1152 lines
264 KiB
Plaintext
1152 lines
264 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import expipe\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import spatial_maps.stats as stats\n",
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"import septum_mec\n",
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"import septum_mec.analysis.data_processing as dp\n",
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"import septum_mec.analysis.registration\n",
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"import head_direction.head as head\n",
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"import spatial_maps as sp\n",
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"import speed_cells.speed as spd\n",
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"import re\n",
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"import joblib\n",
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"import multiprocessing\n",
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"import shutil\n",
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"import psutil\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"from distutils.dir_util import copy_tree\n",
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"from neo import SpikeTrain\n",
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"import scipy\n",
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"import seaborn as sns\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
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"tqdm.pandas()\n",
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"\n",
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"from spike_statistics.core import permutation_resampling_test\n",
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"\n",
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"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
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"\n",
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"from septum_mec.analysis.plotting import violinplot, despine"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"#############################\n",
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"\n",
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"perform_zscore = False\n",
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"\n",
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"if not perform_zscore:\n",
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" zscore_str = \"-no-zscore\"\n",
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"else:\n",
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" zscore_str = \"\"\n",
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"\n",
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"#################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"plt.rc('axes', titlesize=12)\n",
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"plt.rcParams.update({\n",
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" 'font.size': 12, \n",
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" 'figure.figsize': (6, 4), \n",
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" 'figure.dpi': 150\n",
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"})\n",
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"\n",
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"output_path = pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str)\n",
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"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
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"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
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"output_path.mkdir(exist_ok=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_loader = dp.Data()\n",
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"actions = data_loader.actions\n",
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"project = data_loader.project"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"identify_neurons = actions['identify-neurons']\n",
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"sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_action = actions['stimulus-lfp-response' + zscore_str]\n",
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"lfp_results = pd.read_csv(lfp_action.data_path('results'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_results = pd.merge(sessions, lfp_results, how='left')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_results = lfp_results.query('stim_location!=\"mecl\" and stim_location!=\"mecr\"')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"def action_group(row):\n",
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" a = int(row.channel_group in [0,1,2,3])\n",
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" return f'{row.action}-{a}'\n",
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"lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>action_side_a</th>\n",
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" <th>channel_group</th>\n",
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" <th>signal_to_noise</th>\n",
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" <th>stim_strength</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>71</th>\n",
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" <td>1833-010719-1-0</td>\n",
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" <td>7</td>\n",
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" <td>0.001902</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>67</th>\n",
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" <td>1833-010719-1-1</td>\n",
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" <td>3</td>\n",
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" <td>0.003522</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>695</th>\n",
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" <td>1833-010719-2-0</td>\n",
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" <td>7</td>\n",
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" <td>0.004280</td>\n",
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" <td>2.260538</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>691</th>\n",
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" <td>1833-010719-2-1</td>\n",
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" <td>3</td>\n",
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" <td>0.003974</td>\n",
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" <td>6.446883</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>583</th>\n",
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" <td>1833-020719-1-0</td>\n",
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" <td>7</td>\n",
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" <td>-0.002942</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" action_side_a channel_group signal_to_noise stim_strength\n",
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"71 1833-010719-1-0 7 0.001902 NaN\n",
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"67 1833-010719-1-1 3 0.003522 NaN\n",
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"695 1833-010719-2-0 7 0.004280 2.260538\n",
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"691 1833-010719-2-1 3 0.003974 6.446883\n",
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"583 1833-020719-1-0 7 -0.002942 NaN"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# lfp_results_hemisphere = lfp_results.sort_values(\n",
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"# by=['action_side_a', 'stim_strength', 'signal_to_noise'], ascending=[True, False, False]\n",
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"lfp_results_hemisphere = lfp_results.sort_values(\n",
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" by=['action_side_a', 'channel_group'], ascending=[True, False]\n",
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").drop_duplicates(subset='action_side_a', keep='first')\n",
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"lfp_results_hemisphere.loc[:,['action_side_a','channel_group', 'signal_to_noise', 'stim_strength']].head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
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"labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
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"# Hz11 means that the baseline session was indeed before an 11 Hz session\n",
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"queries = ['baseline and i and Hz11', 'frequency==11', 'baseline and ii and Hz30', 'frequency==30']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"# prepare pairwise comparison: same animal same side same date different sessions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_entity_date_side(row):\n",
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" s = row.action_side_a.split('-')\n",
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" del s[2]\n",
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" return '-'.join(s)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_results_hemisphere['entity_date_side'] = lfp_results_hemisphere.apply(make_entity_date_side, axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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zD/d0I+48lD5DB7V9YhFNH81l9qkr/u4Zfdrf6Du42EdUWQuWNfKl6ZeVfP7Vnz+Y1erbbGGpmMWNTVz565dX2PeNA9dn4KBq/G8sHXCsuz+YvzNO0Xw1YVz4zWY2Ls5JUesOIjRpdn7igV6i6v6nu3uLmTlhcYAxWeeYWX9aVxkq+4IJPU2foYPoM6xzgahPv0Xk+i9aYV//0aPot+oanUq3K/Vp/Ij3hpS+nPbAtdajYeDgLsxR5+QWLWPxgBVrT0dtvglDhnRrVzTpZu4+18wOBt4ijIrZhTBJTE1z9zcqnYdaU3UBO3qcELB3IKybm29bQt6XAOVaI1l6mMWNy5k8c/uSz7945qNdmBuRjotBewawNaGKvOYDtrRftQbsGwmzpH3FzE5293l5x4+O2z9olrPeqyXXwvylhbswzF+qfxrSo/SP24/yD5hZP+DrwH6EKViHEyaMeZswA+XP3X2l2kgz+wLwbWB7Qnv5h4S29BuB37j7soxrViUsXfplwhrufYDXCGubX+juC0p5MVmdzlKd8N4D1iYsx3okrX2NZgC/BqZlTXxTrrxVq4oGbDNbl9CG0ZhXPXI/YaWuHYHbzGxfd38vTqryPWB/wgL0F3R3nmtZrqWF5oVz2z4xQ/PC6mtmmr+0kfHXn1vweH1TPyZTeglbym/0VVP7Eta97gnm/eeQH7d3FEtZmNmGhJJ1C3mlazMbRFjE5LNx1yzgOcI64BvFvwPMbCd3fyp13XHA/8WnbwNPE/oGTYx/+5rZzu7enLpmE0KP9TGEET2vEobYbgacCRxsZru6+4udfMl1hHb7A4EFhKbPDQg/LLYnzGY3Ne996K68VUylS9i/I/zDeAiYlOx095yZHRT37wTMjtVBowhzBOeAQ9z9hW7PcQ1rXjiX145bq9LZkF5i9FVT9wUuAUZWOi9l8v7oq6Ye859DfnxTd9zMzPoCqwOfIcza1odQGp2dd+ophGA9B9jN3Z9IpbENcDuhtHoqsG/cvzqtBZ4V1vk2s10Ii6ZMiuffEPcPJszTPiam+a1kjnUzWwv4DaFj3B1mNr6TtZ8jgW8QSsuXuntzXFzqCsI6Eiea2c+SYW7dnLeKqXTALsjdXzOzTxJW5NoL2IIwhOte4Cfu/kAl89eVOjO9aMv8mvx32G0OO2EbRg1bvdLZKJuGhgGVzkIxVxBWyeopRhJeU1cF7AfMrNjxC4AzMvbvTCh5/yAdrAHc/Qkzu4yw2MkWqUNGGDY7n7CUcfqaP5vZ+YR+ROkq8cMJ1cz/BianS97u/q6Z7Qu8QCjRTwFKH6KR7ZfuflHqHkvM7HhCDWs/Ql+mZHnU7s5bRXRpwHb3MW0cn9TG8TnACfGv19D0oq1aWnI0Nq7UjAZA45Jl1DcV/idc39R/pX0NDf3Vq1qqVf447L6E1cPGEoa5fhcYbGbH5wWkCXHkTKFJpJKOHumhIq8T2riHAtPM7Ofu/kwqzR9mpLNP3N6Qvn/qmsVmdjNwIrAnnQ+Kd2bcY66ZfUD48ZT+5d3deauIqi1hS/k0fTSXPv0WVWU7dFsWLlrCuWfeX/B4e9uoV6vv3Hh0aZcj6GFV4sAxXZh+oXHYAwilwkvi/fsS1vX+WFwQaaiZbU8Y+rVB3H6S0JYNqdUZ3f19M7uAUIN5EHCQmb1L6D/0Z+DujKWNN4/bI8xs7wKvIWlz26TNV9u2twrsT6oR0/Gru/NWEQrYvcDsUzdbaQx1Yr0fzaDvkI6tn9J3SNdPmvJhmXt696kr54qyUsx/DvnxTaOvmnoL6nTWKbGn9q/NbG3gbOBIMzvf3d8EMLNVCJ3HDqC1JzmE6ux/E4a+7pqR7ulm9iThR8D/IwS0/eNfk5ndABzj7kmpP2neSDqyFVOOdqfsqrVWdanH3Z23ilDA7uX6DhlR1ZOfSG2LAa7m5r+uUrcTAnZfQsn5zdT+zxJKnhcDjwHPAy/HkvcRZARsaF1ONA6HmkjoaLYboRR6ACEQ7hVPXxSf7+nu08v82jqrmvNWNgrYNSKZXjRritC25Pq1Od26iFS/9HSkdQCxCjwZzrV7gc64o/N3xKFgGwF17v5MXEP8zvh3oplNBc4H9jSz1WIp2wkdvTYDMoOimW1ECJyzu3mhkmrOW9koYFdAW73As3p6J9OLZk0RWss6MvnJ9I2fZGm/5ZnnP7jPdxlaZHrRKu9VLVLM7nGbA56Mj9dPHX+SPGbWQJhQBVb8vj8S+AUww8y2zJiE5D5CwIZQoocQzLcFDjezi/KHRsXJW24HxhGG7B5c4usqh2rOW9koYFdAR3qBF+s4Vu3t0MV0ZPKTG/Y6nFHDs0cLDa1vUDu19ChmVkfoBX1a3HWzu/8nPk5PAnKmmX3f3ZfH6zYl9IZO2nTTvcRvBH5C6Kx1oZmd5u6L4nUjgPPieY+nZpq8lNDZbSxhPPNhyYRXZrYG8CtCQFxGGDfenao5b2WjgF0jinUc623t0EPrBzF8YOmLeojUiIvN7MO8ff0Jk4EkPe2fpHVqZtz9KTO7kTAl6YnAFDN7nTA1aVL6vg/4PLCKma3q7v9193fM7FDgWsLkJIeZ2auEmLAhYYz2HOCw1L3mm9lehNLszsDrZjaTUFVvhKFnTYSJWLp1CdBqzls5qSgiIlIdNidMx5z+25Kw3PB04BBge3fPn1/4G4Qq7icI3+njCQHqTmAPd98FSKZ+3jO5yN2vI3Qyu5kw/eemwHrAy4Tq8HHu/nz6Ru7+r5jPc4BnCT8mxhHm/v4dsLW739Kpd6GDqjlv5VKXyxUaa9/zmdnzY8eO3fSuu+5q++QyapnX2O4q8Y8++cOCJewNLnq3ZkvYc5csbLtKPG/FrWNP+wzrjOgpI4WqWl3bp4hId1GVeJVIeoEDmT3Ba7mnd7GOZfOWrLz/gX2+y7CBobmtceGylZa91OQnItIbKWBXiaQXOJDZE7xYx7JKdxxrS1sdy/ING9jwcRt1fdPSlY6rU5mI9EYK2DWix3Ysy0F984r/DBsXLvs4UC9a1NZkRyIivYMCtlRUffPKbdT5VeAiIqKALWXQ1uQnWe3UIiLSPgrY0mntbaOG1o5lWZ3KRERkZQrYUhFJx7KsTmUiIrIyBewuUmy+8Ky5wqXViVMnMnhw4Tm/NR+4iPRGCthdpCPzhUswePAAhgypr3Q2RESqigK2dFquJUd904r/lG7d7WiGFpngpP/yvixsWqphWyIiJVLAlk5b3Lh8paFZ1858qkK5ERHpmTRllIiISA1QCbsbpecLz1e3uubHFhGRwhSwu1F6vvBaU2xylPlL1etdRKSrKWBLSYpNjlLf1I/JrNiGfdgJ2zBq2OodupeGbYmIrEwBW7pEQ0N/Dc0SESkjdToTERGpAQrYIiJVxsy+aWY5Mzu8ndd9O143pQP37BevzZnZASWcf3g8t6m995KOUZW4dFixBTxWKzJpiogUZmbbAD/twHXbAReUP0dSLVTClg5LFvAYOnDwSsf61Omflkh7mdkk4E/AKu287n/idSv/Z5QeQyVsEZEKM7OBwFTgdKBvO64bBJwa//QruYfTBywiUkFmNhZ4CTgr7jodmF3CdRavOx1oAb4PvNVF2ZQqoBK2iHSZl6b07QsMq3Q+ymTextOam7sg3dHAOsBjwDHu/qSZHVHCdevEax+N1z1lZsd0Qf5KZmb9gOUlnv5bd29Xp7reTgG7AnItLTQvnFvwePPCOd2YG5Gu8dKUvvsClwAjK52XMnn/pSl9j9l4WvNNZU73P8Du7n53O697A/iiu99b5vx0Rg54pMjxEYDFx23WIsiKFLAroHnhXF47bq1KZ0Okq10BrFbpTJTRSMJrKmvAdvdXgFc6cN1LhCrxquHuzcCErGNmtgrwcHz6Z+BH3ZWvnkIBW0RE8l1jZteUK7FYVX4TsCXwArBfDO7SDgrYItJVjqCHVYkDFW0j7kYvAR+0cc6awNgS07sU+AIwF9jD3T/sRN56LQVsEekSG09rvumlKX1vQZ3OatEP3f3aYifEWdiuaCshMzsFOBJYBnzZ3V8rTxZ7HwXsKrHej2bQd8iIgsf7DhnejbkRKY8Y4NoqqUkPZWb7AufHp990979VMj+1TgG7SvQdMoJ+q65R6WyIiJSFme0A/A6oAy5w92mVzVHt08QpIiJSVma2IXAHMBC4jTCpi3SSStjysZZcC/OXNmYem7cke7+ISJqZDQPuJoy5fho4wN1zlc1Vz6CALR+bv7SR8defW+lsiEiNMrN6Qol6Y2AWsJu7L6popnoQBWwRESmX7wA7xcdzgCvMbDDQP+PcJnef1F0Z6wkUsEVEpFxWTT3+dBvn9pYhcmWjgC0iUmXcfUwHrxvdiXs2EXp0l3r+b4Df5O07nbB6mHQBBewOyrXkyC1YXPB4y/zCx2rJA/t8l2EDGzKPDa3P3i8iIuWngN1BuQWLeX/CpZXORpcbNrCB4QOHVDobIiK9nsZhi4iI1AAFbBERkRqgKnEpSUtLjsbGZZnHFi3K3i8iIuWjgF1GI+48lD5DBxU8Xrd64WPVrrFxGeeccV+lsyEi0mspYJdRn6GD6DNMPadFRKT81IYtIiJSAxSwRUREaoCqxKXDTpw6kcGDB2Qea2jI3i8iIh2jgC0fy7XkqG9a8Z9E48Jl1DctzewJPnjwAIYMqe+u7ImI9GoK2PKxxY3LmTxz+xX2XTzz0QrlRkRE0tSGLSIiUgMUsEVERGqAqsS7SK6lheaFczOPNS+c0825ERGRWqeA3UWaF87ltePWqnQ2Ou2wE7Zh1LDVM4+pJ7iISPdRwJaiGhr6qye4SBcxszHA6wUO54D5wBvAPcCF7v5BN2WtbMxsCnAV8Ja7j07tfxCYCJzn7qdXJnfFmdk04GDgIXefVOqxrqKALSJSHWYAH6ae9wOGApsDWwFHmNnn3P25SmROKk8BW0SkOhzr7g/m7zSz4cDVwO7AzWY2zt1bujtzXeAgoAFQp54SKWB3o/V+NIO+Q0ZkHus7ZHg350ZEaoG7zzWzg4G3gI2BXYB7K5urznP3Nyqdh1qjYV3dqO+QEfRbdY3Mv7o++ihEJJu7zyVUmUOoIpdeSCXsXqQl18L8pY0Fj89furgbcyMi7dQ/bj/KP2Bm/YCvA/sBnwKGA03A28BfgZ+7+0sZ130B+DawPaG9/EPCD4Mbgd+4+0pzEpvZqsB3gC8DYwkFv9eAWwgd4xaU8mKyOp2lOuG9B6wNHAocCWwaL5sB/BqY5u65rspbtVLA7kXmLV7EdtddUPB4fVN/9mDrbsyR9HQnnzC9LzCs0vkok3k/uXCP5krc2Mw2JJSsW8irDjezQcBdwGfjrlnAc8CawEbx7wAz28ndn0pddxzwf/Hp28DTwAhCEJ0I7GtmO7t7c+qaTQg91scAzcCrwGJgM+BM4GAz29XdX+zkS64jtNsfCCwAXgI2IPyw2B4wYGre+9BdeasYBexeJGuucJGucvIJ0/cFLgFGVjovZfL+ySdMP+YnF+5xU3fczMz6AqsDnwH+l1BaPM/dZ+edegohWM8BdnP3J1JpbAPcTiitngrsG/evDiS/3r/u7jekrtkFuA2YFM+/Ie4fDNxJCIi3A99y97fjsbWA3xA6xt1hZuPdvTNVdiOBbxBKy5e6e7OZDQSuAA4ATjSznyXD3Lo5bxWjhlMR6SpX0HOCNYTXckUXpv+AmeWSP0KV9hzgDkJnswuAMzKu25lQ8v5BOlgDxOeXxadbpA4ZMJAwzvsPedf8GTgfuBlIV4kfTqhm/jcwOQmI8Zp3CcF9NqFEP6XkV13YL939oqSE7+5LgOMJ49P7AdtWMG8VoYAtIlIdZgCPpP4eA54Hlsbj3wUuiiXvj7n7BELwvbxAuknHlYbUvtcJPwiGAtPMbHxemj90933d/ZbU7n3i9oZ0NXnqmsWEIA+wZ6EX2Q53ZtxjLpBMHpOegrG781YRqhKXolarH1TpLEjtOoIeViUOHNOF6Rcahz2AUCq8JN6/L/Ct9DnuvtzMhprZ9oTS+AZx+0lCWzakCmju/r6ZXQCcRhgPfZCZvQvcD/wZuNvd88dHJ73TjzCzvQu8hmQ+5k3afLVte6vA/qQ6Ox2/ujtvFaGA3csVmyscNF+4dNxPLtzjppNPmLs7V/oAACAASURBVH4L6nTWKbGn9q/NbG3gbOBIMzvf3d8EMLNVCJ3HDqC1JzmE6ux/A08Bu2ake7qZPUn4EfD/CAFt//jXZGY3AMe4ezL72mpxm3RkK6bwl0rpVuqhnqcu9bi781YRCti9nOYKl64UA1zNzX9dpW4nBOy+hJLzm6n9nyWUPC+mtSr95VjyPoKMgA3g7rcCt8bhUBMJHc12I5RCDyAEwr3i6Yvi8z3dfXqZX1tnVXPeykYBW0SkNqSnI60DiFXgyXCu3d39gYzrRufviEPBNgLq3P0Zd/8voc34TkIP7KmEjmd7mtlqsZTthI5emwGZQdHMNiIEztndvFBJNeetbNTpTESkNuwetzngyfh4/dTxJ8ljZg2ECVVgxQLakcAzwLVmVpd/HXBf6nHSyS3pBHZ4DPj59+pHKO0/QRiG1p2qOW9lo4AtIlLFzKzOzL5M6CAGcLO7/yc+Tk8CcqaZ9U9dtylhIpGkTTfdS/xGQhvx5sCFcRxzct0I4Lz49HF3nxcfXwq8Qxg+dYeZrZu6Zo2Y5riYbncHxWrOW9moSlxEpDpcbGYf5u3rT5gMJOlp/yRwdHLQ3Z8ysxsJU5KeCEwxs9cJU5Mmpe/7gM8Dq5jZqu7+X3d/x8wOBa4lTE5ymJm9SogJGxKGic0BDkvda76Z7UUoze4MvG5mMwlV9QbUE4aKfb27lwCt5ryVk0rYIiLVYXNgx7y/LQnjsKcDhwDbx7HIad8gVHE/QfhOH08IUHcCe7j7LkCyMtbHY5Dd/TpCJ7ObCdN/bgqsB7xMaL8e5+7Pp2/k7v+K+TwHeJbwY2IcYe7v3wFb543d7jbVnLdyqcvlVpo/vdcws+fHjh276V133dXua1vmNfL+hEtX2Dfy4W/TZ1iodWr67we8dtxaKxzf4KJ36bfqGh3PcCe9OWceF5/36Ar7jj3tM6wzoqeMupEyy2rbFJEKKVuVeOzccDLwNUJVzEeE6ptfuPs9HUhvDGE2nmKecfet2pt2T9bSkqOxMXv4YmPj8m7OjYiIlEtZAnbssHA/sB2wnDDF3nDCQuu7mNnZ7v6DdiabTJU3D3ihwDkvdyC7PVpj4zLOOeO+tk8UEZGaUq4S9qWEYP00sFdqBp4DgSuBs83sEXf/SzvSTAL2je5+dNEzRUREerhOdzqL67QeQOiNt38SrAHc/Rrgx/Hp2e1MOgnYNdujT0REpFzK0Uv8QMLA+n+4+8yM48kKMjumx8aVIAnYMzqTud6mJdfS9kkiIlJzylElvkPcPpx10N3fMrPZhOECE4Fr2krQzIYQVpsBlbDb5cOlK6/LPn3jJ1naL7vD2ckNE7s6SyIiUgblCNhj4/bVIufMIgTsjUtMc0vCkJK3gTXM7HuEye77AS8B17v7Ix3KbS+0tN9ylvZryjxW10cjd0REakE5AnYyA0+xydSTgf4jSkwzqQ4fCsykdS5bCDP2fNvMrgSOcneNVRIRkR6vHAE7mZ92SZFzknrahiLnpCUBeyDwK8KSca8Q1ms9EDgLOJQwA9C3shJIM7PnCxzasMT8iIiIVFQ5AnYzpXdeK3Vatb/HNJ9y98tS+98AzjOzWYQ5cI8ys0vzp8+TFd2629GMGr5a5rGh9aX+hhIRkUoqR8BeSKi6HljknGS5s8ZSEoxz3F5X7LiZnUVYhWZvwmLtxdLbLGt/LHlvWkqeatnQ+kEMHzik0tkQEZFOKMewrjlxO7zIOUnb9ftluF/iqbhdv+hZIiIiPUA5StgvEEq6Y4qckxx7qdRE47quLe7eXOCU5MdGr+p0VmyucNB84SIiPVU5AvbjwF60jsdegZmNBpIJUx7NOifv/KGEIWJDgclAoeXQPhm3WZO19FiaK1xEpHcqR5X4TXE7ycws4/hRcfuQu89qKzF3nw+8G59OyTrHzL5C6OG9jMIBXUREpMfodMB295eB3xPGSt9iZslEKpjZAcAp8em5+dea2YZmtomZrZ136Py43dPMzjez+tQ1XwGuik9/4u5vd/Y1iIiIVLtyrdZ1HLBF/HvRzJ4jVGmvF4+fVmClrvvjOVeTKk27+zVmtiXwPWAqYaKUl4E1gVHxtN8QxmOLiIj0eGUJ2O4+18x2AE4C9gPGETqDPQRc5O7trrZ295PM7E/AMYT28S0Ja2NPBy5397vKkfdak7W4x2EnbENDQ38A5i9dzD53X7bCcc0XLlLdzGwjQuHk84QJouYBjwGXunvBTitmNhw4nTC8dTQwH3iEUPv4WDvzMAl4ID5dv60mTDObBhxMaO6c1J57SceUq4SNuy8iLKF5djuuGdPG8b8A7VlDu8fLWtxjtz9dvOJc4XmfquYLF6leZvYF4FbCfBWNhI60axCC8N5m9r/uflLGdWsSgvOG8bpnCUF7H2AvMzvS3a/snlch3aEcnc5ERKQDzGwEcD0hWN8AfMLdt3L3UcD+hJkkv2dmkzMu/wMhWN8HjHb3TwOfIJTU+wKXm9m4bngZ0k0UsEVEKudwQn+fWcAUd/8wOeDuvweuiE+PSl8Uq68nEmaa/EYcXYO7t7j7BYSpm/sDp3Vx/qUbKWCLiFTO64QS9i/dfWnG8Wfjdr28/VPi9nZ3n8PKLo/bL5nZoIzjUoPK1oYtlVNscQ/QAh9SOe9u+tO+wLBK56NM5q0186RCMy92iLv/gVC1Xcin4/blvP3JRFUPF7jun0ATMDim8feO5rE98jquteUQd5/WdbnpeRSwewAt7iHV6N1Nf7ovcAkwstJ5KZP33930p8esNfOkm9o+tXPMbHXgO8AhhMB7QepYH2CD+PTVrOvdfbmZvUUomW9MNwVs4ENCR7hCNgCSeTfe6Prs9CwK2CLSVa4AClf91J6RhNfUZQE7di77ATAWqAfeBI5297+lThtK63f3B0WSm0sI2COKnFNW7v4UMCHrmJltSuv01Oe4+1+7K189hQK2iEj12BZILwc8FNjDzP7m7h/Ffek2riVF0krGgHakTez17JmmOyYOQbub8APuRtox/FdaqdOZiHSVIyjvkrqV9j7hNXWli4EhhOFZUwhB9yjgr2aWFLDa246e60A+/kWo2i72V9Jna2YNwJ2E0v4/gYPdvSN56vVUwhaRLrHWzJNuenfTn96COp2VzN3/Ex8uAq42s8eApwkdxw4AphGGciUGFkku6R3e2IGs7NuOmc6KndOHsNbENoTq/b3dvVitgBShgC0iXSYGuGLtrFKEu7uZ3QJ8A5hEa8BeSmjjHl7k8qTtupK1HD8nzNi2CNjL3d9t43wpQlXiIiIVYmbDzGzrOONZIbPjdi0Ik6MAHveNKZBuf0K1OsBLZchqu5nZsYSe7i3A/u7+dCXy0ZMoYFehllwLc5cszPybnzGXuIjUrCcI7cWHFjknmTTlrdS+x+N2B7JtS6hBXQI81ZkMdoSZ7Qn8Ij6d6u63d3ceeiJViVeh+UsbGX/9SsuHA1Df1I/JbN/NORKRLvJnQqeyw83sQndfnj5oZmMIi3lA6LiVuJHQAe4rZnayu8/LS/fouP2Du3frr3wz25owe1sf4Cp3/2l33r8nUwlbRKRyfkroCb4R8Pt01biZfRL4E6Hz2N+AdCn1fkJP7dWA2+KwKcysj5mdTFg4ZDmpCVe6g5mtS/hhMZiQ92925/17OpWwRUQqxN1fM7P9CNOTfgXY08yc0Pt743jaY8Dk9FAod8+Z2UHAQ8BOwGwzmwGMIrR15whTf77Qfa8GCDPbJTOZ1QF3xGFdfTPOfcrdj+22nPUACtgiIhXk7tPNbDxwErALMI4wFOthwqpbV+ZXlcfrXoul8NOAvYAt4nX3Aj9x91Ln9C6nVVOPd2nj3KauzEhPpIBdIx7Y57sMG9hA48JlXDzz0RWOrVavxXhEapm7v0IHqo/jSl0nxL/O5uFBQqm41POn0LpqWLJvUmfzIYUpYNeIYQMbGD5wCPVNK6/A16dOXRFERHo6BewqlGvJUd+04kfTuHAZ9U1LWbRoWYVyJSIilaSAXYUWNy5n8swVh27lV4OLiEjvorpUERGRGqCALSIiUgMUsEVERGqA2rA7KNfSstK+po/m0qffIgCaF84p6/0OO2EbRg1bPfNYQ8OAst5LRESqjwJ2BzUvmr/Svtmnbkau/6IuuV9DQ3+GDKnvkrRFRKT6qUpcRESkBihgi4iI1AAFbBERkRqgNuwyGn3a3+g/elTB432HDO/G3IiISE+igF1GfQcPp9+qa1Q6GyIi0gOpSlxERKQGKGCLiIjUAAVsERGRGqCALSIiUgMUsEVERGqAAraIiEgNUMAWERGpAQrYIiIiNUATp1RAS66F+UsbCx6fv3RxN+ZGRERqgQJ2Bcxf2sj4688teLy+qR+T2b4bcyQiItVOVeIiIiI1QAFbRESkBihgi4iI1AC1YVdAriVHfdOKb/2tux3N0PpBADQ2Lue3M59Y4fhq8ZiIiPROCtgVsLhxOZNnrtip7NqZTxW9pk+dKkNERHozBewici05cguyh1i1LFjSzbkREZHeTAG7iNyCxbw/4dJKZ0NERESdzkRERGqBSthV4rATtmHUsNULHm9oGNCNuRERkWqjgF0lGhr6M2RIfaWzISIiVUoBu51G3HkofYYOoumjucw+dbMVjg1f7eAK5UpERHo6Bex26jN0EH2GNdCn3yJy/RetcKyuT12FciUiIj2dOp2JiIjUAAVsERGRGqCALSIiUgMUsEVERGqAAraIiEgNUMAWERGpAQrYIiIiNUABW0REpAYoYIuIiNQABWwREZEaoIAtIiJSAxSwRUREaoAW/+giLbkW5i9tzDw2f+nibs6NiIjUOgXsLjJ/aSPjrz8381h9Uz8ms30350hERGqZqsRFRERqgErYXSTXkqO+KfvtrW/q3825ERGRWqeAXUSupWWlfU0fzaVPv0U0L5xT9NrFjcuZPFPV3iIiUh4K2EU0L5q/0r7Zp25Grv+ist9rtfpBZU9TRER6DrVhV4k+dfooRESkMEUJERGRGqAq8W502AnbMGrY6pnHGhoGdHNuRESklihgt9Po0/5G/9GjMo/1HTK86LUNDf0ZMqS+K7IlIiI9nAJ2O/UdPJx+q65R6WyIiEgvozZsERGRGqCALSIiUgMUsEVERGqAAraIiEgNUMAWERGpAWXrJW5mDcDJwNeA9YGPgCeBX7j7PR1Mc13gTGBXYCTwAXA/cL67v1COfIuIiNSCspSwzWww8FfgLGAD4HlgEbALcLeZndWBNA34N3AYMAR4BhgIHAj828y+UI68i4iI1IJyVYlfCmwHPA1s6O6fcvf1gIOAJuBsM9u51MTMrB8wHRgOXAOs7e7bAGsDlxAC9w1mVnymki7Ukmth7pKFBf/mL11cqayJiEgP1OkqcTPbEDgAaAH2d/c3k2Pufo2ZbQycDpwN/KXEZA8AxgJvAIe7+7KY3jIzOw4YD+wEnBDT7nbzlzYy/vpzCx6vb+rHZLS8poiIlEc5StgHAn2Bf7j7zIzjl8ftjrFNuhRT4vaaJFgn3D0H/Co+/Xo78yoiIlKTyhGwd4jbh7MOuvtbwOz4dGJbiZlZH2DbYmkCj8TtBma2Ton5FBERqVnl6CU+Nm5fLXLOLGA9YOMS0hsFDGojzTeBZkLJfuP4vFu8s+C/9Bk8j/lLF1PfVPjtq2/q311ZEhGRXqAcAXtk3H5Q5Jy5cTuiHekVTNPdm83sQ2BYiWmWzZW/fpnFA14HaHcb9Wr1g9o+SUREJEM5AnZD3C4pck7SZbqhyDn56ZUtTTN7vsChTd544w123333zIO55ctpbv5whX3z/jGAXF1dW7fM9Nx+v6RPn45dK9LdXnnllTvcfa9K50NEgnIE7GZKbwvPlZhee5SSZiEty5YtW/TKK6+0VaW+Ydy+yqKO3+yjhe91/GLpTq2ft4hIlShHwF4IDCWMjS4kqQtuLDG9xEAKl7JLTtPdNyvhvgUlJfTOpiO1QZ+3iFSjcvQSnxO3xSYxSdqZ329HegXTjBOrrNaONEVERGpaOQJ2Mqf3mCLnJMdeaisxd38bSBqOC6W5DqGHeElpioiI1LpyBOzH43aHrINmNhpIJkx5tMQ0/1ksTeAzcTs7BngREZEerRwB+6a4nRQX7Mh3VNw+5O6zSkzzxrg9xMwGFElzWonpiYiI1LROB2x3fxn4PaGK+hYzSyZSwcwOAE6JT1eaeNvMNjSzTcxs7bxD1xJ66G4A/N7MVonnDzCzi4AJhGrzizubfxERkVpQl8t1ZlRUEFfNegDYgjAs6zlCz/H14imnufuPMq6bFc+52t2n5B3bBriP0LlsIfAiIYAPA5YBu7r7A53OvIiISA0oy/Ka7j6X0N78A0InsHGEHt4PAZOzgnUJaT5BWJXrt8CC+LgF+COwnYK1iIj0JmUpYYuIiEjXKksJW0RERLqWAraIiEgNUMAWERGpAQrYIiIiNUABW0REpAaUY7WuijCzBuBk4GvA+sBHwJPAL9z9ng6muS5wJrArMBL4ALgfON/dXyhy3ebA6cBngdWBd4C7gfPc/a0i1+0ITCVMtToYeBO4Jd5vQUdeQ09V65+3mU0BrmojS//n7se382WISC9Rk8O6zGww4Yt1O2A5MIMw7juZs/xsd/9BO9M04JGYzofAy7RO1LIE+JK7/ynjup2APxOWAp0DzAYMGALMBz7n7k9nXLcfcD2hluMt4D1gM6AeeAOY4O5trdPdK/SQz/tC4HjgdaDQ/Pc3uvtF7XkdItJ71GqV+KWEL++ngQ3d/VPuvh5wENAEnG1mO5eaWFyuczrhy/saYG133wZYG7iE8OV8Q5zRLX3dMOD2ePyCeN2ngU8QJngZCvwxfz70GCyuIbz/xwLruPvWhFnf/k4IRL8v/e3o8Wr6847Gx+333X1CgT8FaxEpqOYCtpltCBxAmPVs/3Qp1N2vAX4cn57djmQPAMYSSraHu/vimN4y4DhCEF0dOCHvuuMIX9KPuftUd2+K130EfAN4jVBqOyjvuu8DA4Ab3P0Sd8/F694DvkQo8U1oTxDqqXrI5w2tAfu5duRTRORjNRewgQMJC438w91nZhy/PG53jG2UpZgSt9fEL+2PxWD6q/j06wWu+21+gjGdK/OvM7OBwFeLXDeP1hXQ8u/XG9X05w0fLzGbzIGv9dtFpENqMWAna2Q/nHUwdvqZHZ9ObCsxM+sDbFssTUJbJ8AGZrZOvG5tWhc3aeu6Hc2sf3z8SUKVai51vNB1k4pmvneo9c8bWkvXLyalchGR9qrFXuLJ8p2vFjlnFuHLdeMS0hsFDGojzTcJq5D1jWm+mcpHjtCRqFA+IHQkWzemn1z3TlIVW+S6MWbW392XF38JPVqtf97QGrBnmNkkYL+Y7hLgKeBKdy+UpogIUJsl7JFx+0GRc+bG7Yh2pFcwTXdvJrQrp9NMrvuvuy9tIx9Z15WS/z6EqtTerNY/b2gN2HsSlqE9GvgfYHfC8LAXzeyoEvIuIr1YLQbshrhdUuScpOTaUOSc/PTam2Z78lGO63qrWv+8oTVg9wG+B4wmlMK3AK4ldEC8LA71ExHJVItV4s2U/kOjlEHmze28f5Jmd1/XW9X65w1hiN5Y4Ap3/3tq/wzgQDNbAhwOXGhmt6idW0Sy1GLAXkgYWjOwyDlJG2VjieklBlK4BJWfZnJdKfkox3W9Va1/3rj7OW3k6WxCwP4EoUPco22cLyK9UC1Wic+J2+FFzknaD99vR3oF04wTbayWl2Zy3Sp5PYKz8pF1XSn5b2HFdtHeqNY/7zbFnu7J+euXep2I9C61GLCTOZ7HFDknOdbmmFd3f5vWDkaF0lyH0GM4nWaSjz60TpFZKB9LCD2N09etXWBGrPR1r8YOUL1ZrX/eAJjZoJXOXlHyf7E3jwgQkSJqMWA/Hrc7ZB2Mk1QkX6ilVi3+s1iahMU5AGbHL3zcfT5h/ulSrns8FXhnEqpX+9I6HrjQdaoarfHP28x2N7OFwEIzy+zFHsd6J8eyJocREanJgJ3MAjYpzsmdLxke