1620 lines
481 KiB
Plaintext
1620 lines
481 KiB
Plaintext
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{
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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": 2,
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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 statsmodels\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": 3,
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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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"zscore_str = \"-no-zscore\"\n",
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"# zscore_str = \"\"\n",
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"\n",
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"# stim_loc = 'mec'\n",
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"stim_loc = 'ms'\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": 4,
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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\" + '-' + stim_loc + 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": 5,
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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": 6,
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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": 7,
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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": 8,
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"if stim_loc == 'ms':\n",
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" lfp_results = lfp_results.query('stim_location!=\"mecl\" and stim_location!=\"mecr\"')\n",
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"elif stim_loc == 'mec':\n",
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" lfp_results = lfp_results.query('stim_location!=\"ms\"')\n",
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"else:\n",
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" raise AssertionError('')"
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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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"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": 11,
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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_bandpower']"
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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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{
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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>1.401239</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>3.920680</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 1.401239\n",
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"691 1833-010719-2-1 3 0.003974 3.920680\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": 12,
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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": 13,
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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": 14,
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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": 15,
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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": 16,
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"from functools import reduce"
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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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"keys = [\n",
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" 'theta_bandpower',\n",
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" 'theta_relpower',\n",
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" 'theta_relpeak',\n",
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" 'theta_peak',\n",
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" 'theta_freq',\n",
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" 'theta_half_width',\n",
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" 'stim_bandpower',\n",
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" 'stim_relpower',\n",
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" 'stim_relpeak',\n",
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" 'stim_half_width',\n",
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" 'stim_p_max',\n",
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" 'stim_strength',\n",
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"]\n",
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"\n",
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"results = {}\n",
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"for key in keys:\n",
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" results[key] = list()\n",
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" for query, label in zip(queries, labels):\n",
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" values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]\n",
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" results[key].append(values.rename({key: label}, axis=1))\n",
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" \n",
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"for key, val in results.items():\n",
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" df = reduce(lambda left,right: pd.merge(left, right, on='entity_date_side', how='outer'), val)\n",
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" results[key] = df.drop('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": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
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" ndim = x[:, None].ndim\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
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" x = x[:, np.newaxis]\n",
|
||
|
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
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|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 525x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
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"<Figure size 525x300 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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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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"text/plain": [
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"metadata": {
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{
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"data": {
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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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"output_type": "display_data"
