, 49.40586853027344, 43.2738...</td>\n",
+ " <td>[0.013299991084257022, -0.31546109771100195, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.20235074425282604, -0.26615717075380924, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>96</th>\n",
+ " <td>[62.35538864135742, 44.597618103027344, 59.132...</td>\n",
+ " <td>[0.05489961083471378, 0.0876649995335069, -0.2...</td>\n",
+ " <td>[62.87530517578125, 64.09046173095703, 50.3667...</td>\n",
+ " <td>[0.05489961083471378, 0.0876649995335069, -0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.06119974519984424, -0.12122381229243022, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>97</th>\n",
+ " <td>[60.198333740234375, 95.67100524902344, 116.28...</td>\n",
+ " <td>[0.09971609072022852, -0.2357405328273232, -0....</td>\n",
+ " <td>[64.03667449951172, 51.58924102783203, 54.6943...</td>\n",
+ " <td>[0.09971609072022852, -0.2357405328273232, -0....</td>\n",
+ " <td>[0.0, 8.342565536499023, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.014309077362570114, -0.23976406030296316, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>98</th>\n",
+ " <td>[78.93936920166016, 50.92294692993164, 74.5402...</td>\n",
+ " <td>[-0.30294716306331876, -0.09579473677464102, -...</td>\n",
+ " <td>[49.127140045166016, 56.811737060546875, 52.44...</td>\n",
+ " <td>[-0.30294716306331876, -0.09579473677464102, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.2452955085616972, 0.22036260935215393, 0.3...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>99</th>\n",
+ " <td>[78.6406021118164, 107.60042572021484, 59.7899...</td>\n",
+ " <td>[-0.2502257842414724, -0.3121836744485205, 0.0...</td>\n",
+ " <td>[50.76772689819336, 48.46454620361328, 63.3520...</td>\n",
+ " <td>[-0.2502257842414724, -0.3121836744485205, 0.0...</td>\n",
+ " <td>[4.285763263702393, 0.0, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.2922565347964962, 0.1391605992799705, -0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>100</th>\n",
+ " <td>[119.61524963378906, 84.64494323730469, 125.15...</td>\n",
+ " <td>[0.023537184968159087, -0.16819879312910715, -...</td>\n",
+ " <td>[60.586795806884766, 53.47188186645508, 55.608...</td>\n",
+ " <td>[0.023537184968159087, -0.16819879312910715, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.3140290217275645, -0.10580112277716092, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>101</th>\n",
+ " <td>[35.014190673828125, 48.636844635009766, 68.94...</td>\n",
+ " <td>[0.0034554883255083655, 0.13592351383099752, -...</td>\n",
+ " <td>[59.63814163208008, 64.55256652832031, 53.6919...</td>\n",
+ " <td>[0.0034554883255083655, 0.13592351383099752, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.624437689781189, 0.0, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.07660858688377657, -0.1471114425429629, 0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>102</th>\n",
+ " <td>[69.81690979003906, 67.22591400146484, 104.189...</td>\n",
+ " <td>[-0.03953536553132661, -0.15291717335810007, -...</td>\n",
+ " <td>[57.80440139770508, 53.59413146972656, 42.5501...</td>\n",
+ " <td>[-0.03953536553132661, -0.15291717335810007, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.42859246262123, -0.35655641351371625, -0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103</th>\n",
+ " <td>[77.95768737792969, 79.76093292236328, 90.0028...</td>\n",
+ " <td>[0.20554872441573402, 0.08575436431803347, 0.0...</td>\n",
+ " <td>[66.73104858398438, 62.28606414794922, 60.8435...</td>\n",
+ " <td>[0.20554872441573402, 0.08575436431803347, 0.0...</td>\n",
+ " <td>[0.0, 0.0, 1.276865005493164, 26.3064327239990...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.2794999993259796, 0.17540419906301868, 0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104</th>\n",
+ " <td>[65.22525787353516, 72.73751068115234, 60.7700...</td>\n",
+ " <td>[-0.22799382214011357, -0.42945423467901594, -...</td>\n",
+ " <td>[50.55990219116211, 43.085575103759766, 46.904...</td>\n",
+ " <td>[-0.22799382214011357, -0.42945423467901594, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.1758377022866041, 0.35720952829369856, -0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105</th>\n",
+ " <td>[62.3677864074707, 77.71094512939453, 71.20282...</td>\n",
+ " <td>[-0.48443602709547084, -0.24628016080916856, 0...</td>\n",
+ " <td>[40.682151794433594, 49.520782470703125, 60.30...</td>\n",
+ " <td>[-0.48443602709547084, -0.24628016080916856, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.79194384...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.2463235497978348, 0.1257521640109947, 0.19...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106</th>\n",
+ " <td>[75.32643127441406, 78.62049865722656, 50.8739...</td>\n",
+ " <td>[-0.09032415395453972, -0.17852165212005172, 0...</td>\n",
+ " <td>[54.88753128051758, 51.61124801635742, 61.2567...</td>\n",
+ " <td>[-0.09032415395453972, -0.17852165212005172, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.051771125230875154, -0.10644662279087692, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107</th>\n",
+ " <td>[48.992218017578125, 60.61746597290039, 48.668...</td>\n",
+ " <td>[-0.11873760946963374, -0.148262305969889, 0.1...</td>\n",
+ " <td>[53.78728485107422, 52.691932678222656, 64.415...</td>\n",
+ " <td>[-0.11873760946963374, -0.148262305969889, 0.1...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>error</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.23296836940969676, -0.1696559648641047, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108</th>\n",
+ " <td>[71.5877685546875, 63.427188873291016, 83.6428...</td>\n",
+ " <td>[-0.08231641622910321, -0.04060588775294994, -...</td>\n",
+ " <td>[55.056236267089844, 56.603912353515625, 44.83...</td>\n",
+ " <td>[-0.08231641622910321, -0.04060588775294994, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 7.658877849578857, 0...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.24978384964776593, 0.1591797183608242, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109</th>\n",
+ " <td>[78.77013397216797, 65.36664581298828, 109.433...</td>\n",
+ " <td>[-0.25505559448628684, -0.36800373418503907, -...</td>\n",
+ " <td>[48.73838806152344, 44.54767608642578, 53.0122...</td>\n",
+ " <td>[-0.25505559448628684, -0.36800373418503907, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.27454116707662285, 0.03674002461317847, 0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>110</th>\n",
+ " <td>[38.5152473449707, 86.25373840332031, 81.69027...</td>\n",
+ " <td>[-0.4528339951832278, -0.6662064551476338, -0....</td>\n",
+ " <td>[41.376529693603516, 33.45721435546875, 37.713...</td>\n",
+ " <td>[-0.4528339951832278, -0.6662064551476338, -0....</td>\n",
+ " <td>[0.0, 7.372641086578369, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.06304974027219329, 0.09722008105097352, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>111</th>\n",
+ " <td>[113.02828979492188, 87.81639862060547, 120.45...</td>\n",
+ " <td>[0.19615098038738354, -0.055762223599005366, -...</td>\n",
+ " <td>[65.47677612304688, 56.127140045166016, 56.325...</td>\n",
+ " <td>[0.19615098038738354, -0.055762223599005366, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.2719967309361023, -0.027659987390843327, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>112</th>\n",
+ " <td>[79.85498809814453, 58.10930633544922, 87.4468...</td>\n",
+ " <td>[-0.16633944404818918, 0.10888349088799196, -0...</td>\n",
+ " <td>[52.002445220947266, 62.21516036987305, 46.889...</td>\n",
+ " <td>[-0.16633944404818918, 0.10888349088799196, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.6220969003674314, 0.15623893908148476, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>113</th>\n",
+ " <td>[45.01163864135742, 83.43706512451172, 81.8867...</td>\n",
+ " <td>[0.013913731854937687, -0.4163141745176622, -0...</td>\n",
+ " <td>[58.603912353515625, 42.63814163208008, 48.843...</td>\n",
+ " <td>[0.013913731854937687, -0.4163141745176622, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.530861083437315, -0.20652647873067792, 0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>114</th>\n",
+ " <td>[61.93214797973633, 53.248687744140625, 43.441...</td>\n",
+ " <td>[0.04493572277938784, -0.6035801534319233, -0....</td>\n",
+ " <td>[59.562347412109375, 35.498779296875, 38.74327...</td>\n",
+ " <td>[0.04493572277938784, -0.6035801534319233, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 1.204552173614502, 0.0, 0.0, 0...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.18096739685133986, -0.1886569438084842, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>115</th>\n",
+ " <td>[89.21119689941406, 59.49201202392578, 98.0487...</td>\n",
+ " <td>[-0.0628079209494439, 0.17664022451254022, -0....</td>\n",
+ " <td>[55.81418228149414, 64.69926452636719, 56.8826...</td>\n",
+ " <td>[-0.0628079209494439, 0.17664022451254022, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.31853245021104715, 0.10504863745001804, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>116</th>\n",
+ " <td>[64.95308685302734, 73.40115356445312, 81.1542...</td>\n",
+ " <td>[0.1502426382514917, -0.16784828398032353, -0....</td>\n",
+ " <td>[63.48655319213867, 51.682151794433594, 51.352...</td>\n",
+ " <td>[0.1502426382514917, -0.16784828398032353, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.30175680225344986, 0.16375730938345276, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>117</th>\n",
+ " <td>[44.58692169189453, 45.08668518066406, 54.8714...</td>\n",
+ " <td>[0.0960102874975894, -0.4004056112616108, -0.2...</td>\n",
+ " <td>[61.56968307495117, 43.14914321899414, 48.8728...</td>\n",
+ " <td>[0.0960102874975894, -0.4004056112616108, -0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 3.5034074783325195, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.2963973444869317, 0.12115764430364243, 0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>118</th>\n",
+ " <td>[53.553985595703125, 50.23899841308594, 77.856...</td>\n",
+ " <td>[-0.11360509205409434, -0.0830051289869477, -0...</td>\n",
+ " <td>[54.04645538330078, 55.18581771850586, 51.4254...</td>\n",
+ " <td>[-0.11360509205409434, -0.0830051289869477, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.97059297...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.0067667134070619715, -0.43233234809760046, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>119</th>\n",
+ " <td>[83.52193450927734, 79.1673355102539, 74.98944...</td>\n",
+ " <td>[0.48339688382461915, 0.36072711238201893, 0.2...</td>\n",
+ " <td>[76.3178482055664, 71.7652816772461, 69.356971...</td>\n",
+ " <td>[0.48339688382461915, 0.36072711238201893, 0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.30551305413246155, 0.0, 0.0,...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.20582980357640282, 0.20434482939978346, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>120</th>\n",
+ " <td>[59.22053527832031, 63.25083923339844, 58.0605...</td>\n",
+ " <td>[-0.35732618412081596, -0.37572977558337395, -...</td>\n",
+ " <td>[45.105133056640625, 44.42298126220703, 49.555...</td>\n",
+ " <td>[-0.35732618412081596, -0.37572977558337395, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.3330145604503587, -0.14132206682762752, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>121</th>\n",
+ " <td>[109.87422943115234, 66.23828887939453, 75.203...</td>\n",
+ " <td>[-0.01965172476274222, -0.08020826439083625, 0...</td>\n",
+ " <td>[57.84352111816406, 55.59657669067383, 61.9486...</td>\n",
+ " <td>[-0.01965172476274222, -0.08020826439083625, 0...</td>\n",
+ " <td>[11.022229194641113, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.3519401221905607, 0.004602235506506332, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>122</th>\n",
+ " <td>[79.05261993408203, 95.03230285644531, 59.7116...</td>\n",
+ " <td>[-0.24078112615830266, 0.08537088858030452, -0...</td>\n",
+ " <td>[49.775062561035156, 61.877750396728516, 47.67...</td>\n",
+ " <td>[-0.24078112615830266, 0.08537088858030452, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 10.619967460632324, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>double_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.10675189996247526, -0.09550797519437028, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>123</th>\n",
+ " <td>[51.110469818115234, 119.89360046386719, 55.19...</td>\n",
+ " <td>[-0.008368330938786342, -0.03842130753140318, ...</td>\n",
+ " <td>[58.65525817871094, 57.540340423583984, 61.882...</td>\n",
+ " <td>[-0.008368330938786342, -0.03842130753140318, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1523734331130...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.30771735952058593, -0.10981481716853166, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>124</th>\n",
+ " <td>[80.1699447631836, 78.87667846679688, 34.03127...</td>\n",
+ " <td>[-0.15543081147981871, -0.2855905214523166, -0...</td>\n",
+ " <td>[53.14425277709961, 48.31051254272461, 48.6112...</td>\n",
+ " <td>[-0.15543081147981871, -0.2855905214523166, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.4342767282191186, -0.1917257202881671, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>125</th>\n",
+ " <td>[90.69183349609375, 83.84123992919922, 94.5277...</td>\n",
+ " <td>[0.05899173422397288, -0.2563027786452426, -0....</td>\n",
+ " <td>[60.76283645629883, 49.063568115234375, 47.207...</td>\n",
+ " <td>[0.05899173422397288, -0.2563027786452426, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.03866226572377788, 0.19141939337343977, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>126</th>\n",
+ " <td>[161.56161499023438, 132.99879455566406, 110.3...</td>\n",
+ " <td>[-0.06751941274281104, 0.5819780988655687, 0.5...</td>\n",
+ " <td>[56.00978088378906, 80.11002349853516, 78.4547...</td>\n",
+ " <td>[-0.06751941274281104, 0.5819780988655687, 0.5...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.83173012...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.005475317961390574, 0.5938895357079891, 0.0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>127</th>\n",
+ " <td>[66.37210083007812, 55.8890380859375, 58.49185...</td>\n",
+ " <td>[-0.4275613453466342, -0.0795234142405778, 0.2...</td>\n",
+ " <td>[42.5085563659668, 55.42298126220703, 68.47677...</td>\n",
+ " <td>[-0.4275613453466342, -0.0795234142405778, 0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.08982622249594478, -0.2626424843578636, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>128</th>\n",
+ " <td>[59.71945571899414, 80.3114242553711, 83.22418...</td>\n",
+ " <td>[0.051009465783387435, -0.35975419880037657, -...</td>\n",
+ " <td>[60.13692092895508, 44.88753128051758, 56.4498...</td>\n",
+ " <td>[0.051009465783387435, -0.35975419880037657, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.17666705668069357, 0.14407729304104866, 0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>129</th>\n",
+ " <td>[88.6202392578125, 114.49066925048828, 51.5933...</td>\n",
+ " <td>[0.40536618024817944, -0.04413966444660514, 0....</td>\n",
+ " <td>[73.11002349853516, 56.42787170410156, 58.0880...</td>\n",
+ " <td>[0.40536618024817944, -0.04413966444660514, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.13272339577414766, -0.3490732114687793, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>130</th>\n",
+ " <td>[49.336551666259766, 63.65651321411133, 56.951...</td>\n",
+ " <td>[-0.17284281990251255, 0.043873549250541946, 0...</td>\n",
+ " <td>[51.831295013427734, 59.872859954833984, 63.84...</td>\n",
+ " <td>[-0.17284281990251255, 0.043873549250541946, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.15696900254311882, -0.16146151075283194, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>131</th>\n",
+ " <td>[81.70460510253906, 76.52861785888672, 139.540...</td>\n",
+ " <td>[-0.2051156853627862, 0.35946644899691343, -0....</td>\n",
+ " <td>[50.515892028808594, 71.46454620361328, 57.970...</td>\n",
+ " <td>[-0.2051156853627862, 0.35946644899691343, -0....</td>\n",
+ " <td>[0.026706727221608162, 0.0, 9.38946533203125, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.028250690057140954, -0.16798914665657105, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>132</th>\n",
+ " <td>[52.215328216552734, 57.2702522277832, 65.0739...</td>\n",
+ " <td>[-0.3461578572783509, -0.12174162024116153, -0...</td>\n",
+ " <td>[45.35696792602539, 53.68459701538086, 48.2713...</td>\n",
+ " <td>[-0.3461578572783509, -0.12174162024116153, -0...</td>\n",
+ " <td>[0.0, 0.0, 4.848875045776367, 0.51783293485641...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.05016901868174861, 0.015429768781672378, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>133</th>\n",
+ " <td>[58.30579376220703, 47.300148010253906, 78.277...</td>\n",
+ " <td>[-0.1682515176082425, -0.15456395556673033, -0...</td>\n",
+ " <td>[51.733497619628906, 52.2420539855957, 49.4303...</td>\n",
+ " <td>[-0.1682515176082425, -0.15456395556673033, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.672...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.18034109172975457, -0.009614545972992442, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>134</th>\n",
+ " <td>[63.71532440185547, 81.97270965576172, 73.7440...</td>\n",
+ " <td>[0.08786407787388432, -0.17868437417727567, 0....</td>\n",
+ " <td>[61.210269927978516, 51.317848205566406, 64.30...</td>\n",
+ " <td>[0.08786407787388432, -0.17868437417727567, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_90&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.4088370983251731, -0.30791523522131586, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>135</th>\n",
+ " <td>[75.25640869140625, 55.06842803955078, 54.0487...</td>\n",
+ " <td>[-0.2021967376662533, -0.06743202423725765, 0....</td>\n",
+ " <td>[50.41075897216797, 55.413204193115234, 64.892...</td>\n",
+ " <td>[-0.2021967376662533, -0.06743202423725765, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.44731166093013214, 0.44970531888534754, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>136</th>\n",
+ " <td>[55.81910705566406, 69.95643615722656, 88.6798...</td>\n",
+ " <td>[-0.5668249380748516, -0.31185606120353443, -0...</td>\n",
+ " <td>[36.80929183959961, 46.26894760131836, 32.6601...</td>\n",
+ " <td>[-0.5668249380748516, -0.31185606120353443, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.0043887192405414124, 0.0971496979710404, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>137</th>\n",
+ " <td>[77.53472137451172, 68.08631896972656, 102.196...</td>\n",
+ " <td>[-0.2304795054900956, 0.06385358870692692, -0....</td>\n",
+ " <td>[49.146697998046875, 60.068458557128906, 55.46...</td>\n",
+ " <td>[-0.2304795054900956, 0.06385358870692692, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>error</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.4581553007353071, -0.5077794535687009, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>138</th>\n",
+ " <td>[88.98057556152344, 112.77156066894531, 79.976...</td>\n",
+ " <td>[-0.03202117467913898, 0.01881579185469454, -0...</td>\n",
+ " <td>[56.330074310302734, 58.21516036987305, 56.002...</td>\n",
+ " <td>[-0.03202117467913898, 0.01881579185469454, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.15122542123654034, -0.21181115986734386, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>139</th>\n",
+ " <td>[54.24409103393555, 41.079917907714844, 52.406...</td>\n",
+ " <td>[-0.30684129625165235, -0.26058806400882634, -...</td>\n",
+ " <td>[46.12469482421875, 47.8410758972168, 51.26650...</td>\n",
+ " <td>[-0.30684129625165235, -0.26058806400882634, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.46105540974307896, -0.30058383635312785, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>140</th>\n",
+ " <td>[88.5430908203125, 60.645084381103516, 50.4617...</td>\n",
+ " <td>[0.3098404670758121, -0.25769622047774604, -0....</td>\n",
+ " <td>[69.07579803466797, 48.014671325683594, 44.946...</td>\n",
+ " <td>[0.3098404670758121, -0.25769622047774604, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.13552526822406252, -0.4820798800603281, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>141</th>\n",
+ " <td>[79.18215942382812, 91.62763214111328, 55.3324...</td>\n",
+ " <td>[-0.3108102758240774, -0.20854692456878854, -0...</td>\n",
+ " <td>[45.80929183959961, 49.603912353515625, 51.220...</td>\n",
+ " <td>[-0.3108102758240774, -0.20854692456878854, -0...</td>\n",
+ " <td>[0.23522087931632996, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_90&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.06851920450846066, 0.319030133589, 0.04637...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>142</th>\n",
+ " <td>[52.050575256347656, 66.22966766357422, 54.569...</td>\n",
+ " <td>[0.15620801022640662, 0.403870506623861, 0.057...</td>\n",
+ " <td>[63.085575103759766, 72.27384185791016, 59.427...</td>\n",
+ " <td>[0.15620801022640662, 0.403870506623861, 0.057...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.94694519...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.30365942138206375, -0.07011455357493906, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>143</th>\n",
+ " <td>[79.55794525146484, 65.02729797363281, 44.7006...</td>\n",
+ " <td>[-0.07974417410614486, 0.14592770554657294, 0....</td>\n",
+ " <td>[54.32029342651367, 62.69437789916992, 65.2689...</td>\n",
+ " <td>[-0.07974417410614486, 0.14592770554657294, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>error</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.11898013577767773, 0.09343762460179239, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>144</th>\n",
+ " <td>[51.3822135925293, 58.575077056884766, 62.3793...</td>\n",
+ " <td>[-0.15740391725071443, -0.27147593571642575, -...</td>\n",
+ " <td>[51.79951095581055, 47.5745735168457, 53.64547...</td>\n",
+ " <td>[-0.15740391725071443, -0.27147593571642575, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.06717073560275591, -0.06953894780202573, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>145</th>\n",
+ " <td>[74.84498596191406, 51.94773864746094, 107.228...</td>\n",
+ " <td>[-0.01820918607240805, -0.015286885737646978, ...</td>\n",
+ " <td>[57.141807556152344, 57.246944427490234, 57.49...</td>\n",
+ " <td>[-0.01820918607240805, -0.015286885737646978, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_90&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2059055774089245, 0.20968368039039287, -0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>[98.53752136230469, 59.3669319152832, 81.53830...</td>\n",
+ " <td>[-0.07075263048698627, -0.14321302983827025, -...</td>\n",
+ " <td>[55.0904655456543, 52.400978088378906, 51.8728...</td>\n",
+ " <td>[-0.07075263048698627, -0.14321302983827025, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.8082059570449399, 0.7297618176851153, 0.238...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>[113.19007110595703, 66.31562805175781, 59.043...</td>\n",
+ " <td>[-0.27854251373066036, 0.2195061242315473, 0.2...</td>\n",
+ " <td>[47.371639251708984, 65.85330200195312, 67.334...</td>\n",
+ " <td>[-0.27854251373066036, 0.2195061242315473, 0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.2590662407604328, 0.06000996370129285, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>[70.78560638427734, 42.672210693359375, 73.129...</td>\n",
+ " <td>[-0.1387883226963254, -0.6539329094909159, -0....</td>\n",
+ " <td>[52.67970657348633, 33.567237854003906, 52.039...</td>\n",
+ " <td>[-0.1387883226963254, -0.6539329094909159, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>D1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&0</td>\n",
+ " <td>[]</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.07282859954108618, 0.01083607695839526, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>[84.0759048461914, 123.96839904785156, 116.421...</td>\n",
+ " <td>[-0.25454560318475217, 0.3316156094474721, -0....</td>\n",
+ " <td>[48.581905364990234, 70.33007049560547, 40.303...</td>\n",
+ " <td>[-0.25454560318475217, 0.3316156094474721, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>10.0</td>\n",
+ " <td>C1</td>\n",
+ " <td>no_answer</td>\n",
+ " <td>10_20&10</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.28085327280336, 0.6028212258930189, 0.09830...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "0 0 [110.48663330078125, 131.6634063720703, 86.054... \\\n",
+ " 1 [67.9014892578125, 84.18050384521484, 79.32645... \n",
+ " 2 [68.5937728881836, 60.977806091308594, 68.2375... \n",
+ " 3 [88.68118286132812, 103.54595947265625, 58.293... \n",
+ " 4 [67.2108154296875, 93.54744720458984, 47.41284... \n",
+ " 5 [61.2387809753418, 62.04039001464844, 93.07856... \n",
+ " 6 [100.87199401855469, 87.00389862060547, 98.347... \n",
+ " 7 [104.04031372070312, 89.15300750732422, 81.111... \n",
+ " 8 [81.16952514648438, 96.71385192871094, 72.9607... \n",
+ " 9 [101.66789245605469, 145.193115234375, 88.2653... \n",
+ " 10 [80.41024017333984, 99.21864318847656, 74.6269... \n",
+ " 11 [94.21552276611328, 86.79850769042969, 85.0309... \n",
+ " 12 [68.03812408447266, 89.03655242919922, 104.884... \n",
+ " 13 [54.27216720581055, 64.89025115966797, 89.1744... \n",
+ " 14 [73.89498901367188, 88.20506286621094, 75.3498... \n",
+ " 15 [49.37717819213867, 40.45200729370117, 64.5281... \n",
+ " 16 [60.29892349243164, 85.16168212890625, 72.3468... \n",
+ " 17 [62.95381546020508, 57.82320022583008, 65.7714... \n",
+ " 18 [57.56557083129883, 51.068687438964844, 58.784... \n",
+ " 19 [42.880428314208984, 65.3575439453125, 82.6388... \n",
+ " 20 [70.03380584716797, 63.43825912475586, 73.4520... \n",
+ " 21 [78.99820709228516, 115.13985443115234, 55.532... \n",
+ " 22 [94.26325225830078, 184.75173950195312, 104.01... \n",
+ " 23 [72.58927154541016, 96.52001190185547, 56.0740... \n",
+ " 24 [130.3365478515625, 119.0087890625, 77.0390396... \n",
+ " 25 [89.026611328125, 87.17903900146484, 115.55131... \n",
+ " 26 [76.88912200927734, 96.81219482421875, 52.2906... \n",
+ " 27 [84.06704711914062, 64.12518310546875, 82.7639... \n",
+ " 28 [70.43943786621094, 67.65316009521484, 91.5920... \n",
+ " 29 [52.862815856933594, 81.0198974609375, 58.4936... \n",
+ " 30 [109.7398910522461, 77.05498504638672, 79.3592... \n",
+ " 31 [61.807518005371094, 56.86727523803711, 54.347... \n",
+ " 32 [88.6243896484375, 66.70386505126953, 73.59136... \n",
+ " 33 [30.058061599731445, 72.42082977294922, 99.798... \n",
+ " 34 [82.01639556884766, 56.73377227783203, 84.5247... \n",
+ " 35 [79.57770538330078, 64.9237060546875, 85.03238... \n",
+ " 36 [58.71508026123047, 43.665916442871094, 48.893... \n",
+ " 37 [71.07194519042969, 37.78615951538086, 70.8722... \n",
+ " 38 [91.78521728515625, 92.76158905029297, 91.2677... \n",
+ " 39 [84.99097442626953, 87.59393310546875, 79.3668... \n",
+ " 40 [34.91205978393555, 50.3759880065918, 54.32717... \n",
+ " 41 [30.715330123901367, 55.607913970947266, 46.54... \n",
+ " 42 [64.60285186767578, 106.04995727539062, 57.325... \n",
+ " 43 [55.19425582885742, 63.48979187011719, 40.7338... \n",
+ " 44 [38.30034255981445, 107.37052154541016, 57.480... \n",
+ " 45 [73.36776733398438, 87.6191635131836, 54.31721... \n",
+ " 46 [66.25225067138672, 70.46928405761719, 81.0243... \n",
+ " 47 [78.35653686523438, 90.84310913085938, 50.7485... \n",
+ " 48 [50.477317810058594, 52.974334716796875, 98.83... \n",
+ " 49 [78.33626556396484, 63.61309051513672, 45.4645... \n",
+ " 50 [77.88352966308594, 79.556640625, 84.604385375... \n",
+ " 51 [82.61448669433594, 44.03900146484375, 72.4047... \n",
+ " 52 [67.33567810058594, 48.28118896484375, 71.0715... \n",
+ " 53 [67.91109466552734, 96.16732025146484, 106.792... \n",
+ " 54 [79.631591796875, 99.23580169677734, 67.710121... \n",
+ " 55 [62.857688903808594, 69.17253112792969, 98.870... \n",
+ " 56 [83.6211929321289, 89.99833679199219, 38.67985... \n",
+ " 57 [48.80213928222656, 67.05059051513672, 69.4643... \n",
+ " 58 [43.20699691772461, 86.48943328857422, 69.5520... \n",
+ " 59 [79.47415161132812, 58.472782135009766, 51.905... \n",
+ " 60 [43.493316650390625, 67.37313079833984, 41.901... \n",
+ " 61 [45.047698974609375, 80.32709503173828, 86.065... \n",
+ " 62 [69.98731994628906, 44.415992736816406, 56.436... \n",
+ " 63 [66.64591217041016, 56.006431579589844, 67.560... \n",
+ " 64 [100.40974426269531, 118.11618041992188, 144.0... \n",
+ " 65 [35.64735412597656, 43.14385986328125, 41.5602... \n",
+ " 66 [71.81153106689453, 57.25579071044922, 79.4886... \n",
+ " 67 [24.20662498474121, 103.0528564453125, 102.228... \n",
+ " 68 [128.7064208984375, 149.93251037597656, 135.77... \n",
+ " 69 [129.32156372070312, 100.10975646972656, 99.19... \n",
+ " 70 [62.72924041748047, 52.49836349487305, 96.7172... \n",
+ " 71 [94.40939331054688, 65.8897933959961, 43.83576... \n",
+ " 72 [66.21219635009766, 97.37837219238281, 68.4393... \n",
+ " 73 [100.31412506103516, 48.935543060302734, 40.20... \n",
+ " 74 [98.16809844970703, 83.87969970703125, 59.1194... \n",
+ " 75 [39.58367919921875, 70.92807006835938, 102.050... \n",
+ " 76 [90.72032928466797, 69.23870086669922, 99.6507... \n",
+ " 77 [69.60657501220703, 59.89699172973633, 33.0299... \n",
+ " 78 [85.4166259765625, 39.24307632446289, 25.82027... \n",
+ " 79 [83.3751449584961, 59.43309020996094, 89.95671... \n",
+ " 80 [42.08283615112305, 48.792240142822266, 53.315... \n",
+ " 81 [143.31285095214844, 241.0919952392578, 139.50... \n",
+ " 82 [45.3671760559082, 61.1575813293457, 45.102962... \n",
+ " 83 [76.06987762451172, 96.85617065429688, 73.2967... \n",
+ " 84 [80.28424072265625, 93.1191177368164, 35.11492... \n",
+ " 85 [60.58255386352539, 64.3961410522461, 96.63408... \n",
+ " 86 [55.03602600097656, 67.45455932617188, 55.5102... \n",
+ " 87 [67.09834289550781, 54.83423614501953, 79.9757... \n",
+ " 88 [75.91631317138672, 117.85779571533203, 112.56... \n",
+ " 89 [100.48384094238281, 58.31154251098633, 34.267... \n",
+ " 90 [74.96868896484375, 63.006832122802734, 73.002... \n",
+ " 91 [91.20542907714844, 83.42327117919922, 84.3542... \n",
+ " 92 [53.37754440307617, 62.96772003173828, 67.4969... \n",
+ " 93 [81.14598083496094, 52.66810989379883, 53.7429... \n",
+ " 94 [57.24399185180664, 40.83292770385742, 74.5320... \n",
+ " 95 [59.50749588012695, 50.100955963134766, 71.551... \n",
+ " 96 [62.35538864135742, 44.597618103027344, 59.132... \n",
+ " 97 [60.198333740234375, 95.67100524902344, 116.28... \n",
+ " 98 [78.93936920166016, 50.92294692993164, 74.5402... \n",
+ " 99 [78.6406021118164, 107.60042572021484, 59.7899... \n",
+ " 100 [119.61524963378906, 84.64494323730469, 125.15... \n",
+ " 101 [35.014190673828125, 48.636844635009766, 68.94... \n",
+ " 102 [69.81690979003906, 67.22591400146484, 104.189... \n",
+ " 103 [77.95768737792969, 79.76093292236328, 90.0028... \n",
+ " 104 [65.22525787353516, 72.73751068115234, 60.7700... \n",
+ " 105 [62.3677864074707, 77.71094512939453, 71.20282... \n",
+ " 106 [75.32643127441406, 78.62049865722656, 50.8739... \n",
+ " 107 [48.992218017578125, 60.61746597290039, 48.668... \n",
+ " 108 [71.5877685546875, 63.427188873291016, 83.6428... \n",
+ " 109 [78.77013397216797, 65.36664581298828, 109.433... \n",
+ " 110 [38.5152473449707, 86.25373840332031, 81.69027... \n",
+ " 111 [113.02828979492188, 87.81639862060547, 120.45... \n",
+ " 112 [79.85498809814453, 58.10930633544922, 87.4468... \n",
+ " 113 [45.01163864135742, 83.43706512451172, 81.8867... \n",
+ " 114 [61.93214797973633, 53.248687744140625, 43.441... \n",
+ " 115 [89.21119689941406, 59.49201202392578, 98.0487... \n",
+ " 116 [64.95308685302734, 73.40115356445312, 81.1542... \n",
+ " 117 [44.58692169189453, 45.08668518066406, 54.8714... \n",
+ " 118 [53.553985595703125, 50.23899841308594, 77.856... \n",
+ " 119 [83.52193450927734, 79.1673355102539, 74.98944... \n",
+ " 120 [59.22053527832031, 63.25083923339844, 58.0605... \n",
+ " 121 [109.87422943115234, 66.23828887939453, 75.203... \n",
+ " 122 [79.05261993408203, 95.03230285644531, 59.7116... \n",
+ " 123 [51.110469818115234, 119.89360046386719, 55.19... \n",
+ " 124 [80.1699447631836, 78.87667846679688, 34.03127... \n",
+ " 125 [90.69183349609375, 83.84123992919922, 94.5277... \n",
+ " 126 [161.56161499023438, 132.99879455566406, 110.3... \n",
+ " 127 [66.37210083007812, 55.8890380859375, 58.49185... \n",
+ " 128 [59.71945571899414, 80.3114242553711, 83.22418... \n",
+ " 129 [88.6202392578125, 114.49066925048828, 51.5933... \n",
+ " 130 [49.336551666259766, 63.65651321411133, 56.951... \n",
+ " 131 [81.70460510253906, 76.52861785888672, 139.540... \n",
+ " 132 [52.215328216552734, 57.2702522277832, 65.0739... \n",
+ " 133 [58.30579376220703, 47.300148010253906, 78.277... \n",
+ " 134 [63.71532440185547, 81.97270965576172, 73.7440... \n",
+ " 135 [75.25640869140625, 55.06842803955078, 54.0487... \n",
+ " 136 [55.81910705566406, 69.95643615722656, 88.6798... \n",
+ " 137 [77.53472137451172, 68.08631896972656, 102.196... \n",
+ " 138 [88.98057556152344, 112.77156066894531, 79.976... \n",
+ " 139 [54.24409103393555, 41.079917907714844, 52.406... \n",
+ " 140 [88.5430908203125, 60.645084381103516, 50.4617... \n",
+ " 141 [79.18215942382812, 91.62763214111328, 55.3324... \n",
+ " 142 [52.050575256347656, 66.22966766357422, 54.569... \n",
+ " 143 [79.55794525146484, 65.02729797363281, 44.7006... \n",
+ " 144 [51.3822135925293, 58.575077056884766, 62.3793... \n",
+ " 145 [74.84498596191406, 51.94773864746094, 107.228... \n",
+ " 146 [98.53752136230469, 59.3669319152832, 81.53830... \n",
+ " 147 [113.19007110595703, 66.31562805175781, 59.043... \n",
+ " 148 [70.78560638427734, 42.672210693359375, 73.129... \n",
+ " 149 [84.0759048461914, 123.96839904785156, 116.421... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ " 5 [0.023354701103315244, -0.0602884150450634, -0... \n",
+ " 6 [-0.019380325737913043, 0.18458343755734835, 0... \n",
+ " 7 [-0.024577687714442492, -0.13861195509594057, ... \n",
+ " 8 [-0.05375759988888351, -0.04555461901997415, -... \n",
+ " 9 [0.365474007550674, 0.032538860639681935, 0.51... \n",
+ " 10 [-0.3700912806005721, -0.18048151937104914, -0... \n",
+ " 11 [0.10451741410126203, -0.12455170707257092, 0.... \n",
+ " 12 [-0.14030594184020395, 0.11681621501625446, 0.... \n",
+ " 13 [-0.04471774756904711, -0.032109036195919816, ... \n",
+ " 14 [-0.10454668169070291, -0.5576836941783401, -0... \n",
+ " 15 [-0.6322334626683969, -0.027267495315954463, -... \n",
+ " 16 [-0.39550408606288084, -0.363552456070871, -0.... \n",
+ " 17 [0.19976095470042038, -0.05398622620535903, 0.... \n",
+ " 18 [-0.005997255257728517, -0.2022945641621877, 0... \n",
+ " 19 [-0.08486048023513294, -0.18244962021015382, -... \n",
+ " 20 [-0.3403347810837183, 0.0880496300975233, -0.2... \n",
+ " 21 [0.1840468480473831, -0.022674418129115716, 0.... \n",
+ " 22 [-0.22139016448293267, 0.14230573772644806, 0.... \n",
+ " 23 [-0.2976526146619502, -0.2058549229338863, -0.... \n",
+ " 24 [-0.12726088658820112, 0.07642245356457941, 0.... \n",
+ " 25 [-0.303403285236063, -0.40443718230751385, -0.... \n",
+ " 26 [-0.12027609235266702, -0.13963080926573726, -... \n",
+ " 27 [0.13275637081627137, -0.5002529915514548, -0.... \n",
+ " 28 [-0.12080809091117684, 0.16048087477853296, 0.... \n",
+ " 29 [-0.08420647129191677, -0.05084344653211932, -... \n",
+ " 30 [0.1845884188792514, -0.02157296613558047, 0.0... \n",
+ " 31 [0.06604966925592491, -0.09479743060955638, -0... \n",
+ " 32 [-0.3233407345214396, -0.12016641033468067, -0... \n",
+ " 33 [0.015579863124009314, 0.0971588280260063, 0.0... \n",
+ " 34 [-0.09417749524349582, 0.27506349218015863, -0... \n",
+ " 35 [-0.18878235652392203, -0.39369606966162674, -... \n",
+ " 36 [0.26346521509705234, -0.09484229177232319, 0.... \n",
+ " 37 [-0.099390634297021, -0.1972715242662317, 0.23... \n",
+ " 38 [-0.05687701978876784, -0.244417231724589, -0.... \n",
+ " 39 [-0.028277544252167736, -0.240024114679574, 0.... \n",
+ " 40 [-0.45521257995033215, -0.2802300644116437, -0... \n",
+ " 41 [-0.07007801749950612, -0.328814752282973, -0.... \n",
+ " 42 [0.15862931905914932, -0.06399512306467645, 0.... \n",
+ " 43 [-0.05467859837029114, -0.10182105954141278, -... \n",
+ " 44 [0.3741567391216821, -0.354969383296631, -0.20... \n",
+ " 45 [0.19683796406183698, 0.08509014106620992, 0.0... \n",
+ " 46 [-0.2951114471823508, -0.26006837876560024, -0... \n",
+ " 47 [0.0027415386495680738, -0.3924749697694411, -... \n",
+ " 48 [-0.170515648519368, -0.08204723069435876, -0.... \n",
+ " 49 [-0.05601202025643073, 0.10586576435554138, -0... \n",
+ " 50 [-0.3631467110904207, -0.07414773281929238, -0... \n",
+ " 51 [-0.038260489836340096, -0.047035223226256216,... \n",
+ " 52 [-0.1442309428776943, -0.3727207795621237, -0.... \n",
+ " 53 [0.009303552100374344, -0.17025002599493916, -... \n",
+ " 54 [0.22314286333295716, -0.24837688663163768, -0... \n",
+ " 55 [-0.03835850195374511, -0.09370537921914046, -... \n",
+ " 56 [0.09419315954663361, 0.08293464780154033, 0.0... \n",
+ " 57 [-0.042127125546975806, 0.1309242041239606, -0... \n",
+ " 58 [-0.1248084634622857, -0.26186370182479807, -0... \n",
+ " 59 [-0.301195984509098, -0.3407307181051589, -0.1... \n",
+ " 60 [-0.15413821610496273, -0.16581109112988884, -... \n",
+ " 61 [0.024156074799225682, -0.29131259425713013, 0... \n",
+ " 62 [-0.6752727451579381, -0.20290599875685558, -0... \n",
+ " 63 [-0.1968960519967103, -0.28216712184208254, -0... \n",
+ " 64 [-0.030046736575592203, -0.13501141369423728, ... \n",
+ " 65 [0.2110368033649102, 0.13835801207285486, 0.03... \n",
+ " 66 [-0.30055408617343826, 0.004062966016386816, -... \n",
+ " 67 [-0.03693458966634329, -0.22616292516821382, -... \n",
+ " 68 [-0.17306684044691387, 0.1467819244998106, 0.3... \n",
+ " 69 [-0.04618558941454977, -0.07786919399351391, 0... \n",
+ " 70 [0.030821484816920198, 0.0383107592037115, 0.2... \n",
+ " 71 [0.20417718744471106, -0.03033553773674708, -0... \n",
+ " 72 [-0.3209433470485387, -0.1581098757030202, -0.... \n",
+ " 73 [-0.06146412558950083, 0.0713148699720483, -0.... \n",
+ " 74 [-0.08479882168349195, -0.2685656852417914, -0... \n",
+ " 75 [-0.43264813603015806, -0.1297134236607614, -0... \n",
+ " 76 [-0.17347125141821915, -0.07810114251272157, 0... \n",
+ " 77 [-0.3262067717026943, -0.20628291626061518, -0... \n",
+ " 78 [-0.09094673203521032, 0.03568569712316148, -0... \n",
+ " 79 [-0.13134106005640503, 0.1728664054324369, -0.... \n",
+ " 80 [-0.16097356093022197, 0.006317768559207317, -... \n",
+ " 81 [0.28761989712564257, -0.006891816149983842, -... \n",
+ " 82 [-0.3804126544092202, -0.4111308949860192, -0.... \n",
+ " 83 [-0.34848006107284624, -0.19417891888034708, -... \n",
+ " 84 [-0.12207049662461984, -0.059592445035989976, ... \n",
+ " 85 [-0.25627909135683047, -0.39599508484936463, -... \n",
+ " 86 [-0.41043758449362056, -0.2803641423462246, 0.... \n",
+ " 87 [-0.24679074133471796, -0.2260667379097021, 0.... \n",
+ " 88 [0.6176921569054851, -0.02168531267157498, -0.... \n",
+ " 89 [-0.2118148036565822, -0.17777621414661174, -0... \n",
+ " 90 [-0.26735093752214306, -0.2862214039136709, 0.... \n",
+ " 91 [-0.09219241885724334, -0.47233935650707887, -... \n",
+ " 92 [-0.3381470058960022, -0.1400723728270602, -0.... \n",
+ " 93 [-0.061336973892297586, -0.4387157206402077, -... \n",
+ " 94 [-0.46692145595952694, -0.012430241109140526, ... \n",
+ " 95 [0.013299991084257022, -0.31546109771100195, -... \n",
+ " 96 [0.05489961083471378, 0.0876649995335069, -0.2... \n",
+ " 97 [0.09971609072022852, -0.2357405328273232, -0.... \n",
+ " 98 [-0.30294716306331876, -0.09579473677464102, -... \n",
+ " 99 [-0.2502257842414724, -0.3121836744485205, 0.0... \n",
+ " 100 [0.023537184968159087, -0.16819879312910715, -... \n",
+ " 101 [0.0034554883255083655, 0.13592351383099752, -... \n",
+ " 102 [-0.03953536553132661, -0.15291717335810007, -... \n",
+ " 103 [0.20554872441573402, 0.08575436431803347, 0.0... \n",
+ " 104 [-0.22799382214011357, -0.42945423467901594, -... \n",
+ " 105 [-0.48443602709547084, -0.24628016080916856, 0... \n",
+ " 106 [-0.09032415395453972, -0.17852165212005172, 0... \n",
+ " 107 [-0.11873760946963374, -0.148262305969889, 0.1... \n",
+ " 108 [-0.08231641622910321, -0.04060588775294994, -... \n",
+ " 109 [-0.25505559448628684, -0.36800373418503907, -... \n",
+ " 110 [-0.4528339951832278, -0.6662064551476338, -0.... \n",
+ " 111 [0.19615098038738354, -0.055762223599005366, -... \n",
+ " 112 [-0.16633944404818918, 0.10888349088799196, -0... \n",
+ " 113 [0.013913731854937687, -0.4163141745176622, -0... \n",
+ " 114 [0.04493572277938784, -0.6035801534319233, -0.... \n",
+ " 115 [-0.0628079209494439, 0.17664022451254022, -0.... \n",
+ " 116 [0.1502426382514917, -0.16784828398032353, -0.... \n",
+ " 117 [0.0960102874975894, -0.4004056112616108, -0.2... \n",
+ " 118 [-0.11360509205409434, -0.0830051289869477, -0... \n",
+ " 119 [0.48339688382461915, 0.36072711238201893, 0.2... \n",
+ " 120 [-0.35732618412081596, -0.37572977558337395, -... \n",
+ " 121 [-0.01965172476274222, -0.08020826439083625, 0... \n",
+ " 122 [-0.24078112615830266, 0.08537088858030452, -0... \n",
+ " 123 [-0.008368330938786342, -0.03842130753140318, ... \n",
+ " 124 [-0.15543081147981871, -0.2855905214523166, -0... \n",
+ " 125 [0.05899173422397288, -0.2563027786452426, -0.... \n",
+ " 126 [-0.06751941274281104, 0.5819780988655687, 0.5... \n",
+ " 127 [-0.4275613453466342, -0.0795234142405778, 0.2... \n",
+ " 128 [0.051009465783387435, -0.35975419880037657, -... \n",
+ " 129 [0.40536618024817944, -0.04413966444660514, 0.... \n",
+ " 130 [-0.17284281990251255, 0.043873549250541946, 0... \n",
+ " 131 [-0.2051156853627862, 0.35946644899691343, -0.... \n",
+ " 132 [-0.3461578572783509, -0.12174162024116153, -0... \n",
+ " 133 [-0.1682515176082425, -0.15456395556673033, -0... \n",
+ " 134 [0.08786407787388432, -0.17868437417727567, 0.... \n",
+ " 135 [-0.2021967376662533, -0.06743202423725765, 0.... \n",
+ " 136 [-0.5668249380748516, -0.31185606120353443, -0... \n",
+ " 137 [-0.2304795054900956, 0.06385358870692692, -0.... \n",
+ " 138 [-0.03202117467913898, 0.01881579185469454, -0... \n",
+ " 139 [-0.30684129625165235, -0.26058806400882634, -... \n",
+ " 140 [0.3098404670758121, -0.25769622047774604, -0.... \n",
+ " 141 [-0.3108102758240774, -0.20854692456878854, -0... \n",
+ " 142 [0.15620801022640662, 0.403870506623861, 0.057... \n",
+ " 143 [-0.07974417410614486, 0.14592770554657294, 0.... \n",
+ " 144 [-0.15740391725071443, -0.27147593571642575, -... \n",
+ " 145 [-0.01820918607240805, -0.015286885737646978, ... \n",
+ " 146 [-0.07075263048698627, -0.14321302983827025, -... \n",
+ " 147 [-0.27854251373066036, 0.2195061242315473, 0.2... \n",
+ " 148 [-0.1387883226963254, -0.6539329094909159, -0.... \n",
+ " 149 [-0.25454560318475217, 0.3316156094474721, -0.... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "0 0 [81.11736297607422, 93.11980438232422, 73.6161... \\\n",
+ " 1 [52.151588439941406, 65.11491394042969, 51.899... \n",
+ " 2 [57.168704986572266, 70.49388885498047, 47.332... \n",
+ " 3 [57.833740234375, 44.317848205566406, 55.43520... \n",
+ " 4 [55.2567253112793, 59.00489044189453, 56.47432... \n",
+ " 5 [63.858192443847656, 60.75550079345703, 62.709... \n",
+ " 6 [62.105133056640625, 69.67237091064453, 70.848... \n",
+ " 7 [61.811737060546875, 57.57212829589844, 53.838... \n",
+ " 8 [60.085575103759766, 60.38875198364258, 61.652... \n",
+ " 9 [75.31051635742188, 62.955989837646484, 80.853... \n",
+ " 10 [47.6577033996582, 54.691932678222656, 43.5452... \n",
+ " 11 [64.93887329101562, 56.437652587890625, 71.378... \n",
+ " 12 [55.66748046875, 65.20782470703125, 68.4523239... \n",
+ " 13 [58.980438232421875, 59.44743347167969, 45.748... \n",
+ " 14 [56.48655319213867, 39.67237091064453, 37.4767... \n",
+ " 15 [36.83863067626953, 59.28606414794922, 44.2493... \n",
+ " 16 [45.48655319213867, 46.67237091064453, 50.3520... \n",
+ " 17 [67.40586853027344, 57.99021911621094, 62.3960... \n",
+ " 18 [59.70415496826172, 52.420536041259766, 59.970... \n",
+ " 19 [56.623470306396484, 53.002445220947266, 42.65... \n",
+ " 20 [47.38875198364258, 63.28361892700195, 50.0978... \n",
+ " 21 [66.75794982910156, 59.08802032470703, 66.2004... \n",
+ " 22 [51.78483963012695, 65.28117370605469, 72.7334... \n",
+ " 23 [48.95354461669922, 52.36185836791992, 49.6308... \n",
+ " 24 [55.51100158691406, 63.068458557128906, 68.633... \n",
+ " 25 [48.73105239868164, 44.98288345336914, 49.3325... \n",
+ " 26 [55.45232391357422, 54.73838806152344, 46.0782... \n",
+ " 27 [65.09046173095703, 41.603912353515625, 56.581... \n",
+ " 28 [55.70170974731445, 66.13936614990234, 63.3838... \n",
+ " 29 [57.039119720458984, 58.27383804321289, 45.249... \n",
+ " 30 [66.94621276855469, 59.29829025268555, 63.6894... \n",
+ " 31 [62.55256652832031, 56.579463958740234, 41.591... \n",
+ " 32 [47.98288345336914, 55.52322769165039, 52.1246... \n",
+ " 33 [60.65525817871094, 63.68459701538086, 63.1026... \n",
+ " 34 [56.51344680786133, 70.21515655517578, 55.4987... \n",
+ " 35 [53.05868148803711, 45.454769134521484, 49.953... \n",
+ " 36 [69.77995300292969, 56.484107971191406, 67.701... \n",
+ " 37 [56.51100158691406, 52.88019561767578, 68.7481... \n",
+ " 38 [57.97555160522461, 51.01711654663086, 57.3251... \n",
+ " 39 [58.711490631103516, 50.853302001953125, 67.33... \n",
+ " 40 [42.84596633911133, 49.3398551940918, 47.74816... \n",
+ " 41 [57.334964752197266, 47.733497619628906, 42.45... \n",
+ " 42 [65.74082946777344, 57.47677230834961, 59.9437... \n",
+ " 43 [57.586795806884766, 55.836185455322266, 57.94... \n",
+ " 44 [73.31051635742188, 46.25428009033203, 51.8606... \n",
+ " 45 [66.78239440917969, 62.640586853027344, 59.699... \n",
+ " 46 [48.51833724975586, 49.81418228149414, 55.4621... \n",
+ " 47 [59.50611114501953, 44.8410758972168, 51.18581... \n",
+ " 48 [53.371639251708984, 56.6577033996582, 52.0782... \n",
+ " 49 [57.44009780883789, 63.44743347167969, 57.8606... \n",
+ " 50 [46.2420539855957, 56.96577072143555, 49.34474... \n",
+ " 51 [58.122249603271484, 57.79706573486328, 59.684... \n",
+ " 52 [54.02933883666992, 45.55012130737305, 57.8606... \n",
+ " 53 [59.71638107299805, 53.05379104614258, 51.1980... \n",
+ " 54 [67.65525817871094, 50.1589241027832, 48.75061... \n",
+ " 55 [58.22004699707031, 56.166259765625, 46.251834... \n",
+ " 56 [63.21516036987305, 62.79706573486328, 62.0782... \n",
+ " 57 [58.26161193847656, 64.6845932006836, 52.63814... \n",
+ " 58 [55.38630676269531, 50.30073165893555, 55.9853... \n",
+ " 59 [49.151588439941406, 47.687042236328125, 55.01... \n",
+ " 60 [54.811737060546875, 54.37897491455078, 53.264... \n",
+ " 61 [61.78728485107422, 50.0831298828125, 66.60635... \n",
+ " 62 [35.831295013427734, 53.359413146972656, 52.18... \n",
+ " 63 [53.61124801635742, 50.44498825073242, 40.0366... \n",
+ " 64 [59.63080596923828, 55.73594284057617, 55.6063... \n",
+ " 65 [68.5843505859375, 65.88752746582031, 62.07334... \n",
+ " 66 [49.53545379638672, 60.83863067626953, 57.5305... \n",
+ " 67 [59.54278564453125, 52.52322769165039, 46.1564... \n",
+ " 68 [54.76039123535156, 66.62836456298828, 72.4547... \n",
+ " 69 [59.30073165893555, 58.12469482421875, 73.9951... \n",
+ " 70 [62.163814544677734, 62.44009780883789, 69.493... \n",
+ " 71 [68.54278564453125, 59.8410758972168, 49.48899... \n",
+ " 72 [49.0, 55.04156494140625, 54.69926834106445, 5... \n",
+ " 73 [58.61858367919922, 63.545230865478516, 58.892... \n",
+ " 74 [57.581905364990234, 50.76039123535156, 54.638... \n",
+ " 75 [44.44743347167969, 55.687042236328125, 58.254... \n",
+ " 76 [54.04645538330078, 57.5843505859375, 61.12958... \n",
+ " 77 [47.980438232421875, 52.42787170410156, 46.547... \n",
+ " 78 [56.45232391357422, 61.15403366088867, 57.9779... \n",
+ " 79 [54.86307907104492, 66.1515884399414, 59.00489... \n",
+ " 80 [53.89731216430664, 60.105133056640625, 44.838... \n",
+ " 81 [70.90708923339844, 59.977996826171875, 53.369... \n",
+ " 82 [45.985328674316406, 44.84352111816406, 41.322... \n",
+ " 83 [46.84352111816406, 52.56968307495117, 36.9021... \n",
+ " 84 [55.2493896484375, 57.567237854003906, 54.6161... \n",
+ " 85 [50.5745735168457, 45.391197204589844, 43.7334... \n",
+ " 86 [45.085575103759766, 49.91197967529297, 63.486... \n",
+ " 87 [51.246944427490234, 52.014671325683594, 61.50... \n",
+ " 88 [83.33985137939453, 59.61369323730469, 57.0342... \n",
+ " 89 [52.82884979248047, 54.09291076660156, 58.7799... \n",
+ " 90 [50.98777389526367, 50.288509368896484, 64.132... \n",
+ " 91 [57.628360748291016, 43.52322769165039, 46.449... \n",
+ " 92 [48.35207748413086, 55.70170974731445, 55.4156... \n",
+ " 93 [58.92176055908203, 44.919315338134766, 56.347... \n",
+ " 94 [44.07823944091797, 60.943763732910156, 46.579... \n",
+ " 95 [61.60635757446289, 49.40586853027344, 43.2738... \n",
+ " 96 [62.87530517578125, 64.09046173095703, 50.3667... \n",
+ " 97 [64.03667449951172, 51.58924102783203, 54.6943... \n",
+ " 98 [49.127140045166016, 56.811737060546875, 52.44... \n",
+ " 99 [50.76772689819336, 48.46454620361328, 63.3520... \n",
+ " 100 [60.586795806884766, 53.47188186645508, 55.608... \n",
+ " 101 [59.63814163208008, 64.55256652832031, 53.6919... \n",
+ " 102 [57.80440139770508, 53.59413146972656, 42.5501... \n",
+ " 103 [66.73104858398438, 62.28606414794922, 60.8435... \n",
+ " 104 [50.55990219116211, 43.085575103759766, 46.904... \n",
+ " 105 [40.682151794433594, 49.520782470703125, 60.30... \n",
+ " 106 [54.88753128051758, 51.61124801635742, 61.2567... \n",
+ " 107 [53.78728485107422, 52.691932678222656, 64.415... \n",
+ " 108 [55.056236267089844, 56.603912353515625, 44.83... \n",
+ " 109 [48.73838806152344, 44.54767608642578, 53.0122... \n",
+ " 110 [41.376529693603516, 33.45721435546875, 37.713... \n",
+ " 111 [65.47677612304688, 56.127140045166016, 56.325... \n",
+ " 112 [52.002445220947266, 62.21516036987305, 46.889... \n",
+ " 113 [58.603912353515625, 42.63814163208008, 48.843... \n",
+ " 114 [59.562347412109375, 35.498779296875, 38.74327... \n",
+ " 115 [55.81418228149414, 64.69926452636719, 56.8826... \n",
+ " 116 [63.48655319213867, 51.682151794433594, 51.352... \n",
+ " 117 [61.56968307495117, 43.14914321899414, 48.8728... \n",
+ " 118 [54.04645538330078, 55.18581771850586, 51.4254... \n",
+ " 119 [76.3178482055664, 71.7652816772461, 69.356971... \n",
+ " 120 [45.105133056640625, 44.42298126220703, 49.555... \n",
+ " 121 [57.84352111816406, 55.59657669067383, 61.9486... \n",
+ " 122 [49.775062561035156, 61.877750396728516, 47.67... \n",
+ " 123 [58.65525817871094, 57.540340423583984, 61.882... \n",
+ " 124 [53.14425277709961, 48.31051254272461, 48.6112... \n",
+ " 125 [60.76283645629883, 49.063568115234375, 47.207... \n",
+ " 126 [56.00978088378906, 80.11002349853516, 78.4547... \n",
+ " 127 [42.5085563659668, 55.42298126220703, 68.47677... \n",
+ " 128 [60.13692092895508, 44.88753128051758, 56.4498... \n",
+ " 129 [73.11002349853516, 56.42787170410156, 58.0880... \n",
+ " 130 [51.831295013427734, 59.872859954833984, 63.84... \n",
+ " 131 [50.515892028808594, 71.46454620361328, 57.970... \n",
+ " 132 [45.35696792602539, 53.68459701538086, 48.2713... \n",
+ " 133 [51.733497619628906, 52.2420539855957, 49.4303... \n",
+ " 134 [61.210269927978516, 51.317848205566406, 64.30... \n",
+ " 135 [50.41075897216797, 55.413204193115234, 64.892... \n",
+ " 136 [36.80929183959961, 46.26894760131836, 32.6601... \n",
+ " 137 [49.146697998046875, 60.068458557128906, 55.46... \n",
+ " 138 [56.330074310302734, 58.21516036987305, 56.002... \n",
+ " 139 [46.12469482421875, 47.8410758972168, 51.26650... \n",
+ " 140 [69.07579803466797, 48.014671325683594, 44.946... \n",
+ " 141 [45.80929183959961, 49.603912353515625, 51.220... \n",
+ " 142 [63.085575103759766, 72.27384185791016, 59.427... \n",
+ " 143 [54.32029342651367, 62.69437789916992, 65.2689... \n",
+ " 144 [51.79951095581055, 47.5745735168457, 53.64547... \n",
+ " 145 [57.141807556152344, 57.246944427490234, 57.49... \n",
+ " 146 [55.0904655456543, 52.400978088378906, 51.8728... \n",
+ " 147 [47.371639251708984, 65.85330200195312, 67.334... \n",
+ " 148 [52.67970657348633, 33.567237854003906, 52.039... \n",
+ " 149 [48.581905364990234, 70.33007049560547, 40.303... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ " 5 [0.023354701103315244, -0.0602884150450634, -0... \n",
+ " 6 [-0.019380325737913043, 0.18458343755734835, 0... \n",
+ " 7 [-0.024577687714442492, -0.13861195509594057, ... \n",
+ " 8 [-0.05375759988888351, -0.04555461901997415, -... \n",
+ " 9 [0.365474007550674, 0.032538860639681935, 0.51... \n",
+ " 10 [-0.3700912806005721, -0.18048151937104914, -0... \n",
+ " 11 [0.10451741410126203, -0.12455170707257092, 0.... \n",
+ " 12 [-0.14030594184020395, 0.11681621501625446, 0.... \n",
+ " 13 [-0.04471774756904711, -0.032109036195919816, ... \n",
+ " 14 [-0.10454668169070291, -0.5576836941783401, -0... \n",
+ " 15 [-0.6322334626683969, -0.027267495315954463, -... \n",
+ " 16 [-0.39550408606288084, -0.363552456070871, -0.... \n",
+ " 17 [0.19976095470042038, -0.05398622620535903, 0.... \n",
+ " 18 [-0.005997255257728517, -0.2022945641621877, 0... \n",
+ " 19 [-0.08486048023513294, -0.18244962021015382, -... \n",
+ " 20 [-0.3403347810837183, 0.0880496300975233, -0.2... \n",
+ " 21 [0.1840468480473831, -0.022674418129115716, 0.... \n",
+ " 22 [-0.22139016448293267, 0.14230573772644806, 0.... \n",
+ " 23 [-0.2976526146619502, -0.2058549229338863, -0.... \n",
+ " 24 [-0.12726088658820112, 0.07642245356457941, 0.... \n",
+ " 25 [-0.303403285236063, -0.40443718230751385, -0.... \n",
+ " 26 [-0.12027609235266702, -0.13963080926573726, -... \n",
+ " 27 [0.13275637081627137, -0.5002529915514548, -0.... \n",
+ " 28 [-0.12080809091117684, 0.16048087477853296, 0.... \n",
+ " 29 [-0.08420647129191677, -0.05084344653211932, -... \n",
+ " 30 [0.1845884188792514, -0.02157296613558047, 0.0... \n",
+ " 31 [0.06604966925592491, -0.09479743060955638, -0... \n",
+ " 32 [-0.3233407345214396, -0.12016641033468067, -0... \n",
+ " 33 [0.015579863124009314, 0.0971588280260063, 0.0... \n",
+ " 34 [-0.09417749524349582, 0.27506349218015863, -0... \n",
+ " 35 [-0.18878235652392203, -0.39369606966162674, -... \n",
+ " 36 [0.26346521509705234, -0.09484229177232319, 0.... \n",
+ " 37 [-0.099390634297021, -0.1972715242662317, 0.23... \n",
+ " 38 [-0.05687701978876784, -0.244417231724589, -0.... \n",
+ " 39 [-0.028277544252167736, -0.240024114679574, 0.... \n",
+ " 40 [-0.45521257995033215, -0.2802300644116437, -0... \n",
+ " 41 [-0.07007801749950612, -0.328814752282973, -0.... \n",
+ " 42 [0.15862931905914932, -0.06399512306467645, 0.... \n",
+ " 43 [-0.05467859837029114, -0.10182105954141278, -... \n",
+ " 44 [0.3741567391216821, -0.354969383296631, -0.20... \n",
+ " 45 [0.19683796406183698, 0.08509014106620992, 0.0... \n",
+ " 46 [-0.2951114471823508, -0.26006837876560024, -0... \n",
+ " 47 [0.0027415386495680738, -0.3924749697694411, -... \n",
+ " 48 [-0.170515648519368, -0.08204723069435876, -0.... \n",
+ " 49 [-0.05601202025643073, 0.10586576435554138, -0... \n",
+ " 50 [-0.3631467110904207, -0.07414773281929238, -0... \n",
+ " 51 [-0.038260489836340096, -0.047035223226256216,... \n",
+ " 52 [-0.1442309428776943, -0.3727207795621237, -0.... \n",
+ " 53 [0.009303552100374344, -0.17025002599493916, -... \n",
+ " 54 [0.22314286333295716, -0.24837688663163768, -0... \n",
+ " 55 [-0.03835850195374511, -0.09370537921914046, -... \n",
+ " 56 [0.09419315954663361, 0.08293464780154033, 0.0... \n",
+ " 57 [-0.042127125546975806, 0.1309242041239606, -0... \n",
+ " 58 [-0.1248084634622857, -0.26186370182479807, -0... \n",
+ " 59 [-0.301195984509098, -0.3407307181051589, -0.1... \n",
+ " 60 [-0.15413821610496273, -0.16581109112988884, -... \n",
+ " 61 [0.024156074799225682, -0.29131259425713013, 0... \n",
+ " 62 [-0.6752727451579381, -0.20290599875685558, -0... \n",
+ " 63 [-0.1968960519967103, -0.28216712184208254, -0... \n",
+ " 64 [-0.030046736575592203, -0.13501141369423728, ... \n",
+ " 65 [0.2110368033649102, 0.13835801207285486, 0.03... \n",
+ " 66 [-0.30055408617343826, 0.004062966016386816, -... \n",
+ " 67 [-0.03693458966634329, -0.22616292516821382, -... \n",
+ " 68 [-0.17306684044691387, 0.1467819244998106, 0.3... \n",
+ " 69 [-0.04618558941454977, -0.07786919399351391, 0... \n",
+ " 70 [0.030821484816920198, 0.0383107592037115, 0.2... \n",
+ " 71 [0.20417718744471106, -0.03033553773674708, -0... \n",
+ " 72 [-0.3209433470485387, -0.1581098757030202, -0.... \n",
+ " 73 [-0.06146412558950083, 0.0713148699720483, -0.... \n",
+ " 74 [-0.08479882168349195, -0.2685656852417914, -0... \n",
+ " 75 [-0.43264813603015806, -0.1297134236607614, -0... \n",
+ " 76 [-0.17347125141821915, -0.07810114251272157, 0... \n",
+ " 77 [-0.3262067717026943, -0.20628291626061518, -0... \n",
+ " 78 [-0.09094673203521032, 0.03568569712316148, -0... \n",
+ " 79 [-0.13134106005640503, 0.1728664054324369, -0.... \n",
+ " 80 [-0.16097356093022197, 0.006317768559207317, -... \n",
+ " 81 [0.28761989712564257, -0.006891816149983842, -... \n",
+ " 82 [-0.3804126544092202, -0.4111308949860192, -0.... \n",
+ " 83 [-0.34848006107284624, -0.19417891888034708, -... \n",
+ " 84 [-0.12207049662461984, -0.059592445035989976, ... \n",
+ " 85 [-0.25627909135683047, -0.39599508484936463, -... \n",
+ " 86 [-0.41043758449362056, -0.2803641423462246, 0.... \n",
+ " 87 [-0.24679074133471796, -0.2260667379097021, 0.... \n",
+ " 88 [0.6176921569054851, -0.02168531267157498, -0.... \n",
+ " 89 [-0.2118148036565822, -0.17777621414661174, -0... \n",
+ " 90 [-0.26735093752214306, -0.2862214039136709, 0.... \n",
+ " 91 [-0.09219241885724334, -0.47233935650707887, -... \n",
+ " 92 [-0.3381470058960022, -0.1400723728270602, -0.... \n",
+ " 93 [-0.061336973892297586, -0.4387157206402077, -... \n",
+ " 94 [-0.46692145595952694, -0.012430241109140526, ... \n",
+ " 95 [0.013299991084257022, -0.31546109771100195, -... \n",
+ " 96 [0.05489961083471378, 0.0876649995335069, -0.2... \n",
+ " 97 [0.09971609072022852, -0.2357405328273232, -0.... \n",
+ " 98 [-0.30294716306331876, -0.09579473677464102, -... \n",
+ " 99 [-0.2502257842414724, -0.3121836744485205, 0.0... \n",
+ " 100 [0.023537184968159087, -0.16819879312910715, -... \n",
+ " 101 [0.0034554883255083655, 0.13592351383099752, -... \n",
+ " 102 [-0.03953536553132661, -0.15291717335810007, -... \n",
+ " 103 [0.20554872441573402, 0.08575436431803347, 0.0... \n",
+ " 104 [-0.22799382214011357, -0.42945423467901594, -... \n",
+ " 105 [-0.48443602709547084, -0.24628016080916856, 0... \n",
+ " 106 [-0.09032415395453972, -0.17852165212005172, 0... \n",
+ " 107 [-0.11873760946963374, -0.148262305969889, 0.1... \n",
+ " 108 [-0.08231641622910321, -0.04060588775294994, -... \n",
+ " 109 [-0.25505559448628684, -0.36800373418503907, -... \n",
+ " 110 [-0.4528339951832278, -0.6662064551476338, -0.... \n",
+ " 111 [0.19615098038738354, -0.055762223599005366, -... \n",
+ " 112 [-0.16633944404818918, 0.10888349088799196, -0... \n",
+ " 113 [0.013913731854937687, -0.4163141745176622, -0... \n",
+ " 114 [0.04493572277938784, -0.6035801534319233, -0.... \n",
+ " 115 [-0.0628079209494439, 0.17664022451254022, -0.... \n",
+ " 116 [0.1502426382514917, -0.16784828398032353, -0.... \n",
+ " 117 [0.0960102874975894, -0.4004056112616108, -0.2... \n",
+ " 118 [-0.11360509205409434, -0.0830051289869477, -0... \n",
+ " 119 [0.48339688382461915, 0.36072711238201893, 0.2... \n",
+ " 120 [-0.35732618412081596, -0.37572977558337395, -... \n",
+ " 121 [-0.01965172476274222, -0.08020826439083625, 0... \n",
+ " 122 [-0.24078112615830266, 0.08537088858030452, -0... \n",
+ " 123 [-0.008368330938786342, -0.03842130753140318, ... \n",
+ " 124 [-0.15543081147981871, -0.2855905214523166, -0... \n",
+ " 125 [0.05899173422397288, -0.2563027786452426, -0.... \n",
+ " 126 [-0.06751941274281104, 0.5819780988655687, 0.5... \n",
+ " 127 [-0.4275613453466342, -0.0795234142405778, 0.2... \n",
+ " 128 [0.051009465783387435, -0.35975419880037657, -... \n",
+ " 129 [0.40536618024817944, -0.04413966444660514, 0.... \n",
+ " 130 [-0.17284281990251255, 0.043873549250541946, 0... \n",
+ " 131 [-0.2051156853627862, 0.35946644899691343, -0.... \n",
+ " 132 [-0.3461578572783509, -0.12174162024116153, -0... \n",
+ " 133 [-0.1682515176082425, -0.15456395556673033, -0... \n",
+ " 134 [0.08786407787388432, -0.17868437417727567, 0.... \n",
+ " 135 [-0.2021967376662533, -0.06743202423725765, 0.... \n",
+ " 136 [-0.5668249380748516, -0.31185606120353443, -0... \n",
+ " 137 [-0.2304795054900956, 0.06385358870692692, -0.... \n",
+ " 138 [-0.03202117467913898, 0.01881579185469454, -0... \n",
+ " 139 [-0.30684129625165235, -0.26058806400882634, -... \n",
+ " 140 [0.3098404670758121, -0.25769622047774604, -0.... \n",
+ " 141 [-0.3108102758240774, -0.20854692456878854, -0... \n",
+ " 142 [0.15620801022640662, 0.403870506623861, 0.057... \n",
+ " 143 [-0.07974417410614486, 0.14592770554657294, 0.... \n",
+ " 144 [-0.15740391725071443, -0.27147593571642575, -... \n",
+ " 145 [-0.01820918607240805, -0.015286885737646978, ... \n",
+ " 146 [-0.07075263048698627, -0.14321302983827025, -... \n",
+ " 147 [-0.27854251373066036, 0.2195061242315473, 0.2... \n",
+ " 148 [-0.1387883226963254, -0.6539329094909159, -0.... \n",
+ " 149 [-0.25454560318475217, 0.3316156094474721, -0.... \n",
+ "\n",
+ " spks target_stim \n",
+ "roi# trial# \n",
+ "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... C1_10_90 \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... C1_10_20 \n",
+ " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... D1_10_20 \n",
+ " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... D1_10_90 \n",
+ " 5 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 6 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 7 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 8 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 16.464019775390... C1_10_20 \n",
+ " 9 [22.30367088317871, 19.749446868896484, 0.0, 1... D1_10_90 \n",
+ " 10 [0.0, 0.0, 0.0, 0.0, 0.0, 1.8619872331619263, ... D1_10_90 \n",
+ " 11 [0.0, 0.0, 0.0, 0.0, 0.0, 5.640011787414551, 0... D1_10_20 \n",
+ " 12 [0.0, 7.206940174102783, 0.0, 0.0, 0.0, 0.0, 0... D1_10_90 \n",
+ " 13 [0.0, 0.0, 31.858652114868164, 14.622788429260... D1_10_90 \n",
+ " 14 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 15 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 16 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 17 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 18 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0834541320800... D1_10_20 \n",
+ " 19 [0.0, 0.7954126000404358, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 20 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 21 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 22 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 23 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 24 [9.151693344116211, 0.0, 0.0, 0.0, 0.0, 5.0895... C1_10_90 \n",
+ " 25 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 26 [4.5889716148376465, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 27 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 28 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 29 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 30 [2.113368511199951, 0.0, 0.0, 0.0, 0.0, 0.0, 0... C1_10_90 \n",
+ " 31 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 32 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 33 [0.0, 0.0, 20.99272346496582, 0.0, 0.0, 0.0, 0... D1_10_20 \n",
+ " 34 [0.0, 0.0, 0.0, 0.0, 23.926652908325195, 0.0, ... C1_10_90 \n",
+ " 35 [5.771840572357178, 0.0, 5.634783744812012, 0.... C1_10_20 \n",
+ " 36 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 37 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 38 [7.951125621795654, 0.0, 0.0, 0.0, 0.0, 0.0, 0... C1_10_90 \n",
+ " 39 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.4415662288665... C1_10_20 \n",
+ " 40 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 41 [0.0, 0.0, 0.0, 0.0, 0.0, 11.688969612121582, ... D1_10_20 \n",
+ " 42 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 43 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.02... D1_10_20 \n",
+ " 44 [0.0, 6.112779140472412, 0.0, 0.0, 0.0, 0.0, 0... C1_10_20 \n",
+ " 45 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 17.804487228393... C1_10_90 \n",
+ " 46 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.2597560882568... D1_10_90 \n",
+ " 47 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 48 [0.0, 0.0, 12.839415550231934, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 49 [0.0, 0.0, 0.0, 2.0834388732910156, 1.31890356... C1_10_90 \n",
+ " 50 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 51 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 52 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 53 [0.0, 2.5876357555389404, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 54 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 55 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 56 [0.0, 0.0, 0.0, 0.0, 12.035335540771484, 0.0, ... C1_10_90 \n",
+ " 57 [0.0, 0.0, 0.0, 0.0, 10.164849281311035, 0.0, ... D1_10_20 \n",
+ " 58 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 59 [0.21878375113010406, 0.0, 0.0, 2.707182645797... C1_10_90 \n",
+ " 60 [0.0, 0.0, 0.0, 0.0, 1.8363639116287231, 0.0, ... C1_10_20 \n",
+ " 61 [0.0, 1.9810150861740112, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 62 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 63 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.1722745895385... C1_10_90 \n",
+ " 64 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 65 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11.23... D1_10_20 \n",
+ " 66 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 67 [0.0, 6.1036810874938965, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 68 [0.0, 0.0, 0.0, 5.692770957946777, 0.0, 0.0, 0... D1_10_20 \n",
+ " 69 [0.0, 0.0, 0.0, 0.0, 6.817755222320557, 0.0, 0... D1_10_90 \n",
+ " 70 [0.0, 0.0, 3.947056770324707, 0.0, 0.0, 0.0, 0... D1_10_20 \n",
+ " 71 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 72 [0.0, 0.0, 0.0, 0.0, 4.024373531341553, 2.6950... D1_10_20 \n",
+ " 73 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 74 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 75 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 76 [0.0, 0.0, 0.0, 0.0, 0.0, 19.95299530029297, 0... C1_10_20 \n",
+ " 77 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 78 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.219... D1_10_90 \n",
+ " 79 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 80 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.71974372... C1_10_90 \n",
+ " 81 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 82 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 83 [0.0, 0.0, 0.0, 1.9594131708145142, 0.0, 0.0, ... C1_10_20 \n",
+ " 84 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 85 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 86 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 87 [0.0, 0.0, 0.0, 0.0, 4.056626796722412, 4.9645... C1_10_90 \n",
+ " 88 [0.0, 0.0, 0.0, 3.646857500076294, 37.41185379... D1_10_90 \n",
+ " 89 [0.0, 0.0, 0.0, 2.820892810821533, 5.042201519... D1_10_20 \n",
+ " 90 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004... C1_10_20 \n",
+ " 91 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 92 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 93 [0.0, 0.0, 0.0, 0.0, 0.0, 14.115710258483887, ... C1_10_20 \n",
+ " 94 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 95 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 96 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 97 [0.0, 8.342565536499023, 0.0, 0.0, 0.0, 0.0, 0... D1_10_20 \n",
+ " 98 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 99 [4.285763263702393, 0.0, 0.0, 0.0, 0.0, 0.0, 0... D1_10_90 \n",
+ " 100 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 101 [0.0, 0.0, 0.0, 0.624437689781189, 0.0, 0.0, 0... C1_10_90 \n",
+ " 102 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 103 [0.0, 0.0, 1.276865005493164, 26.3064327239990... D1_10_20 \n",
+ " 104 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 105 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.79194384... C1_10_90 \n",
+ " 106 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 107 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 108 [0.0, 0.0, 0.0, 0.0, 0.0, 7.658877849578857, 0... C1_10_20 \n",
+ " 109 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 110 [0.0, 7.372641086578369, 0.0, 0.0, 0.0, 0.0, 0... C1_10_90 \n",
+ " 111 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 112 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 113 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 114 [0.0, 0.0, 0.0, 1.204552173614502, 0.0, 0.0, 0... D1_10_20 \n",
+ " 115 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 116 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 117 [0.0, 0.0, 0.0, 3.5034074783325195, 0.0, 0.0, ... D1_10_20 \n",
+ " 118 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.97059297... D1_10_90 \n",
+ " 119 [0.0, 0.0, 0.0, 0.30551305413246155, 0.0, 0.0,... D1_10_90 \n",
+ " 120 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 121 [11.022229194641113, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 122 [0.0, 0.0, 0.0, 10.619967460632324, 0.0, 0.0, ... D1_10_90 \n",
+ " 123 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1523734331130... C1_10_90 \n",
+ " 124 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 125 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 126 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.83173012... D1_10_90 \n",
+ " 127 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 128 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 129 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 130 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 131 [0.026706727221608162, 0.0, 9.38946533203125, ... C1_10_20 \n",
+ " 132 [0.0, 0.0, 4.848875045776367, 0.51783293485641... C1_10_90 \n",
+ " 133 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.672... C1_10_20 \n",
+ " 134 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 135 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 136 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 137 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 138 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 139 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ " 140 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 141 [0.23522087931632996, 0.0, 0.0, 0.0, 0.0, 0.0,... D1_10_90 \n",
+ " 142 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.94694519... D1_10_20 \n",
+ " 143 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 144 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 145 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 148 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 149 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ "\n",
+ " target_amplitude frequency_change \n",
+ "roi# trial# \n",
+ "0 0 10_90 80.0 \\\n",
+ " 1 10_90 80.0 \n",
+ " 2 10_20 10.0 \n",
+ " 3 10_20 10.0 \n",
+ " 4 10_90 80.0 \n",
+ " 5 10_20 10.0 \n",
+ " 6 10_20 10.0 \n",
+ " 7 10_90 80.0 \n",
+ " 8 10_20 10.0 \n",
+ " 9 10_90 80.0 \n",
+ " 10 10_90 80.0 \n",
+ " 11 10_20 10.0 \n",
+ " 12 10_90 80.0 \n",
+ " 13 10_90 80.0 \n",
+ " 14 10_20 10.0 \n",
+ " 15 10_20 10.0 \n",
+ " 16 10_90 80.0 \n",
+ " 17 10_90 80.0 \n",
+ " 18 10_20 10.0 \n",
+ " 19 10_20 10.0 \n",
+ " 20 10_90 80.0 \n",
+ " 21 10_90 80.0 \n",
+ " 22 10_20 10.0 \n",
+ " 23 10_20 10.0 \n",
+ " 24 10_90 80.0 \n",
+ " 25 10_20 10.0 \n",
+ " 26 10_90 80.0 \n",
+ " 27 10_20 10.0 \n",
+ " 28 10_20 10.0 \n",
+ " 29 10_90 80.0 \n",
+ " 30 10_90 80.0 \n",
+ " 31 10_20 10.0 \n",
+ " 32 10_90 80.0 \n",
+ " 33 10_20 10.0 \n",
+ " 34 10_90 80.0 \n",
+ " 35 10_20 10.0 \n",
+ " 36 10_20 10.0 \n",
+ " 37 10_90 80.0 \n",
+ " 38 10_90 80.0 \n",
+ " 39 10_20 10.0 \n",
+ " 40 10_90 80.0 \n",
+ " 41 10_20 10.0 \n",
+ " 42 10_90 80.0 \n",
+ " 43 10_20 10.0 \n",
+ " 44 10_20 10.0 \n",
+ " 45 10_90 80.0 \n",
+ " 46 10_90 80.0 \n",
+ " 47 10_20 10.0 \n",
+ " 48 10_90 80.0 \n",
+ " 49 10_90 80.0 \n",
+ " 50 10_20 10.0 \n",
+ " 51 10_20 10.0 \n",
+ " 52 10_90 80.0 \n",
+ " 53 10_20 10.0 \n",
+ " 54 10_90 80.0 \n",
+ " 55 10_20 10.0 \n",
+ " 56 10_90 80.0 \n",
+ " 57 10_20 10.0 \n",
+ " 58 10_20 10.0 \n",
+ " 59 10_90 80.0 \n",
+ " 60 10_20 10.0 \n",
+ " 61 10_90 80.0 \n",
+ " 62 10_20 10.0 \n",
+ " 63 10_90 80.0 \n",
+ " 64 10_90 80.0 \n",
+ " 65 10_20 10.0 \n",
+ " 66 10_90 80.0 \n",
+ " 67 10_20 10.0 \n",
+ " 68 10_20 10.0 \n",
+ " 69 10_90 80.0 \n",
+ " 70 10_20 10.0 \n",
+ " 71 10_90 80.0 \n",
+ " 72 10_20 10.0 \n",
+ " 73 10_90 80.0 \n",
+ " 74 10_20 10.0 \n",
+ " 75 10_90 80.0 \n",
+ " 76 10_20 10.0 \n",
+ " 77 10_20 10.0 \n",
+ " 78 10_90 80.0 \n",
+ " 79 10_90 80.0 \n",
+ " 80 10_90 80.0 \n",
+ " 81 10_90 80.0 \n",
+ " 82 10_20 10.0 \n",
+ " 83 10_20 10.0 \n",
+ " 84 10_20 10.0 \n",
+ " 85 10_20 10.0 \n",
+ " 86 10_90 80.0 \n",
+ " 87 10_90 80.0 \n",
+ " 88 10_90 80.0 \n",
+ " 89 10_20 10.0 \n",
+ " 90 10_20 10.0 \n",
+ " 91 10_90 80.0 \n",
+ " 92 10_90 80.0 \n",
+ " 93 10_20 10.0 \n",
+ " 94 10_90 80.0 \n",
+ " 95 10_20 10.0 \n",
+ " 96 10_90 80.0 \n",
+ " 97 10_20 10.0 \n",
+ " 98 10_20 10.0 \n",
+ " 99 10_90 80.0 \n",
+ " 100 10_20 10.0 \n",
+ " 101 10_90 80.0 \n",
+ " 102 10_90 80.0 \n",
+ " 103 10_20 10.0 \n",
+ " 104 10_20 10.0 \n",
+ " 105 10_90 80.0 \n",
+ " 106 10_20 10.0 \n",
+ " 107 10_90 80.0 \n",
+ " 108 10_20 10.0 \n",
+ " 109 10_90 80.0 \n",
+ " 110 10_90 80.0 \n",
+ " 111 10_20 10.0 \n",
+ " 112 10_90 80.0 \n",
+ " 113 10_90 80.0 \n",
+ " 114 10_20 10.0 \n",
+ " 115 10_20 10.0 \n",
+ " 116 10_20 10.0 \n",
+ " 117 10_20 10.0 \n",
+ " 118 10_90 80.0 \n",
+ " 119 10_90 80.0 \n",
+ " 120 10_20 10.0 \n",
+ " 121 10_20 10.0 \n",
+ " 122 10_90 80.0 \n",
+ " 123 10_90 80.0 \n",
+ " 124 10_20 10.0 \n",
+ " 125 10_20 10.0 \n",
+ " 126 10_90 80.0 \n",
+ " 127 10_90 80.0 \n",
+ " 128 10_20 10.0 \n",
+ " 129 10_90 80.0 \n",
+ " 130 10_90 80.0 \n",
+ " 131 10_20 10.0 \n",
+ " 132 10_90 80.0 \n",
+ " 133 10_20 10.0 \n",
+ " 134 10_90 80.0 \n",
+ " 135 10_20 10.0 \n",
+ " 136 10_90 80.0 \n",
+ " 137 10_90 80.0 \n",
+ " 138 10_20 10.0 \n",
+ " 139 10_20 10.0 \n",
+ " 140 10_20 10.0 \n",
+ " 141 10_90 80.0 \n",
+ " 142 10_20 10.0 \n",
+ " 143 10_90 80.0 \n",
+ " 144 10_90 80.0 \n",
+ " 145 10_90 80.0 \n",
+ " 146 10_20 10.0 \n",
+ " 147 10_20 10.0 \n",
+ " 148 10_20 10.0 \n",
+ " 149 10_20 10.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 4 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 5 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 6 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 7 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 8 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 9 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 10 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 11 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 12 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 13 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 14 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 15 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 16 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 17 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 18 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 19 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 20 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 21 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 22 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 23 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 24 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 25 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 26 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 27 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 28 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 29 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 30 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 31 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 32 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 33 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 34 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 35 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 36 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 37 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 38 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 39 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 40 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 41 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 42 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 43 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 44 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 45 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 46 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 47 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 48 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 49 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 50 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 51 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 52 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 53 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 54 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 55 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 56 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 57 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 58 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 59 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 60 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 61 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 62 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 63 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 64 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 65 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 66 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 67 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 68 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 69 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 70 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 71 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 72 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 73 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 74 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 75 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 76 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 77 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 78 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 79 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 80 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 81 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 82 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 83 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 84 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 85 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 86 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 87 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 88 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 89 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 90 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 91 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 92 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 93 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 94 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 95 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 96 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 97 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 98 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 99 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 100 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 101 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 102 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 103 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 104 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 105 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 106 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 107 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 108 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 109 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 110 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 111 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 112 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 113 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 114 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 115 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 116 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 117 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 118 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 119 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 120 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 121 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 122 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 123 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 124 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 125 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 126 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 127 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 128 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 129 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 130 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 131 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 132 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 133 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 134 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 135 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 136 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 137 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 138 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 139 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 140 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 141 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 142 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 143 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 144 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_amplitude nontarget_onset nontarget_whisker \n",
+ "roi# trial# \n",
+ "0 0 10 10.0 D1 \\\n",
+ " 1 0 0.0 C1 \n",
+ " 2 10 10.0 D1 \n",
+ " 3 0 0.0 C1 \n",
+ " 4 0 0.0 C1 \n",
+ " 5 10 10.0 C1 \n",
+ " 6 0 0.0 D1 \n",
+ " 7 10 10.0 C1 \n",
+ " 8 10 10.0 D1 \n",
+ " 9 10 10.0 C1 \n",
+ " 10 0 0.0 C1 \n",
+ " 11 0 0.0 C1 \n",
+ " 12 10 10.0 C1 \n",
+ " 13 0 0.0 C1 \n",
+ " 14 10 10.0 C1 \n",
+ " 15 0 0.0 C1 \n",
+ " 16 10 10.0 D1 \n",
+ " 17 0 0.0 D1 \n",
+ " 18 0 0.0 C1 \n",
+ " 19 10 10.0 D1 \n",
+ " 20 0 0.0 D1 \n",
+ " 21 10 10.0 C1 \n",
+ " 22 0 0.0 C1 \n",
+ " 23 10 10.0 C1 \n",
+ " 24 0 0.0 D1 \n",
+ " 25 10 10.0 C1 \n",
+ " 26 10 10.0 C1 \n",
+ " 27 0 0.0 D1 \n",
+ " 28 0 0.0 C1 \n",
+ " 29 10 10.0 C1 \n",
+ " 30 0 0.0 D1 \n",
+ " 31 10 10.0 D1 \n",
+ " 32 10 10.0 D1 \n",
+ " 33 0 0.0 C1 \n",
+ " 34 0 0.0 D1 \n",
+ " 35 10 10.0 D1 \n",
+ " 36 0 0.0 D1 \n",
+ " 37 0 0.0 C1 \n",
+ " 38 10 10.0 D1 \n",
+ " 39 10 10.0 D1 \n",
+ " 40 0 0.0 C1 \n",
+ " 41 10 10.0 C1 \n",
+ " 42 10 10.0 D1 \n",
+ " 43 0 0.0 C1 \n",
+ " 44 0 0.0 D1 \n",
+ " 45 0 0.0 D1 \n",
+ " 46 10 10.0 C1 \n",
+ " 47 10 10.0 C1 \n",
+ " 48 0 0.0 C1 \n",
+ " 49 10 10.0 D1 \n",
+ " 50 0 0.0 D1 \n",
+ " 51 10 10.0 C1 \n",
+ " 52 0 0.0 D1 \n",
+ " 53 10 10.0 D1 \n",
+ " 54 10 10.0 C1 \n",
+ " 55 0 0.0 D1 \n",
+ " 56 10 10.0 D1 \n",
+ " 57 0 0.0 C1 \n",
+ " 58 10 10.0 D1 \n",
+ " 59 0 0.0 D1 \n",
+ " 60 10 10.0 D1 \n",
+ " 61 0 0.0 D1 \n",
+ " 62 0 0.0 C1 \n",
+ " 63 10 10.0 D1 \n",
+ " 64 0 0.0 D1 \n",
+ " 65 10 10.0 C1 \n",
+ " 66 10 10.0 C1 \n",
+ " 67 0 0.0 D1 \n",
+ " 68 0 0.0 C1 \n",
+ " 69 10 10.0 C1 \n",
+ " 70 10 10.0 C1 \n",
+ " 71 0 0.0 C1 \n",
+ " 72 10 10.0 C1 \n",
+ " 73 0 0.0 C1 \n",
+ " 74 0 0.0 C1 \n",
+ " 75 10 10.0 D1 \n",
+ " 76 10 10.0 D1 \n",
+ " 77 0 0.0 D1 \n",
+ " 78 10 10.0 C1 \n",
+ " 79 0 0.0 D1 \n",
+ " 80 0 0.0 D1 \n",
+ " 81 10 10.0 D1 \n",
+ " 82 0 0.0 D1 \n",
+ " 83 10 10.0 D1 \n",
+ " 84 0 0.0 C1 \n",
+ " 85 10 10.0 D1 \n",
+ " 86 10 10.0 D1 \n",
+ " 87 0 0.0 D1 \n",
+ " 88 0 0.0 C1 \n",
+ " 89 10 10.0 C1 \n",
+ " 90 0 0.0 D1 \n",
+ " 91 10 10.0 D1 \n",
+ " 92 10 10.0 D1 \n",
+ " 93 0 0.0 D1 \n",
+ " 94 0 0.0 D1 \n",
+ " 95 10 10.0 C1 \n",
+ " 96 10 10.0 D1 \n",
+ " 97 0 0.0 C1 \n",
+ " 98 10 10.0 D1 \n",
+ " 99 0 0.0 C1 \n",
+ " 100 10 10.0 C1 \n",
+ " 101 10 10.0 D1 \n",
+ " 102 0 0.0 D1 \n",
+ " 103 0 0.0 C1 \n",
+ " 104 10 10.0 C1 \n",
+ " 105 10 10.0 D1 \n",
+ " 106 0 0.0 D1 \n",
+ " 107 0 0.0 C1 \n",
+ " 108 0 0.0 D1 \n",
+ " 109 0 0.0 C1 \n",
+ " 110 10 10.0 D1 \n",
+ " 111 10 10.0 C1 \n",
+ " 112 10 10.0 D1 \n",
+ " 113 0 0.0 C1 \n",
+ " 114 0 0.0 C1 \n",
+ " 115 10 10.0 C1 \n",
+ " 116 10 10.0 D1 \n",
+ " 117 0 0.0 C1 \n",
+ " 118 0 0.0 C1 \n",
+ " 119 10 10.0 C1 \n",
+ " 120 0 0.0 C1 \n",
+ " 121 10 10.0 C1 \n",
+ " 122 10 10.0 C1 \n",
+ " 123 0 0.0 D1 \n",
+ " 124 0 0.0 D1 \n",
+ " 125 10 10.0 D1 \n",
+ " 126 10 10.0 C1 \n",
+ " 127 0 0.0 D1 \n",
+ " 128 10 10.0 D1 \n",
+ " 129 0 0.0 D1 \n",
+ " 130 10 10.0 D1 \n",
+ " 131 0 0.0 D1 \n",
+ " 132 10 10.0 D1 \n",
+ " 133 0 0.0 D1 \n",
+ " 134 0 0.0 D1 \n",
+ " 135 10 10.0 D1 \n",
+ " 136 0 0.0 C1 \n",
+ " 137 10 10.0 C1 \n",
+ " 138 10 10.0 D1 \n",
+ " 139 0 0.0 C1 \n",
+ " 140 0 0.0 D1 \n",
+ " 141 10 10.0 C1 \n",
+ " 142 10 10.0 C1 \n",
+ " 143 0 0.0 C1 \n",
+ " 144 0 0.0 C1 \n",
+ " 145 10 10.0 D1 \n",
+ " 146 10 10.0 D1 \n",
+ " 147 0 0.0 D1 \n",
+ " 148 0 0.0 D1 \n",
+ " 149 10 10.0 C1 \n",
+ "\n",
+ " behavioural_result complete_amplitude \n",
+ "roi# trial# \n",
+ "0 0 no_answer 10_90&10 \\\n",
+ " 1 no_answer 10_90&0 \n",
+ " 2 no_answer 10_20&10 \n",
+ " 3 no_answer 10_20&0 \n",
+ " 4 no_answer 10_90&0 \n",
+ " 5 no_answer 10_20&10 \n",
+ " 6 no_answer 10_20&0 \n",
+ " 7 no_answer 10_90&10 \n",
+ " 8 no_answer 10_20&10 \n",
+ " 9 no_answer 10_90&10 \n",
+ " 10 no_answer 10_90&0 \n",
+ " 11 no_answer 10_20&0 \n",
+ " 12 no_answer 10_90&10 \n",
+ " 13 correct 10_90&0 \n",
+ " 14 no_answer 10_20&10 \n",
+ " 15 no_answer 10_20&0 \n",
+ " 16 no_answer 10_90&10 \n",
+ " 17 no_answer 10_90&0 \n",
+ " 18 double_answer 10_20&0 \n",
+ " 19 no_answer 10_20&10 \n",
+ " 20 no_answer 10_90&0 \n",
+ " 21 no_answer 10_90&10 \n",
+ " 22 no_answer 10_20&0 \n",
+ " 23 double_answer 10_20&10 \n",
+ " 24 no_answer 10_90&0 \n",
+ " 25 no_answer 10_20&10 \n",
+ " 26 no_answer 10_90&10 \n",
+ " 27 no_answer 10_20&0 \n",
+ " 28 no_answer 10_20&0 \n",
+ " 29 double_answer 10_90&10 \n",
+ " 30 no_answer 10_90&0 \n",
+ " 31 no_answer 10_20&10 \n",
+ " 32 no_answer 10_90&10 \n",
+ " 33 no_answer 10_20&0 \n",
+ " 34 no_answer 10_90&0 \n",
+ " 35 no_answer 10_20&10 \n",
+ " 36 no_answer 10_20&0 \n",
+ " 37 no_answer 10_90&0 \n",
+ " 38 no_answer 10_90&10 \n",
+ " 39 no_answer 10_20&10 \n",
+ " 40 no_answer 10_90&0 \n",
+ " 41 double_answer 10_20&10 \n",
+ " 42 no_answer 10_90&10 \n",
+ " 43 no_answer 10_20&0 \n",
+ " 44 no_answer 10_20&0 \n",
+ " 45 no_answer 10_90&0 \n",
+ " 46 no_answer 10_90&10 \n",
+ " 47 no_answer 10_20&10 \n",
+ " 48 error 10_90&0 \n",
+ " 49 no_answer 10_90&10 \n",
+ " 50 no_answer 10_20&0 \n",
+ " 51 no_answer 10_20&10 \n",
+ " 52 no_answer 10_90&0 \n",
+ " 53 no_answer 10_20&10 \n",
+ " 54 no_answer 10_90&10 \n",
+ " 55 no_answer 10_20&0 \n",
+ " 56 no_answer 10_90&10 \n",
+ " 57 no_answer 10_20&0 \n",
+ " 58 no_answer 10_20&10 \n",
+ " 59 no_answer 10_90&0 \n",
+ " 60 no_answer 10_20&10 \n",
+ " 61 no_answer 10_90&0 \n",
+ " 62 double_answer 10_20&0 \n",
+ " 63 no_answer 10_90&10 \n",
+ " 64 no_answer 10_90&0 \n",
+ " 65 no_answer 10_20&10 \n",
+ " 66 no_answer 10_90&10 \n",
+ " 67 no_answer 10_20&0 \n",
+ " 68 double_answer 10_20&0 \n",
+ " 69 no_answer 10_90&10 \n",
+ " 70 no_answer 10_20&10 \n",
+ " 71 no_answer 10_90&0 \n",
+ " 72 double_answer 10_20&10 \n",
+ " 73 no_answer 10_90&0 \n",
+ " 74 no_answer 10_20&0 \n",
+ " 75 no_answer 10_90&10 \n",
+ " 76 no_answer 10_20&10 \n",
+ " 77 no_answer 10_20&0 \n",
+ " 78 no_answer 10_90&10 \n",
+ " 79 correct 10_90&0 \n",
+ " 80 no_answer 10_90&0 \n",
+ " 81 no_answer 10_90&10 \n",
+ " 82 no_answer 10_20&0 \n",
+ " 83 no_answer 10_20&10 \n",
+ " 84 no_answer 10_20&0 \n",
+ " 85 no_answer 10_20&10 \n",
+ " 86 no_answer 10_90&10 \n",
+ " 87 no_answer 10_90&0 \n",
+ " 88 no_answer 10_90&0 \n",
+ " 89 double_answer 10_20&10 \n",
+ " 90 no_answer 10_20&0 \n",
+ " 91 no_answer 10_90&10 \n",
+ " 92 no_answer 10_90&10 \n",
+ " 93 no_answer 10_20&0 \n",
+ " 94 no_answer 10_90&0 \n",
+ " 95 no_answer 10_20&10 \n",
+ " 96 no_answer 10_90&10 \n",
+ " 97 no_answer 10_20&0 \n",
+ " 98 no_answer 10_20&10 \n",
+ " 99 no_answer 10_90&0 \n",
+ " 100 double_answer 10_20&10 \n",
+ " 101 no_answer 10_90&10 \n",
+ " 102 no_answer 10_90&0 \n",
+ " 103 no_answer 10_20&0 \n",
+ " 104 no_answer 10_20&10 \n",
+ " 105 no_answer 10_90&10 \n",
+ " 106 no_answer 10_20&0 \n",
+ " 107 error 10_90&0 \n",
+ " 108 no_answer 10_20&0 \n",
+ " 109 no_answer 10_90&0 \n",
+ " 110 no_answer 10_90&10 \n",
+ " 111 no_answer 10_20&10 \n",
+ " 112 no_answer 10_90&10 \n",
+ " 113 double_answer 10_90&0 \n",
+ " 114 no_answer 10_20&0 \n",
+ " 115 no_answer 10_20&10 \n",
+ " 116 no_answer 10_20&10 \n",
+ " 117 no_answer 10_20&0 \n",
+ " 118 double_answer 10_90&0 \n",
+ " 119 no_answer 10_90&10 \n",
+ " 120 no_answer 10_20&0 \n",
+ " 121 no_answer 10_20&10 \n",
+ " 122 double_answer 10_90&10 \n",
+ " 123 no_answer 10_90&0 \n",
+ " 124 no_answer 10_20&0 \n",
+ " 125 no_answer 10_20&10 \n",
+ " 126 no_answer 10_90&10 \n",
+ " 127 no_answer 10_90&0 \n",
+ " 128 no_answer 10_20&10 \n",
+ " 129 no_answer 10_90&0 \n",
+ " 130 no_answer 10_90&10 \n",
+ " 131 no_answer 10_20&0 \n",
+ " 132 no_answer 10_90&10 \n",
+ " 133 no_answer 10_20&0 \n",
+ " 134 no_answer 10_90&0 \n",
+ " 135 no_answer 10_20&10 \n",
+ " 136 no_answer 10_90&0 \n",
+ " 137 error 10_90&10 \n",
+ " 138 no_answer 10_20&10 \n",
+ " 139 no_answer 10_20&0 \n",
+ " 140 no_answer 10_20&0 \n",
+ " 141 no_answer 10_90&10 \n",
+ " 142 no_answer 10_20&10 \n",
+ " 143 error 10_90&0 \n",
+ " 144 no_answer 10_90&0 \n",
+ " 145 no_answer 10_90&10 \n",
+ " 146 no_answer 10_20&10 \n",
+ " 147 no_answer 10_20&0 \n",
+ " 148 no_answer 10_20&0 \n",
+ " 149 no_answer 10_20&10 \n",
+ "\n",
+ " nontarget_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [] \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [] \n",
+ " 4 [] \n",
+ " 5 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 6 [] \n",
+ " 7 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 8 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 9 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 10 [] \n",
+ " 11 [] \n",
+ " 12 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 13 [] \n",
+ " 14 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 15 [] \n",
+ " 16 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 17 [] \n",
+ " 18 [] \n",
+ " 19 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 20 [] \n",
+ " 21 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 22 [] \n",
+ " 23 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 24 [] \n",
+ " 25 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 26 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 27 [] \n",
+ " 28 [] \n",
+ " 29 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 30 [] \n",
+ " 31 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 32 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 33 [] \n",
+ " 34 [] \n",
+ " 35 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 36 [] \n",
+ " 37 [] \n",
+ " 38 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 39 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 40 [] \n",
+ " 41 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 42 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 43 [] \n",
+ " 44 [] \n",
+ " 45 [] \n",
+ " 46 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 47 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 48 [] \n",
+ " 49 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 50 [] \n",
+ " 51 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 52 [] \n",
+ " 53 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 54 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 55 [] \n",
+ " 56 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 57 [] \n",
+ " 58 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 59 [] \n",
+ " 60 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 61 [] \n",
+ " 62 [] \n",
+ " 63 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 64 [] \n",
+ " 65 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 66 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 67 [] \n",
+ " 68 [] \n",
+ " 69 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 70 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 71 [] \n",
+ " 72 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 73 [] \n",
+ " 74 [] \n",
+ " 75 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 76 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 77 [] \n",
+ " 78 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 79 [] \n",
+ " 80 [] \n",
+ " 81 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 82 [] \n",
+ " 83 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 84 [] \n",
+ " 85 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 86 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 87 [] \n",
+ " 88 [] \n",
+ " 89 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 90 [] \n",
+ " 91 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 92 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 93 [] \n",
+ " 94 [] \n",
+ " 95 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 96 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 97 [] \n",
+ " 98 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 99 [] \n",
+ " 100 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 101 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 102 [] \n",
+ " 103 [] \n",
+ " 104 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 105 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 106 [] \n",
+ " 107 [] \n",
+ " 108 [] \n",
+ " 109 [] \n",
+ " 110 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 111 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 112 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 113 [] \n",
+ " 114 [] \n",
+ " 115 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 116 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 117 [] \n",
+ " 118 [] \n",
+ " 119 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 120 [] \n",
+ " 121 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 122 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 123 [] \n",
+ " 124 [] \n",
+ " 125 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 126 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 127 [] \n",
+ " 128 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 129 [] \n",
+ " 130 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 131 [] \n",
+ " 132 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 133 [] \n",
+ " 134 [] \n",
+ " 135 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 136 [] \n",
+ " 137 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 138 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 139 [] \n",
+ " 140 [] \n",
+ " 141 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 142 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 143 [] \n",
+ " 144 [] \n",
+ " 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [] \n",
+ " 148 [] \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " complete_stim target_whisker nontarget_stim in_D1 \n",
+ "roi# trial# \n",
+ "0 0 C1_10_90&D1_10 C1 D1_10 True \\\n",
+ " 1 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 2 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 3 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 4 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 5 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 6 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 7 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 8 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 9 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 10 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 11 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 12 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 13 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 14 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 15 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 16 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 17 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 18 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 19 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 20 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 21 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 22 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 23 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 24 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 25 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 26 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 27 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 28 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 29 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 30 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 31 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 32 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 33 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 34 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 35 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 36 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 37 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 38 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 39 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 40 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 41 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 42 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 43 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 44 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 45 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 46 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 47 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 48 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 49 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 50 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 51 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 52 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 53 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 54 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 55 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 56 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 57 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 58 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 59 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 60 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 61 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 62 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 63 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 64 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 65 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 66 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 67 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 68 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 69 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 70 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 71 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 72 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 73 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 74 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 75 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 76 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 77 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 78 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 79 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 80 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 81 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 82 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 83 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 84 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 85 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 86 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 87 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 88 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 89 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 90 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 91 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 92 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 93 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 94 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 95 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 96 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 97 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 98 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 99 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 100 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 101 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 102 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 103 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 104 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 105 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 106 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 107 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 108 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 109 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 110 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 111 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 112 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 113 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 114 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 115 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 116 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 117 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 118 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 119 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 120 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 121 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 122 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 123 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 124 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 125 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 126 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 127 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 128 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 129 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 130 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 131 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 132 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 133 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 134 C1_10_90&D1_0 C1 D1_0 True \n",
+ " 135 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 136 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 137 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 138 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 139 D1_10_20&C1_0 D1 C1_0 True \n",
+ " 140 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 141 D1_10_90&C1_10 D1 C1_10 True \n",
+ " 142 D1_10_20&C1_10 D1 C1_10 True \n",
+ " 143 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 144 D1_10_90&C1_0 D1 C1_0 True \n",
+ " 145 C1_10_90&D1_10 C1 D1_10 True \n",
+ " 146 C1_10_20&D1_10 C1 D1_10 True \n",
+ " 147 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 148 C1_10_20&D1_0 C1 D1_0 True \n",
+ " 149 D1_10_20&C1_10 D1 C1_10 True \n",
+ "\n",
+ " in_any_barrel in_C1 is_neuron is_VGAT in_target_barrel \n",
+ "roi# trial# \n",
+ "0 0 True False True False False \\\n",
+ " 1 True False True False True \n",
+ " 2 True False True False False \n",
+ " 3 True False True False True \n",
+ " 4 True False True False True \n",
+ " 5 True False True False True \n",
+ " 6 True False True False False \n",
+ " 7 True False True False True \n",
+ " 8 True False True False False \n",
+ " 9 True False True False True \n",
+ " 10 True False True False True \n",
+ " 11 True False True False True \n",
+ " 12 True False True False True \n",
+ " 13 True False True False True \n",
+ " 14 True False True False True \n",
+ " 15 True False True False True \n",
+ " 16 True False True False False \n",
+ " 17 True False True False False \n",
+ " 18 True False True False True \n",
+ " 19 True False True False False \n",
+ " 20 True False True False False \n",
+ " 21 True False True False True \n",
+ " 22 True False True False True \n",
+ " 23 True False True False True \n",
+ " 24 True False True False False \n",
+ " 25 True False True False True \n",
+ " 26 True False True False True \n",
+ " 27 True False True False False \n",
+ " 28 True False True False True \n",
+ " 29 True False True False True \n",
+ " 30 True False True False False \n",
+ " 31 True False True False False \n",
+ " 32 True False True False False \n",
+ " 33 True False True False True \n",
+ " 34 True False True False False \n",
+ " 35 True False True False False \n",
+ " 36 True False True False False \n",
+ " 37 True False True False True \n",
+ " 38 True False True False False \n",
+ " 39 True False True False False \n",
+ " 40 True False True False True \n",
+ " 41 True False True False True \n",
+ " 42 True False True False False \n",
+ " 43 True False True False True \n",
+ " 44 True False True False False \n",
+ " 45 True False True False False \n",
+ " 46 True False True False True \n",
+ " 47 True False True False True \n",
+ " 48 True False True False True \n",
+ " 49 True False True False False \n",
+ " 50 True False True False False \n",
+ " 51 True False True False True \n",
+ " 52 True False True False False \n",
+ " 53 True False True False False \n",
+ " 54 True False True False True \n",
+ " 55 True False True False False \n",
+ " 56 True False True False False \n",
+ " 57 True False True False True \n",
+ " 58 True False True False False \n",
+ " 59 True False True False False \n",
+ " 60 True False True False False \n",
+ " 61 True False True False False \n",
+ " 62 True False True False True \n",
+ " 63 True False True False False \n",
+ " 64 True False True False False \n",
+ " 65 True False True False True \n",
+ " 66 True False True False True \n",
+ " 67 True False True False False \n",
+ " 68 True False True False True \n",
+ " 69 True False True False True \n",
+ " 70 True False True False True \n",
+ " 71 True False True False True \n",
+ " 72 True False True False True \n",
+ " 73 True False True False True \n",
+ " 74 True False True False True \n",
+ " 75 True False True False False \n",
+ " 76 True False True False False \n",
+ " 77 True False True False False \n",
+ " 78 True False True False True \n",
+ " 79 True False True False False \n",
+ " 80 True False True False False \n",
+ " 81 True False True False False \n",
+ " 82 True False True False False \n",
+ " 83 True False True False False \n",
+ " 84 True False True False True \n",
+ " 85 True False True False False \n",
+ " 86 True False True False False \n",
+ " 87 True False True False False \n",
+ " 88 True False True False True \n",
+ " 89 True False True False True \n",
+ " 90 True False True False False \n",
+ " 91 True False True False False \n",
+ " 92 True False True False False \n",
+ " 93 True False True False False \n",
+ " 94 True False True False False \n",
+ " 95 True False True False True \n",
+ " 96 True False True False False \n",
+ " 97 True False True False True \n",
+ " 98 True False True False False \n",
+ " 99 True False True False True \n",
+ " 100 True False True False True \n",
+ " 101 True False True False False \n",
+ " 102 True False True False False \n",
+ " 103 True False True False True \n",
+ " 104 True False True False True \n",
+ " 105 True False True False False \n",
+ " 106 True False True False False \n",
+ " 107 True False True False True \n",
+ " 108 True False True False False \n",
+ " 109 True False True False True \n",
+ " 110 True False True False False \n",
+ " 111 True False True False True \n",
+ " 112 True False True False False \n",
+ " 113 True False True False True \n",
+ " 114 True False True False True \n",
+ " 115 True False True False True \n",
+ " 116 True False True False False \n",
+ " 117 True False True False True \n",
+ " 118 True False True False True \n",
+ " 119 True False True False True \n",
+ " 120 True False True False True \n",
+ " 121 True False True False True \n",
+ " 122 True False True False True \n",
+ " 123 True False True False False \n",
+ " 124 True False True False False \n",
+ " 125 True False True False False \n",
+ " 126 True False True False True \n",
+ " 127 True False True False False \n",
+ " 128 True False True False False \n",
+ " 129 True False True False False \n",
+ " 130 True False True False False \n",
+ " 131 True False True False False \n",
+ " 132 True False True False False \n",
+ " 133 True False True False False \n",
+ " 134 True False True False False \n",
+ " 135 True False True False False \n",
+ " 136 True False True False True \n",
+ " 137 True False True False True \n",
+ " 138 True False True False False \n",
+ " 139 True False True False True \n",
+ " 140 True False True False False \n",
+ " 141 True False True False True \n",
+ " 142 True False True False True \n",
+ " 143 True False True False True \n",
+ " 144 True False True False True \n",
+ " 145 True False True False False \n",
+ " 146 True False True False False \n",
+ " 147 True False True False False \n",
+ " 148 True False True False False \n",
+ " 149 True False True False True \n",
+ "\n",
+ " neuronal_features \n",
+ "roi# trial# \n",
+ "0 0 [-0.29373020232037816, 0.07303959775594149, 0.... \n",
+ " 1 [-0.13887659963863402, 0.4147024933008791, 0.4... \n",
+ " 2 [0.15659750000313963, 0.19433377025185258, 0.1... \n",
+ " 3 [-0.06945498179116452, -0.19292336493298068, -... \n",
+ " 4 [-0.29605039572842456, -0.21797866295520857, -... \n",
+ " 5 [-0.09585280544451584, 0.08839419743372925, 0.... \n",
+ " 6 [0.1586406918515107, -0.07239579586479972, 0.0... \n",
+ " 7 [-0.17903471929644427, -0.11314780519622668, -... \n",
+ " 8 [-0.056415259345327345, -0.38576577276236984, ... \n",
+ " 9 [-0.11893396048355051, 0.19793689371662737, 0.... \n",
+ " 10 [-0.13360868692080818, 0.18285725484724114, 0.... \n",
+ " 11 [0.06606075047913067, 0.03045827186574529, -0.... \n",
+ " 12 [0.020764577031397124, 0.22413008417466124, 0.... \n",
+ " 13 [-0.18366586265757104, -0.09561425035674856, 0... \n",
+ " 14 [-0.32084606454126974, 0.21170013194548504, -0... \n",
+ " 15 [-0.2233702461580103, -0.10584663877430595, -0... \n",
+ " 16 [-0.10200682795348072, -0.3634837937046822, -0... \n",
+ " 17 [-0.30381872957173545, -0.2875727317407065, -0... \n",
+ " 18 [-0.05333520181619443, -0.037444531879346, -0.... \n",
+ " 19 [0.057095191872701845, -0.047725034214181755, ... \n",
+ " 20 [-0.1158566759531461, -0.24487215771161036, -0... \n",
+ " 21 [-0.2484370567745084, 0.11998574722982666, 0.2... \n",
+ " 22 [-0.07128055945243081, 0.1258235445609875, 0.1... \n",
+ " 23 [-0.31407936104983597, -0.1496493146878156, -0... \n",
+ " 24 [0.2559409678391272, 0.11771191691850143, -0.5... \n",
+ " 25 [0.25648891043416605, -0.06470452797842258, 0.... \n",
+ " 26 [-0.44187417553120845, -0.18892344423372182, -... \n",
+ " 27 [0.12941679310412216, -0.1998458966893841, -0.... \n",
+ " 28 [-0.00535585894090174, 0.13174976772022057, -0... \n",
+ " 29 [-0.42875129629467834, -0.4122965672741163, -0... \n",
+ " 30 [-0.16490452819679824, -0.285950893498107, 0.1... \n",
+ " 31 [0.12314718773306868, -0.16273326443163694, -0... \n",
+ " 32 [-0.19784900826085494, 0.062175475457198905, -... \n",
+ " 33 [-0.09310310438392891, -0.046343915663542755, ... \n",
+ " 34 [-0.06742932492396372, -0.42743440044955994, 0... \n",
+ " 35 [-0.43925763920793215, -0.12073934696734709, 0... \n",
+ " 36 [-0.22095575502509116, 0.3264835900107608, -0.... \n",
+ " 37 [-0.3216613023238765, -0.05579784277374154, -0... \n",
+ " 38 [-0.24295755943595615, 0.3099773600145452, 0.2... \n",
+ " 39 [-0.35026443795160744, 0.05911030048999485, -0... \n",
+ " 40 [-0.07131691493638509, 0.017797547110946624, 0... \n",
+ " 41 [0.09884544639752846, -0.3788618921501363, -0.... \n",
+ " 42 [0.31382009716959736, 0.047844504856988615, 0.... \n",
+ " 43 [-0.12571834442119553, -0.11715802496472115, 0... \n",
+ " 44 [-0.22173434971107372, 0.15077624019543714, -0... \n",
+ " 45 [-0.39907746867152893, -0.34447767683243824, -... \n",
+ " 46 [-0.27907480779008736, 0.03636179081996047, 0.... \n",
+ " 47 [-0.4198751922383796, -0.29187665550714936, 0.... \n",
+ " 48 [0.04819120535954457, 0.09834841579284731, -0.... \n",
+ " 49 [-0.15709045775573335, 0.11013780258789338, -0... \n",
+ " 50 [-0.3195126924143639, -0.357946587662354, -0.4... \n",
+ " 51 [-0.12023647923594184, -0.06421172611519112, -... \n",
+ " 52 [-0.0749953255950385, -0.11083596483827954, 0.... \n",
+ " 53 [-0.46087358930812883, -0.20997387152137403, -... \n",
+ " 54 [-0.2976068960015559, -0.2337016056030365, -0.... \n",
+ " 55 [-0.34675582872189553, -0.06147218810392419, -... \n",
+ " 56 [0.12088726780346111, -0.032241339133646, -0.0... \n",
+ " 57 [0.02221208985969802, -0.028490028173294924, -... \n",
+ " 58 [-0.28427237817009676, 0.07280642829347027, -0... \n",
+ " 59 [0.2898785447083861, -0.3538369410897002, -0.2... \n",
+ " 60 [-0.17100977900144365, -0.23644636047852982, -... \n",
+ " 61 [-0.14430546202944736, -0.19186351520268025, -... \n",
+ " 62 [-0.21930540692070996, 0.17883559573003496, 0.... \n",
+ " 63 [-0.3039832100466536, 0.1915875160209097, -0.2... \n",
+ " 64 [-0.36986341769212033, 0.2856213246909113, 0.1... \n",
+ " 65 [0.041238023850725, -0.1455926229558748, -0.11... \n",
+ " 66 [0.20142496914011293, 0.3958612359215496, -0.1... \n",
+ " 67 [-0.034808481432044326, -0.3988412414450435, -... \n",
+ " 68 [-0.3635190905722923, 0.12402280399234121, 0.0... \n",
+ " 69 [0.2871885478249861, -0.06660201335551236, -0.... \n",
+ " 70 [-0.10678601420513019, 0.08652861199023938, -0... \n",
+ " 71 [-0.3237963303789482, -0.08130754748958187, 0.... \n",
+ " 72 [-0.11188509357738591, -0.01005331679667346, -... \n",
+ " 73 [-0.08120320192335687, 0.09236620888870556, 0.... \n",
+ " 74 [0.23791490254987677, 0.16111473391310682, -0.... \n",
+ " 75 [0.0740241454336928, -0.2526596912926837, 0.07... \n",
+ " 76 [-0.22818338488193032, 0.10502950680758155, 0.... \n",
+ " 77 [-0.27503738004609635, -0.38200986066603304, -... \n",
+ " 78 [-0.20029733825104348, -0.019619538606299676, ... \n",
+ " 79 [-0.3120616635082773, -0.09372354484998649, -0... \n",
+ " 80 [0.10694041577938958, -0.45814948334946415, -0... \n",
+ " 81 [-0.2806819954356836, 0.028434918729493223, -0... \n",
+ " 82 [-0.0922527143204685, -0.13774746044514272, -0... \n",
+ " 83 [0.16074487625299158, 0.2769847034155389, 0.54... \n",
+ " 84 [-0.2571475549627524, 0.17345825854257613, 0.0... \n",
+ " 85 [-0.12107095006439643, 0.1493125472712992, -0.... \n",
+ " 86 [-0.37360834950963556, -0.044459555646546516, ... \n",
+ " 87 [-0.013602904575762508, -0.015408833029702288,... \n",
+ " 88 [0.03798425597520885, 0.403944756721094, -0.16... \n",
+ " 89 [-0.3160092699178422, -0.2899910985571208, -0.... \n",
+ " 90 [-0.46502922599479735, -0.16776150114350868, -... \n",
+ " 91 [0.17241031721870484, -0.03714868882900112, -0... \n",
+ " 92 [-0.0982104121630473, 0.17067831176903206, 0.0... \n",
+ " 93 [0.07788084503784974, 0.22043836401472844, 0.5... \n",
+ " 94 [-0.4004910839027843, -0.0615911497822542, -0.... \n",
+ " 95 [-0.20235074425282604, -0.26615717075380924, -... \n",
+ " 96 [-0.06119974519984424, -0.12122381229243022, -... \n",
+ " 97 [-0.014309077362570114, -0.23976406030296316, ... \n",
+ " 98 [-0.2452955085616972, 0.22036260935215393, 0.3... \n",
+ " 99 [-0.2922565347964962, 0.1391605992799705, -0.2... \n",
+ " 100 [-0.3140290217275645, -0.10580112277716092, -0... \n",
+ " 101 [0.07660858688377657, -0.1471114425429629, 0.2... \n",
+ " 102 [-0.42859246262123, -0.35655641351371625, -0.1... \n",
+ " 103 [-0.2794999993259796, 0.17540419906301868, 0.4... \n",
+ " 104 [0.1758377022866041, 0.35720952829369856, -0.0... \n",
+ " 105 [-0.2463235497978348, 0.1257521640109947, 0.19... \n",
+ " 106 [0.051771125230875154, -0.10644662279087692, -... \n",
+ " 107 [-0.23296836940969676, -0.1696559648641047, -0... \n",
+ " 108 [-0.24978384964776593, 0.1591797183608242, -0.... \n",
+ " 109 [0.27454116707662285, 0.03674002461317847, 0.0... \n",
+ " 110 [-0.06304974027219329, 0.09722008105097352, -0... \n",
+ " 111 [-0.2719967309361023, -0.027659987390843327, 0... \n",
+ " 112 [-0.6220969003674314, 0.15623893908148476, -0.... \n",
+ " 113 [-0.530861083437315, -0.20652647873067792, 0.0... \n",
+ " 114 [-0.18096739685133986, -0.1886569438084842, 0.... \n",
+ " 115 [-0.31853245021104715, 0.10504863745001804, -0... \n",
+ " 116 [0.30175680225344986, 0.16375730938345276, 0.1... \n",
+ " 117 [-0.2963973444869317, 0.12115764430364243, 0.4... \n",
+ " 118 [0.0067667134070619715, -0.43233234809760046, ... \n",
+ " 119 [-0.20582980357640282, 0.20434482939978346, 0.... \n",
+ " 120 [-0.3330145604503587, -0.14132206682762752, -0... \n",
+ " 121 [-0.3519401221905607, 0.004602235506506332, 0.... \n",
+ " 122 [-0.10675189996247526, -0.09550797519437028, 0... \n",
+ " 123 [-0.30771735952058593, -0.10981481716853166, -... \n",
+ " 124 [-0.4342767282191186, -0.1917257202881671, -0.... \n",
+ " 125 [-0.03866226572377788, 0.19141939337343977, 0.... \n",
+ " 126 [0.005475317961390574, 0.5938895357079891, 0.0... \n",
+ " 127 [0.08982622249594478, -0.2626424843578636, -0.... \n",
+ " 128 [0.17666705668069357, 0.14407729304104866, 0.4... \n",
+ " 129 [0.13272339577414766, -0.3490732114687793, 0.1... \n",
+ " 130 [-0.15696900254311882, -0.16146151075283194, -... \n",
+ " 131 [0.028250690057140954, -0.16798914665657105, -... \n",
+ " 132 [-0.05016901868174861, 0.015429768781672378, 0... \n",
+ " 133 [-0.18034109172975457, -0.009614545972992442, ... \n",
+ " 134 [-0.4088370983251731, -0.30791523522131586, -0... \n",
+ " 135 [-0.44731166093013214, 0.44970531888534754, -0... \n",
+ " 136 [-0.0043887192405414124, 0.0971496979710404, 0... \n",
+ " 137 [-0.4581553007353071, -0.5077794535687009, -0.... \n",
+ " 138 [0.15122542123654034, -0.21181115986734386, 0.... \n",
+ " 139 [-0.46105540974307896, -0.30058383635312785, -... \n",
+ " 140 [0.13552526822406252, -0.4820798800603281, -0.... \n",
+ " 141 [-0.06851920450846066, 0.319030133589, 0.04637... \n",
+ " 142 [-0.30365942138206375, -0.07011455357493906, 0... \n",
+ " 143 [-0.11898013577767773, 0.09343762460179239, -0... \n",
+ " 144 [-0.06717073560275591, -0.06953894780202573, 0... \n",
+ " 145 [0.2059055774089245, 0.20968368039039287, -0.4... \n",
+ " 146 [0.8082059570449399, 0.7297618176851153, 0.238... \n",
+ " 147 [-0.2590662407604328, 0.06000996370129285, -0.... \n",
+ " 148 [-0.07282859954108618, 0.01083607695839526, 0.... \n",
+ " 149 [0.28085327280336, 0.6028212258930189, 0.09830... "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "with pd.option_context('display.max_rows', 150, 'display.max_columns', None):\n",
+ " display(subset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "36a90f52-d5fd-46b8-b134-e87b8d1e0934",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "randomized_subset = subset.sample(frac = 1)\n",
+ "subset.loc[:,'target_amplitude'] = list(randomized_subset.loc[:,'target_amplitude'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "edfac67f-47b8-4856-8686-4722fb8cbb41",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>frequency_change</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>...</th>\n",
+ " <th>complete_stim</th>\n",
+ " <th>target_whisker</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_D1</th>\n",
+ " <th>in_any_barrel</th>\n",
+ " <th>in_C1</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " <th>neuronal_features</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"11\" valign=\"top\">0</th>\n",
+ " <th>0</th>\n",
+ " <td>[110.48663330078125, 131.6634063720703, 86.054...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[81.11736297607422, 93.11980438232422, 73.6161...</td>\n",
+ " <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_20</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.29373020232037816, 0.07303959775594149, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>[67.9014892578125, 84.18050384521484, 79.32645...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[52.151588439941406, 65.11491394042969, 51.899...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.13887659963863402, 0.4147024933008791, 0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>[68.5937728881836, 60.977806091308594, 68.2375...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[57.168704986572266, 70.49388885498047, 47.332...</td>\n",
+ " <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
+ " <td>[0.0, 0.0, 9.140109062194824, 9.33862686157226...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.15659750000313963, 0.19433377025185258, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>[88.68118286132812, 103.54595947265625, 58.293...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[57.833740234375, 44.317848205566406, 55.43520...</td>\n",
+ " <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>D1_10_20&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.06945498179116452, -0.19292336493298068, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>[67.2108154296875, 93.54744720458984, 47.41284...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[55.2567253112793, 59.00489044189453, 56.47432...</td>\n",
+ " <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0....</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>10_20</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>D1_10_90&C1_0</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.29605039572842456, -0.21797866295520857, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>145</th>\n",
+ " <td>[74.84498596191406, 51.94773864746094, 107.228...</td>\n",
+ " <td>[-0.01820918607240805, -0.015286885737646978, ...</td>\n",
+ " <td>[57.141807556152344, 57.246944427490234, 57.49...</td>\n",
+ " <td>[-0.01820918607240805, -0.015286885737646978, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>10_90</td>\n",
+ " <td>80.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_90&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2059055774089245, 0.20968368039039287, -0.4...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>[98.53752136230469, 59.3669319152832, 81.53830...</td>\n",
+ " <td>[-0.07075263048698627, -0.14321302983827025, -...</td>\n",
+ " <td>[55.0904655456543, 52.400978088378906, 51.8728...</td>\n",
+ " <td>[-0.07075263048698627, -0.14321302983827025, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_10</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.8082059570449399, 0.7297618176851153, 0.238...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>[113.19007110595703, 66.31562805175781, 59.043...</td>\n",
+ " <td>[-0.27854251373066036, 0.2195061242315473, 0.2...</td>\n",
+ " <td>[47.371639251708984, 65.85330200195312, 67.334...</td>\n",
+ " <td>[-0.27854251373066036, 0.2195061242315473, 0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.2590662407604328, 0.06000996370129285, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>[70.78560638427734, 42.672210693359375, 73.129...</td>\n",
+ " <td>[-0.1387883226963254, -0.6539329094909159, -0....</td>\n",
+ " <td>[52.67970657348633, 33.567237854003906, 52.039...</td>\n",
+ " <td>[-0.1387883226963254, -0.6539329094909159, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>C1_10_20&D1_0</td>\n",
+ " <td>C1</td>\n",
+ " <td>D1_0</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.07282859954108618, 0.01083607695839526, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>[84.0759048461914, 123.96839904785156, 116.421...</td>\n",
+ " <td>[-0.25454560318475217, 0.3316156094474721, -0....</td>\n",
+ " <td>[48.581905364990234, 70.33007049560547, 40.303...</td>\n",
+ " <td>[-0.25454560318475217, 0.3316156094474721, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>10</td>\n",
+ " <td>...</td>\n",
+ " <td>D1_10_20&C1_10</td>\n",
+ " <td>D1</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.28085327280336, 0.6028212258930189, 0.09830...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>150 rows × 25 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "0 0 [110.48663330078125, 131.6634063720703, 86.054... \\\n",
+ " 1 [67.9014892578125, 84.18050384521484, 79.32645... \n",
+ " 2 [68.5937728881836, 60.977806091308594, 68.2375... \n",
+ " 3 [88.68118286132812, 103.54595947265625, 58.293... \n",
+ " 4 [67.2108154296875, 93.54744720458984, 47.41284... \n",
+ "... ... \n",
+ " 145 [74.84498596191406, 51.94773864746094, 107.228... \n",
+ " 146 [98.53752136230469, 59.3669319152832, 81.53830... \n",
+ " 147 [113.19007110595703, 66.31562805175781, 59.043... \n",
+ " 148 [70.78560638427734, 42.672210693359375, 73.129... \n",
+ " 149 [84.0759048461914, 123.96839904785156, 116.421... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ "... ... \n",
+ " 145 [-0.01820918607240805, -0.015286885737646978, ... \n",
+ " 146 [-0.07075263048698627, -0.14321302983827025, -... \n",
+ " 147 [-0.27854251373066036, 0.2195061242315473, 0.2... \n",
+ " 148 [-0.1387883226963254, -0.6539329094909159, -0.... \n",
+ " 149 [-0.25454560318475217, 0.3316156094474721, -0.... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "0 0 [81.11736297607422, 93.11980438232422, 73.6161... \\\n",
+ " 1 [52.151588439941406, 65.11491394042969, 51.899... \n",
+ " 2 [57.168704986572266, 70.49388885498047, 47.332... \n",
+ " 3 [57.833740234375, 44.317848205566406, 55.43520... \n",
+ " 4 [55.2567253112793, 59.00489044189453, 56.47432... \n",
+ "... ... \n",
+ " 145 [57.141807556152344, 57.246944427490234, 57.49... \n",
+ " 146 [55.0904655456543, 52.400978088378906, 51.8728... \n",
+ " 147 [47.371639251708984, 65.85330200195312, 67.334... \n",
+ " 148 [52.67970657348633, 33.567237854003906, 52.039... \n",
+ " 149 [48.581905364990234, 70.33007049560547, 40.303... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "0 0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 2 [-0.17911552203251063, 0.18014861315202807, -0... \n",
+ " 3 [-0.15754997708637508, -0.5217095653778792, -0... \n",
+ " 4 [-0.2178897487180029, -0.11685176608670593, -0... \n",
+ "... ... \n",
+ " 145 [-0.01820918607240805, -0.015286885737646978, ... \n",
+ " 146 [-0.07075263048698627, -0.14321302983827025, -... \n",
+ " 147 [-0.27854251373066036, 0.2195061242315473, 0.2... \n",
+ " 148 [-0.1387883226963254, -0.6539329094909159, -0.... \n",
+ " 149 [-0.25454560318475217, 0.3316156094474721, -0.... \n",
+ "\n",
+ " spks target_stim \n",
+ "roi# trial# \n",
+ "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... C1_10_90 \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_90 \n",
+ " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... C1_10_20 \n",
+ " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... D1_10_20 \n",
+ " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... D1_10_90 \n",
+ "... ... ... \n",
+ " 145 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_90 \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 148 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... C1_10_20 \n",
+ " 149 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... D1_10_20 \n",
+ "\n",
+ " target_amplitude frequency_change \n",
+ "roi# trial# \n",
+ "0 0 10_20 80.0 \\\n",
+ " 1 10_90 80.0 \n",
+ " 2 10_20 10.0 \n",
+ " 3 10_90 10.0 \n",
+ " 4 10_20 80.0 \n",
+ "... ... ... \n",
+ " 145 10_90 80.0 \n",
+ " 146 10_20 10.0 \n",
+ " 147 10_90 10.0 \n",
+ " 148 10_90 10.0 \n",
+ " 149 10_90 10.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 4 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ " 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_amplitude ... complete_stim target_whisker \n",
+ "roi# trial# ... \n",
+ "0 0 10 ... C1_10_90&D1_10 C1 \\\n",
+ " 1 0 ... D1_10_90&C1_0 D1 \n",
+ " 2 10 ... C1_10_20&D1_10 C1 \n",
+ " 3 0 ... D1_10_20&C1_0 D1 \n",
+ " 4 0 ... D1_10_90&C1_0 D1 \n",
+ "... ... ... ... ... \n",
+ " 145 10 ... C1_10_90&D1_10 C1 \n",
+ " 146 10 ... C1_10_20&D1_10 C1 \n",
+ " 147 0 ... C1_10_20&D1_0 C1 \n",
+ " 148 0 ... C1_10_20&D1_0 C1 \n",
+ " 149 10 ... D1_10_20&C1_10 D1 \n",
+ "\n",
+ " nontarget_stim in_D1 in_any_barrel in_C1 is_neuron is_VGAT \n",
+ "roi# trial# \n",
+ "0 0 D1_10 True True False True False \\\n",
+ " 1 C1_0 True True False True False \n",
+ " 2 D1_10 True True False True False \n",
+ " 3 C1_0 True True False True False \n",
+ " 4 C1_0 True True False True False \n",
+ "... ... ... ... ... ... ... \n",
+ " 145 D1_10 True True False True False \n",
+ " 146 D1_10 True True False True False \n",
+ " 147 D1_0 True True False True False \n",
+ " 148 D1_0 True True False True False \n",
+ " 149 C1_10 True True False True False \n",
+ "\n",
+ " in_target_barrel \n",
+ "roi# trial# \n",
+ "0 0 False \\\n",
+ " 1 True \n",
+ " 2 False \n",
+ " 3 True \n",
+ " 4 True \n",
+ "... ... \n",
+ " 145 False \n",
+ " 146 False \n",
+ " 147 False \n",
+ " 148 False \n",
+ " 149 True \n",
+ "\n",
+ " neuronal_features \n",
+ "roi# trial# \n",
+ "0 0 [-0.29373020232037816, 0.07303959775594149, 0.... \n",
+ " 1 [-0.13887659963863402, 0.4147024933008791, 0.4... \n",
+ " 2 [0.15659750000313963, 0.19433377025185258, 0.1... \n",
+ " 3 [-0.06945498179116452, -0.19292336493298068, -... \n",
+ " 4 [-0.29605039572842456, -0.21797866295520857, -... \n",
+ "... ... \n",
+ " 145 [0.2059055774089245, 0.20968368039039287, -0.4... \n",
+ " 146 [0.8082059570449399, 0.7297618176851153, 0.238... \n",
+ " 147 [-0.2590662407604328, 0.06000996370129285, -0.... \n",
+ " 148 [-0.07282859954108618, 0.01083607695839526, 0.... \n",
+ " 149 [0.28085327280336, 0.6028212258930189, 0.09830... \n",
+ "\n",
+ "[150 rows x 25 columns]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "subset"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Documents.ipynb b/Documents.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..69e32383ea5df30624ca7f2edb5e729933bc1b0b
--- /dev/null
+++ b/Documents.ipynb
@@ -0,0 +1,141 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "75f54bdf-d3e3-49b5-8aea-a2394c758321",
+ "metadata": {},
+ "source": [
+ "Cross validation:\n",
+ "bootstraping :\n",
+ "graph theory application:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5cf8de25-b5d8-4daf-badd-4184937e0572",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.utils import resample"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ac8e889-e03f-47ce-8cce-7bfa62db70d6",
+ "metadata": {},
+ "source": [
+ "For estimating the final accuracy of a classifier, we would like an estimation\n",
+ "method with low bias and low variance.\n",
+ "E.x : leave-one-out is almost unbiased,\n",
+ "but it has high variance, leading to unreliable estimates\n",
+ "(Efron 1983)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "96bcd7f9-3857-466f-bc88-18f159270fe5",
+ "metadata": {},
+ "source": [
+ "Bootstrap is a statistical resampling technique that is used to estimate the accuracy of a model on new, unseen data\n",
+ "In machine learning, bootstrap involves randomly selecting data points from the original dataset with replacement to create multiple \"bootstrap samples\".\n",
+ "These samples are the same size as the original dataset, but some data points may appear multiple times in a sample while others may not appear at all"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b57455f0-2d06-400b-895f-e3dde884b7fb",
+ "metadata": {},
+ "source": [
+ "Both cross-validation and bootstrapping are techniques used in machine learning and statistics to estimate the performance of a model on unseen data.\n",
+ "\n",
+ "Cross-validation involves dividing the dataset into several \"folds\", training the model on a subset of the folds and then evaluating its performance on the remaining fold. This process is repeated several times, each time using a different fold for evaluation. The results are then averaged to obtain an estimate of the model's performance.\n",
+ "\n",
+ "Bootstraping, on the other hand, involves repeatedly resampling the original dataset with replacement to create multiple \"bootstrap\" datasets of the same size. A model is trained on each bootstrap dataset, and the results are averaged to obtain an estimate of the model's performance.\n",
+ "\n",
+ "The main difference between the two techniques is that cross-validation uses a fixed partition of the data into training and testing sets, whereas bootstrapping resamples the data randomly.\n",
+ "\n",
+ "Cross-validation is often used to select the best model from a set of candidate models, by comparing their performance on the test set. Bootstrapping, on the other hand, is used to estimate the performance of a single model, and to obtain a measure of the uncertainty in the estimate.\n",
+ "\n",
+ "Both techniques can be used together to obtain a more accurate estimate of the model's performance. For example, we can use cross-validation to select the best model, and then use bootstrapping to estimate its performance on new data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86a846d7-0de3-4974-a62c-9572f6595a3e",
+ "metadata": {},
+ "source": [
+ "We do not have to implement the bootstrap method manually. \n",
+ "The scikit-learn library provides an implementation that will create a single bootstrap sample of a dataset.\n",
+ "\n",
+ "The resample() scikit-learn function can be used. \n",
+ "It takes as arguments the data array, whether or not to sample with replacement,\n",
+ "the size of the sample, and the seed for the pseudorandom number generator used prior to the sampling.\n",
+ "Ref = https://machinelearningmastery.com/a-gentle-introduction-to-the-bootstrap-method/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3db7c8ea-bbcc-4bc4-a854-cf8b412b6f90",
+ "metadata": {},
+ "source": [
+ "Cross-validation is used to estimate the generalization performance of a model, which is its ability to make accurate predictions on new, unseen data. By using a validation set that is separate from the training set, cross-validation allows us to assess whether the model is overfitting to the training data or if it is able to generalize to new data. It is a commonly used technique in machine learning for model selection, hyperparameter tuning, and comparing the performance of different models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "662d0d4c-eff1-4b07-81bb-9dd05a9fe577",
+ "metadata": {},
+ "source": [
+ "Variations on Cross-Validation\n",
+ "There are a number of variations on the k-fold cross validation procedure.\n",
+ "Three commonly used variations are as follows:\n",
+ "\n",
+ "\n",
+ "1.Train/Test Split: Taken to one extreme, k may be set to 2 (not 1) such that a single train/test split is created to evaluate the model.\n",
+ "2.LOOCV: Taken to another extreme, k may be set to the total number of observations in the dataset such that each observation is given a chance to be the held out of the dataset. This is called leave-one-out cross-validation, or LOOCV for short.\n",
+ "3.Stratified: The splitting of data into folds may be governed by criteria such as ensuring that each fold has the same proportion of observations with a given categorical value, such as the class outcome value. This is called stratified cross-validation.\n",
+ "4.Repeated: This is where the k-fold cross-validation procedure is repeated n times, where importantly, the data sample is shuffled prior to each repetition, which results in a different split of the sample.\n",
+ "5.Nested: This is where k-fold cross-validation is performed within each fold of cross-validation, often to perform hyperparameter tuning during model evaluation. This is called nested cross-validation or double cross-validation.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9072900b-1a08-46e7-a3ab-66ef5a33f8ae",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "270f3489-209c-45f2-8531-1849721e4458",
+ "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": "3.8.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Imaging_data_test.ipynb b/Imaging_data_test.ipynb
index ddc9e8e49a41b91b51042cae01541a098faa1002..da00f99a00f8c2523480e1983723f31ebd251b04 100644
--- a/Imaging_data_test.ipynb
+++ b/Imaging_data_test.ipynb
@@ -10,21 +10,12 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 109,
"id": "9bae2feb-e2da-469e-8da1-e53fbfe7adca",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\mohay\\anaconda3\\envs\\Analysis\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#you must import 6 libraries!\n",
"import numpy as np\n",
@@ -38,7 +29,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 110,
"id": "2e0196d8-7de8-4dda-af54-3f3fdddb663d",
"metadata": {
"tags": []
@@ -61,7 +52,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 111,
"id": "cab36de5-f1f3-4536-96cb-015f8f07d8b4",
"metadata": {
"tags": []
@@ -107,7 +98,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 112,
"id": "b9780e55-533a-405f-ae29-fe0556895022",
"metadata": {
"tags": []
@@ -243,7 +234,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 56,
"id": "7ba3475a-1141-4b56-bc97-8efd0789d6d2",
"metadata": {
"tags": []
@@ -272,13 +263,13 @@
}
],
"source": [
- "session = sessions.iloc[0]\n",
+ "session = sessions.iloc[0] #gives us all the details about row #0\n",
"print(session)"
]
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 57,
"id": "62b01f68-48af-4f01-b5cf-385d166075b9",
"metadata": {
"tags": []
@@ -306,7 +297,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 113,
"id": "aebe664f-1911-42ce-b5ab-78174922c22d",
"metadata": {
"tags": []
@@ -793,7 +784,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 114,
"id": "801c37cb-6799-4431-b109-f9110c2724b4",
"metadata": {
"tags": []
@@ -805,48 +796,19 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 60,
"id": "fe362400-1c97-4b9d-9daf-db7256b6fc98",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[94;20mINFO : roi_signal_corrections : Correcting signal F of 35 ROIs with rough neuropil correction and slow trend correction \u001b[0m\n",
- "\u001b[94;20mINFO : slow_trend_correction : Pooling processes for slow_trend_correction\u001b[0m\n",
- "\u001b[94;20mINFO : roi_signal_corrections : All ROIs corrected successfully\u001b[0m\n",
- "\u001b[94;20mINFO : roi_signal_corrections : Correcting signal Fneu of 35 ROIs with rough neuropil correction and slow trend correction \u001b[0m\n",
- "\u001b[94;20mINFO : slow_trend_correction : Pooling processes for slow_trend_correction\u001b[0m\n",
- "\u001b[94;20mINFO : roi_signal_corrections : All ROIs corrected successfully\u001b[0m\n",
- "\u001b[94;20mINFO : rois_df : Generating signal variation measurement using method 'delta_over_F'\u001b[0m\n",
- "\u001b[94;20mINFO : rois_df : Generating is_neuron label based on iscell column values at 0 positionnal indices\u001b[0m\n",
- "\u001b[94;20mINFO : VGAT_values : Generating VGAT_values columns in the ROI dataframe supplied\u001b[0m\n",
- "\u001b[94;20mINFO : VGAT_labels : Generating is_VGAT label based on VGAT_value for every ROI\u001b[0m\n",
- "\u001b[94;20mINFO : VGAT_labels : Threshold absolute values are : up=26.26 and low=18.08 obtained from relative values : up=70 and low=30 percentiles criterion\u001b[0m\n",
- "\u001b[32mSAVE_INFO : preprocessed_data : Saving processed rois_df data at wm24\\2022-08-22\\001\\preprocessing_saves\\preproc_data.rois_df.pickle (overwriting)\u001b[0m\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"#rois_df = adaptation.pipelines.generate_rois_df(session_details = session, refresh = True, sigma = 50, F0_index = 50, upper_perc = 70, lower_perc = 30)"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 115,
"id": "1f2192db-3a34-4b29-bb97-984ebc569aa3",
"metadata": {
"collapsed": true,
@@ -2116,7 +2078,7 @@
"[35 rows x 34 columns]"
]
},
- "execution_count": 9,
+ "execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
@@ -2137,7 +2099,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 62,
"id": "8df2601b-54e5-4955-83ea-a879eda202d3",
"metadata": {
"tags": []
@@ -2171,7 +2133,7 @@
},
{
"cell_type": "code",
- "execution_count": 123,
+ "execution_count": 63,
"id": "5e8d66cd-a3a8-46ee-80b3-0644ce2fdbd8",
"metadata": {
"tags": []
@@ -2214,19 +2176,20 @@
},
{
"cell_type": "code",
- "execution_count": 120,
+ "execution_count": 64,
"id": "a0483ed6-76b8-4b24-98bc-3e2865959b77",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
- "series = trials_roi_df.iloc[0] #only take the first ROI as a test dataset"
+ "series = trials_roi_df.iloc[0] #only take the first trial of the ROI as a test dataset(trial zero)\n",
+ "#why we extract features of one trial?"