85C7zyoxzRvj9pACpd4kzWkFrjsy/4KYzqH518UhQbfHp9/MuG4YYZxu1v16o5r+vAnjrAcS/q9lTVkKcErcznD3GQVzLSK9Ws0FbHd/mdDrti9wi5klE1pgZgfQ+uV3bv61ZrahmW0SZ61Ku5YwycUGwO/NbJV4/gAzuwiYQKhGvTjvuouABcBOZnZR8uUfr78upvdafJz2I0LV5wFmdkqcfQszGwncBqwKPOzuD5b2rvRctf55xxL61fHpeWaWTEuLmdWb2TnAtwm9yr9b2rsiIr1RrS6vOZwwAcUWhOE2zxF6EidTR57m7j/KuG5WPOdqd5+Sd2wb4D5CZ6OFwIu0Lre4DNjV3R/ISHMPwkpNA4B5hC9sA1YhfLlPcPfnM677JnAZUAe8S1hiczNCaWw2sIO7v1PaO9Kz1frnHZcHnU7rVLPvEdq4N4r3bwK+6e5XIiJSQM2VsAHcfS6hHfEHhE5B4wg9fh8CJmd9eZeQ5hOECS5+S/jiHU/opf1HYLusL+943XTg08AfCKXmrQgB4Gpg66xgHa/7FWHu6+lA/3i/9wjLO26rYN2q1j9vd18E7EyoSv87YfjXloRS/DRgKwVrEWlLTZawRUREepuaLGGLiIj0NgrYIiIiNUABW0REpAYoYIuIiNQABWwREZEaoIAtIiJSAxSwRUREaoACtoiISA1QwBYREakBCtgiIiI1QAFbRESkBihgi4iI1IB+lc5AJZnZFOCqDlz6kLtPMrMxwOtx30bu/kq58laMmfUDNnD3l7rjflJ+ZvYF4B7gYHe/psRrzgbOAh5x9wkZ+7MsA+YAzwLXA9e4+wor/phZf8KSpR8AE929pV0vRkS6Ra8O2ITlLB/J2L8usA6wFPhXxvHnujJTxZjZLsBFwM3A6ZXKh3ScmQ0lLKv5BHBtGZPO+vc6EFgb2DX+fdXM9nb3puQEd19uZicAdwOnAOeXMU8iUia9OmC7+z2EUs4KUiWWd9MlmSpxKmCVzoR0yo+BtYD98ku7nVTw36uZfQa4E9gNmAqcmz7u7veY2V+AM8zsD+7+WhnzJSJloDZskW5kZpsBhwN/dfe/d9d93f1R4Mz49NsFTjsHGIRK2CJVSQFbpHudRfh/96sK3PuhuF3LzEbkH4w/IGYC+8YfFiJSRXp1lXiZ1ZnZwcCRwJZADnDgt8Cvsqo+zaweOAr4GrApMAB4A7gL+Km7v5M6dwordpA7zcxOA6529ymp87YCvgXsBIwitGHOB54ErnT3m9v7wsxsTeBEYA9gDNAMvAjcAFzq7kvyzj+bEJguAP4XOAPYC/gEsAB4ADjP3TP7ApjZeOC7wGeBNYGFhLbZX7v7HzPOfxCYSKju/TRwDLAK8BrwFXd/MZ73aeBkYHtgDWA2cE3MowPrAeu7+ywzu57wudzp7nsVyOf+hDboF919XIG3L33+KGAf4CPgtgLn9AEOBo4g/JuA0M/i7LbSL0H/uM0R3tMsvyNU2R8DHF2Ge4pImaiEXT6/IXQkGge8BCwnBI/LCEF7BWa2NvAY8AtgO2AeoXSzLnACMMPMdkxdknSQ+298/mZ8/lIqzaMJgfkIQqB7BXgVWA34InCTmZ3XnhcV8/A8cBIwlhAE3wC2JgS6x8xsrQKXrwc8DRwbn78AjAC+Gq/7VMb9vh1fw0HA0HjvhcAuwM1mdp2Z9S1wv9MI1bofEd6fVYGXY7pTCO/3vsAQYEbMy7nAXwk/bNKujNtds0qj0cFxW+pIg68QfiT/xd2X5R80swHAH+O9dyD07n4N+Dzhs965xPsU8qW4/Wv+j6yUe+N2v/jjQUSqhP5Dls8EQil0TXffmtCp6Jfx2CFm9nEJzMzqCL28twIeBsa5+/qp664EhgG3JcHQ3e+JHYqeisn8zt0nuPuPYpobAf9H+ExPj/n4VCz5fQK4MV53Uuyl3KZYIrwdGA5cAYx0983dfTNgI+BxYHwq7XxfIwTbbePr24pQavwP0EBrm2pyvy8CFxNK8N8BVo+vYT1CsHof+AaFS5s7Aqe4+1h3N2Brd282s01j/vsS2mfXcvdtCO/1aYTguGZeWvcTfpj0j68j6735n5jXkoZl0RpwHy5w/CRCUP0Q+Hx8HZ8i1Go8Gl9fu5hZvZmtb2ZTCR0WGwn/Tgt5lvCDZxiw0g8qEakcBezy+a27/9zdl0MYKgN8j9YS8U6pc/cCPgO8Dezq7p4ccPcPCZ2SHieUAE8o8f67AE3Ak+5+XpKPmOa8mBcIAajUXubfIwTrO9z9SHdfkErzVWDv+Pp2isE2yzfc/V+p6xy4MD7ND0A/AuqAqe5+kbs3p667H5gSn55oZsMz7jUb+Gnqmg/iw7MIJdub3f3UpHTr7k3xB8/l+QnFscjT4tMDM+51IOH/z73ppotCYml1Ynw6I+N4f0J1PcB33P0vqby8TahKn9vGbdYzs1z6D1hCKKWfDywGJrn7M4USiE03Sf4+19brEpHuo4BdPiu1rbr7YmKVLKHNNLFP3N7m7osyrsvROj53z1Ju7u6XunsDK/4wSGtMPW4oJc1UPjPHCrv7e8B98WlWPt92939n7H8hbldPdsRJaLZq4373EKqJBxFKt/kezZgUZAChbRtC80SWXxTYfxWhvXdbM8v/kXNQ6pxSDCe0q0Noqsi3E6EKfwmhb8AK3H0+8Ic27rGUUHWe/nsSmBWPr0KotdmjjXSSH5AbtHGeiHQjdTorn7cK7E869wxK7ds8bveMHayyJNXWG5tZXTvG6y4zs23jPTaMf1sAm6TOafOHmpkNIbRBA5xpZt8pcOqYuN0k41ih92Rx3Kb//W2eenzryvHxY0lbc9b9skq6Ywht1gCZJUt3f8nMPqI1oCb7Z5nZA4SS5oHEiWrMbBtCX4W5hLHNpRiZerwg43jygl9x96UF0ni6jXsUG4c9itBkMpnw/k5y96xJg9L5JDM5gAAABjhJREFUG1nguIhUgAJ2+RTqxJNltbhdJ/4V05cQSP7bxnnEXurnE2a2Snud0PHtiA7kEVYMpoWsnrFvpY5VJd6vlLbarPstztiX7jBWqGc0hPd3lYz9VxIC9gFmdkb84ZR0Nrsuq/NYAel+A41FjhfL4/wS77USd3/LzL5GqN0YS2gm2KXA6UmtT0l9HUSkeyhgV0byhXisu19SjgRjsJ4Wn94L3Epoi3zB3efHNtL2BOx0Vf0W7r5Su2uZJfeb6+6FemV3Jl0IVc4fFDgvK1gD3ELoBLYesKOZPUZrJ7T2zEOf/kG3OvBu3vGkfXrVImkMKnKsTe7eZGZ3ETr0fbrIqUmgzvoBJCIVooBdGU7oXV1wcgozW4cwjvqN2OmoLd+P29+5+8EZx0e3K4PuC8zsPULv6c3I6CgV87kFoRZgdmxn7aik3XS4ma3l7vkBLbnfBEJwmxX7CJSS7jLCGPctCb2/89McQ4FA6e6L45jso4Avx3SGA0+7e1tV1Gnp1zOClQN28vo3MrPBWX0bKPLvpR2ShT3qipyT/GB6rwz3E5EyUaezykjaPb9mZoXaCa8E/kFYYSmt0Bfu+nH7ZIH0Dk89LvWH2vS4PSZrTK6ZrUaYBOUp4PgS08zk7i/Q2hnr2Kxz4pjwZDau7UtMdwmt88UfVuC0b7aRTDKOfh9Cz3ho/ypvb9Fa3Z314+nvhGFr/VnxswLAzBoIQ9o6LI5f3zU+faLIqUn+XuzM/USkvBSwK+MPhBW/Vgf+lJ4G0sxWMbNfEsbs5lh5XufkS3+9vP3Jl+s3YwejJL1V48xjU1PnltpL/MfxfhOAa9MTiJjZeoQZ2YYTqowvLTHNYs6I26lmdnLs4Z3cbwJh7DrAY+7+QDvS/SFhvPTXzeyM2DyAmdWZ2VG0DnnLFIelPUvowHYoocR+XTvun/T8/0d8+pmM4820vv4fx/ZmYj6HE8a6t9XfoSAzW5XQSz6ZDyCzZ3ycfW/r+LTQeHERqQBViVdAXM7wS4SS31aEWc2c0BlpY2BwPPUEd7837/KnCEOo9jezLYG/u/u3CROA3E6YmOS1mB6ECU4GEsbi5gi9xkuqHnf3V8xsP8IPjK8DXzGz5wnVwhsT/v0sAnZz9/fb+TZk3e+GOAHMDwjTmp5qZi8RhsSNSU6jtZRbarpPmtlxwCWEmdCON7NXCD961iSMed8unt6UnQpXEcaPDwH+6O5tjYnOcg9h1rLMntzu/uvYxHAMcL2ZXUAodW8O1BOmM/1S1rXRWmaWH2TrCD/QxsU0csAP3f3uAmlsE8+bC/yzlBclIt1DJewKicsXfoowu9XjhJ7dWxB6K98MTHT3/8u49ALCNKhzCUFzy5jedMKX7W2E9tFxhGlOnyO0b4+ntVRY0tjumO49hLbTCwlV1kboZTyLMJPbFnElqLJw9x8SZh67jvBejCcE7KcIJdBPd+THgbv/kjBxyXRCs8JWhF7XpxCmSk1k9eCGMDY8mcilvdXhiesJPwh2iM0JWfk8llD1/lfCj4NxhOrrL5Ax1j9PPaGHffpve8J46peBXwPbuftZRdJIJsC5IT35johUXl0uV87leEVqT2ySmEGYeGRQgYVatiBUi78DrJOeha2d97qKMGPbMe5ejmaEsjGzfoTZ4kYC5loTW6SqqIQtPZ6Z3Wtm/zKzXQucksyE9nSRCWqSIXFXdTRYR+cRSurtGWLXXfYgzDt/rYK1SPVRwJbeYCahI9XPzGzDZGfsdPYlwiQikNdxzsw+aWbrmdm3CEtNLqV1QZcOcfdXCOPlx5vZ5zuTVhf4HuE1nlvpjIjIylQlLj2ema1BWO1qLKH9+hVC+/i6tE6/eZG7fyfvOif0E0iclqyO1sn8rE7oWzAX+FRcaKSizGwyoe/E99z9Z5XOj4isTCVs+f/t3bEJQkEQRdHXkIJVWJMd2IxNGBhYhwVYgsEOfDAW9Mk58QabXSbY2b83v3YdsjZ83bPWoO6ypslLkuN7rMdtzjySnD4R67nPM+t52D7bmtOvmWdu5yTXbD+pAT/GhA0ABUzYAFBAsAGggGADQAHBBoACgg0ABQQbAAoINgAUEGwAKCDYAFBAsAGggGADQAHBBoACgg0ABQQbAAq8ABz9iKhbbZ0MAAAAAElFTkSuQmCC\n",
|
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"text/plain": [
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"<Figure size 525x300 with 1 Axes>"
|
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]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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D9nPAhoQm8swHbGm7eg3YNxJmSfu2mR3v7jPzzh8WtzdolrPupSXXHVr6RDpFv7j9NP+EmfUF9gC+A3yRcDuxmbAs6j+AX7v7Eq2RZvZ14HBgM8L98o8J99JvBC5z9yWau8xsaeCHhHXJxxIm4HqNsN77ee7+USUvplCns1QnvP8BKwD7AwfT2tfoOcJa6ZPcfYkOx9UqW72qacA2s5UI9zCa8ppH7iUso7kF8Bcz293d/xcnVfkRsBewEDi7q8vcVp3VYWzUSQ/Qb9TIDuXRZ3D93b/+eL5+f3Uno648sQ8wrNblqJKZb+33y7aOYqkKMxtDqFm3kFe7NrOBwB3AV+OhacCzhFUOV4t/E8zsy+7+VCrdkcBv4+47wNOEvkFbxb/dzWwbd1+USrMGocf6aMKInlcJQ2zXBk4B9jWz7d39xQ6+5F6E+/Z7Ax8Rbn2uSvhhsRlghHUr0u9DV5WtZmpdw/4j4YNxPzAuOejuOTPbJx7/MjA9NgeNBJYnDHfYz91f6PISt1Gh2cWq0WFs+Of3rbv7zyJpo648cXfgQmC5WpelSt4fdeWJR7y13y9v6oonM7M+wBDgS8CvCLXFM9x9et6lJxCC9YfADu7+eCqPjYFbCbXVnwC7x+NDaK3wLLbuuZltB/yF8J28O3B9PD4IuJ0QEG8Fvu/u78RzywOXETrG3WZm63ew9XM5YE9Cbfkid18UF5e6lLCOxLFm9n/JMLcuLlvN1DpgF+Xur5nZBoQVuXYB1iUM4forcI6731fL8nVENTqM9erdq0qlqX9TVn+C+X0XFj3/z92OYWgP7VTWHo2N/bvqqS4FlumqJ+sCyxFeU2cF7PvMrNT5s4GfFji+DaHmfVo6WAO4++Nm9nvgdMJ3aMIIw2ZnEZYyTqe528zOIvQjSjeJH0hoZn4SGJ+uebv7e2a2O/ACoUY/EWjbcI4l/c7dz089xzwzO4rQwtqX0JfpjhqVrSY6NWC7++gy58eVOf8hcHT8kx5qft+FzO/bXPR84+D+DB6gXs+SefnjsPsASxECUQNwDDDIzI7KC0hbxpEzxSaRSprz0mMbXyfc4x4KTDKzX7v7M6k8f14gn93i9vr086fSzDWzm4FjgZ3peFC8vcBzzDCzDwg/nobUsGw1Ubc1bOl+WnItzJpf+lbArAL3sG/Z4TBGDi9eURvaQ8dYZ8BBdLMmceCITsy/2Djs/oRa4YXx+fsA309fExdEGmpmmxGGfq0atxsQ7mVDanVGd3/fzM4mtGDuA+xjZu8R+g/dDdxZYGnjdeL2IDPbtchrWD5u1yj7ast7u8jx5EsiHb+6umw1oYBdA921w1g5s+Y3sf51vyh5TUNz3yWGbQ1tGMjwAW0bey2199Z+v7xp1JUnTkadzjok9tS+xMxWAE4FDjazs9z9TQAzW4rQeWwCrT3JITRnP0kY+rp9gXxPNrMnCD8CvkIIaHvFv2Yzux44wt2TWn/yqznpyFbKkDLnK1F8QoYgfV+wq8tWEwrYNdBn0HB1GJMeIQa4zM1/XaduJQTsPoSa85up418l1DwvAB4FngdejjXvgygQsAHc/RbgljgcaitCR7MdCLXQCYRAuEu8fE7c39ndp1T5tXVUPZetahSwRUSyIT1GtBdAbAJPhnPtWKQz7qj8A3Eo2GpAL3d/xt0/Idwzvp3QA/tE4CxgZzNbJtayndDRa22gYFA0s9UIgXN6Fy9UUs9lq5re5S+RYnItOVpmNpX++6jUCqEiIhXbMW5zwBPx8Sqp80+Qx8waCROqwOIVtIOBZ4BrzKzQkJP0dIN94jbpBHZgDPj5z9WXUNt/nDAMrSvVc9mqRjXsDuisecK7q1xLjobmxT9yt+xwGEMbWv9/NTUt5PKpi41MYZmGJf7/ifQYMaDuRuggBnCzu78VH6cnATnFzH7s7gtjurUIvaGTe7rp3pk3AucQOmudZ2YnufucmG4EcEa87rHUTJMXETq7jSWMZz4gmfDKzJYF/gCsSbj33NVBsZ7LVjUK2NJl5jYtZPzUxTuUXTO1/FTwvXupIUh6hAvM7OO8Y/0Ik4EkPe2foHVqZtz9KTO7kTAl6bHARDN7nTA1aVL7vgfYFljKzJZ290/c/V0z2x+4hjA5yQFm9iohJowhjNH+EDgg9VyzzGwXQm12G+B1M5tKaKo3wtCzZsJELF26BGg9l62a9E0oIlIf1iFMx5z+W4+w3PAUYD9gM3efkZduT0IT9+OE7/T1CQHqdmAnd98OSKZ+3jlJ5O5/InQyu5kw/edawMrAy4T712u6+/PpJ3L3/8Ryng78l/BjYk3C3N9/BDZ098kdehfaqZ7LVi29crliY+27PzN7fuzYsWvdcccd5S8uoGVmU7uaxIfd9T36r7xiu54zy978cCYXnNH2lbNO+fm2Wg6yNnrOdHoiGaAm8Q4otLDHkOu+Se8hAz7bXzRnBm+d8ZXFrhm+zL6dXjYREeleFLA7oNDCHm+es+GS84T3W3y3J80DXs4BR2/MyGGl5zHowrmvRUTqlgK21FRjYz81d4uIVECdzkRERDJANWypSCULd5RTaGEPERGpjAJ2lVWysEd3XbijnEILe4iISGUUsKtMC3uIiEhnUMAuIdeSI/dR8WZczRMuIiJdRQG7BM0VLiIi9UIBW9rtvt2OYdiA1vUEci055jYtLHq9FvYQEWk/BWxpt2EDGhk+YPBn+7Nnz+ecM+5vUx5a2ENEpDL6thQREckA1bDbaMTt+9N7aGjGbf50BtN/svZi5zVPuIiIdAYF7DbqPXQgvYeF+7a9+85ZYt5wzRMuIiKdQQFbOtWxJ27FoEHFF+/Qwh4iIpVRwJZONWhQfy3uISJSBep0JiIikgEK2CIidcbMDjGznJkd2MZ0h8d0E9vxnH1j2pyZTajg+gPjtc1tfS5pHwVsEZE6YmYbA+e2I92mwNnVL5HUCwVsEZE6YWbjgL8BS7Ux3ddiukGdUCypE+p0JiJSY2Y2ADgROBno04Z0A4GfxD9VwLo5/QOLiNSQmY0FXgJ+Fg+dDEyvIJ3FdCcDLcCPgbc7qZhSB1TDFpFO89LEPn2AYbUuR5XMXH3SokWdkO8oYEXgUeAId3/CzA6qIN2KMe3DMd1TZnZEJ5SvYmbWFyi+AtDiLnf3NnWq6+kUsEWkU7w0sc/uwIXAcrUuS5W8/9LEPkesPmnRTVXO9y1gR3e/s43p3gC+4e5/rXJ5OiIHPFTi/AjA4uOyrQiyOAVsEekslwLL1LoQVbQc4TVVNWC7+yvAK+1I9xKhSbxuuPsiYMtC58xsKeDBuHs3cGZXlau7UMAWEZF8V5vZ1dXKLDaV3wSsB7wAfCcGd2kDBWwR6SwH0c2axIGa3iPuQi8BH5S55nPA2Arzuwj4OjAD2MndP+5A2XosBWypSK4lR0Pz4h+XptkLaGie/9n+nDkLurpYUsdWn7Toppcm9pmMOp1l0c/d/ZpSF8RZ2C4tl5GZnQAcDCwAvuXur1WniD2PArZUZG7TQsZP3WyxYxdMfbhGpZGsiAGuXE1Nuikz2x04K+4e4u4P1LI8Wadx2CIiUnVmtjnwR6AXcLa7T6ptibJPAVtERKrKzMYAtwEDgL8QJnWRDlKTuADQkmth1vymoudnzZ/bhaURkawys2HAnYQx108DE9w9V9tSdQ8K2ALArPlNrH/dL4qeb2juy3gWv4d9wNEbM3LYkJL5Njb2r0r5RKT+mVkDoUa9OjAN2MHd59S0UN2IArYAhXuBpzU091viWGNjPwYPbujMYolItvwQ+HJ8/CFwqZkNApb8AoFmdx/XVQXrDhSwBSjcC1xEpI2WTj3eqMy1PWWIXNUoYIuI1Bl3H93OdKM68JzNhB7dlV5/GXBZ3rGTCauHSSdQL3Fpt2UaBta6CCIiPYYCtrRb7176+IiIdBU1iUtR5XqBqwe4iEjXUcDuIdozzlq9wEVE6ocCdg/RnnHWIiJSP3QTUkREJAMUsEVERDJAAVtERCQDdA+7B7tvt2MYNqARgKbZC5ZY31rjrEVE6ocCdg82bEAjwwcMBqChef4S5zXOWkSkfihg9xCFFvdomr3gs0A9Z86CWhRLREQqpIDdQxRa3CO/CVxEROqX2jxFREQyQAFbREQkAxSwRUREMkD3sHswLe4hIpIdCtg9mBb3EKktMxsNvF7kdA6YBbwB3AWc5+4fdFHRqsbMJgJXAm+7+6jU8X8CWwFnuPvJtSldaWY2CdgXuN/dx1V6rrMoYHcD5VbigsKrcYlIXXkO+Di13xcYCqwDfAE4yMy2dvdna1E4qT0F7G6g3EpcoNW4RDLgB+7+z/yDZjYcuArYEbjZzNZ095auLlwn2AdoBD6sdUGyQgFbRKSOufsMM9sXeBtYHdgO+GttS9Vx7v5GrcuQNeolLiJS59x9BqHJHEITufRAqmGLiGRDv7j9NP+EmfUF9gC+A3wRGA40A+8A/wB+7e4vFUj3deBwYDPC/fKPCT8MbgQuc/cl5iw2s6WBHwLfAsYSKn6vAZMJHeM+quTFFOp0luqE9z9gBWB/4GBgrZjsOeASYJK75zqrbPVKAbsbKDRP+C07HMbQ1GpbTU0LuXzq44tdo9W4pLMdf/SUPsCwWpejSmaec95Oi2rxxGY2hlCzbiGvOdzMBgJ3AF+Nh6YBzwKfA1aLfxPM7Mvu/lQq3ZHAb+PuO8DTwAhCEN0K2N3MtnH3Rak0axB6rI8GFgGvAnOBtYFTgH3NbHt3f7GDL7kX4b793sBHwEvAqoQfFpsBBpyY9z50VdlqRgG7Gyg0T/g1U58qcnUrrcYlnen4o6fsDlwILFfrslTJ+8cfPeWIc87b6aaueDIz6wMMAb4E/IpQWzzD3afnXXoCIVh/COzg7o+n8tgYuJVQW/0JsHs8PgQ4O162h7tfn0qzHfAXYFy8/vp4fBBwOyEg3gp8393fieeWBy4jdIy7zczWd/eODE1ZDtiTUFu+yN0XmdkA4FJgAnCsmf1fMsyti8tWM/rGFpHOcindJ1hDeC2XdmL+95lZLvkjNGl/CNxG6Gx2NvDTAum2IdS8T0sHa4C4//u4u27qlAEDCOO8b8hLczdwFnAzkG4SP5DQzPwkMD4JiDHNe4TgPp1Qo59Y8asu7nfufn5Sw3f3ecBRhPHpfYFNali2mlDAFhGpD88BD6X+HgWeB5LF6o8Bzo8178+4+5aE4HtxkXyTSRoaU8deJ/wgGApMMrP18/L8ubvv7u6TU4d3i9vr083kqTRzCUEeYOdiL7INbi/wHDOAZPKY9DSNXV22mlCTuIh0loPoZk3iwBGdmH+xcdj9CbXCC+Pz9wG+n77G3Rea2VAz24xQG181bjcg3MuGVAXN3d83s7OBkwjjofcxs/eAe4G7gTvdPX98dNI7/SAz27XIa1g+btco+2rLe7vI8aQ5Ox2/urpsNaGA3U2VmyccNFe4dK5zztvppuOPnjIZdTrrkNhT+xIzWwE4FTjYzM5y9zcBzGwpQuexCbT2JIfQnP0k8BSwfYF8TzazJwg/Ar5CCGh7xb9mM7seOMLdk9nXlonbpCNbKaW/fCqzRA/1PL1Sj7u6bDWhgN1NaZ5wqQcxwGVu/us6dSshYPch1JzfTB3/KqHmeQGtTekvx5r3QRQI2ADufgtwSxwOtRWho9kOhFroBEIg3CVePifu7+zuU6r82jqqnstWNQrYIiLZkJ6OtBdAbAJPhnPt6O73FUg3Kv9AHAq2GtDL3Z9x908I94xvJ/TAPpHQ8WxnM1sm1rKd0NFrbaBgUDSz1QiBc3oXL1RSz2WrGnU6ExHJhh3jNgc8ER+vkjr/BHnMrJEwoQosXkE7GHgGuMbMeuWnA+5JPU46uSWdwA6MAT//ufoSavuPE4ahdaV6LlvVKGCLiNQxM+tlZt8idBADuNnd34qP05OAnGJm/VLp1iJMJJLc0033Er+RcI94HeC8OI45STcCOCPuPubuM+Pji4B3CcOnbjOzlVJplo15rhnz7eqgWM9lqxo1iYuI1IcLzOzjvGP9CJOBJD3tnwAOS066+1NmdiNhStJjgYlm9jphatKk9n0PsC2wlJkt7e6fuPu7ZrY/cA1hcpIDzOxVQkwYQxgm9iFwQOq5ZpnZLoTa7DbA62Y2lTIG68MAACAASURBVNBUb0ADYajYHl29BGg9l62aVMMWEakP6wBb5P2tRxiHPQXYD9gsjkVO25PQxP044Tt9fUKAuh3Yyd23A5KVsT4bg+zufyJ0MruZMP3nWsDKwMuE+9druvvz6Sdy9//Ecp4O/JfwY2JNwtzffwQ2zBu73WXquWzV0iuXW2L+9B7DzJ4fO3bsWnfccUfB8y0zm