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAFICAYAAABwe9bhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeZhk4/X4P1W99+wLxhhjMBwzGLET2xBbrLGMLISRkA2JLEQQkUVEkh+SkIj4MiERRKxjj9iFjJ3BmbENZgzGrD29V93fH++tqlvV1dXV3bfWPp/n6efe9973fetUd3Wde8573nMinudhGIZhGEb5Ei21AIZhGIZh5MaUtWEYhmGUOaasDcMwDKPMMWVtGIZhGGWOKWvDMAzDKHNMWRuGYRhGmWPK2jAMwzDKHFPWhmEYhlHmmLI2DMMwjDLHlLVhGIZhlDmmrA3DMAyjzDFlbRiGYRhljilrwzAMwyhzTFkbhmEYRplTW2oBjOpARGao6ksZ1+YAJ/jN9VV1adEFC5BNxnJBRGYD1/jNL6rqDSWSI1Ez9z5VPTDL/eHAT4AjgA2ANuAD4GBVfadYcvYHETkfJzPArqr61ADmeAfYCHhEVWf20ifr50tEHgb2AjpUtbG/r20YYMraGCQiMgr4GXAKZfp5EpEJwG+B3YCNSyxOxSIiUeB+YNfA5UZgGLC4JEKVAfb5MopBWX65GhXFxcBXSi1EH/wd2AdYVGpBKpz9SSnql4DzgKVAs6p2lUyq0mOfL6PgmLI2BktNqQXIg0qQsRLYMnD+A1V9oGSS9ANVPR84v4AvYZ8vo+BYgJlhGPkyLHD+dsmkMIwhiClrwzDyJfh90V0yKQxjCGJucGNAZETYJq4lIomzRsyKyBTgDOBAXCTxauBVYA7wV1X1MscExtYBs4FZwAxgLLASeBG4GbhGVTszxswhFY0OsFFAxr+q6uyM/usBJwOfAcR/jW7gE2AecAPwr1xyhoUfdX06cBQwFYjjrNmbgd+r6uocY0cCX8WtMW+Nex8RYDnwPHALcF2+68yBSOggb4tI4nzjwUaCi8giYDLwsqrO6KXPo8AefnNHVX0mS5+TgSv95nRVfS2faHA/SOzbwMHAJkAH7m9+iare34s8c+jH5yswbmvg+7h17vVI/V2uVNXbso0xDLOsjWJxFPAa8C3cl2EDsA5uS8s1wF0ikvXhUUSm4pTylcB+uC+4On/8vsAVwIsS0B79RUROwCnDnwMzgfV9GYfhlMhRwD+B20Wk0GuUWwDzfVk+BQwHRgLb+NeeE5HJ2QaKyAHAO7jAv8RDURMuansiThn9H/CYiIwo6LvoH3f5x638h6Y0RGQYsEvg0sxe5jnIP76hqq/l88Iish+wAPgR7kFwODAO9/u7T0QuyGeePF/r2zjFfAKwIVAPTAA+C9wqIlfmGG4MYUxZGwPlCmBb4M7AtW39n5Oy9L8MZ91djvsS3Bs4B1jr3/8s8L3MQb7F8xgwDej0xx8M7AQcDvwViOEU3EMisn5g+Hm+PM/67Q8CMp4XeI19cNZ9E87K+Zkvzy44JX25/9oAh/by/sLkJ7gHhDv919/Nf83EOvGmuN9/Gv7Dyh3AGNz+50uBQ3Dv43DgQqDF774zcHae8hyE+539OXDtYFK/yyV5zpOLuf4xgnsAy2Qv3ANagpmZHUSkHucVAfd76BMR2Rb3oDAC6ML9zvYBdgfOBVbhfk8bZhme1+crQAPwO9xn/pe497k/cJH/2gAni8gx+chuDC3MDW4MCD/ByVIRWR649kKOIR3APqr6ZODawyLyH+C/fvsE4NcZ467AWR6rgX1VdV7G/TtE5Gbcl/P6wCXAF3x53gXeFZGEgursRcaf+cdu4IAs7tVbROReUg8ms0hXXIXg+6p6caD9pIjcgls2mAAcKCLrqupHgT4/xllqAF/K4lK9Q0Ruwrl3a3Hv40d9CaKqrwKISDCpzashJ0H5D9AKNOO8J3/PuJ9Q4F04pb27iNSoaizQZw+c0oU8lTXuIbIOt8xwqKreF7j3hP/ZehwYnzmwH5+vIMuAPVT19cC1B0TkBeAffvsE4KY85TeGCGZZG8XisgxFDYC/fvic39wi6AoXkc2Bw/zmBVkUdWKOuTgLG2CWiEzMVygRacZZPMuBO7OtgwZeY6Xf3CDf+QfIMxmKOiHDCuB6vxkhfSsVuGWBj4Dnelv79JXJfL9Z6PeRN6raDjzoN7NZ1olr1/rHUTgLNkjCBb4cp2BzIiJbAZ/2m1dnKOqEXAqc2ddc/eCnGYo6wY04RQ6wVYivZ1QJpqyNYnF3jnsL/WMU9yWc4CCcUgLoa09vYv4ova9n9kBVW1V1R1UdBxzdR/eEZdmQ7/wD5J4c9xYGzscGb6jqAaq6HrBjH/MX6330l4QrfAMRmZ64KCLrklJgl5JaOtk7Y/xn/ePdGRZ3bxwUOL++114usLAtj/nyIev/gR+0+KbfHJutjzG0MTe4USzez3GvPXAe/EwGLafn+hE/tkm+HYOoahySwUwb49aGt8AFdu1Oat2y0A+5A/ldJQm8j0ZgCu73Ibjgqd2AzfyukWzjS8hdgfP9cC5/cFZ1BFiqqq+IyNO4deWZwG8gudNgmt8/Xxf4FoHzXt3XqtomIi/j4iQGSz5/W/teNnpgHwqjWKzJs19QgfRYJ8yTMf0dICKTcNtpDqN3ZR+nON6ogfyuABCRsbgtX0fjFHQ2eYv1PvqFqi4WkedxD2n74YKxIOUCfyhw3If0deuEldwJ3JvnSyaizuP+EkMuPsxzzlx0Zm4v7IVye4gyygBT1kaxGMje5ODncydSEbN98XF/XkREDsTtXw5m6FqD22o2HxeQ9W/gVnquExeCAe3jFpHtcW7WdQOX24DXcVbqMzhFdwEumrscmYtT1nuJSJ2/FzwR4f1QxnEksB3u75NQ1g+rar4PO8nfs4hE+tg/H0bu84LvzzeqF1PWRjmzPHC+WFXD2CKUhr817Aacou7CbW/6By62yMvoOzzs1w8L3+X9L1KK+g/A1bgkI7GMvmX7PnDK+se4vc67iMgHuG1skFLS/8NtQRsOzPRd1In163xd4OC2WoHzMowjFeCVDVtHNkqKKWujnHklcL4LLvNWVkRkZ9wa5jvAE6qaa20wyHGkgtp+oao/y9ZJRBpIuU3LkUNIZRm7RlW/naNv1oQqZcI8nMt5PZwrPFHJ6j1VfQNAVbtE5HHcfv19gJdxW76gf8p6fuB8R3oJ7PNLg27dj3kNI3TKbt3KqDjiBZw7uJXmm330/Q3wK5yVnLnmnEvGqYHzZ3vt5ZKTNPrn5fiQm9f7EJFPE6i53FvWuFLhezMSEdP7AXv65w9ldP2Pf9yd1Pa+F1T1vX68XPDh76s5+h2Ms7x7o5D/A4YBmLI2Bk9H4iRs96q/5/lRv7mviGTNuCUi3yeVM/oFXMazbDJmky/o+vxslvuIyE44t3KCctvyBPm9j82A6zIul+N7SWzh2hGX4Qt6V9bDgRP98/5Y1YmkJv/ym0eJyOzMPv6e/d/3MVWuz5dhhEJZPVUbFckHgfNfisi1QExVnw9p/pNwgVEjgQtEZC9cbutFuFzXxwJH+n07ga9lCRRKyDhORH6E27Pd6mfmuhmXTjICfMtPkvIvnPLbAJem84ukp7ocmUdAUrGZSyoD2MEichsu5/pSnEv5AOB4eiqUUaT2LZcL9+P+lom82ZBSzgmeB1bgIv8THo9+KWuf03Dr3WOBq0Vkb9ye61W4pZcf+jKsJT0AMUiuz5dhhIJZ1iVERO4QkYF8wZQTd+Byc4P74psHhFY5SFUX4vJCv+Nf2h+X7ekpnBvzKJyiXQEc3kuWs6C785e+jH/y538BlwMaf54TcYrvKZzSPh6nqO8G/uL3qyfd7Vxy/PSv3yD1tzgc93d4CrgdV0BlOPA0LoguQTGi2/uFqrYAjwQuveVbwcE+8Yw+i1U11zJGb6/1Ac4rswj39z8et/Xrv7jUtRNwBWQezjFNr58vwwgLU9alZdOpU6ceitvSUZE/qvr8lVdeWbPtttvS3NxMY2MjkydPntz
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 525x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"xlabel = {\n",