]
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 65,
"id": "8f4cb3a1-1bd6-44b2-9bc4-bc0e24d5668d",
"metadata": {
"tags": []
@@ -2238,7 +2201,7 @@
},
{
"cell_type": "code",
- "execution_count": 124,
+ "execution_count": 66,
"id": "e9a95534-50e6-4fb4-86fa-45858065b4af",
"metadata": {
"tags": []
@@ -2247,10 +2210,10 @@
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x26513418df0>]"
+ "[<matplotlib.lines.Line2D at 0x23ba1603970>]"
]
},
- "execution_count": 124,
+ "execution_count": 66,
"metadata": {},
"output_type": "execute_result"
},
@@ -2272,7 +2235,7 @@
},
{
"cell_type": "code",
- "execution_count": 122,
+ "execution_count": 67,
"id": "e85059de-e17b-45a2-800e-fda57103ed82",
"metadata": {
"tags": []
@@ -2305,10 +2268,10 @@
" <th>Fneu</th>\n",
" <th>Fneu_var</th>\n",
" <th>spks</th>\n",
- " <th>is_C1</th>\n",
" <th>is_VGAT</th>\n",
- " <th>is_D1</th>\n",
+ " <th>is_C1</th>\n",
" <th>is_neuron</th>\n",
+ " <th>is_D1</th>\n",
" <th>target_stim</th>\n",
" <th>nontarget_stim</th>\n",
" <th>in_target_barrel</th>\n",
@@ -2317,7 +2280,6 @@
" <th>Result</th>\n",
" <th>target_stim_info</th>\n",
" <th>nontarget_stim_info</th>\n",
- " <th>features</th>\n",
" </tr>\n",
" <tr>\n",
" <th>roi#</th>\n",
@@ -2339,7 +2301,6 @@
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
- " <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -2363,7 +2324,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[-0.29373020232037816, 0.07303959775594149, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@@ -2384,7 +2344,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[]</td>\n",
- " <td>[-0.13887659963863402, 0.4147024933008791, 0.4...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
@@ -2405,7 +2364,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[0.15659750000313963, 0.19433377025185258, 0.1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
@@ -2426,7 +2384,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[]</td>\n",
- " <td>[-0.06945498179116452, -0.19292336493298068, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
@@ -2447,7 +2404,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[]</td>\n",
- " <td>[-0.29605039572842456, -0.21797866295520857, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -2469,7 +2425,6 @@
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
- " <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">34</th>\n",
@@ -2479,10 +2434,10 @@
" <td>[81.04872131347656, 76.43589782714844, 87.0641...</td>\n",
" <td>[0.28543534597878606, 0.14659601694516844, 0.4...</td>\n",
" <td>[0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
- " <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>False</td>\n",
" <td>C1_10_90</td>\n",
" <td>D1_10</td>\n",
" <td>False</td>\n",
@@ -2491,7 +2446,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[0.12998113130297562, -0.04041878154425823, -0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
@@ -2500,10 +2454,10 @@
" <td>[68.32051086425781, 74.994873046875, 89.817947...</td>\n",
" <td>[-0.09959991718158123, 0.10129027462650247, 0....</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
- " <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>False</td>\n",
" <td>C1_10_20</td>\n",
" <td>D1_10</td>\n",
" <td>False</td>\n",
@@ -2512,7 +2466,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[-0.17850266845055784, 0.13588496412603587, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
@@ -2521,10 +2474,10 @@
" <td>[50.0487174987793, 59.089744567871094, 65.0923...</td>\n",
" <td>[-0.6525859803693786, -0.3806188309835738, -0....</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
- " <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>False</td>\n",
" <td>C1_10_20</td>\n",
" <td>D1_NaN</td>\n",
" <td>False</td>\n",
@@ -2533,7 +2486,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[]</td>\n",
- " <td>[0.8367421288529879, 0.5799961483465563, -0.25...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
@@ -2542,10 +2494,10 @@
" <td>[74.46154022216797, 68.3974380493164, 61.12307...</td>\n",
" <td>[0.08084930191997584, -0.10169227498872888, -0...</td>\n",
" <td>[1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
- " <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>False</td>\n",
" <td>C1_10_20</td>\n",
" <td>D1_NaN</td>\n",
" <td>False</td>\n",
@@ -2554,7 +2506,6 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[]</td>\n",
- " <td>[-0.03735654613817374, -0.16723968654290852, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
@@ -2563,10 +2514,10 @@
" <td>[78.14871978759766, 68.52820587158203, 57.7435...</td>\n",
" <td>[0.1908002463908141, -0.09864775120552391, -0....</td>\n",
" <td>[3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
- " <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>False</td>\n",
" <td>D1_10_20</td>\n",
" <td>C1_10</td>\n",
" <td>False</td>\n",
@@ -2575,11 +2526,10 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[0.3758403148406946, 0.1221455348751175, -0.08...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>5250 rows × 18 columns</p>\n",
+ "<p>5250 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
@@ -2639,33 +2589,33 @@
" 148 [0.08084930191997584, -0.10169227498872888, -0... \n",
" 149 [0.1908002463908141, -0.09864775120552391, -0.... \n",
"\n",
- " spks is_C1 is_VGAT \n",
+ " spks is_VGAT is_C1 \n",
"roi# trial# \n",
- "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... False False \\\n",
- " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... False False \n",
- " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... False False \n",
- " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... False False \n",
- "... ... ... ... \n",
- "34 145 [0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ... False None \n",
- " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False None \n",
- " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False None \n",
- " 148 [1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ... False None \n",
- " 149 [3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ... False None \n",
+ "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... False False \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... False False \n",
+ " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... False False \n",
+ " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... False False \n",
+ "... ... ... ... \n",
+ "34 145 [0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ " 148 [1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ " 149 [3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ... None False \n",
"\n",
- " is_D1 is_neuron target_stim nontarget_stim in_target_barrel \n",
+ " is_neuron is_D1 target_stim nontarget_stim in_target_barrel \n",
"roi# trial# \n",
- "0 0 True True C1_10_90 D1_10 False \\\n",
- " 1 True True D1_10_90 C1_NaN True \n",
- " 2 True True C1_10_20 D1_10 False \n",
- " 3 True True D1_10_20 C1_NaN True \n",
- " 4 True True D1_10_90 C1_NaN True \n",
- "... ... ... ... ... ... \n",
- "34 145 False False C1_10_90 D1_10 False \n",
- " 146 False False C1_10_20 D1_10 False \n",
- " 147 False False C1_10_20 D1_NaN False \n",
- " 148 False False C1_10_20 D1_NaN False \n",
- " 149 False False D1_10_20 C1_10 False \n",
+ "0 0 True True C1_10_90 D1_10 False \\\n",
+ " 1 True True D1_10_90 C1_NaN True \n",
+ " 2 True True C1_10_20 D1_10 False \n",
+ " 3 True True D1_10_20 C1_NaN True \n",
+ " 4 True True D1_10_90 C1_NaN True \n",
+ "... ... ... ... ... ... \n",
+ "34 145 False False C1_10_90 D1_10 False \n",
+ " 146 False False C1_10_20 D1_10 False \n",
+ " 147 False False C1_10_20 D1_NaN False \n",
+ " 148 False False C1_10_20 D1_NaN False \n",
+ " 149 False False D1_10_20 C1_10 False \n",
"\n",
" target_amplitude nontarget_amplitude Result \n",
"roi# trial# \n",
@@ -2695,38 +2645,24 @@
" 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
" 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
"\n",
- " nontarget_stim_info \n",
- "roi# trial# \n",
- "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
- " 1 [] \n",
- " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 3 [] \n",
- " 4 [] \n",
- "... ... \n",
- "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 147 [] \n",
- " 148 [] \n",
- " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- "\n",
- " features \n",
+ " nontarget_stim_info \n",
"roi# trial# \n",
- "0 0 [-0.29373020232037816, 0.07303959775594149, 0.... \n",
- " 1 [-0.13887659963863402, 0.4147024933008791, 0.4... \n",
- " 2 [0.15659750000313963, 0.19433377025185258, 0.1... \n",
- " 3 [-0.06945498179116452, -0.19292336493298068, -... \n",
- " 4 [-0.29605039572842456, -0.21797866295520857, -... \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 1 [] \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [] \n",
+ " 4 [] \n",
"... ... \n",
- "34 145 [0.12998113130297562, -0.04041878154425823, -0... \n",
- " 146 [-0.17850266845055784, 0.13588496412603587, 0.... \n",
- " 147 [0.8367421288529879, 0.5799961483465563, -0.25... \n",
- " 148 [-0.03735654613817374, -0.16723968654290852, -... \n",
- " 149 [0.3758403148406946, 0.1221455348751175, -0.08... \n",
+ "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [] \n",
+ " 148 [] \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
"\n",
- "[5250 rows x 18 columns]"
+ "[5250 rows x 17 columns]"
]
},
- "execution_count": 122,
+ "execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
@@ -2748,7 +2684,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 68,
"id": "6eda5f3d-761b-40a0-bdcb-05a3f7e68ad0",
"metadata": {
"tags": []
@@ -2771,7 +2707,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 69,
"id": "bb4d2b2a-5f09-498c-ac7a-4e40bf928225",
"metadata": {
"tags": []
@@ -2783,19 +2719,20 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 70,
"id": "2f1e11ab-8659-4943-949d-3ddd62356fdb",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
- "training_data, test_data = adaptation.classifiers.get_sample_and_training(first_roi, frac = 0.75)"
+ "training_data, test_data = adaptation.classifiers.get_sample_and_training(first_roi, frac = 0.75) \n",
+ "#get 75% of all trials in first roi as training data"
]
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 95,
"id": "40533e0a-990f-4c37-bb96-2cd15ec3c132",
"metadata": {
"tags": []
@@ -2807,53 +2744,75 @@
"(112, 38, 150)"
]
},
- "execution_count": 61,
+ "execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "len(training_data), len(test_data) , len(first_roi)"
+ "len(training_data), len(test_data) , len(first_roi) \n",
+ "#training data is the data we will get to the machine to learn how to predict, each has 10 features\n",
+ "#then we need to test whether this data helped the machine to predict or not, so we will get 25% of trials as sample,\n",
+ "#to see it it predict based on the feature son these 25 sample that what is happening \n",
+ "#each time randomly we change sample and training data, to check which one works beteer and why?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "id": "eb9616f9-b224-44fa-8c76-d8ae38b0fdb6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#From Chat GPT: Bootstrap classification is a type of resampling technique used in machine \n",
+ "#learning to evaluate the stability and reliability of a classification model. In this technique, \n",
+ "#multiple random samples of the original dataset are created by randomly selecting data points with replacement. \n",
+ "#Each of these samples is then used to train a separate classification model, \n",
+ "#and the predictions of these models are combined to make a final prediction."
]
},
{
"cell_type": "code",
- "execution_count": 197,
+ "execution_count": 96,
"id": "d62aa56e-394c-454c-89a6-2f07f40bc9b8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
+ "#give the machine features of trials and ask him to predict what was the target amplitude\n",
+ "\n",
"def get_score(training_data, test_data):\n",
" \n",
" training_inputs = np.array(list(training_data['features']))\n",
" training_outputs = np.array(training_data['target_amplitude'])\n",
" \n",
" test_inputs = np.array(list(test_data['features']))\n",
- " test_outputs = np.array(test_data['target_amplitude'])\n",
+ " test_outputs = np.array(test_data['target_amplitude']) \n",
" \n",
" classifier = sklearn.svm.LinearSVC()\n",
" classifier.fit(training_inputs,training_outputs)\n",
" \n",
- " score = classifier.score(test_inputs,test_outputs)\n",
+ " score = classifier.score(test_inputs,test_outputs) #to know how much your trained machine is good at predicting you ask him to give you a score \n",
" training_trials = training_data.index.get_level_values(\"trial#\")\n",
" test_trials = test_data.index.get_level_values(\"trial#\")\n",
" \n",
" result = {'score':score,'training_trials':training_trials,'test_trials':test_trials}\n",
- " \n",
+ " #you created result dictionary, gives you the score, tells you this score comes from which training trials and test trials\n",
" return result\n",
- "\n",
+ "#100 times get 75% of trials as training data and 25% as sample data, give them to the machine , ask the score, each time randomly\n",
"def bootstrap_classify(dataframe, frac = 0.75, iter_count = 100): #https://en.wikipedia.org/wiki/Bootstrap_aggregating\n",
" scores =[]\n",
" for _ in range(iter_count):\n",
" training_data, test_data = adaptation.classifiers.get_sample_and_training(dataframe, frac = frac)\n",
" score = get_score(training_data,test_data)\n",
" scores.append(score)\n",
- " \n",
+ " \n",
" values = []\n",
" for item in scores:\n",
- " values.append(item[\"score\"])\n",
+ " values.append(item[\"score\"]) #then you need the meadian of all the scores you got from different trials \n",
" \n",
" meta_result = {'data' : scores, 'scores' : values, 'average_score' : np.median(np.array(values)) }\n",
" return meta_result"
@@ -2861,28 +2820,7 @@
},
{
"cell_type": "code",
- "execution_count": 203,
- "id": "ac4f99ab-7ae4-462b-86a6-ab39797a1258",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.5526315789473685"
- ]
- },
- "execution_count": 203,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 159,
+ "execution_count": 90,
"id": "f82c57bd-0d95-4acf-8fb0-cf951511aa64",
"metadata": {
"collapsed": true,
@@ -2919,10 +2857,10 @@
" <th>Fneu</th>\n",
" <th>Fneu_var</th>\n",
" <th>spks</th>\n",
- " <th>is_C1</th>\n",
" <th>is_VGAT</th>\n",
- " <th>is_D1</th>\n",
+ " <th>is_C1</th>\n",
" <th>is_neuron</th>\n",
+ " <th>is_D1</th>\n",
" <th>target_stim</th>\n",
" <th>nontarget_stim</th>\n",
" <th>in_target_barrel</th>\n",
@@ -3251,33 +3189,33 @@
" 148 [-0.1387883226963254, -0.6539329094909159, -0.... \n",
" 149 [-0.25454560318475217, 0.3316156094474721, -0.... \n",
"\n",
- " spks is_C1 is_VGAT \n",
+ " spks is_VGAT is_C1 \n",
"roi# trial# \n",
- "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... False False \\\n",
- " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... False False \n",
- " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... False False \n",
- " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... False False \n",
- "... ... ... ... \n",
- " 145 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 148 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 149 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... False False \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... False False \n",
+ " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... False False \n",
+ " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... False False \n",
+ "... ... ... ... \n",
+ " 145 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 148 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 149 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
"\n",
- " is_D1 is_neuron target_stim nontarget_stim in_target_barrel \n",
+ " is_neuron is_D1 target_stim nontarget_stim in_target_barrel \n",
"roi# trial# \n",
- "0 0 True True C1_10_90 D1_10 False \\\n",
- " 1 True True D1_10_90 C1_NaN True \n",
- " 2 True True C1_10_20 D1_10 False \n",
- " 3 True True D1_10_20 C1_NaN True \n",
- " 4 True True D1_10_90 C1_NaN True \n",
- "... ... ... ... ... ... \n",
- " 145 True True C1_10_90 D1_10 False \n",
- " 146 True True C1_10_20 D1_10 False \n",
- " 147 True True C1_10_20 D1_NaN False \n",
- " 148 True True C1_10_20 D1_NaN False \n",
- " 149 True True D1_10_20 C1_10 True \n",
+ "0 0 True True C1_10_90 D1_10 False \\\n",
+ " 1 True True D1_10_90 C1_NaN True \n",
+ " 2 True True C1_10_20 D1_10 False \n",
+ " 3 True True D1_10_20 C1_NaN True \n",
+ " 4 True True D1_10_90 C1_NaN True \n",
+ "... ... ... ... ... ... \n",
+ " 145 True True C1_10_90 D1_10 False \n",
+ " 146 True True C1_10_20 D1_10 False \n",
+ " 147 True True C1_10_20 D1_NaN False \n",
+ " 148 True True C1_10_20 D1_NaN False \n",
+ " 149 True True D1_10_20 C1_10 True \n",
"\n",
" target_amplitude nontarget_amplitude Result \n",
"roi# trial# \n",
@@ -3338,7 +3276,7 @@
"[150 rows x 18 columns]"
]
},
- "execution_count": 159,
+ "execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
@@ -3349,7 +3287,7 @@
},
{
"cell_type": "code",
- "execution_count": 158,
+ "execution_count": 97,
"id": "2f31861e-ab0e-4127-952f-c8ab6d60bfff",
"metadata": {
"tags": []
@@ -3361,7 +3299,7 @@
"150"
]
},
- "execution_count": 158,
+ "execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
@@ -3371,36 +3309,23 @@
]
},
{
- "cell_type": "code",
- "execution_count": 160,
- "id": "dc05bfc9-a75d-4091-86f0-80ccd7dbfd57",
+ "cell_type": "markdown",
+ "id": "f49fe32a-3f59-461b-8c72-9e877c8f55cc",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.5694736842105264"
- ]
- },
- "execution_count": 160,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
+ "#we added this to the function we wrote \n",
"value = 0\n",
"for item in scores:\n",
" value = value + item[\"score\"]\n",
"average_score = value/len(scores)\n",
- "average_score\n",
- " "
+ "average_score"
]
},
{
"cell_type": "code",
- "execution_count": 200,
+ "execution_count": 116,
"id": "4779a44d-ed3b-4256-8df7-131bbbb6cfd6",
"metadata": {
"tags": []
@@ -3412,13 +3337,13 @@
"(0.0, 1.0)"
]
},
- "execution_count": 200,
+ "execution_count": 116,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -3435,31 +3360,22 @@
},
{
"cell_type": "code",
- "execution_count": 165,
+ "execution_count": 101,
"id": "ab42b08a-276c-4f71-b22d-16bf247529cc",
"metadata": {
"tags": []
},
"outputs": [
{
- "data": {
- "text/plain": [
- "(0.0, 1.0)"
- ]
- },
- "execution_count": 165,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "ename": "NameError",
+ "evalue": "name 'scores' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[101], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m values \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m \u001b[43mscores\u001b[49m:\n\u001b[0;32m 3\u001b[0m values\u001b[38;5;241m.\u001b[39mappend(item[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscore\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(values,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'scores' is not defined"
+ ]
}
],
"source": [
@@ -3473,924 +3389,22 @@
},
{
"cell_type": "code",
- "execution_count": 156,
+ "execution_count": 117,
"id": "6faa81d8-957a-419a-b56b-9280f979fd22",
"metadata": {
- "collapsed": true,
- "jupyter": {
- "outputs_hidden": true
- },
"tags": []
},
"outputs": [
{
- "data": {
- "text/plain": [
- "[{'score': 0.5789473684210527,\n",
- " 'training_trials': Index([ 13, 73, 81, 63, 112, 26, 77, 72, 107, 8,\n",
- " ...\n",
- " 50, 20, 149, 103, 142, 64, 96, 101, 89, 141],\n",
- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 4, 7, 12, 14, 16, 24, 27, 32, 33, 38, 39, 42, 43,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 8, 9, 13, 14, 16, 17, 20, 21, 28, 38, 42, 46, 50,\n",
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- " 'training_trials': Index([ 57, 61, 46, 21, 11, 93, 89, 112, 83, 35,\n",
- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 5, 7, 8, 10, 13, 14, 19, 28, 29, 32, 39, 40, 44, 45,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 11, 12, 13, 14, 15, 26, 32, 35, 46, 48, 64, 67, 69,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 3, 5, 14, 15, 18, 19, 20, 21, 23, 25, 26, 35, 43,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 5, 8, 9, 18, 21, 24, 28, 29, 35, 36, 38, 40, 48,\n",
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- " {'score': 0.4473684210526316,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 3, 7, 8, 11, 19, 22, 32, 35, 37, 38, 43, 48, 49, 56,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 7, 26, 29, 30, 35, 36, 37, 41, 50, 52, 53, 57, 58,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 4, 5, 12, 13, 15, 20, 22, 26, 28, 31, 40, 41, 42, 44,\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 11, 16, 17, 21, 27, 38, 46, 49, 52, 56, 60, 64, 65,\n",
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- " {'score': 0.5526315789473685,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 4, 5, 11, 16, 17, 22, 30, 35, 36, 38, 43, 49, 51,\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 3, 4, 7, 10, 12, 16, 17, 18, 23, 27, 35, 36, 37,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 3, 8, 11, 15, 18, 19, 26, 29, 33, 34, 45, 46, 51, 66,\n",
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- " {'score': 0.5789473684210527,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 6, 7, 8, 12, 18, 22, 23, 24, 35, 38, 43, 44, 49,\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 3, 11, 14, 16, 17, 19, 26, 32, 34, 37, 43, 48, 51, 52,\n",
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- " {'score': 0.6578947368421053,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 8, 10, 16, 21, 22, 27, 35, 47, 48, 53, 57, 61, 64, 72,\n",
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- " {'score': 0.5263157894736842,\n",
- " 'training_trials': Index([120, 91, 73, 71, 51, 45, 93, 44, 66, 139,\n",
- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 11, 13, 14, 18, 28, 30, 33, 36, 37, 42, 43, 46, 48, 49,\n",
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- " {'score': 0.5789473684210527,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 2, 4, 14, 21, 22, 23, 24, 29, 33, 35, 36, 38, 45,\n",
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- " {'score': 0.4473684210526316,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 3, 5, 6, 11, 13, 14, 17, 24, 32, 36, 37, 40, 49,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 3, 13, 14, 16, 19, 23, 26, 28, 31, 32, 34, 35, 40, 42,\n",
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- " {'score': 0.631578947368421,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 10, 15, 16, 17, 23, 29, 30, 31, 32, 36, 40, 45, 52,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 1, 3, 7, 9, 11, 24, 26, 28, 34, 36, 46, 49, 52,\n",
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- " {'score': 0.5526315789473685,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 2, 5, 13, 15, 19, 22, 27, 31, 35, 43, 44, 51, 56,\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 8, 12, 16, 17, 20, 21, 22, 23, 27, 32, 36, 39, 43,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 5, 7, 13, 15, 17, 18, 19, 22, 24, 29, 44, 45, 47,\n",
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- " {'score': 0.5526315789473685,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 2, 5, 11, 18, 29, 30, 31, 33, 43, 44, 48, 50, 52,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 13, 21, 23, 25, 31, 35, 37, 47, 49, 50, 54, 60, 63, 70,\n",
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- " {'score': 0.6578947368421053,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 3, 10, 11, 25, 33, 37, 41, 42, 43, 44, 62, 64, 66,\n",
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- " ...\n",
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- " 'test_trials': Index([ 3, 8, 11, 12, 13, 18, 29, 34, 36, 41, 42, 50, 55, 58,\n",
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- " {'score': 0.5789473684210527,\n",
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- " ...\n",
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- " 'test_trials': Index([ 0, 11, 14, 17, 22, 27, 30, 33, 36, 37, 46, 51, 55, 56,\n",
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- " {'score': 0.5526315789473685,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 6, 7, 10, 12, 15, 19, 24, 25, 29, 37, 39, 46, 55, 63,\n",
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- " {'score': 0.5,\n",
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- " ...\n",
- " 143, 89, 120, 80, 103, 25, 61, 123, 116, 66],\n",
- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 3, 6, 7, 10, 12, 13, 14, 16, 20, 22, 24, 29, 30,\n",
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- " 119, 124, 128, 131, 132, 133, 135, 136, 141, 149],\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
- " 88, 14, 1, 99, 27, 118, 128, 31, 83, 18],\n",
- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 5, 11, 17, 19, 22, 24, 26, 30, 33, 34, 36, 37, 43, 46,\n",
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- " {'score': 0.5789473684210527,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 1, 5, 6, 7, 8, 9, 17, 21, 24, 27, 36, 46, 48,\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " 'test_trials': Index([ 0, 2, 4, 11, 13, 15, 17, 18, 24, 34, 38, 39, 42, 48,\n",
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- " ...\n",
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- " 'test_trials': Index([ 0, 7, 14, 17, 20, 23, 24, 26, 27, 32, 33, 38, 43, 44,\n",
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- " 'training_trials': Index([ 95, 23, 39, 30, 8, 50, 59, 6, 126, 32,\n",
- " ...\n",
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- " 'test_trials': Index([ 3, 9, 17, 19, 20, 21, 26, 35, 36, 41, 42, 46, 47, 60,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 7, 10, 11, 13, 16, 18, 19, 20, 22, 28, 32, 33, 35,\n",
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- " {'score': 0.6578947368421053,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 6, 13, 17, 21, 23, 24, 27, 34, 36, 37, 38, 47, 50,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 9, 16, 19, 22, 26, 28, 34, 36, 37, 42, 49, 53, 54, 58,\n",
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- " 'training_trials': Index([ 92, 81, 22, 145, 60, 144, 75, 3, 68, 97,\n",
- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 2, 4, 9, 13, 16, 17, 19, 20, 29, 32, 33, 34, 35,\n",
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- " {'score': 0.5,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 2, 5, 10, 16, 18, 20, 27, 29, 34, 35, 37, 40, 56,\n",
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- " {'score': 0.6578947368421053,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 6, 12, 13, 14, 19, 21, 26, 29, 31, 40, 42, 44, 54,\n",
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- " {'score': 0.5789473684210527,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 14, 17, 21, 23, 24, 28, 30, 32, 33, 35, 37, 42, 50,\n",
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- " {'score': 0.5526315789473685,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 2, 12, 14, 17, 19, 23, 28, 30, 36, 37, 46, 48, 59,\n",
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- " {'score': 0.5526315789473685,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 4, 5, 6, 16, 18, 22, 23, 27, 29, 30, 31, 41, 48, 51,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 5, 10, 23, 26, 30, 34, 36, 37, 40, 41, 42, 44, 48, 50,\n",
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- " {'score': 0.631578947368421,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 2, 6, 14, 19, 20, 23, 24, 29, 31, 32, 37, 38, 42,\n",
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- " {'score': 0.5789473684210527,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 5, 10, 13, 16, 22, 23, 25, 27, 37, 41, 45, 51, 53, 55,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 2, 3, 4, 6, 13, 17, 20, 21, 25, 29, 42, 45, 47,\n",
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- " {'score': 0.4473684210526316,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 2, 5, 18, 19, 21, 22, 31, 36, 40, 47, 52, 54, 57,\n",
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- " {'score': 0.5789473684210527,\n",
- " 'training_trials': Index([ 6, 98, 8, 102, 101, 136, 137, 106, 56, 18,\n",
- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 10, 12, 13, 16, 19, 31, 35, 45, 47, 50, 57, 60, 67,\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 15, 18, 19, 22, 33, 36, 37, 47, 52, 54, 55, 66, 68,\n",
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- " {'score': 0.631578947368421,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 2, 13, 15, 19, 29, 37, 45, 51, 53, 54, 57, 58, 63,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 1, 8, 10, 19, 25, 32, 33, 44, 54, 57, 61, 63, 64,\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 8, 12, 16, 17, 18, 20, 32, 33, 36, 38, 42, 47, 49, 58,\n",
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- " {'score': 0.631578947368421,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 3, 4, 6, 7, 23, 29, 34, 38, 48, 51, 54, 61, 62, 66,\n",
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- " {'score': 0.5789473684210527,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 11, 16, 17, 19, 22, 26, 29, 30, 33, 34, 42, 46, 48,\n",
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- " {'score': 0.6578947368421053,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 6, 7, 10, 13, 15, 16, 20, 23, 24, 34, 40, 41, 50,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 4, 6, 7, 9, 12, 13, 20, 22, 27, 28, 41, 44, 45, 47,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 4, 8, 9, 10, 20, 27, 30, 37, 45, 46, 48, 49, 55, 69,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 4, 5, 7, 12, 27, 29, 32, 34, 45, 50, 56, 57, 58, 59,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 3, 4, 5, 14, 15, 22, 31, 35, 42, 47, 48, 53, 56,\n",
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- " {'score': 0.5789473684210527,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 1, 2, 13, 20, 22, 23, 24, 25, 31, 35, 36, 38, 45,\n",
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- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 5, 12, 21, 24, 27, 35, 39, 44, 45, 47, 52, 56, 61,\n",
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- " {'score': 0.6578947368421053,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 3, 5, 11, 12, 16, 18, 20, 22, 25, 26, 44, 48, 52,\n",
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- " {'score': 0.5526315789473685,\n",
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- " ...\n",
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- " 'test_trials': Index([ 0, 2, 9, 10, 12, 13, 15, 19, 23, 24, 45, 48, 55, 57,\n",
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- " {'score': 0.4473684210526316,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 2, 5, 7, 9, 14, 15, 16, 18, 23, 35, 38, 49, 56,\n",
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- " {'score': 0.6578947368421053,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 4, 7, 10, 16, 24, 26, 30, 34, 37, 58, 65, 68, 70, 73,\n",
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- " {'score': 0.5,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
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- " ...\n",
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- " 'test_trials': Index([ 6, 9, 10, 14, 20, 23, 26, 34, 38, 44, 47, 49, 53, 57,\n",
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- " ...\n",
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- " 'test_trials': Index([ 4, 12, 13, 15, 19, 24, 25, 26, 36, 42, 43, 48, 49, 50,\n",
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- " ...\n",
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- " 'test_trials': Index([ 3, 9, 10, 14, 19, 22, 24, 26, 28, 33, 39, 40, 41, 43,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
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- " 'test_trials': Index([ 10, 15, 16, 20, 24, 26, 27, 31, 37, 40, 41, 49, 50, 53,\n",
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- " {'score': 0.5,\n",
- " 'training_trials': Index([ 54, 79, 118, 7, 39, 43, 50, 145, 142, 105,\n",
- " ...\n",
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- " 'test_trials': Index([ 1, 6, 11, 16, 18, 25, 46, 47, 48, 49, 51, 52, 55, 56,\n",
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- " {'score': 0.5789473684210527,\n",
- " 'training_trials': Index([ 32, 121, 20, 71, 115, 4, 99, 24, 60, 12,\n",
- " ...\n",
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- " 'test_trials': Index([ 0, 3, 7, 9, 11, 15, 19, 22, 29, 30, 37, 50, 51, 52,\n",
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- " {'score': 0.6578947368421053,\n",
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- " ...\n",
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- " 'test_trials': Index([ 0, 3, 7, 12, 16, 17, 22, 27, 31, 38, 40, 43, 45, 52,\n",
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- " 'test_trials': Index([ 3, 6, 9, 11, 12, 14, 16, 28, 29, 34, 41, 42, 44, 46,\n",
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- " 'test_trials': Index([ 1, 2, 6, 9, 10, 20, 23, 25, 26, 33, 35, 37, 38, 44,\n",
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- " ...\n",
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- " 'test_trials': Index([ 5, 16, 23, 29, 34, 35, 39, 52, 55, 57, 59, 61, 70, 72,\n",
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- " ...\n",
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- " 'test_trials': Index([ 1, 2, 8, 9, 14, 15, 26, 28, 36, 37, 44, 49, 52, 53,\n",
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- " ...\n",
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- " 'test_trials': Index([ 2, 3, 4, 9, 11, 14, 16, 17, 18, 28, 32, 35, 36, 37,\n",
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- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 3, 9, 12, 16, 21, 22, 27, 28, 37, 40, 43, 46, 47,\n",
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- " dtype='int64', name='trial#')},\n",
- " {'score': 0.5263157894736842,\n",
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- " ...\n",
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- " 'test_trials': Index([ 1, 2, 5, 10, 11, 16, 18, 26, 39, 40, 43, 44, 48, 53,\n",
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- " ...\n",
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- " 'test_trials': Index([ 0, 2, 3, 6, 9, 14, 18, 19, 28, 31, 37, 38, 39, 42,\n",
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- " {'score': 0.5526315789473685,\n",
- " 'training_trials': Index([ 61, 58, 96, 109, 45, 72, 108, 137, 4, 42,\n",
- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 1, 2, 3, 9, 10, 17, 18, 19, 20, 22, 31, 41, 55, 62,\n",
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- " {'score': 0.5789473684210527,\n",
- " 'training_trials': Index([129, 28, 42, 45, 59, 76, 3, 20, 38, 74,\n",
- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 5, 10, 12, 15, 18, 23, 24, 26, 34, 35, 36, 39, 40,\n",
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- " {'score': 0.6052631578947368,\n",
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- " ...\n",
- " 78, 9, 42, 92, 112, 85, 2, 41, 95, 12],\n",
- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 3, 4, 5, 6, 14, 15, 16, 17, 23, 28, 32, 40, 48, 51,\n",
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- " dtype='int64', name='trial#')},\n",
- " {'score': 0.5263157894736842,\n",
- " 'training_trials': Index([ 32, 42, 98, 82, 108, 33, 59, 130, 116, 96,\n",
- " ...\n",
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- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 2, 16, 18, 19, 23, 24, 28, 30, 31, 34, 38, 43, 44, 51,\n",
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- " {'score': 0.7631578947368421,\n",
- " 'training_trials': Index([103, 1, 132, 10, 51, 141, 23, 131, 142, 80,\n",
- " ...\n",
- " 57, 73, 116, 144, 128, 53, 126, 32, 130, 65],\n",
- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 4, 6, 11, 15, 20, 28, 33, 35, 40, 42, 43, 47, 48, 49,\n",
- " 54, 58, 60, 62, 64, 67, 68, 69, 78, 82, 83, 86, 88, 97,\n",
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- " dtype='int64', name='trial#')},\n",
- " {'score': 0.5526315789473685,\n",
- " 'training_trials': Index([ 67, 98, 136, 40, 112, 54, 110, 103, 61, 80,\n",
- " ...\n",
- " 145, 36, 93, 73, 19, 46, 148, 17, 140, 122],\n",
- " dtype='int64', name='trial#', length=112),\n",
- " 'test_trials': Index([ 0, 9, 14, 15, 18, 23, 24, 28, 29, 34, 35, 41, 43, 48,\n",
- " 50, 51, 59, 60, 62, 63, 75, 77, 81, 86, 87, 92, 100, 104,\n",
- " 114, 120, 126, 129, 130, 131, 137, 138, 144, 149],\n",
- " dtype='int64', name='trial#')}]"
- ]
- },
- "execution_count": 156,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "NameError",
+ "evalue": "name 'scores' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[117], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mscores\u001b[49m\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'scores' is not defined"
+ ]
}
],
"source": [
@@ -4399,13 +3413,9 @@
},
{
"cell_type": "code",
- "execution_count": 148,
+ "execution_count": 102,
"id": "8404239f-58ea-4281-8d20-c4f33aecdf56",
"metadata": {
- "collapsed": true,
- "jupyter": {
- "outputs_hidden": true
- },
"tags": []
},
"outputs": [
@@ -4436,10 +3446,10 @@
" <th>Fneu</th>\n",
" <th>Fneu_var</th>\n",
" <th>spks</th>\n",
- " <th>is_C1</th>\n",
" <th>is_VGAT</th>\n",
- " <th>is_D1</th>\n",
+ " <th>is_C1</th>\n",
" <th>is_neuron</th>\n",
+ " <th>is_D1</th>\n",
" <th>target_stim</th>\n",
" <th>nontarget_stim</th>\n",
" <th>in_target_barrel</th>\n",
@@ -4476,33 +3486,54 @@
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"11\" valign=\"top\">0</th>\n",
- " <th>107</th>\n",
- " <td>[48.992218017578125, 60.61746597290039, 48.668...</td>\n",
- " <td>[-0.11873760946963374, -0.148262305969889, 0.1...</td>\n",
- " <td>[53.78728485107422, 52.691932678222656, 64.415...</td>\n",
- " <td>[-0.11873760946963374, -0.148262305969889, 0.1...</td>\n",
+ " <th>27</th>\n",
+ " <td>[84.06704711914062, 64.12518310546875, 82.7639...</td>\n",
+ " <td>[0.13275637081627137, -0.5002529915514548, -0....</td>\n",
+ " <td>[65.09046173095703, 41.603912353515625, 56.581...</td>\n",
+ " <td>[0.13275637081627137, -0.5002529915514548, -0....</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
- " <td>D1_10_90</td>\n",
- " <td>C1_NaN</td>\n",
- " <td>True</td>\n",
- " <td>10_90</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
" <td>0</td>\n",
- " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[]</td>\n",
- " <td>[-0.23296836940969676, -0.1696559648641047, -0...</td>\n",
+ " <td>[0.12941679310412216, -0.1998458966893841, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>89</th>\n",
+ " <td>[100.48384094238281, 58.31154251098633, 34.267...</td>\n",
+ " <td>[-0.2118148036565822, -0.17777621414661174, -0...</td>\n",
+ " <td>[52.82884979248047, 54.09291076660156, 58.7799...</td>\n",
+ " <td>[-0.2118148036565822, -0.17777621414661174, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 2.820892810821533, 5.042201519...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>3.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.3160092699178422, -0.2899910985571208, -0....</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>45</th>\n",
- " <td>[73.36776733398438, 87.6191635131836, 54.31721...</td>\n",
- " <td>[0.19683796406183698, 0.08509014106620992, 0.0...</td>\n",
- " <td>[66.78239440917969, 62.640586853027344, 59.699...</td>\n",
- " <td>[0.19683796406183698, 0.08509014106620992, 0.0...</td>\n",
- " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 17.804487228393...</td>\n",
+ " <th>61</th>\n",
+ " <td>[45.047698974609375, 80.32709503173828, 86.065...</td>\n",
+ " <td>[0.024156074799225682, -0.29131259425713013, 0...</td>\n",
+ " <td>[61.78728485107422, 50.0831298828125, 66.60635...</td>\n",
+ " <td>[0.024156074799225682, -0.29131259425713013, 0...</td>\n",
+ " <td>[0.0, 1.9810150861740112, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
@@ -4515,36 +3546,36 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[]</td>\n",
- " <td>[-0.39907746867152893, -0.34447767683243824, -...</td>\n",
+ " <td>[-0.14430546202944736, -0.19186351520268025, -...</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>121</th>\n",
- " <td>[109.87422943115234, 66.23828887939453, 75.203...</td>\n",
- " <td>[-0.01965172476274222, -0.08020826439083625, 0...</td>\n",
- " <td>[57.84352111816406, 55.59657669067383, 61.9486...</td>\n",
- " <td>[-0.01965172476274222, -0.08020826439083625, 0...</td>\n",
- " <td>[11.022229194641113, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <th>29</th>\n",
+ " <td>[52.862815856933594, 81.0198974609375, 58.4936...</td>\n",
+ " <td>[-0.08420647129191677, -0.05084344653211932, -...</td>\n",
+ " <td>[57.039119720458984, 58.27383804321289, 45.249...</td>\n",
+ " <td>[-0.08420647129191677, -0.05084344653211932, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
- " <td>D1_10_20</td>\n",
+ " <td>D1_10_90</td>\n",
" <td>C1_10</td>\n",
" <td>True</td>\n",
- " <td>10_20</td>\n",
+ " <td>10_90</td>\n",
" <td>10</td>\n",
- " <td>2.0</td>\n",
+ " <td>3.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[-0.3519401221905607, 0.004602235506506332, 0....</td>\n",
+ " <td>[-0.42875129629467834, -0.4122965672741163, -0...</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>56</th>\n",
- " <td>[83.6211929321289, 89.99833679199219, 38.67985...</td>\n",
- " <td>[0.09419315954663361, 0.08293464780154033, 0.0...</td>\n",
- " <td>[63.21516036987305, 62.79706573486328, 62.0782...</td>\n",
- " <td>[0.09419315954663361, 0.08293464780154033, 0.0...</td>\n",
- " <td>[0.0, 0.0, 0.0, 0.0, 12.035335540771484, 0.0, ...</td>\n",
+ " <th>145</th>\n",
+ " <td>[74.84498596191406, 51.94773864746094, 107.228...</td>\n",
+ " <td>[-0.01820918607240805, -0.015286885737646978, ...</td>\n",
+ " <td>[57.141807556152344, 57.246944427490234, 57.49...</td>\n",
+ " <td>[-0.01820918607240805, -0.015286885737646978, ...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
@@ -4557,28 +3588,7 @@
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[0.12088726780346111, -0.032241339133646, -0.0...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>133</th>\n",
- " <td>[58.30579376220703, 47.300148010253906, 78.277...</td>\n",
- " <td>[-0.1682515176082425, -0.15456395556673033, -0...</td>\n",
- " <td>[51.733497619628906, 52.2420539855957, 49.4303...</td>\n",
- " <td>[-0.1682515176082425, -0.15456395556673033, -0...</td>\n",
- " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.672...</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>C1_10_20</td>\n",
- " <td>D1_NaN</td>\n",
- " <td>False</td>\n",
- " <td>10_20</td>\n",
- " <td>0</td>\n",
- " <td>2.0</td>\n",
- " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[]</td>\n",
- " <td>[-0.18034109172975457, -0.009614545972992442, ...</td>\n",
+ " <td>[0.2059055774089245, 0.20968368039039287, -0.4...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -4602,109 +3612,109 @@
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>89</th>\n",
- " <td>[100.48384094238281, 58.31154251098633, 34.267...</td>\n",
- " <td>[-0.2118148036565822, -0.17777621414661174, -0...</td>\n",
- " <td>[52.82884979248047, 54.09291076660156, 58.7799...</td>\n",
- " <td>[-0.2118148036565822, -0.17777621414661174, -0...</td>\n",
- " <td>[0.0, 0.0, 0.0, 2.820892810821533, 5.042201519...</td>\n",
+ " <th>18</th>\n",
+ " <td>[57.56557083129883, 51.068687438964844, 58.784...</td>\n",
+ " <td>[-0.005997255257728517, -0.2022945641621877, 0...</td>\n",
+ " <td>[59.70415496826172, 52.420536041259766, 59.970...</td>\n",
+ " <td>[-0.005997255257728517, -0.2022945641621877, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0834541320800...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>D1_10_20</td>\n",
- " <td>C1_10</td>\n",
+ " <td>C1_NaN</td>\n",
" <td>True</td>\n",
" <td>10_20</td>\n",
- " <td>10</td>\n",
+ " <td>0</td>\n",
" <td>3.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[-0.3160092699178422, -0.2899910985571208, -0....</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.05333520181619443, -0.037444531879346, -0....</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>32</th>\n",
- " <td>[88.6243896484375, 66.70386505126953, 73.59136...</td>\n",
- " <td>[-0.3233407345214396, -0.12016641033468067, -0...</td>\n",
- " <td>[47.98288345336914, 55.52322769165039, 52.1246...</td>\n",
- " <td>[-0.3233407345214396, -0.12016641033468067, -0...</td>\n",
- " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <th>46</th>\n",
+ " <td>[66.25225067138672, 70.46928405761719, 81.0243...</td>\n",
+ " <td>[-0.2951114471823508, -0.26006837876560024, -0...</td>\n",
+ " <td>[48.51833724975586, 49.81418228149414, 55.4621...</td>\n",
+ " <td>[-0.2951114471823508, -0.26006837876560024, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.2597560882568...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
- " <td>C1_10_90</td>\n",
- " <td>D1_10</td>\n",
- " <td>False</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
" <td>10_90</td>\n",
" <td>10</td>\n",
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[-0.19784900826085494, 0.062175475457198905, -...</td>\n",
+ " <td>[-0.27907480779008736, 0.03636179081996047, 0....</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>7</th>\n",
- " <td>[104.04031372070312, 89.15300750732422, 81.111...</td>\n",
- " <td>[-0.024577687714442492, -0.13861195509594057, ...</td>\n",
- " <td>[61.811737060546875, 57.57212829589844, 53.838...</td>\n",
- " <td>[-0.024577687714442492, -0.13861195509594057, ...</td>\n",
- " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <th>60</th>\n",
+ " <td>[43.493316650390625, 67.37313079833984, 41.901...</td>\n",
+ " <td>[-0.15413821610496273, -0.16581109112988884, -...</td>\n",
+ " <td>[54.811737060546875, 54.37897491455078, 53.264...</td>\n",
+ " <td>[-0.15413821610496273, -0.16581109112988884, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 1.8363639116287231, 0.0, ...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
- " <td>D1_10_90</td>\n",
- " <td>C1_10</td>\n",
- " <td>True</td>\n",
- " <td>10_90</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
" <td>10</td>\n",
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[-0.17903471929644427, -0.11314780519622668, -...</td>\n",
+ " <td>[-0.17100977900144365, -0.23644636047852982, -...</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>54</th>\n",
- " <td>[79.631591796875, 99.23580169677734, 67.710121...</td>\n",
- " <td>[0.22314286333295716, -0.24837688663163768, -0...</td>\n",
- " <td>[67.65525817871094, 50.1589241027832, 48.75061...</td>\n",
- " <td>[0.22314286333295716, -0.24837688663163768, -0...</td>\n",
+ " <th>1</th>\n",
+ " <td>[67.9014892578125, 84.18050384521484, 79.32645...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
+ " <td>[52.151588439941406, 65.11491394042969, 51.899...</td>\n",
+ " <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>D1_10_90</td>\n",
- " <td>C1_10</td>\n",
+ " <td>C1_NaN</td>\n",
" <td>True</td>\n",
" <td>10_90</td>\n",
- " <td>10</td>\n",
+ " <td>0</td>\n",
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[-0.2976068960015559, -0.2337016056030365, -0....</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.13887659963863402, 0.4147024933008791, 0.4...</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>49</th>\n",
- " <td>[78.33626556396484, 63.61309051513672, 45.4645...</td>\n",
- " <td>[-0.05601202025643073, 0.10586576435554138, -0...</td>\n",
- " <td>[57.44009780883789, 63.44743347167969, 57.8606...</td>\n",
- " <td>[-0.05601202025643073, 0.10586576435554138, -0...</td>\n",
- " <td>[0.0, 0.0, 0.0, 2.0834388732910156, 1.31890356...</td>\n",
+ " <th>11</th>\n",
+ " <td>[94.21552276611328, 86.79850769042969, 85.0309...</td>\n",
+ " <td>[0.10451741410126203, -0.12455170707257092, 0....</td>\n",
+ " <td>[64.93887329101562, 56.437652587890625, 71.378...</td>\n",
+ " <td>[0.10451741410126203, -0.12455170707257092, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 5.640011787414551, 0...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
- " <td>C1_10_90</td>\n",
- " <td>D1_10</td>\n",
- " <td>False</td>\n",
- " <td>10_90</td>\n",
- " <td>10</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
" <td>2.0</td>\n",
" <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
- " <td>[-0.15709045775573335, 0.11013780258789338, -0...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[0.06606075047913067, 0.03045827186574529, -0....</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -4714,148 +3724,148 @@
"text/plain": [
" F \n",
"roi# trial# \n",
- "0 107 [48.992218017578125, 60.61746597290039, 48.668... \\\n",
- " 45 [73.36776733398438, 87.6191635131836, 54.31721... \n",
- " 121 [109.87422943115234, 66.23828887939453, 75.203... \n",
- " 56 [83.6211929321289, 89.99833679199219, 38.67985... \n",
- " 133 [58.30579376220703, 47.300148010253906, 78.277... \n",
- "... ... \n",
+ "0 27 [84.06704711914062, 64.12518310546875, 82.7639... \\\n",
" 89 [100.48384094238281, 58.31154251098633, 34.267... \n",
- " 32 [88.6243896484375, 66.70386505126953, 73.59136... \n",
- " 7 [104.04031372070312, 89.15300750732422, 81.111... \n",
- " 54 [79.631591796875, 99.23580169677734, 67.710121... \n",
- " 49 [78.33626556396484, 63.61309051513672, 45.4645... \n",
+ " 61 [45.047698974609375, 80.32709503173828, 86.065... \n",
+ " 29 [52.862815856933594, 81.0198974609375, 58.4936... \n",
+ " 145 [74.84498596191406, 51.94773864746094, 107.228... \n",
+ "... ... \n",
+ " 18 [57.56557083129883, 51.068687438964844, 58.784... \n",
+ " 46 [66.25225067138672, 70.46928405761719, 81.0243... \n",
+ " 60 [43.493316650390625, 67.37313079833984, 41.901... \n",
+ " 1 [67.9014892578125, 84.18050384521484, 79.32645... \n",
+ " 11 [94.21552276611328, 86.79850769042969, 85.0309... \n",
"\n",
" F_var \n",
"roi# trial# \n",
- "0 107 [-0.11873760946963374, -0.148262305969889, 0.1... \\\n",
- " 45 [0.19683796406183698, 0.08509014106620992, 0.0... \n",
- " 121 [-0.01965172476274222, -0.08020826439083625, 0... \n",
- " 56 [0.09419315954663361, 0.08293464780154033, 0.0... \n",
- " 133 [-0.1682515176082425, -0.15456395556673033, -0... \n",
- "... ... \n",
+ "0 27 [0.13275637081627137, -0.5002529915514548, -0.... \\\n",
" 89 [-0.2118148036565822, -0.17777621414661174, -0... \n",
- " 32 [-0.3233407345214396, -0.12016641033468067, -0... \n",
- " 7 [-0.024577687714442492, -0.13861195509594057, ... \n",
- " 54 [0.22314286333295716, -0.24837688663163768, -0... \n",
- " 49 [-0.05601202025643073, 0.10586576435554138, -0... \n",
+ " 61 [0.024156074799225682, -0.29131259425713013, 0... \n",
+ " 29 [-0.08420647129191677, -0.05084344653211932, -... \n",
+ " 145 [-0.01820918607240805, -0.015286885737646978, ... \n",
+ "... ... \n",
+ " 18 [-0.005997255257728517, -0.2022945641621877, 0... \n",
+ " 46 [-0.2951114471823508, -0.26006837876560024, -0... \n",
+ " 60 [-0.15413821610496273, -0.16581109112988884, -... \n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 11 [0.10451741410126203, -0.12455170707257092, 0.... \n",
"\n",
" Fneu \n",
"roi# trial# \n",
- "0 107 [53.78728485107422, 52.691932678222656, 64.415... \\\n",
- " 45 [66.78239440917969, 62.640586853027344, 59.699... \n",
- " 121 [57.84352111816406, 55.59657669067383, 61.9486... \n",
- " 56 [63.21516036987305, 62.79706573486328, 62.0782... \n",
- " 133 [51.733497619628906, 52.2420539855957, 49.4303... \n",
- "... ... \n",
+ "0 27 [65.09046173095703, 41.603912353515625, 56.581... \\\n",
" 89 [52.82884979248047, 54.09291076660156, 58.7799... \n",
- " 32 [47.98288345336914, 55.52322769165039, 52.1246... \n",
- " 7 [61.811737060546875, 57.57212829589844, 53.838... \n",
- " 54 [67.65525817871094, 50.1589241027832, 48.75061... \n",
- " 49 [57.44009780883789, 63.44743347167969, 57.8606... \n",
+ " 61 [61.78728485107422, 50.0831298828125, 66.60635... \n",
+ " 29 [57.039119720458984, 58.27383804321289, 45.249... \n",
+ " 145 [57.141807556152344, 57.246944427490234, 57.49... \n",
+ "... ... \n",
+ " 18 [59.70415496826172, 52.420536041259766, 59.970... \n",
+ " 46 [48.51833724975586, 49.81418228149414, 55.4621... \n",
+ " 60 [54.811737060546875, 54.37897491455078, 53.264... \n",
+ " 1 [52.151588439941406, 65.11491394042969, 51.899... \n",
+ " 11 [64.93887329101562, 56.437652587890625, 71.378... \n",
"\n",
" Fneu_var \n",
"roi# trial# \n",
- "0 107 [-0.11873760946963374, -0.148262305969889, 0.1... \\\n",
- " 45 [0.19683796406183698, 0.08509014106620992, 0.0... \n",
- " 121 [-0.01965172476274222, -0.08020826439083625, 0... \n",
- " 56 [0.09419315954663361, 0.08293464780154033, 0.0... \n",
- " 133 [-0.1682515176082425, -0.15456395556673033, -0... \n",
- "... ... \n",
+ "0 27 [0.13275637081627137, -0.5002529915514548, -0.... \\\n",
" 89 [-0.2118148036565822, -0.17777621414661174, -0... \n",
- " 32 [-0.3233407345214396, -0.12016641033468067, -0... \n",
- " 7 [-0.024577687714442492, -0.13861195509594057, ... \n",
- " 54 [0.22314286333295716, -0.24837688663163768, -0... \n",
- " 49 [-0.05601202025643073, 0.10586576435554138, -0... \n",
+ " 61 [0.024156074799225682, -0.29131259425713013, 0... \n",
+ " 29 [-0.08420647129191677, -0.05084344653211932, -... \n",
+ " 145 [-0.01820918607240805, -0.015286885737646978, ... \n",
+ "... ... \n",
+ " 18 [-0.005997255257728517, -0.2022945641621877, 0... \n",
+ " 46 [-0.2951114471823508, -0.26006837876560024, -0... \n",
+ " 60 [-0.15413821610496273, -0.16581109112988884, -... \n",
+ " 1 [-0.32539554129285003, 0.023858377160785173, -... \n",
+ " 11 [0.10451741410126203, -0.12455170707257092, 0.... \n",
"\n",
- " spks is_C1 is_VGAT \n",
+ " spks is_VGAT is_C1 \n",
"roi# trial# \n",
- "0 107 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \\\n",
- " 45 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 17.804487228393... False False \n",
- " 121 [11.022229194641113, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 56 [0.0, 0.0, 0.0, 0.0, 12.035335540771484, 0.0, ... False False \n",
- " 133 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.672... False False \n",
- "... ... ... ... \n",
- " 89 [0.0, 0.0, 0.0, 2.820892810821533, 5.042201519... False False \n",
- " 32 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 7 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 54 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
- " 49 [0.0, 0.0, 0.0, 2.0834388732910156, 1.31890356... False False \n",
+ "0 27 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \\\n",
+ " 89 [0.0, 0.0, 0.0, 2.820892810821533, 5.042201519... False False \n",
+ " 61 [0.0, 1.9810150861740112, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 29 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 145 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ "... ... ... ... \n",
+ " 18 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0834541320800... False False \n",
+ " 46 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.2597560882568... False False \n",
+ " 60 [0.0, 0.0, 0.0, 0.0, 1.8363639116287231, 0.0, ... False False \n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 11 [0.0, 0.0, 0.0, 0.0, 0.0, 5.640011787414551, 0... False False \n",
"\n",
- " is_D1 is_neuron target_stim nontarget_stim in_target_barrel \n",
+ " is_neuron is_D1 target_stim nontarget_stim in_target_barrel \n",
"roi# trial# \n",
- "0 107 True True D1_10_90 C1_NaN True \\\n",
- " 45 True True C1_10_90 D1_NaN False \n",
- " 121 True True D1_10_20 C1_10 True \n",
- " 56 True True C1_10_90 D1_10 False \n",
- " 133 True True C1_10_20 D1_NaN False \n",
- "... ... ... ... ... ... \n",
- " 89 True True D1_10_20 C1_10 True \n",
- " 32 True True C1_10_90 D1_10 False \n",
- " 7 True True D1_10_90 C1_10 True \n",
- " 54 True True D1_10_90 C1_10 True \n",
- " 49 True True C1_10_90 D1_10 False \n",
+ "0 27 True True C1_10_20 D1_NaN False \\\n",
+ " 89 True True D1_10_20 C1_10 True \n",
+ " 61 True True C1_10_90 D1_NaN False \n",
+ " 29 True True D1_10_90 C1_10 True \n",
+ " 145 True True C1_10_90 D1_10 False \n",
+ "... ... ... ... ... ... \n",
+ " 18 True True D1_10_20 C1_NaN True \n",
+ " 46 True True D1_10_90 C1_10 True \n",
+ " 60 True True C1_10_20 D1_10 False \n",
+ " 1 True True D1_10_90 C1_NaN True \n",
+ " 11 True True D1_10_20 C1_NaN True \n",
"\n",
" target_amplitude nontarget_amplitude Result \n",
"roi# trial# \n",
- "0 107 10_90 0 1.0 \\\n",
- " 45 10_90 0 2.0 \n",
- " 121 10_20 10 2.0 \n",
- " 56 10_90 10 2.0 \n",
- " 133 10_20 0 2.0 \n",
- "... ... ... ... \n",
+ "0 27 10_20 0 2.0 \\\n",
" 89 10_20 10 3.0 \n",
- " 32 10_90 10 2.0 \n",
- " 7 10_90 10 2.0 \n",
- " 54 10_90 10 2.0 \n",
- " 49 10_90 10 2.0 \n",
+ " 61 10_90 0 2.0 \n",
+ " 29 10_90 10 3.0 \n",
+ " 145 10_90 10 2.0 \n",
+ "... ... ... ... \n",
+ " 18 10_20 0 3.0 \n",
+ " 46 10_90 10 2.0 \n",
+ " 60 10_20 10 2.0 \n",
+ " 1 10_90 0 2.0 \n",
+ " 11 10_20 0 2.0 \n",
"\n",
" target_stim_info \n",
"roi# trial# \n",
- "0 107 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
- " 45 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 121 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 56 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 133 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- "... ... \n",
+ "0 27 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
" 89 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 32 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 7 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 54 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 49 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 61 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 29 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ " 18 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 46 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 60 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 1 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 11 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
"\n",
" nontarget_stim_info \n",
"roi# trial# \n",
- "0 107 [] \\\n",
- " 45 [] \n",
- " 121 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 56 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 133 [] \n",
- "... ... \n",
+ "0 27 [] \\\n",
" 89 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 32 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 7 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 54 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
- " 49 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 61 [] \n",
+ " 29 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ " 18 [] \n",
+ " 46 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 60 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 1 [] \n",
+ " 11 [] \n",
"\n",
" features \n",
"roi# trial# \n",
- "0 107 [-0.23296836940969676, -0.1696559648641047, -0... \n",
- " 45 [-0.39907746867152893, -0.34447767683243824, -... \n",
- " 121 [-0.3519401221905607, 0.004602235506506332, 0.... \n",
- " 56 [0.12088726780346111, -0.032241339133646, -0.0... \n",
- " 133 [-0.18034109172975457, -0.009614545972992442, ... \n",
- "... ... \n",
+ "0 27 [0.12941679310412216, -0.1998458966893841, -0.... \n",
" 89 [-0.3160092699178422, -0.2899910985571208, -0.... \n",
- " 32 [-0.19784900826085494, 0.062175475457198905, -... \n",
- " 7 [-0.17903471929644427, -0.11314780519622668, -... \n",
- " 54 [-0.2976068960015559, -0.2337016056030365, -0.... \n",
- " 49 [-0.15709045775573335, 0.11013780258789338, -0... \n",
+ " 61 [-0.14430546202944736, -0.19186351520268025, -... \n",
+ " 29 [-0.42875129629467834, -0.4122965672741163, -0... \n",
+ " 145 [0.2059055774089245, 0.20968368039039287, -0.4... \n",
+ "... ... \n",
+ " 18 [-0.05333520181619443, -0.037444531879346, -0.... \n",
+ " 46 [-0.27907480779008736, 0.03636179081996047, 0.... \n",
+ " 60 [-0.17100977900144365, -0.23644636047852982, -... \n",
+ " 1 [-0.13887659963863402, 0.4147024933008791, 0.4... \n",
+ " 11 [0.06606075047913067, 0.03045827186574529, -0.... \n",
"\n",
"[112 rows x 18 columns]"
]
},
- "execution_count": 148,
+ "execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
@@ -4876,7 +3886,7 @@
},
{
"cell_type": "code",
- "execution_count": 79,
+ "execution_count": 81,
"id": "2554b555-9b5d-4224-85bd-88e1ddc46957",
"metadata": {
"tags": []
@@ -4885,12 +3895,12 @@
{
"data": {
"text/plain": [
- "TimelinedArray([-0.2960504 , -0.21797866, -0.27632232, 0.19377695,\n",
- " 0.34522772, 0.40969928, 0.39594327, -0.16101162,\n",
- " 0.52444186, -0.14588925])"
+ "TimelinedArray([-0.40049108, -0.06159115, -0.13542947, -0.09375131,\n",
+ " -0.15822192, -0.26564429, -0.14321994, -0.1337692 ,\n",
+ " -0.49653342, -0.0535423 ])"
]
},
- "execution_count": 79,
+ "execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
@@ -4902,7 +3912,7 @@
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 82,
"id": "b8379309-d691-4ab7-b8b9-28db0920daeb",
"metadata": {
"tags": []
@@ -4914,7 +3924,7 @@
"'10_90'"
]
},
- "execution_count": 78,
+ "execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
@@ -4926,7 +3936,7 @@
},
{
"cell_type": "code",
- "execution_count": 111,
+ "execution_count": 83,
"id": "ce84f0b4-0e12-4b0e-8ddc-df338e2ba53f",
"metadata": {
"tags": []
@@ -4939,7 +3949,7 @@
},
{
"cell_type": "code",
- "execution_count": 112,
+ "execution_count": 84,
"id": "38fbe2e7-8e99-4ea1-9f7f-f5035345843c",
"metadata": {
"tags": []
@@ -4962,7 +3972,7 @@
},
{
"cell_type": "code",
- "execution_count": 114,
+ "execution_count": 85,
"id": "1cc0ec2f-62b4-42ae-8881-b59e755b206c",
"metadata": {
"tags": []
@@ -4971,13 +3981,13 @@
{
"data": {
"text/html": [
- "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div>"
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div>"
],
"text/plain": [
"LinearSVC()"
]
},
- "execution_count": 114,
+ "execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
@@ -5000,7 +4010,7 @@
},
{
"cell_type": "code",
- "execution_count": 119,
+ "execution_count": 86,
"id": "d9f099e1-4f93-4b41-8017-70da97679df2",
"metadata": {
"tags": []
@@ -5009,10 +4019,10 @@
{
"data": {
"text/plain": [
- "0.631578947368421"
+ "0.5526315789473685"
]
},
- "execution_count": 119,
+ "execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
@@ -5023,7 +4033,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 87,
"id": "a2db6962-7ff2-42ff-95a2-24e8b2ae602d",
"metadata": {
"tags": []
@@ -5037,10 +4047,10 @@
"Fneu [81.11736297607422, 93.11980438232422, 73.6161...\n",
"Fneu_var [0.44800236099089397, 0.7714516639019757, 0.24...\n",
"spks [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0...\n",
+ "is_VGAT False\n",
"is_C1 False\n",
- "is_D1 True\n",
"is_neuron True\n",
- "is_VGAT False\n",
+ "is_D1 True\n",
"target_stim C1_10_90\n",
"nontarget_stim D1_10\n",
"in_target_barrel False\n",
@@ -5049,10 +4059,11 @@
"Result 2.0\n",
"target_stim_info [{'pulse_freq': '10', 'peak_voltage': '10', 'o...\n",
"nontarget_stim_info [{'pulse_freq': '10', 'peak_voltage': '10', 'o...\n",
+ "features [-0.29373020232037816, 0.07303959775594149, 0....\n",
"Name: (0, 0), dtype: object"
]
},
- "execution_count": 12,
+ "execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
@@ -5073,7 +4084,7 @@
},
{
"cell_type": "code",
- "execution_count": 201,
+ "execution_count": 88,
"id": "74422f66-0d35-4c49-ab0d-3c0995a7e937",
"metadata": {
"tags": []
@@ -5100,12 +4111,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.478\n"
+ "0.5\n"
]
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -5134,12 +4145,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.643888888888889\n"
+ "0.6666666666666666\n"
]
},
{
"data": {
- "image/png": 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",
+ "image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
diff --git a/Imaging_exercise.ipynb b/Imaging_exercise.ipynb
index 3b3d0165f8e9bd8a2d47b7b812c94e58bcc96151..9149e4a551a161b1a1e3afde71c6f9f5ac0dabeb 100644
--- a/Imaging_exercise.ipynb
+++ b/Imaging_exercise.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"id": "be0bb47b-5926-4b80-ad70-5b3967df9fe6",
"metadata": {
"tags": []
@@ -30,7 +30,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"id": "b7eb9c77-b853-4231-9978-649925633624",
"metadata": {
"tags": []
@@ -45,7 +45,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"id": "175ab49e-7ac8-4590-990a-0d0284af6037",
"metadata": {
"tags": []
@@ -83,7 +83,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"id": "3847986b-b5b4-4c15-829b-6020f5b2191b",
"metadata": {
"tags": []
@@ -219,7 +219,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 6,
"id": "df29f78f-0904-4194-8e1a-c15c6ec22b3b",
"metadata": {
"tags": []
@@ -254,7 +254,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 7,
"id": "f2f00eb2-0123-476b-822a-90729b7d3753",
"metadata": {
"collapsed": true,
@@ -738,7 +738,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 8,
"id": "e59a2b4a-4ad0-4ecb-b98b-cb8ff642e846",
"metadata": {
"tags": []
@@ -750,7 +750,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 9,
"id": "646962ea-8d98-4d41-8b9c-d537b8d1fd3b",
"metadata": {
"collapsed": true,
@@ -2020,7 +2020,7 @@
"[35 rows x 34 columns]"
]
},
- "execution_count": 15,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -2031,7 +2031,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 10,
"id": "feca6fae-2908-41e0-a6c4-c7686670987a",
"metadata": {
"tags": []
@@ -2041,10 +2041,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded timelines_dictNone file. Skipping processing\u001b[0m\n",
+ "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded timelines_dict file. Skipping processing\u001b[0m\n",
"\u001b[94;20mINFO : suite2p_tiff_verification : Suite2p output length and total lenth of the fiff files provided in the trials_df are valid.\u001b[0m\n",
"\u001b[94;20mINFO : trials_roi_df : Splitting and aligning ['F', 'F_var', 'Fneu', 'Fneu_var', 'spks'] by #trial and #roi multi index.\u001b[0m\n",
- "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded timelines_dictNone file. Skipping processing\u001b[0m\n",
+ "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded timelines_dict file. Skipping processing\u001b[0m\n",
"\u001b[32mSAVE_INFO : preprocessed_data : Saving processed trials_roi_df data at wm24\\2022-08-22\\001\\preprocessing_saves\\preproc_data.trials_roi_df.pickle (overwriting)\u001b[0m\n"
]
}
@@ -2055,13 +2055,9 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 11,
"id": "8f5575ad-256a-44de-9cb9-31757060b3b7",
"metadata": {
- "collapsed": true,
- "jupyter": {
- "outputs_hidden": true
- },
"tags": []
},
"outputs": [
@@ -2092,16 +2088,18 @@
" <th>Fneu</th>\n",
" <th>Fneu_var</th>\n",
" <th>spks</th>\n",
- " <th>is_D1</th>\n",
- " <th>is_neuron</th>\n",
- " <th>is_C1</th>\n",
" <th>is_VGAT</th>\n",
+ " <th>is_C1</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_D1</th>\n",
" <th>target_stim</th>\n",
" <th>nontarget_stim</th>\n",
" <th>in_target_barrel</th>\n",
" <th>target_amplitude</th>\n",
" <th>nontarget_amplitude</th>\n",
" <th>Result</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_stim_info</th>\n",
" </tr>\n",
" <tr>\n",
" <th>roi#</th>\n",
@@ -2121,6 +2119,8 @@
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -2132,16 +2132,18 @@
" <td>[81.11736297607422, 93.11980438232422, 73.6161...</td>\n",
" <td>[0.44800236099089397, 0.7714516639019757, 0.24...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
" <td>C1_10_90</td>\n",
" <td>D1_10</td>\n",
" <td>False</td>\n",
" <td>10_90</td>\n",
" <td>10</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@@ -2150,16 +2152,18 @@
" <td>[52.151588439941406, 65.11491394042969, 51.899...</td>\n",
" <td>[-0.32539554129285003, 0.023858377160785173, -...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
" <td>D1_10_90</td>\n",
" <td>C1_NaN</td>\n",
" <td>True</td>\n",
" <td>10_90</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
@@ -2168,16 +2172,18 @@
" <td>[57.168704986572266, 70.49388885498047, 47.332...</td>\n",
" <td>[-0.17911552203251063, 0.18014861315202807, -0...</td>\n",
" <td>[0.0, 0.0, 9.140109062194824, 9.33862686157226...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
" <td>C1_10_20</td>\n",
" <td>D1_10</td>\n",
" <td>False</td>\n",
" <td>10_20</td>\n",
" <td>10</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
@@ -2186,16 +2192,18 @@
" <td>[57.833740234375, 44.317848205566406, 55.43520...</td>\n",
" <td>[-0.15754997708637508, -0.5217095653778792, -0...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
" <td>D1_10_20</td>\n",
" <td>C1_NaN</td>\n",
" <td>True</td>\n",
" <td>10_20</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
@@ -2204,16 +2212,18 @@
" <td>[55.2567253112793, 59.00489044189453, 56.47432...</td>\n",
" <td>[-0.2178897487180029, -0.11685176608670593, -0...</td>\n",
" <td>[0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0....</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
" <td>D1_10_90</td>\n",
" <td>C1_NaN</td>\n",
" <td>True</td>\n",
" <td>10_90</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -2233,6 +2243,8 @@
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">34</th>\n",
@@ -2242,16 +2254,18 @@
" <td>[81.04872131347656, 76.43589782714844, 87.0641...</td>\n",
" <td>[0.28543534597878606, 0.14659601694516844, 0.4...</td>\n",
" <td>[0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>None</td>\n",
" <td>C1_10_90</td>\n",
" <td>D1_10</td>\n",
" <td>False</td>\n",
" <td>10_90</td>\n",
" <td>10</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
@@ -2260,16 +2274,18 @@
" <td>[68.32051086425781, 74.994873046875, 89.817947...</td>\n",
" <td>[-0.09959991718158123, 0.10129027462650247, 0....</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>None</td>\n",
" <td>C1_10_20</td>\n",
" <td>D1_10</td>\n",
" <td>False</td>\n",
" <td>10_20</td>\n",
" <td>10</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
@@ -2278,16 +2294,18 @@
" <td>[50.0487174987793, 59.089744567871094, 65.0923...</td>\n",
" <td>[-0.6525859803693786, -0.3806188309835738, -0....</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>None</td>\n",
" <td>C1_10_20</td>\n",
" <td>D1_NaN</td>\n",
" <td>False</td>\n",
" <td>10_20</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
@@ -2296,16 +2314,18 @@
" <td>[74.46154022216797, 68.3974380493164, 61.12307...</td>\n",
" <td>[0.08084930191997584, -0.10169227498872888, -0...</td>\n",
" <td>[1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>None</td>\n",
" <td>C1_10_20</td>\n",
" <td>D1_NaN</td>\n",
" <td>False</td>\n",
" <td>10_20</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
@@ -2314,20 +2334,22 @@
" <td>[78.14871978759766, 68.52820587158203, 57.7435...</td>\n",
" <td>[0.1908002463908141, -0.09864775120552391, -0....</td>\n",
" <td>[3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
- " <td>None</td>\n",
" <td>D1_10_20</td>\n",
" <td>C1_10</td>\n",
" <td>False</td>\n",
" <td>10_20</td>\n",
" <td>10</td>\n",
" <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>5250 rows × 15 columns</p>\n",
+ "<p>5250 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
@@ -2387,52 +2409,80 @@
" 148 [0.08084930191997584, -0.10169227498872888, -0... \n",
" 149 [0.1908002463908141, -0.09864775120552391, -0.... \n",
"\n",
- " spks is_D1 \n",
- "roi# trial# \n",
- "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... True \\\n",
- " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... True \n",
- " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... True \n",
- " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... True \n",
- " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... True \n",
- "... ... ... \n",
- "34 145 [0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ... False \n",
- " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
- " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
- " 148 [1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
- " 149 [3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " spks is_VGAT is_C1 \n",
+ "roi# trial# \n",
+ "0 0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... False False \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False False \n",
+ " 2 [0.0, 0.0, 9.140109062194824, 9.33862686157226... False False \n",
+ " 3 [0.0, 0.0, 0.0, 0.0, 0.25519195199012756, 0.0,... False False \n",
+ " 4 [0.0, 0.0, 0.0, 4.29819917678833, 0.0, 0.0, 0.... False False \n",
+ "... ... ... ... \n",
+ "34 145 [0.0, 12.766802787780762, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ " 148 [1.2654484510421753, 0.0, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ " 149 [3.0910704135894775, 0.0, 0.0, 0.0, 0.0, 0.0, ... None False \n",
+ "\n",
+ " is_neuron is_D1 target_stim nontarget_stim in_target_barrel \n",
+ "roi# trial# \n",
+ "0 0 True True C1_10_90 D1_10 False \\\n",
+ " 1 True True D1_10_90 C1_NaN True \n",
+ " 2 True True C1_10_20 D1_10 False \n",
+ " 3 True True D1_10_20 C1_NaN True \n",
+ " 4 True True D1_10_90 C1_NaN True \n",
+ "... ... ... ... ... ... \n",
+ "34 145 False False C1_10_90 D1_10 False \n",
+ " 146 False False C1_10_20 D1_10 False \n",
+ " 147 False False C1_10_20 D1_NaN False \n",
+ " 148 False False C1_10_20 D1_NaN False \n",
+ " 149 False False D1_10_20 C1_10 False \n",
+ "\n",
+ " target_amplitude nontarget_amplitude Result \n",
+ "roi# trial# \n",
+ "0 0 10_90 10 2.0 \\\n",
+ " 1 10_90 0 2.0 \n",
+ " 2 10_20 10 2.0 \n",
+ " 3 10_20 0 2.0 \n",
+ " 4 10_90 0 2.0 \n",
+ "... ... ... ... \n",
+ "34 145 10_90 10 2.0 \n",
+ " 146 10_20 10 2.0 \n",
+ " 147 10_20 0 2.0 \n",
+ " 148 10_20 0 2.0 \n",
+ " 149 10_20 10 2.0 \n",
"\n",
- " is_neuron is_C1 is_VGAT target_stim nontarget_stim \n",
- "roi# trial# \n",
- "0 0 True False False C1_10_90 D1_10 \\\n",
- " 1 True False False D1_10_90 C1_NaN \n",
- " 2 True False False C1_10_20 D1_10 \n",
- " 3 True False False D1_10_20 C1_NaN \n",
- " 4 True False False D1_10_90 C1_NaN \n",
- "... ... ... ... ... ... \n",
- "34 145 False False None C1_10_90 D1_10 \n",
- " 146 False False None C1_10_20 D1_10 \n",
- " 147 False False None C1_10_20 D1_NaN \n",
- " 148 False False None C1_10_20 D1_NaN \n",
- " 149 False False None D1_10_20 C1_10 \n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 4 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
"\n",
- " in_target_barrel target_amplitude nontarget_amplitude Result \n",
- "roi# trial# \n",
- "0 0 False 10_90 10 2.0 \n",
- " 1 True 10_90 0 2.0 \n",
- " 2 False 10_20 10 2.0 \n",
- " 3 True 10_20 0 2.0 \n",
- " 4 True 10_90 0 2.0 \n",
- "... ... ... ... ... \n",
- "34 145 False 10_90 10 2.0 \n",
- " 146 False 10_20 10 2.0 \n",
- " 147 False 10_20 0 2.0 \n",
- " 148 False 10_20 0 2.0 \n",
- " 149 False 10_20 10 2.0 \n",
+ " nontarget_stim_info \n",
+ "roi# trial# \n",
+ "0 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 1 [] \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [] \n",
+ " 4 [] \n",
+ "... ... \n",
+ "34 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [] \n",
+ " 148 [] \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
"\n",
- "[5250 rows x 15 columns]"
+ "[5250 rows x 17 columns]"
]
},
- "execution_count": 14,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/Machine_Learning_exercise.ipynb b/Machine_Learning_exercise.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7af5e10feb49e340f7feb13b9298c5a7732b4b0f