3h/y4sWO7bcg4fTe1hoWWr+5ANeO3L5xc6vev579F162c4pcBFvfjiTC854eLFjPzjpS6w4oruMppEaKXRvU0RqpGpN4rFzw/HA9whNMZ8Smm9+4+53tSO/0YTZeEp5xt2/0Na8s6alJUdTU/EhiU1NC7uwNCIiUgtVCdixw8K9wKbAQsIUe8MJC61vZ2anuvtpbcw2mSpvJvBCkWtebkdxK5ZraVniWPOnM+jddw4Ai2bnTwTUOZqaFnD6T+8pf6GIiHRb1aphX0QI1k8Du6Rm4NkbuAI41cwecve/tyHPJGDf6O6HlbyykyyaM2uJY9N/sja5fnNqUBoREenJOtzpLK7TOoHQG2+vJFgDuPvVwC/j7qltzDoJ2Jnt0SciIlIt1eglvjdhYP0j7j61wPlkBZkt0mPjKpAE7Oc6UrjuoCW3ZNO8iIj0LNVoEt88bh8sdNLd3zaz6YThAlsBV5fL0MwGE1abAdWw+Xj+kmutT1n9Ceb3Ld7Z7PjGrTqzSCIi0sWqEbDHxu2rJa6ZRgjYq1eY53qEISXvAMua2Y8Ik933BV4CrnP3h9pV2g4addID9Bs1suj5PoOHd0k55vddyPy+zUXP9+qtETkiIt1JNQJ2MgNPqcnUk4H+IyrMM2kOHwpMpXUuWwgz9hxuZlcAh7p7l45p6jNoeJePsxYREalGwE7mp51X4pqkTbexxDVpScAeAPyBsGTcK4T1WvcGfgbsT5gB6PuFMkgzs+eLnBpTYXlERERqqhoBexGVd16rdFq1f8U8n3L336eOvwGcYWbTCHPgHmpmF+VPn9cT3LLDYYwcvkzR80MbKv1tJCIiWVCNgD2b0HQ9oMQ1yXJnTZVkGOe4/VOp82b2M8IqNLsSFmsvld/ahY7HmvdalZSp3gxtGMjwAYNrXQwREeki1RjWlUz3Vaq3VXLv+v0qPF/iqbhdpeRVIiIi3UA1atgvEGq6o0tck5x7qdJM47quLe6+qMglyY+NTE+k3ZJrYdb80g0PswoM6xIRkZ6lGgH7MWAXWsdjL8bMRgHJhCkPF7om7/qhhCFiQ4HxQLHl0DaI20KTtWTGrPlNrH/dL0pe09Dcl/Fs1kUlEhGRelSNJvGb4nacmVmB84fG7f3uPq1cZu4+C3gv7k4sdI2ZfZvQw3sBxQO6iIhIt9HhgO3uLwPXEsZKTzazZCIVzGwCcELcXaIaaWZjzGwNM1sh79RZcbuzmZ1lZg2pNN8Groy757j7Ox19DSIiIvWuWqt1HQmsG/9eNLNnCU3aK8fzJxVZqeveeM1VpGrT7n61ma0H/Ag4kTBRysvA54BkmrHLCOOxRUREur2qBGx3n2FmmwPHAd8B1iR0BrsfON/d29xs7e7HmdnfgCMI98fXI6yNPQW42N3vqEbZ69F9ux3DsAGt46ibZi/ggqmL3/5fpmFgfjIRySgzW41QOdmWMEHUTOBR4CJ3v6dEuuHAyYThraOAWcBDhNbHR9tYhnHAfXF3lXK3MM1sErAv4XbnuLY8l7RPtWrYuPscwhKap7Yhzegy5/8OtGUN7czJteRoaF78n2FAc18amvt9tt+8aMn5Znr3qkb3AxGpNTP7OnALYb6KJkJH2mUJQXhXM/uVux9XIN3nCMF5TEz3X0LQ3g3YxcwOdvcruuZVSFeoWsCW9pnbtJDxUxfvAZ5fmxaR7snMRgDXEYL19YT1ET6O5/YE/gj8yMwedfc/5yW/gRCs7wG+6+6zzKw3oaXzl8DFZvaIu7/QRS9HOpmqaSIitXMgob/PNGBiEqwB3P1a4NK4e2g6UWy+3oow0+SecXQN7t7i7mcTpm7uB5zUyeWXLqSALSJSO68Tati/c/f5Bc7/N25Xzjs+MW5vdfcPWdLFcftNM1OHl25CTeIi0mneW+vcPsCwWpejSmYuP/W4YjMvtou730Bo2i5mo7h9Oe94MlHVg0XS/RtoBgbFPP7V3jK2RV7HtXL2c/dJnVea7kcBuw4dcPTGjBw2pOQ1jY39u6g0Iu3z3lrn7g5cCCxX67JUyfvvrXXuEctPPe6m8pd2jJkNAX4I7EcIvGenzvUGVo27rxZK7+4LzextQs18dbooYAMfEzrCFbMqkMy78UbnF6d7UcCuQ42N/Rg8uKH8hSL17VKg+Bqw2bMc4TV1WsA2s/HAacBYoAF4EzjM3R9IXTaU1u/uD0pkN4MQsEeUuKaq3P0pYMtC58xsLVqnpz7d3f/RVeXqLhSwRUTqxyZAejngocBOZvaAu38aj6UXu59XIq9k1aDGEtcU83rhmabbJw5Bu5PwA+5G2jD8V1qp05mIdJaDqO6SurX2PuE1daYLgMHA5wkdy+YSeoj/w8ySClZb76MvOZFDef8hNG2X+qvo39bMGoHbCbX9fwP7unt7ytTjqYYtIp1i+anH3fTeWudORp3OKubub8WHc4CrzOxR4GlCx7EJwCTCUK7EgBLZJb3DS6/fW9jubZjprNQ1vQlrTWxMaN7f1d1LtQpICQrYItJpYoArdZ9VSnB3N7PJwJ7AOFoD9nzCPe7hJZIn965r2crxa8KMbXOAXdz9vTLXSwlqEhcRqREzG2ZmG8YZz4qZHrfLQ5gcBfB4bHSRfPsRmtUBXqpCUdvMzH5A6OneAuzl7k/XohzdiQK2iEjtPE64X7x/iWuSSVPeTh17LG43p7BNCC2o84CnOlLA9jCznYHfxN0T3f3Wri5Dd6SALSJSO3fH7YGxVrwYMxtNWMwDQsetxI1x+20zK9RH4LC4vcHd5xY432nMbEPC7G29gSvd/dyufP7uTAFbRKR2ziX0BF8NuDbdNG5mGwB/I3QeewBI11LvJfTUXgb4Sxw2hZn1NrPjgb0ISxyfTRcys5UIPywGEcp+SFc+f3enTmciIjXi7q+Z2XcI05N+G9jZzJzQ+3v1eNmjwPj0UCh3z5nZPsD9wJeB6Wb2HDCScK87R5j6s6tX6rqQ1pnMegG3xWFdfQpc+5S7/6DLStYNKGCLiNSQu08xs/UJy2JuB6xJGIr1IGHVrSvcfWGBdK/FWvhJwC7AujHdX4Fz3L3SOb2raenU4+3KXNvcmQXpjhSwRURqzN1foR3Nx3GlrqPjX0fL8E9CrbjS6yfSumpYcmxcR8shxeketoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkQN9aF6C7a8m1MGt+U9Hzs+bP7cLSiIhIVilgd7JZ85tY/7pfFD3f0NyX8WzWhSUSEZEsUpO4iIhIBqiG3clyLTkamou/zQ3N/bqwNCIiklUK2J1sbtNCxk9Vk7eIiHSMmsTr0DINA2tdBBERqTMK2HWody/9s4iIyOIUGURERDJA97Br4ICjN2bksCFFzzc29u/C0oiISBYoYNdAY2M/Bg9uqHUxREQkQ9QkLiIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAX1rXYAsa8m1MGt+U8lrZs2f20WlERGR7kwBuwNmzW9i/et+UfKahua+jGezLiqRiIh0V2oSFxERyQAFbBERkQxQwBYREckA3cOusvt2O4ZhAxo/22+avYALpj682DXLNAzs6mKJiEjGKWBX2bABjQwfMPiz/Ybm+Utc07uXGjZERKRtFLA7INeSo6F58bewafaCxYL0nDkLurpYIiLSDSlgd8DcpoWMn7r4kK385m8REZFqUNusiIhIBihgi4iIZIACtoiISAboHnaVHXD0xowcNqTkNY2N/buoNCIi0l0oYFdZY2M/Bg9uqHUxRESkm1GTuIiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZEDVhnWZWSNwPPA9YBXgU+AJ4Dfuflc781wJOAXYHlgO+AC4FzjL3V+oRrlFRESyoCo1bDMbBPwD+BmwKvA8MAfYDrjTzH7WjjwNeBI4ABgMPAMMAPYGnjSzr1ej7CIiIllQrSbxi4BNgaeBMe7+RXdfGdgHaAZONbNtKs3MzPoCU4DhwNXACu6+MbACcCEhcF9vZsOrVH4REZG61uGAbWZjgAlAC7CXu7+ZnHP3q4Ffxt1T25DtBGAs8AZwoLvPjfktAI4E/gUMAY7uaPlFRESyoBo17L2BPsAj7j61wPmL43aLeE+6EhPj9uoYpD/j7jngD3F3jzaWVUREJJOqEbA3j9sHC51097eB6XF3q3KZmVlvYJNSeQIPxe2qZrZiheUUERHJrGr0Eh8bt6+WuGYasDKwegX5jQQGlsnzTWARoWa/etzvEu9+9Am9B80Mj2d92lVPKyIiPVw1AvZycftBiWtmxO2INuRXNE93X2RmHwPDKsyzaq645GXm9n+9K59SRESkKgG7MW7nlbhmbt61leRXtTzN7Pkip9Z444032HHHHQuezC1cyKJFHy92bOYj/cn16lX0uZ6e2ki/vn3KFUmk7r3yyiu3ufsutS6HiATVCNiLqPxeeK7C/NqikjyLaVmwYMGcV155pVyT+pi4fZU5pS/8dHYHSiPdRevnRUSkSqoRsGcDQwljo4tJ7kk3VZhfYgDFa9kV5+nua1fwvEUlNfSO5iM9gz4vItIZqtFL/MO4LTWJSXKf+f025Fc0zzixyjJtyFNERCTTqhGwkzm9R5e4Jjn3UrnM3P0dILlxXCzPFQk9xCvKU0REJOuqEbAfi9vNC500s1FAMmHKwxXm+e9SeQJfitvpMcCLiIh0a9UI2DfF7bi4YEe+Q+P2fnefVmGeN8btfmbWv0SekyrMT0REJNM6HLDd/WXgWkIT9WQzSyZSwcwmACfE3V/kpzWzMWa2hpmtkHfqGkIP21WBa81sqXh9fzM7H9iS0Gx+QUfLLyIikgW9crmOjIoK4qpZ9wHrEoZlPUvoOb5yvOQkdz+zQLpp8Zqr3H1i3rmNgXsInctmAy8SAvgwYAGwvbvf1+HCi4iIZEBVltd09xmE+82nETqBrUno4X0/ML5QsK4gz8eB9YHLgY/i4xbgz8CmCtYiItKTVKWGLSIiIp2rKjVsERER6VwK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAdVYrasmzKwROB74HrAK8CnwBPAbd7+rnXmuBJwCbA8sB3wA3Auc5e4vlEi3DnAy8FVgCPAucCdwhru/XSLdFsCJhKlWBwFvApPj833UntcghWX982JmE4EryxTpt+5+VBtfhohkRCaHdZnZIMIX46bAQuA5wrjvZM7yU939tDbmacBDMZ+PgZdpnahlHvBNd/9bgXRfBu4mLAX6ITAdMGAwMAvY2t2fLpDuO8B1hFaOt4H/AWsDDcAbwJbuXm6dbqlAN/m8nAccBbwOFJs//0Z3P78tr0NEsiOrTeIXEb58nwbGuPsX3X1lYB+gGTjVzLapNLO4XOcUwpfv1cAK7r4xsAJwIeHL9fo4o1s63TDg1nj+7JhuI+DzhAlehgJ/zp8PPX7ZX014/38ArOjuGxJmffsXIZBcW/nbIWVk+vMSrR+3P3b3LYv8KViLdGOZC9hmNgaYQJj1bK90LdTdrwZ+GXdPbUO2E4CxhJrtge4+N+a3ADiSEESHAEfnpTuS8CX7qLuf6O7NMd2nwJ7Aa4Ra1z556X4M9Aeud/cL3T0X0/0P+CahxrZlW4KIFNZNPi/QGrCfbUM5RaQbyVzABvYmLDTyiLtPLXD+4rjdIt5jrMTEuL06ful+JgbTP8TdPYqkuzw/w5jPFfnpzGwA8N0S6WbSugJa/vNJ22X68wKfLVGbzKGv9d9FeqgsBuxkjewHC52MnXamx92tymVmZr2BTUrlSbhXCbCqma0Y061A6+Im5dJtYWb94uMNCE2iudT5YunGlSy8VCLrnxdorV2/mNTKRaTnyWIv8WT5zldLXDON8OW4egX5jQQGlsnzTcIqZH1inm+mypEjdAQqVg4IHclWivkn6d5NmlJLpBttZv3cfWHplyAlZP3zAq0B+zkzGwd8J+Y7D3gKuMLdi+UpIt1EFmvYy8XtByWumRG3I9qQX9E83X0R4b5yOs8k3SfuPr9MOQqlq6T8vQlNodJ+Wf+8QGvA3pmwjO1hwNeAHQnDw140s0MrKLuIZFgWA3Zj3M4rcU1Sc20scU1+fm3Nsy3lqEY6aZ+sf16gNWD3Bn4EjCLUwtcFriF0YPx9HCooIt1UFpvEF1H5D41KBpkvauPzJ3l2dTppn6x/XiAM8RsLXOru/0odfw7Y28zmAQcC55nZZN3nFumeshiwZxOGxgwocU1yj7GpwvwSAyheA8rPM0lXSTmqkU7aJ+ufF9z99DJlOpUQsD9P6BD3cJnrRSSDstgk/mHcDi9xTXL/7/025Fc0zzhRxjJ5eSbplsrr0VuoHIXSVVL+Fha/ryltl/XPS1mxp3ty/SqVphORbMliwE7maB5d4prkXNkxq+7+Dq0dhIrluSKhx286z6QcvWmd4rJYOeYRegqn061QZEardLpXYwcmab+sf14AMLOBS1y9uOT/skYUiHRTWQzYj8Xt5oVOxkkmki/ESpsG/10qT8LiHADT4xc27j6LMH90JekeSwXeqYTm0T60juctlk5Nmx2X6c+Lme1oZrOB2WZWsBd7HOudnCs0OYyIdANZDNjJLGDj4pzc+ZLhLfe7+7QK87wxbvcrUutN8pxUJN3B+QliPvvnp4tDem6Nu4cUSDeMMM620PNJ22X680IYZz2A8H+10JSlACfE7XPu/lzRUotIpmUuYLv7y4Res32AyWaWTEiBmU2g9cvrF/lpzWyMma0RZ51Ku4YwScWqwLVmtlS8vr+ZnQ9sSWgGvSAv3fnAR8CXzez85Ms7pv9TzO+1+DjtTELT5QQzOyHOnoWZLQf8BVgaeNDd/1nZuyLFZP3zEmvoV8XdM8wsmdYWM2sws9OBwwm9yo+p7F0RkSzK6vKawwkTSKxLGC7zLKEncDL140nufmaBdNPiNVe5+8S8cxsD9xA6C80GXqR1ucQFwPbufl+BPHcirLTUH5hJ+MI1YCnCl/OW7v58gXSHAL8HegHvEZbYXJtQm5oObO7u71b2jkgpWf+8xOVBp9A6Ve3/CPe4V4vP3wwc4u5XICLdVuZq2ADuPoNwH/A0QqeeNQk9du8Hxhf68q0gz8cJE1RcTvjiXJ/QS/vPwKaFvnxjuinARsANhFrzFwhf4FcBGxYK1jHdHwhzV08B+sXn+x9hecZNFKyrJ+ufF3efA2xDaEr/F2H413qEWvwk4AsK1iLdXyZr2CIiIj1NJmvYIiIiPY0CtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBihgi4iIZIACtoiISAYoYIuIiGSAAraIiEgGKGCLiIhkgAK2iIhIBvStdQFqwcxGA6+XuGQB8AlhZac7gAvd/ZMuKFpRZpas0rKtu/+9g3mNIyw3WUiO8PpnAVOBW4BL3X1+kbz6A/sB3ySsIDUCmAO8E5/jKnf/T5G002hd4jKtBZhHWH7yBcIKWH9097llX1wJZrYe8G/gDHf/eYVpJgJXAm+7+6gCxwtpBmYQltz8M3Cxuy8s8zzHAecA33T3W83sVOBnwHR3H11BOacR3svT3P3UcteXyOcrhFXMDnH3S9qbj4hUn2rY8BzwUN7fM4Sg8yXgDOBZMxtbsxJ2rv+w+Gt/mPCe5ICtgQuAf5vZsvkJzWwM8DxwMfB1YC7wFPAGYW3oI2Lai8ysV4kyvJlXhseAV4DBwLYx/2diwG0XM+sH/ImwhOm57c2niPzPz1OEHxxbAecDj5jZUmXy2IHwQ6lDP8Y6yt0fAG4EzuvGn3mRTOqRNew8P3D3fxY6EWuitwIrEdYr3qLritVldnf3aYVOmNl3gasJNeffAHulzvUH7gLGEt6jw9397dT5AcCBMd33gQ8JNcZCrihUK4xB/quEgL0a8Dcz+5K7l2odKeZHwDrAPu4+rx3pi3L3LQsdN7O1gNuBDYHzCO9HoeuWJny27o9rX9faicBuwEWEH2IiUgdUwy4hBvIfx90vmdmGNSxOl3P3GwgBF+C7Zva51OnvEILoG8B308E6pp3n7hcCp8dDx8bA1Jbnz7n7P4AvA28ByxOCSJvE1oGfEGrtf2pr+vZy96nAkXF37xKvf1ugH3BnlxSsjPiD6GpgOzP7Rq3LIyKBAnZ5t6Qeb1azUtRO8vr7ABuljm8ct08Xu78dXRa3gwg13DZz9/8Bx8Tdb5jZJm3M4jhC8/ql7t7SnjJ0wP1x2x9Yvcg1SVCsi4AdXRy3p9ayECLSSk3i5X2cerzEfcjYSedIQpPmcEJnrUeA82PtcAlmNgQ4lHDfci1gGcI981cJTai/dfdZlRQu3me8H/g88A9gZ3dvquiVVabY618Qt5uZ2XB3n1Eosbu/Y2YbxHzeLnRNhf5MaFYfQejg9u9KEpnZQEJTdA64psR1uxHuua8PNABPAGd2oLyJfqnHnxa55hvA6+7uVXi+z6Q6rlVilfStEXd/3MxeBDYxs03d/bFqlk1E2k417PJWSz1+M33CzH5JCJbjCV/y/yX0cN4VuDeeJy/NavG6s4DNgQ/ifjPwRcIX7CNmNrhcwcxsVUJP7M8DfwN2qnKwhuKv/29xuxyhY9nhZrZ8oQzc/Wl3f93dFxQ6X4lYM34k7o5rQ9LtgKGEloB3Cl1gZhcBkwmd7JoIvbs3IbzGfdtZ5MQ34/YVwqiD/Of+AuHfrzNq12+wZIe49F/yWfmYxX+YJf4at9/rhLKJSBuphl1e0hS7WA9eMzsEOAH4CDjC3f8Uj/ci3N+9HDjBzF5298tT+V0KrAg8CnzL3d9NpZsATAKMECiK3q81s5UJNepRwBTg22WaptvrqLj9AHg8OejufzezPxE6oq0KXAhcYGZTgQfi373u/kEVy5J0NlupDWm2idsHC500s70IneIWAPu5+7Xx+BDgEmD3thYydshbFtgJ+BXhR9wR7p4rcPkOcVv1gO3uVwBXFCnjXoQWh2ZCx8NCLToPEv79t6122USk7RSwC4jNqGsABwP7xMPnxXupyRfyafH4/u7+2X3u+KV8g5kNA34HnGZmV7l7c+y0tXa89KAkWKfSXR3H924NrFuifCsSatYrE+4xf68jtdcC+Q+Oz38MrbXZUwo8x77As8BJhObyXoTXtzZwGNBiZg8AJ7v7Q1UoWtKkPLwNab4at88VOX9S3J6ZBGsAd//IzCYQeshbqSdIjZEvpJkwtvpvRc5/gzAErNi4+JXL5N9m8TZOEsh/6O73FLn02bhd28w+l3z+RaQ2FLDhPrOS38cQOk79NLX/JeBzhABya5E0fyLUOkcSmrr/Hb/wljWzgYUmATGzPoQJWwAai+Q7ktAhaBXgL8B33L253Aso4fUyr38RIZhdnH/C3RcBZ8cm5V0ItcWvEpp4IdxyGQf8y8xOdveO3hPuH7dtCWCrxO0r+SfiLYU14+6k/PPuvsDMLidMaFJK/o+RfsAwQstDX+AqMzsur6UlqcVvDtxTYlKY+YSx8uVsRLgtU5KFf+xbCO/lBe7+uxKXv0JoHehNeB8VsEVqSAE71LzS9+9yhBrPDMK95b/E4TlpSW/n/sADJQLeIsKX3RqkOkm5+9zYpL0xYRxzEji+QOjNDMX7F/weGBgfL0/4Qu2I/xCCQiJHuLf5AWECkJvdfXqpDNx9NnBt/Es6wn2NcG9/W0LN+wwze9Ld/1o0o/KWidtKO+QNovWHz0eFLonbT0u8xqfLPU+JcdjDgJ8TmtwvM7PZcahc4uuE3velmsPfK5Z/3nNNo/Cscelrlo3PNYxwf/7oUte7e4uZfQIMIfRVEJEaUsAuMXFKCUngaKCyyVSGJA9iDediluw49QnwL0LtdP0SeQ0EbiMEws2AY+nYzF1FJ05pL3d/hVA7+4OZbUUo79KEwNWRgJ3Uhl+o8PqhqceFOuMl50tNVlLRj4NC3H0mcHicQGUcIXinA3an3b/OFyeyuY3w43AqoWVmUQVJ5xA+v0PLXSginUsBu32SL/gn3H2jklemmNlyhM5YyxF68F4CPEnolTzN3XOxI1epgH0VsD/h/vK5wOlmdru7v9j2l9E+ZvZFwjzaw4DVSs0c5u73xybzH1PmXnCZ5xxAuLUASzZBF5Mu15AC55OhaKWmDR1Y4lylbiUE7NXMbGl3/yR2MtweeMndX63CcxQVn+tqwg+8DwmjCSqdGz8J1B2ax11EOk4Bu32S8bKrm1nfQveQ45fkOMIMXdNjh639CcF6JrChu39YIO9RBY6lXRObKs8jDLfZELjSzLbowklBZhE6Y0Fo+r6jzPVJ57qO9BjfgzD5CsB1FaaZRej93Z8wfjtf8u84yMxWd/clhl3R2kmwI9L/Lsmc6hsSPgvXLnl51Z0DfJvwXuxW6dSusfNlcktB969FakzjsNvnAcJ976UIK1UVsidh2NWLhGFc0NoBanqhYB2bTjePuyV/TMXmzAMIvZCTpvEuEb/wH467Z5WactTMehOGuUH5wF4sj+VoncRkcqWtCfE9SjqbLfFDKN4KeDLuHlbgeXsTfmR11I5x+7K7J/0lkubwu6qQf1FmdihhHnUIIxMKDm8rIv2edVkLkJVANwAAA0ZJREFUjogUpoDdDnGBhrPi7m/NbL/45Q6Ame1K69SON6aaPJMvvfXNbHzq+l5mtj3h/m4yM1axXuLpcjxDaw/m081sjXa9oPY5hnBfeF3gMTPbNQ53+0wsz1+ALYGXaeM84GbW18x2JDSBL09YsvPI0qmWkDSff6nI+WSu+CPN7Kjk39HMGgm3LDYukq4sM2uIs41tFw/9JnV6B8Ktlfvz01VLnAf8wrh7irv/sY1ZJP0zXtGQLpHaU5N4+50DjAEOIoxpPcfMXicMu0qGNT1IqAUnLid0vBoL3Gxm0wnNxCsRmkcXAv8kNKWXaxpPnE7ojW3ApNg0Xklnog5x98fMbGfCPfUkMM+JvZWbCO/ByHj508D4EvdN9zezbVL7fQitF6vSeg/5RcJ45rZOb3oX4d+oYE9rd7/bzE4AfklYUetEM3sjvqalCEOgdiv1BGZWqNY6IOaRNONfSfwRZ2YjCD8E7uikyW4SNxDey7nAF83sb4T3s9AP9SviRCtpyXtWT3Oci/RYqmG3U1xJ6mDC0JxbCE3TGxC+5B8l1AS/lp4qNAasjQnB4XnCbFjrEHqIX0G4r5k0wa5vZmVn9Ipf+Mlc2ZvStU3j/yD8UDiUELDfJzT/rx/LczthcpWN3P21ElmtSKjNJX+bEW4fvBfz3QdYr51zbd9J6DOwihUZf+fu5xDGj98eD61DuL+9B/DrCp5ji7y/LxGC9VuEzl7buvv+qT4GXyf83+vsQJh0phtImCJ1O8LKZ/nl3YK82eNiS0PSMnB1J5dTRCrQK5er6iRKInXHzH5GWHXqV+5+XI2LkwlmtgOhz8F97r51rcsjIqphS8/wW0InwX3z77NLUQfH7WklrxKRLqOALd2eu39EWIRjWTq++la3FzsL7gT83d07rVOciLSNArb0FGcTplo9PU5ZKsWdS+g4eFCtCyIirRSwpUdw94XA3oSZu46vcXHqlpl9lVC7PrraU9aKSMeo05mIiEgGqIYtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGaCALSIikgEK2CIiIhmggC0iIpIBCtgiIiIZoIAtIiKSAQrYIiIiGfD/Kz+tba1exEcAAAAASUVORK5CYII=\n",