|
||
|
|
" 'theta_bandpower': 'Theta bandpower (V$^2$/Hz)',\n",
|
||
|
|
" 'theta_relpower': 'Theta relative power',\n",
|
||
|
|
" 'theta_relpeak': 'Theta relative power',\n",
|
||
|
|
" 'theta_peak': 'Peak PSD (V$^2$/Hz)',\n",
|
||
|
|
" 'theta_freq': '(Hz)',\n",
|
||
|
|
" 'theta_half_width': '(Hz)',\n",
|
||
|
|
"}\n",
|
||
|
|
"\n",
|
||
|
|
"for key in xlabel:\n",
|
||
|
|
" fig = plt.figure(figsize=(3.5,2))\n",
|
||
|
|
" plt.suptitle(key)\n",
|
||
|
|
" legend_lines = []\n",
|
||
|
|
" for color, label in zip(colors, labels):\n",
|
||
|
|
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n",
|
||
|
|
" sns.kdeplot(data=results[key].loc[:,labels], cumulative=True, legend=False, palette=colors, common_norm=False)\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",
|
||
|
|
" 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": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" ndim = x[:, None].ndim\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" x = x[:, np.newaxis]\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
|
||
|
|
" y = y[:, np.newaxis]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
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"text/plain": [
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"metadata": {
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{
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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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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 480x300 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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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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},
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 480x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 480x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"xlabel = {\n",
|
||
|
|
" 'stim_bandpower': 'Energy (V$^2$/Hz)',\n",
|
||
|
|
" 'stim_relpower': 'Relative power',\n",
|
||
|
|
" 'stim_relpeak': 'Relative power',\n",
|
||
|
|
" 'stim_half_width': '(Hz)',\n",
|
||
|
|
" 'stim_p_max': 'Peak PSD (V$^2$/Hz)',\n",
|
||
|
|
" 'stim_strength': 'Ratio',\n",
|
||
|
|
"}\n",
|
||
|
|
"for key in xlabel:\n",
|
||
|
|
" fig = plt.figure(figsize=(3.2,2))\n",
|
||
|
|
" plt.suptitle(key)\n",
|
||
|
|
" legend_lines = []\n",
|
||
|
|
" for color, label in zip(colors[1::2], labels[1::2]):\n",
|
||
|
|
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n",
|
||
|
|
" sns.kdeplot(data=results[key].loc[:, labels[1::2]], cumulative=True, legend=False, palette=colors[1::2], common_norm=False)\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",
|
||
|
|
" 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": [],
|
||
|
|
"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 \"{:.1e}, {:.1e}, ({})\".format(statistic, pvalue, len(dff))\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"def summarize_wilcoxon(df, keys):\n",
|
||
|
|
" dff = df.loc[:,[keys[0], keys[1]]].dropna()\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" return\"{:.1e} ± {:.1e}, {:.1e} ± {:.1e} ({})\".format(dff[keys[0]].mean(), dff[keys[0]].sem(), dff[keys[1]].mean(), dff[keys[1]].sem(), 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 \"{:.1e}, {:.1e}\".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": 22,
|
||
|
|
"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 bandpower</th>\n",
|
||
|
|
" <th>Theta relpower</th>\n",
|
||
|
|
" <th>Theta relpeak</th>\n",
|
||
|
|
" <th>Theta peak</th>\n",