--- /dev/null
+++ b/Machine_Learning_exercise.ipynb
@@ -0,0 +1,4707 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7c16aabd-5acc-47f9-9de3-81000a7346a1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\mohay\\anaconda3\\envs\\Analysis\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "#you must import 6 libraries!\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import Inflow\n",
+ "Inflow.logging.enable_logging()\n",
+ "import ResearchProjects\n",
+ "import pandas as pd\n",
+ "import one"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "fbbd82db-7fcc-44d6-a181-4a511b896838",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#adaptation is a sublibrary within the ResearchProject\n",
+ "from ResearchProjects import adaptation\n",
+ "#inside adaptation experiment there are different file, we import the \"aliases\"\n",
+ "from ResearchProjects.adaptation import aliases"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c0ab43eb-3d60-4cde-8562-785b6181928a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<function ResearchProjects.adaptation.select.cells_labelled(rois_df, iscell=True, **kwargs)>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\u001b[1;31mType:\u001b[0m module\n",
+ "\u001b[1;31mString form:\u001b[0m <module 'ResearchProjects.core' from 'C:\\\\Users\\\\mohay\\\\anaconda3\\\\envs\\\\Analysis\\\\lib\\\\site-packages\\\\ResearchProjects\\\\core.py'>\n",
+ "\u001b[1;31mFile:\u001b[0m c:\\users\\mohay\\anaconda3\\envs\\analysis\\lib\\site-packages\\researchprojects\\core.py\n",
+ "\u001b[1;31mDocstring:\u001b[0m <no docstring>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "connector = one.ONE()\n",
+ "connector.set_data_access_mode('remote')\n",
+ "ResearchProjects.core?\n",
+ "ResearchProjects.adaptation.select.cells_labelled\n",
+ "display(ResearchProjects.adaptation.select.cells_labelled) #cells in all mices???"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e56c81cc-21d1-4c68-a7e0-5c22bdc2e8cf",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "subject wm24\n",
+ "start_time 2022-08-22T15:27:00\n",
+ "number 1\n",
+ "lab HaissLab\n",
+ "projects [Adaptation]\n",
+ "url http://157.99.138.172/sessions/04f92e4a-da64-4...\n",
+ "task_protocol \n",
+ "date 2022-08-22\n",
+ "json {'channels': ['R', 'G'], 'whisker_stims': {'St...\n",
+ "extended_qc {'exclude_whisker': ['C1']}\n",
+ "rel_path wm24\\2022-08-22\\001\n",
+ "alias_name wm24_2022_08_22_001\n",
+ "short_path wm24\\2022-08-22\\001\n",
+ "path \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202...\n",
+ "Name: 04f92e4a-da64-4018-aa6c-d9a79a91c831, dtype: object"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#sessions = connector.search(subject = \"wm24\",date_range = \"2022-08-09\",number = 1, details= True) ###Did not work(OSError: No result exists for trials_df in session wm24_2022_08_09_001)\n",
+ "sessions = connector.search(subject = 'wm24', date_range = \"2022-08-22\", number = 1, details = True)\n",
+ "session = sessions.iloc[0]\n",
+ "session"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "542d9b56-1f51-4330-ae24-69dbb7182810",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Trial Start</th>\n",
+ " <th>Stimulus Ref</th>\n",
+ " <th>Stimulus It</th>\n",
+ " <th>Stimulus right</th>\n",
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+ " <td>C1_NaN</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
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+ " <td>\\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202...</td>\n",
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+ "text/plain": [
+ " Trial Start Stimulus Ref Stimulus It Stimulus right Electr Stim \n",
+ "trial# \n",
+ "0 62894.0 3000.0 65893.0 1.0 0.0 \\\n",
+ "1 70443.0 3000.0 73442.0 0.0 0.0 \n",
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+ "3 86129.0 3000.0 89128.0 0.0 0.0 \n",
+ "4 93858.0 3000.0 96857.0 0.0 0.0 \n",
+ "... ... ... ... ... ... \n",
+ "145 1153883.0 3000.0 1156882.0 1.0 0.0 \n",
+ "146 1161725.0 3000.0 1164724.0 1.0 0.0 \n",
+ "147 1169237.0 3000.0 1172236.0 1.0 0.0 \n",
+ "148 1176793.0 3000.0 1179792.0 1.0 0.0 \n",
+ "149 1184567.0 3000.0 1187566.0 0.0 0.0 \n",
+ "\n",
+ " Curr water duration Timepoint of valve opening Data acquisition \n",
+ "trial# \n",
+ "0 100.0 3800.0 1250.0 \\\n",
+ "1 100.0 3800.0 1250.0 \n",
+ "2 100.0 3800.0 1250.0 \n",
+ "3 100.0 3800.0 1250.0 \n",
+ "4 100.0 3800.0 1250.0 \n",
+ "... ... ... ... \n",
+ "145 100.0 3800.0 1250.0 \n",
+ "146 100.0 3800.0 1250.0 \n",
+ "147 100.0 3800.0 1250.0 \n",
+ "148 100.0 3800.0 1250.0 \n",
+ "149 100.0 3800.0 1250.0 \n",
+ "\n",
+ " Free Choice On current TDMS_p1 trial ... Start of Dec Per \n",
+ "trial# ... \n",
+ "0 0.0 0.0 ... 3000.0 \\\n",
+ "1 0.0 1.0 ... 3000.0 \n",
+ "2 0.0 2.0 ... 3000.0 \n",
+ "3 0.0 3.0 ... 3000.0 \n",
+ "4 0.0 4.0 ... 3000.0 \n",
+ "... ... ... ... ... \n",
+ "145 0.0 145.0 ... 3000.0 \n",
+ "146 0.0 146.0 ... 3000.0 \n",
+ "147 0.0 147.0 ... 3000.0 \n",
+ "148 0.0 148.0 ... 3000.0 \n",
+ "149 0.0 149.0 ... 3000.0 \n",
+ "\n",
+ " End of Dec Per End of trial Trial + ITI Result \n",
+ "trial# \n",
+ "0 5500.0 5800.0 7549.0 2.0 \\\n",
+ "1 5500.0 5800.0 7735.0 2.0 \n",
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+ "3 5500.0 5800.0 7729.0 2.0 \n",
+ "4 5500.0 5800.0 7429.0 2.0 \n",
+ "... ... ... ... ... \n",
+ "145 5500.0 5800.0 7842.0 2.0 \n",
+ "146 5500.0 5800.0 7512.0 2.0 \n",
+ "147 5500.0 5800.0 7556.0 2.0 \n",
+ "148 5500.0 5800.0 7774.0 2.0 \n",
+ "149 5500.0 5800.0 7327.0 2.0 \n",
+ "\n",
+ " tiff_path target_stim \n",
+ "trial# \n",
+ "0 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... C1_10_90 \\\n",
+ "1 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... D1_10_90 \n",
+ "2 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... C1_10_20 \n",
+ "3 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... D1_10_20 \n",
+ "4 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... D1_10_90 \n",
+ "... ... ... \n",
+ "145 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... C1_10_90 \n",
+ "146 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... C1_10_20 \n",
+ "147 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... C1_10_20 \n",
+ "148 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... C1_10_20 \n",
+ "149 \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202... D1_10_20 \n",
+ "\n",
+ " nontarget_stim target_stim_info \n",
+ "trial# \n",
+ "0 D1_10 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ "1 C1_NaN [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "2 D1_10 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "3 C1_NaN [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "4 C1_NaN [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... ... \n",
+ "145 D1_10 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "146 D1_10 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "147 D1_NaN [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "148 D1_NaN [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "149 C1_10 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_stim_info \n",
+ "trial# \n",
+ "0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "1 [] \n",
+ "2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "3 [] \n",
+ "4 [] \n",
+ "... ... \n",
+ "145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "147 [] \n",
+ "148 [] \n",
+ "149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ "[150 rows x 24 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "subject wm24\n",
+ "start_time 2022-08-22T15:27:00\n",
+ "number 1\n",
+ "lab HaissLab\n",
+ "projects [Adaptation]\n",
+ "url http://157.99.138.172/sessions/04f92e4a-da64-4...\n",
+ "task_protocol \n",
+ "date 2022-08-22\n",
+ "json {'channels': ['R', 'G'], 'whisker_stims': {'St...\n",
+ "extended_qc {'exclude_whisker': ['C1']}\n",
+ "rel_path wm24\\2022-08-22\\001\n",
+ "alias_name wm24_2022_08_22_001\n",
+ "short_path wm24\\2022-08-22\\001\n",
+ "path \\\\Mountcastle\\lab\\data\\ONE\\Adaptation\\wm24\\202...\n",
+ "Name: 04f92e4a-da64-4018-aa6c-d9a79a91c831, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "trials_df = adaptation.pipelines.get_trials_df( session_details = session) #Getting all the trials for one session\n",
+ "display(trials_df)\n",
+ "print(session)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "70517f38-461a-4848-b5c2-4edc40d53e30",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "rois_df = adaptation.pipelines.get_rois_df( session_details = session )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "57dea3df-8578-4f57-b560-453233fa137a",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
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+ " <td>[0.350585996819315, 0.6918782055416933, 0.3494...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>17.418802</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>[133, 134, 134, 134, 134, 134, 134, 134, 134, ...</td>\n",
+ " <td>[196, 191, 192, 193, 194, 195, 196, 197, 198, ...</td>\n",
+ " <td>[86.511665, 97.57231, 121.71201, 136.07935, 13...</td>\n",
+ " <td>[139, 195]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.432848</td>\n",
+ " <td>4.351917</td>\n",
+ " <td>1.004287</td>\n",
+ " <td>1.116667</td>\n",
+ " <td>158</td>\n",
+ " <td>...</td>\n",
+ " <td>[1.0, 0.12854916895398194]</td>\n",
+ " <td>[0.0, 0.4756465256214142]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 2.7272820472717285, 0.0, 0.0, ...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.5691486631506817, 0.6781690150630921, 0.293...</td>\n",
+ " <td>[0.5691486631506817, 0.6781690150630921, 0.293...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>17.863403</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>[135, 135, 135, 135, 136, 136, 136, 136, 136, ...</td>\n",
+ " <td>[317, 318, 320, 321, 317, 318, 319, 320, 321, ...</td>\n",
+ " <td>[119.56799, 122.02602, 134.26067, 96.63066, 14...</td>\n",
+ " <td>[137, 319]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.564749</td>\n",
+ " <td>1.635435</td>\n",
+ " <td>1.053321</td>\n",
+ " <td>1.583333</td>\n",
+ " <td>22</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.04225060687144257]</td>\n",
+ " <td>[1.0, 0.6516932249069214]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 6.874102592468262, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.4469539885137614, 0.3397471620427446, 0.281...</td>\n",
+ " <td>[0.4469539885137614, 0.3397471620427446, 0.281...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>34.789767</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>[141, 141, 141, 141, 141, 142, 142, 142, 142, ...</td>\n",
+ " <td>[276, 277, 278, 279, 280, 275, 276, 277, 278, ...</td>\n",
+ " <td>[124.626335, 108.03297, 181.62836, 120.1023, 1...</td>\n",
+ " <td>[148, 278]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.367490</td>\n",
+ " <td>4.104028</td>\n",
+ " <td>1.016371</td>\n",
+ " <td>1.133333</td>\n",
+ " <td>125</td>\n",
+ " <td>...</td>\n",
+ " <td>[1.0, 0.59403637207295]</td>\n",
+ " <td>[0.0, 0.49555009603500366]</td>\n",
+ " <td>[0.0, 4.527439117431641, 0.0, 0.0, 0.0, 13.525...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.3447188232432147, 0.32276301024105575, 0.44...</td>\n",
+ " <td>[0.3447188232432147, 0.32276301024105575, 0.44...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>18.963004</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>[177, 177, 177, 178, 178, 178, 178, 178, 178, ...</td>\n",
+ " <td>[315, 316, 317, 314, 315, 316, 317, 318, 319, ...</td>\n",
+ " <td>[136.54901, 98.068665, 111.988014, 102.38901, ...</td>\n",
+ " <td>[182, 316]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>3.050270</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.211009</td>\n",
+ " <td>71</td>\n",
+ " <td>...</td>\n",
+ " <td>[1.0, 0.6350150704091108]</td>\n",
+ " <td>[0.0, 0.4965686798095703]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 1.1192702054977417, 0.0, 6.603...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.42712841434253623, 0.5596610374297426, 0.35...</td>\n",
+ " <td>[0.42712841434253623, 0.5596610374297426, 0.35...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>13.393158</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>[185, 185, 185, 185, 185, 186, 186, 186, 186, ...</td>\n",
+ " <td>[246, 247, 248, 249, 250, 245, 246, 247, 248, ...</td>\n",
+ " <td>[94.352005, 97.25834, 112.49667, 98.12432, 103...</td>\n",
+ " <td>[189, 248]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.823255</td>\n",
+ " <td>2.511150</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.323529</td>\n",
+ " <td>67</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.23703065094056855]</td>\n",
+ " <td>[0.0, 0.531902551651001]</td>\n",
+ " <td>[0.0, 0.0, 20.364572525024414, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.5979055526302492, 0.46991087817612653, 0.77...</td>\n",
+ " <td>[0.5979055526302492, 0.46991087817612653, 0.77...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>16.534836</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>[226, 226, 226, 226, 226, 227, 227, 227, 227, ...</td>\n",
+ " <td>[461, 462, 463, 464, 465, 460, 461, 462, 463, ...</td>\n",
+ " <td>[77.01567, 77.573, 79.867325, 78.52466, 51.992...</td>\n",
+ " <td>[231, 463]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.008665</td>\n",
+ " <td>3.071492</td>\n",
+ " <td>1.001696</td>\n",
+ " <td>1.218182</td>\n",
+ " <td>86</td>\n",
+ " <td>...</td>\n",
+ " <td>[1.0, 0.5569769694576615]</td>\n",
+ " <td>[0.0, 0.5071209073066711]</td>\n",
+ " <td>[0.0, 9.91696548461914, 2.587313652038574, 0.0...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.31598268340817015, 0.1559547081931097, -0.1...</td>\n",
+ " <td>[0.31598268340817015, 0.1559547081931097, -0.1...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>12.698708</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>[232, 233, 233, 234, 234, 234, 234, 235, 235, ...</td>\n",
+ " <td>[195, 195, 196, 195, 196, 197, 199, 195, 196, ...</td>\n",
+ " <td>[89.65165, 80.839005, 107.42232, 83.21235, 114...</td>\n",
+ " <td>[235, 196]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.457185</td>\n",
+ " <td>1.358220</td>\n",
+ " <td>1.026740</td>\n",
+ " <td>1.857143</td>\n",
+ " <td>16</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.12247908321998226]</td>\n",
+ " <td>[0.0, 0.5557902455329895]</td>\n",
+ " <td>[0.0, 0.0, 11.1720609664917, 0.0, 15.451468467...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.17878273718770565, 0.3562295478287571, 0.37...</td>\n",
+ " <td>[0.17878273718770565, 0.3562295478287571, 0.37...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>24.034887</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>[245, 245, 245, 245, 245, 246, 246, 246, 246, ...</td>\n",
+ " <td>[325, 326, 327, 328, 329, 324, 325, 326, 327, ...</td>\n",
+ " <td>[97.38135, 106.39031, 121.905, 89.459656, 83.7...</td>\n",
+ " <td>[249, 327]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.013710</td>\n",
+ " <td>3.092090</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.225225</td>\n",
+ " <td>76</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.31518866035777654]</td>\n",
+ " <td>[0.0, 0.6001868844032288]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 9.435110092163086, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.1253581893085868, 0.43417906458645533, 0.29...</td>\n",
+ " <td>[0.1253581893085868, 0.43417906458645533, 0.29...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>28.536033</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>[248, 248, 248, 248, 248, 249, 249, 249, 249, ...</td>\n",
+ " <td>[236, 237, 238, 239, 240, 234, 235, 236, 237, ...</td>\n",
+ " <td>[97.9397, 103.28203, 152.8473, 131.3603, 104.0...</td>\n",
+ " <td>[252, 237]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.001734</td>\n",
+ " <td>3.050270</td>\n",
+ " <td>1.001734</td>\n",
+ " <td>1.211009</td>\n",
+ " <td>74</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.27568337357308575]</td>\n",
+ " <td>[0.0, 0.5360695123672485]</td>\n",
+ " <td>[0.0, 25.326162338256836, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.8711807509724349, 0.2996534638086047, 0.799...</td>\n",
+ " <td>[0.8711807509724349, 0.2996534638086047, 0.799...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>23.928035</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>[266, 266, 266, 267, 267, 267, 267, 267, 268, ...</td>\n",
+ " <td>[157, 158, 159, 156, 157, 158, 159, 160, 155, ...</td>\n",
+ " <td>[101.42234, 90.23266, 136.36002, 95.29129, 124...</td>\n",
+ " <td>[268, 158]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.564162</td>\n",
+ " <td>1.692638</td>\n",
+ " <td>1.016665</td>\n",
+ " <td>1.500000</td>\n",
+ " <td>23</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.0812641652292859]</td>\n",
+ " <td>[0.0, 0.4896278977394104]</td>\n",
+ " <td>[0.0, 0.0, 5.292405605316162, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2811353291712975, 0.5383316295043573, 0.476...</td>\n",
+ " <td>[0.2811353291712975, 0.5383316295043573, 0.476...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>19.343984</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>[272, 272, 272, 273, 273, 273, 273, 273, 273, ...</td>\n",
+ " <td>[305, 306, 307, 303, 304, 305, 306, 307, 308, ...</td>\n",
+ " <td>[92.97467, 110.365326, 76.94368, 109.505005, 8...</td>\n",
+ " <td>[278, 306]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.225295</td>\n",
+ " <td>3.716985</td>\n",
+ " <td>1.005514</td>\n",
+ " <td>1.166667</td>\n",
+ " <td>106</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.3551817466873422]</td>\n",
+ " <td>[0.0, 0.4980297386646271]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 11.847755432128906, 3.36206650...</td>\n",
+ " <td>False</td>\n",
+ " <td>[-0.1251134853395897, 0.09527923850691102, 0.3...</td>\n",
+ " <td>[-0.1251134853395897, 0.09527923850691102, 0.3...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>16.356201</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>[296, 296, 296, 296, 296, 296, 297, 297, 297, ...</td>\n",
+ " <td>[240, 241, 242, 243, 244, 245, 239, 240, 241, ...</td>\n",
+ " <td>[84.87599, 149.15967, 100.07534, 127.11835, 11...</td>\n",
+ " <td>[299, 243]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.954412</td>\n",
+ " <td>2.889354</td>\n",
+ " <td>1.007566</td>\n",
+ " <td>1.229167</td>\n",
+ " <td>62</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.3620637409560754]</td>\n",
+ " <td>[0.0, 0.5504989624023438]</td>\n",
+ " <td>[0.0, 41.399085998535156, 0.0, 0.0, 43.0873756...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.25744066175173336, 0.3001019756083122, 0.16...</td>\n",
+ " <td>[0.25744066175173336, 0.3001019756083122, 0.16...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>28.372598</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>[303, 303, 304, 304, 304, 304, 304, 304, 305, ...</td>\n",
+ " <td>[226, 227, 223, 224, 225, 226, 227, 228, 223, ...</td>\n",
+ " <td>[83.44468, 100.25865, 149.66637, 125.811356, 1...</td>\n",
+ " <td>[310, 227]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.348367</td>\n",
+ " <td>4.052854</td>\n",
+ " <td>1.014812</td>\n",
+ " <td>1.142857</td>\n",
+ " <td>119</td>\n",
+ " <td>...</td>\n",
+ " <td>[1.0, 0.5729795959203344]</td>\n",
+ " <td>[0.0, 0.5129371881484985]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 17.434009552001953, 0.0, 0.0, ...</td>\n",
+ " <td>True</td>\n",
+ " <td>[-0.0711396902237596, 0.5727540243990961, 0.43...</td>\n",
+ " <td>[-0.0711396902237596, 0.5727540243990961, 0.43...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>27.890615</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>[324, 324, 324, 324, 324, 324, 324, 325, 325, ...</td>\n",
+ " <td>[304, 305, 306, 307, 308, 309, 310, 302, 303, ...</td>\n",
+ " <td>[145.95636, 193.80801, 164.54097, 169.56934, 1...</td>\n",
+ " <td>[330, 304]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.564387</td>\n",
+ " <td>4.686828</td>\n",
+ " <td>1.018131</td>\n",
+ " <td>1.123188</td>\n",
+ " <td>158</td>\n",
+ " <td>...</td>\n",
+ " <td>[1.0, 0.5185813445220834]</td>\n",
+ " <td>[0.0, 0.43891873955726624]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 8.143514633178711, 22.779...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.17702394161205348, 0.2297014840258989, -0.0...</td>\n",
+ " <td>[0.17702394161205348, 0.2297014840258989, -0.0...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>27.928532</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>[329, 330, 330, 330, 330, 330, 330, 330, 330, ...</td>\n",
+ " <td>[223, 219, 220, 221, 222, 223, 224, 225, 226, ...</td>\n",
+ " <td>[138.75534, 163.43802, 178.58865, 156.22168, 2...</td>\n",
+ " <td>[336, 224]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.502636</td>\n",
+ " <td>4.413583</td>\n",
+ " <td>1.038486</td>\n",
+ " <td>1.078125</td>\n",
+ " <td>147</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.4863324314103594]</td>\n",
+ " <td>[0.0, 0.5507894158363342]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.5328021266560912, 0.30939625155891576, 0.29...</td>\n",
+ " <td>[0.5328021266560912, 0.30939625155891576, 0.29...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>36.552379</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>[353, 353, 353, 353, 353, 353, 354, 354, 354, ...</td>\n",
+ " <td>[340, 341, 342, 343, 344, 345, 338, 339, 340, ...</td>\n",
+ " <td>[93.87101, 126.815994, 110.26731, 85.311005, 1...</td>\n",
+ " <td>[356, 342]</td>\n",
+ " <td>1</td>\n",
+ " <td>0.985086</td>\n",
+ " <td>2.982562</td>\n",
+ " <td>1.007449</td>\n",
+ " <td>1.247525</td>\n",
+ " <td>72</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.41391623892189955]</td>\n",
+ " <td>[1.0, 0.7544840574264526]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.2301474713828739, 0.16546045480804852, 0.50...</td>\n",
+ " <td>[0.2301474713828739, 0.16546045480804852, 0.50...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>80.087524</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>[382, 382, 382, 382, 383, 383, 383, 383, 383, ...</td>\n",
+ " <td>[347, 348, 349, 350, 345, 346, 347, 348, 349, ...</td>\n",
+ " <td>[95.458, 94.66168, 80.534676, 76.242676, 66.90...</td>\n",
+ " <td>[386, 349]</td>\n",
+ " <td>1</td>\n",
+ " <td>1.006957</td>\n",
+ " <td>3.071492</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.218182</td>\n",
+ " <td>73</td>\n",
+ " <td>...</td>\n",
+ " <td>[0.0, 0.32998067402752457]</td>\n",
+ " <td>[0.0, 0.554786205291748]</td>\n",
+ " <td>[0.0, 0.0, 0.0, 21.49405860900879, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.5679862561165399, 0.3468873788565367, 0.703...</td>\n",
+ " <td>[0.5679862561165399, 0.3468873788565367, 0.703...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>22.438936</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>[385, 385, 386, 386, 386, 386, 386, 387, 387, ...</td>\n",
+ " <td>[184, 185, 183, 184, 185, 186, 187, 182, 183, ...</td>\n",
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+ " <td>[0.0, 0.0, 0.0, 33.52047348022461, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
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+ " <td>[0.4788335155797292, 0.516987579730329, 0.0919...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>22.286501</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>[425, 425, 425, 425, 425, 426, 426, 426, 426, ...</td>\n",
+ " <td>[328, 329, 330, 331, 332, 327, 328, 329, 330, ...</td>\n",
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+ " <td>[0.0, 5.386137008666992, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.7384792119745599, 0.5925478691694359, 0.366...</td>\n",
+ " <td>[0.7384792119745599, 0.5925478691694359, 0.366...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>26.382097</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td>[427, 427, 427, 427, 428, 428, 428, 428, 428, ...</td>\n",
+ " <td>[291, 292, 293, 294, 290, 291, 292, 293, 294, ...</td>\n",
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+ " <td>1</td>\n",
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+ " <td>1.004338</td>\n",
+ " <td>1.312500</td>\n",
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+ " <td>[0.0, 0.12061438316332257]</td>\n",
+ " <td>[0.0, 0.5817732810974121]</td>\n",
+ " <td>[0.0, 4.279571056365967, 23.245716094970703, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.6194280584484223, 0.3238257032467561, 0.185...</td>\n",
+ " <td>[0.6194280584484223, 0.3238257032467561, 0.185...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>28.628524</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
+ " <td>[459, 459, 459, 459, 459, 460, 460, 460, 460, ...</td>\n",
+ " <td>[193, 194, 195, 196, 197, 192, 193, 194, 195, ...</td>\n",
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+ " <td>[465, 194]</td>\n",
+ " <td>1</td>\n",
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+ " <td>3.774605</td>\n",
+ " <td>1.002795</td>\n",
+ " <td>1.160920</td>\n",
+ " <td>107</td>\n",
+ " <td>...</td>\n",
+ " <td>[1.0, 0.20595270888845352]</td>\n",
+ " <td>[0.0, 0.5029698014259338]</td>\n",
+ " <td>[0.0, 0.0, 8.385239601135254, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>True</td>\n",
+ " <td>[0.3347138584366429, 0.4375312905535715, 0.011...</td>\n",
+ " <td>[0.3347138584366429, 0.4375312905535715, 0.011...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>21.779781</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>[468, 468, 469, 469, 469, 469, 469, 470, 470, ...</td>\n",
+ " <td>[55, 56, 55, 56, 57, 58, 59, 54, 55, 56, 57, 5...</td>\n",
+ " <td>[105.954, 81.938, 118.78168, 90.30934, 90.2623...</td>\n",
+ " <td>[472, 57]</td>\n",
+ " <td>1</td>\n",
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+ " <td>2.511150</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.323529</td>\n",
+ " <td>58</td>\n",
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+ " <td>[0.0, 0.18570381110579157]</td>\n",
+ " <td>[0.0, 0.5029839277267456]</td>\n",
+ " <td>[0.0, 18.88400650024414, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>[0.5870835397580942, -0.23022304812531788, -0....</td>\n",
+ " <td>[0.5870835397580942, -0.23022304812531788, -0....</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>23.604597</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>35 rows × 34 columns</p>\n",
+ "</div>"
+ ],
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+ "\n",
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+ "\n",
+ " iscell redcell \n",
+ "roi# \n",
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+ "26 [1.0, 0.5185813445220834] [0.0, 0.43891873955726624] \n",
+ "27 [0.0, 0.4863324314103594] [0.0, 0.5507894158363342] \n",
+ "28 [0.0, 0.41391623892189955] [1.0, 0.7544840574264526] \n",
+ "29 [0.0, 0.32998067402752457] [0.0, 0.554786205291748] \n",
+ "30 [0.0, 0.04981382444036565] [0.0, 0.5327021479606628] \n",
+ "31 [0.0, 0.02072980819769298] [0.0, 0.60174161195755] \n",
+ "32 [0.0, 0.12061438316332257] [0.0, 0.5817732810974121] \n",
+ "33 [1.0, 0.20595270888845352] [0.0, 0.5029698014259338] \n",
+ "34 [0.0, 0.18570381110579157] [0.0, 0.5029839277267456] \n",
+ "\n",
+ " spks is_neuron \n",
+ "roi# \n",
+ "0 [0.0, 0.0, 0.0, 0.0, 2.942286491394043, 0.0, 0... True \\\n",
+ "1 [0.0, 0.0, 0.0, 27.431922912597656, 0.0, 0.0, ... False \n",
+ "2 [0.0, 0.0, 0.0, 19.099365234375, 0.0, 0.0, 0.0... True \n",
+ "3 [0.0, 1.0597600936889648, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "4 [0.0, 0.0, 0.0, 0.0, 0.0, 1.5610218048095703, ... False \n",
+ "5 [0.0, 0.0, 0.0, 21.451704025268555, 0.0, 0.0, ... False \n",
+ "6 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... True \n",
+ "7 [0.0, 6.787431716918945, 11.328902244567871, 0... False \n",
+ "8 [0.0, 22.53038215637207, 0.0, 0.0, 0.0, 0.0, 0... True \n",
+ "9 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "10 [0.0, 0.0, 0.0, 0.0, 18.23613739013672, 0.0, 0... True \n",
+ "11 [0.0, 0.0, 0.0, 0.0, 0.0, 7.39235258102417, 0.... False \n",
+ "12 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... True \n",
+ "13 [0.0, 0.0, 0.0, 2.7272820472717285, 0.0, 0.0, ... True \n",
+ "14 [0.0, 0.0, 0.0, 0.0, 0.0, 6.874102592468262, 0... False \n",
+ "15 [0.0, 4.527439117431641, 0.0, 0.0, 0.0, 13.525... True \n",
+ "16 [0.0, 0.0, 0.0, 1.1192702054977417, 0.0, 6.603... True \n",
+ "17 [0.0, 0.0, 20.364572525024414, 0.0, 0.0, 0.0, ... False \n",
+ "18 [0.0, 9.91696548461914, 2.587313652038574, 0.0... True \n",
+ "19 [0.0, 0.0, 11.1720609664917, 0.0, 15.451468467... False \n",
+ "20 [0.0, 0.0, 0.0, 9.435110092163086, 0.0, 0.0, 0... False \n",
+ "21 [0.0, 25.326162338256836, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "22 [0.0, 0.0, 5.292405605316162, 0.0, 0.0, 0.0, 0... False \n",
+ "23 [0.0, 0.0, 0.0, 11.847755432128906, 3.36206650... False \n",
+ "24 [0.0, 41.399085998535156, 0.0, 0.0, 43.0873756... False \n",
+ "25 [0.0, 0.0, 0.0, 17.434009552001953, 0.0, 0.0, ... True \n",
+ "26 [0.0, 0.0, 0.0, 0.0, 8.143514633178711, 22.779... True \n",
+ "27 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "28 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "29 [0.0, 0.0, 0.0, 21.49405860900879, 0.0, 0.0, 0... False \n",
+ "30 [0.0, 0.0, 0.0, 33.52047348022461, 0.0, 0.0, 0... False \n",
+ "31 [0.0, 5.386137008666992, 0.0, 0.0, 0.0, 0.0, 0... False \n",
+ "32 [0.0, 4.279571056365967, 23.245716094970703, 0... False \n",
+ "33 [0.0, 0.0, 8.385239601135254, 0.0, 0.0, 0.0, 0... True \n",
+ "34 [0.0, 18.88400650024414, 0.0, 0.0, 0.0, 0.0, 0... False \n",
+ "\n",
+ " F_var \n",
+ "roi# \n",
+ "0 [0.44800236099089397, 0.7714516639019757, 0.24... \\\n",
+ "1 [0.2883108210421972, 0.5932362809663284, 0.192... \n",
+ "2 [0.5608495401495428, 0.7443431223037524, 0.407... \n",
+ "3 [0.24668316397308548, 0.4711971469352052, 0.10... \n",
+ "4 [0.03368734128291486, 0.2728357744731917, 0.16... \n",
+ "5 [0.4880555886878001, 0.42163433406968576, 0.48... \n",
+ "6 [0.5580097968938957, 0.23501575931973562, 0.29... \n",
+ "7 [0.3395221176386508, 0.6257711399229017, -0.13... \n",
+ "8 [0.3185552548066303, 0.26840759667618413, 0.13... \n",
+ "9 [0.3427790067639393, 0.39612290259181304, 0.03... \n",
+ "10 [0.48875698009025026, 0.4032714989805199, 0.39... \n",
+ "11 [0.6323846377753256, 0.3075497911254068, 0.181... \n",
+ "12 [0.350585996819315, 0.6918782055416933, 0.3494... \n",
+ "13 [0.5691486631506817, 0.6781690150630921, 0.293... \n",
+ "14 [0.4469539885137614, 0.3397471620427446, 0.281... \n",
+ "15 [0.3447188232432147, 0.32276301024105575, 0.44... \n",
+ "16 [0.42712841434253623, 0.5596610374297426, 0.35... \n",
+ "17 [0.5979055526302492, 0.46991087817612653, 0.77... \n",
+ "18 [0.31598268340817015, 0.1559547081931097, -0.1... \n",
+ "19 [0.17878273718770565, 0.3562295478287571, 0.37... \n",
+ "20 [0.1253581893085868, 0.43417906458645533, 0.29... \n",
+ "21 [0.8711807509724349, 0.2996534638086047, 0.799... \n",
+ "22 [0.2811353291712975, 0.5383316295043573, 0.476... \n",
+ "23 [-0.1251134853395897, 0.09527923850691102, 0.3... \n",
+ "24 [0.25744066175173336, 0.3001019756083122, 0.16... \n",
+ "25 [-0.0711396902237596, 0.5727540243990961, 0.43... \n",
+ "26 [0.17702394161205348, 0.2297014840258989, -0.0... \n",
+ "27 [0.5328021266560912, 0.30939625155891576, 0.29... \n",
+ "28 [0.2301474713828739, 0.16546045480804852, 0.50... \n",
+ "29 [0.5679862561165399, 0.3468873788565367, 0.703... \n",
+ "30 [0.4788335155797292, 0.516987579730329, 0.0919... \n",
+ "31 [0.7384792119745599, 0.5925478691694359, 0.366... \n",
+ "32 [0.6194280584484223, 0.3238257032467561, 0.185... \n",
+ "33 [0.3347138584366429, 0.4375312905535715, 0.011... \n",
+ "34 [0.5870835397580942, -0.23022304812531788, -0.... \n",
+ "\n",
+ " Fneu_var is_D1 is_C1 \n",
+ "roi# \n",
+ "0 [0.44800236099089397, 0.7714516639019757, 0.24... True False \\\n",
+ "1 [0.2883108210421972, 0.5932362809663284, 0.192... True False \n",
+ "2 [0.5608495401495428, 0.7443431223037524, 0.407... True False \n",
+ "3 [0.24668316397308548, 0.4711971469352052, 0.10... True False \n",
+ "4 [0.03368734128291486, 0.2728357744731917, 0.16... True False \n",
+ "5 [0.4880555886878001, 0.42163433406968576, 0.48... True False \n",
+ "6 [0.5580097968938957, 0.23501575931973562, 0.29... True False \n",
+ "7 [0.3395221176386508, 0.6257711399229017, -0.13... True False \n",
+ "8 [0.3185552548066303, 0.26840759667618413, 0.13... True False \n",
+ "9 [0.3427790067639393, 0.39612290259181304, 0.03... True False \n",
+ "10 [0.48875698009025026, 0.4032714989805199, 0.39... True False \n",
+ "11 [0.6323846377753256, 0.3075497911254068, 0.181... True False \n",
+ "12 [0.350585996819315, 0.6918782055416933, 0.3494... True False \n",
+ "13 [0.5691486631506817, 0.6781690150630921, 0.293... True False \n",
+ "14 [0.4469539885137614, 0.3397471620427446, 0.281... True False \n",
+ "15 [0.3447188232432147, 0.32276301024105575, 0.44... True False \n",
+ "16 [0.42712841434253623, 0.5596610374297426, 0.35... True False \n",
+ "17 [0.5979055526302492, 0.46991087817612653, 0.77... True False \n",
+ "18 [0.31598268340817015, 0.1559547081931097, -0.1... False False \n",
+ "19 [0.17878273718770565, 0.3562295478287571, 0.37... False False \n",
+ "20 [0.1253581893085868, 0.43417906458645533, 0.29... False True \n",
+ "21 [0.8711807509724349, 0.2996534638086047, 0.799... False False \n",
+ "22 [0.2811353291712975, 0.5383316295043573, 0.476... False False \n",
+ "23 [-0.1251134853395897, 0.09527923850691102, 0.3... False True \n",
+ "24 [0.25744066175173336, 0.3001019756083122, 0.16... False True \n",
+ "25 [-0.0711396902237596, 0.5727540243990961, 0.43... False False \n",
+ "26 [0.17702394161205348, 0.2297014840258989, -0.0... False True \n",
+ "27 [0.5328021266560912, 0.30939625155891576, 0.29... False False \n",
+ "28 [0.2301474713828739, 0.16546045480804852, 0.50... False True \n",
+ "29 [0.5679862561165399, 0.3468873788565367, 0.703... False True \n",
+ "30 [0.4788335155797292, 0.516987579730329, 0.0919... False False \n",
+ "31 [0.7384792119745599, 0.5925478691694359, 0.366... False True \n",
+ "32 [0.6194280584484223, 0.3238257032467561, 0.185... False True \n",
+ "33 [0.3347138584366429, 0.4375312905535715, 0.011... False False \n",
+ "34 [0.5870835397580942, -0.23022304812531788, -0.... False False \n",
+ "\n",
+ " VGAT_value is_VGAT \n",
+ "roi# \n",
+ "0 14.059807 False \n",
+ "1 20.949542 None \n",
+ "2 24.833536 None \n",
+ "3 15.331578 False \n",
+ "4 15.483268 False \n",
+ "5 19.285923 None \n",
+ "6 15.558809 False \n",
+ "7 33.543051 True \n",
+ "8 23.304562 None \n",
+ "9 25.789192 None \n",
+ "10 17.051609 False \n",
+ "11 34.336797 True \n",
+ "12 17.418802 False \n",
+ "13 17.863403 False \n",
+ "14 34.789767 True \n",
+ "15 18.963004 None \n",
+ "16 13.393158 False \n",
+ "17 16.534836 False \n",
+ "18 12.698708 False \n",
+ "19 24.034887 None \n",
+ "20 28.536033 True \n",
+ "21 23.928035 None \n",
+ "22 19.343984 None \n",
+ "23 16.356201 False \n",
+ "24 28.372598 True \n",
+ "25 27.890615 True \n",
+ "26 27.928532 True \n",
+ "27 36.552379 True \n",
+ "28 80.087524 True \n",
+ "29 22.438936 None \n",
+ "30 22.286501 None \n",
+ "31 26.382097 True \n",
+ "32 28.628524 True \n",
+ "33 21.779781 None \n",
+ "34 23.604597 None \n",
+ "\n",
+ "[35 rows x 34 columns]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rois_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "d5a87a8d-5e31-4795-93a2-76cdb944eaba",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded timelines_dict file. Skipping processing\u001b[0m\n",
+ "\u001b[94;20mINFO : suite2p_tiff_verification : Suite2p output length and total lenth of the fiff files provided in the trials_df are valid.\u001b[0m\n",
+ "\u001b[94;20mINFO : trials_roi_df : Splitting and aligning ['F', 'F_var', 'Fneu', 'Fneu_var', 'spks'] by #trial and #roi multi index.\u001b[0m\n",
+ "\u001b[32mLOAD_INFO : load_preprocessing : Found and loaded timelines_dict file. Skipping processing\u001b[0m\n",
+ "\u001b[32mSAVE_INFO : preprocessed_data : Saving processed trials_roi_df data at wm24\\2022-08-22\\001\\preprocessing_saves\\preproc_data.trials_roi_df.pickle (overwriting)\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "trials_roi_df = adaptation.pipelines.generate_trials_roi_df(rois_df, trials_df, session_details = session, refresh_main_only = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "3e3b4702-d819-4467-a513-322f16487200",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AMml89d2IAkfY0G36ehc6rczlfUlSkZ5UUnmXhcBTKqnkt3/kw5K484U3kSdPntjP/dzP2c/93M/d6vvf933fZ7/wC7/wWm1IJZVUUlHR1L73XcLU05syivl8PvL9i4uLyJk5wAWAKS41kpTR+XzuxVni3rVer63T6fi5QgW2PBfWUBlF2MRisWjj8diy2awNh0N7+PChTadTOzs7s0wmY61Wy8bjsZ97pC3KjgKOm82mdbtd6/f7frxhNptZo9FwwIgOAfRCEA7o7Pf7sff9kobLe8NxY+4KhYL1er2Nc8U5xfC8400kn8/fO1AEHH8o6y+V90OwMa+zvlJ5PyRlFFNJJZVU3nN5V1NP9SqLu0rNe11GUaueZrNZ63a71mq1HChyTpF3KLuIZLPZCOMVx5jO53Mrl8vW6XQcSMIQAt70egzuVIRRBKyuViubTqd+dUZ4j+NqtXJwOxqNvMI2V16gF7CBepcv72OeisWijUYjZwDDFFzOcVJkJ5TpdGrFYjEWOGUymUgFVQByEtt808qncfKmGEUNNKSSypdBsCGppJICxVRSSSWVL7GMRqPIhh4HuN7V1FOABoDnrp5522I2ZtcLn8xms0iFTIAiIAWWL6wsmsvlbDKZeFtChmw+n1ulUrHRaORAEWALU5bP5z2VFMAGmNT5HI/Htre3533m3WZXVURfvXpljUbDCoWCmZmVy2UHmYx/sViMACdNXzW7rNDd7/cjbGTcfZL0KxSK4yTNN2OpKbNJjMaulU836RVgeb1eJ6bLvq4w/m8iUKMgP5VUtkm73U68uiUFiqkgKVBMJZVUUvkSS7fbjVSajKvI+a6mngIs7rIqqwK92zxXU1fH43HkInszc8bs6OjIAVs+n7ezszN/BgCn1+s5MAuvSKBYDYyfthdAlc/nrV6vWy6X8zmlb/P53DKZjKeF6pyXy2VPSaWYymKxsHq97p8hPXY2m0WAIuBLATZnGalqShrqfD6PgBPms9lsxqaOknoax3Dn83kHrWZRhjFOuDpkm2yqUAujuFwu7+1ORRjFN7H+jo6O7v0dqbw/osWwVLCBKVBMxSwFiqmkkkoqX2oJiw7E3Rmnjvm7VH1Ri5vclSP9OtdjmEVTV8fjsRWLRS/oYnYFumezmS2XS0/xVCau1+vZYrGwfr/vdyqG6ZgUllEQD1vY7XatXC5bq9Wyg4MDv8ZC+6eppbCOCIAOsDUajazVakX0AuaOAjUAKq2iCoDVvpdKJU87nU6nEeaQsUtKCwX4xQHFXC7noFXHKIlR3JWF3nTnpTKX9wXk3iRQfFP3QqbyfgjnpEMhaJQCxVTMUqCYSiqppPKllhAoxjEo6ph//vnn70zZ8/tIPTWzWxezMbsCYjqmetaRZwIuSJdUp2s4HFoul7PRaBQ5b6dt4f/lctkdfL2Oo1wuO4iuVqsOsEitVScunG8uvs9kMjYcDu3x48fXUmpJbR2NRg5QlYHlagyzqyse8vm8NRoNT9cMGT3mU68Y0XHV8YxjFLU4TS6XizCMm+Zrk2y785L+3ReQI/X0vp3uNFUwlZsK6zqUFCimopICxVRSSSWVL7Hswijq/Xbj8fidcQDuK/UUuUkxGwAYfw8GA6tWq9eqkep7AIwwl1pdFKBDZc0Q3C2XS58Lvs/fsFAIRWV4Fldl8PlwvjlHeXR0ZNVq1crlcqToznq9tlwuZ8Vi0UGtgjwYUM5OAmrz+bxVq1UHviFrGNeWly9fmlmUHYwLDORyuUg1WYDjJpC3yzUam9JXzczn5b7WBKnF980o4vS/K0GgVN59SdKXN6WzqXw5JAWKqaSSSipfYgGYIJsYFACLnml8m0Jb7+v6gJswigAKwE6v17NyuexXVwACeS4sYgjSP/vsM2cDAZncpaifXSwWdnFxYYvFwiqVioMuwCfVSc2iQBEGExYS0KcCsO10OlapVBxY6ueoYjqbzfydsLvz+dzPSdImgGI2m3UnctsZQYrEwD4CfpNST0OgCNBOkl0K2oSVWUPhPXz2riUp0HDX8i6mlqfybguZAaFo0CiVVFKgmEoqqaTyJRUuZd/GKCLz+dzq9fpORUDehNxH6qk6Nzdx0BUoKigEyNJe/bmeUYSJ6/V6ls1mbTAYONCazWbO6pldAqh+v2/9ft9Wq5U1Gg0bDAZ+jjEEf7BxVOlkzjf1DSYRfQiv8eBi++VyaUdHR85U5nI5G4/HNplMImcUAYpmZvv7+5HKr3ESnv/r9/teTGdT6in9LhQKOzGK2wIM2xjFQqHg13Z8mRmUFCimclMJ0+H155uCK6l8WJJqQiqppJLKl1RIUQzPviU5xty3p4ziycnJvbczSe4j9VTlJsVsABSr1cqGw6HV6/XIdQ0AQVJE+T9AEWAzmUwiF9KvViu/MxGgyM/MLgEb104cHR35OwBvtE0ZRYDiJnBA32HpNICgYJcUUi0s1G63/ewiz1e9qlarkT7ECVeykMY6nU696E5S6qmynqTZbmIUd7n2ZVPghPfAdt51+umbTANV/U0llV1kUzEbDY6l8mFLChRTSSWVVL6kslgsIhU5zZKjwdls1mazmVUqlQhzMhgM3hqTch9VT1VuAkCVURwMBtZsNv1nCgz1nkaNyHMHoQIozr8BFOkjVVP39/cdDHFNA2BF/w2jyOeoEDoYDCyXy1m/37fxeGzT6dRWq5U9f/7cZrNZBFSGjKKZeTos138wF6Sjhqln/B8wRypznLOp6bG9Xi9yNUdcShv3RNJGrq7YBvK26c22Yja85z4YxZDNvE/gSHZBekYxlV0lae2yDt/Va5VSebOSAsVUUkkllS+pxF0fkJRqR0phoVCInN0jPfBtyH1VPUVucsZGGRnSMhU8Afr4nN5RCKPY7/e92ik/h/nTKoIAykePHjmYovgNxXHm83mEgdTUU9oJmzkej63X69nJyYktl0sbDodeKGdvb8/6/X4ss8ZZS3SA85nD4XDjfKBfANFNQJE7Jjcxg2bXQf0uOrEro7gNKHK/412vA12f98WaIwDFlAFKZRfB9m4DiumVK6mkQDGVVFJJ5UsqcUAxiVHUQih6z93bLKuvxWzUwZ1Op9btdm/8vNdhUwAUk8kkwn6RagowBMjhRJFems/nrdvtWjabtX6/7ywkwFNBDecYK5WKg8JCoWCTycTBKSmbtEFTdPmbYi8whsPh0BaLhe3v79tsNrP5fG6tVstGo9E1RpGiRtVq1VNPLy4ubDKZXAOKejelmXlbAIp6dlPTWrliI67S7ra52uWM1K6M4jZWknVx1+tAQWpcAZ+7flcKFO9WFotF5J7Q90k26YsCxXelQnYqb09SoJhKKqmk8iWVmzKKOAXlctnPKXIdw9sQPaOoTjRn/W77vNsI4zYej61Wq/mF9SFQpPAJY6jnEM/OzrygDWwd7CFprWbmgNLMrF6v28XFhTOKei+jFr8J+8W84cyRpskZSYIBhULBqtXqtTRO2szZPKrn6rnLpHGlbwoUYVBfvHhhZldsH33fBpJCMLoL6N+FpdvGKDIv95FmF14JkgLFL5dMJhPr9Xpvuxn3Ipx73sYopkAxlRQoppJKKmaWHlr/MgqpfZpKGjrciF6CXiqVHIjBKAJ63qQoUFRJugh6l+fdtqS7pp7O53Nrt9uR858waBQ+KZfLNpvNvErpcDi0QqEQYeMAS9ommEJ+Vy6XbTgcRqqT5nK5a05aCKIUnPHMZrNpg8HA353L5ezw8ND29vYcQCKTycRarZY/G5BIf8Jzr2FAApDG2UJYxfF47O+fz+fe13A+w6tL6As6uCk1Tp+xi87uohP3AeS0OM99MJbhu1KgeLdCxsD7KKzdFCimsk1SoJhKKqnYdDq14+Pjt92MVG4osDWhIx7nGCuI0tRTzt4dHR3dm5M5HA5jf54EapPKtu/yntueqdHCNavVyiaTSaQNcYyiOpLj8djW67XV63VvQ9y1FL1ez9brtZVKJS+i0mw2rd/vW7FYtOl06ncVxgFFBTTL5dImk4mzkc1m04FqJpOxRqNh7XbbBoPBtfEcjUb24MEDPz8Jo6gBB/6GfQiFlFqdL8aENsXdN0k/tE2wb3wO4HPfjipjdR9AUcftvguDwDqnQPHuhGyA91FSRjGVXSUFiqmkkooX6Ujlyye7OrmaPqgXyOMM3JbF20VuGoSIAxa7SK/XuzVQVCDNBfF6ThKgyEX0VDldr9degbTRaDhQ1JRLTX2kjbVazYFivV630Wjk4JHiOTB7Cqi5zoH/c+0EBVlIQ+Wz3/jGN+zFixeWyWQ8jY530H4q4XJWURlFPYOpomyfFsPh+blczobDoTWbzVjgH+pseCco/79vR1Xfe9f6HzKK921j3yegCDP9NiUFimnV01RSoJhKKqnY7VP9Unk3ZBcnlCIsCMVYuBbgPoMFN3X2b8soUjX0tkL6J3cLapEWgBGAjP9nMhnrdDpWrVYdFBSLRRsMBg72wtTa8Xhs9Xo9krpZLBYdjHI+0syckeT/VOnk/XpHI2m8o9HIHb1isejVVbkzczgcWrlctvPzc8tkMlatViNAMayKG1dYiLN9fPb58+dmZg52qf7abDZjxzo8lxpWhuX/24D/64Kj8L13Kcoo3nfqKedo3xc7/i5kuCTdM/g+yKazu3ok4H3tfyq7SwoUU0kllVszOKm8PQmvEtiFUVSgWCqVbDweuzNwX8GCXa/fUIfktm153T4oUJxOp2Z2/ewuDBRtns1mNh6P7fHjx37eE5Cm112YmZ9vXK1WViqVzOyqOijnCwGOfAdQqIyiVqzlehF9T7vdtlwu50zZkydP7OHDhzYcDq3dbjuQw1kEzDBXpMzSXwCkirKe8/ncgXWlUvGKq1SHBUzqWHJ/YTiumnoKQ7pJNgGwXZxc7iK9j2sAlMV/U6mn74tj/y6kPL4vYxknm4CiZjC8z2OQym6SAsVUUkklBYpfQomrqLhpU9c0OLPLyqcKFHcFdDeVXdlBLUwSd+ffNqF/r+PY6DMADsvl0vr9fiQNVJ3/fr9vjUbDGo2GfyaXy1m5XHamdrFY2GAwsE6n46weV2QAFCuVihUKBavX654WzO84O8k7Z7OZgy8Ap84daXuAsVwuZ48fP7ZPPvnEvv3tb1ulUvG2Ano5q8rZQPQJpjlJN/iMprHO53Pr9XpWKBT8DO1yuXTW0cys0WhEKkqGqaa7Mn2bANguVXCXy+VOgPR15T5TT5UBel8YxbsaqxToxMs2G3vTCsSpvL+SAsVUUkklTT39EkocUNzkGIeFY7jeQQuuqA7ACr2uaCXNTe3RVMTbVC+dTCZWLBYjTFy73fbnbRLex9m90PHmOfSFMa5UKg6EuHsRgLder21/f98mk4l1Oh3r9/tWLpctn8/bwcGBjUYjazQaXvQln8/bgwcPrFarebvy+bzPUZh6CvPIGdM4RlaLUXBvY7VajYBLCtnAzHGnI0BSz06qaKqjgkmA4mAwiJzP43PMBbpL+2D2lFGkYNAm2cQo3gQowoDepR0M9fu+bCzs0PsCFMlwuAvhupabyrZrVb7ssmv/4ioWp/JhSQoUU0kllfd+U3yfZD6f+317IVBMmse4iDAgQgGIOmeDwcD6/f5rt1fBn0oIBnep3LpJJpOJFQqFyNk9ZUy3tRHAAUOlxRy0wAssoJk5yNL3mpkXiSgUClYqlazZbFqhULCHDx86YCLdlGsk+CzMKufbqByqjCLXojBuWnwmZOQYfz736NEjB0TK4hEwgFE0u6yMenJy4mcgQ9H7HmEkSWmmL5y/hHnUOW42m84qhsxe2P4k2cQo7mLXaLNeWXIXEqdzt726ZZu8b0BRr1t5XUmquLxNdI29j0LmwzbZVvl0tVq9t3dNpnIpKVBMJZVUNlZAS+XdktFoZGdnZxGHfhujqKBS51gvkzeLgjkKuryu4MSHuhU6KnFO7k30cTqdWqFQiIA7zu3tChQBCwA3WDv6oAVszK6ugqhWq+7Y5vN5nw89/zkejy2fz1uhULDRaOTgMpvN2mw2s0KhcO2Se00zZY5gXgGPvE+BIv/fNL6aekp/AXIU6zk7O7PhcGjFYvHamGnFXL6P/g0GA9vb2/O+8f0QKDYaDQ9GhMBQC8Fskk0pnbukMPNexvuumKww1fs+RfXhfbDhWk33dYQgxm3GBJ1+n2WXwMU2oLhcLlOg+J5LChRTSSUVd/rSc4qXQmGOd1FWq5X1+31PPzSLMopxjjHVTUOnWh39s7OzyO/uCigCWOIYRW2rpp7epoKjFpGZz+fOju3ynE6n42ySAqfwjkBNTTw5ObHJZGJmZrVazR1SUlEBlDBtjGexWLTxeGyVSsXMrorZhEBegaIyinyGdNR8Ph/pI3OtbDHvATjTj0wmY8Vi0d/PmJVKJcvlcvby5UsPMsQ5jIBqDTQNh0PLZDLOKNKeuIJJ3Bc5nU4jFWDp4y6ObC6Xs06nE+us7sKaKOt4l+cIk4DufQA5BYrvg2jF3dcRPWd8m+9q0aoPVXZhFN/VvTKVu5EUKKZya1ksFu4opfLll7Bc/Ycs7Xb7ndXt1Wpl1WrVK2SaXbFFSY4xACV0hKn22O/3rVQqRYDhXTKKcWx1CBQVTN708nC9pgGQdBNG8fz83IEiwI3rKgA4nLOkTZ1OxyujlkolH1cYPj2XCds5m82s3+9fSx0NGV/WIqBVzyjyXH5G0RzapsVoGN+48eRnMImweZpKq/dBFotF7y/fp+2asnt6emoPHz50kA5QhJkN57TVavn1G0lAZ5MeZDIZ63a7NhgMrv1ul9RTxmnX+0h3lThG8b5SQ9+3owN3xShSMfg2z2EdvS8s7a4S9nUbUIw7f57K+yUpUEzl1jKZTNKUg/dI0kPrV/IuV4Fdr9e2t7dn/X7fwYWea4tjFEltDB3hQqFg/X7fCoWCVSqVyCXXCtpeRwA8cddMhIwiYPemjOJ0OnUwAvhVdnLbc3AoNQVVr3XQc3P6HcAZYA1QBTBW4JnL5axUKnlxGk3VpM8KAgFf2hYzi4xPPp+PnK8bDofX2qBpsNp+raxKRgHps/wMoM35yTigyFhns1kbDod2cHDgn81msz43jFU4F9Vq1UajUeLcaCXUOLm4uLB6vR5b9GaXYjZm5swsc3YXEsco3lfl0xQoxosGKW7zXQImH9K+GK6ZlFFMJQWKqdxa7qucfipvR+6zfPuXTd5l3eZcmbJb+rs4h1FTT3XTz2az1uv1rNFoWKVSiQABzqq9juN8eHjoKYXbGEWADH24CcM9nU6tXC47G0bqKQBrE9gFHPA53hu2WYEVhXL4TCaTscPDw2tFRQCKMHONRsNGo5Fls1l3YEMWLQR3tI3P8UzeBRNYLpet0+n4c7Q6qp5bRDTIt16vvT0APmX/AKRJjCJnwZbLpTWbTWcfAbqawhfqbCaTidU9Ld6TpINU5tXCOSo3AVDM5V0yinFA8T6u4XjfgKIGUF5H5vO5lcvlWzOK71OBIJXQ7qiduylQjFvTqbxfkgLFVG4t77IzncruogzF+z6fu1bSUxbpXRM28kqlco3RT2IUcXrCc6idTseq1aqn3Skrs16vr6Wj3rSd4/HYQdc2UAuQwem9CcM9mUzcIST1VKttbnoOgAigqOf3+L+CxkKhYIPBwEHldDq1V69eRcZOU1YBe+FZQxhFdcz4jp4nZJz4nj4TYGx2eVVHt9t1x5azfzh62Wz2GmOs5yh1TjgbyTgUCoVrl9LrWUcKWjQaDe8bIBW2k+/EzUWz2fRUb841ArJ4Rpycnp7a48ePzezyuhe9p1HHKknUFhB8ucsziiF4u69z4O8bUFSG+3WEIMJtn3Obs9JfBtF1EQbS4o4EbNLZXffU913W67WdnZ297Wbci6RAMZVbSwoU3w+5jWP+JuQ+Np9OpxN7limUdz311OwSGITXV2xyGMMzWLPZzKbTqdVqNd/s9bwJTNBNgOLR0VGkLdiIXRlFPn/Ty8M19VSv+UCnN+kS5/LMokVitJiKMnvlctnvRJxMJjabzezhw4c2Go0clDIHYd+73a6znARmOIPI+8M+hGBO5xgQB7MIW6lnEhUoxjkytEXnvlarOdMajkn4XbMroA5jViwW/boSZXaTgKIWDdIrQcySU0/H47HPB58LAyfbitmoDgLckwJEqtu7SBKjmALF7cK8ve5+hD7ddMxVz99XoKg2ZBNQ3CUj430bn9vIcrm8k+uk3kVJgWIqt5YUKL4fokDxTc7naDTaeMfVixcvbr0BxRXN4Oe79PEumYW7FoCPFhwxu3JCQwZF04w09e34+Nj29vasUqk4IIC9mUwmdnJyYsvl8kZAUecTx58UxThGMS5yramnNylmowAJxwaQtsnRYTwUEKkAWhUottttKxaLNhwOI+wb3+ePgkDYWb3mQtNTaWN4TQTtD1NPAV3oAdVVVQ9gyWBYeWaYeoYe8ZzxeGx7e3s+H+HVHDqHpJ3C5GphIAWpPDtpXdbrdev3+5EzncxhCN7W67WdnJzYo0ePzOwqCNLpdCLt2wag9Bwh70nSlW0BptFoFEmfjStmc1+pp+Fa+rIzPNiiu+jHbQCn6s37CBQ1gLINKPKZTc9638bnNvIuB5dfV1KgmMqtJTUQ74fouao3aeim02nEsQplNpvdOu3x4uIiNrq3q85uO5fxNkWZF72wHGc/dE71jBjR9cFgYIVCwYuUADYLhYJ1Oh07OjqyRqPh9+DtKupoAzJC8IYkFbPh57sGLtQhBwApMN4l9RQgGALF8Gwg7ep2uw7W8/m8DYdD/z3f49m8fzabWbVatfF47PO0Wq0idxBq1VHaTNu0vfQLAAbYYbz5TK1Ws9ls5leg8Pv5fB4BbVp4J5PJ2GQysXw+7ywlwjnFuMqIgF50KewHjn8SUESXYRQ3pZ4OBgOrVCqRe0Tn87n3FVGAnTT325hLHZ9NMhqNIqm9ZnbN4X4TVxC9D8AmKbh0E9EjFTd9jt5R+z6MZyibgHAcUDRLDj6kVU8vJQWKqaQSIylQvL2s12s7PT299vOLi4s33pa3lXq6jbVbrVYbgeQmoWR/3DN36eO7fB8Z45bP561erzvTAVsRbvIUstHPnJ2d2aNHj2y1WlmlUnFHnKIsX/va1yLgY9d2hUCRf8c5W0mppzdlFClkQ/s1xXGX1FO92gHghQCc6Fs2m/WzdI8ePXKHlufoFRbaH86v9Pv9CDuHnoZAkSqqfG40Gtlnn31ms9nMq8jyDjPzuYI5pK3lctmm0+k1oMjY8/8nT57YYDDwc560rVwuWzabdbsEUFytVp5qW61W/Vn9ft/XrFZPZSw3AUV0lDnVuw31O+v12s7Pz+3BgweReaJdYWBj01rWIMMm+6dzkySb0la1ne8zUJxMJvbpp5/aF198Yc+ePbPDw0M7OTnZmDkSJ3eResq6us14aHDtfQeKuzCKm5jwD/GMYlwQOimL6X2QFCimcmtJijylsl2Wy2Xs5qlVC99kW+JST+/b6G0Dba8DFEmHi3vmNkfty7DpAW5gk0jVC1MKza7uUDw+Po6cgePzgJT5fG6j0ciazaafKdP0x20SnnHUjTPunMumM4o3AYqTycRKpVLkZ1oxc5ue6ZUY4flEWFrA+Xq9djaLn2ezWavX6xE2SZlIro+oVqv2ySef+Duy2awdHh56wQ1lYLWqLaCt2Wza4eGhDYdDy+VyVi6XI5VamS/AEqmn9J8CMQokmYdWq+VtXywW3j/0ZD6f22AwcKAYRs95fjabdScqdPT1faEA2Pb29uzk5MTOzs6s0+nY+fm5dTqdCPjrdDrWarWuFUKaTCZ+RciuEneFRZwwL+8qUAzTiN+Wwzoajezhw4f2ySef2Ne+9jV79OiR1ev1Gxf5uItiNnq9xU3HPAWK15nwTWz7h3bX5NnZWWyGzPsKmlMvP5VbC+lv75sRvW85Ozvz6HcobyPdMa4M+HK5tMPDw3t97zbQFl7wfRN5HUbxyxAA0dQoLixPisBzh2K/3/e7/3AS9PzbYDBwliYshrKNTTG7umNQdcjMIpU4VfScTPgcmL1dHDwKqagwf7BYuzCKpGMqG2lmXrmVd+Xzeb/OAV2p1+t+TpH3k8I5mUysWq3aN7/5TTOLFqchVZLiNSFQxKFbrS6vKvn444+t0+l48R7sr46X3q2pf4cX3/Nv5rpcLvt1E5xbLZVKzhoOh0MrFov+GZ2v9Xpt0+nUms2mA2bOTGpBIOZ9MBhEUlo1Vfbjjz+2UqlkrVbLSqWSZbNZ63a7kfmu1WqROczn835dyaY7GePmXnUwqeBOqNNxoumpSTbmPkBHGBx6m8BmPB5bpVLxdhQKBatWqzd+zl0winp37C42X0Wr7r6vQFHT5Lelnm4CincxV182ifMjsL3vY/rpu+0NpfJOy/sOFO+rghVn85JSsN50RAonLbxX6b7nddM7bnJGJQkIxBnsXdJDdKO8y7mYTqeJZf5vKlpRsVarOcsU116i45zhUr1mwzs4OLCnT5/6VRB6AT0VLF++fLmxTUlAUe8oVIljPxWkhfOfVHUyrrok79NqmkkCWIBl01RJvf4hm806m0hhGkAWLJtGlClmQ2omz6Lf3PWIvsG6aISesVDwvLe3Z4vFwnq9nrON9BNmmfFQRlhTbJlX3kEbqtVqZBwAsJp6CygM02snk4k1m00/A6nnPhkP5n04HF4DirPZzM7Pz61UKjlorNfr1mq1Iux2XIEaWM9KpXLtnGDcfKuOqu5oGrFKqNNxtkUZxV2ZyruQcDySCga9CdEA1uvINvu/y7lpZRS3jUd4rcqHxCiG58dvwyi+z35gnCQBRS1CdpfyNo4kqaRAMZVbCwbifYygmFnsGcK7EBy6OMP6NkpNxzleb+KA+iYHns1ql5SWXq8XYRyQpO9tG9+bMlq7Sr/fv7PUYpz+fr9vmUzGSqVSxAFWUdZoPp9fuzQdxgjgUCwWbTweu4NEdU/O5oXPxjEHAGnqKU5rkrMVd34sLvW02+1eq2hJ+zXNE1FGMSkdaDKZ2Gg0ilTsnEwm/kxtA2cveZZWZKRKKAxa2JfwvkEAGGcGGTvuZqTMegjuFouFHR0d2Xq9tocPH3q6J6BkNBpFqsYyZyHo5DvcL6fpvowdLDAOoLKqAGMNpvDearXqBZEohEQAg3YANENW0iya3aDOqjqqmxxZUng32dFOp+MFoELbBzgI9UyDovP53L744otrz9X3baq2etfnn8N3vQlgE15DQjvuKhNjG/g4PDzcui/cBCiGmStp1dObM4rvY8plkrxJoLher+3Vq1dvtbheChRTeS1501cqvEm5K/YnFJyNTU79mxTdFHn3m2jHpmI2OB2kum2S0Onc1O5dgCcb310HQZbL5Y3S4jYJwI9gRqvVsl6vl+jQzOdzr4AZMnkADRiQQqFgR0dHDrIAinEb1WQycaaP54TsSwggt4kGCZDDw0MvyqLC+ctQAGabqh4Oh0Nrt9sOAiaTSSRyq3chUgwGQA7byPO5v5DxzeVyPh+AHgAZ71PwxTsAUqw/0uf4PyAvk8l46ijtYXwAZcvl0quH8n+9AiIEisVi0Z3rdrvtQJF2IOiDghP6WSqVIgEMre6q80JfEAV39E3nn+CFzm8434DfcB5C0cqsYeqppvyqMGfL5dLG47FNJpNrOsW489xNjOJd2lY9Y2v2ZoBNXBA1LgX8trItnXGXzBC9v3OToIvhnGjq9vsGFG96RnFbReAPMfU0tBGazn+XQkbO27yjMQWKqdxa9NLo902I5t8HWOLZcY5GUiQccHlfEm4Mb4LZ3PR8HAWKZ2wSUviQOOB703Zls7tfz7CrAAbualz1TFS1Wr3m0PNO7qWrVquRypc8g8/oeTcYLbNLJ2E0GsVGjblzcbFYXAOKgBAcaHSYDS/JgYtLSSUYEK6BuEI2Oj5x/9a2A9b0bBtjqlfGhOnZ6lxxPQZnDqkGylzTf8bI7Or8LWAz1A0Fivx8tVpZu92OVBXlKomLi4tIiqjZlb4p+wlQBFSxznFy+HyhUIhkPWhKfKlUstFo5ABddahYLDrABNQSZcfhiQscKONqZvb48WN/9unpqZXL5Y1BFsaHtFXmNekMLOuEgAui906GzycNdzweW71ej2XYtUJuEqN410COwMGzZ8/8+fcd5Ivbi/R84usK85LUj7CYUpLswt6i25qmrfI+Mooa1NjljOK2qqcfEqOo6foqaiPuUhaLhe3t7aVAMZUvp7zPkaRt6UuvIwr6wkie/q0C+3HfohH/N5F6mpSqpAzFbYBiPp+/NdCDzdy0Od5GlstlooN5EwlBHvL48WMbDocR/YGRgg1jXnVucajZ7A8PDyNgkjOLlUrl2njO53NrtVo2GAx8owx1Wi+9D89IhpLkHHLtQhxQhMUI+xQH6sK2N5tN6/f7fjYPlg3gx1iORiNrtVqRs0v8jrsG0bfxeGzdbteZKwAO55I5B0r6puoZ4wuAnE6nfiXGer32dFJsU6VSseFwaMvlMsKSaR8JDlBcRMEq46ZnFvXco6aEIqVSyQG6Ok0EGyjuMxqNbDQaORCGvYVl1HnhObyn2Wx6+7vdrl/zkSR8X4Fikn1ZLi/vEY1bhwQ64hhFxm4ymdj+/n7sWUjGbNMZxfuwK8qw3Tew0VRslbtkFBXIJLVhUx9vsneF507Dudt1PG96Bci7IrucUdwEBN9nPzBONgX07yPDjr3bbHMxrfuUFCim8lryvqaeatrcXQtnxcIzGOo0haLpUvcpeqbrTUQIN7FKpJ5uA4phRBRWZNO5ik1yX6mn6/Xa6vX6azkUzI2mMiqgq1QqkbQwKl0CGGkHAtOljCKFSsJ03rjxnM1mtr+/b/1+/xqjqM47jmycE64SOi39ft8+//xzZ6nCYMl0OvVNVB0cPS+lhVDCsWw0GjYej71/pEvOZrPINRnz+dwajYYXTDG7Ak+VSsVGo1Ekze3x48dWLpe96iJVeHmuplTT58Vi4Wwkc0VRIcZuOp06wMlkMl6NlDZqoIezhKSkcu6S+dF7MgEbpPzyLH6mbCDFjQDV6CBzBPiFbWY+0IHxeGzD4dBZRyQunRBGtVKpbAywUDyHQAXgOo7VW6/XNh6P7fz8/NrvNqWeKshOKprDGtnEKN5HSntYcfc+nfakvXETOL7p9Qlx6ecI87Opj5vaEvdZzYzSYJDZ9WybJOH6oS+bhHOzDaSHAlD8Mvb9NoJuxunffWTYUSBK70x+05ICxVReS1KgeHPR80JhykfSOzVd6i4lNO7MpzJKb0Mwxtucqrg0RQWK+l1NedvkdN5X6qlZsoO5qwDg484CLpdLv/+Od3CGj81Gz6jxvJBRpJqnMlThvYoIl7LDMoSpNzjYYTpk0roKI9Oz2cyre37f932fHR0dXSvGo9cuaGVNvScyaR7Z8Hu9np+LQzcYGwVB9NfMPHW0XC7bZDLxwhnT6dSBMywd65e2FwoFr5Ta6/UchAEwABmseWVxdP7K5bIDXSqM0lcAuqZ9ahVT5huwx/cZEwBuWKCInwOYcbR5JvNdKpUid1AypjCrnIlk3pJSjtfrtV9HkmSTaLveJaoVV0NZLpcOsFUYkySgCGuWZJc0tXcTo3gfQPFNVemM2xuTQDlyU6AIWIn7DjZw0xiGYG+TLJdLPwfMd29TsXaXc5PvosSNcxJAT5L3MT03SbAFSYziXY8DgctGo/HW0k9ToJjKrQRD/r6eUYwDcncpsAYho5hkcPV8011K6Jwxn/eVdquiZ9dCUUaIz8ZJnEOgDJkyYHqWIumqBX33XaeImW2+dHwXAcCHZwGXy6Wn+T19+tSOj49ttVq5A8lYa2EURJ08PWcHSBmPx34GcrFYRAq+8FxSDdfrtbXb7YijF6ZW6pUQceOjbVssFl5R9ODgwIrFoh0eHvpz1Dll/bBOACxxTj3tI+10Op36mcTJZOLtCBlQ7hAkHTefzzsggtkEZOsap8AKYAfWDoeXOYIZY2xI0yS4AVhnztAD5or5W6/XnhoKw6VjwrNCZlork8L4afVhnqPP1zbC6pGqTFtIBaboDiAa5o85CpmJ2WwWSbtKAiSTySQS7ABAbzonyHdCiVv3pFDrObyQEeVn2xjFXYDiTbJHeNabuvePudQ+jMfjjWmnN23TJlYL+7zpeXH7QtIeslwuPaiS9N1dBJv0rku45+9arGfTmO/yjLgz5l9GifMN0a3XCQIlXZ81Go28uNzbCkakQDGVW4myLu9jJCmMMt6lYFTD5yuQCQW25q7bEzpebzr1NGnzYSzMbGPlUzZ13aiSUk/R2W3R6PtKPUVeh1UEvLBR4bB1Oh07PT31IjT7+/t2enp6rfJfCBQBAApgAQ6kSY7HY6vVan6+7NWrV9fAZqPR8JRaBUNmUYeaZ+j8bhLaQUojF7G/fPnyWiEb5vf58+e+vsKqmwhgbzAYWKlUilyHQaEWxkFTaQFZgAnO+QF8GFOuF2EcKHjDuUTmAVDBd/m3AkWcVr7LGHION5fLWb/fj4BsgJwylWZXIEgDMbB8MKI4JIBKTVnV9cD3wwqfnGEE8FLUxswclDP/miIal8JGNJ3gRBhAQqbTaeTMJWc3w89OJhNncAkOhBJnk9AnBYq6jjX9m7FOYjN3CUBtu7NURRmwTcHGuxJ0Xd+xrZDNTRlFvhMHFjcxOkhc+mjS51lz6Ch77U1lW1r9uyLhnr9r8aNNRzl2ecZgMHhrqZN3Kbr/Ihpcvq0OdLvd2PsSR6ORB76q1eqdVU6/iaRAMZVbyX2m570Lcl9AUQFYuHltOhS+Wq2sXC7feUQuTJF606mnSYy0Mk6bCtqQWqmgjj4lAUWz6xdqx737LnVbx7JWq936nCJAjo2KvweDgT1+/NhqtZq9fPnSZrOZM1g6xzjiCijC6DIgg9Q9qjwCUDjjpc8ljdDMHBjgPOg8LJdLL/QSOtKMNe0BFAMUzcxZqmazaYeHhxEWg75o6h/AOQ4oajVXnCeYP02Xpa2MmwI2UjlJpQRU9ft9nxuYt9FoZJPJxIE7z+McKe0C4PFuCr8o+OI5BDTQJwV/mkaqgJWxwBGnbTj6gChlonkOY8L3eLbaMtbrarXy85vMbaPRcMccVlUd13DNEdQBeAP8Q1kul54CDVA8OzuLBJhms5m9ePHC2VPueAwlrnBOmMqMLipQZHy3gcBdAlA3OWYwmUysWq1G0nfvGyiG6dzbCtm8DngN96G494eiqbi8P+nz4fPiGMW4eyPDNur6eJclBIq7gvhNur2Lzi2Xy3eOURwMBtf6tC2Iy76r+sSY3iYggkyn09h3T6dTe/r0qQ0GAy++9qYlBYqp3Epwpt9EKe63IfcJFHG8Qgc2LlKl3yOl6y4lLrqII3Xf8xoW11DZFSiyqWuUPolRVDZi04auRv+u5l/HmejgbQRHGN2keiYAsFqt2kcffeQsbK/XizhNem0ITqXOM+NDhB1wWK1Wbbm8vJePqGbojFF4SIEiz9S5IY0yBIrPnz+/NmZ8F+aQqxlarZY9ePAgwmKgMwDFQqHgjkA4jwCw2WxmtVrN9RD9R3f6/b7/jrXHGUaCCcwlY6vpwev12tkX0laZK8ae9EpAI04/Y8C7YDKZc1hWABef42d6npA5V4dZxwR2lgJStJs0Q8ZVgSKOcRh8QA9Wq5U1m00bjUb+PQIO4fnJOKCogHY8HvvcJwFFwDagjuJBZpfA4fDw0B49euQBgaSzaHt7e9ei9gq2Ec6mqu7tAhS3BaBgrne1vxRYYk95G4zitgyBXW2pFuUx28zubnpeyApuynwK+xOX3rwtsKe2412XcK7uAiju4geyl7xL0uv1IiznarWyFy9ebPxOnP6xZ7wOUNT9C+Hf1WrVbfSms9r3JSlQTOVWoo78+yh3DRTZaDTSHwLFuNQrFVLp7lK2pZ7e1iDtCoSSnBoFGrsyiuoQA0J1496VUVRmqtPp7NSPbaLjjCO/i24BHhDSmwAhhULB+v2+NZvNSKCh1WrZN77xDb/SQdMiaQ/9VOc3ZJ5gELXQDZVCw8g76X7qEIRRVtg4/lbhO7RHKwPznmKxaKVSycbjsT148ODaWU09+xemvIbvopIwgFMLq1C1s91u+5rgXJ+yYLwHoe1cPM/YaTVOdF7P/WnqablcvsYoal8oqqJVWjVwx5gD5AF7tE8dYj3zCCAEKOr5Sv2crg+YR+07xZOWy6UDeWVD0VNAXMh4s2YZK65lKZVKsemkBDQA28w3TOl8PrfDw0P76le/6uPDuMeBG0CD2giCG8wT7eMMpo7/NrCwLfWUrIFdbe9sNrNyuexje9/7cngMYpfiL5uAhP78/Pw8Yuvj9oe4M2KhhOOwCTwDFJPmDVZ72/s2PeNdkrjgMH3bpHPbgOK2/exdZFyXy2UkKDSZTHz/SJKk1NNdjlIkCTY1rIre6/WsVqtF9qRtFaDvQ95fTz+VexUcnfdBzs7Orv3sroHi0dGRbzZ6tjMuKhu+EyOi6X13JXFAEWfwddhizpAliabfJqWe0q5NqVp8Lu4zYXRPnYe4dMTwuzjGdyHhOO961mCxWNjx8bH/P0xbhjWr1+vuQCkLxXkxPb8VHopXh0rBGY45wANwAJAApCNU8uRvs6vAg4I/Uvb0vQBinS8ceTZms0tdKJfL1u12/XOvXr2KfBdQpoGVcB6pDJvNZr2KJeARoEwRHQAZxYJIP4XRU9Cn/1cgCYihOqqOierlcrm0RqMRGQOt4KsOgzKdvFuvvKCNzIOmixK5prgMgDI8q8o1GYAb/mAbNAiB4FAy14BZ2pLJZOz4+NiBoKaThkDRzPx8bFKBGuaY6zoAw7C9L1++tK985SsOYDUdN8m5q1arER0zs2tA8eTkxFl23su7NwGnbUBOU+h3Efpx30wiEgKryWSy8Xyi2WZG8YsvvvB/EwhDNjGKN7HNm8ZGUwlD1tjsKqi46X2MybsGhOJkU+rpJr9uW+rpNl/hLvfTu5KQ5WRP2BYUCAP6Oqa38YsJ6oVXYHQ6HavX62ZmDiIbjcbWVOi7lhQopnIreZ8YxbgDxJvSQG8jOIkaeQ4Zu6QzijhwbzL1lHa+TopD3LgiyvptSz3V7ySJbmJJhloN/C7nhIgk30WaRxxQ3OWcYsgshA4pfQ3PzCFcBg/rgy5perG2S88OVqtVGwwGkbL7AJdyuWzD4TByNyPzyRk3s6szkcqe1ev1a6mnei6W/gLa6IeZeZoxzjnf1e8zt7rhxzGK/X4/knYKOKTdpJ3ihONEAED4Of1DZ0kxhYnTdNDpdGrlcjlS8CS0AXpWEJ1VAKXVSElRVOZVq4sqqwnQ12fQF86k6hySBqjndZfLZeRsn+oPY6xZCbSn2+06KOTZgMzRaOS6QIDGzPx8bb1e93bFAUXaTQADneDs5qNHjyKVU7WgU9Ie1mg0rmUTjMdjfwd3U3LOVc9h7sIuYFviMhZUr7eJzsV9n01EwiDqtoqnZsmM4nw+jwTMSEXW78Xth7w/LtMkbu/Y5PyHZ3DjAhGql3FyG/D6tmQbUExaE3fBKL5rPqMG3swudZlz1EkSpu6aXR/Tm/oM3AdMcJN9cD6f+9oCRL7uFVu3kXdr1lL50ki46L/M5xRJ3QrlLiO0bGqhMxXHKIZjidOz6ZzFbWVT6ummNNhtoqXv4wSQs20DR+LSbpUB0fvL4oqz8E4cll0K1TAOd7H5h+O8a/pInI7ovwEe/D8OKKozqWl/CqwQBYrcXcd3YbTz+bwX5AmBIiAIB05TDXECa7XatYqTYdqwmfmGCatndsn+A4BgImAh6A96peMXt6Zms5lVq1XrdDoOSPSCeE29VJBDYRmqkyrbyvvU0WCMyuWyLZdLP+8ZOs/oqqYoozeaiqrfw6ZQfAc21ewKRCho1VRigCL3QXL2BXCnzDRMpKbPav9DUTsHANd0UxyhQqFgz549s8FgYOv12s7OznzsYEorlYqD5aSUZQXTCrC//vWvR9YMYzSZTDaubQ1IaHuxj2dnZ/bkyRMHr4PBIJLmvEsa2nw+j2UG+P4udmc4HPr6f93A3q4SBjS3FbKhbXF2fjweR9bnrowiAZCXL19e63NcMZptKcFqc8Lvol/bwMOXCSjqGtIAw02BImO/S5Bi1333Tcp6vbZarebBil2Y4bgxCo+W3NRPAyiaXZ19Ho1Gls/nXR81uM058DclKVBM5Vaii+VNpbzch+AAxaV03mW/stmsOydm5gyAPl+ZAZWw/PxdStJ5hddNPTUza7VaiSkSPH/XjSPunKJu6poaF1fF1ex2QJH3vK7ERXG3Ram5jxDnHEZHQV94LQDOyqtXrxzsKDPHzzRqqe0KnTSzKxAHUOTcGABD+0f6H0BUz1ICoOr1uoON8L3qNOpmre8BAFH9Tc/OKVBUdonxQSg1bmY+VtVq1cd5Op1arVaLpHtqEIIUUgAkdgTgRnsAMKROhmmfyrbSD+74ZC3q50JHWq+awMlhvAjGaFou4EtZVwA8rBjgr9frRQJGPAPHmZRYZbUR3q1FpRR48R1+BghnPngPbdFzgyEIQx80PZe/G43GNcZKg05JDmEul7NarRa5lqNYLFoul/PMEArIEKBSu70t/YzAQlyGyE1Ax3A4dAZ617NmdyF6RGETM4skpSZS+j/MDtDvxTGK6EBcOmMc2Ns1yBp3NQY/24VlehsB8/V6bScnJzt/Pm7P34VRjJvDMENqm2wbx7chHAPR8+CvCxRvCoYVKMIc9vt9KxaLEX0ERL7p6qcpUEzlVhICxXcpSnQTYfOIi5TdZYQWJ5v3aVl9bUvchqbO0V2D8jgAY7a9sM4m4TvNZjMRKOoGs0t/koAijjGOy8uXLyNGXI2+6myS4xCmeWaz2TuJ3MWlo1WrVfv8888Tv9Nutz2Y0Ol0rhWQgd3TNrMWB4OBnZ2dRe6wox2kGSpzjGjRE87vcVaLtDtdGwrG+Fk+n3dnGiChARBYR50nPTum7FnIEAOaOQMHQGMt8Q7OUCkToM4QKUb8DGYjl8t5FVJNr+Nz6CzghDRKxh3Hlj+aCaCsnj4X3SWdCPCgDKOCXd6zXq9tMBhE2BzYQBXGCJ1RwKdrQosJ8WzmU0E4oE6dU34fgiWAMjYMgM3P6A+FgjSdUKvdAijjgjyj0cjP0ir4NbtuN8LU06S1ncvlIszjdDp1YHh+fm4PHjyIAGSt1rnLGSUCDHFBqNVq98Ioen/hmwrYaibIrpfTJ7WNcdX1sC31VHV2V6CY9H61N0nf5cqabamnbyutcrVa3eh+wpDx1v7ftB/YhF3TnrUo19sW2k7WDWvpdYHibTK/yBozuwKuHM/QwDcgctejK3clKVBM5Vayi9P9ZRCMphovZSXuul9sNmxGIaO4KfWU7921oU1yLG87rwoCqVB5fn5+7TM3GeM4oKjXMyjTo/0J7/CLOwuloqmP6/XaN5HXlTigWKvVrNPpxIJxnDAABw6ROjLD4dBarVbk+zjLjBcFNxCcHpx0BYq8AwZyvV5bo9HwyKWyOvoO2stzOEOBU24WjSQrcFImgc/EMVM6joBCnA4FivSpXC5fA3AKkJbLy3v31uu1NZtNm0wmnoo6mUys1Wr5O0PGDBYJB0kZUP7PH9IzFcQCeHSdXVxcOJDU6zfQR2wAjjRgRxkl1dsw0IUOhDql13XMZjPvE/0xs0gwApCdy+Vsf38/kl6uAI2+wShq30ulkusp79DUL36+XF5VTdXqrcvl0p49e+bzA8BmnAFuyizT/pD9SgKKsJP1et36/X5knLm3EEHfb2IrlV0N7RBrYRtQBGSrDVSwetfsllbu5vkKVDdJEjMY7gHY6/Dsdfg95jUJKIZnP5P2mdAu652t+rxt6YhhOueblLjrfzZJ3F6EhCAylFCvvsyMogbvcrmcn/+7DVAM/eGbECfhuIXHNVQfuZeWdt9k3ler1U4F9OIkBYqp3EpeZ2G8S0L0Vg3DTdmubYIhoBgNzkq40JPSVzSd8q4L2iQZ910Z1ZAVNYvqxv7+vrXbbWu327HP31V34ox3XMVF0tXivqcbQxJQDFM7N13NcROJcyTK5fI1xk8/j0OPQwRIwzEcjUbWbDYj34MRy+Vy9ujRo2sAnesXFFjRX0A21T7n87nV63UHFwoo5/P5tbMd/B6Qpm0F1IWXzXN3oqZ3KeBRXWI8mHdlCxUkrtdrK5VKPg4hy0RRGnSBc32TycRqtZqziWFaHQA3nC+YRTZ3bXupVHJd0wI2IQDudDo+NrBYvG8wGPizVQcAaVpNVIGagmQCA8pmonu0HSeZda/FbbQ9ep+jRrYB0OHYAHp4HqznbDbzs6roHACRlFpACH3ns7omAYUIusu4xK1h+pVUFAK9abVa1ul0rN/vWzabtcFgEKlKa3bpvGHXkW12k/WADVBZrbanngLSsKFmUTB0H0Dx5OQkYkPNbGegGLeXkP6tvwvXRtIerAGAUOfQq/Dz2+w9AYaQUdxlPnRc3nTQPG4P3iZJrPc2RjHcrwkahPdfJsl9BLpvKzr31WrVer2eV8DetvZ0j0I0nf4m/nASK69HDhD2Ldp+k3mfz+cbCwxukhQofkBylxvH+wQUS6VSxHixCHdJIdr1HaTxaTVInHKNdse9U4HifVyRESe7pp72er1rufKqG4xteEnsJjAe9844wxxu6hTkSGIUcTT1TFgoITAJdeO2EheppZ1xQJTzP6RwKtOiUdkw+g07XigUIunOyixpCqmCG5girong+gvuauSzpH3q+a/QHjC+ekZR79yjLeg/zAgAB0Ck40T6IvfG0VbsD0Ayk8k4WGXT16yBXq/nl8ujCxTYAYCqo01ggZ9pQZVwvfIZwIqm1QGKwjS2yWRiBwcHvuYmk4mndQLMwnEtl8t+hkWZSfSGtgAEqS6pabgEVVQXCoWCVSqVCAtHn5hLLUSUz+e9sELotOuZHzPz0u56PYjeTcl3tPqsBhpIY+10OpFUxXB9amqw2RXjF5ctkBRhJwjBWlutVtbv91339F16dhfZZjfRR+7vVNkFmCwWC3v+/Lmn8dKWEHCFwvnPfr9/owAY4xyyUXq2apPEtUeBotqoXYAin4tz6uPsdZJTHaYMJrFt28AD62+Xatp3LTdlFDfJLkAxLqje6XQ26hNzexepp7tU/NwlLTMEiprquY313JQNddOjWBx1UEnyT8wuM5EGg8GN/e64IOeukgLFD0juMtKlBuXLfkYxZBQ3pWbcRnBQOIiMM5zNZq3X6zlDmGSk9YziXTKKScCU+dyFUYwzPmE/uKBd9U/PNsQ9M24cwrSVMI1Mz08h4caGgec9oSjzpymMdyFxKb65XC72UDpjenZ2Zu122xkW2LR+vx+5mF37goM7nU6t0Wh4KX/9HRuRMoqj0cg3n8Fg4ECr0Wh4NUoqV+Iw8wxdM4zxcrl0Jpn/Axpw5ilAFFbspP8K0Eh3XSwWDmoBd7QD3VHnHScOB2UwGESKvphdbr6c34tLCdTUTaq9hmcfQ9AOoMMJHgwGfiZGmT+zqJOKE8DvNI0Stq1YLHolUQX8ml67WCy8OutsNvNzL7RTgayuRxiicI3SRlJ6OXuqQTCuHQHo53JX9/vBgAOUK5WKDQYDZwsBI/RNz1vynkwmY8Ph0PtM8ETvUMxms5E7JDU1FMaHtZ1UeVgdfljhk5MT29vbi6w15knt5C77Ifp4W0ZxsVj4ucwkRjHOvnHuFF3YRTSjQde5BhK2SRzDybUatFv3HPUtNu1BcamMBIxUkpx63VvjwCRrYNucYnfeRtD8pkBx03huA4ohENag2KY28LldQNg2Idix6V0UBNskSX7eTdj4uLm+qQ6EwZbDw0P38+LmgnOKKVBM5V7kPhnFN51uEYoWabiJaEoXEldZ73WEsSIFCudc2RmzZOCmG+ddpm4kbQp3DRRha7TdmzakpN9pCtmmdiUxinxvU+qpMn+kvt313ZXalnw+H8tqAKoWi4U1m03rdDrW7Xadeev3+5GUL5ijo6MjT7tjAyoWi35mh3eSJqi6RUEExgbQXavVrNfr2WKx8PHAoUcfw9RaAKXOl56n0isTNNWSdsAa6Tmzi4sLdwIvLi68j6xfrrnAYQPYMud67QKgVNNgQ6cf8EmKJ8BWL6g3u2JAdLNnDSkjTcomz1Xwq6mL6/Xa9vb23AHU6x/o/2g08jb3er1ra4Z3dLtdm0wm1uv1bDKZOFAEEDcaDQcBWpkWoKFOE30aDofeJ2XeCoWCnZ6eOkuoZ1JhG4vFogPFUqnkc086KXrPHZeqTzC9WimTMarX6z73pIjSRtVRBdMUVdqU2ohkMhnr9/vWbDYjdiM8U4fzuc0ZhlmJy1hYrVbXzhaHgh4mAcVNwAi929Vp7Ha7vh7VuWY+k0TPCYbtQf81XTMu62Lb8Y844GJ2PQiY5PyHqafh9/j9NqdcgeKbPoO36169i9yWUdzmf+nafN12bvN/NBtlk+jcTyaTrQWLQun3+x54UbmpP6xAcb1e2+npqdVqtUjgVEWDoClQTOXOJU7pDg8Pb7Vw1cF8F1JPT09PNzr0SSlGcYbxrhlFHC4MCGPHJqf3b20y0mZ3C8qT+qm58dt0g8i+StgPUji1MtvrAsW4tscxlKFu4owkGdm41NP72vh5fpze4oQBrh4+fGiz2SxS/Eaj7rBHvV7PptOpjcdjvyC8Xq/byclJJEUR1kUd3PPzc9/Q9foWTU0lRQcwSoU2dVDRb4Ii/EyfBVDkeXyXd/b7/Qio52d8DlYN8GVmkWsW0N1wjGDnQodnsVj4PYraBthT5gvgCJvKd80sck1JHFBkLlkHOCf8m8+E57Y01VPTXyeTiY3HY+t2u84a0/5sNuvgbX9/38wuAfJwOIwwitVq1YEn14HQrjigyBlCZVEJrKELtFdZYf4GUJPJoXOPjmazWT8viigTPZlMrFKpRGwnwF3PeWrqMueowrvIGN9NwpjVarVrzpYGJBRk78IoMgZxQBHwniQ45hrUVBYxziHnuZrGvovARitLzM833Z94fHwcYR21P3r3oq77MKU7DmCqhMyrVk8OPxsXhNUshzBDxezq/Ng2Jkzt5ttgFJNARSis6U2/vw9GMS4IcBsh6wA7Fyfo9rbxUB9iPB5bq9WK+Im7BMmxhzpmtzmjiA4ul0sbjUZWr9c3HnsB1KZAMZU7l7iFFR7C31WU/XoXgOKm9IvlcpmYrsAiV4foPlJPMSQ4AMoQaEpcnNzVWclQNgFFTdPaJLswisvlZVEILWgTnhcK27UNKMYVsqHt2p5w7OjzpgizAsVdzmluk6TvAxTjNoPlculVRgE2jUbDSqWSvXjxIlJQQ0HPfD632Wxm/X7fryLgHFm3243onhYBubi4cMdTHXvGsl6v28XFhbPD6DTgAEaTsYMxSYrwc/VAtVqN3K0HoMQxVcaBM3Y4bTBP/J73henIPGOxuLwrkPsPaTP93tvbc+CyWq0ijGyxWPRnaaoj36XPzHeYasX7YIP5jlbSJDgAG6vjETr+fJ4zf+gIIE4LxmghLGUfFIRTpAVHBVCk71VGU+0Yn6OPgHfmHD3CydR0XIIWjPFkMvH0WmXMtSIuz2LOlcnBnoUMR6VSiVyhYWZ+HnXbeSYcOSoOhkARhnq1WkXScjcJ4xO3dzKHm/ZUwDJjYrb9jKIy4Di4+rwkIdWbsdCMi6SrMQDxScB1NBp55Vh+p6xTEjMaZt2EAT+t4ruL3Q5TaZOA4rYALd99W4wie8Uun93k2+wCFENGMe7fm567i1+RJMz1+fl5os6yfrbNg+71s9nM9vb23BbsEpDH/oRjeht/WOsV8LxN5zk1qLmrpEAxlZ0kznDGRd52kU3G+m3IpohW0n1VZvEpI/fBKLLhkRLFO2mfmfk9eeF3Q7mr8U5KsaWtu6aebmMUl8ulnwFTZ2UTo5hUUIDxiatOZ7Z9E9plo9zkONxGkvoKUIybS0CFnpExu3SYv/d7v9f29vb8s9ls1lkvooxsYlxPUC6Xr6VmKqPYbrcdtOp5Odp2cHBg3W7XNygERoTP4aRNp1Nvk6Yy8zktbqNn/ZT94Xn8HwdXAQ/n/s7Pz/1KDuYPMKFAURkogkv0JwR/FERZr9dWrVYjKZl6rjkEnDqfrCUcj1KpZL1ez1lDnt/pdPz8IkBRr9BRphZWk3Gh38zfer12tkaLrISf0Sj2cDi0arXqDjv/DtkxdAv90mJV9JGiECrKXFOAh/YoGNjb27PRaHRtfQPQNdthuVw6a66l5JkHTTMF5GFDYDaLxWLiXa86zgRXQueMuaJdnK3cZjd3AR6bfj8ajazRaESc4W2pp2FKseqvXjcSSlyBItqYZB/1Lkyz66mfcfc/arZDEuANg4hxwA6bcVPgFGen0aNtTKGepX3TvpAGTnb57KbKl9uAYgiCAOSs6V2e+zpXZNDHTVVW0aNdgKIGxbXYYBKDrH2EuQx9im3p0iqhD0YgE1CXBDoJ2G7TNc0c2DX4HycpUPyAJE5B4hz9m8q7cEZxE6OII5vUf9K0koDiXTBKbMyZTCZytkSdZyLS+k5NjUHu6pziJkbxJqmnuzCK6gCHnwkNa9JmpQxkHKOogDz8Hj9TRyMu6q/v1n+/jg4kjTOAI04vGbOQ4TSza4xFLndVqEWBBGCTMVEGQh3c8CJzvaweEARALJVKsWcywvOOzBH/Z0NUJknZEIR+sRZhxkajkacBo5+sHYCOnnEzuwKKevYOkADYnE6n1uv1XJe0iihjsF6vPdWRNaFnGxlnBenaX2wT4Gw6nVqz2YxUHR0MBl7JlVQugFLIjgCSFouFVatVn2O1E7A1jCPBBAVS/M24KQOoIE0BPP83u7qTU+eUtE+YofV67UCcsdIxrtfrkeJKpVLJms2mrVaryBk31jA2k/XA3Wca3KA9mtYNUGSt0MdisZjIKGazl9UcYR7jbKIymfl83obDoReZ2tVmxH0W/Ut6xmg0slardS17YhOjqCmyahPJQkg6usHc8tlNwAoJ07PjMjs0nRg/RPUm7nthIE//pn0EYML+J2WQ6PPC92nq6S4+ztvKrmIMF4tFYuDDLOpjJP3+powiWQKbWNwwgHdb/wUbvIkY0IDYtmcR/GHNavZDElDUPSCOUbxJBlhYyIb/E4hrNpuxx6bQ8W31EwaDgXW7XTOzyF54U0mB4gckcYt41xSNTXKTTfE+BIcnaQFQ5j/OcGAY1XjpwtcNdzgc+jmfTcaYNoXsGc9nM8OgaLtxYhhPNneVu7oiYxNQxJnYhVGMc3J0s4FJwbCaRTflXYGi2dVGFcco4rCFG7U6h9sYRQVmtPF104nCKDiiTFFc+q0a9dFoFCkLrp/nQnCcb5x1Ir2MrwZDuGA+n8+7M8wzFaBqeiA/C6sEV6tVj/Cq7sD+mV1uWOoQ0jfWnqZr4pSTipnNZr3wDX1nDkmVDFkJsyunjb4BFLPZrK9fvl+v153RY+3yHoCi6rquD4IWyp4wf6PRyFlPGKf5fG7dbtefpRcoMxbMtQaMFIwBlGHkzK7WEQwo7aR9gMFyuWzj8dgmk4kdHR1FWFueo1kWymSq489z0VEFE8zjahW95Hm1ujqnis3WwA16U6vVrgE45pZ5XS6XnoKpgI1+hOdfYdhpHwVyko5f5HKXhYL0CopKpRI500jAhblQwL/LmTaeERb64jxuEuiYz+d+phQJs3zC/VCDQCoEHDal4LKOk85EhjKbzZyFj/tdeK2RBmY2MTIhsAuF9R3nECftZ3H7sD5vF0aR576N1FMyTmDzNzGGmwCW2mCVV69eJTLV7DkE2jaxfHfFKBIo3/SuXWoLYE+U3a5Wq77XhN+nkjTZDRTDivMpdvWH44CiFurSK6hC2dvb2+qHagCQsbtNICMFih+QJDFqtwF5bxMYhsKmu4lRrNVqsYYjjlFUZzMEilT82xbJGY1GdnZ25s/DscKxxClSZoJon25ocZcH3yWjGIJQ7fOujGJc1DeOUcRZDz+zLb1IhXlSEKLfi4skqzOn+h4H0HTuza7YlZvcORZKEjjFAY9LJVaQQruUgWN++LfeUwkwZrxD4AToYP65uJvna8pru9123YSV0iIkZtE0GN7FhoQDwflIwIE6Xvw7ZBE4C6iMKc9Vtg3nPAQsPJf0XtZXNpv1qqHL5dKdWvQBRtbsir1lTJSJQ5/op17Nws8ODw+tVqs5EN3b27N8Pu9MKd9hPlknOCWMB5F0TafUAJmmJ5tdXemA88g8aABgOp1G+q0VaqmMy7zwHWVGNZgBMFTwAFtN2xkrPSOq4GC9Xvt9bJucSdYj81mtViOpp1qtFz09Pj52QEhmB3Oq+qVyfn7uxX4IaFQqlYju891sNuvXcJRKpZ2Ln9AfDaBpKnecU8fa3MRyJTGKmi2AzOfzyPmsuHep/iujvwkowsKHoucTta2M8yYAGhdEDNlRxiZ897aUwE2Mo7Kcm+QmaYfbZFuBGLOrNiujqHczh6IZPSq614f7eXi1lQrvxJ4lvTcEirf1X0jt1jUSyq6MIjqsQHG1Wlm3241dv51Ox4bDoQedFCjG+VG7SAgUNfi2Xq83AjvS9Dfppe6r4b57E0mB4gck98Uovm1hA9wEFKvVaiKjGB4cTmK7SFXD4dnWJr6HQdLqVvycd7Ah4XTw3fF4HEnBMru7uxR3ST29zaYXBxRxPLXCK2McsoAhWFNRsBeXzqTgSvtzdHTk71cWJZzHk5OTa+mm9wUUGae4KLRuujjoONz6XX6v44rzvl6v3fnjeeHGgaNtZtcKiEynU+t2u3ZycuIbWrfbdVDAOwFJYZv03zCcyijSBgWKmrIJ86DMHX3TapsAIZ6tKW04DQA8XeOAR/0ODjxRXA3maJCBayaYF64NAbAtl0sPUKH72l8ABfdBakEc3qNn38gsUDaH76EXmh6Kww1DpwEJKsZiFwFO4/E4kuLJuxknLRCjDCp7iI6hmTmjGqbpYm+5ZkUZG7IF4hwvnsN3YYjD1FPmGPaPKolqv82u7FypVLLhcHitKnO73fbxASjq1SC0Cb2vVCoRoLINKOZyOet2u9bv9+358+f2xRdf2He/+113RpXBVUEvGPs4ibPd7JN6jo81AagLn8caC8EcNn2Tsx5mHiBxQJG+np+fXwte67/jGEVth/4s7P82EJf0O11XcaJ70U3SDrdJXMGW5XIZ0VPslQJF1oeySYgGTFU27fUhYNU+Mm/Y6U1McBKDfhM5PT31/WHTu3ZhFDVbQ7McuNs1/D42naAqKaJx+/uuehAeoWF/UBuV1A/NYkgS9UNTRjGVnSSJUbwpGFAn/12QbUCR1IhNqacawU5K4YF9uClQ5B0YBc1tByhggEJ2lPexYZycnLyRM4q7Mopm8edIQqAYAiIFEttSTzudjhtDxihO/5IAZjab9QtqYWr0PAKCcx5GrLXaaijhxp30mU1AMWnj1DNhsC/6M20nbIlGITVNkM0tdD77/b7V63V3LiqVSiQlslwuW6PRsBcvXnjFztVqZY1Gw9NeAC1a0ASmhoAGIAMwoECdv4nmquPIBq4AAEcgZIkV5OgYcqm59h27x8asZ4Pz+XykIAtjx+/NLqvEakEcdIR3LxaXVVRD5oV7H6kyitOuuoC9onooOsu9ldoO+hUy6bo+GCeexeeKxaJfQ2J2BX4Bago0AVx8V5k7/q/9zGQyfk+iAlBlw7jSgzOAABXdX9T+oCfMPSCZwkCsM9Y33z87O7PHjx/7uIcBjUqlYv1+305OTvxdpErDtHINCY5qmCqqKdSMx6Y9Yrm8rAS9Xq+t1WrZ/v6+ffLJJ/bkyRO/h5LPhaJ3hyZlXmxKPUXnGHPAerFYvBaA1KMPmvkQph2rbBsDPX+rbeUPadFxon3W9a7vUaY7HJPQ3ocSgqC4fsX19z78oTigx5lq/YyuPdIVx+OxnZ6eelYTovZGJYkx1ayF8OdmV2cUtwFFtcuvk3rKGt/2rrgU6zgJi8nonhm2kfHFl+O8fhJQ3MWvVt1R24oNS1rfSK1W25hqrDqUAsVUdpI4wxdG73aRuA1qV1DxunJ4eHjtqottQNEsOYrFQk06hB4CRZzxbUYAJ4Z3rFYrT6nSlCxlMzX1Qx3B/f19P5CMo34bpi+UJEdDHddd51Q/F/dcop4Y3+l06gYu3MDD75NqymdDZpb3J7UZYE7qpNnVofgQoMZFh+McKIS7DTfJNqAYpnKG7WeTVQc6dHaVDWADUwYgm726N1H1ks+R8hg67GYWSbkjFbVWqzlAXq0ui5J0Op0IcEPPzaLFbF68eOF9YSMLUxvV8VosFj4+nO0D0KijqGCBdgFYAKgKIpgbTWXkObCDPH86nUaAo17TYmYRRpE/9Xo9ci7O7HL9FotFZ28ZA00tZT4YM9qdy+Ui0Wc2fcZD+6Ysohaxmc/n1mg0rFKpWKFQsNPTUzO71PVer2e1Ws0ZK1Jkdc1oKqL+H2CpugXg1iCLsvSAvXK5HAELjHsINBh/1iPjzbvpB21SAFytVq1Wq3kKtH4HQKv7w/n5uZVKJTs6OvJrYb73e7/X9xrsMX3CLilw3uQMLxYLazabtre3Z81mM8LCYqtCu4jACKOrcTY67ufoOjqJPgA2dE1rOwFd6nxvAoo8L4mVge3WtjL3tEnPZYXrNTwWwntC3dnGKMbtU0ns5SZRtoz23oUvpME+/ZmOKUBegWKz2bTxeGy9Xs+Ojo4ie9dsNnMmTCWpzfgl+nn1lTQratfU09dJz8XGxYFd/YwGwJIkk8lE7vM0u9JrGNmwDwoU0f84PUlauyqh3rDu2S+Srp4J36P7bNw7FCjuWpTp2ntu/I1UvrSSFCG7C6CYBLTuWjCA2uZNQFHB2KaNW9OjVEKjpo7dJgkZxfF47BdKky5H29SJ11QvM/PKV5w7IAJ8V5IUCQ2drSRRcBF+NxQ17hjd8Odm1w2oblSMVdz5RDNLLE2PESbdLmSyaJO2Q9PskoDiLrqQxHSyjsJUYnUAcXw0xU2/q9/RFED+recaYZa0qBIlwblzUcEamwrnXg4ODmy5XPomDCsEq8NmFbJOCkbn87mnw9IWwAJOqzofzAlMEuPNsxUo8jMFigCaZrNpw+HQWVXGCJaFM546V+h/uBHrzxhTnGgdb2yAzq8WltGCCAqgGCNNQ9fnanop40V70BUFrbPZzOr1uv/s4ODAGSXOC5pd2lbO5BHNVoZYgZzqHHNNn9frtfX7fev1etbr9SIASgM+gAIYQZ7PfIaZE+g86zeO/dd1sVxeFrup1WrOHHL/pKaooyO8CxAKq1wul/1sKXOhUXzWK0wfurgt9ZQx088SGKCIUxJQZC0kMQRxzjiAlv73ej0vgpXJXJ7pDAtnAERY42o7kq72AcjGtSFMOzW7WmesseVyGTlzTdvNNjOKGlCLc4jj9plNQDA8B5+0V4b2/XWAUPj+8DkagDaLnqHETpbLZa9t8OjRIzs8PHT9Yt52BYrMv/5O93t9jgaCQ0kKSoeyrf6DBqeSxng+n9v5+fnG99AfbB6yWFwW0jo/P7/Wjk2MYpw/vE0HAO3abuxy0vVfcXJwcJDIKoY+7W399BQofkASx1TE/XybvE2gyIaui3ATUGTB7cqOhX0L00DVOd7WTmV/YF36/b47FWz0bJLZbNY3b96Joaa8O+dydolYvY7o2axtErcxxm2qev4qBH+bIr0K4GBIwqsx2KCSUk6IRJKaow7P4eGhvyeuv5sOxivbmSTbGEUt48/nGaswnTKOUVRHSUG6Mm1mUf2l76RMcs0APyeyyXMpIsC1DrPZzFqtlvV6PW9LpVLx/5tdXW1BWixn8tRBJnJbqVTs/Pw8ArYAcMwbzBN9ClP0cLgVTANO9UJ3wDnjBUjF0VLd5zNULuVdPJ+x7PV6nroJW0WhAU1VBDiuVpdptnt7e5FgAwxcq9XyyDnrBUcfWxayepo2FaY0KQAEBMIM8h3mQdeaZlvwZ72+TOdUJlQd1fX6stru06dPbTgceuopzD5zj45rP+gj/dP7M5l3zh4Oh8NrmQXqYJEKzBoimwObqoAYm7xer63dblulUrFms+njkpTyqwI7AWBMsguMWxiQYK4Y+12AYtyRCsYwbj+k/zijanc0KIAo+8gZTMZ5057LEYtwjOKAIv2+uLiIBGu0oBTvCRlFZd2xQxrkUgnHRNMO43yaXVmd8Lt35QuFoNDsemAyZBTNotWD8/m8PXnyxF6+fOnrSAt/IUnAK445Vt3WvWlTv3dlXQeDwcZKntiJTWBcj5ckfQY9Go/Hzigq6Pvoo4+s0+lEMn0UKNLv0NYiu+hAWMiGwBcBT2zbpn5wtELPXoe/T4FiKjeSpAjjTaNfcUb1tsBFGZ1dBMc8NJZJm1Z4WHiTEClKig7i8OxSSEaNB5HzSqVitVrN+v2+sydEpTivQxvUccjn89ZqtTy1T4sS3ER2HWfevQtQ3JS2GwoOhzIUZvG6sw0oxl2NkclkIhXDtD/q+FMBF8eVd6uzqvO36UzFLoxiqFParySgqBFJ/s961e8yrgrA6XvI4AKm2PwYk9lsZqPRyM8nEukEBFBghGgnII0rDGhfuVz2/9OuTCbjxTlgZrlzS5n1fD7vTCVjUSqV/JL0xWJhlUolcmYR8KrjofO0Wq18bAGV9F3HDL0cjUbuKDC2CsI0ZQ4mjHWrd1HiQAC4sRnqxGYyGRsMBq6LsF8K3tQG4CwwVgAp2Drex78VNK5WK2eMeS7zGLKyBLC07Rocow045xpU0AAQ+k6/M5nLYjnMj6Ymaqq72dXaB2D3+33rdrv+We7n0+AG3xuPx55GfXZ2Zk+fPo2wiI1GwzMSWE86X/P53AEmabi6hyhrreuIeapWq37dSZxdWK/X9uzZs9jgERkAgFc9OwuIYkxZn2EQizUd7uvK0mu79CyumVm5XPZ0ZLMoq6ZBuCTAQfvi9lx0QFP9EGw8+lWtViPnSdWO8f9MJmOnp6ce0ApB5DagqJ+Hwdc9L+mIg8rR0VEkLd1se9pxnCyXy2vnCXWPREKWEb0O+xYW2jo4OHBmMY5RRC9CwBPHQLL2+T36sa3fu4yRMvtxovYnyTdRtm/Tvo091j2VccvlcnZwcGCvXr3yq4rYC7FnrNO4db4rUNR1oqn06uNsehbvr1QqkTPWyG0AbOx7bvyNVL60EscoblpwSZLEKN4m3aLdbkfuh9v2XlKbQocwTI9AdMFtA8U40bqB42iFkc5d0ii0fWzuhULBKpWKdTodB3ywKJPJxJ4+fRphTHknd5/BftymoA0OitlmFlmZh21ziqFNMj7h5qVAmN9t053ws0mMIinG4fkC3RRgFtThV0BGmxUobipMsYtDcH5+HqsvrKMQ9LMRwrDh2Md9l7lScIvjAnOmDrxWjGTNsKYA2fP53A4ODiJnGrWf+XzegyaAOZztZrMZucMxl8t55Utld9gQu92uM46wc4DSYrHoBXXUqWM9A34UYKgeKVDEdpRKJWf1FShms9nIXZUKzHTuFYgBQPSKCLVFmi6sxYY08q9M42KxsAcPHkRYU/SWaLcykuqwad9VL3TdYOt5NumqGpjI5XI2GAzs5cuX7rigk9ixTOby/CXAEscpBJIAQ9hktduqC9oPxoX2Ad40tRDHdTweW61W8+9p8GA+v6x0Wq/XIwC7Vqs50FPHmHG5uLiwvb09DwTM53OvTGtmEfurFYWZZ67IQE/i1nx4lQxtXy6X1xxE9OP8/DzinLNvhEBR5z58L/pE4KfRaPgaYu1Vq9XIPqGsm2bloOdxdjG0z2HgMWlcmGOCQoPBwJ49e3aNUeT/enaX7ybZAn4W7gsAwfF47HOifQ+L7oT9hb3KZrN2fn5uw+HwVs74eDy2b3/729f2/bAPtJm0d/4fAj36z/cbjYZnRsQ9V4NhYf9CoKjzjg/DO3cFyEk1IwaDwcZAvLJ6ScI5zW1AkXYgyp6y937lK1+x4+Nj38O2BR/059t0IC71lAAR+zNtTOqHHrs4OzuL+HWhHdgEbLdJChQ/ILkNUFytVtciXUlA8b4Zxbg0K7NLI5tkXOKiwUlCqlMco6iGRc/cxEk4nhgsUp/K5bLVajVrt9sOEgaDgVWrVXdOQnBqZm5UeNZNrshYrVaeumq2+XwGY7yNUYxjruLey3tgUnAeQ8d2U9sZf003UlEj2+v1rh345/MYVT1vpE4Im0EcwxEnChQ2fSapX7Q5ZMiXy6Wfs9ANXMFGyBwBxjTYoSl2ZlfXGmj/iJyrw65XDjAu6APOsJl5+inP2tvbi7CE9AcgiINLgAFHC0cVVkhBLc+hzTB5o9EochZK2VQ+D1AECBB9hwFE12E7dW2rswULquy0pt8BJmijbsroGfPHuoW90bWuZ/wYF+aY78VF8BXwaAphyHLyGeablCX0F0aYYjfKJjNXWuGQVGJ0R/WTM6HL5VXqptpR9ErtnOqqsnwKQOkLbWHOCWDAdJHiyHzruh8MBhHQwft6vZ41m81IloraSj3Pyr1qgNJSqRRZG3G2UzNidA8B3LJGmCM926pBBdbRYDCIpMclBfdYj/x+NptZs9n0caFAXJiSqoxLGBRM2h/U5uveoM+K+47+0XkK+0QbCFqge2on4/aUcJ/SeWXda//C1FP2nvF47HOMfeR3yrbfRHDyKZK0XC5jj1Hw816vZ4PBwNcf71fgo+vazDzgpkdbEA12qLAOwvRF9SPUHu3qA8Zl6cCqJvlouucl+S48h0BW0rNYf/ocZVsJ9piZffzxx3Z6ehrZM3Sc49aABnmSJBxv+sd3d2EU0WkCiHrGONSHTetvm6RA8QOS0DhoKfokWa/X1y7ivUugGBqzTYJBDiMs4VkLFU0fCY1T2A8idZuAIt/bBKLUSaZ/GFS+9+jRI2cRiQ5Wq9VIcQuNApuZO8hsFjdhFHEm6Mc2oKjOQZLwuRC4h2OhoMXseiW1bbqjOoK+xqXIwCiGDJhu5LSZKpUKFDHOvE8dVtoR179tdzbFpRDxc5g1/f1icVkso9FoRFLJYAtPT08TmVz0Nc5hwhEiIkv6Is4A6ZBcw8D5RU1pNLuMTne7Xb8iglQ7syvmCpBGH2Fm9E5D5p0zj+gJ60FBhI6XpqVp2lXIgrN5KljLZDKRs5Vml9evhFVGlTFZLpdeEIV5UAZU514/oyxJXGEa1jBrbLlc2vHxsTPBCmzW67WzHpp+G+odc9zr9dwZ4kwfc65gD9uuAQIAV6FQiDBwpCkC8AGatIfxwJnSM2ZaPMgsGpQDyLMWGHeeA1DUNalOO3ZQwSqfMTPPxkC/YPX0rPd6fXlWtVqtOgjVVHneu1gsbDQaWb/ft/F47HrYbretXC77WDMXof1UW6QSAkXWsOoQ44neY+fa7ba9ePEikjkRijqNzA1AUe0I1XjjrqegTaSSxwVLw3erbdfMiHDv0jWLXUI34nwE7AbB0zComsQobgKKtFcDhyHry/wzPiGrCYi4qS/U7XatXC57UB5/IW7fAEiMRiP34bCxzHHcfYkEGEajUezYxDGKGsBBtH8KFG8CkOOC9mRoJOmwBhdp52q1ilQd53sAPtrDnopgG7W/Ghwaj8c+Jrlczj766CMbDAZuwzRLLU5yuZy9evUqVg+waXHrhzbpHr4Lo7harSJXZXQ6nYj+JjH5u0oKFD8gCRffLkAxzkjHAcXbUto3ZRSJXuvmgzMXJwp6kqroIfw+BIrq4ChgSXonm7IaWAwuzgOO+XQ6tU6nY/V63aOJOJlhioUyAttST8O2jUYjj7Lx+ySgGLc5JH1uMplEIpRxjtE2oLiJURwMBl6Zzyw5ig27w2YVl5qs56WoDBcyiuqY69hvSqvZ5hhsCoawMSgbBrusDAAOup4Vw6HlOcvl0jcIsyuHMOyXsmKlUskddJxALomnb3qOifTSer1uZpdndNrttp2dnfnzYec0rYbCLjB86AB9yGQykaqrOMGq5wp8hsOhA0PdALPZrJ9JDNePOnIUh1LGiLS/ECiaXd5Xpc5FJpOJOGMaBKIPysQRhKC/6mijG7VazRqNhoNS2oqDTUqv9lsBsqb3ct6Tvp2dnXkqKEzYYDCIFEFQ9gSgjrNKAI02TadTK5fLfobU7IqVof9qn0gRZjwZby67Zw2whnGmmDe1W9g/+h4Gkfr9fiQoyFwri8lF1eoM1mo1a7Va/h49/6q2t1QqWb1et8ePH1uj0bBqteprV6uQxjl4y+XSg4Gz2czPFcUxivSN8+2aJq+/AzA9f/48cT/VrAp0C8ZKbT3VjaksrvaWz5yenl5jiPU9qkc6BrxrNBrZd77znWugTYG57p9x9pP+aDZBGCCj3djATUARGxT6FqFtUUaYZyio1wDYTUSDLJx7TqrgzZgqmA8ZRQ0CIgCsuMCl2qVQwnFTQKjz/bqMIv1J8m00QKK6oVfV0AfNOqDvyraxpuOAIrqg38cmadB/kzBGcQGX2Wxmn376aSQ9OynFW5+V9B7sI2uV4JE+M85nv4mkQPEDkrio3La0vzijEkdh7wIsktp0E6AIa6SGKindJjSyYRQrjlHclHqqjpeCx1Aw2Lp56KbU7/ctn897mhcpD5zFUaAYpkZwriSpz4zTs2fPIj8bj8fWaDRuxChuM/o4cyGQDp1zZZvUAVSnR58ZjmXItuH4humlGF6MpT4Tx5R2wiiqY8W8oZPajzCVRKOVmwy5OjFJokwfYO/Ro0cRZ5HPwCrhvJOeStBBAQUpzXHsCH3b3993B4k+1et1d1IoUKMMM05IrVaz/f19W6/XdnJy4uc6VK8AuwRgAJHdbtc3XEA+0WL6A3PK2mMMWaeh3tCP6XTq94cpgFIdpYgMjg76ruBNzxpq8RM+o+l5qmf8f7G4LPjB3XQadIpj7NfrtacwKvMGONMCM/qHZzK36MhkMrHV6pLZrdVqVqvVLJfLWaVS8fPS6ALzBDuMPod6o1H9er0eKcigDhbjo+1XO4ENV7aPZ8YFyrjChZRRAhyh7SFgoo6ovoM2lstlP6vKfIRzp+df1UbQVoIogJrBYBDpJ+tCBaC4XF6e7wQg4ByHZ+phzmB2dUzYl5bLpTUaDfvqV79q7Xbb+v3+NRujTDTMEmOlPsDFxYUdHBx46rOmA7O2h8Ohtzc8lhKeT1TbiB6dnJxE9iJdb8ypFhWJ8xGGw6FfqxIGklTW68viQXEFhkJGMbQT4bNoSxi4YE5Yg7cpZoONrlar1m63I/MV9kdBSMgYYify+bzbWhgyzpzH+XyqC/q8MEuDcdDggQYVkxi0UOLAILaPvToU9EH1I9xbR6ORByj1OeyDCO0MgSL7Ua1WiwBF+old0mrRccIcPX/+/NrvlstlJPhtFj0iFQYoNgFw9E5ZRbIsNHCQAsVUdpY4J3xTCqXZ7ozibVNPb8oownpgZEg1int3aGy3pZ4mMYpsDhgK2pDUbmUU6Z+COxY20cNSqRSJ9LH5hRG+xWJhe3t7W6/noHojwnM12r0JKJJaswujqIY0bkwx7owlTqgyaOEz1UiG+seZtdFoFLnbjvnBWQg3ajYNzmnijCsTqofZw3boeSGzS+B9fn6+NdVIgf8mYVzOzs6sVqu5gx2yRxz253msg2w262cDm82ms2qwiuguawiWEF0EhKruml1Pu0WnSWEi9Y55J7pL+iLrB+eUNcgfdJ0zkbSVlFaYu5B5VEGXlHk7Pj6OtFkBgQK++XzuF56jjxpVVpDD80NQgTBW4Vky2D3GVJlIjQTrXFFkhHejH+ooqUOh+pDNZp2ZAxii1ziE2Bd0i/E5OzuL2HLGKTwL2O/3/W5GdEtTwnCW9bydrnlNeeY80GKxsIODA+8vQbv1+vLsFg7g+fm59fv9SJowa202m9ne3l4ss5fP572YR6PRiLANIdMC2KT/ajdJL8WpRB9g/GDNzOxaoTbWDqw6usLeowEPZQ05u67phQBtvlMsFu0rX/mK9ft9v7MX0WDRYrHwarFqtwkuUG5fA2fK5ND3fr9/za6FlRzVNs7nc7u4uLCvfvWr187fxQHF6XRqtVrNWW/tz2AwsHq9HgH/yvgj5+fnPv9xqZh8Fl0IbaIKz0Hn+MNn2de2+ULdbteDR7ybrA7sXeiH0F50A0ZahT0QwEVq+2QysYuLi4iuhvujAkX1rXa5WgwblNTvOH8xLhCNPWDth4L+hcF6ndPJZOL6p9XET09PrxWMo80IaysOKGqAh2DO+fl54t6OHuo863hQHRnRAFHo9+3CKKKLpVLJ/QAlGkJ93jSfcZICxQ9YWMCblCbOod914YfCgfnwWTcFippaGAIVbWt4jcI2RlGdMf2ZAkX6SNQ+Tmazmac1rVYrP0OJI4JDSBqfggmi5Umpc9wlF54tUSHNT9uD47gLUNQI7SahvfrZOEZR3xOmy8U9Uz+vDjxtY8PWzUSjqeok8l0cHBgXdQwVROn9etoPnDttFw7mJseASPU2HYdh5voIZaPZxPnD+TOzK/0H9BAQ2N/f9yJPIVMKKFbwxbjopq9t0/Nms9nM0/nY7HA+z8/PIyCbYEIul3NwwLgTFSd1Dmdbo96tVityFnAymXgBBwWICgoJBvAz5igJ/Icp2TgYtJXiKAr2AHEIhVMYU8ZYq4JqapCmMukz1U6QlYAOx7HzmgaLfaCfzC2MIe3ibxhoPk8ARq8aymazfm6PseZZgEctkIRowAGdRELGSBlA7nHkTk6Yt8lkYgcHB7ZYLKzX69mDBw+8HcwdDJ3et6nvNDNf+9xTiVOlAQ0+z1grUMQZY2/Ra1Gazaa3+Tvf+U7E6UawG91uN5L2yziEwYrVauXAjD7ybwIsof4/evTIstmsByiYD2XEuKKCvsE6abCNd2HTNQgH6GYdIkmM4mp1eU/igwcPru1FqhP8G3v94MEDa7fbzsCwfieTiZ+nxQ4r2KPPw+HQ9vf3Pd06FJ6nwAHbFDJ6yhpiixkvfkcbN/lU6DMyHA6tXC772O7t7Vmn07m2P/M+xjRkULEZnAVXO4be5XK5a9cx0Tf6xfUo7LVJWWdhH5MCpnH+Yvi5MEASBxRpj9phXf9mV3eZmlmkdgBsvb4/DCpgj2DweT6/AygyjgDFuLlW5jcU3q0FCembZt9sGleYyjCTD98Rv5NnhnvHTUmdFCh+wKLR7CTZlVHcJYVVIyjh83Ztbxj5VSc4bIPS+bRR+xrXjxCoKEsAAGFTSmr3YDCIpLpoEQKN/FerVXvy5EkkfRZHlYIeoYOVzWa9mAgXiB8fH0fer1E1s6tLjncFikQetxkTjVLvChTpB2O7LQgRxyhiHNvttn8Gw8w8qYHHUaZfsLvKNIRAMWxHCBQXi4Ub902pRlrEZJPk83k7Pj62J0+eRNJ6zKLVN9frtV+twvPZZBXgUoGRKx/YbAhS8B02JfRZmUSdGyL86AXAlqsxmGsKLtEOwCqRacB6GOzRtcn6ymQuC9fgVOHA9Xo9f56OC//nPJqOOc9Xx5bPcGZM2dLl8jIFGL0ArDLnpIljDzhXqMGT0WhkJycnkYIbSdkPjPlisXA7CQMfMo6hY8/c42SpA8n31dFXW8Z3AVf8HxCjzoWOM+ua9+CcMrYANn6vhTnQnxC4AxDRo5cvX9pgMPACS4CXRqPhjBjzYHZ5XhbdCZ0rnDIqAtNfgiuMcbhOw9Q7AgfKPgCaKZI0m838zts4oNjr9TwlWcGIjilnqFkzChS1GisBCmWACbAoC6egZr1eR9Jb0W2udlgsLtPPLy4urgEhDXaEqZrT6dTvsUSwjYeHh17sB32KYxQ1sLdcLq3VankqHeOj7+b56pSjs91u1548eeI6gd2LE9YP72WNqPAu3atZRxr42SY8H+EOW77bbDad+Vahnwrmw6CqZiMASAFTfD8OKIbBELMrnydp/woD2UlAMWSzVquVPXv2zNbrtRdfQa81FTjufZrRwbNDRhF7q1fHKHvP98zii9Ggi/o5AkTYfNZA0tiobQ1/z/cfPnzoPgygLo6siAuosyeG60h9EfZ2npkCxVRuLJrmuA3g7QIUk3K19Ttx79nFiUbiFh8RTI0KIyHQimvTLv2YTqc2GAwi55TUIQyFyCXOVZjahMOHg4nh0GhvHFBEWq2WdbtdT8/jbBhjtF6v7ejoyE5OTtzxBCiqc54EFEOWNknG47FHtJPGNDyjo3MUl74XBxT1M8xzPp/3TYb24rhpRVg2T5x7wLXZVbrXLkAR8K7twBBvMrwKRDcJ/VInS9OwcHTUOVV2SMcFxzWXy3mFUPqPM4ieq5MS6gTjvl6vneFhAyUFlqqXerYDoAhzgiOl78DxYz1pKiRzQQEPBcEUJVCHiTEABLFWwzmB2dL2mEVTTZfLpae86nPRGVLFlVVgMw7TQTOZjJ8B1bkCmNA+fQegjX5rgR9YQGWZFUQqi0vf+Jn+nmCUPgPAEY6XPpNUZoIk/J7f0Vb+hNkc6JMWZVFwijMGE9Tv9+3o6Mj29/d9Xp8+ferpnKVSKZLZwXUBABy1G8ViMXIuiGAKgSMFwCqqF4wjNoK1pg41e5GmS+t4Mn/1et3BgNpugCn2Rh1HGF9YAzNz4KUgibapHmAfsFnorwIL2NbFYuHsqDK/asM6nY41Go1ItgGVYMO0uXa7bdVqNRJIiLOZGizifaxxdCabzfoZSQWK2DeCIrRd2eUQHKl+KKPF+0JGkd+xJrCzutY2Bd1VpxS0cKULbUfHmOMQ7GsWQNgHvs8f5orjKvl8PnIGGtFMHw3Ca5GucK7CO6eT9rkw+M7+lc1mfQ+HCaRvScCUMUoCigry6CfBVP0c4xP6f5oNov3muegS+qjjnNRW9Rtoczab9RR29jD6FZd2rEL7CI7HzaXZFVAMGcVdSIBQUqD4gYouxl0YxW1M3C7vC9+joGsXYRHzPLOrS0vjQG9cVFAd0YuLi1hmNGSHZrOZl4sPQUucaHGO1WoVYegwxmwCvF+ZMgqtxLXf7CqlgnNi1WrVer2emZlHdLlq4+XLl15wQMdnE6MYOkdJAmugoroBG6BzoqkQ4cbCvCQxigqIwg2NZ5O+pucsNKqm7wxBA21SZx4JryNRRnHTGYIkoBg+HxAbOvxmVw6KbszdbtfHQ4EE4Bf9C8Emc4YeD4fDSPqatkmDSbAwbDy6WZldnQfB2Wcj1QIsjBXjpfNJYARnNpPJWK1Wc2ae1CFlF3Cm0VN14NAPzrJyZlGjqqw5ZTiJbCt7oCBOdY/249Qps8f8wTSxTpgv3qmOHSyezg+BAz2LCitLPzSwoc42beedBGZ07Pm3Bkso9MPcrNdrbwff0bnF5vBZXRe0Q/vz/PlzG4/HPg4KyqfTqQ2HQ/voo4+cyV2vL7NRnjx5YvV63avV5vN5T3d++fJlJD1Vx4F10e123f6gz4Av7IHqNP1V2x3uWQQLAEH7+/sRR5h5Gw6Hdnp6aoPBwAsLoX8KQplj9gB16ghyUSkWPSVAwlizjtVG8juAJkEb1q2yVDCKALJwv89kMtbtdq1er0eOcxDsMTN78eKFrwXmJQSCWq1SxxV9UceWgEAul/O+qy0JAUa73bZms+ltD8GZ2jqdb8YbkKQSZo6w9lm/GpzYBBpDXwg2XtMRK5WKV6XXfS7scyisPbUzFxcXkTOngBGVMBjCswCKoZDiqmA6yZcL9Ye2ZLNXZxEJwOleGo4f+sl+hA1jPHW/pI3Ybg246fNoF/8maJR0JZwCRV1rodBWtS8I/mEmk/Ggv+61jHuSX4yeK7AnMIWdD/cE7WvKKKays+waBQuBGf/exiCamVfv4jmh4qvzsk0Wi4VfvqvtAChiFPUdccaetESYiUwmE6ncpkBRr09g8Wpb48ABmz8OJUZF70ckLYS+cx5DgQ6OWlyJdgAODuqDBw+80h2be6FQsGq1al/96let0Wg4w6ggJAnsq3O0SfQ8gOoJc0Daoc4ZG0sYpVbjFqbTMC9sfmZXesumrs/QlBPAH99XYKdpVbQDhzmMNoZgj3Yx10lRRS2QoxKOv6Z/qgOjzIUyDoAyNlzaA0hmI9LxUj3WlEE9g6dsWGgb9vf3vdBOGMlF13HmcJ41Osol7mZX0U51JjVAkc1mrVwu2/n5uc8vAAEQCvOmgI5nkwb37W9/205PT91pVDaIsZhMJp4CBHDUgAFABaZHHRNdv8qCqD1SZjIMDuhYL5eX1SsBR4yNMlMwQGEbWSPMl+qLOufoHrrJu/UMYwgKWQu0BecOdlpTIxVM03+NqlcqFWu1Wp7tQPsYy8PDQ8vn83Z6eurj+erVK6/UytUoOOaZTMa++OILm0wmXjE1DPhgK2HW1aFSEEiEX9cmfZ/NZnZ2duZ9w6FTHUAnWQeapjYcDiOBmkLh8p5SMkJCIfU0DGwBFAF8tEcDCeGaV+EMKvtSsViMVMNkf2S/CtkIxkPT7tGHi4sLH49er+fOdhxblc1m7ezszJ4/f27Pnj2LjGUYRNG7/5QV1fZic1ary+sSHjx44HOCfQ8DALoeFJSqnVQJ2R70jH+rc78peIhwPU3ISIeZKhqU0PREBaaMG+cddT8j2LZaXRbXevXq1bW26Rjx3ThW1cw8/T8MtCftg3FAkeeit9hP+hR3nENTjnmXsvHqe5ldFZShMrHu82FQj/HE7qht5j1hFgt7ATadisxm5ufkc7nLM/MKPAHFZpdpxgRMNKgRAkVdO6oPtI+5YI71czxTgWISCE2SFCh+oBIXgU76XByYiwOKoWPZ7/dtPp/b0dGRO8Eqm1itUNhkzaKpGAoGw4iWOr4IUWM28dFo5EBxvV678TYzOz4+9gqiyjzhDMbdkTMcDq1UKvkGj0NGeXMz880ZQ0PlPzYJjX6Fhszs6iwAjgNpK/P53Fkn3j8ajWx/f98+/vhjj5bpfJmZO20quzC9cRvJarWyw8NDOz4+jgBtJAko8r7FYuGH6fkcTqoaeb5DdFLTKZX908grQKFcLkfuBmSsmVeNxmm7FTizqcZtpMjp6akz0XFAMQTQOPdsluPx2MdJi2aYWeScoT6T58BKaTRaHT++x6YNoGce+IxuRpoWhV5ragvOEfOlwJe2o9vMC2PIVQV8lkp9uVzOHWJSTnmupglrZHU8HvvZTIoDNZvNCGuBE1YsFr10OuBaN1X0DGdLgSL6kM/n/Rwd7dLMhaTosz5DHR/YNnQ9dBIYJ37P2If6TOq6Ojlm5u/Q/nGZNH8AR6PRyItx0Q6cHO6ypBgQbGIYUMNR0bk6ODgwM/OiLlwVBGj96KOPIk4aAA+AwdoYDAbWaDT8LCi6owwvIETTwRjHTqfjBZIAipo+iZ0djUbW6/UibAUsF0wQc4leciUH/2ZvoD+AXpxIHTMNovBzTZUPrzsIU0/5tzJLZlfBFph5ACl6Sxo2ATW1QapbjDVtMjM/l0mBKAr7qPNLXyaTiXW7XXv48KF99atftcViYefn576nKstJOjugKazAq9kptLfRaESAoo6tOv7YRu0fP4tLPdWfKzhUu63tShJsBvPBPCrwPjg48HURtlMDUvp/9Jh558w47WV+4/y5EIiEwVIEW4hOf/HFF/bixYvYa1l4b5h6WqvVfE3yLt1jwpoA9D9kFJVNpp/6LtauBoV4n+7xzCsBQw0+o3cEJjjvSFCIZx0eHnobsA35/OVZfhh2s8s9F3DInsLez/vCwLQGHhQcM1/6u5A9DYM9KaOYys4SRgmTRKOlSBKbGMfoDQYDOz8/j0QMkdAZ3yQ4GfquyWQSiU7pJpnUJ5gEHOder+dntACEpHGenZ1FIsMYp/V67U5FKP1+31Oa9JnqTOmF5ADF6XTq6UYKVuI2M5xHnNp8Pm+tVss6nY4bBTYwUlOZ77hxAWwhGOxNxiRkKXDqKNJTKpU8chkCRe2bRmBxqkOwr3OrDjTRQhx79EmrnWm6CptppVKJHAanjYCWOKAYOvAhkI8TDPiujGIYkNDS2gBFIs84d4BlvoNjgSPH+tWrL8KUx/X6qgImUWk9H6Xjjm4RwVXQAuBgfDj3hiPE8+krGxql9hl7zh/V6/XIJqgsMHOFszCfzz3Ywtm1xWJhH3/8sTNejIOuBUCqpv0CwvUPesSaVd0k0AOACs8qAhQ1Q0AdDNYO6w6Qw3d4F59T2xKuLXUeNHCgLJ8y83yfirf0VYErDC3t4CoTGCS9XgQHirmmHQpEF4urwiIHBweuX8Vi0VqtVsT51nvVMpmMR+3VEVNbhM5qpdzxeBw5t5rNXlYEvbi4sGKxaPV63TqdjoOmL774IjLGpAPCWDDWVJdWpoF2ZDKZSGDDzDztlDagHwBbTfc1u77PEgALHUYN0IbZEVoEBl2EMSF7hc/k8/lIUIs+hgG6+XxujUbDQSs2gr14Op1avV73dQkLiB3s9/t2fHxspVLJzxBWKhXb29uz9fqSve90Og6aKBRFMSIqUtJGBQsXFxf26NGjyFio7UfnlQ1kffA5xjMcf0351IBSHFBMcsZpj16Rgh3Qgl9m5oBPgYH2Qd+nAQRlFPUaKNYTQDH0p9R2bBIKSuXzeXvy5Il98skn9uTJEwdZSX1GAKzYC60TwNhqRVD9HoEktU98dzqdWqfTiXynWq165oNmD4RzzN7Cvsq7Vb91nObzudv7bDbracKsTWy+rn+ANLav0+n4eg+PNoSB+tC26/81mKu+nwaMdA5SoJjKzrIrSEMptxkPs+uU9nK5tHa77ZuKWRTAJbXh6Ojo2vtwMFikZpcRbaK2ZtFNMi7t1CzKKI7HY2s2m+5AsAlRVpy0EDYpjBjObdzheAyIMifr9VWlPBwiPkd0VqO7agTj0lzMoqCcqP7FxYVv/BgL8uFfvHgRcdx0Eww3I8ZSQVEoAAt1ajDWpVLJ9vb2rNls2nw+jxhvwLZu4Dp3FK3QMdVNnHfxM+YI54eouG6OypxnMhlrt9vOGuBMqWMdN0aMpRpY3WhCNl31QR0kjaCGrBXtpS8K0NjAqtVqpM1sMmyC6qzSB4CYpjXze/RMWRvAmQIYxp7P4fxpRd/l8uoi4Ww26/fZhcEj9B79yeWil5U3Gg3vj6YgqQ4om0eVX+5pVOaetcGZJhxyHFh+hs4ASnRz1WInWpCDeSTAoxu86oOmMcE2hkBR51/TCEOnhu8R7MAuMaewTMwtdkqDS8x52A/sJXO/Wq2uXSXDXPPcXC4XSXVScKprQ8ENQTrGhmqYsBRmZi9fvnSnkO/N53N78eKFt51nrVaryBUni8XCz1niiM5mM7dH0+nUer2ePXnyxIrFoq8J7DXrCXuNfUYvzK7SQNEZrbSMXnImUqub4qQxNrVazYvwEHwhUJnL5SJXXPBdvXcydPh0X2AtaGomn1kuL69BqFarPh+sB/ShVCpF0unUplCNmHdoISbYdfZSHPBCoWCdTsd6vZ49fPjQn6fjUa1WrVarWbfbdcDP9Sxk+3DHHX1UsAAIiVs/ekduyCgmZa2ooNdq/2CH+LkGPrmPWIW5YP0Q0KL4DoEq2gbrzBoI9099Lu/HdgJIWVcEAlib6r+ovYrb/1Qmk4nrDe1UXyvcB7WtyrjyHrKi+Bxpt+E5SgLa7CnosQJF/kYqlYrbMICczr0CRd0fGSPWGe/Tn5GNYnaZdv3w4cOIXUMPzC6PbcAi8v6TkxMbj8fW6/U8uKlBV903Q0ZRMxiUidR2IvQ7BYqp7CxqDJIMQfj5XXOaQwVcLpdu2NX50N/HMTKc51BhEagDjWGIYxSTKoZiKIk0HxwcWD6fty+++MI+//xzm8/ndn5+bp9//rmfs6DCo0aBcdZV9JJsIj6MB+CQDVg3bzZbjBSOY7iZKVCMc2gxovythUTUqYgbW/2dAmKzZKCo5+BohxZLAYh961vfshcvXngf1EFWoIjjp2lxCsZ6vV6kXTjGXKqsQHE+n/t8KrDE+WXMcGKOjo48Aoh+hUEMAJRusJsiyDiebGx60bJungpQcRK0qIYyW1Sv1c0coLxer52NUiCkKTW0hfbyHfrAc9Qp5t1szIwnzjPfoXKinj/UqHe/3/cqqxp8UeALOKB/rVbL36mprrSn2Wy6AwRQDdfN3t6eO1ucdyJFmzFC95SB4/MwGplMxtONFMTrRczKCChQpM2MFfOr0WAzc/DBmuC9Oter1coODg6ufRZnXdcVqWY6Jrw/DFTQDk0LVnaR8VcGm78BTugKTJQGI9B11j82XKP3pIEqaGGtnJ2d+VlR3kM/CDAQBGy1Wg4S+dNsNp11Pjg48P0BppgKjHweB5FAgN5LpkwD9hVHECAPi8kRDNYuY0x2AynSPJP1SNqaBkdw+tEldE7BAboEGOHnJycnEda13+9HWFb9/mJxdU4RO8Hcl8tlL6zDfsOcAWxKpZKfv+JMPqwyqaYEZ/S9rO9Wq2Wj0cgGg4EDRfRLfQZNLw0BoAJFxkMDiMqyAv50XSOHh4c+H8wbNhrAFe7PzF144Tqfg1EEwGmwSttGsRP0KgyY6ppFD1RXsNW9Xi9S+Zy5ROi3sv5x/hOfpQhSyCKGAEfbwliT3cG8jUYjt6HsByGQNbsEYwRf1C5i89WmIAQ0FJxjt1Tm88uaFVwrg11RcMkcqP+GPwAoD32s0GfUvQE7M51O7eHDh34dk+qi6pPaUH0X84HuhIHKMJ2etX4TSYHiByRh1EgNYxIQVCcWiQMOZlFnmcWml+KGgDOMivHskLkxuyqpruAIx0iZLZ4fdxjd7ArgdTodrxrHwmk0GvbRRx/Z/v6+PXjwwCqVih0eHnokMtygwnEgkqpAUSOMWhVSUwRgH3XxYwTpV7gRFQoFr/4GYMUQrdfryHkF2hayo4gCRQWXtCNuvvWAOH/I0dfy/tls1prNpp2cnNhv/MZvRIyUGkN0AxCvesR32LARHGccJ+acSquz2cwBAn0i3QfHhM0DB5R+h7qJ0zufz+358+fXWE6N+IXf0VRNQHDc3AK+cELYEBVoAM5xdhTMKVtDpTWq6ZEaQ5t5x3x+eWVGNpuNpD4NBoNI/3U+eJ/qCk4MDjZ6CRBnAydyns1mPb0M/dIINbqg14UsFgtPow43b9qoTk4mk3GGW6sKkurHGgZUYOt4Dk4pKUa6JlR/iYbjIDMu6KDaMnXuQnuI84Y9DtkWM/OzWjjPMGGNRiPidKNf2Jp+vx95t7YRveB3ujcwj+p00yfVw3q97vaYeywZxzB7QSP2rF/exSXW2DplQLFvCpw1fRJwofPHsQeADxe+45RiW5rNphergdVjPVOhFLuq7UJYR+gj2SGlUsn6/b7rlgYwlNXgqgzm0+wqqKnOZyZzWcVyuVx6VWANtqhNIgjE905PTyPFpHQucH7RA00hVoA2m82sWq36WlegSJCUFFTAED9DT9AhTTHW4A+25NGjR3Z8fOxpiuxnajuV7ebf+jv8m0wmE7l/chNQRNcQwB7+DQE1DVxjt8O1HBeUJbCF7gOkCeqor4E9UV3T9UnwDr3RQlToNmM6Go3s+PjYut2uny1k7F68eBEZxzj/SQO8mUwmNqBPe1S0P9hj9Qm1Ei/7kAaXEHRZGUX2Khh56g8gBDsAclrTgLaxZ4zHY2u1WhEdBCjqmkKvmYPRaOQBS2X9eAZ2an9/387Oztx+EXwilXc+v7xOan9//xqY02eHQBE9Qa/xa2gz92mmjGIqO4lGmtXJpHBEnGgUnP8nMZGq3MfHxw6GNIqlBk/boD9T4ILwfY2EKyiKA4rqZGobV6uVg7pwHCqVipXL5cg9hTAgbEQ6Buro4SDxB0ecjRwAwyKmLcqIKlDcxChq29isOCtFW4fDoZ8TwyDFAUU2R94TjlcSo0j0l89ogQQ2N8Z2b2/PptOpvXz5MsKa8mzGAsdIo4m5XM6Oj48j54IYI3Wq2dyIzD148MCj2Bh9Tf8No4IqGok2u0r3gBULo34a8UMUbKijxfM1+osTiWOF84Je0l+AFZsb+sO8kWp0fHzswIhn0B/aslwuPW2MPuAAAR55rzJDIYBB5/gdAE//5pkENTR1W50t2ksfGRv+TcVIHCP0COeT1FAdu0Kh4OeaWH9aCZC0RcZSn6tzpoEfjdjTXjPzs1m8v91uR3SEfqnjgR4TUNBgHvOKDtMWZeUA4cr84MxpCroyYGpbAAYajNOIv9pc5kFBCQBAU34ZNwWuGsygnYBL0kVXq5XVajU/UqAMkp4Lx/nL5/PuZF5cXFi/3/d3FAoFe/XqlTtJ9B37T/GV5XLpz2aMldHQM8aAS7IP+L7aLBhFdG0wGFir1Yr0RR1O9hv0Cp3CNmFXaBd2VoGiMlzoD2lt6BBs6mq1cmDC+BOAYa8ivTCbjaZeA7jVbi2XSweH2g7WOWOjQSGed3Z25vODHeYsIs8hQDiZTDwgErKGup4UKGLfACBJjCKiQUyeB6hmbNVOanBK91RseLifYhsYN61JwM/UJyLYo1UzAV58R9lvDQRoejRHQT7++GOr1Wq2t7fnQIs9TYFimJFF3wF6/X7f9Q7RoI+K7nXYaN3jGBPVv3DclEDI5XJeKXg8Htve3p6vZbWxjB86USwWrdvtepE0s0v7DCM5Go28CBLzyfqmT5pNQN80eKD2WvUT0kQL66Dv2AXWOmn4YRAR3cQX0eCt7pH8H7tKarSui5tKChQ/IFHHXI2NGlqzyxQ/nEf9HP9PAor6uZOTEz9LoEZB36ORJto1n88jxUgQjElodHCIQqCYlHpqdnV33Gq1sna77eOCY0t6GkAMB0CjaDiRbDIaYdeNRAtp0F4izMru0HY1MLpphBsRbQSUsbFrZBjgq+wEG5+OuwJUIms4jTo3KtpOHT/6gwFks55Op/bgwQNPpQkZCf6QTqwOdi6X8xQkNgsFi7yXzZH+UuEUJ4zPslFrCl1c0CJkFEulkm8s9Jtxi4vS6XPZTOhzCBTZaMIzZ5lMxnq9no+rpvxpQQ90J5vNWr1e9znU8vfIcrl0h5ENmGfqBsVaV3DDPOMYKhuIkwDo0bQ42A0FkswHmxsbvQqggvfn8/lr6Z86h4wZgkPMPHAOGXCrNkWdWI2ca8VP1TE+q7quzwOAoZ/VajXizBO0UD1n3BWAhDqmzgdAs1wuR0AyfdFURxwJ/q82s1wuR6r46e/W67VXOGW8YY8U7NAe9DRkOFWHmCt+T8Td7HL/OT09decNHWm32x60w4YSACDFuNlsWi6Xs4ODA8tkLgvKdLvdSAR+NpvZycmJB09w2GEB1+u1X0FhZs5oYROYJ0BqOE86N7xvb28vsnfCcGHPdHy1yJMyK6xj2ChNqwO8q7Dm1LYrQNcLvzlzhg4DCAAT9Im0QF17APPwHlizy/2c1Fu1Jegm5w6z2axXH+Z32AIAD9VUQ6CITeP/6hD3+33PpuB52CCeMZvNrNvtWrfbdcAU6j/Pp8/YC8Y8jlEEYKqw/zBGtAP9hPFSm6B7GHqg4DIOKAImer2eNRqNiF/CGGWzWb+6odfrRWwsABZhzrGnnG/VwISm06voXqf31KJPum4A9Iyn6j62LJvN+p44mUxsb2/P2u222x09p676xppW2xdmpmDX1P4r+OPfzB/+qto5ldBXJnCqR6CUqMAGhs/axCgybuwZuq/he8XZh5tIChQ/IFFGEcdQGRlkPB5H8uJDoJgUkWBh41iwieMIxAFFFFwBHtW+VDQHH2dQjbP2hc+HTifvZJMMnUDdBEhFpO04FOpIkV5idskmchaTjQAnkvL+ROQwyhrl1gi2/g7RaLTZ1eXLMIqwh/V63TdeNm0dO5gD3TRJfTGziIMTsiU6F8o4EIHEkWFueAZODQ4YrAvz1ev1bDAYeMn0XC7nrCDC85rNZiSdlDHBWcnlcl4MIpPJeGEEABX6xb9pv6Z8xQnONIECAiDoaVwUVMEbjhSpMbqOaLeCV2VxcJKUxczn816ghnYoiwRY4HyCzqk66WGKIHoDA66OmAZFKJLEZsfPaQeOQDgezCWbq1bhY5Nnc2f9oL+MJYwNhT40CKP2RCP2VP8loks7Wq1WZAxpv44nQFoDP0gmk4kwvui0gj4cRtKgcKg1SAOwLJfLkfO/agf1SgIqjubzeU8vZ70yRjiyPIfxD6PkOFiMnQYB1FEjnZd1bHZ5BojzL2pzeUe4rrRdzDX7Aim89Xrd702sVqvWaDRssVh4YRT0G6BBwIRI/HK5tIuLC1+n/X7fz38z72pfVquVdToda7VanhqNzphZJDNFQUQ+f1ltmrHX5/Ee0srYs+g/IBtHlbFCh1erlWf58DwAW7Va9bln7XY6ncjZbl1jmg7OHAGMNbCDHmKHSEXHRnEHo7aJPQX91DPLfA5gxrOYe96D/VsuL9Npc7mctVotazQaPqeahRMX1ENPNWjMvqQBZdoUMorYSgKkIQANmX3miDlTlop3h6CB52iQFltBMZPj4+PIuVT1MxRsYYPYu5WJpo/0if02ZJTQcXwCbU8YaNdgH36HBi51rELfjXnRuSdop2fhQ0ZRr8jodDoRQMY8qd9I2zn3yzPRbfRTj0wAsLW/4RUiYRBd1xDvRA8Yew0IqZ0HIGLz6A9rWyupqv5sYhSVMaVNBIrp32p1mV58W0mB4gckGjXXCEgIFJfLZaToBhvSyclJZKOMez7RURYiG2QIFDHgIYuRBBQ1qq6OoDpHtBeJYz7b7bYfWOZ9ZlcGXCPw9IeUKBazsgcYJEqfhwBVHSxSPxuNhrc5BAw4XDpXcVIqlbxQEGBAD6ur4YNFAYBotBBHg8CARjMxcmE6CVXKFFxwHxggGUYQZ//BgweuZ+gGzirpoZ1OJxItZYM3u4rmtVqtSLVBHDQ1zKT48q5qteqpxDjs+nuMcBJTjl7V6/VIkQ411nGMogYV2PjYxEJnBMeRTUzZIsAKG+x6vfY0Y9rGGLHmVqvLQjBsGEkRzzCiqw4t7ArMDM4SmzuspgZb+AxRXXRQ5511jw4QSDG7SvMD8HK2LgSQzWYzkrpDYIe1RgCFtfTq1SsHauVy2c/U8RwFRqx77RPzwNlcJJPJ+FlLsyunMwwELZdLnwt1KPSdjLWCVAXbmhar+sN8804t5qDPBJTQPgJ52Dx1aNQp0SADthxwSsZFu9123QMIYUuUUeSZ6DxAX/XN7MoWEnTgZ7QDXalWq/bw4UMHrzrW9Ad7pbYvZAAVSOGwqU7RbsZL17TaDj7TarUsm83a+fm5swQayCRzRZkw0qFpE2wh39FzRsyFOuDHx8duE4bDoVcPPj4+dsCAk8k+i/1mPHK5nF+PQyZMNpv1da5jjM4yZgQ5YKZpF0WumEeCTIAc5lp1Bd1gL6hUKn61iAZiGAtdo+oTLBZXFY71Z/ybOSOgo9kB/C7uWEEICrRipf5ehb2OcdB2ZjIZ+/zzz21vb88/y+8YW2wBcwOLhn3XTAcCsARcGB/6ppVzZ7OZNRoNTzEOjz7Qjul06oXAuNZExzME1UmiY6cAmPmPA4q9Xs9tO/uj+lbY5hAo8hwCJwBFbCsstl67EmaMaGYA+3ylUrHT09NIGjaioJzvEyDWa9D0e59//rmnf5tdv0EgDAKEwZhsNhu5ExUfSPejuOvcdpUUKH5AsolRVFCC8wawAXycnZ1FFkEoLKJ2u+2bqzoMqtwXFxeRssUahU1iFNWxByiykYR92WSoqaRIZJvNjk2x1+vZgwcPPKpEuhMABWPLBmh2dUhbNxkMGhXw+LduELrhYqRh5tSxCAXjl8lcFilQJ4+rMpgD2kY0C8aCuda/lUWgDWdnZ5EzfHFAUSu/LZdLOz8/d+fUzCJpVMq2LJfRdBtYYtJmaBv9JRWHu5F4Dpsbmz5zCwNxfHzs46MH3NUZSdJr3qHR6dUqmv7BGomTXYAi6UfMqTKuIfNiZn49AnOqbOBqtXKns9VqRYp3hI6PgqLQQVKgo3rR6XQ8KKEsI+wibR6Px9ZoNCKOJQETAMBkMrGHDx/698MoNv1grgEAMA2wy7QTe6Kl8M2unFOCO4wVaXgUQeF3jFMIbDV4QRsHg0HEgdG1rFFjjXJjF0OgGLImGjBAlN3ASQodS9rAO2azWeSOy3w+b81m09e5Akg9P8p6UkcefWE8NTjIXLDGlAXWNimjyJzBpDAOGiRinNUe0ib2Cw3ccK+lAv5ut+vn9GgHAAnHmrXK/LAudYzZJ1ir3IeGLpmZO9L8XoMCrA9NkUNHNbDC/1lTMHrqRGtQDSbV7BIo1ut1Z11xhFX3sOGavsj+xxlRgjinp6dmZpF9g/GmT2HwkrkrlUqRAAsp8Ywp6wffQNcO+g1wZM5C9k7BIIJN5ncE2ACDei6UfQGmSoG/HoUJg7fYstBf0bajNxRe0bYz75z5C4Mi2GzSjNFttR8wghwz4V18H51RPwuARiBlb2/P1zxr/NNPP/XnZLNZa7fbdnx8bE+ePPE1ks/n/forbGDSPhjOGzafYDTrD11QO6RVqtVu61GD4XBohULB9ZBxpCAQ76Z9ADdAJ/ZQ7ZjOK7YfXWm3287uq8+E36E2AqC4WCw8K4rvEZzRrIe4oK7qFuOkukagBr+dPjLWGhwz23x/eigpUPyARFkqlI1NWZUSNgLHAiBA5GwTUGRD4HlshqGjqylluihY2HHsjDINLDI1gmZXh8/j0k75vKbqsfEANvv9vjUajYhTZWZ+Pk2ZKDZYnJ4QnGLM8/m8X+GgY6fOF89TRgCjE5cWqQzLfD73yBasG0bTzLz0NuOqqSshUNQ+Y7ABlwhRYz5DqgqOFZFvTStSZoXvLZdXd1f2ej2/hgBm98WLFxGQhbGv1WoOFNGtUqlkp6enDoKJVtKeUqlk3W7Xx0oZBGVzwjFAALFalVId07jCBQh6prqnQFEdUp6hwRtlaRhHnBfdFHV81SlnflnH9F+ZJrUNClQ0QMCzer2etVot7wvnm+LGqtls+niRVoNzTNETihtoyiP6g7PFJq9puzwXpl9BjJlFgl04tugvgYPvfve7dnx87FcFqLA+Q8ZIHVqcAcZf08dZowQVFNArO6ggH7CsQJG2MMZaiAcnWgOAPBtnWNPKWNOZTCbCKOoeoDYqLjCoacUaeVdgByhhXPi96qUWOGG+aQd2gHfAPqktUfabzwCuqJDJ/weDgZ2dnUXAKYCBduKs83sFhjj8um5hJ2azmZ2dnZmZOfOmwZ1KpWIvX770vqMP9E3nXkEsawYbj92FDaRipp6bJVgGGEOHZrOZZ7wwhuwHrD210WQNsCdxWXgIFNED2jSfzyNVYpmncNwAitls1gPQiI4/QR/60ev1YoGirtHw5xpIhlVarS7TjZn/9XodOafJ3CtQDHWY4AL2XYVx1iMJ4T6uz3jw4IH3UbNTwrWzWq3s5OQkYjMIYAJa1uu1DQYD29vbi2RJqQ1gTMhMIYCNjSJdm3nPZDJ+TZgWgstmL69d0WAaVXlD0QC1zhE+lKahomusMQ00YuNCgkMzGxhf1gHPUftBcAVbpGnkBBkIwvC37rG0Iwwiqa9HuwGKBCvUzhG00fW0CShq8A0BKC4WCz8GhT+rqc5hAGJXSYHiByQatTO7Al9xzBXOuNkVUCQSsgko9nq9SKpNCBRRTvLqp9NpJCc/3KS1rRgwFiDPZ+PgOxiHUPhcmCJCFFEjrZyHWa1W/jM2KYw0wJm00xBc4FANh0P76KOPYjcy3UDVkVWG4tWrV7HjTZ/YJOh7tVq1jz76yD+Dg6FgUpkj5p/NJAROGFJEU/WYM5070p5wPjBKmsvPHFIwCOeJzzNuvJtCIGxOnBFgQ+N5/X7fnS0tDFOr1dyQnpyc+Kal6W46L3FAkQgqZ6I0oBGyriq6MZtdBVSI/uOsaXVFHBh1TNTpQDdx8gg28D7mFFCB441+KFDkd+g6/SXVFX1hw1wsFu6UmJmnb+IIrddrT1FSBo4UbmUzqawIYGSMWJeMDT8vFotul3gu9gVnOXT29Rqd5XJpX/va12y9XtvBwYF9//d/v33ta1+z/f39CLihfZoej84rGNT50Y2e8dP1QT/jglh8hsAV+qyMIsVXFNThsDOfZuaAnDkBkKszrcCd/8etfXXe0JEwdbNarXrVQcYj1GUNCAJomDt0hL7ybA3+8VxSHwkYspb4vRYvYo0AMjXtL2RNzcwrpvL5cJ9QAMvawd40Gg0vCsI7aWe1WvXrN4bDobOaupeoDjEuBDZxUjkbxhzD+mkKbLPZtMFgEEnnZs2yLvhbHf7Dw0Ofl1ar5eADMMUeSb8UYKJbrD8CNBqY0OwDwAbjCuunc828EBRDP5gj9UH4Xdz+ih1UcKwBCexfNpuN3GOsAVTGFjvId0K2XUVZJc2u0YAOwUXSlPEXNCikY1Gv1x2sKQPK0Q1schjMZkzDQAf2iJTifD5vp6en3q6vfOUrEVt7cXFhT58+tU8//TQC1tmrmSeKF4VCZhP9Z8z1OAf9VbA2Go0iGWrMnwZVGCOCprBrrA0NHFOzAMEOoJMEJ3jPaDSKnG1cr9f28uVLHz/dA3q9nh0eHl7bO/Eb2EvYazTAHgYGQ51SQIrwXmzHYrGIHEPiCjXWoDKWKVBMJVZU0Vary3L1LBJVQAVCGtnmzFMSUMToYzDMLOLETKdTu7i4iBhZFiCbMJtr6KizSNms1HmmjRh+Iq2hKFDEqdXqjBgrzdFnMbPx6kLOZC7PbrXbbavX6xEwYHYFFJ88eRIBqLSXTUwj5GbRtBDGJxTGFMcSIwcjcXBw4GNI+7UstY6JWRSYhJuLMiVqzLUtOt6DwcCazaYXDgkje+rgc76Symxs2uPx2Or1ulc3VBBJhI7KtJw5JPLNZwH79Jc7vA4ODuzs7MwdcfpEu5KiebybzVAdfpxyFXV6VKfZHEkLwSnV1GzAKBX/0CcFj7xDi/LwM9pEYIf1ohFy1R/0czgc+ufK5bK/k3UMW6wslt5DSH9xMHCcaT/t4t9hwEHTgzjrpPZmf3/fWcLQnqnzjR4VCpdXJKA7bMrYH0CoFvfgewrMaSsOh84vbSBopc4xbdPgCgEL9ALR9CACIXER4EzmshKuMs266aPLYRScdmoASHWTMVNGSRkIHVcc/0wm4xfZ5/N51y2N+NMevk8fNUgXBjD5ntpl1p7qDunLmtWQzWY93ZH2AkZgSPTMF047Y1wqlTwdkt/rPKlNw/bmcjl78OCBHR0dRUA0LMfnn3/uTj12kXWn9ob2zmazSFEyjj0oUNYx04qjFxcXkSMOzLXOhYInKmij2xzNUECEQ64po4VCwc+lK3DAzsMYExwJ75DVYI6yNOgrc1kul/1n2EXdZ5n7UEL2DKAYd6ZaAxm5XM5OTk68Xeg065LxYGwUPDKHjFU2m7Vut2vT6dQDbWoTuNqFfigY1TUNoMR/4qzbeDy20Whkjx8/9j4qyMVXUhuA/jIGfC6bvUzvPj8/t729PV/n2P31em1Pnjxxm44uAeYIREMC0E8EP4R/A5gI5Gl/1ab1+33/nuoW9lozangne6zaXTKpZrOZFzfLZrOx5861SBDjr5kC6JsGZV+9euU+lgI/DSzxb/a20WjkxYZCG69jp4F2RO2zriX2HgoeYjN0P0sZxVQSJXQwza4AR5xQ8AGFR7mTPq+GWR1MNuRXr1458CGlhigbm3UcwKOdms6j7QZELpdLZ2o2MYpm5oYbR4NoKJuYRhZ1w2OTUQCAoVEH2OzKMQ7LhquRxuho+p+CKXUWVRgrBXAwbNqGXq/nEVwc7jBNUCO+ABYNFNC32Wxmp6enkWgwbVGnmLYxHjiEpE3RDmUVNO2S+Xn48KFXiNUrDtTxNLuqtgfbRF9Ju4CpIBpfKBTs4cOHkTNRzAv9imMU+Xk+n48Ud2FMw3G9uLiIjCGGmbL09P/i4sLbyLO4/4zIo0YE9UylVo5j3tlglCnn2ZoOpQ4O46QOKxsMmxBVaVV3zcw3bJ0Tormk8ywWl/c5kZ6m1d3C8WfjZePTlG0ctjCNiHEFXLB+8/m8t1nBk6YFYQNCm0T7tMiDOjT8jPaqU8ZzdM3zR20T/VS2HZ0OHWjW52q18oBdyGjgEOHA6TVH+plcLudVPWm/2iVtC3qFMw1AQneq1Wokyq9t1vUaOrJadZP1yf+V+WDec7mcpyczxgpWOJcN+xJWAaVfgHXtNz/n2dg71rHqJw4k5yAJRlWrVU+R51xir9fzC8Vx3LRPjI/ZlXOYzWZ9f2LtUdQJML5cLv2cMnNjZn4nnDrNqre5XC6SconTyru0QAjgmmdpQREz8xRfPX/OWgXc6udVB8P1rumZei4TdofvaZYKEp711PFlPlkjME26RgGQMH/MEzqOb8DzsEEa0DC7YkA1MKrrEEZLx4Jnsz5CvwZhvilCo2f3qtVqhMHCvhHs5j2araBBZPYo2pXJZNxGEazlqAdMlQaS1ut1JGCLLvA8+szd1bRbWdw4oIhfNxgMPFhAqqzaW4Cj7jWMlTK73H+8XC690E82e5UBhfAe1j96ovuW7juZTMbOz8+tWq3awcFBJACkPqcG3HSfrNfrPp7qg6i02+3IVTdmUbCnvgNrTs8x83kFinHB8CRJgeIHJKFDYRY1bvpzsyugiLHL5XJucOJEoyIovf4f55rzDBgHZRcBiuFCwfBq5ARgdHh4aIeHh3Z8fOz33G1iFHFMzC4XDDn6tEOjRESs1AlE2ICUzVOQpmOr0fvwHIKm++EMZTKXbOXh4eG1AhpmV4UP9Kxeu932y1UR0kByuVzkjKW20eyqVLbqiUZMeWev1/PIIWMYpkuok0AEjbQTTavF4D948MCfg+NEVBBmSoGWRtLm87k7TGyKGGk2Bg0A5HI5e/78eeTKCGVS4nTPzCIpP2zaZ2dn3l8ciPV67cUgiHQyV4w7bIY6r3qnG9c+aEAFwbnE6dCop7Z7vV5HzhgzJxrtROeYk/F47HOrGzF/s1HhqKDrbHw8UyPuZ2dnHgQgCk603exqs9PIv6YP8n76iOPa7XYdpGlKXchCEURgXJfLpbOmfFafo0CBMeadYSop7Wdewt/BCOn4hAEI5izcuPm/6ijMlQaecCy1P7qOmCtlPHgPbLM6vHoOFh3QDAPYLg2kMS6aAql9od+sEfqle0Amk/GsDHRIA0LoBg4m7YB5LRQK9tlnn7leaQVBDUBqsIZ3s241PTOTyfj1MNzpyNhdXFx4MTZlUbFv2WzWz65xNhz7o9UxAVasFWW8uftPnWXWG3sV94HiEOL0FgqFyPVWmiqczWb9PCVjSSo6QVH0gGAWOk4VWASWUPc/dYo5G8+4cWZUwVJoPwBqyhBqeh/v0SCCBtB0fWnbeZ/uI6PRyPdHABE6rMEQnqFBU34HCFEQgC5pCjgBPV0rzPlqtXKQSkDAzCJjoOmJq9XKC3A9evQoYv/Pz899XejZVf7POmLtssbZDzVwbGbWaDQ80IYtBkirj6d7nPo2GvDhzmozi4BmGEm1GfiZPJs2w/Zzpm+1uiqGVCwWvdAd32dt4d+oLhNQHo/HDjLxBdBF2oTdAoSp/0zaLef20U/1fREN4iigZJ0nCf6u+na6d+rnNOsKoGwWvcomZRRTSZQ4pkQdDbPowg7LBLMYk8rsxkX59d1ESXEYNepOZCiMtms7NYecBfLo0SN79OiRffLJJ3ZwcOCperpB6DNYIKHjQ3vCyLoCN37OvzGSeumzAkU9y4SzqaCbRYtBA4Cw6WMY4g6Hk7evgGEymdjBwYEdHR1FQNx8PnfHRh1GsyvDxWZB3y4uLpzVwvBwTgYwoeMKg8QGQhUv+t/tdt1wafTbzPx8ZVgOXktc8xyMq57LJMpIVFVZJQVEfLbX6zkwU4CYFM3TOWLMzMwePnzowBzHc7FY2OHhoVcJRA80Ks4GCysBo4WedDod11OACxs7Y0TfdINVB0ADHYypOlNhFJz3s250w9Rqufxf9YB3qCPHe87OzvxdBwcHrtOwguoo8wdmCpZVNzXO4+i5FQ386OdXq8s752BZcIb0vJWuB3V8zK5SSXV8Q2YudA55jtoIHBUFOAjfVz3V76uwDpWFUt1Wpp7fY181GKX2lnlTB1HZp9CW0ybYEmyLsmHKyJpdB8OMOY40uqZVOZXJQd9Xq5XbKBwyfqd2n36wtmH79N3YJgWKnCFiTWCr1Cahu7QRJ5H2ciYYpg5AERdg0YCqsku5XM5ZUtYSwNHMHNiRmcBaKRaLXpQJoKPzN5/PPeWONZ/JZCJnh7FLpKPqWg4DxNgaxlyBGdkdMEHMsYJ21ZUQYMEcq34wRmHgVJ3zOKAY7gHYv/F47MGvZrPpYwZDxVzTv5BRVLvHWNAPgnysC/ZwxhImWANiPFP3OO3L+fm5nZ2d+ZGLBw8euC7ofs+6VhsXBkzRbz4zGo08mKvXsKCL1GCgPwBPXQvhO9WerVarSMaK+lTsyUpkaFBJWclyuew1IVh3ZKoQbKJa+mAw8DHVwnX6Huyx1tXAHmtfF4urYxz6Oc0kYAxC1g5dRf/V/iqYVYAaCnZKWWbNcOMZuib1uI6uN76bAsVUYkUZH3V61cBqBM3sMgVqOBy6sm8CigqAwpQadQLJVcc4YmjY7Myu0+rqwPEudfTpgy7GUMKFrBF3TbPDuGm0jM/ztzqPmjajQBHHlI2DZ+LY8FwiYmz+Gk2nXefn5+44v3r1yo6OjhxgYCybzaZHsDGWAIC46LnOPxHk5fLyDs2HDx96tA7WlPQZTT1lzPWaENquKZKAJeaNwEGtVvO+A5AICpCqwsasRnA0Gtn+/n5kDkknU6dBC13gsJtZJN1DnWgk1B+AEqBpsbgsc53L5ezzzz/3wjudTscePnzofVF95/m9Xs+dGDY9Uqpms5kXowDcMW9cdqzjrsEIBYraft3cwuglGzVjrbZBmTTGCd3FqWDuNHquDhHMKm0+ODiw1Wpl7XbbHRoYL1gcmDMFXrpW+A6ggXayPlif6/XV3VHYF/6Njmk6jzoJ9FPZc3RVN1gt7a+btrKramsAEBr8wInSMVddNLsCrehU6FDwXpxODUao86xBAoJM4frg86SShcEDBdbYDCLtWiiE76ge0l+z6P2NuVzuWt9Yz+xbgO3QlrBO6C+fZw5U13Hc0F1NvaMfmv6nwRIc/slkYuPx2Gq1WuSidfa+fD4fAZAaYFytVpG712gr4Ay9VdCtATbdI9hjCRRybrtQKFi9Xrdut+vjn89fVt7mLkW+A6PI2mXdEWBBP5gr1TkCh+fn567/2FfuoSSgwHhyHk3nWfcK+outVYBIP8I0U01TVCG7CL0L1wFXVi2XS2ezCUaEgR/GR9lZXQ/qh/AcMjHIosKmEPCD9VGWHTuGbRqPx9bpdKzdblulUnE95N96dQd6pGwf7WVeNBUTe5nP592O6RpjnFQfGYe9vT0bDoeRM3iMgfpujAm6RlsJBBA40L1e51SBFLYT9pJ9SJlp7jqm7XpsIHyWBsbCIjtm0foa7P+8h89+z/d8TyQ7Qe1neBSE7IdMJuNnTZl37EdSxh52Q+9aVV+btUXwiOwY5gC7x7ykQDGVRFFnAmVnsSQBxXK5HLlQOs6JQfTcnEb7NUd6uVxGzpmYXd0JqN87Pz+PADDebXZlENkoQ1Y03BQQNWKhgdcFrgY9dGbCMdB3hUARg8UGqIaWcslm5hH5MPKdz+et1WrZYrGwRqNhz549s08//dQqlYrl83kH7ZlMxu+2y+fz9vDhQ49gnp+f+zvVOH73u9+NbACwcp1OxwFns9l0ppKNS0G0mTlAY/Nmg1EnDUCr6XT8Deg8Pj6OBBp4H3oSpoAg6uTp3W04J1r0Q0HE3t6ebyK6wceJbvZEyOnvwcGBV8xcr9f2/Plzu7i4iETL9coOxghdomorYAGHi76y6edyOf+csoaqg5peF/5ON8cwkNJoNCJsPmOH/pNeR5+U0cTZDlkwdYABERrh526rbrfr0Ww9A6mOGmtIxw591cBAWABEmVXGk/EJgWLYdv6tDr7qEZ/DWYNd0t+FLHQ4rqrDOj/MW5gVEccQMCar1VWFW9ae2ktEbRDtSAKKOM/h89BH1gS6oYBCA2MheNazU7yXzAl1OLnXjHlUdoif0VacOA2y8G5YIh1nnqWVZM2uF1diDcMOKouSz+fdceVzoQ1Tpjac++l0Gkk1ZEy0uNVyuYzcK9zr9SJBJ3SPz2GruXKIsQK8stY1U0b1knVZq9U8dV9tKoWkZrOZffrpp5EUQGVP2JvCoySks4bjo7rKmVet1gvoCpk2xo1x5zmDwcDXo4Id2gK7hR3Xaq7YR3WkdU51D0KnFAQoUFAnHb3HB9L5W61WrgsEUE9PT+3s7MyazaYdHBxYo9FwG8of1ooGZ7S6ph7HYO3p+gUYk87J3BH4HY1GdnBwYL1eLwI22Ys426rAQ8eOd7P/M94c92FMlBlnPsIAMXqKrSFFWY8F6V5DPQzapOtfg4kayFAdVvZa92QEe6x7HfNwenoasa1q88bjsXW73UiKNfYx6bztarXyisYamFJ2/cWLF37UAvtEm8MgShJzmSQpUPyAhMXBYgg3cLMoUMT5DC9TDs8QIOp8cRYK8EREGAeONvAOzZ9frVbOXpmZb1gaGVawp45QXLQ97Js6hhrdVMDHXVBEnuPAZfi+OKCov1OHTO/VYeMGKLKRcF0AdwDSb61iBdjUDSubzdqjR4/s4uIico8cv89kMtZut/2MIJEs0j9qtZpv/kR35/O5b0Dab1IQmQPmViPB4ebOeT+i251Oxxk5NlJlBUiNIShAtJ40RPSEjUdTm/QSX3WwOc8A+KVPOpaqU2yK5XLZNz6zq+BHo9Gw/f19KxaL9vHHH3vqEVFRTeNWxoL0rGz26sJxvpPNZu3g4CAy5vQrdODVGQ2DJLrOGS914jUirKBSnS/+aMSXeQIo0i6exWdhKhVQklrXarXcYS2XyzYej10XQhCAfrGRsj40aKRtXy6XnmZndhXEwLHHQVFmSsdOx1JBAv1DL7TKMzZF28L3WX96Tk3fxfjP5/PYSnwa1NMUYtZBqA8aGAzfhfOj9li/o/2KCzbw92w2czunqYI4NAq4cXLRbU29LxaLXmSFP/V63cyil0aHzpTaHeZdgQv2kICLMnw49xrUigt24HjRB87yMk4fffSRLRYLPxdIuwB0ChCZQ/Y4ZQh0zTBGy+XS9vb2zOzqjjYFtjiGrEnVAw2cEQhivvP5vJ2cnHib1F7DWmqqpYLLbrdrr169snq9bvv7+z6nqtN6yTl7vAIzPdMWBkjwL4rFor+fc9tqgxCCJ9ig2eyy6Fqz2XTmS4GU6oFmaNCeMNOEsUUP9FlxQJHfKTBCBwDHACdAPu+jf3o/rvomfB79ZR/ANpmZ2xf0X/d4AmOz2cwuLi787OuTJ08iIAx/jAwZgvvqe3ElDPvEycmJj9disfBzl+onYHN6vZ49fvw4kv2jY45O6c9zuVyk6j4+gdooMqr6/b5NJhMf/xAgApJ17+BzPF+rtNNO2qRptrR/OBx6AB6grWwf9uDVq1cecA4DJWrbwr2cSvLKKKKT2WzWOp2OB+01kEAAmv7x3RQophIrCqzU+TO7Yh5CoMgi0oiL0vsqek6CiIY65wqadGPRSJLZ1Zmufr/vIIIFaXbltLHQtA/Q73FRGXWWWYR6qJpFpGfKcJTVaQ4XH0Y9ZAowarPZ1UWwvBcjxHyow0uakDJEs9nlPZZf+cpX7P/9v//n0VaiZhqdMzO/XHm5XHr1L2XdCoWCXz3BBjCdTq3RaPgmhNF+/Pixl81WXclkMpFqkapDjBmOum66FBDgbAwVAvUz4Xgzlpxz1cPqfJafVatVbx9jrG3VKJymRYZAATk8PPT3wNrinKmx1k1ZI5E4zXFAkYBAuVz2s6XKZIQOMeuR/4fOO5uZpsmETk3IgKje6Dxq4RD9HVeXsKZZQ/SR+caBor8KHpRx4nwJv9N7r9jMVS9YfxphDjdCqgHSb40KMzanp6eR/irDxdyqo9HpdCLjiei5atZSeBYU5xIdVVBHH9WO6f9pS2g/aK86oCr6XNUfQMF6fZlOC4BYLBaRtC1lEMzM7YcGAwC1pIIBKGDVte3KthUKBXckaQfv4EwpDDHP0hS6ZrPpjiDP5TM4phrc4zPqmLFWdF9E13Q9qGMOi62pmJVKxZrNpusHY4xesOaYP2w97J8GLufzue3v7/sYATg1y0WZK1L0crmcO/3Md7PZ9LVer9d9DwgDFoyrAkMFudr2s7OzSKVGPTqgFU6ZC77HvkNQIjy3rXuzMkUABs0q0jXKXCnIOjw89Llnn9SAYCaTcbuvgJX9Fnu+WFweJUCHdQ5CNl5TedE1dB3d0u+Tkt9ut50x1fWrRy7C9az2ibk8Pz+34+Njm0wmnnJ7cXER8b2oQ3BxcWGlUskqlYrt7e351VkazC6Xy/bs2TO3LYyZ2kj8touLCw8maLCx2+26nccez+dzOz09vZYejU3SedYUSgWK9Jt0Wd2/sUOkFYf7pAaxWW+aRaABWAXr2BCyN/DzNI335OTE9vb23C4oAYGN7HQ6HkTWvR690UAo7UTizo9qujcFtPDpsAs8j3FTQmZXSYHiByQokQJFdbI0wsL/M5lMJHd8tVp5WkooLFTOZyn4wcE3u3TiqLyGg6/peLPZZanj0WhkJycnXq1KmSFSC1TZe72el4qOYzzV8cJwaIVFBaLaft6pkV+MUDabdfChjiH9YANhw2G8S6VS5E5JPbxN3ygIMBqN7KOPPrLJZOKMmqY8MO7KppldRVoBgcvlZaU8AD3FbRg7NgS+S3uy2ax9z/d8T6Qyp4KE0FirrsEKaKRQDddqtfJ3Y7zVyKljhQO6t7cXiXoyrsrwsHkrm8tGzv2hWtKbTSqMsq1WK98IebZGpNVYa/STtVQoFOzo6MifhV7QT/rF+QPaoeuBv3FAw41I07J4D7qo461jpGeFYEnpO20KnWj6p/de8nzdeBTY055MJhOpOqfMgqb1sKbr9bq3dzQaRWxUyAax4cIOwqLTJwWDBG7y+Xzkouc4oBjaRpwetQWslbjosAbZ9Pm6FlgrykqGkeRw3kM7wzgp+I5zwPmsSjZ7dWm0AtUwwMD8UB0QB44xJwUXJ0UdGgVm6LzaJuYK0AEz12q1PIig1Rtpd61W88uzFdih3wrIcTQZD3RIv2NmXpyK8QhB/Xw+j5wxZP4rlYqzXoypMpSAsFCfGo2Gtw1AtL+/731SO8G/NVNFx5WS/wpk6Bd7lAZY+Bz7B+1cr9eRVEzNBMjlcpFrHwAYysro2LBeOTuFDrMH8U7GwOwqcExWkrJl9Ev3d+3n2dmZPXjwwN9lZra3t2e9Xs+foYEmBazMM/soKfFmV2nBekaROWZe2B/5fmgrSLk9Pj62wWBg9Xrdi9GEQSH0Uv0MraLM59ABzrq3Wq1IBU61AZzpI5tGA6PoQb/ft263a7PZzJ4/f26NRsMLrg0Gg2uAG31UPWHedV4JDPd6PQ+OMg/oL0EfhOcxbqHPqmBL/Rb6FNpDdA/9IWCRlO2jGRGI2g/9TK/XswcPHkTO+bMvmpkDeCU36Avs8nQ6tbOzMx8D9Ac5Ozuz4XDoKeWM2Xw+d3ZVA5t63Ie1qzYtBYqpxIo6G2ZXC10dgzhGUVMCNJKU9A4ikPl83jqdjp2ennq6IcYNZ4DUSiIuZpcGotFo+JklFB6DgiOujg337akBCkUXPf/GaOmhYDWE9FvHzCxaBVMjTyo4B5oCwTtwstRRWi6Xdnx8bK1Wy51wSrO/evXKarWagw6AD+mQZnYtXY0Nf7lcukPXbDYj/ZvPL6tsPn361J1mxlmdzXw+b5VKxR0axh+DGOfckpKpY8rcaHoYTB0bC3rAJsOcrtdr6/f7EaCIDvF5nVv0FqeCMSXCv1gs/MwOm1TI5JBCwt9EuYlosgkyn/xBZwgknJ6eus7g9GmVQ8p683vWobINbCyhLtI3NgPGWoM0ytjxDMZfU7x4FhuJbnR8V8+oKGCjXaxHjbhjFwDJtI+NDj0yi1YrpI3aJsZBnVm1X7A+/ExZY3QCJ5QL2BXo8S50Ttkn1WPV9RCQ6dhpJBcdU0CBvrOOtD3hPOvYIQpEGY/QwQk/y7/pG89WXQtBaSaT8Yi5Oto6nspY4nTxGd1XlsulF16BKQjHluCJjrGCHM4Z47ipjgAulGkM2Sj+TaYBxYtWq5Wzg3wOPVVHVIEHDAa/g2ng/5pGF4IldIR9E5ut4Ji2cpG4pjoCpsfjse3t7XnfAbXNZvMaI6O2G9aIYIu2H5uMU07QmPbomdnRaOTBR2UzMpmMX6yu4FMZSHwD9sIw0KU6qUculsulnZ2d2Xp9yWCpTUBnYZmoso1zzpqjbwpYuIcP0WyRENgyR6Rehhka2sejoyPL5S7vI+QeP9UvAvOAQlg8DbTlcpfF3AiGKLuFb0a/+Td9w1YrI57JZJzZXK/Xtr+/70c7zs/P7Tvf+Y7rMGm8pLsznqQkh7YOG0AfTk9P/TiF2nX+r34bukmKtgZMdS9HlMHTQCdtIGg8n8/d7ofs2uHhodsw1gnrjGfie/G+TqdjtVrt2p6hc9Ptdv1cps6LMtHD4dCvaNF+Ii9evLBms2m9Xi9yfc5oNPLjOOrrEARhjkM/6SaSAsUPSFiwGtFQx4AFrgZQo+Ma1cnlch7ZMIvmPmse98nJiYMwFtZisfBUFBw+3TQBPxoZ1oWHw8ZmtV5fluGnxDGpJotF9NwI7SQV08x8M9V0Hu23ghYdR5x50odwRvU9PEc3QX6njjnjizPOPHGOi+j5+fm5tdttG41GNp9f3pu4WCw8Eq8lxdU4cP6Qe/WYQ00/Il1Tjc9qdXnwWgGYAh36oWfedKPHsWDu+e5isYicW9MoIe8CfBPBVvCjh7QVGLN5EQFlnti0KEixXq+dpSoWixF2Q50gM7OjoyNPmdGgyWq1sm9961uRzRY2iwAAwOXRo0d2enrqc4cjzAYFOwOw4znMabhOFSSwVtUpVpaMZ6nehk6CBkvUOWasAeX0EUfP7Cr6r4wkY8hn6dd6vfaCJXrWJwzqoH+6PtAr+gG7q0ynbrBxDgR9Y8wzmYzfb6ZBAk3BUtCoaT78rUyEMpj0We0sDmRcJFfHQPWW/2v6J2PM88NiIiHADINgyoTQNsaYz6m953lqc+fzuR0cHHhbFCSGUXyCA5o1QDVlxvnx48feZ8bu2bNnrv/6XPQX20KKsbJorFHmWYMY+m8NsKBDCvDJ7oBJ0etWVK/5Pe0DUNJ/wI6uT/SNcRmNRg5alD1VJ5oz45rFQpYIbCSgAx3R0v2Ac56P3gMkAHOs+9ls5kASnaJ9hULB7VYmc3nueDAY2Oeff+6gGrBVrVYjQFF1TW2Nzi/rDP1lbSpQJD1Zs0G0qBM6zn2AvBc7rTrDWmKv0OAv88aYhYEhnglIxm9Q8EJaMe/jnBprSPcM0q1hzDOZjKcW67ypHdDUVnRQz2DqWmas0Xd0Us+bl8tl+8pXvuJ+XiaT8WJjvV7P9Z0MLk1b16waBYnhOxk7BU1qi7j2SmtW0H8df51DdEIDDXxH0731iiz6osCa749GI9vb24sQEPgYJycnHkDXwBHtwA4Q0NJz8tgcfqdsreocekEGVLVatX6/b4PBwMd5Op361WrYJuwE60sZxZtKChQ/IGFhHR8fm5lFFglGJGQUMWTVajUCDKvVqj179uyaA8+CxWEPL/HG6aGipkZq9OJeUlpYWJlMxtM3Q4e22+362Tqzq2pV0+nUwREGG0OO8V0uryq96fkO7aeZ+Wavzo06lzputFEdJa3MpVF3PsNG0Gw2vfoZRmm1uqzSCWjkomCYEzYcdWQxZESh6/W6FQpXFzErEKTSKsAPRwODg7EnmknVLsB9u932zV1TvJgLQD/GS518nXfGdbm8LFeOoeP8ZRilQ+fYTAG9sNPKiIzH4wgY0rLZWpI6BC1Eq9EJ+kXbu92uj6NuQMw7G1qz2bTDw0NPyVQgowEP+sXPOYcR6jCfV0efOcNh0T+ML46WspqMF30EvLM21NFU9g/dUj2lfcryaOBFz0VRvEZZRhxWmAkNMOmYhXqDI632xyzK5vEzdIi5v7i4iKxXBdOIrg+eqzprdlW0h77yt7Jayg5pAEDTtnTcEMYIh1JZC02d04CV6pM+WytSs05UP3SMsSuMAUBf+47zhSOiNlRth1br1CAPoK/ZbEbOI2mfdR54N4Ef9EADkfp/3qHzz/ls1Tv6z7orl8uWz+et3+9HyvAPBgO/l5b36YXlzLkCF1Ld+LyyZAoEdW1q4FH1Fj3E0SXQmc1mrdVqOUhkXWowDwaNtmlAgrNdzKMGxZgrAL6mDPKM4XDoxai0UrbZVXVPXVdqF9Qh5nmwn/Sb+UHPza7SOfEp5vO5V75VPYXFQ38BWQr4AMjr9dqznHR/Zl6xFaENYM8OGUUdr263a+12220nooHrxeLqrDBMuwJF7LHqEXYcXaIGATqi6wcwjs6x/+lccLyFehOAUIAi88Raw45rUIl+0x8tTkXATMeO1Fgz84q5sOQKRDV4gGiQbjKZRKqDwqzRPoLi/X4/wmayp2vAK5e7vEeSoNh8flXMCh2HfQ3JBPQAX5j1wnuYS2WPNVMtDsCiU1/96lc92w77TnCdFFbWOkBSfbSbSgoUPyDBqBDlCzf1ECiqc1QqlSKpGEQI1cHWdBtKf8PGqNFmMZpdOSOk2phdMW6AM3XgzK4cWTPzC4RZqCxujCjto1+kPOndkFpSWKPmCurIMVfjRnswuPTp+PjYHQeiSHERfNplZs5y9no9d8y0AtZisbBHjx75GQMtkqIGQMcF50eDAXt7e/b06VM/K7BcRsuv4xgyfhg8xnY8HtvLly+t3W77nANs1RlmHjm7pMCavqlzpBFPdIF7uLSABkZR38UYMc5aYYxn6oatTpwyIYAs2o5xJ9igII1nYtwHg4H3iSqx9AXdowy5sjv0SYECztlqdXl+kw1SHUnaqOM2mUz8bis2cY2OxkWWM5nMtU1EgWupVIqcpeSZjDlFk9jcFFzxfHXIlZFgfPSuT2wPaWL68xCka2VFfq7MPP9nnDU4w7oh8KLzYXblXCvzgw7E9U8dfvRbo7iMAY6zPkcZYvqs0WcdVz3XxVhqyf7wWfo8RBk/ZbJpC3OvwRn6zxlFHGL+Tfqy2kXV8/V67RWd1eFRVgJnlN8BetEvtQ98h5/TB81o0WAFz0R3Ly4uIjafuVSQaXZZSXkwGHhbKDzFXGvwEduBbmm2BJF+giCkj6oOqcOPPmWzWd+rFJzxTJz+YrHoDCsB2sPDw4hdZtw1sMA4o0fYMQ3SsB6Y60aj4c6tijIusKPqa9B+nOQQnNAXxo10w8lk4gXQwowPAAQ6YGYOcvRnBCp6vZ6n+GtGh+4Rq9XK90CAGu/UdrOPML4AbVgw+j2bzSI1Ch4+fOjrQYMXjI0G+haLq0I+7OnsbaxBnkV1+tUqep2LAn/GBDtCmq0GLjgqk81mrd/veyCCNrDG+v2+B5zRSc7/Mx7L5dL6/b4znFQk1T1Yg3rYR/Z+M/OiTIyHVt3nOaxjUjwBowQduA+8Xq/7umJ/1/lX/4I9czabOagfDofWarVcnx89euR7ivpJ6Itmt+l5WGydBkK0aJQy3QTB6/W624jZbOZ1Kwiosx/gN3L1lgbrFChqkHObpEDxAxK9N87sqhodgsJrFBajQuQDQfEXi4UdHx97BAjlu7i4sI8//jgS1WYBsIgqlYrnvLN5qLOGs60ONdF3jQpSgQ1DHxp73o9R4ec8F0dLjZdGf2E3WXTKSvFZoqjr9dra7baDCy5TZ2MJo6nKShK94soE+s/naAMGHoeCMddIEdFQTUXFKXj69Kk/j++y6UynU3dk2GQmk4m9evXKZrOZVatVazQaHllVJkpZC/4maobB1Xx/TUlGJzBoRP84XxKyJDhXunFr8SDVbYATm2GYWsYGiW7x/PPzc3v06JGdn59HwKQab6KeencVYwdAVuCv1SD52Wx2dXm8MiLoGP/HsWUuEcaA+YMtC1kwxoLzDbyDedagBt998OCBs3u6PlhvOP/osOq3rmVSVEPnAGaE9tBunqkgh7+VzQwj5Aok1KEMQRtjD7Op0XH0ToMnIehQtkbL0fN73klfQxumY6Vjxvv0XegcwF2Bhf7RbIcwIMU7sF08Q48AaAAAPQwBMjaJisW6FtU55m/tG/ZP26ht14yI9frqDDC23OwyQIkd4X3MnQYqeb72XYNF3W7XarXatTRH9I4gBHYV8MdeqNc1AEyYW9IDWRNq23O5nIMJBWpkdRBQUkeVccC5BCArMG21Ws5yLJdLB8KaeotTrJeLm5kXVYPRiQseaMEmdEZBDO9YrVZeKA2AQQVOxpHAggay1JkF8GAXNBgNUGQ8SAtmPNThNjO/ToT5efDggdsu/iirSh81UKp2gOfrZxV8sRegx5VKxZlpzusrQGOO2Qv5HTaDfYb9DbuFT8Dv6vV6JPtE9ZqfcTRDg/Lq17DOqYrKuVMNxmLDNaOo0Wi4bjHnMJEE5qlB0Gg0nBnV8dUg4Wq1ch+EjCJ0BdDHdzRgzLwpQOYdBODJGiNAqrqh9pv1h/7r9+lL6NOij5oGm8vl/A5KMuvwPUlZVdusZ4PZh8mM29/f9/X38uVLMzP3rQCy9KNer3sQH6Y5zNBg7HaRFCh+QKIshwJCs/jogrI7Gi03M3v+/Lk7wZzz0XM7+/v7viDZ2AAVOAWAT5xhjCRRVT3HZBa9xBujWq/X3YjybJ5/fn4ecbaUESHqgoFXo6Ns3WKxiETCyQ+fz+fOMAISSbHQKDCMqr4Hg8emyLOJeHJGkz4xrlqsRp1F+qeRczZOHHScCuZGN7+QGaYfOGPn5+dWLpft4ODA0yy08AqbWujYsnkwV0QFza7SRzQNSh02jXJrhVfd/Ki2xtyywYb/174DXtEhTWU1uwJAmUzGLi4u7NGjRxHnOgTjvV4v4lATKCAVz+zyOpCnT5/6fZgKfIiwEhUPjTebCnOjmyRrlnXyta99zR1afk8AgH+vVqtImnYY2dcNis2Q4kph383Mo5zqWGqgiT7RB+aQvzWQo3OjY00/1XGl7fxOnR3VJewK609BF+N0cHAQCQCZRSsGa19oEz8HeCiwY6xxYBSwhiyM2oNwvEKnRcGPjjFBIdaJ2nF1dhQ8hmctFQhrwCIEnZRgXy6XkQvNNWUxnC/er+f2wjEjEEW/teqyMnSsAfYdDRjq3WQKTFX3aNNkMrFarRbpn+4vFIBhPLXgGZ9hfMmcQbeUKeDZ+o7lcumgBlt1cHBwDSiZXTr8WiQEZgZ95TOAW5zCs7Mzb4sG4HK5nD158sR1J2QbV6uVFzZRx517I9EVAiTqdKLzpL9pJVXNRgLUh0EL1h2ARgvWrNdrL/jFXGN/tZib2n3epeu5Vqu5U828ose6TnSvYU9k/GgDuqfzyBioHeEqIfVnANSsO57PPKnuacBCA0KLxcKL+LFPM1+IZuuordUzlFqpk75opg/BzNVq5XNSKBQiKd+aEVOtVi2bvSzygj/Fem61WhHgQtBFUzEZ306n44CVtat+pAY1NSAS2tH5fO6V+Mm6wW9Ad5kT1XHAZDab9X2bP5odRdsVoOp88zk9gkWAUu04fWMt0S6u1MBelctlOz8/9zoLFMhB5yARisWiPXr0yANP2BH0gKDVLpICxQ9IcIJZTCyuyWTizkYYhYbqV0edRaq52d1u1zePRqPhxgVQhkHCeWLjqFQqfsh8Pp9HjIdGzvi+Lmazq7NCs9nMzx+w+XFwPwQPGuVVQ88C7fV6HhVkUzIzP4dG5SmAz+npqadrEPnRcsXa/v8fe/8dZmlV5f3D33Oqqit27iZnUBCQQQEBHQERURlMjCiKOqCAoqKiICqmUVBBQTAAMioCio5ZkaBEJQqICszTDmg3TTcd6Bwq1znn90e9n13fe9W+T1WjzvM+Y6/rqquqzrnvHdZee+W9Nswe5oJ3TRplvqtXr1a1OlbgB8FA+XXWZ8aMGYUIjzOW1atXJ0FKCq8rMTB45gwzlcbumHPjVhozavB4w2xcAXBDkbYd1+6FZ82giRhpQCChGCG83ahsNMbSqMGFK/eusMPEXVFlvd1QxJhifESbXBDRdqVSGcd8BwYGUqSRuTB+j3SjpFDZMabBuOIWvdYoxlFIoqB6pMY9pXh82cOVSiW1F/Hmzga/7oY1YB8R1XMjzQ0EcMC+Jcrq6+O043sScCM80hp4dYXYo0qkfLG/nH5YfzziOCecN7hx4evvylj8DrxLYxFSPgePvrccV7Ttc2Uf4YBiPaFdTzlz/g0+3QmIUw38+nzduHFj3vemFyNyrztKm/fv0UlXnMCVG3VcRcA7nKmGX7rjBT5M2psrr24Uurc+0owb1dAC6+JzZ949PT0pyuQRKQzFmTNnpr4c504n4JeKlczP146MBJcJyAg3aj0F09Pv1q1bl5xJZMMgbzAO/Iof5Iyfd924cWOiT2gZPshYMbDcycQc16xZk6KnrLVn99Afc/f1QjcgaswehlYozFav17Vw4UJt3LgxpXDCk9zoY097hoXTAvRJZM5TQKGtZcuWFaLuHtV0/st7Ps9Vq1YlOnSnk+9rdxzX62MFk/jOHay8S7SP/cJ3KP5uUELTjhdPzXXnM8439hT0Aj6Yu+956MuLxbW1tWnmzJmpfgBj6e7uTvwH3RKji/scMX5pH/zB4z3LwOWl813wAA1Uq6N1AqTRLK+Ojo6UtRUNxehQ4uyfZ8xFJz0Oec/6YS+hP7KejJHIPrTiOrnzBZxwZJPRPw4VcMO6u1OXaHatVku6kvOp1atXazKw2VD8BwJP5XRGgsIdlR8MJgxFNizCAUOxr69PS5cuVb1e18qVK5MgptwvRAyz9pSGnp6elB6CcCCFg2cRejAFvDrONFBqnfE6I+F9Twvp6upKAgJ84LV2IecGEgrG1KlTkxGI95YLdKdOnVpIn2FupCwwVhglHlupeI2ENFbAoKenp1DKv1KppPZYW/C6ZMmSdBcSCgYeQbypMAkYEIatOxNQECQlx4Djk4hwFL4INjcY2trakocRPLO+0CX05YLbmaDTp9MDOMFgcQ81wpSoKmMlNQZBQV+Mi7QaosdRSeB51tENRXcKoIB7RTiUfQTjwMCA1q5dW1BoUDwAaJz1xNGAko2SQiqKKyUeVSPVE4CW8LR6VkCj0dCTTz6ZlB1S6vBYsv98jPTpxh2GiCvO7OMYVeC5eHaMtrwvTw/LpVrhuEHJAXf+DGuG8eu4j8aC/+9zpcy9713nta5MxlRAn5cbAjE6Stt+iTuKnVfvc8U7Kqb071FqIlu+h5hvjBBKY44bHAyAR5R8nfgutg9vdHx6JAje7PthZGQk3T3X19enrbbaKinHXV1dBaURnDN/5AVyzKPR7G2nOWRAxCFGVMykQImLe8ENunq9ns5bwZvduMGpR8Sj0WgkxxNjYW4Yem4oSmPn2DzKyng8Bd1lMTIXg5j5xZoB4AolGN7nazsyMpKyhHAwYRyDKxxBMW2Vs59ePGl4eDhlt7BOXAa/evXqQlVHlHmUaJRjdwa7owe9p6urSytXrkwZQi0tLYX7QeEPfO50w/O+Vzxy5+nB0JjfQe1ZEtArV3NAo6Q0Mn/O72EAuTymWB3PIxecJuEDrK07AV3HY72IlrujtVIZq7LOc93d3QXHJHvCeaDrbZ4hU6vVtHbt2uQMw1Dy6KzLEpyMnqXGuBkXNEdmFWcbkdHoBO6woi/wOjg4mHiO81s3EqWxehKsFXpjX19fqsaPzHceg5zw40k4nxgDuqSnnE+fPl1r1qwp6DlUCndj1+mSTAyng9x96DnYbCj+gwEbHcZE2igHqB3wkLDBPS2ho6MjMdHe3t7EEGFQRPhcqHpUxM/X0ffg4GA6L+Fedw/zxw0dvYCufHlKAN4U8t6lsVx/Knl5upWn3bS2jl38G8ugw2zY7DAphDvMjDMbMNtVq1Zp6tSpibFihPkcYGAY1O5ldAMKBkW/nZ2d2m677VJue1RomDvpFFyq60IOpY9CMhiKHoFkHTFAEGReuIG16OjoSJ7CSqWSzuqhGHhkr6enJyk8bnS44eSpyp5S7YYUDJGxtba2aubMmcnI92JB7r2t10cvHp41a5YWL16chJUroi7g+/v7NW3aNK1cubKgkMLQOYPoDJx1Zp8QLfZUQPc8Q4s4JVyh9yqv8TynC1uUDzeYeYb0adbA00y5foUxE83hffAQDR6UA4yiaDBHg4soQ3RY+b6XlIQtBW/c6HOh75Fgf8494ihcGNjuiOJZV358vm74OM5cQON4cUABdAXH5xz/j8abG7GueHiE0vEaAeNA0rjzqvRFf/w4sKYx5RqjBhzgVIMveOXQaLy5Y4/3PdXOjxbAj9esWZPG7ca88w03kv07319uyDmvxPiFz8DPPUKDMeFr4oYCa8HzGDbu3KlUKsmogmc7rbnDj3eI5OOM8kwV5JtXUXSZRl/sT+ZWrVaT4u6ph4zFdQCcRhs2bFCtVksOma6urhS5cdkOXfg+dt7rsok919/fX+BVRLZqtdH7edevX6/Ozs6UNYACjwGIbHIj2rN/4EusBXIIRd3TAFln+LQ7t1zB9/0CD2KNkNlPPfVUQaYxR2gNHu573e+/pLhKPJ7jzgOvwk1fHl2DFmq1mubPn5+isk4XOOLIQvH5gS8MNWS880nnG5zJdGcYMhi9kM85n+jZL75PaRcnlxdjQ/dAN8HIIjuopaUl7S+Parsx63ITmcpn6FisOzLTs20kJedxe3u75s6dW9ibMfME3gcfHh4e1tKlSwu058b9wMCApk2bls6JIgtpizaiQ4DIKOuD3JsMbDYU/4EA4pfGziK5YHNilcYEHIqxew1RrGAmpJdgKJHC6Qf/IWKEHUKHQ+tLly7VunXrUilllEfGS3+xBDzfuaCHocAMNmzYULj01A3AJ598MjFBFKjo9fR8cy6AdUUEYQkO3EvLeUMYzsaNG1PxAYQDyqNHr1gDP/PiBjeMYP369ck7R1VNDoGzFijgrK8XsfAUDAQ0jJM5u1cOWsGI90PiGJGe6iQpCTbG7Gd0GJsLWOjIFT2PIMIgiXZ7VULoESbpxiM4aTQaqaKhPwuO+/v7NX369KSERGPAjSBoxqttYtRz5gIjGdwPDAykqoSuRLgXPyrA0H/8zlNZ3ICFRthrGPcxXQlDsaVl/JnZ3t5ezZkzJ+Hc9zy0gZLjkdky48PvPoQX8DcGaHzf8cF6VqvVlKbtkRJPWS7DF+MlhXrjxo2F9HoUCwQvigDjANxQh2b53BVBd6I0Go3EF+L8+B0NM3dOUDHQI/uuxHk6U64tSSnl0Z0HzstcWXLDG4CnupIVPfNxTmRkMGePtkCruUinK20A///lL39RT09PwZD0efj8wbOnAUbcuFKNfHB56cqzZ7JQlMTxxZ6NCiRj8UIz0tje8iijyzyPRBNBjXschZMsFegbvDCGRYsWJTygbGIUSaN7iJR6dALn3ez11tbWdH0Q/Au5A2/s6elJ8snp3mnXdQzwjjG9YsUKSUoXmjNnl7/gywvbVCqVxIP7+/v11FNPJeOJtqJuwbMYGm78I1/J6nEDiXVzw5o5RlmGcz7qDs5LN2zYMK6egWd7wEPc2Qj++IwrQjD4vX2chX7GVRqrJu+ONQws31PsfW+bKL07eNzZCP5c72Mf+T6HhgcGBtLZeAx6N7gxovifua1fvz5V2yXzYPbs2SmFlHVB5rNevO8ywKthu0PAU93pz3WZ6ERiv6A3Q8PO1zyLzSvkYuh69gy4wdiDPslUcF7m2SKuJ5MJt9lQ3AzjAMaBR829YmwMTyXzje6MiNx7TyPwojYwUc4r4oVjI3L3HBGGSqWSKqCuWbNG69evLxwAxwPuDMg9eG70SsUURYQ7BWo8okjKGAaaKwG1Wi3dSYWxhjfTz1vAZDx6wHMwdvfwcsiYIi2MPaZlRO+dKw8wM4+gwdARhlTia2lpGXe+hLnzmRvkRD55Dq8s3jAYi+f2u9LnXjI3FKlWKBUjg/5eFJw+XgSKM1Zwi4FB9MIZPwoM43JDEe8gigE0j/Dwggye7uLneVj7efPmJZxWKpVE4wgkhA57qKOjQ1tuuWUq+uRnG8AH4AoJCiI4cAE+bdq0cZGpWKGPvRT3ikdvmD/j6ejoSJf5uueVvUE/brD4GNzh4lU7AebjqX5umOXadOMgOhRc4fbIH8WPUN5JP8bQY4zs4bJUVR8jyqVfng0+3dPvvAujijHGtXbcxDnz28vbx2fie/6bfhkXf3vFUufVvibejjtjoPMYVXaDEeU1AuNlbXifys+MwfktOMfxEBUp+oWPRIXJDVqP1nsWgzR2jQRtecaHG+ZevRKeg1MSPuJRRt970BDj9LOIKJruyCLy7/eXViqVwl2NTkN+VhpDoKenJxlUnlbp48MZCm34FQdOs9xPC31zr1yj0dCyZcuSQ5RrFmjf+XN01GGYDA0NafHixerq6kqGG3SGE9qrc9fr9ZSyTLZTtTpaVIV7m5G9bsR3dXVp7dq1ibf29PQkucC6ekSZdffsI+dT/LAvMMQdvzGajgzFwPW7axuNsQgha+qGCamoOGCp+wC9+hlD8P3kk0+mudRqNW255ZaF7B6uXEDHgA6gS3QcDJS2tja1trYmR2Sj0UjZYfALUh9bWlq0YsWKJJvc6YJTQpKmTp1a4HEuk5Cxnq3gtIWznP3hxqb/jxzBmQs/qFarevLJJ8dlEeEkY6yLFi0q8H5ww34Hp/TJHuBv2kavYK4Y3c6Tli5dKkkpokybvA89SGMOU+iXfVCtVtNdp2XZOznYbCj+AwEFZiSlS4QlJeIlqgDEVCAEGlEzmIIzMzYzz5HW6Eq/K2RUtiK1sl6vp7NzkpKnanh4tMqoKzm0hdcTZQHm5NGVdevWaWRkRL29vSmq4Yf63QNLtG369OnpO4Qf8+KAsXuOYVQwUYQHSt2aNWuSQYhSgOIBM+AdTwVGEQDfeBNRZlkrzrt1dXWltBzGFhV2V4JrtVoqz46gATAGUFrcA0W1OJi8t+eKNgwKXGKkEdWRxhRAhDGMFxy4N5VxIhw5xwLevbR9pAMqsvE/AtrTXDCyFy9eXCgjjlD0aDeCBBph7sx/1qxZqew8SiHtI7TcO+rFCHzsQM4TzfNdXV3pGY9qOCDceY7/3fjxd3H6QEuk+uJYYQ6+D2ibcVWr1XEeeJ7JRfykYpGkaOx4lKdSqRQcErTjjh3G5veWRecP50gAFGCn6wjuJMMA9gii8zyeh+ZyEUrAI6QOHoXh7JlHBdyQkcaitY5veA+foVizvr5u9OOOJHDP+viZ1mjMRaeH33fHWKrVajJ62Pcob7zrRcww6DgD77j2FE08/4wF2vKL5/keukahYw4e3XMlFB5OO36OUpK23nprSaNy1e+XBEesCxEG5kl0wI1vN2pQmt14c6OQKIrf++p73Z3DpAHSZ602VtiG6K9fIYKugA4RZVzc/+Cjr68vXVYez3YxR8YKP0DBZz0wEmfMmJF4FbICnYN+udcVZwI0jtJPNK9eH7vmA6OS+WGkujFLv579wWe+hozN+Y4bWX7EIsoK9g/rhPGFzuH3PQP0BU0ih6TR+5wZH2MF/1QaZa4YHj09PZJGz+JOmzatILOh2yh3MCbh8ziRMXzRzbhHmIgftOwp0r5G06ZNSxE05oqjE4c94Fle0AN7neJH7DmPFOMsRhfhO45TsReJTrsjp1KppLsK6dcj/NC1Hw1B90QXcWcBxm93d3faQ+6EmzZtWuJZnlXCvJwWvMYDY8Up1NfXp1WrVqXz0pOBzYbiPxDgWZLG0vsqlUpimF7YQNI4pQNiJnpAyiICkqhJTMvxy3kh5np99MA5RgmboNFoqLu7W0uWLEmMAwUIwYqQck8KhoifkfEUB4waL3iD4j44OKgZM2YkYxKlCeXPFRA3BpmTM1FpLErG5iVNF8HPeF0xdk+4R0t8XlTAg+kg7FBo+I2ywP1DrhzGqyIwlFgnjBXmhocRYYDRW6lUtHr16kLlM2d6HunBs+tRTc6GIcDcS+cR6KGhIa1YsaJQsZIxeXSKCBEKj6d3uNHJWkgqlNfnXrh6vZ6MbBdQHR0dKbWYMtV4n6EBlByPPhL98TMDGIquaKB8+P6MRiIKLX+zho53/mZMrvhXKpWUBiaNXVHiXlA3/KAlcOpGDY6M9evXJxz5927coSywdoyRfe2eePaAR8rY686XnMd4ShvtsP7gyfma0//cuXM1MjKSzgv7PDzaHefIfNzjzOcxpY61dMUYxcrBeaT/lkajW/BKnFoO8EkX/NHAZizQGMoo6+vGmHvdaZ91gW6ZF3ONRn80HP0eXngz/NGjMuCKfiiSwdoTofGU90ajkeSPG3VuZPmeQmGK0XkMQI8QwLs8bY718qJY4HnmzJkpyuIGMw5Wnx/7AHnMZ4wR2sZgdFy440Yaq9DoRYA4k4RMgT4x3lAowSlyZN26delicXd4uAEAbXkk18eOUwweiMHj+EaxBU9uHMc9QoSptbU1RdU8nbnRaKTrFLgqAv5FJo9nU8U0f8bGGTbffx45c+eeO6D4YR96hMcjwtAjso35RP7j11Zg0HLnJsA7bpS5rsK6sjdZQ3Q+aAmcRxnNmrlzibadb/t5ZNr0/Qf9EyHjTKuDG+/VajUVkEF/iHuFv1tbW9MdkkSCWS8ii8graJR9gGGKHoRMWr169bjIraRxchAHPuN3naOlpSVFvYmE8j58zfEEnWL0k9ZK5I93169fn3DC/nMDmDm4fCVzZvvtt0+FpqZOnVoobNcMNhuK/0Dg55PYTL75pKKH24UWDKBSqRTOdSGs8OC4t49N6gqEM1Q8oG6sNhoNTZ8+PV1UT79sCN+oMGsX3O5FhkHiaaJ9NpkrRBhK0liEVRq7EBxAEMAQnFGCV573sxwIOFc4vJIZjAuBRToD48ZQZMxuiGMQ8oOygMHrqYYIUNYPB4FHhl0g+OFn5uLK1bp168ZVYmS93KAFB9wr6UWG3Pvq0QRJKS3E02D++7//O62rpIJQZ7x+AB26ZA2gQ4SLG38onMwF7zyCsL+/PxV3aTQaqXBDozGaGuIR17a2tiQkPLLh5zWYEzjyaLsrrNCBC3x3JCDUaQfwCAm0zzx5x/uUxlIqUeLdKeJj6+zs1MKFCwtGa4wouhfW15bPHS+eCgON+fryHQ4j5hSFN7Tpihu0xPjd0SJJW265ZXoeZZP9yBh8Xh45ccOcdact3nGjDfr084Teh0eBHHxN4jNOC0A0RB2v4M2v8XHcRN4ajRPPguA9j24yT4+iOe3GNWNuvp7sQ08B7evrS04d+kf2uMESeQC0gnwhvY523GHBbzJM3Mj3FF2fkzs2iLgRCeN5xwV82WnHFVJ3mjiPcCcVkQ6MEiIcRDEZH8oj64YzlOriKMsYijwXnTzQl8tD+uU574fxUsirp6cnGVc846n8MVIV5RGKO84pTwXlGdKVu7u7C4WDeJfjH+wj8OyyAZzFtRkaGkpOTNYSQxGaY5x9fX3pagjWAnmK4ffkk0+md10uudOJz9xhwfoyDgykSqWSeNfw8LC23XbbgjORuTj9uqHeaDRSPQlJSZ5ReM4jrNHB585oacyhw3f+mUfJXIdyHkCac6VSKdwxjJPMM4zQ0XD4IieZFw5ljEUin9AxawlNrly5suDE8nmT9cZ7tVqtUFTN77pGzrvjBmg0xrJCoD8/6tLa2qo1a9Yk3Y62SYv22gno8i6bmCdR0tbWVvX29mratGnpc+qHTAY2G4r/QODeb4iLzcQm9HMknhrjTApjA+aFp80LCvCOP+dXBuAZQ1B4kYtKZTTtwA0PjMB4lsaNIAQVXhkUqvXr16c+W1pakrfIPeakdaDAAhz8doEoFb318RwYETmEDXj3yoobN25MjKrRGL2knfSGnJIT77l0ZZ95PPnkk2ksXmyGNR4ZGdGCBQsKgocrTNauXZtKowMI2ShoXDmK68b4EZB+uBoBgIGEYuLG6NSpUwtR0JGRkZR2g8CvVCop/ZOKsi0tLYV7PVEyoE+UkOisYB5Ea3p7ewvnUVgTVyCptkj/KF8Y526gVCqjh9OZn+8rFA0XIP53VPTBvdMagKPFP2PP87y3iZPBFSX2Gm2T0kuBG95nfTkLixLFGvs8fM0x2oGcseP05/OO/7ujx6PNKHU+Bow+Vy5YG/bmU089lebnBjvji/sfWkDRYv6ki/FsrVYrVLrjveiAiuvtzjtXTKG5WICG1Cl4ec5IBG+OTxQFz/qAfuv1uubOnVvApe9v+mIc7JuYVut72R0KLne8XxR4DClPCR0YGNDMmTPTerrD0mkZ4H+PSvf09KS1wYjy791QhI+gxGHcOE43bNhQiJQjH+NeiA5Gzvm7Q8AjErTnzkTe5VgIhg+4ZW7QsaeTQj/QJLKDCBzRDz+vHR0P7gBAJ/D9NzIykqJ37ozgefQEnM0ub32toCHG7JEpNzTBszuNWlpaUsSU8XDMhGsEcGIzl3q9rhkzZhTmDN49i4PrJ4aHh9Mc4t20FDlZs2ZN4fweY8XYphqm70s3wNAZAJR+jEHmBx5cn2k0Gpo5c2Y6B+f73tc0V/kS/rh+/XpNnTpVy5YtS8Yne439B23TvuPMK9a63Hcd1GmKNNDIH9Dn0CdwFvnewtnP335EhOM6rBdHaDyjB9ziHKPqbTzC4HKXeXDe0w1FjDEKGcJH4tlB6MFlFvKB99D9cLiQSu3jcYerO6diAbFKpZKuX0F/ngxsNhT/wQCm4UoZgqqvry8pwZIKyhWERvqGNHYX3cjISNoQtIui5fe0UKDGmeDw8HBKn5TGyqw7s6atqAhXKqPXLPB5o9FIlRoxFMk/Z+N6pSrPI8fD6941vvNDxi44XEHy82oUBcLg42/uzRscHNTy5csLqVKeRuPeOdrwseKVQ8jCcKkC6Z59BBUeNJR/SendRYsWJSELfbDeHu2TlDz3MCcv+ON0UquNlUxH2XMFh2IVbnS2tbWl80o+fwxFjwhLSvPCAGf9Sa8E71ER5bOZM2cmRwCGOYICz6R716EJIrWAn0GCDuhvzpw5hX3hXmA8n05L0dDzsZMe43vWlWsu8naDjbEgPJg7Asf7wXjlPY9+84x7lLu7uwtpgzkjjXWizRihYS6Onwh8j2D0z3DEuKHo7zluPaLY2tqqjo4OLV26VMPDw1qxYkXKgvA97vhx48uVM3dCMV9fH68ayDt+/g2IY/fvoJV6va6pU6eOMzD8/Wio+feOK9p144TnUPo8Pc/BlXxXQL3gheN+xowZBedMpEv2mLfhjjHfH45PjEhokrR0j1jGIxXwSDJHmAP9Ox/wtEiPlPLMyMjouXccai0tLXrqqadSH75GHgGt1WopYsoP/QBeqdHPNWNkQtMcmcD4Ax9EZfy+O+dTzu+JMGBgoNCDSx+//zgdDAwMaPXq1el8L7h3JZYozerVqwuRPqkYUUfhJcLpchlFV1LBWJHG0jVJnY2KOU47+Ab9SSoYjvQPXdE2MgWeTkqoO+JaW1uT88AzatyYBj/RCPH9h87A/17R3I9d4IRnHuhtrInrEDGKWKlUCmvkTgr2GllDnnrt9OrOHuYAv3IHB89CM+x12sJx7WuPYch4cWixNhg80A/rgkysVEaj7BwRQYd1uUERQNYN+sKIZs+R3uu8ABqieB24Z/xEFJmHF+B56qmnCgUQXSeFrkiDRsYMDQ2llHD0XJfp7pzCuKxUKonXuCPiiSeeKJzvbwabDcV/MEBJcq8gzI17DAE3zNx7CFGT5oKH0j0tbBhXbKkK6alLAwMDSfAjeEhH9HQH92RJY+k9RH78HB3KAwf+YfAYHOCAPrwNxxPKAF45xuGRQPeQs9FhbO7hlJQqbmJEMbeRkZFCNM+VaIwfxuZn22CoPItnmXMhMKuWlpZklElj92gBjJ+0RBcWbrBJ4+/LW7VqVSG1GMHoyhTnEaEpZ9Ksqc/FDQbmBt7AN+m5ePD4bNasWRoYGEhV26Bh2nVB0tPTkxwUK1euHHeOgf4dH6yPH6qP57tcoUIhQyFwA8DPFuS8e/Tn3kc/i8JYoHWUgpyXkPXxs1SOX/fsMyY3QikCBD03Go2kdPG+t+c0wt71KBl7zhW9aCQ7fbrRwJ6BVr1fN6xdgfB9AtTrda1atUqVSiUVp4pRH2/TDUU3kIaHh1PJffa8r0309EujvMB5g38njSm8fO77xKMEPp7IF6Jx53uYdj0y6/iNBp+PEeedG3Gu+DmNekSOcTqts460y55xZZUsF19Hxu37h/9RjF1xcsM03r/pDhzHN/hFaSMa5U46xkybLS2jZ7xRPpmHK2S+Nm7o+hzBC+lpXswMw469vmTJkqQ8+n2tnkrp9OoOrUqlku6VJV3T9wrnlJmb8xF4KsVIUF5dPnKXXZQ3ZGAgm1kHjyC70UGEC17LD9F6lz21Wi2V/nf5DH7cIML4gD+7HgPvwnD1NMR169YlRwN7xNeOqCXKuhtY7INVq1YVDAVktju+3QlHAcKWlpbCFSTwLfY/xVvWr19fSIN2ncpllus+rLcHBXw9wCfvemTRDSiPFDrPwFnNeuOcA99uyCxfvjzRE7jBWKYYT6VS0YwZM1J7zgNZ58HBQfX09BT0WQIJRL+HhoaSLgpt+1g8ldZxgDPF92x0KkFbtdpoUUXWE0eOzx9aQ29jLYnE42jv6+tLxazoE57KZ8yJOSCzu7u71dbWlgILk4HNhuI/GLg3xj+TVCB8NocXVUHZQ4CRWuDFXqQxA9ONKknjiqtQpZO0T4QTf7vhICkxaGl0o3OwP6Y4ekoSRhMChbQNZwSME0ZDn0888UThnhqpGMJHgBGNgcH6vUnk+EtjFQIxoFHsYWyuCMGMomHvHiSYIcwApr1gwYKCol+r1QrCntRb1r6trU09PT3q6uoqCCkXsu4NJa3VL4cGZ6w3xjDVSF15rFRGU4v9klhPX/L+XKjxg3AdHh4uFDjgXRitH9BHWC1atCitpUf/nLHzPuvtirCkpCTg8XQjPHq/mRsKEDh3Bu/nH30/elQGQOFGwLqhhxLk6UjR+PJ97/N0OoNu3cPr9EYb69evLxgSrpj7Z9Ci8x0UNFeEWN84bhekbmS7IZh73vHohjD422677RKfg05cGWdcrtwxD8YNn0JRd1p0HLsyzG9/jjHl1snPcKEwxvM/jkcUw7gWcY0Yf3SOuJOIlFxf+1i639vzNFHnP0QCoiHrP+wLj2LCrxnb8PCwVq5cWYhAOk36fuB/nGvgEqcAPIkUbNpC6XaZQgVG51HMpdEYS1NF6aRPjyjxvBe2cKdidMZ5JosbLdVqtbD3qtWqpk6dmvY+Dj832l2mIEfgxX4+Gx4Enx8cHCwcA3HnB7TmEUQvkkIkg2Mm7gBE4Y1RfGQxvMgdSjh9OYJCv+6IwXEJzXmBHubFGjAXihoRXXKapP+Iuw0bNiSns591wyni6YZO+9B5a+towSDeZVz04UYln2PQ1ev1gtPZaZn5QAs4ZYiMOY9iPD09Pem+SmidceJwQQ9y/dGNdY/Asz/oz3kehpvvH+fppJliFHu7fC+NFjYCN0RXoVn2MOuHowf+An7gYzhs0efWrVunSqWSjkY5T3QnPs9TQwHnzcaNGxN+uT8Z2qCif1tbW9LJXK4B7qhBViD7+/v7NWPGjORk8XoPvMu6w8NIL16zZo2mTZum/v5+bbvttpNOPc3X4d5E6O/v1z333KPf/e53mj9/vpYtW5ZSJWbMmKEddthBe+21lw488EA94xnP+Ft0uRmeJuSUrhhB2rhxY6EKpCvrMDC/u869ai7w2EQAVSlJVcXImTJlihYuXJiYCgzRjaboJaL94eFhTZ8+XcuWLZM0Vvber5dAcUJgI9xIeYXZwwjZnOvWrUsMHabtCkpUgjs7O5OnC4HT1tampUuXpmfApZ/pqtfriQGSOhujIO6Z87XEIKOd9vZ2rVmzRrNnzy6Ui+7q6koG2IYNG9IZDtaAKLGnHHrEgHGsXbs2MVnWk7XwKCGCGqXWhSnCJnrDWltbNW3aNC1btqxgPDhtSaPRP6LTMEAElHuMPeKMIdre3q4nnngi0TxjokAARoMXxwDAP6nLGzduTOkpzAvvJ8+j0HP5r9O1pELaMgY/4MoC4IqO71nuKUMwsm6ehuyCnDG5ku2KEMo6zgAXSl6sibZZywg+H98rzM8VlpgKzvs5QeppeF52P9e3R58AP4c6NDSU9os7ndxA5H14H+1Hgy9GNOnbHT++ltHAdaWqbB7QKv3B11wBK0spcqPVlVd4CGOPTgrf2/A5d+jQbzR+3QB13hUNLV9fNxS5X9fHkZuTG4YojcwHHLHnPf1ZGoty+jOsHcqtG0f04+2joHq70Bd/41TyfgcGBpKhxn4lbVDSuOgssmn9+vXq6upKfIoIcM4B2traWkhzzclRaewuS48uSUpRTd+/KNa1Wi3xYviX1xFwGeQyBSPOeQC8sa+vL1WdxqHU19enqVOnFrIT3Ijmbz9PTSSTfeFRbOeHKOB+5IF2Odvmc2YMKOaMEwPW6wQwd4D3kR/u3IMOwU80WNEd1q1bpy233LIg+8CnR7zBiZ/Xgz59LV3nqVRGz/97ZNejhn71FD/QHjKctUcmbNiwQVOnTk16AUVZ3HBmT6EvkqZJbQJoaebMmapWq1qzZk0y7j1VnbHyHfNwumfPeXou8m3dunWp6nlHR0eBfqvVYp0Nzp760RtoESMQoxMZ5evsRprrIq63wGfQC1tbWwuRbYqiOR8Fp7FYIkbs8PBwcgAtXLhwHD/NwdM2FDdu3Kgf/OAH+u53v6s77rhj0pbpDjvsoGOOOUbHH3+8nvvc5z7d7jfDXwGuGDqjhlgxjFzpd88S77Ih8URCjCjkPO9Clx9n9E899VS69gEG58UVGDObwJn8yMhoaftFixaNU3q5s8cFNBsHQ5fPvRR+vI8HRZmN7pvRvdAwAPDlQkAa86I6PmgPAyznKcfrzIWwMDe8t+7BdW/SU089VVAyYJS9vb3aaqut0hhg/F61FibrwqXRGM1rh+l7Kq+vD2s8ffr0pFC60GT9PAWFs4bRKBwcHNTq1asLHmmYMWdpXInEq+cpRNJYOq97U/E+t7W1acWKFYU5wPzdWyuNRYOpdjo8PJzO3jJ+F/gICEpRowTx7tq1awt3FLpi4QqnCyJp7D4z1sajrOwRcEsbOWPL5+xGDN5kIhR85goCkROHaAz5fizjP75GuX6iISsp7V+MZF/rqJyx5u60Yi3idTluRLjh5Lj0s13xTIkrUbwL3bkR73wk4oU+aCtGyVwh9XdQ2pvJ4hg59OuGfC1wwjBeN6DdcPDItfMN5hONROfhOPVwCLnjjXlNnz5da9asSe0PDg5q6tSphbRnTyeXlAwkFFeML+iC8/RO924UMnZXguP+8D3qyl00RuMxBa90ShZBjKj5GvpaugLc0tKinp6ewt2LKPwu50i78zS1aADzGUV44ENESZ3O4VPoDdGAh9aREURvmQftuWONCBZ7oq+vLzluWHeMC8bM+36kwfcc73BEplqtpoqeZPyQ1oeBOjQ0VCj+wZriaMb5TISbs/4Yom7cEWECn7yHnHBnNvh1GmVPQjfQBPNy5R9573IL453vnP8i1xirF2fr6OhIMh2HkDujwIfzZvAxMDCg2bNnp7Vk7NCgR8yd5uG/RPaYx8DAgGbMmKHVq1enNe3q6kpGYqQ9ZBKVdqPRjzHNfKBFXz+c5vDdSG+sC8de2JdeDbVWG702a/Xq1YXoN7zOI83eLu9zbMadi9QfIPMLHd3nB06QZei98BqM9vXr1yd+Hitvl8Emp54++eSTet/73qfttttOJ510km6++eZx3o+Ojg5tvfXWmjFjRoHJNhoNLVy4UBdddJEOOOAAHXzwwfrBD36wqUPYDH8FRO+eK98AROnKk6RkECKA8PqhhLvn3j2cMDWEBcKbzbNs2bKCAMXrFNMBpeL5KVc23Ltdr4+W5XZD0RUAacwL554bN6TcMxpz1aPxxHgwQGgLryPtUMAFYc/cpDGFwBUixwfrwJkDcOyKCAyHMXC2By9ftVpNAgwlgHcR6uA5pl6OjIxo4cKFaX+7ERMVc8bOoXAqmTKver2eSlBLY2XPndYY1+DgoAYHB1PE2Rk8ihFjRrmGbpkHilGj0Uipsh0dHSmtDsbvBonft4Wwo82RkRHNnTs3jSUqv6wXSg/02NPTU1D8WEtSXFAUGUeM6sJDmYuPmwqkvBsjOy5U+N8jeJKyZ2/r9dES/Cg9MXIYlWtvDwAHbogA0aDzucaxR0OXccbIXGzDDRagpaVFa9euTc94wSEvhAIO/DcGtEf1PL0s8huPKAKsvc8zzi33t6RCarvvYWkM937euqxt3o+04t9BI36Whuej4eT7AID3Ax7Jdz4K7sAzdIZTBWdXrVZLaWc+Ruef7HfG5PMg+ubywouPYTzHvRyjEq5s+yXuzMl5B/Mly8KjujwX15i97s5FlDtoHueZO1hQZCnk5g465yVPPvlkwbhvbW3V2rVrC2NHRmBsoMh7JVXao//oXIxyXVLKXPHCQ752w8NjhYb8/kjkmUfePVW0UhktbudOPsbv1ya1tLRoyy23LFxzJI0vwMJ4PJLm84QWMPZx/LJH/Iyq39Hnsjr24fyV8fg+GBkZ0bRp07R06dLCmq9fv76gV7hh6TTl7fN3R0dHKjbV2tqajIcYEY973nUpHKh+3CM63mLqa3SUsMc41kTWF7qaZ6F5JgrrAE14xofPlXO44JX6AbznFbnZm16dnHWEPpznT+7qRgABAABJREFUutOfjDJojnXzKKBUzH7y9UBndHxt2LAhGc7wrsg/nQejHzAPz5xjzfyakYlg0obi2rVr9b73vU+77babvvzlL6foxmGHHaYPfehD+vGPf6yFCxeqv79fvb29Wrx4sVatWqXh4WGtXr1a9957r7785S/rzW9+s7bddls1Gg399re/1XHHHad99tlHv/zlLyc7lM3wV4B7FN0DHJVtvO+E33nXL/rs6ekpnI9zIsWT4d50F+ZDQ0OpqAoRMpQBDCw3vNxodGXa23amgHfOz6f4eJgvfdEWDEsau1zYv3MPmG9WHw+/Mb6424mDxK4gRiPLmah/58Y3Ahulg3ZgZMPDw8kbSNTHUzFIXYoeK1eIaVNSOnPhVS5dCYueO3DvVbxynjPAz35FI8QNak9/4zylK1rMk3GRSsqcW1pa0hmBSmWsuFKtVtNWW22VhGG9Xi9EqaADP78Gfc+aNatQRTZG2aBrT8nxu8swehHOHm1AcERvMF5plMdKpZL2KekwPOvveftSsSKxpALdSyrccekFDdwIAh/RiAOgJy9oxHji3OJYwSmKhws13vVUsLJ2eD/uM/+MlDWiFj4+/xs69TkTXeD/WPzBxwG4EgF41M35me8J9r9HJ6QxJRzeUrYeUlHp8xLwbuCxj9m/FDmRpNWrVyfc86zPMRqQrqAyNmgBXPt4/EyY8zf4g19EDh92BxOl8xm3n/Gu1WrJMIxyQSoaHvwgX3zvuBIa9xFOUWjF5SJ9+XlgV+rAA+uH0uhGiRuDtAd+mBN49GyR4eHRq6LWr19fOE5BxIyzVb6ubuTxHfzVC+f4HmEsLmu9OBK0hCzAMeM0THog9EyUCPrlc9//vm8xBOD1VJtkvKTv+drkjCL2sKefs1+QLaS4+n4l4k/78Emnn7hXvTIpzqdoKJI18NRTTyV57s5IZCmy19eqXq+nLAqnT8/4iYbLnDlzJKlgmEVe6HwQ/DNfInSe9cTzLS0tae+4AcgzXN1FZBhHgOsoMYjAfFkz5zfopYzPs8B8L9I2KanIiUajkYq6QWPoJehkzk8YG+3j4GMuXlQLPua8xvcEPG7WrFmFKD9zJ9vAnVW87+nYXoHcK8ZOBJM2FDEQBwcHdeCBB+pLX/qSlixZoltvvVWf+cxn9OpXv1rbb7/9uFBmpTJaleh5z3ue3vWud+nKK6/UE088odtvv10nn3yypk+frkceeURHHXWUvvKVr0x2OJvhaUD0JjlhQTB4ULhYtrOzsyAIYSitra2pkpSkAoMj0uHCFMXYhQWKrW8q2uA7xuqpLW6csLFhTDDXgYEBdXV1qbe3t8DU3QODwHGBAT5QxD06KI0VWHBDkXHF/yWlCpzSqLOFlBzfzG64g4NKZazIgEdjXQnBMGHsjIFzDC4wEeYwl+XLlxe83kQ7UVL5nPWdMmWK1q1blzyyzlgRLE4LrmRxxsOFixvZjmfGCiPEyPL0O9aHVCHG4wf2oTcXeqQhwWh9vTmv555u3xukkLoR32iMlpJnf3hk2b150AOCB+cEKSYeFXU6yync0Ief48J7LY2VeM+lG0WnhAtpfrMWLoBz9M3z0FWZwOEdP1cXFfsIsUKkz8HbpX/nM96nP+9XgdDWLrvskvrg/Z122inNled8LXx/unOEa2/c+8zz3hafuZCPc3VHRFxDxkL0GL4YMxIcHxF4xg3j3PNugBOdkVRQjH1ukWc4buO4XA65scG+pk94jvN9p21oCZ5RqVQS73bcOe1jXNCmR8VJTWOMGDLIoLge0cDzDAval8YyG5gvuMXh5/igf+bG/P1qIlesc4q5RwanT5+ers7o7OxM0TnmTbETHBCexuhOWArH8DlrG4tSgW/nD37tka+DVIxQg5Otttoq8XPHGwYy+5/KqQDpdrQF3UZe50aEj5PP/Hwe68eacFQAmUImDgYiOMK4xuHh/Ajcue4F/SIfKQSHoe3Pe+o970pj1ZJpA3nFvYD0u3z58gKdepE0oK+vL/FN0lEdoGPGja4I3tCvOjs7UwVW8E0k2R1GGEh+XQfOC4IJyGhfF8bF39FR6o482nK6hb5iWi3RQ9cJWUd3Mnmkn/UhFRX+AD3C2yuVSqri6zSIUetOGOcLjGnatGkFenJ9lH7d+MQoxaEgjZ2tnQxM2lBcvXq1jjzySN111126++679e53vztdxvt04JBDDtHXvvY1PfHEEzr33HM1e/ZsrV69+mm3txkmhqhse7RHGt1QpJNCWO7Jow02HClzbijQh5/XQ/FEwGBIesEcqZhi5mkvbkS5wIZBLF++PPU5MDCQDol3dHRo9erVKX0RD5IrBp4OwAbD2MLAdIXJFRb3CK5du7ZQVAOmjEBH+OBIwePFvD0y5oYP6+KGBYLQI2ZumFERjc8QOO4AcHySugWjhumhJDFejxrDsFwoudKIsd/SMnq2krm4gQzwPOvDPNyj64YwwrTRaGjlypUJj7yDQ8CjJI1GI6V+4iRwZu80KI0JXD53Tz3r4XsK+o+M1ysgki5WrY6W+YYePNLmhSB8T0AH7qUEN75mzDmOI2dAQAuuvIMbztEAHh3IKQzNgD3jgo31zL3rn/nfrsQ4z4qGrTsgwGU0vBGetOkFL8ra8/nESFgsyuPrFvEGT4sRBqdVIgg5XHgf0QEAXh1XEbdlNOufgyP69DahORQXPnMj0fHvCr4rTLznypQ0lgYM73TnnjTqcANnGOfep697zghuNMbu1GxrG727lXechzMf50+sI7/5jjGiiMLPXWn1PYl8qdXGjgiwHl6plPbggR5p5vxTo9FIZ9x5jjHwHe+sX78+te0GJYYASq4b0hiVzMPltEc2fE94ZgW8t1arpaIc0phi73RGgTyuj3FDB0Xdi+AMDAwU9goGrSvHnokS94pH8Z0fOY7BAe/ijPNjAy67yXSJdA6eOD/r+OTHo+UYnJFHu3GH0s+zOEkwttizrDtzxXEHXvjeZToRMMB1HsbuwNpGx1BHR4e6urpSKrQbse4w9qqeGDSzZs1Kafq12mhF1u7u7oKB6OPwfQq+oWlkG45WsobcuCfDAnnt10tg4LI20AO6o+PXI4HgEKc9ujE6KnvH2/b3nWZxDgHottCa7yVkLqnc4IdAhzuoJ4JJG4r33XefbrjhBh188MGTfWVS0NPTow9/+MN6/PHHdeyxx/5N294MRVi8eHGBMNig0SPLhm5paSnk5rviLCkJIJgRXhCP3PGDYELoIQRQNpxhw+gQEq74ufGCgOWwOsyBd1G+3AjCuGLMzJ0zET4OhJZ7sKLCACPs6+tLF727J9KVQXCGwRwZXPRs8T3P4hWGEVBZOJaknzlzpqQxY4cUDr5n/KQmecqP//Z5+rko/7tWG7ub0Isb1Ov1tC54qvncz2KAH58Tffu4nOFCI6xtPOMijR0Id+WXaB4Gmhvufr8R4wEP4ADhijewWq2mFN641k6PeE4xAHBEuJB2OvDf8W8fD+3jeJFGi394ajCA0ufRHncAgXM3RD3S7kVavF3eceUrKuZ+ninOIRonPO/PeV9O5zhTfJ38WX8nGriVSkXLli1LEXLGAs3G9vxvd5hIY3TH/85PXQmcaO6uAPheinhzJ1R0slWroxUBywrauIFeqYym5HlmiYPzZVcqfPwYJdALtOB8AJyw/yJtet/OjxyHbkg/+eSTaVyxcqAbvW7MuaxjvzQaY6lkPs/oVEE5i3ThewPwDIno2PA1Yy8ibxkDtOKl7f3oQDxPH9NPwR3pnUTy3EFJtg48iXTE4eHRCsAYILQVC9KAT7/6CjpAzsdoBQYtSnWkQ3571MnPClPsDtmNHBsaGkpXlzh/cmOY8TkfjXIHGmXNGDuyjpRfl+3QNHNx5y9Gj8/X6YB0UcZAu34vJLKK7Bj0E7/+BJqBNhg78h4jy89XQgOehukGkTsQfGxOn66rgEs3Uhg7uMAAAxfg1Q14HNbgCKOKOVcqo9Fx+IjLaqcjcODnewloeCS8o6MjOZ3AA4Zwe3u72tvbtXLlSjm44e7GqBvH0SHqGV04YJkThj04w7BDZ2aePIcOjTENnWH8ubMs0iXG+KpVq9L+L3MoRpi0obj//vtP9tGnBV1dXXrWs571d+3jHx1ym8vLzLOBXCHz1CGiUmzwFStWjPMs+pmlqBD5odpKZeziaC8EgcCEiF0xpD2IHubnVcD4nA2CAUieNncX+bkIPHGuUMBonLk43vx/vifNlbuepLFcdX4/+eST49K1vB2YpDN+cMs6gC88Se45dOYFPhCYMBOeccbmBgpjce+lpFQxa/r06Ql3nrKSUzRdYQJn7gH2aAEOAtqD5mCcMFxw5l5v3nUmPHPmzHG4YR4YiqzdggUL0llSVwYYpzTmEazX61q3bl3yWrtS6jhk3fCmco8ogr+trS2l5OS8o/EzfrsCRtqYO0aYgwsB1pS19Igt4PQHX6CPOEfAPeZANHBi5LCZFxO6zT3rhoA7M1wZ9Od8n0Zju9FopIiqpwtSeCnO0w2+3Pi82Al9wetyEM9OYzB4f7l5O/9zg4S+v/71r+sd73iHTjvttKalz+ENPT09hbYdHGfQAAD/yEVG2Sf8Dx8h0sO+ZhxuPNIm78C/WSeMKNYot0+dJ7Jm/j980OfpdOWGvRvAHmH3+fn4/Kw3Pz43n6MbntGhyH4mojo0NJSu14G/eIplTHlGyWcNPTuFKAQRFa4daGlpSTwb2dzb25tSEL0wmON7ZGQkOYkpUjc0NFSoBOn7ET4UaScadTE6wveeBUSU0g1F12e8oqevAW0yNtp3owd8UjSOLCfucnaaYC/jFCMqTDvQiDS69zlW4rwxOkZ87h558/OFOAgxsMjiwbhHZrtMQj76URMqBNfr9UIdCuZMtC3KJ2iXsbrTCHwSzSOy5Vk0OCt43tNB+R6a4YovxgnO+I2M80rrTkO+VyUlGidqzJ6pVqupFgMynPHgyOY59kJ0irhjjecwFF0+uUOTdgYHB7V+/fp0XQ173dtCv3TZwPdOP+AHBwxHbLhyZrKwyVVPN8P/u4BQBlC+Y+oIkURp7BoMztqhZJNq6d5VF/6uWPLjUSFJiWHAwFF+vChJ9BL5ZnNFVioqv6R3wMA5g0d1Lw500waCDvBUBPdy068/y+bE2ITpgFs3Aj0FJCrqvunxyLlgcMHiBo2nrFWr1UJZc7xpFKJBUYpeTi+QgTLqRn5LS4tWrlyZzka4Zw1G6h4tcOjRO9beq6bCvBm/32fpOEZguxLHb/dKenTRz5LyGYLRGTU4cmXKvaq8PzQ0lNLUKDLR19eXBBK0jAE/NDSUDMLBwUHNmDEjPQPdzZ49O3kyfawO0chxHKNM+9nh3DtRGfXKcI5jp0sfiyvctBOV2zJgP8Z+ojFLm/69t+GCD3yhRPnco0HrdAxwzgiHFc/hXPBxSsWzY77W9OVXyzj+coZiNNr5zJWLGMFz/MC3o0Pj1ltv1XXXXSdJWrRokc4++2zdfPPNWXx6n1z10gw8JdMVRBQ8j7a4Mu74wxFDex7Jg2+604Tn4KGeYeDVnx1PTrcxa8MzJPx5jwRJSvfrAlExjcoZRg94IR3QPf7ROIwRQMAVdze8KOjhUVuvyAivhbb8aibnr7RJVoM7TDHCkRGSUhEqPzaCjKCvrq4uTZ06tTAfZKGkxEcd38hVj1I5DqMh3dnZWXCQAv4uNIlzATyXGVzRQQItI0cajYamTZuW8EyFdy9sNGXKlBQhZNzufGc9cei4zsXcPE05F+lEzjAHnAX1ej1FxP18HvKLfuBN8G5wiHzEyMTB7WcfN2zYkNKY/WoJX3/neW4UgUsMH6KinHt0J7oHE1yGezomfNqLK0Y6gObBq2cIeO0Bdwz4+VNolPRWd06DN9cdIn+Hdpy/R/6NAejy06OcGHTooO5M92gi86XoHHhALkbdhQBPZ2enttpqKzUajUI2UzPYbCj+gwEeQ4idg7gwkOitXLx4ser1upYsWZIEGOmOfp0CTBKC5tJ0voPB+FkeV/6iNzGXDhUNRfcSSkqFEPDs4l1zg8G91R4tW7p0aYqkuMD0s4qMz9NCXRh3dXUVFGo/9E67CLvoaY2KvTRWAMHLbHvENuJWGlNio3B0hdzXwBmRG5AYEs50YWBce+Nj9zk4DmCIMGfGxNpF+ollxF2BkIoRCFKDEcTQLEIXXLN2nl4Tq7yxhngvXXlAaRweHi5Un8NoZk60xTwRdGvXrk1VY13oMB+vDpuDnGK6fv36QhSB+Xj6TE4JZV4ogjnjq9FopLRulCrmk4sgRdrN0bIrd66gRUGbS8n2efM+f7uxkQMXqLEf1tMjf+DQxwl4tAEFMRo6zldzRiLz5p2IC9rzdHLfu+7EcD66cOFCff3rXy/0Mzw8rP/4j//QxRdfnC1cBG+IUTQfpzt//MxPS0tL8u4DRLZRorwdeAr4ceeNNGZsRZ7P/DFI6Bta9LX1veXz835cTvlaROMRqFQq6fyeK/Euyzzy5HN2eRPX143PmOLueHCHA9kSbjDDC/v7+9Pdktzx5sowvKirqyudy4TPuYxxAxbDnWfdkIrPdXd3JwUZnDmdUcUc5Zeq1c7PXSHG0HF8RVmA84mooeswMQrmugJGg68xa+aGfXRO0LfLBOZP1My/Yyysk/MHd4hgAMQCVqwPeg1tgEPnQ148x6unMm7m7u/jbAIXGPde8RsHBetRxq9o2yuxwwdx7LBX/AypH8HgxzMPent7U+o041ixYkXq0/mXG8HIf9aIzBvSlGu1WiqEBP5Yc/RAP27BeFweuR4XDfuenp6kXznvwVB0h5MbnV6jwOnfHYeetuyOCOc1vq88owtHx4wZMwoV0ieCv6uheNddd+mUU07R8573PO2+++464IADdPLJJ+uOO+74e3a7GUoglqGGscB0YCau5HN+YuXKlYUDxNyRJ41tACd+r9jlgtKrgNEW6XkYsRg6ePGiEHeFjc/5zabBC+t57tXq2Jky94AiEAFnrpEx8r9XIcUgoS2YtHuWYzU9TxEA+Jv3YW4YOO4NBT+tra3JqHMly5k1/7sBR18wGE/JcCXImU+s4ObP844r7TA/Ty0GeN/bpzBFa2ur+vr6CkYMY3QGCPOMxXIoie/CG7w6eBGcGCVm7iidju+oWMSKdE6v7BU3It0Y99SonHHHdy6gXFmB5ikiwPUfzMvf8f8xHFDM3BBAmDAHV6YdT7HQQhnEZ5wWcnvA97jjMp5FRBB7Ojvt+7xza0IKUTQycYz5sz5unCVRQcBD7/u6zIB1J4Eri9LYmnnE0o1O5y/sn40bN+qLX/xi2vMvfvGLdcwxx6T+7rnnHn3oQx/Sn//85wJeMfTg8dHAj2NzI8yVEOaLUeDOA1+riA8UMiAakWVjcMeXG7hxjb1Yho8hl3YfnRA+bgzF6MxkvD4vpwtPR/b2APgLkSqfo+9xotzeP7zHC2Ugj+M1C/TlvJgL0KMiDJ/H0IS/VSoVzZ07t2As+fu9vb2Fu2dXr16d5oRRgCHiKfyuH4AnZEyMNrqu0mg0kkFDFI+94lk2rEl0cHlaX1w3d3j7/bEc3/BI9rp16xK+nA9FPcGNP/QjpyWMdzeY2W8YU64vgQ9P02QdYzqq70nWlLF4hBLj3o+04LhFvjt90obrJatWrSroY6whewo6YI5e5dhxBJ8D/+CB2gA+Bq/SDu1zzAPnrMvyjo4OtbW1pavLmK8bsswH3ct5GmNlDFFGsg6Vyli1dL7HsM05jN1gxEgeGRmtccCYhoeHk57svNh1esbn+mA8zwqNcCftRPB3MxTf85736JBDDtE3vvENPfDAA3rsscf0u9/9Tt/85jd12GGH6d3vfvffq+vNUAIQFsTvFcQwpGI0wgnajQ8n/pwCENM13YPmSimbBoXBN5KXHHaid0bkQsYFJMaie/5mzJiRmDweUC8s40aIt+MRPJ+3pGRk0n804nzDooTwXs577vMhaoWRR+qEMyaEE4IXDxzCAOHpRjyM3c/quLdYUipB7go46+XRDp+DC61KpVJIjYLxS2PnEmJUxI1QF3D8JvINfn3NvPABwsDPgEkqCAA/U1KpVNI9iblIgxtSpCi7h510EYSh76F6vZ4qEjqNIgAiDbhx4vvK/3alQRorduHziG2BR6BM6EWDNxpyvla+V5uB7/P4eRn4vqdfHytAOfzc+7F/b4c1IrXP24tGKuAKanSuNBrFiFL0PjuwlwE3GLu7uwvKF9+78QPd895ll12WLuDebbfddMIJJ+jd7363zjjjjJTmtXz5cn3yk5/UX/7ylzQGd275XAB34vjYwanTbjQ6MEgcP9EJEg1FL7hCH+whH1utNnqtRxwTfzM396T7nF2ximf5wIWP1XmLj92NZHcs+tpHPodccGOEv3289O9KJ+ehweXGjRsLqacxeuo4wTHS39+vrq6uVFTLFVX+HxkZSdkatVotnU+Ez0Wc1+uj9+3BXxuNRsp4wFHb29tbONrghj7j9XWODgB0FefH0BiODjd4WQuyZFzH8UiUG6cO7AtPmScSynh8PXFYgTOvB8AcXCfwjBbkt8tc6IExuwMK3aharWrFihWFSFi1Wk2GGFFij14zb8D3mFf0hi4i3uLaQMu12mjNCpzbrrO5LPcziOwfIn5uKHq6r68lhqY7hmMxPww8frN2Ph+yGzZu3Fi405YjKOwlHHbgirm7Tuey2Z0c0ijvcGM3HkUCL3Ff4QjHcMWg27BhQ8FRwdq4PuYZIu78YUwjIyOFjLHJwN/FULz00kv1la98RZ2dnXrXu96l7373u7rhhht0+eWX65BDDlGj0dCll16qSy+99O/R/WYoAQgdQkYYsFnxVCAoOjs7tXTpUq1duzbdmRi9KK5w0IekguCiTwQoG8Y3Fd5BFHxnxlIxyueM0f/2jQGTYFM1Go1UarlSGStN7MwPhTIXHXSFwJUIr6wZvXf+rnt8XOnLRZBg7gicmPYaPeKSCkwgnkvB8+iGmgsoAG8d4wX3fhk8XkbahknBtJypurfevaTuaXfjFKGOd9jTEGM6Kl54PJ2sPR5lPw8FuAJOu258+tkJVy4p3uP9RgPB0xX9jIUbjj4G3w8eLXCFJRo7kUaYC1H0anU0Cu1e8Dj/XHs5gzQayvEzT1+OSnQEaC0KJTeQcgooNORC0IG+oU/aj1Ek/+307oqa48iN4TgHSak6JHudPpkP+GpmKNbrdS1btkxXXnmlbr311vSdC3R4GfvB32d/33TTTbr77rsljUauzz777HQm+KCDDtJ5552n3XbbTdIorZ9//vmpWnW1Wi0YdHEdHZ/8zT5kX7uB4xEjN4SAjRs3Fqr8gUvvLzoEGZfzr6iceaq3VORv/nk02nzt3DiNfBVl02nH5V00SHE+OR9xYyhmsDhtRiMhGoyOH4oxsUfcGPV3BwcHCwaPyzPaBmfwaZf1nJeC5j2aSBtEaTA2/O5Ljqp4hJfzarVaLaUgehSRcfFDyj88zp1Vricwb8bm1004/mJWTKQTaNedro3G2JEcXxccfhg/HjF1gwbHLzTiESHSAH3uka75nPoLGJc+f8YzZcoU9fb2FuSo6y++r3iXs7lE5KRiJVjoOkYwR0ZGksHV2to67i51DzbQN/16lJK1qFarSQ90xzORYx8TRjNrzDvsAdfzWE/w0dbWlirER/kzMjJSoJFoDPqP7wU3vN1pAO+B33k2XDREaQPdpFKppOtiyByKzmJ3vEhF/sRvD5qQ/fJ/1VC87LLLVK1WddNNN+lLX/qSXv/61+ulL32pTjrpJN1222064YQTkrG4Gf7nwCMw0lgpcGns/iIESm9vb6EilROiR96iZ8KFM0xcGiNcGAXKlh9Wpj0Ypt+n5V5g2nChX61WUzUylPWRkZFU/r2lpSX97Z4t5sHmZC6NRmPchb9sZDeIAIQ/yhJKGHPJveNCMTJyhI+nNbqAAvyORD9f6QYt+PCIF32ilHj/jcZo1MSFL3PAiUC7cc1dMUIZ8jFjKPKMQ84BEQ0H+uQsHh41cIDHFqHnfTvjZ13i4XRo0A16j15Ap66w4bn1KIbTqSuYnOtFEYz3IpUZXdFokcb2HLSMxz9W4gOiJznOydegVqtlz5v5O5IKEaEycM+xv+tKgNOQKz05b26EXATQFX3vm8/BhRcCkJR4iCtQgBuKft7I+4wGXQ4X9Xpdt9xyi8444wxdf/31uvTSS3X11Ven+cZiT+5MwZhauXKlfv/73+tb3/pWavvUU0/VVlttVeDjW2yxhT75yU/qmc98piRp1apVuvDCCwtZGxFn0fiNuHZnnBu2KOa+HnFdvT1S9iK+/KwWziP3xoMHwPkC0UyUfW+X9sCpyxSXM24o1uv1pEy6MojHP9Iq5/FQEuFHLjswtnxs4Nrpnf/jGF0euLPJceBZJGT3sAZ+Rop5+jn4KGdQ6D1dPfIjFE8cdJ4pxHzBjUdYarXRi+X9WgjXMXiPzxmH82AqiGJEshb073IOGvIqvj5nlzmMDxrFAGa9WFf/jHb4DiMBB6Mbrg4dHR0FI5x1hFbjmPzYCfyZ51pbW9XZ2Tlub/Mu7ft8MTB5lgqjTl8e3QNwAnDuLeo5cQ/iyHD+ymeMberUqQUZzJxwNA0NDWn27NmpHyKC7BU+r9frycBynSzuzcinPD0XcBnoDmzw6HuBoAvr6TwPvuXZRDEg4dFg133RJSig5/vUdTHwSX/ME5yj2/rVdxPBJhmKV1xxxaSee/TRR/WMZzyj9M7Ft771rZKkxx57bFO63wx/JbjR48wRw6S9vV19fX3prIETaLzfiI3tkSwAonXh40oiGxWl2YUjwqhaHb2U3O+LcWbHhoK5kBLpDKhSqSTPJekvnnLogphN6p5rT6XxeflcYcb9/f0pbQFB5VHQaOxFhgvwjEc2/F1X3DHSOQMHQ/Uznu6Bg1mAP36iIgN+/FC+KzR+v1oUQM4UvVAODBgjXRo7ixCZ2+zZs8cpI4wjRi28fU/HiCWqETQuFF15kkaNOBQ5F+RRgWMNoSUcBE7/Udl0AeIRa4pJ+fpPBC7UwDP45MxZDnfx3kRfQxc0GN+unPjcHHIRcQf3sOb6jf1HTym4dG+rjyFGIIDYH9/FqKrTpjSWFh1pwJUDDCRX4KTiHaBxDsDGjRt1wQUX6JJLLikYSddee62+853vFDzQwPDwsG655RZddNFFOv300/Vv//ZvOv744/WpT30q7ZmXv/zlOvDAA9Pectpva2vTBz7wAc2ePVuS9N///d/65je/WUgHLuNHbvS5QegyxHnDROvg+IhKJXNx+sAQxSjK0YArhxgpTi8+x1zl5GhIuAHoZ5Jj2r/zDlfE3KDjOSJGHr31yJ7zYsaFo4t0d+dv8D7n645nnzd06uvmlcLhDb7v2bPIMe4K9qig7ym/Tscjd/CkkZERbdiwoaCYuhHsDlb0keg0jQ46+nKa8POevg6uq6DPON04vUVj2Y1KxsT6udNhcHCwcE1IpVIpnJNvNBrJII507HLN9TSMa58vOEAn8sixy343wFiL6JzNRQfdmBgaGipUpnf6kkaj2hSFweD3tiNNVypj1wDVajWtWrWqMA7fc+DDaXf9+vXJ+AN/XgUUXQ/dgywh9o1XJM/xO3DMGV3m6noGzit3xPic6/V6qhzrwQ7WjDnCK+K6sB9dN3JHiB+riU5XcOV6J3hCn3Me5bpHM9gkQ/Ftb3ub/vmf/1l//OMfmz43depULVu2rDSs+cQTT0gqeuo3w98fYrpUo9FIjMyJkk2G0B0YGEhGHUzazyv29PSM89ijEPPjG5l2SXX0zUjZcwQMShlCvqVl7O46r0ZJRTzGlBPAXhI/Fq/h8xg9id4mqehpd2+3e8pg4Aj3KKii51AaY6yulPn7LkCdsSO8Sf2hX57BawvziMo98/YIM0YZc2TtcoqQG2M5eqNt5kRKjM8XmqlUxq4r8FQzx1+1OlYOHOaLowBaigU68LC6cHbls1odve8pJ0xd6fazKQ6xcibgwhLPviun0L/TW2w3h9sY/eFvV5gd3ECXisZM9O5G50/Eh/8/kaDJGV3RQIjPR+dRxJFDTOMra8/pGuBzz5hwRdTH7JFT+IUbuAhiMiLid5L0+9//Xu973/t0//33p7b22Wef9Pe1116bsnGAxx57TKeffrouvfRS3XPPPVqwYME4g3+33XbTm970poKzKyrUM2bM0Jlnnpnmccstt+jGG2/MOngi/nIR5agE8qwrpvF9P/8JHh3g9zF93w04wO8Ai8aqGwdxT9C2GztugMBHfZ5EUpzuo7Hpc8LgYDx+Dsnfi+Bn3MFtWbYBeGVc8Yy282nnFY1GI93r6v0475SKZ07XrVuXjMsoU30c8GU30oaGhpLSPDg4qM7OzoRTd5jFSFtLS0vSD+CtyDrXVTBQwa/LSWSD70PmBS+OjiM/p+6/kS9eN4E1Ix3Qz+eBK85moiP4vaA+XxyGOTrziBk0jGFKJhgynLRUcIhs5F0HxspeYI+1tIxWjWcfeMVa8ARd9vX1FVI7/TnowA3lSqWS7giMOgTfc9bR14nrzKZOnZr68H3OeIgi8r3TBxlsZGGxfjG44Gvie9llCPsLnTXSGNdhkYHlkUrfO06DUQ9wHsu6l+lODk5bLodcRwHHMXJaBptkKB544IG6++67tf/+++u0005LaToRDj/8cK1fv17HH3+8nnrqqcJ3d9xxhz7wgQ+oUqnoiCOO2JTuN8NfCR5x42fNmjXpkmk2DxvRvYtEWyqVSqqAKo1VTmUTNRqjpfVhjq6EwIwwXjBm8A7BRFFeMFw8YkUbLoxcEEtKzCUKvQ0bNiTm6AKEZ2F00lj5fPe642GKxqOniLjR7YqKMwHGHtMGaM8BwevzcaYGo3FvKEYJkbVGY9Q72CxN0BkXNOD44bd7HJ05glPoAsHo5yPBhTMuxwvKClczuKCGDjDUoEVSbxC0KCC9vb3pbKGkgvBwLyTjgKnG84aVSjHKzf2JPj4vM+3z4x36jUqLe6bLACESFZp4ptFTTer1eiHCyfuRtmIfPmaPlkXFpdl4c+AGO224QG4GbshLxSgE/8frLKJC7f/njF6nS5SsmJIT97w7PvjesxR8HI899pi+8IUv6HOf+5zWrl0radSZesYZZ+jss8/W29/+9tTOD3/4Q331q1/V+vXrdemll+rss8/WggULCvPdaquttO++++qoo47SySefrE984hMFZcYdbA677rqrTj311PT/JZdcokceeSTJBZ+L95dzRriRFI00d+o4n4iGIUppxDM8Q1K6PNvlU7VazTqZY0RscHBwXKEjV/Kikcu5rEg/3DUWeZ5H7CqVsQqHGCpuoEY80gZjYd7OW8FnPNbhBpqncQJETNg37sR1RRYZxNg8bRj6b21tTVEjP1flPJh944XxGF+1Wk38s6enp3CNT1wLsjTq9XoyTt2R7I4GfnuGDLhiTPV6PSnWjhPO/ccIItEinnedBv65atWqgiHkGUiuoMfIKTiNKbbeFvoJMt31J8biZ+yk0QwFv+qIzClprH6Cp647nXDekjGShu+6jDSm46HHQK9OP35uMF49Ag1CG34EhPkxjmq1qm233bZwFo8I3cyZMxNeckEoaIDxOR3QL+PDwHM9h/GyN/iO95y2WGf0RMBpuru7uyCv6IesPdbTx+qGe9RhXS67Yc33joeIf6loKG7cuFFTp06dtCzfJIl/zz336PLLL9eMGTP01a9+VbvvvnvhjARwzjnnaMaMGfrhD3+oHXbYQXvttZee//zna4cddtBhhx2m5cuXa+bMmTrnnHM2pfvN8FeCKwROjEQE2ZRemYtnPe3GN13uqgQP97siJhWZLm1jaHr1SJite5Tpv7W1tXBY3jeOKy3xfwwQ9wIyHja3p3Ui7MFDrGro44dZYpRhOHmVQmdgeB1pwwFmxZpFYKz87casM173JOeMAVfCfX3cCHdG6VEUV5Q8nSsaivTPPMF1jBJ52yhckfkh9Dz9yQ3xlpYWzZo1K82XFL5KpVIoA12tFi/OBWcxlQfB5imCXvyD8XqVUfp25wLjJ52lWdQjt9aRDlzB8DmRHZBrLxpMruzHtqZNm1bwmucMzMkYeUBZBDuOJzdWaCUqqf5eLBDQbKyuMHlqte8nn28UpP6/O8hYV35L0gMPPKBPfOIT+shHPlKIIj7nOc/RF77wBR1wwAGSRq+zOOWUU9L33//+9/XOd75Tt912W/psxx131Mc+9jF9+9vf1te+9jV97GMf0ymnnKIjjjgi0Z97wOM8mPcLX/hCvfKVr5Q0uq4XXXSRFi1aVFAiysD3iitlgPPPHM48nRHIPe9yykvXuzI5NDRUSHPje+crHgVifIyBefgcaCPuNS945YZKNJa9QBX3qDEeV66dV+f4nLc5MjKS5Eks9uKKuiujjmfkG20ip6AVFF368nV15dUziFzWMrYlS5YUZLU05myk6A74pj0fu/NDUhK9yio832UoNOXGFv2AI49wbtiwoWB4xqM4Pmand/52J7E7sx33pKnirHM9BEPVjTCgr68vZUUxFva1n7EbHh5O6zVz5sxxOh3jcAe2z8HnFusfcPd1a2trcrySIeC8zvHl+5IryXw8jms3xMFDlA20v9VWWxU+4/5Pv0LNs0CIBDs940hBfkNrTncxBbVSGTtXWq8Xj7O4PsU+9jsunZbQ/5xngg/XP11HQiYSoXVjGie2G+g5mRjlJuMZGRktOMQc1qxZU9AbJ4JNLmZz0kkn6dFHH9Upp5yilStXZtNRd9ttN91555065JBDNDQ0pHnz5unee+/V4sWL1Wg0dNhhh+nOO+/Urrvuuqndb4a/AtiU7mEaGhpKhp1Xj3KPRdxcnutMuoJvNveEuQCPwiV6TNkg0bDk75GRkeQlc2OmVqslQ8CV37gJRkZGEoOhmI5vKJ6Rih5jNqR77nLjwyvHPWsIEmlMiWOjezSNMeMJxFBE8JSduXHG5XP25/zQuysprrw5oyFV1L33CLuBgYGUysL6Ol25ohiNIPcy57yBLuxR1NxIckXCBYQbrZE5RqEL7vntAou1dw8cEIsCxLNI8TL5qHC7gsAztVqtYIA6+F7yaELOmHTDxyNrjMFpIxpjPicHT7+K0UBgU6KK0XD1yE9uLnGeMXIXodmB/EajUTAo3JERn5OaVyr1saFQ+djZf/fdd59OP/10nXfeefrTn/6Unpk5c6be+c536qyzztKMGTMKbUVjkXF2dXXphBNO0Hnnnae99947KbKuIDsNSMVUYnDthuAb3vAGPfe5z5U0GrH73Oc+V7hOJ847rotHdz1ileOdjnsfc3yOvtgnzjfiPnUlrAxwLqCY4rgiDQxe4UZRjqZ9PHHuLtf4DR5xKDmfdNlU5hzyMfjYpGI6KPzYjWIfrxsosX9vO0Zhfby+Ju3t7QXeD134VQyesVSpVJKx4XwZw9ezhcoKKrmyTGE5nHD0m+N7rpxHQ5nxRacK3+fwz7gYu+MrFiuq1+uFzJGYnbVixYq0/s5jBwYG0tEY+nO6ZGwjI2NXNBEBB9xB4DQG/dKmr1F8n+84csR5Pww0qtvGDAQMYd8rLrPRQ9zYcePcda21a9cWjkhJo87LWm3sfmw3SCVleXxra+s4B7EHCKA9d+w2Gg3NmTMn3evt/Mcd45JSIMD5lR/XWLNmTaIbz+RzHu7Rymq1ms5fsgaua4BTP0LDunoWh+8j8EDQhXXv7e0tZFVNBE+r6unMmTN12WWX6d5779X++++fTUd91rOepdtuu01PPPGEfv7zn+vqq6/Wz3/+cy1cuFC33nqr9thjj6fT9Wb4K4D0HfcCwaBccXVh7kzNFRvf7FHhJQfcFXh+opeJTYowiEzejQqYI5+5AIhpjBEqlUryUOL1X716dfoup/TnzmhFY82FTqPRSBfnSmOXcEvKeq7ieKNyDlNw5YQ+MbgQHszJFQT/371PPobYP8zHU1D9ecbtZe69HLwLSMbKnNzxkMMrEJVJBBgM2wVETAH0iCV06Up9pGHGhsLg53N93P4846Ndj254RIn+fK+4ouUGcTMjDrzHVEJ/tl4fPZvh0V//3h08UlGpjwp3VEByxkNZ9K4MogKWa9MVh/id49zB78Bi3eO+8vTDSGeRP7nS6L9d8fJnnZ42btyoz3zmMzrnnHPSOXxJ2m677fSud71LX/nKV3TYYYdl96I0Glk89dRT0+eHHnqovvrVr+rlL3/5OAPFlVqej8qVO4ec71erVb33ve/VtttuK2m0ZsD5558/zkiQioWGIk3l5gBP4hlPZ3SHoz/v64FccAXNjTTabmtry96fGUv4ezShUhmNFrjSlpuf45W0dt/HTr9Oqxgecd5+FIHnmFfO2OUz3vNoHucYJSW+420zbp9DNHbgjcyHqqGsh7dF2/FaCL7r6OhQf39/ga/xjMsozs8xB99LnlbMu0NDQ4UIIMdSPCWTMcR1cedFxDt7j4wkP0/sssnpwNeMd2nb15y5OG/AwMXIpFZATFdsa2vT2rVrk5FZqYyl57rc8ywraNsdnG6UxkhjpLtly5YVjKSRkZF0tQap3S4jK5XKuGs8WHfX5/iO5+nX912tVkv6nuO5Wq1q7dq12dR/+mtvbx9nKHqwAgO3r69Pa9euTf1jbNbr9ZR26k5o+pk6dWrCi88H3MW95XoMf/sZZXdK81lXV1cyXH2PxowlP7dN/5yZdtqIeIxrAN5ct94U+Kuux9h///1177336rLLLitNR91uu+109NFH6/jjj9fRRx+t7bff/q/pcjP8FZBjgmxuDhc7QIie6sc7vnl8o1QqlRTi9g3gTMsZkytaXonMBYB7DKnqGaMS3pZD7ntKeLsRAMQIYIzaOe743BlgR0dHYoC0A2N0pT5nsOX6isqbCwLmNDg4mBi4C4PoCUMQgJeofHo6p3uXHT/engtrBHgctytfzmSj0uF4dWWSzyiCwLjpE4Ef08Xc6HevrAs+xohQcIXD1ype9OyRHAQkkQtJiZE7LhynRG3xtEamnTOicp/HPjo7O9McoofRcenvRvqXxiKojDNnPERjshlEYZqbjxsEcZ7uHIpjjY6kaCxJ+YijG0/QAOvuxmY0GBlfpN/7779fH/jAB3THHXekz3bffXd98IMf1AUXXKDDDz+8cIYYWnKoVEbP7V9yySW6+OKL9d73vlfTp08ftwdjlDnyEj9nDfi5v5aWFnV3d+tDH/pQyia599579aMf/WgcfqF/+vN1zPFPd+rQF39jKPq4cqlnTguuePve9PNVcbx+lxoKpO/NXFSQd+mfsXv00X/n+nXcu2LsKX7wgRyt+hydPlyZdeXaaTDuKedfGETOG4j0MU9wHSMR8CjnLT5nrkfgbze6okHtstXXuF6vp3OMjIkKnqwZz7kj1w0j77elZex6A+evLjtZcyLMLnuiYU/fGL5l59F5H5mAseXRQbKmHJcYfRTp453odGE9mAfy3Au7tbW1JYM88geXa5XKaFEZN4AZC3qbOz2d7lweeIG2Wq2m7u7ugh4Tf5xe4/ED3vE7Pb0fjOeurq5xWWzR4TR16tRkjLGXaJP1IzobnVcrVqwYFxhx517kgY67OAb+dt4FvXE9hRvS8K5cEMT3LJ97f1FmRdqPuuimGIt/laEojSLulFNO0X//93/rpJNOSumoL3jBC/SHP/zhr21+M/wNIXfAutFopJRgfjwqg0HijBPmAGF6OfFqtVpgvq4YucCnb+8DBZvnfEO7UHdjiHaGh4fHFWuJClTcGC44eAYlmdQDH2vOkPN3MXapuOl9e3EDxl62Uf3zKHjdK1qv11MaT1Q66vV6gRExL2cucYwuwPjtDMvXISoVCFgMCPp1A909qS7wAE8B9Yge/XtaDOBed77DQ0sqr6SCguH4xasYjfcYUYzCNipM0DiKlX8HrlyhclqOqbi5yLj3B7jQQgFkrNF5A84jDnLOFS9S5kqOw2TuTwTi/HLGcdk4JSUldTLg0QTaLDvnGz3acX96loLzHM802Lhxoy655BKdd955hUI1H/jAB/SpT31K++23X8FhwhgR2jGFq729XXPnztVWW22V1jF6imkrGkGxvVgd1flOvV7XNttso/e9733p/R/+8If67W9/Ow5XRMujIY/il+OH/r971+NeinTtfJ7vfX85Xyoz2MAZypgb9p4WGI0BNxakYgpvrProhpjjlmeIeMD7nXc6DuLYaRfHmPNuH6Mb4hF/UfmPThqvhJnjKw7gyM97+3fO81pbW1M6m68Vawc+3FhDDjiPAI8x1Tg628AtaxZlsRvXPl5fA78vGF7hMi/i1h3dklJKJmNg7pVKRd3d3Wl/sFcdj7RDhGz16tXpPspoxOfWknc98tjSMnqfrqcZxrk4z4gVfqvVaqGIn+MyJweQO8yfo0H0E/km+5Pzgz42p20CGHyOfofO0NIyVu0a49J1VejRs4p8XKTBxjtX29raUqaZOzScz/mZ5ZhdxJxHRkY0ffr0NH7XNaH5aHxj/Pu6u7EfM/fQI6IBCniU3nnbyMhI0kUmC3+1oQjMmjVLl19+ue655x4997nP1T333KMDDjhA7373u0uro26G/1lw5d69Du6RksafdfGyzS50MX7cGMCrCHF6ypsTPO3AQMn1jlEpxoog6O3tVb1eT3c90nas3Ed/zIPfMKqocETlFK9VzF93xRJwwbNx48Z0r6E0Wl0KD6cz7dgOuImenmYRzXq9nqKIpIL6u6RNDQ0NadWqVUmQ8K57p3wcGHusnUefc4YSY3KjxwsiuWfezz76b8brHjb/3ovPxNRV9yj7eQEXEu6ldCMVr6njxnHibXt/KOWOczfMHWd8zz6KXtsYcSuLskSDKxoJeBxpL+71sr0VAQHukfJIk95WzrCNY3f6Bf+TNf5yUcJcv3Gcub4dwEFMBXNDxSNE1Wq1kFL4pz/9SWeeeaZ+/etfp3f3228/XXbZZTr88MML/TptlRmKUrEScM4Y8DWnLf5nzZlLvNIj8pyRkRHtt99+euMb35g++/KXv1xIm5XGHIyRruKZ3kajGD3jmRihdQ9/Thnnt+8h/46/Y7Vb2vR5u4MNcCPPx+o8JL7rRyRckY98kP9xTHmkLdKxz9/n40qnpJQmBy5ieh/PuRHET05+xHsi4TfR+eMZFI4HBxROzvzH82txfbwas8uMXJl+l89EySLePA3SceFGe9wf/J2jC66ziDiL4+V/ZJbrAO78Zu2c17hOAd495deNM4wjxuzfk8IYM7OgMzcsIm8EV5GHIrM9qiWN3THshp/rcOzr6KRwvdEr3nqlcDeOGCf3XzN20knRLfnt44AW0IViAUa/moJ2c9kaW265ZYoeO52wn7ir03UG52k8P23atMLeZHwjIyOpIqqvKzqwy3rGhkPCjXWfh68LzyBjqUZP9Wa/yzN3nCUHfzNDETjggAN033336atf/aqmTZumSy65RLvvvruuuOKKv3VXm2ETwc8CuDIC4UdG7ILCN5NfGhqZKYpCrVZLCmc88O33ReH1lcanZMLwPP0N5sCm8Y1U5n3Lpb3mvGPOOKOxDP6k/BkdGDRMMwqGqBBFfEvFA9muxLigcCHJRkdxj/1UKpWU/kJ6pCs40QD2Nh1/rJ17+XKRAKKbzJuxuYPCjaCcQeSMzvtBEXF84YHzdSeF2qMpUalsNBqFczUe8YmKIOuXi2z6M6yfe5Udd+7xc9w5g3eIAjxnLMX/WetolAC5jIIcsL895TDCZI28CPSbW/syD2eZUZmr1DlZL6mvQ65tpzH/DJzcdddd+uhHP6oVK1ZIGo0AveMd79DHPvYx9fT0FNbflUjGyNjjOuOA47mozEblzvlIvM6HveG0mzPQXvGKV+iQQw6RNLr2559/fuFKCH/feZufm/F5RaMkOmAiP83Rou+hyP/Y335MwfHjjim+YwwYRjgBPA0rprM53t0g9xRLB19nTxsETx5xiE4AeJnLBPa8RzPi2bZYSCvi040P8Bnv6/OzbnENeJeKmHF/ceaupaUlpdrFdQOYq6eS5pxY4NL79+iT011ub8T9FlMCXaGPn+NAzNEkCjx7ijUjBZT3MNwIkDg+MJI8SoxRAk+KkXOvMO/tDQ4OpsJ54IWIEddGOY+LcwZn/HYjFiez85ZGozEu+6ZarabPqADq+GOt3MmKLhKznGJKpO8F7jgeHh7WunXrUqYAPy4jKLjDejk+nU4wNnMZKFOmTEnnSz0TAdwTkXS9wnUeSensr/Mn1jee7cQJ6fop+MI4pR90t8jfowPX+/SjB67fTxaelqH43e9+V0ceeaS22GILtbe3a4stttCRRx6pa665Jg3k1FNP1aOPPqoTTzxRK1as0EknnbQ5HfX/MuDhksZ7pyC+6C1lA8bPndjwmtKeVKxe6YadNLqR3aPChvTLT9kAvAt4GN0LGiAwyxRKn3P0ijnwvkejgFgohLnxPwrEqlWr0vdE1KJQi2mAzNnH5wwAfMNYovfby2b7OrgAiwV16MOVS/caozw5RBrxtlxJdAHF2KKBFJUOaM3PRng/RFtckFAwiH77+/tTarQrkn19fQV6dQOeKKOP3ZVZb9/n5kqqFyFgLniDmat7+qITw4tU+PoBMfLKOPy5eDA/l448EXDg3fGQM+qcLiZrnPk8mhmasb0NGzYUBGdsC4i0X/Zc2XhyjqYcnV999dX60pe+lPjbM57xDH3hC1/Qi170orSfJhLGMWoIDA0NFdK3416LNBIdFtFJ5Xw5Z/xC96eccop22WUXSaNndM4999x0nZDvI+8PXuBz8OihvxsdFj7OOL9qtVqgL55tNBrJ2eU8MMdzfO9JKhjm9JcruOWGWzSSpbFz3jmlzBVXz2yAR3Me1Nt3hTruM8YcHUkuH9y4dDnBT3RYMAffKx7JzJ0fZawx7VJSKjjjRUt8PN63R1QkpYyksgwKH6MbVn4WPyrhzM8dItHwyO3JXNZUbkyeskvqYnwnRn3cUCTqmuPLGB5Oz248Rp0BWefzpPJobk1ydAGvgf5IHY7pt+zBWm2s6KHTMGPEYc+6erTQ97Uby74uORplrsuWLVOtVks1JqIu4g4EcOX7NY4l7ivXIWi7Wq2mIojeNoZdrNjrVV1jZNp1Dp+fX+/kugJt1mq1dIaXveCOhKgjReCzeJ1QdFo3g00yFGu1ml772tfqTW96k26++eZ08frKlSt18803681vfrOOOeaYtEizZ8/WN77xDd11113ad999C+monOfYDP9z4F4XV8S9cqR7YymSEpUT99zxfGT2OQZO+3iG2HxdXV2JcL0QQ85r6wq9p7WSmpLzBOL98cII3gb/+zw3btyYnsnhzPHhKVgtLS2FC9qdkTq4Euq/3TvmaTr+jBusXgQgVgGEMeEdJAXB55UTim50u9CV8lFV/vc0Jp8fZ8xiZCgKeoRhNAb5nYvIEbHg8+HhYQ0MDIy7kNsj6d5me3t7uvPMldeID1f8Pa0MoG8XeoyJ9xAu4N3pItJIpLGYrhT7Bxc+h9w7ufYd3JigXz535afZlRQ5cIV9Iohjy+1p2swZhfGzGCmhD/dgl2UjOP42bNigj33sY/rZz36WPnvxi1+sT37yk5ozZ05hX0VDwv93/hMVldzZIsc94464KcMRaVhR+Yy8ra2tTWeccYZmzpwpSXr88cf16U9/Whs3bkwRisinco62WIVYKp459nHnxuyGoM8N/oKzKKfgScW0fFf8vS14dfws0kHcOz4OSeOUtBxPrFarhQhaDg+MOzoSo/HhbTLmXMq08176Q7mWipVNMbjLsl5y8/RxO0/H2GAcUSdAYXdHBnN0Psi84Kn+bDQ2PWLq8t3bi5kULpOj082NKObFesfP/dhCznDt7u4uZJ/4WVUfD0aFpx76fHx9+Y1ROGfOnHFteXqy9xX3rztG3KFM2/FcP3qez9lpFIew6y0YxYzZeW7MtsoFI7x/jvHU6/VC5JJ95QEBnytz9EwZjNac3kqGHe16lpwbqJ4WHAuVMV4fj/MZcOnRTnRY358Y29RbcCeDR7YjX4syAvpzOihziORgkwzFCy+8UD/+8Y8lSaeccopuu+02zZs3T7fddlu6A+pnP/uZLrzwwsJ7Bx10kB544AF9+ctf1tSpU3XppZduTkf9vwC5KA4bw5+BSUflKSrx0fPkhAfT8+/4GRgYSEyQMTnx8tsVLr53RurFHPysZYRKpaKurq7C5mP8/I8CgDAAJ+4VyglLF4hRGXQcRSOpLFrhfbkHCIhzxBAcHBwcV7QDQxovXFRCc55KZ9yO86hY5iDiiL76+/vHKRvuZY3zj/OL3r8YiXAFyRVVpznez50ViwafCyjo2Pt0h4ErJF61Ls6Jv6OnNNJk7r2I+0hHAJ5GTzMpW6+cF98/z0WNfW/mCsRMBiJPcSiLvrmh52PyMbjCPdn2UVjAWS5zgXeWLVumD33oQ7r//vtTW29961v1zne+s2D8+hltH1ukab7PXfHAeSDa8/H7upTN2fHltOD95+hi9uzZ+sQnPqEZM2ZIGjUWzznnHK1Zs2ZcG26QxWhhBNJEpXx1Pge/pw6IMoE5+sXYAOvpUV3GSf+xXX8315crem5IlPE7V4yr1dES+K6YosjFyFPc1678eUQ9KqQOZfRPlCfyXeRJ5K3MB5qL92w6DaLIesaKj9mNIRTfKDejYYu8ZB3h6+wvxxPjceeOjyPKGJcTjq+43tFQjGmp0biPssGd2tJY3QNvG+M3l0IYjSkfqx/r8LEzPzcU3YCMtIueg/HE/PyYirefOxfMd6Qhu/Od3+AFQ4fzgpGfuREJ0FfMFqI9AHz7feC8H9eN2g3gl+daWlq0ceNGDQ0NZc9mxwhv3A9+JnXFihXjaMXHBZ2yhjNnzlSj0RhnoKMz+LnOiK8yAxuIDu7INyeCTTIUv/Wtb6lSqejMM8/UZZddpkMPPVS77767Dj30UF122WX64Ac/qEajkTUAK5WK3vWud+nRRx/VW97yFq1cuVInnXTSpnS/Gf4GkBOOLtxJA5XGn+PBO+MbvMxL3miM3RPnm0sq3l+GJ4Xy2TmhH4U2beVKn+eAdmEwXLqK8HQjwhliNBgiDqPCw1hiykFOmPvmBtzbD2Pwz/19B+YSz/B5lMsZr+M4enSZO5CrOJljMDmlj+dy50ddKPrzUbGNDDDSpDQW/WUeCCKfXxTafDcyMloi2xm2P+t0QVt+r5or/K70R6+m07IrbDkFNuIzrk8Zg/f015aWlqwRkhMiDo5b7i6L43EFZVPalsoN1GZtxKiJQ/zfjfoIUSmMhnd0MECP8+fP18c//nEtXbpU0mhV07PPPlsvfelLC/yHPevr7P25Jxj6jGOi35wxlWvT2wVcQW9vb0/06rylDLbZZht9/OMfTxX7FixYoE996lPjCtJ5On2MwjWj1egsieDpjbk7QX3fxRQ+f7/s/KMra9Hgpg/A18D79sJksX3Wn7Egp6rValJMaTMaNN6Wj9MdC260RgcaQIqrZxSU8R1o3Gk4yi1ki9Ofv4NyjqHgEY4yXYGx8WxMf3XeSoQFuer3LUNL0dByQzvyHJTmeJ6W+Tpvc5yxjrRN2maZnHdFnnajweeyyaOjft7Q22P8fB+L5NFOdCI4ThgTuhfrCY2CnzKZ7rTqRyuYi/MvpzOfu8vnyCNyY/cxOa14RVs34HyuUV/wMUVdivfjWcw4HrLiPH2dGg2cTfQjSNHZA85ixg7XmwA5R4HzwBhwcEcc4Odffa2ijtsMNslQnD9/viTpVa96Vfb7V77ylZJGhUsZzJkzR1dccYXuuOMO/dM//dOmdL8Z/kqIwgmo1caq/sUonTMvqVgYxpkcm87venHhhkCif/+N8u3RMh8nTMwFDbn4DmWGYmtra7poOY49KvHRUAXo2zc8zJr/OQRNJDDn9Yrr4RDTaXLeUgDcwKxciHs6A+sKbriDknabFTBgjnHMubQWfzeOM2ek5YriuILtxi54ivcTMtd4f2StVtPatWsL9IMAjEYyQs+N8mioepUw8OqKCYqOp9zEubkClcNdNAgiTGRg0QYCBWUi90wEFBP/PqZ+RyMlB5MROmWpp2XOE6ncMJ7s/Hjf+3bBG6MOPqbf/e53+vd///dkKO244446//zztc8++6TnfN7sp5zB6vvTS9rH8ft+d+887/qz8ZydG4LQNZGY2HbECe1su+22BWNx/vz5+sQnPpFSqaTyQkc5nkL/DmWRZS6gzimQ7gzztLGYJeDGaIxwuOLn0Zgc7XnaoPOhmIoujc9ekIqplWURCIDUQ6Ber487w8Vz9JdzJkQnAzjKOSaiIed8wB1kPn+nV8bgyr8bBJ6GivyPRjfjjLzGFX1pLKsF44x2477wOefW1Q3wWKE16gC5YxDevn8W1xKHQq6QW855kjOqXX47OM2tXbu2MD5P78053X39ycLy9cQBQzGrHE9n78Trg5CDMaUyGuPVarWg60TeleNTlcrY/dwum+Ie8PZdB4pGMHvY9zX9t7S0ZCub+hzAnztjeR++6RF2d1rwm7m4Du3OWcc1tN7MQZvjLcy3VquNK1qVM5TLYJMMxWnTpkkaTUnJAZ9T/aoZPP/5z9cDDzywKd1vhr8BRA9EZHKkHLji5kzHjRfejWXYI5PCeERpwQB1QTBlypT0WdzARJm8gha/vWx2mUKJsoCQ4bmcQsNvNhGM1FOZ3ACJSkTOyz2Zc1nuJYJRcP4wjrXMSMspOjBTZw7+rjNBH4Mboi4sESQ5AVmWjphT3qXmaYKuADgDjG3xnStVFG1i7i6wouODCLoretH76YwY3LuHrtFoaN26dQXvbtwDUnGfRGbeDF++T8rw5O8h7MqiqBO9y/hrtVrBY9uMb0wWyvZos/aazd2F/NMZlxvvHiluNBq666679OlPfzoJ2N13311f/OIXNXfu3NL+qM7oZ7A9c0HSOEUqzjUqyE6L8VqNHG04r/AUsmjINMPXdtttVzAW//KXv+jiiy/Ovuv7PirSlcroJeP+nit4EWLamlTMPoip97m7c/lxR16UX/QVP/OsF/h/lFVx/v53LFLhqXoe5Ypyo6WlZRx/cNnjn0c5FPex83ogF82K56ocf1F5dpx6X14kqK+vrxBxc5no0RSnkciPHZfRyG5tbU31DPx78AdEZ4DjChqKRq5DjOJF+nD9w9sF+BvdxCOjvq5x3k4PjKu3t3fc9Rv83dLSkgxF+gHXFG9zfDoPx9iKx4MajUaqpBodWRGvHR0d44w5143AnetVESdR7rrTPdKEG1c4HuK6O72yRpHWYraRr623447+qOtyV7i3RfTPr/3wuTNHP+YQ+XmOn7FubhRHHbnsWfYsBm00NicrLzfJUDziiCPUaDR0xhln6L777it89+CDD+qss85SpVLREUccMbnOJ2nNboa/HeQIo0xhHBoaKlxd4c9GBsE7btxJ46sURiYhqaCQwlhykT0/rEy7/f3945TFCIODg+rv789GFFypiecRo3IcBbQr0YyrUqmkVEYghvhz48zhNSoiPq5osDvOIj5aWloKBXfinBziu5Tz9rZdIEQPpbedo5E4nwiRPl25YjwRcES4Mubr6spfxGmMnLlRTXt+5sGZujPj1tZWdXZ2Fmg0l26bSxfxNnMwkaGYcxpQtj1ndOYgKhV4InMKUI5vNxujQ7MiONBx2RjLxlz2mfMXafy4WaMcLV9//fW64IILEi3sv//++uhHP6rZs2cXno3Kd+5sDcoASoDfi1cWKXbDJqfouZEQ95nzo5iS51ki3k4OorH40EMP6Te/+Y2k4oXQZf3zP/wHiMakQ+RvjotYWMuNfMAjdxgyPt6o3MeIURlP8n3hezvONfL6yLfL8B2dbBgUub0N5HAYeR8Qj3X4+57eRhvelzvEvJ9GY6wKrRudkcdR9r9Wq2XP3cd5R5lGdGfKlCmFfpxWyipGerv8xkCMe4Fn/OhNlPmOt1j9M35PURk3fnLppNBszDph3ZyeXW9xZyB4Zz0wLuO65nQvfjvfoI1IM14wqLu7uyATXPb5mKKsyznH/O+y4le+3uxdryTs2RXuiPN9ztj86o+cvuS4dr3RM+884gtP8+h8vMomZxT79WYY+HHeXmmX36y9p5rmMr3o16P7scr9ZGCTLLVzzjlHs2bN0tKlS3XwwQdrl1120Qte8ALtuuuuOuCAA7R48WLNmDFDn/70pzel2c3wPwg5wvCN6il61erYHYcQnzM5P6cS24ngRll8nsP+MCeP1nif8Z7BKBTL+u/s7NTQ0NA4z3fEBxsS5uupFd6nz4m2XFBFZTinBOQgMq4o8HNeU57LXVdQq42dxywzRnJMmfbBRUypdGbkOMhFNYFoAJSNxz3SPMe7lKl2QAGJ6WeshSvUrvDx28vos97RKI6VHGMBHJwqbW1thWqrcY2cYcczr80YdkwXi78jzqEFIugOufWJig7tlo0r91mujRyUzROFKWf4loHjBd7gtBnpKxfZj3ymUqnojjvuKJyzP/LII/X+978/VRDNKcxA7nxQfM5pP3d+Kr7rCo4rJvzOGcfucUahp1BMHFez/7fbbju9613vSv9fddVVWrduXaHtuGZR8cs5HJqtM8ZlHKvzobI19giJ8wV3JHm2TEz39LHH/lwBpc2y571fV1gjDbL+ZRGUXLaBGzp85t/Hs/vMLeLU+VyMQPjnrJfPnT54Njo/MKK8GBlXRcW5+N+siTuD6dPPSfKTk0XMw/mXG7/oLVFuutHmBl50LjgOyowN8OBjwlCMsoPnPGXW1zbqSzEKFY2FiBt3cnifjjsKDOJMyhnIUX7mih36+pGtE3Ush9w+9hTlHHB+0I24RqNY1XNgYKBwzjA6GGJxJx8Lc2GtPRjhTmTOKUpjBn21WtWGDRvSs7znBruvl68NmVA879FI9BznQxG3bkRGHkU1eD9C49lrE8EmGYo777yz7r77br3oRS9So9HQ448/rnvuuUcLFixQo9HQIYccojvuuEO77rrrpjS7Gf4HIbf5XLlw4nFPcC5NIzKi6PXgMzZmLI/tCpozSRemnt6ZU66d8ZQpoe49cohMy705bKTIyDy65saHC1f33khjTMeNnwg5xh4hRlp9rDnvc61WG3exL325p93vWmPMnr4UKy/mhJd/51C2Jk4HcS45AwXGHBkb+HfcuIcujjEq9f6ZK0OufOToJ+6HoaGhwpUqPOP9upLgtOepLTlwvOb2YATGEL2T8flm9FiWGlYW+fTIXDMoo4cYnQcmm3XidJGL9pS15fuVsd14443p+2OPPVbvfe97U5sU3vK9msOp/+2OGz5v5tGN0XDnMTGduIwe+HzatGmqVqvpeoS4t3KOorjG+++/v17wghdIGi0cdeWVVxaqZboBGudT5kBotq45ucJv5xvcoZrjQVGBJwIQHZtxHK4I+h73vVuW9utGM2P280c5enE6cMcOcjh3htRxUYa/3Gf1er1QUdHbJjWtTE6W8Zn+/v5CpsT06dNTFMQNIHfA+rxzhlXsC6W5Uhm9xD7yVeYWZYAbTL4v+Cx3/UR0FvC/Zxsx5pzMdbkR26pUxs4MR/yyt0ldLHOq85nPz8fkBneUle7cyclX9rE7xFwn8D5aWloKxVrQQWq1WkpJxYjCiRcdzuyvZgZ+BGiYMbgc9b3qjgvXExuNRuFie8eFr50/7+Pj85aWllRUyXVUnok4cxrOVZHHCejRQ753vhcdVj626BjjN1ejxUBH5J3NYJNzP5/5zGfqlltu0RNPPKGf//znuvrqq/Xzn/9cCxcu1O23364999xzU5vcDP9DUKbk+UZxZu5MO0bXpHKB7ozZhSQVNKOnipB7d3d3oW2YAZvQGR9CZTKKJIw4KnGRyZNi6JvcmQ+fuUcKwIvmwiF6jSYyFH3uueeiN5bfCAVnQHyHVw0Gyfxc+XLh7dFk2o4pu7GfOKYcRCZV5smK54EYg9NapJ04XvfYRcUvwsaNGwt9xChcNDhRfHJl2qPjIVfcxvEOYASXgXu3c0I7jpf1orJfGUQlJHrm3TMJeKqPQzMHx2TAFbpGYzQK9dvf/jad+Ws2fil/hcZknBbxmaeeekp//vOfJY06Ro899thCwQJ4iY85zqMs8h+fzSlzkXf4+yhgzdp0OnHF11MvmxnQuTk1Gg2deOKJiT/fdddduu+++wp7scxJgJISFdpcUQYHH0e1OnYtQtz7cU/H+UVnEB53PsutFXLF+/MITFkxL+8r8gZX7nJQr9fH0Vmj0Rh3H6zzwrJovxuscXzNop7O66Jx6waWvx8vHO/u7h53XYyfsXN+PJEcBMDbrFmzCgaIv+8VZf09fjudeiG8nNEZcQPu4jPRwRQV/DhGINI+dwEja3NrR/voaL63+Ywx+dijrPbnymjIM2jcMRP3enRouYM859SO+8KPffC3G5wOGJsYcbS1evXqwrg9gujODzcaqYfh+GNeUd67cZ1bD+bB/9yVGflSzETgt9MyjmaXheCGtfJaDHEd0HtytTdy69yMH417dlJPZWC77bbT0UcfreOPP15HH320tt9++6fb1Gb4H4QcA4re7Zwg5T0v1kLlq5yyEBmSe2jLzlC4oThRNAgGVuZ5ciANJqaAuqIRU2D8M58PED1DzNHTUaJXsEyZmug7wMefS5lwJYi5uLKTi0xFQZATlLnIGG36+jer0Aat5L5zcKWF/3MpMf484404jMpxVP54H5quVsfOPDgzj3c+oXBFZ0guJTU3XwREbg1yEM868E6uDyLAvuccch7wMiEYjVmp3NkUI4/AZAUR9MHzF154oS688EKdfvrpWrlyZfadyA/i53HuuarIrgRUKhXdc8896bvDDjtMUj4tyqNTZTh1hT7uVz6LCqtHLijgxThxtLmCE/c8bTEm977nFIO4v3POgUajoWnTpuktb3lL+uzyyy/XwMBAqRICeOGrqCxFB0Xs0/HFXOPYml21wffN5utjh6dHQ8D7YU3KsidyRhqfx0Jn7vhqNBrjMlqk8Wf9idTFs1/epht4Dm1tbeOcsT4OjPHcvqLNaBygeEOXvb29BXokPQ9eFB06OWPCx+WGOVe95FITy5R5N1wAcJcrPBf/Z95eFdvHFvEYcdvs+Ei1Wk0GEtXIqURaFkn01FJw6xkp7ux1oxKeEOV61FFwgpbJZu8DnLhcg/48LbKZs8/5X9xrDl7DgjnMmTOnQD9cSs9cSXX2PY++Es9kQp+Rz1QqlXHV1uMzXrPAHdfg03EHzdM/6dTcERnPTqJXRLxgDOf2S3RI5wIlfPd3NxQ3w/97MFmiANrb28dd/hkZZM7YkoqGRqyAFT04PIegcsbHRmimVEwUVfR7FKNRm1PWYxqpg3vs3VDkwH3uzreoRPs65CJf3pfjyL9zvOXac8gpfv53jEIAMJIYyc0pQWXgXq/JRH/LHAkwvDLwd5xZR2YIg+WdaHzGqmMoZa78S0UDGYbNFS/+bnSelCn3k8HhRM8BHoGMxqJHTON3cW9FnOaeAVzg1+t13XbbbTrttNN0xhlnpPsHJwLW5f7779cf/vAHSdKqVat07rnnpnLtDmU8gbFGx0RZaXEUqFqtVjAUX/KSl0ga72Aq4wmSCsYde4Pf8dxRmffX//a2c2uVmxN8WlKhsFZu7aKynItAQ9+HHnpouhZk5cqV+t73vpf25dDQkB577DHdcMMNmjdvXgFXZcZ07m/+dxy7kyd6+yNPacZrXRHlvSjffI/nnH7wlNzaRWdF3OPuwHFgfn79iI8hziPiJwe5dYw81I1Al2X06fTHM3Ec0ihPcR3AeSDGDHgD3363Z8SFywzXHySlqqcR/1ERdrkWP/c+ypyL3nbMqInPl/FEeL1DNJQ9E0YapZFmRxFcHpOi6nTm8sz3Sm6+/szIyIgGBgaSDhb5kP/m7xiRpn3W2A0oaXxGR+SbPJtzrOXk6IwZM7J1J9wApG0/akOhN3+WfpADblzmeCyyvlKpFKKdtBn5jxtrjcbYUSH4I/2W7Vt3UtN/zjnCZ64zulHszzcrLDduvpN+cjP8Pw8TeRCioeCHdaPRArS1tSWPkW9mf5fNv2HDhkTs9OUpB2x633CuZLj3Nc5rsnOPhogzKzeY6C+CC042OMatMyA/T9dMyY4bOKY+5Pr2Mcb5TZkypVSx5fOcQeAH/GkfQeQX2vO+eyBzEI3MiQxKBxfmjp9mAtnX1lNDo7IT22GdnCZzNOZ7A2Mzp+C64jmRQZETVjkoi4CUAQZwLuoRPdo5hT1nYIPHiVIG58+fr69//et69NFH02fnn3++zjnnnEI0IwJ9Dw0N6aqrrip8t2TJEp133nn62Mc+VkjPA3AE5YouOTTDXaPR0LJly/SXv/xF0mja6Q477KAlS5aMi6LwO/ISV568/+hM42JmH5/vJ3AfFXvfh82Mv+jIwaue26sdHR2FSKvzc8CzSt7xjnfofe97n4aGhnTjjTeqpaVFjz/+uB577LEC7b/mNa/R6173ulQOP+6taMD5Ps0ZUq2trcmzTzv1er307lzGnZtzGf/3d/Dk+zk7ns85erztKVOmFO6r9b5ykUBw4/gDmJ/jqBkvBNra2sZdA4CC6mNFCedMlz8b5UGMVLoC39nZmaKJjl/S/OIdijnnJ0C6Ms/jvOOu2tw+ztG387PcvaU5xTyee/OU0ByU0TTgY0UuxTGDf5xJzdJzofv29vbC9zj1Pcrvd1rznu+xXKYK88+lZOecBLnPkT387cZSnIsbyBSNibiMjhH2ZGdnZ+n+Ba+RR+d0wIgDCjo6TeX2LOMAF/V6PRX7zOlY0fHHGnhfMWrI5/4Z+IrVc90ZCbj+HHGV41FlMOmI4itf+Uo9+OCDk318k6C/v19f+MIXdNlll/1d2t8MozCRF1IaH2GKBkHcxDGED0QFtK2tLZ1zdA+JP4OC4nnYCG36ygmWZvPylAMfezQKc+/lSslHIxaIm9AZiEcYIuSUIv/bUxHKzj75/znB0t7eXvD05lLA3OvGWCuV0QPuuYvu/bmJvI88F6N1EwlWh9bW1nGl1aMDgR9fZwxeH1OOTvntjDo6KbwvZ9zQJZXjfC5xr5RFjptBVDYmAgz/SKM+D6lIT8uWLdO9996r+++/X7///e/10EMPad68eXryyScL75Qp5oODg7riiit01llnFYxEadTQu+iii5o6FcDh9ddfr+XLl0uSnvGMZ2jmzJmSpD//+c+F6yokaenSpbr88sv1b//2bzrhhBP0hS98Qffff/84AdoM3Mng0cSDDz64UNFOGqWf7u7uJPSjsotHOhdB484vT2/yNFMgenkjj/TriqD3mGrk78BzUSLiuCIfzjmT/O+tt95axx57bBrbL37xCz3yyCPjjJyf/OQn+vznP5/Swbw6ofcdP8PozylmzcYVIcdbynikv+PGUK4oEpGEspRXHBa57zgbF+cdnWoOUf7Ez6R8pDkWfuO5XMaLNGYgeFsYLdBYdIT4GHHgRGMozp9xgL+c/I1pe6wJdwPmZJ/vB58fexKcRCdMxKU7xmljIgdT/NvxHZ+NtR7g1eg57kDKrSvPQWOMz2mqtbU1RSX9nJ7zAB8HPMn7yK2z48Gv33Bc837ZncE+F8dBvJs5yujcPihbF+iB8TUajYLuQKXzCG6kgtfo2AByGV04Hsp01NyVHLSFEV92/ZdUjF7HDC3GGIMMOXwCzRxeESYdUfzFL36h6667Ti9/+cv1gQ98QC960Ysm+