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"text/plain": [
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"<Figure size 525x300 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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I8Zx3Ccl9PqFGP7XkT13YD939B9kavruvBb5CqLD1IfRlSiq2RFS0hu3uW3dwfFIHxxcBZ8UfEZGerO047AZgKCER9Qe+Cgw2s6/kNlO7+z5x5EyhSaSyc1XkjnV8jfCFYAQww8yudvfncq757TzXOTJuZ+beP+ecNWZ2F/A14FBavih01X157rHYzN4jfHkanmBsiUi6SVxEeq5p9LAmceDMCl6/0DjsfoRa4XXx/g3A6bll4oJII8xsT0JtfNu43Y3wLBtyWlTdfaGZXQlcQBgP/Xkze5fQf+gPwAN5ljbO9k6fZmaHF/gM2edGO3T4aTu2oMD+bHN2bv6qdmyJUMIW6aKtLnuRhiGF155pXrqWJZ+a2Wpfw+ARlQ6rZrx1whV3jr3lvLtRp7NuiT21bzSzzYGLgVPM7HJ3fxPAzIYSOo9NoaUnOYTm7L8Thr4elOe6F5rZM4QvAR8lJLRj40+jmc0EznT3bK1/WNxmO7IVM7yD46Vo10O9jdxV2KodWyKUsEW6qGHIaPpstHHB482N7WfNrauv2W4jFRETXOrmv65R9xASdgOh5vxmzv6PEWqe1wJPAi8B82LNexp5EjaAu/8a+HUcDrUfoaPZpwm10CmERHhYLL4qvj/U3e8v82frrlqOrWyUsEVE0iF3OtI6gNgEnh3OdbC7P5znvLFtd8ShYNsBde7+nLu/T3hmfB+hB/Z5hI5nh5rZsFjLdkJHr4lA3qRoZtsREuf8Ki9UUsuxlU3v+rovIpJeB8dtBngmvt4m5/gztGFmgwgTqkDrCtopwHPA7WZW1/Y84MGc1w1xm+0EdnJM+G3v1YdQ23+aMAytmmo5trJRwpZUyzRn6N/Yp9XP6pXrWblyXeGfFe3nEm9csZjG998r+NO0UusTSDLMrM7MPkPoIAZwl7u/FV/nTgJyUewtnj1vR8JEItlnurm9xO8gPCPeCbgmjmPOnjcauDS+fSpnpsnpwDuEXuv3xomvsudsHK85IV632kmxlmMrGzWJS6qtWb2Bo+bs2Wpf25W4SjH//IkMzLxfrrBEuuJaM1veZl9fwmQg2Z72z9AyNTPuPtvM7iBMSfo1YKqZvUaYmjRb+34Q+AQw1Mw2cvf33f0dMzsRuJ0wOclJZvYKISeMI4zRXgSclHOvpWZ2GKE2ewDwmpnNITTVG2HoWSNhIpaqLgFay7GVk2rYIiK1YSfCdMy5P7sQlhu+HzgB2DMuBJLrc4Qm7qcJf9N3JSSo+4BD3P1AIDv186HZk+LkU5MIE6QsI8zXvRUwj/D8eoK7v5R7I3f/W4zzEuB5wpeJCYS5v38KfMjd7+7Wb6GLajm2cqnLZAqNte/5zOyl8ePH7zhr1qykQ5EuenPREq69tPM16raOXXZcp2vY2/7g3eK9xJesZuE+01vt2+SxM6gfWXit7hqT79mmiCRENWwREZEU0DNs6XFOOmt3xowsPDdC44rFzD9/Yqt923/ncfoOLVxbzqdhyKguxSci0hVK2NLjDBrUlyFD+hc83tjct13zd9+hGxdt3hYRSZqaxEVERFJACVtERCQFlLBFRERSQAlbREQkBZSwRUREUkAJW0REJAWUsEVERFJACVtERCQFlLBFRERSQAlbREQkBTQ1qfQ6meYMdRsGt9rXvHQtzY2ry3qf5qVryno9EendlLAl1TKZ5nb7mlcuobFfU8FzGt9+m6Gzv9Fq35JPzSx7bCIi5aSELamWWbWs3b5l39qddUXWtq7bMJihfKPgcRGRWqRn2CIiIimghC0iUmPM7AtmljGzkzt53hnxvKlduGefeG7GzKaUUP7kWLaxs/eSrlGTuAgw/BdH0G+LMRW/T93wgRW/h6Sbme0OXNWF8z4CXFn+iKRWKGFLj7PR2b9niy22KXi8eenadp3M+o7ZnPqRgyodmkhRZjYJuBsY2snzPg78ChjcUVlJLyVs6XHqB4+kz0YbFzyeb/hWXb2eDklyzGwAcB5wIdDQifMGAufHH/0j7uH0H1hEJEFmNh6YC3wz7roQmF/CeRbPuxBoBv4fsKBCYUoNUA1bRCpm7tSGBmBk0nGUyZLtZzQVHuDfdWOBLYAngTPd/Rkzm1bCeVvEc5+I5802szMrEF/JzKwPsKHE4j929051quvtlLBFpCLmTm2YDFwHbJJ0LGWycO7UhjO3n9F0Z5mv+xZwsLs/0Mnz3gA+5e6/K3M83ZEBHi9yfDRg8XWHrQjSmhK2iFTKTcCwpIMoo00In6msCdvdXwZe7sJ5cwlN4jXD3ZuAffIdM7OhwGPx7R+Ay6oVV0+hhC0iIm3dZma3letisan8TmAX4B/AZ2Nyl05QwhaRSplGD2sSBxJ9RlxFc4H3OiizKTC+xOtNBz4JLAYOcffl3Yit11LCFpGK2H5G051zpzbcjTqdpdG33f32YgXiLGw3dXQhM/s6cAqwHviMu79anhB7HyVsEamYmOA6qqlJD2Vmk4HL49svuPujScaTdhqHLSIiZWdmewE/BeqAK919RrIRpZ9q2JJuzRkGrm+z9sDydTQvaT+b2b9PWbqmwkGJ9G5mNg64FxgA/IYwqYt0kxK2pNuK9Zz28LzW+x6ex8JkohHp9cxsJPAAYcz1s8AUd88kG1XPoCZxEREpCzPrT6hRbw+8Dnza3VclGlQPohq2iIiUy5eBfePrRcBNZjYY6JunbKO7T6pWYD2BEraIiJTLRjmvP9xB2d4yRK5s6jKZ3vtowcxeGj9+/I6zZs1KOhTpovnPvUT/Y1pPwbz+ho8zdqcdOnWduuEDqauvK2doPYF+ISI1RDVsqRmZ5maaVi7u1DnNq5a2v86w/tSPHFSusEREaoISttSMppWLefVLm3XqnLWNmzGQr1QoIhGR2qFe4iIiIimghC0iIpICStgiIiIpoGfYUtO2uuxFGoaMLnh8wWsLoU0v8bpBwyodlohI1SlhS01rGDKaPhttXPB4/eD17fbV1Wk0koj0PGoSFxERSQElbBERkRRQwhYREUkBJWwREZEUUKczqRmZTHO7fUvXraJ+7cCC5yxbt5bCXdJERHoOJWypGcvWr2m3b9LdV7O8X+F5wTd9vy+zWi0QJCLSM6lJXEREJAWUsEVERFJACVtERCQF9AxbatqvDz6VUaO3LHh89VvL4Uc/b7Vvo36FO6mJ1BIz2xp4rcDhDLAUeAP4LXCNu79XpdDKxsymArcAC9x9bM7+PwP7AZe6+4XJRFecmc0AjgcecfdJpR6rFCVsqRmZ5gx1Gwa32jdsTQMjVhduCOq7pp7VbfbVa2pSSacXgeU57/sAI4CdgA8C08xsf3d/IYngJHlK2FI73l/P0NnfaLWr+fB7WZhQOCJV9kV3/3PbnWY2CrgVOBi4y8wmuHv7MZDp83lgELAo6UDSQglbRKSGuftiMzseWABsDxwI/C7ZqLrP3d9IOoa0UaczEZEa5+6LCU3mEJrIpRdSDVtEJB36xu2KtgfMrA9wDPBZ4D+AUUAj8DbwJ+Bqd5+b57xPAmcAexKely8nfDG4A7jZ3dutX2tmGwFfBj4DjCdU/F4F7iZ0jFtWyofJ1+kspxPev4DNgROBU4Ad42kvAjcCM9w9U6nYapUSttS0+ls/yehx4wseX7VqHd+74pFW+84epl7iteLcs+5vAEYmHUeZLPnuNYc0JXFjMxtHqFk306Y53MwGArOAj8VdrwMvAJsC28WfKWa2r7vPzjnvS8D/xrdvA88CowlJdD9gspkd4O5NOefsQOixvjXQBLwCrAEmAhcBx5vZQe7+z25+5DrCc/vjgGXAXGBbwheLPQEDzmvze6hWbIlRwpbaNrw/9SMLT01a16+BNf1a/zOuq1cv8Vpw7ln3TwauAzZJOpYyWXjuWfef+d1rDrmzGjczswZgOPCfwPcItcVL3X1+m6JfJyTrRcCn3f3pnGvsDtxDqK2eD0yO+4cDV8Zix7j7zJxzDgR+A0yK5WfG/YOB+wgJ8R7gdHd/Ox7bDLiZ0DHuXjPb1d3bzzVcuk2AzxFqy9PdvcnMBgA3AVOAr5nZ/2SHuVU5tsToGbaIVMpN9JxkDeGz3FTB6z9sZpnsD6FJexFwL6Gz2ZXAN/KcdwCh5v2t3GQNEN9fH9/unHPIgAGEcd6/bHPOH4DLgbuA3CbxkwnNzH8HjsomxHjOu4TkPp9Qo59a8qcu7Ifu/oNsDd/d1wJfIYxP7wPskWBsiVDCFhGpDS8Cj+f8PAm8BKyLx78K/CDWvP/N3fchJN8bClw3O1VBblPVa4QvBCOAGWa2a5trftvdJ7v73Tm7j4zbmbnN5DnnrCEkeYBDC33ITrgvzz0WA9nJY4YnGFsi1CQuIpUyjR7WJA6cWcHrFxqH3Y9QK7wu3r8BOD23jLtvMLMRZrYnoTa+bdzuRniWDTkVNHdfaGZXAhcQxkN/3szeBR4C/gA84O5tx0dne6dPM7PDC3yGzeJ2hw4/bccWFNifbc7OzV/Vji0RStgiUhHfveaQO8896/67Uaezbok9tW80s82Bi4FTzOxyd38TwMyGEjqPTaGlJzmE5uy/A7OBg/Jc90Ize4bwJeCjhIR2bPxpNLOZwJnunp19bVjcZjuyFTO8g+OlaNdDvY3czirVji0RStgiUjExwaVu/usadQ8hYTcQas5v5uz/GKHmeS0tTenzYs17GnkSNoC7/xr4dRwOtR+ho9mnCbXQKYREeFgsviq+P9Td7y/zZ+uuWo6tbJSwRUTSIXc60jqA2ASeHc51sLs/nOe8sW13xKFg2wF17v6cu79PeGZ8H6EH9nmEjmeHmtmwWMt2QkeviUDepGhm2xES5/wqL1RSy7GVjTqdiYikw8FxmwGeia+3yTn+DG2Y2SDChCrQuoJ2CvAccLuZ5RsH+WDO62wnt2wnsJNjwm97rz6E2v7ThGFo1VTLsZWNEraISA0zszoz+wyhgxjAXe7+VnydOwnIRWbWN+e8HQkTiWSf6eb2Er+D8Ix4J+CaOI45e95o4NL49il3XxJfTwfeIQyfutfMtsw5Z+N4zQnxutVOirUcW9moSVxEpDZca2bL2+zrS5gMJNvT/hngtOxBd59tZncQpiT9GjDVzF4jTE2arX0/CHwCGGpmG7n7++7+jpmdCNxOmJzkJDN7hZATxhGGiS0CTsq511IzO4xQmz0AeM3M5hCa6g3oTxgqdky1lwCt5djKSTVsEZHasBOwd5ufXQjjsO8HTgD2jGORc32O0MT9NOFv+q6EBHUfcIi7HwhkV8b69xhkd/8ZoZPZXYTpP3cEtgLmEZ5fT3D3l3Jv5O5/i3FeAjxP+DIxgTD390+BD7UZu101tRxbudRlMu3mT+81zOyl8ePH7zhr1qykQxFg4Tyn+fB7W+2rv+cwNtnOCp6zcuU6LvnGg632XfTtTzBkSP+KxNjLaI5XkRpStibx2LnhXOC/CU0xKwjNN99399924XpbE2bjKeY5d/9gZ68tIiKSNmVJ2LHDwkPAR4ANhCn2RhEWWj/QzC5292918rLZqfKWAP8oUGZeF8IVERFJnXLVsKcTkvWzwGE5M/AcB/wEuNjMHnf3P3bimtmEfYe7n1a0pIiISA/X7U5ncZ3WKYTeeMdmkzWAu98GXBHfXtzJS2cTdmp79ImIiJRLOXqJH0cYWP8Xd5+T53h2BZm9c8fGlSCbsF/sTnAiIiI9QTkS9l5x+1i+g+6+gLAOKYS5ajtkZkMIq82AatgiIiJleYY9Pm5fKVLmdcL4vu1LvOYuhCElbwMbm9nZhMnu+wBzgV+4++NdilZERCSFypGwszPwFJtMPTvQf3SJ18w2h48A5tAyly2EGXvOMLOfAKe6+4ZSAxUREUmrciTs7Py0a4uUyS44PqhImVzZhD0A+BFhybiXCeu1Hgd8EziRMAPQ6fkukMvMXipwaFyJ8YiIiCSqHAm7idKfhZc6rdr/xWvOdvfrc/a/AVxqZq8T5sA91cymt50+T0REpKcpR8JeSWi6HlCkTHa5s9WlXDDOcfuzYsfN7JuEVWgOJyzWXux6E/PtjzXvHUuJSUREJEnl6CW+KG5HFSmTfXa9sAz3y5odt9sULSUiItIDlKOG/Q9CTXfrImWyx+aWetG4rmuzuzcVKJL9sqFOZzUo05whs4eKzY0AABTjSURBVGxNxwVzLVtXmWBERHqAciTsp4DDaBmP3YqZjQWyE6Y80dHFzGwEYYjYCOAooNByaLvFbb7JWiRhmWVrWLjP9KTDEBHpMcrRJH5n3E4ys3zrIJ4at4+4++sdXczdlwLvxrdT85Uxs6MJPbzXUzihi4iI9BjdTtjuPg/4OWGs9N1mlp1IBTObAnw9vv1O23PNbJyZ7WBmm7c5dHncHmpml5tZ/5xzjgZuiW+/6+5vd/cziIiI1Lpyrdb1JWDn+PNPM3uB0KS9VTx+QYGVuh6KZW4lpzbt7reZ2S7A2cB5hIlS5gGbAmNisZsJ47FFRER6vLIkbHdfbGZ7AecAnwUmEDqDPQL8wN073Wzt7ueY2e+BMwnPx3chrI19P3CDu88qR+xSPaPvO5H6EQMLHl+0+E2WX7x7q33DNjq60mGJJM7MtiNUTj5BmCBqCfAkMN3dHyxy3ijgQsLw1rHAUuBxQuvjk52MYRLwcHy7TUePMM1sBnA84XHnpM7cS7qmXDVs3H0VYQnNiztxztYdHP8j0Jk1tKWG1Y8YSP3IwpPd1TX1J9N3Vet99XWVDkskUWb2SeDXhPkqVhM60m5MSMKHm9n33P2cPOdtSkjO4+J5zxOS9pHAYWZ2irv/pDqfQqqhHJ3ORESkC8xsNPALQrKeCXzA3T/o7mOAYwkzSZ5tZkflOf2XhGT9IDDW3T8MfIBQU28AbjCzCVX4GFIlStgiIsk5mdDf53Vgqrsvzx5w958DN8W3p+aeFJuv9yPMNPm5OLoGd2929ysJUzf3BS6ocPxSRUrYIiLJeY1Qw/6hu+ebOej5uN2qzf6pcXuPuy+ivRvi9ggzK9xxRFKlbM+wRUTaenfHqxqAkUnHUSZLNptzTqGZF7vE3X9JaNou5MNxO6/N/uxEVY8VOO+vQCMwOF7j/7oaY2e06bjWkRPcfUbloul5lLBFpCLe3fGqycB1wCZJx1ImC9/d8aozN5tzzp0dF+0eMxsOfBk4gZB4r8w5Vg9sG9++ku98d99gZgsINfPtqVLCBpYTOsIVsi2QnXfjjcqH07MoYUtFZJqb2+1rXLGY+j6r8pQOmlcurmRIUn03AcOSDqKMNiF8pool7Ni57FvAeKA/8CZwmrs/mlNsBC1/u98rcrnFhIQ9ukiZsnL32cA++Y6Z2Y60TE99ibv/qVpx9RRK2FIRTauWtts3//yJ7YZtiUgrewC5ywGPAA4xs0fdfUXclzs2cm2Ra2VX3yk8lrKw1/LPNN01cQjaA4QvcHfQieG/0kKdzkSkUqZR3iV1k7aQ8Jkq6VpgCGF41lRC0j0V+JOZZStYnX2OnulCHH8jNG0X+ynpv62ZDQLuI9T2/woc7+5dianXUw1bRCpisznn3PnujlfdjTqdlczd34ovVwG3mtmTwLOEjmNTgBmEoVxZA4pcLts7fHUXQpnciZnOipWpJ6w1sTuhef9wdy/WKiBFKGGLSMXEBFfsOasU4e5uZncDnwMm0ZKw1xGecY8qcnr22XWSrRxXE2ZsWwUc5u7vdlBeilDClg5lmjNklq3puGCO5mXtv0SPveBR+o4dk6d0sHTdKibdfXWrfY8M7imVM5H2zGwksA0wv8B4aoD5cbsZhMlRzMwJ6ytsXeC6fQnN6gBzyxZwJ5jZFwk93ZuBY9392STi6EmUsKVDmWVrWLjP9G5fp2HwKPpstHHB4/VrB7K8X+v+MXX16mYhPdrThKFOXwe+W6BMdtKUBTn7niIk7L2AH+c5Zw/C3/e1wOyyRNoJZnYo8P349jx3v6faMfRE+msoNSPTnKF/Y59WP6tXrmflynUFf1atWp902CLd8Ye4PTnWilsxs60Ji3lA6LiVdUfcHh1r6W2dFre/dPfONY91k5l9iDB7Wz1wi7tfVc3792SqYUvNWLN6A0fN2bPVvmvnPFGgtEiPcBWh49Z2wM/N7LRs07iZ7UZYEGQg8CiQW0t9iNBTe2/gN2Y22d3/FTt5nU1YOGQDOROuVIOZbUn4YjEY+D3whWrev6dTwhYRSYi7v2pmnyVMT3o0cGh8Pj2AMEMZhHWxj8odCuXuGTP7PPAIsC8w38xeBMYQnnVnCFN//qN6nwYIM9tlZzKrA+6Nw7oa8pSd7e5frFpkPYAStnTJ6PtOpH5E4TUFGlcsZv75E1vtGzWs6AgQkV7J3e83s12Bc4ADgQmEoViPEVbd+om7b8hz3quxFn4BcBiwczzvd8B33b3UOb3LaaOc1wd2ULaxkoH0RErY0iX1IwZSP7LwBEr1fVa1m9Wsrr6u0mGJpJK7v0wXmo9j8/lZ8ae7MfyZUCsutfxUWlYNy+6b1N04pDAlbKlpJ521O2NGDu/UOYMG9atQNCIiyVHClpo2aFBfhgzpn3QYIiKJ07AuERGRFFDCFhERSQElbBERkRRQwhYREUkBJWwREZEUUMIWERFJASVsERGRFFDCFhERSQFNnCIdyjQ3t9vXuGIx9X1W5SkdNK1cVMmQRER6HSVs6VDTqqXt9s0/f2K7ucJFRKRy1CQuIiKSAkrYIiIiKaCELSIikgJ6ht2LZJqbaVq5uNPnNa1qf87YCx6l79gxnbpOw5BRnb63iIgESti9SNPKxbz6pc06fV7dhsEM5Rut9jUMHkWfjTYuV2giItIBJexeJNOcoW7D4E6fV9c4qALRiIhIZyhh9yKZ5esYOvsbHRcUEZGao05nIiIiKaCELV3SMHhE0iGIiPQqStjSJXX1+qcjIlJNeobdyw3/xRH026Jzw7MA6oYPrEA0IiJSiBJ2L1c/fAD1I9ULXESk1qldU0REJAWUsEVERFJACVtERCQFlLBFRERSQAlbREQkBZSwRUREUkDDuqQimjPNLF23ulPnLF23pkLRiIiknxK2VMTSdavZ9Rff6dQ5/Rv7cBR7VigiEZF0U5O4iIhICihhi4iIpIAStoiISAroGbZUzcNHfpWRAwrPW7565XqunfNEq33D+muRERERUMKWKho5YBCjBgwpeLx/47p2++rr1AgkIgJqEhcREUkFJWwREZEUUMIWERFJASVsERGRFFDCFhERSQElbBERkRRQwhYREUkBJWwREZEUUMIWERFJASVsERGRFFDCFhERSQElbBERkRRQwhYREUkBJWwREZEUUMIWERFJASVsERGRFFDCFhERSQElbBERkRRQwhYREUkBJWwREZEUUMIWERFJASVsERGRFFDCFhERSYE+SQcg1ZPJNLfbt2z9ahrWriz7vZasXV32a4qI9GZK2L3I8g1r2+074v7reWP4gASiERGRzlCTuIiISAqUrYZtZoOAc4H/BrYBVgDPAN9399928ZpbAhcBBwGbAO8BDwGXu/s/yhG3iIhIGpSlhm1mg4E/Ad8EtgVeAlYBBwIPmNk3u3BNA/4OnAQMAZ4DBgDHAX83s0+WI3YREZE0KFcNezrwEeBZ4DB3fxPAzI4DfgJcbGaPu/sfS7mYmfUB7gdGAbcBX3D3NWbWD/gf4ExgppmNd/fFZfoMvdKtnziekeO3q8q9RvQfVJX7iIj0RN1O2GY2DpgCNAPHZpM1gLvfZmbbAxcCFwMlJex4vfHAG8DJ7r4+Xm+9mX0J2BXYFzgrXlu6aFj/QYwaMCTpMEREpAPlaBI/DmgA/uLuc/IcvyFu947PpEsxNW5vyybrLHfPAD+Kb4/pZKwiIiKpVI6EvVfcPpbvoLsvAObHt/t1dDEzqwf2KHZN4PG43dbMtigxThERkdQqxzPs8XH7SpEyrwNbAduXcL0xwMAOrvkm0ESo2W8f31fU/OdeqvQtKm7Fm28xus2+NasbWblyXSLxtLVq1fqOC4mI9FLlSNibxO17RcpkO4a1zRfFrlfwmu7eZGbLgZElXrPb+h/zQDVuU1H98+z7yY3zWNPvtarHIiIinVOOhJ3t+tt+Gq0Wa9qULeV6ZbummRWqHu/wxhtvcPDBB3cYVGPTog7LpNGSv/QjU1eXdBgFvTD5h9TX1258PdnLL798r7sflnQcIhKUI2E3Ufqz8EyJ1+uMUq5ZSPP69etXvfzyyxVvUi/RuLgt9nihvFZV7U5dsmLlv5IOISnV/7cgIjWtHAl7JTCCMKlJIdln0qWsCJG7EsUACteyS76mu08s4b6Jy7YEpCVeqRz9WxCRtsrRSzzbVjyqSJnsc+aFnbhewWvGiVWGdeKaIiIiqVaOhJ2d03vrImWyx+Z2dDF3fxtY3sE1tyD0EC/pmiIiImlXjoT9VNzule+gmY0FshOmPFHiNf9a7JrAf8bt/JjgRUREerRyJOw743ZSXLCjrVPj9hF3f73Ea94RtyfE+cMLXXNGidcTERFJtW4nbHefB/yc0ER9t5llJ1LBzKYAX49vv9P2XDMbZ2Y7mNnmbQ7dTugduy3wczMbGsv3M7MfAPsQms2v7W78IiIiaVCXyXRnVFRgZqOAh4GdCcOyXiD0HN8qFrnA3S/Lc97rscyt7j61zbHdgQcJnctWAv8kJPCRwHrgIHd/uNvBi4iIpEBZ1sOOS1zuBXyL0AlsAqGH9yPAUfmSdQnXfJqwKtePgWXxdTPwK+AjStYiItKblKWGLSIiIpVVlhq2iIiIVJYStoiISAooYYuIiKSAEraIiEgKKGGLiIikQDlW65JuyBmLXswId19W+WgkCWb2BeAGYJq731ygzCDgXOC/gW2AFcAzwPfd/bfVilVEkqOEnSAzG05I1k3Ak0WKNlYnIqm2OEHQVR2UGQw8BHwE2AC8SJjn4EDgQDO72N2/VelYRSRZStjJ2iVu57n7PolGIlVnZpOAu4GhHRSdTkjWzwKHufub8fzjgJ8AF5vZ4+7+xwqGKyIJ0zPsZO0aty8kGoVUlZkNMLOLgT8SpvAtVnYcMIUwy9+x2WQN4O63AVfEtxdXJFgRqRlK2MnKJuwXE41CqiYujjMX+GbcdSEwv8gpxxEW1vmLu8/Jc/yGuN3bzLbMc1xEeggl7GSpht37jAW2IPRZ+Ii7X9pB+eya8I/lO+juC2hJ+PuVJUIRqUl6hp0QM2sAdopv3zazs4B9CauTLQBmAXe6e3NCIUplvAUc7O4PlFg+u1ztK0XKvE7ovLh9N+ISkRqnhJ2c7YEB8fWDtO94dBzwVTM7wt3fqWpkUjHu/jLwcidO2SRu3ytSZnHcju5SUCKSCmoST86uOa+fBj4KDCYM15kCvAvsATxgZv2qH57UiEFxu7ZImTVtyopID6QadnLmAz8g9P79Wk7T92rgZ2b2NDAb+CBwEnB9IlFK0poo/Yu11soV6cGUsBPi7n8B/lLk+Fwz+xkwDTgSJezeaiVh6NeAImUGxu3qyocjIklRk3htmx232yQahSRpUdyOKlIm++x6YYVjEZEEKWEnyMzqO