|
||
|
|
" <th>Theta freq</th>\n",
|
||
|
|
" <th>Theta half width</th>\n",
|
||
|
|
" <th>Stim bandpower</th>\n",
|
||
|
|
" <th>Stim relpower</th>\n",
|
||
|
|
" <th>Stim relpeak</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>8.5e-04 ± 8.0e-05 (44)</td>\n",
|
||
|
|
" <td>8.6e-02 ± 4.6e-03 (44)</td>\n",
|
||
|
|
" <td>2.3e-01 ± 5.2e-02 (44)</td>\n",
|
||
|
|
" <td>3.2e-04 ± 3.1e-05 (44)</td>\n",
|
||
|
|
" <td>7.7e+00 ± 1.3e-01 (44)</td>\n",
|
||
|
|
" <td>1.7e+00 ± 3.0e-01 (43)</td>\n",
|
||
|
|
" <td>9.6e-04 ± 8.0e-05 (44)</td>\n",
|
||
|
|
" <td>1.1e-01 ± 7.4e-03 (44)</td>\n",
|
||
|
|
" <td>1.6e+01 ± 1.9e+00 (44)</td>\n",
|
||
|
|
" <td>3.4e-01 ± 2.5e-03 (44)</td>\n",
|
||
|
|
" <td>2.1e-03 ± 2.3e-04 (44)</td>\n",
|
||
|
|
" <td>3.3e+00 ± 3.8e-01 (44)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>30 Hz</th>\n",
|
||
|
|
" <td>5.3e-04 ± 6.3e-05 (34)</td>\n",
|
||
|
|
" <td>5.3e-02 ± 4.2e-03 (34)</td>\n",
|
||
|
|
" <td>3.0e-02 ± 5.3e-02 (34)</td>\n",
|
||
|
|
" <td>1.9e-04 ± 2.7e-05 (34)</td>\n",
|
||
|
|
" <td>7.4e+00 ± 1.4e-01 (34)</td>\n",
|
||
|
|
" <td>1.2e+01 ± 1.9e+00 (34)</td>\n",
|
||
|
|
" <td>1.8e-03 ± 3.3e-04 (34)</td>\n",
|
||
|
|
" <td>1.6e-01 ± 2.3e-02 (34)</td>\n",
|
||
|
|
" <td>8.6e+01 ± 1.4e+01 (34)</td>\n",
|
||
|
|
" <td>3.0e-01 ± 2.3e-03 (23)</td>\n",
|
||
|
|
" <td>5.3e-03 ± 1.1e-03 (34)</td>\n",
|
||
|
|
" <td>1.3e+01 ± 2.8e+00 (34)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Baseline I</th>\n",
|
||
|
|
" <td>2.2e-03 ± 2.2e-04 (46)</td>\n",
|
||
|
|
" <td>2.6e-01 ± 1.6e-02 (46)</td>\n",
|
||
|
|
" <td>6.5e+00 ± 6.7e-01 (46)</td>\n",
|
||
|
|
" <td>1.7e-03 ± 1.8e-04 (46)</td>\n",
|
||
|
|
" <td>7.8e+00 ± 4.0e-02 (46)</td>\n",
|
||
|
|
" <td>7.7e-01 ± 1.8e-02 (46)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Baseline II</th>\n",
|
||
|
|
" <td>2.2e-03 ± 2.3e-04 (32)</td>\n",
|
||
|
|
" <td>2.7e-01 ± 1.7e-02 (32)</td>\n",
|
||
|
|
" <td>6.3e+00 ± 7.4e-01 (32)</td>\n",
|
||
|
|
" <td>1.6e-03 ± 2.1e-04 (32)</td>\n",
|
||
|
|
" <td>8.1e+00 ± 4.7e-02 (32)</td>\n",
|
||
|
|
" <td>8.4e-01 ± 3.2e-02 (32)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan (0)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Normality 11 Hz</th>\n",
|
||
|
|
" <td>2.1e+01, 2.6e-05</td>\n",
|
||
|
|
" <td>5.6e+00, 6.2e-02</td>\n",
|
||
|
|
" <td>1.2e+01, 2.3e-03</td>\n",
|
||
|
|
" <td>2.4e+01, 5.3e-06</td>\n",
|
||
|
|
" <td>1.9e+00, 3.9e-01</td>\n",
|
||
|
|
" <td>1.0e+01, 5.5e-03</td>\n",
|
||
|
|
" <td>1.7e+01, 1.8e-04</td>\n",
|
||
|
|
" <td>3.9e+00, 1.4e-01</td>\n",
|
||
|
|
" <td>5.8e+00, 5.6e-02</td>\n",
|
||
|
|
" <td>1.6e+01, 4.2e-04</td>\n",
|
||
|
|
" <td>1.5e+01, 5.8e-04</td>\n",
|
||
|
|
" <td>1.2e+01, 2.3e-03</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Normality 30 Hz</th>\n",
|
||
|
|
" <td>3.1e+01, 1.9e-07</td>\n",
|
||
|
|
" <td>7.9e+00, 2.0e-02</td>\n",
|
||
|
|
" <td>1.2e+01, 2.3e-03</td>\n",
|
||
|
|
" <td>3.8e+01, 5.3e-09</td>\n",
|
||
|
|
" <td>7.2e+00, 2.8e-02</td>\n",
|
||
|
|
" <td>1.9e+02, 2.2e-41</td>\n",
|
||
|
|
" <td>1.7e+01, 1.7e-04</td>\n",
|
||
|
|
" <td>1.5e+01, 4.8e-04</td>\n",
|
||
|
|
" <td>6.2e+00, 4.5e-02</td>\n",
|
||
|
|
" <td>6.1e-01, 7.4e-01</td>\n",
|
||
|
|
" <td>1.9e+01, 6.3e-05</td>\n",
|
||
|
|
" <td>4.3e+01, 5.1e-10</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Normality Baseline I</th>\n",
|
||
|
|
" <td>4.3e+01, 5.8e-10</td>\n",
|
||
|
|
" <td>1.6e+00, 4.6e-01</td>\n",
|
||
|
|
" <td>2.1e+00, 3.4e-01</td>\n",
|
||
|
|
" <td>3.2e+01, 1.3e-07</td>\n",
|
||
|
|
" <td>5.9e+00, 5.3e-02</td>\n",
|
||
|
|