2opLFu2TFdccYUuuugirVy5Up/4xCf+6jY3w9MHj7xIY2l4MNVcuNs9fGUblw07MjKSDrdHj3y9Xk9pUu455n1pLA1wIvB3W1tbk2fNN0Zra2vB8+VzipEob9eZlHveUJQQWBhGXva8LKUy/u9956JCOcZVxjwRHBz2B+8xmhXTXWCQ4CxXLIT3cvPK4S5XhTMCQgGaiR5tcOrgnj03FN04dSXIcecpL1Eg5YxdfvvdiiiPpB/7XBx8TtBFmYIY39sUoBok8/ZxeNQN4fT444/r4x//eDaiIY06CY8//vjSscyfP1/nnXee1q5dmz7beuut9ZrXvEZXX321NmzYoIceekhXXXWVTjzxxNJxr1ixQj/+8Y8ljeLulFNOUaVS0Sc+8Qn19vbqoYce0iWXXKJXvvKV+tnPfqZ77rmnMJ77779f999/v6ZOnap//ud/1pFHHqltt912EhgbBTcUDzrooMSH4l1Z0hiPyBkuTqfQSHd3t9avX58Ul5yi4IqmZ2QA8M+BgYG0J11B472YfimV884Y2fTIOm1Eb/7RRx+tu+++WwsWLEifz5kzR7vvvrtmzJih66+/Xo1GQw8++KA+/OEP64Mf/KB23nnnUueWJK1fv15f+cpX9NBDD+lf/uVf9KY3vSl9557yuL8mUhbjZ9E4K3uvWh27HNz5iqcWl0FOGZSUnG7x8xwtADEFciJe20wW5yIOOUPHIdJTNHLQDcARCnHMDmppaUn8xWVQdIRE4xEdAUcy+kZOJkYnXnTYxD1WrVbHRdCjM9GzVHIQjW8H5uJrkTN0fc9hdHsass/P+3WjwB2dOKTcGeXPenEUPqMNIvcuY8torsypBZ7jezE6xzhGRka0fv36cXzIIV7/NWXKlJSh5vhBziPbvP6Az9Xpiv85VwndINfL9hKy3/dUb29vyhjJzZ13a7Waurq6UuQcp0WOP3F20fspi/pK4/lEsyygiRypqc1JPSXpox/9qL7whS/o+uuv1w033KBtttlGb3jDG3TMMcdov/32K/UiRVi4cKFuueUWXXPNNfr1r3+diOqFL3yh/vVf/3Wyw9kMfwdwJdsZYPTCOZQZijlG7p7HGMlpNBoF5bnMI7upAINg3JFZuVEYGR2KV+5chzMjB/cGIjAnihg5lBmnZd5XnycGSAQX9FFQ0W5UIlzo+pyikI7n8qKwlsbSd+LccgyM9sGfr1cUHjkF1+nJaZn5S0U6cq8yd226UsjcPJWrXi9ejJtLJSxTVGM68mQgp0w1A3A3ODioP/3pT9pyyy01Y8aM9B3Q0tKi3t5efelLXyo1EiXp2muvTffmxv0wPDysL3/5y8lInDJlio455hgdffTRamtr09y5c3XOOeeoVqvpxhtv1HbbbZfO/kW4+uqr0zhe8pKXaIcddlBLS4s++MEP6pxzztHw8LDuuusu3XXXXYX3Ojs71d7ensawYcMG3XDDDbrhhhv0zGc+Uy996Ut10EEHFSIKAOuwePHiZPjsuuuu2nLLLVNqjtMd6Z/sGTeqeNYVZdpHKXIlIgppPx+TMxSpjiiN359u4MT0smq1WlCqAMYb6YvftJtL+/rIRz6i2267TXPnztXuu++urbbaKo1333331cUXX6yNGzdqyZIl+vCHP6zTTz9d++233zhjTZKeeOIJff7zn9dTTz0lSfr5z3+uer2uN73pTaXywA045hLlD07JHDhfjZ+THgxviM4AfnLORHDEGBgba5qLNtNWLuIb+ai/kwOe98i/OzDKjGPWuUy5p+1oKNJeT0+Pent71dvbW4hmusGIQeZ7xvkO9Bhx6nSdO6ssjfHnHB3wf4zmlhn80SBjnjnadeejR6N93HEu7lyNDnHo1vuIfzMXL7BTJuPcyUVfHR0dWbxTLKher6dKxWWyJud0YCw5vOTA+VSZzkdfEV8tLS2JX/i83TgHR9VqtSDzy+7THh4e1rRp07Ru3bqs48H7gq5iqjV7uL29PTka3fntfIdzitHA9T4i8AyVYV339logkaa6u7tTdkfE7WRg0obipz71KZ188sn69Kc/rSuvvFJPPvmkLrjgAl1wwQVqa2vTs5/9bD3nOc/RFltsoZkzZ2rmzJnq7+/X6tWrtWbNGj366KO6//77UwU7BrjXXnvps5/9rI4++ujJDmUz/J0gMkIIy4kxx8R5PnqwAc+7j54wZ5oQe8ydZ0yTiSbGefgYct+VbUa8nY4PV5r8kH6uf+9zUyAy1qiklY07l1YJ4CnLReNoqyytN6dE+rxy6TURJlJqcp9hpAMwx7KzIjBtp5WyA+JOA+3t7QU693MdTos5BRrFDxy0t7cX7tbKnZnJeQyBsjNVuTVtJsRRPK+44gr94he/UGdnpz784Q9r9913H+eVvPrqq/Xkk09KknbYYQf98z//c8LD4sWL0zUI3/zmN/XpT396XJ8/+9nPtGTJEkmj5/rOPPNMzZ49O32/55576uSTT07HCr75zW9q66231t57713A5aOPPqrbbrtNktTd3a3Xve51aS577LGH3ve+9+mCCy4o4GLatGk66qij9NKXvlTt7e16+OGHdfvtt+uBBx5I6/boo4/q0Ucf1VVXXaUjjjhCL33pSzV9+vTUBvvNjc+DDz5Y0pgi7EIX4e/7FCWVtYuKmDvY3KlAhAnAMVKmNOHtl8anRUe64jvS9YaGhrL7v8zRFdPMHCqViqZNm6ZXvepVBSOEvvfZZx995jOf0ec//3ktWrRI/f39+sxnPqNnP/vZevWrX6299torPXvffffpK1/5yjhHxS9+8QtNmTJFr3/960ujKZ4ZMZHjJVZzRfbk+CGRJj8CgRzi75wh7335mNyQz4015wSbCCaab4ykeV858HOVzZyVuXa5jw1+6TIBh5rjn3WL2TL8HbMtWGsU6rLzZdGQBaLcdMdrbAseHJ2zjDemw0bZFiNVOchFgN3x3CwqTLuDg4OpQrfL7ji36DDAaPDjJHFezWQU0NHRUTBU3ImMsZkblz+LIddsL/EuAI3xjtNkdJyhr1UqlcJe9jk6vXd0dBQcbmU0As7j99BrpEPGBI/jf85CAn7uMOdU97FGZ5RDLoLteo7D39xQlKTtt99el19+uT75yU/qq1/9qq688kotWbJEQ0ND+t3vfjfhGUYG1dbWple+8pU65ZRTSr3Lm+F/HpzAohcGiMpsWZQvEivPOROdLJE6REE2mQgL/ZY9Hz2t0cvn83BDpKxvPptM/ndkQh0dHYVolfcTqyb6eGPKWG5+RNziu7n1oI/c2QAXJGUKy2QM8mbPe3/MoVmFOfrIeeSZd654zMjISCF6UJZ6Ssqf0wXCCsXGmXGZgGk235wHswyPzfBZqVR022236Re/+IWk0TPgn/3sZ/XhD39Yz33uc1Obv/3tb3XTTTdJGlUUzzjjDG299dZJqRscHNQZZ5yhJUuW6LHHHtOvf/3rwpGDJUuW6Cc/+Uma76mnnlowEoEXvehFWrx4sX7xi1+oXq/rwgsv1FFHHaV99tlHu+66qyTpiiuuSM8fe+yxmjp1apqLNHrp+6mnnqqvf/3rmjZtmo4++mi96EUvKpw92XfffbXvvvuqt7dXd999t26++WY9/vjjkqR169bpRz/6kX7605/qTW96k4466qjCGGPaqeObtB8UDo/+eIVZ1i96hiuVscgeKVPSaCR0/fr1aX96lemcY6tSGYtIumPEI1IAbfb29mrLLbfUhg0b1NHRUeDXOccE/GaiaEIOnOa33HJLnXPOObrkkkv029/+VpL08MMP6+GHH9Yuu+yiV7/61Vq8eLG+//3vp/d33XVXPe95z9N3v/tdSdKPf/xjTZkyZVymkUfmmhlMzgviXHFKRvkFfqPjzBW0yfAh+ve/3Qnl4Eb2RDww93cZ5DJlyiAaKXE+zaLP7e3t6uvry9Ih9ByvSckd+8jJVn68qExuHDne6eOPY6bNeOaaKHTUhThHF+Wh73PvK/7veG6m3LsMi051/75SqRSiftAQkcFYyMj7wLCPRnWtVkvyC/yU0Uyj0Sh13LpDvczYjXujrC8vpCWN7U+PxsV2Hceetuxjj387bfF/1Cd8P/nfnuUxPDxccBTm9C14D07suO9imqlDpA/GAW1OFL2NMNlgxtOqerrNNtvo3HPP1TnnnKM777xTt912m+644w7de++9WatVGi1M8MIXvlAvfOELddRRR2nu3LlPp+vN8D8E0Ujwzx2cWfmmcmaPUtPS0lI4LyiNV45zBO0KSC61JCoBzQyU2H5OWSgTlvF/PE85L6x7UDfFII5KiHsuy+ZVZiAC7vUnpTgae6yNA/NqaWnJnkVspiw55DzUE+Ekh1MMurI2Go1GwSuHcIfGcvQcU3C8T2eiM2bMSAq+VLyUmPWhahuCL/YXBUaEMkMxFxHwsca+/vu//1sXXXRRoW2MxU9+8pPaaaedtGrVKl166aXp+xNOOEHbbbddElrsq7e+9a0655xzJEnf+c53dMABB6i7u1uNRkNf//rXE45e+cpXaqeddipd1+OPP15LlizRgw8+qN7eXv3gBz/QD37wA3V1dWnHHXfUY489JknjUlM9MnzooYfq+c9//oSKend3t17ykpfopS99qR555BHdeOONuv/++xNPu/LKK1Wr1fSKV7xC0qjBu3DhQknSbrvtpi222CLtbxfCzs9QLFzZIuqdUy4wMKnGG9eSeQK5+aHE+iXm3o+fIffMh6g8RcjxkpxhEMGjFzknTUdHh97//vfrtttu009+8pNUpGj+/Pm68MILC8++4AUv0Hve8570Ho6D733ve5oyZUpaq4ibZs6YiaJ0U6ZMGXduuix65m16UYoc5Ip/uXOh2btx78eIxWQhF+1qpvS7EyxntHp6vrdHURWikRGn1DZwR200rnJzj3ONqYxxbXO49f2aw0FOJjA+f5fxx/F6JCy3NmWOjJi+H8eV04U8og3AA2J67NSpU0v1cNrysfnvkZERtbe3pwyEskgf7zAP16NGRkbU0dGRxpzjPeCANcid64vFhxzQO3Nzi1lYubO9Od3Mncl8l5PjTlNDQ0OaPn16ovlarabu7u50xjQ6q+gPY9ANUj/a4mNyo55nkTVxPjnASZWDXAQ+B3/V9RiVSiUZf8CaNWu0YsUKrV69Wh0dHZo7d67mzp07rnT5Zvj/b8hF4HJRkmYVBt3DA8N1T1vOE5hTkp2Yc8yhLGUvtieNKVvRM+jP5c4k+sFr2i1TkKRiVapNFfA5Ayie84vpuaRxOEOIHmX/Pxrq0cPnY2DtcofXJ4N3BK+PK/depLWc4tzMA+bePQeM3VwUeHh4WJ2dndlD67ky2YCntDI/x/1EUZlmc4ie0TIay8Hq1av1+c9/Pq3VEUccoRUrVuiPf/yj+vv79clPflIf+tCH9P3vfz8JswMPPFCHH3542muuFD372c/WwQcfrHvuuUfr16/Xd7/7XZ100kn6zW9+o//6r/+SJM2dO1fHHHPMuLHE9TzttNP0xS9+UQ899FB6pq+vr3Dv3gknnDCu+q5XzCxLPc45FqrVqvbee2/tueeeWrlypa699lrdeOONkqRvf/vbqlar+pd/+Zdx1U69b2m808cVoDhfqXjnntM6a8r/KNde0j225fQDfaFUcQ4G+nADxvtGISsrT+/73j+P/UdwZRq+6u8yryOOOEKHHHKIHnjgAf34xz9OUV7a+Ld/+ze97GUvS/N62ctepuHhYX3729+WJF111VWqVqt62ctelt1TZfvM8Un0w9eC4mk+/5g6lvueKx/KcJNzgvpxhmaQM3bKjKNm/GVT+I7znInGlTN8/Bx3fIZxOL7c2ZejyVwBLmgrl3rqxh08LJd+F3ES9QBJhfRAxl2mu0aHroPTkEPOyRWNwJzx6ZF+aGHatGnq7e0dJyO22GILLVu2LDtmadRpSBEfxxXOT3CZMxLBY04e+tw92yHXhmfxgJcycKM23mkZgwzx+qeYyu/6aISoP5QZ+pIKemA0TDHEI+Qi4YzF1yFmpET9zfcifB4o04XL9KbJ1pb5m9+jyPnEzfC/AyJzjQQc03kieEqBR28wapp5uj1vP3pWHJp5JnPKo0OZ8HavGwzJPYGNxtidhZMJ90/WaIhMxplDM8ZV5tH072AuzkwmYuZ4xzel34meywnK6N3NCVQXLJPtF68vbUVvLh5v6CKuGcJm3bp16TNXhBm3j9+NCW/LBVuZoPI1GxgY0Gc/+1ktXbpUp556qp7znOekZ3Ne8qGhIV1wwQVas2aNpNHz3yeddJKGh4d1/vnn6+GHH07GImObPXt2qi4KjXNfGfN6y1veot///vcaGBjQzTffrOc973m6+uqrU/9ve9vbsndaRZrv6urS2WefraeeekoPP/ywHnroIT3yyCMFg3XfffctTRNvtodyhqI/O2fOHJ144omaNWuWrrnmGkmjBkhra6vuvffe9NxBBx1U2kc0rBqNRjqf6FWDUbCoPhjfjQYc4EpKbgwYh3znVTSp7tfX15eUNIwTeHCZc4b+okE0EcQzlrn14ndLS4v++Z//Wc973vP00EMP6brrrtOqVav0pje9Sc9//vMLd/E2Gg294hWv0NDQUEpN/da3vqXHHntMJ5988ri9OhneGvdcLsLn/N5T3qIDDSV6MkbaZCOCruzm2pHGO289KpNzLk4WP8yH9crJzLL33alUpiDHaBFtxbvgaCMq1MyRyH40EOLfEXL8WBrj37mzh74eOSO6mUycCN/xO/Zo7jydv8MVO+z5eH1Lbky5cQwMDBSu5vFnOjs7U7SuGQ+IuM9F8hzncY0wUGImRYTooHZD0S+9j84pH6NHudkXObyMjIyos7OzsNbRGVDmXHO85HSq6BQBPzkc5/QEnHG5SsGum+b040ajUXDO+PuTDeD9zQ3FzfC/F3IRGRh3junDhAGPDjbLp3aIG5pqjg7NwufNyoEzjphiEZkBfUSG5kwsx3hyjCUHrqyx6SOzi2c/Yj+SxkUUade9dvEANX2g1Dqjoz++i2s/WWOtbL4O0ROWi17moqMTAYIFhTaX9oOHNeetdkYdlQ33wBLNYR5lNBnpIZc2TZvXXnut/vSnP0mSLrjgAp155pn6p3/6p2y7jcZoKuif//xnSaNRvve///2p6ttZZ52l8847Tw8//HBByL373e9WT09PaieOu1KpaNasWXrta1+rb3/722o0GvrsZz+b8HLQQQfpOc95TnZNy9Zoiy220Itf/GK9+MUvljSairhq1Srtu+++494vSymPUJbOFd857rjjVKvV9J//+Z+SRovrAM985jM1d+7ccYI47kWnQfgfZ4t9vrGASr1e14wZM7RkyZICHcBXys7ReMSxWQEW90i7IsI9qvGM4kQOi4n2d2dnZ1NDscxo/Kd/+qdEx/4OuGBM//qv/6p6va4f/vCHkqS77rpL8+fP1+mnn64dd9yxEPGdDC+IayONP2PvezeHA3gEiltZSleklYlK1OdwHTNSWM9cdkeu/5zs4TO/Hio6x+J4mvVHgSX2CVXNHaZMmVKIXvm6lYGPa2hoKN0NmNvTOQdOhJzinjPgcwa36ywOubG4nOA7P3PmxlyMFsFLymilUqkUDLwyo9BxnUsfHRwcVGdnZ7YPCsXUajV1dnaOS2GdTEo6z/mc4zijzpObM9Ey6LVWK1Z19wKJsf6A00/MUCmLzOM0dh1ooowpeJbTYC6bLPLamMoc5YSPy/ERn2W/uq4XAwTQSu7s42RTTyf31Gb4h4XoUYnM2NOsyrya0YNU5kmJkPOuOuOO300GcgpFvMhdykfj4vfNUomikuiKkv/OjS96vsvGjfFW5gl2psAcywQR43Xh7Qeyc2PGqN1UiF6sSqUy7rPoSCiLVsR2cucsHXLjrVbzhZuiUI/v5pSqsnX1Sog+prII+dq1a1MxGmlUgH/+85/XI488Mu75wcFBffvb39avf/1rSaNrfeaZZ2rGjBlJ4LW3t+uDH/ygnv3sZ6f3XvOa12jPPfcc17c0nt5f/vKXa8cdd0xzkEYNhX/7t38rfJZrqxlMmTJFz3jGM3TAAQekqBjgXv2JYDKH8lmr1772tdmrmHLRxJwzyPcbyrZfgO0RFvhcLvLjtCUVnSNl/NHPCkdlw9OeYnqsO3uAnPIPf3WFy8foQMEh3ptoncq+9+yN+Myxxx6r008/PVV4XLp0qc4++2zdcsstTQ0Dhxh5oc+yCHiZY8y9/M3o3emkzMhwKBt/bI+2mq0JgFNhoj7duZCTrRP1g+MWfHZ3d497xgvXeFu5vqDpHO8nXbGM57L/PPW3bA6+V30cOSOIcUdHw0RZNnHdgDJDOY4ljjlmkZQZ70TPYntALKrkf7NWbW1t2TT/nJFFG94XWTpl8iTyrbIjI9HoHhgYSP1wXCQ6mCN9eGAh5zhxOe78O+fAifw9ftZoNJKxHR0B0XCL+It0HNcux0M8dTXyt4j7KVOmjKOpydxtLW02FDfD3wiaCRO/HwzIeUqk8cZhbrPEvjblGorc1Q45huxCDaPQGTNzKjOUXJCUMRwH9wjllMncO1EZzOHBldpoUPr8PfWFvtx7mUu1anY2tBnkxpnzzMa5T8YhkGvbcVqmHOfmkvPglY073tPZLJJdRu/+2Y9//OMUrSHiRwqpn+l76KGHdOaZZxaMSiIuXmkQReuDH/ygXvva1+r444/Xa1/72vQO647BHGmltbVVp512WmGcb3jDGzRr1qwJcZQD2i8zDuNz8e8Iuf5zn2EEHHvssXrNa15T+M6rnXobZXNzR5g7m6LiybNTpkxJdz1KY8UaeCen1EXjriyFtF6vZxUBnCcTlaB3gKd535Mx+ssAh1pOCUZpgpflFNGDDjpIn/vc57TTTjtJGt0Hl19+uc4991xdf/31WrBgwaT2qM+BIwM+JtaaFL/4jhuzuYhOhGYpm5MBN2Zjf2XRet+3ZWloUrnSGY2L6PCMEA2m3LPwsagU5wxZj5rxPw6QZpUdJ3JU5L7LZUjlIFet1veuz01qLjfK0h5po8y4zVXHjM5N3o1nb8vmxHhiyrvfp9gMnAabzSnHO6IMdFpwGvZiXPSTkxGRt0zWUHRDjn79THyZXGHu6EYOuWvTHA9OH83WKOfcyI1FKu5nN3r9mVxAZLKO/s2pp5uhKUyGqZUxA0/J8/vomuXR067/dsh5/Db1vkL3HNfr9VKGG/8vM1wm49HOXc2Q6yNXMU8arQSYSwPxcZUpDjlFKT4/Z84crVq1qlSg+pp4208H95E5lXmyXZA7vssUME/19f4oZFF2h2R/f79aWloKZcXph984CuJVCdH7mMM1yk4OcvNYvny5br75Zkmj637++efr61//uh588EENDg7qc5/7nN7znvfo7rvv1p133pnea2lp0fHHH6+DDjpIg4ODKS3MS8pPmTJFxx133Li1i7ScG++znvUsHXnkkfrVr36lffbZp1CdNKeYlCkaZUpMxJNfBRHfi22UtRefIQJSqVT0+te/Xu3t7frpT3+qF7/4xYWrPXg3l/Id9wIGThxDV1dX8thiCHolPypBwye9b3hGpGVPHfczjt3d3eNS8mmbqwvK0iTj3DwCl3PyARPdfRbHTlsxM6XZXYjgequtttKnP/1pXX311frVr34ladRJQmGk7u5u7b777nruc5+rww8/PMmd6L13Xh75EM9R4CrH86TyYxPNnByTTZWP7+fOFsV5lY0BmptILkjFKpb+uTsLaTOeRWOcfsm4t5FLxZPySmqj0SikS2IAkioJ7bhjj3uCGV8uGtlMXjTjz4w/vu8yyI3lMmcPUHbPbm6MPg9Pbef9uN+Z69DQUFMj2PuKDlLWslqtjrs6JNcO70R6hG9P1sHk57qbOTHoz3GOU4T5ltEC7+ZwwnpSudzn1+z9arU6rtgZZ8kd3LiMUfyy/iLuPCofDfTc8ZU41tx+m+z6bDYUN8PfBCabhhGBzcPviZ5/OhGsCH65sDR5QzOmfpQZyLHNyeSBuwKT8yo28+w2U6A9zaWsqEKZIKXv3OXIzH0iIzmCXw/gbZVF1lwxcQabO1/jz/j/nLcYGhrKKv2tra3q6elpWlIcpTx39oA5+Hzi+QzHVVkBKJ757ne/m545+uijNXv2bL3//e/XBRdckIrKnH/++YXx7bHHHjrppJO0/fbbJ4GANza3ThMpELn16O/v11vf+lYdc8wxmjZt2rhoTIRmPIEx5Tyh/pzTbTNFGyU19hNpZHBwsMBvXv/61+u1r33tOL7iXticgeVXVDQaDW3cuDHRqtNszmCC3olcYbQ4T3EcOa2hBEoqpOJxXjDHO+B1FM1odgbKx+gOnNz6Tps2TUuXLs1+H/FOW2Upe+4Eit/jnJkyZYre9ra3ac8999QVV1yRCkxJUm9vrx588EE9+OCDuuuuu/Se97wnRbuBHG/IzaulpUX9/f2laXbx8vUyaObImIh3RtniEUJ3eJS9i8JcJp/KjJIcjmij2RwYL06JKIOiwQt958D3pzR25cCGDRuSIesp2O4ImEwUhmdj+p9HifzdHL3mahrwd6QNL/rk38U9XiYHaWPjxo0FXlFWxbisIqfjJDpn3DgfHBwsFM3KzdPxk3OoebZSbj9H2nXHgNOvRxQdtzE9Pkfruf/L8MsRg8HBwdJ97f24oeZjYS09hTrSUzPnhX8fdYqy/ezr6Z/F9cqt6WT16c2pp5uhKZQZOc6Umwm9SNzxuZh6lnvfYVMNkxzkQvARyrxO/n1MC3PwMtsTeW2aKRTgL8eEc8pZLA/t0MwgJsri+I6HwKOnebLeqDjGyabASWo6n9wZD3/OCyw5s84VJWI+kVadxnORzNxaRI9tzljI/V2tVvXnP/9Zd999tyRp+vTpOvroo9NcPvKRj2jvvfcu9N/T06O3v/3t+sQnPqHtt99e0lhakd9TNZG3Mqdgxc8Q0HPmzJlUWe1m9JEzmnP9lbUxkUIYz3MBOCqczplLrq8y/ueKUKQB0ls9BYl5ehQa48cNRL5zvumKpUf3PdrjSkxUTDBovc2ya0ZidKUZv3XHSuTzOaUmt/fgOdEz7xD33cEHH6yvfe1rOv/883XCCSfoec97nqZNm5a+nzdvnj74wQ/qwQcfLOxfIDpSfCzS6NlbzpxGAD+5qsxlEJVacNEMcg7TqJw2A2TGZMfo/eRS48sMSY8Wkp1TpqA77eba8rHEaxBwcETjjvFG3t9sfv5cDpeR3iaS4dGBnDPggMiL4T0TRd+cX5aNx/lKs7ZyjkzHhzvAyubh7zQbb3yuUqmoq6trUndXS8Xzxe5Uk4pVgOP4mhlW8TN3TETZ4OA4chqOFX1bWloKgQh4cjTKNsXRxPu5TAg/K172vjRa4Ty+O9kgyWZDcTNMCLnNFvPam3n7mwnFyPhjX083r3oyAjIymthHTih5+wi/icawKcZtzttaht+YfpB7z/9upnjnhMNEqWWTgRxTzlWPi4ZHM6+bK8pROeV7fiOQ/ILenIfTFRmAQ+nuFJisIpIbV+67+B5XN0ijVR+9Ol1HR4fOOuss7bfffmppadFhhx2mL37xizr88MML44L5ozDENcgZ+dBYM6cF7zZzkDg8nVS7OKZmUQeHsvOl8bnoTGk0GoVCTwDVG8v2LjTBj6cjQieusFUqo6mfRDQlpbtPmaf/OB+oVCoF5Yi+45liNzw9TWl4eLhQBKQsJUsau05moqhPpVIpVGltdibH24/4jOXuJ3Ko+O+ddtpJL3/5y/WBD3xA3/nOd/Txj388pQ9v2LBB5513nq666qrC+eGnnnpKDz74oO644w4tWrSogAfPMMk5hfzzic5nO6+ZTEbJpkA0qKOx45GLyUQMIp5zcqMMXIYS4fb+fXzRSC5LbaRSswP061FGABw300Ny9Jm791HK6xyT0WEcnK5zlSh9DJMB5Be050ZIBCrjlo3Nx+E8h9T41tZWTZs2rcAnJyvvY/vxb/py/DhPjsZdvC86OlziXLyf6JhyR54/X61WUx2AZvPMtQXe/D30CedVOYfPRHLUx+t0Gmk5F4zJObLWrl2bDURMBjannm6GplCmrHNmDmWijOCafcf3zZTTnJCdDNOajHFWlopJv5HB5dJGJhpDriTxRO9ERaRM4JellTjkzgXlwNO4gL+loRgZvCurjUZjwkuNc0LAz3vlAOVZKp5fjGPCixi/ywmg3PjKlMZNVRD/8Ic/pEvst95663R9hAMFadwoiONxA6OMtidSIMqeczyWQdn38fPooc2lf07WyTJZgRfbjEqLt4dxV9ZfjHLQnlQsNIXTqbu7O6XmgVsvHNMsWkBqbeQPrnS3tLSkPeEpWz7nsoiWO0OcV7jxG/EWr+KJ30cjzKMtfuSg0SimEsY5T1QBFv6211576fzzz9ell16qBx54QJJ07bXX6v7771ej0dCKFSvG0cm0adO05557as8999Q//dM/aauttkpnFKOx6wY66+Q8YjK0uinOkzLcT2ZfwPsms0+dNnDaeU2BZvLZ8eBpe/G9XBpvmaGYuw8ud41Jbj5lPHcihxHvS6N0l6sGWbbG3KcsjeHHZX6ZHPX2JlpTztHHaGFuTN5WGf/3852+F6Wxa7RiP26oOI/I6XiuJ8VUfIxexh7lmP/d19eXxuWOiLgvMbrpJ87JIRpw1Wo16R/NnCIcWcrhMscLcnIhvpvjB5H3Rl4T23XDl2fgifF6lNjnZHWUzYbiZnhaENNTypTpskII/m4zxTXX7qZGKiIgoHIbDyClJmdgMK7JKAfNzgRFKGsv9t1Myfd1wUNYq9WyDNYBwQDT53eZETTZu8sitLW1aWBgYNy7OaHdaDQKZ8RynurcOOK65ubg6+IKTsSRK+Q5YyamFgLNDKqyz6666qr0/3HHHTfOgIkXD+fw77TK+uWKNTUTUBONM37nilJuHGV7JF5bUKaEShMXA4nvuqfcxxOVtjLelUtriooAikuM8LgBJI0pnrxDQRrezV3HUIY3ztLggIKOBwYGUho/CrNHYWivra2t6dk2ohEelc7hNypIEykcrsBgnDvNwKN87jlHjX/mNMG4e3p6dMYZZ+iGG27Qt7/9bdVqNS1btqx0XOvXr9e9996re++9V9JoUZxdd91Ve++9t7bcckvtuuuumjNnThqDy4VmBlScw9OBMkNRGq/g5fqfCFyB9zYoIhXbyRmNjg9oucxAjc7Psv2cc2pyN2CzuU1Eh3H8ZcWMyiJB/rfL9EjLkVflKobTfpmBlJsb43IDqMxRuClOSucNFNsZHh6eMAWdz3L3bMa9Hfuq1+uF93L6AE4g1smrkdJmLHxEH54dECG3zrkjIjk8OZTJ/sHBwYIzz+fjOJgsf4h6R9ldiH6vay76uHLlynFtT/aM4mZDcTM0hbLN09/fX9jcZQZRTvHxv7nkdKLL5B3+2oI2zTzh3ofnxJO+BUx2o0+m0qCDM9KIsxygkPKOewVdCW5mJPI8qZYTecK8/YmAtZ0oQpErROK/mSspoi0tLVmD0wGFlOiHtwm9VqvVQrVThIwzeAQvVURjH2VV2srOXOU+u/vuu/XEE09IknbbbTcdeOCB456ZKNUtGitSUXgAMQoG5LyNsSgDUaFoTDuQQhiLfsQxxjOkzfZSMwNHyhuZvieYF8oJ/3uUyyF6o739GAnwqyfcUEPhnTp1qvr6+sZdE0EfU6ZMSfuYd6vVakHBbG9vLyjLra2t6u3tLXiT3SnU0dGR9jPRZ09HLXMySGNnLCfaW5FH+TuuCFYqY2mJvItihhMmF1GMzhtXsCgEBG13dnZqcHAw0eZRRx2l3XffXZdeeqkWLVqkzs5Obb311tp66601d+5c9fT0aN68eZo3b14hetTb21uopipJO+64o9797ndrhx12SOsXjZkcvsqcS5OFZns0OpFyRo2nwOX2VpmDLbZbNgZ/pl6vpwJA0G8zQ7fZXof2HdyR0Oz9ZoVIfC3iHvbfcW65qFEZrnI6QdlY6/XyuxB93E7zGHBl10DlIFfZNI6jXh+9XscLxZS1N5HTgj4iLvw8No4d5lOGM/8uOi2YW3RMeLS4TLfz75rRqYOn2vM+tO76EAXHPP03nrn13xP1W4YbcFBmbMe55apoT9aRtdlQ3AxPC3JXHDSDsu/d65+DTb1+waFM2ZmI4UvFzY3An4xgje13dHSMYzBl4MZcFPqRmblx7Uw558GaaLzMVxpT3p1R58CV42aQO2uSG0uZh9w9h56K4Up1M880KdLRkeEeN9rySpYA/dfr9aSsO+RodyL6yjH+a6+9Nv3/hje8IdtuDmc+j7hWZZ79MsdM9KTn0sU6Ozu1YcOGcQLHwSOMzfa2R93K5utrvilQpiDnhGXZ3Zq+53NGNLwLpwXvsRbMDz5C3962OyFQitw5wRp0dXWNO2PihURaWlo0ZcqUFLmMaazuvXdjx/HgzrGJlKeoBEY+juGKocjZVhyDPgb2Hpdpu7Hpa+lXBIF/V6Qj7LrrrvrCF76g/v7+dBE86WMdHR3J0fTEE0/ooYce0p/+9CfNnz9fq1evLrSzcOFCnX322TrxxBN1xBFHJNxPBj/xCMPTgeiUaZZ9k1M+J3LiRuDeTSI6kZflnIkY+zFa02xsm4IXr/Zbhu9mUcoIHnWK78DjI28qGy+OhmbGYw68iFaZnuM8wKNFZXSFDPMUzFybPj7whjPV5WZOJk4ku8t0Otac9nw+0aHnOHcZzG/2lvczmSyZOE6e2bBhQ2kbQJQTrju4rIwVXMucaRPRSzPZKI3J8VwBqTiWHL1syv77qw3F3t5ePfDAA5o3b54WLVqkjRs3qr+/X52dnerp6dH222+vZz3rWdp///0Ts94MzWHDhg264IIL9KMf/UgLFixQS0uLnvnMZ+q4447TaaedVnqe6+8BZRsnModNSUNsxnQibEqFzAhlmyfXZ87gcAa1qcLeheGmeG6iZywyFWc4uXcAlPWJGKV71SYyevzvWq2WlLv4Xa4NxuwKohssuTMhcVwxgj0ZvDaL7DEPFFVX+P19SUkRigZZDm/+THweJcvHM2/ePC1YsECStMsuu2ivvfbKjjVGOh0wNCKOcvdHdnd3Z88HlZ37cdorE24OfpYjKiVxPrSbg4i7idbbDY8yYz2+j4I0Uf+Ox3q9nhwqMR0uKgTeBgoRxqCklEaKN9/HHg0EFJJqtVq4bxVjt6OjI33mhqIbCuAHoy1CozG+1HoOorNqZGRkXPTZxw99SmNnC4kceeEFf8eV//b2drW3txcqrcKDarXauIwP2mo0Gum8Ic+hfILr3XbbTTvuuKNe8YpXqK2tTf39/Xr00Uf16KOP6s4779SiRYs0NDSkr33ta5o3b55OPfXUcbRbxgM2xXjhvcjzvW0vsBXfcWUwV3gjQjMjt7u7W+vXr886oOJccQaUpTs6z59oz/u44h7OpULyDHy+WRQsZ+CUOZMcJrN+sdjUZAB8NDueEmmAPdfMWcA46CNGn/3dyGek0ewFdMxmjr44xjLwCDxRVKcr3veskRzdsZ/8HSJ6HmXFGC2DuOaNRiNlyG2KnufXlDiecLi5o+jpZBU00yHdUcb+myhLaFN5kcPTNhR/85vf6POf/7xuueWWpilBQHt7u4444gideeaZeuELX/h0u/1fDwsXLtRhhx2mxx9/XNKoJ3lwcFAPPPCAHnjgAX3nO9/RLbfcopkzZ25y25PZ9JOFuBH/Gq9psyjYpkDcTJsyX9+MbG73pG3K2Jp5IFGQNpUBxw1eJnB5bjLl23PtN2Ow/jwVG3PfOeQO36N0++dlSgZCxaMcjUZjXIW9MuDsVu6cXpxLTmlwRSVXTr9Z5G+yDonrr78+/X3UUUeVGuwOHk2S8pWAo+FEv5uSwho/z70blRw/q+RznYzgj326FzmXApabb075KZtfs71I1LysP0+bd4VuaGhIs2bN0urVq1O0wI08Hx+KP3sJxSf2h0FJtIdn3FBzT7vjuL29PY2VfZQ7E8wYywzr6FiKxR1iiq8bf7kIl7dNv25Qep+xEiYKFHuyGS+A50A74M/3h8PMmTN1wAEHaP/999dRRx2lq666SjfddJOkUb1n/vz5Ou2007TTTjuleXvfa9eu1f3336/f/OY3WrRokV7zmtfoVa961aQKm+VoMXf+OfdOVFahqbI908xAwVDwyHwsoORzn4wByBgneoY+3FiIcilnTG6KjEa+53CQMygnUrCbGd1l42KOzeS08253MJVBzqjPZZrEcUjFS++JqpaNrRlOoiyPfDkaZHwW6TdClCXR8PE2PUMjN74cTiazzg4+n5iODm+OWUhl+kXuf3ewlPUPTTh/d4g8eFOCFg6bbCgODQ3prW99q7773e+mwU4GBgYGdN111+m6667TG9/4Rn3jG9/4H42M/b8AIyMjesUrXqHHH39cW2+9ta666iodccQRqtfr+sEPfqCTTz5Zv//97/WmN71J11133Sa3D9EMDg7qtttu0+DgoI466qhJl2iOY/1bQVm6ShnMmzdPg4ODeuYzn6murq6mz052Y7jyVqlUdNNNN+mqq65SZ2enTj/9dO29996Fg/u8U9aWp+fw3C233KIrrrhC06ZN00EHHaSDDz5Yu+22W/IMudczKhWRsRHNazYGaXLr5EI3phRFwygywonABQDzyxmFOQHHeIi2OBDRmShFDsW4mRe1WTpXXHOeRwj5mrkHkL6jYI9K5bJly1KFxpkzZ+rggw/OjgNvbGyLz3Jn/Wq1WorsxKhEDmLEN/bvxmkuRdBx5jgoA18H9xDn+IErjs0MAseBP8eaxbnlIg3xO08Rc0dSTglEQens7CxErcAFnmY+GxgYSFegxL3hSvrg4GAyFDlvKI3tg+Hh4ZR26nu00Wioo6MjRezhK5EGSA3Npas7TbnREA23ZrwDzzffuVLl+zNWkKT/qCt4NMb/zymOGIV8n3OiOQ/09Z0yZYpOOukk7bXXXvra176m/v5+LV68WGeddZamT5+unXbaSTvttJN23HFHDQ0N6e6779bDDz9coKlrrrlGlUpFr371qwt95aDZPOL/zZw3kT/n+vRzow6+ts6/+/v7NX/+fD3zmc8s8IFcJHeiKGQziHyVcUxkJE1kcOUMjRxEnjUZI7GZ86rZuCZKTY77eSIoo4Uc5PikO7Z8T8f5TYSTsuwU14ngneCHKsc8m2szzqXMMHLeOdkxTpZGHTfMIXcUC56T+3wyBin8JxeIcx7gezECc+T6j0ajscl1M6SnYSj+67/+q66//vq00EcccYQOPfRQ7bHHHtp+++3V3d2dikP09vZq0aJF+tOf/qRf//rXuvnmm1Wr1XTNNddo3bp1+vnPf77JA/7fDFdeeaUefvhhSdKPfvSjpDRWq1W9/vWvV71e1xvf+EZdf/31uuWWW7Ll85tBo9HQXXfdpe985ztatWqVJGn58uU65ZRTNqmdpxP5m4zBNhlGeMMNN+hb3/pWanPHHXfUHnvsoT322EN777136eWjk4WhoSFdccUVuvXWWyWNKnLnnnuuPvShD2nPPfecdDsxenPnnXfqP/7jP9RoNLRq1arkNJkzZ44OPvhgHXHEEdp6663T+znvc1TYyr6TxnA50Vkah1yaYhlMpl33tjEm0vaksZTeSmX8OTEXoLn2PUJQNo5ooMaISKxKVhYdRAEnauFRD7zSKATRe18myIAbbrghPfvyl7+89CL0MjwAOYHu+PV3JypSkDOqwaFHjKCVuHZE4WJbEcCb3xHm6UdlTgqH3J7IKc8YQWWVDpv10dXVlWiAuTv9u1EUo1RldwO6glar1bR27dq0X4iiOy48Au+8gfmgUETjDWWFs0bQfS4KjNyONOoGukdEybgoU3w6Ojq0fv36NFe/zzDSGLTa3d2tjRs3Ftphz/k7Po5maehuAEpjZ42dv3i02und8XDwwQdr55131sUXX6z58+dLGr1S6I9//KP++Mc/jus3wne+8x319PTo8MMPn/DZKVOmFFLDy45F5Ax7h7h3I7iD2PFXdl3Mpz71KT322GPaZ599dNZZZxVou14fu4c211dONjUbN20Cca5xfaRNu85pU6KEEz3XTE5NNNeJxpEzeDYlKtTs2RghdicJ/z+dPnPGXM6Y3lQ9Ml5i7xkUrBN9NEvPzTlBy1Lxc0AfLgfK1p4K194nsjMnmyPkrr7gf6loHDYbvzvenw5s0kVf3/ve91Iki8jXDTfcoA996EN69atfrf3220977LGHdt55Z+2xxx7ab7/99OpXv1of+tCHdMMNN2jBggU6+uij1Wg0dN111+n73//+0xr0/1a48sorJUkvetGLspGF4447TjvvvLMkFUrpTxbOOeccfelLX0pGojQa5ZqMoHPYFO/ghg0btHTp0kkxhGZEXKlUNH/+fF199dXps0ajoccff1w33nijLrroIp1yyim67LLL9NRTT6X2cmMt80yuWLFCn/zkJ5ORCAwODurcc8/V/fffn33PPduMCyE8PDys3/3ud/rqV7+aZcArV67Utddeqw984AN65JFHCvONY5/Io+oQUz7r9brWr1+fNQTLFAQYaA5Q9sqUMwdfV6IHCxYs0Cc/+UmdcMIJuuaaa8alovoZp6hs85lHItwgBarVsQuEc+OPn+fmeuedd+rkk0/WSSedpC996UvauHFj4TkXXB7dAOK4nZn39/frtttukzTqTT3yyCPH9d8MYsQg9u0Fh/zzMoUqt96Vytg9gzlBlYNoCDCW3DsYRWXeUPrMed5zikcZlEU/pXJFz8/UxX5cOXGl1aMvjNv3CfuaudXrdfX19SUDjaI0GDTA0NBQ4X8fM8+j7NA+PMhxX6/nq7wy3xzfyeHKDdoyHoFTKCr+bmR75KparY7jA6SER8dZnO9ERV587ODEo/FeoRCjJLa31VZb6ZxzztEJJ5zQ1Cm5xRZb6HWve52++MUv6vWvf336/PLLL9cdd9yRdfI5+FlTx5uDGzD1el3r1q3LRoObGX9ujEajwPljo9HQrbfeqscee0yS9NBDDyX9w5VdPyMWaYKxltGKw2SjYs2icXE+ue9zxqZDM6ddhJwsnChayDOTVd43Re/ycZW1UdYv+3AiZy0Qv8v9H3Uk7z/yTD5z2UM7Ph72QIzaTiSnXKYAniqfg1z7Pp+4/j4+59uSxjk8mvXlciWOwfk1e7Zs7PCziRzXZbBJEUUiOS960Yv005/+dJMJd7vtttNPf/pTHXHEEbr99tv1zW9+U6973es2qY3/rdDX16e77rpL0mhkIQeVSkUve9nLdOmll+pXv/rVJvfx5z//Of29ww47pHL8X/va1/SFL3xhwjROYLKM7dFHH9VnP/tZ9fX1qbu7W3vssYee9axnJWdCzjCR8t6rvr4+XXTRRWkz7Lnnnurt7dUTTzxRUERuu+02/eY3v9GLXvQiveY1r9EWW2wxbly5zfnII4/o4osv1vr16yWNGipve9vbdP/99+uBBx7QyMiILrzwQr3zne8cd8YWhSe3Uf/P//k/+uIXv5hwduSRR+r1r3+9HnjgAd1zzz16+OGH00HsL37xi/rc5z6nOXPmJO+Zt7ly5Uo98sgj2n///dXR0TFuDAi+er2u//qv/9Kjjz6qRYsW6cknn9TixYu1YcMGTZ8+XR//+Me13XbbjcNJLgW5zBNcr9c1ODio22+/XZ2dndprr73GKU65dVy3bp2+//3v66abbkrf/exnP9PDDz+s008/Pa2X4zTHZ6iQ6PiJQhclNaYO+vN8jkfc4bbbbtPXvva19M5Pf/pT3XzzzTruuON02GGHpQI4jDF3FjL26+fUbr755hQxOuSQQ9TV1TUpxUIanzKZM6Lq9fq46ARjiJE4F54RD25w5rz9EaKTgnbjO/5czkPqUOb0iX2UAXPKpePl6DR6mKPy4RHt+O6UKVMSH4leczcQaZd1ivhx/tje3l4oUOHKUWdnZzq76HyDlG2POhIZjVFR0mUrlUohWlmr1Qopux5lGRoaKlR0dUCRITWWu8UYvytb8BI/gxnbixGCSLtlhgR9RWeVRw95Nn6X44ctLS067rjj9PKXv1yNRkNr1qzR4sWLNX/+fA0NDWnffffVM5/5TE2bNk2Dg4PaeuutNTAwoJ/97GdqNBq65JJL1NHRoQMPPLDUAKTCbTNww/+CCy7QAw88oCOPPFJvfetbC3uqGT/JVYEG/PxeX1+f/vM//7Pw/S9/+Uttv/32eslLXlKIVoDzyRiEZZCLnuX2Z3zW3/G55+hpMo7ryVb2ZhyRd7IOzQrVTCZa6Y6OTYkITRSxy0XRGo3RNHccNCtXrtSTTz6pZz3rWU/LUKXNMkefNJbdE/do2b7OQZQjkx0XEFPpvd24ts7HXIfw56UxGvV2NzWq5/KpWQbBRNHJWq1WqptMBjbJUPzjH/+oSqWi9773vU+baKrVqk4//XTdfvvtmxzJ+t8M8+bNSwS09957lz7Hd8uWLdPq1as1a9asTepnm2220Vve8hbtu+++Ovfcc/Xwww9r1apVuuqqq/SOd7yj6buNRkPz58/XLbfcogcffFB77LGHTjnllKyBuWjRIp133nnpbExvb69+97vf6Xe/+52kUSVo1qxZmjlzZvqZPXu2DjroIG211VYFJaZer+s//uM/tHz5ckmj98ydffbZ6S6xRx99VI888ohuvfVW9fX1qVar6eabb9btt9+uF7/4xTrqqKO01VZbFeYBrF69Wj/96U910003JfxvscUWOuOMM7Tzzjvr0EMP1Ve/+lXdcccdqtfr+spXvqK+vj699KUvTW2UeUAXLFig888/Pwmb5z//+TrxxBNVrVZ12GGH6bDDDtPGjRv15S9/WX/4wx+0YcMGXXjhhfr3f//3wtmyRqOhBx98UF/84hc1NDSk7bbbTh/5yEc0e/bscXMaHBzUpZdeqrvvvju7huvWrdNFF12kc889V+3t7YUqnDlmVybkKpWKvvSlLyXnRqVS0c4776xnP/vZ2meffbTddtulAhQoqrfccot+8IMfFCoXAvPnz9eZZ56pE044QYcddpikcqOIKEYUhJEBk7rnnu5otPFe9C7fdNNN+vrXv16Yb6PR0MaNG/X1r39d1157rY499ljtv//+if5dsS8zxIBGo6Ebb7wx/X/UUUeVMvvYFjjwtSm7C3AyXvFmAgaFH8A4apZ6nIvulClduWqt3jfgUR8gl4420bx8DLmLonP9RyXH8RrnhXFEtI1iBjg+oqHNmhMVc1w4Pbqy6/Oq1+vpnkbWKkY8MfTa2tq0cePG5NQglZr/KUIETsucBh4Z7OjoyKbH8d6UKVPU398/7rx1LhKAxzue00R5hScyT/eqNyvMQp+Mb2hoaFzapb87depUDQ4OZmky7sXZs2drq6220j777JPtr1Kp6A1veIP6+/v1q1/9SvV6XRdddJHe9ra3ab/99tP06dPH0eim7NnbbrstnXH+1a9+pS222EKveMUrJCmr3Drk7q6Uxpxw8O0f//jHyfGxzTbbaMmSJZKkK664Qttuu6323nvvQuEn+gYPcdybAmW8KfLr+E7Z3gGgmzKDNlcYpBnkxuP/R0eZR2En00/MYIjtxvYnY6hzBUbOMMehe8MNN2hkZEQ777yzTjrpJO22227pWaL8k80Wi+sI34Bn5RwXub0Rq7nnDOhmjs/4DP35fcqAp6Q7wDOiw6LMWeD/T7TeERderMb3s+tFZI2UyTN36pXhoBlskqGIl2ubbbbZlNfGAe9P5DX7RwKYryRtu+22pc/5d0uWLMkaioODgwWCh8m/8Y1v1L/8y78kgnn729+uM888M6XAHXjggXrOc54zrr2NGzfqzjvv1K233qqFCxemz++55x4tWrRIZ511ViFy99RTT+ncc89NZ0222mor9fb2prtqpFHF76mnnkpposB//ud/6jWveY1e8YpXaMqUKWo0Grr99tuTQdLZ2an3vOc9aQ7d3d16znOeo+c85zk65phjdN111+n6669Pyskvf/lL/fKXv9Q+++yjI444Qvvtt59aW1u1Zs0a/exnP9PNN99c2Fz77ruv3vOe96i7uzsZJO985zvV2dmZorjf/OY3tXz5cr3xjW9MKQuRISxcuFCf+cxnUjrFvvvuq3e9613jLp7u6enRaaedpo985CNavny5/vKXv+hb3/qW3v72t0sa3ch33323vvzlLycGs3jxYn30ox/Vhz/8Ye2www6SRhnUqlWr9PnPf74QOQZmzZqlWq2mdevWadGiRbriiiv0jne8o2CcbEqBovvuuy+tCeOcP3++5s+fr5/97GcTvt/Z2aljjjlGe+yxhy655BItXbpUAwMDuuyyy/SHP/xBp556qqZNmyZJhSgDjDaXZhgZMMIpMm4iKv4/Aqtareraa68tpHYfddRR+pd/+Rf94Ac/0K9//Ws1Gg0tX75cX/nKVxJud9llFz3rWc/S1ltvrZ133lmzZs1qavTdf//9WrlypSTpuc99blOemmtjIuHsNJmL5rlAc2XDPaTRoObZ1tbWpmXMpTFjqhlNucd1onQzBHOz6GdOKY1t5KJSOcHtxlqZgsM6uMCu1Wrq6elJCmDOmdFojBUpIMWv0Ri7PxHFxyvx0Z5Hyfh71qxZ6u3t1YwZM7R8+fIUfYSv0b+vGYaA3+2YoxH/25W9nOPG5+lrFR09ruw4DUqjhnyM5Pnaez8Yln5G2MGdYM5zK5VKwkWj0VB7e/u4+ZTxw0qlUrjKh8hj7q45NyhPPPFEDQ0N6fbbb9fIyIi+9rWvSRrN7Hn2s5+tZz/72dpmm23U0dGhDRs2pCq3PqfoIFizZo2+/e1vF8b3ne98R9tvv7323XffwufInQULFuiJJ57QypUrtWbNGi1fvlwrV67U9OnT9Y53vENbb711ou96va5ly5alqsxtbW36yEc+ohtvvFG/+MUvVKvVdOGFF+qzn/2sdtxxx4QDDPcc/U+mUvVkAB6ecxTl7nkE2Gs55d8hdya2mYI/UcQnV/3SnUNlMFE0qtm4JsKzR6poY2BgQNdff71++tOfFuh8wYIF+uhHP6qXvOQlOu644wpX3cHD4h3JkXeW8VHfK+xjd0Q2m1t0LMVzl74mzRyiQ0NDSd9waDaOer2umTNnasWKFdl26dszMjbF+UAfrvtEePzxx7VgwQK94AUvaOr0kPLOjL+Lobjllltq0aJFeuSRR7TffvttyqsFoGDLlltu+bTb+N8GbkQ1SwH17/wdh89+9rP693//93Gfv+QlLyl4FebOnas3v/nNuvzyyyWNpaD29PSo0Who3rx5uvXWW3XvvfeWeioWL16ss88+W2eccYZ23313rVu3Tp/5zGe0Zs0aSaOXHn/sYx9Te3u7lixZonnz5mnevHlauHCh1q5dO65wwfDwsL7//e/rN7/5jU488UTNmTNHV1xxRfr+7W9/eynddHd363Wve52OOuoo/eIXv9D111+fFIKHHnpIDz30kGbMmKG99tpL9913X2FO7e3tetWrXqW3vOUt4yJe1WpVJ598srq6uvTTn/5UknTdddfpscce03vf+15tscUWBUXn2muv1Y9//OPU/u677673v//9487a8XdPT4/e//7366Mf/aiGh4d188036xnPeIaOOuoo/fCHP0xnV6WxqoCrV6/Wxz/+cZ1xxhnae++9tWjRIn3mM5/RihUrJI16C1//+tdrt91200477aT29nYtWrRIH/nIR1LV27322kuHHXZYwdPe19en7373u5o3b56OPfZYHXjggePw3Nvbq2984xvp/4MPPlhLliwpOBGawaGHHqo3v/nNKVX1c5/7nK688sp0NvTee+/VH//4Rx155JE66qijNGPGjHHn5LziK4pdNAra29sLlRZRLlzRRyh1d3drw4YN+slPfqJrrrkmtfOqV71Kb3jDG1SpVPSud71LL3vZy/S9731Pf/jDH9Izq1ev1urVq5NnX5KmT5+uXXbZRTvttJOe8YxnaM6cOZozZ06asxfyOuqoo9LfEzHuyTL2p9tOLqocPeNuKOaEF8KRaxlyz/l6DA0NlXqQ43ijQh/7baYMREVrMl533omGTiwmQtXQ4eFh9fT0JL7mBrhHrhy3fudWjFBCo0Rr3JMN3aO0kTrKeygXlUqlgOPYH6mfOOaAHK5yik5OMefKGdKdJlt9WSpGfTGgK5XKuCrPGLddXV1ZxT86FnjHFWPnA/zf399fSHmPSnAuylGWNQDgcBwYGNC9996bPn/iiSf0xBNPZKuYk6Vx6qmnaocddij00Wg0dMUVVyRlfs6cOVq5cqUajYYuvvhinXvuuel4QaVS0caNG3XRRRfpt7/9bRb3S5Ys0cc+9jF9/OMfT87Hjo4Offvb3040fvTRR2vu3Ll605vepMWLF6csmPPPP19nnnmmFixYoMWLF2vhwoVavny5dtppJ5166qmFfpo5YDYVchEgoOzYQs7ZEL9HoW4WscyNPRr1biBwP6d/z08z44HvJ4rE5lI3aT/KSQCnBG3fcccduuaaawo1LKZMmaLZs2dr6dKlajQa+tWvfqXf/va3evOb36zD/n+ZP9HJ5McxcnwbYG+7AdUM78zL9y/7kXfq9bo2bNigO++8U3fffbdGRka0//7765BDDskeQwLKjrhMRKfTpk1LOhf9N8PDZCBmCvlZc6Cvr0/f+9739Ktf/UqNRkPf+MY3kkM72g/oPDneNdkIf6WxCTN485vfrO985zvabbfd9Nvf/vZp3eW3Zs0aPe95z9P8+fP1xje+sVCc5B8ZrrnmGh1//PGSpMcee6wQ4ne46aabUtGLu+++O1v0JhdR3H777XXFFVeMI6JGo6HPfvazKQ344IMP1i677KJbb71VS5cuHdf2brvtpsMPP1y77LKLLr744vRMa2urTjzxRN18883p8vBtttlGn/nMZ5ICkyO1oaEhrV27VqtXr9Z9992nG264obDZqDgoSS9+8Ys3qULr+vXrdfvtt+vmm29OaasR2tvb9dKXvlSveMUrNG3aNE2fPl3r1q2TNLaJ3Gt800036Vvf+lbazFOnTtVpp52m5z3vefr973+v//iP/yhEh3faaSd9/OMfT8pcLsrB/7/+9a91ySWXSBr13h5++OH65S9/mZ59yUteomOPPVbnn39+ihq2tLTo6KOP1q9+9askiObMmaMzzzxTO+2007gzRN5He3u7zj//fG299dZqNBp69NFH9aUvfSkxvmq1qve9733jjMXLLrssFWF5znOeo7POOkuVSkXr1q3TI488ov/6r/9KxRVITanVapo+fbpe/epXa9ddd034dLj//vt16aWXFgz1trY2HXrooTrmmGNS5GT16tVatWqVNmzYoBUrVqivr099fX3q7e1N786ZM0fbbbedpk+fri222CIZah0dHRoYGEgpcygMtVpNF198cSFl97Wvfa1e+9rXjlMY58yZo1tvvVV//OMf9fjjj+svf/lLQQloBp2dnZo1a5aefPJJSaMRhc9//vOJFqTyczcYVh6JiQadv+uVLKPX2oUtf6PwllWgbTQa6Wws5x5j1BBhRmQNgwm8+3ymTJmiadOmae3atalP2iuLxnq6aFQUPYIU51iGn4kis62trers7ExOOQw0zpP29/ens7CNxmiK5HbbbaelS5emtFBSpRqNRjL4MJApdNDa2poigVz+PGPGDK1evToZdn43JQ6jKVOm6AUveIH+z//5P9piiy30yCOPpDOLnFP0q2SGhobU2dmp9vZ2DQ0NpbGNjIyoo6Mj7VlJBUPPr634/9h78zBZr6ref701z9XV85mSk4EQkygQIRCUYAYijldGcYKrAk4MIqABlAARlABiuCpeQdALXngQEcWLigQhgPwAkSESEMhwMp5zeq6uqp5q+P3R97Pr+65+q7tPQNTL2c/TT3dXvcPea6+91ncNe21tuVzOpqamAj8zFlJwM5mMdTqdGO0VQCkw1yinRhrT6XTgzfX19RgITqVSVqlUbH19PWYMe37w71GDm+Ix7Xbb0um0VSqVMN9K61F8o3xOy+fz1u/3E88bRkZ+/vOft9tvv31PEFmpVOzFL36xnXvuueHaz3zmM3b99deb2bYO+p3f+R37oz/6o1Bw7eDBg/bKV77SSqWSffrTn7Y3velNQa/5pnNTKBTshS98oV188cV2yy232Ete8hIzMxsbG7Pf/d3fDUe5tNtte8lLXpKIEbQ9+clPtic84Qkj37dX2y1ilk6n7R3veIdlMhl76EMfakeOHLEoisL+OvhYKzTncrlwTiDrt1AoxJwn/X4/jFPPE9yrzyqPuL7RaNjS0pKVy+WYXoN/S6VSLP3TR8jUQZNkePBZkj5NkncqH6empmxubs6y2ay97W1vs7/8y7+MvReHbqFQsL/927+1d73rXTF5++AHP9ie+cxn2sTERGye6IsarxgrXt4WCoXYulUdlKQP1bDRzykaeNNNN9nnPve5RLl+4YUX2mWXXWaPeMQjYjUemAucxdoGg4HNzc3Zl770JfviF79oX/va12xqasqe8pSn2AMe8AA788wz7dZbb43NBa1cLlun0wmY8v44RsiW2NraClkon/zkJ+1P/uRPQjBGW7lcth/8wR+07/u+7ws8zO+tra0dDrtsNmuPe9zj9uzHKRmKn/jEJ+xRj3qUDQYDO+OMM+z666+3xz3ucfvKqe92u/ae97zHrrnmGrvjjjsslUrZxz72MXvEIx6x39f/P93e97732Q//8A+b2fZeUL/ngfZXf/VX4Tymm2++edf9jLRms2n1ej3RUDQzW1hYsBe84AWxVANt5XLZLrvsMrviiiuCt9FsOyX19a9/faxaJ21iYsJe8YpX2OTk5CkphjvvvNPe8pa32Je+9KXY54cPH7ZXvepVOypI7adFUWSf//zn7R/+4R/sn//5n4OH7+qrr7Yf/uEftrGxsXBtsVhMBP0A3UwmY//2b/9mr3/964NBFUWRXXjhhTE6pFIpe/zjH28/9EM/tKPwjO+b0ubNb35zONxZ2xOe8AR70pOeFPZl3nDDDfYv//IvO647++yz7brrrovtoVKlaRY39M444wx7xSteYf/n//wfe/e7371jntLptL3whS8MKclf+MIX7JWvfGWg1Wtf+1qbnJzcdWxJyh6gqu/LZrN24sQJ+4u/+IuQoqXPGnWm0H7b0aNH7TGPeYw94hGPCKm46+vr9td//df2V3/1V7FnP+UpT9khQFF84+PjtrCwEJTaxsaGLS0t2bFjx+zLX/6y3X777XbbbbftAI9J7Rd+4Rfs8ssvj3lFR6WxACL02ARV/goWUH5EVZOOQ/AgGqUEgFalhxIsFoshAmW201jD0AOAI1OS9tCkUik7cOCA3Xfffbv2T8dTKpVCxEedSJ5O2mdatVqNAYH9GooUieEes23wvrW1ZZubmwEEUixmenraFhYWwpyokaeAcDAYWLFYDJ8Vi0VLp9MB0NfrdZufn98BYonWbmxsWKlUsoc//OF26623Wj6ft9tuuy0YTt1u14rFoq2vr1u5XLaNjQ1bX1+3Uqlk5XLZ1tfXAwBpNps7QFq1Wo0V96IiqfJnsVi0o0ePxuQ11+bzectms7HzHdkX56Ob7BFKp9OxOcVIhE8xeKFHJpMJBZvgIXV8EK3QtaVzisMI4yGKIhsbG7Pl5eUwZ6OMZFqtVgt0opXLZdvc3IwdYq4gn3XSarXsi1/8ot1yyy2hKnW/v10Jd35+PqSnF4tFu+aaa+z888+3Tqdjz3/+821xcdHMzJ71rGfZox71KFtbW7Nf//Vft7vvvtvMtoF8sVi0T3ziE6Ff1WrVfuiHfsgOHjxo09PTNj4+bqlUyq6//nr78pe/HPr6nOc8x9773vcGp+8v/uIv2qMf/ejYGO+77z578YtfPBI3MBfXXXdd4t62vZqP/vHZYDCw5eVl++3f/u3QP7PtDLWHPvShdumll9r5558fk2vMXyaTCce2/N7v/Z59/OMft1wuZ7OzszYzM2Ozs7N24MABe+hDH2pTU1N7FifzY/V6DvnmHWVzc3P2gQ98wObn521xcdGazaatrq5aq9WyM844w371V3/VpqamAp/TNF3Tv1M/T6KxNyZZ3//7f//vWIbLQx/6UPuJn/gJO3jwYOzg+vn5efvTP/1T+9SnPhWuLZVK9tM//dP2qEc9Kqwx5P4tt9xiH//4x63X69nRo0ft0KFDduTIEWs0GuHaYrEY2w/MeqcoV9Lew89//vN211132YkTJ2xubs7m5ubs5MmT+y48VCwW7Rd+4ReCAxw5oYXf5ufn7d3vfrfdfPPNYQ1qi6LIrrzySnve854X9LzHjsjomZmZkYGKU2knT560t771rTHcl8/n7SEPeYh9+tOf3qHrnvrUp9pll10WHGFJ5+eSSbdXOyVD0czsZS97mb3iFa8IE12r1cLCPHz4sFUqlSBYW62W3X333fblL3/ZPvGJT4QzlczMrr32Wrv22mtP5dX/T7fPfOYz9tCHPtTMzN7//vePrHz6B3/wB/ZLv/RLZrZt4O2nmI0aiuVyOVHIffjDH7Y3vvGNsc8uuOACu/LKK+2SSy7ZceAxrdvt2lve8ha78cYbw2fVatVe9rKXhdSX/SoGWiaTsQ996EP29re/3VZWViyfz9srX/lKO3LkyL6foU0B4eLiov3bv/2bnX/++YkRcQ+2zYYpEoCVXq9n7Xbbfu/3fi/RWDv33HPtGc94hj3sYQ+zxcXFXcGoVzpbW1v2spe9LLbP8L//9/8e+EHB4lve8hb74Ac/GK572MMeZs961rNsenrams1mKE7hld3m5qa9+MUvtrvuusvMdnrEv+3bvs0ajUaIrmWzWfu1X/u1kEKLgfz0pz/dHvOYx4wcGy0JkGez2R0eRi2wsbS0ZH/7t39rf//3f7+jaufX2zKZjF166aX2wAc+0N773vfGFEG1WrWf+qmfskc/+tGJEakoiqxWqwW+JEqTzWYtl8sFrzFjuPXWW+3YsWM2Nzdn8/PztrCwYPPz87a1tWUPeMAD7Nprrx15nlnS3+l0OhiK/G9moSCARhrxridFNtiz5CMwZjv3RSiwVZ5KMt5RjqRijgLYvOvQoUN27733xvbjMUc+UhlFUQAW7ClRgO6BpV9bXmHvlrpG0z2b7MXBWYDhheOAyCNGGPdroQSis6SlVSqVsJ+wWCyGCqF8t7CwEM79w4jDYG+321YsFu2ss86yhYUFS6VSNjc3F97f7XaDQ6ZUKlk6nQ7RDehIQZrFxUXLZrPBsEun0zY5ORnoxXwocDTbXi+HDh2yf/u3f9sR5cjlcsF4hq9yuVxsP5/ybLlctm63G3MgURBLq3DSh1QqZdVqNRhcHjzrnk9tRJw2NjasWCwGpwVOBHQAayipcrA25KeP9JjFj1ihwBGOSp9izL3gp06nY69+9auDAZfP5+2FL3yhffrTnw6ZJg960IPsRS96UdBVJ06csBe/+MWJTqqHPexh9vSnPz3mGIWv1tbW7A1veEPiEVBHjx4NhdS0pVIpu+WWW+zP//zPrVKp2OHDh+2MM86wI0eO2Cc/+Ul75zvfaWbb0c1Xv/rVoRgRMqPb7drb3/52++pXv2qHDx+2o0eP2llnnWVHjx61QqGQmO6cSqXs+PHj9spXvtKOHz8+ck6mpqbsV3/1V+3ss882s2FGTSaTsVKpZG94wxvsb//2b0feb2b2gAc8wL7ru77LHvnIR1q9Xg/3s36TDMalpSXr9/s2OTlpg8EgpASrEcFxWUlF3WjT09P28pe/POx1h5fhp06nY3fffXfIgioWizt4qt1u2/z8vK2urtrhw4dtamoq9n06nbY/+qM/ilXQ/9mf/Vn7kR/5kRBxVScJPPbP//zP9qY3vSkW0brkkkvsZ3/2Zy2VStlNN91k//AP/zByfsrlsh05csSOHDkS+OXw4cNWrVZjhmJSxegbbrhhZPo0bXx83B71qEfZZZddZjMzM/Y3f/M39pGPfCQW/c5kMvaSl7zELrjggti2ll6vF7b1aEopzc95pVKxJz/5yXbVVVdZo9GI6SP6j+OJjAhk2n5bFEV200032Zve9KaYvrr44ovtGc94hp155pl277332tve9ja76aabYv17ylOeYk984hOD3vI4oFqtjrQ1Yn04VUPRbHsv2zXXXLMjRW+3xmvq9bq9+tWvPuVD3v9fb51Ox6rVqvX7/ZDzn9R+8Rd/0d74xjfa7OzsnmkfNDUUK5VKYirHYDCwd7zjHfb5z3/eHvSgB9nll18eOwB+tzYYDOz973+//dmf/ZmVy2X7tV/7tZgHcT+ATBtMvbq6av/yL/9i55xzztdVQMl7sXdrGulS4EqpebN4tcz3ve999o53vMMGg+29Qj/2Yz9mV155paVSKavX69ZqtfZVFUzbwsKCvfzlL7eVlRV75jOfad/93d89Msf//e9/v33oQx+ySy+91B7/+MdbKpUKlek4Q5Aqdjr+e++916655prYvKRSKXviE59oT37yk21zc9P+x//4H8Ebnc/n7aKLLgpVay+44AL7jd/4jVMSeNqS9mOpocg1zWbTPvCBD9hHP/rRoHjxhE9PT1u5XLZyuWyNRsPS6bSVy2Xr9Xq2tLRk8/PzdvLkSVtcXLQvfvGL4RywpJZOp+17v/d77QlPeIJVKhUzG13RE6AD6MWAKJVKQVEoWPXzPxgMrNPpBPCeBGZpPhWT1Cm8sByr4CMfOi9Jz/cROz9e33+NFDJvSXOoKZE6l/799BvjzRuKHvTgqMJb3e12Ew1F3fPjDed6vR4roOYjjEm0xyhSevT7fTv//PPtzjvvDEbjyZMnLZ/PW7vdDsCDNFnSrjFmNOUXXsPwp0poKpWyWq1mCwsLVigUAs9piu7m5qZVKhX7zu/8Trv99tttYWHB1tbWghFoZiG1N5vNWqVSCaCVSBrGJIYiY4miyM4888wQsVGHgdkQeI+Njdn09LR99atfjUUziH6XSiVrtVphzD4KrMY9aWhmFnMYwD+aHokzolarWTqdjoFS5irJ0cL7zCzsTeZa+kUUnvVFtFGfr30kWqQ8qyljZnHjEwdPUqRMr4UvXvva19oXvvCF8B3rKp/P2+te9zqbmpqK8e3NN99sr3rVq0L/KpWKPfOZz7RLLrlkB1bTyHGv17M//uM/jjl9zbad+o94xCN2RE13a7lczl7wgheEtLzv//7vt6c97WmBXgsLC/a6173OvvjFL+64N4oiO3DggD384Q+3q666Kpaxcs8999h1110XjJTJyUl77GMfa5/73Ofslltuiem4YrFoL3rRi+yCCy6IyYEPfehD9od/+Idmtj1PyKBRejqVStl3fMd32HnnnReM3XQ6bcVi0RYXF+3ee+8NP/DJRRddFPb4r6ysBMfOu971rliKJ61QKFitVrO1tbUgk6ampuylL31pqIGALseJrjxZKBSs0WhYuVy2ZrNpS0tLOxwR3/7t327f9V3fZZdccokVCgX7oz/6o5BZFEWRPeMZz7Arr7zSxsfHQ7Q6aYuC2bYR+ta3vtU++tGPhnf4KPqptunpaXvqU59qj3zkI4NjSAtf/c//+T9Df7Xlcjmbnp62s88+2x71qEfZRRddFNbo2NiYNZtNi6LIvvzlL9t73/veUEugXC7bddddZ4cOHQpyfXV11a699toQlc/lcnbeeefZBRdcYBdeeKF9+7d/u73vfe+zd77znTH6n3nmmfYLv/AL4ZxzXc+lUsnuuOMOe8tb3mKf+9znLJVK2fj4uE1MTNjExIRNTk7aRRddFOs3bWtry9761rfG1mSj0bCf/umftksuuSQ4ywhe3Hffffaud70rtoXmsY99rD3zmc8M+l7XyNjY2L7Ob75fhqLZtufkzW9+s733ve+1T33qU7uC4XQ6bZdccon9yI/8iD396U+/X3sbvxXaZZddZh/96Eftiiuu2CGszbYXy7nnnmu33XabPfWpT40VOdmt7ZV6+o1qgCSfHqpKej/slpQueSrNv0fB9KhraBrpUnCo+1q8wXnHHXfYV77yFbvkkksSvbWnaijyHrxrng6j9m0wpsOHD9vdd9+9axpHFEX2kY98xH7/93/fzLaF9LOf/Ww777zzgveTsx21SIvZNj1f85rXxI4cOdVGaiJ7jnhuEn2TUgjb7XasYIFWlfNADgPinnvusQ9+8IP2kY98JGYgPOhBD7Jf+qVfsrGxsV35k71lCFvoTZpbpVIJhgjzlrR3xI/NC2/epR5d/vdRF3jbF1cBzPDcpOeb7V4chsgHURyAbj6ft/X19V0jiuzP0P1x2tgPRzTOG4o+3Y/oLYZGt9u1crkci5x4WuvaIbqqzwQQKXhX41mdRpo6mMlk7KKLLrIvfvGLwUExPz8f3r+5uRn2PbFXEMCCkY/BguOONaol69mjWCqVQt87nU4sFbNQKNjFF19s99xzjx07dizwOv0HzOqex1KpFCp9kiK6uLhouVzOGo1GMBTPPfdc+8pXvrKDXzFmzba3GNRqNbvjjjtihg5yr1qtBocyqbYKsHzqKYY5fExkDkNRjc9sNmsTExPW7/ftxIkTu+oZTd1mLjEEWTuAU3gd/s9kMjsMRZXrjEFTwj0/akSZfvg1oSmzKvM3Nzfthhtu2CGHf/Inf9Ie//jHJ8qXm266yd75znfaeeedZ8961rOsVCqNjO6rwySVStk73vEOe8973mNmZg9/+MPtV37lV3ZElvaSk4VCwW677Tb71V/91XDfS1/6Urvwwgut3W7btddeGzJa9moXXXRRMGBe+9rXhr4eOXLEfuM3fiNE+1qtln32s5+1v/u7vwsZORisD3rQg8xsO5r32te+NtD+53/+5+2KK66wXq9n8/Pzdvz4cbv99tvtk5/8ZDByv5528cUX2+Me9zg766yz7NWvfnUo5Gi2XQ/ix37sx6zRaARH2OLiol133XWhzsHExIS99KUvtQMHDtjy8rL97u/+rt1yyy1fV5+y2awdOHAgnKMdRduF2i677DIbDAY7MozIgvBOuMFgYP/8z/9sb3zjGxOdCN/+7d9uV111lc3MzNg999xjd9xxh91111121113xQrm+PakJz3JHv/4x8cqz77zne8MBnY6nbanPe1pdtZZZ9n09LTNzs4Gh5xvk5OTtry8HGTM1taWXX/99aEmx/T0tP3Wb/2WVSoV29jYsFe84hWBdzSqS5udnQ1ZQX/2Z39mH/nIR2Lve9SjHmU/8RM/EWycfr9vN954o73tbW/bM1hy8OBBu/rqq+3Rj360lctlu+eee+z1r399mCez7TodP/mTPxlwPNsxstlswB6pVGpHUb5HPvKR9ku/9EthK4SZBZ5/9rOfvWu/zL4OQ1Hb1taWfeUrXwmHelMsolqt2uHDh+28885LPLz2dIu3P/7jP7anP/3pFkWRfeITn9hRRORd73qX/eiP/qiZmX3wgx+0K6+8cl/P/WYZima7bz7f7Tvf9toX4psaE/49vgCI2Whll7R3C7CLsk+KzowyBvdjHI/yLpPmR/RGKyzutqdreno6pE2MMhZ4x80332x33323XX755VYqlcJGfoARghWPtpnZU5/6VPuBH/iBXce0V2OviEZJ+N9Hufx5d6TWaeQAb6vZToMBOgD819fX7fOf/7x96UtfsgsvvNC+8zu/M4x5N/7Eq0vpelLSMCCKxWIsKrGxsbFnNN0bbD7i4O/Ha0tUAS80AFtpx9z7fT76vRqW3sAeDLYP/15fXw98qOmJfszM4fr6egAbzFVSEYPBYBDKi/vvfd8A9OVyOfCmj/RoJMg/g7lTmUJa2Kjor09B5Ln5fN4e+MAH2i233BIKQc3NzQU+bLfbVigUgpNmc3PTxsbGrNPpWCqViqWlZrPZUB0WOcWYAPDlcjmkLRG56na7YZ/kWWedZcvLyzY3NxcbY6VSsUwmY81mM0RgO52OFQqFsG8aJ8TKyooVCoWQ7ppKpezw4cN27NixHVEFdcpMTU3tiOjhLKSQw/LycniGl+sYbjgKoDXrV79HFpPim81mbWpqyjY2NsJeTuVdbcwlRix9IGWcVDuMWfbzJPGNTysdxT8qpzUzgB/P85rem+Qk/d3f/d2Q4XH22Wfbb/7mb8bSnEc1+GSUHFJ5yVg+/elP2+23324/+IM/GDIfMKh3y4DgGTiT3v/+99uf/MmfmNk2rzz72c+2G264IRgK1Wo1GKLHjh2zW2+91W6//Xa74447dpXFZ599tr3iFa+wUqm0I51yfX3dXve619lnP/vZ8Nlzn/tcm5qaspe97GWBDo973OPsKU95SqKOLpVK9rWvfc0+8pGP2Mc+9rHEfWraoiiyyclJO3jwoB0/fnzHnjTVqalUyn78x3/cHve4xyXOc6vVspe+9KWhQNT4+Lhdfvnl9r73vS/Ghw972MMsn8/b0tJS+FlbW7NyuRwiVmNjY1YoFOyzn/1s4j65dDptz372s+2Rj3xkLGIPT+m86zrAoT4YbO8XffOb32yf/OQnrVwu2/d8z/fYVVddFbLAfOZJFG1X4cVg/MpXvmJf+9rXYtttHv7wh9uznvUsy+Vy9v73vz8ERaIosuc85zn2yEc+MjZX6ETfarVarECY2XaBomuvvTZUaT/33HPtRS96kd1www0B54yNjdnLX/7yHc7wAwcOxKK1t956q73xjW+0O+64IzbXT3ziE+3CCy+0N7/5zbFxjY2NWb1et4WFhZE1DAqFgl1yySX2yU9+MvBqLpezn/3Zn7Xv+77v2+G0IjDDHmswykc+8hH7wz/8wzDuiy66yB772Mfa1772NfvKV75it956ayKeTWrfEEPxdPvGtG63axdffLHdfPPNdujQIfvTP/1Tu/LKK63f79tf/MVf2NOf/nRrNpv2fd/3feFso/20b6ahuJuBdyqGoq8StldT5eoV9n6UG80rDRaegvb9jmE3I1G/GwUMdM+Ygv2kVFo1FPGiaapg0vlVeKOgs08zpG1sbNj1119v//qv/2oXXnih/fqv//op59gnATe8qLzfj2tU3wHUGH70mz77wgE8i4PJt7a2LJ/PW6lUsqWlpVg6qU9Z8w1Qr0AbY4Jol0ZO/J7XpDnwUT0FnT5VT9MK+/3tqppbW1uJHtWkaKVGXdT40bP8dA/Z+Pi4LS0tBeWTTqdD1CuKoliFSPq3vr5uExMTNj8/H2ju+0YKV7FYtFartWf2QKFQsG63G5wg8IXex7uT5o4IqxY8IR3MG+dKI7OdRnyxWLSZmRm799577cCBA9btdm1paSlE0BYXF4MjgegvvK58Sp+mpqaCkqfAjBrhtVot7MOjwiG8sb6+bhdddJFtbm7aV7/61RAZi6LIDh48aKurq7a6uhrWdT6ftyjaLtiysbERDCT2O2az2ZgRds899+zgYZUZ9F0rN2YyGSuXy7aysmL1ej2kfiVFy8yGB9yz11cNOeZKo5UYiplMxiYmJmxlZSVEuL2sZA6ZeyLZmhKqUVycIwDPpH3ePq2U9zDHSQYZugN94qOUPNc7KWlEK9/97nfbsWPH7Kd+6qdsdnY2seKqbzhlRhmK+3HKjlojSQ0HCO972ctetqM4ndn2nuEXvehFdvDgwR1jXl5etg9/+MP2oQ99aIeBc8EFF9gLX/jCEEn0hiJOsxtuuCEY1qlUKlbV8tJLL7XnPOc5I/WYpkT3+3277bbbQnoucodsjoMHD9qBAwdia/ymm26y97znPTvOiR4bG7PnPve5dsEFF4TxelxUKBTsxIkTdt111yVGXaempuyZz3xmKHiofMNedbOdx7p87Wtfs49//OP2T//0T7aysmKZTMZ++Zd/2R72sIfF1vgox3qhUIg56cyGRbnS6bSdPHnSarXajqAQ/ydFvtm/NxgM7K//+q/DNh6z7b2xl112WexM45/+6Z+2xz72saFPyOOkgm1mFs7DxlkJnRcXF+3Xf/3Xg8NC11G5XLZrr73WzjzzzNh7zLZ5ttPpBMOTLKIbb7zR3vnOd+66Fr/3e7/XnvKUpwQMvr6+bouLi3bs2DG78cYbY9FmbUeOHLFf/uVftiNHjgSdoEEMMwv1AMiIAP987nOfs9e97nW7ru/ThuJ/wXbHHXfY5ZdfHjwURHmY+Ic85CF24403nlL67jfTUNTjJXw7laI2p2ooqsfOv+dUDEW/v0VTlfCSjkprMtt/6W+9JykiiaJSBcJzkwCxKotqtRpSR0kVTPIcodD1oORRRnC/37c777zTDh06dL+yAzxd8vl8ADC+sqKngdnOw9TZI9LpdGLzwph8RdVUKmWTk5NBcVN0C1APz+jBtklGHfuoSAHFSANAo5B88Qr64I02H13w/OeBoAdqrOdRHtUkp0kSr+qRBppiNjk5GfYDQRsMhSga7vvjWbVazVqtlh06dMjuuuuuxCgx88/YSc/U5tNGST2dnp4OUUA9KmEwGCR6wkc9M+koEAxrM4utTdYVtCuVSiGafPbZZ1uz2QxFqyYnJ+2+++6zVCoVgCPVklOpVEg/xEufTqft6NGjtrS0ZIPBIBzzoGB7amrKWq1W2OtHaj77Hs8991xbW1uzY8eOhUJLFKPZ3NwM64v+Ybiur6+HuSFd1sxCNdBqtRpSauEJHDQYAY1Gw9rtdiy9OJPJWLVateXlZatUKjEjLMlZOD09bZ1OJxSEwWmDIa3Fc3DqrK+vxwrRaDEbnw6tWxmq1WrMGOf4E95NoSB/vIjqIt6p603PoPRHvfi/c7mclUql2H5Zs3gFSC8v/XqAllpZeFTT8fv0XPh035EFSW1O0hXoWjU8Tpw4YS94wQtic3LOOefYr/3ar1m9Xt81I8fM7F//9V/tQx/6kH3uc5+ziy++2J75zGeGvaNE7WnsG8/lcpbP5+01r3lNOJ+Xdv7559tLXvKSkQX6aHtt+/Cy2c9Zt9u1j33sY/bud7/b5ubm7Nu+7dvsuc997g7sluQU2Nrasmazadddd10sRfT7v//77clPfnKskvooB5kvHIUMWltbs1tvvTXsL0a+qnPVyz3kq0ZFNUq41xyO0qk4kuDJz3zmM/aGN7whsfL8E57wBHvyk58c/mcufCaJtmw2G8OSKu/vvPNOe+lLXxp7Vy6Xs5e85CV2/vnnx/pO/yYmJsJ+wK2trRhNKDB44403xsZ58OBB+7mf+zm74IILRuLEbDZrt912m/393/+93XTTTYGnH/3oRwd+J+NC9zoT2VUnmZ7Zmclk7Ktf/ar95m/+5g4jdnJy0h74wAfaxz72sZ0T5tre51pIg2HZ/Hm6fePb0aNH7Qtf+IK99rWvtfe85z12++23WzabtQsvvNB+7Md+zJ797GfvKeD+Pdp+DaDdjAj16O/VTrXSpQqJpL1Wo8q7+4ZA8woVT7e/dq8I0X7aqAjIKI9j0j16DUYS95MykhS50siJTz30zz969OieYxmVRptEFxTZbnQY1R+zYUGNbDYbMxA0AqGNFA0AoV7jDfJRio+xaLpcUt+T3j/qM1XSo1LnaN7pwT5IbaqUdR/nbtFtfb7vrzoQBoNBmDONVnKtFh/AsE5ay9q3pNRY7ZPyMR5z9Q4nRRd1Hx3NFxvyHm6MME8nns+8k/KK0cLzkBP8zfEURAuJjOucMD59L/ynxhH8qjKBeV9ZWQnROI5+SKfTNjY2ZnfeeWfM4cDexLW1tXBQPXTTNaGVB3XfJv2hAZj8WuIzL4s9r7G/FhDL59CQFHU1IvVcSu8A5B4Fjj7CqHRn3LpfkUgivOnlftI6KpfLwWmk46N5Q88/08wSD2XXde6bj/po03txAGnfdZ73myHDczXFV5vyCvtN+/2+zc7O2lOf+lR705veZGbb+/ae+9znBmNnN32ZSqVCoQ/fD/rvnQLwR7lctuc973mWzWZDldiZmRl78YtfvCeGGqVDNP3S4wl/fTabtauuusq++7u/2xYWFmxmZibxXaN0R61Ws5e+9KX2x3/8x7a2tmZPetKTEs/X5jgc3wqFQiwTgHWbTqftvPPOC5/rGlJHpZfLKlNVN+zlhFee8c4PT7+LL77YfvM3f9Ouv/76WCT5yiuvtCc96UmJdBqFKZHxalhpO+OMM+z5z3++/dZv/VbQK89//vNjRqK+x9OB56sh/XM/93N2xRVX2P/6X//Ljh07Zo997GPt8Y9/fDD0dgsoHDlyxJ7+9Kfbj//4j9vNN99suVzOHvKQhwQZpM4pXduKq73zs9fr2QUXXGCvetWr7C//8i+tUqnYeeedZ+eee66Nj4+HYmF7tVMyFI8ePWqpVMq+8IUv2AUXXLDj+06nEzZcX3bZZafy6NNNWrVatZe//OX28pe//D+6K6Ht1/jZzcBLUo5fTxslpJOcGKpYRhUXARAnLeIkUDJq7xd9SAK/gL2kSKG/X/uifUrqvwcGmoKFQku6R40IgP/9MXZpWuxF+7WX0k1qCMek+zVC4flgFODS+fCpu964TWo8Ay88ykK/H+U51c8U7Gs/PN3ZF5L0DA8CPVhWY0oN21FpqPQlic6el3UcnhcxWEiPwWjxawEFl+Q88kBUHRlEC3zlTLM4YPAGCntU9X8fTfRGr/KfGhf9fj9EFs2GqeH9fj9EtjTyq/v6oijakcpJ1ApDkvfxTuSZ8iVRIjOLRdCRvxjprA+cETgWVldXA1jWQkVq8GIM+/13yiP0W2UahqI27vF7x9kzzFlfahxrn3ASMLdE/jjiQ+/1hpg/tob3Mi5NR4aPSD/VNa+y0RtKGxsbISXYj5m2m1FhZns6dPxz/T7epPvUGE9q6jBMaknP3St6pGOhXXnllVar1azdbttll10W09G7jXcv51aSs4d5LBaLtrS0ZD/zMz9jZ511lt1666323/7bfwt7NvdqoxyF+v1uzm91MrPXbZSu13fp86rVqv3yL//yrv308p93e+dTkuOGBt2Uz3U9E73VNhgMQjTay+xRzm3Ps96Zl8vl7PDhw/bKV77Sfu/3fs8+97nP2aMf/Wh7xjOescOJ7/XnqDF5h7jqvQc/+MH2/Oc/32688Ua7+uqr7cEPfvBIOkMTLSTj9/M3m00755xz7BWveEV4v8qlUfRRDFAqleyqq64KadLQzafAc5+uUe8A5fODBw/az//8z48c217tlAxFfXFSu/322+17vud7dqRlnG7fOm03Q/H+Gh9me0fq/DELmiLhFeFuz0L4sSBReIBDf603+vQd/n0q4BRsJBmcCGeftjjK6EpSFDSvtGnqtdeUy92ap53/H/pphC8JiKhhOqqxp2nUeEkH1u81Nc/3C8+zRhR1Tnw0ICkyyndUk9RonoIUhHtS2inv1T2hScY8RoW+X40I7b+fE96lINePQ38rgPAODaK0jFHv84qr1+tZo9Gw1dXVHeDdv9MrUqW90h+gy/5Q9gbp9X6+90qP9jTDkFP6ArRYI3ynESj4kB+9X+ee/bQKbEibarfbsdRF/Y49sKxhnsucQT+K3pBa1uv1bGVlxQaD7T087JnE0CaCx3MA7uo4YNwYjPRBgV5SRVszC9F2irMkOYT4HNmj54Pq0SI6HxjT9COVip/V6UEktKT56KfZziwEQF+73Y6l5vOdNjUGAG007bvHQ0np4Jr6rXpC168C7d2cjfp/UgRQQbNv+j5fKZj1MEomq1POF/C5/PLLT2k7SdK4aGrYaNOiSJqxcPnll9vll18ertmrqbNhlGMMnt3teeqMGBWl3Evv7oV9Rjlbdf8a40iS82Y702w930VRlLjH1Tvp1NCkqXzWqOQo7GS2HU190YteFI6RGkWH3aqKM1acTkn4LZVK2Xd+53fawx/+8ERHqX8efK1rUAv7aCpqkv4b1bzDR6OgURQlRv5HOXk8FhhVEXY//Qrv2tdVp9i+HoPgdPuv3XbzNO7mxfMMu5fH0jf1GCUJH1IEuHa3dyelS41aUD56mQRauWbU+5PC/6RAqVLm/iQlr6BUQb6mc2lDmSutNEoxqu1HsBSLxdh7klq/399hnCbtKRv1DOjiU4gGg8EOLyWgQQ1FNbpR5mbx1E5v7Ot7uU/7porGK4gkI5B3jkrTU5p43tHnjtrfStOx6XdeoZjFQRQAN5vNxjzKPjLrn6FGBZFFn8KndMKo1jF5XgRIs79ODWilpR+zvk/H5qN0nhZ+7tVoZ650nyNjyOfzMdqr46JcLpuZBRBgZsGpwp4rXbP0UytyqnHI90tLSxZFUYjK0deJiQkrFouxNECMRYxuHaN63r0XXunjAaU+Q/utUcGk52jTdDf6qd95OahpX+qA4b1+DriP9Fz/Hd/T8Oarw8Asnr7ux+NBK3PpDbV+vx+rHql6S/nW6zMvQ/ZytHFPEsBWXvLAnkJT/p3QYZQO0DnSNTkqikXb6/tRLSmap+uHytj3pyHLR/Era195aRRd9uqDl6GjMMWo5rNnaEm8NGo8Xuf5Pnk6e57ib5+VQf/VaNP+JWW4aH+1pkaSbk3aEqTPZ42owzlJD+1lJJpZkKM6Lm26jpLW217YSeWMYiN0n1/r0FJ1Dnyp7/Vzd3+2rv27GIqn23/edip7S3e7djdlMap9PQ6E/fSbxeI9oPrbAzFtoxSzT8XYj7G0WwqS9ywmGcXp9HaxFsadBAhU8GkfNaKWJPh9n5I8xqPafoDJKPDln0OxCH23Xr9X/jyRB40eJaW8mA2rBnKNjhOASf+T+qJ/azqvn2d1FGg/kiJcPJMUpiTl0m63d6RpqWFFH5K8pWbDaIn2aTf+V94kOoVBvtcaBBT7/bCjjGA8s96QZD+H9mswGAQjkSJIfJe0HpPepwrT74/iHYAXz3tJxiNRuU6nY5VKZQd4ItqF8mYuMLoHg+HZexsbG9bpdEJRCr6ncqoCLY1maloh0Q2MWI5nYez0IZfLhfQ7+Mg7D9RBMWr+FEB6ecO7OCYkSaZgRCK72F+pDiClM+NibljrPp3VZ1DoXLL/kO+JEvj54Zk+0q+RdW1EFpJ4UZ1LzJMeR6HX+WcqrX0EbT/6dD8FRgDJjH2U3FMQm/QsHaPu19TPkvhoL50yqo2Kpui+5yQDYL9RFL/m9TlaJTrp+6T3jcpy8PyV5OTard++CA33eMe811dJzhS/l1VbrVaLYaIkh6p/njafYqvz5x2VXo6OarsZiurs0jWfFJnbz1qiYrLX19DMbyPxbbc+mMUNuP1keKmeHoXzvDND+zuqH0nttKH4Ldbuj1Der2Ddq309huJ++qCLdJRCSgKWvl/+0G7fB13sSQDdP8/v7fHKa9ReCL/HYDcajIqI7Abu1QvF7ySPJs/c6700VVy7AZVut7vD0PIGhqevzocKShpz4gGOp6UfV9J3SYoUZcA7FJRq1If/9/LoMyZNlfONPSgKxvT+3fbSAC6Vd5Oiy4zP875GqDz/e55QQxEaKW38XCqQ03nVwia8J4qiYHR4AB1FUWz/oVk8FYrrlE4YdnynKXJqqCktPX9GURQqVzYajQDOCoVC4A810JgL/QHMcDSFgnX6QV8Gg+2iNBiYRFQwjpQfqdTJM82GZ8wVi0Url8thb1ESH0ALTbtM8pZ7YOQdcklGlr5Po7JKf6Kj+hx1fjDHmvYF70AL/16MCKUJhjYRSq6Df7rdbkyeJQFZM4sByKSm14+S66pPksCrrkfaqehFAKIaE8gWLy+9w4B3sY72Mr50LPo7iT73FxckGYr0GSdI0vP3i4G8rNS/i8Xijkisb15Oq4zS+1RfjXIIJ+kQ/Z6mESWOjNHrRjlxk7aneGecd1rS31GOJB2nj/7zTi9b9f/daML9uzmT4V3WpaZva/+1n7s1ZM3y8vKOyCjP8hlA2vbCcP4+j0WS5p/1qHLBy1xvdGoK8WlD8XRLbKcSUdyL6U+l7dc7cir3+n4BKHZLoRwVgUjqg198SWkVo0BzUktK0xv1/ygvvDfkkgTEbgZK0vgUTO+X1qOaAi6z0YYiKXWahkffaf477vPX6jsAit7gTKKFAg0tLuL7of+jzFD63jD0XmZo6r3wg8EgHGTf7/etWCwm8h9n36mS0flNAm1eIfnqckleZb2H37x3P5EMony6r0+dFaP6SL+Ur7Ufuu44UsMbiv1+f4eh6FMezUbv6fLAmP+VP9Q5QF/X19eDAY1xiHGmfKD0SHLgcMwDBovSkPNGtcIv8855h4xVzwZkb+HY2JiVy2WLoihWvEb31CkIoX98hmEFH6gDIkkOevom7Qn0aWjKHzxXjWXtqxqLRPF8vxmTd6ToGNQ4ozH3PtLBc/w+Uh07tNLmU7Zp/lxYL1sw9P07kuSIb0mRcA+GdS3rHCgvcK2OibTeJMMgCdQmOQ++kbUrdgPehUIhOBu+nuZ1E/SFF/Y6f9IsniI4qqkOTkrzPJVGHzlXT3XFXoV3vBGhsthHhL1jadRz2VPu0yK9gycJE+3l6EZ2Jl1HXzmjNck543XVbg2ZMypFfLfCOmZDY9k70PR7bZrZkrTPGPrhFAEzKi/pmGm7ZbuNaqcNxW+xdioRxSRlen/bfiJbu/Vjt9RL/3+SkjIbKu7dhIIuLv17lOfKp2z5sSS9K6nfnj5Jhg1CYJRH2YOtvVIq/DVJApP/91tGeZSX1z8zCcAkPSvpXt9U+Wl0VJsaAx4EJdFTr+d//Rujwe898iBq1BhVgScZOnwXRVEA+t5TOCot0P/vo+JJexSSDE7dM+f5zI8nn89bOp2OHQ+h4Dapj9CflEOu0bQ8b0BGURRLB+QzD6w5i1BlmPa50+mMVKh+r2AUDc9x9OBIjd1cLhf2T3pQrBFOdVxgCFIt1szCMQ2pVCoAUjWulKbwTzabtVarFVJL+bxWq8XSe8fGxoKHXY10bzwRrWS/sUbpkmTLqLWVlF6mayxp32uSo0sjh/BEFEWJ5//5taLgVx00AEwFePo+7Rf/j9KFg8HOvUDQycv2pCwE/vYGsaeJ0i+pJe3ZHtWUr/xv/tZ5URnlnTVJLSn6lHTt/THmkvS7tl6vF87i3E8blUUzSgd0Op1daasOhiTnkHeyaLRrNyM8qXnjTff1MYYkPbSXwYAs4R1+/7VZPNNmFNZR505SaiTfJbXdcAfjGpXmqjyixp13GHpdn9QwtJOcU0lFfpL6pHI3yYG+G7bbDf/SJ+ir8jZpze3XOIz17ZTvON3+S7f9CHiaN4K+nnYqkUzfRinIUUq73+/HDqSlacpRUtvNU6olqJM8sv5zf79v/lpvKCSNDQ9TUrqr/gZQKeBNAvdJ9/k+n+rcJ3mtfUulUrHDvXdrSSBQm3rnUc6jwIgHkEnnmilw1u+1pdPp2PEM6g31kYXdnC3MD+B1FM3gPaUF0T6MDW+00JKMYV9wg357pwjP08gObRQ49/ypXtQk411p56ObrEeN6HjDE2WYZPx6A1Dp2263Y04QBVQK8gD6Y2NjViqVdtBaz9Ni/xvXMXdU98zn8yEqqk1TUKEJhiYGqkam4JtSqRTWdzqdDkc0sJcIo5rUo16vZ1NTU6ECoAI3pX2/3w9e6kKhEK7zAMn334MzHznzQE3lGWP3PMA15XI55igjc4D5UEPOywEvz3Rdq7PFR7SVHrsZityvacY8JykzhOuSnIyj5AXzr0ZFUkvSe6Nav9+PRcTU+ZWkL0bpQO800JRBvW63iOyptv2kJSYd2j7qfUmfjYrijLreN10ve+EflYEeo3nntvJOUks6vsE7IDwf+ei7me2Q+f7d6uzSMfjreQ8ycpSR7/8fFSnUZ8O/ewUbRmEmfddu/5tZSNVnTUMrHJej1o223c4tTfpc+5FUqIbfyF8fBabdnzUW6/f9uenTn/60zc/P7/j89ttvD39/9KMf3RfIPH3e4n/e9vUyl7a9Skjv1kh3USGdxFt81uv1wgHTvg97Rdl0zOn08Kw1POoYoZT5HpWKsVfz3tpRfVIwC2DxHnKlB8ICD5+mLyS904OoUUBkL5p5o3OvRj98WW2a9/TT+J/r/R4q3Wum/WLs+plGtHQsXKPl3T3gXV1djUV5vLHKO5NSYXUcGr1RGgAQVPDr2NWAUsXgPaTQRCM8msqo7/QgUJWeP3/U00TBr4+4ETHTEuz9fj/sk+M+3VOioBReUAMeUD3KI63rgvt9IQXGBS21eqmCfc4jRB5wT7lctpWVFUun0+GICwwrPw7GzZ5D5X8K2HQ6nfAO+lMsFm1hYcHMLFQszWazYY+URv0Gg0EoIsKY1tbWrNvthv18nU4nGAhJTjHomc/nY0dpqFwYJSO8w4XxJxmj/M+Y8LZrVJln1ev1mCz3BTugmXrQ1SgZFS3R9GS/h0kNaeS6HptEg9+z2WzYE6agv1AohH2syntEjVWv7BZt8A6opLYXsE6ihxoh0EEj+qO2S3CcTFLjOhwnyKNR+ns/OnM/93jZ7vU/LYkf9nqev471nHQUQtK1Sld/vc479FYnDnvxkpx+fK9j02rhPFf5G9mgOmAUz6nTUNdxUiRvlLGN7KbAljdkRkX0vKGTZFiSGeL1k4+kaWaDxxo8a6+xrK2txfSSN7Q1o2gUT2NUJr3P93kwGMSyTTwPaePYKPRX0lFfu/Vrr3a/DMWf+ZmfGfkdA/2e7/mePZ8DiDndvrlNmXq3pkyb5Ok6lWjTbgyKck0SPEkLSkFwUvRL0wy0+aIyuzXv+aH6I9/puKBNUiqbfrdbNNdH1niGF0YKVhQscx3fqyHhvfX+HfRVFbs2VSpJTRXNKMPPvxdwjEfS75tDaPpneONIU17oJ86AJCOYH2/QeCNV59N7lvFkViqVHfOGIYBCTDLu6Y/2qVgsxkACBr4qEK5PpVLBu+k9mcp3HqAltSTPKetHDxz3NND3+QqAHpBmMpkQMfV8wX263nTM3kOqPKs0IVuA61GcvV4v/E461kEjWkoTBTHQWQ+hR4kD4lqtVkg71XlVo4x0tUqlYouLi2a2ncKWzWbt7LPPti9+8Yth/yDed5Q//eFw+nK5HDvCQYHbysqK5fN5GxsbC06jzc1NK5VKlkptV2IlIq6GNvPN+Y261zKKtlOgKewyysHEvawfrTKq8wcNiQjCZ/A8TqLBYLuwDY4Z5pd7NjY2wnpTXi6VSsHA5n0Y1qlUKswl8tJHP9XI3Ut3mVnsfDili490Mwb4TmVPklGl9yXt99T/R+1r1+av8VkTXoZo28tQ1XcQteZIATV0d9Ppo9qp3sNc73We6qjn8wzfoFOhUAhOl1FN5Q3rTv9Xnakp2F4n6vr3DlD9HseJGqi67nQMOCfNhueZJtEEWaiGa5IRxfVJn/Ec7yjScY0yFD2O8uPGCco60ywd/0x/JuhufVfDnO8JEmB06ThUB4+iDy3JyaL7H3W89Be5PCrlWWWG8oNPd94Lh45qp5x6ygR8o35Ot29e8wt1P+1UhMKo+/ZKC9jLIPQGD2B5lHBRoOf7s5s33Gwo3D3oNRumh6oAUYCxGyjfTcGbJaca+aiKWdyAVWOGxsZx75lOohV7EJJSULSNolnSs1F0u23c174yjqS0xKTmqw4CCLSfSUaqByl+M7oqV/WiJvVFPZgYJRoJ0QguBoaCQb5Tw7BSqcT6g5BHoSd5V9XopZ9JytgreK9skz6LomGBFiJUo4osaKVJXaMoPyJyGJzwBkoQGul610puClhYY8oHSosk2eBTHhWUoZw9qFNwo39zvYJfdQZpUSJohne3Xq9boVAI+wTxsg8GA5ueng5GFYYMNNKxMRcYbfDW+vq65fP5UD212+1au90OTgV489ChQ1atVncYmXpcB1E+v+cF3kryxquzRv/XvY36t94Hr2ulSmjMfSqXB4N41ETXK/RQAwGeUP5UxwLzq2shCZ/o+vK8pQWC/Pd6j9LJN+Xz/ThNfdtPlKdWq4XP/Xf+3f4addr4Ijf6Ph1bUvVg/977gwOT9q6pbN0Lc/i23z7AF97g0Pu9AxYeGLU/z8x26Ayf5aFrUOW/fsba0GwidUpwrY8867uTmvY9CVd53abXqRGVtP6THIL6Xl9kThuGouK9UduLvFxK+s6PyzeVk4onk4zGUW0U1qXPXsYqH4zCvDwDR5LKEd+3pGDBftopRRSvvfbaU7n8dPtP1jSdyntqktqpCHC/UVm9NzDobh5JVSL+b7/Id/Pa7GUMI6x8XxScKtBVAJnP5219fT2mJBTEc3i2B/XQR5/lUwiSUvsA1tpnlIEHWDoO79nie039438iCbvRzEf8aEmb07UfOhZ9NkCuUCjsKDiwWw6/WbwSrzeIPd31vQoIAZTQRUEpAjdJWeuzzYYVHXWsSaCwWq1as9nc0Ufmgn1rHgwoD+ZyuQD4lSe8wahrR6PL3rmhRpefH54F2MaAGTUvPgKMccH/atShyDc2NmJ86YGfFqyBz1OplJVKJdvY2AgOGgy0zc1NKxQKYc8gaVzKH/QxKU0J40qbyh4OqFdaYNzi0cYoJi2dOcPgL5VKtrq6Go7SUF730TvmYG5uLjYvjEFlOZHAXC5n5XI59LNWq9nm5qZtbW2FPa35fN4qlYotLS3F+IA+AiT5nHmCpsx1knd+lHPP7wn0HnkMUzzo3lhpNpuxojLdbjdU2WVt8C59judR+FJBPJUhNeVXgZuC/iT5yFwBynV9QyvNAmHtEglROTUKyOq7dmtJqWl+bWsFWzUedL3qvOjRIOrY0XuZG6LJXpZoH+6PUcjYRhmnNO9MoH/aF/qwG+6heb2q0WyelzQer+vhWZXNXm96eo76nvsxWLrdbpCBRLWV95WX6UNSmr7ZMEqqDUNMtwzslgKp+EnXTBRFMayUxBOelqqXR/ENOoVnIhuTcEmSrktquvXA359KpUIxLDPboXP3ej7bCUY1f6/SPWn96/tU36nTYD8ZEXu104bit2BTZtzLUFSgTVPlSPPPKJfLsRRJv09lLyFtNjSe1KvNe5OMXu5JErRc6/fN6Fh9U2GhqSMeDKigSwIK+xlv0vekVQEsuAaBgGdQFYMaDnt5bgG3XrH7NsrT6I0TPgOwjXov/5dKJVtZWQn59z6ldj9GaFLZ6yQQ44Gfzq1PewXI+kqVCl4xuvv97aMt6DsROP6OosjGxsbsnnvuMbMhiFA+LhQKsT1jCkwxpjWyhZGre1sUwKPci8Wira+v76CjOiD8fNJvwLsazgp+6D/9AVDCT+l02sbHx+348ePhc77L5XLWbrfDGLvdbsyg5V0YhbxvbW3NqtWqFYvFkAJKH6CR5/tRkRt4hbF555SCisFgu5IgBiDXUjQGYME9pG1CF41+sWajKArzrodc03fGUq/X7c477wzfQSv2Rup+MhwOlUrFms2mRVFkBw4csDvuuCNcA18Xi0VbXV0NcwpP6PzD0xjJpIay5pIMvyQDUKOvCuB7vV5wtmCkAXDUgdRsNnfINTX+da8h71CHnc4z88Rcsgb5nQRedY15fcl7eT70Zc63trasVCol7pfTKKc6AbwMxgigz6MavOc/06Z8bmY7nAXeONKm+6h9RJ/rvaGgc76bY3Gv5u/d7TnKjyo//Drf6x3a1MhEZnmDkMYecN6Vz+eDc4tnKY14rlncIFC96XUs/JpUcI1tHTyP7CRfTVrp5d/BM3XNJ+nOvRy7vI/3gGfU8anvUyPey4tR79GsF82yUvqif1R3jhr3qIg0mRuaNZSE+eD7pKY4Qu9BNiU5J8CrowxQHaeXZarHknh+Pzjc7H6knp5u/3WbVs3bbdM7bZRQTfKuekYcVSExKXyuzKqCExCqe17oT1K6g/ZDjQX9fFRk0yt/s3j0SsdcLpfDtYA+TdNSUKPjSarMmPR+3p20sH10xgsBAFKSYPVKrd/vx8ai/dB3j9qf6JWdfrZbWWvmZDev5KgWRVEsvSTp3YxDn6MKxwvyJIPJg4ok0KmVLfU9avQl0Ud/Y1Co84N+RlEUipbU6/XA1xhcCsbgLWgTRVGYW19lkef7PintMA6g1+bmZqJHH75WIxVAUC6XrVgsWq1Wi42tXq+He6kESkVTD5TVII2i7QPulbYodU1RYj7UMNL+ejoQnTIbAh/APuOnkqDKgUajEQw+onEYcoVCITyLyD3PwSNNmmpStELBldKcdPFisWiVSiX2DipeasEcAKsCFOYXAOKNVZ7B/KdSqbDPVKN3Smv4UqPwvlKoB19RFIU9N/At/VXZu7m5GarGRlFkpVIpFv3z0Rr4gvmjD7lcLhZd4XMFqEnOPWjpz5FTfuCYDjUUe72edTqdkOar84l81wiylw1Kp7GxsZFgj2uSMiv8szDAFUDD47ovC7rrumV+95IjquOTUpfvT/Pj9v1Icg6pQQtPjKKx/0yfl4SVVI74vum2C+RXknNan+sxmZdTXi8nyTH4yGfp6LOhg+oscERSRoUvAOjfi9zmb99fNWDgd39tFMWLT7EmfYR4FO8TXS0Wi4n70JVuScdneb7Eca2N/8nMYDyeXvvB1b54Hvd6fkIeqXMmSTboPOm4veNjlNN0P+2UVu7znvc8+8d//Mf77RU63f5jmxoO7InabS69oeEB3Kim0Tf/HN2L5O/xDSDJMxRQqVBJ6rNXThiIms6Q1NSYVA8c95FCRgMEesVltvPcJJ9yoMLG08R7kPC263gpna9GAMLEGyhqPCutFOxxv+/PKGEyKh1it3v0OzyKGgEeDAaxypFJ4FLTp/Rzn1qmffPR71ER5ySPvjfy9XgBM4t5+pVPAIO6cT3JwaF84A18ADQHC2ME6Pj1mXovfKrREH0uY9Px0zCe4Du/90N5BsVXKpXCuicSqs9h7hgL95KyBu3hK42c089isRgr2OCdP9CIvxVw6DtZt/Sb57EvU+dGDWddR2tra4FmgDGMBIqyYNhks1lrt9vWbrdD9FMBm6bhIqegwezsbDAOee7m5qZNTEwEfms0GmE+oYHKMLIzqGQK4DEbpj7r3LImAWnQHZqx1tSoQl57Z4yCLOYABwEglb7rGmZ+NzY2rNfrWalUCgaxOg+8bjCzGFjmWVoYiPRVlRt+jaiBq04abdxHZFT3Xatx6p2PmhEAwFSD3htg0G5UBIB36Lu8DkBuqt4gw0YdFtyrvxmTd2Dq39BRM292u/5UmneYeP2i/KL01KY0TdpDP0oWqkzhO2Sqf65Z3NCAb5SnkwzMUfPnn6vfex7R73Qsuk/abKfeBjN4xy0GSFLGg16T1B+llRpW0GWUg5+m87Ob8aW1AiYmJswsGdPyvN0cBdpGZdtoVFT1FU11W1Lz10Mf/dzTGN4DL/n91zoe+F6dZIoTRvHsXu2Urr7hhhvsqquussnJSXvKU55ib3/720P1ttPtP39TZlev/n7v02uTFJYK61HVpUYB9CRDSxePNwz8O0e9w3tXk6qgeUHnveMsZv5PirCx2H0Ec7e2nyp1mmI1KjVBFZlXmL4R+dBxIkSTjCTek/T5qDGOUnT+PgSfRoSIuii49M9WwwAFnCQAvXEIgB2VMqR8oHxCxIb3QXeeT8Q76XlRtB018QaP0jSdTsfAus4DClajlMyh5x+dc7y0OgdqQCmdvFEeRdEOg4lUUX0XoBhvphZGqlarAQx749VHWLziwyADjDEGjoZoNBoBfNFv/d/PXRRFsTOovGEEcDazWGTTzEIhHq/gU6lUzEGAbOl0OuEe+JwiMzpu7zVeXV21dDod9hiWy+VQMZLIMP3u97f34BGR7Pf7dt5554W/GS+pUkQ0uC+fzwd65PP5YDDBUz76oXPt9zOzPgGxavwlVQFN4lfWJvMIH+k6B2TSF51zrtUorUaYVTZDG+ZWZbbK2lH6xusvjR4qWE+nh+ceaqqazr0W1PFOH+UPjK/dALPKF13Hnt4UO4ImvV4vzGG1Wt0BpEcBSvqn684b+foZz/RZE/e3qe5JMkSTzvbTdZ3k5BzlHFUe4f8kp4HvA39jcPlne2xiFncUJuEhlafKizhZyM5QHlA9gANT8ZmPRPt+eSeHp41+n6RvvBGbNDZPS+9IVcesv4Z7kZPqfNJx6BhGzR3f+bFo9hBymbErPXxUU9sorOTXrV6Lk07x0qg15Ne+TzXW9bcf3K/tlFbqVVddZdls1lZWVuxd73qXPe1pT7OZmRm77LLL7DWveY196UtfOqWXn27f3KZgvFwu70tQjzIe9rrHM+Jez/HVN83im+z1eap0/XtYwOwJ0e9HjVeVNO9TEOEXsBeyKih1oXoFM8rjNqp5RaAGFf31kUb6740INahUaO7m8d3N6B1lUGrf9krDwCDgmAEaZ7fxtzZN1RrlUfUeXB2Dj4T4MXnDk3FBdz3/zB/2re9SDx/pwKPoSL+SIrxm2+sWfoS28CFGoxqvCvoVOKlXtVwux84Q9OCgVCqFcURRZO12O7yLOSNCg9d5bW0tzGOtVgue3s3NzXAtKanaSNOE1r6AAvzNWYzQXdcnyp31YDYswqPzaGaxsSk9+ZsoKN+ThqmR2Ww2a6urq6Ef3W43lD0vFoux8W5tbVmlUomlbkZRZFNTUwH8kVJbLpctl8sFPtcoZqPRCM/1hsn8/HzM8YORpcf6pFLb5yrCBxod5HvkBmOlOBDXq+NGjTRoiKGshpk33LmX38yTGog+moecwijTyrUKgvGo+6g/NDezHcAZvvZRKF3DPiJB06ig8hiOAy024vkQvh8MBiHCi0PIOyXUyZDUmDM13LzRqfJB76PSrR+/PsuPVYGsfqfv838TofZ04P5TaUlGFp8nfefH4vvi2yhjV+8ZZRDovnldG/TPrwGe5R2YScaTroUkHab6Xe9jvOpk1P6M2iriMYfvt2IeX8hIo+T8nbSn0uM7lf3Iej9/ZsPsC74nM2QwGIQ0fBrP0/oB+j5/ractMhI9OsrQ3G0voTdW/fiVR9AL/O/77J9JU92nMlbfnYTP92qnZCh+4AMfsPn5efvzP/9ze+pTn2oTExPW6/XsYx/7mF1zzTV20UUX2bnnnmvPe97z7EMf+tDpFNX/ZM177pOEw26GS5KxN8pLMmoxJBkQAKpR7wQsYKDtxVcqcL1iHJVSwH0AQZ/6APACDGlDWKry8QrCbOdZiUnRTd6v7zPbmTIBjX1KoCqoJGGA8QD49kLRAysVYmpQ8GxVIiiypLHzPH0uvJAUaVDANar556uHMcl7zWcKiDzfAeq8wla60jeUWFKpdMaPcvTgTfd/6XpBacPDnU4npN1BM+imhodGNTwNlebQyXt5/Ryo5zGdTodIFP8T9Y2iKBiIAHgiO7yXSCDPZSzQc3Nz0yYnJ3c4MhQAwKekgDLmpEi68kG5XI6N22y4zwSQrPNHVTp4HgOw1WrFvMi5XM5arVYAXax95gbQgpHJGPToCdaiRhQZvzrIGBtgnggh6bqZTMYqlYq1223rdDqx/ZR6/mCpVLJmsxnbz6o8g4NG96NigNBPL7v9OscwVCCrBj/XqqFHI20LXkIGq8edcev6VucczZfNhy+gl/fiY6gqgIZOW1tbVqvVEmWRrut+vx+8/+rc8KBP/+b9PjKbZETtFgXxDp+k36yhpAJO3vBgXXod5PcxJvVD72E98D4F8D4Fb7fms0MwfpLej6PA4xXvePW0803HrUZ3klNbWz6fj60THFzaPx0T+kb1jM8GMRtGr+lP0p5HZJo29kTre/3WIC//lTa7Nb0vaQ653xfOAzcg85SeXifrFgr/bOSAj8qjf7x+TYqoqTMavlKspmsDvlJdoM9YXV0diWWTHBPIIMXFXkbqteogTaKJOjTos9LWR4/320459l+pVOwJT3iC/cmf/ImdOHHCPv7xj9s111xjF1xwgQ0GA7vtttvsDW94gz3mMY+xiYkJ+9Ef/dHTKar/SZoqet1vknSdNl1kSUaeb0ngGeYeJZS9EcV7dRHvx3hIMpAUVCQZLx6YemPQKxs1CDUSZLZzUarQ9mWr9Uf7r+AVgcp+IzUYAWUqQPhby5rzPkCtF6oaafa05HMFr6MatAeA+muT3qng0WyYDsZnno+SNm3r+KB/NpuNRdQAaB4cKz9rCtyoTfGqGHWfUdI4mR9VKqlUKqR1emNSo4Hcv7q6GhwRKFtVuGpweLCn/EJKH7TR9ca8ci8pzozVR3gAGqwrIoTQQx0F6gygeSDZ7XatXq+Ha3UPptKDaB/v1r2A6rlOcnb4MaiBQGowY9Bn9no9q1QqNjc3F1vnmUwmnFPIs7vdrm1sbFir1bJCoRBLveW+er1unU4nlho3GAys1WqFvmvqE2vK05/MnsnJyTDearVq/X7fVldXw5xqdViOadF9o9BN96ph8A8G23vu2F+ZTqd3HGyPcZW0npT2OBsAP1roBv5QcJdKDffl+iN9iKSr84bfGrHwa9PLWuiomQEqR3X/KOcP+qayFJ5UfgPcaxQJGmg6OQ4W3UdJwym0m95j3pAN3nmI7Nc5URmblG6cFH1Uuvp1zfNVrqnTTOeKd+gYd2teBu83Gqh/q9zUqHTSFhDFBd4AJ716lLHoix7pWtPPlScGg+E+VP9+fY7qf6WDVnv316njaFTq5SgjWv/e3NyMOZahi+4B9/yiTg6ls4/s+XUKryE7PGZU2aVYzH+m8tnMgqNCx+WreXusw7uiaDv9kyrMGJUeH44yrtX5qJ8pPWkqVxRv+t+evzVyqo4O34d/d0NRWxRFdumll9qrXvUqu/nmm4ORSIpqs9m0P//zP4+lqF5//fWnU1T/g5oyNftrkphavarKVEkLNumzJCNBjZokhZckdCnj7j3G3hPqhUyS93GUkaNRRI1u6Vl6qty5zgtE9Wz5z7nP00WNPe2Pvku9Slqd0Wyo7LSADc9VI0HH72no9yv4efCKfzcBwzPZzO/nOQmoo3AZJ0cOIChV4PEMrdJI0wIcKHOv7Pk9CnCpQlcgogoeGmDswQve2GesRJbUkPPVanW+PH0xDvUsPE2rw7Gg801fFTSroRhFwxQ+D+BQzmtrazvSfYhK8U4UP9EWPSKAQi9E5NRw9KnbRPEUUCpI0DRLjNwkwz9JHjA2z8fa/PEKGOX0oVwuW6vViq0Njr5Qw5hqp/V63TY3N4OzJoqGR2ZMTk7axsZGmFf6xbMUAAAetTQ90bB+v2+dTsdqtZplMpnYPtjZ2dlwLAWRuV6vZ+vr67a0tBQzvFUeYKTwHoxH3gltNNKnvxmrPofnc2SAd4boGoDmaix5wA3PVCqVWGSL+dMjHLz8hGe4j+I+yKp0Om1jY2MxvuH9mvqtTdcIlWHVIcp3STJWI47sG9Ux0ucoimJOGN/ol6/+Sp/4v1Ao7Kjmy33pdDp2hIdGlHWuGK+nhc4rz/YVcs2SHcJcM6olrVldiz5zBIeS0iHpb93ekGSU0TQKSnbEbmBbs2x0D6u/R425KIqCs2zUmOFRlfPoQTItMBbUEaUpp76KqZeZPoqlWyxUd9Ef9BJj9fhE16Y6TDymS9oCow4Nz3fewIyiyBYWFmJywD8j6b1quCXxtNkwOpdKpazT6djW1lbYiuANUzDrqObXrxqK+n7VA51OZ4dx7h1yXKu010h+0rhOpX1dhqJvR48etWc961n293//9yFF9WlPe5pNTk6GFNUXvehFsRTVG2+8cWSu9+n2jW26GNm7kiSMlPFUMLE4vLBT0D4qYtjv90OJb8+oozww/f72sQAo0CTjUwW+2eizkBA2qjj1GT41Q4WDjglhSb/9WPz+ySTPD/+rQaTKWPfDqIfKAzI8/Fy3W3UyHav+7dee/u/TQgHSnn785jpSb0YJJO0f42BslMsHvHh+U6Gqz6cyovKKeuU0oryXwes96T6NAyCtx7Z4g4r3a38R8lq1EcXur9Nn4XRTAxDlr+DMLH6ulR7yDRCkX+p0AAyqEtWKnqpkSa2Lou3CGEQUNQWUPvhoKrTXiBfjga70hXU9GAwjTYVCwVZWVsKeT96DkYxhqXzG+NVwIbKl60vXPPTFuNboAX0m6uoNFOhy9OjR8Hm/v31uX7lcDgaO7sslEjk+Ph6jQxRFsegvFUvZH8kYiDjBCzhJMEIwmM444wwbDAZ2/PhxazabtrGxsQN4qgxkrACjUqkU2+s0GAwrXDJ2dYjBn8hd/uZelaW61tLpdCzNGXr49abGieosvscJwXhIHyadW88+VV2i60y9+kn6R5saisyjnkfKPaoT1CDr9/s7jq9QoAxI9/qSdaDyknVI63a7NjY2Fs7m9VEyL0/1vdpGGYoqS5gzdUz565Kcy6NakhHpDUXfHzX+VLcmPXcv4FypVGLP132/SX3HSOEdqVQqdnyK2U7eQc7t1iedUz9n6OZerxcrSoRxSBEjs3hlZ9aTN8D0nZpFoKnHPF/f72kMLllfXx8ZhfUYCR6C14m867ogDV/lzuLiYljDvuqx5wGlT1JEjzHRP/Qj2we8ga17JUfxMu8ZlTKt2MrjWHAZayrJgabNZwf4tptjJql9Qw1FbaSovvWtb7Xjx4+PTFG9+uqrbWpqyt72trf9e3XldPu/TQ0OfzYhDWZPYq4kgauLxWzn5n69VyMwHtCNYlwMRd6l72fh7XY+oXqjFHQiPFRg6kJVjzZeM65XRawCRxWWGis6RjW6kiJsFL/QcWqqkgcQWvnQp1EqfXmO31+y255N9Qj7vms/vJGBYhw1H/p+6KZCFIWSFPlQGiVFFdU4UDrRf4wA5l/Hr/3wwI50IuULjWBq45rBYBCAozfSuU7nwc8h17AHTVMZ1SD0EXMF8Rgb6tBRbyPv0LkcDAY7DrTnubp/UgutKK+YDSvQqRcbhdvpdGJGjk+RZb4xTBS01mq10LdUKhWqeiqwUN5Xg5w5YE50LuAf+gzoKZfLoQ/eiaZrgoPVo2gYHTLbBmWVSsXW1tZi0UAibFG0XXiLgiJ45dUI4joMO42mMZccBI0hz3c610RgZ2dnrVKp2MbGRohOQ2NAJe/f2toK0VAqZauxyLrw6brKC8rn8K4WslJa4lyoVCoxwEhfMJjIiNH39Xo9KxaLsfQ6NVZU1itvmO2MpPA8vh+1dUYNY6WNrmc1iFV2Kt2r1WowBLUqr9IWPZYkI7wsVMch9CN10Kdrw+u6brSfqn/5WyOp9FX5m7llDJ72GBZKN9+SMIiOj6Ypm8hINRSSKqCaDcE3uspjGVpS9FL77u9RvtKx+3Xtx6lOCl0z+m497kbnSLEB65TryOrQfbCqU3erVYFM8Kmc/K0y0AcJkNH5fH5H5V+erbTy+AJ9PTExEXOeeZrxt1bm9npD8cUonJSUEQa91DBXPZnEv6Owzyha0xR/qtGta1P39/vmMSX99lg7ybGyV/t3MxS1RVE8RfX222+PpaiurKzY7bff/s3oyrd0814mFKlPz1Eh5Y9j8KDXbKf3IslrhMDx0ZfdGsqN56mnjv6YxVNDVFApwNVUBH2+7ilhHBgiLCjAm6aZeY8ttFGPNwte36cAdRQ9dDyDwSBWFt/vJVDhrM9ROqhBoeXtFWTpM2mknfnPkwxiVRpJnkXtFwqAd3hvmtJY36njUQXB96P2MijY3k1RwPveYFSjxnsnk4Cb0kvTgn0EJJUapjhqZMavR6KXWpZbFafyt9lwvxPpjKpcoQFzq2PCWMGY8zIAxaP7YXSuiMaRigqt1VAExPniPLonkvml7xRk2dzctHq9HlKrMB7U0FDgDn00dQpe0LljXgCUWvyhWq2G/5UP1ZkDnbQ8u4JmHRc8hzFBFJw5J82I+VB5C4+MjY2FiBvnAbZaLWu1Wra1tWV33323tdvtEM0iFRy6srcym82GvZQAzV6vZ1NTUzHjBMBP+qUaDuqk4n/lRQWVqls0JRk6ttvt4AApl8th/lgreg6krk/lP7YqwAcarajVarEIqj4/CfRrSvPCwsKONe73dOozmWcPVL1O6Pe3o/uaReJTKTVawfrW5iN36rzTveCMif8xMFm3HqirLFadhsxROkBTHhYAAQAASURBVJrF0yzN4gaY7mP+RjSV28gsNVKJ4OncalaJ0mIwGIQIFfSjoac1PdGnqvr58HqaokBgBm9QqQNP8YHXK4yLPiuv6XYMZKf2TXUszjXlF+VPpZmnIWuIa7UfKkP1e3USMcbdIsjQXJ3y3rGnRremgZrFdS7XKv18YEGvSzpag2u8EznJeWi2+35bzi/3TbEYDtmkgozI2aSIoToOkO/eieRx637bN8VQ9O3MM8+Mpai++93vtksvvfQ/oivfUg3B7yMjfOevMxsC191SMb0XZpRC071KKmiVef27jx8/HlN2PrUziqIQJfSCXr1dPNsbm3yuJeN9/ne/3w/FBPz3XKOebx0TIAqaK3DxRg3v9xW3kjyzZvFUYA+Qk3L+NZ1BBdyoplEYb7R5kKDXEPnQvuqcmsUBi0/VWVlZiV1DX7T5ufTRcfhS+dQDBL0PQ8ZH6+B/wISnCSDLj9NsmBKmUS0dC/drVIf3M0aAJMBHI2fqKPBR13Q6HaJYPJMUIowO5VmemU6nrVqthrQ93SMHyNE9ln4tAPIBjWZDcIhxiPPDbHjciO7n00gLyh26Li8vx4Cq99irgsWQ1ArGgGcFFVyrhjS8AF2VN1KpVKxYFEaWyhDohexIp9PWbrdDXzlORGVps9kM0TwMqSiKwr5Q+qoFGbgul8tZtVq1Q4cO2cGDB61SqQRjEiDN2YvNZjPMValUCgVdiNJh7FO0wzukMMbUe69rSyMQ3iBnvgDPpIz3+9tFeACTapgPBoNg0K6vr8fWvRpAatzyHnWUkHapzetB76DarXmgzHrm3bomlY90nRJtZEyDQfzgc+SDgmt9BvJJ14DfQ6dr3mzb0aD6yANtXyCt3x991q4aJDyD92kKshYv0t+76SA/D6MAuIJ7+qcpx0obfbbqQpwpvk/IxCSDc1Q/SdVWp1LSs2n+jNJRY1VspWP1ffXOfXiUz4m2JuEPTX/lnSo/NYtKHaBqoOhcsB40U8pnNnmjWKta53K5HfuquQ9HGzJQncLIA9XX/O+rnGpT3lca6m+VUR4HoX+8wZz0tzblU7JQdC15HKvp7CorlafprzeMVXYn8UBS+w8xFLVVKhV7/OMfb495zGP+o7vy/3wDSCkA9Z4gFq33mPtqkcrwunHce0/13Vpi3BtK3nNF84U7vPGhfVdwYhY/foJFreeCqRDVpsUa6HMqlYpt2lbgpt50FZQKZr2wSfLK0ZQOapRwrxYj4HwtBKfS1x+dgaGuqVG7NYDqKHBA8/1PKjaT5HU1s1hEiPllL5H23UcB1bD0gM1sp5cdfleA4IGUOjiSPHZalVHTXHVsCkrwPKvByv/Kq+w/9OCYHyp9qrJjzEm/AQEY7Dwb4BZFw6q+up4xhJALgB1+o/C1uAkAQI1GZEylUgkps6zTdrsdPPT0N5PJ2L333hvjeWjJ+3DQnDx5Mjh22BvJnAK6NRuAMyiZY/qrURqlHzy4trZmuVwupLkyJ+rRVVpCH2jNtYPB8Gy8wWAQ6J7L5cK+RWQm8wr/wcOAIKKHKl91bsfGxqxQKMQivfAaUelyuRz60Ov1QkXETCYT3rO1tRXO2dU+QWdfrEH5To0Zla+6dzid3j4+hD2uGLScUba5uRlzcsA72Ww2yAZdmwp8dK8y8897AZC6FvVaMwt7PeE/vX83kIfs1Xnnc12bPgpBdF/TZXUdeDmtcoqGLFL5pfJYnXNqtKshuLGxEftMaatA3t+nMhRjhPt0jXtDKQlHeJ3h26jvlZ7wGgaB8qR3pimNoJnyEp9XKpXwLG8k+aYGGOsnm83uODtWx4/+WFtbC997PuEe7Rt85udL97zTtGquOhm9EeLHqfjDLO6cTpKfalQqbyh285V9kwxgdCx08ZiDfqiOQN7hfNQ9vf59vEdpiMPGN/qHnNAqr/o8nZdRvIEzjqZYQjEeWIVx8Q51qPogj8pas3jWi75vv8ZhrJ+ncvEVV1xhV155pR07duyUX3S6/cc3NRSjKAoHT5sNz6oZFfnyAEmFgwIxv2iUKb2CUaOK7/UeQJb2X5+PV0cViBpLepYb41BvD5570pm8IDEbeitV6ZHOhQLCIPOREO2z0kGv8coLQeeNaDVMmQ8FjSgHNVo46w4aaEprEuDQpgrCG4te0Ph5TxpXksHJeBWsmg2jTJqm4SOp6s3zio+mSgA+9fynz+deCurwbPrN/i+AlfdyMk5+M0dKs3Q6HYw+M4ulnOq9CiRLpVLY48H6VbDnPeY8S40kDD51FMGvyl8YJ8yBnikIEFAjBONvMNhO06T/4+PjlsvlQiEIxkGaXb1ejxk6ut8RpY2yJyVWFWa9Xt8RzcAZwLN4LvxF3zY3N2MFCXgmRgTrjKJMGP187/kMx4hGaTUqhuygKJGZheIO0JsoHgYqckAjShsbG1ar1cI6VwfaYLC9/1pBErwJGOl2uyHayxg5Z1INVf7n7EfkHfdoJWoF1oBtxurXP/KSMWDUqXzGCdHpdGLpZ+l02iYmJmxra8tarVYA1WoM9/t9W1lZib1bC9KoExOwqhkZqVQqZiiyXnm2B6vwlcp63eMJT+m6VlmhTqPNzc3YcRlqoBE5UZmXpKu84aa059pUKmVLS0ux/iAr4AXexbwkAXmehzPRp8szl9DQy334zMtgWhKgTTpb0GwIiP3Y0R/IL6+TfF+077RSqWQbGxs79v6aJRuu3vjBwcw6V76g0b/V1dXY/Ul9VeOAdzA2ZK13jqjRrrRNen5SwSR1rigO4jpN0da0XDXE1LGqNQjU2KRvuibhvySjmet1bn3Wmc4vulCNWfrFOldDUedbnbfIY+232RBvcEyR76v/m/+9oYijzCzu9EE+8X6dE+0vf4MzfIRZ18FewYJw376u+r/twx/+sH34wx/eYRXTvvrVr9rZZ59t55xzzqk89nT7JjUVhniQAVV61o5flApEUYaDwSAIYha2RrBU+ZpZSH0zi4Nmr2B4D429WWY7zydCAGhhA+9ho19mcaHAuPw+KrPhwgN88TfCDUCNQceiJO0AwYnAVrDgDUZPY+/VxVhGgNCSDBlANU2VmzbvNU1qABTGrUIRwaOeOq94PLjgev/Z7Oxs+A4loUeTJLV+vx/ApjYF/Qqm6RcOgaRrvbGrfeZ6vMLwAe8fdWYl/M1YAE8aRVUa837lYQXsGu1TMJDkjYWHMd7UsIDfFWgwHjyZgDLGpqmD/M11rVYrFJ4BaMIPuVwuGJv0y4OQdDptzWYzlq5M9BtQgYHT6XRC5U/WHNf0er1Y8RnWhR51ofOt8wvdUczsM9TxQAc+UyCRz+dtY2Mj9AWjESMAA/uOO+6wKIrsAQ94gPX725WgcQLwXuYRo4+ol0bzNItCQQzX6RxBR+5HXkFnxs610NDMAu9hMOo74St1Lur7dJ+p7gs9dOiQFYtFq9VqQa6x7omCm20DfdJ66YOmBDN/KsN1L7KuIa8XANUYrDpXaixrNNufpehlqxYJoR+qA9UBquA2lUoF/vcy1BekYc15x5znZ+94InKbTqdjh4Kr3NGUVb3fG6MqC/T5CvBVLqnu1LGZxfcsJhlP2kYZTz4KxDN1b/9uwBjeVacDjarDWmAIHlEnDw4bpZMatt7hrMYUdKMeA/I9ybjWKJHKObAKe5C1H7xD8Ys6HWg4lHRs6CvG5ve9J8lPlbPquOJ9nn90TlSWI3M0G4F35nK5oAs9xlIsxdyqcYwR5jPOeJauG7OduCHJCaR4zDvklb5+b6aucXWyqRPK41n0gS8g541WaKxrTA1z74jZrX1DU083NzftjjvusDvuuOMb+djT7RvUNH1TF2kqlbJGoxGu043dMKcyGP/DzKrcdX+V/m+2LWS0yIMuKK9IzGzHogWkqvGZyWRCoQEUkvaBn3Q6vSMdURe5joEcfkCTgm9VxJoH70ug0z+e6dMdtV/0Qa/zdEDgY+ADRvUcMG8AauQKg1P7sFcrFArBOFKBpXnwOk8+PUmbN654JgoMGg8Gw6IWo/Yo7mZA0icMdt0r0m63Y9EGD9h4n0+fUUWJMlFae4+hPleVJEpL05yICqsxqak99B+eVHCCY4LxwAfdbjd21iC/AfA8188H/G4WL4LA/wBxlD/rhjXH+sa44b5KpRL25HmgCU0YJ2PBCYNhxBljKkNYs1EUBYChhU5YfwqAAAxKN1XAAC4Kvaii1eidRuqIfq+vr4f3YzQCoM2GlW2r1WpYw0QJmWONQPNslTNaUMhs554i3qfHb3A/z83lciEVjcOj4dnBYBB4cjAYWL1eD0CGNHf2BAEMAdjMi+6pS9IDMzMzses1lZkoONFPXc8Y1qT60l/18vMuTTdk/StNFCRirOLIVCPUA1Qv0xVccl+73d5hCCIb9Dp9T6VS2QGgGRdzqnytTdOU9fkKnDWTCDCqvKzGCfylDiw1+gHvCr5VJygv6dzpZ0qP/TblJ23+Ocr3Kk9oPjJsNnQie4enRpShg+pVGryh/zP3mczwnFOfNo2BqJglyaDw41SHN21zczNUWPbjUKOSNaHzY2bB6YbsYRxqiCN/FQ+o0aE8ofJY07t1DaKv9F6u0TXqI3R+ztl/rfOMA0kzHXS8er8a4erw4n1aX0MjnSpzoKs/ekr7nYRdNNNH+Yh3JD0jlRpGRr2xSr/gU89Lygv7bf/hexRPt29e814TPX8MQKapm2YWUwQqBMyGwleNJGVys7hh1Gq1YqWsOe/He6dUCJEmBqj1QqJarcaqQzFOfRaf018qX6mRpkpCQTTv1hQg+qogl+do9EGvUwVMvwBl6o1KpbbTC7ziVUClSghgxfgRZhgHuo8SgJWkKH1DQeC9VhCvQIC+qJL0PASPKF9AA00/U97RioiMVRWNRvH0c56PQkMA80wAsjo+PJ8wftra2lospVH5lev85nUUh4J8xlYqlcK7VQHBO8rfGEcARd1rwpjVGNFoC8aT9k8VqaYT+jXt06q4rtfrhT2T3W43GIE4Lig64pWuRt2Uf70Dhb+ZM59ui3GIXGDsyK9WqxUz4li7NIq36BpXg6NYLIbiMXzGXKmRBu8zxxiKpB2SjgpfZTIZm5qaCtEJ+CeTyYT9gB54KX00Mgegg7dUfkAPnVsFaowRWYDxr84J5nkwGGaNEMFPpVK2trYWomvwKrIR+sAT9I3npNPpENVUoxg6axEf5o3vs9mszczMxJwQGDDszdRsD/oHT0EbeFJ5jmq66rjS8TFGzYLRIiXqLNTUXAWTSRFDdXTwmQJx+DTJ8NTnqJ6FZnpdtVoNa8aPT6Og0F73qkE7eEojPDhD9Pgg5kqfq3Sgf9AkqSVFO7yhyWcefDM23avno8Fc5+fE7++En1ROMi7di89a1j6q4wQd4J25/M13hUIhyC2vR5GjZkPZrfO+sbFhExMTO/SaRtpV5rFGVAYr3VUeME7VgYxZ5ZXSCV5WHlY553Wwri3lO+Ur/12/3w8yt1AoxByF3KtnWBKJ5DmKS3BWqqOY52g6uXfKqDG2W7TOG33Ia9aU6n51dqgxqtFRzXbjefxPEAf8xnceI+7XWDxtKH4LNfXyDgaDcJ6ZCke/eLmPSAPMDhgwi0fBdIGoQlYjj+tVkHsvpzK3B63q1dF9EABvnkd/eLemTKpAVyCg9w4GA1tYWAhARAUp6QsIQa+YlVa8w2yYDoqC9jn9aizQAEDQTmmKh5Nx8R1edRXCKD0FANo333TPjL9On8t3OpdJnjO85to2NjZiSoyme0B9S6VSwUPr+6DpTwh9+kIUxGznQcPcr3PAdThVWq1WuI5jDLzyoC8K1DEMoQ0OEhXsGhWGp+grkRz2TjJ/ADR4Q9NRdCw6Ri3IpIpClYcCWAxy+rO+vm6rq6sBVOpeuI2NDatWq7E+wXd6IDyfk/ru06B0zQK81SiiH4wXIzSVSoXsAtYjz1Bjg+crjTGu1PGhvKBRMgXKONvK5XI4S1ENRfZEE1nV6MHY2Fh4JxE0nkeaoEafKNSjhiLfwXtqKGoKN/NJ1BgjBwDN/OGoUh7i71QqZRMTE7a+vm6NRiO2fpDl4+PjgS4KdtVxx9ywH1PBKPORy+ViaaCsk7W1tWCEsNbYA8a8kD0C3VUnACyVdjgONWrHd2qUwnPwPPIR8AbtAX1qJDE+lb3Ibw8g+QweVOcqtPYON7/H18tg5hzeQy4o7/T7/SAj+/1+qLyohiXzofzIemIds854LrRP0uPe8FOHo4+cquOLluRsgm5aPVz1jneOqbzxskjXOTRSftQUXjVeVAcRKU8aM9fpnNJXryv1e56j+p458bhCHUGKrVTea99U3jE2pbfqMtVV/f52ETDFT+hIaKU4yesmxQAqM5KOcNOItzrv9Tp4jv5QlIjnajqxGqlJx2foc/Q+7xQpFArBCce9SbTjXpWHujVKjyvyEVif7cPfyi/IflLsla46P/ttpw3Fb7GG9yKdTodUqWw2G6uCWK/XY4uPBQCTwcwHDhyIGYNeqStoRaBrVTQiAgoiVHApCFUFZTb01rFoFeApyFOlqX3T96LcfVqI2XbJen2m2RCQqVeO/uFBg9Zcz/s1DdHMdqTKeSGl48dTrWPScfBcPl9bW4uNOcmAHNU0/QJFkOQFhg7644EP80MFWxqKhvH51B3lQVUqnufoj27y5v0acVbjGMHsaew9uYwvlUoFQ1Gv57neoCb6NRgMQrQDeuo4Gb+uARQ+4yeNVIuw8E6ex3gwzACl6vyAX+FBgLHS0s8rdFdjrNFo7OAlfRZOGeUHokW5XC6kc/sKnawxQA90VABOVJh1wNxroSLWt4LwJADC/2oYYxAQHcSw0znSrACMK864xDHBsRyMT4+Mge9KpZK1Wq0wfs49jKIoHJqtkdxcLmcrKyu2uLhozWbTtra2QiEhaA3va+QI2mFQaL8YD2NBL0DffD4fjCMK7hDB1QiCWfz8SB9FwbEDP2SzWVtaWrJarbZDfvT7favX69ZqtWJzura2FtKCkaXMg46DNUJfqtVqWBesa8YIvdgP5Y06Diqfnp4OoFj3prNWkAnwgjo84Xtv+OgaVpmjBgB9UT7HSanv13QypRn0JioWRdtbRtAN9AGjr1AoBPmqRaAwADSNuVwuh724ZqOdxPCvLy6n8oamOlgjZsyZf4/KOHQLkV/kL8/yTdcMumPUGYm6bpSPobk6wGjwiRaAScIY/E3/k56l42O+VXZBE92fCG+pXPYGs+odlZXqXEKeaz95p49eagYEDjPFYdBD9bBZfH843yld/VnZyCmz4fpgjSkW1AwCXUfIQv8OddDomKC94lM1HHUs3rFD85jLr3EMTnSlv8ZH7hVLaVaR4qUkgzVJ3+/VThuK30KNBQ9YRLmQlqhKRRfyYBAv6oIyBtyYWaIAVO+RjxABHvR6M4uBC5gZoMyiVe/L8vJyEAQINgW5KGp9v6YzKG00VZPvOHtL04FQ7PSVe7UPjEtBhDde1TvEtT71xdNWo4t40KCxRuw08sh48J7Th6Sqbfqe9fV1a7VaMQcBc6TzmCTYfdPrtKly1PnlUGDmSwU085eUesdz1PhURabeZJ0rFZ6qMIkAs8cOA0eVvx+PCnH1cvKdRkP1vC+aN058Kg1N54XxsC7ZX6eRg0wmE6t0rMrZ8yk0QsFmMplYtV9kAtdDj4MHD1q327WVlZUYkNB+jo2NxfqvzgLm0EcqiHDwPXvzmEMq/BKpxEAYDAZhDcNX8LDyhf7kcrmwX1AjCR4Ip1KpEFWir7p/WPmSdcvzB4PtqKEel4Fc0bUC0GFOzznnHJudnQ3zOxgMI9TqGOJ5ADNkmN8PQ5/UQNB9SOgD+qDbAOAJjYBBG2+cYxDncrlQuKXT6cSO2lA+rVarscrC3W43rEXGoinJahTpsU/wD0edmG0b6Arm+v3taqm6/hkTqWpTU1Ohj9DDgy/eq6nmrItyuRxLh02SpTyfSKrumyPCrI5IBfHocV3bOB1Yw0Sak+QXoBp5hVHI9yqf6FexWIztAfe6Bt4325ZzpB+rjPXGPnypDkiljcpT+qJpu6rDlU6tVmuH4QV/IG90rDoWfnO+rPKa1yHacIjpHjxdF4xPo3LqwNHnefogV1XfZTKZUFU5KZVco/D+N9fBe8yNn389row+0iftI799JWiVGfpcs2HGj2I9b3xCRzWszSwm21R/w4tK89XV1bAekH96Pc9RHuFv9Anv1H6p7k8yFJVuSleNIEJT/vdrANlCxgX0MBum9xeLxVB0yGd08A51Hu6nnTYUv8Var9cL3hkfqgZMacoGys9HWRREmVlsoZnFjUWEdxQNN8yrgeOVFQ1hhTLSBYgXp91ux4CJCikAKBFUFhYAAK89dPHKn2fgjWUBm8WPxFBhjEKHdtovhIiOUwUHtPJVhTWtB9oxZ6qUFRD7Q8ah2cTEROy5+m4akc1cLhcAuBeYOkcIM1XiPm00iqId+0RQjPSRqm/QmbEmeayhq+6bUQOavmqlWAUqCuZ4NnOpSpjv9AxFpbkWl1H+53t4Vo0JiuooQNF1gxFBg/6aLqpgCwCJAyiKIpucnIwZ3iiG5eXlsK7IHFCDkjWt9MAQY0yk1qixBhiqVqs2MTFhrVYrpOeq3ID2GklljlSRQk8tDEIEEkNMPduk0Xe73diRIGZmKysrwUDWqLOnJXyFo4wzH5lvjBKN4qohG0VRLKJIn6CP7nHjPhwyURTZ0tKStdvtAMYALfAifVaDhHkvlUrWbrcDUPSOBozora2tkL6p6xQnAkCK8a2uru4wcAqFgrXb7bA+mFc89vRVozhkfmSzWVtcXAzRHpXd9FeNKgVxGk2GF9Wg9bKIeTGLH/8Er2v2xurqaiylTOUohhU8SlRYQZfqHq1IzDXwpOokrmdefJqzyih4j78ZC2tFHZnMFfdC516vFzOSk5xdOFXhYX0eNOFcOw9iVZ76Q92TjB7orE3HqBFNaOibVsnUd3ijEH3iATy8BcbwBhaAvd8fHuMEUFdDt9+PpzQnGSvesGA9sNYzmUwsNd0737X/GjE2s9jnRKS0D1wH//GdGkQY1OqYROdo1FZ5RHlcZY3yDfRF/qnO1+b3Jh88eDCWhaZ8o84R3qn7DdPp7YwqTd1sNpshcwD6qqHo540+drvdmOHW6/Vi52JqVdF+fzv91ldCp6kc533MBw6otbW1WLRX30uhMb+vXfEZ+62RJcyl4m14N8mgTez3vq5ybb9W6On2n6sp2IH5KMKgAkWrQ2Ls6KGnUbSdBokyMBuG/1VIqCcbI0ZLVycpPvVEopB1rwoL2m/+RzioQYlgomkaFwuWZ3kvEPTQ1DoMEww9TRtlHJR8V5qr0PEgg3epIvZGFmNTAa/7NZgHjP10Oh3zIKsw0jz4pCiS2TAisLW1Fbz40N6vfY2uqhHLO/g7SUkrfdRogg7010cUFbiroagCVz2g9BmgAUj0kVG+U/CsYFjBlRo/ZhZT8GrwAIihISkzrCuNenONOnN0Xfq9OIxX5xFaAmR0zorFos3MzMQAMuPTNFfWrL4DOjYajcD/eIA1MkV6KIbk4uJiLIquTg6AgYIQ+EgNKZ0zwK5GunO57cPr8/l8SJ9TI5C+8j8GpcofnkUfCoWCLS4uhr54Y5U1Qj9RzAqyMWrgTYxYDHRk7crKivX72/v7pqambHx83FZXV0OfyAJR2Ucf1CiZnJy0lZWVELH2QA4+aDabIaXSgyI9DoOon+7zRe7Nzc3ZoUOHwn28QzMBvIMP+mu6KeNT5xkyB+ANDUljg09ZW+yJZc02m80gD+Er9s5CM5XDmsYHjXiPFkBjnLrmoB39x6HD2mWMm5ub1m63A/3gc1Iycapwnwdx2jcF5tAZWeiNJXVY0W94Q0ErfdZCKppW6jNmoJs6o7Spg44+85nqBTVK4FH0pDcMdfsLDbCvmTTICn0mckf1Pt+psaGGIum48ILKZNagRl1Vx6XTaWu1WmHdeqNd3+91SRKfwYe+/3pNPp+3arUa433la404k0qsdRu841vlqDc49dlKR5wrOl6cgZo54ueRTBClIcaQyjieSTZLJpMJMkrrOKDDKA7E8zHgdM2bWZB5ZkPnE3+bDfGtOqShHUYrdFceSko3h940rWdAwTfooNiB8aJHGSf6ngAP2wU2NzfDeNmrqPygRvle7X4ZildffbWdffbZO34e+9jHhmuSvvc/p89b/OY3GFAVQz6fD2lLgB1l6CjaPntGmZ4S6ygOrTrHIiAtS71c3lunKaGauw64UsCjYJ7KhB7ME3HQiEJS1BNwDg28J5Hf6XTaZmdnd0R1AH0aMeF6pZMakoxRDRafPsHnSc4Yja6pp0jTwVBwuj8jybDx0VsVGBj03juI0lPjC2OUd6sRooqeiG6SoalA0xuxUTQs5qDvhO4abeV/BZDwchRFAUDqj3p5vXBWftMUQ99/BLD2WfdoEZkCgMG7GvHhhzFoRBEgpoBbjWWtXkw0DFCmRhrphVyjxh3jI9qkYy+Xy1av1y2fz9vs7GysUEUmM9y/pkZqr9cLBhFzg8GG9318fDx8rmvcAyEAPoBufHw8GNwYN1pxdGZmZgcwUocDxy5oCiZrkbFriq4el8PYVP6l0+lwHAb8o+coatRAI9A432q1mpVKpQCMGo2GNRoNW15eDgexo/yVZ3DAqfw5dOiQra2tBUPTA5Jer2eLi4sxHkRuQvtUKhUKJ8HHWuim0+lYqVQKGQIARI7rgVa6B0idiuVyOYyV9/jKkqnU9pERGqlXhwryCN4DoMGbCtgGg0GYc91ywXP5raCfNQXQ1D38yuv0V9c8UXC+y2azNjs7GwxgHy1l77E69Ehz5J30WR0GPuLg6wioAacy0+8LV8exrg14ROWc2XAPlj+Ggbn3+pTvNZWYhpHnDVIt3MRYccap3qLSstdlfh+c6hea0oe1r9EueEv1gTcgNLXPH9ECBmEu9L30JckoUZ2rejlJjyOL+GxhYSFm3CnmQn7Bg9VqNUZfjBB0pp9/pZ+mo3M/8oRMEzV2iVQSLcOZps9T3gAv8A7NXjOzYBgyb+p0oj/qzFP5NMphrdkKqje0f4xXK/ar3td1ok4AWhK2Y82p85VnI+c9rc0stj4Us3e73XCuMfdoaiv0VQfLXu1+GYr33HNPOC9Rf+65555AhKTvk35Ot29eU4bWim0UJgCsIgQ0vUQXKt/peVZasEOFKMajCrl+f7tYQalUigl+jcqgFFkAGoFQJmcx6uekBuhC5f0Ie/bf0ViUXjEiMGnQh5RUFrh6DfWZURTFlAmCVJ9H/zqdTgCPauhqf3geabPr6+tBoAP4NDVNvfq8C2WugsN7BrkPeiro9Pn0OB6YM57j0ye1iipygogkeyd5lkaSklKOvNLUCLM6IvSsIZwh0JCUPW0aXVGBrke7aN805UkBHKAIJamA1CyuaNRg1TGhtLXSI4AFZZbJZEIURw0fBav0Gb47cOCARVEUoodEAJkjjBL6Rhoy4J+I0MrKihUKhWDAaDTbbJjGBljBAO52u1atVmMVN3XMCpR4pxaBqlargZ+JzioIUrmTz+djR1WYmU1NTcUMe6U7ckP3PkEnBVyMD2OZqB+Ne/x+bwAw6Uz0j3lgjWD0Ukl2MBiEM9LUqFVjjPUzOTlpy8vLMT5mfM1m07LZrFUqlcBPyAeKdfAueAhDCV6s1+tWLpdjZ8/inNIUSz7HkNZoMk4cLUTBPYuLizGnEv3XiICCUI2AqyHonSR87x2M+hwadCX1VCsZYlwTpVAewqhW56Q6ZwCcyAPAa6VSCc45ZJ6udzVikRvKb0TgWNeqF80spDd3u92wX1RBK2tX94lBe7P40UPcC7/wvRooqi/gVwx5lavqXKLBkzgTmDeKGame5X8F6Npf6Jekn5kbPtcIO/RQ57fqSOSRT0lUXaAyHZnkHZ6ke2uEW/WkGvqqK9S5pbhOcZH/TMegaYy0QqEQi5z77Ss6n8hk1adq3DIfaqzwnS8GxfPL5XIs8q1HHXknM1kT6kAzGzor1OFrNtxOo7RTPlD9rEX3uA7HG/Snn6qL6TfbIrxByPh13aE3cZoxTyq7wOPwMLhMC9uow4F1wjyb2Y51B012q1MRu3ZfV/3fdsYZZyQO/nT7r9GU2QBWGxsbIf1UN89zPcKPNESzodGiXmKzIdjynm6eCxBisfkccfX2pFKpACoRTKVSKQgygIZ6wfE8kWqE117HpGmqRF0wZDUaqmPSvSsKrFRxIGRVePo9ZYBtVSCckYdwKJfLsX1R2hhbJpOxWq1mi4uLQQlF0TAdWEGGFnFQL7N6x5hr3fNIn33VMm18pmkTSidV/kRiPADx0SI1nhCS6qH0yhFe9umNeJqpCqnCl78rlYrNz8/HFB1pYGoU8B1p0N5QhLYKEhD4GDrQYH193ba2tqxcLsciOWpAmA2BQaFQCNWJPS/3+32r1Wq2srIS5pB+6Zzzw/oFAGihAV23ekwDgEkNSWjD/zhMGDNrDOeOZhYUCoVQzVIdJvSLeVNPfre7fXQE44Q+7Mubnp6OgRSVWZVKJRznAd/X6/Ud/MzftVrN5ubmgvFBtgXjYl40wkx0U1PTSA2lYA1GP+mx9JeiBBw1wvwjF5iTbDZr1WrVOp1OzJjV+VBQQKVPQHi327Vms2nT09N27NgxO/fcc0MBF/qgWRz0d2Njw/r9vi0vL9vk5KRNTU2FsZ44cSJ2PEmn07FKpWKdTidWsAnDEf0zNzcXM4DT6e0K2gsLC1av1wNPNpvNAOrgP40+9/v9GKheX18PdCQVGV7gmcjeUqlkS0tLYR1RCEx5guuZX/4HJKujUo0oDv9mDeJUBSBrij3rTM93BcAhn9UQYm153u12t4v9ACJVvkdRFJ6/sLAQiyazxpGZ9A1eUsDr96aNjY3Z8ePHg+7z+kH1I2uiUqmEfbSsbeQf6xo6eIMHY0qbGgVqPCkve7ml/VTjCScD8k8NB493FPco4OZZ0BZH5KjsJTValI81mumfi67UOVbc4seJs2N5eTnILOQTzTtKzOKptmrc0BQ/qOHI2HkHWEV5gbmm7yqHcOJiXNNIrVQdiGwpl8uxPeWafUHfV1ZWgrPJO84ZD1gVfcz72OdOTQyf7s2aQdZqQIF5UiMTvsTxBY9qVor2n3eSkdbr9cI2C8bAc9vtdnDCqqPQO7l9sGC3dkqG4ukI4H/tpiDHbJhiqCX3FbD7fSHaiPogHBSE0xASKysrAXAArLhHwa0aR1tbW1av12MCRr1auVwuVqkVjwr71SqVirVarfBeFKwaNyo0EQyqnGh6n/69trYWBDfefu/RBlwOBtvpT3oWnz4fUAl4R7FwLdfzfK3ax/xAS+YMw1q9pvyvhoC+y8yCwalGjgouVRCDwSB43Pv9fhifN/gQ6CgDVYAqCBVQK0CmwbMKELhfwT4REIxXFJcqWo2Ia3qdvkv31eAp9I1x0mfohKKjXxiKZsNy6/p8pTFApVgsWqvV2lGtlL8BANAAg0YLUrHWKEhDvxQIQ3f2eyiAwuMJPTT6pOAVoAqPQGv4lb6oE0ObGqoUIoA/S6WSHT9+PKakoRtGCDTDGCe1amVlJRjbWuhG1zJ9I421VqsFowfnC+k8yCb4hX43Gg0zixczITLHGCgCBijAGNMsjM3NTVtZWbGZmRlbWVmxdrtt1WrVKpWKzc3NhXGnUqlAJzUs0ul0ONYnirazQebn5+3IkSPBIFTa69YDZBHGca/Xs6WlpSBTkT+5XM5arVaofIlhVCwWY5Em5hqapNNpW15eDkCGCqj0h0Ozy+VyAEFEw31qJ+9Qx5vyI99VKpWYk4p1p3JPjyrhWcgsdRCgJ7xs80aRGqKDwcBWV1etXC7b8vJyzBhHFs3Pzwf5jZOUa/w+a65TvYyxixzgM/hBU2fVGPM6e21tLfBDsViMVYNFZinNzLaj8Dhj1PnF+0qlkq2urgZ9D61YuwBf3sEY6DPP0bRbaKOGWxRtp842m82gZzA4kK3IMjUS1NhCZqkjCKe0VhX2KdC8Xwtz6XEjPB9egj5JzmnorIaUH6uZBUcRekydO8gk5CzR0iT9qe9lHKonPZ36/X44YoW5UcMQHcbaUH5Uo0nfncvl7MCBA/av//qv4bq1tbUgY4vFYlj3PuqtfAd2zefz1mq1Ytt5kCPgI/qiUXFN/81ktvc/esyTyWSC0xaZoFs91OHNWKCTYljFb+poVXp6euFs5XP4nJRe6M/fHGHTbrd3GMba773a6aqn30KNRQDzs2dCjRoWFP8T1VNvRCaTsfn5+eCB8R4WBa14NFCw7PlQoYxwUiNNN6MjYDUFDUEKQPRetGw2G1JqvfcVQ1FBLeBChRz0wkNkFvfWaal9NX7N4gcY+6gTnipNZ9MonwoF9XYxHtI0PWBQAxbApPOOIcd80Cez+BlVAJV+vx8znhRU6HwhbDi+gPH7Pvr3Qn8MR51v5rlcLoeDnzV67PP2qWiqDWWdTqeDh1aVGZ5gaK0ePvqj6dA0QK3yrzcgiYQqT+phxFpdzt+v70Up0Hw0gftZd5p+Cd0ZI/1uNps7Upn4Xr2qzDfpPRi6g8HAGo2GtVqtAMLNLBhmzKPuI1EA4vus3nLGoIADwMc+afYGMrcAfvXi9nrb6dl+3aqs0/WFTOG5xWIx7MODJ+r1elhbpDFBL/XuKoCHB+gHtG232zG5oXJsdXU1RAQxopRX4U0iVEQgvGHAs9vtdhgPnnEMIGiggIF10Gq1rFAohFRf1oHyga5p1jx7bhTsENmEN2q1WnA8ZLPZwOuk0nNEB3NHJFYj1GogqvzV/cFmFqLK8ADrQFPR4FXlFaLfapxopFudHirjMYxpAFQz26GPmGNNVdb9rsh7jTIkGXjoGuVnvs9ms7GCdZp2rH2Cl9jrqLqI9+l7FbDruJReOImgnWYNMQ7opUXIcNIoVlBnGfebWawiM/IPemvRE5o+Q9cNc4hxqfPH+1S/8RkYCP7nHTisWAf+Po0o61x4va9RMeQXRiF0BR95/UCf4C8ceHo970H+IDe9M1d5RaOuSlewFWtMo1bQyuvKTCYTjnTQ/ZHqbFS80e/3g0ymqJXSS+fQf6540/OrGpmsfzAaWAja8WycZoxDq7FSWEjnT/sET5I5oTgXetFPH4Chv+A05gWdoFFZvz55JrppP+20ofgt1FDIbNol/Y0FAFDSyAtgBs+a2VCYaRENryh5Hw0AR7Si0+mEg7u9Z5+FpcKaXH6uY7GwsNR7g6BloXuji+YNLN8HvHREDlnMjJmojaYYAEg0rYN3oKybzWYQ9Brd9c+ivyqo+F/z4AFjqpAAPrp/ByGpx1Toe/TZ7BcAyOn33MezUc7KK2bxvYxKK55DnxW0wnMIS/XAMf9mQ+8nyklBJTQllZNnogjUwUDkRFNn4GPdx7u5uRnbC6r98d5hQK/3nAKGstls8PKiwHyacqfTCRUydX1pNFBBAn3SdCF+A4bpvx7PAa9Ce2iuXlEUytTUVFDKjUbDNjY2rF6vh/TearUaeyb72NSjurq6aqurq+H5qsBQbgB91nm327X19fUwR5qaZbZtwClttXw/hiogkzWnSpS+AJBJDS4Wi4G/kV1ra2uxY280aqr7QqDl0tJSAKvKp0lncUHPmZkZS6VS1mq1AigkJZMD16vVqhUKhdAnb0TiaFB+bbfbIeqk/A6tMaCJLFcqlVDASL3b+XzeVlZWQgosXnrmW4F7FMULpMCjOreaKs4YcWboPZrGRT+UbxQUq9HR7Xbt5MmTgSfQgyqrWAOqJzBYPXjnndBQZQtNHW/ewEBmo9M4408jCnwGf+oc8ZkHlNyjUQz6oOnf3INeNLPYO5AhGBIaAcEZgzyjaUqqymT+hraNRmMHMFe+om+sWwXqrHNkCfdCX50j7qMP6DDVh7yLpvve0Am8u9/fTvNXWa4yGCNYdXG/3w8OH+7j/eq4U/mn+EJ1rcpJf+QB40bfqcFgZkHeKbZD9+i7NQJpZkGuMxZoqvrI9xN96ytiq6NM5497C4VCOJbHzGLbeLied7F21ehjPMh5PdKGdQNmVT7R+YcWk5OTMQMd3iZ9eXJyMqZfNWCiOJL5UazDu5HNZtuyAhmZlEmmBjPP0Oep7EAuIQORmWrUqwPP49RR7bSh+C3UVGnDrL1ez5aXl2PeD91DgWKAWc3iiwNFCuPB6ArU8NiyKAEV5HubDfP8VRCpUgNAINw2NjZCpEO9aPQVoYhwU6GlEQgKAKiXmEXOczh/h34ARFVRIwCI1rKQFfSqwqWpsa39RNEqyEDIpVKpkObH52YW5kK9m2p80F8tEuA93FxXLpeDUeKjgGZxTyz9bLfbAQADoFSJeM8u4FaNQy3tzBwsLCzEDBD6meQVJC3EbAh0isViSFtCGCuYVuORRr+4HuOO59EH76lWRaGpOXyGNz+dTodDvFWBML/r6+vWaDRie4YAfNCTMeh7SQeDHvAtKXisRwwlBcvqONECLpwZhWxIp9MhDbNarQa+88WL1FmxtbUV0pN1HNCfMbLfyZdKX15ejjm6dM0pn8G/zCU8oIb0xsZGMN7gfwAScqdcLocxYgCdPHkygBTdc8f6Y/woacYJ36hDhHWuThD27SHD8DQz98yF7qGt1WrW728fGA9QhS9SqVQAqhsbGyFCSP8xhEgpJRKAEwEaZbPZEIUEZOLI6Ha7VqlUAk11zyAyDJ4slUohRR/nG3PTbreD8wQHDhFErvEOLeZUnSGa6m22LRsXFxdj6wBe02i77n/iB0NOo+IAfT6DJ/U+1YM8h+crbegfThCVDVRo9o48+JMx8F06nQ5OSpVdeq2uCRxaPM9nFPFbo8XICtampgCrXGZs6CD4VXU4tFD9pOCccy11Ljc3N3fQcX19PXZ+MH3yjjd9pz6T+9Qx6A0N+FSxBc8vFouWzWZDphTjRKdoZUrmA6MGGaPGusojeEkNEOVxorXMKeePqnGFfELHMlfIMZ0rpVk6nQ5n7qqTQ9eVN7aor+D766Pw6qTgd7vdDtlOykd6LfiBcUCLKNp2YoJJwKzqtPAyQp2iNNYN8hq+gd/hx3q9HuMh1hJ9Z7wYZz46Tb/UaaFZPNo0aKKYi3UAzzAfOFpxvOnYGD9zT9r/ftrXbSj2ej37//6//89e85rX2DOe8Qx74hOfaN/7vd9rT3rSk+znfu7n7Hd+53fsU5/61I5w8+n2zW8q2BAyvmqmCgxKoSOAFNAhQIg8aTpXKpUKQpOULNJ82JPAviuiW5VKJZzRpoaiCgz1uOL1R5gDfHyqA9EQn57GgeSAGECAggSA8NLSUgx8eu8y3kToyvMAh7ofSAEjBSv0/T4qpWBZjSoUEH3mfwX+CqZVaOLRp+++pdPp4FVTxa/jRqDiaUMpAjS1qA/3egGuQAE6wJ8Ivm63G/M0qmGmkS+fzsvYMOx0HDoWjVpg6NMnBD3jYdw+tRDZpnv79F00vLqawqJ8pCCm2+3a+Ph4MIpQVvSPfrEW+YzrWJOMg/c0m82QwuybXk+aZy6Xs7GxscB/WgIfg17XG+sfPiCKzjyMjY2FiJVWCmXNY3T4yqvsh8OIoemzKWDFWaZ4yVOpVKgCSkSRM/qYf4w2TceiiJK+V409+J99XcgH9jRqYQQtGANQgaeQbXqkRq/X21Fghj7BA8Vi0Q4ePBiOGllfX7e77rorlmKdSqXCHrHJyckgJzBM2I/JmsNg1FTAXq9ntVotBk7gl3Q6HRwQWshBwY2mUZJCvL6+HovYU+wlnU7bzMxMMBQ1PTmVGmZxaJYAfdKolkbeAFBqNAA+MehXV1d3gDXmiJRx5Ud18Kk85X0qVzVzJAmoanEp9EBS9VE1ylRXqMNT5QG00XQ45I8a3swxae4YFoyRfoIBeBfAHtCu41d9o9XKFQeqIayGaqFQCGtUHa16RBfrqlQqBQcUz4+iYYVrnA0+kqo6UmUJMhj5hjxAZjFvGg1Etyk/8x1zxVi1eidFZnQ98Z3iHxpzrbIW/IIsoyXhbegDPdVJxzog6qZYB55SPePpZjaUl3ymhq3yPrqLz7LZrNVqtR39R9bq85gT1Ss8Rx1v7G+l78hd3Q+r86hN55xnsqaz2awtLCzEnEKMk7lnjO12OzjviDgyr2Bn8DZ7zdXR5Y1q5Ey73Q7ZHD4Cq8EB3frhz7ZUR/F+2v02FLvdrt1www129OhR+67v+i675ppr7C1veYv95V/+pX3wgx+097znPfbmN7/ZXvjCF9qll15qZ599tv3+7/9+jBlOt29ug3lguuXl5bC4NZTPggLsqGcPxqUoQ6VSCQABL5x61TSNEUBARAJPPcqEpsYg70TIIWAAkypkNedaBSVCBg8xAAiDjHfqb5RhvV4PhQ/UY6X3aTVS9RbRDzWaS6VSLG1FlQF05G9Nu/ERR5Q/96OEADcKXJK8ebzTGzQYn9PT0zHBou/iM96lkV2EJZ4tBS3wAM9QGuqmdRXkCEDmXdP+dDxEtXQOScMA4KhnFAGqYAhAg4LQqCifAfh5z8TERIxvoY1GI/QzAAVHw3g+UGXHRnTlWU0tVu+yGjGsFZQdnys9eJcqWhTHwsKCLS8vB+NfU4CYR/q0uLgYA/M4VCqVit19991mZiGFUftNhEuLmKj31hugW1tb1mg0ApiF/9TrjNHE3J48eTJU+qNIDgBYwamuB+SXRkILhYLVajUbHx+PAUiewXEhND0mQueWfXfwHGuccRIZwYmEfPLGCzwwNjYWUkA7nY6NjY1ZtVq1EydOhHkATGxuboYjGNg3jfwlislYiVYgR5DDgEmKRGxuboZ0PDUQFKxghNN/DFLAHmtbjzDBMYmTJJVKBdoRMVJveSqVCkWY4Huux5hUuQMQVH7DUFU5Cf1J81dwpbIO3kaeKDiGLhg/rEnN+EC+a+XVycnJEGFUGc76Vcct6wq5xNho3vBljy+Nd8CzKudUfpdKpZheIm3fp2GiKzR1U9exyhyzYUYMaakc/QQfKg11XJqNpKnGUTSsUEqWijp64UNtKk+gBc9G7mpxFOZQHSPwmepqIqPoEHWWwmN+HnSN0x9v2DFOdbyqvtUsBOQpn+OQUUeVYjYcs0SooQM4UI025XH4mcIpqhO4j/Wj22igo3eKIY/ggX5/eEYj74fuc3NzsbkYGxuzwWAQc8qqTtdUUW3IJuaB+YSni8WiLS4uhveyThQ/QxPV3WSAsdZZY1Rphg8WFxdjfK00xAHIGMB8YErmG52qR3rgVFFe9wb/bu1+GYoLCwt2xRVX2K/8yq/YvffeG5hgt5+77rrLnvOc59jVV18dKlmebt/8BsjK5XJ28ODBWDojgqPf74f9g+p1QBDjIYcpWXQIQvX2EpXD0MIYYEHrYlLPZza7Xb1LgbY+G8NUBbQCFi3BDQ8i2CgYALDwApuWyWRsfHw8jF0VgBbTAdix+BAg+n4Uaq1WiylLhAt9QBCUSqUgXNTQo1/qwe/1hhUtVckh9PyRBlq63o8d4+D48eMxhe4NSw/81DiFJ9RpgOJL8j4jBNVIYsyAEYSceu9VyGlkWZUe/O0dB/CKAgb9jLlAyWBgkF7H3LHny2x4MDtKQ9cMPI6yyOVysb2iXkGyHvHuqlEHjRXEK3jlWt4NICc9UVOMFPDihV1ZWYmtNQqv8ExoXSgUbGVlxcbGxgIfdTqdsA9xfHw8jBmQjYwgyodxoJvy4V/4VsejeytZa/BXsVi02dnZMO58Ph8iomRQpNPbhVpIdaTxDhwTOC7o0+TkZDAAcYAp/yjfYjxEURTWJSnR9J1ooedVeAd5os4wmgJO5qJer1un07FMJmOzs7Mhvbzf74dUWBrAmTULaFGwpjyFwcDRGOyF63a7NjExYaurqzEHkVbcVZBF3zEsAFgqC6Hj5ORkKGOfTqeDx535AGBBB2g3GAwCnfG6a9YAfULe6JFPrCmNuAAEvfGAwadrCH2nY0LWYBizBjCENQsHvcKcQS8ANjyvjjkFnyrLNWKGjiE9UaN0uq4VmPJcpZ1uQdHMI3Xs8U5t0Jo514YxToryYDCw8fFxy2S2qxWjN9ibq7RSh9KRI0cCr+Gw1kge/cLoUiNV+UodoEQIebdmgGC4ohuQC7pNw8xC4Tl4TLETRhvPo18ayVRjj2dwrc9I0AJT0IWxUXlZr+d/xo0caDabIQU8KduCaJlm4qRSqbDu4Hd0KJ/RH3WOITfIYIBW9I/sF+ZOgxa6JYIUfN6rDiwzC/vN2RqkwQLFNxqtVFpBe40WIz+63W6oSs19yA6uIz1ZsShjR3YMBoNYCjO/+Y45R1eAsfQIFrL3ut1ucDyylvUoDQxtr1tGtVM2FHu9nv3AD/yAffzjHw9Eufrqq+3666+3D37wg/bZz37WvvrVr9q//Mu/2Ac+8AF79atfbVdddVUg7oc//GH74R/+4X138HT7xjWUfjabtfHx8ZBSBpCEsc0sgFj1TCOkOP9qdnY2tuDVUCOiosoPTw4CRyvb4WVShcfCwMhCcBNi94vdbFjiWPc/IjxR4LrIFTCbxXO+Nf0TgctY6SfvUAWii1y/VwCG4gYYquLl+QgSwAWLW8EBz8TjByAmegQ4JoIFDVASzAmN58IbZsO9V/CDNzKZC69IaToPumdDvZ4ITC3lrMAThUpfUOj0WR0RvBtege+VvihjrqM/qgyYk0ajEQCuppoqf+p8mg3PKNS1x3jgN00dVmcA86bgnQqAyr/qYVZjXEExtEmn06GIDGlZGGhEJfv9YcEGeJJ363ySauxTXVKplN199922urpqU1NTwUikf8gAQDlHODBPgNBUanjWIREkMwu8o+mqgAdAGEYh5yESfYE/GBNVNxXEmg1loZ4xyTzTN6oZTk1NBQBFnzygZK8v8g0ewhmAoscYgR7ISMamvEQ2h9m2nF5bWwupp2RoKEAmQkfKIfNG+v7S0lKQ/axjXcOkXo6NjdnJkydjUexGo2Grq6uWTqfDMSREo7VBS50reEdT8ZHDGnVDPhFh5If7uQ4DlDkGwHO+Iu/a2toKfMX8IMfgfRw7yELAGvT0DiXVkfAUNMRZp8ZsLjc8qoVnKZhrNpsx45W1gZxnfvk+m82GaIuPFOg7fEE61qOueQC9OmeZk1QqZUtLS7a1tRXbHkAE3APvKBqm8Kvs8Q1ZqI42+Fgdj+pQ887jwWBgtVot6BKVPfCgOiVUPppZ0JGsE+8E1y0QAHV0BPJNHb88H1r6rCscx9AJ3a1balTPqSGLHFBdgHGoFZPhFdaXrh1kBXMDbiHrgVRI3quOUxxZSk/opphOnU8YhKon6Rs8jNxg/FznCycOBsPIcyqVCtWJeQ9rAb5n3KxBDE7FM/Du0tJSDNeqEc+cQHNoOTY2FsNJFENTQ1ENPxxdeswZ/dViUXyWSqVimFrPy0ZnKMaA96AxGTw6X9y7n3bKhuJv//Zv26c+9SkzM3vIQx5iN998s/3d3/2dveAFL7ArrrjCHvSgB9k555xjD37wg+2qq66yF77whfaBD3zAPv/5z9uDH/xgGwwG9olPfMJe85rXnOqrT7dvQMtkMqGCHoBKc7JRGpxd1+/3w+I2G5afJi2Aa8rlcqj0hsJjkajwxGvU6/WCB4rFj5HH9bpJnfLuGDacTwaoVHClqVteYfGbsL96UWmpVCpEFmh6Dd+jtL3g41pVaHzebDbDUQ5qzFEgAoWI0GQMauSZxc9q036rwgBI6L4ADHwKrfjGu06cOBHzOuFZ1ZQYdR5wLQILI34wGO73hPYqUOk/Bq0eB4GS5Tm8A9rBJ0ojnW+uqdVqweCn7/C/AmqAoXrbo2i7YAgeVtaIvsPMYsqIMXtAhPFXKpVseno6tneQ8aGQ8YACjvA2qjOGfR06dkAZ/ddUSt1XA08Q4WYdsgcO/mEfM+sL5c89uVwupHfOz89bJpOxsbGxwHeqhHWcZhbLJqDPOLKgISCbIizq7IHu6lknCg9gZH8hIMjMbGJiIrzXzGLOoM3NzdiRQAD0tbW1kGLK+uRdytNE9ZgjLfDQ6/VC5VGeSzYAhiKRAZ5Bn1WhE0Ez2z6ShOJatVrN5ufndzhAJiYmLJPJhFRfQEKn0wlnjjIHCvThAUAhaxF90Gg0rFQqWT6ft3q9boVCwcbGxmKFkKAh7wSg+TWk6dBmFs7xI10YwKvrXp0UZhb2vdPgoX6/HypNE6VgLWlUgrmjzxQ+gj8o/qCGuK5HeFLL9ZtZ7Ow2GnKGhlyBLzkLUKMvfKf6RYv9IDP12czhYDAIZ11iSCEjNNqL3EY2KZjEkaRnXMLzGJEaSdGIo0Yo6btG2ph3s2FRtsFgu/gXgBidxZz7CBNjZI4ymUxMhypfqwGCMUn/C4WCzc7Oxt6DQ4E54t38rcYWfYQGfO7fj6OG6/lNZBUaJGEKjcAipxkPtFDeSQrMqKNF5ySTycQKtqmOMhsagBqhxPmlWQreCcr/6A5dC+l02paWlkKfdG++8iiYzztDiDZjaMGz4B6yE3Qe0ul0YlYDtNb0bjWoFQdqurTu0WSOlFe4R7OriJj6jAcMbqWxypi1tbVY/6Dv5uamzczMBNown4xZadbv92MBgd3aKRmKW1tb9oY3vMGiKLKHPOQh9vGPf9wuuOCCfd170UUX2T/90z/ZQx7yEBsMBvb6178+EOF0++Y0GEUjTnjhtXopCgnPEikrLDrAEtfiCYOJe71ezFDU/GmULgUg9B4AgxoKCL7Nzc2wqFmACEoENQpJPTcKVP39CDhVWtBJjQL6qIuWsetxBAogEHRmFgO3/X7fpqeng/BDESOgoDl7WnieepRIKyDdgsb80Ff65z3H9Xo99F+VJjQfDAYhSqCgl7GqN5r7UVYKDKEHKRmMX0EfzwMAaXoLDWHH9zQFafAcQEe94FRkZC6YF4CLRgFV4eM8Aahq3/nRM5AA04BxTYM1Gx6PUSgUwlET3vDBiPPFBQBivAPvOfyIUUR/zIb7a7LZbDhSAY8jnl72hPJ+olWsKWQF6wd6rqysWKvVCnKAQjXT09Ph+A28t9AKrzPpfBrpY2706AtNL9S+MDeAJRQhtKjX68EIxLFEZJH3qDNLo9TwM3/DixiK+Xx+R1qTNxSjKNqx55bnEJVQemqWhV4D/9Fn39RjDm9ijOo6Ra5gKAJA19fX7eDBg4GPGLcC90wmE47f4H4iEVSunpiYCJF2+B5+xfhXZwxrTcehafT9/vY5aTgAcVIpr6tzQNetGvfwIE4L1h5917QtQBbOCPQkegf9xeHWmrLpae33y2latRpgCv68PlhbWwu8rU4R1j/9UV2nVZz9HjyzYbERD2YVbCIr1CHGs6AhMpM5VaOAiBS8DA8oUIYurEHmiDXT6XSsWCxaPp+38fHxUKldjSDojaHFutNoFt95/evXEOMnY0H36uocar/NLOYM1GvV4Q3O4jveRZ90raqxovOkRj94gIqp6tBEd/i6D4wbujEG+sVcq2HC3ns1Lhi3Gst8hpzT7DGz+JmUSiv4QKO+ZC6gl3ESKn04sgNDW+WK0gxHuhrcyJdut2utVis2t9o3zTBgvCqj+J81zV59+E3nTNcp70PP9XrDs34Zp+7NV55VYxY5hCxl3SivaWRRo6vaJ3D9ftopGYrve9/7bG5uzqIosre//e0xr9J+WqFQsLe97W0WRZHNzc3Z3/zN35zS/afb19cQHghZCs2USiWrVqsh3QVhpKmZZkNDhIWCwaIGBIJyYmIiGIWaUqrFSjisWyMF6tElwkR58U6nE/PWHj16NHi2FJhqhM5s6LFEgKkS5ew3TS3VfUcIVBSP9zDxPgxdlATvRKipciGVkXGwJ9FsqCTw/mpaC+9Fsfq0AeZKU3I9YBgMBqFanRp6NMbro11qYOGpNRtG9gaDQdizwedqXPIcwA7/q3cPZa2b/aEp86Hn8qGoEKhEyjXCm8lkrNlsBoNZo5DKM2YWIjm8k2rAZhb2IqhXL4oiW15eDvTGyDGzUBlNgYWCX1WyNJ0HTU+i7wAyNTLUW1ur1UJ0T+cTpUAlQeYHUKf8xVh4P0avmYX0XQqH1Go1m5iYCEYqhVEwFIlEEXnPZrPWbret3W7b0tJSzLmDoc+4iOzhIYYmWiyEtafAnXnRPXmktWnUH+OWSDbP1JRx0q/YT4b3F74FUJgN08Aw7FHOuv7IdFhfX7dcLmetVivscWV+FSiYWaKhSJ9IZ6zX66FwQaVSsYWFhXCPGorqnOMZ9Xrd6vV6AGmMiRQ5UjepVE2kG/q1Wq2wJ7LX64WoHOuVlFClPfzPWNTjbTYEeRMTE7GURnhA1w96TL3yjJs9lRTrQU6tr6/bGWecEa71YJ++YtjBXzgvqDrIXGmqLnOuz1Rehn90P77KCXVyadqtGmWqc/keoEnpfo3oavEkDGhoplErHbOuJ8bHOOARzV5QAwgZo/xKfzWiyDqFB4h2MhYK5dXr9ViKoPYLXaHp8dBQo0OMDd4h+0mdtOgNPdKG52mWA7RhLTAnus6VXzHgGb86D+fn581sWMyL9zEWrvXyP5VKBUcdxgrv9Ge0okcwWNinShqwGoqKgXQdqMOc79XJocXK+EwdnvA3c8Z7NDqpeID7yAhgPvkeWij2gHaDwcBOnDgRk83wU6FQCPMGD6rsQTek02k7dOhQ4DfopLwHXbXID02fpw5K/a7Valm5XA77F82GdSXUAFanPDKfPhaLxdj2jEajYSsrKzFng8/iYD6Zn/20UzIUP/axj5mZ2VVXXWXnn3/+qdwa2gUXXGCPecxjzMzsox/96P16xul2/5ouwK2tLTt58mRIOzp69KiNjY0FgXzvvfcGwT0+Ph6Y1yu2ZrMZvmch9/v94JWMoigARjzB/X4/eLE1hU092WqosCeSnG4V/HjjAZkcQp3JZGJnY2mKHoZAOp0Oee4qCFEcjIMcdO/1VWMIzyiRGgXrPD+KIjt8+HDMEFYhrspKlZoWtEA4aCqYeioB7HhG1XuHEMSAxpulAAva6/9mFkuFhU66rwnBw7sAIqTJMRbmCTDKHNM/on8KVhVYaFqSOjWIOKkCYCyAfN6hBgXgnD7DT/AWYFcNAe5VpQrvoohYK+oJBPilUqmQQqN0VkDQ6/WCYUnKr3qUMWQ0clqr1YKRpmAcui8uLsacM16RMqeqLPXcPfipUChYtVoNPNFoNGL7KlgDFKMCZGez2VClk/Xb6XRsc3Mzlt7I9Rrxm52dtXa7HUthRp6srq7GwIfZ0DhUsK2gHu83aaCpVCoWCcPr2+/3Y+cDek+zOhKQeVtbWyGK7tcQMgXDCzkICFlZWYkVSKLfmvHQ6/VC6j37nFjTGv1WgEekCK/41tZW2F6AMd/pdEKhOVKtSb8kBblcLlulUgn0JQqHV355eTmsa+SZRoE13RseVtmPYyebzdrMzEzgE6U1vylM0e/3Q8VQfbamses6w6GFPKGfrGntu5kFPahAyzuaFCRqlDyKtvfZorc0U0BlPDKXNaQGNfepgYZcV6Nsc3MzVnxNDRIF6ji9VAZwD04M3UOq3yOTWQeeVvpOeBd+QicpOOf5a2trwQnc621vS8FQ7HQ6ViqVYvKUe8EGahSqE4LnMe/oE+inlV4Zk+pMnqUOZuRKOp0OZ5BqgSc1wOmb6iU1ypaWlmI8ReRfdToyWfkbBwk8AV8l9R/+4vmsa2TzKIcxjfXk+Zu58ymQNHVE8kwcNsp3yJGk7TQY0Goc4sAFe6mjjf7Oz88H/aCFxXCCaL0CTT+mn5lMJgQRdC31er3Y/nUwA1l5Gg2ErshWZCG4rdlsWrvdjmUs4PBl/UFbrY2gjlx0aCq1XTWZI48UW+l+TOYSubLfYN8pGYqf+cxnLIoiu/LKK0/lth3tyiuvtMFgYJ/5zGe+ruecbqfeiEQBoGZmZmxmZibsn4L5KpVKAKksFvb1sZAoPYzHD4Op398+ABrhpcAb4Aezq8JUIW829LwcPHgwLECiiz6tQI0sTXfQdBUADal1AMUkAIPQu/fee0M0kQWu1QmhG15FiuioR1KrjjYaDWu1WjEvOQoa5aCFJ9SIRkAjfFQ5Q+NqtRqqEA4Gg9Af6NrtdkOxGE0XQuiaWUzZILzUg6wCDC+rGi4YfChIs2GkVEEOxrI3vn1KD2NDoeg+WSobmg2VrU93UaeB7tODJ5kbaK90hh7QQotBMHfQATCgylDTWhRgYCjSP/pKvzKZjM3NzcXOyVLgwcHlKLBarRb6rnvi8JBXq1VbWFgIexJJx4S+qjQxlgFHGl2jz4AA7xihYh6RR+YXAwxjA1rTb1WCvB8nQy6Xs+npaWs0Gtbv90MlQeav3W7H9s/yTvZwsla5B36Db+FDrmV8nM+mXnyNpsMHGi3VqK0CF41OrK2thaMpuJb9mCsrK7GCHOwX1dbv98ORGJrOrJEY9vYxvwoGASys93a7HdYHaw3jWFMQzbaddhxtBCBBHjDH/PB+rfAKUAPAARCZt2KxGIrraLRX70un0zvoojSj/61WK6RKY/D3+/3gENV1qPMDnUgBJtMAGY/+Yg4Bw/oc5Q90IbTUYjjQTI0kBarMi24XAaCqXMABBLiF5hol2tjYCPthlTeQKziSVPbyDuVn9K+uCzUUqD0APQHyRKdZR8g71iDZIuqUofw/OCTJeVksFmOOJjWmwB9qKHCvOgE1lRh6qENRgb/q0yjaTl1E3xFhUoekOp41QoTTVh1kjEnfj6xVQ4xrWNvoEeiN01Cdi8g1dZip7IZeuVwuFqVSQwndqk54sFEUDc/W7Pf7sToX0A3aoYd1vrRgG/OMA0wNSJ6D7EulUmGtK09DB7ANc02UVyPJzKfqWI2M4iTwzkicQMViMVbFnPUMH4DJoG+hULBGoxHDP8wzPKpbttAPrAFkV7PZDPNKdBIMwrzxXP1f53o/7ZQMxbvuusvMzL7jO77jVG7b0bj/2LFjX9dzTrdTayykycnJUFUQYGg2BNUAcgQ6nnX1huJFg0EHg0GILAwGg9jhtAhgwAGHarOgWfQYGAh/FKfuqzEbpjRhtGlaIEIYIUKKCfep0VWtVkMqro8y5XK5AOIwgFmAGFcIwCjaPreHRY9A8t5WFv3i4mIwqPmOYgvME4BEPYpRFMWUImPmHYPBIHi1UGpeaeNFSqfTAVQo7fifiIvZ0LjSlM5KpRKEuO5n5V0oPmhlZgF88Vz13jPPFJtgjhU4aKRJ+VAVciqVCkIV3lM+wPBC8aEIvAcYEOfnTxUASkAFOs9VXkXBacU3gK4a6OptT6VSQbnQX1oms11wgaIsgF8AnQJBrTLZbrdjBXCIPtMPwBp8r1505XWUFTTjN+ljlJNfXl4OxU3YNK9RCObGzyP8gHKDj1KplD3wgQ8Mc4nSw3kAeFJwrEY6vMh1GCLMtfJkOp0OBgZrgffpulM+6nQ6YW+ZRtfhMXiLKpy8y8xCMQiN9uJwA9zT6A9pnXi0WY/IEAWVPBOQhzHPWZOzs7Mx0DI7OxuMINJcoygK1fxmZmbs+PHjVqlUAlgl0gg9VP7TF5wwGC04s5jfcrkcO6+ONDndf7exsRHbK8h1zOHGxoZNTU3FIh+aIqaGlFbe1gYQI4VagTrjItWd61gbRMKVzxcXFwP/sb6ISEIj1hjgHX5R+U5UmHXEvNRqtVDJV3mctcS1Ol/IRZ7f6/Xs8OHDtrW1Zaurq7H1g75ZXV0N6wI5pWthc3MzyDZ1ZKqRpFEMdejB48hI9nHBV9BHHRE4yXBSQk91yinAVllM3zEE1OFBv3iGOrL4jWGrsiWV2s4WQYdBa3VO8T5oqNE9zQbR+VcjmEbf9RqcLYpBcISxHuBlNTKV78k+gM7gFMUVpOCaWRgT64K5gM91uxB6CToSFR0bG4utceYLQ8jrVfQZa4WoGvhmdXU1thUGeYsBq3ynclhpje4h5Vz1ErQkHbtUKsXOcoY/cNTCk9AAXsOA1W1TVMdWXKw4TVNTwTMULNPrzCymm8GSip32207JUFxZWTGz7TzYr6dxP8873b45TQ8rJbIG45sNDUW8ghsbG+GHw6bNhqF5BB2LCMNDmVkVKwZHtVoNCgVQsbW1FStOo55ks2EKAguMKoEs4kajsSMiUq/Xg+cLIYrHFC+sCkkaAJY0BzzR0EoX3+zsrM3OzgagizEBUFJBHkVRTNHyme5bU4XAMzGa19fX7cwzzwzgSj29GInj4+MBcGH48CwAolaJxVuloJ+xasVBQKZGGlEA5NjrHj6UoQpgFJB6k6EjESRVEsyF7peLomFxHPhPAZl6LXnvxMREqJjn965xL9fqOkAZqqBXkIQx4SvEKS0R6KwbeGhjYyOktDFW7mXetaARSi6V2i7XjzODwip46zVCpnOjEWvoOTY2FqImGsECyBEpg4e9dxp6IQ/oN1EkdSwoL2h1SeSIKm/6z3wBcsy2DQIfXQGE8hlGEMp+aWkpFHDSiCAOC+7R9Qr/Y5gQ5fcGnq4jIptaIZex00eMaT6n8XzANJkTpCHpHij6SNQTx4FGu7vdrjWbTVtbWwt9IY201WpZvV63u+++23K5nB06dCgAVnTCxMREmD8ce6VSKSZbzYYgMZMZVoBOpYaHx+veK+QzDiPmAQ8/Tkc943JqasrMLNBsa2vLDh8+HI78IJKInlEwiHwnkqQpWFS1XltbC05T1iCOBwxA5HG/37fx8fEgZ0jd1yqvmUwmVhafCKKZBT3qj3nCKNjaGh45QcYAKd7qSEVnIgNx+kADzWZBhgJytegQ86pyU1MxeTbpnZlMxs4444yQtsy7kAUYJhwbw1rWuffyVg0p+ohcWF1dtZmZmZj+1Eh1tzs8uggZhLGhcpC50FR96KLjVDmHzMcA5H3MN88iOoxsZZ1DM/oMX1KAjv6Bn/r94ZEJKl/VuOD9YB9fOAbjhQJAqksYF/pcnWo4NpSvoA3rWrEG8lH7RUMusVZVXun9ur8VYwocgGOn2+3GIpRaC0MdyGAj3oE8wWHMMzHCcLSpY1KxL/3H4drtdmP1KdSpQECCauGMjyAGGEH3W8NLzNVgMAhF6OBl+gOdNKVZ/65Wq2EtMkb6y1oiG4O+Q0/F13u1UzIUOcSc8v73tyE48Vqdbt+cdujQoSC4YXL19mpKwMLCQgDLpPcpeGfx45kErCKAALgII7OhUNHiHygbCmSwADTs3+12w6ZmFTCA0UKhEAwkjUSo59VseEgugttvMjYbRisxRjVVjj2QqhS0HL5PX/BRqiiK7OTJk5bP52MHYgP6mYtOp2PLy8shOoixiDCtVqsBCKoS4igADBHGwzvUi8Zv76FE8CJoUWqaYsazNAUPWtIXFb4IZvV40bfBYLBD2cID2nctMKAeaXVIaOqren6r1WoAKSgHjeYpDQA48FGpVIoBe6LrqsxViei4iFjoc0k7hnc03Zvn8T/eaTWs6TNRFnUy4J2Fzhh/hUIhpPUA0PH4Kr010sGaU0+0AlHtj84hkQDmhQ37ChgVOPB+Ih3qad/c3LTp6ekAkOHRdDptk5OT1mw2A9BlrWvFwvX1dVtbW7OZmZlY9V+84wAtdWzAQ4A65E2pVLL77rsv5g1HdpBChmxTAAE98cLDF/6cTXgWo7DT6QQ9ixFJgz567IyZBbBitu2EHR8ft2KxaCsrKyHSybNnZ2ftzDPPjJVHZ/5Za6xJjGwACTym50hqFBFAomltzD198elrzD1AB52hkX2OX5meng5zoOBWIw5m20Ym60UdXKwTZDzGE9+bWQCVgEbG2Wg0YrJBozU612qkUDAOuaJbEVQu8T8ZLBj5GgVkfBoV4j6z4VmjqoN8dB19o84fXfd+XznPmZ2dDcUMNfKlawFZoin+ZhZS9OgzTZ03atDyNzIIXaIRU81qwqiBd9HJ6ujSSIwaKFTAZS2xn5u+IO9w4OBUgle1Qqoe6aK6EJ2ga5bPdPuDYgY+I7KKoU/mDeOgb2okq1MXXcDziISpPjEbOuH8+8F/6sRVI5ejKZCt6oTgPfAadGK+GP/s7KyZWUwmgDmgI7IZZwzPxiACUzEG3RIzGGyfA7u2thYyOtR5iZGmxRZ5LrKPcTBW+gb/aQo9MkAdpzjskHerq6tBp2Sz2YA1PKaBB5Cp9AunBX3GcGRuoCP6EacPY1C5sVc7JUNRgeE3oqmQOd3+/Rsph2bD/H4FyaQAEEmZnJyMpcUhqFmcCGS81eo1wlhRwK/pI+l0OkSXAEKagqFAC5CLQIyi7ejS4uJieK5GONX7pQaumQVPt1m8yp2CdJ6Jdx3hjsAwG56Jd+LECVteXg77maAz6TcKgtQjpYYl/aHCK4KeKC6GopmFQ8QVfONt0zRDrRaIsU2/2X+A0azrWg1swDVKiOcBZqGtGmiAIIAeYIx7MXK8wca7UZRKJ/UY6rMRdAhFX1iCucTDRv+gN0aPKkcv4KluqHSlr1o9jaYCXsES/dLy3gh5hLjuFSSiQdqh8qUqCZxu7Fdk/oiS8S6UWavVCuAsnU6HfVDQVdO8y+VyUEwa/VKwB61qtVrwwpO21O12w3l48CqggL6ixFgv6vne3Ny0sbExi6L4vidkTrFYDGuPNaNHeLTbbRsfHw/0RZkqqGfuMBgUxDJ3OCgOHz5sCwsLod/0pdfr2fHjx216ejp27Afzj5zEoUBGhQJ/wOzGxobdfffdNj4+Hjz5Cgx1DjTd0sxCgRv23SFLONZjfX097E+G75RvS6VSmEf4ENlL9AR5oUYC/MR1pO9p9Lbb7YbiO0TEdZ3CY5razXe8kwiwAjk+1yi0mcXS8wGXfKZ7TzXyi/FULBYDEGVdILcoFoNcZoyaBaLg2Gxb5mpRDk0Nh5b6vxqVrHEMEnUiQQNdj0QcNbqvvAw92F8JD0NjPZ+Ncc7Pz9vk5KQNBgObn5+PRVvhAZwvGrFUZ6kCeYwOBdTwk56dqtiBNEKwCfLj4MGDsfd1u90dxUB8yp5mDpBho9flcrmQ8ojcR66qEQhP+yMG4FOa6jvmgTUI36u8VD0Af1DYiXfl83lbWVmxbrdry8vLgXcymYwtLCwEnZ/kLABnmQ11LnyvOksNV65l3ni2rjd0B9FpxU0a9WR9o8/hYU2zVKc06wfZUigUAo9SQEwj6bo9CIcgDgHkkTpFoUu/3w/6RucQo5WsNcbDPYo/wKLQC8OMDAwMs1wuZ2NjYyELD+cDOhhdYWYBY6Gz4R8wM/OZy+UCvkWusXbA5BRlVN7cTzslQ/F0+6/dUqlUKAyD98czTaPRCPtl1OuIwMLLQ9hcjTEENuAbwIIgUC8bQoRrzSzG0Jubm1ar1QK45XOt3kVJeDyZCF/1SukY1DBT4QlgI+oFfRCygCf1LEGLwWCwYy8Z/VQQCJ11sas3kFQSBUoKBlDmc3NzMYMQw03phgIC4KghyN5ChBMGNo18e03j06MYGANgh2qPgAR1DvA+Nc4Q9OqxVG86Ch5jSSNRCmChNbyEosUbToO2CHDSxBSEoFQAy4xRU7XUyweIPeuss8J36ikFlBCxQ6nonjd4BEMQb756mZvNph04cCBmBOp6JeKRyWRC2qM6ZKAFTplOp2Nzc3OxPUQzMzMBbBM9gr5aEAX6Tk5OhiIq3psL3Tc3N63RaMQKOzAXmUzGxsfHg1HEGlUgo+njFDYC5Gtfomg7fZYUzPvuu8/a7XaopMoxOX69o9yZYzz2us/RzOzOO++MeYqz2WwwBhU09Xo9W1lZsampqSA31VDUvXHZbNaOHDkS+Ji+AHhOnjwZ9nqrx907V+jPkSNHAj8g60hhBbANBgM7dOiQnXHGGXb8+PHESnfZ7HbVXA5rZhxk/Whkh3miYXyQOqppb9Ahm80GA5E1BH00iqfpnto35EStVrN77703RgcinvQPenJGHToJMKsVYnWemKNSqRR4QXUaNFYwq9EwBcNcq3KIKCHygKJq8CL9U7mockQdB6wlUsrReTxTo3LQGMOfcWt1acAlhqI6y2q1WkhZ9nOvIF/1pzpfuY7CIsg4jWwwz4ovoNvGxkYo1gNN1RGi13e73XAcCjoVOqhxqOOrVquxlFt4Gb7LZIZHd6A3zSx2VrKOAYcHOg/Dt9/v29zcXNBJmvKJTENHmA2P/1An/Nramq2ursZSqXFAsk7Z5wtPQyPeoSme6AaNuCLX1RGL/mGszJvKCjK7mB+er04Tla/MWa/XC4Y5Y1W+UPyjDkzWOYYh61edx/A3hiKfq5MDPiMKyZx7zDg5ORmiyFEUhYwC1js0I50UHs1mt/dTTk1N2WAwPN4sirarRLNWeB+GZZJzkOtw5DMWnklkEkzBesMIr9frMef2fg3Fnbu499H+4A/+wKanp+/PrWZmdvLkyft97+l2/5uCY4SGeqRJrel0Ora0tBRLo2FxA6QLhYIdPHjQjh8/bmbxA3QRtL1eL3hDFRiZbe9TJK0TQVmtVm1+fj4IGTxFlJFXcI1i1igUxop6EAFLCB7Gol5YBSIsOpR3KpWK7WvU1FNA+OzsrK2urgbBaLYNesivp5VKpVi6IXNC3nytVrOlpaXgWVJByT2qEPmf8aTT22l/0Iem6QrlctmazWagufaF8akQBXiyFw6Fa7a91xjjiyJF99xzT6gCNhhs7wMlfUor9THHCvrVMEVJMr8aafRKSp+DUlehqtEE3bugwhleUgGMYyCVSsU2qvNsopReMdGXRqNh9913X+ChlZWV4LUnIkw6IBvwdZwUaiIKAb8qAGHOWq1W2PsNEGEP1vT0tN13331Wr9etWCzavffea4cPH7aNjQ2bmZkJxjBKCrpg/PEdwAveNrOQjg1fatqTGrfwDRFmjFgcJ5VKJYAFIh7z8/MBwFEkh36Uy2VbXl4O9x85csQWFxft5MmTYd/w1772tVgEVp0yAEJNZde0V7zVGOHQFqNhc3N4EDv9AWwh+zR9EkOvVqvF0u9JW11YWAjOCowUAB0GlI+a8Wwv46enp+3uu+8OnyELisWiTU5OxmQSDW96qVQKkUn2Wq6vr4e9gkmNvmqqKWBLI+/QB8cOAE+Bc7PZ3BFBZdzwK+CM+0gtVxnW7XZtcXExOCoA+KwjjAuejeGnkShk/GAwCMBfZRdOOWT/zMyMtVqtYEBopC6bzVq9XreVlZVAV90fiBNMMy6Qs6wf6Kmp5NCP9ciWERybRDfVuOl0OnbOOeeEMWEwrq2txQoEqQOtVquFKswYJrrnikhoLpcLjl2VlQpIvSGJQ45Ktawv5oe992oAMz8Y89BUjRLG7R1EyEUcZDyPeeVa1Y04X8y2K+zOz8+HPZSrq6vBIILPceIB4OmD8p8eYwO20e1A6qzmOvCNrhkzi52jeuDAAfunf/qnQH/Go9FujYQpzZH93KvrRA1/lUMY2+qAAY9hPNFXxYL6HI3+QSct4AVt0c9qALOf1xeP0XT2KIpsZWUl0NI7682GRqWmnNJ/eKNQKIQILnPUbDZj8kpxGvqPyuPoN7YsPfCBD7SvfOUrsQAF71f5xxx4LK3BBBxJlUol8CQBFRynWgVWHUx7tftlKL7xjW+8P7edbv/BTQ1FftTrCRPjzVCvCguzVCrFzrFB+QL0Nd2o0WgEL6rZsGy9mQXvMgxcr9dDHvbY2JgtLS3FwG65XA5gGk81/VKQS58Hg+E+SYQfCwrBgDJl7IAYAKBGDlmECCMiOLwHL5d6tugjAoXDyLUsP9ESAA9KDyFoFj+kWDeW81wFLxhlADQKD1BVlaglXiY//6rUyeOngpjZtgcKA4k9paTLwDekTbCXAEWMUtfN1cwfwo8+674N+lSr1cJ+COZLU6hRahiaCHoMHo0CAIJ0vyRzQWVZnQs9ZoRroaOPApsNlSfPLhaLwUBHqZkNow6lUinsAY+i7X2q9GNra8s6nU44w44+ME8claDgVtc7Sou5QNmy3kizg7asVY2WQCeikMeOHQt7+KhKSGQvn8/b3Nyc1Wo1a7VaNjExEYsUk46KQsdQxFuLZ1sjwWtrazY5ORnmsdFo2MLCQpBF+Xzems2mnXXWWTY2NmYLCwuBP3StqQOF9Q/45X8FhxhrABrdZ7K4uBiqgOJJR55plAPe6Pf7wWjnuVT+5jlEC5Q/WQua4u3bYDCwpaWlsC9PQZB6zqG3bwAN5pHKjUtLSzFeZjxKRwXgCt41uqTAiaiLpv5i4B0/fjy2JxPZzt/oBl17lUol9BNAmkptVw3WQlOlUimkXtMvpRO8oDKCg9wxpprNZpD7Kj9Tqe1snfvuuy9EYTEUkcHwEe9HP2Go0hfArkZV1fDCkMZJl06n7eTJkzYzMxOKQvkD6OFdZC/pzzR0q1ZjBvCyJtbX1+3QoUMhZZ651owhzdZQ2jJeeForcjKmQqEQqvwSYdfoLO/CycT8mA2PD1G+ZA1BN40ELS8vh+eiF9HbrGPGpM4lnK04CvL5fMy5zfpSsA9PEjkj4si7fcZD0l5K1j7fqZONiBk6kr2GrBHkN3oQ2uEsUwNLM8hYL8yfGneKu9D3ODkwjLxurtfrtrCwEPhZDde77ror4BmCA0RhNWKoUV7mmrEqroHfSc1MpVJ24sSJmBNcI6vMl+ISMBzzrkaz8u3y8rItLi4G+YejU+sBkCECP4CJVP42Go2wB1ixNPKIbSv6HA0csF7y+bwtLy+HDC94o1qtxpx3ZhY7FWC3dsqpp0zY1/tzun3zG0BWPUoIN037o5ob12I4mQ3TQ80sGFYAKqJ9eOIOHDgQgC0LSBepGnxbW1s2NTUVPErsB2OxaBoJwgSvTKfTCV4xlFW/Pyw0w7gRhrongEgTgkAra0EnAJSZBY8reyZSqVQ4EFgFj3rXCoWC1Wq1UEIZLxcNwTY/Px/uYQysF00VwRBSQwABpt7AycnJUBJdgTeABQNXBR/vUvpqGgipGQhtBVwawVNwyrzAX+rNhO5mFpwB8JnuU8EDz/WaZsb5Rer1RaGop433+QgzwFkjQkRnmXuMQe7DeKRv9EfnQb2wgEvSc9hzR2QATx+06XQ61mg0bGlpKawp+qLGrZmFfYYoaejEOtOo2sTERPBMst8laT+TRmLUeMSQnpmZscXFxdhZjqTLYrSNj48HGiIT6vV6LDqIgp6bmwvectYD9AQIKJghwofTBY86xVnw4hMpYJ61eMiJEyesWCxaOr29V7PVasVAMsa3eqkZL2NiHknBBjwoX7VarRgPAkI3NzdtcnLSxsbGwvWaGgkv+1TFJEMRoxmwBtjwhiVr1TcFtqSc6sHsCmoUCMPralSoLDCz2O/BYGBTU1Mh0wQDgAijRpKgN7yIzPHzyd4qnAgAUC1UAy9TGEYdArxfHVrQDRnIe5eXl8O6QTfAh8ViMaSjqeGkxYE0WwE+YN7od6PRCP3v9/sh02BxcdHMzJaXl0MWCn3QCAjGLvMAvVg7ajyAAdhGsrKyEipqY4h1Oh2bn5+3fD4fjElkKz/wjhq3Ks+guUZCkDu5XC62v0wNEYowkRmEwY2hoHUOGBtzA6/CO6wfdJhmQWgadK/XC7gEXodXNEJFRBAnIsazRqnge+YJBwBbQBTPwDPqqND5Y5418kSfGTc6gggt6xc9DU9rkEAdCYrRyQRAvzF+La4TRVHYmjM1NRWizPSN98KXKn+1eGGr1Qr9VKcSNNAsANW58BGOW82wMhumKMMr6sxCNygOVseVyhT6olF86D0YDILzj/u0cCRp3poJhFHKNiBS9uFH0tChAzwCfmFt0ceNjQ1bWFiwO++8MxQM02wTjEOwT6PRCHp1P+2UIor/+I//eCqXn27/yZoKL92bYWbh8GQiW+qB0zLE7Ikys7D4MbbUywWwhYmpFqdeKd5BtASly3sU+BJRQAgRmSBiyf4GIjepVCqkAGGQIkzMLBgzWlwEQcTfCix5pwJfBDoGCn2ZmpqyhYWFYHiwERq6p1Ipq9frQeD0er2wiVoNEACiRsvYwwMo53sEBlEZMwsAhIgNAFxTdaAN80KlVTMLaWD9/vDMKPXCI2jUMEeYke6JVw8BSuqVKgAUH57a1dXVEFVQUKPzroY0VWTZO6cC1DuoNA2SlE2KM/B5FEXBI6hrhL7CU0S3qUwKX2NUsv9AvYebm5s2MTFhCwsL4b2adgL/9/vbhRra7XboB2CaCC4GYLlctrvvvjtUACRt0GwIbJQ/MpmMLS4u2gMf+EAzGxryyAf2B5sNHSYANSpmTk5O2uLiom1tbdnKykr4GxBApA9ZwN/IDXiAOSVbYXl5OYBxZA57bonG6hmrRPs7nU7swGMiFsgj1o96m7PZbFjHgEc9P25rayscnYChqGeCMU8o8hMnTsSqiJpZACiMF+CGQ0LXJKBmdXU1FLJiDMgZ3a+k7yD1GADHO7zcR6b5poYtEQ8K6ihdeYaOg4YTQ6M4mo6KDKboAmnLyGsFYDy71WqFMQ8Gg9gRJep4ZFwARww8jBvAdC6XC5Vg9aw76AhwpG1sbNjs7Gx4T6vVskOHDtnCwkKQ1/C7ygqexxyrk4nUUcaAbGLMyAGtE6DFXMbGxkJkk/GS9bG6umpTU1NBjqmBg2xCpmuWBQZutVq1e+65J0SfMMKQzTh6SL/U9zA+olZmQ0cg9GdOMKyWlpZ28DfXw9fT09Mxnkfm93q9cC6vRv3UEe5TAgeDQQDkRKQ1bTWVSoUIDDJD+QPjiQrjJ0+eDMY+12hfoA195hqczUtLS4GPuF6dm8hd3SvIPkywjtkQv4FvNO2VFGd0DbgBxwxrFKxCKjDPAUeBG1ZWVgLvYjSTWcJeYGQudIeW0IcMLIwijsUByyk20PWhTjwcU4ol1anE5xpggD7MO0456KV0Vqc6WE/xGAECnKInT54M61F1FnJHHf/wC8dTYcyiB3FS0li/XMN85XI5m5ubs2w2awcOHAhG9Nramh06dChEu5XmZH+Bz/bTTslQfPSjH30ql59u/4kbXg4Wsz8oGhDtFWc+nw97QogorqysxFL+zCxsrF1ZWbFCoRAUD4qL6xBOGilCwWqEiupvCCWzoSLVQgPVajWAcEAlKRR42wnD93q9UIjFg552ux0iNWbDNDQigrpQAZG6f1HT5wC2mkLG/j4iKAgRCgeQW47yUqA7NjZmi4uLgS4IHqJPePUQLmbbRWXm5+djwIE5RLiS+gZwLxaLIYWPBqjASFLljIGrESsiRaRJaIoqdMKzSForhiqgSfeSbm1txVI6oDvAU885hG7s30G5wWPwg0a2zIbpXRphwkgxG6bN4NVUoxlDNoqiYLhpRB6exYBWMKepy+rg0CiXKleq9eFlJ111cXHRarVaUPqtVit4c+FZACD0UxqpE0k91Ri2akg0m02bnp4O0QgcHqVSKVxbq9XCnonV1dVwUDn8hIFUq9WCk6rValmr1QrrhzWKcoYuAO6DBw/aXXfdFfiU8alHnrVKNKfRaNiJEyeCdxmAquuROdB71fAhkqhnp2o0vdVqhTQ/5giZSfovshKDYG5uzhqNRpBbRGvX19dDdoA2PRIA+aZpXxpBVG+/NuQMc00acr1eDxX7lLYAp/vuuy+kk5kN5SZzi6Gj0XV1TJFuSFQBGYnxQ1Mvuu7BVgelpo+xBjEGNAMBp9Ly8nKQOwpGB4NBrHIp74CHMFw18sUebU2ppNAG0X62UYyNjdn8/HzQQyrrBoNBMH4wTOBDskOYV3gUgwGHHPLVR5oUqKoh1Ov1QoSy0+nY5ORkKI5CFExlszp2ms1mmNu1tTXb3NwMchNZCtCmT3zOD87nVqtlk5OT4cgAnAhnnXVWuJff8OjS0pKdeeaZ1mw2Y2mO6ACiqERztXo2AB6+Uf7UfYTImmw2G4xM5mlqairIfd3qgsxhvBp9hhbwEMYC70ZXdjod29jYCMf8mA0j7IwJ3tza2rLJyUlbXV2NOSCQKzgS4Bt16LINYHV1NfDX1NRUwHN6NJHKP7AZepw0Ty2wpE4q9OXm5qYdOHAg6HBNZYZWuu7ViNfAhb6L9arvU6xDFFf1F5lzyDT2Y2v0TrOwfHRVMSnv97IZx0w+nw98rU6P5eXlHZFdxoycQ0bBM/BHu92O6R3mizXHs6CF4tNKpWL33ntvDNvt1k5XPf0WbYPBwA4ePBiMLlLKYFbADGCLRZdOp8OhsSwiDdOjOFEc5M1rgQIEJO9SAwoDEq80IAoDBUWHgaV7lGZnZ4NnlPdhAHkPGkYxBg+GBQuSPWoIL0171I3v9E8rg/IM9bQilAH5aiSkUqng5cO7h6GGMNVUEby6mlarRj7GrNnQ+NBcduhF4RvmQ6NfRDlppMnogew05kZTP0gJ430qROmT7kVkvtSAoN8KcDqdTsxYN7NQQKLf74cUTviMyB7NC9Ferxf4Vq9BgS4uLgbeQGFCK64F1DAXevA9/WT8ugdPj2Tge3gFZa6GIusW7yRAB37EGYLS5r5ms2m1Wi2MNZVK2RlnnBHGm8lkQpETonzwC2MH8Gvfx8fHA9BrNBo2MzMTDkLXqBKyAW8zkTw1SjWVB15nU74WL9DzADOZjN1xxx129OjRHQo6iqJQNdk7RzCoAToK6DDOdH8Ma4Fy9KxPNSAXFxdDRTmNCi0vL9vExEQAWZTTx8jc2NgI1/t0fKK/fA6P+4giGRnIJ6WpglbookAsqXHvyspKKHakjTFOTk7a7OysTU1N2ZlnnmlnnnmmTUxMBJDFfEJj5JM6c4isRFEUQByfz83Nxc5YZe3CpxgMvro2awQZgyxi7GSeIGfQZSo3kOHMEXs2WbPILUArMo0qtBpVAgwjo1XfaSqdGrnIPFJXAam6vQGaQj8iQdAI+Yb8gS7dbjeWJo2+63a7oaiSgnqzoSHLvKKXNMUbemtlSNVxRGHVgFXdRH/Qe+godCa8gxMolUrFzhtl7jWiqHqZ9yBriW6yBqEpa5AfDBkzC85U1hg6i60S/X4/FL8jRRfdgk7RzAL0PM9HbtBfMiYYH/3GaU1NBRxq6qjUdQJtcdpBg3q9Hqu1gFNFswt4DpFXdfxoFgB9xIEJ7zDXWh1eaUINBW9g45DFEYaDq9lshgwk6A1f6JYedAX4MZ8fHlOjWEWxCcWgoKPKAwx5nDi8P5PJBIc64/ZGXbfbjWUewWuMjWuhs8pr+N/MYhVf+/2+LS8vBwyHQ4c+qx4H22nWBXJnP+20ofgt2gaDQdgLwf8sKLNthiRSqEaOmYUKpBptQWgDVAeDgd1999127rnn2tjYWEyoojxZGEQUUZxmFpQqezfwiKiXF8WIEYcgQHjRR4qyUOkMoKCLF/CKYkIAQw8UI9dpegLRGoxqH1VQQxQgoAY4xnej0QjeVSJO3hjXwjoIU+iJAtL0Lwwts22hQzVbxjc2NrbDQONQYc6pjKLIGo1GACCMX1PcKMahnjgMDPqjxg/9x+OJ4c84oDvCXKNCRKxQru122+r1evAImg2L02AoQStVnGqUeaCJkmYzP8BPU1oUjKvhr6ATfqFPRBq4H4Clho7u3+M3z1PhbxYv109aNGuKFGUqrgEGFLyYDffRIgcAe9ofUq4ACyi4AwcOhHsZExEUPesTzyr8rulF7GOG9wAs4+PjweOqUTXKv7MeiHj5cflsCKLEZhbAbFJKG9F3dZ5gtALemQtkgAJ7QAu0Zv0TuQE4YHgAwhXga98AxkR9vKGI8akATvkKOptZjDdHNS3cBQjXpgYI/dPvAFXpdDp29hj3AGy5Xx0dpGRF0TAFHbngU9I0EuUjDkTa2u12oLU6pyqVSsx5wxrS9PYoGqag4wSAf6CvHiTP2mT9Im+IxHMdlZehHfOmjgn+5zvlY5wV0B3giCG5vLwcGw8RDdasmcWKuajzYWZmJtAEvTgYDELkBpp5gA1I1gi0OlrVyKHPzCO0YE6IjpOGSKESjR7r3nEMJBr0w5gyG6aZFgqFEC0FJ8zOzoYUUNazFrqCDvCVGmLocrI3uEeNX2/4MC84erPZbHDUMbeMgfel0+mwjxR8ogYsvF+pVMK2F+QSDQOW9e3XIA4EDFE1rs0sdgQS40BuY6igX0mDRQ7wObyGbNTMDzJ8kFXKZ8gc+IqIIPJKnW2MS1O3ebdWjWV+4MF0Om3Ly8u2ublp7XbbTpw4EZ6padro2Hq9bgcOHLB0Om2Li4uhT+jZSqUSnIKa7QD9FAMpnTT6z/c4jzgnke81IoreBFOR2s+aAaMzJs3W2qudNhS/hZtGVNRLYTY8bw8lg1I2sxgANBsCWw647vf7trCwYOPj46GSJ+9CMcKkeFw0Kkc0AO8UxQoQaqR/aNoiQs0rI8AbqUwAFwAphmE+n48Vm8Fw63a7Ib0G4Ml7iHBo4RUzC5u9ofHBgwdjgJNnqeBMp9N25plnhkO0NU1XFbT3pqLgAZYY/urpVdAIiCCFBdCq9zJGhDR9wAhEwAF4ENJUfkQRAQqnp6et3++H1DI8dXgF8Squra1Zs9kMStpHV88777wwFvoOAEA533fffeHZ0A/hyDigOfsTdJ+D2XDz+NTUVOzQYOaQPqmCQqEy7wo6VZmbWQAqExMTYc1p2qkaItBflR1ASqPM6tE2G57nBTjAmCM6oevXG1Q+aoXzBQcDeySz2e39j3p0hEY6SENlDLlczhqNRljzNJQmihD+wtjFaQL9mbsDBw7EzsZS+UV/dBwYWxi9mgJJv0mx5Qc+whBImmvGhxxFji0tLYU1wfwiBzUKramjrB89joV1R7qgN25VrkI71j/PBBCop1ubGtjwNdFB3xRg8G69V8+lRDbDV1EUxQpeAN5woKBL0D1EnaNoOzOEfcg652QaqCzsdrs2NjYWCrPAV/RbjymhAjdNnTikouH02trashMnTliz2Qy/Scn0AFSjfABwjFX0ImNgDnQ7CM/hHuiuTjQ11pHpGEfci/xUByTHMDFH7HWDxhMTE5bP54PzFYcGMhIjD7131llnhb1RyArkAWBXC3TgcDKzkCk0Pj4ec4Cyj2xpaSkYjfCVX5vIZNaTpjUCqLlPK3n2er0A7pkDdepqUa9UKmUnT56MnQcKf5DJZGaxo4rUEGfdISOQV+pkVpnO/xim6GRkI7zFGmTM8Llm4zBeiplhZPf7/dBX+st4eSfXqzMZ3oG27LvF0NcIIPJBjUN4FFmeyWRsZmbGzCzwkK4BZCbj6vV6wTmGHlcnvRrEJ06cCIY1slSj0+hcsCRYkEwFDHj6Qb+pxn/48OFw7Mz4+HhwDJHiPhgMbGZmJhYooaK5nmtIXxgLhdKQO2pYomMIzDCfpFqD68C5yJl2ux34i+ys/bTThuK3YEMQ0Eg7RXmbbRtjLCQV3mY7qyRSjpcoBBGHM844I+Z9VkCjYAXDEWXIgudzhDaRISqIIuhZFAq0EEo8n8U2NTUVFCceMLPhmYAaqgecLCwsxFJ4arVaTElzpAP3oXT5X8fPc3UPDUINYWEWPxJD+6lGBd4qPGxaxUoVFO+gL0SYUK4IXy1GwjzwHNI9NALX7/dj6VREpgDAAPtsNhsqoiHYda8V13DwMUJXgb568qCFByP6PASs2VBBMTbA09zcXOzdOi54RCuKqgJHITJP3MfzdN8Bc4CBxtxDN4As/KKgHqXHWsAApFKm8ny/39+xBwc644DR4kM0FCeKFfpoIyrTaDSCIleeou8oMNLCSZMmpQz5wPoHYKks0fkdHx8P1R4VqGKMaKEV7YvZtrNL0yYzmUzY8O/lDfNHOi2OB5WTGHcYNqwlDrlmjUAD9kjTN12zjJX5xxGnGRY6P/CLNxQ1SqSGMrIY3tOIIl5mHZc21t2hQ4csqXke1f5gEGhKpK6PdHpYTRhDkaqJmjHQarVClgByqVwuB4OBuYFWWnhL5QG6AUcDa4qsGPqlXn3uy2QywcBivQPSDx48GI6KGhsbC0BO91OhL3gH65X+sB5VLqG7dA2nUsPtBPoO1j7951l6LA/9xkjjMHDorZFaNX4YP2OfnJy0ubm5wDeqnzTKNRgMwr5a5gMHDEYu32EswQve8KvVaiEllj51u12r1WoB1GcyGbv99ttjUcL/n70/j7OsKu/98c8Zqk6N59Rc1UP1IDOCBgRvjBg1BgyoL7livCIKaCIxarwOqN8k4pSIximXxCExQdCo0Ze5SLxJJI6oEDWCoAYIidhNN9Dd1TVXnZrPOb8/6vde9exV+ww1djW9Pq8XL7qqztl77bXXep7n80yLbsw4V+w+Z99zH2SKJfvW+cGcUBvf1dXlUvzt2ube6XRag4ODrvGedbKxh1ljvGcrk6k/lOR0qW1AxDqxTgacLdR021RM3pGkSFqpdQ53dna6tcNzMwbWEDLDOjZtNhCN/HB28DzMJ/KQtWLtCJodWvvLyj87d9h8ODBSqZTrvYBtif6iQQ7EDDsW2WgbxrCWmF8cDDQBtGVVvCcaYpExVywW3b8hr7wba7dIcnvSyhxsKXsGp11D6Da+S1YVzYXQSVbfQyRZx+ikw4cPR/RpNQSieALCRt1SqZQTfHZzYtBY4WY9x/azNpqGxw8y5RsKKGeUAYYiniu6n0LSIFj19fWuiyQNXyRF6sEYn615QtFyD5QAxBGvHal5GJ42NQPlCmnu7Ox0QhlCgaBNJpNOICaTSUe4MdYgMNaDi1CTlgx++3m7oa2HUJJrlMH5dTZKa5U574DUCyKrKAKbziAtpTIgeBcWFiIpRfzfdmeT5M7DsynDyeTiuXe28y1E1ZIGjBM8engMGb9NX7XKqr5+6ZgCSAkkhLFbooiRVCgU1NfX5+bdzgMKTVrqAmnPv8TAsvPBWmpsbHSGBHOJ8LaeQd87ihLD6UFKHXsPw5UxYRQzfmmp2YFVaHjA6+qWuuZaQsUc++lCFqSSdnV1OQXHd63nmFRSa7RS38U+sQqfRjyWHLJv0ul0hNQwdtYpx7vY57DPFQf2A1EeG1UpFAquwyJygnFNT0+rtbVVHR0dLlUOpd7c3KzW1lYnnzAsbL0sBrE9HN4a6TZtau/evc6A4busMRsFleSa47D+pEWZAFmwqW7AOpSk5RFk1r4fdQTIQebAjyiyv2z6Mk6VdHqxJTzvXZI7ssJG4ayRZiOKltTRxKpQWOpAaNNI8eTbKBTygJ8hrsgenh3d09DQEHG6cW9rKDY3N7solq1jppaf942zBQcD7w+5xPuyhMoacshi9C161BrI09PTrlMv32e/E+Vva2tTXd1S12yiG0RCWHvsNfQjUXL2ri1x4J1ztqof2UZPMBc2pdjKVJuahxGOfgOQX94HTsXx8fEI4YGIcz8bKeQz1k6g26QF808UeGRkRJOTkxHigeFeKi2m6M7Ozmp4eNjpLGS4Nfbb29vduHmflF/Q4ZZ3V19fr+7ubqcPisWi+w62BfOMQxNHImsLfYP84DtkZUFWIHvIGL4P4eA50L3MGU4c3o3V1ziAWOPJZNKdiQsBs+UD1gHCWJCPdXV1zl5lDaD7cfZyb9Yluof9kEotHrNiG/zYPYhuYL8QhWau0fFzc3Ou4zryk4wUWzcK8fRtC+aGdW9rRXEc8664r22mg4ziXpTeQNbZW6w/PtvT06M9e/a4NVELAlE8AWGN3Pr6+siB6oANhNCXorUotq5wYmJiWfqNbYRiPfY2haFYXDzXDeGFQMK4oXsbHil7ViKbjOtbsoKis16cdDrt6owQOPb4BX7X3t4e2UClUskZNswRaR94KjGAuQ/CHkHqN8KhSx1KpqurK5KSY1MJrGeW8ZC6hUDE8ON5UTB27hEWdKD1/wb4mRRDDFzbthpPl41yItAxFFBCeOETiYRLg0QoStHoEvMqKSLcuYdPFJkbzusj+socI0itp42oGw4KalD4D7Jn1yzvEOXEe0T4Mg/MP+sYosF6hiiyB3kGG0XCgKJ1N99PpVKufs4aHoA1Yr2RVjFLcl5dWyzP33lm3m0cUeQ5eBc8h40oYgzznBBeInG2zgdCaIkiSpPnkKS2tjYVCgUNDAyoubnZpcvgUAKViKIfQWAskhw55H3wb9LEJWl4eFgdHR2uHpf3TFo8MoT0H1tDwxwTCbMeXuQEz4133jYbsvvXGgrSUn2ihe80sxFAfmeJInvD3o91GAe75m0mh7TUMAgZhDxijtijtiSBfQ2hgfTxjtiDHPfDUTbMH2ude1j9hjzHAcWYef5CoRDptgqJopOvJEdGWWO8o6mpqUjjDogia6atrc05SnhntkMxRMVmJaCHeIfoNpq2oWMx7lmDftSS+UGesccwqOn6bQmpnyJr00MhvvPzi91fcUwin0ZGRiIkjYg8Tlmcdla+kDqJ/kDGQUAh893d3REdA/FkzFNTU65OnYg2dgNyi+tbfcrvpWiXZPYKsoXv7tq1y93PRtzm5+ddp/V0Oq2TTz7Z2TnIGPuu2Q820whdjzyx+wx5QY0gOiyVWjxKCwdmJpNxZTK5XC5yfeSNJcY22mRT3yEzlhShd5gT/o+ex+5hHNhGvGPmAL26a9euSO0pBBh7ys4PRJJ7UHtnSwCsExzk83n19PREMub4j0wbzjG07xy7BWKNk2FgYCDifGFv4mRIJpM6dOiQWx/szUKh4LIokHm2BIBAAfKLDCN0ho1eT05OuvmGWPIdUklJObX60L5PO4/MYTUEongCwkYUrafNAkFIBMCSD0lu8x8+fFh9fX3uenS2ssQTwYHH0bZPb25ujggEK4gQwgh1SAubzI4JQ43ns8Yvh4Dv2LHDNa/h71wnn8+7CAAbbH5+8QzIgwcPqlBY7JCKMESp2NRXxoAiQ8HYAnUEIS3h8f4hVG0aDukj3Id54RlsRHPPnj0aHR11yhRBhtDnXVsyw++tpwsjZefOna6Op7293T23JVIIdwQtAo8aRAQ690FgM/82zTGRSLimQFyztbXVCVwbGcWQweCwKTaWxFmyx1ghMiha65yw9a6sI5seatMDbTtvxse6wYBhjVjvsI1UWYOed8tagpwzXtZNV1dXRPj7+xrDj3m0kR2MKOrA/O9ZQ8ISMDyR7DUIM2vHGtE+keGeth5WkjvKpaurS9JS/RpzYDvEYdSOj4+rpaXFdVO2tTX+WHz4Rpd17tgUU4w41q4kJwtwyNBBk3di04epp7IpZNyfubVzyfdxVPiNHOy7IHLOdTEQ7BpiLu2eRwYxx7VEFIkuV4OfCovxjaFso158lr1knT58hnmHaNhUx/r6xU64NFnDYLL7xkYGeTacLrzb8fFxDQwMOILe0tISiWS1tLS48gauCcnhc+xhyAkOVc7lTSQS6unpcWPnPRI9RV5aMoFeRifzHmdnZ7Vjx44I6eM5eT7GyF5HviAT0aGsXyLeVt4iL1kv6G3rPETPzczMRGqTOZ5oaGgoYpTbNcjYuR5RY+aiq6vLGdY4kOfmFo+xaWtrczKb94584zMY6xjZ/GczPbi3nWtreKN3WVPYAB0dHW59klXBOXSZTMYRXmv7UL9t7QPrsASJREKDg4PK5XIRncazQl4Yoz2OZ25uznW0Zr+Rhutn3/h2hM0sYZ5sVN7qN9Y8YL2z121k3I63VCq5iBv6lneEHGCdQMqIyrGGSTFlX3OUh3X2kBmAA4MMEBxe6Csc/lbmcm/Gjy6k5IQxJ5NJ56TEGY1NiHNk165dLlJq7UAi9qwRnF+s6e7ubifn+R7vn+PAEomExsbGXKYChA+9QZR/fn7eRSR5v7xX3sPU1JRrZlgLAlE8AYFnW5ITcsBGidgYKBxrEBDtI80OgjM4OKjW1taIUMEzbL2ckpx3HlhBw/2s0CYiRjoBJMl6ZBk/3hZJ7jBtDDxrEEMi8MJYoWINnW3btjkvFwYHqZQoPRSXVfLcg2vhsbZnAWEEEQ2zJNnmqvMdvIgYib29vdqxY4eLFNTXL53LhoK3BpQ1MG10yEY8WltbnSFBgx2e1SoeIo2MEYIqyUXuksmkIwaQSWmp7pDP20iUjYzyjjhk1x79wDzhTbRpJ7xDvo93k3XDXNiIDl52KWrcs2ZQYMw/Y2Vd0NyAeWIM1jlhnSKWPHBP1gOEiDRMasZs5AD40Xrm1NawoTR9omiNdetxBqw97pfNZiOHVvupp8wTqcvMjXXekEaKYYsBhbFga4wxnjBKSAvyI2mViCLfR1kiRyAo1qC0xqMktx9419YhRfSK8ZPSF0diMdRwmEjRuiHfgLSOM0mufpd3bN+thd07NPshhRPjyM5THNnr6elxazIO5SKK1pFiSaJd8/ybubbOmJmZGe3evdv93jqlUqmUKxFAThHBSKVS7sxKDG6em6g2xKS1tVWdnZ3q7+93pQ7WKGbOu7q6XDYI0XaehXRVG10mMmQjd+gx9o/VS8g2a3ATWbAOy2KxGHGuIkOYd+aSfQaYXz6DjkZW9vX1uYYc7GP0QqFQiDTKYDzpdNqlp7LGfSJpu4WzDu2h7daB2d3d7XQHOrpQWEwBz+VykfMciRrbtY9RbFOXbbaJtSewA2wKZWtrq/bt26eRkRHl83mNjY25eYWU2W7eEMOuri73N87jY755F8wNhI73YHUDv7PzjjMHXY/s4B0mEotHb3APSBdkBYcx69CPKNo5so5jIvTsa+uUJs0SWYHjBB3OO7SZWPw3OjrqGvpJS9kv7e3tEYc178OmifLubaZJLpdTa2urhoeHXfYG6aHsLd6fdYAzNiJ0OE7IUpmZmXH7lAgm9hnvy5bUtLe3u6OUkCukTHd3dztbL5lcKolBdpVKS6cO2BRdayPjMGMd4XTn3XDOptVHEEbfRoL48rnBwUG3XmpBIIonINrb252R1dDQ4Oq0gPWeskl8A5x/d3Z2SlKkKQXpMwCiaIkcQg+laA1oBAqGJ8SMNEOEFrVwtkOoTUOQFhXV7t27I55szsVCCCwsLDhvJiSiVCqpu7vbkTq8ztZ4sV4vSC1EkzGycRGIKBGrjJgz2/WVz1piTLoeP3Pdbdu2RZ4fwmfHa0mOtNSEgrGg0KzBQMdGhLSdZ6s4idBhnGFg2HESySIdlzXQ1dXlBBoKh/thZDLfw8PDruMu3nu8urbYnrVHBNrOC4YyabCsFxt59Nunc10bybUEVlKkkyP3Z01TD4qywSjGuEUBJpNLKSysU9Ym3lGu6xvoeMyZN4woDBtpqRutTfFlnmzUlfVn/848SYv1yxgUfkSRtcPhxtYQ4FpEGDE4rNEPUWTN8h4giDZCaY1ifyzARoetRx/ZgkGPUc+7tZE9W1sGOWSOiO7weSIffiq/3S+MX9KyJhIW1tkkKUJuJTmHkYWNBFDry3rz1wyfs++Wa1D7XA7MlT9G5tYeWWANdktULEFk7+NosalsyCX2M5kGrA+rJ3wnDHqM+jKILHKLZ+H/1iEKWbWZFDgGRkdH1dvbG9E/GK3oMEsWbdSUa1hHlb0f74mIBfNoHS1WXtv0RtYY64foJPdB3uOkoRacdY9zB91jHVDIJd4J794SWmqi+Tzr0DZvszKVTqfU+aEzcc5a47etrc0Z+VZ/+z0CeEabOl1XV+fksLUxMPYhqdlsNlJnz3E8RIUg7NKiXcN47RFd9pxQyK2Vr4VCwTlN+Z2dW6JqPAvrxUa0mW+iuchIS4Ds2rY61+4P7Ba+y7vL5/MuM4g1busyGQ9p2Fb2z8/Pa3Jy0tkrktTd3e32MJkn9G9gLJb4MkablWPHzv4gIpbNZp2jiL3CnsTuwIlp5RKyB92YyWTU09MTsSnsu+Mds+6kxXMdGxsb3Xq1jgrsGlK60fHFYlG9vb1uLnFiYj+hN4mUNjU1qaenx2Uq0eMBx6GNDtvaYZslhe5IpVI6dOiQK6WoBYEonuDwI4V2UVkvvBRNPWhqatLu3bsjgsgqdZ9UWk+UtGggQTJt6qDdyNaTjGFO44BcLueMRasQEGSkn0xPT+sXv/iFCoWCq0+cn5/XyMiIEzJ4MImcEdmyzQ5sjYj13HJ/PFp4oVCwzJ2N5NChFNJkIwTWk8bcQTrZ6EQ/rQfYpk4kk0s1B5boo9h4Zu7rn/skySl2Gw2gvgbhhiLr6OhwipKIIWNraGjQ6OioSzHE6EDo+54vhCWC1pKaI0eOqFgsujx863WFYJNOZNNFSqVSJK04kUi4oymsN5tILJGBQqGgw4cPOwVtIwrMA+McHBx0SpJ9hAFua6OsMdvQ0KCxsTHV19e7WgmULu8ZI8oC49DuR9a8bfFNvZxtTsF9rUfbkliMZd/4t55aC2uw2/QyarOYX3stahZZB9YgZ21bD+j4+LhrzoBRZrMg7FgsUbSkibFZw5/fQ0yYB/a/3TfMJQSXvTM2NqahoSHn+bfE1B8b6421Ky1lNFgZDHwCZyPuklwTCQv7ef/ZLVG0aZ0YNha+oe3DRiV9wk4aIPMMaWbdUvOLcYuxOTc359a1TS1Np5da1/uykfRsDDCbkSDJGcFELxgLspZ5tpEn5tDuE+tUQtZbA7murs55+O2aJiuDeWb+7ZzjmETGY7DSyTKXyznCaGvUbL0v2RaNjY0aHh6OROjYB8gf9A/ROPSudVbaTBOuhRwgzdKWgNiICA1+mDdbs8beIZJuI4WkVVtZ29XVpccee8zJQXQ+jkiiLPboA2l553OyBFiDjI/5hGwXCotZUb5MKxQKriZVUkQPTk1NuZ+poeWZLBGAIM/Pz7smKehIUhNZX1afsU5wnCDT0ZPouWRyMXMnk8k4ZyfrzZbCWPuFaDvXYZ4HBgbcfPNebZmQlWe2Vg4ZDqG3uos5mJub0+joqGtWhdMB2c6YsWE4wxm5yR6ZmJhwDlQyDTKZjCO9XM82oeP5IFPsecZGDwccBbxX6+xHZuEwHRoacll1PCcyAblkM4bY70Qc8/l8JICAHWHloB0PUVDWAu/epmSzz6zcpN57bm7OOQEmJibKifgIAlEMiABhAflhE9lIibScYLJBbSMDCz+1ioYFtl7HetD4DvdCYKHE8Ej5XcpIP2xsbHT56A0NDa7hSVNTk+rr6zU8POyIIh0n2ZgYoRgVGB3MB8YA30FJIUTpkmrrMWiGYkk4ee7MMcIE5WuNFus9JmJjhbNNUSEqg3BF0NuOqowfgWQFO8aHjaRYoW9TYGyEhXeIICRCgOLjOXinlnxYUruwsKDR0VHXYQ4jhKZCLS0tziPOdUdGRlzKs412+SmaKGLO+MOYmJmZcWeAplJLHfRISUHwMm923SLMWRv2zC9r3PIeWOMNDQ2ufpYCeGvw2oga64DxWUOX57LEwB6dYlNT2KsW7DvrlV4JUbQRRd4f68Yqft47jS2skc0zohRLpZIOHTqkyclJDQ0NOU/30aNHnUKvNBbuxRwie6wjyj4j78RG3ufnF8+FI0poI5k8U1tbm3bu3KmxsTGNjIy41LW4aKdP/m2Uze4LxuwTOD81237P3ifudz5RtM1qrMy1c1cJcdFb0N3d7Qx41pKtqSLVi/WGPMOIsWQBeWw7YLMWm5ubHYEiFZLsBubP1ouedNJJzptPJMOmObNG7D5hnrmnn0bLXGHwsW/Zf7bpCp2ubaQEY85GLWZmZpTP5yM1emROQKbsnhwaGnLduuvr693h8dJSpg9zRzQOko1DiTmy699GuVi//N7qMSIb7HnrMOFZWfM0KUKXc0+iLji5IDo7duzQ0aNHIymZpVLJddu0teKQMJwNrM9isehqKFmLLS0tqq+v1yOPPOJS8LAr2CtEE63hzj7BAUwkFNKRSqUccUTOsg9snSJH9ywsLLj3a2tI+Q6GPmPC2CfNkfUFGefcUBysfgaBdWCix4moJpNJjY2Nud+j35hPW+eG/CZjxTrV+vv73R6CLKEf29ra1NjYqHw+7+r7II7UHRYKBdfgj2expFSSs/vINMPm8R3EnM1q55DO0xD7YnHpuBzWum2mZ4klWTs4nTKZjCYmJiI2jnVIMge8d66NjMpms+54C+QzDjO777gXziHeD8/B3rTBCcp0iD6ShfXLX/5SiURC/f39ZfW6j0AUAyJAcOOts8QiLj0KoJiIIPiftS3lpSWiaGsCEabcj/8TUbQG8+zsrLLZbKRlPtcvFBbrl/DYcE5PQ0OD60SH8shms+6g10wmo87OTicM8M6k02l3TlcikXCF2ygGvLsYDHhpIbNsfFvMXCgsHvSLUiDSIC0ZI9KSF475n5ubc94sjNlkMum66EF6bETRej+J/ElLHv5kMuk8rDb9dHp62ikHCCFrwipB6wRAsEKURkdHHQHjGawxgjGH0uGaPT092r17t3bv3u3IJweA29oe1hadEBGi9l54sFGG1suG4rNRKgwV1pjfVIh5s2ldzAFrZHx8PBKt5JkZU0NDgzsnjA6zEM6WlhZX0+OvexQD17VOHIQ+68oabKwl38vLfFtD2CcJ9v4+LGGwMgCni21Kw+f7+vrcnFn5wrWIJJRKJVc7NDAw4IxTajYrjcV/rlJpsVamUChEGhLwd+7LOq2rq9PExIQj3XToY91zfzzQLS0t6ujo0KFDh5zS98dmHT1kCviRfKvo/ewKK+fK1Sdah4b9XaWIYhxRZP7KkcFKEUeuxxq1ZJhoPvIShxpEh3eDrGPNEr3D2UKEY2xszBnLLS0tam9vj3jobRowTiT2DKmEVnbbuWAvY+BbzzzvjbUC8YLQcP4q859OpzUwMOCaY9BVFaKRTCY1MTGhkZERzc3Nqbu7W+Pj485QxOlG+jyNfdA3bW1tymazGhoa0vj4uNPBNIKy+yWfzzvnFHuUyAxyk71GeQjPaucBXQJR5MgD1p9da0TYSqWSa7SBjEI22s7hOA3T6bSy2azGx8dVV1fnjv7A+GdNofeQpRA+5nv79u2am5tzc93U1OTsABxbe/fudXVj7DNIEIepsyfZo7YxlN2P6H5rxLM3rPOQdWxT2JFv1mGHDYG+spFp0iAnJiYcYeTedg2jo+07hijaSC+lSOxvxsZ3sNus45Rn6evrc8Sa94MNUigUtG3bNpfquX37dqXTi3W5ELWOjg51dnZG1vnY2JizU4is5fN5t/75HPvX6hYb9WZckH9JrhFQNpt18zo+Pq7e3l6nw3C4p1Ip9ff3O8cP9pQfSLFE0drOOA9sWj9HSWG3YgdBzsmmsA2isCO4F3sLmW2j6Mlk0jnhbE0j2XOBKAasChgHCE88RpWMRYAgsYsVQL7YoCgvq3T52QqgUqnkauXYKKlUyhXhYzAhuImiYMBZIQwxIbcdkkg6STKZjBQrT09PO1JEpIBUDfvMQ0ND6u7uliTXjECSSy/gbCWMAsYNAZIUMbo6OjrcXFPDMT4+7pScTbWU5IwX/s17YAzMpU3HQYhbo5l3gmBlbvHqWWN1+/bt7hoof5SgbRkPuUN44uHCYMNA9FPhMB4wAhOJxS6CpGVBrv2oJsawNSLwIvOOqTFgrVuvJfdkzvGqo4SYX9YpiorxcP3e3t5IUweIKIqbz6F08IiSkk3ExTprJGlyctIpOxS/deqgsOx/gLS/OFLAXsNgsIhLTQRxZILPkx7jRyQt+bUkk3vw7lCGp59+uvr7+9XT06P6+nrXaCDuGeKeSZKL0szMzLi0G96zFCWKrIeRkRF1d3fHpvHiYeddkjJLxgIREcCzsYcxtP3orh8JsU4dmy4cdyyGnTv7XfaGje5aomc/a8EejgMGTdz32Fv2vWPQYNQwD9JSd+Te3l4Vi0UdPXrUZWpgZGWz2UhUGHnGvsKQ849p8utFMfyHh4c1Pj7uOjtjVGNQM0d8nghYIpFwhjUOILJpIIo88+HDhzU+Pu5+zufz7nw9DD9SLuvqFo+YQH9kMhln0B49elSdnZ0RB5uN6NEhlAwZCGY6nY6k9yEfGTPvf2pqytXg26ghOsoalOxt3gGfLRYXm5OwRplPyB46iPklm4Z5JEsCtLa2Oj3b3d2tmZkZJ/vJoEG+8I6wJXj/nOsHmeZ5rH2wbds2dXZ2uswj1rbN3GAtQrpZ++jDhYUFZydZeWCzftAbRMkPHz7sZL+NhBF5RW7yneHhYUlLWU7MvXVCQpQhcRxiDwHEOcX7J0rF+qMxIWvTZiMxp9KiI5J3hcOBd0hUlzGAhYXFTr/YckTIeV84QyjxYS2iS0dHRyP1+xyVg30CcWO8kDt0ArKIe7CeuT/ZIti+2WzW7UPeIc9bKBScLhofH3cOK+v0s/e2+w37EhmPXUxnU2xf9gkOMOuchlTjwMKmxQFinw252NDQoO7ubhfNJOJczaYHgSgGRICQlJaKmG3KYiXYTe+DugWbxiFFPeN2U1tYTyZKxkZ0EJbSUkQRrwuCEiHMc9ionW8E8x2IIiQG5WvTXUnhopC5paUlkoqJF5Jnpm2zVTTWSEcZMk7y5fEq9fT0OGMWYW69hZBdUlAAitvel2e0jUV87z9zbCOGKF+MAKKj/H1hYcF5YDHweH+QPN453n8EKe8SA5HvMNc2ncs2SJqfn9f27dsjXmRSwVBCvNu2trZI5BoCZetESqWS62hGgbxtDINQn52djdS6QmqJXszNzWlyclJjY2NO8eJBBDREYC3QURhHg917GIGMs1AoRAwklGRdXV3E8yjJ1VWV28e2O64ldjxXHBKJhIaGhiLKFBBls5E+P0PBrmWrWFGWrFW8u6yDcsTVwpIrGk1ZhwyGLM9ox8O6Zv+xppg7vON2zDa10jqTpOhxQ7yTckQRg8wqchwRzCUZGT6QUTYyFkcUMSK4T9x84liKg3WW+EAO2DQ91gayF2cXhhlkq7+/X1NTUzp69KgzwBsaGpyx5Kd1+Q4O6rBJnadeCiSTSdekbGBgQGNjY5E0NJxOfJa9aYmAdU4hu5hPnjOVSqmjo0O9vb1qampyRyANDQ05uc3awimUzWZd1MhmXljSbKNJw8PD6unpiRihyWT00HQ+z7h5DvYbZN/qWPaqjV7xdxutsZFa5J3dzzgXrbzhb7bun7/jXEkkFlMUSdUlwkZUlZo+HGG8I0ti47JepqamXKdynAxW3kGWbabO/Py8enp6Ipk2ltwyT9aRgLHuzwfrHTuEjCfrdLDj5r3h5ERHMWfoDPYc69VG32xEn2dGP9lGMpb0suZwlnMPbAV7Bif12+jz2dlZ50hhLuvqlrreEt2iRAPihLy3Nfbspfr6epeujk7YuXOnyxBB3tFDAd1ix2WdVna9o7tnZmZcdI+zVxsals5CZi4sMa+vXzyDPJfLuX1hAxzMK/OEXYozyToxhoaG3Nr3iSbzQ+dUnJE27RTCSS0237NrAX2HU9M6xaohEMWACBBAbBQMqVoiitQexBkP/I4QOEaOFXbSEjlloyF0raeIMeLxRTCQvsnnMORt+2qUAALET8thrCgWNjPjItUUkK5KJCebzUYieLbrHc+BYrNk1Qpv+3cEEEq7WCy61tAornw+7wQR18IDa41l5gghjseUtA3r9UchQjhQDJaMWKPfetIfeeQRd0Yen7EEnWdKJBIuZdSmAVpnA9Edvo/CQ4HiNMBLz9wwj9Q02jHayCoGPsoMz6QkjY6OOoMGoWs9+ihR27qetci7YDwzMzORSFihUNCjjz6qdDrt6mxQKIzBEnKMlrm5xbOPrIOE9YwSwIDyDXnauZeDNYQs6au29223R0soU6lU5KBk1qY/T7wDFBdr39akYOxxjmQ5omjHbSOKhULBGYaAuiRpSQ5Zw46ohh2bfWZrfDAmZFZcRJGohzWqbGaAtDz11JI99i6OhrioajabjTht+C7zbqNlNvW0XESx3FmKEMW4MTD3NmLIM7L/2bsYdhjM3d3dLuNgdHTUHZotLRF/K+esN58UNIBh6afoMh/d3d3q7u52URZkinUgoi9KpZIjNsh0GwGyn0fO2fQzMgCGh4fV0NDgnp+/UxIxNTUVeXcdHR1qamrSoUOHXNZGQ0OD06G+7B4eHnYR1mQy6er4cDDMzc1p27Zt7v1SF0iXy4WFBR05csSlpVqHjjW0bRSDyIsln7YEwxIp1hp1gvZ61GXSlIt3zRi2b9/uGtKhZ+1c8g4lOSM/mVzM2hkdHXXOVinaJM5GEGkyYw1ysgqQ0dZZwLzYJipEtW0kln2LfLDOT0tK0Y12nc7MzOjkk092Ne3sOes0hUATeWKvMU7mBRli7QJkA45j7B+uyz1IbSUbiHeKLcG+HhkZiXQaJTvD2h0QRebYNnvBycDcIT9okJZKLR6TMzExobGxMbW2tjqih67k3jYbzu5V3mVPT497bpwozC9rmvmD7DKXyOGmpiaXrst68OcXGxJibSOE/A6dgQ60kWjItQ2WWGc3GQjWUWJrGLu6upzMlhSJsNeCQBQDIrBpChiwlsxUAsrQGmMWLFxLFK1xiTLG6ESoSoqE4jGkCKkjRCYnJ5cZdHixMAbwYNtGNf5mQcHYGhNC9hgAkAXr9WP8Nu0CRbWwsOBaeltjmOuj6KzysgXplqQiBG3ki5QIFJVNDbNeRObbpor4UVdJTihBxvHOM6eQFD6DknvssceUy+Xc+BkDitgaltZLZ9cW8yrJGR1EEHk/lkjYlKLGxka1tLQol8spm826on/eBcatjV5isCBgIWQYmUTsIKSsl4GBAW3bts2lACKkbcoHhglp3BDv5uZm5wG1ygtvrm3djwE0Pj6uYnGp8F6SM/5YPza6wntl/0jRRjZ+BJC9ZSOA3KPc3mfOcEzYlFFgo1e8A+QEY66vr3d1tlwHg5i5TSQSLhW7HEGxz8Xc8Xk82Iy7u7vbzY+NKEIa+/r6XPqxfU8YT3afMw8oces44blZW4yBebWkF6cQ1/QjitTsxNUn8jlL+Lkm17Ofs46ZckSxUuppuYgigKgzFtYGsgX5xjWs3G1ra3MOHnuWGBkpyIS9e/e6+bBRDMg6+9Gfo/n5eZdqR9orhpTVd3wWgtbc3OwOX+fZrBy3RBEZDVGEmBERYy9QCoFBSRYCjgye1TYy4h6Q40JhsVunTVHFEce5kug+m27PODgyCydVLpdz5yTaqBXzwl60pBpZPzY2ppaWFpfmjqFva/Tn55cOBadODucbOp3aRMZKpsrCwoJzhqTTaXV3d0eOYrBOFa5DQyxsCN4PZAB5h8y2z2yzBpg7CBLPTBp6Op12ziXWBe91ZGTE2QPYFrlcLuIAsvsynU67Rmo4E2xmBvMCUWaObPaMfV/IWvYlv8Muw+Fna0UhfKnU4lFORK5s1g+dfdFFEEpbczk1NeXeq+3eSeptPp+PyDqCDuh91nwul3N7hXdp17ElV/yNd0Q5lbQULccJjKywwRGaY7H/FhYWIrL/yJEjLgrNXmHukc92/6bT6YjzEVtOWooWY6tZ2W11B8475I91LPKMLS0tLpBgSbJ1hNlzkGtBIIoBESSTSZeCZ8P/1UiitFT8X44ocp6ijczY+0IMEVRWmEGsEBxEGBBaNCCxBh3t4zHSUeyQsXJgk9rIpj22gE1rx03nU4wrK0yp3ZiYmHBF5jbFhKjU5ORkRBDZ4nmUExsej2qpVIocZsyc4J3G8Lbvhblj7Ahd+1maAVjCaqM/zAHCeHZ2VuPj485DSDSSCC/PYdPg8OQhSBFsGDrS0oHkvE/+bWsnMbbwgtsUO2soIpx9o4s1x7U6OjpcyiRrAAJG1BEiyeHgpC6xzlE2eOtPOukkd03WhyXQeBaZB8igFfZ06mUeWc+QfdY1xgRrTZLrzGYPybbEgXHYyJj9W7n9jxd2ZmbGrQ2fxFlHAvsXxclYu7u7I++tublZ3d3dkXcGUPLl9q6de5uyY/cH69JGBG1EEQPHphGhVMfGxlxtG+MolUruvWIUUwspLclP5BP3tQ4j5qoSUcQ7H1efaBFHFC35tyj3bu37ifubLSGodF2eEeKAcwgD1dZ9sqYZ7969e931eLdEa4gIFIvFSLQeDzvX8Y0h1qCt5+H6fm0V8y4pkirJekJeYqTxHYxhfmbdobswzHHqtLW1uQiZJA0ODrpumH19fe54H6J4tklNOp12TaNoJkKzm7GxMWdYM3c4X3BuIK/ZvxjtVm8gG9gPGKkQ+7q6OheVIZMAHWCbvSBHicRIcp9JpVIuw4TMGWq3IJNkXXAN3oW0GCWhgyf7mb/39fVp27ZtkSgv+4nnYQ3a8gPWgrVDsF/QBzbCWFdX56Ke1jFLwyv0KeSHaJgl2rwniKytwWVtQazT6bQ7JgJSxLq3WSlWt1qHLWtlfHxciUTCNQOyEWPWr1+GxJggu+ginpm5xcnO+6ZvRKFQcA4U3j/XhrBBoO3eJa0feUtKLc+IM5bOttgp2AuAtFPmLZfLRWxXZDG/S6VSTvYODw+7/gtWfjIPNp3WBiXI/Kqrq3P2Cuvb7xRu1zBzy3phzfKO0KnYjYODgxG5hr5DJpGJUItdLwWiGOABRc5CsukmtXyX78UBgWEjg5a82JQblBjCA6FkUx5QRFZpW8PDHoeBguYz1Ygi44EgEQVCCGPsIBTtwae2SBtlbhUMc2oNpGKxqJ6eHh09etQRLM5K5O9sapQ3c4Thg+EGaSECyrvDiIV44PHGMLbNB7i2tBSF4rv8nSgQqT2lUsmdqUj6o62B4XcIfwrwGSPPxDxKS+l5qVRKExMTTujibbc1RAMDA8tSLrkenmIcBaQpU9M0MzOjk046KeIJ5/gW2xiJQ855bvtOSLXFawlJ4d69vb1uXdn3h8GB4rJ7CXJeLC6mbNuzR3k+7m8ji5K0a9cut9YmJiZUV1fn0vj8qCFjYm34qaflgLFViSiynlF8KC2bLgbR5711dHS46A2NHECliKJ9Ltbr1NSUiwrbaI/9jE8UcQ5xkLOdF1J4LclDBrBuW1tbNTo6umx83Ac55ZPeckQRQ803usrBEkVrfFpg/FZD3PvH8PIJO5/N5/MulQpSwPE1pGFjqNh0QJ6d1Cv7nPY5bP2VPT+UMbB/bFTdjpF9CtlArvlpuHY9Wb1mdY79mesQURkYGHBpZbzr6elpTU9Pa2ZmRu3t7U7WI88SiYTGxsZ09OhRJxOIvNg6NdYf0QO6QBLta21t1fbt2yNNpQqFgiYnJ50jY35+PhKhQRaxNpkDdC9zJy0Z6hjiBw4ccBkdtr7RNv5C96MPuD7EC7lqjwuyz4shLC3JdvYjnaqtcwWHVC6Xi0RomQsb+WXdoIssqeXz6I+6ujrnnPYjyZB26+AlZZO5JB2X+UMmcL9isega9lnHpiSXuopMYN2gp3k/rAXWP45/5ts2b8FxwrqA3JJRwFqD2CAreTauxTP49ZM2mocdhQ5g/1iHMr+nJMQ6KCYnJ5XP550tRSkJehIyC1G1zl7so/n5eddIr6mpyT0bto205Cjj3UIUeT6OnbCAKDJ+65CyJFZasiF5fhzerAtLMplDGgmxZltaWlzmDc4PnJjWEWUzqLC9WY+1IBDFgAgQyGwa61WqhlQq5Q4hjYON/kiKdDy198DDgyBis2IMU5iLIqJ9PU0MICDUKVhj3np5KxlJCGyMSOvZ40xGW5c1NTXlSDJCEU+PtFRrZ6NUVhguLCy41BJqRSAKKHGL+fn5SAMW5sueUWeVF89s58I2QaFpC0LaRlJQevwODyAkjfdB91gUtyWKxWJRXV1dLpXF1hei0KzhwJht9I1aTGkp+sB/3FuKRseYA+oVS6WShoaGnEe/qalJHR0dznmA4kmn085DbdM5pSWlyfpBueHpxiAmVcp29+N7NsrGOrHPAKmjZgmSbJ0LjMWua+aQMVtj1u4xf/3Hec9BJUJhvcyse5/EMBeQIxtRtPdGmVkSSGQG4sAzV4oo2ueFYEtL7cIxXhkbTgNLFFH2RDUYJ2ub92jnc2pqyhlrbW1tGhsbWzY+3j37xye99r1Y0sIaYV9ViuT537WpcxbInUqOAEtcyz2L/7tkMqnJyUk378hP0vO4Hvva7mne7ZEjRyKpjXYOiFawBqgztMCwIwpogQyzzkoMJ/YTn5Oih6vb77NOpKVulMiG+vp6bd++Xf39/Wpra1N3d7eampqUzWY1OjqqyclJdXR0RAzv2dlZDQ4OOgLe2tqqnTt3uvljHInEYkopTbImJyddNoONuOVyOe3cuTOS4cF+QP5Jcuf+Iovs2rCRMrsOIHDsJWrEIDYYsugAS8L9KDXyHj1KyqmkSFMb5JdNyy+VSk42ooulpeilv35xWlACYtNwITm2E6nd58gGG8myadA28kaElaiZTfuz90VG22yhhYUFTUxMuOwPIlPsN1ueQDookUrr+KIJFETINrqBkExPTzs9yHiYQzKJSqWSGzsOEfvM6H+IInIG0s06Yr9iR+FMgdRZIgNxt/0EeLZMJqNHHnlEra2tkRIm3jONr3Cqsdbt3sF+kBadeqShW73O/ZARRLynp6cdybRZKdISUUS24dyzthP60nZit/uPlF/SdaWl2keO1mANtra2ukY/rHUI/+joaCRt1r4He4RaLQhEMSACm9pnlWMtIepUKuW8mnGAbNgjITB8rRGLJ8sauwClSH0RRBEjFcI2OjrqzkSUltqi29SiapEJlGsqlXIePOYBTxdpOhBYm+KAoED4WQ8pc8y18Bjmcjl3wLBtt433m5+tcMfwxEhgDAh1hBpCXZITVDz/7OysduzY4RSQJWF423gOiHgqtdjZb2JiwhmBkiLGtPVkAcaLYQZZRbjac/IQbhgathYkkUhocHBQ2WzWCXob2bLpwdQiEWHCA0fqlFWydXV1zhD1jVpSRjo7OyNpizYijqHA2K1Ra1MgIYq2Zg+Qijo5OalicbGdPwXzrM9icakl/NjYmDNwLBmze8bfi356qTWoMebiIowW7GHWOqneFigwnB+8F5wZ7A2b2mzR3t6ukZER97M1aH34EUUMY5uamE6n9cgjjzjFbccD4eVvGH8869jYmEurY04Yj+2yl81mI6mngDHZZi6W9Fo5azMsmN9MJuOiwpXgR/vYtzYyQfS9kmyvVKcYF1Hkdxho0pIRx9zYaGQ2m3XGHusknV7sLL19+3bXKIrvIb9pDCFJvb29kfQ03pkl2RalUimSTu9HJvbv36+HH35YBw4c0MTEhDMwiU7yHUsUMbpsxIYx9/X1Of3U3Nzssgzsfm9qanJGfSaT0VlnneW+D0mglotoQaFQUHt7uzt3DkcGOgsZTBSD+Z2entbk5KSSyaS6urqcTmAds1Yge9gBPCvG7/T0tOsmTDdRDFvkPmTKkhSch+wP5CwOSO5dKpVcB2HS7DiGCrlhI6R9fX2OzKGrcYja9QlJsuQX3WHfoc384F4QRXSpjUIyV5CjwcFB10REUmQc6Fa+i34jmoxDiHePo409yxonMs6aZt5sIz7WoyVivL/p6Wl1dHS457NOAXSsdYhbssc6sd1luQ6fhzBbAsbaJO2UqKbdv8BG9SQ5Zzhkj07BrDlJrsQDHUvNL2visccec3/jmtzLjhNAZHmHXV1dy+Q0YCzICwgxz4QMsZHOuro6HTlyxMkhopX0V7BZJYybY9aKxaLLHsMBwfsmtZU9QfTb7rNytrqPQBQDIkBoWc9irRHFWj7HwaZSNKJoFbutU/RTnGiEQ82G9VRbpcZhykRpUA7UmEnL64Ms2NC9vb3OIystGfrFYlGdnZ0aHx93XjnIBt+3USMr4Ek5sNEhWwTd3t7uFC3zSnqQNcQgsFNTU4584l1kPBjvVnBDErgHHkPSSGixzOfwRtp0EYwdDpbGqJCWzgzEAPCJBl5oUlBsnj7REqsYuC7Ggo185fN57dq1y0WSUTw2dQpBblNix8fHHfnnLCT7WQxU5hrFXCqV1Nvbq5aWlki3UyukmRvrVecZcFhAECW5w6Tt3mFtULOwsLB0KDlzyHurr6+PNBqgZgNQw2JhCRXPjWHFXHP/SukpEMV0Oh1pKGFh1xhrkjHwflkjfmdSadFDbGuXKsEnihiyEEXeh43a22YJNgLLXJBKR7MHnEA28sd9Ma6YBzte5NP8/HzZiKKFjZBYglIuwmdhSSbfb21tddEZKdo+vRwqHZEhLXcg2vWCfPFJtJ8ezvtGDiLjbQYEgFjX19c70mCjNPZndIp/jUwmo46OjojzIpFIuIO/+/r6tHv3bu3evTtSK8z/rUyyDhWMybioeKFQcCmydGf01z+Ov2RysQZxx44drmNyobB4KHhnZ6cjRWQ9YIwuLCxo3759bi0zrzYCjTOGpjC2oRPrDX2D/CKSxLPOzc2ppaVFjzzyiCPtGPSWdCKvcaLimISQPfroo87gJ/JGWQDp/cwlmRlc35J1nB02a4R17TvECoWCO9uYyBZrgjo5m10AAbaywTpsIeL8m3Wcz+fduXXINq6DA9F3eCFzJyYmIhHipqYmTUxMRPYq8p+zEnFEEL3leCyrd+x90RPMl5UF6GP2DzWX0lIgwTZfsg5+61CAkPLu7P7MZDLKZrMRIuPLIebYymRsOAiibT4DobUOewgxJKlYLGpoaMiRa2QK8gJbya4XnoNmhP4YrTwm0mcdgBBs5op9QSYLxM3uT0vKkWHYDjiId+7c6d4j75Dx4uy2fTawO+2cBaIYsCoQ0QFWiVcD5KoSaFohlY8o4tVBcVlC5xtAjY2Nymazamho0MTEhOuARmtugLfNHhFQjSi2t7ertbVVfX19zohGsCFMaDiAwcfvqW8iv76jo8N5texZSNzLeqaZc4xjPEkIGUs29+7dq+HhYec9woOI9xFvpCVb1puWSqVchz3G0tnZGYkEku7C2IgIIvy6urqUz+fds2NQc9i0b6gRQSXCh/IklYL7Ms94yyGpkDHIuo1M0oHNzhWkmGcmVYp3YLvwsjay2WykFsN6BUnnQOn6RHRsbMylkOVyuQgBQyHRXQ6ya1NbuQdRhEOHDrmznez6JDLFHKEcaC7E+0YJ2+/HRRSZL6uwqzUsYd5xzlQiHTSXwusN8baGvT23CyQSCeeQ8NPWfPipp7RPxwDC+MDTjIHEfXifvle7VCo5Ly73YX6tAWYdXsgkYCOYrPc4GWQJsV03kiIGeyX4BJ/onT22w+7jcrBGjA9Lzux9MUQZv3U4Ic8wqOw4kUuVnBP+HAPqh7knY4+LlhL5WlhYcOSGvSYtRSakJaLP/vHXl42ycd6ezTzhuSBCEEVbT+6nn1qnw86dOzU4OOjWCX/bsWNHRHZiiJOqLili3HMv5ChNMWx03Ka22rIH5pLPQNyQt8haDGCbtYJhjcOB7yLHBwcH3fc5242zhnFq2DQ8ZAMkARmKDNmxY4e7HusN49u+J9u8LJlMumtBJqwut6mzVp9B7u3etBFWnAd8nnVtyzGsXPZTztHhlJjYWk9ITWdnp3OwsAaIJtlIHETROidIfea7yGWbSWRtFBwGrA8/4wGdZG03muNwjiifraurc7WzPHscUeQ67EkcA729vZqYmHD2Kp+xUd1icfGsyqmpKeeksE59wJpOpxePYLF7lz2BQ6arqytyjjaOXECfAObeyjaCG6SQooNs2q4UjTbzzqhx5fkhxOhe5ADyqKGhwdXQ8k6szYfsQuZVQyCKAcuwc+dO92+8FbUQxfr6+qppUXh4pGitkW+g2lQMWyBd7hy4lpYWjY+PK5fLufQwf2ySXO2if38fRL3wTklLqTnkwGPUdHR0uFQMhE6ptNhQhrOCenp6nNAhGggQHowTA3ZoaMgZB62trc4AgWyWSiXXJnx0dDRydpRN/SHNiHvhccTowONnaw8YK0qT949g4hwsUn+lpbQLIkPlIhEoNpQoytxGmBFspJog9BFykDubXsXxLOTx45nDAGhtbVVHR4ceeeQRF5lBQVmjzSoSCvXpwGkNEBsJtsSNGsidO3cuq9dDyQ4NDUVaYfvGJcojk8lofHzcnX0GMIbm5+edIoa005yH9U7k1u5hnyhCKP39ABGsBNaov6592FQuxsCzowzjiKIkp/BtDVkc/Iji6Oioent7JWlZYxS/ZpLr+l2R+Xl8fNxFdTG4cMTwfJbE5HK5ZQ1tUPaML27OrePIjpe/lUsDtvANLwxM6wG3xKQcKqWeliOK1PthsGHY2vHYKKy9f6Gw2MnUkjUf1oAFpCgyLmQjteT+3LD/GSvXZU8B5or72fVlUxdtBMvXlXwHY72zszPiIPBJVGNjY6TbJ3WNNiLBGsFAtF1WMdT9/c7YaKyFbGctS9HmTjbKA5nFCdvf3+90DGQDwmXr/0jftnWg6GCikdYh2tjYGDlrEoOZrrA2jZNsGhvVqq+vd6mBRPMSiaX0U555dnbWETDWJzKSuWIeIIq+0xWCzdzV19fr6NGjrnSCSK8lTj5RRMdaxyM2lz1rk7RjIkI0VWOf8a4gc1YuYQfwvrgPR0PgOLHyzKZgYnPZqLqfTmrXn42kkTWRy+WcswEZZLOb/Ogq92OukPvUB2Jboe8lRYg+spyINvJwYmLCOTBstBJnihRt3sW1WK/Yg6VSSWNjYxoaGnK6FtmBjB8fH48cvWV1DA5tCCzzji1lM05ssMY6t+y5z7avh30f2CiWhPL81pFTDYEoBiyDNcTwXFaLFPK9ShEFaSnFw7+XTxRt9y3ubw/p9WEPucYg9ceGIWDT+ipFFPF8+mmQnOWTz+ddrYitzbBKkQgmgmlhYcEpp7g0OlvL0dbW5gQUjSx8xUyhOtEaWy8WRxQxzlGwtsYSJcWz4hWz3nSrmDo7O91129vbNTY25gQiKUQ+IWFuEc5cC8+craWAiOO1ZQ3mcjl1dHS4z6bTadcsghQtFEKpVFI+n1dzc7OGhobU09PjDD28v3Q59VMI+Q9jwRppPAdKCCOnrq7OKVY/qs06hLzaekqMDsC8FotFFzH3r8N7xiMMrOFHkwk/cuUb+awB61BA8ZUjiozXvsdK+x+CbSMb7PtkMumitnH3Q0lPTExUlEXWkGcNoXyRZUSI8dDzboHNdJiZmYkcMcD7ZAwQI/95pMV1aiOKxWLRpRzZZ/TXCE4xCztPlYiyhf1cNptd1pwHI6LSO2PNx6W7xo3TEkXWNJkINnpgCaId57Zt25TNZsumGJeLKFpCa1Ow2tvbdfToUR06dCgyVmQ08oV3i6wFfiMOK88gmzYCZGWoP08QdRxayDmMWQzG1tZWDQ4OOocq2Sn2eA4+yz62Kd1xmQHojfr6+kiXR/7G+rXRLasfWQeNjY067bTTImcBYnSyh61+Qx/RqM3WLEpSZ2enO18wlUqpvb1dQ0NDkpacyjaiYyOKNl0UApPP59XW1uaaunFdm9rHnrd1ibxPSCHzhzMBuYIzE5nN/DNGztjkuzwne8FGiXwHE++BrAvkutW/0tKZe3atY9swF9TC8jfII8SD8yp5T1Y2sybJhrIRN9YCXUeto9RmVtiUXmkpLZXf+2sUvWp1IO/Nyin0tSXdNFCztbB+Fg/3s/Wd2FHMo60rZhy8W+ZndnZWuVxOg4ODGhkZccfzIPfQN8gCq7f9QEgqlXL2F05q7ETsPdYQ64MSiNbW1kgvCEuI2ZMzMzPq6OhwHZZZrzZlN0QUA9YFbKBaPQ/VYBWRn55jDUAWvvXy+QaxBQY9jSviYCNYUvXUU4QRm8966dmA5ItboogBAVFEseDBhxD5hjoeWNsdjTGnUksd/BCo/J3vQ1SsB44ImY1e8MwoLIQHggOFw/NCvGm4EGfEkUpBPQP3s1E3fw3wd5S47YCKkb+wsNTFy6ZBEZljftvb2yNnPGE4otCsorZRULp/MQfWO81YJLlzpqzThPeJAbNt2zanTKz33wKBbY0X5tf/LNFnIthxQGn4YA4xAvy1bte1VaLME+sRw8kqT8D7w4iqFOlCifufsRHFuro6d9ZYHNrb2zU4OFgzUZycnIwY7TwbhrUlBFyzVCq5/Tw0NKSBgQH19/ertbVVPT097ju8D5vuYw1MaUmGWUcTzp5Kx/Pwviz8SFU5IgX8eaY2nPNauWalNE/Q1dWlo0ePRn7H9yoRRYxTaliRvTb65sM6V+LWk58SB5AHEAebJrlz505ls1k98sgjGhwcdNeABORyuUjXTL/myOomu76Q82Q3kOLo6yjkANEIK1chX6wr5o2jK6Sls/2IiPsE0KY0s9/9SC/158gvGsD41+HfOOnstWZnZ935jeivYrGo7u5uR2Yw1G3dOe+e7BBJzviFgCBnIKA4yGxNrU2Dx9li3wVyP5lcTEekEVFdXV0kJZf1lU5HDz3v6OjQ2NiYW6/IRLvWbRMQ1jDvkQgd92D92b3LHoDo2zVunSDMAc6zhYUFtbe3u1RFxi8tpfgShUomk2pvb3cEwRJC1iFObpuSzLoqFAqutlBaquFDR3Ed61TBbrBjkuTS/ok0Q15tVJa5sDqJNQuhxyFq9WRTU5PGxsZc9Bi5A7m0UV3rEMBmsmUHtlu3lWs2Io2twt6kw38isXg0DO+MkoOurq7IXrX/to5PmyrOerXZc7akCDJKHw/STyF+1nlbLBYdacXuwLnN50ONYsC6wBrY6wWiXHaRNjU1qaury/1sPb58zk/P88eZSCQ0Pj4eazhLS14tUIkospGtsY8ws8QQ5WG7nKK02YwY06Tn2fQE/3lRTihPlHEmk9GOHTsiRAalgpCHjCLMUUp4l2x6CkLY1p5YA8DWqPAO+Lz9vUVTU5MeffTRiHfOeu18cF1IhiWK/Myz+2cW8V2rAEjVkJbaqiPoEezT09PK5XIqFAratm2burq6IkrPegEtUeSoA7yRKEVaVTMvRBb5rK3jsKmmGLUYK5b82zVIbU65dT83N+eex343k8m4dGSM93KppxBdG9XAQPRThCxQ3DYKW+5d2/QtX3mydrPZrPr7+2O/Ly05esrtWeaMNWDbiwPWLZENaaljpSTn+CFltL+/3xkdllyy79g7EF37Dn2HQF1dnYuEr5QoMs5Kf68FlizzvNVkOxEOmwliZYSFrc1B1tojkzCCiQaUQ7lnJFJZjmRi6CMDebbm5mbt3r1bqVRKw8PD7vr2MxhOPBPXsI4mG4mPI4oY2RY2skH9WDK5dD4gqfJEFWyqIPexhB494ztaGZf9LsDYZJ/68gCSjK4jSs6+51xDGx0lXc6+W2QAf29sbFRbW5tLI6UuDUInLWUDIVszmYxGRkYiXaxtuiLjs04w5BA17qwhHKj2HEtb78nnmDcyZXhWIkPoMe7FGrMRfgidJVw8E5/jvtaRx/dwYBE1Qu5Y3W7T8+06I/q6e/du9w4hCNSN8k5Ztzbya0kYutVP5aQ8hWwkdBdzaKO1lswxbt4nz8acUVtp17Bd59hR2FnoIcbIusIRYaPM7JO5uTm3DjmbFPvBz9KwssdmAKXTaY2MjKi+vl67d+/W4cOHVVdX544CIqPNZnPZiKm/X7GDbW0w90W+2r2LLrGBAIglHet5x8wfcwJBtw3UqmUAWQSiGFARpFOtJ1FsbGx0Cr0cEK4zMzPq7u6uesg0yqe9vb3s4rfF01J8ATXAi2uVpyUUqdTiUSAc5o2HdGFh6QgAxiRFmzrgRfaJolXutrsqwhLl5Y8ZwQkBtJ5T670kOoUC6urqihiOttYFpWANA2vs4/W2Y8C7lUqlIkZ9OaPWKl28cTYli0hoKrVYo2KJCkLPdk/0HQlEI5nH+vp6jY+PuxoWoqQoKcZgj1SxKSgoTOYfokike25u8ciSzs5Ol7ZjDdHBwUFXv4JStN0A/fWI0egrMgBR8RsLMPbBwUEXAfbrca3BiwPApiDZiAtrrhaiGFfPZg1ODClrXHF9v2GPj1Qq5Q7OLgf7fdKrLHDoWIMDIiAtrpl8Pq/e3l51dnZGxokTR4pmRBBBtil4gHozacm5QQTLz6oA5fYMteM2hSsOlSK7ktz5oLV8FvT09ESiiuxdfxyWgDAnHOEgLRl/lRxIfDfu7/bIIB/Nzc0aHR2NpNn50TcbnWf92ywXDCkcVTZ11o96YHCzzpDtlbJv6OaJkZ3JZFxapk1ho+U9c8G+QQdZ+cLzMC4/MsM1kOXIeT/jwXceca9CoaChoSEX7bfzyru06Xmsc4xjojzIYWv4SkvdrDGCGxsbNTg4GCHMcdFvskW4X3d3t2sYgkwm5Xpubs7JMnsckk3BleRq/m2WidX71ilkU6jRtVaHsbasc5fr8lkbrWSubDqkJRqkSdNV254hCFEk4svampqaUktLS6TBCvKPNH5sDHQhz+vLfTqFEo22zh773lkf7AUimhBF29na129+6qklNzbbiO/SxApdTYqyJfrWPiB1lPllLq2csFF06wyA7JK9lE4vdu5tbm52nWlTqcUu6rZGG/hEMZvNugiljUDbcUiLUVmaRo2Ojrp74Wxk3di+C9JS+ilrGvsEvVPJ/vURiGJAVcSl+qwFGE+Vwt7JZNJ5XNrb2zU5ORnpxuoDwWDPmvNhjUNQzjBtaGhQT0/PMgPZpjeR3oGhjRGE0EokEuru7l6WTgEZsUTRRmNsZJDoHeOE2PnnVSLsrCJnDMVi0dXqkO5QX1/v0uBs9AjP4969eyOdCbmX9b5iyEhLnQRpugOxra+v1+DgoI4ePaqBgQHnQcMrRrTBdhJjzCgUm44hLSlN5tIXwKVSSUePHlVnZ6daWlpcBBgPJOQT4x9ljSFgm1vwOeaKdWY9wpa04jyw6cooKEvs8ChKS9E23+vIWVOVQC2OL/S5drmIojUmbc0Qe92m36Dw/OgRRiJ7hPn1wXEpeOSZQ0nuDMxagGe9ElG0wFjCs856Zc3Mz8+7SAmf59BwjHH+Yw45ioYICL+rr693Di289XjzOeKAqBxrmfPh7H24F+eg2d9zXbz6/vf4j/VX7u91dXUaGRlx80G9bKX/WGN8j0YS/nc57oBxlrtOOp3W2NhY7PMzrnw+v+z3EB06F9v/8OQjA0jT8sfX0NCg4eFhdy37fm3nU8gYRNGPenAf9gjrqpIDFAdcOp1278kSV2SbPR/ON74xMJEr7GvkC/LSEm2bemibYPAslrzwXHackCP0gY2WpdPpiFPF6gtJkUg88tXKgFKp5GpnydQg1ZDDxK1sY85sxoBNBcXohvgjy3AqIhekpWOsAOPgemS1WMcZ82sdX7wbIoO2u7CNcNfX1zvCje5AHtgMJqtr+BxRps7OTuecYP3xvpHVMzMzLoJG6YaNspOOy+8KhYJLU2Q9s9ZZF5B57BHkPv+xjlgTXJt3wLPZNct6gcxbW4nP8y78rCJJrnkhc2Cj5onEYkdx9Avyg4ZkRBTR58ylddRBFKkFtGcWQgzZ0zSgotO53du+bmfNNDU1aXBw0DmKreOa7zI2dBad9n29yVph7BDFZHKxASPzzOfQI7WgeivLgBMe600U2fjVIoq2eYXf0tgHAqpS5NN6yKo9DwTKplFIS2ckpVIpl5LFpmcDQm4SiYQT6ozRpoP6EUWUC3OTSqWcYWONEwS7rXtAMfN8pDhJS4p4ZGRE2WzWHeaKYUPuPUIZUmFTFCxpkqLda6Wluobx8XFHnGyaUGdnp0v9HBwcdIqht7fXKXbrhbXpMMy7TZez0S/7PhmDLW4nskjklxRX62G2DgGbFsNazWaz7lwvBDf/QZKpB+FzKPdUKqWjR4+qu7tb+/fvd9fFILOpWnZdZrNZjY2NLSNogM5vNoUJZDIZbd++3dUt2fXCHNsUJSI1KHg/jTQuSjE/P+9qyvBCx0WB8vm8stmscxLYaMJKap/91KRK4NkmJyf1i1/8wv2eyA3kYP/+/REFjmFz5MiRZb/nfe/bt8/tHdqPFwqLXe6YIxpyoLh/+ctfqlAoaN++fZExFovFiFHJ7zHQ4hQ5Yykn67iuvZcPCG6hUNDAwIAGBwfLftZed2xszBk1jMWOEWcAUVQitPa+0uJ7oL19pWf0x8X3yz1bobB0rt78/LzGx8cj9+e6R44ccb/bt2+fFhYW9Oijjzq5ZIki3ZaROTaigawF5aKd1ti2Rh/A6SDJpSaT/kykgGtgrCK7cCpagkctant7u6Sl6AnkB0Jr6+jYX1wHp9bIyIi2bdumfD7vDHFkJ5EVn8hZ52ZTU5OGhoacY4Hn5T58tqmpKVKSMjY25o4SsXsRByYp/6RCcl3mmwgc98GJYaNKfX19kbFjvDMvEDhLmmwqKnqDqBeH2EO8pEVZjFMEeYdTgJrHhYUF9fb2unfDZ2wEDXLe2trq0kkhfjZ1sampSePj42pra3O2BOteispyIsdEJX3Hq40qNzQ0uHRLa+dwbBFjTafTzkE3NzcXKSlKpVIuos67hyz7tpYll+hM3xFpo2g0+UF/JRKLpUh9fX169NFH3Xdx+jJ21ghzmU6nnW3D/u7v79fQ0JCL0o6NjSmZTOqUU07RAw884DIFUqmU66xu05B9AszvduzYofvuu89lctm1Z2W8TeXlXGGfKOLQotN3Q0ODxsfHnQOM/c+6xlFa7aQCKRDFgBpgUyDWA4lEwh22Wg421QshVIkEkhpQCXjSSD2pdaw29dTWltl0E5uiYAlVXV2dK8jHyIBElUs9RcFgmFGDZq9hj5Kwz24VmVVsENt8Pq8zzjhDQ0NDkZonvPj2PSM0IX62rsMaMtJSo44jR464tCaME9K4Ghoa1NzcrNbWVmdM7ty50+X9ozxRHn50zypMmxJmI2IoyZ07d2rfvn2OKKGss9mspqennYKxhgVKDmA0EK3wyZhNRauvr3cHJTP3pC1h5LH+7PELRJzioqO8g3LHE3Bfm8Jkx86RMTblyK5rMD8/r9bWVmfwMVYcIlwvjijaNShFoxpgampK27Zt09jY2DKiuBLEkdVyIKWILAPSyP2GH75ziUgGaxgwx+wtHBmQY9vV2MoqIji8JyvzMID9mkv2C3vIh41ExKHa37k342Wf1wJrHBNltkRpeno6Uptjm2/ws91H1snlP4ON8ElL7eclVXQych8MLvtZyJif5TE1NaWhoSGNj487EmLrn3lm31nBO7URlThdyfd4VuSPJYc4L1pbW93fMXrRQX6Uj4YWtjZeWoq0QBTtOmUerA5CR1jiyFrv7e11ZInn4N3YiB3PbbMRJDknXSKRcOSbuSKbYmFh8Yy6Bx54wNWNT05OukiOTZ23RNfOrxQtt7ApsDhicRzyXuMyNtrb23Xw4MFIlNg+I44cjjJB/xw9elRTU1OuKQ73gJxKi/Ke85QluXeysLCg7u5uPfLIIxG7wq5RW4KC/WGfBSdlS0uLjh49qrq6Olezyfrl2sh8HDqJxFINIj/b5m62tIBoNOvRdu+GIFNe5BNF6kXJhGFs6EN7xJa1AdFLcXurtbVVjz32mDo7O139L3NC1hLvkL1ko/iWPLHGrUOL71FrifOqra1NmUzG2VX5fF67du2KRLVttB+whiQ52w69aZ0+rB37fUi4n1HG38gW6enpcc5wq//t3hgZGYkchVcJgSgGVIUVDOuF7du3V/w7XrJkcvHQbGt8rOWathi8FmPVN5ARlihem7bJpmbsfN7WHExOTrqmNNY7ZtMdbCpjPp/Xzp07I0SuUChEur4RHbTK0qZKWQ8bBi4ClHv7BIXnwLuGAOUekEJARBECx3VHRkZc2qs1tJhHm1qKNxBFZg0rSxIg5tQ34vWV5IrZEYrk//NdDnL2iSLC1DoQUqmUqwkhKoHBzzjse8b4wuvIOhkfH9euXbuUz+fV0tKiiYkJ59VsaWnR4cOH3Xvy17hdSz4wUIiSxe0PjLFKYC2RPo3iJELKdXySFndPIjK8D5vCJmnNRLGWiGIymdTIyIhSqZR6e3sjXZBt1B+i5zsHSCfyI4rsT5wW7AdSk1i3XMMfO/OK88AafD6sY8EHhKkcWWI9VJLZrH+eoVb5bo/vQQZaBx1ywGYH2I7FvsFqjTr/Gf3zLG1aZDWnIE4q9iPg/dh3QEplOp12hrptXMI6j3PkAFvLVo4ocr1EYvHYjoGBgWVryhJFxsh+sY4KomozM4vnpM7Nzbk0TTJWRkZGIhG7YrGorq4uFwHymxHZdW0djjT/SKfT6uvr0+DgoJO5RGNsyYVPFJFRyWTS6RLmxDoSWAs4OOvr6zU5Oelqsjo7O52BOzs7q23btrkoiV1fjNXW1KN74yKUcWsHEs66IxrKejhw4IBmZmbU2dmp2dlZ12GZo09sBodNC8V4Z35t+j715IwbG4G9ZokqsAQackY2DTLCZuzwfQjcI4884p6P1E0+x5qyady2iy1jwqGCruFID5ylrB+7xqyzCaJonc82Y4jnZu/6yGazOnLkiEv9t84e2wm+vr7eNb3h/taWsM54nyjyHqVFO3N4eNiNpbOz0+0Jein4TjT73qwupZuxlXWsDXtvu09TqVRsZ3+b2cZxafY9WVuQNNlyjR+XXbumTwWc0Ghvb6/qwV1vsAkbGhpcqmI1olgtSphKpbRr1y6Xq16LsWojij4Bw6uKEEPgWGPBCgg8i9TL2c1v72Ovm06nI6mnXI8NPzMzo/b29mXGNwrJ1jvMz8+ro6NDg4ODThnxn1W2ktyz2BRNvLt4pawxwPxS+8gYpqamXIG9jQhipNgaET5jlaI1KixB429+hz+ELalZpEtxUDS1oTYlR1KktTXo6OhwxglzadN4UDTMLwaJVcZEn+vrFw9MhtTHpbvEEa9yhqe01ISmElGshVxBUHfs2BFRNrZ1PIqv2nVIwQVEPABrezVEsZJxZwE5L+eBtteKI7+VPm/TjjHAbZpd3DiJkPNdSw4qPVOlv1ljcaXflaJyqdq1/OuyP20U2f7fnwt7fWSHn3lRy/j9SHUlcF0MLv/avsFt3yEExnZZjFv/yGBSVUulUtmjbGzqfCqVcq3zWQuMJ51OR+Qssg0HJ59B1uBss5230RukwwGIIiQYZwHPgjxifuKyeHiOo0ePOtliI4M2jdA60ZhDfo/cQ4Zxb2q8k8nFhiujo6NKpVIaGRnR4OCgHnvsMUcSkedxqb/ILfRsc3Ozu24tThEaktlsDZsmir6Qlo7hyeVykUiYJa08L+uIeSbF2Tbaw8HY3Nzsoo/MNWvT6kz+TbQSfT4wMBAp/7ARNZrXWB2GnkL+FwoF9fb2uvFOTk66HgTWOYbjwTrRgV8bb9cX88e4uCf34zlsF3jese/g6OvrUzq92KAP+2phYbHXAusAgsWaZU360XXWoiV2XIM9zjX4fWtrayRFGfvIOjCsDWdtoaampkgdLzLHOlgJDlTTnciP4eFhSYtyg0ixtRGPHj3qGuTUgkAUA7Yk8H5YpVnroq4EUkX8mq1ysAalTXdBwDQ1Nbl0R5RkXPqRX/flE0ULBFc6nXYpgVaoWvJHPZzvmS9HFMmBpxkCQtWOFy9eW1ubI6npdFq9vb1OiSDsfCOzo6MjQhSJSmHoMJekAKEkFhYW3NlNKAcrFC1BxujGE+lHzHK5nIt2Qhg5X5PnSSaTzgMvLaXEWaJoPcAQQ+pC+BkvvyWKrINCoaDJyUl1d3dLWkozkha7SPLczE+cIcx1H3roIT388MM6cOCAHnvsMQ0MDERSX63ismCd1mJg24gi17VRckuifGKKoe0TRY4O4Brz8/MramDjoxZSw33KkQ3GG3c9SwDjvusTRXutcuOzBriNLKyWJFZ7l7USP0umVgJL8PxnjyPJPiHj/5WIor2mf72VgD1myazVJb4DgO/Mzs666AjP7DsR6MQ9Ozvr5AeHgfuwDhv0gN/QjCwPm00TRxRtFJi0ZqK83IsIFd1tkUk0LCEi6Kee+mnBvo5MJpMuamObu1jiRydnX3Zb8snnrZzHOUhEjOuPjo4qm81q586d6u/vV0NDg6uNbmhoiD2OhLorIoDMNbV21dDc3BxJCbTPZiP29hxfv3kQsGnu6A6uOTc3p5GREdcVGOIF+aCzq7QUZbNdQy0Rg1iRbggJkZayRiCKzDsZAqybXC4X2Zd0oub9kDKK7uXdWv1jHRl+FN5GEW2aqy1xodYbJzLPaM+XttlAdm4aGhoi3+MzOBR4f9g4ONvR84xHWqoRhZjyPDhl2DvIcur8uS5zwnwVCgU9+uijjgRKS6n51B1jB9kOu4zFdhmPg802YJ9ms1nnwMDWozFkpWwlH4EoBmxJsMEymYz6+/sjqZVrARsfD2itsOkZXGNhYUG7du1SLpdzTS34uw3pYyRYcuh7tPkdisTWHdgaFWsgDA0N6dFHH9UjjzzijAf/ngh9q8y7urqcEJSWjARrKFnPoE8KrEHhk11b/2nHwAHI0lI6BwIUAUwzA5Ql9Ri8N8aKIG5tbVV7e3tsZNMe1sw4mUs+09vb656Zuhff22yjDHjhMSBIE7IeUp6ZNCLeoU2ztcapVRTlIoqQ9t27d2vnzp3q7u5WS0uL+vr63P3K7Q/fA+3DJ5jWSCD9iTm0hrKtT+R7RHhtSjX1KFwPA3kjMxTYIyslFdJSdNVHHCmEXPhz69+XvU7qVK0OqtWMv9wY4rBaeWqjgtISmYtbvz5RtJ+v5T7+92qdExs1tNkP9t72PTBOnIGk7vlnolpMTU25DqREFDHWffhEkU7AGNmlUsnVl9nx2SiDTc0nAmP1me8wtE6bfD7vGopBVIrFosbHx3XkyBHX6AVdYMftgzRQ5khShCgi7/x58ImidQjaiGtvb68mJyedk3F2dlY7duxwc2PXgS+HeJfoX0gLTjDISDUkEgnnVMVgt01GJLlD3m39M7Bj4n1ZRzHPMDY2plQqFTk+BpkMeSENGb2Jc0CKpp7ymaNHjzpyALgOTmHsH5t+DPmbmZnR6OioW8+sGVKi0WXWJmH98J+vb4GvLwHviQYsvDMcqESpuZ/v/PQzxPyoI+sRR28ymYwQPt9xj7y2z2OPB6FzKu+a/YzTluvxPtPptGtyNDo6Gln/RBBTqZQ7+zjORrWlAuVAw8DOzk4NDw+rsbHR2WM4ZGrdAxaBKAZsSbAZ0ul02WjLatHQ0OBSNGqFTXdEAS4sLLZa7u3tdYKBa9pOUhgecUaThVUkcYaJtDgv7e3tam5udmlEu3fvVldXV2zeuvUaYQQ0NjZq165dy57NjiNO0dtr2jQYCzsH1ptItJDPWK8+yhfBKckJUCsUEbooKiKifkQxlUo5bzrvi8iltBTNQEmifMulJKHEMGqsF5736gtvUkup18jn864mFNJOSq7t3FYuomgj2XV1i+c/NjY2RohiuYii9e5bQFTtczc3N0caYKyEKOLttdEbG5VcLXlbKfCcxyFu/9nf1ZLSaSOK/rzHfd8a8LXKnHLzdPXVV+vSSy+tKEtqlZU4Ruj8eu+999Y0NvaCTafl97VEFPft26f6+vqq97Pfvfrqq/WiF73I/b4WWOeNTUG1zp84GQux9Ped/SzELpfLOcJkHXM+MG7tfTnTliOWcDAB21kaA53xZ7PZSJ2qJRLsSwjK/Py8I4oQzIGBAddcKpvNau/evW7c7GErM3kPEAI/ewC5D1Ek+mPnNJFYOsfSr1NDjhGZkZacd6T822waEEcUeXaen2eBINVqJLe3t7vInB2rJbi2jMKuf0uk+NmmCs/Pz7vzCKkP5z3zH2uX9x0X9WXeiVY1Njaqt7fXRVBZs0SQITs4XG12EGvTdig/cuSIOjo6JMk1IMQRa7to0lzJ1teSbWVhazK5ZzabdUd9cD30McQLBwxrxa9X9COrfrYTjhxbL4uNYdefXT9EbpEHZFTZLBk+WywW3XmFfkSReZ6cnNT27duVTqc1OTnpdDMRdFKtOXvSHt3E2rJ1i3EgdZsGSVYuzc7OamJiQm1tbcuc69UQiGLAloRVVmC9jEy/81gtYNOTGgNQzHT2ilNaKIa4dL24z2FYxqWmItCy2axGRkaUy+XU3t7uiIm0RKj89C57f7xMNg2mXEQxjrzYmgTgG4pEsrg+UVg86DZKg+LAixrXedInivzON5hRFDaCZT3t1lBg3jiMNg44ASypY7y+wYNytykiiUQiQhStl9HWCsVFs6xHPA7cs1Lqabk0axSV7/3mOhiy9jqgHFHk3wsLC8s6XtoI5WqBUq4EjJVq8gJjciWpl/7ny5Ej/+eV1mWWIxw33HCDbr75ZknlSe1KInariSrafcCz3nzzze7cWf8e/nh27typgwcP6qyzzqo6vmrvuhLs2rOpZQDZ4ZMP9juRDD7r1xiRio5BbomEDxsxI1LT3d3t0hfZTzYtsrGxcVk3auajtbXVEUW7BonCAJpn2WYZnZ2d6urq0o4dO9TY2OgaftjxFwqL58FxX97h4OCgWltbVSqVXJt+xuVHFO3agjxY+eLPj5Wrra2tmpqacg1oyFLwoyzViKI1tJubm1fkrIHASEuOa1snhuGOI5g5SiaT6uvri6x7ulQyx6Ojo+48RavnaOpCaYbtjsvc2QwhSMnAwIDa2tq0Z88eVxduHc/z8/Nqa2tTR0eHI0akS1OOwzwlk4tpy+3t7c7OYN4gTqRK4mxCx7e2trr30dTU5CKlgLVq9zYkGv3X1tamoaEhp/vQXzguIF92HVkdZzMIJEU+ayN96HxsA/udZDLpuqPbvzF+a3dwhBnz7UcUeX/MHdE+SxT5Ti6XcwSbY0SkJcc5Ddh8MBYyHKTFMiAaP9EvgnOrA1EMeFyAyElclGytYKOsJqIoKZJ2IC11SkQY+MCT6Cs534AipdT38vn3YuyTk5OxZ+BYr6NPPuLSufzIgB9R9Mdp0xco+McDZ40UUjsQ1LTG9uv6GCteT67pOwosUazkFWb81ji3ka3e3t6IwWVTfOJga24gvpBejEme0XppSSlhjm1E06ap2giGb0TRSa/c2KqlnvKZuLWOl7EceeOMSz5bKaLIHpCWUt4sOZbklP1a4Ht+44ABVwtRlCo3Y4mD/7la7rMSWSMtpen5yOVyamtrq0pwV+JUW8lniRJbL7wfMfSv7Xv2k8mktm3btmxO/OeJe8aVjNUa1LwDf+0gZ32iaLuY2s9Ji2sQIxIZa0lk3BjtXJHWOD097WTV9PS0mpqaInPiE0WbHse+JSsA+UC9M2hpaXHpjZZM+uNEtvJ73xAnampLIYgS2lRzS6Ts922dHe/UykQrCyFf6Li2tjZXRuA7n8oRRUmuA6m0VPe/kvXT1NTk5pKxcWSQPUoC3c+75f3atYbzqlgsanp62pFDolyAbCF75AmNlUhHtE1T0O/UtnHAOtfn3UD8yULB1uD3ligydxCWk046SWeccYYkOWeGtVGsnQE5j5N5zA9Oae5FxAsCRgYZRBFHG/uD+fYzOXi36FP2Ps2GrBPEJ4q+XiFKaiOKklwnVXvfsbEx19gH8m5tgvn5eU1MTLioMJHdfD6vubk51xwHEswc2/OdbU1znKOF92DrnmngRB1toVBQW1tbxf4Y5RCIYsCWBQJvvYEHayVGq2+IWwMGI7pcyheRQj9dwieDNuUiLlKG4TIzM6OJiQlX1O/DRrb4OW7c/IwwtjUHVkj5gskSRfLuk8lk5DgFaanZA89Nyq8VpjQtwPiwnjh/zvHY+aTOjzykUinXJhyDzBqD1tPa1NSkbDZb0ZiHKKJ8MQYsecSDblNDKBjnbDk7PpQI17KGRhzK/d4SxXKfsdFU//eVUm79z1YiirY2C6Lop+jQGXAtKJcu6KNaiqkUTxRrNST5XDlisBb8wz/8g84++2w1Njaqs7NTv/mbv+kiN37q6bOe9Sy94Q1v0Nve9jZ1dHSor69P733veyPX+8///E9dcMEFamho0JlnnqlvfvObSiQSuvXWWyPPYvEf//Efuvjii9XS0qLe3l694hWv0ODgYMQYxuD7zne+o1e+8pUaGxtzxvi73/1uSdLevXt1/fXX68orr1Q2m9Xv/d7vaf/+/Uqn0y71tFAo6FWvepX27t2rxsZGnXbaabrhhhvKkuGVkkXWC9+zRpJdT/a5bGdaP5qXTCY1MTERMTIxlOOMODsO20EauTA7O6vZ2Vnt3Lkzsl9om4/TA9mI8UsdHt04peVEysoie11/H1qHkKRlkYuhoSEVi0V1dnZGSCdHH9n0Y+SN/X42m3Vzze/tWK3shCjmcrnIcRpkmvhEsZzctqULkNyVZDRA+BYWFlz9KO/DpiQzZt69TfkFnGuXzWa1fft2t2ZIPQWp1GINWn19feSYJ/QLJNOuUWmptnpoaEhdXV0ueoldYh08HNpOJBs9BbnCzrAZMTadeceOHZIWyS8dT2m6B8HjWSzssR++w4A1hZOvs7Mz8p452sJmJpXbaxA7nA6kS9vuutbB0tHRsUymQBT9iCLN53g+orLMF2PDKYCsYOxkB1HXSAZZIrGYcs1YIeK2MZFtoOPDzqG16dra2lyHYtYzcmRFcrTmTwYEPI6wffv2soImDj5hsj9Xi3JAiPwol/89hEe56A/CYHh4WG1tbRUJAMKLugKEf9w4Edo8j40o+kXidiykGHEvnyhS4A1R5HoYTLbVuI22IUj9e6N8fWPITytLp9MaHx93HmWK8e3z+imylYgiCpAW2NJSyi6KD6Vux5zJZNTd3e3GYufPdr+kXiSOlFcjL3GGsI9yEUU8zrUYT77R7r+b1tZWd14WRNFPeU2n0zWf21QOtUQUq9Xu8Sw+Uaw2j3HRspXIkFpw6NAhXX755XrVq16lBx54QLfffrte9KIXVYysfeYzn1Fzc7N+9KMf6YMf/KCuv/56ffOb35S0aEBceumlampq0o9+9CN96lOf0h//8R9XfK7R0VH9xm/8hs455xzddddduu2223TkyBG95CUviThC2DNPfepT9dGPflTZbFaPPPKIDh06pGuvvdZd7//8n/+jJz3pSbrnnnt03XXXLXuWYrGo7du36wtf+ILuv/9+vfOd79Qf/dEf6ctf/nKsY2sliHPG+bVIfvYCZNH+ZyMgqVTKEUV+tun1cbAklbp75CNdU+nkab/T2tqqoaEhF2nAoUTnQhxWNuLoZx90d3dH9h2OUn8+E4mlWjgrIxcWFjQxMaFcLueMWJ6V1Na4ebZ7gzRW+xn/DEWbZWEjkPPz82pvb9fo6Ogy2R+Xrh8370RkVtrEo62tzdV4kh3DOsGx6WeK8Dz+3OIsxcFQLBaXkVeIp23eQhfMVCrl6s/8dVwqlSJ1eBBN21yMvUta6szMjEtJ7enpcVFbIuTlHOnMaS6Xc506bXSdZmVxRBHdDom147Kku6OjI6IzmSN0sSWKcaUnEMX5+XlXS2kjsdYJTg1m3DUIKNjO5yCVWmw+g9OFPWjP25SkkZERdXV1RaLmxeLiUTUTExOOrFOeVFe32J2YqC8EvlJGjl2XvFtp0UHD9/wOtishiivLhwkIeJxgpQarH92ynsxyhAogQPFqWu8tuenSUjc8rkvUzn62vr5eO3bscC3D4+Cn8djUED83nXQWarpsyocfIbPPYz1c3IN0LIDS4kDeUqnkuppJSynARKcw3jlr0Pdq24iiVfj83nqnUaaQJKt0bGoIpLgSUUTBk6ZqBTE/2/QOoryMaWJiwh2RwXdYL9zTnhFl4TebiRtbNTJZjiiiMFabDuobtTZiy9lg641aiKJfP8v88P9Dhw45Jc7/bfqv9aZb2PTick6auFS4dDqt7du31/R8hw4d0sLCgl70ohdp9+7dkqSzzz572eesUfqkJz1J73rXuyRJp5xyiv7yL/9S3/rWt3ThhRfqG9/4hh566CHdfvvt6uvrkyS9733v04UXXhi5lsXHPvYxnXPOObr++uvd7z796U+rv79fDz74oJ74xCe632PUtra2KpFIaNu2bcsM92c961l685vf7NbqAw88sGx+rrvuOkmLzpW9e/fqBz/4gb785S/r0ksvjX32WsE9bS2iNfYw4G0kRFrqEIjxar/D8/GeyTSoJbURmcPnkI9+qibIZrMaGxuLHOSODMeIxUGHvPfXn591YiMkzIedA6I67IHBwUFJcvVmdg/W19eroaFBw8PDEePTN2ptCif3nZubc3rY6kSIx9GjR9XY2Kjx8XH19/frwIEDbr1Vg08eaEayUllHV+3x8XE3Ns43ZK6t469SpMaSw0wmo8nJSTU1NS2r4/aPWOHd831LXiwGBwfdO4JokrbL2qFRDtkk1G42NzfrkUcecXKyWtMUaanu1K+vh5DFEUWypUiRlZZHFP3nqqtbPH6EWkmfKMZlHmEjQJ6xRVi71fapdYiwr2yGlLSUudTd3a2RkREnA1grOHRwDPm6C6f6+Pi4stmshoaG1NraqoWFBbW3t7v3PTMz41KGy70T65TlzGYcEieddJJ+9rOfOX08Ojqq6elp59itBYEoBmx5VEqr2yxYBUx+u/Uk2jPyyqFYLEZqF3zBgTDnulb48lkK3TlAPg5cBwWGwEE5WYKJoQBRsoXj/niAJbhEB23aDfOF4CT1giYFAwMDkuSKs/HMQih7enqcIrTGk/Wox/0eZDIZ9fX1RRoW2VQQlP3MzIxLJ8LTVm4+EdRStEsrhpU9Y6u1tdXdO84TiCJLpVKuwQKOB99Y9D3oq8GOHTtijdBajVsftaRoUlOy3mBNVkK5dDTGDZmxKb943jGWy5FA0o3KGax+ne5K8eQnP1nPec5zdPbZZ+u5z32uLrroIr34xS92nWiBTxQt+vr63B578MEH1d/f70iiJD31qU9ddi2Ln/70p/rOd74TK1/27du3rAlNNfL+lKc8pWL958LCgj71qU/ppptu0sGDBzU9Pa25uTn9yq/8yrLPrjSiyD0tUfRh11SpVHLp2Byq7kfC2LeAzIhaCIw1wHEa5vN59fb2xn6efTQ5OenGPzk56cgpTipqsG0tdjnwbDyLrU3ku8inhYXFcww7OjoimTDIE3sWn81OyOVyywgQ84hhH+cE43lIw0NOl0qLXTRHR0fdOOIyMKTlUWT2+WoaaVHbNjY25pypNBrByQCZsA1tuK91Ulm9TKMRMlJAKpWKEHIioaQhs2aILlqnciKRiJz7WSgsnqM4NDTk9K99dzSjYT5t1k8tDkTSJaemppTJZCJR0DiiSeTMzoO0PDvFlyXMKXKZBjc8h99vAv1KfS5riXMOK8kCW0fJXE1OTmp2dlaTk5ORek6/ayzzwe+LxaKGhoZi6yDZW9lsVsPDw2ptbXX3xEbD8Uz2ANeIg53P+vr6yPnNBCmwgWZnZ92eqhUh9TRgywJjqJxC2EzYMfhEUVpKQykHPEyWNMQZWD4htUKcz3LvSsSG60AU7c++d5y0KSvoUezlIoo8C+mLeDoBBrMt6i4Wi5HDZCGKfv1NY2OjU7z23nZ8Vsjb3zM+DDnGiDEnLXXAo/U881kpdTObzTrDGYKMR47x23swb75RyZjwvmcyGTcPcet8PYhipUjpaoynWoyIjo6Oso6MtaCWiGI1osh79tNpK5EZ/zMr/VutSKVS+sY3vqGvfe1rOvPMM/WXf/mXOu2007Rv377I5+xz+O/Qj5pVg/+8k5OTesELXqB777038t/999+vZz7zmbHf9f9v4ad+W5RKJX3xi1/U2972Nr3yla/U1772Nd1777165StfGTkeYS1zi9EWl24sLREL6s/y+bySyaTa29tdtM+PBFhvfCKRqLnW165N23ykUsflhoYGTUxMOBk+Pj4eIYo2q8Oui3KwEUWbwmhTSnEQDg8PS1Kke2Vra6szfm0jsPHxcTcHdPQEGNSk79msFwsb8SkUCi6NcXBw0DXisHMZN+f++q9FZpQDepEmbET7bPYKWSXoWhtVtTrHZujwzuy1fHDmH2mKklyaJNlHrO18Pu8+w7ggx/acylQqFTmD0a6VXC7n5rOWiKK0uBfy+XykjEKKTwnGMcBY4lJPfUcg6wAbBSdtJaJo7ROcKvzeplP7z+dHynnXw8PDGhsbUz6fVzabde90enra6XZklO0GPTc35wge5JNnrq+vd7Jlx44damhocE4RsqmwibhPpYiifR6uDZBNpVLJnYGKg7RWBKIYsGWB0DjWRBGPpCV4PlGsZnSTLtLS0uJy2uMUmF/76EcUJWlsbKxs2imfZbzWk0lNyeTkpKRoSh7eOp8oxglUqwilRVJoi7z5Hd5YvG22xocIHJ3FmDtb4+FHkn1CCOy4LRDcpPfYz9gzwPzUFR+NjY1qa2tzkSKUFYcao3xtNzEUGW3GK42XOs44L2e1Dq8852pQaxqXj0qdBoFvKK4X/DTAOJTrLlyuRrGalxnUEnldj8yHRCKhpz/96XrPe96je+65R/X19frKV76y7HPsbwv/59NOO00HDx7UkSNH3O9+/OMfVxzzueeeq/vuu0979uzRySefrJNPPlknnXSSnvCEJ5RN2SeaXu55yq3RYrGoH/7wh/q1X/s1vfa1r9Wv/Mqv6OSTT9ZDDz207LuVyGgl2AhTHJHCYYNBzTlyXV1djsjZZ6MTsb1+tRRxKWrcS0vnJLa1tZUlC8iUbDbrshRIqbY60abDVYMlMH6Ug32D7M3n85FsCr6PEzOZXOzqyDEK5eYgnU4rm81GSK0P3g3GcTqddsd3cOwA6dg8c9z9IAr259XKIptpUyqVtHPnzsi7pjEQuimuJEKKnnfInCNvyjnyiABZ0obeT6VSrkwFcuEf28V7zeVybn/ad+zXs2azWXV1dTl7p1pkWlpymkJ4/Wiuhc1WsjWK2CiW+Nn5Zw6wUbAJLemLGytzY+0oENcMx98Lzc3Nro6YLC6b1eRnldl3zDVs/aO9PhksiUTCdcRtampyWU44XYjuMre1pp5SRiEt2ouU84yNjamtrc3ZiLUiEMWALQsrUI4lUcRQsBFFv+lMNaMbImEFddxmtcLECk9rIOfz+YrRGltHxXeJzuVyOY2NjUlaEtB4Ay0RY6yVPIsILo7+8Due2p9R3niJEbpEzCBfKHjr/QOkTPnjQaDaMx19+OmpNspQLbWZA3AB6S9EQ1Op1LKmBJBPv6udJHdAs71+OaJYCymTVkdQ6uvrKzoc4u5BGstqCOZ6oNaIYtz4fKII4n6OQyXCw/XXih/96Ee6/vrrddddd+nAgQO65ZZbdPToUdee3sI3iOPGcOGFF+qkk07SVVddpZ/97Ge688479Y53vMM9Txxe97rXaXh4WJdffrl+/OMf66GHHtJtt92ma665pqwRuHfvXk1OTurb3/62BgcHNTU15f5m580fX6FQ0Kmnnqq77rpLX//61/Xggw/quuuuc2TWjyiuR+ppre+xoaHBdd2s5JxAvlYzrK1xLy3JpErp38jkjo4O5fP5SCqszQbBMKxlX8alwPFvW6NIClwcifXJHt0Uy90/kUhEzmXkfhY2WpTP511aayqVUm9vr44cORKZp1ojiqvNnLDj4h3T3IY5YR6pvevo6HD3tvp0enraHXbP+vNTQX3YMzp5Z1anQ1SGhoaW1ZsRGZYWiSIknd/bekkLq9NrJdc0tbM2Qxzs/vPfEWOiy6cdjy2BsUSxXEQRcP24coA4omgdKHx/fHzcnScJYaX2EPLFHvQJbE9PT8Qx5UcsaUJlnxN7AaLIZ1KplOuCHQc/9TSRWDq2ZmxszNXakoVAN/haEYhiwJYFAqLWNIiNAh62Sqmn1Ygi3c4s4oQqCon7+Gmq1PFVMkosUWTMEEWrjEllIqJoFRstvOPSgyxoGuJHFG3KJIISQUudJuMhRRUhzDP4ghxiGZcKWywW9ctf/rKsl8w2K7GCG0Fci/fUjgPCxP3b29sj0ZZKa9bWsxDNQOH4qKUeMG6uakE6vbIupMzz8UAUrafXIi71EKNlramn1b5bC7LZrL73ve/pkksu0amnnqp3vOMd+shHPqKLL7449l7VSE8qldKtt96qyclJnX/++frd3/1d1/XUNxK5zvbt23XnnXeqUCjooosu0tlnn603velNrsFC3Die/vSn6zWveY3+1//6X+ru7tYHP/jB2GvzeWnJkHvNa16jF73oRbr88st1wQUXaGhoSK997WvdZ/3nW80c+1HFSnOG4WnT5m0NYxyoDay0D5E3Vi5w7XLXhcil04vt/engiKEMeWBf1pKmbp/fRkA4NxBSbcsH4p7XGqY0HLFnzPkg7VBayvbwn5V7z8zMOON+586dqqurU1tbm2usI1WOKNpMmPUgig0NDa68xKZQ2gycurq6SC2wjT6NjIxEMkt4zkp2DQ5F+46tDSItvksOUrewqb4W7B0csXHZKkSJa7G5EomES9G2TXb8Perrsbj0ePSwdfjiuLDN3/w+BeWynjhCjKOtLKqlnkpyOhqHan9/v6v9JjJXKBTc/iyVSu7fzIG1n3yiiC3HeElBn5qacmuMyDrvs5yd4qeeSnJ1iERF8/m8cyjY2t9aEJrZBGxZ2KjPVoso+oJmx44dVdMXfSXqCyYUh00j8lNPOdy10nwwPiKFRGURMtlsVuPj464WEAFko25cvxLhYbz5fN55VC1sDRXGEMqOe/s1FCj0uJQc1kPceEitHR8fd+keVhFRr2C9j42NjRobG1sxUcSgsRFFXzFWI4q8H94xSsWi1iZOlbzS6wn2wfz8/JqPuVjrGCohLgOhXATRkpZqc13t7+sRUTzjjDN02223lf37zTffHPn529/+9rI6y//7f/9vxDA+/fTTdccdd7if77zzTknSySefLEnas2fPsgyJU045Rbfccou7Jo6fuDlg/X7yk5/UJz/5ycjf9u/fL0nOu10qlbRnz56IAZxKpXTTTTfppptuinj/3//+9zvn0o033rjidCl/jMiIakTRGnc2+oGx7u9rdBSZBuVgiQtobGx0cikOdr6bmpo0MDDgaj6pg+KMv9U0UkqlUi4Fnxb9ELW2trayTWBs9BIZTnOUcoBISIuGbZzjFGeerb/kmXK5nA4ePOiesxpRjCvhWA0gwlNTU5HsHEvELAkG6BzbjASw9qs5fNELPlFkzY2MjLijFPzvVoqE07E0jtij02shiqlUSj09Pe7zzE9cqqe9HoTbRy6X06OPPhq5vrS0J200tlJEMZ1Oa2JiwqV4WpIO8fTXqm+PTU5OOuIPwWKvJhIJNTQ0aGxszDloiIbatFI7Nj+Sb23Kuro6NTU1uTNVsRFmZmaUzWZrWr98BjlNF3qCGLOzs86hQERxenq6pqZzIaIYsGWBcj/WqacYCtYb5zc/WU3E0xdMtHTGaLBpiyhRUjarea5RlBBFqzghipAZSFtctKZcPQmKi7obK6jjlIJt8iItRSy7u7vd8/ipp3E1BFJ8cxbSsyYmJmLnhDFYI9ASxZV4nDEey9UF+cX2Png/GJfS4rv2FWetkbvNIooYHscyohjnJa8Gv/7Q92zzGf935a5ViWisR43iShBHnP0xfOUrX9E3vvEN7d+/X9/85jd1zTXX6OlPf7pOOumkyHcqRcviUsFBtUga8K8fJ9fLpQXXElmvBDv+lRBFjvBhP8c5KWztdaUxxmWCUP9UjQBD1jkAfmxsTA8//LAGBweVSqXU39+/IqLIHPgyn98XCotn2ZXb69YR1tLS4iJuldDS0qKWlhanM+I6nhYKBc3OzkbOGrRj6+vr0+HDh130Jk7v2ggU912LY4ux+GcSMn7kOAfRWwfp/Py8hoeHy57VV02OWl1RLBZ18OBBPfzwwzp06JDrItrQ0BAbUbM184zTPlNdXV3sellJ6ik64QlPeIJzfMZ914922+Z1Fi0tLZFGLOi2Sr0S4rKebFSUiJq9Jk4jC5t6WiqVNDU1taw0o6enx6Vkcx2caGSaEcVk3HFEEVhHA/eirGh6ejpCkGsF95ufn3dkeWZmJuLoa2lpccd61IJAFAO2LBBCW4Eo+sXxK4lAlYNv9Nqzf6TFM5xImcDz29jYWLX5Cl5Iqyz8VBxb6M0RDZWu58OmpVJ3CPxUD7+o36YdcWg017REsZxiiEuvQlnbjl8+uYcwWyMQ7+dK3ydEkZQRf14qrVnr6eUZGxsblxmLtXY8XW3q6UrBHB7rCH8l+JFBSwTj1rGfBlkJ9vvrET1cD/gRsrhxTUxM6HWve51OP/10XX311Tr//PP1j//4j8uuU4korjX13yd8tl6q0jhWSuQr3d+mwteSeirJGX3MQdw6sY1Jqjnw/HlsbGyM1LXFARk7MzOjjo4OJ1/6+/vV0dGhpqYmZxBXO6LJjsUegQEYPw1syhFFK98gitVADVYymXSGqz8mnyj6hnV9fb1aWlqcgVsuwm0do5VqJ2sBTkaajJBNgiPXdpCVFNFh09PTsUQOHVhtXBj5hUJBnZ2d2r17t3bv3q2dO3eqs7NTnZ2dZfcnJQ3lrkvTJh/sy1qjWJY0s2Z8feqvI0iZT/JsxJmfidTzN3//xsl2GsBAXO29ibBViihynIY/B+l0Wjt37nRRRZttQdSO2mafKFp7z8oTK7sTicVa3unp6UgN40rAfWxUcXR0VC0tLW49dHR0uLMpa7rmikcRELBJQAhVis5s1jh8MrAeRFGKKjo/ouh/Lp/PK5fLVSXOGI9EynyiKMk1tbEGA9+txQhGqCJwLVG09SVStKgfoypu/qiPkcp3qisX/UPwtrW1aXR01P3OjyL4gtt2Ml0JUEB+nYlNKy1n0FrjnvvGHSFQS8dTxrJWQ74W+B7+rYhyqXLViGKtEatKnztW5NE3qP3xXXnllfqv//ovzczM6JFHHtHNN9+8rClCuX1PJGOtkVKfKFaKBpVbY7XKpkpjALVGFDnOB+dIOaIoVXfYxOmNZDJZ9eBr5GehUFBDQ4M6OztjI7y1nOULkFM++UU2IXfKNQqJ08m1rJHOzk73juNKCyAPpEbGzXdHR4fLiIkD62u9bAb2V2trqyYmJiLkIy6F0mYgTU5ORvYafysWi8pms1UjnY2NjRFHhUV/f3/FvbRjx47I8Rx2Purr692RC3HPW6s+8dePTc218MkadgkOIz/aScM9zpD078kzlQO6k2icfXYcFr4MsE4TUnrj1jSNpbBpbM0q0cu4iKK0JHfo6UBqPX/Due87kish7jns31gjvuOAcdaCQBQDtixsRHEzDOFyiBM060UU/Ygiue7+BobkYAhUU8qkbJQjis3Nze68MJtG5Hf+qnR9DKrm5uaIZ9JPgaqVKFrhXe7A9mw2W3buS6WSO/bCr0EDvuCmXmql79NGLuMiitUMWp8oNjU1xRLFWiKKjY2NNRuIa4Hvqd+KiIuAxKUuMv+WfNRSo1jLe91sWGNttSma5Z4NQrBeRJF/l0vTjEstjos6rnUslWBlhM2GiCOKpGlCDCrtjbq6Onc0UtyYyq0tIhWMGxlt0/kYR61yjEhY3Nz60Y64+Sqnk6vtj2w266K7cal/kIxKmUSJREKdnZ1luzaWS1VcLXi/9fX1amhocEct1dfXx5YMMH465tpxWP1QLZNHWjLm41IlK6VeSnJpynzGjiOTyWj79u2x91yJ49FPpyxHFH25DBmyRJHnaW5u1vj4uCS5zrcWtiFfufVGplBcNDmVSsXqVVtawc9xSCQS6u3t1aFDhyQtzb89IoX3Vc7RMj8/77LDmD+73jljNW6P+vCdNshWZAbH1/jvynfoV8LW1fgBJzy2Uo2iFRr19fVl2xSvBaRs9Pb2amhoyNWgzM3NaXJyckWNCjDuEH6+okgkEhFS6NdVSJUbfFii6KdolEsz4Zpx3mRJkToXm7Mf92wWRPUQqvasSD/91O9W66e11Ap7xIVvCNRCtDGIGUsmk1kWWai1FjCuUdJGgPS2Y1WfCCo1tKl2NEbc7/zUn2qIS4eM+3mzYKN1qx1DHFFBbqyHU8yOkdSvWschbczclrumlZU2+h+37kZGRlyb/EpZBNLiui1HFCs56Oh+aMckLTrkSK+zHV1rQbn7IVshW+UQt1fiarDiYDM5/O9PTEyosbHRXYfz6+I+WynyvJ4OLQgJnV2bm5tdFCmOKOJQGBwcVC6Xi8wJf6uk3yxs6nO55ylXCmDXa1xztUq6vdY97++JckTRz/SwPQN4l7YZi+2r4O8raqKxDeOil5A1yLxFa2trbCSXe4yOji47+9hHQ0ODcrmcy15iXHQ15nlWQxSpc+QZqq3juPTdRGKx0RWp3HFEcWpqqmo2AwhEMWDLYqvUKPopRclkct0iONZLjVBpamrS9u3b1d/fr0wmo6NHj+rw4cM1dafyx4yXO87DSsc9C7+esJzCQFDTBAfERWdslKBSRLEWD2ucMTY3N+cOZZYUOSuS+/NstiW1HfNKoxS2k6ufelqrsWTnIS7CstYGHusNaouONVGkhXgcfKJYLrXUJ4grQbW04mOB1ZAFi7i1x1yuxzP5qaeV5q9aRHE9UOma/p6mbtyPKNIswhqVq9VT1kEX9zeamDE3qVTKnTVYq3PKwm/cAWzJgu9U87FaolhXV6fu7u5lv2fumpub3dg6Ozs1NDS07LOQkbi9u96ZDzSTqaurU2Njo5qbmx1BIbro339qakrJZDKil6SlOar1KBMckquxgez7qdSF28dKiGJc6mkcOfLlL/rPpj/biHljY6MmJibcM/s6lu/E3WtyclK5XM7Vlfp2k40yxjn78vl8bO2mj1wup1Qq5RwZkF8isnG2KxH7+fl5d0wY80cE/7HHHtOOHTt08sknq6mpqaaIop0f7L7W1lYXYCDDzCeKtQY8AlEM2LLYKjWK1VKK1gKrXH2hl0wm1draqh07duiUU06p2n7dv66NpMXNYTKZdA1ZgJ8mWk65NDU1aXx8fFm0tVoUzLaoj7tmLcrTB80PGLdf/2CbGsS17l4NbL6/n/ZRyz38lF/fQF6PurD1RjKZ3BIRxZaWlrLdbSutv3Kpp3E/lwNrd631cusN1tN6jQmDZ73k3kpSfKWNj85Wen/+7zH2/RTfw4cPq7e31+3VcpGdWmAddD7q6upc8xfIdiaT0djYmKvD4ry1ldzPZpNYJwPZJn5dWTXUShQTiUTZDIhMJuMiijR8kRTbEdonYWA9U09x5kEUfcQ9cyqV0tGjR9XV1RVpdmM/v9JIfS0piJWwEhuqrq5O27Ztq+mz/vMnEolldkPcPmOd2X1lSWFDQ4OrB+V3wHZojyOK+Xy+pi63cWnuxWJRLS0tNc91R0eHFhYWNDU1FXESl3MY2/TWTCbj5ktafMdDQ0Pq7e11TZ+y2WzVTLK4bLFMJqOuri6VSiXXcdfKL0hprQGPQBQDtiwQHGtRwOuBzSKK1Z6zVkUsRaOgEKc44dfa2hoRtLUSxXQ6rb6+Ph06dCiiVMq1aEcor5X0xz0DRNGS6NbWVldQbiOK/r1XG6223n0/LaaW1uK2A2McavU4byZIiznWRDGTybgGIz7KebN92Hn3U1BrQdznjyVxtEbPWgxKGoj4UbW1oly6brnPHkui6IPuyNJSA43x8XFlMhnX4dA3dlcKn1BY+PuOjBaaiaXTaRddrBWWKFqjlmgEdZEbQRQrYc+ePcuOdejo6NDw8HDkcxwNYY9SANZBulbMzMy4iBT7oZoO4/1A4i2ZtVHpWsdnG6T5qJZuzjpfSURxJWOLa6Tlp4OWuzfpkUSGLVFEJnPYvV8uUo4o+j0tKjn/4mqOm5uba07HZKzbtm3TwMDAshTcuP3sjxunRqlU0qFDh9TW1hZxojQ0NFQlc3HzS2ZaLpdzKc52f9JBv1ZdEYhiwJbGehg/a4WferqeWIlyXelnLVEsF4lsa2uL1EpYg6WaV5YOfEeOHHHvqVyLdsZg20HHoZoBF/f3OHJKEwA7D3HnmJWrIagGmjHEpV/Nzc1VXS8dHR3LPmM9+7U2stlMsBaONVGUFDnTzKJS0xoLfx2tNKLIveL+fqwQ5yFfCZiD9Uw5LXefSljrc9Q6hnK1kP6+tMSFdLeRkRF1dXVJWjx+hLqqjUg95b5EGajxIyKRTqc1NTW14hp221mR59voiGK195pMJpfVuXEQub02RDGufjEuXdHH5OSH2IWJAABHFklEQVRkTbqU6JTdD74Tz3/ulpYW9fb2SlruAODZpJXVk/qZP6Bafb2NYK6n48deP67Bk9+zIE6XUdNHppTV03SZ5T35EUUIqm+j+AfIVyL1Vt+Cnp6eFWVuNTU1qbm5Wdu2bdOjjz4aGWOcw9g2M4IIz87O6tChQ+5a/uerjaWSnUYarL23pJrTa0EgigEBVYDy2gj4gqPaZ2tNnczlck7Jc6B9HNLptDvgVYoaELWkx3CYMYcgl+ukBVGsRj4rNSoBvrGBIvHrzjhqhHmN64AWd+ZTLbA1oP7v44r5477v39d69rciUeSZN2ovrAQYERaVUgnLeeMtqjUjkZb20laLKEq1tVKvBFLBaCm/3qj1mnEGnLS+NbuVHFJxTT8w2AqFgo4cOaKenh6Xio0zai3p4rV0m2Z9sgf37Nnjxjs9Pb0iomhhDchUKqXW1lbXpKVcY6hq1ymHWsh0KpVy8wr8qGKpVCrb3r9aRLFYLOrRRx9dJj/iMDU1paamJpe+J8Uf9WBTY20DFT+iSG1ppfH54Bpxn69GAO15hRsht+Oicj7KlQMQtY5LPV1YWHDHkVg7pFQquaNRBgYGHNEEk5OTzp6ploJcySFVaxZba2urix53dXVFSK8/NvtsIJPJaHBwUJlMRs3NzcvuWcueKmen+c9mbavp6ekVNcALRDEgoAoaGxs3pMupFD07qpogXwlRtEKnElGshFqVS1tbm9LptIaGhsoKLeo8yjWyAStNX7LGmRXChUJBuVzONRWQFufBf4/lzt2rBe3t7WXJRi3vMo4o2rOZtiJR3MhI00rQ1NTkDC5Qbr3WElFcacfTrdbMRip/xmitqHXtruX6tX5uNe9mpSgXUSx3xivdp1OplItajI6OqrW11cmt1UYUK8k9ukQj6yCmvCeadK2FKNp6RQ4PL0cUy5G9Wo3aWmSjH41paWlRPp+PkJJyhn45Jx4YGRlRZ2dnVaJI9ovv1PMjZHGdT4Ffq5ZMJjUyMqJ8Pl/zmiZ7Iu7z1eaTd7tRR4xVisyDcuuIKDqOIT+iyHhpqlQoFPToo49qYWHBNXo5cuRIxFkwMzPjMpqIWJbbk5VI7mqyA1paWtTV1RVpEhVHFK0jubm5WdlsVp2dnbH3XO2esk4/ey2b1bWS5wtEMSCgCjajRrGWyJYvZGpFHFGs5AVEcK/EC9nV1VVWWTIGW+hdDrV6pYElVDbNp1AoqLGxMdL9LplMLuuAttrUU54jTnnX0gSkHFFk/LUejbGZSCQSsd0KjwUwBv10tFqPxvB/RnGulsxsFazWGKSGeSPX3FqIov3/eo2l1oiitHRMgd0DpVJJ09PTqq+vrxj1WStol2/ParT3QW6slCiyf3wyw3m+5YzlcoR4vYhiXFZGIpFw3azjok8WlZwdxWJRExMT6uzsdLVh5eCnMdqyALtPKhFFf52lUinXebvcOZA+OFs5bs6r6S/e7UZFFKXl9d61ymVb52/fqSVwRBXn5uZ08OBBdXR0qLu7W6lUyh1Xks/ndejQIRfBYzykf24WUeSZIOYNDQ2xRNF2Drc1iHH3rCXDKu7d+vfhd4VCwdUnrgSBKAZsaWxVg2y9YIubqxlpK4koWsSd2VRJcSDsVtI5LpFIaNu2berr6yt7TQydWhRbpfvY9WBrIv1GPOl0Wu3t7RUNqNWmnjLWuHfhHxlS7rv++/Zb1m+FyJ2PlRT6bzSam5sjUcU4g6RaBNBipSmXGymXbrvtNl1wwQVqa2tTZ2ennv/85+uhhx6SJP3ar/2a3v72t0c+f/ToUdXV1el73/uepMV9ce2112rHjh1qbm7W//gf/0O33367+/zNN9+strY2ffWrX9WZZ56pTCajgwcP6q677tKFF16orq4u5XI5PfOZz9RPfvKTyL3+8z//UxdccIEaGhp05pln6pvf/KYSiYRuvfVW95mDBw/qJS95idra2tTR0aEXvvCFevjhh1cUtbXzu9bUznL3iEOcPGhqatL09LROPvlkt7dJS6SpzFqJYjldZ4kiJNHeh3+vlCjadv5W5vI8lYzoOPm2XkQRoud/DqJo9zkHk1tUSo8fHR1VW1ub6wwZl7oKbB2X1Uu+nIk7IqMcSIdsa2vT0NBQTTIEfbKW1NPNOmKMtVELUbRR2XJ7O5vNanh4WGNjY+rv73fE3cqDHTt2qKWlRQcOHIiU0dhzDeOwUURxfn6+ZqJo05bjony11CiW+x6dkgER+JXWJ0rS2k/SDQjYQKw0FfF4g98Fq9pnOTNwJSCdyAq/akQRYbcSLyTKNw42d38tEUVfuM/OzrpW2HgQpaXna2hoqDhna0k9LVf0Hmfk+Ghubl4mrEkh24pHY2xFtLS06OjRo84JUqlTLPN53nnn6fDhw5G/bUbDrL6+Pt111101fz6fz+vNb36znvSkJ2lyclLvfOc79T//5//UvffeqyuuuEIf/OAH9YEPfMCN+Utf+pK2b9+uZzzjGZKk17/+9br//vv1xS9+Udu3b9dXvvIV/dZv/ZZ+/vOf65RTTpG0SHT+7M/+TH/7t3+rzs5O9fT06Je//KWuuuoq/eVf/qVKpZI+8pGP6JJLLtF///d/q7W1VYVCQZdeeql27dqlH/3oR5qYmNBb3vKWyNjn5+f13Oc+V0972tP0/e9/X+l0Wn/6p3+q5z3vebrnnntqev5qqcIbhXLdXpuamnT48OHIuEZHR9Xd3e3OOJyenl7TGkL2+fJxbm5Ozc3NGh8fV319vZqbm2ObaK00YgSJ8OsjGUO5+S4XUaylXq1WAzzueZLJpJqbmzUyMhKJyExPT0cIAs8Wd+/x8XHt3r1b0qL8mJycLNtVcnp62kWPiVwSBfMjupWIIlEhrsGxH01NTZqYmFjmxI37Po2MfBQKhYoOAjriSpuTFo+TwerwcmmOHPciVU5n7uzsVH9/f8Xxt7a2qqmpadl7mZubW7Y2QLla6ErjqQbIaalUUnd39zLbwieKNn16tamncaAWNo4QTk9Pr7iUKhDFgC2NzfCCHUtYoljNI7za1FNpSYnXQhRRfOvdPAJjrxpRrOTl9VMxZmdnXffBuro6dxC7JYrlztyTVq8QuF+cgVBLRLHc9RYWFrbk0RhbEaRksU7phOjDNqk5fPhwpDvdVsVll10W+fnTn/60uru7df/99+slL3mJ3vjGN+qOO+5wxPALX/iCLr/8ciUSCR04cEA33XSTDhw4oO3bt0uSrr32Wt1222266aabdP3110taJHSf+MQn9OQnP9nd5zd+4zci9/3Upz6ltrY2ffe739Xzn/98feMb39BDDz2k22+/3WUPvO9979OFF17ovvOlL31JxWJRf/u3f+vm/aabblJbW5u+//3v66KLLqr6/H7dkv/zeqBcp9u4vcuZfXwemV1fX6/JyUnV1dUpn8+vaXzIkzii2N7erpGRESUSiWXEhsZdqyWKHLUhLRqR1coOyjkQa9EVtaZAlpOh7e3t+u///m/t2LFD0iJRHBkZWfa5nTt3Lvvd6OiocrmcG2dTU9OyYzfsOK3cIKK4mk7HNnI7MzPjGq91dHTo4MGDam1trXiNVCrlumz7WFhYWFZO4X+3WpOk9QB7My6VuRxsBodPJu3erLUu3l8vtaSelnOGrNZZW1dXp8nJSdelOE62+Gdy8qyrTT0tV6frRxSlpfe0UjkViGLAlsZG5dVvFbBha0mBJG1lNcaI7+2tFlGEcK0naiWKlRSbH1mwwjXuDMh0Oq3BwcGq41oNyqVn9fb2ruqaPPtWbGSzVZHJZFzNRbnUU/suyqVGb3Sqb7n7lsN///d/653vfKd+9KMfaXBw0K2zAwcO6KyzztJFF12kz3/+83rGM56hffv26Qc/+IH++q//WpL085//XIVCQaeeemrkmrOzsxFPcn19vZ70pCdFPnPkyBG94x3v0O233+7OBpuamtKBAwckSQ8++KD6+/sjz/PUpz41co2f/vSn+sUvfrHs0OuZmRmXPlsN1ojjHW4GUSwXUSSdkVR3GwkiirLWsZXrfMrh3OWM14WFBfX09KzqfjT7KBQKGh4eVj6f186dOx2hiUOl91At6lsoFGqSbeUipOl0Wp2dnc6pWi7i4usYoom7du1yv6vkNJ2amoo4nWzTuTj9VWl94njNZDLuzE2umc1mNTo6qvb29opz0dTUFDtv1TqT10ra1gLSI7FPbESxkp1hCYs/d9grley/YrFYMRuI/VTp/uUiwWuJKFY6/oQ6+LiuueWIYiVUqrOOy1IrFosrTjuVAlEM2OKo5PV5vKBUKtWceiqtLsq6UqJYa83FSkDnwGpd2mpVbHHKxZ53hlCulA61lrXV0tISK3RXazCy1sudRRmwHByT0djYWNawsAaWTf+0Cn01Xla68fHe1jMS/IIXvEC7d+/W3/zN32j79u0qFos666yzXGr1FVdcoTe84Q36y7/8S33hC1/Q2WefrbPPPluSXGfOu+++e9l82DSsuAOXr7rqKg0NDemGG27Q7t27lclk9LSnPa1ioyofk5OTespTnqLPf/7zy/5WazMkonbs4Y1IPS13zXLrgFTBhoYGjY+Pu6jWwsKCGhsb10wUK8ldiGpc9Gi1ja9ISyyVShoZGVFTU5N27twZ2zHRAmK8GtTaGbatra3sPXxSbFM7y2FsbEzZbHbZvZubmzU1NbXMqZHP5yP12JDqah08yx0NhQPAP++yra1NBw4cUC6XKzsv9fX1amtrWzZGqXqEtlzUfD1hU6b91NNKazOdTrv0U1+XQ/IqNRirpYylUiPCSqnSK+0Kau9ZKRpJGZB9ZzRDWg05rZSyWy6iWSkCXQ6BKAZsaRCqf7yjFsG0WoNWiieKlQS4PX9wvdDc3Fz1uishirOzsxHDvFxny3KGxFojFH60ar0wNzdXtW4lYBGNjY2rjhhDRso1JaoGPxK1XhgaGtKDDz6ov/mbv3GppXfccUfkMy984Qt1zTXX6LbbbtMXvvAFXXnlle5v55xzjjtnjO/XijvvvFOf+MQndMkll0habEpj5/e0007TwYMHdeTIEXeo+I9//OPINc4991x96UtfUk9Pz6rXMQYuRu5KmhKtBCt5bzRTwahHniBL1zq2dDq9LJPDEqt0Oh3rmFoLUZyentaBAwfU2NgYIfGVShwqyc1qxKTWuvc4UlQONKUpZwCXSiWNjY1FoomgpaVFw8PDy+43OzsbIXTopXLZHhj7cUSR92odwqy7ZDKp9vZ2DQ8PuxIKH7Yzpo9adFi1yNtaYW2LlRDFTCajXC4X22zHluSUWy9xncMtaJy32q6nGzVnyE3A+l1Numu5iDJdYX10dHSsuMeFFLqeBmxxrKZI/3jDSoTDaudjJRFFUkjWe95rqd2rJrxtFKBc5M03/ohk+ljL0Rgbia14NMZWBY6T1Tg2iDav1sDfqAYr7e3t6uzs1Kc+9Sn94he/0Le//W29+c1vjnymublZl156qa677jo98MADuvzyy93fTj31VF1xxRW68sordcstt2jfvn3693//d73//e/XP//zP1e89ymnnKK/+7u/0wMPPKAf/ehHuuKKKyKGxYUXXqiTTjpJV111lX72s5/pzjvv1Dve8Q5JS3LsiiuuUFdXl174whfq+9//vvbt26fbb79db3jDG/TII4/UNAdxnU/XmySWO4ajHLLZrPL5vMbGxiLRJozK9Ygo+qmnlpiUSyNcrbzA0N6xY8cyOQpRjJuTSlHBao6+jTimIa7zqcXY2JhaW1vLNlTxa+LRfXa94TwtlzVQ6YgM3iv1cvZYCGlxXU1OTq4qRbTWLsIbaUPZd05qMT+Xi8ACUjXjiGKlVF+pekSR65d79o1oZsN1K8kS33lm185qiGK5FO04x0m5fVANgSgGbGm0tLQol8sd62FsKFZKFNcjoliLZ3e9lUtdXV1VwV4pSuenk8YdMh1nTFHH5mMtR2NsFFC6oetp7WhpadHo6OiK3+V6RKk2gsgkk0l98Ytf1N13362zzjpLb3rTm/ShD31o2eeuuOIK/fSnP9UznvGMZdGSm266SVdeeaXe8pa36LTTTtOll16qH//4x7FRFYsbb7xRIyMjOvfcc/WKV7xCb3jDGyKpfqlUSrfeeqsmJyd1/vnn63d/93f1x3/8x5LkyEZTU5O+973vadeuXXrRi16kM844Q7/zO7+jmZmZFUUY/ejUekfwV0r0M5mMFhYWYrsJrjYqbRFXn+2fExuXmrpaOZZIJLRz504XDbX6AbIUNz+VdEc1orgRxzQ0NDSUJYqlUqliDSCpgJYsxr1fG1Esd9RDubRhyND09LTrYGrXcSKRUFdXV9XMiNVio53ttuSDBk+1RBSl6Jr2iXktEcVqz1WuWyzjrtTMZrXrdKWdSmsp9anUgThuDurq6lZVt1wOW8tKCgjw8HjveiqtjPytF1Gs5tmtq6s7JkSxEvyurXFpQHGtyst1xlvL0RgbhXJHbgSUR0tLi/bt21exIUQc1koUrXG93pHF3/zN39T9998f+Z1/j4svvrjsfevq6vSe97xH73nPe2L/fvXVV+vqq69e9vtzzjlnWSrpi1/84sjPp59+eiQV9s4775QknXzyye53fX19+sxnPhN771rB/K6lNrva9f2IYqW1QBOKTCaz7HPJZLJsG/6VjMcHR2NISwalnzq2HnIMw9yvE4uLHq4loiitf2S40vEUk5OTam5urrh2OCYDp2M+n1+WBor+LBaLsTrMnofnA0JiiaI/Ry0tLRoZGVmW8loJtcqc1XbhrhVxEUXkYi1EMa7LOY2jKkUNc7lcVXuiv7+/7N+qZS+tdzf0cqiWrl3u2BxJG5L5FYfHvxUeELDFUS3X3qKzs3PVRNEqp2qCcK2kLg4NDQ1r8nJBbhGccXWdKJ64YnEfWzH1lAL/gNqBU2M1xvJajFafaJwoUeCvfOUr+sY3vqH9+/frm9/8pq655ho9/elP10knnbSu9/FTg9cjvdO/vkUthndHR0fZroHr4cH3x2SdYRznMD4+Xrbz82phjX2M1nIdqCvdb7Xnvq0FlZodDQ8Pq6Ojo+L3m5ub3VmDUvnzWCs5lmppuocOKreOe3p6dOTIkYrXsKj1vW8mUfSjzdUylyodMVXtfOm4A+1XglrKXFaD1WQXVEqDrXRExkakcseOYcPvEBAQUBErIYrVzlwqhzihWOk6G0EU/bbQlT4XJzRRjJwnGSeM6+rqlrV2L2dEbMXU0/r6+tDxdBXI5XI1e+IBaWerxUY1s9nqmJiY0Ote9zqdfvrpuvrqq3X++efrH//xH9f9PuzvzSLgtXSHzOVyEcN2vaP/vlFoIwaZTEa7du3SzMyMDh48WLEub6WwpJB7cnSDj0qN144FUZTiaw3pMFpLiQWdN+lkHIdq64Na6XKwBDzuOplMRplMpuK5vxa1RpMaGxs31PlYichUQ7locC3NbNaKjapRXE1jK7q/xoFOqnGodjzKemFrWUkBAScgGhoaNjzFtpKwiUM1L+xGAiXhz4kV3lNTU7GEKp1Oa2ZmJrbGxPdObsXU05V0+wtYQrmOgdWw1q63J2JE8corr4x0Wd0obFRXYf8eKzk+oLGxUWNjY+7n9TbUMJx9RxdIJpPq6enR/Py8BgYG1m2ObG036YKNjY06evToiprlpFKpdSWwtYI6RasThoaGaj6/lGMy5ufnK6YQV5qHtrY2jY6Oxsoizufk/+XkTnd3tw4cOFA1XVaqfe2tNSW6GsrZFrWQrXLHSdha/Y0iQpX2zWqPx5BWF1GMc