3g+nf3vs6Ea8UhN+kfcbl2kTPbY3IpGIiKJUsJOiJk9CqwHvlek2H/Ebb4JM6R3eCpu98p30MzGAtkJU56oSkQikggl7OS8SJjB6jNm1m4uaTPbCviv+HZmNQOTmnJn3E4yM8tz/NS4fcTdX69OSCKSBCXs5FwDrAPGAL80s82yB8xsV+D3hGFejxIWiJBeyN3nAT8nfLm7O05tCoCZTQG+Ht9+J4HwRKSK1Es8Ie4+z8yOBW4HPgXMN7O5hP8mO8RifwOO1Gxnvd6XgJ3jzz/N7AVCz/HsOuoXaKUukZ5PNewEufuvCOOsbwLeJsx+tjlhuNcXgb3cfUlyEUotcPfFhGfY3yJ0LJtA6DX+CHCUu1+WYHgiUiV1mYzmWhAREal1qmGLiIikgBK2iIhICihhi4iIpIAStoiISAooYYuIiKSAEraIiEgKKGGLiIikgBK2iIhICihhi4iIpIAStoiISAooYYuIiKSAEraIiEgKKGFLt5nZ5Wa2NrtWs5nNMLOMmf25xPMz8WdqN+M4zsyazeyT3bmOiEgtUsKWbjGzfYBzgR+4+8sJh3M78FdghpmNSDgWEZGyUsKWLjOzPsANwDIg8TWZ3T0DnA1sBlyRcDgiImWlhC3dcTIwEfi+uy9LOhgAd38M+BNwsplNTDoeEZFyUcKWLjGzvsCFQBPw44TDaesGwr/ti5IORESkXPokHYCk1lHAGOAP7v52uS9uZjOA40sp6+51bXbdA7wPfMbMxrj7gjKHJyJSdaphS1f9V9zOqtD15wKPF/lpiuXeaHuiu68nNIv3AY6uUHwiIlWlGrZ0mpk1AB+Lbx+rxD3c/TIKdGQzswuAvYGVwKEFLvEYcATwCeB/KxGjiEg1KWFLV+wGDAOagTlFyu1nZply3tjMjgG+He99jLs/X6DoCzkxNLh7U4FyIiKpoIQtXbFN3C5w97VFyr1PS+IsZu9SbhrHfN8C1AFnu/v9RYp73A4BNgbeLeUeIiK1SglbumKTuO1oKNdsd5/U0cVKqYWb2XbAb4D+wM3ufnUHp+TGtglK2CKScup0Jl2RnUVsdTVuZmajgd8Co4CHgdNLOG1VzmvNeiYiqaeELV2RbQYfXukbmdkAwjCtccA84Gh331DCqblJek0lYhMRqSYlbOmKbPPy6ErexMzqgFuB/wSWAoe4+5IST8+N7V/ljk1EpNqUsKUrsh26RpjZoAre53Lgs0AjMNnd53bi3LFxu5o8Y7VFRNJGCVu64lnCM+J6YM9K3MDMpgFfBzLASe7+UCcvke15/kRcFEREJNXUS1w6zd03mNlDwGHAPoRZxcrGzDYFfhjfLiZMMXocMIAwpKutS939t2327RO3D5QzNhGRpChhS1fdRkjYnwIuKfO1B9Lyb3M0cHgH5TfNfWNmGxGeezcCvyhzbCIiiajLZNRaKJ0Xpyf9JzAe2MndX0o4pH8zs9OB6cAt7n5i0vGIiJSDnmFLl8SpPi+Nb6clGUse0wiLg1zaUUERkbRQwpbuuJ1Qyz4hTm6SODM7APggoXb9StLxiIiUixK2dJm7NwJTgcHAN5ONBsysHvge8BZwTsLhiIiUlRK2dIu7PwVcAXwhzvedpOOBXYAT3L2jec5FRFJFnc5ERERSQDVsERGRFFDCFhERSQElbBERkRRQwhYREUkBJWwREZEUUMIWERFJASVsERGRFFDCFhERSQElbBERkRRQwhYREUkBJWwREZEUUMIWERFJASVsERGRFFDCFhERSYH/DxeiPovy4XfeAAAAAElFTkSuQmCC\n",
|
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"text/plain": [
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|
"<Figure size 525x300 with 1 Axes>"
|
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]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 525x300 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"density = True\n",
|
|
"cumulative = True\n",
|
|
"histtype = 'step'\n",
|
|
"lw = 2\n",
|
|
"if perform_zscore:\n",
|
|
" bins = {\n",
|
|
" 'theta_energy': np.arange(0, .7, .03),\n",
|
|
" 'theta_peak': np.arange(0, .7, .03),\n",
|
|
" 'theta_freq': np.arange(4, 10, .5),\n",
|
|
" 'theta_half_width': np.arange(0, 15, .5)\n",
|
|
" }\n",
|
|
"else:\n",
|
|
" bins = {\n",
|
|
" 'theta_energy': np.arange(0, .008, .0003),\n",
|
|
" 'theta_peak': np.arange(0, .007, .0003),\n",
|
|
" 'theta_freq': np.arange(4, 12, .5),\n",
|
|
" 'theta_half_width': np.arange(0, 15, .5)\n",
|
|
" }\n",
|
|
"xlabel = {\n",
|
|
" 'theta_energy': 'Theta energy (dB)',\n",
|
|
" 'theta_peak': 'Peak PSD (dB/Hz)',\n",
|
|
" 'theta_freq': '(Hz)',\n",
|
|
" 'theta_half_width': '(Hz)',\n",
|
|
"}\n",
|
|
"# key = 'theta_energy'\n",
|
|
"# key = 'theta_peak'\n",
|
|
"results = {}\n",
|
|
"for key in bins:\n",
|
|
" results[key] = list()\n",
|
|
" fig = plt.figure(figsize=(3.5,2))\n",
|
|
" plt.suptitle(key)\n",
|
|
" legend_lines = []\n",
|
|
" for color, query, label in zip(colors, queries, labels):\n",
|
|
" values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]\n",
|
|
" results[key].append(values.rename({key: label}, axis=1))\n",
|
|
" values[key].hist(\n",
|
|
" bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
|
|
" histtype=histtype, color=color)\n",
|
|
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n",
|
|
" \n",
|
|
" plt.legend(\n",
|
|
" handles=legend_lines,\n",
|
|
" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.grid(False)\n",
|
|
" plt.xlim(right=bins[key].max() - bins[key].max()*0.025)\n",
|
|
" despine()\n",
|
|
" plt.xlabel(xlabel[key])\n",
|
|
" figname = f'lfp-psd-histogram-{key}'\n",
|
|
" fig.savefig(\n",
|
|
" output_path / 'figures' / f'{figname}.png', \n",
|
|
" bbox_inches='tight', transparent=True)\n",
|
|
" fig.savefig(\n",
|
|
" output_path / 'figures' / f'{figname}.svg', \n",
|
|
" bbox_inches='tight', transparent=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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7eonr7QIc6u5buvsoYL9Y7p6EbsgWQpDcwN13ANYjBMEvFynrJkLgXgfYq/Ckme0MjCW0Um+v6t2Az8Xj9BLnLyC8P/8CPhlfx9bA5oSAuF2V18mvZ18Lfk4I2nOBM8tkSeq2jZmtU+v1RKQ66g5tDNeYWTWtnN3d/a9Fnl8OHOjubydPuPtjsbv0RELQAsDM+gBnx4dHufv/5eXJATeb2VBCa+9sM7vW3VcWXO9hd785L9+8+OuP4/EX7n5x3vklwIlmNhpYpTvT3T8ws/8lBI4jgDsLrpWsy7vB3ZcVee2riAElacnNLHJ+PeCo+HCiu8/Iq8vsOLvzeVq7NIvZrcIaz38T1iW+WSqBu883s7eA9YHdCeOEIpIytQQbw4vAI1X8LCqR/+/5ATDP8/GYv8D+04SZiR9QumV1PaE1N4LQ1VpotRaWmY0gjMNBGPsr5sISzyddovuZ2Vp5Za5Ba9dmtV2ho+IxBxSbFfolwv+LOe7+YOHJOJP0vgrXeJ/VP5t/AEnQ2wC4x8x2qlCOx+OmFdKJSBupJdgY2rtOsFSLY0k85v87SFpJfYCHzKxUmc2EYPEJwi4o+d4qkn5LwhjgYnd/pUSZfy/x/EOEgDWaMC73m/j8foQA/qy7P1WqogWGx+PiuByhUPKCV2sl5nmaECxLKbdOcCxhLeBuwFQz26bM+5GsERxe4ryItJNagtmwvML5przfk5bWGoRu0lI/SXdgsW3alhR5Lpnqv7hMPd4v9mTshk1aekfknUq6QqttBQIMicePKpwvV8/3arjeKtx9NmFy0SJgIHB6meQfFtRJRFKmlqAUSr54n4qTYdIud80yaQaVOXctYVLNrma2MSHQfgFYARTuulLO0nhcq8T5+VXUs18N11uNuy8ys78SgmG59zgJfsX+qBCRFKglKIWScaixpZYkmFmTme1uZpvFiTTVSPbS7B8nwBSzTclKuf8LuJfQaj0A2JfwR9xd7v5ulXUASMZG+5rZwGKXSupiZk1FzkPo2m2vlngsdQ1obT2/k8L1RKQIBUEp9BChq24QMLlEmsOA+4EXgNXWGBYTx73+GR+WWlpxbInnE7+NxwMJrSiorSsUWoMcwIZFzt9BaF2OIC7tyBdnj+5T4zULyxhIGBME+FuZpEn9XmjP9USkNAVBWYW7fwicFx/+yswmm9nH/07iEoEr4sNbatx3M1kXd4qZHZO0tMyst5mdReWF4XcQFt/vStj55h3g7hquT2w1vhgffrrI+QXA/8SHV5vZHsm52A17OzCglmvmM7PhhNm1Q4GVhPWRxdKNIswihdLrGUWknTQm2Bi+Z2ZHV5n2HHf/czuvdz5hJuYxhOUJ55vZq4TWUf4Xc6kWXVHufruZnQ+cSpgh+SMze52wLdkQ4AlgJ+LWaUXyLzez6wl3WegJXFpkjWI1/hyvOZ7W5Rf5zibMEv0yYQbni4SJMlvFut1N+dmh48ysMHA1EcYZN491Xwl8y92fKVHG+Hh8ptx6QhFpH7UEG8NmlJ+pmf+zbnsv5u45d/8GYeLJ/xG+sMcRukgfJ+z2soe7l5phWa7s0whjetMI99jbBpgDfIPWnWvKlTsl7/dau0IT18Xjnvmt3Lw6LiMsxZhM2Ct1XcJavamE9/ixCuWvyeqfy06E7s1/EtZDbuXuV5UpI9k04LoyaUSknZpyuVpvXi7SMcxsb8KOMC+6+9gSafYldIv+zd13bMe1HgAmAPu4e7k9PDtd3JHnTcJM1lHuXmoTBBFpJ7UEpdOY2Uwze8zMiu0yA61djP8oU8wx8ViuFVWNZGu4Y8qmqo8jCa3kCxUARTqWWoLSaczsNsLMzgeBr7r7W/H5XoT9Oi8j/GG2e7JlmZn1JHSZLgAmESbXzCW0kNq1fs7MphJag1u5+/MVkneKuOTkBULX82a6s7xIx9LEGOlMpxMmfOwGvGZmLxEWgo8izJZsAU4p2LOzBXiUsINN4rvtDYDRMYQxuvMJ6w67ghMJt5b6igKgSMdTS1A6lZmtTbjJ74GEG/AOIOw1+jBwmbs/USTPNELwfB04z92Lzehsa32OJnStftbdH0ir3DbWZSjhXo93ufsRldKLSPspCIqISGZpYoyIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGSWgqCIiGTW/wfPPD7fzy4ceAAAAABJRU5ErkJggg==\n",
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"text/plain": [
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"<Figure size 480x300 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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\n",
|
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"text/plain": [
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"<Figure size 480x300 with 1 Axes>"
|
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]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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\n",
|
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"text/plain": [
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"<Figure size 480x300 with 1 Axes>"
|
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]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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t1BFWRGSlWiciieMyz0q+RO2tVYPgB8AnCKsSFJO9FvhGiTQto9brfePOeJp+Q/rMZc+a9IVOJPm631rGW/t+fO7wYb/eg85hpX73SdrFMdQ3UuVEJGa2PWF6usENKFZba7kg6O7dZubAZ4DxhdLEhUI/Ge/O6aWiNUW/ISPpv9qazS5GU3Uvfo83dr6y2cVouFXHrkPniLKXuCWFap2IxMwGAt+Lf614+avpWvVFyQ6cLbba9ZaEAL6MMP5IRKRPihORzAFOjQ+dAsyv4DiLx50CdAMnEdb9lBwtVxOMrifMNrO3mZ1QYAHNo+L2Os0WIyJ9XO5EJMe4++NmdlgFx42Nxz4Uj5ttZsc0sJxlmVl/wkLJlfiFu1fV8acWTQ2CZrYuYYjDe+7+Ss6uewnzDG4N3GRm+7j763Eg/XHA/oQXsq0v8lZyva/fkFKXRUXa25wp/foBI5pdjoQs3vCKrmp7t1ei1olIXgF2dve7yqbsPRnCd3sxIwGLt8vWdpPQ7JrgVYSJsmcAk7MPunvGzA6Kj28LzDezp4F1gNGEF3Kquz/X6yVOkK731W7krYfQOXxg+YRtpGNY33o+5cyZ0m8f4ELChNp9wRtzpvQ7ZsMruqYnmWmtE5G4+xxarM+Eu3cB2xTaZ2ZDWTlV5u+BM3qjTM0OgkW5+1wz25ww8e5uwKaE4RB3ERbVTXRVcOkp050h81bzW5u7l/QsQ+fwgepE0v4uBVZvdiESNIrwnBINgi3qajO7OqnMYjPpdEKHyOeAr8WA2XANDYLuPr7M/sll9i8Evh3/pJdl3nqfN7a5qNnFEJHWM4fS01pCWAx9YoX5XQR8mTAd5q7u/nYdZatKy9YE+5JMdzdd71Q+ubJIShxGH2sOBZra8aQX/dDdf1kqQZzN5tJyGZnZd4HDgQ+Br8ZJ13uNgmAv6Fq6iPknb9rsYoi0lA2v6Jo+Z0q/G1HHmNQys32AM+PdI9z9gd4ug4KgiDRNDBpqJkkhM5tE6BzZAZzl7lc0oxwKglKVVumVmbaelCJ9iZlNAG4hzA99E2Egf1MoCEoP2V6h3Yt7zk2uXpkiUg8zGwHcQRgT+ARwgLs3bTUgBcEGyq4cUahTzLjTn2rZgfDqFSoijWBmnyDU/DYE5gH/6u7vNrNMCoINVGrliH5D16Sjs1WnbhURaYh/J0yAArAQuNTMBgOrFEi7otwwuiQoCIqISG9ZLef2FmXStv9geRERSV65iUhKHDemjnOuIPTkrDT9z4Gf5z12CmFVi5ahINgApa4FtquRt0ylc8Qg9coUkT5FQbABSl0LHHf6U/QbumZLdoop2St0xCD1ChWRPkdBsJf1G7pmy64coV6hIpI26p4oIiKppSAoIiKppebQhBVbMaKVrwWKiKSVgmDCiq0Y0crXAktRr1AR6csUBAUIPUPVK1RE0kbXBAUIPUMX7nZ5s4shItKrFARFRCS1FAR7QSuvGCEikmYKgr1AK0aIiLQmfTNLUSNvmapeoSLSp6l3aMqVmy+0o7PiSeNFRNqOgmCCig2Ub2WaL1RE0kzNoQkqNlBeRERak4KgiIiklppDRURanJltAJwI/AswGlgMzAIucve7Sxy3BmEl992BMcAS4EHgbHefVWUZJgN/iHfXc/d5ZdJfARwMzHD3ydWcqzcpCCak1a4HZju8lNO9pGcazRcq0jrM7MvAb4GBwHvAs8CahMC2u5md6+7HFzhuLULAmxCPe5IQCPcEdjOzw939st55Fq1LQTAhxa4HNmugfD0dXjRfqEhrMLORwK8JAfBa4Eh3fzvu2w+4CjjOzGa5+w15h19HCIB3A1939yVm1gkcD/wYuNjMHnb353rp6bQkXRNsMA2UF5E6HAoMB+YBU7IBEMDdrwEujXePzD0oNl1uBywF9nP3JfGYbnc/C/glsApwcoPL3/L07Swi0rpeJtQEf+ruHxTY/2Tcjst7fErc3uzuCwscd3Hc7mFmqb7uoeZQEWma1zY5px8wotnlSMji0c8e35Vkhu5+HaFZs5gt4vaFvMcnxe3MIsc9CqwABsc8/lhrGauR17mmnKnufkXjShMoCDZQq02cPfLWQ+gcXv5HnzrESG94bZNz9gEuBEY1uywJeeO1Tc45ZvSzx09v9InMbBjw78BUQjA7K2dfJ7B+vPtSoePdfbmZLSDUIDekl4Ig8Dahs04x6wNrx9uvNL44CoIN1WrXAzuHD1SHF2kllwKrN7sQCRpFeE4NC4JmthdwGjAR+ATwKnCUuz+Qk2w4K7/bS3VZX0QIgiMbUNSC3H02sE2hfWa2CfBQvPtf7n5fb5RJQVBEpH1sCXwq5/5wYFcze8Dd34mP5f7SXVYir+z4qFp+Gb9sZjUcVlgcznEH4UfR9cC0xDIvo3WqKSKSNocBbzS7EAl6g/CcGukCYAjwSULnl/cJPUPvM7Nspaba65KZGsrxGKFZs9RfRe+tmQ0CbiXUSh8FDnb3WspUE9UE65Tp7qZr6aKWGigv0g5GP3v89Nc2OedG1DGmYu7+13jzXeBKM5sFPEHo3HIAcAVhWETWgBLZZS/+91xCprx9qpgxplSaTuAa4AuEpt3d3b1U7TVxCoJ16lq6iLnHjm52MUTaUgwa+gVZI3d3M7sR2A+YzMog+AHhmmGpnnnZa4HNrI3/D2Hmm3eB3dz9td4ugJpDRURalJmNMLPPx5ljipkft6MhDIgHPD42vki+qxCaVAHmJFDUqpnZtwg9XLuB/d39iWaUQ0GwDWW6M3Qvfq/0X4E5QUWk7fyJcP3tkBJpsgPlF+Q89kjcTqKwLQktgcuA2fUUsBZm9hXgvHj3RHe/ubfLkKXm0DakhXBFUuP3hI4vh5rZ/7r78tydZjaeMCE2hM4lWdcTOunsbWYnuPvivHyPitvr3L1XfzGb2ecJs+B0Ape7+zm9ef58qgnWodjKEeNOf4r1f/JaSw2UF5G2dA6hB+gGwDW5zaJmtjnwO0IHlweA3NrUvYQemqsDN8UhCJhZp5mdAOwPLCdnkH1vMLN1CcF6MKHsR/Tm+QtRTbAOxVaO6Dd0TfqvtmYTSiQifYm7zzWzrxGmTtsb+IqZOaHX54Yx2Sxgr9xhBe6eMbODgBnAtsB8M3saWIdw7TBDmJast1eQuJCVM8J0ALfEIRL9CqSd7e7fanSBFARFRFqYu99mZpsRlkDaEdiYMKxhJmE1iMvym0njcXNjbfFkYDdg03jcXYRFdSudwzNJq+Xc3rFM2hWNLEhWRybTa2MSW46ZPTNx4sRNbr/99pqOX/GPNwsOj1j/J681tCbYvfi9HtcEK5kXtGPYQDo6OxpWLkkdfZik7akm2GayPUPzaV5QEZHqqWNMwhq9ckTmrfdZuNvlDctfRCRNFAQT1morR4iISHH6thYRkdRSEBQRkdRSEOwDRt4yVavBi4jUQEGwjRTtGTpikIY+iIjUQEGwjahnqIhIshQERUQktRQERUQktRQERUQktRQE25x6hoqI1E5zh7agTHeGzFs917kstFq8eoaKiNROQbAFaeV4EZHeoeZQERFJLQVBERFJLQVBERFJLV0TbAH5HWEKdYAptnK8eoaKiNROQbAFVNIRRivHi4gkT82hIiKSWgqCIiKSWgqCIiKSWrom2KLyO8KoA4yISPIUBHtZoSnRCk6Hpo4wIiINpyDYyzQlmohI69A1QRERSS0FQRERSS0FwRplurvpeufNZhdDRETqoGuCNepauoj5J2+aSF6FpkRTb1ARkcZTEGyAYovignqCioi0EgXBBlAPUBGR9qBrgiIikloKggkad/pT9BuyRrOLISIiFVIQTFC/oWvS0amXVESkXeiaYC8ptiguqCeoiEizJBYEzWwQcALwDWA94B3gceA8d7+zhvzGAy+XSfYXd/9stXk3SqY7Q/fi9+he/F6PfeoBKiLSehIJgmY2GLgX2ApYDjwNrAHsCOxoZtPc/bQqs90sbhcDzxVJ80INxa1LprubrqWLCg6Uz7z9AW/sfGVvF0lERGqUVE3wIkIAfALYzd1fBTCzA4HLgGlm9qC731NFntkgeL27H5VQOevWtXQRc48d3exiiIhIAuruxWFmE4ADgG5g/2wABHD3q4Efx7vTqsw6GwSfqreMIiIihSTRlfFAoB/wsLs/W2D/xXG7tZmtW0W+2SD4dD2FExERKSaJIDgpbmcW2unuC4D58e52lWRoZkOA9ePdtq4JjrxlKqNmHq0eoCIiLSiJa4IT4/alEmnmAeOADSvM8zNAB/A3YE0zOw7YnFDeOcCv3f3BmkrbAONOf4rOwSPhzY4e+zpHDFKvUBGRFpVEEBwVt6XWFVoUtyMrzDPbFDoceJbQ3Jr1L8DRZnYZcKS7L6+0oI3Sb+iadK4YzBu7a75QEZF2kkQQzFZzlpVIk106odIqUTYIDgAuAS4AXgRGE65BngocAnwAfLNcZmb2TJFdEyosj4iI9EFJBMEuKr+2mKkw3R9jnrPd/Wc5j78CnG5m84BfAkea2UXuXizIiYiIFJVEEFxKaLYcUCJNtldIz6lUCnD3XwG/KrXfzE4FNgB2B0oGQXf/VKHHYw1xk0rKJCIifU8SvUMXxm2p5ROy1wLfSOB8WbPjdr0E80zUyFumqleoiEgLSyIIZqc0G18iTXbfnEozNbNVzKxfiSTZsje3Y0ymg+4lywrPFzpiEB2dPXuMiohIa0iiOfQRYDdWjhf8GDMbA2QHyT9ULjMzG04YbjEc2Au4sUjSzeO20AD9XtOxYhCLd762mUUQEZEaJVETnB63k83MCuw/Mm5nuPu8cpm5+xLgtXh3SqE0ZrY3oWfnhxQPkiIiIiXVHQTd/QXgGsJYvhvNLDt4HjM7APhuvPuj/GPNbIKZbWRma+ftOjNuv2JmZ5rZJ3KO2Ru4PN49293/Vu9zEBGRdEpqFYljgU3j3/Nm9hShOXNc3H9ykRUk7o1priSn1ufuV5vZZ4DjgBMJg+NfANYC1onJfk4YLygiIlKTJJpDcfdFhGuCpxE6v2xM6C06A9jL3c+oIc/jCbPD3EwYbP8ZYBXgNmBXdz/M3buTKH/SNF+oiEh7SGxleXd/l7Bc0rQqjhlfZv89QDVrEPauTAcdKwb3eFjzhYqItIdEaoJp1bFiEEOe+k6ziyEiIjVSEBQRkdRSEBQRkdRSEBQRkdRSEKxVkU4xmi9URKR9KAhWIdPdTdc7Ye3gYp1iNF+oiEj7UBCsQtfSRcw/edNmF0NERBKiICgiIqmlICgiIqmlIJggdYoREWkvCoIJUqcYEZH2oiAoIiKppSAoIiKppSAoIiKppSBYodyB8iIi0jcoCFZIA+VFRPoeBUEREUktBUEREUktBUEREUktBUEREUktBUEREUktBUEREUmt/s0uQNvJdNCxYlDBVeVFRKS9KAhWqWPFIIbO/n6ziyEiIglQc6iIiKSWgqCIiKSWgqCIiKSWrgkmYOQtU8OCulpVXkSkrSgIJqBzxCA6RwxqdjFERKRKag4VEZHUUhAUEZHUUhAUEZHUUhAUEZHUUhAUEZHUUhAUEZHUUhAUEZHUUhCsQKa7m6533mx2MUREJGEKghXoWrqI+Sdv2uxiiIhIwhQERUQktRQERUQktRQERUQktRQERUQktRQERUQktRQERUQktRQERUQktRQEq5HpoGPF4GaXQkREEqIgWIWOFYMY8tR3ml0MERFJiIKgiIikloKgiIikloKgiIikloJgnUbeMpWOYQObXQwREamBgmCdOkcMoqOzo9nFEBGRGigIiohIaikIiohIaikIiohIaikIiohIaikIiohIaikIVkrzhoqI9DkKghXSvKEiIn2PgqCIiKSWgqCIiKSWgqCIiKSWgmAdNG+oiEh7UxCsg+YNFRFpbwqCIiKSWgqCIiKSWgqCIiKSWgqCIiKSWv2bXYBWlunupmvpIrreebPZRRERkQZQECyha+ki5h47GoAONG+oiEhfo+ZQERFJLQVBERFJLQVBERFJLQVBERFJLQXBSmhBXRGRPklBsAJaUFdEpG9SEBQRkdRSEBQRkdRSEBQRkdRSEKyRFtQVEWl/iU2bZmaDgBOAbwDrAe8AjwPnufudNea5LvADYCdgFPAmcC9wprs/l0S5S8TZEI0AAAvKSURBVMl0Z+hYPrhgz1AtqCsi0v4SqQma2WDgPuBUYH3gGeBdYEfgDjM7tYY8Dfgz8G/AEOAvwADgQODPZvblJMpeSubtDxg6+/vqGSoi0kcl1Rx6EbAV8AQwwd0/5+7jgIOAFcA0M9uh0szMrD9wG7AGcDWwtrt/AVgbuJAQDK81szUSKr+IiKRQ3UHQzCYABwDdwP7u/mp2n7tfDfw43p1WRbYHABOBV4BD3f39mN+HwLHAH4FhwLfrLb+IiKRXEjXBA4F+wMPu/myB/RfH7dbxGl8lpsTt1THwfcTdM8Al8e6+VZZVRETkI0kEwUlxO7PQTndfAMyPd7crl5mZdQJblsoTeDBu1zezsRWWMxHDfr0Ho2YerZ6hIiJ9QBJBcGLcvlQizby43bCC/NYBshGmWJ6vAl1V5JmYzmED1DNURKSPSCIIjorbN0ukWRS3I6vIr2ie7t4FvF1FniIiIj0kMU5wUNwuK5Hm/by0leSXWJ5m9kyRXRu98sor7LLLLgV3ZpYvp6vr7Y891u+wu+lYZZVypxTp81588cVb3H23ZpdDpB5JBMEuKq9RZirMrxqV5FlM94cffvjuiy+++GqZdBPi9iXmv1XH6aQNrXzvRaTPSSIILgWGE8buFZO9xvdehfllDaB4bbDiPN39UxWct6hsTbLefKT96L0X6duSuCa4MG5LDVzPXrd7o4r8iuYZB9OvXkWeIiIiPSQRBLNzeI4vkSa7b065zNz9b6zs9FIsz7GEsYkV5SkiIlJIEkHwkbidVGinmY0BsoPkH6owz0dL5Qn8U9zOj0FTRESkakkEwelxOzlOep3vyLid4e7zKszz+ridamarlsjzigrzExER6aHuIOjuLwDXEJonbzSz7OB5zOwA4Lvx7o/yjzWzCWa2kZmtnbfrl4TeeOsD15jZ0Jh+VTP7CbANocn0gnrLLyIi6dWRydQzwiCIqzn8AdiUMMThKUKP0XExycnufkaB4+bFNFe6+5S8fV8A7iZ0gFkKPE8IiiOAD4Gd3P0PdRdeRERSK5GllNx9EeH63WmEjiobE3p2zgD2KhQAK8jzT8BmwC+At+LtbuAGYCsFQBERqVciNUEREZF2lNSiuiIiIm1HQVBERFJLQVBERFJLQVBERFJLQVBERFIriVUk+iQzGwScAHwDWA94B3gcOM/d72xm2aR2ZnYEcDFwmLv/vEiamt77OF72FGB3YAywBHgQONvdZyX5PEQkGaoJFmBmg4H7gFMJA/SfAd4FdgTuMLNTm1g8qVGcgOGcMmlqeu/NbC3CPLr/AawFPElY63JPYKaZHZLQ0xCRBCkIFnYRsBXwBDDB3T/n7uOAg4AVwDQz26GZBZTqmNlk4HfA0DJJa33vryMswHs3MMbdtwA+CZxImFLwYjPbOInnIiLJURDMY2YTgAMIs9Ps7+4frTrv7lcDP453p/V+6aRaZjbAzKYB9xCm8iuVtqb3PgbY7QjT++3n7kviMd3ufhZhLtxVgJPrf0YikiQFwZ4OJPxyf9jdny2w/+K43drM1i2wX1pEnMx9DqFpE8L1uvklDqn1vZ8Stze7+0J6yh63h5kNrKTsItI7FAR7yq5hOLPQTndfwMov0u16pURSqzGEBZhnEeabPb1M+lrf+5LHEdbHXAEMBrYoUwYR6UUKgj1ll4J6qUSaeXG7YWOLInX6K7CLu09y98crSF/1e29mnYQONEWPc/flwILc40SkNWiIRE+j4vbNEmkWxe3IBpdF6uDuLwIvVnFILe/9cFb+H5U7bhz6zIi0FNUEexoUt8tKpHk/L630DbW897mfAX1mRNqMgmBPXVWk1TpUfUst7301x+QeJyItQEGwp6VxO6BEmmwPv/caXBbpXbW890tz9ukzI9JmFAR7ynZxX6NEmux1nTcaXBbpXbW890uBD2o4TkRagIJgT8/F7fgSabL75jS0JNLbqn7v3b0b8FLHmdkqhNljPjpORFqDgmBPj8TtpEI7zWwMkB0o/VCvlEh6S63vfcnjgC0JPUiXAbPrLKOIJEhBsKfpcTvZzKzA/iPjdoa7z+udIkkvqfW9vz5u9zazEQWOOypur3P39wvsF5EmURDM4+4vANcQps+6MU69BYCZHQB8N979UROKJw1Ux3t/L2HJpNWBm+KKEphZp5mdAOwPLAfOauwzEJFqabB8YccCm8a/583sKcKg6HFx/8nufk+zCicNVfV77+4ZMzsImAFsC8w3s6eBdYDRhGERU939OUSkpagmWIC7LyJc3zmN0JFhY0LPvxnAXu5+RhOLJw1U63vv7nOBzYHzCFOkbUoYMnEXsL27/6rxpReRanVkMhq7KyIi6aSaoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaWUpKqmFm5GddXAO8AcwkrKJzr7m8lXIZBwKjchW3NbApwObDA3cckeT4R6btUE5RavUBYSDb/74m4//PAycAzZjY2qZOa2X6EJY52SCpPEUkv1QSlVme4+xWFdphZJ7AfcBnwSUINLamgdQZhsdp8vwVmEVZwFxGpiIKgJM7du4FfmtmGwPeB7c1sA3d/oYHnfBt4u1H5i0jfpOZQaaRbc25/ummlEBEpQjVBaaTunNsd+TvN7LPAN4FtCU2cA4AlwOPAZe7+m5y004BTcw6/1MwuBU5z92nlOsaY2fbxXP8ErEGoNT4GXOruN9bxHEWkjakmKI309bjtBh7N3WFmRxGC3WHAWsCLwEvA6sDOwHQzOz3nkFcIHW8+iPdfjPdfKVcIM7sAuAf4KrAKofPOh8BOwA1mdp2ZrVLD8xORNqcgKIkzswFmdizwnfjQZe7+15z9GwDnEz5/pwBrufvn3H1jQkea62PS481sOIC7X+bu2wCvxX1nufs27n5ZmbL8J3AMYejG0YShFVu6+zrA14ClcXtu3U9cRNqOmkOlVt8zs0PzHusEVgMmAp+Ij10HHJuXbkdCUHrS3XNre7j7YjM7jhCYVgGM0OuzamY2gBBkAX7g7j/NO9d0M+sH/Bo42sz+N3fsoYj0fQqCUqsN4l8hLwB3Ate6+8P5O939IuAiMxtY5Pj3cm4PqqOM2wLDCAH3oiJpriPUAtcBdgUurON8ItJmFASlVlOz4wTjuMDxwPHAkYQmzRcKBcA8H5rZloSeoxPi36bARjlp6mmyz+bzgrv/o1ACd8+Y2Z8JQdDqOJeItCEFQalbHBc4FzjKzF4n9OK8wMwGuHvBa21mdjBwJrB23q6XgV8QOszUa7W4LTd+MBsghyZwThFpI+oYI0k7Dbg73j4rDk34mBgAryAEwLuAI4CtgRHuvj6hA0sS3onb1cukG56XXkRSQjVBSVRsXpwKPEMIPlea2SZ5zZEnxe1V7n5wgWySmgD7+bjdwMxWK9QkGptyN493GzajjYi0JtUEJXHuvgA4Lt5dBzg7L8l6cft4kSxye53m/1DLDsDvMfi+gJmEwff9KV67/AahRpoh1EpFJEUUBKVRfgE8EG8fbmbb5uzL1tCOMLOPJsM2s9XizDAn5qTN7x26NG7HlSuAu79HuO4I8F9m9s1Y88ueby/gknj3/9x9Trk8RaRvURCUhnD3DHA4YYaXDsI0Z9mxgycTanSbAHPN7EkzexLIdqqZR5g9Bno2jc6O2++a2WwzO4XSzgV+RqgNXgS8bmaPmNkC4DfAkLj9j5qeqIi0NQVBaRh3dyA7GN6AH8THbwO+ANxEmAFmY2Bd4CnC9cLNgF/F476Sl+1xwA3AuzHPjcuUIePu3yQM0L8J6GLlNcBbgT3cfR93X1bbsxSRdtaRyZRbKFxERKRvUk1QRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERSS0FQRERS6/8DLfXQADbVPkAAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<Figure size 480x300 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"density = True\n",
|
|
"cumulative = True\n",
|
|
"histtype = 'step'\n",
|
|
"lw = 2\n",
|
|
"if perform_zscore:\n",
|
|
" bins = {\n",
|
|
" 'stim_energy': np.arange(0, .7, .01),\n",
|
|
" 'stim_half_width': np.arange(0, 10, .5),\n",
|
|
" 'stim_p_max': np.arange(0, 4, .01),\n",
|
|
" 'stim_strength': np.arange(0, 160, 1)\n",
|
|
" }\n",
|
|
"else:\n",
|
|
" bins = {\n",
|
|
" 'stim_energy': np.arange(0, .008, .0001),\n",
|
|
" 'stim_half_width': np.arange(0, 0.5, .001),\n",
|
|
" 'stim_p_max': np.arange(0, .06, .0001),\n",
|
|
" 'stim_strength': np.arange(0, 160, 1)\n",
|
|
" }\n",
|
|
"xlabel = {\n",
|
|
" 'stim_energy': 'Energy (dB)',\n",
|
|
" 'stim_half_width': '(Hz)',\n",
|
|
" 'stim_p_max': 'Peak PSD (dB/Hz)',\n",
|
|
" 'stim_strength': 'Ratio',\n",
|
|
"}\n",
|
|
"# key = 'theta_energy'\n",
|
|
"# key = 'theta_peak'\n",
|
|
"for key in bins:\n",
|
|
" results[key] = list()\n",
|
|
" fig = plt.figure(figsize=(3.2,2))\n",
|
|
" plt.suptitle(key)\n",
|
|
" legend_lines = []\n",
|
|
" for color, query, label in zip(colors[1::2], queries[1::2], labels[1::2]):\n",
|
|
" values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]\n",
|
|
" results[key].append(values.rename({key: label}, axis=1))\n",
|
|
" values[key].hist(\n",
|
|
" bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
|
|
" histtype=histtype, color=color)\n",
|
|
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n",
|
|
" \n",
|
|
" plt.legend(\n",
|
|
" handles=legend_lines,\n",
|
|
" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.grid(False)\n",
|
|
" plt.xlim(right=bins[key].max() - bins[key].max()*0.025)\n",
|
|
" despine()\n",
|
|
" plt.xlabel(xlabel[key])\n",
|
|
" figname = f'lfp-psd-histogram-{key}'\n",
|
|
" fig.savefig(\n",
|
|
" output_path / 'figures' / f'{figname}.png', \n",
|
|
" bbox_inches='tight', transparent=True)\n",
|
|
" fig.savefig(\n",
|
|
" output_path / 'figures' / f'{figname}.svg', \n",
|
|
" bbox_inches='tight', transparent=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from functools import reduce"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for key, val in results.items():\n",
|
|
" df = reduce(lambda left,right: pd.merge(left, right, on='entity_date_side', how='outer'), val)\n",
|
|
" results[key] = df.drop('entity_date_side',axis=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def summarize(data):\n",
|
|
" return \"{:.1e} ± {:.1e} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
|
|
"\n",
|
|
"\n",
|
|
"def MWU(df, keys):\n",
|
|
" '''\n",
|
|
" Mann Whitney U\n",
|
|
" '''\n",
|
|
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
|
|
" df[keys[0]].dropna(), \n",
|
|
" df[keys[1]].dropna(),\n",
|
|
" alternative='two-sided')\n",
|
|
"\n",
|
|
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
|
|
"\n",
|
|
"\n",
|
|
"def PRS(df, keys):\n",
|
|
" '''\n",
|
|
" Permutation ReSampling\n",
|
|
" '''\n",
|
|
" pvalue, observed_diff, diffs = permutation_resampling(\n",
|
|
" df[keys[0]].dropna(), \n",
|
|
" df[keys[1]].dropna(), statistic=np.median)\n",
|
|
"\n",
|
|
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
|
|
"\n",
|
|
"\n",
|
|
"def wilcoxon(df, keys):\n",
|
|
" dff = df.loc[:,[keys[0], keys[1]]].dropna()\n",
|
|
" statistic, pvalue = scipy.stats.wilcoxon(\n",
|
|
" dff[keys[0]], \n",
|
|
" dff[keys[1]],\n",
|
|
" alternative='two-sided')\n",
|
|
"\n",
|
|
" return \"{:.2f}, {:.3f}, ({})\".format(statistic, pvalue, len(dff))\n",
|
|
"\n",
|
|
"\n",
|
|
"def paired_t(df, keys):\n",
|
|
" dff = df.loc[:,[keys[0], keys[1]]].dropna()\n",
|
|
" statistic, pvalue = scipy.stats.ttest_rel(\n",
|
|
" dff[keys[0]], \n",
|
|
" dff[keys[1]])\n",
|
|
"\n",
|
|
" return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n",
|
|
"\n",
|
|
" \n",
|
|
"def normality(df, key):\n",
|
|
" statistic, pvalue = scipy.stats.normaltest(\n",
|
|
" df[key].dropna())\n",
|
|
"\n",
|
|
" return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n",
|
|
"\n",
|
|
"\n",
|
|
"def shapiro(df, key):\n",
|
|
" statistic, pvalue = scipy.stats.shapiro(\n",
|
|
" df[key].dropna())\n",
|
|
"\n",
|
|
" return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n",
|
|
"\n",
|
|
"def rename(name):\n",
|
|
" return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Theta energy</th>\n",
|
|
" <th>Theta peak</th>\n",
|
|
" <th>Theta freq</th>\n",
|
|
" <th>Theta half width</th>\n",
|
|
" <th>Stim energy</th>\n",
|
|
" <th>Stim half width</th>\n",
|
|
" <th>Stim p max</th>\n",
|
|
" <th>Stim strength</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>11 Hz</th>\n",
|
|
" <td>9.1e-04 ± 9.0e-05 (44)</td>\n",
|
|
" <td>3.9e-04 ± 5.1e-05 (44)</td>\n",
|
|
" <td>7.5e+00 ± 1.5e-01 (44)</td>\n",
|
|
" <td>5.5e+00 ± 5.6e-01 (42)</td>\n",
|
|
" <td>4.3e-04 ± 4.7e-05 (44)</td>\n",
|
|
" <td>1.8e-01 ± 1.3e-02 (44)</td>\n",
|
|
" <td>3.9e-03 ± 4.5e-04 (44)</td>\n",
|
|
" <td>5.8e+00 ± 7.3e-01 (44)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>30 Hz</th>\n",
|
|
" <td>5.7e-04 ± 7.0e-05 (34)</td>\n",
|
|
" <td>2.5e-04 ± 4.3e-05 (34)</td>\n",
|
|
" <td>7.7e+00 ± 2.1e-01 (34)</td>\n",
|
|
" <td>6.7e+00 ± 8.4e-01 (29)</td>\n",
|
|
" <td>1.0e-03 ± 2.1e-04 (34)</td>\n",
|
|
" <td>1.5e-01 ± 1.6e-03 (34)</td>\n",
|
|
" <td>1.0e-02 ± 2.0e-03 (34)</td>\n",
|
|
" <td>2.2e+01 ± 4.5e+00 (34)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Baseline I</th>\n",
|
|
" <td>2.3e-03 ± 2.2e-04 (46)</td>\n",
|
|
" <td>1.8e-03 ± 1.9e-04 (46)</td>\n",
|
|
" <td>7.7e+00 ± 6.3e-02 (46)</td>\n",
|
|
" <td>1.2e+00 ± 2.0e-01 (45)</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Baseline II</th>\n",
|
|
" <td>2.3e-03 ± 2.4e-04 (32)</td>\n",
|
|
" <td>1.8e-03 ± 2.3e-04 (32)</td>\n",
|
|
" <td>8.1e+00 ± 4.7e-02 (32)</td>\n",
|
|
" <td>9.1e-01 ± 3.9e-02 (31)</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Normality 11 Hz</th>\n",
|
|
" <td>27.15, 0.000</td>\n",
|
|
" <td>57.06, 0.000</td>\n",
|
|
" <td>1.61, 0.447</td>\n",
|
|
" <td>11.89, 0.003</td>\n",
|
|
" <td>16.86, 0.000</td>\n",
|
|
" <td>82.50, 0.000</td>\n",