" <td>1.9e+00, 3.8e-01</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</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>1.4e+01, 1.1e-03</td>\n",
|
||
|
|
" <td>3.6e-01, 8.3e-01</td>\n",
|
||
|
|
" <td>4.9e+00, 8.8e-02</td>\n",
|
||
|
|
" <td>2.5e+01, 3.4e-06</td>\n",
|
||
|
|
" <td>4.7e+00, 9.7e-02</td>\n",
|
||
|
|
" <td>1.6e+01, 2.8e-04</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Paired summary 11 Hz 30 Hz</th>\n",
|
||
|
|
" <td>8.2e-04 ± 8.8e-05, 5.4e-04 ± 6.6e-05 (32)</td>\n",
|
||
|
|
" <td>8.4e-02 ± 4.5e-03, 5.5e-02 ± 4.2e-03 (32)</td>\n",
|
||
|
|
" <td>2.0e-01 ± 5.0e-02, 4.9e-02 ± 5.4e-02 (32)</td>\n",
|
||
|
|
" <td>3.0e-04 ± 3.1e-05, 1.9e-04 ± 2.8e-05 (32)</td>\n",
|
||
|
|
" <td>7.6e+00 ± 1.5e-01, 7.3e+00 ± 1.4e-01 (32)</td>\n",
|
||
|
|
" <td>1.5e+00 ± 3.5e-01, 1.1e+01 ± 2.0e+00 (31)</td>\n",
|
||
|
|
" <td>9.7e-04 ± 9.3e-05, 1.8e-03 ± 3.4e-04 (32)</td>\n",
|
||
|
|
" <td>1.1e-01 ± 8.6e-03, 1.6e-01 ± 2.4e-02 (32)</td>\n",
|
||
|
|
" <td>1.8e+01 ± 2.4e+00, 8.5e+01 ± 1.4e+01 (32)</td>\n",
|
||
|
|
" <td>3.4e-01 ± 3.6e-03, 3.0e-01 ± 2.3e-03 (21)</td>\n",
|
||
|
|
" <td>2.1e-03 ± 2.7e-04, 5.4e-03 ± 1.1e-03 (32)</td>\n",
|
||
|
|
" <td>3.5e+00 ± 4.9e-01, 1.3e+01 ± 3.0e+00 (32)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Paired summary 11 Hz Baseline II</th>\n",
|
||
|
|
" <td>8.2e-04 ± 8.8e-05, 2.2e-03 ± 2.3e-04 (32)</td>\n",
|
||
|
|
" <td>8.4e-02 ± 4.5e-03, 2.7e-01 ± 1.7e-02 (32)</td>\n",
|
||
|
|
" <td>2.0e-01 ± 5.0e-02, 6.3e+00 ± 7.4e-01 (32)</td>\n",
|
||
|
|
" <td>3.0e-04 ± 3.1e-05, 1.6e-03 ± 2.1e-04 (32)</td>\n",
|
||
|
|
" <td>7.6e+00 ± 1.5e-01, 8.1e+00 ± 4.7e-02 (32)</td>\n",
|
||
|
|
" <td>1.5e+00 ± 3.5e-01, 8.6e-01 ± 2.5e-02 (31)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Paired summary Baseline I 11 Hz</th>\n",
|
||
|
|
" <td>2.2e-03 ± 2.3e-04, 8.5e-04 ± 8.0e-05 (44)</td>\n",
|
||
|
|
" <td>2.6e-01 ± 1.6e-02, 8.6e-02 ± 4.6e-03 (44)</td>\n",
|
||
|
|
" <td>6.3e+00 ± 6.7e-01, 2.3e-01 ± 5.2e-02 (44)</td>\n",
|
||
|
|
" <td>1.6e-03 ± 1.9e-04, 3.2e-04 ± 3.1e-05 (44)</td>\n",
|
||
|
|
" <td>7.8e+00 ± 4.2e-02, 7.7e+00 ± 1.3e-01 (44)</td>\n",
|
||
|
|
" <td>7.8e-01 ± 1.8e-02, 1.7e+00 ± 3.0e-01 (43)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Paired summary Baseline I 30 Hz</th>\n",
|
||
|
|
" <td>2.3e-03 ± 2.8e-04, 5.4e-04 ± 6.6e-05 (32)</td>\n",
|
||
|
|
" <td>2.7e-01 ± 1.8e-02, 5.5e-02 ± 4.2e-03 (32)</td>\n",
|
||
|
|
" <td>6.6e+00 ± 8.1e-01, 4.9e-02 ± 5.4e-02 (32)</td>\n",
|
||
|
|
" <td>1.7e-03 ± 2.3e-04, 1.9e-04 ± 2.8e-05 (32)</td>\n",
|
||
|
|
" <td>7.8e+00 ± 4.4e-02, 7.3e+00 ± 1.4e-01 (32)</td>\n",
|
||
|
|
" <td>7.6e-01 ± 2.0e-02, 1.2e+01 ± 2.0e+00 (32)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Paired summary Baseline I Baseline II</th>\n",
|
||
|
|
" <td>2.3e-03 ± 2.8e-04, 2.2e-03 ± 2.3e-04 (32)</td>\n",
|
||
|
|
" <td>2.7e-01 ± 1.8e-02, 2.7e-01 ± 1.7e-02 (32)</td>\n",
|
||
|
|
" <td>6.6e+00 ± 8.1e-01, 6.3e+00 ± 7.4e-01 (32)</td>\n",
|
||
|
|
" <td>1.7e-03 ± 2.3e-04, 1.6e-03 ± 2.1e-04 (32)</td>\n",
|
||
|
|
" <td>7.8e+00 ± 4.4e-02, 8.1e+00 ± 4.7e-02 (32)</td>\n",
|
||
|
|
" <td>7.6e-01 ± 2.0e-02, 8.4e-01 ± 3.2e-02 (32)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Paired summary Baseline II 30 Hz</th>\n",
|
||
|
|
" <td>2.2e-03 ± 2.3e-04, 5.4e-04 ± 6.6e-05 (32)</td>\n",
|
||
|
|
" <td>2.7e-01 ± 1.7e-02, 5.5e-02 ± 4.2e-03 (32)</td>\n",
|
||
|
|
" <td>6.3e+00 ± 7.4e-01, 4.9e-02 ± 5.4e-02 (32)</td>\n",
|
||
|
|
" <td>1.6e-03 ± 2.1e-04, 1.9e-04 ± 2.8e-05 (32)</td>\n",
|
||
|
|
" <td>8.1e+00 ± 4.7e-02, 7.3e+00 ± 1.4e-01 (32)</td>\n",
|
||
|
|
" <td>8.4e-01 ± 3.2e-02, 1.2e+01 ± 2.0e+00 (32)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" <td>nan ± nan, nan ± nan (0)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Wilcoxon 11 Hz 30 Hz</th>\n",