3SASs6XEFEMCDhB0NLSsqqzbVaCSmkWcdgMI60cyqU82dTTfD5f9syquMOi4xoebERjhbXiWM57QMBWQq0ZCGu9B2S/FsLoN8tab0PNdj6tdtbtjh071NbWFunAulpYY9QSw87OTg0PD9d8nUoHvK8lna8afPlOfVut64c6xbhGNiCdTle8Htco1wCIDIZqZwO2t7draGio6pg3iyRUQzkis5bu3ezLzarB87EWopjJZFZcL5/JZFYVpQ9EMSAgYN1gieJWT5MrJxht6un09HQsUcRD6QtPziryEUhZwHpgq++p4xWbYQRZolgLrCNrI4giqXj+ObFxaGpqWpczV+0zWQO/qalJMzMzNacWVjomolITnLXCl+9DQ0Pq7Oxc0fd5znIRLA4xL4dEIrGs3hFw1ENdXZ3q6+srnmWXzWY1PT1ddh7BViGKttGPTRefnZ2tudSkHNZC2GrBRuj/ZDK5Ysd/fX192bVVKbV3s5zdgSgGBJwA8A/F3WqRNItaiGK5+jJSVeIiiuVSOwIC1gIIRnA6HH9YTfpwY2OjpqenJa1/10Fbs1XucPeNgB9R5L6JREIdHR01RxWrNQjZyHozaXEvzs/Pq1AorKjWm+h1pflubW2telh5W1tbbIftUqnkiH8mk6lI7hOJhHp6ejQwMFDxXlulGRtzb/cCztxaiGI6nT7hZWddXZ16e3tj/1atbGgz5m7rWosBAQHrBmsQbRVPZDmUS19CEaVSqdgW9SDuaA+/aH4j06ACThywr0JE8fjESs9SlKJEcb1rFG3q6WYSRb9G0T5TS0uL8vm8ixpVk5vlyOJG6x2isbV0Oo1DtTTe5ubmqsSHRia+rkmn01UPn7ego+fk5GTZz2w1PW73ArWqtTxvuS6wq9mbK8XxILePVYMoi0AUAwJOAFjlvtUUjI84wTg7O+vOmEqn0xVrAOKIovU4S1uz42nA8Qf/4OyA4wurMUZtPdx6y9J0Oh0hiivt5Lta2PQ2P90vkUi4SFktUcFyaf4brXcaGho0NTWlmZmZVdX8Nzc3V40Y1gI/qmibq62E+Hd3d1eM5G41PW7HA1Gs5XnLNX9ZTVOYxyMCUQwICNh0bDUF48MXjMViUYcOHdL27duVSCSUTCYreoy3bdsW68m09T9bseNpwPGHzfB6B2wcVhMR5sy8Uqm07rL0WGV+1HIsyMTERE1lC5WI4kYa/g0NDTpy5Ija29uPqdOGDqq8x+npaTU3N1dsWBIHuqSWW5vHqtFLOfhEsdZU43LHSazk2LDV4niQ3+VqFDczKyoQxYCAEwzHA1G0XU8HBgbU0dGxohbtcQLURgK2Sn1HwPENSzS2QkTx6quv1qWXXup+ftaznqU3vvGN63Lt/fv3K5FI6N57712X620FrPad0bRlo4z1Y2W8liNziURC2WxWw8PDNUUU447I2MgaRWkpXfNYHzGUSCTU0tLizkOcnp5WY2Ojdu7cueJr1dfXx54zKG18o5eVwq9RrFVfNzY2KpfLLft9XK+B9UalsxS3CnBM+dhMO27rrLKAgIBNwfFAFBHek5OTKhaL69LZz3q6A1EMWC9sdY/0VsZ9992nyy67THv27FEikdD/+T//55iMY6VEnzrFjSA/yWTymKXGV8q0aGtr08TERFVyUq7z6UbrnWQyqSc84QlbwmHDmYrSUu3eatJa44512opIpVKam5uL1CjWmmabSqVi52YzIopxJGyrEcdy6zkQxYCAgA3DVieK9gylwcHBmg9Nrgbb+TTUKAasB46H1KWtjKmpKT3hCU/QBz7wgXXb5ysF73AlBKOpqUlTU1Pu++uJdDqtqampTWtkA5LJZMUjDUj5ryY3j2UEZCuQRGkpGjY9Pb2mqF+56OxWQzKZ1NzcnHu/6XR6zfW1ra2tsZHG9URc46WteL5yHAJRDAgI2BBsRF3NRuHQoUPq7e1dN6Ftax9DjWLAemAjDNNisagPfvCDOvnkk5XJZLRr1y69733vkyQdPHhQL3nJS9TW1qaOjg698IUv1P79+9flvnv27NH111+vV73qVWptbdWuXbv0qU99atnn/vM//1O/9mu/poaGBp111ln67ne/u+p7nn/++frQhz6kl770pZvWuCUOK40i2Hrn9UZdXZ3y+fymE8VaOlV2dnbWZLzHNeDY6scyrTfa29t16NChNTXIKRdR3Cqp7oCIInZFJpNRT0/Pmq+5GRFFf+9vtZTectjMGtWtPxsBAQHrAoTi8UAUC4WCmpqa1qULnQU1CSea0RKwMdiIiOIf/uEf6gMf+ICuu+463X///frCF76g3t5ezc/P67nPfa5aW1v1/e9/X3feeadaWlr0W7/1W1UP564VH/nIR3Teeefpnnvu0Wtf+1r9/u//vh588MHIZ9761rfqLW95i+655x497WlP0wte8AINDQ25v7e0tFT87zWvec26jHW9gExYieGdSCQ2LJq8lYkiz10NcQ1tNrpGcauhsbFRxWJxRec5+qgUnd1K+iuVSml+fn5Zt9ytjrgaxa0YUYxrarTeR/NUQsi9Cgg4QQBRPB4U9rZt29akYMuB+plaDZ6AgM3ExMSEbrjhBn3sYx/TVVddJUk66aSTdMEFF+hzn/ucisWi/vZv/9at3ZtuukltbW26/fbbddFFF635/pdccole+9rXSpLe/va368///M/1ne98R6eddpr7zOtf/3pddtllkqRPfvKTuu2223TjjTfqbW97myRVbXazHvXG64nVEEUpep7ieoIz944FUaz1kPRqIGXSHlNxvERq1guJREI7d+5c83skOmt19maShFqQTCbLNpHbyjheUk8Zp78GNsJGisPWWWkBAQEbCoTNVktbicN6RxIBqTyhrixgK+KBBx7Q7OysnvOc5yz7209/+lP94he/WNbVcWZmRg899NC63P9JT3qS+3cikVBfX58GBgYin3na057m/p1Op3XeeefpgQcecL87+eST12UsmwVk4WqI4npFci3q6upiz4LdaKTT6XXLNmloaIhEmU9UrIchj87iLEZp6/UZSKVSW2o8taJcM5utRhTjjhvZzDUQiGJAwAmCcufxnEjIZDIaHR3dcoog4PjFejpdKjlIJicn9ZSnPEWf//znl/2tu7t7Xe7vR5NW0z6+paWl4t9f/vKX66/+6q9WPLaNwmrfX1NT04ZEddLp9KZFCizoVLke67murm5DSPSJCKKzlihutTMUj2eieDxEFIkqW/m8mWsgEMWAgBMEcULxREMmk1E+n48o3YCArYJTTjlFjY2N+ta3vqXf/d3fjfzt3HPP1Ze+9CX19PQc0/TNH/7wh/r1X/91SYvGyt13363Xv/717u/HY+rpashRMpncEEKXTCbV39+/7tethpWcfVcNWz1j5XhCQ0ODxsfHI7/biqmnW2k8tSLOeb6ZB9nXirjmUJtZQnT8vdmAgIBVwZ5PeKKCVJPQ8TRgvbCeRkVDQ4Pe/va3621ve5vq6+v19Kc/XUePHtV9992nK664Qh/60If0whe+UO9973u1c+dOPfzww7rlllv0tre9bVUHeq8GH//4x3XKKafojDPO0J//+Z9rZGREr3rVq9zfV5J6Ojc3p/vvv9/9+9FHH9W9996rlpaWTUthTSQSsYbYscSxiGjU1dWta8p/Op1259WGVP/Vo66uTgsLC5HfFQqFTa9hrYR0On1c6tTjpZlNHKHdzBKiQBQDAk4QJJPJLZeycizQ0NBwXHo/A7Ym1rv75XXXXad0Oq13vvOdeuyxx7Rt2za95jWvUVNTk773ve/p7W9/u170ohdpYmJCO3bs0HOe85xNjdJ94AMf0Ac+8AHde++9Ovnkk/XVr35VXV1dq7rWY489pnPOOcf9/OEPf1gf/vCH9cxnPlO33377Oo24Ourq6rYUUTwWqKurW/ORBhakTLa0tGxJ4/t4AoSGOdxqNYp1dXXH7BzUteB4OR7jWDv5E6Xg6nncY3x8XLlcTmNjY1su7Sdg8zAxMaGpqSkVi0Vt27btWA/nmGFoaEiNjY2RjnwBjy/MzMxo37592rt374bXexUKBZVKpeB8OM6xmWvmRMDk5KRmZ2fV2dmpubk5DQ0NndB6Zy04cuSIcrmcW5ePPvqoent7g8xZI6anpzUxMRFxkAwODqqxsXFLlafYvQQOHDigXbt2bcr9txZtDggI2DAkk0nNz89vKU/ksUBHR0cgiQHrBlrDBwQELIGIorT1zv073mDnUtp6EcXjFcdbMxuw2fG9rTUbAQEBG4ZkMqm5ubkTXsFstUL1gOMbx8OZnN///veXHXxv/wsIWG9QoyhtbuONxyM4IsNiq8uc4wHHC1H0axQ321EQ4tYBAScIQkQxIODExHnnnVe1G2lAwHoCIlMqlUIEbI3IZDKR40ZCxdj64HhpZuPXKAaiGBAQsCEIzWwCAk5MNDY2bloX0YAAUFdXp/n5+UAU1wgaZm1mp8sTAeUiilttjv3U082247YWbQ4ICNgwcDTEVvOWBQQEBAQ8/tDQ0KDZ2dlQo7gO4JiMQBbXD3FrcitGFP3O2pvteNlasxEQELBhSCaT7sywgICAgICAjQRNWEKN4tpBnWKIzq4v4tJ4txpR9FEoFDa14+3Wno2AgIB1A003gpIJCAgICNhoZDIZF1EMemdtgHSHuQwIEcWAgIANQyqVCkomICAgIGDDkU6nVSgUArlZBxBRDH0G1hfHYxpvIIoBAQEbhnQ6veXTKgICAgICHh9IJBKhRnEdQOfLzU47DNgasB1aQzObgICADUN7e/uxHkJAQMA64N3vfrd+5Vd+ZUXfSSQSuvXWW9fl/jfffLPa2trW5VoBj1/U19drbm7uuIzcbDWEI65OXNgjMkJEMSAgYMPQ0dFxrIcQEBBQAc961rP0xje+sernrr32Wn3rW9/a+AFtIm655RZddNFF6uzsVCKRiD378VnPepart+a/17zmNZHPHDhwQM973vPU1NSknp4evfWtb3WHvwdsLjKZTDj3b53Q0NCgfD4fiOI64nhZm/4RGZsZoQ/x64CAgICAgOMEHGDe0tKilpaWYz2cdUU+n9cFF1ygl7zkJXr1q19d9nOvfvWr9d73vtf93NTU5P5dKBT0vOc9T319ffq3f/s3HTp0SFdeeaXq6up0/fXXb+j4A5Yjk8kEYrNOyGQyGhwcVG9v77EeyuMGHD3hH0Gx1ZBMJh1R3OxxhohiQEBAQEDAFsDVV1+t7373u7rhhhtctOzmm29WIpHQ1772NT3lKU9RJpPRHXfcsSz19Mc//rEuvPBCdXV1KZfL6ZnPfKZ+8pOfrGoc+/fvVyKR0C233KJnP/vZampq0pOf/GT94Ac/WPbZW2+9VaeccooaGhr03Oc+VwcPHlzt4+sVr3iF3vnOd+o3f/M3K36uqalJfX197r9sNuv+9vWvf13333+/Pve5z+lXfuVXdPHFF+tP/uRP9PGPf1xzc3OrHlvA6pDJZEJN3TqhoaEhNAZaZySTSZfSuZXPqLSpp5uNQBQDAgICAgK2AG644QY97WlP06tf/WodOnRIhw4dUn9/vyTp//v//j994AMf0AMPPKAnPelJy747MTGhq666SnfccYd++MMf6pRTTtEll1yiiYmJVY/nj//4j3Xttdfq3nvv1amnnqrLL788ksI5NTWl973vffrsZz+rO++8U6Ojo3rpS1/q/v7973/fRT7L/ff5z39+xeP6/Oc/r66uLp111ln6wz/8Q01NTbm//eAHP9DZZ58dibo897nP1fj4uO67775VzkTAapFKpdwaDlgb6urqlE6nA1FcR1iiWCwWtzRRLBQKx4TMBjdPQEBAQEDAFkAul1N9fb2LmEnSf/7nf0qS3vve9+rCCy8s+93f+I3fiPz8qU99Sm1tbfrud7+r5z//+asaz7XXXqvnPe95kqT3vOc9euITn6hf/OIXOv300yVJ8/Pz+tjHPqb/8T/+hyTpM5/5jM444wz9+7//u5761KfqvPPOi60ztFhpGt3LXvYy7d69W9u3b9fPfvYzvf3tb9eDDz6oW265RZJ0+PDhZdfk58OHD6/oXgHrg9DxdH2QSCTU09OzZcnM8QjbTbRYLG7ZtZpKpTQ/P39MxhiIYkBAQEBAwBbHeeedV/HvR44c0Tve8Q7dfvvtGhgYUKFQ0NTUlA4cOLDqe9rI5bZt2yRJAwMDjiim02mdf/757jOnn3662tra9MADD+ipT32qGhsbdfLJJ6/6/nG45ppr3L/PPvtsbdu2Tc95znP00EMP6aSTTlrXewUEbDWEhnTri2Qy6Wr+SqXSliWK1Cgei9TjrTkjAQEBAQEBAQ7Nzc0V/37VVVfp3nvv1Q033KB/+7d/07333qvOzs411eXV1dW5fxPFWEmdzEalnloQzfzFL34hSerr69ORI0cin+FnorQBAQEB0vLU061KFEk93ewzFKUQUQwICAgICNgyqK+vj7RBrxV33nmnPvGJT+iSSy6RJB08eFCDg4PrPbwIFhYWdNddd+mpT32qJOnBBx/U6OiozjjjDEnakNRTH1yfiOfTnvY0ve9979PAwIB6enokSd/4xjeUzWZ15plnruleAQEBjy8cT0SxWCyqUChsenOoQBQDAgICAgK2CPbs2aMf/ehH2r9/v1paWmqO4J1yyin6u7/7O5133nkaHx/XW9/6VjU2Nm7oWOvq6vQHf/AH+ou/+Aul02m9/vWv16/+6q864rjS1NPh4WEdOHBAjz32mKRF4inJdTd96KGH9IUvfEGXXHKJOjs79bOf/UxvetOb9Ou//usuTfaiiy7SmWeeqVe84hX64Ac/qMOHD+sd73iHXve61ymTyazzDAQEBBzPOJ6IYkg9DQgICAgIOMFx7bXXKpVK6cwzz1R3d3fNNYY33nijRkZGdO655+oVr3iF3vCGN7iI2kahqalJb3/72/Wyl71MT3/609XS0qIvfelLq77eV7/6VZ1zzjmugc5LX/pSnXPOOfqrv/orSYvR1m9+85u66KKLdPrpp+stb3mLLrvsMv2///f/3DVSqZT+6Z/+SalUSk972tP08pe/XFdeeWXk3MWAgIAA6fhpZsM4jwVRTJS28gmTAeuC8fFx5XI5jY2NRc6bCggICHg8YmZmRvv27dPevXvV0NBwrIcTcBwgrJmAgBMP4+PjKhQKam9v18jIiJLJpHK53LEeViwefvhhNTQ0KJvNbni2iMXWpM4BAQEBAQEBAQEBAQEbhOMl9RSE1NOAgICAgICADcX1119ftgvpxRdffKyHFxAQELApsERxKx+PAUIzm4CAgICAgIANxWte8xq95CUvif3bZqY0BQQEBBxLHE8RReoUN3uMgSgGBAQEBAScQOjo6AgHdwcEBJzwOF6a2UiLpHZ+fn7z77vpdwwICAgICNgEhF5tAbUirJWAgBMPyWTS7f3jgSjWelzSut530+8YEBAQEBCwgairq5MkTU1NHeORBBwvYK2wdgICAh7/OJ5ST1Op1KY3spFC6qnD1NSUvvvd7+ruu+/WT37yE919993u/Kp3vetdeve7313TdY4cOaIPfvCD+qd/+icdOHBAjY2NeuITn6irrrpKv/M7v6NEIlHx+w899JA++MEP6utf/7oOHTqk1tZWnXvuubrmmmt02WWXrfUxAwICAh73SKVSamtr08DAgKTF8/6qyd6AExOlUklTU1MaGBhQW1vbMTHEAgICjg18oriV9UQgiscY//7v/65LLrlkTde4++679dznPldDQ0OSpJaWFk1MTOiOO+7QHXfcoX/4h3/QV7/6VdXX18d+/1/+5V/027/9286zmc1mNTw8rK9//ev6+te/rle+8pW68cYbt/RCDggICNgK6OvrkyRHFgMCKqGtrc2tmYCAgBMDvj0dIorLEYiiQXt7u84991z335ve9CYdPny4pu+OjY3p+c9/voaGhnT66afr7/7u73Teeedpbm5Of/M3f6M3velN+td//Ve98Y1v1Cc+8Yll39+3b59e8pKXaGpqSk9/+tP16U9/WqeeeqomJyf1oQ99SO9973t100036fTTT9fb3va29X70gICAgMcVEomEtm3bpp6enmPSACDg+EFdXV2IJAYEBGxpJJPJYyKnEqVQwS0p/hDLPXv26OGHH64p9fS6667Tn/7pn6qxsVH33Xef9u7dG/n7+9//fv3RH/2RUqmU7r//fp166qmRv7/iFa/Q5z73OfX19emBBx5QW1tb5O+/93u/p0996lPKZrPav3+/2tvba3628fFx5XI5jY2NKZvN1vy9gICAgICAgICAgMcrDhw4oF27drn/b1XMzMxobm5u0+34rRtj3WSslaV/9rOflSS99KUvXUYSJekP/uAP1NLSokKhoM9//vORv+Xzef3f//t/JUm///u/v4wkStIf/uEfSlokfbfeeuuaxhoQEBAQEBAQEBAQcHygoaHhmAR7AlFcBzz44IOu8c3FF18c+5mWlhY94xnPkCR9/etfj/ztjjvu0PT0dMXv79mzR2eccUbs9wMCAgICAgICAgICAtYTgSiuA/7jP/7D/fuss84q+zn+dv/996/p+/fdd9+qxhkQEBAQEBAQEBAQEFALQjObdcBjjz3m/r1jx46yn+Nv4+PjmpycVEtLS+T77e3tamxsrPp9e784zM7OanZ21v08Njbm7hsQEBAQEBAQEBAQIE1MTGhsbEwTExMnpJ3c2tpa8TSFQBTXARMTE+7fTU1NZT9n/zYxMeGIIt+v9F37d3u/OLz//e/Xe97znmW/7+/vr/i9gICAgICAgICAgIATA9UaXR63RPHmm2/WK1/5ylV//2tf+5p+67d+ax1HtHXwh3/4h3rzm9/sfi4WixoeHlZnZ2c4gzEgYIthfHxc/f39OnjwYOhKHBAQ8LhCkG8BAVsbra2tFf9+3BLFrQQ7yVNTU2WF4dTUVOx3+Lf9e6XvV3upmUxGmUwm8ru4TqoBAQFbB9lsNhhSAQEBj0sE+RYQcHziuCWKl19+uZ7//Oev+vu5XG7dxrJ9+3b370cffbSsMHz00UclLQpM0k7t90dGRjQ9PV22TpHv2/sFBAQEBAQEBAQEBASsN45bohgXNTtWsJ1K/+M//sMdY+GD7qZnnnlmxe+ff/75Fb//xCc+cU3jDQgICAgICAgICAgIqIRwPMY64NRTT9WuXbskSbfddlvsZ/L5vL7//e9Lki666KLI3y644AIXRSz3/YcfflgPPPBA7PcDAgKOX2QyGb3rXe/aMo6vgICAgPVCkG8BAcc3AlFcByQSCV155ZWSpC9+8Yvav3//ss98/OMf1+TkpFKplK644orI35qbm3XZZZdJkj75yU+64yws/uzP/kzSYn3ipZdeur4PEBAQcMyQyWT07ne/OxhSAQEBjzsE+RYQcHwjEEWDkZERDQ4Ouv+KxaKkxSYy9veTk5PLvnvttdeqr69PU1NTet7znqe7775bkjQ3N6dPfvKTuu666yRJ11xzjU499dRl33/ve9+r5uZmHTp0SC94wQv03//935IWI5Hvfe979Vd/9VeSpHe84x1qb2/fkOcPCAgICAgICAgICAiQpESpVCod60FsFezZs0cPP/xw1c9dddVVuvnmm5f9/u6779Zzn/tcDQ0NSVqM/s3MzGh+fl7SYsroV7/61bKetX/5l3/Rb//2b7vuprlcTpOTkyoUCpKkV77ylbrxxhvDERcBAQEBAQEBAQEBARuKEFFcRzzlKU/Rfffdpze96U065ZRTND8/r+bmZl1wwQX6m7/5G33ta1+rmH5xySWX6Gc/+5le/epXa8+ePZqZmVF7e7suvPBC/cM//IM+/elPB5IYEBAQEBAQEBAQELDhCBHFgICAgICAgICAgICAgAhCRDEgICDgGGBiYkLvfve7dfbZZ6ulpUW5XE7nn3++PvKRj2hubu5YDy8gICBgxRgaGtJNN92kl7/85TrzzDPV3NysTCajnTt36tJLL9VXvvKVYz3EgICAFSBEFAMCAgI2GQ8//LCe9axnuQ7JTU1NKhQKmp2dlSSdc845+ta3vhUaVwUEBBxXqKur08LCgvu5oaFBqVRK+Xze/e7iiy/WP/zDP6ipqelYDDEgIGAFCBHFgICAgE3EwsKCXvCCF2j//v3atm2bvvGNbyifz2tqakpf/OIX1draqnvuuUcvf/nLj/VQAwICAlaEhYUFPfWpT9UnPvEJPfTQQ5qentbk5KT27dun3/md35Ekfe1rX9Pv/d7vHeORBgQE1IIQUQwICAjYRNx444363d/9XUnSv/3bv+lpT3ta5O9///d/r5e97GWSpG9+85t6znOes+ljDAgICFgNvvOd7+jZz3522b+/5jWv0V//9V9Lkg4cOKD+/v7NGlpAQMAqECKKAQEBAZuIz3zmM5KkZz/72ctIoiS99KUv1d69eyVJn/3sZzd1bAEBAQFrQSWSKMlFFSXprrvu2ujhBAQErBGBKAYEBARsEqampnTnnXdKWqzTiUMikdBv/dZvSZK+/vWvb9rYAgICAjYaDQ0N7t+cER0QELB1EYhiQEBAwCbhgQceULFYlCSdddZZZT/H3w4fPqzh4eFNGVtAQEDARuP22293/z777LOP3UACAgJqQiCKAQEBAZuExx57zP17x44dZT9n/2a/ExAQEHC8YnR0VO9///slSc94xjN02mmnHeMRBQQEVEMgigEBAQGbhImJCffvSq3h7d/sdwICAgKORxSLRb3iFa/QoUOH1NDQoI997GPHekgBAQE1IBDFgICAgICAgICADcP//t//W//0T/8kSfr4xz+uJz3pScd4RAEBAbUgEMWAgICATUJra6v799TUVNnP2b/Z7wQEBAQcb7j22mtdBPHP//zP9apXveoYjyggIKBWBKIYEBAQsEnYvn27+/ejjz5a9nP2b/Y7AQEBAccT3va2t+kjH/mIJOnDH/6w3vjGNx7bAQUEBKwIgSgGBAQEbBLOOOMMJZOLYvc//uM/yn6Ov/X19amjo2NTxhYQEBCwnnjrW9+qD33oQ5KkD37wg3rLW95yjEcUEBCwUgSiGBAQELBJaGpq0tOf/nRJ0m233Rb7mVKppH/913+VJF100UWbNraAgICA9cK1116rD3/4w5IWSeJb3/rWYzyigICA1SAQxYCAgIBNxFVXXSVJ+s53vqMf/ehHy/7+5S9/Wb/85S8lSVdeeeWmji0gICBgrbj22msj6aaBJAYEHL8IRDEgICBgE3HVVVfp7LPPVqlU0mWXXaZvfetbkhbbx3/5y1/Wq1/9aknSxRdfrOc85znHcqgBAQEBK4KtSfzoRz8a0k0DAo5zJEqlUulYDyIgICDgRML+/fv17Gc/W/v375e0mJJaLBY1MzMjSTrnnHP0rW99S+3t7cdwlAEBAQG148CBA9q9e7ckKZlMqru7u+Lnr732Wl177bWbMbSAgIBVIn2sBxAQEBBwomHPnj362c9+pg9/+MO65ZZbtG/fPtXV1emJT3yiLr/8cv3BH/yB6uvrj/UwAwICAmpGsViM/PvIkSMVPz85ObnRQwoICFgjQkQxICAgICAgICAgICAgIIJQoxgQEBAQEBAQEBAQEBAQQSCKAQEBAQEBAQEBAQEBAREEohgQEBAQEBAQEBAQEBAQQSCKAQEBAQEBAQEBAQEBAREEohgQEBAQEBAQEBAQEBAQQSCKAQEBAQEBAQEBAQEBAREEohgQEBAQEBAQEBAQEBAQQSCKAQEBAQEBAQEBAQEBAREEohgQEBAQEBAQEBAQEBAQQSCKAQEBAQEBAQEBAQEBAREEohgQEBAQECDp3e9+txKJhBKJxLEeyjHBt7/9bSUSCfX29mpqaupYD2fd8KEPfUiJRELPetazjvVQAgICAo4rBKIYEBAQEBBwgqNYLOqNb3yjJOnaa69VU1PTsR3QOuL3f//31dnZqe9+97u65ZZbjvVwAgICAo4bBKIYEBAQEPC4xc033+yihPv37z/Ww9my+OIXv6if//zn6urq0mtf+9pjPZx1RUtLi9785jdLkt75zneqWCwe4xEFBAQEHB8IRDEgICAgIECLqaelUkmlUulYD2XT8b73vU+S9Hu/93tqbm4+xqNZf7zuda9TQ0OD7rvvPt16663HejgBAQEBxwUCUQwICAgICDiB8Y1vfEP333+/JOnlL3/5MR7NxiCXy+mSSy6RJP3FX/zFMR5NQEBAwPGBQBQDAgICAgJOYNx4442SpHPPPVenn376MR7NxuGKK66QJH33u9/VQw89dIxHExAQELD1EYhiQEBAQMDjDrfffrsSiYRe+cpXut/t3bvX1Svy3+233+7+Xq3r6Z49e5RIJHT11VdLkn7yk5/oiiuuUH9/vxobG3XyySfrzW9+swYHByPf+7d/+zf99m//tnbt2qWGhgaddNJJevvb366JiYmqz1EoFPSZz3xGz3/+87V9+3ZlMhl1dnbqggsu0Ec/+lFNT0+vfHIMZmZm9NWvflWSdNlll1Udy80336znPve56uvrU319vXK5nE455RQ95znP0fXXX+8ik+Vw6623Ruaira1N5513nt7znvdoZGSkpjH/y7/8i17+8pfrCU94gpqbm9XQ0KC9e/fqsssu080331y2Y+vznvc8NTQ0SJL+/u//vqZ7BQQEBJzQKAUEBAQEBDzO8J3vfKckqep/3/nOd9x33vWud7nfx2H37t0lSaWrrrqq9NnPfrZUX18fe81TTz21dOjQoVKpVCp96EMfKiUSidjPnXvuuaWJiYmyz/Dwww+XnvzkJ1cc/8knn1x68MEHVz1Pt99+u7vWt771rbKfm5iYKD3jGc+oOp+XXXZZ7PeHh4dLv/Ebv1Hxuz09PaUf/OAHZccwODhYes5znlN1DDfddFPZa/zqr/5qSVLp137t12qeo4CAgIATFel15JwBAQEBAQFbAueff75+/vOf6x//8R/1jne8Q5L0r//6r9q+fXvkc3v37l3xtX/605/q7//+73XyySfr2muv1dlnn62JiQl9+tOf1uc+9zn913/9l6699lq96EUv0lvf+lb96q/+qv7gD/5Ap512mgYHB/UXf/EX+pd/+Rf95Cc/0Z/+6Z/qAx/4wLJ7DA0N6YILLtDBgweVyWT06le/Ws985jO1Z88eTU5O6utf/7puuOEG/eIXv9DFF1+sn/zkJ8rlcit+lu9///uSpEQioac85SllP/fud7/bffb5z3++rrjiChcVHBgY0D333KN/+qd/io3Gzs7O6jd/8zf1k5/8RKlUSi972ct0ySWXaO/evZqfn9f3vvc9ffSjH9XAwIAuueQS3XPPPdq9e3fkGlNTU3r2s5+tn//855KkpzzlKbrmmmt01llnKZPJ6ODBg/re976nL33pSxWf96lPfap++MMf6t///d81MzPjIowBAQEBATE41kw1ICAgICBgo3DTTTe5SNO+ffsqfrbWiKL+/xGpfD6/7DMvfvGLS5JKqVSq1NHRUbrssstKCwsLkc8sLCy4yFZnZ2dpfn5+2XVe9rKXlSSVdu/eXfrlL38ZO56f/OQnpebm5pKk0h/90R9VfLZyuPjii0uSSieddFLFz/X395cklV784hdX/NzQ0NCy3/3RH/1RSVKpra2tdNddd8V+b//+/aVt27aVJJVe9rKXLfv7m970Jjf3r3vd60rFYjH2OrOzs6XDhw+XHd9nPvMZd50f/vCHFZ8lICAg4ERHqFEMCAgICAhYARKJhP72b/829lB6ziAsFAqamZnRpz71KaVSqchnUqmUrrnmGkmLkUO/rm///v0uMvaxj32sbNTznHPO0ete9zpJi+dFrgaPPPKIJKmnp6fi5w4fPixJesYznlHxcx0dHZGfJycn9fGPf1yS9Cd/8idlo5a7d+/WddddJ0n68pe/rHw+7/42Ojqqv/7rv5a0GEm84YYbytaR1tfXq7e3t+z47HP+8pe/rPgsAQEBASc6AlEMCAgICAhYAZ70pCfpjDPOiP3bk5/8ZPfvCy+8cBlxivucT1j++Z//WYVCQU1NTbr44osrjuXXf/3XJUmPPfaYDhw4UNP4LY4ePSpJam9vr/i5bdu2SZK+9KUvlW0WE4fvfve7GhsbkyS9+MUvrvhZnmV+fl533323+/23v/1td883vOENy4j3SmDfB+Q3ICAgICAegSgGBAQEBASsAKeeemrZv7W1ta34c37307vuukvSYl1eOp1e1qnV/vf85z/ffW81xGd4eFhSdaJ41VVXSVrs4Lp37169/vWv11e+8hVHNMuBZ5EWyWalZznrrLNin+Wee+5x/64W0awG+5w2ahkQEBAQsByBKAYEBAQEBKwAcSmnIJlMrvhzhUIh8reBgYFVjWslkT5AM5dqx2xcd911etWrXqVEIqGBgQF9/OMf14te9CL19PTorLPO0rve9S4dOXJk2ffW41nscSNENlcL+5x1dXVrulZAQEDA4x2h62lAQEBAQMAWAsSxq6tL3/nOd2r+3mo6uHZ3d2t8fNxFFsuhrq5ON954o97ylrfo7//+7/Xtb39bd911l+bm5nTffffpvvvu00c/+lF97nOf0wtf+MJlzyItnjtZKznbuXPnip+lFtjntFHdgICAgIDlCEQxICAgICBgC6Gzs1PSYkrqGWecsaaavGro7u7WQw89VPNh92eeeab+5E/+RH/yJ3+imZkZ3XHHHfrCF76gz372s5qcnNTll1+uhx56yEX+eBbutRoC2NXV5f596NChVRFiYJ9z165dq75OQEBAwImAkHoaEBAQEPC4RbnumFsZ55xzjqTF8wdtjd9G4Oyz/3/t3V1IU38cx/GPYNOakxFdzLyYEBIkizIouiiSZheFkklREGEPKkRFEniXQnohBWIP4E0EdeG6qVj2hDPLTfOhFFQkUCwjKUU0ipGKkF0Mhwd16Or/H8336+qw8zu/h3O1D+d8f8chSRoYGNCvX7+WdW18fLycTqfu3Lmja9euSQq82vnkyZNgm9m1SFJzc3NYc0xPTw8ee73esPqY1dfXFzxOS0v7o74AINoRFAEAUWvuB9WnpqYiOJOly8rKCgbcqqqq/3Ss2c1h/H6/3r9/H3Y/e/fuDR7PrSl0Op3BWs0bN25oZmZm2X1nZGTIbDZLkm7evDmvpnM53r59KylQ68gTRQAIjaAIAIhaczc/GRgYiOBMlm7jxo06fPiwJOn+/fuqrKwM2f7jx49yuVxhjTV3F9H29vYF24yPj6u2tjZkyKurqwsez3011Gq16ty5c5ICO6YWFRWFfHI5MjKi27dvG36zWq0qLCyUJHV0dOjixYuLzmV6ejrkBjqza8zMzFy0DQAggBpFAEDU2rp1q+Lj4zU5OanLly9r1apVstvtwV1Hk5OTtXr16gjPcr7q6mq9e/dOHz580KVLl+R2u3XixAmlpaUpLi5OY2Nj6urq0osXL9TQ0KCcnBwdO3Zs2eOkpKRo8+bN6u7u1suXL3Xy5Ml5bX78+KHs7GylpKTo0KFD2rFjh+x2u2JjY/X161fV1tYGw11ycrLhkx2SdOXKFTU2NqqtrU3Xr1/X69evlZ+fry1btshsNuvbt2/q7e1VfX29nj9/LofDoTNnzhj6KCsrk8fjUU9Pj27duqWWlhYVFhbK4XDIZDJpaGhIPp9PLpdL5eXlysvLm7eO/v5+ff78WZKUk5Oz7HsFACsNQREAELUsFosuXLigq1evqrOzU/v27TOcf/Xqlfbs2ROZyYWwdu1aNTc368iRI/L5fPJ6vSHr8xITE8MeKz8/X+fPn5fb7dbPnz8X/azH4OBgyKebSUlJcrvdSkhIMPweFxcnj8ejvLw8PXz4UF1dXcGnjAtZaC1r1qxRQ0ODcnNz5fV61dHRoYKCgiWuMKCmpkZS4N7u379/WdcCwEpEUAQARLWKigqlpqbq3r176u3t1ffv3/+ozu3/YrPZ5PV69fTpU7lcLrW0tGh4eFjT09OyWq1KTU3Vzp07lZ2drd27d4c9zvHjx1VcXCy/36/Hjx/r6NGjhvN2u13t7e169uyZ3rx5o0+fPmlkZER+v19Wq1WbNm1SVlaWCgoKFg2sFotFDx48UFNTk+7evSufz6cvX75oYmJCiYmJ2rBhg7Zv364DBw7MC/Oz1q1bp8bGRj169Eg1NTVqbW3V6OioYmJitH79em3btk0HDx5Ubm7ugtfPBsXTp0/LZDKFfb8AYKWImQmnshwAAESNs2fPqrq6Wk6nUx6PJ9LT+euampq0a9cumUwm9ff3s5ENACwBm9kAALDClZSUyGw2q76+Xq2trZGezl9XVlYmSTp16hQhEQCWiKAIAMAKZ7PZVFRUJCmw+Uw0aWtrU11dnSwWi0pLSyM9HQD4Z1CjCAAAVFxcrNjYwN+CUJva/GvGxsZUWlqq9PR02Wy2SE8HAP4Z1CgCAAAAAAx49RQAAAAAYEBQBAAAAAAYEBQBAAAAAAYERQAAAACAAUERAAAAAGBAUAQAAAAAGBAUAQAAAAAGBEUAAAAAgAFBEQAAAABgQFAEAAAAABj8BursYCQk6eHfAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "adaptation.plots.show_traces_averages(trials_roi_df.loc[1:1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "4b51e8ae-4307-49e8-85d0-8acd7c849276",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "series = trials_roi_df.iloc[0] #Extracting features from first row of second roi???"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "c62ff987-817a-4625-9318-aac32b360324",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "features = adaptation.classifiers.extract_features(series)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "a305d275-98b1-4f8c-98e9-0740250e4923",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x1a5b3a46340>]"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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4CwqmXz4wBgB45eBYbPtDEAQRNxS8hMRVXqrBi8mUF+9Nh40H6MynAZDyQviTvOfF8gQvQcE0S19RwE0QxGQm3eodmCos6mnD9/5qNf9/OsDzwtJGnbk09qBA/gHCFx68lBIqla5o0kaBwYs7E4kgCGKyQsFLSDpyaZx21Fz+f27Y1fRwYYbdznzG2YZuBIQPSaSNCkraSA1Wgo5HFnhT8EIQxGSG0kZ1kjacj047HqB6A+jKpX23IQggmbRRQUkbRTHsssdSwE0QxGSGgpc6CSyVLjo3gM4c87xQqTShh6V4Yi2VrgQbdoM9L5Q2Ighi8kPBS50wz4tuFcs9L3lSXohgmPKiziBqBNHzYttyMAPUCF6qxy4F3ARBTGYoeKkTP8+Lbduu54UpLzTbiPBBVO7i6vWiPo86eoCUF4IgpjoUvNSJ6dOkTlxBd5HyQtSgJAQa8QUvcgpKTUkFDWfkygsF3ARBTGKaErzcdNNNWLZsGfL5PFavXo0tW7aEetydd96JVCqFd77zncnuYB24nhf5hiOOBnA9L3QjIPQUhSAhLt+LmiZSzcBUKk0QxFQn8eDlrrvuwtVXX43rrrsOjz32GE466SScddZZ2Lt3b+DjXn75Zfzd3/0dTj/99KR3sS78xgMwv4tppJDPUIddIhhxZlZcFUeetJHy/6BgmqqNCIKYCiQevNxwww249NJLsX79eqxYsQI333wz2tvbceutt/o+plKp4MILL8RnP/tZHHHEEUnvYl34VRux7rptGRNpU6/OEARDDl5iUl48nhd1UCh12CUIYmqTaPBSLBaxdetWrFu3zn1Bw8C6deuwefNm38f94z/+I+bPn49LLrmk5msUCgUMDQ1Jf5qBX58XNtconzEDhzcSBCAHLxOleILcQq20kY/npWLZvGkeBdwEQUxmEg1e9u/fj0qlggULFkg/X7BgAfr6+rSPeeCBB/Cd73wHt9xyS6jXuP7669HT08P/LF26tOH9DoOf8jJaLAMAOnMmzGqAQxI8ocO23WABaH3aSFR+KOAmCGIyM6mqjYaHh/HBD34Qt9xyC+bOnVv7AQCuvfZaDA4O8j87d+5MeC8d0j6DGUcKTvDSkUuT8kIEogYRiaWNlOf1M+yKJdUUcBMEMZlJdLbR3LlzYZom9uzZI/18z549WLhwoWf7F154AS+//DLOPfdc/jOrGhyk02k888wzOPLII6XH5HI55HK5BPY+GLdUWv75WMG5UXRk04HzjwiiVCO9Uy/19nkRX58CboIgJjOJKi/ZbBYrV67Epk2b+M8sy8KmTZuwZs0az/bLly/HE088gccff5z/Oe+883DGGWfg8ccfb1pKKAyuqiLfGEa58kKeFyKYUllRXmLyvNQqlfbr8yIqNBRwEwQxmUl8qvTVV1+Niy++GKeeeipWrVqFG2+8EaOjo1i/fj0A4KKLLsKSJUtw/fXXI5/P4/jjj5ce39vbCwCen7caP8+LmDby6wVDEIA3yFD/X/fz1kgb+QXTomGYAm6CICYziQcvF1xwAfbt24dPf/rT6Ovrw8knn4yNGzdyE++OHTtgGJPKehMKvw67Y1XDbkc2LfhipvaN4JWDY5jflUc2PfW+p8mMJ21USsrzEjZtJCgvEQPu/SMF/HrbXpx70mLe34ggCCIpEg9eAGDDhg3YsGGD9nf33Xdf4GP//d//Pf4digFeSVRRlZeq5yWXnhbVRs/0DeOsG+/HuSctxjfe99pW7860IjHPiycoClcq3Yjn5eubnsN3N29HqWLj/asPjfRYgiCIqNBSuk78/CxjQqn0dPC87OgfAwBsPzDa4j2ZfpQqarVRTH1eaqSN/PwsjVQbHRgpAgB2D4xHehxBEEQ9UPBSJ7U8L+2S52XqBi8s8CIDZ/x4lZekOuyGU14mSvX3eWEB08B4MdLjCIIg6oGClzqpXW00Pfq82NUbnd8Nj6gfr+clIcNuRfW86B8nKjZRA26WqhocL0d6HEEQRD1Q8FIntWYbdWTNaVFtxMpqp3IANllJyvOiKjiqEdjveGykwy57jYExUl4IgkgeCl7qxE9VkTvsVucfTeGUC3t7FLzET1Ht8xJX2qgaFKVS7HmjG3ajBtzsNYfGS5EeRxAEUQ8UvNSJ6TOYkaWNOqeJ54W1kvdrbEbUj6q8qOmeemHP014tWVaf1zdtJHpeIgbcRe55oeCFIIjkoeClTszqJ+cNXpwbQHvWnBZ9XixKGyWGqm7EPR6gLet0QpgohZttNBHC87Kzfwxf3/ScJz3EXnOQgheCIJoABS914tfDxZ0qPT2UFxa0UPASP960Ue3gZWiihO8/ujMwSGCBREfO1D6v71TpEB12v33/i7jh3mfxo9/vkl9TSBv5BUcEQRBxQcFLnfj2eRGa1E2PaiPn76n8HiYr9XTYve2Bl/H//eCPuP3Bl323YYFEe1V58YwHCDPbyOf7Hp4oVf+Wq4pY4GPZwHCBKo4IgkgWCl7qRFdJVCxb/MYhTZWeBtVGVCodP/VUG71y0GkauH+koP29Zdm8+V171qy+ju3ZRkeYDrsl3vfHfy4TmXYJgkgaCl7qRKeqjAorzvac6VYbTWHVggUtUzn1NVmpp0ndwTEnMBgv6rcVgwgWvKj4po1CzDZiRt6S8hyiKXhgjIIXgiCShYKXOjGrZlyx8yzzu2TTBjKmMS08LxZ5XhKjWD122HESRnk5WDXKjvukmMIEL76G3RCeFxbUlJR9FYMXMu0SBJE0FLzUiV55cW4onbm0tI1t+98wJjtst6fq/k9mWADAjpcwHXYPjjrBi1pBxBCDiDaf6c7+npfa1UYsBSX+3rJsKWiiEQEEQSQNBS91oqs2chvUOTcNps6o200l+GyjKbr/kxmmYvDgJVTaqIbyUg1AsqaBtKk/vf1UFdEw7Bdwc+VFCFbUKdakvBAEkTQUvNQJV16EVSybKN2RlZUXwLnof/XeZ3HPk31N3MvGsciwmxhMxejKO8eLGgSoVCybN4Hz9byw4CVtSMef+jw61LSVTqFhaVIKXgiCaCXpVu/AVMVgwYvoeRFGAwCulwEAnn51CF/b9BwOmdWGs16zsIl72hjs/kXKS/y4/VjCpY0Gx0v8+5jw2ZYFEtm0wZskMrKmgWLF8g1e1FRUxbKhZp7KvNrIfQ61g+8gGXYJgkgYUl7qJK0x44rddZ1t3I+3f9S5oPvddCYrbPVt2+6EaSIemHrBg5caht3+UddLUsvzkjUN6fgDnIAGCDfbCNAHrKxEWqw28gQvpLwQBJEwFLzUickNu+6FW+yuCwCias9UmamWfhH3lyqO4oUFL10hPS9iS34/z0tBSBuZStqIBS/+pdJK2kgz34g9Vqw2Uh9HpdIEQSQNBS91olNeRpS0USqV4tux3021AEA0bVLqKF6Y56VTUF6C1C1Realp2NWkjXJMeQnR5wXQ93op82ojuTmjCCkvBEEkDQUvdWJqSqX5aAChv4apBC9TreRY3N2pphpNdpjy0lk17Nq2txuuyEFReanRpM5JG+mVF/9qI3X6tHe7Eq828k8b0WRpgiCShoKXOkkHlkqnhe2cGwhLG/n12BD59TN78eK+kUj7Y9s2/u23L2Lr9v5Ij6uFeAObaqrRZIcHL8LxEpQ6OiikYwplSxsIF6W0kXx6Z8xaaSNVeQlbbSQ/Ls7xAD95fBfe/rXf4uX9o7E9J0EQUx8KXupEp7yo1UbidmyQXa0A4Pm9I1h/2yPYcMfvI+3P4zsH8E8/exqf/smTkR5XC5s8L4mhpo2AYNPuwVG5+duEJtARg5eM4ZM28jPshlBeKppqI/a4bDU4Er05jfLTP76Kp18dwu9e2B/bcxIEMfWh4KVOdJ6XsaI3bcQahYU17O7oH63+PRZpf5hJMm6zZIWCl8RgKZ6MmeIpnaDgpV8JXnSpI6ae5NKG1CQRCJE2ClFtxBQXsbdLofrveV05AMBoseKZ21QvbF/p2CMIQoSClzphNwarRtqIKS+sEqnWRfjASJE/V5iOqwxWOutXQlsv4u6GSXkR4WEVO5m0gVw1yFX9IyIHx1TlxbutXCotBy8sbaQ7BtUW/852GsMu77jsNezOrQYvQHypI11fGYIgCApe6sRM6ZQXf88LSxtZNfqlHBBW1+pKOwiWQvCrQqkXizwviVHiyouBXIYpL+E8L4BeeRGb1Kmel1yA8iIGLmwmUpDyIjWpK5XweuMpnFn6Dc7IPwMDVmymXRoMSiTJDfc+i69veq7Vu0HUAXXYrZO0ps/LCKs20ikvVVUGcAIYU9+5XQpYDowUsainLdT+sOZ346UKbNtGKuXzAhGhPi/JwYKDrGkgl3YChqAuux7PiyZQlTwvPqXSOgVNfK6OnInxUkWrdrBjgAc7T92NMzf+Hc7N7gEGnB/tzs3GxNNfAOa9z/e9hIWnjUj1I2JmaKLEA5fL3ngE8j6DTInJCSkvdWJqO+yyJnWC50UplQaCg4D9IwX+70jKS/XmY9u1O7VGQcwkaLIIRAOwQCNjGjywCPS8KGkjncpWENJGfk3qdFVK7HGmkeKBlO445X1eKjbw1N3A9y9C28QeaZuF6Mfhv7rc+X2DkOeFSIqRCfeaXGuuGDH5oOClTlgDsIombdSe1Skv7o0myLTbX2/aSFix+/UAqQdxX3VNy4j6KWkNu/rvrmLZvPkbM8Zq00YBgxmDSqWZ4pMTmttp00bVY8Aql4CNHwdgQ9X4+MtuvAawGjsWmeJCnhcibtj1GqDjaypCwUudMD+BeNCPFOTxAIDbDyas8sIMu4Dsf6mFKPvH6XsRgxdqUhcvrFTa8bwEp43EoYyLevIA9N+zNJjRx/Oi+x6lKiVNGwDAUWzYQ19TfgoY2u373lKwgaFdwPYHfbcJg6u8UOBMxIu4oIyrOo5oHhS81ElaucCXKxZXP3SeFyl4CTLsSmmjgu92KmLPj6SCFzq/40Uy7NZIGzEVrjufRle1I6/O8zJWPc60U6UDDLsTXHkxhTYA8r6UhP/PskM2QxzZU3ubANjxR6MpiLgZFZQXCl6mHmTYrRPV8zIm3EjapT4v3lWs34gA27aVaqPwFRuFhNJG4jlNaaN4Efu85ILSRlYF5Rfux3nGg0jlF2AsvQqAN3ixbRv3PbsPAHDcwm6P5yWoVJq9bj7jVimp24kq4x6rF558kY7OBSE28oe9JnleiLgZk5QXOr6mGhS81Am7EYwVyyiWLW7WTRvujQiA5wYC+F+IR4sVaeUdSXkRbmRx9noRy7opdokXrrykhWojVXl56m5g48exfGg3vp4FMAEcfGUerjEuxHhxhbTp4zsHsP3AGNoyJt66YgF++5zclTZIeWGvKysv/sHL5sqxwOzFwNCrADR9YwAY3UuAw9YGfwg1IOWFSApSXqY2lDaqk8PmtGN+Vw5jxQp+8adX0Tc4AcBRXcQyZdU0CfinjfpHZI9LPdVGQHJpI1Je4oUFA1khbSQ1qatW86jekt7yPnwzcyMW7r5X+vlPHne2e9trFqAjl/Yce7yKSHP4cc9LRvC8KBtKjekqAM7+EgBv6GLZzg/Hz/w8YDRWfjrTqo36R4v4067BVu/GjGCsSJ6XqQwFL3WSMQ1cuPowAMB3N2/HTb9+HgCw5sg50nY65cUvBtivKC3RDLvJp43IsBsves9L9buzKryaR4UdUWuf+zImCkX89rl92DM0gf/5gxO8vPO1SwDAMx7AbVLnPQClaiM/5UVMfdpAZfm5wHu+i8HMPGm7vak5uLz0UTwz+4zA9x+GmRa8XPG9x/Bn33gAz++NNpiVcLj5Ny/gGyGbzom9tyhtNPWgtFEDvG/1UvzLr5/D1u0HATiByt+ftVzaRps2qqG8tGdNjBUrdXXYBeJVXmwy7CYGU1nSZsrtsMuC0O0PBlbzGCmgu7gH/7PxR/jI5k6kUk6PnzkdWZx+1FwAQMYzVVpfRQS4x08+Y/L9UrdTV6eligVzxXn4pycOwSt/2IRLTmrHW1ediKv/18CDLw3grXtHcPLS3jAfhS+VSZ422nFgDL/feRDnnrgYhuZcj8qfdjuqy56hCRw1v7Ph55tJFMsWvrRxG2wbWP+Gw6WqTx2i8lKmi9uUg5SXBpjflcfbj1/E///+VYd6Ljhqi3bA37B7oKq8HF19joGxUuiTKinPS4XSRolRktJGiuclZJXO4L5XAICXMJ938mI+DFQNnNlr6L5GfZ+X4CnTLKAoWCk8ZK3AjiXnAIefjqMW9gAAnts7HOo9BDHZS6X/4cdP4Mo7H8ej1QVMIwyOlfgYkckarE1mShWLnwdBM8IYoueFmtRNPSh4aZCL1y4D4PR2uXLd0Z7faz0vvsGLo7QcOc9ZSQPeeTZ+JNekTvg3nd+xEpg2Clmls7vUDQD48BuPwPX/5wT8/VnH8t/5lkpr+7y4hl2/aiNVWmeDJYtCjxgAPIB/IYbUBwv0J+vNfH9VLY1irvdj50F3krzfAofwRzxGwiy0qNpoakNpowZZedgs3Lb+dZjXmcPczpzn91HSRqxB3byuHHraMhgYK6F/tMg7qjIGxorozKX5ChtQDbvxRRnSYEbyvMSGbdv8YpsxU2irltez+Vg4bC3QHVDNYwMDmXl42DoWwDBOOWwWznrNQmkbNXDOBnXYFQKQ8ZKf50Xf90Xs6gsAR81zgpc4fBvlSe55YYFbMYab3ysHx/m/J+v7ncyIn1mYz29U6rBLK7OpBikvMXDGsfNx/JIe7e90yovfqop5XOZ0ZjG7IwvATSUx9gxNYNUXNuGv/99j0s/FEtvkmtTRCR4X4kovkzYwq935vgfY/CLD5NU8akMV1pD/ztl/gwNjznfNHi+idtgNnG3E0kYZ07fDrtpCnf2fSe5ceVngBC87+scaTmGy4y+pm/mugXH85PFddT9/kU/ZbvzceEVQXiar0jSZEYPrMO3+x6jD7pSmKcHLTTfdhGXLliGfz2P16tXYsmWL77a33HILTj/9dMyaNQuzZs3CunXrAref7ERRXthQxtkdOcypBi8HlUZ1L+wbQbFs4Zk9Q9LPxVRRrJ4XaTUT29POeMSLZdY00NueAeD4nDgrzgPe812ge5H02PG2Bbi89FH8LruWB7yzOzKe1/AbzBhk2A1bbSS+Bxb4MGVnXmcO3fk0LBt4af+o57WikHS10T/+z5O48s7H8cDz+2tvrIGpTnHc/ETlhSr7otOI8hKHckY0l8SDl7vuugtXX301rrvuOjz22GM46aSTcNZZZ2Hv3r3a7e+77z68733vw69//Wts3rwZS5cuxdve9jbs2rUr6V1NhCieF53youbSi2VZqmdI1UYJeV5Iyo4P8WaXNlLorSonB5XJ0VhxHvDRP+Hzc/8v/ra4AQ+c9u/47Tt+hXusVRiZKGOoavDUKS+ZCOMBXOVF7PMiH2OqulBSlBf2/KlUCkcv6ALQeOqIj99I6Nhj59z+4fo8K27wEkfayFVe6FyLjqi2hPK8ULXRlCbx4OWGG27ApZdeivXr12PFihW4+eab0d7ejltvvVW7/fe+9z38zd/8DU4++WQsX74c//Zv/wbLsrBp06akdzUR9NVG+m2Z52VORxazOxyfi9rrpeAXvDShSV1SF9SBsaJUkj0TYDf8VMpRSGbplBeGYeLx9PG421qL4YWvR1vWCVRerTZGTKWAnrYQyosZzrDrp7yoN+iy4nlh1UwAcOjs9uo+jqMRklZe1AAsKoWElBcKXqIjG3ZDKC8F6rA7lUk0eCkWi9i6dSvWrVvnvqBhYN26ddi8eXOo5xgbG0OpVMLs2bO1vy8UChgaGpL+TCbCdti1bVtQXty0kdrrRae82LYtVxslFbwkEGA88cogTvncvfinnz0d+3NPZsSJ0qlUiisnHuWlCg8uMgY39+6tqgU9bRnJvM1QPS9spIXW8yKkjQw/z4tq2C1Xb/yKYRcA38fxYmM3haT7vKgBWFTY48J4LIKwbRs7+0l5aYRKVM9LkaqNpjKJBi/79+9HpVLBggVy2eeCBQvQ19cX6jk+/vGPY/HixVIAJHL99dejp6eH/1m6dGnD+x0napdTQH9hGi6U+erPUV6YYddHeRFWCuo8nIlYO+wKXVUTuKA+u2cYlg08uXtmtURnZcZMDWGel0LZ0qb9CsLU57aM3HJ/tiZlBMjHXtpIeYaJSs/P1ZMInpfqzaKgC16q+9hoIM3uR0mZxbnpuI7gxbLcirFG+4QMjJUwKnzvVNkXnXJEz8sYzTaa0kzqaqMvfvGLuPPOO/GjH/0I+Xxeu821116LwcFB/mfnzp1N3stgtNVGmgsT667bkTWRz5iY05mVfs4Qc+ws1VJQSqPj7bDr/juJ1S+7yHgGEk5z3B4vzvHRKcwi0qkvojKSV4KXWR364CUjHHuGELzoLuxFIXhhqU41WPWrNioogRjgBi+NmseZMtKosuH//PUHH+JjGt0/MWUEkPJSD7LnJUzaiKqNpjKJ9nmZO3cuTNPEnj1yt9A9e/Zg4cKFPo9y+PKXv4wvfvGL+OUvf4kTTzzRd7tcLodczttfZbIQdqp0f/WGxW5Es33SRgXBmFusWMilTcmsC8QbvCStvFR8ArDpDpOpWbonlXJMu/tHCjg4VsTi3jZp+wlReckqwYuf8mLIygsLjnTBs5j68Z8q7R0P4DzWHerIYPvYSPBi2zY3jCdVfVPm7yH68ScG3I3e/ESzLkDBSz2In1ktA27FsqXrJKWNph6JKi/ZbBYrV66UzLbMfLtmzRrfx/3zP/8zPve5z2Hjxo049dRTk9zFxAnb52WkWjXC5nGwv0cEUxkgX2TZv9UbRLzVRtFWM1HhLebL8e1zEP2jRVz3kz+1fHIvu9mJagVLHQ1qTLsFPnvI8KaNNGXSgOx5MY0UjJS/8iJ32PXzvOhLpYua98J6vjQSSFsJq35AY4Zd8VwsNZjW2qkEL1QqHZ0oaSP1uCTlRc8vnngVz+1pfMxHEiSeNrr66qtxyy234Pbbb8fTTz+Nyy+/HKOjo1i/fj0A4KKLLsK1117Lt//Sl76ET33qU7j11luxbNky9PX1oa+vDyMjU3PKqq7aSJfPZs73rrwTtLAbj3oRK2iDl+akjZLIw1sxeQbC8vMnXsXtm7fjlt++2JTX80NNGwHgFUe6kRBicKEGL35pI3E8gBkybRSovKiG3WrqUkw5MVzDbv3HYtS+HfXQiGFXPGaZeble1LRRUmmy6YyottQKdseURSF93l629Q3h8u89ho/e9Xird0VL4uMBLrjgAuzbtw+f/vSn0dfXh5NPPhkbN27kJt4dO3bAEG7w3/zmN1EsFvHud79bep7rrrsOn/nMZ5Le3dgJ2+dluHoydVQVF/aRqNtKq73qCZek8iIGLEmkjbjy0qS0EQsSxXx3I1iWjaf7hnDMgi5ezROGIg9eROXFv+JIrDYSgwQgwLBr6A27+lJp11Nj8unT+r4ujHLFQtlyUztxG3blNEAyNxf2GnWljYT31ujQUjV4IeUlOlGC3dEiKS+1eKXfOSb7qi0ZJhtNmW20YcMGbNiwQfu7++67T/r/yy+/nPwONRGd50V3YWI3VZYuSvsMx5M8L0rayEg5UntSpdJJSPcWTxs15+Khq9ZqhB8/vgtXf/8PuPLMo3HVW48J/TixVJrh9nqRg5dyxeLHAStlzqUN/l58lRcjvPJSCOV5UauNbOmmrwteGvG8VJrQY6jUQLWRpLw0eDyxnj1zO3PYP1Igz0sdROnzMqqm4yl48cB8mEMTJdi2jVTKey9rJZO62mg6oFdevNupnhd2T1NXyZLnpeLcGCaqP2Mr9zjHA4hqS6LKS5M8L26fnHhe77lqB1m1k+zO/jFc/f3H8dRufd8hViqdSYvBC1Ne5LSRGNixRnCiaTec8mLADPC8FCXPiz5w9igxZUsOXoRALM8Nu/XfFCTlJbFS6fqDWZ0KWi/seOzMOZ8blUpHJ4phd0xRXiht5OVgtVikVLEbOo+TgoKXhAnb52WkKAcvfuZK8UZWUJQXZvgsVezYZFBpPEACF9SK0CukGV12iw1Ul+gYEFYnIv+5ZQf++7Fd+H8Pb9c+jt2MxXJmv7SRHLw4p6zoe/FTXlIpt8LIMPxTkeJrBCkvug677PM0jZTUKC/utFFSQkSpgbSRHLw02Iyvuh98hAPdTCMTSXkpysoLpY28iIso9fo2GaDgJWHC9nlhyktHjbRRULWRWDIbl/qStGmSXTNsuznlisynEJdMzNr5D457B2jqfs4oBqSN1Goj9l1mTbf7rRi8zPYJXgBXfUkbhq8JHJB7tbizjWobdtWhjAzWiyYuw25SyksjfYbEc7HRlXtZDV5IeYmMqAzWulaNFVTPC33eKgeFNh3DFLzMPLTVRpoTS6028lslFzRSNbuBdOfTYLFSXL6XpGcbiRecZqSO4lde9MHLi/ucacrDE2XPYwB92qiXVxvplRfRqCs2qvNLGwFucGQaKX5M6ValYq+WsMpLqWLx1GVWMRHH4XmRjr0Ebi62bbuG3QjBLG8OKTym0WCYpWSzASMciGBKEZrUjZHyUpN+4To0OK6/jrUSCl4SJrTyUtArL6FKpXkPELeMdqLBmTK6fU0keBGevxmmXb/BlvUyMO4NXiqWje0HnL4dIz4rFrfPizdtpA5nLAQ0gTONFA94dbjKS4p7XmwbnhSd2KvFNfbKn5Gn70vF1gZWQALVRgkce+LNLqwH6mu/fA6rv7AJuwbGpQq5RqcSq8pLUn1tpjOyShzN80LBixexcIDSRjOQsB12R5RqI79Vstxht2rYrd4g2jJu99X4lJfg/W4U8f1NxeBlkHlexkt8tbzr4DgPBtQmg4ySplTabzijONeIwYKDWe0ZnkrSwT0vqZTUtE78Lm3blkqxQ3fYtSypP4xIPus2qavXy5R0ylJMRYU9Hn71zF7sHS7g8R0DSrVRY/vH3l8mYPI3EYx4vNb6Pryel3Cfd7liNRyoThXE7u5DPunvVkLBS8KE7fOiBi/iKlmUkPWeF3bjMV2vQVzBi3gDScKwK1w0CjFWSfnBq43i8rxUT2rLdi+IL+53K4/80kaBnhchEAKC00a9ASkjwG1UlzbdtBEgf5dly+bNCHOmCdPU+61K6v/Ltm/wwoIr267/s05aeRGfM+w+shvXeKkSr2HXZqXwzudGaaPo1ON56apeb8N8f6WKhbd+9X6865sPNqW4oNUMSIZdShvNOML3eamWSioddgH/1EpRaVInto6Pq1GduK9JzjYCmqO8sBtOHK9VKFck+ZmljpjfBXCN2CrsJpjWpI0sW5ZpmdqW1XSwDfK7AO5xJPZ5AeSLu1TNFKC8eMcFWNqhjIDsyak3hZl4g8SKflEQBLvJTcQdvFRY8EJpo3qpp9qop7pgCGMIf+XgOF7aP4o/vDKYWPXbZMGybEkBJsPuDES8ObEeP7rrHFuhd2TltJGzfTjlJS+kjWKrNkq4SZ3fTTQpWCAQR9pIrQriwYugvIwUy9obr262UTZtoKP6/YmrnoKgrDHy1ZvcLJ+5RgwWsJgpOXh5fu8IPv2TP2H3wLinV4tfMzv1Bl2q6EcDAI6ixIKgelVAK2nlpeI9l2o/xl0wiD6ZRvePPZ6NiyDlJTqRPC/VxWJPW7W9RIjxDnuH3E6zSVW/TRaGJkpSgDZEht2Zh1ht1J7xb0ClVhuJNxpLUic0HXaZYTedRNrI/Xcy1UatSxs1Kv0OjOuDl5f2u8qLbSv5dasCvPRbzHnpf/B64ynkTXkfdL1e3O9Xo7wElEkDbvBsCoZdAPjOAy/hu5u344dbX+HHVMZMwRCmT9fssFtx+7yIfhy+jw0ei1EG7dVDyWdREAR7v+PFipRqajQYVtNG5HmJTjlCtRE7J1mFX5i04Z7hAv/3NI9dPI0yJ6NhtynjAWYyouelI5fGaLHiWVWVKxa/wHfkvMGLX26erfwmik1KGyXSpK7JaaPq52fbzuea0TQRDItaFTSkSRsBjp+pK58Bnrob2PhxYGg33gfgfVmg8Oy/AU/9X2DFeQCci+mugfGaysuhs9sBAEfN7wrcR3b8pU1Zedk/4lyIhwtl17cilFUD3tWrutosV/wNu4DTZXe4UK77WEy6z4ukvIT2vDj7pAZkjSovniZ1pLxERjxGapXWs3Rvb1vW81g/vMqLN2CfahwcLaIjl/acv6JZFyDD7ozEVIIXwHthEoeEdVTbg4urZMm8WfJecHWl0kn0eUk6bRRXBVAQurRbvagziAbHSxgrlvmcGhYMjEyUncDl+xfBHtotPSY3tgf4/kXO76GvONIZdi9euww/vHwtLlpzWOA+mtzzYiCVSvHUJQuOxosVqbsu4PpkVKWF/Z8dY8WKrfXjMBo9FuXAOf5USj2VbiXRsBvTbCOx30zGxyxN1EaqNgo526g7StpIUF6mw/ezb7iA11+/CZfc/ojndwfV4IUMuzMPWXmpVhIoCgarNMqaBpeNw5gr3anSzPNixO55Ec/RRAy7LSqVBmIIXsZV5aXMU0az2jNY0JMDAAyPFxzFBTa8Ok/1/W+8BrAqQqM6r2FXDF4ypoGVh82qOck6zT0v8v958CIYT9Vjz2vQrQYv1WNMUl40+8GCl3rTgR7DcMzKXz2G3aKPYbeRDrvi22S9fGZINW6siGpL2D4vfKRKCOVlj6S8TP3gZfuBURTKlmcuG+Aunthihwy7MxAxCGnP+igvrExaaDaWSqV4t1w/dUKdbZQXS6XjShs1sVy1KR12y9FTBX7oDLsseDl8bgc6c86F0di5GVAUFxkbGNoFbH+QKy8DWuUlukztel6cU53NzGIXp/FSxaOe+HfYdfaDBSVlS+4Po5JvsOeQGizHvdotKWmjMB6osrBgKMR0LIkpiyxXXih6iUpd1UZMeQnjeRGCl+mgvLDjV/dZsevDou48AEobzUjEkmfWw0VdQfJKo5x8c+Ir4FqG3RJbmSebNppOpdLqv+thYNybNnppHwteOrn52h7qC/eEI3vQ3eY8RuwP43peop+u3PNiuMZdwF15TghpI6bssGGiuo66gKPwAc4Nu6ipmmIwg3G9x6J3qnVyaSPmgapFycew20jjMvF9ubON6n66GYtUbVTL81Jgnpf60kbTQXlh9xLdecWU30PnON46ShvNQGTlRd+AiisvObnslT2W3TTKFUuSmL2l0gbaso3dMFSkC8I0aFKnm8pdL+wEZ0HK4HgJOw86YwEOm9POG2AdNGeHe8LOBciazE8i7qc3bRQWUwla1L5DYtqolvISmDbSeV6yjamA6vEW9w1DVTdqBbO2bfN98DapC963b/3mBbzj67/1+KSc/XAfy75jKpWOTj3KS2+EPi97h8Rqo6n//bBFkS7wZp6Xw2Z3ACDlZUYi9nnp4GkjeRu3u66ivFQlfqZ+qDfbkmLYbRNnG8UUCIj3j0QMu1NYeWFpo8Oqq5PB8RJ29o8DAJbObuNpwJfaTwS6FwMax4tDCuheAhy2lgcB4r6JfXyiklEqiHTBi0d5qVFtxNNGwmyjIMNuvcdi0sqLGnDUOh7E7aN22P2vra/gyd1DeOjFfs/vdMrLdO8jkgTiTTjo87NtmyuPzLBb67sfLZSlUR/TQ3lx3rPuvGLVRofNbefbNiOtHwUKXhKG3QgyZgqZtDcNBHhHA6iP5ZNvy/qVYkG4ubEb3Gihgp88vgvP9A03tP9JdzltpmHXtm251LxBzwtLG7Gy5aEJV3lZOqudKzJDBRs4+0uwAU1nzmowcfYXAcPkpdulmJUX17irBC9FnfKi7/Lqpo1cdUg1+4o0msLUdfSNE9VkW+t4EL+TCcErpP5OB1u5qiWogPw5u9VGgU9HaAjbF2hwvMR/75ZKB1/bxJSR8/zJfEGD46WmBQnsdXTvnRn6l85qF0y7kyt1RMFLwrCbRj5jukqKcrCMTMgTpRlq8KLe3MXKB/YaTKq/96k9uPLOx/F3//WHhvY/6anSzTTsej6/hkulq3nhqrTaP1rkZdJLZ7fzNOBIoQysOA9j77wNfVBSSN2Lgfd8l/d5yWmUl4YMu4riog5xnCiJpdKs2sj5nV/wICovtfq8OK9R3+esVuWx/YlrroxaYVLreBCDHTHoA5ygNOj8YE2++kcLnt+x64HYSDCJnkrTHakvUEAa79YHXgIAHLugi3eorhV8imZdIBnlZbRQxulf+hXe862HYn9uHUGG3f5qenNOZ5Yvqidb6oia1CUMC0i68+70X1V5UbvrMlTDrp/yop1tVP3Z83tHYNs2Uim/lEUwSXfY9ethkwTqyjqu4GVZNW20o38Mtu3cyOd15vj3ycoMdy1ah7MLX8cZ+efxnXctBToXAIetBQw3KGErb33wUo9hV04FqYNC5VJptycM4N/nhXm3ypbleaxIwx12Na9/2+9ewtc3PYf/vOz1WL6wu67nZaimzlrKn3j8TJQrnuOpVLFgGt4As1Cu8ABu/4i/8iLOn5oO1SzNJozycmCkgO9Ug5er3no0P99KFTvwOqkqL42UxvuxZ2gCQxNlPNM3FPtz62DX24rlfe/M8zKrPYvufAbDE+VJZ9ql4CVhjpjbgU+csxxHL+jC757bD0CjvNRIG7ETRVUmeKl02U0btSm+iPFSBftHipjXlatr/yXlJYHVoJgKUG8elmV7lIKaWBVg+4PAyB5PcOAJ/iqNKT1sHABz5LOP55DeNhhGigcv7PvdMzQBCwZe6VkJnPBG7XNyz4uYNmLVZHVUG5mmorxo0kZ+pdJ+U6XzQpO6sZJ/SotVJdVr2FXVB8u28Ztn9+HgWAmPvnyw4eBFTUPVVF6E7ceLlmd7v9W4KLcf0KSN2Ocszp+i4CU6YirH77v41v0vYrRYwfFLunHWaxZKN+Sgjtt7FeUlyVEptczfcSHeTyqWzf2ZlmXzHlazO7Ju+puUl5lFKpXCZW88EgDw0AsHAHgPfHZz86SNQhh2SxWLP18+bXKpXmTnwbG6g5dKwmkj8f4hnkzP9A3j/JsfxBVnHIUPv+nIcE8mtN/ndC8Gzv4SsOK8WNNGpYrFv7fD5nRIvzuk6oFhwSi7ee2pVivM7/b/LnSG3TjSRmr6iDFR8qonpl+1UUU2DpcrFleVmPFRpHHDrvz/smVLU50bxWPYreV5Ecpp1SZ1zu8tQPPVihf9AyPetBH7nNNGylVnKXiJjFxt5P0uJ0oVfHfzywCAj73tWKRSKSlYKVUs36aPatoomYWc85wVy+m4rJ6rcSNeD8uWDXZ5GZ4ou56g9gw/tyfbfCPyvDQRv7SRn/KiXsh0N1/xIp7LGMLMm06ccmgvAGBn/1hd+2vbtlRtlIznRa+8PLq9H0MTZTzw/P5wT1Rtv+9pBjf0Km+/r95sGjEIs5RRKgUs7M5LF5qls9oAuN+nqLwAwIJq4ycdrowdj2E3bcipIDVtVKxYvPIi51Fe5M+Hff88bVSx+Y25O+8NXhodEqqeJxXL5gFEHE0YoyovYnAjVmkx/Lq0iqt7nWGXKy+mOxSTBjNGRxrMqFEvnt0zjImShdkdWbz5mHkAIAUrQYqH17CbbPFCI+MmwiIev+JrswZ1HVkTubTJz20y7M5g/Ay7oz7Biyrfe9MeFs+lp1LOzefIeZ346UfegP/68Bosm+soAq8cHK9rf9XzM5ETVnhK0fPCPpNQuWWrwtvve6n+bOM1KBbllQP7PPcNFyKbQAerlUbd+QxMI8U7dQKOWReAM4wRovLCgpcQyotU2VJ/qbSruDj/16XhWPpLHQ/g12GX7UepYvH3xprriTTc58UzCNKtFoujj5Gn2ihC2qhi2VLpLOB/8xOVF53nRUwbsbQeKS/RkXpSaT6/ba86lZfHLeri/g4xmA8KGDyG3QRSO/XM2moEsa9WWRO8sAn37NyebGkjCl6aiJ/ywm4Anflayot8wRaVl1za4Cfk8Ut6MKsji6WznJtovcqLznMQN+INSrxhjxRYGV+Ik3j7g6Ha76df2Sz9tFix8Jtn9+F1n/8lPv2TJyPtN1NeWJMrKXiZxYKXqvKiBC8LA5SXbMyGXVMZD6CWSgPuRUktlfadbcSCF8viUrJOeeFpozovxOq9pCKkjWIJXpT3V8sDpXZhHVQu5n5ddkW5/eBY0TtVvnqMk2G3Mco1PC9PV42woldKTB0FBS9igzogKeUlfN+gOPBTXngauepZY+c2pY1mMCZfVck/Z90eVc+LKiF7cuwVt3GQblXOFADWeyQqfjevOBFXMGJwxpSXYpgVzsieUK+VUrYrli08uXsQAPAfD23Hlpf6Qz0PIAQv1aClW1Je/NJGzPMSELyk3eoHRiNpIxZAMPOsLo8+wJWXcOMBmKJSKtsYGq8qL0HBS72GXc1NPk7Pixps1GxSpwTSXuXFJ3gZd7erWLYn6GFPmzbctBGVSkcnrPKyfGGX9HOWOgpSU1jaSKy0i5t6BoU2gux58QZ+GYMFL0x5obTRjIVJ9359XrpUz0tK73lhNyBHealK+RozJ/NesK6vUVGvn0k0qRMv0vq0UYiTuHNBqNcay86V/l8sW1JK41M//lPoFQ+74fdUpdVA5aXgGODCeF60ygufbRQ9bfS+VYfiva9binedcggAffDCbqbstf2qjdQ+LxPlCldA1DJ/oHHPi2eqtGXzoC4Oz4vXsBt8fJeUG4p6fvimjZQV6wGl1wv7XA3BsJtEWmK6Ix4v6nls2za2VZWX4xbJVWrsePczbIvddRf1OOdu0j2v1P0fGCvG1jWdoVYbMdjnwKqPyLBL1DTs1mpSx25o7EZRENJGeU0ZLVNedg2M1zU4Tl39JT9VWkwbOZ9JqGDisLWh2u/vm71S+mmx7JpVAeCZPcP43kPbQ+03m1HDlZfqd9KZS/NUkpgGHJko89VbUNqI93mRDLv1p42Omt+JL77rRH4s6Dwv7L1kNdVGohdIVV4GhKnaSQQvOsMuOwfGYghe1BtQbc9LjeDG51hVlRbV98L2I01N6hoiSHnZM1TAwbESTCOFo+Z3Sr9z1c7gtF9a8LY1M3gZmijhDV/6Nd777Xib14mLRZ3ZmV2LyLBL+HfYDWnYZTcxtl1RKNdVAx/AWeFnzBQqls07v0ZBd/OIG6lJnSZtFGoFaphOOTQAbwDjtt8vWP6VNuzm+8jLB0Pt946qj2hRrxOIsIvaIbPauPcolza5mrG9fxQVy0YqBcztzPo+r75Uuv60kYqujcVgVQ5Wq40A2bTNLqhqL6GOrIm0psS0UcOuep7E7XmJ2mHXb2XOFg5hDLsAcMAneCHPS2OIN3w10GR+lyPmdnhS7GmfpowMdo1oz5qJfj8Vn8rLV/rHMVIo48ndg7F1lwbk41nuTuz8PMOVFzLsznjUjrmAc6Cw1E+tUuli9SbGg5ey5Rv4sNdb0ltNHdXhe7GVa3USq0E/5WW0atgNPX9oxXlOm/3uRfLPhfb7ug6740VZDg57U3xuzwgA4Oj5Tv7cDV7ape1YUPT8Xmf7uZ057Y2ekdWWStdfbaTCLtQirmFXrjYCvBU2uv3o0vhdgPgHM5Yt2zMSoxG81Ua1DLv6Y5Gde/4rd3nFqo4IoOAlHoKUF+53UVJGAPjMOb9rzTgPXtK+lXhxIB6P8hDQMv9ZHEE7Q1JedGmj6rWCnd+qgthqqEldE9GVQbKbNBC+SR27IZYqluuX0cj2gJM6evnAGF7pHwdC9npjqMpLIlOlfcYDjERRXhgrzgOWv8O3w25BOfELZQuj1QvTnI4cgBGMFcNJo8/vY8GLI0GfcugsGCng9KNlX01nPo0Do0UevASVSQNe5cUSUiVxKC+a2IVfrHJKtRGg9J5g1UZKI0RdmTQQ/2BGp89LnKXS3tYDgdv7HP/t2TSAou+xOiQYogtly5M2cscDGO5cqRmQNmpkbIkOuUmdErzwSiPZrAvUNuwyFbg9a/pW4sWBruIHkFOkg+Ol6vHWOH6eF542ql4PuIIas+emUSh4aSKmppJguOCWqarD7dQo3/W8uGPcg5QXoLGKI0+pdNLBi5g2qgYRkV39hgkcfrr2Vx7lpeIadmdXUznjIeYrDYwVsa/qXzmyGrysW7EAT3zmLE8Ayr6XF6rBTpDfBXCDl7JlO4GLsM/1GHZVRFVlVnsGBwXfiup5YfvB/+2TNvJTXvJZ5/kmSpW6blS64DlOw66nVLpWtZFPcNNRU3lxPuPD53ZgW9+wx7DL3mfaSPGS9umuvFz23Uex8+A47t5wmm9X26joUh8Mprys0CkvRrDnhY3AaM+ZySovPp6XcSV4WdTTFsvr+VcbVdNGBkt/e1PZkwFKGzURXetvpryolUaA/1RpdkMsW26HU7VHDKORXi8ez0EifV780kbVUukYTxjdYEumtMztqAYv1f//adcgzvjyfbjl/hc9z/P83hEYsPCOrufR+eyPgZd+C1gVre+IfVdbtw8AABb2BAcvYrvyYsWS1KhYPC+CqjK/S94XneeFDS+0LJv7X9Tgpdvn2GPpJcuOkP4T8CovlpA2avy4iB68eI9/IwW0ZYJvfuwcPXyuO31chH3GhmDYnc7Bi23b+OXTe/D0q0Poq8OL54eonKgqBls8HKtTXsKmjTJp3+7TcaCr+AFkxWNwLL7UjW+fl+rnyKqN2HWhGY3zokDKSxPR9XlhqzJd2sdTbVR9oBiosEFvnTn96pf1HNlZR5ddT4fdBMo3xYBIV20U5wpHH7xU00adTjqHXSjuf24fXto/is///GkUKxauOOMo/riJP/4YD+Q+h8WlfuCH1R8KM5REmCqxvzrT5h0nLA7cR1F9Kwp9fIyUt7V/PYiG3fndOTyzZ1h+basCY/uDOM98EHvtXpTLZwDISt8DU1QYurlGgBzkTBStyLOZ1OBZPD5iMewqN6tCrdlGmt9n0wb3MPmXSjvHMgte/NJGzmwj52fTOXgpVdxAOM7zW1JehH+/cnAMZctGe9bk3jaRWoZdtpBqkwy7se02p6wEXAw1bRQXfh12XcOu87mw85aUlxkM7/Mi3LBZ5cHsDm8Fikd5KXlVGtbK2c/zwp63Hqe4p9ooCeVFWS2VK5ZkYo6z06RuNhS7Cc5haaPqhWJM8CL933uewY9+/4rzn6fuxmmPXYWFUBraCTOURMTvZcWibrz+iNmB+5gRlJFS2ZKGMsbhDxBTQuqwzoW77wVuPB64/c/w9cy/4M7sP2H2LacAT90tycretJH+2MuYBg+46gk21BubKJ/HkjaKOB5AG7yYBjdZ+6U42bnHxnWowxnZ9UA07E7nUmkxPRzn+a0zlwPA9gOO6nzYnA7tOaQzyYuwY7cjJwYvSSgv7nMGpY3iwk95UUuldSNLJgMUvDQRnWGXBR/a4EUx7LKDR0xPsODHz/PCzWh1rHC8paqRn6ImakBUKFs8xww4q7S4ygPZzYl1yWSl0gYsHD36OM4zHsRrin8ErAr33DDJ9JdP7xVmKDlKiEx1Hzde42xXRfxeLnnD4TUDEMNw25WLyktO08enHtjFN58xpK64ZxlbcOxvrvCMWTBG+oDvXwT7qf/hP8umDen967rrMhox7ao3cPE5xqs+mkZwjbJu08cgdMpKNm1yeV33+AlhgOMRLHgZ9THsplK+DQLjZnC8hO0HRhN9DT/ElF+cq3m/qdIv7Xfe57I57Z7HAG7ayNfzUg0e2jIJVxv5KC/ica9WrjWC5HkRu/vyaiM5bcQWl5MFCl6aiG5VxfLfs9r9lZcyV15Yyay72usfDQ5e2AFYzwpHvXkksdpQLwKFssVlWvd1YwpeWNpNKDU/rfggHsj9LVbdfxG+nv0X3G78I+wbj8cR+34FAFhS7VI8NF7iM5T8ww8bGNrlbFeFqRJzO3P4s5MW+T1Qgk+WLtuBHZTrgR1TXfkMryIwYOG6zHehG2yZqv4sv+kfYIAZ+Qyp3NsvbQQA+QZ6vajfuxoANZqDZxfijmw4WVx3DuXSRuACgTX2SqWclT/gNPcTbwLsvEqb7mDGJG6OIh+6bQvO/MpvsHcoPs9JWCZKXuXl3qf24I6HdzQUkPp5XliQxpQvlbQRnPYT+7wkGVz6TZUWKyDjVV7EtJE3kGHVRmoqe7JAwUsT0fVwYMFHUNqIKSAsJ5813cok7nkJkO6B+tqNezwvDZ6wg+Mlz8VJ1+VUDV6CRtVHgaXd2Gd18sj9uAFf0aaAPrDjkzjL2ML75AyMlULPUBK3W3vkXOTSBv7ubceE9ny4Mm0lduWF3Ry78mmuiqwytmFxqj8wKDOHd2GVsQ1GylGHskLw4pc2AuRRAlFRjw11RlKjXXZZgMBKT2uWSvsGL/4LBO5py6UxuyPLFav+MVd9YQ+T0kYJBy87+x0fyI46h7Y2ghh0snP7qrsexyd+9AS+tum5up9Xvvm7/365mjbyVV5qpI3GWKl0zuSG96T7vIijKsaL7n7F1ShOHLUB6FUrVm0knuuFGIzycUHBSxPRpo1CBC8VRXnJZUx+g2Ot3XXVSoDrGK9nkJh682jkfH1q9xBWfu5e/ONPn+I/s23b8xqFcoVPlGaonVDrhd2cuvIZGLBw8eA3AXhTQCnYsAFcl/kPLO4WGjSFnKEkbveGo+fi6X88G+9ddWjo/WQXi0LZrTaKXGlkVZwqqCd+wKuhAEV5qQYW8zEQ6innY4CvUtOC8zcobcS6z9YznLGW8tKoaZfdrNpz4ZQX3eyjrKC86IJsdrPpbsvANFJcYRW77DLlxUwJTeoS9rywAGK40PyW76ryUrFsbtC/8ZfP4fuP7qzrecUbsE55YcqXCgs+/VIivFRaqjZKVnmR00a1lZefPL4Lj+04GPq11GO9oksbsVlnpuGmVmea8nLTTTdh2bJlyOfzWL16NbZs2RK4/X/9139h+fLlyOfzOOGEE/Dzn/+8GbuZONq0UTX4mBUQvPA+L4Lywk44drz7KS9u2ij6yaaqJI1MUn12zzDKlo0/vjLIfyae/8wKoksb+XU2jQpv8pdLY5WxDfPsAxrvioMBYHHqAE5NPQOgetE4bC3G8gsCgjhnhhIOWys/V8QqIfFmKBp2Q/PU3dx4ix9e4vx94/HAU3fzY6o7n+Ypnb3oDfW0e9HLgxaxkV0Y5aWeQEO9gXuCl0aVl+o50ZF104hB+FYbBfQJYR4FFuAxY7hYLi16b5rVYZft60gL5tWIykuxYnk+t0/9+E+hm0WKVDS9SkoVC69UKy2X+QYvTOl0P/M/vjKAp3Y7je3GxfEAPhPX48Cvz0utaqOd/WO48s7H8eH/2Bo67VZQlFC52kg27AJCufRMUl7uuusuXH311bjuuuvw2GOP4aSTTsJZZ52FvXv3ard/8MEH8b73vQ+XXHIJfv/73+Od73wn3vnOd+JPf/pT0ruaOIHKi87zonbYLbkpBLWhnV+jMLcMsA7lxdOkLvJTuM9Vfc/iiSh+DuwGUii5jfcYcUm0Yp+csGrDIWkn2BqaKMGCgU3Lrgagd4cAAM7+Iu/oWy9iU6jIc42eutupelKMt6wa6qTh+wHIaaMt1nLstmfD9kkcWTawz5iLLdZyHgxnReUlyPPSiGHXU20kH4CNjghw00augTtwe8UzBbAUrv/K3VVenMe4Q+7cmxB7n2mzeX1e2GJGPdeagViiWypbns+9ULbq8nbolJfdA+MoWzbyGQPzu/Tdrd3UOushVMEF33oI7/32ZpQr7mKqPed6XpLpNq43MteqNmIDUvcNF9AX0sOk+sWCZhsBcip7spB48HLDDTfg0ksvxfr167FixQrcfPPNaG9vx6233qrd/mtf+xrOPvts/P3f/z2OO+44fO5zn8Mpp5yCf/mXf0l6VxPHlYTdnzHPSlTlJat0pfQ17Jr1n2xqsNKIlM1OjnFhRSWeMMw8WihXNJ6XmNJGwniFsGpD25wlAADbdsyXj7a9AZeXPorh7Hx5Q2GGUqOIOXiuvITxvPBqKN335Pzsz/u+AQMWunJu2siCgc+WLqpuJwcw7Cu6Mf2XsGBIUjIjsNqoAcOuesyqwUqjaSO2Om/nx164aiNRaRKVF11aiXle2GfEFFKxaoS9TyMllkp7lc+4qFhuurYVysuEVCptS8pqVqhsiUpZ4+FgfpfDZnf4KqCqZ2mkUMZ4qYKhiTKGJ8r8OGvPmsICNNniBalUuhQcvIjB39OvDoV6LVVBEVV1NgZEVFfZ/SaO5pBxkWjwUiwWsXXrVqxbt859QcPAunXrsHnzZu1jNm/eLG0PAGeddZbv9oVCAUNDQ9KfyQrv86JRXuaEMexKnpfwvTaAOoMXm8mH7mqw3gsqC3xE5UU8YTqEG0hShl0WvHTm01xt8PtYLAC77TkwDzuN+zYGx0s4MFrEPdYq/OD0XwAX/xR413ecvz/6RCyBCyDPN3I9LyHUnGo1lD82ekt7scrY5igvQrO5e6xVqLz7ds9gyz7MweWlj+KH46cAcNOQsuclhGE35lJpIL60UXvEtJEYrEnVRpoge1DwvACuQioGDRV+s0hJfXiSUl/EG6OoADUL8cZZqlhSaW6mgbSZ+Bjbdv7v+l30Zl3AmzYSn2dooiSVSiervOg9L7XSRiUpeBn2/F6HmjaSzM7V12Yl5IC7eJpMnpdEO+zu378flUoFCxbIRscFCxZg27Zt2sf09fVpt+/r69Nuf/311+Ozn/1sPDucMGraaKJU4YMBdcqLO07A+b+kvAhphLSR8k0riAazqPNl3ODFQKkqF1q23KU1LGWuvLgnjbh4aWNpI41hN67eAuyE7cqludrwzcyNsCBH8U76xMZnSx/E/9eWRU9bBhOlAgbHS251WGeb7wylRskK7bjZPufDKC8hq6HmYwBHzu+UpkOnUoD5mvOAFX8GbH8Qn//+r/HEYBu2WMthweBfFm9cFbJUmh2X9azYPIbdYrzKCzfsRiyVVpWXwGqjcdnzwhTSYY3yYhqGpA5UbDuRC7R4A2qJYVe4cRYrFkpl5/1n04Z2hEpYVE9e2bLw8v5qpZFPmTQgqNPVz0UMTAbHS3wx1ZETPC8JdBvXTXYGaqeNxOPuqbDKS1n9rLyqVUajvMwoz0vSXHvttRgcHOR/du6sz6neDFTDLstVpo2UdvWqztEQy2ZFz0FnPu0blIjyflQFgx3PonGrXtMuU4/GhOZi4nNx6b7kVV7iivZF5QVw1IbLSx9FvyFPgp5oW4CPlK/CPdYqtGdN9LS5FUdBpe1xId4MJ6IoLyGroa76i9PxnlOXSp1yc2nDOYaqgy3vz74ZD1krnMBFgBt2q39nTP/AGXADMbVizLJsvDoYPLLC02FXCVYa9bywGyRr+ljb8+JsLwZr2bQZXG00oXpenL9HCu5NqMKDF3kERJyZiVLF4nNxxDRNSwy7PsqL2JE5avAizt5ihFVe1A674mJpaFxOGzVLefFLGxXLlue4F4Pu0GmjIOWFVxsJyks6nC+smSQavMydOxemaWLPHnlFuGfPHixcuFD7mIULF0baPpfLobu7W/ozWVHNeGy67KyOrDb44EoN67Bb1isvfn4XQDZdRQ082H6KwUu9F1R2slcsd1Iye1/OcDs3beQx7MaVNuKGS/fmc4+1Ch9ZcDtw8U9x0+xr8d7iJ/E/Z2zEz8qvA+CkFJodvGSFWSKRDLuHrXW8N74dW5xqqMNXvhWmkeJ+FAAeD5Xp4w9gP2f58O58JlDNExvuiXz5f5/Bmut/hfue0Rv3Aa9h1+N5aTBtxHL7bSGVl6JOeTENfpHXKy+K50WjvFRE5SVV//kaxCW3P4rXX78JB0YKUpDVCsPuhGrYLbvBS72l4rrty5aNl1mDOp9KI/a6gBt8ioGJmjZifV6SGN8g9XmR0kbyd6T2ehGPu5f2j4aq1PJ6XrzBi3jd52pwDDPF4iLR4CWbzWLlypXYtGkT/5llWdi0aRPWrFmjfcyaNWuk7QHg3nvv9d1+KmEoJ+bBUecg1FUaAaLy4vyfSX1ih10gOHgRb0JRlRdb8byI+x4V0eDGbjruRTslTC71GnbjuoizE1YtK2/L5YDDT8cfZ63DQ9YK7Bt1T1BHeXG+n4NjxcBxDnGRFW6Gbql0iFPVMJ3hkAC8AYy3GkpSXpR5RWmf3CCTktnxF5QyAvwbgD3ycj8A4Lk9I76PZYcrO4RjN+zW2WHXmzbybwTJS6W556UavBS8aSPV8xKn8vL0q0MYL1WwvX9M+i5aErwoTerY/uTSbvASdcGiU2pKZQs7+x11L0h5UYPPipI2GhPSRs2qNhKv1WqQrqaORKO4bQPP9NX2vXiqjYRjwi2VFpWXyed5STxtdPXVV+OWW27B7bffjqeffhqXX345RkdHsX79egDARRddhGuvvZZvf+WVV2Ljxo34yle+gm3btuEzn/kMHn30UWzYsCHpXU0cdmKy+7/b40V/A1CHgLnKiykpL0F9NsS8ZVTviE55KVcs/PSPu7EzYmdO8aXZTUcKXjKuz2NUWTkUy/EqL2pDP7byZjdzNgGa3ZiY8rJrYJxfVJJVXtwLxQQvjw9Zfr3iPKfqSTHe6qqhxOAlrPKipo2Cjj1ASBspxx7r7BrUJZcpL+w54m5S5zHshkwbdXkMu/7KS39VXWWfU6fGsGsJ54GZanyhoIPtW1EpTR5ucdpI7POSMVNc0YuqbPj12GHvdW6nvkzaeV35GBWfa3C8xJvUSVOlk/a8aGYbsfNADV7UPlhhTLtBfV601UZCEcFkIVHDLgBccMEF2LdvHz796U+jr68PJ598MjZu3MhNuTt27IAhfEhr167FHXfcgU9+8pP4xCc+gaOPPho//vGPcfzxxye9q4mjGnb7qzdJvxuhatgVy2bFaqMg5cUwUjBSjn8lch6Ze17cC+rmFw5gwx2/x5uOmYfb/3JV6OcSVxVjqvKSSvGcqtPnRT2x4i+VFmErb2YaZt1P2c9Z8PLSvlH+83zYYKIOWCBRLFvc0N2ejfB6K84Dlr/DqT4a2eN4YQ5b6+k/k8+KyovibfELXgzmdXHTRkGIQyYZE6UK9gw5x/5Yyf/myb73rGlgomR5DbsNjweoKi+hO+wGVxuVlPPrpf2j+NOuIaRSzkRxQEwbuTcgsUmdYaSQSjkLnDjTRuwGVxTSNECrlBe5w66YNqrYzu+iKhvitY19fqKCG6Rcuh12vdVG+4YLfLHZnk12MKNUbSQEUmzBtLA7jx39Y97gpaIGL7V9L0F9XtixovO8NDpPLE4SD14AYMOGDb7KyX333ef52fnnn4/zzz8/4b1qPmr3zP6qgc4veBENu+VqG21A7rALuKs5P9KmgWLZ8lxca8FWP2IEvnvQaYLUNxhtoJukvBTlC1SttFFcfV7YidehBHts5a0qL+znPHipTqed3Zmc6gKIpZvuDZs18QtN1XgbRH3Ki1Hdx2qn3rbg/cqY3hWbqNoFjQ1gX3suYwITZU/FUpjgZaJUQd/ghLbahB1/bSFLpV3DrtLnhft65Mff8fB2AMCbj5mHpbOdtIVr2BU9L26pMOAE82Xbjtmw6/rmJmupdDZtSN64KIjBRC7tBLvsM+ZmdB/E8019LvE615YxPUUUcSKmith3JKqLtYIX00ihYtnhgpcAz4u4aGDkZqLyQrioHXODuusCsmFXXLnmMoa0kghSXgBnwFYR0dNGbD8NI4W0kULZsnlgEXXFplNeLG3wknyfl3z1IuTevJybOFM3mPLSzpUX5/Nl5r/ZHf4SdByIEi37LNqiKC8hYdUdZcv2el6EgJUpd87PZcNuVy6650UcBhiYNrLdYB3wpnWC0kZjxTJuf3A7vvPAi9g/UsS7Vx6C685dIaV83PEAbiVFUDsB1/MiVBuZbuWfqJRMlCr4r62vAAA+8PrD+M+Z30o27Dp/s4DRqH7gcaWNbOH6obbjnxRN6oR29EZKVmVr8dTuIZQqFhb25AE4n2HGMDABixtXa6mkqmdJ9Nuwirh8xpDGNzSrwy4L0E0jhbldzn1CDV7YomzZnHa8sG+UN+YLIrjaqLpo1Y0HqGPAalJM+VLpqQS7H7jKi393XUA27IoRr9pht5bvgK8M6zTBsUnCgCvFRl2xiRdidlER+1uwm2dRqDby80vUC/sMc2m5Wqs9w9JG1eCl6lNorwaFvdXgkl0kZrcH37AbRbzhsxs0S23EDVNfcgHKi6ha8BLpNDPs1vC8aI49KXgJCEDY8ecn+QcFL9f95El8aeM27K8Goj/Y+gre8fUHeOmys0/O99kuqFpBvhe+fcbkJmK/Drs//eOrGBgrYUlvG958rNuNWd+kzl05A+55H9dkadVLIXrIRouVxEcRqKiDGcUqSvZZhtmnimXjfbc8hAu+vZkHg6aR4r1YWPq5ltld9SyJQShLb7JjhH83SVQbaSp+WHDfnpFbNoiUFC9WmHRjUJ8XXan0ZPS8UPDSRNQ+L/0jwZUrhiBRsoPNNFJIRyiVBoScbkSpk52fopFwRFBeonTbFU8Otdoo7aO8zKoGCXGUSourz6wSvKiGXVYOrXpeGEkrL6JEK5ZpJgHzvaizskTPy7ELuoSfy2kjv5laDJ3nZUfotJHXMC4S1OeFvcbfvPlI/Oelr8fczhx29I/hV0+7pdlunxc3MAy6OHOFIG3wYyWbNnggJyqbP/q9o7q8f/WhUiDIztWRYpkHJ2L6FHAV2rhW92Lwrxp2AXgM8nEwWijz9KuKeOMsCUpQ1CZ146UKBsdLmChZvAowXVWJ2T4A4ZUXnjYSrjd7qrOC2PdtGoZnm7jQddhl18p81uQVa35pI6YUh9k3r+fFW22kSxtNJs8LBS9NRO3zUqvs1tQoL+wgihK8NFp+mEp5LwiWHa3aQ1xFegy7Hs+L8/veaolyHMqLeNJl07JyxTww7ORnu8r+r5YDz0nY8+IqTm6aLpJhNwJceUnrlRfTSOGIea7ywoKRc45fhGMXdOHM45QZTwrsxi76QXYcCJc24sqLYiZm+xrkeWHluKccOgtrjpyDkw7pASDL3upFH6gVvLhVMSzgzaVN3tJePE73Dzvn9kmH9ErPwVRS23aDBtdbJqSNEN94ALHHTqFiebw5SaSO3vOtzXjd53+J99/yEO55Uu6OLg9mtIUmdalITerE4HVEVF48wUvwbS6tpI2kIYVKgMsuG0lPlWYq3njJPf/ZIop1bWaox3GY66Xar0WuNpI9WAApLzMe9aLEVvizavZ5cZuVsYNIXI2qfUu8z1Nf+oVdVE0xbVT0XjDCIJ4cLFXAUklO8OKceMNCeWNPVXmJw/MieYbUtFFWThu5P5cNu4wky6QB97stlJuXNvIoL9UgZW5nVlKa2LG0bsUC3HPVG/GaxT2Bz692LwUipI1s7woQcIPJoOCZKTrsO1Xn1wDuMZk1XSNmUNrI3d7gq3mxz4v43KLKJyKWVjMVk9002TkWd2qiqCgv6nUgiYqj5/eOwLaBB184gA//x1bJpK0qL2K1USTlRbgWsb45jvLifOauYTf43Ml60kbe12ambq68JD7byHlvrvLqnzZi3y/bxzD75u3zoksbicrL5Ks2ouClibiGXSeNUUt5cQ278DQrk/q8hEwbRS+VZp4X72oGiDYXRVRe2GRpXZ8X9pkAbtAQR8moxzMkpo0yctqIwQKGZgcvsmG3OWkjr/Li/H9Bd56n7wBwP0FYRBUJcI77qGkjNQBgFTvjAXNWJpSZULqVIwsaTDMVamXJfpc25bSROhtH3Fb9XFOpFE+1MZ+GmD4F3CBGp5Te/YfduPa//xhpIVIrbZRErxf2mouqRtqHX+rnvxMVk6KSNorSBE58HnZdMg33+2A3/lrKi1rqritsYL64escXhEH2nVSVFyEId5UXJXgpu14stm+1UvqBs400aaMspY1mNmKp9HChXLPhmThVmh007CDKRlFeQhp2D44WpZOS/dPQSLFAA8oLK5WuCMFL9X2xSp9s2gg9MC8M/PMznbJJ8fPjpdJhlRcfpSwuJMNu0e3umQRtGW8wDLgX6fldOUkZzBjRghfVT7BvuCBdAIP6vPgGL9XvIyjw4c390rLyIt7IWVCcMcIFL3x7IW0kmufF51aVUhF1RICrQDrbqlWJIjf87zP4zy078YedA777qSK+J0d5kZ837nLpijBn6E3HzAMAbHnpAP/9hFIqzYMX0wh87yrjmuAlY7rXqrDKi1rqrguc3LRRNP/gL5/aw7tJ10LusCuXSqsz1kRKXHlx32et4I8dn7r341YbedNGVG00QxHHA7Ay6faAhmfigVUSbr4AIpVKp0OccC/sG8HrPv9LXHnn7/nPxGoj1bCr/rsW4sWInZBuWirFUxMv7HPaxXfm0lz+jUOiVVfC4uenlkozOgQzq/i7pPu8iBcKsbtnErieF/n52bE3ryuPXkF5SfuYZ/1Qg4btSmfmIN+KWirNYE3igtJG40raKJuupgaqx4E4yC8tBCBBK0uxpPf8lYfgpEN6sOrw2R7PhPg8ukoXPiKgGjRww271XqH2gxJhqebRCA36JOWlUvEEaHGnjcTXW3uUM/R0i6C8FJRSad1sozDnvHjsjBRcz4vXsBut2kj3ubep1UYhYpeBsSIu+49Hcdl3H629MfSzjUTDvm/wUvU0ideoWv5G1ueFPUY3mFE7HoCUl5kJX1VYNj8A1VW9tL1g2BUvnOLfQO1S6aDZK4wnXhlE2bKx6em9/ODVp42EPHMU5UUzq0OssnjTMfOwqCfP32dHzhQ6X8aXNspq0m5sVaWmZtqEElrxe5qTcNqIlS0PT5R5xVfkJnUhafOpNmIXtcU9eV4qDvh33vVDvTEws+6S3jYA4Qy7fspL0GOZYZd7UxQFSJxynRbSRkHpmJJwk/3gmmX4yYY3YHZHVltRpR5vIrziqHpzrfD0VVV58bmBW1XFFojWXbim5yXmtJH4/GuOmINUCnj5wBj2Vit3VOWFeYXEFFyYMnGd8uLMhzKkn9VSXvj1sfqaumOgPRNdeRkaL8OygYNjpdCl3wz2nYlB+PwuJwW3b6QgBRE643mt/eMNO5lPRvSCVdzjnEFpoxkO7/Ni2745cRGxtFqd9ClXG9XqsOuthlDZO+xcWMZLFWyrzsaQSqV1aaMIKza5z4vcpI7dPP76TUfybTqyaa0Rsl5UGV/u86JPG4mpGjF4SdywW1UJBsbcFZbqx4mLvI9h9+K1y/CXpx2OC1YtlTwvfgMb/RBHHQCuWXf5Qqf8erxU8c3P1/K8iJ6HHQfG8B8PbUeh7PQtYa/HPregcth0yLRRSUgbiaiLA9v2pnlFuvL6tBHvsOtj2BWD2SjyfUlZ0Sdt2BU/29kdWRy3sBsAsKWaPhH3Xdwfp0ldfZ4X1tNFVl7YXLBwnhf23esCjXYlbRQmGCkpTQtroZttxNNGGRMLunPozKVRsdxp2YDXsAuEUF6q3wF7X5Lywq/LXsMuKS8zFKa82DYkqbTW9mXL9kh5UvBSs9qo9gVh37Dbk+HR7c5FRiyV5sFLUfS8hM+ViyeHqrywC9YFr1vKB6h15tJaI2S9eJQX4XP3SxuJzcvYaj9rGjXTdI2SNZ39YOpcW8bkKce4Yf6dWUrjvSPndeLT567A/K48uvMZ3pRN7LwbBl4qXb2Y7jzoBC/HVIMX2/ZfzblN6uTvRaw2YoHP53/+FD714z/hl0/tlW6OfoZd8VxIG4ZvF18RVf3k79Ez2M99bt2qX21UJ874AvxvkGKDvSjKi5w2St6wy17PSDnvZdXhswG4qSOP8iKcm1Ha74+XvGmjtGF4rlW1+7zIaoruOsmuDVHSWuLnHiZ40aVuWEPPtqyJVCqFo+Z3ApCnsesWwqWoyosubaQplSbPywxFbFY1EWDoU7e3LLeFNouG2cU2lXIlTT/ClErvlYKXg87rCqXS7mrQfcxIoYxSxcI3Nj2HP74yELgP4ok5xquNqtUb1efOZ0x8+I1HAAAW9bZpjZD1EtQnh12Y1ItchxDM9FZvmLM6MoFzUuKAXUwHxuQxBUlw6RuPwD+ccxwuOPVQ320MI8WVJ1V1qIW6qmU3bFaFAvinf5gioaqTzPNSEc6L7dV01IHRgnRzzPsYdsWAOGOmaub0Lcv2bZqnKptqWb4KN+yyUmmfJnVq8CJ6HcLcDBliX5dC2ZL6vgDxKy9FRSX2Bi/qeADm50t5htEGIX7PbCFlGilPKXrtDrtygK2tNuKeF6O6f7WDF1H9mAihWIgBhGU7rzFelM24R7PgZa87OVqs1lKHTPrh53lxKpWcbTKS8jL5PC8026iJiKtndlAGKi+iYVdw5APuzbczm665Kk+HKJXeO+QGL1tfPgjbtmXPi+aGPVwo4zfP7MNX7n0WD710AN/7q9f7Pr8cvLAmdc7/xf2/5A2HY2FPHqcum4X/99B2APH0efFUawkrYpZaUIMEMY3Ebt5Jd9d19q2as1dMp0mwoDuPS6sBYxC97VkcHCtxP0FYVM8Ll7gzJrJpZ2Con/HW3/PiXrbGSxVk0wb6qn6K0UKFP5/YsZV7Wqo3bjFgSKVqp41Uj4yIOgJBbACmmo0Br2FXTJ8C/k3qpOAlwk1E9bwUK/LnHbfnpawoVK9b5gQvz+wZxuBYybfDLpu1BYRUXoqi56VSfQ5XJR4rsFLpcJ6XoGqjRpWXMEqZ+p6dc6PapK76Hpjy8vxeV3lhxx0br1Cq1B75UKjuG2vQyVQncZ+14wFiGtUSB6S8NBExAJgoRVFevA5wdlGslTJyHlPbsLtPaOXdNzSB3YMTUqm0LkAamShjd3Vwmdr1UUVKG5VY2khWXthrnXvSYizqaROMdI2fMGKptPi3mJIRL56APH2aBS9Jm3XFfeP7kZBZNwqs4iiq8pLjaSPn82crvlzG5EHjuE97enGKukhHNs3PjYlSBROlCvcHjRfL/NzKC+eWx7Bb/dsUVD/AXwUSA2h1f9JK2kHsGKs7b9g5y4IG9jiDp42q79/2D16ipY1Uz4usaA0X4i2VVq9V87pymNWegW3LDQoB57MSFxZRxwMw5Goj1bAbfJtj53wpRNooiqFY/Nyjel4A57MZUxYwRy/wBi9iQ8Qw/kbADbBV5aUkKZJe5UWdRt1KKHhpImLaiK8OQygvFdubNjp8XgcyZgrHLequ+brqyamDVQKwVeGjL/dLpdK6KpORQpkPvqslJ+qUF56W8lGO3BVRDKXSFZY2kg2qQWqL+Ds2PDPp0QDivun2qVWwXi+RPS+qKlF2h+Wxz9cvYLB8lJesMFtovFjh82fYc01oysvViiCeAqoee+y4ZzfBwbEStm7v554aMZWgngvie7QFM77fuV2rSR1TtyqVIOWlAc9Ldf9YIB6/58Vr+GQVa+J3xfZN9BLV3aSu6PW8jIT0vKiNFINKpaMoL+IxE9XzAlSVFzV4me94xV7cN8qfX6dc1dq/oo/nRVzgatNGpLzMTIyoyovGsMsuiIt62rD52jPxrQ+urPm6tZSXiVIFQ9UL2FtXLAAAbN1+kF+4zQDlhQ1fq3VQi6vIcU2TOh1hgq6wFJTPm52M3sZ07v9FxeMdJyzCm46Zh/ev8veGxIV6TCTVoC4Kc6tBW62eGSpqlY+4ymafvZ+KwMcDKJ9HRmjPP16q4NVBIXgpucGLeNNSZyypN1j2XbPg5RM/egLv+uZmbK36v9j+p1Le4zUjBHRlTUNJlS61VNrjeZHfP0PsrBrUoE9F7bDL/s8C8qT6vIjBGzNZ7xlWgpey7ZagpyM2qZPSRt4+L+wpwnpeKpYNy7K110nmf/PzI+koCdtMhFAs1NcVp8qz69KS3jbkMwaKFQs7D47z7Zz3kRIakoYz7KrVRuxaqx7nfDwAKS8zE8mwW3JztLW2tzTVRgAwtzMX6Jlh1JISWaVRNm3gzcc6g/b+tGtQrjbS7OZwoYwDLHiJpLzoB9KpqKv2RlBnzfgqLxm98rJsbgdu/8tVWH3EnIb3pRbqd5rUaIAo/OUbDsf7Vx+KPz95SaTHiUZZW2kRwD5rv/lGfmmjjJni3814SVZexosVfqPIC74mNW1UVsqeOxXlhZWissBI9HGohm1W2s7ep/se9UGn6nnxBC/CeW9ZtqsGSYbdCJ4XpcMu+z8r+U+qz4t4rWKG9z2DXuWFq6JRm9QJxw1Tj9KC54VRS3kRvR0lyxLSeO42bR7PS+3PXzRK16O8lCreqfKGkcKR81jFkWPaFZU+piTW9LxUlTu1zwtXwRSFlTwvMxzxZHBNhf4nllgyqZNiw1LrgsAqjeZ15njJ7HjJ4p4XM5XSpgsc5cVJG9VqXqRLG6ml0ipxNqnrr+4nu3Fwz4viJxH/355wSbQfk1F5Wb6wG1/4ixOwUKgSCgP7nO1q9URBuLG311Je/NJGwmyhiWIFfcINcbRQ5s+XF4JPtQmdqvqxCiC2gh9W/Ci68lGGeG6UKrWVl5rVRkK6+G/v/D1O/ad78ergeN1po6KSNmLvZXZiyosubVQNXoTCAHV/MulU3U3q2GfuVBvJn3sttVAMjksVm38fYnNGXm0UYU6cGOAEdYN2t6+dNgLEiqMRvs+Ao1yFHQXDFJQ2xfNS1gSegOh5oVLpGUkqleIBDFs5hVFeKpa3SV0UWBTtFwTsq0q587py0krZrTZyG+yJjEjKS/BBLZ6YhbIllZ76NT6LU3l56tUhAOANs9iNpcOjvBjCv1sTNKhKQ5Kl0kkjqxK25HlhgWKt4EWV/TNpgwcm46UKrzRi/+dDGXWGXaWihAUeatdb3rqfr0jd1IbnPZqy8hI01wjwGnYryr6I5/0TuwYxUbLwx1cG6zfs+sw2Yj6m5JQXIXipKi99Gs+LNFW6ziZ1DNHzwqg520jYvlyx+PchNqNk14konpyohl1PtZEmbQS4FUcvVIMXUXnh+xcybcTelxuk6xfJpLwQ/MRiB2VQPtadKm3zgzEbsdoDEKsh9CccSxvN78pJagcPXgyvFAsoht0aB7W6khovVQRDsI/nJcY+L0/udoKXFYvl4MWvMV1bxvT14iSNt1V/69NG9SLewIplS5r5wwJFv7QRO2R0nhf+WK1hV15VivvBujWrK0xWWTYyUYZtu6kat2W8HGCIpFIp4aZRu3t2t49hlz21Ifgq2Ap5z9AE96UB0Uql1Wojdq4yw+5IsRxK6QiLmpIDgB7FsMsWBuWK7aZ0hZtvmP3Rpc7SgueFUUt5ccrlnX/rlClATBu5/phaRG1Sp1NeeJO6jBi8OKZdV3kRDLshlCHLcj9zpi6r1UZe5cWs/t6O9VhpBApemgy7ME2EqDYSD8RiA2mjWoZdljaa352T1A7ehyXlzgsRGRgr8ot8sWwFjmFXT8yxYgVqlYV3v8Pnl4MYmijxEs3XVIMXdmGa1yX3bWEXqVamaqaT8iJ+t2Kli5M2YsqLfuXPvneP8mKm+GMHx0tS2mis6PZ5ET0vtQy7LJ04Wiw7jdyUIKdUY/EgKpZh00bjpYq00meBUdoQz3vnuV4dnJDTRnXONipoDLu2LXfObpRiWe7zArjKC7vWMPWpLARoGaFUOupgRoap8bzUUl5SqZRy3asqL7q0UYRSbqlJXQiPktsEkXkUbW3a6PC5HQCAV6rdqt0mdW56vxSwf+Lx4FdtpCr84rE8WdSXqbukm6KI/SmA4DSQuAJrJG1Uq2qHNaib15nnB39RUF5MxbCbMVNOBC6cH5btnAB+aTC1emBcCF78Gp/FVSr9dFV1WdLbxvPY5520GJZl44zl86Vt2QqnleXJampiMpRK10sqlULWNPiKlisvGbHayEK5YqF/tIj53a6nhh2uOs/LsQu78Ktte/GnXYOSj2K8WOZ5eXHF7WfYZecGrzaaKEulwyXVyOgTkKTNFFBS2t37nKtib6aRQtnf82K5Ks6ewQm52qjOUmlx/7ryaaSNFMpVUzAr4W4UnfLCPC9sKnZXPs0VX7FtRJTgQOcjSRspTxo6TIVcxkihCCdYZd/HrA4xeHGO1SjBVSmq56V6jLVlTJQqZamBo1QFmXNTpoA8aibMSBWxYqjDp9rIrxEje3wtE3QzIOWlyZhceQlenQFylF9r5RdEuobywhrUze/OIZsW0kaCnC2uZub4dJkNqjhSX3usJF60ffY7plJpNWUEOBUI7111KBZ0ywZUdpFoZWO4ydikrhHYMS6mCuVqozK+vuk5rPrCJtz/7D7+OF4qbcoXyoxpYOWhswAAj7zcL6WNRosV7WqVHdeqYZddpMVqo2FhhpAqp/uphGKXXa4u+VzgnVJvZ/vhibKn2khMFzP/TN/QRP3jAXwMu1nT4O87zl4vWs+LMjtLDJSYehu1SZ3uMxCb1DFqKS+AOIPL4jf+Jb15rDliDtYdt4Crf1GCq6jVRizoY8ftWLHMg+Z2oeKQm9VLljQ6RuzzEuQTZMeUkXI/G54eZYGQ8hlmTDe1VqiEP/aShIKXJsNOTnaBDTMeoNFqo1pVO2yi9PwuOW3Ezk9xMCPg9AZQja5AcPCiKi9jYZQXpUKkXnjwEqKhX95nVEAzUdWrqay8AO77EY2hWaFJ3Xixgkdedvqp/M8fdgNwJjP7VRtl0gZOOcwJXl7YNyqtgseLrmFXvGn5tYDXGXYl5cWSgx2/81VsR6B2c9YhNqpT06fu6tk97/uGZOUlzEqevweP58V9L2wQ6l6lCqgRSpq0UU+b3NyxS6jkG6sGL1Gb1PkpL95S6RDtJAz3usdeO2Ma+M/LXo9/u/hUXh4vXpOD0uTqe6iVNrIs93rLghPR45TPCoUEwvVgolxROuzW9uSIFX8ZxSMjvncRpqACk6fXCwUvTYanjUIMZhRXYI2kjUyuYPh4XoaYYTfPD35P2ki4IOTTptQ6nxGUC1UvRnLaSP8Yt0qqsbQRqzR6zeLawQtXXlpUJg3IFwpg6isv7JgdFUpys6bcpI6pf5tfPABAHgCqSxvN7sjiiGruH3CP8bFi2TPMTnwOnjZSjIluqXRFKh32VBv5HKyi54VV3uUCbprs5j08UfKkjdh5L96cd/aPRboZiqh9XkqCJ+eQWW0AXP9EHJQ0Yz/YeA1GW9Y1xI/yhVyq7iZ1jLQy4gMIp7xkheAzqHmm+Ny11BcxaKwVbIoNCdsEPxfbD/F6IHq5xAVBxhQGMwao1QXh+OT3BsXbpasAnWxddil4aTL8wlSMZtj1c4GHwTXseg+6imXjQDUPPc+v2iglVwS1ZU3tTKVA5SXQsOt3Q5BPrHoolCu8mdNrlvTU3J57Xlqc09VNvZ6qsOOP9TVxfAlykzo2nuKVg+PY2T8m3RhUwy47Z5j6AgBLqzdhy3YM2oBi2FVKpUvKsdchKC+iwsHOmWKN88+dw2VLTdf8aBd8C2qzRlNRZwFvGqDeDrtOB2BX9XWDl/HQz1fz9YSOuQw1bZQTJiCLP2MqbEPKi/q8YTwvgsrrNz0ckAMatQOyivi51+qPIh7vrJJukE2Vz5hSY0TDcAeJDgkpzqzpfn5BaSNuWagOchRfvxyg8LOeZJNlsjQFL02GHRO1KhIA2bBbS7YOIkiK7R8tomLZSKWcFvDsxmDZ7glgKOWHbRlTkn0ZQY3q2GuzG9FYscxP/tql0vUrL8/tGUHZstHbnsHiEA3WFvU6F/PF1b9bhXhhn+rBCzvG2SqRHQPsfQ2MFSWJfPMLB6SVtzdt5Hw2K4XgZZmgwjBTqM6wq1YRsQVClxCMix6aWlUYfJ9YoF22uKwedG7zdutl12PBUsqmRnlRqdewC7gTmLPpFA6Z1Q4gXuWFfWbiwkxVXvIZ0/NZZkyDXx/ZYmesWJYUOxF/z4uaNqp9/oheEaZa6JUXd59rKS/iYrGW8iJem1llE+uJ09vhNVKzwF/0QWXMlNBh1/9azIzSszuyngamQc0YeaO6SRK8TG09egpiKspLUDAi9jwoNpA2CjLsPrfXUSXmdGQdyVV0lXNjlzzbKJ8xYGsOnaCInJ3oXfkMCiMFKW3k36SutnO+Fk+/6vpd1LbuOs47aTE6c2msacIYgCBk5WVqn6bc8yIYMwFXHlcnDW9+8QDOOXER/793PIDzfzF4WdLbhmzaQLFscSUxMG2k+EycVX8KFcvGq5rgpVbaSCxRdYeABgUv7o3AO5jRTYGpzO7Ion+06NxkK1YoD5wa/I8IHpMklBf2/sUgImMa6Myl+WvnM4b2e1WVlz//l9/h4FgJ9/9/b5bOg3LF0i5qHM+LatgNobwIaT83UPVeL8SnrqUOybONaigvwnthwRYbTcF8SSJtGROD4yUMjTufJ5tF5Hqv/PeNBapLZ7d7+sKUAlQnnjaaJMELKS9NhgUBYTwvYlTcWNrImwcdmijhvd/ejPff8jAAZ9Cj+vxiy+20FLyY3CMgEpQLdYMX53GyYTdYii9WbOwdmsCHbtuCTU/vCXqrHtjKZH6XvkJKJZs2cPbxC9HTHk/ZaL2IF4/2STAeoBHYe2E3LqY6sNXj7gG56+rmFw4Epo3YsXjUvE5+PC3sznMl5yBTXjRpo0q1s7Mqj6dSKX5Mi31jVC+A3/nHzuNCqeKWSocJXkoVrkCqpdLMuyMiHsdio7o/7BzAfz26U/ta6s2G9XRxPC9MeYkzbaQvKxfVF8csqqQD03KTOsuy8dzeEewfKeDxHQPStn5N+kzDkFSDVCo4Nc8Qe2G5HiTv4yTlpYYiLFcbBd/wxWszC7pZ8KKr7mTbsLRRtjpzK819gv6vx77rQ2a1ufcYxQumW1DyYzyC6pckFLw0GTWfHRSM8LJBu9G0kTf9snX7QTz0Yj9SKWDNEXPwyXcc5zy/cHKy1YI6YdQJXrw396C8LrtAs5uNWDZr+igiYtC1adte3PfMPvzHQ9trvFtg/0iBVwKIzvqpxLT0vLC0UUZOG7Hj4LhF3ciaBvqGJvB8tXsoIH8W7CINOOfH6sMdhezweR1orwZDPG2kUV6A6upa04uEBS/ilGp1YJ2f0iEOigyTEhbTRp5Sad6F26u8iH1w2Pm5a2Ac77/lIfz9D/6IbX1DnseoiwqWkROVlz3DE7GtqPlnqyxKRN9LPmN4bpAZU25SJ7ZIYNO9GX7jEVTPSz5thlJc2XFQrHiVMBHxR7WUF6kKLqTnxSlfdo6bVwecIINNdBdh6gzzZ7EArVY3dUAMXto9pd9BCiMpLzMcdqMW26T7EV/ayJt+YXnk1y2bjf+87PV8WrI4CoArL6mUbNjNmJJHgJ3QYZQX3l20WAnMLQPyaoj13qjVj2LzCwdw6j/9El/+32cAuBf4MOWSkwlxtdg+CaZKN0KWKy/Od5jjaSM5KFs6qw0nH9oLANjyUj//uXjMq8H+5975GtzwnpNw1msW8ufjqYm0/nFFIeUgrq7d4MVVIdS0kd8qnqU0xoqVUAEzC+AKZctTbcTO+zHNDbq3LcM/v/FiBbZt4x9+9ASv2Nk/XPQ8xs/wnjUNzOnIOmlgW37fjeB3rRKDl1za9HyWapM6Mc29dYccvLDzui1jSkGGqhKHMeuK+1oS+uDo1AdxFETtaqPwfV54GtM03Oq86neqTxsxw241Bcj70NQ2PLO0kaS8WHKQrltUiwH3ZGBqXdGnAYbhXW34bisMKWskbaQz7LILo65fC3sNZjw0UophNyunjRZWV4NhPC/scU7ayPmdX/Ailm0zs2etIXJs5fn0q46Xxw1eppZ6Ia7ap3yfl6rBln133POifCfzunLcVH2wWmlhpOSbiJqKWNTThv9zyiHImIbHGyR+59Lk4LLFDY2iOsC6je4ZdHueqOMB/PxZ7LGjhXKktNFEqcKVEHbjUSsSRbrb0vx4KJQr+Mnju3HfM25jP51Pxi94yVRVrLhTR1wlVt5/r9DrJZ8xtGkjwyd4eWz7QalikSkZbVlT+p4zpux5yYdUXMWFUq2xJeLU7yCiBC/ia6oL2jka5YWnjarKC7tmh/EJimkjb7URO8511UakvMxo1BRJGM8LILeAjorOsMsaQ+nMoOw1uGHXUAy7abczZyoFLOiJELzwtFGZ30B8lRchH8vKbEd8Kg8Y7KLGLhZhFK7JCLvZZsxU4DEyFXCVF9nzoqbD5nfluYmXKWxpw5DOmaDjX30+Mehz5te4qQE3DSSkjaqN40QF0bsirZE2KlbcqdIB+8o+AzHYYO9THSEi0tOW4TfkiZKFb973AgBX/dSpNX7jNdjnsaQ33l4vfhUrPVLayJQmjhspWTUpW7bi0SvjhX1uKpF3Uc7IwYtpGHUqL8KxUaN5Jldeanheosw2EtU3dYE6x8ewC7iePnZcun1b9Ps2VixzQ/shs9ol5cW2hW69gdVG5HmZkajKS9AFTryps5tyPcELDwKEiwGTJHV+Ch68cOVFDrrygvIyqz3LT6QwaSM2UTeM8sL2w7LBXfW1ghfW/0INYvxatU9W2Huf6pVGgM6wq08bzevK8eORpQnV0RRB54t6LKurbnFWlq6fRafGGF1SjIz+wQsb7FgRxgPUVl5Y2TIA7tVQ00ZqszeWAh0vVXg57bI5HdXX954ffuclC4rjrjjy803Ihl1ZeeE3X6FJnZr6EH0v40I6WEwJqx12wyovaUl5CVbZDM31VEcpkufFrdBSP7dAzwsz7KbZYoeljfT7tqv6HXfn0+hpy0jHlmW7DQZ1xzkpLzMc9ZjwG/TmbCtU/rDJqw3MNpI6PgYGL8zz4mxjprwmOOZ5mduZDVX/r08beUsq5f12fz5QTSOMFMqBbbndoMWS/p6qaaOpbtYF3GPcE7xkVOXFDV7YtmYqJY2nCDr+1UCvLSufW2K5tDqYEYC2gs5rZPRJGwnzaIo1/DGAG9iIqSG2L4YSvLDgAmDBi5uiYjcv1pdIl2oKShs5zx9v2sjP3Nzbpigvwu/Zd6OrsGQ8qgle1LSRqlyEVV7cPkBuqbTvdSms5yXCbCOxwklVWv1KpQF3UccNuwE9vQDZrAtAuq6XLUsb1DMmW58XCl6ajCdtFKLPC+AGEvXMNnId6KLyUk0baS7YbtrIbZ4l7ndb1sTyhd0wUsBJh/SGisg91UbFiseo6LcfgOuBqFh24MnDgpWCorxMOcPuNApeeNpI8byowca8rpxruq1uyxu3Vf8OSqGpSo5qmBW77KqzjQD9SAh2Iy7WSBu1iYbdCE3qRKWEeV3c4a3OsbuwJ89vyN15N3jZO1zgfplF1dStqOS476FW8JJQ2kgJ9GTDrtznhf2bfc+W4nkBgIdePICt2/sxUhAmh6dNWXmp2/PC0i1eA7WK2Itm7/AEX1ipiNfbQtnydBmXthUCJvUYm9NRW3nhypUwF0uHaNZlr8eo1ck9S8HLzMaTNgrRYRcIN07AD92MIK68aBQJT/CizDZqy5g4dmEXHvmHdfjSu06s2TZaHLLXKZRKW3b44GVgzO0kGVRx5O95mVpBAPuep0faqGrYVTwvpiH7eeZ15fiK0vW8yKmUoLSpaj5X1TZ5de29SOu6Rpf5YMZwhl1ReQmsNuKdpr3Ki6koL/mMifldTnDS057hn9Geakl3Zy7NUzJ6w673RmYK6ZW400Z+bR3E4Yy5jCl99qpno2y56lg2bSCVcvbvXd/cjDO/ch/3erRlTSlAqbfaSFSng8YDAO73NFooY91XfoNz/+UB7Xbq5x5GmVbnGBkpJzWv4mvYrdHnxaO8CJ9VWZlQrULBywwnivIiHlisKZNocguLOPGWwT0vWuWFVRu5o9PlPi/OPs/pzDlzNoSqIB3igkMqlQ4YgMZ+zn51UFjdBPleePBSZmkjUl5ajZ/nBZBTR3M7NWkj5YYeFLy0edJGSvAipI10hl2d8qIOZqxVKj1aEDwvIaqNWLCRSnlVJnYsZ00DV731GLzz5MVYedgsfiyzTsA9bRl+HusMu2x/2pSqHAa7kfUNxdPrJUypdD6tTxu57SHcm393Po1P/9kKrDp8NtJGCnuGCtjW51QT5jOm9D1nDEO6noRdtEgddmsUErCf7x0uYGiijJ3941p1S/1ZUOrILZWWA/rZHTnPghdwA3MWxPHPr0afF7HSCPA23QsK0nM022hm4ymVDlRe3H/XGmIYREZzQI9XL5r6UmmN8iIadtUVLe8Wqj+oRfmUBS8TovIS0ESKrYgGhBkefrNOAMGwW5SDmLDy8WSBfWfTKXhhKT1xNcze36z2DLJpgwcgLHgxlAqcaIZdeVtx/pB7gxIMu5pho2y7oLbp4muL1UaBwQsbSll0fWUM9aaZTRt498pDcON7X4tc2r1Zs07Ave0Zfh7rDLvsJioGZ+LnyHxrcfV6CZU2yhjStY9tK5phxWve+tMOx/c/vAZHze8EALywbxSAE5DlApSXsIsWscSYK0d+1UZMSRQUYJ0hV03dBJl2K1K1kfzd6OCeF9bnhVcnelV2ETVtpDbdcyvFgpSXGVBt1N/fjwsvvBDd3d3o7e3FJZdcgpGRkcDtP/KRj+DYY49FW1sbDj30UPzt3/4tBgcHk9zNphJFeUkp6Zpa2/u+piZtxHLjuh4iaTV4UUulleDFHZWuP6hF4ztXXkq1PS+AWyklenSD0kZs7MJE2WngVeDVRlMrTvfzhUxF1NRo1jQBqwK89Fu8I/U7vN54Cgs7nRsbS2OOFHzSRgHKoxi8ZMyUxx8mKi88NVDDsMuVl3Jw2qhdCB7C9HlhN9VRRWFS/617HhaIi8ELe/0xjeeFKSFiNZX4nKlUCguqvZr2jxTQKH5pI6nPi9Kkjm3LzbC2vrcOK+t+odqB2SmVVj0v9SsvRWk8gI/ykpLToIB+ynfJiqK8uAZy8bvRmXUBt0kdO45Vz5D62gw1bSS2EKhY9pSabZTolfHCCy/Eq6++invvvRelUgnr16/HZZddhjvuuEO7/e7du7F79258+ctfxooVK7B9+3b89V//NXbv3o0f/OAHSe5q01AD2lrBCBsWx6grbaQp7RsrsSZ13kMgq1QbGSnZ2KVWidQy7ErKi248QFDwkjYA5cIQmDaqbmvbzkWbBWBTrdrILZWeWvutQ12BnzD8G+DGdwFDu/FJAMgCB0bnAk99Fe3ZtQCEdumR0kbuZ6VT2kTDrq4iRhe8sIu5blKySIeQtgnXpE5WXqQuscoCR70BM9WGTx1uz7odfjU3SBZ4icqL+jnO6shiR/8Y+kdLaJRQHXYzhlwVVP2s3CZ1rnFWfJ4lVcVgV7V1flvWlJrFqcpFeOXFTRvVGhjLjkXxOqRTVbxpozCeF/lz0TWoA7yLTjX40ykvYo+XJUIFm2mk+DTtMLONpn3w8vTTT2Pjxo145JFHcOqppwIAvvGNb+Ccc87Bl7/8ZSxevNjzmOOPPx4//OEP+f+PPPJIfP7zn8cHPvABlMtlpNNTfxVqRGhSB3gvZPWljbzKi9ukzv8iXxI8KZJhV/USmMEHtU55CTOYEdC/X9ZmXse4cIGYKFmu52WKpY1YsKXzYUw1sqb72Z9lbME7n73Rs83syn7g+xdh4Vu+CaCb/9xtmS/L4jrEQFzX10c8rnWGXb3nxdlONylZpI2ngcr8PAtj2GXKi6hsqqllNf3EXovNcOpty7iGYU1gz87joOBldjWwYEMtG8HvBpjPmOhtz2BwvITetoy2z4t48y1pPnOmvIjPKS7u1D4v4ZUXN22ke10R9vxi+loXmERJG4lTzuWUnl55URdjLAUnDiBVYT1euvJpqeeOc25Z1WojGg+AzZs3o7e3lwcuALBu3ToYhoGHH3449PMMDg6iu7vbN3ApFAoYGhqS/kxm1Bt1rb4tcaSNdIbdsQDDriq3p5TZRmogIMrxOkTlhV1AndUvu0j4v6es5vMZ0UjjDFGaLQhD8qaaYfcdJyzC2iPn4J2vXdLqXWkYphYasHBd5rvabdi3vPThf4QB93hRu84GHf9iUK32eAHcIKAkdFEN3eeFG+ZrKC+FOJQXeVtP2kg5lnvbM9wrNKqolLZtC2kjMXiRX4RVtPT7lP1Ggd0Add/Vtz6wEv/6/lMwpzNXu0mdxtAvKgaAN22kdtiNqrwUpfEANTwvEZWXwMG1wnsVv29f5cWnkk53rWccrFZszlMCIl1vnRndpK6vrw/z58+XfpZOpzF79mz09fWFeo79+/fjc5/7HC677DLfba6//nr09PTwP0uXLm1ov5NGVFKMVO2+LZ5gp460UUYzrItVOeiUFzVgMFMpaRWVV24MtZoXMVk3lZJXxywICfoIdJ9P0HwjMXgZL1XcDrtTTHk5fkkP7rj09Th5aW+rd6Vh2IV1lbENi1P98D+CbWRGd2OVsY3/RB1WGNykLnzaiN8sfNJGbpv1aql0DcOuaJgNNVU6I5+TonHYNPXnF0O9cfW2ZfnrjyuGXfGclwy7yuczq9pL5GAswYv/DXD1EXPw9hMWVffB/b2uSV1Z0+3Vq7wYkgqhel7CpovTGsOu6Zs2klUzwK85YH3Ki2TY7fDxvHgq6eTzRFdtxBRr1Zguql3hmtRNUcPuNddcg1S166Xfn23bttV+ohoMDQ3hHe94B1asWIHPfOYzvttde+21GBwc5H927tzZ8GsniSgJh2n1rwYv9aSN3BPTO5hRF7yor2Gk5HSXX7WRX0TuN3SMnUx+M0TEfRcJThu5J5aUNppiyst0gh3n8zEQantxO3b8Gzx48f8e2yXlxb9ztNhhVzLsChd11g1WnSrtFzyx17NsCH1egpQX+Xfi26pl6lfPP8ewq1dexBW4ZNhV3sdsFrzEkDaqNcSSEdTnRWxSJxl2VeVF6bCbNuSFVtiZZlIPIM2xIcJu9pJhV1ttJJeoB3lexPJsybDbpVdePGkjnnZz34cKK3RQFUaxt47fXCpALMyYHMpL5IT6xz72MXzoQx8K3OaII47AwoULsXfvXunn5XIZ/f39WLhwYeDjh4eHcfbZZ6Orqws/+tGPkMlkfLfN5XLI5fTR6WREvDCFGbinemTqGw8gO9DLgpFVO5hR2S/DSEE8VaIadrn5MuVULeUzBiZKFq94CorhdNKzrosoQ1wBjRRKvMfMVJttNJ1gF9a96A21vbgdO/7daqOg4MU9lnXKi9hMsZZht7c9gwOjRU+fl1qzjeTXq502YogLBvUlPGmjrBq8ZKVSbRFxKKOoenoMuyxtFINht9YQS90+MOVAVl68JctzO3LIpg1+rclnTOm645RKi4bdqNVGFr9m1Ko2Gq5RKs1Sk135tKQC6xBNwuLnMsdPefELXoTKIRUWbKnBizjuIEy1kV9LjGYTOXiZN28e5s2bV3O7NWvWYGBgAFu3bsXKlSsBAL/61a9gWRZWr17t+7ihoSGcddZZyOVyuPvuu5HP56Pu4qRGNpPVDl7SklLjKFtRYSezbTsrGrEiIWi2EcNIpbhGp2tfXatJnTpivj2bxkSpyIfvRVVe/EqlLWV0gNiVl5SX1sGOpy3Wcuy2Z2NR6iBS0PWhSMHuXowte5fzn7DvP2qfF11pvNwC3quk5NKOX6Js2ZjdkcUL+0bdPi+8fbv+9U0hKGcET5VWlRfB86K8hrqt2r+mtz2D9pybtrJtm18nxHOyPajaiBl2Y0gb6czQOqTgRVEOLMF/IV4DDCOFJb1teGm/2+dFVBnSniZ14c579hrSrCmf748bdou10kbOfnXl09g7XAhOG1Xc9KF43Ph5XmopL7pqI5ZuV9NGfKSAFdykrpa3sdkkdkU/7rjjcPbZZ+PSSy/Fli1b8Lvf/Q4bNmzAe9/7Xl5ptGvXLixfvhxbtmwB4AQub3vb2zA6OorvfOc7GBoaQl9fH/r6+lDx6SEy1WgkbVRPygiQD8SSZfETzUjpT261QZFpuKsN3UqmVpM6teyVrRp4NVNAQKb7jPzSRqrnhgUvqVR9RmciHtjxYcHAZ0sXVX+qfufO/1NnfxE5QWlVlZews43UlSkgG3bZOSAqIKlUivtCeqtKBLsJhOmaq6ovwU3qgoIXeVvVn+JJG7VluKpi23J6gncGThvS/qif46wY00a15kAxdGkjdukpW3rDLiD7XtoymvEAoj8vovIiBhi1BsaGbVLXmXeO51DKS9g+Lz7do1WVXYQt+tQxGOy+Uqlh2OXVRpNEeUn0iv69730Py5cvx5lnnolzzjkHb3jDG/Dtb3+b/75UKuGZZ57B2JjT9e+xxx7Dww8/jCeeeAJHHXUUFi1axP9Mdi9LWMSANkzayFSUl3oQg5FyxeZGs45sWqvkqKZgQzDs6i4GtXKhqvKiqiDBTerCp43UCwjryptLG3UpVkQ8iBfCe6xV+NMbvgF0L5I36l4MvOe7wIrzJAXFOx4gyLArpI0CSqWLZYtL6N3KKpRJ6kyJYBfzMBVE4n5nzeBjTk0biedArXYKamAmzjsC5PlG4lgDXVM4xuwYDbs6o60O/XiAaqBr6w27gBK86DwvdSgv7LgSA79a4wHEqkddYMI+e3aMhRkPYBopdLc528/pyPoGX95qI/n8CEobdeVlGwZP1VWCZxtNNsNuok0kZs+e7duQDgCWLVsGW2gw9OY3v1n6/3SkEeUlTLCjQ1yJlCu2UCatPzHU/ZLNut59qOl5UQYwqquGIGOfGEjN6cjiwGgRwz5N6tTgZbB6IZ5qDeqmG+rxNHz424G3vB/Y/iAwsgfoXAActhYwnO+pLWsCTlaAq3Jh+ryIF3Sd8iKWww77SOhu8FJVXjyGXf/XFz0ltc5V9aaaDlBYPWkjTbWRYaTQljExXqpgrFjBnOrvRKOxXN0jn3Ps/Q6Ml1Cx7MAFRS14WXmEtJFr2HX+Lw4JVBUQ0bSbz5jIZ9zz3ulJVb/nRUz/hBnMyAgKXrp48BLUpM79zBb1tOGG95yEhT3+lglfzws37Hrvo37HvOh54Z1+Nd8du27r5me1gqnfAWuKIRl2QwQv4nlbd9pIeJKSZQmVRvqvXxe8sN3W3RRYEzLfDruK/Ks+h7rSlPfd3ZcF3XkcGC1iZEKfNlLzzoOC8kK0DvVGnkubTqBy+Ona7YOVF//vUvSdBAXZpYrFJX91FcpuNCyNwo7dMOXPYlBe65hTfy+eA7UMu2IPm46syX/fkXOCF9GLUSi7QZf4PH7db23bOW+YElMPYQ27WY3yYgopjLJPwLi4Vwxe5FLpjCn3eQk7FkSXNvKL39TBmeq/Acezw8SPrlzGs03FsmGkwNU5tWT+/5xySOD+qsePt8mf91rMS6WDqo3K/nOdWHA+WYIXuqo3GSkNFMqwK1xw6ujxAjgniFjLH9TjBfCumEzD3e8gz0uttBEL3NTpv365ZXVfFlVXImKJYrFs4b8fewV9gxOe1Q9rykTKS2tRj6daN3bx+FCDl1oBPwvIg9JGE6UKRor6youL1y7Dm46Zh7csd3pUsZVoMYQJtcNndpCOtCkbSyVTao20kZhyYt4cQL8yFgOJoLRRxjR44NbfgO/Ftm3ut6hZKp32LuTYNaIiVBupz+PxvGTkYFculY7WYZddQ9KGf3GETpUaL8rXPtFz0qWkjQrlCtbd8Bt86LZH+DZqar0WrGqT7z/3vFQNu4Fpo6BqI//jXJzfNRkyJBS8NBkxbZQLo7xETDP5IXZeDOrxonudVCqFk5f2YklvG856zQLP9rUGdvG0kcmUlwieF2FfFlSDF9HzsvHJPlz9/T/gSxu3eYIX5nmZaqMBphtqwFEreGkXbkbe2UbBF3em6mmD7OpjB8ZLfNCneiE/96TFuP0vV3GjpGXLlS9hDbth1D5xG79ARvdcosIjtnnnK+OCGLy4ipGuKZwIU1sGGvC9VCybf7a1Ak1t2kisfPHpdHvILNXz4v5eHQ8QtcMuU0eCAi9ddaSqvIhpmy5u2HW+i1cHJvDS/lH85tl9/JoVZkitiqhgs3sJ7+mlMeyO1OzzEtykjlWr2fbkGBFAaaMmI6aNwigpYiTeSMWMOL+inrTRgu48fnfNW7Tbu6PSQyovyo0lcLaRsC8Lu13lxbJsGEYKfYPOvI7dA+MBnheK0VuJejzVWg1LaaMUe45q8FIr8MkGBC/Vx/aPFPlz+gUZ4jFZsizXsGv677tk2A0ZvOjGA6jKS5DnZVaHG7yIK2O+74L/RFeaLDKrPYvtB8YaUl7EFX8Uwy67FvLxAGLZrnJ9WNiTR1cujULFQk9bRlKa1D4vYZUX9hhXefHfd506oi6cxLRNp6K8iKXduwfGccS8zsjKC+BcRw/CWaBl1A67QZ6XUNVGGs+LcNyNFsotV7QpeGkyZsRgxAhYkUVBjMjrSRsF4Rp29blQdQCjx7AbMm0kGthGi2V05TP8hBwcLwV4Xkh5aSWe4KVGMNkmeV5cD8CB0SLeeHRwjyl2TKvqnrgf7ObcmdNX2znbyib3Ip9tFK7aKVzwYgLVm4/seVHSRkrAJPZ56W1z00bs9cXzQJzwnJM8L973EVRxVCxbmChX0J33bxgqvh5Q+3qV1QRT8pwdfdooYxr43qWrUSxbaM+ma4wHCLdwYQbmkk95tog+baRXXpyRKKzDrrON+BntqgYvfmXhQaheH6CGYbdWtZEVXG0kespEU3iroOClyYgXqVDVRsKx3FDaSDiooyovtcqMwzap8/PNqFN0pX0RVkBzOrK8idhooSIFL0PjJUyofV5Y8ELKS0tRK1tq+1bE4MX5+5wTFuGcExb5PEJ8rL/nhQUUB6rBi3oRFxFX3k5TO/9hg4wOybBbO2AWj0vxBu0ZDxCUNmqvobyInpcAwy7gmnZ1XXY/8G8P4+lXh/DANW+RUlUqJeEc1Jk+RXSl0uLNuxhQ4XXiIb3833kl/SYPZoxWbeT+P6iIQBO8eNJGrM2+wb+vca68uIEFm/TMqo2iKC+64CUTIm3k8bzw8mp980YR1mB0Mph26areZMRzJMzqTDLsNhC8uCPfbYwVgpUXNd8Z1EQOkAcz6oxcFcVJr75u0AkrXtS78hkuwTLnPA9eJsqY8FFeWi1vznSiKi/tGsNuWE48pAemkcJxi7p994MpC7op0gzxmBRvTFH6vNRC9rzo/S/qdoDs4ept8wYvWs9LiOBldru/5+WPuwYwXCjjhX0jAe9I9m4ELUqcffAu5MT3zpqh1bqhi8FcxnDeZz7jGJQ7fBZoKmqaKKry4k0buaoRu/7o0ka7BpzgRTegsxZtmuONXbsrivJSrlj8OPb1vFRsHnz6pc34MVbUt6toJqS8NBkjctrI/Xe9TeoAufPiaI0+L+rQtqBSZsC9oNu2cxKq++kGL87/o5RKixfZjpyJzlwaA2MlHrSwEQMjhbKn/wuLo6hUurX4jZPwQ5c2Css1b1+OvznjKK06wF6XHY/qClTEMFIwUo5hVzSIBw6GzNWTNnIQTxlP2kgza4zN95klVBux15erjQTDrkbpEGHl4arnpVi2uNn0wEiwH6YYsscLIPuX3PEAQvBSZubZ4M8ynzZx6Ox2FMoVdObTyJgGvv3BU1GxbO2ATh2qOhjkeQkTvJSEBntu8FIdNVH2Bi8Vn8qqIMTrqNvkz73Oi4jHcEeI2UZ++zGZyqUpeGkyUQczyh12G1BehJkX7MDzW5V4DLshPS+Ac/FSH68qL6oSEtikTlRechm+amAno1g2vXd4QvscpLy0FvF4cEpZaygvGf1NPQypVMo3raGeb0HBC+DcNItlS1plhk8b1a+8qIqF7jqRrwYvYtqoQ7MqFoOJrCZYEPHzvAwLfZX2jxQC3pGrINRKGan7kNGkjQohAyHDSOEXV54Oy7b5sfbGY2rP3xNRg5XgaiN92qhi2bjgW5uxoCePj7zlKL7vbYryInleDqrKS4OeF0FhFxmacHteqccTn4ckmKT9jnMWDI76NAptJrQkbTJRO+aaMaWN3IO6tmFXvbnUVF5MOXhR4Sdm9WnU1VDY2Uad+TQPXtS0EQDsHXIurKpJj6qNWot4nIe5qYvHR63UQxTU8yfI8wIAmeprs2A/YwanQtqiGnaVEl/dvwH9Z8Y+IzFt1KZZFft7XrzvYxb3vKjBi3uOHagRvHCvR4j3L1c/ySXxgHuzD3ND78ila36fgfsSMChTxc/z8srBMTy6/SB+9sdX+XUwbRj8+jOh87yoykuUaiMxXWayaiM3EBFT+H49XgC3PL1Ydidq+y0wWC+joCGTzYKu6k2mMcNu/RdyUzioaxl21bRRrYtH2jR4N0qdadey5X4N9ZZKd+RM7nlhF1RReekbdJQXsQIDoD4vrUYMbsMEL5LnJcaZVOr5E+R5Adxjb5wHL8H73hG5VNrbSRio3aQOAK/6mdflDu7r0Bp23WBCp3SI8BEBY7Jhd0hSXoLTRkGltiriNtywmxKDl3AzkuJA3d8g5Ujb56VocY8d4F6fMmnX8zKu8bz0DU5Ibfmj9XnxLmzF4Eecb8Suk7pjnj1mQqgW9VOe2jKy8t1KKG3UZFqlvIgu9Nql0qryUvv5s2mnhC5IeWFvxWvY9XlfVgWHD2/Fecaf0G/MQs44m+dr2ckoStp7hqrBS3sGfUNuComqjVqLeGMIU4Ujtr9vpD2ASuS0EZthUz1fap2vouclVLWRT5M6b6m093WvO/c1+P2Ogzh5aa/n9UXDrtufRi2V9k8b9XvSRm4wVCtt5M4jiqa8uFOlnVEkTiM0bw+cpFCDlajKy0SpIgUv7N8ZwxDSRlXPixC8lC0be4cn6u7zwlCnSrPnZoeh3zgMwH2v4uwlv7QRU17IsDsDiephETdprFTa7WNQq8Nu1LQR4FysJ0qWtlGdpXTK9JZKa57wqbuBjR/HXwztxl8wIeXG7+D1vX+Dn+EInnMdEi6sLHgRTYwAKS+txhRuSGECdra6A8Ide2FRL8jqgDoVdiNg50uUEu+onhe/tFHWZyL6G46eizccPVd+/ep5NSZI+uJYg1rpO2bYHRwvoVyx+HVAXCDUMuyKBuFa+FU/makUyrbtVhs1Q3lRfSB1eF5ExYoFL2mhESILxtQF3q6D40Kfl/DvNa+pNhI/x1LF4tfa4TDKi3Dc+AVRbrVR65UXWpI2GfFiHOYCJwc7jVQbCYbdQq0+L9GqjYDgydKu8uI8j5o28qzSnrob+P5FwNBu+edDr+IDOz6Js4wtGC6UUShXpNdjVVRi11GADLutJpVyu7uGSxvp0ymN4lVegj0S7LhkrQVqLR6id9jVe3uijhBh8FVxQZM2CtPnpU0ezsgYGhc8L6PhPC9hFARdnxfA/c7Zzb6R615Y1P0NbN+g+V3FsiU/EFdeTIO/T8t2PIdqA7ldA+N1KS/ioozPNvJJG7EAVBews4BpXOlUrKN9ElUbUfDSZGQlpfaBmkjaqFRNG/mUSquvE+YGwiL/gqbLrqWcmKphV4pdrAqw8eMAdIO/nJ9dl/kPjI4XuBSq0qsqL5Q2ajns+AiTwhODgDiVF49ht4bnhZ0zTMmoFXh1RJ1t5GPYjVqRyGA3llFNqXStwYyAs8Dp1gxnjOZ5CTdR2tlG3zbCDV6Ce47Eibq/Qa9p+Khkrwqpald5kYPGYsWS0kYA8MrB8fpmG2kMu9JYCyFI4mmjEMpL1tSrfYC+oq1V0FW9yYgX4zB9XhLpsFsITht5O+zWfv6g4YxcefGZbSRdKLY/6FVcxH0BsDh1AAsPPiaZdUVmtcsrahoP0HrYxTWc50Vo955otVE0w25Nz0tDTer0npcoPYrY649rOuxm04ZkrPdbOC3qcYYevjro3ohFz8vBsaI0t0elXGe1kVpOD4jBS/LKi6kMdAxKG4n705VP88ftGfQGLxlDTteVyrYneNktKi/19nmpfn6plNthWOyyyw27WuWFGXZrTwNnFW2TwbBLwUuTkdJAEQ27jZgX3Q67Vog+L9GqjQAhbaS5sFXsGsqL+PQje2q+FgBkxvdJF1UR1fNCht3Ww25OYW7qjXTYDUINBGpWGzHDbiFctZFk2A1xzPlVG0U19fPX1ygvatM49nx+z8smNr9S7T8CyMqLbQMHx7zjAxhuW/z600aqEhCnaTuItM93oCL+ri1j8iCiT6O8ZEwD6arnCwAKlQq/RrLn2TUwzj+3eqdKi5+lrteL31BGwH3f45ohoSpuqTQpLzOOqIMZpXECDSgv7HWLQptov+6TuqnStQjyvFSUE1M86Rwzp/D8nQtqvhYAbC92SRdVEbVJGXleWk+m3rRRC/u8sJsAu1DXCiR0K+EgwigvUYKXIM8LG+6YrRFEusHLGP+ZukjQVRz988ZtuPS7j0YqbzarXYyd7b3vnysvTTDsAvJnEtbzks+a/PrSJygvQ4JhN5VKufPfyhZKZSeoWNLrfNa7DtbpefHxWIm9XhgjPkMZAbfPC0v5Bx1z7Bgn5WUGYrSow65bOeBeiEJ32A1TKm0GBC/Vc0g3Vdqz0jhsLdC9GE6CyIuNFHbbc/CbiaN8PS+s5JNB4wFaDzvWozapizdtJD9X7VLpqmE3ZLURm7oLANkQ6TFpGnIMwQv73MZKFd6gzO3zwpQXZxu/a8khs9oByMrLsLJI0FUc3fa7l3HvU3vwdN+Q835CqiVvWT4fyxd2YWG3OzHeVJSXMCpOHIj7HBQwmcLv2jImL+33U14AeXHHvpPD5jif9e6B8fpmG/kcP2JDUga7VurSRh6lK2AfWKsKdYp2K6CrepOJrryEy8PWgl0A2IoglfI3sjZUbaRLGykNmPLSTBfluQ0TOPtL1f+or+v8/7OlD2LvaFkqkxbxGnZJeWk13LAb4qYujgeItVQ6Yp8Xdh7wtFGIQIItCKIadkWFyZQqEsMfu+y1bVvsKSJPw1575BzM78rh8Hkd2ufQKS9itRHgrTiyLJurufuGC9Lr1eKWi07FL648XQoW2PtvtvKSqUd5EdJGYp8UN3ipBo2me31kwcviqr9otFgRGiFGTxupBlvR38gYrnYk1xl2WcDkHudBnhdvI8RWQcFLkzEjd9iNFuz4wS4ALNXSnjF9HeWNpI10fV5YPMOCF8Nwex9oLxIrzgPe812ge5H88+7FKL/7dtxjrUKpYvML7Hyhyyggm+gACl4mA+7Kv/YxnBYqY+L0vKjHtTqgToW9NqusCOXXqaZuwpVK62+WUYe3MsSVOLu5FCtyGudr7z0ZD17zFt6hV0WrvLAbXzXYYwEKQzzn2e/CqsSpVMpzHWJpjHpMrI2gMw3rEK/JbRlTe30Rq40AWXlh38nszixXtVkqLlq1kVHdb/kxGeXzAwTlJcDz8uqg853P68x5tmGwAHkyKC/UpK7JGBElYSOmtBE7oNlJ1R5w4a6nVDqo2ogrL8JJ3541UShb/p6GFecBy9/hVB+N7HG8MIetRcYwMav9f3FwrIQX940CAJbMasNe4YLaljGRTxvcuEil0q0nSp8XwFnhFcetePu8KHJ/rfOJ/Z6ljUL1qKk22Is+HkB/44ySNjIMZwjgeMldybuGXaEaJSAYYMrL3uECJkoV5DMmTzUfMbcDf3hlEAeU2Udi2SwLXhoJOFQ1thnVRoAcBAQdG/LCyECx4g1emMKdVYKXkqC85NIGetuz6B8tYl81eInkeakGTX5zmcTJ0sMhqo2YEZtVnOloJ+Vl5iIZcMOsQmNKGzEpkUnAfmXSgDeSD6Pcs1y6Pnhx/jZNecXi7FfAkxsmcPjpwAnvdv42nMeweS4v7BsB4BrfGPmMKfkmqFS69WQipI0A9/iMM3gxDLeMtFZ3XcBbhRFG0n/Nkm6kjRSOnt9Zc1vJsCvJ/intNmFgpl22SIkyawhwRmuwXh67q0MD2Y142Vwn1bRfUV7EIX1R00Y61O+8GX1egPDKi3gdFtNGInzAoSGnjQqCYTdjGrytAwsQo3hejpzXieMWdePtx8sKNXsfZU2flyDlhbGwJ+/ZhsE77JJhd+YRdTBjXMoLO+HYqsnPrKt7nWhN6vyVF9WlD9RXTcKCl5f2O8oLk7r5c2cM6SZJykvridKkDnBz63EOZgTcY7uW3wVwJf+ws40A4MvvPgmPfnIdjpgXIngRm9QJN8Sopn4Rdi78zfcew7a+oUjt+gFHmRFTR7Zt8xvr4dXgRVVexBQCm4vUkPKiXBOa0WEXkL01QYsqv1LpoOfUGXazpuFp6xBVefnFlafj+v9zgvY5JMNuVXnRpQvVz3tRQPDCUq2iKbxV0FW9yURtQCXNOYlhttH2A84Nf3GvvzQYe6m0LTepA0IqLz7M73JOLibnz+3MSp9NPmNKAQvNNmo9qnGxFu0NBLdBsOO0VnddwKu8hE3zqoZxP+Lu8wIAX3zXCThkVht29I/hXf/6IHb2O+pJlIWP2OtlomTxShgevIz4Ky/sftbIQktVH5pXKh1O5fYYdgNUbPacOs9LxvQeK3EojXwUTPV7E3t7BVUbMYKUF/ZeK5atXag2Ewpemkwjht2G0kZK/n7pbP/gRey/AIQdzMjc9M7zv3JwDKf/86/wb799kZ9EacMbvNRzss5TDLrd+Qy625yTMmM6c3REEx0ZdltPlD4vgOsdidvv4CovwT1eAK/y0sgNWYdf2kh8y1FTnssXduN/NrwBxy/pxmixgh39jqm9vuBljBv8TSOFpbMdRUYdEaCbc9NY8CL/v3meF1F5CfK8CN6prN6wy59H6asjVhtl0oanG3gc71XtsCv2ZOnQjIQxlQ88SHkRKwFbbdql4KXJtMywq5wUS5VUi4q42gnV50VRXn73/H7s7B/Hxj/18dlGktzagKdBdcN35dPorjamYxcS8YJCfV5aD1NSanW1ZbQlpbxUFwBh9oOdM6wENqoKUgs/5SWVclvV13PszurI4qNnHiP9LErqRUwbsR4vXfk0P+/2jxSklIGovNTzeiqq8hJ30OiH1OclMG3k/tvP86I+p5w2cj0vak+qeJSXqmG3+jqsWiybNrTBsHpvWBhg2BVnNbXatEtX9SYjKy+1D9TY0kbKY9kqyg/doLQw27PgpW/QkZYnyhXt0LFGlJf53WrwkuFddVnQIvZAiPsGSETnr04/AheuPhTnnLCo9sYAN7wumxN8nEaFp41CeV7k4ybKhOcw+HleAPc6UW/AdMby+ThUOMejXDtk5YV1Zk1jTqdzoy2ULWkEwUTMyosaOMRp2g5CMuwGXJvF4CqfMXjJMuBd6KkdjYtli18js6bhSRvFURaeqe4fK5Xmc418AnY5cPa2nlDp4DO0SHmZUUTNZ0sTTBtKGynKS0DaCJADK79+MCJqkzrWbbJQsoKVlzoMmary0plPcyNaG1deoqUpiGQ5fkkPPv8XJ2BuQA8JkWvPOQ73//0ZOP3oebHuB7tBhak2SloB8BsPIP6/XtXQNFK4aM1h/P9RgiBReWGVRl25DNqzaX4DvHPLDr69Lm3UiE9FvSY0y7BbT5M6py2Dq2YwP567rbdUWuy9o6aNolQb+cFLpauvM1Atge5t06dKxXvDvM5c7RlemhlarYCu7E0magOqdGxpI0V5iZA2ijKYkZm49lSDlySUF9Xz0pVPc+WFPW+O/U1m3SmJaaRwaMyqCyAqL7U9L+pNM9G0kdqoTSmxrYfzT13Kz4cwwRpD7PXC/C1MqVp/2jIAwD/97Gl89d5nAejTRtkYq42a12FXTBuF7fMiG3ZVs6uaNioI1UY6w24snhdlMCMLXnra9ce8GDAF+V0Ybrk0pY1mFGbEMsiopdV+iNH17I5sze6i2YieF7VJHRtSNlGyYFXz4+J7z8cZvOTc4IWVYLPVEJVJEyLcsBuq2kg+duIPXvwXCOy/jbxmT1sG3/zAKfjEOctx7IKu0I8Te708uXsQALin7Oq3HoOPvdXx03xt03PYPTCuTR80pLyopdItMexGUF4Ez8viXvnm70kbKYbdJDwvvM+LxZQXJwBVy7IZ4vsJqjRisAanOsWtmdCVvcmI18Owk1fd7RtIGwnPs3RWcMpIfa16SqW58lKq8BWAmEdm0Xs9K42etowUXHUJ1Ub56n6woIUqjQgRdtyE8bx42643z/Oi9geplzcfOx+XvfHIUKlfRiqVwmsW9wAAfvPsPgDu55VKpfCRM4/G4upNbt9wwcewG1/w0orZRsGeF0V5Ea4xandarWG37M6bSrbayHkd1jm311d5cV8zqLsug1UckWF3hsHUh1Qq3IEaV9pIvAAcUsOsq24fKngRmtQVyxZvZFUoW7zPizoTBKhvpZFKpbj6wib58rRRVjbskvJCiDCjepgmcuqxGbfykg04x9j/W5X2PHXZLADgIzjU5mYsDTVSKDchbdQs5UW41oZOGxlS2khNu7DrKLt2F6W0kdewG2e1EU8bjTvX4t62eJQXVm5Nht0ZBjs4M8okUD9i67ArPM8hoZSX+kul9w67o+GLZctVXmIqlQaAudXgpSufRiqVwjFVWZw10sqT54XQ8Pm/OB4bP3o6Xle9OQehrvjjLrlPmwY/L9UUFXvpuAOmsLxu2Wzp/92KUsWMu8MT5cTTRs3q8xLW5xfUYVdVLlgQJ6bV5SZ1qvLS+PftTpWupo1GHeVFVXkYsvJSO3hpmySGXRrM2GRYMBK27DJqabUfYjBSy6wLyCunMAEGUz72jxR4yojBBrfpJj3XG7zMF4IXAHjTMfOw6WNvwmHVlbWbNqL4nHDJZ0wsX9gdalvVa5FEv5Fc2kC5WPFWGzVYKt0opxw6C6mU2zFXNTh3Vv8/UtAHLw2ljTyDGZvzGYhKWHCHXXe7tqwJYXgzetszaM+a3A+iqzYSlZeMaaArn3ZnG8WgMnHlhaeNqspLh5/y4r6fhd0hlBdeKk1poxlF1IuSGVvaSPC8REwbhVGI2A3hxf2j2H5gTPod6/AorqCY9Fjve2Jpo85chu/jkfM6+X7zZnWkvBB1oqoHjVT++JHzCeJNZcXebHraM5LJV/UIMcPzyEQJY3E3qfN4jZqfNopSbSQukHraMtLQW+55kQy7Vc9L9bsVjbRxel5Yn5eB8eBS6ciel0mivFDw0mSYa9+vbE0ltuDFEJWX8IbdsMrIgu4cZrVnULFsPPDcful3THkRU2BvPmY+3rZiAS5esyzU86uwXi9+xkuWGgua4UQQQag3kiRUEBaceBqztVh5AVzfC+BetxgsbSQqL/Fdq2TVN4rZuBHCDmZUq41Ez4sTvLjXJF5tJJZKl13lBZDTOXHONnL7vNSoNhKCNrUBqI7JUipNaaMmc/jcDnz5/JNwxLyOUNvHVW3EHptKAUsieF7CnkupVArHLerGgy8c4BUKjNGiV3mZ1ZHFty86NdyTa1hSDUrm+Eihb12xEHde9nocv6Sn7tcgZjZq6iCptBHgbw4Oaj2fNK9bNhv/7yGnGZ26SGCG3eFCGRNV5WVhdx67BqIPglSR5rk1sTt2JmTaSBqumzFg2e53xNJG7vPIwYvqeQGcayHfPo5SaVZtVAlXbcRec05HNlR1ZntVNW91qTQFLy3g3SsPCb1tfGkj57ELuvKhTKxu8BL+ZGLBC6s0YoxWI/Qoz1WLd5y4CLsGxn3bzZtGCq8/Yk5sr0fMPDxpowRUkFkdWbx8YMwTHGx4y9G4/9l9OOXQ2sbipDhVMO16PC88bVTmyuqiHjd4aaRCKK5rXlQyiuLjh2rYzZoGjJTz785cWps20lUbZU1N2iiG9ytOlbZtG4M1gpcVi7txxrHzsObIcNdLVird6uAl0SOjv78fF154Ibq7u9Hb24tLLrkEIyMjoR5r2zbe/va3I5VK4cc//nGSuzmpiTqF2o/jFnVhbmcWZx+/MNT2bFUQNXjRwYKXOEseO3JpXPXWY3DswvDNtwgiCqphNwn/yef+/Hj80zuPx8lLe6Wfn3fSYnz5/JNamjZa0tuG5Qu7kDUNT6q5SyqVdm7GYoq2EX+QGTKIiJtMWkwb+e+/qI7kMyZ627P4+vtei3/9wEqkUqnAtNF4qcINvux6LgYVsfZ5qVgYK1a40uOXNsqlTdy2fhUue+ORoZ7fbVI3jdNGF154IV599VXce++9KJVKWL9+PS677DLccccdNR974403Ni3XOZmJK200vyuPLZ9YF3pIIYveo1w8jlukDyRY2ihO5YUgksbT6TUBFeD4JT2TOrV512VrMFwoYY46T0xQXljVySKhu2wjN+G4rnlRCZs2mt2RxRFzO9DTnuGP+bMTF/PfS8qLMqNqVPCJZDSG3Vj7vFg2rzTKmoa0X43AnqfVht3Egpenn34aGzduxCOPPIJTT3W8Dd/4xjdwzjnn4Mtf/jIWL17s+9jHH38cX/nKV/Doo49i0aJwU2inK+xgTsdgXIsyXZmtGKK85FHzO5E2UrxEryuXxnChzF3vzSp5JIg4UIOVVqograKnPaMtLhA9L6xJ3RJBeck08FmJN/BmXjPkaqMAw65p4J6r3ug7VFbrean+LaZadJ6XegbVevZP6PMyIKSM4hIDOqrK0rRtUrd582b09vbywAUA1q1bB8Mw8PDDD/s+bmxsDO9///tx0003YeHC2imOQqGAoaEh6c90Qmxq10yiVhsBjvx41Hy3c6lakt3kt0AQDaGuvmdi8OKH7HlxbmJimW1caaNmddcFlPEANa57GdPwXQy25/zTRiOC8uJ6Xpzg0EhFW2D6IZZKD9Twu9RDG1depmmfl76+PsyfP1/6WTqdxuzZs9HX1+f7uKuuugpr167Fn//5n4d6neuvvx49PT38z9KlSxva78kGV16aeBI7rxfdsAu4vpfe9gxvXMeIY9w7QTQLddXfzBTGZEf0vLBqI7E7a0OG3Zh8flGJaxRLe0bT50VJG2VMV0lnaaO4VCa3VNpNG6ljCBphSW8bPrR2Gc5f2dp7beRP65prrkEqlQr8s23btrp25u6778avfvUr3HjjjaEfc+2112JwcJD/2blzZ12vPVnh/R6aLFtk6w5eHN/Lwu68p7stKS/EVEJNHeRManjIYM0hD44VedM1MXhpqFQ6ZPombkRlrRHviai8ZJQmdaxhp/j5MFUkLnNyhnteLN6gzm80QD0snd2Oz5z3Glz+5nAG36SI7Hn52Mc+hg996EOB2xxxxBFYuHAh9u7dK/28XC6jv7/fNx30q1/9Ci+88AJ6e3uln7/rXe/C6aefjvvuu8/zmFwuh1yudmOdqUqr00ZRz6c3HTMfX77nWbz+iDmeMQGkvBBTCUob+cM9LxNl6WfL5rRj18C4b/+lMKhN6pqFeI1tRGUTPS/sOZkHqFiRG9QBwGFzOtDTlsGyObU7n4fBrTayMTAaPJRxKhM5eJk3bx7mzZtXc7s1a9ZgYGAAW7duxcqVKwE4wYllWVi9erX2Mddccw3+6q/+SvrZCSecgK9+9as499xzo+7qtIBPJU23Jm0U9eJx7MIu/OG6tyGfMXD19/8g/S4OMxpBNAt1wUBpI5fOnHzrMFKOunDHpa/H4HipoTTFZEgbNbLQ6ggw7DLE99WZS+O3Hz8jNnXd5H1eXOWltyM+5WWykFi10XHHHYezzz4bl156KW6++WaUSiVs2LAB733ve3ml0a5du3DmmWfiu9/9LlatWoWFCxdqVZlDDz0Uhx9+eFK7Oqk5fkk33nzsPJx+dO2AMU7qaVLHYIYutS9GM1dRBNEo4s3MSMXTQGy6oAYv7Vlnuvvi3raGR3KIgUNTDbtSn5f6X7dN6PPCegWp18Ks8r668/EFF2KH3YM1RgNMZRLt8/K9730PGzZswJlnngnDMPCud70LX//61/nvS6USnnnmGYyNjQU8y8wmlzbx7+tXNf11s8I4gXpRW01T8EJMJcRghVJGMqaRkqYnh2krH/653X9nmlkqHVPQ1KFJG6nHTyOl5LXghl2x2shnKONUJtHgZfbs2YEN6ZYtWwbbtn1/D6Dm74lkqDdtJJLzGHYpeCGmDnFVn0xXOnNpHrzE1QANaKHyEpNRWDTsqtVG7msldzyx91GxLIxMON9PnNVGkwU6IwktjaSNGPk0KS/E1EW8cSYxGmCq0ynMY4pzgKR4X29mqi4jVRs1UCqtU14CPC9xw66zpYqrvMRZbTRZoDOS0JKts9pIRFVemln2SBCNIlef0KVSpUtQGNqSUl6aWW0U0+sGVRsxVM9LnLB+MeWK5XpeGqj+mqzQGUloqbdJnYiqvNBsI2IqId7AyPPiJTHlRbhMNDV4ESo6G0lXscGMqZSrgjRTeWFpo1LFxuA4eV6IGUYmBs+LauJrdpdggmgEseNps5tETgU6k1JeWqR4id93I9e9+V05dGRNaZhlK9JG/aNFPsF6OnpeKHghtGR4tVEjwYt8gpLyQkwlxGCb0kZeWJddIObgJRWPAhIVMcBopMqpI5fGPVe9UVq8GUYKGTPFuxEnWW3EjtX9IwVnf7LmtFQOKXghtLjKS/3P4VFeyPNCTCHEG+d0vPg3SldCaSPxOtHUqdJC2shsMGg6ZJa3W27WNFCqVKr/TtLz4jx3oex08102tyOx12oldEYSWuKoNqImdcRURlx9U/DiRUwbxVsq3ZrZRmKglER/GfEYSlLJUyu0/u5txyb2Wq2ElBdCy2sP7cVR8zvxjhMW1f0c1KSOmMqIq2/yvHhJrlS6NWmj9qzpdFI2jERet2nBi/D5vW3FApyxfH5ir9VKKHghtMztzOGXV7+poefwTpWm4IWYOpDyEkxiht0WNQfsyKXxz+8+CRkzlcjrNqv0nk2pzmcMfPrcFYm9Tquh4IVIjBw1qSOmMGlSXgJJyvPSqrQRALx75SGJPbcYAGcTHLR72JwOfOX8k3DonHat92a6QMELkRge5YWqjYgphDQegJQXD81QXqbTMMxsE0vA35VgEDZZmD5HBjHp8Cgv1OeFmEKkUinfJmOEErzE2qROTBtNn2tGrkmel5kCfYJEYngMu6S8EFMMpr6Q58WLZNiNtUmdULI8jVLNzTLszhToEyQSgwy7xFTHHaxHx65Kl9CkLtZS6VRrDLtJI3le6HhqmOlzZBCTDjLsElMdZtol5cWLqLyoKmsjpFto2E0SGvQZL/QJEomRMVPSVGpKGxFTDUob+dORcwMWNowwDmaCYZeOp8ahT5BIjFQqxVdkqZQz34MgphKs6yqtlL3k0u7MnKRKpaeTYZc8L/FCnyCRKCx4mU7yLzFzoLRRMKsPn40F3Tksnd0W23OaLZptlDRS8ELHU8NQnxciUfLpxmckEUSrcA27dLPRcfv6VShbdqzBXSub1CVJjgy7sULBC5EoOVJeiCmMSZ6XQAwjhWzM53arZhslTTOb1M0E6BMkEoWtNsjvQkxF0tSkrulMW8MueV5ihT5BIlHI80JMZXjaiJSXpiGNZZhG1w0qlY4X+gSJRGGN6qjHCzEVYWkLutk0D9EfN12VlyQHM84Ups+RQUxKWKM6Cl6Iqcjhczukv4nkESuMppXnhdJGsUKGXSJRmPIynUoeiZnDl951Iq5+6zE4ZFZ7q3dlxiBeKqZTupkMu/FCnyCRKMzzQrELMRXJmAYFLk1GUl6m0YWDpkrHC32CRKLk08ywS4caQRC1mQkddql6rXHoEyQSJZdhTepavCMEQUwJpmuptFRtRIbdhpk+RwYxKXFLpelQIwiiNtO1wy4ZduOFPkEiUfLUpI4giAjIaaPpc4uSpkpPo/fVKugTJBKFxgMQBBEF8VoxnVoskPISL/QJEoniVhtNn4sQQRDJITapm66G3en0vloFBS9EorDyQFJeCIIIQ3qaGnalUmkaN9Ew9AkSicKUFzNFwQtBELUxjBQPYHLT6CafNU3h39PnfbUK6rBLJMrC7jwAYE5ntsV7QhDEVOHvzzoW/WNFzO3MtXpXYkMsjybPS+NQ8EIkytoj5+DbH1yJk5f2tnpXCIKYInz4TUe2ehdih6ktRmp6GZFbBQUvRKIYRgpve83CVu8GQRBES+lpyyCVAma1kwodBxS8EARBEETCzOnM4V/ffwpmdVDwEgcUvBAEQRBEE3j7CYtavQvThsRcQ/39/bjwwgvR3d2N3t5eXHLJJRgZGan5uM2bN+Mtb3kLOjo60N3djTe+8Y0YHx9PajcJgiAIgphiJBa8XHjhhXjyySdx77334qc//Snuv/9+XHbZZYGP2bx5M84++2y87W1vw5YtW/DII49gw4YNMGguDkEQBEEQVVK2bdtxP+nTTz+NFStW4JFHHsGpp54KANi4cSPOOeccvPLKK1i8eLH2ca9//evx1re+FZ/73Ofqfu2hoSH09PRgcHAQ3d3ddT8PQRAEQRDNI8r9OxFJY/Pmzejt7eWBCwCsW7cOhmHg4Ycf1j5m7969ePjhhzF//nysXbsWCxYswJve9CY88MADga9VKBQwNDQk/SEIgiAIYvqSSPDS19eH+fPnSz9Lp9OYPXs2+vr6tI958cUXAQCf+cxncOmll2Ljxo045ZRTcOaZZ+K5557zfa3rr78ePT09/M/SpUvjeyMEQRAEQUw6IgUv11xzDVKpVOCfbdu21bUjlmUBAD784Q9j/fr1eO1rX4uvfvWrOPbYY3Hrrbf6Pu7aa6/F4OAg/7Nz5866Xp8gCIIgiKlBpFLpj33sY/jQhz4UuM0RRxyBhQsXYu/evdLPy+Uy+vv7sXChvmHZokVOCdmKFSuknx933HHYsWOH7+vlcjnkctOnhTRBEARBEMFECl7mzZuHefPm1dxuzZo1GBgYwNatW7Fy5UoAwK9+9StYloXVq1drH7Ns2TIsXrwYzzzzjPTzZ599Fm9/+9uj7CZBEARBENOYRDwvxx13HM4++2xceuml2LJlC373u99hw4YNeO9738srjXbt2oXly5djy5YtAIBUKoW///u/x9e//nX84Ac/wPPPP49PfepT2LZtGy655JIkdpMgCIIgiClIYh12v/e972HDhg0488wzYRgG3vWud+HrX/86/32pVMIzzzyDsbEx/rOPfvSjmJiYwFVXXYX+/n6cdNJJuPfee3HkkdNvSBdBEARBEPWRSJ+XVkJ9XgiCIAhi6tHyPi8EQRAEQRBJQcELQRAEQRBTimk3VZplwajTLkEQBEFMHdh9O4ybZdoFL8PDwwBAnXYJgiAIYgoyPDyMnp6ewG2mnWHXsizs3r0bXV1dSKVSnt8PDQ1h6dKl2Llz54ww9M6k9zuT3itA73e6Q+93+jKT3isQ/v3ato3h4WEsXrwYhhHsapl2yothGDjkkENqbtfd3T0jDhrGTHq/M+m9AvR+pzv0fqcvM+m9AuHeby3FhUGGXYIgCIIgphQUvBAEQRAEMaWYccFLLpfDddddN2OGOc6k9zuT3itA73e6Q+93+jKT3iuQzPuddoZdgiAIgiCmNzNOeSEIgiAIYmpDwQtBEARBEFMKCl4IgiAIgphSUPBCEARBEMSUYsYGLy+//DIuueQSHH744Whra8ORRx6J6667DsVisdW7lhif//znsXbtWrS3t6O3t7fVuxM7N910E5YtW4Z8Po/Vq1djy5Ytrd6lRLj//vtx7rnnYvHixUilUvjxj3/c6l1KlOuvvx6ve93r0NXVhfnz5+Od73wnnnnmmVbvVmJ885vfxIknnsgbeq1Zswa/+MUvWr1bTeGLX/wiUqkUPvrRj7Z6VxLhM5/5DFKplPRn+fLlrd6tRNm1axc+8IEPYM6cOWhra8MJJ5yARx99tOHnnbHBy7Zt22BZFr71rW/hySefxFe/+lXcfPPN+MQnPtHqXUuMYrGI888/H5dffnmrdyV27rrrLlx99dW47rrr8Nhjj+Gkk07CWWedhb1797Z612JndHQUJ510Em666aZW70pT+M1vfoMrrrgCDz30EO69916USiW87W1vw+joaKt3LREOOeQQfPGLX8TWrVvx6KOP4i1veQv+/M//HE8++WSrdy1RHnnkEXzrW9/CiSee2OpdSZTXvOY1ePXVV/mfBx54oNW7lBgHDx7Eaaedhkwmg1/84hd46qmn8JWvfAWzZs1q/MltgvPP//zP9uGHH97q3Uic2267ze7p6Wn1bsTKqlWr7CuuuIL/v1Kp2IsXL7avv/76Fu5V8gCwf/SjH7V6N5rK3r17bQD2b37zm1bvStOYNWuW/W//9m+t3o3EGB4eto8++mj73nvvtd/0pjfZV155Zat3KRGuu+46+6STTmr1bjSNj3/84/Yb3vCGRJ57xiovOgYHBzF79uxW7wYRkWKxiK1bt2LdunX8Z4ZhYN26ddi8eXML94xIgsHBQQCYEedqpVLBnXfeidHRUaxZs6bVu5MYV1xxBd7xjndI5/B05bnnnsPixYtxxBFH4MILL8SOHTtavUuJcffdd+PUU0/F+eefj/nz5+O1r30tbrnllliem4KXKs8//zy+8Y1v4MMf/nCrd4WIyP79+1GpVLBgwQLp5wsWLEBfX1+L9opIAsuy8NGPfhSnnXYajj/++FbvTmI88cQT6OzsRC6Xw1//9V/jRz/6EVasWNHq3UqEO++8E4899hiuv/76Vu9K4qxevRr//u//jo0bN+Kb3/wmXnrpJZx++ukYHh5u9a4lwosvvohvfvObOProo3HPPffg8ssvx9/+7d/i9ttvb/i5p13wcs0113gMUeqfbdu2SY/ZtWsXzj77bJx//vm49NJLW7Tn9VHP+yWIqcoVV1yBP/3pT7jzzjtbvSuJcuyxx+Lxxx/Hww8/jMsvvxwXX3wxnnrqqVbvVuzs3LkTV155Jb73ve8hn8+3encS5+1vfzvOP/98nHjiiTjrrLPw85//HAMDA/j+97/f6l1LBMuycMopp+ALX/gCXvva1+Kyyy7DpZdeiptvvrnh507HsH+Tio997GP40Ic+FLjNEUccwf+9e/dunHHGGVi7di2+/e1vJ7x38RP1/U5H5s6dC9M0sWfPHunne/bswcKFC1u0V0TcbNiwAT/96U9x//3345BDDmn17iRKNpvFUUcdBQBYuXIlHnnkEXzta1/Dt771rRbvWbxs3boVe/fuxSmnnMJ/VqlUcP/99+Nf/uVfUCgUYJpmC/cwWXp7e3HMMcfg+eefb/WuJMKiRYs8iuFxxx2HH/7whw0/97QLXubNm4d58+aF2nbXrl0444wzsHLlStx2220wjKknREV5v9OVbDaLlStXYtOmTXjnO98JwIn4N23ahA0bNrR254iGsW0bH/nIR/CjH/0I9913Hw4//PBW71LTsSwLhUKh1bsRO2eeeSaeeOIJ6Wfr16/H8uXL8fGPf3xaBy4AMDIyghdeeAEf/OAHW70riXDaaad52ho8++yzOOywwxp+7mkXvIRl165dePOb34zDDjsMX/7yl7Fv3z7+u+m6Wt+xYwf6+/uxY8cOVCoVPP744wCAo446Cp2dna3duQa5+uqrcfHFF+PUU0/FqlWrcOONN2J0dBTr169v9a7FzsjIiLRSe+mll/D4449j9uzZOPTQQ1u4Z8lwxRVX4I477sBPfvITdHV1cR9TT08P2traWrx38XPttdfi7W9/Ow499FAMDw/jjjvuwH333Yd77rmn1bsWO11dXR7vUkdHB+bMmTMtPU1/93d/h3PPPReHHXYYdu/ejeuuuw6maeJ973tfq3ctEa666iqsXbsWX/jCF/Ce97wHW7Zswbe//e14shyJ1DBNAW677TYbgPbPdOXiiy/Wvt9f//rXrd61WPjGN75hH3rooXY2m7VXrVplP/TQQ63epUT49a9/rf0eL7744lbvWiL4nae33XZbq3ctEf7yL//SPuyww+xsNmvPmzfPPvPMM+3//d//bfVuNY3pXCp9wQUX2IsWLbKz2ay9ZMkS+4ILLrCff/75Vu9WovzP//yPffzxx9u5XM5evny5/e1vfzuW503Ztm03HgIRBEEQBEE0h6ln8iAIgiAIYkZDwQtBEARBEFMKCl4IgiAIgphSUPBCEARBEMSUgoIXgiAIgiCmFBS8EARBEAQxpaDghSAIgiCIKQUFLwRBEARBTCkoeCEIgiAIYkpBwQtBEARBEFMKCl4IgiAIgphSUPBCEARBEMSU4v8HtXlY3p6msw4AAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot( *series['F_var'].pack )\n",