|
|
" <td>14.96, 0.001</td>\n",
|
|
" <td>15.95, 0.000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Normality 30 Hz</th>\n",
|
|
" <td>30.23, 0.000</td>\n",
|
|
" <td>43.50, 0.000</td>\n",
|
|
" <td>4.85, 0.088</td>\n",
|
|
" <td>5.64, 0.060</td>\n",
|
|
" <td>19.73, 0.000</td>\n",
|
|
" <td>9.69, 0.008</td>\n",
|
|
" <td>19.72, 0.000</td>\n",
|
|
" <td>25.38, 0.000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Normality Baseline I</th>\n",
|
|
" <td>41.33, 0.000</td>\n",
|
|
" <td>32.43, 0.000</td>\n",
|
|
" <td>25.20, 0.000</td>\n",
|
|
" <td>74.91, 0.000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Normality Baseline II</th>\n",
|
|
" <td>13.17, 0.001</td>\n",
|
|
" <td>20.78, 0.000</td>\n",
|
|
" <td>0.96, 0.618</td>\n",
|
|
" <td>13.33, 0.001</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Wilcoxon 11 Hz 30 Hz</th>\n",
|
|
" <td>118.00, 0.006, (32)</td>\n",
|
|
" <td>134.00, 0.015, (32)</td>\n",
|
|
" <td>164.00, 0.247, (32)</td>\n",
|
|
" <td>143.00, 0.269, (27)</td>\n",
|
|
" <td>173.00, 0.089, (32)</td>\n",
|
|
" <td>25.00, 0.000, (32)</td>\n",
|
|
" <td>157.00, 0.045, (32)</td>\n",
|
|
" <td>105.00, 0.003, (32)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Wilcoxon 11 Hz Baseline II</th>\n",
|
|
" <td>11.00, 0.000, (32)</td>\n",
|
|
" <td>5.00, 0.000, (32)</td>\n",
|
|
" <td>60.00, 0.000, (32)</td>\n",
|
|
" <td>3.00, 0.000, (30)</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Wilcoxon Baseline I 11 Hz</th>\n",
|
|
" <td>42.00, 0.000, (44)</td>\n",
|
|
" <td>16.00, 0.000, (44)</td>\n",
|
|
" <td>329.50, 0.190, (44)</td>\n",
|
|
" <td>14.00, 0.000, (41)</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Wilcoxon Baseline I 30 Hz</th>\n",
|
|
" <td>12.00, 0.000, (32)</td>\n",
|
|
" <td>5.00, 0.000, (32)</td>\n",
|
|
" <td>221.50, 0.427, (32)</td>\n",
|
|
" <td>8.00, 0.000, (27)</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Wilcoxon Baseline I Baseline II</th>\n",
|
|
" <td>264.00, 1.000, (32)</td>\n",
|
|
" <td>256.00, 0.881, (32)</td>\n",
|
|
" <td>0.00, 0.000, (32)</td>\n",
|
|
" <td>203.00, 0.544, (30)</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Wilcoxon Baseline II 30 Hz</th>\n",
|
|
" <td>17.00, 0.000, (32)</td>\n",
|
|
" <td>9.00, 0.000, (32)</td>\n",
|
|
" <td>129.50, 0.020, (32)</td>\n",
|
|
" <td>11.00, 0.000, (27)</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Theta energy \\\n",
|
|
"11 Hz 9.1e-04 ± 9.0e-05 (44) \n",
|
|
"30 Hz 5.7e-04 ± 7.0e-05 (34) \n",
|
|
"Baseline I 2.3e-03 ± 2.2e-04 (46) \n",
|
|
"Baseline II 2.3e-03 ± 2.4e-04 (32) \n",
|
|
"Normality 11 Hz 27.15, 0.000 \n",
|
|
"Normality 30 Hz 30.23, 0.000 \n",
|
|
"Normality Baseline I 41.33, 0.000 \n",
|
|
"Normality Baseline II 13.17, 0.001 \n",
|
|
"Wilcoxon 11 Hz 30 Hz 118.00, 0.006, (32) \n",
|
|
"Wilcoxon 11 Hz Baseline II 11.00, 0.000, (32) \n",
|
|
"Wilcoxon Baseline I 11 Hz 42.00, 0.000, (44) \n",
|
|
"Wilcoxon Baseline I 30 Hz 12.00, 0.000, (32) \n",
|
|
"Wilcoxon Baseline I Baseline II 264.00, 1.000, (32) \n",
|
|
"Wilcoxon Baseline II 30 Hz 17.00, 0.000, (32) \n",
|
|
"\n",
|
|
" Theta peak \\\n",
|
|
"11 Hz 3.9e-04 ± 5.1e-05 (44) \n",
|
|
"30 Hz 2.5e-04 ± 4.3e-05 (34) \n",
|
|
"Baseline I 1.8e-03 ± 1.9e-04 (46) \n",
|
|
"Baseline II 1.8e-03 ± 2.3e-04 (32) \n",
|
|
"Normality 11 Hz 57.06, 0.000 \n",
|
|
"Normality 30 Hz 43.50, 0.000 \n",
|
|
"Normality Baseline I 32.43, 0.000 \n",
|
|
"Normality Baseline II 20.78, 0.000 \n",
|
|
"Wilcoxon 11 Hz 30 Hz 134.00, 0.015, (32) \n",
|
|
"Wilcoxon 11 Hz Baseline II 5.00, 0.000, (32) \n",
|
|
"Wilcoxon Baseline I 11 Hz 16.00, 0.000, (44) \n",
|
|
"Wilcoxon Baseline I 30 Hz 5.00, 0.000, (32) \n",
|
|
"Wilcoxon Baseline I Baseline II 256.00, 0.881, (32) \n",
|
|
"Wilcoxon Baseline II 30 Hz 9.00, 0.000, (32) \n",
|
|
"\n",
|
|
" Theta freq \\\n",
|
|
"11 Hz 7.5e+00 ± 1.5e-01 (44) \n",
|
|
"30 Hz 7.7e+00 ± 2.1e-01 (34) \n",
|
|
"Baseline I 7.7e+00 ± 6.3e-02 (46) \n",
|
|
"Baseline II 8.1e+00 ± 4.7e-02 (32) \n",
|
|
"Normality 11 Hz 1.61, 0.447 \n",
|
|
"Normality 30 Hz 4.85, 0.088 \n",
|
|
"Normality Baseline I 25.20, 0.000 \n",
|
|
"Normality Baseline II 0.96, 0.618 \n",
|
|
"Wilcoxon 11 Hz 30 Hz 164.00, 0.247, (32) \n",
|
|
"Wilcoxon 11 Hz Baseline II 60.00, 0.000, (32) \n",
|
|
"Wilcoxon Baseline I 11 Hz 329.50, 0.190, (44) \n",
|
|
"Wilcoxon Baseline I 30 Hz 221.50, 0.427, (32) \n",
|
|
"Wilcoxon Baseline I Baseline II 0.00, 0.000, (32) \n",
|
|
"Wilcoxon Baseline II 30 Hz 129.50, 0.020, (32) \n",
|
|
"\n",
|
|
" Theta half width \\\n",
|
|
"11 Hz 5.5e+00 ± 5.6e-01 (42) \n",
|
|
"30 Hz 6.7e+00 ± 8.4e-01 (29) \n",
|
|
"Baseline I 1.2e+00 ± 2.0e-01 (45) \n",
|
|
"Baseline II 9.1e-01 ± 3.9e-02 (31) \n",
|
|
"Normality 11 Hz 11.89, 0.003 \n",
|
|
"Normality 30 Hz 5.64, 0.060 \n",
|
|
"Normality Baseline I 74.91, 0.000 \n",
|
|
"Normality Baseline II 13.33, 0.001 \n",
|
|
"Wilcoxon 11 Hz 30 Hz 143.00, 0.269, (27) \n",
|
|
"Wilcoxon 11 Hz Baseline II 3.00, 0.000, (30) \n",
|
|
"Wilcoxon Baseline I 11 Hz 14.00, 0.000, (41) \n",
|
|
"Wilcoxon Baseline I 30 Hz 8.00, 0.000, (27) \n",
|
|
"Wilcoxon Baseline I Baseline II 203.00, 0.544, (30) \n",
|
|
"Wilcoxon Baseline II 30 Hz 11.00, 0.000, (27) \n",
|
|
"\n",
|
|
" Stim energy \\\n",
|
|
"11 Hz 4.3e-04 ± 4.7e-05 (44) \n",
|
|
"30 Hz 1.0e-03 ± 2.1e-04 (34) \n",
|
|
"Baseline I NaN \n",
|
|
"Baseline II NaN \n",
|
|
"Normality 11 Hz 16.86, 0.000 \n",
|
|
"Normality 30 Hz 19.73, 0.000 \n",
|
|
"Normality Baseline I NaN \n",
|
|
"Normality Baseline II NaN \n",
|
|
"Wilcoxon 11 Hz 30 Hz 173.00, 0.089, (32) \n",
|
|
"Wilcoxon 11 Hz Baseline II NaN \n",
|
|
"Wilcoxon Baseline I 11 Hz NaN \n",
|
|
"Wilcoxon Baseline I 30 Hz NaN \n",
|
|
"Wilcoxon Baseline I Baseline II NaN \n",
|
|
"Wilcoxon Baseline II 30 Hz NaN \n",
|
|
"\n",
|
|
" Stim half width \\\n",
|
|
"11 Hz 1.8e-01 ± 1.3e-02 (44) \n",
|
|
"30 Hz 1.5e-01 ± 1.6e-03 (34) \n",
|
|
"Baseline I NaN \n",
|
|
"Baseline II NaN \n",
|
|
"Normality 11 Hz 82.50, 0.000 \n",
|
|
"Normality 30 Hz 9.69, 0.008 \n",
|
|
"Normality Baseline I NaN \n",
|
|
"Normality Baseline II NaN \n",
|
|
"Wilcoxon 11 Hz 30 Hz 25.00, 0.000, (32) \n",
|
|
"Wilcoxon 11 Hz Baseline II NaN \n",
|
|
"Wilcoxon Baseline I 11 Hz NaN \n",
|
|
"Wilcoxon Baseline I 30 Hz NaN \n",
|
|
"Wilcoxon Baseline I Baseline II NaN \n",
|
|
"Wilcoxon Baseline II 30 Hz NaN \n",
|
|
"\n",
|
|
" Stim p max \\\n",
|
|
"11 Hz 3.9e-03 ± 4.5e-04 (44) \n",
|
|
"30 Hz 1.0e-02 ± 2.0e-03 (34) \n",
|
|
"Baseline I NaN \n",
|
|
"Baseline II NaN \n",
|
|
"Normality 11 Hz 14.96, 0.001 \n",
|
|
"Normality 30 Hz 19.72, 0.000 \n",
|
|
"Normality Baseline I NaN \n",
|
|
"Normality Baseline II NaN \n",
|
|
"Wilcoxon 11 Hz 30 Hz 157.00, 0.045, (32) \n",
|
|
"Wilcoxon 11 Hz Baseline II NaN \n",
|
|
"Wilcoxon Baseline I 11 Hz NaN \n",
|
|
"Wilcoxon Baseline I 30 Hz NaN \n",
|
|
"Wilcoxon Baseline I Baseline II NaN \n",
|
|
"Wilcoxon Baseline II 30 Hz NaN \n",
|
|
"\n",
|
|
" Stim strength \n",
|
|
"11 Hz 5.8e+00 ± 7.3e-01 (44) \n",
|
|
"30 Hz 2.2e+01 ± 4.5e+00 (34) \n",
|
|
"Baseline I NaN \n",
|
|
"Baseline II NaN \n",
|
|
"Normality 11 Hz 15.95, 0.000 \n",
|
|
"Normality 30 Hz 25.38, 0.000 \n",
|
|
"Normality Baseline I NaN \n",
|
|
"Normality Baseline II NaN \n",
|
|
"Wilcoxon 11 Hz 30 Hz 105.00, 0.003, (32) \n",
|
|
"Wilcoxon 11 Hz Baseline II NaN \n",
|
|
"Wilcoxon Baseline I 11 Hz NaN \n",
|
|
"Wilcoxon Baseline I 30 Hz NaN \n",
|
|
"Wilcoxon Baseline I Baseline II NaN \n",
|
|
"Wilcoxon Baseline II 30 Hz NaN "
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"\n",
|
|
"stat = pd.DataFrame()\n",
|
|
"\n",
|
|
"for key, df in results.items():\n",
|
|
" Key = rename(key)\n",
|
|
" stat[Key] = df.agg(summarize)\n",
|
|
" stat[Key] = df.agg(summarize)\n",
|
|
"\n",
|
|
" for i, c1 in enumerate(df.columns):\n",
|
|
" stat.loc[f'Normality {c1}', Key] = normality(df, c1)\n",
|
|
"# stat.loc[f'Shapiro {c1}', Key] = shapiro(df, c1)\n",
|
|
" for c2 in df.columns[i+1:]:\n",
|
|
"# stat.loc[f'MWU {c1} {c2}', Key] = MWU(df, [c1, c2])\n",
|
|
"# stat.loc[f'PRS {c1} {c2}', Key] = PRS(df, [c1, c2])\n",
|
|
" stat.loc[f'Wilcoxon {c1} {c2}', Key] = wilcoxon(df, [c1, c2])\n",
|
|
"# stat.loc[f'Paired T {c1} {c2}', Key] = paired_t(df, [c1, c2])\n",
|
|
"\n",
|
|
"stat.sort_index()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"stat.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n",
|
|
"stat.to_csv(output_path / \"statistics\" / \"statistics.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Plot PSD"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"psd = pd.read_feather(output_path / 'data' / 'psd.feather')\n",
|
|
"freqs = pd.read_feather(output_path / 'data' / 'freqs.feather')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"freq = freqs.T.iloc[0].values\n",
|
|
"\n",
|
|
"mask = (freq < 49)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 750x300 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
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"\n",
|
|
"fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
|
|
"axs = axs.repeat(2)\n",
|
|
"for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
|
|
" selection = [\n",
|
|
" f'{r.action}_{r.channel_group}' \n",
|
|
" for i, r in lfp_results_hemisphere.query(query).iterrows()]\n",
|
|
" values = psd.loc[mask, selection].to_numpy()\n",
|
|
" values = 10 * np.log10(values)\n",
|
|
" plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])\n",
|
|
"# ax.set_title(titles[i])\n",
|
|
" ax.set_xlabel('Frequency Hz')\n",
|
|
" ax.legend(frameon=False)\n",
|
|
"axs[0].set_ylabel('PSD (dB/Hz)')\n",
|
|
"# axs[0].set_ylim(-31, 1)\n",
|
|
"despine()\n",
|
|
"\n",
|
|
"figname = 'lfp-psd'\n",
|
|
"fig.savefig(\n",
|
|
" output_path / 'figures' / f'{figname}.png', \n",
|
|
" bbox_inches='tight', transparent=True)\n",
|
|
"fig.savefig(\n",
|
|
" output_path / 'figures' / f'{figname}.svg', \n",
|
|
" bbox_inches='tight', transparent=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Store results in Expipe action"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"action = project.require_action(\"stimulus-lfp-response\" + zscore_str)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/statistics/statistics.tex',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/statistics/statistics.csv',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/data/psd.feather',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/data/freqs.feather',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-stim_energy.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-stim_strength.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-theta_peak.svg',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-theta_freq.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-theta_energy.svg',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-theta_freq.svg',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.svg',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.svg',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-theta_energy.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-theta_peak.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.svg',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd.svg',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/.ipynb_checkpoints/lfp-psd-histogram-theta_energy-checkpoint.png',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-stim_energy.svg',\n",
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-no-zscore/data/figures/lfp-psd-histogram-stim_strength.svg']"
|
|
]
|
|
},
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"copy_tree(output_path, str(action.data_path()))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-lfp-response.ipynb\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|