|
||
|
|
" <td>1.1e+02, 3.3e-03, (32)</td>\n",
|
||
|
|
" <td>4.1e+01, 3.0e-05, (32)</td>\n",
|
||
|
|
" <td>1.3e+02, 1.4e-02, (32)</td>\n",
|
||
|
|
" <td>1.2e+02, 5.6e-03, (32)</td>\n",
|
||
|
|
" <td>1.2e+02, 4.5e-02, (32)</td>\n",
|
||
|
|
" <td>6.7e+01, 3.9e-04, (31)</td>\n",
|
||
|
|
" <td>2.1e+02, 2.9e-01, (32)</td>\n",
|
||
|
|
" <td>2.0e+02, 2.2e-01, (32)</td>\n",
|
||
|
|
" <td>3.0e+01, 1.2e-05, (32)</td>\n",
|
||
|
|
" <td>0.0e+00, 9.5e-07, (21)</td>\n",
|
||
|
|
" <td>1.6e+02, 5.0e-02, (32)</td>\n",
|
||
|
|
" <td>8.8e+01, 1.0e-03, (32)</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Wilcoxon 11 Hz Baseline II</th>\n",
|
||
|
|
" <td>1.2e+01, 2.5e-06, (32)</td>\n",
|
||
|
|
" <td>2.0e+00, 9.6e-07, (32)</td>\n",
|
||
|
|
" <td>0.0e+00, 8.0e-07, (32)</td>\n",
|
||
|
|
" <td>3.0e+00, 1.1e-06, (32)</td>\n",
|
||
|
|
" <td>1.2e+02, 9.3e-03, (32)</td>\n",
|
||
|
|
" <td>2.3e+02, 6.7e-01, (31)</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</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>3.5e+01, 7.9e-08, (44)</td>\n",
|
||
|
|
" <td>1.0e+00, 8.2e-09, (44)</td>\n",
|
||
|
|
" <td>2.0e+00, 8.7e-09, (44)</td>\n",
|
||
|
|
" <td>3.0e+00, 9.4e-09, (44)</td>\n",
|
||
|
|
" <td>3.6e+02, 4.7e-01, (44)</td>\n",
|
||
|
|
" <td>4.3e+02, 6.2e-01, (43)</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</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>6.0e+00, 1.4e-06, (32)</td>\n",
|
||
|
|
" <td>0.0e+00, 8.0e-07, (32)</td>\n",
|
||
|
|
" <td>0.0e+00, 8.0e-07, (32)</td>\n",
|
||
|
|
" <td>0.0e+00, 8.0e-07, (32)</td>\n",
|
||
|
|
" <td>9.5e+01, 1.6e-03, (32)</td>\n",
|
||
|
|
" <td>7.1e+01, 3.1e-04, (32)</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</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>2.4e+02, 7.1e-01, (32)</td>\n",
|
||
|
|
" <td>2.4e+02, 7.1e-01, (32)</td>\n",
|
||
|
|
" <td>2.4e+02, 5.9e-01, (32)</td>\n",
|
||
|
|
" <td>2.3e+02, 5.5e-01, (32)</td>\n",
|
||
|
|
" <td>6.0e+00, 9.0e-06, (32)</td>\n",
|
||
|
|
" <td>1.4e+02, 2.3e-02, (32)</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</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>1.6e+01, 3.5e-06, (32)</td>\n",
|
||
|
|
" <td>0.0e+00, 8.0e-07, (32)</td>\n",
|
||
|
|
" <td>0.0e+00, 8.0e-07, (32)</td>\n",
|
||
|
|
" <td>3.0e+00, 1.1e-06, (32)</td>\n",
|
||
|
|
" <td>5.0e+01, 9.9e-05, (32)</td>\n",
|
||
|
|
" <td>7.5e+01, 4.1e-04, (32)</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</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 bandpower \\\n",
|
||
|
|
"11 Hz 8.5e-04 ± 8.0e-05 (44) \n",
|
||
|
|
"30 Hz 5.3e-04 ± 6.3e-05 (34) \n",
|
||
|
|
"Baseline I 2.2e-03 ± 2.2e-04 (46) \n",
|
||
|
|
"Baseline II 2.2e-03 ± 2.3e-04 (32) \n",
|
||
|
|
"Normality 11 Hz 2.1e+01, 2.6e-05 \n",
|
||
|
|
"Normality 30 Hz 3.1e+01, 1.9e-07 \n",
|
||
|
|
"Normality Baseline I 4.3e+01, 5.8e-10 \n",
|
||
|
|
"Normality Baseline II 1.4e+01, 1.1e-03 \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 8.2e-04 ± 8.8e-05, 5.4e-04 ± 6.6e-05 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II 8.2e-04 ± 8.8e-05, 2.2e-03 ± 2.3e-04 (32) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz 2.2e-03 ± 2.3e-04, 8.5e-04 ± 8.0e-05 (44) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz 2.3e-03 ± 2.8e-04, 5.4e-04 ± 6.6e-05 (32) \n",
|
||
|
|
"Paired summary Baseline I Baseline II 2.3e-03 ± 2.8e-04, 2.2e-03 ± 2.3e-04 (32) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz 2.2e-03 ± 2.3e-04, 5.4e-04 ± 6.6e-05 (32) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 1.1e+02, 3.3e-03, (32) \n",