+ "plt.plot( *features.pack , 'o' )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "805024c9-14f0-4bf0-9e2c-ae47cbdbe02a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "all_features = trials_roi_df.apply( adaptation.classifiers.extract_features , axis = 1)\n",
+ "trials_roi_df.loc[:,\"features\"] = all_features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "1e5a4905-bbd1-470f-9b70-693106bed47f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "second_roi = trials_roi_df.loc[1:1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "ff5951ef-664f-4431-b79e-e8ef000fbea4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "training_data, test_data = adaptation.classifiers.get_sample_and_training(second_roi, frac = 0.75) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "30ad970d-f8ad-4e80-be7d-e0704b787071",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(112, 38, 150)"
+ ]
+ },
+ "execution_count": 73,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(training_data), len(test_data) , len(second_roi) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "161d503c-a5de-49d1-a082-2edc71097c39",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>is_C1</th>\n",
+ " <th>is_D1</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>Result</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_stim_info</th>\n",
+ " <th>features</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"11\" valign=\"top\">1</th>\n",
+ " <th>0</th>\n",
+ " <td>[209.49720764160156, 196.16419982910156, 163.7...</td>\n",
+ " <td>[0.2883108210421972, 0.5932362809663284, 0.192...</td>\n",
+ " <td>[94.13381958007812, 108.01216888427734, 89.793...</td>\n",
+ " <td>[0.2883108210421972, 0.5932362809663284, 0.192...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 27.431922912597656, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.14208435053957136, -0.04467766920909842, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>[158.18783569335938, 89.54745483398438, 110.91...</td>\n",
+ " <td>[-0.37860951812428595, -0.09118932585200633, 0...</td>\n",
+ " <td>[63.766422271728516, 76.84671783447266, 87.138...</td>\n",
+ " <td>[-0.37860951812428595, -0.09118932585200633, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 12.860980987548828, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[0.18317441572002485, 0.6089883355764498, 0.27...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>[208.61781311035156, 141.9474639892578, 127.91...</td>\n",
+ " <td>[-0.03890399501502499, -0.19722410025020307, -...</td>\n",
+ " <td>[78.98297119140625, 71.77615356445312, 68.0291...</td>\n",
+ " <td>[-0.03890399501502499, -0.19722410025020307, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.28473706526705966, 0.6401952631874557, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>[65.38990020751953, 97.3988037109375, 78.23871...</td>\n",
+ " <td>[-0.6413238419292778, -0.5933089554122087, -0....</td>\n",
+ " <td>[51.401458740234375, 53.57664108276367, 69.479...</td>\n",
+ " <td>[-0.6413238419292778, -0.5933089554122087, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 9.399063110351562, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.09234676619335945, 0.17328275696398882, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>[112.6033706665039, 127.02738189697266, 135.33...</td>\n",
+ " <td>[-0.07971632302949747, -0.31289708216540224, -...</td>\n",
+ " <td>[76.63746643066406, 66.02676391601562, 68.2822...</td>\n",
+ " <td>[-0.07971632302949747, -0.31289708216540224, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 26.755979537963867, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.38352857755051395, -0.02409812337578938, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>145</th>\n",
+ " <td>[160.49502563476562, 100.42060089111328, 144.7...</td>\n",
+ " <td>[-0.11440516678545243, 0.06607001015022987, -0...</td>\n",
+ " <td>[67.53771209716797, 75.7493896484375, 65.95864...</td>\n",
+ " <td>[-0.11440516678545243, 0.06607001015022987, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.9535269141197205, 3.284...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.12718914083330662, -0.19491205012189783, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>[155.9180450439453, 162.44467163085938, 96.349...</td>\n",
+ " <td>[-0.23688705374362884, -0.0093043096092575, -0...</td>\n",
+ " <td>[61.83941650390625, 72.19464874267578, 68.3527...</td>\n",
+ " <td>[-0.23688705374362884, -0.0093043096092575, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.22872979086860398, 0.015237631903899545, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>[148.58978271484375, 126.00932312011719, 120.3...</td>\n",
+ " <td>[-0.12257307039707485, -0.22470088768804625, -...</td>\n",
+ " <td>[67.03163146972656, 62.384429931640625, 64.430...</td>\n",
+ " <td>[-0.12257307039707485, -0.22470088768804625, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.04876766581601869, 0.5481617164147937, 0.2...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>[107.55477905273438, 147.9124298095703, 98.443...</td>\n",
+ " <td>[-0.22314441989074893, -0.18957804955818716, -...</td>\n",
+ " <td>[62.58880615234375, 64.11679077148438, 63.9659...</td>\n",
+ " <td>[-0.22314441989074893, -0.18957804955818716, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.5479384824166067, -0.1350689360739867, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>[75.85784149169922, 95.86479187011719, 131.299...</td>\n",
+ " <td>[-0.5684309448517589, -0.3047014356799419, -0....</td>\n",
+ " <td>[46.89051055908203, 58.89051055908203, 70.8783...</td>\n",
+ " <td>[-0.5684309448517589, -0.3047014356799419, -0....</td>\n",
+ " <td>[0.0, 0.0, 11.951688766479492, 2.0843546390533...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[0.04623678528617768, 0.24733918871087354, 0.0...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>150 rows × 18 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "1 0 [209.49720764160156, 196.16419982910156, 163.7... \\\n",
+ " 1 [158.18783569335938, 89.54745483398438, 110.91... \n",
+ " 2 [208.61781311035156, 141.9474639892578, 127.91... \n",
+ " 3 [65.38990020751953, 97.3988037109375, 78.23871... \n",
+ " 4 [112.6033706665039, 127.02738189697266, 135.33... \n",
+ "... ... \n",
+ " 145 [160.49502563476562, 100.42060089111328, 144.7... \n",
+ " 146 [155.9180450439453, 162.44467163085938, 96.349... \n",
+ " 147 [148.58978271484375, 126.00932312011719, 120.3... \n",
+ " 148 [107.55477905273438, 147.9124298095703, 98.443... \n",
+ " 149 [75.85784149169922, 95.86479187011719, 131.299... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "1 0 [0.2883108210421972, 0.5932362809663284, 0.192... \\\n",
+ " 1 [-0.37860951812428595, -0.09118932585200633, 0... \n",
+ " 2 [-0.03890399501502499, -0.19722410025020307, -... \n",
+ " 3 [-0.6413238419292778, -0.5933089554122087, -0.... \n",
+ " 4 [-0.07971632302949747, -0.31289708216540224, -... \n",
+ "... ... \n",
+ " 145 [-0.11440516678545243, 0.06607001015022987, -0... \n",
+ " 146 [-0.23688705374362884, -0.0093043096092575, -0... \n",
+ " 147 [-0.12257307039707485, -0.22470088768804625, -... \n",
+ " 148 [-0.22314441989074893, -0.18957804955818716, -... \n",
+ " 149 [-0.5684309448517589, -0.3047014356799419, -0.... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "1 0 [94.13381958007812, 108.01216888427734, 89.793... \\\n",
+ " 1 [63.766422271728516, 76.84671783447266, 87.138... \n",
+ " 2 [78.98297119140625, 71.77615356445312, 68.0291... \n",
+ " 3 [51.401458740234375, 53.57664108276367, 69.479... \n",
+ " 4 [76.63746643066406, 66.02676391601562, 68.2822... \n",
+ "... ... \n",
+ " 145 [67.53771209716797, 75.7493896484375, 65.95864... \n",
+ " 146 [61.83941650390625, 72.19464874267578, 68.3527... \n",
+ " 147 [67.03163146972656, 62.384429931640625, 64.430... \n",
+ " 148 [62.58880615234375, 64.11679077148438, 63.9659... \n",
+ " 149 [46.89051055908203, 58.89051055908203, 70.8783... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "1 0 [0.2883108210421972, 0.5932362809663284, 0.192... \\\n",
+ " 1 [-0.37860951812428595, -0.09118932585200633, 0... \n",
+ " 2 [-0.03890399501502499, -0.19722410025020307, -... \n",
+ " 3 [-0.6413238419292778, -0.5933089554122087, -0.... \n",
+ " 4 [-0.07971632302949747, -0.31289708216540224, -... \n",
+ "... ... \n",
+ " 145 [-0.11440516678545243, 0.06607001015022987, -0... \n",
+ " 146 [-0.23688705374362884, -0.0093043096092575, -0... \n",
+ " 147 [-0.12257307039707485, -0.22470088768804625, -... \n",
+ " 148 [-0.22314441989074893, -0.18957804955818716, -... \n",
+ " 149 [-0.5684309448517589, -0.3047014356799419, -0.... \n",
+ "\n",
+ " spks is_neuron \n",
+ "roi# trial# \n",
+ "1 0 [0.0, 0.0, 0.0, 27.431922912597656, 0.0, 0.0, ... False \\\n",
+ " 1 [0.0, 0.0, 0.0, 0.0, 0.0, 12.860980987548828, ... False \n",
+ " 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 3 [0.0, 0.0, 0.0, 9.399063110351562, 0.0, 0.0, 0... False \n",
+ " 4 [0.0, 0.0, 0.0, 0.0, 0.0, 26.755979537963867, ... False \n",
+ "... ... ... \n",
+ " 145 [0.0, 0.0, 0.0, 0.0, 0.9535269141197205, 3.284... False \n",
+ " 146 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 147 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 148 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 149 [0.0, 0.0, 11.951688766479492, 2.0843546390533... False \n",
+ "\n",
+ " is_VGAT is_C1 is_D1 target_stim nontarget_stim \n",
+ "roi# trial# \n",
+ "1 0 None False True C1_10_90 D1_10 \\\n",
+ " 1 None False True D1_10_90 C1_NaN \n",
+ " 2 None False True C1_10_20 D1_10 \n",
+ " 3 None False True D1_10_20 C1_NaN \n",
+ " 4 None False True D1_10_90 C1_NaN \n",
+ "... ... ... ... ... ... \n",
+ " 145 None False True C1_10_90 D1_10 \n",
+ " 146 None False True C1_10_20 D1_10 \n",
+ " 147 None False True C1_10_20 D1_NaN \n",
+ " 148 None False True C1_10_20 D1_NaN \n",
+ " 149 None False True D1_10_20 C1_10 \n",
+ "\n",
+ " in_target_barrel target_amplitude nontarget_amplitude Result \n",
+ "roi# trial# \n",
+ "1 0 False 10_90 10 2.0 \\\n",
+ " 1 True 10_90 0 2.0 \n",
+ " 2 False 10_20 10 2.0 \n",
+ " 3 True 10_20 0 2.0 \n",
+ " 4 True 10_90 0 2.0 \n",
+ "... ... ... ... ... \n",
+ " 145 False 10_90 10 2.0 \n",
+ " 146 False 10_20 10 2.0 \n",
+ " 147 False 10_20 0 2.0 \n",
+ " 148 False 10_20 0 2.0 \n",
+ " 149 True 10_20 10 2.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "1 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 4 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ " 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_stim_info \n",
+ "roi# trial# \n",
+ "1 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 1 [] \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 3 [] \n",
+ " 4 [] \n",
+ "... ... \n",
+ " 145 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 146 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 147 [] \n",
+ " 148 [] \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " features \n",
+ "roi# trial# \n",
+ "1 0 [-0.14208435053957136, -0.04467766920909842, -... \n",
+ " 1 [0.18317441572002485, 0.6089883355764498, 0.27... \n",
+ " 2 [-0.28473706526705966, 0.6401952631874557, -0.... \n",
+ " 3 [-0.09234676619335945, 0.17328275696398882, -0... \n",
+ " 4 [-0.38352857755051395, -0.02409812337578938, -... \n",
+ "... ... \n",
+ " 145 [-0.12718914083330662, -0.19491205012189783, -... \n",
+ " 146 [-0.22872979086860398, 0.015237631903899545, 0... \n",
+ " 147 [-0.04876766581601869, 0.5481617164147937, 0.2... \n",
+ " 148 [-0.5479384824166067, -0.1350689360739867, -0.... \n",
+ " 149 [0.04623678528617768, 0.24733918871087354, 0.0... \n",
+ "\n",
+ "[150 rows x 18 columns]"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "second_roi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cad2a8a9-0ed8-4f97-89b9-698274005e4f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "571e41b9-1fe2-45b5-9e00-d446ef7e3f2f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def get_score(training_data, test_data):\n",
+ " \n",
+ " training_inputs = np.array(list(training_data['features']))\n",
+ " training_outputs = np.array(training_data['target_amplitude'])\n",
+ " \n",
+ " test_inputs = np.array(list(test_data['features']))\n",
+ " test_outputs = np.array(test_data['target_amplitude']) \n",
+ " \n",
+ " classifier = sklearn.svm.LinearSVC()\n",
+ " classifier.fit(training_inputs,training_outputs)\n",
+ " \n",
+ " score = classifier.score(test_inputs,test_outputs) #to know how much your trained machine is good at predicting you ask him to give you a score \n",
+ " training_trials = training_data.index.get_level_values(\"trial#\")\n",
+ " test_trials = test_data.index.get_level_values(\"trial#\")\n",
+ " \n",
+ " result = {'score':score,'training_trials':training_trials,'test_trials':test_trials}\n",
+ " #you created result dictionary, gives you the score, tells you this score comes from which training trials and test trials\n",
+ " return result\n",
+ "#100 times get 75% of trials as training data and 25% as sample data, give them to the machine , ask the score, each time randomly\n",
+ "def bootstrap_classify(dataframe, frac = 0.75, iter_count = 100): #https://en.wikipedia.org/wiki/Bootstrap_aggregating\n",
+ " scores =[]\n",
+ " for _ in range(iter_count):\n",
+ " training_data, test_data = adaptation.classifiers.get_sample_and_training(dataframe, frac = frac)\n",
+ " score = get_score(training_data,test_data)\n",
+ " scores.append(score)\n",
+ " \n",
+ " values = []\n",
+ " for item in scores:\n",
+ " values.append(item[\"score\"]) #then you need the meadian of all the scores you got from different trials \n",
+ " \n",
+ " meta_result = {'data' : scores, 'scores' : values, 'average_score' : np.median(np.array(values)) }\n",
+ " return meta_result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "04584880-c143-43c2-bd58-d6873a5bee7c",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
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+ " .dataframe thead th {\n",
+ " text-align: right;\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>is_C1</th>\n",
+ " <th>is_D1</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>Result</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_stim_info</th>\n",
+ " <th>features</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
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+ " <th rowspan=\"11\" valign=\"top\">1</th>\n",
+ " <th>127</th>\n",
+ " <td>[65.52728271484375, 77.4063491821289, 100.2077...</td>\n",
+ " <td>[-0.4205346940317808, -0.3358639854490272, -0....</td>\n",
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+ " <td>D1_NaN</td>\n",
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+ " <td>0</td>\n",
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+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
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+ " <td>[-0.39471145404451674, -0.11223986886923122, -...</td>\n",
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+ " <td>[0.2883108210421972, 0.5932362809663284, 0.192...</td>\n",
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+ " <td>[0.0, 0.0, 0.0, 27.431922912597656, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
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+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.14208435053957136, -0.04467766920909842, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>62</th>\n",
+ " <td>[100.78144836425781, 125.46564483642578, 117.1...</td>\n",
+ " <td>[-0.42390780989672217, -0.5171286881027128, -0...</td>\n",
+ " <td>[57.92457580566406, 53.68369674682617, 65.1459...</td>\n",
+ " <td>[-0.42390780989672217, -0.5171286881027128, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.2553529573892071, -0.10574781774943136, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>94</th>\n",
+ " <td>[135.84402465820312, 114.00334930419922, 144.5...</td>\n",
+ " <td>[-0.5942826063724249, -0.15949349643079397, -0...</td>\n",
+ " <td>[50.6399040222168, 70.42578887939453, 65.38442...</td>\n",
+ " <td>[-0.5942826063724249, -0.15949349643079397, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.2758669203106033, -0.4010856645218715, -0....</td>\n",
+ " </tr>\n",
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+ " <th>122</th>\n",
+ " <td>[197.59129333496094, 69.22527313232422, 107.39...</td>\n",
+ " <td>[-0.282181910326863, 0.049330039406084644, -0....</td>\n",
+ " <td>[62.69343185424805, 77.7785873413086, 71.07542...</td>\n",
+ " <td>[-0.282181910326863, 0.049330039406084644, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
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+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>3.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.2738690413785103, -0.10799840780801394, -0...</td>\n",
+ " </tr>\n",
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+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
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+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>96</th>\n",
+ " <td>[147.0696258544922, 177.9226837158203, 222.881...</td>\n",
+ " <td>[-0.04089773299556593, -0.004114914322431476, ...</td>\n",
+ " <td>[75.73722839355469, 77.40876007080078, 84.1265...</td>\n",
+ " <td>[-0.04089773299556593, -0.004114914322431476, ...</td>\n",
+ " <td>[28.720787048339844, 21.22627830505371, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[0.015861500290614804, -0.05331720739471966, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td>[102.0448226928711, 119.07066345214844, 73.027...</td>\n",
+ " <td>[-0.2704176389242353, -0.3702954833507052, -0....</td>\n",
+ " <td>[63.5328483581543, 58.98783493041992, 56.90997...</td>\n",
+ " <td>[-0.2704176389242353, -0.3702954833507052, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
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+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.03927499074910555, 0.031822879612714565, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>144</th>\n",
+ " <td>[92.19116973876953, 141.11790466308594, 114.27...</td>\n",
+ " <td>[-0.41343722359243557, -0.13032959131784663, -...</td>\n",
+ " <td>[53.785888671875, 66.66909790039062, 62.296836...</td>\n",
+ " <td>[-0.41343722359243557, -0.13032959131784663, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 2.2366623878479004, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.055606165319110834, 0.5615506202122859, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59</th>\n",
+ " <td>[126.74130249023438, 138.35012817382812, 106.9...</td>\n",
+ " <td>[-0.12705361897387324, -0.15456271938304442, -...</td>\n",
+ " <td>[70.91240692138672, 69.66666412353516, 59.4671...</td>\n",
+ " <td>[-0.12705361897387324, -0.15456271938304442, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.39170789420148233, -0.12876790290860435, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>[208.61781311035156, 141.9474639892578, 127.91...</td>\n",
+ " <td>[-0.03890399501502499, -0.19722410025020307, -...</td>\n",
+ " <td>[78.98297119140625, 71.77615356445312, 68.0291...</td>\n",
+ " <td>[-0.03890399501502499, -0.19722410025020307, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.28473706526705966, 0.6401952631874557, -0....</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>112 rows × 18 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "1 127 [65.52728271484375, 77.4063491821289, 100.2077... \\\n",
+ " 0 [209.49720764160156, 196.16419982910156, 163.7... \n",
+ " 62 [100.78144836425781, 125.46564483642578, 117.1... \n",
+ " 94 [135.84402465820312, 114.00334930419922, 144.5... \n",
+ " 122 [197.59129333496094, 69.22527313232422, 107.39... \n",
+ "... ... \n",
+ " 96 [147.0696258544922, 177.9226837158203, 222.881... \n",
+ " 40 [102.0448226928711, 119.07066345214844, 73.027... \n",
+ " 144 [92.19116973876953, 141.11790466308594, 114.27... \n",
+ " 59 [126.74130249023438, 138.35012817382812, 106.9... \n",
+ " 2 [208.61781311035156, 141.9474639892578, 127.91... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "1 127 [-0.4205346940317808, -0.3358639854490272, -0.... \\\n",
+ " 0 [0.2883108210421972, 0.5932362809663284, 0.192... \n",
+ " 62 [-0.42390780989672217, -0.5171286881027128, -0... \n",
+ " 94 [-0.5942826063724249, -0.15949349643079397, -0... \n",
+ " 122 [-0.282181910326863, 0.049330039406084644, -0.... \n",
+ "... ... \n",
+ " 96 [-0.04089773299556593, -0.004114914322431476, ... \n",
+ " 40 [-0.2704176389242353, -0.3702954833507052, -0.... \n",
+ " 144 [-0.41343722359243557, -0.13032959131784663, -... \n",
+ " 59 [-0.12705361897387324, -0.15456271938304442, -... \n",
+ " 2 [-0.03890399501502499, -0.19722410025020307, -... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "1 127 [55.43309020996094, 59.284671783447266, 51.861... \\\n",
+ " 0 [94.13381958007812, 108.01216888427734, 89.793... \n",
+ " 62 [57.92457580566406, 53.68369674682617, 65.1459... \n",
+ " 94 [50.6399040222168, 70.42578887939453, 65.38442... \n",
+ " 122 [62.69343185424805, 77.7785873413086, 71.07542... \n",
+ "... ... \n",
+ " 96 [75.73722839355469, 77.40876007080078, 84.1265... \n",
+ " 40 [63.5328483581543, 58.98783493041992, 56.90997... \n",
+ " 144 [53.785888671875, 66.66909790039062, 62.296836... \n",
+ " 59 [70.91240692138672, 69.66666412353516, 59.4671... \n",
+ " 2 [78.98297119140625, 71.77615356445312, 68.0291... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "1 127 [-0.4205346940317808, -0.3358639854490272, -0.... \\\n",
+ " 0 [0.2883108210421972, 0.5932362809663284, 0.192... \n",
+ " 62 [-0.42390780989672217, -0.5171286881027128, -0... \n",
+ " 94 [-0.5942826063724249, -0.15949349643079397, -0... \n",
+ " 122 [-0.282181910326863, 0.049330039406084644, -0.... \n",
+ "... ... \n",
+ " 96 [-0.04089773299556593, -0.004114914322431476, ... \n",
+ " 40 [-0.2704176389242353, -0.3702954833507052, -0.... \n",
+ " 144 [-0.41343722359243557, -0.13032959131784663, -... \n",
+ " 59 [-0.12705361897387324, -0.15456271938304442, -... \n",
+ " 2 [-0.03890399501502499, -0.19722410025020307, -... \n",
+ "\n",
+ " spks is_neuron \n",
+ "roi# trial# \n",
+ "1 127 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \\\n",
+ " 0 [0.0, 0.0, 0.0, 27.431922912597656, 0.0, 0.0, ... False \n",
+ " 62 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 94 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 122 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "... ... ... \n",
+ " 96 [28.720787048339844, 21.22627830505371, 0.0, 0... False \n",
+ " 40 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 144 [0.0, 0.0, 0.0, 0.0, 0.0, 2.2366623878479004, ... False \n",
+ " 59 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ "\n",
+ " is_VGAT is_C1 is_D1 target_stim nontarget_stim \n",
+ "roi# trial# \n",
+ "1 127 None False True C1_10_90 D1_NaN \\\n",
+ " 0 None False True C1_10_90 D1_10 \n",
+ " 62 None False True D1_10_20 C1_NaN \n",
+ " 94 None False True C1_10_90 D1_NaN \n",
+ " 122 None False True D1_10_90 C1_10 \n",
+ "... ... ... ... ... ... \n",
+ " 96 None False True C1_10_90 D1_10 \n",
+ " 40 None False True D1_10_90 C1_NaN \n",
+ " 144 None False True D1_10_90 C1_NaN \n",
+ " 59 None False True C1_10_90 D1_NaN \n",
+ " 2 None False True C1_10_20 D1_10 \n",
+ "\n",
+ " in_target_barrel target_amplitude nontarget_amplitude Result \n",
+ "roi# trial# \n",
+ "1 127 False 10_90 0 2.0 \\\n",
+ " 0 False 10_90 10 2.0 \n",
+ " 62 True 10_20 0 3.0 \n",
+ " 94 False 10_90 0 2.0 \n",
+ " 122 True 10_90 10 3.0 \n",
+ "... ... ... ... ... \n",
+ " 96 False 10_90 10 2.0 \n",
+ " 40 True 10_90 0 2.0 \n",
+ " 144 True 10_90 0 2.0 \n",
+ " 59 False 10_90 0 2.0 \n",
+ " 2 False 10_20 10 2.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "1 127 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 62 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 94 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 122 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ " 96 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 40 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 144 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 59 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_stim_info \n",
+ "roi# trial# \n",
+ "1 127 [] \\\n",
+ " 0 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 62 [] \n",
+ " 94 [] \n",
+ " 122 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "... ... \n",
+ " 96 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 40 [] \n",
+ " 144 [] \n",
+ " 59 [] \n",
+ " 2 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " features \n",
+ "roi# trial# \n",
+ "1 127 [-0.39471145404451674, -0.11223986886923122, -... \n",
+ " 0 [-0.14208435053957136, -0.04467766920909842, -... \n",
+ " 62 [-0.2553529573892071, -0.10574781774943136, 0.... \n",
+ " 94 [-0.2758669203106033, -0.4010856645218715, -0.... \n",
+ " 122 [-0.2738690413785103, -0.10799840780801394, -0... \n",
+ "... ... \n",
+ " 96 [0.015861500290614804, -0.05331720739471966, 0... \n",
+ " 40 [-0.03927499074910555, 0.031822879612714565, 0... \n",
+ " 144 [-0.055606165319110834, 0.5615506202122859, -0... \n",
+ " 59 [-0.39170789420148233, -0.12876790290860435, -... \n",
+ " 2 [-0.28473706526705966, 0.6401952631874557, -0.... \n",
+ "\n",
+ "[112 rows x 18 columns]"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "491a2e3f-1141-4f7d-8e3c-3d1dc09e65dd",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>F</th>\n",
+ " <th>F_var</th>\n",
+ " <th>Fneu</th>\n",
+ " <th>Fneu_var</th>\n",
+ " <th>spks</th>\n",
+ " <th>is_neuron</th>\n",
+ " <th>is_VGAT</th>\n",
+ " <th>is_C1</th>\n",
+ " <th>is_D1</th>\n",
+ " <th>target_stim</th>\n",
+ " <th>nontarget_stim</th>\n",
+ " <th>in_target_barrel</th>\n",
+ " <th>target_amplitude</th>\n",
+ " <th>nontarget_amplitude</th>\n",
+ " <th>Result</th>\n",
+ " <th>target_stim_info</th>\n",
+ " <th>nontarget_stim_info</th>\n",
+ " <th>features</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>roi#</th>\n",
+ " <th>trial#</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"38\" valign=\"top\">1</th>\n",
+ " <th>3</th>\n",
+ " <td>[65.38990020751953, 97.3988037109375, 78.23871...</td>\n",
+ " <td>[-0.6413238419292778, -0.5933089554122087, -0....</td>\n",
+ " <td>[51.401458740234375, 53.57664108276367, 69.479...</td>\n",
+ " <td>[-0.6413238419292778, -0.5933089554122087, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 9.399063110351562, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.09234676619335945, 0.17328275696398882, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>[112.92879486083984, 159.3483123779297, 143.16...</td>\n",
+ " <td>[-0.1322782887656356, -0.405838058847386, -0.2...</td>\n",
+ " <td>[73.60826873779297, 61.14841842651367, 69.5109...</td>\n",
+ " <td>[-0.1322782887656356, -0.405838058847386, -0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.0656671524047...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.3588153210373761, -0.07913098427899713, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>[95.08174896240234, 99.19114685058594, 166.550...</td>\n",
+ " <td>[-0.2991491067228387, -0.320097658013037, -0.3...</td>\n",
+ " <td>[65.15084838867188, 64.19464874267578, 62.5839...</td>\n",
+ " <td>[-0.2991491067228387, -0.320097658013037, -0.3...</td>\n",
+ " <td>[0.0, 0.0, 4.953240394592285, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.07941622959378912, -0.09063082994244048, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>[91.68379974365234, 73.52933502197266, 133.646...</td>\n",
+ " <td>[-0.37410173259197227, -0.31330928355173904, -...</td>\n",
+ " <td>[60.57664108276367, 63.33576583862305, 55.6034...</td>\n",
+ " <td>[-0.37410173259197227, -0.31330928355173904, -...</td>\n",
+ " <td>[0.0, 0.0, 7.434715270996094, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.22193644585795141, 0.19844592301018588, 0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>[161.94859313964844, 199.63211059570312, 163.2...</td>\n",
+ " <td>[0.49128682746196506, -0.03387934328756773, 0....</td>\n",
+ " <td>[99.12408447265625, 75.22628021240234, 83.2116...</td>\n",
+ " <td>[0.49128682746196506, -0.03387934328756773, 0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 15.629164695739746, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.017383238160832516, 0.22389958264939533, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>[114.15812683105469, 60.72441864013672, 113.02...</td>\n",
+ " <td>[-0.3455050937378926, -0.6683925005850538, -0....</td>\n",
+ " <td>[60.51338195800781, 45.822383880615234, 53.211...</td>\n",
+ " <td>[-0.3455050937378926, -0.6683925005850538, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.3201763713513183, -0.3955343377506322, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>[93.15322875976562, 132.84658813476562, 109.91...</td>\n",
+ " <td>[-0.5687271666521604, -0.3708823790829836, -0....</td>\n",
+ " <td>[50.10218811035156, 59.10462188720703, 60.2773...</td>\n",
+ " <td>[-0.5687271666521604, -0.3708823790829836, -0....</td>\n",
+ " <td>[0.0, 5.189079284667969, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.1304386206530661, -0.44067560038990133, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>[154.97335815429688, 121.49047088623047, 98.42...</td>\n",
+ " <td>[-0.037199821126239256, 0.10605499537727486, 0...</td>\n",
+ " <td>[74.41849517822266, 80.93673706054688, 96.5085...</td>\n",
+ " <td>[-0.037199821126239256, 0.10605499537727486, 0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.5365144063056656, 0.15290344578927692, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>[114.42371368408203, 90.07608032226562, 109.80...</td>\n",
+ " <td>[-0.3725754118230143, -0.6310386155947286, -0....</td>\n",
+ " <td>[59.5036506652832, 47.7469596862793, 73.287101...</td>\n",
+ " <td>[-0.3725754118230143, -0.6310386155947286, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[0.0913854583373718, 0.5600446153017025, 0.256...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td>[78.85540771484375, 135.93475341796875, 86.567...</td>\n",
+ " <td>[-0.12236088083651446, -0.3749699620905525, -0...</td>\n",
+ " <td>[71.25790405273438, 59.76398849487305, 73.2627...</td>\n",
+ " <td>[-0.12236088083651446, -0.3749699620905525, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.19493923585241107, -0.051277351385884615, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>[158.4487762451172, 103.57232666015625, 76.137...</td>\n",
+ " <td>[-0.2144747908904325, 0.1699798286569285, -0.0...</td>\n",
+ " <td>[66.0997543334961, 83.59367370605469, 72.15814...</td>\n",
+ " <td>[-0.2144747908904325, 0.1699798286569285, -0.0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.15290837128792856, 0.030170095040596506, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>[85.32022857666016, 150.54190063476562, 94.415...</td>\n",
+ " <td>[-0.5128304740100976, 0.13118952105758835, -0....</td>\n",
+ " <td>[52.72992706298828, 82.03406524658203, 69.6788...</td>\n",
+ " <td>[-0.5128304740100976, 0.13118952105758835, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 2.6340558528900146, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.5362513141979057, -0.09783228371971471, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>53</th>\n",
+ " <td>[67.57147216796875, 135.45388793945312, 118.60...</td>\n",
+ " <td>[-0.3691023700569555, -0.33471972321091714, -0...</td>\n",
+ " <td>[58.90510940551758, 60.46958541870117, 65.2287...</td>\n",
+ " <td>[-0.3691023700569555, -0.33471972321091714, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 15.68...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.4519261231026052, -0.2556200423341208, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55</th>\n",
+ " <td>[120.72547149658203, 142.8217010498047, 123.10...</td>\n",
+ " <td>[-0.48167830594657995, -0.20217789316479096, -...</td>\n",
+ " <td>[54.036495208740234, 66.7566909790039, 54.5961...</td>\n",
+ " <td>[-0.48167830594657995, -0.20217789316479096, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.45551729...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.0018365379992391897, -0.28596247526676294,...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>56</th>\n",
+ " <td>[203.07302856445312, 146.7401123046875, 184.91...</td>\n",
+ " <td>[0.13401924290622025, 0.22940755187193504, 0.0...</td>\n",
+ " <td>[82.44768524169922, 86.78832244873047, 80.5523...</td>\n",
+ " <td>[0.13401924290622025, 0.22940755187193504, 0.0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[0.0016229673481693136, -0.219862864718235, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>67</th>\n",
+ " <td>[131.05352783203125, 162.49517822265625, 137.5...</td>\n",
+ " <td>[-0.241543605339258, -0.10094089967319672, -0....</td>\n",
+ " <td>[66.63746643066406, 73.0364990234375, 75.39173...</td>\n",
+ " <td>[-0.241543605339258, -0.10094089967319672, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14843799...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.0846015906470225, -0.2570581533140372, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>74</th>\n",
+ " <td>[103.39415740966797, 125.89250946044922, 151.2...</td>\n",
+ " <td>[-0.07452704954347095, -0.209369470395866, -0....</td>\n",
+ " <td>[73.88077545166016, 67.74209594726562, 76.8296...</td>\n",
+ " <td>[-0.07452704954347095, -0.209369470395866, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[0.06700071921008612, -0.35799371488326326, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>87</th>\n",
+ " <td>[141.526611328125, 210.92494201660156, 130.647...</td>\n",
+ " <td>[-0.14264467911588816, 0.04301696280835992, 0....</td>\n",
+ " <td>[70.10948944091797, 78.55717468261719, 90.2189...</td>\n",
+ " <td>[-0.14264467911588816, 0.04301696280835992, 0....</td>\n",
+ " <td>[12.288996696472168, 22.157291412353516, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.3692539477877848, -0.6074426602039235, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>89</th>\n",
+ " <td>[130.03794860839844, 170.2689971923828, 136.39...</td>\n",
+ " <td>[-0.5560033991495117, -0.30869868254217386, -0...</td>\n",
+ " <td>[51.435523986816406, 62.69099807739258, 59.552...</td>\n",
+ " <td>[-0.5560033991495117, -0.30869868254217386, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>3.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.5481321173580741, -0.18114484351270632, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>97</th>\n",
+ " <td>[138.26419067382812, 231.31321716308594, 117.8...</td>\n",
+ " <td>[-0.00673451744319432, -0.31882068909606553, -...</td>\n",
+ " <td>[76.94403839111328, 62.74452590942383, 65.7420...</td>\n",
+ " <td>[-0.00673451744319432, -0.31882068909606553, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.049297393554554834, -0.02865096549342668, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104</th>\n",
+ " <td>[62.128299713134766, 137.3159637451172, 183.67...</td>\n",
+ " <td>[-0.46493053525788747, -0.2570831931387614, -0...</td>\n",
+ " <td>[54.98540115356445, 64.44282531738281, 62.7274...</td>\n",
+ " <td>[-0.46493053525788747, -0.2570831931387614, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.021254173659933366, -0.06416608441494862, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106</th>\n",
+ " <td>[168.42068481445312, 210.0718536376953, 144.65...</td>\n",
+ " <td>[0.2650907584022125, -0.06241719670789813, 0.2...</td>\n",
+ " <td>[87.37469482421875, 72.4720230102539, 85.98783...</td>\n",
+ " <td>[0.2650907584022125, -0.06241719670789813, 0.2...</td>\n",
+ " <td>[11.072638511657715, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.4038540835009023, -0.23507432913562598, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108</th>\n",
+ " <td>[100.39228820800781, 94.58695220947266, 91.541...</td>\n",
+ " <td>[-0.42270450987088837, -0.3471214700152542, -0...</td>\n",
+ " <td>[56.01216506958008, 59.45255661010742, 64.4330...</td>\n",
+ " <td>[-0.42270450987088837, -0.3471214700152542, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 5.550724506378174, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.34894090094509606, 0.05837936656690834, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109</th>\n",
+ " <td>[57.468666076660156, 106.03108215332031, 101.8...</td>\n",
+ " <td>[-0.05183069634235161, -0.1740395826337219, -0...</td>\n",
+ " <td>[72.87104797363281, 67.30900573730469, 54.9075...</td>\n",
+ " <td>[-0.05183069634235161, -0.1740395826337219, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[0.13936681509445512, 0.3092582486996589, 0.04...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>112</th>\n",
+ " <td>[151.0594940185547, 104.39273071289062, 88.254...</td>\n",
+ " <td>[-0.004069848935951793, 0.02047246920190751, -...</td>\n",
+ " <td>[75.34306335449219, 76.45985412597656, 64.4501...</td>\n",
+ " <td>[-0.004069848935951793, 0.02047246920190751, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 9.792997360229492, 4.7922...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.4219618704021598, -0.47645232887852157, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>115</th>\n",
+ " <td>[114.57542419433594, 89.72770690917969, 155.71...</td>\n",
+ " <td>[-0.09828607189161205, -0.3248465540676223, 0....</td>\n",
+ " <td>[71.04379272460938, 60.73479461669922, 88.6885...</td>\n",
+ " <td>[-0.09828607189161205, -0.3248465540676223, 0....</td>\n",
+ " <td>[0.0, 0.0, 5.4040093421936035, 16.236265182495...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.14611476782141777, 0.0826343050156583, 0.1...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>120</th>\n",
+ " <td>[174.27081298828125, 137.41981506347656, 129.1...</td>\n",
+ " <td>[0.0053415983330048575, -0.3174968192428005, -...</td>\n",
+ " <td>[75.2798080444336, 60.59123992919922, 60.76642...</td>\n",
+ " <td>[0.0053415983330048575, -0.3174968192428005, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.061369397789512004, -0.5878573768823497, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>121</th>\n",
+ " <td>[128.32443237304688, 111.56602478027344, 138.4...</td>\n",
+ " <td>[-0.0011906872684789393, -0.22857295953556406,...</td>\n",
+ " <td>[75.39173126220703, 65.04622650146484, 68.9562...</td>\n",
+ " <td>[-0.0011906872684789393, -0.22857295953556406,...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.048330496855457726, -0.11524928363291319, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>124</th>\n",
+ " <td>[96.57644653320312, 84.94210815429688, 105.585...</td>\n",
+ " <td>[-0.5650162480314755, -0.3934966566802378, -0....</td>\n",
+ " <td>[49.725059509277344, 57.52311325073242, 57.445...</td>\n",
+ " <td>[-0.5650162480314755, -0.3934966566802378, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.379700610014295, -0.18456262455411357, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>125</th>\n",
+ " <td>[127.83878326416016, 161.52891540527344, 161.8...</td>\n",
+ " <td>[-0.24073100951818352, -0.1088606940212012, -0...</td>\n",
+ " <td>[63.965938568115234, 69.96836853027344, 64.065...</td>\n",
+ " <td>[-0.24073100951818352, -0.1088606940212012, -0...</td>\n",
+ " <td>[2.4704697132110596, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.20725339938934803, -0.03469486408251092, 0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>132</th>\n",
+ " <td>[77.16002655029297, 92.0215072631836, 82.97117...</td>\n",
+ " <td>[-0.27669162704232997, -0.4167638554515489, -0...</td>\n",
+ " <td>[61.04623031616211, 54.67396545410156, 55.9026...</td>\n",
+ " <td>[-0.27669162704232997, -0.4167638554515489, -0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 28.846420288085...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_90</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.02783308220847001, -0.18538207111580676, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>133</th>\n",
+ " <td>[181.25515747070312, 74.56159210205078, 145.67...</td>\n",
+ " <td>[-0.2885751174517671, -0.542771193014224, -0.1...</td>\n",
+ " <td>[60.3892936706543, 48.8248176574707, 68.800483...</td>\n",
+ " <td>[-0.2885751174517671, -0.542771193014224, -0.1...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.010039112629721493, -0.4624830999941455, -...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>137</th>\n",
+ " <td>[136.2071075439453, 164.5403289794922, 145.822...</td>\n",
+ " <td>[0.17822915077119758, 0.14455497966698427, 0.2...</td>\n",
+ " <td>[80.87591552734375, 79.34306335449219, 84.1630...</td>\n",
+ " <td>[0.17822915077119758, 0.14455497966698427, 0.2...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>10</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.0741812332536874, -0.28310114671782116, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>138</th>\n",
+ " <td>[147.11309814453125, 159.74964904785156, 94.93...</td>\n",
+ " <td>[-0.4598040294270339, -0.2609018041904224, -0....</td>\n",
+ " <td>[51.64720153808594, 60.695865631103516, 56.296...</td>\n",
+ " <td>[-0.4598040294270339, -0.2609018041904224, -0....</td>\n",
+ " <td>[4.786396026611328, 0.0, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_10</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[-0.3637218442405162, -0.11152140030432998, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>140</th>\n",
+ " <td>[133.0036163330078, 76.26187896728516, 97.6623...</td>\n",
+ " <td>[-0.186675940853228, -0.14447920474078346, -0....</td>\n",
+ " <td>[64.24574279785156, 66.16545104980469, 70.3844...</td>\n",
+ " <td>[-0.186675940853228, -0.14447920474078346, -0....</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 1.0861608982086182, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.23747423176658447, -0.3412223260222124, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>143</th>\n",
+ " <td>[123.27689361572266, 126.47122192382812, 141.6...</td>\n",
+ " <td>[0.02564648405645122, 0.15790876888271843, 0.0...</td>\n",
+ " <td>[73.28953552246094, 79.30657196044922, 72.9732...</td>\n",
+ " <td>[0.02564648405645122, 0.15790876888271843, 0.0...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5933207869529...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_90</td>\n",
+ " <td>C1_NaN</td>\n",
+ " <td>True</td>\n",
+ " <td>10_90</td>\n",
+ " <td>0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[0.05379129100743235, -0.14728104263451638, -0...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>[107.55477905273438, 147.9124298095703, 98.443...</td>\n",
+ " <td>[-0.22314441989074893, -0.18957804955818716, -...</td>\n",
+ " <td>[62.58880615234375, 64.11679077148438, 63.9659...</td>\n",
+ " <td>[-0.22314441989074893, -0.18957804955818716, -...</td>\n",
+ " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>C1_10_20</td>\n",
+ " <td>D1_NaN</td>\n",
+ " <td>False</td>\n",
+ " <td>10_20</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[-0.5479384824166067, -0.1350689360739867, -0....</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>[75.85784149169922, 95.86479187011719, 131.299...</td>\n",
+ " <td>[-0.5684309448517589, -0.3047014356799419, -0....</td>\n",
+ " <td>[46.89051055908203, 58.89051055908203, 70.8783...</td>\n",
+ " <td>[-0.5684309448517589, -0.3047014356799419, -0....</td>\n",
+ " <td>[0.0, 0.0, 11.951688766479492, 2.0843546390533...</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>D1_10_20</td>\n",
+ " <td>C1_10</td>\n",
+ " <td>True</td>\n",
+ " <td>10_20</td>\n",
+ " <td>10</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[{'pulse_freq': '10', 'peak_voltage': '10', 'o...</td>\n",
+ " <td>[0.04623678528617768, 0.24733918871087354, 0.0...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " F \n",
+ "roi# trial# \n",
+ "1 3 [65.38990020751953, 97.3988037109375, 78.23871... \\\n",
+ " 7 [112.92879486083984, 159.3483123779297, 143.16... \n",
+ " 8 [95.08174896240234, 99.19114685058594, 166.550... \n",
+ " 10 [91.68379974365234, 73.52933502197266, 133.646... \n",
+ " 12 [161.94859313964844, 199.63211059570312, 163.2... \n",
+ " 16 [114.15812683105469, 60.72441864013672, 113.02... \n",
+ " 19 [93.15322875976562, 132.84658813476562, 109.91... \n",
+ " 21 [154.97335815429688, 121.49047088623047, 98.42... \n",
+ " 22 [114.42371368408203, 90.07608032226562, 109.80... \n",
+ " 32 [78.85540771484375, 135.93475341796875, 86.567... \n",
+ " 44 [158.4487762451172, 103.57232666015625, 76.137... \n",
+ " 50 [85.32022857666016, 150.54190063476562, 94.415... \n",
+ " 53 [67.57147216796875, 135.45388793945312, 118.60... \n",
+ " 55 [120.72547149658203, 142.8217010498047, 123.10... \n",
+ " 56 [203.07302856445312, 146.7401123046875, 184.91... \n",
+ " 67 [131.05352783203125, 162.49517822265625, 137.5... \n",
+ " 74 [103.39415740966797, 125.89250946044922, 151.2... \n",
+ " 87 [141.526611328125, 210.92494201660156, 130.647... \n",
+ " 89 [130.03794860839844, 170.2689971923828, 136.39... \n",
+ " 97 [138.26419067382812, 231.31321716308594, 117.8... \n",
+ " 104 [62.128299713134766, 137.3159637451172, 183.67... \n",
+ " 106 [168.42068481445312, 210.0718536376953, 144.65... \n",
+ " 108 [100.39228820800781, 94.58695220947266, 91.541... \n",
+ " 109 [57.468666076660156, 106.03108215332031, 101.8... \n",
+ " 112 [151.0594940185547, 104.39273071289062, 88.254... \n",
+ " 115 [114.57542419433594, 89.72770690917969, 155.71... \n",
+ " 120 [174.27081298828125, 137.41981506347656, 129.1... \n",
+ " 121 [128.32443237304688, 111.56602478027344, 138.4... \n",
+ " 124 [96.57644653320312, 84.94210815429688, 105.585... \n",
+ " 125 [127.83878326416016, 161.52891540527344, 161.8... \n",
+ " 132 [77.16002655029297, 92.0215072631836, 82.97117... \n",
+ " 133 [181.25515747070312, 74.56159210205078, 145.67... \n",
+ " 137 [136.2071075439453, 164.5403289794922, 145.822... \n",
+ " 138 [147.11309814453125, 159.74964904785156, 94.93... \n",
+ " 140 [133.0036163330078, 76.26187896728516, 97.6623... \n",
+ " 143 [123.27689361572266, 126.47122192382812, 141.6... \n",
+ " 148 [107.55477905273438, 147.9124298095703, 98.443... \n",
+ " 149 [75.85784149169922, 95.86479187011719, 131.299... \n",
+ "\n",
+ " F_var \n",
+ "roi# trial# \n",
+ "1 3 [-0.6413238419292778, -0.5933089554122087, -0.... \\\n",
+ " 7 [-0.1322782887656356, -0.405838058847386, -0.2... \n",
+ " 8 [-0.2991491067228387, -0.320097658013037, -0.3... \n",
+ " 10 [-0.37410173259197227, -0.31330928355173904, -... \n",
+ " 12 [0.49128682746196506, -0.03387934328756773, 0.... \n",
+ " 16 [-0.3455050937378926, -0.6683925005850538, -0.... \n",
+ " 19 [-0.5687271666521604, -0.3708823790829836, -0.... \n",
+ " 21 [-0.037199821126239256, 0.10605499537727486, 0... \n",
+ " 22 [-0.3725754118230143, -0.6310386155947286, -0.... \n",
+ " 32 [-0.12236088083651446, -0.3749699620905525, -0... \n",
+ " 44 [-0.2144747908904325, 0.1699798286569285, -0.0... \n",
+ " 50 [-0.5128304740100976, 0.13118952105758835, -0.... \n",
+ " 53 [-0.3691023700569555, -0.33471972321091714, -0... \n",
+ " 55 [-0.48167830594657995, -0.20217789316479096, -... \n",
+ " 56 [0.13401924290622025, 0.22940755187193504, 0.0... \n",
+ " 67 [-0.241543605339258, -0.10094089967319672, -0.... \n",
+ " 74 [-0.07452704954347095, -0.209369470395866, -0.... \n",
+ " 87 [-0.14264467911588816, 0.04301696280835992, 0.... \n",
+ " 89 [-0.5560033991495117, -0.30869868254217386, -0... \n",
+ " 97 [-0.00673451744319432, -0.31882068909606553, -... \n",
+ " 104 [-0.46493053525788747, -0.2570831931387614, -0... \n",
+ " 106 [0.2650907584022125, -0.06241719670789813, 0.2... \n",
+ " 108 [-0.42270450987088837, -0.3471214700152542, -0... \n",
+ " 109 [-0.05183069634235161, -0.1740395826337219, -0... \n",
+ " 112 [-0.004069848935951793, 0.02047246920190751, -... \n",
+ " 115 [-0.09828607189161205, -0.3248465540676223, 0.... \n",
+ " 120 [0.0053415983330048575, -0.3174968192428005, -... \n",
+ " 121 [-0.0011906872684789393, -0.22857295953556406,... \n",
+ " 124 [-0.5650162480314755, -0.3934966566802378, -0.... \n",
+ " 125 [-0.24073100951818352, -0.1088606940212012, -0... \n",
+ " 132 [-0.27669162704232997, -0.4167638554515489, -0... \n",
+ " 133 [-0.2885751174517671, -0.542771193014224, -0.1... \n",
+ " 137 [0.17822915077119758, 0.14455497966698427, 0.2... \n",
+ " 138 [-0.4598040294270339, -0.2609018041904224, -0.... \n",
+ " 140 [-0.186675940853228, -0.14447920474078346, -0.... \n",
+ " 143 [0.02564648405645122, 0.15790876888271843, 0.0... \n",
+ " 148 [-0.22314441989074893, -0.18957804955818716, -... \n",
+ " 149 [-0.5684309448517589, -0.3047014356799419, -0.... \n",
+ "\n",
+ " Fneu \n",
+ "roi# trial# \n",
+ "1 3 [51.401458740234375, 53.57664108276367, 69.479... \\\n",
+ " 7 [73.60826873779297, 61.14841842651367, 69.5109... \n",
+ " 8 [65.15084838867188, 64.19464874267578, 62.5839... \n",
+ " 10 [60.57664108276367, 63.33576583862305, 55.6034... \n",
+ " 12 [99.12408447265625, 75.22628021240234, 83.2116... \n",
+ " 16 [60.51338195800781, 45.822383880615234, 53.211... \n",
+ " 19 [50.10218811035156, 59.10462188720703, 60.2773... \n",
+ " 21 [74.41849517822266, 80.93673706054688, 96.5085... \n",
+ " 22 [59.5036506652832, 47.7469596862793, 73.287101... \n",
+ " 32 [71.25790405273438, 59.76398849487305, 73.2627... \n",
+ " 44 [66.0997543334961, 83.59367370605469, 72.15814... \n",
+ " 50 [52.72992706298828, 82.03406524658203, 69.6788... \n",
+ " 53 [58.90510940551758, 60.46958541870117, 65.2287... \n",
+ " 55 [54.036495208740234, 66.7566909790039, 54.5961... \n",
+ " 56 [82.44768524169922, 86.78832244873047, 80.5523... \n",
+ " 67 [66.63746643066406, 73.0364990234375, 75.39173... \n",
+ " 74 [73.88077545166016, 67.74209594726562, 76.8296... \n",
+ " 87 [70.10948944091797, 78.55717468261719, 90.2189... \n",
+ " 89 [51.435523986816406, 62.69099807739258, 59.552... \n",
+ " 97 [76.94403839111328, 62.74452590942383, 65.7420... \n",
+ " 104 [54.98540115356445, 64.44282531738281, 62.7274... \n",
+ " 106 [87.37469482421875, 72.4720230102539, 85.98783... \n",
+ " 108 [56.01216506958008, 59.45255661010742, 64.4330... \n",
+ " 109 [72.87104797363281, 67.30900573730469, 54.9075... \n",
+ " 112 [75.34306335449219, 76.45985412597656, 64.4501... \n",
+ " 115 [71.04379272460938, 60.73479461669922, 88.6885... \n",
+ " 120 [75.2798080444336, 60.59123992919922, 60.76642... \n",
+ " 121 [75.39173126220703, 65.04622650146484, 68.9562... \n",
+ " 124 [49.725059509277344, 57.52311325073242, 57.445... \n",
+ " 125 [63.965938568115234, 69.96836853027344, 64.065... \n",
+ " 132 [61.04623031616211, 54.67396545410156, 55.9026... \n",
+ " 133 [60.3892936706543, 48.8248176574707, 68.800483... \n",
+ " 137 [80.87591552734375, 79.34306335449219, 84.1630... \n",
+ " 138 [51.64720153808594, 60.695865631103516, 56.296... \n",
+ " 140 [64.24574279785156, 66.16545104980469, 70.3844... \n",
+ " 143 [73.28953552246094, 79.30657196044922, 72.9732... \n",
+ " 148 [62.58880615234375, 64.11679077148438, 63.9659... \n",
+ " 149 [46.89051055908203, 58.89051055908203, 70.8783... \n",
+ "\n",
+ " Fneu_var \n",
+ "roi# trial# \n",
+ "1 3 [-0.6413238419292778, -0.5933089554122087, -0.... \\\n",
+ " 7 [-0.1322782887656356, -0.405838058847386, -0.2... \n",
+ " 8 [-0.2991491067228387, -0.320097658013037, -0.3... \n",
+ " 10 [-0.37410173259197227, -0.31330928355173904, -... \n",
+ " 12 [0.49128682746196506, -0.03387934328756773, 0.... \n",
+ " 16 [-0.3455050937378926, -0.6683925005850538, -0.... \n",
+ " 19 [-0.5687271666521604, -0.3708823790829836, -0.... \n",
+ " 21 [-0.037199821126239256, 0.10605499537727486, 0... \n",
+ " 22 [-0.3725754118230143, -0.6310386155947286, -0.... \n",
+ " 32 [-0.12236088083651446, -0.3749699620905525, -0... \n",
+ " 44 [-0.2144747908904325, 0.1699798286569285, -0.0... \n",
+ " 50 [-0.5128304740100976, 0.13118952105758835, -0.... \n",
+ " 53 [-0.3691023700569555, -0.33471972321091714, -0... \n",
+ " 55 [-0.48167830594657995, -0.20217789316479096, -... \n",
+ " 56 [0.13401924290622025, 0.22940755187193504, 0.0... \n",
+ " 67 [-0.241543605339258, -0.10094089967319672, -0.... \n",
+ " 74 [-0.07452704954347095, -0.209369470395866, -0.... \n",
+ " 87 [-0.14264467911588816, 0.04301696280835992, 0.... \n",
+ " 89 [-0.5560033991495117, -0.30869868254217386, -0... \n",
+ " 97 [-0.00673451744319432, -0.31882068909606553, -... \n",
+ " 104 [-0.46493053525788747, -0.2570831931387614, -0... \n",
+ " 106 [0.2650907584022125, -0.06241719670789813, 0.2... \n",
+ " 108 [-0.42270450987088837, -0.3471214700152542, -0... \n",
+ " 109 [-0.05183069634235161, -0.1740395826337219, -0... \n",
+ " 112 [-0.004069848935951793, 0.02047246920190751, -... \n",
+ " 115 [-0.09828607189161205, -0.3248465540676223, 0.... \n",
+ " 120 [0.0053415983330048575, -0.3174968192428005, -... \n",
+ " 121 [-0.0011906872684789393, -0.22857295953556406,... \n",
+ " 124 [-0.5650162480314755, -0.3934966566802378, -0.... \n",
+ " 125 [-0.24073100951818352, -0.1088606940212012, -0... \n",
+ " 132 [-0.27669162704232997, -0.4167638554515489, -0... \n",
+ " 133 [-0.2885751174517671, -0.542771193014224, -0.1... \n",
+ " 137 [0.17822915077119758, 0.14455497966698427, 0.2... \n",
+ " 138 [-0.4598040294270339, -0.2609018041904224, -0.... \n",
+ " 140 [-0.186675940853228, -0.14447920474078346, -0.... \n",
+ " 143 [0.02564648405645122, 0.15790876888271843, 0.0... \n",
+ " 148 [-0.22314441989074893, -0.18957804955818716, -... \n",
+ " 149 [-0.5684309448517589, -0.3047014356799419, -0.... \n",
+ "\n",
+ " spks is_neuron \n",
+ "roi# trial# \n",
+ "1 3 [0.0, 0.0, 0.0, 9.399063110351562, 0.0, 0.0, 0... False \\\n",
+ " 7 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.0656671524047... False \n",
+ " 8 [0.0, 0.0, 4.953240394592285, 0.0, 0.0, 0.0, 0... False \n",
+ " 10 [0.0, 0.0, 7.434715270996094, 0.0, 0.0, 0.0, 0... False \n",
+ " 12 [0.0, 0.0, 0.0, 0.0, 15.629164695739746, 0.0, ... False \n",
+ " 16 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 19 [0.0, 5.189079284667969, 0.0, 0.0, 0.0, 0.0, 0... False \n",
+ " 21 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 22 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 32 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 44 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 50 [0.0, 0.0, 0.0, 2.6340558528900146, 0.0, 0.0, ... False \n",
+ " 53 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 15.68... False \n",
+ " 55 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.45551729... False \n",
+ " 56 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 67 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14843799... False \n",
+ " 74 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 87 [12.288996696472168, 22.157291412353516, 0.0, ... False \n",
+ " 89 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 97 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 104 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 106 [11.072638511657715, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 108 [0.0, 0.0, 0.0, 0.0, 5.550724506378174, 0.0, 0... False \n",
+ " 109 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 112 [0.0, 0.0, 0.0, 0.0, 9.792997360229492, 4.7922... False \n",
+ " 115 [0.0, 0.0, 5.4040093421936035, 16.236265182495... False \n",
+ " 120 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 121 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 124 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 125 [2.4704697132110596, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 132 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 28.846420288085... False \n",
+ " 133 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 137 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 138 [4.786396026611328, 0.0, 0.0, 0.0, 0.0, 0.0, 0... False \n",
+ " 140 [0.0, 0.0, 0.0, 0.0, 0.0, 1.0861608982086182, ... False \n",
+ " 143 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5933207869529... False \n",
+ " 148 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... False \n",
+ " 149 [0.0, 0.0, 11.951688766479492, 2.0843546390533... False \n",
+ "\n",
+ " is_VGAT is_C1 is_D1 target_stim nontarget_stim \n",
+ "roi# trial# \n",
+ "1 3 None False True D1_10_20 C1_NaN \\\n",
+ " 7 None False True D1_10_90 C1_10 \n",
+ " 8 None False True C1_10_20 D1_10 \n",
+ " 10 None False True D1_10_90 C1_NaN \n",
+ " 12 None False True D1_10_90 C1_10 \n",
+ " 16 None False True C1_10_90 D1_10 \n",
+ " 19 None False True C1_10_20 D1_10 \n",
+ " 21 None False True D1_10_90 C1_10 \n",
+ " 22 None False True D1_10_20 C1_NaN \n",
+ " 32 None False True C1_10_90 D1_10 \n",
+ " 44 None False True C1_10_20 D1_NaN \n",
+ " 50 None False True C1_10_20 D1_NaN \n",
+ " 53 None False True C1_10_20 D1_10 \n",
+ " 55 None False True C1_10_20 D1_NaN \n",
+ " 56 None False True C1_10_90 D1_10 \n",
+ " 67 None False True C1_10_20 D1_NaN \n",
+ " 74 None False True D1_10_20 C1_NaN \n",
+ " 87 None False True C1_10_90 D1_NaN \n",
+ " 89 None False True D1_10_20 C1_10 \n",
+ " 97 None False True D1_10_20 C1_NaN \n",
+ " 104 None False True D1_10_20 C1_10 \n",
+ " 106 None False True C1_10_20 D1_NaN \n",
+ " 108 None False True C1_10_20 D1_NaN \n",
+ " 109 None False True D1_10_90 C1_NaN \n",
+ " 112 None False True C1_10_90 D1_10 \n",
+ " 115 None False True D1_10_20 C1_10 \n",
+ " 120 None False True D1_10_20 C1_NaN \n",
+ " 121 None False True D1_10_20 C1_10 \n",
+ " 124 None False True C1_10_20 D1_NaN \n",
+ " 125 None False True C1_10_20 D1_10 \n",
+ " 132 None False True C1_10_90 D1_10 \n",
+ " 133 None False True C1_10_20 D1_NaN \n",
+ " 137 None False True D1_10_90 C1_10 \n",
+ " 138 None False True C1_10_20 D1_10 \n",
+ " 140 None False True C1_10_20 D1_NaN \n",
+ " 143 None False True D1_10_90 C1_NaN \n",
+ " 148 None False True C1_10_20 D1_NaN \n",
+ " 149 None False True D1_10_20 C1_10 \n",
+ "\n",
+ " in_target_barrel target_amplitude nontarget_amplitude Result \n",
+ "roi# trial# \n",
+ "1 3 True 10_20 0 2.0 \\\n",
+ " 7 True 10_90 10 2.0 \n",
+ " 8 False 10_20 10 2.0 \n",
+ " 10 True 10_90 0 2.0 \n",
+ " 12 True 10_90 10 2.0 \n",
+ " 16 False 10_90 10 2.0 \n",
+ " 19 False 10_20 10 2.0 \n",
+ " 21 True 10_90 10 2.0 \n",
+ " 22 True 10_20 0 2.0 \n",
+ " 32 False 10_90 10 2.0 \n",
+ " 44 False 10_20 0 2.0 \n",
+ " 50 False 10_20 0 2.0 \n",
+ " 53 False 10_20 10 2.0 \n",
+ " 55 False 10_20 0 2.0 \n",
+ " 56 False 10_90 10 2.0 \n",
+ " 67 False 10_20 0 2.0 \n",
+ " 74 True 10_20 0 2.0 \n",
+ " 87 False 10_90 0 2.0 \n",
+ " 89 True 10_20 10 3.0 \n",
+ " 97 True 10_20 0 2.0 \n",
+ " 104 True 10_20 10 2.0 \n",
+ " 106 False 10_20 0 2.0 \n",
+ " 108 False 10_20 0 2.0 \n",
+ " 109 True 10_90 0 2.0 \n",
+ " 112 False 10_90 10 2.0 \n",
+ " 115 True 10_20 10 2.0 \n",
+ " 120 True 10_20 0 2.0 \n",
+ " 121 True 10_20 10 2.0 \n",
+ " 124 False 10_20 0 2.0 \n",
+ " 125 False 10_20 10 2.0 \n",
+ " 132 False 10_90 10 2.0 \n",
+ " 133 False 10_20 0 2.0 \n",
+ " 137 True 10_90 10 1.0 \n",
+ " 138 False 10_20 10 2.0 \n",
+ " 140 False 10_20 0 2.0 \n",
+ " 143 True 10_90 0 1.0 \n",
+ " 148 False 10_20 0 2.0 \n",
+ " 149 True 10_20 10 2.0 \n",
+ "\n",
+ " target_stim_info \n",
+ "roi# trial# \n",
+ "1 3 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \\\n",
+ " 7 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 8 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 10 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 12 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 16 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 19 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 21 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 22 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 32 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 44 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 50 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 53 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 55 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 56 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 67 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 74 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 87 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 89 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 97 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 104 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 106 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 108 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 109 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 112 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 115 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 120 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 121 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 124 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 125 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 132 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 133 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 137 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 138 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 140 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 143 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 148 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " nontarget_stim_info \n",
+ "roi# trial# \n",
+ "1 3 [] \\\n",
+ " 7 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 8 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 10 [] \n",
+ " 12 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 16 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 19 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 21 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 22 [] \n",
+ " 32 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 44 [] \n",
+ " 50 [] \n",
+ " 53 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 55 [] \n",
+ " 56 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 67 [] \n",
+ " 74 [] \n",
+ " 87 [] \n",
+ " 89 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 97 [] \n",
+ " 104 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 106 [] \n",
+ " 108 [] \n",
+ " 109 [] \n",
+ " 112 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 115 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 120 [] \n",
+ " 121 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 124 [] \n",
+ " 125 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 132 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 133 [] \n",
+ " 137 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 138 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ " 140 [] \n",
+ " 143 [] \n",
+ " 148 [] \n",
+ " 149 [{'pulse_freq': '10', 'peak_voltage': '10', 'o... \n",
+ "\n",
+ " features \n",
+ "roi# trial# \n",
+ "1 3 [-0.09234676619335945, 0.17328275696398882, -0... \n",
+ " 7 [-0.3588153210373761, -0.07913098427899713, -0... \n",
+ " 8 [-0.07941622959378912, -0.09063082994244048, 0... \n",
+ " 10 [-0.22193644585795141, 0.19844592301018588, 0.... \n",
+ " 12 [-0.017383238160832516, 0.22389958264939533, 0... \n",
+ " 16 [-0.3201763713513183, -0.3955343377506322, -0.... \n",
+ " 19 [-0.1304386206530661, -0.44067560038990133, -0... \n",
+ " 21 [-0.5365144063056656, 0.15290344578927692, -0.... \n",
+ " 22 [0.0913854583373718, 0.5600446153017025, 0.256... \n",
+ " 32 [-0.19493923585241107, -0.051277351385884615, ... \n",
+ " 44 [-0.15290837128792856, 0.030170095040596506, -... \n",
+ " 50 [-0.5362513141979057, -0.09783228371971471, -0... \n",
+ " 53 [-0.4519261231026052, -0.2556200423341208, -0.... \n",
+ " 55 [-0.0018365379992391897, -0.28596247526676294,... \n",
+ " 56 [0.0016229673481693136, -0.219862864718235, -0... \n",
+ " 67 [-0.0846015906470225, -0.2570581533140372, -0.... \n",
+ " 74 [0.06700071921008612, -0.35799371488326326, -0... \n",
+ " 87 [-0.3692539477877848, -0.6074426602039235, -0.... \n",
+ " 89 [-0.5481321173580741, -0.18114484351270632, -0... \n",
+ " 97 [-0.049297393554554834, -0.02865096549342668, ... \n",
+ " 104 [-0.021254173659933366, -0.06416608441494862, ... \n",
+ " 106 [-0.4038540835009023, -0.23507432913562598, -0... \n",
+ " 108 [-0.34894090094509606, 0.05837936656690834, -0... \n",
+ " 109 [0.13936681509445512, 0.3092582486996589, 0.04... \n",
+ " 112 [-0.4219618704021598, -0.47645232887852157, -0... \n",
+ " 115 [-0.14611476782141777, 0.0826343050156583, 0.1... \n",
+ " 120 [-0.061369397789512004, -0.5878573768823497, -... \n",
+ " 121 [-0.048330496855457726, -0.11524928363291319, ... \n",
+ " 124 [-0.379700610014295, -0.18456262455411357, -0.... \n",
+ " 125 [-0.20725339938934803, -0.03469486408251092, 0... \n",
+ " 132 [-0.02783308220847001, -0.18538207111580676, -... \n",
+ " 133 [-0.010039112629721493, -0.4624830999941455, -... \n",
+ " 137 [-0.0741812332536874, -0.28310114671782116, -0... \n",
+ " 138 [-0.3637218442405162, -0.11152140030432998, -0... \n",
+ " 140 [-0.23747423176658447, -0.3412223260222124, -0... \n",
+ " 143 [0.05379129100743235, -0.14728104263451638, -0... \n",
+ " 148 [-0.5479384824166067, -0.1350689360739867, -0.... \n",
+ " 149 [0.04623678528617768, 0.24733918871087354, 0.0... "
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "a1088427-d525-4346-9067-14dfb7e24b85",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "inputs = training_data['features']\n",
+ "training_inputs = np.array(list(inputs))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "89715335-c039-438f-a5da-6ec643c84830",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "outputs = training_data['target_amplitude']\n",
+ "training_outputs = np.array(outputs)\n",
+ "test_inputs = np.array(list(test_data['features']))\n",
+ "test_outputs = np.array(test_data['target_amplitude'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "82ce3799-7c92-4f14-b8ed-bc8e5953981c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "LinearSVC()"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import sklearn\n",
+ "classifier = sklearn.svm.LinearSVC()\n",
+ "classifier.fit(training_inputs,training_outputs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "ebef86ae-544a-45a9-aec2-2ab516fbf121",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.4473684210526316"
+ ]
+ },
+ "execution_count": 78,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "classifier.score(test_inputs,test_outputs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "9f0b6b0d-1d79-4597-ae3a-7927ad0f328b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "in target barrel is (False,) for this group\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.5\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "in target barrel is (True,) for this group\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.7777777777777778\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "selection = trials_roi_df.loc[1:1]\n",
+ "for condition, group_df in selection.groupby(by = [\"in_target_barrel\"]):\n",
+ " print(\"in target barrel is \",condition, \"for this group\")\n",
+ " #display(group_df)\n",
+ " adaptation.plots.show_traces_averages(group_df)\n",
+ " score = bootstrap_classify(group_df)\n",
+ " print(score[\"average_score\"])\n",
+ " plt.boxplot(score[\"scores\"])\n",
+ " plt.axhline(0.5, ls = '--', color = 'gray')\n",
+ " plt.ylim(0,1)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "1a069660-af60-494a-825b-34f45d1a958f",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "in_target_barrel is : True for this group\n",
+ "amplitude is : 10_20 for this group\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "in_target_barrel is : True for this group\n",
+ "amplitude is : 10_90 for this group\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for (in_target_barrel,target_amplitude), group_df in selection.groupby(by = [\"in_target_barrel\",\"target_amplitude\"]):\n",
+ " if in_target_barrel == False : \n",
+ " continue\n",
+ " print(\"in_target_barrel is : \" , in_target_barrel, \" for this group\")\n",
+ " print(\"amplitude is : \" , target_amplitude, \" for this group\")\n",
+ " #display(group_df)\n",
+ " adaptation.plots.show_traces_averages(group_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "f56a747d-a798-4f47-a0c5-a4872476acfe",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "target amplitude is : 10_20 for this group\n",
+ "nontarget amplitude is : 0 for this group\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "target amplitude is : 10_20 for this group\n",
+ "nontarget amplitude is : 10 for this group\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "target amplitude is : 10_90 for this group\n",
+ "nontarget amplitude is : 0 for this group\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "target amplitude is : 10_90 for this group\n",
+ "nontarget amplitude is : 10 for this group\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for (in_target_barrel,target_amplitude,nontarget_amplitude), group_df in selection.groupby(by = [\"in_target_barrel\",\"target_amplitude\",\"nontarget_amplitude\"]):\n",
+ " if in_target_barrel == False : \n",
+ " continue\n",
+ " print(\"target amplitude is : \" , target_amplitude, \" for this group\")\n",
+ " print(\"nontarget amplitude is : \" , nontarget_amplitude, \" for this group\")\n",
+ " #display(group_df)\n",
+ " adaptation.plots.show_traces_averages(group_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "5322070c-4f14-4628-98a4-8e5a1339ab47",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8f34f532-7454-4786-9d21-b0aef075d78e",
+ "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": "3.8.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chance_level_exercise.ipynb b/chance_level_exercise.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6cf0e14f5f16b537f88e99fc21073f1cb8d21c5f
--- /dev/null
+++ b/chance_level_exercise.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0de90813-758b-4d03-89b3-066ed0f34b27",
+ "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": "3.8.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}