|
||
|
|
"Wilcoxon 11 Hz Baseline II 1.2e+01, 2.5e-06, (32) \n",
|
||
|
|
"Wilcoxon Baseline I 11 Hz 3.5e+01, 7.9e-08, (44) \n",
|
||
|
|
"Wilcoxon Baseline I 30 Hz 6.0e+00, 1.4e-06, (32) \n",
|
||
|
|
"Wilcoxon Baseline I Baseline II 2.4e+02, 7.1e-01, (32) \n",
|
||
|
|
"Wilcoxon Baseline II 30 Hz 1.6e+01, 3.5e-06, (32) \n",
|
||
|
|
"\n",
|
||
|
|
" Theta relpower \\\n",
|
||
|
|
"11 Hz 8.6e-02 ± 4.6e-03 (44) \n",
|
||
|
|
"30 Hz 5.3e-02 ± 4.2e-03 (34) \n",
|
||
|
|
"Baseline I 2.6e-01 ± 1.6e-02 (46) \n",
|
||
|
|
"Baseline II 2.7e-01 ± 1.7e-02 (32) \n",
|
||
|
|
"Normality 11 Hz 5.6e+00, 6.2e-02 \n",
|
||
|
|
"Normality 30 Hz 7.9e+00, 2.0e-02 \n",
|
||
|
|
"Normality Baseline I 1.6e+00, 4.6e-01 \n",
|
||
|
|
"Normality Baseline II 3.6e-01, 8.3e-01 \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 8.4e-02 ± 4.5e-03, 5.5e-02 ± 4.2e-03 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II 8.4e-02 ± 4.5e-03, 2.7e-01 ± 1.7e-02 (32) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz 2.6e-01 ± 1.6e-02, 8.6e-02 ± 4.6e-03 (44) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz 2.7e-01 ± 1.8e-02, 5.5e-02 ± 4.2e-03 (32) \n",
|
||
|
|
"Paired summary Baseline I Baseline II 2.7e-01 ± 1.8e-02, 2.7e-01 ± 1.7e-02 (32) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz 2.7e-01 ± 1.7e-02, 5.5e-02 ± 4.2e-03 (32) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 4.1e+01, 3.0e-05, (32) \n",
|
||
|
|
"Wilcoxon 11 Hz Baseline II 2.0e+00, 9.6e-07, (32) \n",
|
||
|
|
"Wilcoxon Baseline I 11 Hz 1.0e+00, 8.2e-09, (44) \n",
|
||
|
|
"Wilcoxon Baseline I 30 Hz 0.0e+00, 8.0e-07, (32) \n",
|
||
|
|
"Wilcoxon Baseline I Baseline II 2.4e+02, 7.1e-01, (32) \n",
|
||
|
|
"Wilcoxon Baseline II 30 Hz 0.0e+00, 8.0e-07, (32) \n",
|
||
|
|
"\n",
|
||
|
|
" Theta relpeak \\\n",
|
||
|
|
"11 Hz 2.3e-01 ± 5.2e-02 (44) \n",
|
||
|
|
"30 Hz 3.0e-02 ± 5.3e-02 (34) \n",
|
||
|
|
"Baseline I 6.5e+00 ± 6.7e-01 (46) \n",
|
||
|
|
"Baseline II 6.3e+00 ± 7.4e-01 (32) \n",
|
||
|
|
"Normality 11 Hz 1.2e+01, 2.3e-03 \n",
|
||
|
|
"Normality 30 Hz 1.2e+01, 2.3e-03 \n",
|
||
|
|
"Normality Baseline I 2.1e+00, 3.4e-01 \n",
|
||
|
|
"Normality Baseline II 4.9e+00, 8.8e-02 \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 2.0e-01 ± 5.0e-02, 4.9e-02 ± 5.4e-02 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II 2.0e-01 ± 5.0e-02, 6.3e+00 ± 7.4e-01 (32) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz 6.3e+00 ± 6.7e-01, 2.3e-01 ± 5.2e-02 (44) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz 6.6e+00 ± 8.1e-01, 4.9e-02 ± 5.4e-02 (32) \n",
|
||
|
|
"Paired summary Baseline I Baseline II 6.6e+00 ± 8.1e-01, 6.3e+00 ± 7.4e-01 (32) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz 6.3e+00 ± 7.4e-01, 4.9e-02 ± 5.4e-02 (32) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 1.3e+02, 1.4e-02, (32) \n",
|
||
|
|
"Wilcoxon 11 Hz Baseline II 0.0e+00, 8.0e-07, (32) \n",
|
||
|
|
"Wilcoxon Baseline I 11 Hz 2.0e+00, 8.7e-09, (44) \n",
|
||
|
|
"Wilcoxon Baseline I 30 Hz 0.0e+00, 8.0e-07, (32) \n",
|
||
|
|
"Wilcoxon Baseline I Baseline II 2.4e+02, 5.9e-01, (32) \n",
|
||
|
|
"Wilcoxon Baseline II 30 Hz 0.0e+00, 8.0e-07, (32) \n",
|
||
|
|
"\n",
|
||
|
|
" Theta peak \\\n",
|
||
|
|
"11 Hz 3.2e-04 ± 3.1e-05 (44) \n",
|
||
|
|
"30 Hz 1.9e-04 ± 2.7e-05 (34) \n",
|
||
|
|
"Baseline I 1.7e-03 ± 1.8e-04 (46) \n",
|
||
|
|
"Baseline II 1.6e-03 ± 2.1e-04 (32) \n",
|
||
|
|
"Normality 11 Hz 2.4e+01, 5.3e-06 \n",
|
||
|
|
"Normality 30 Hz 3.8e+01, 5.3e-09 \n",
|
||
|
|
"Normality Baseline I 3.2e+01, 1.3e-07 \n",
|
||
|
|
"Normality Baseline II 2.5e+01, 3.4e-06 \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 3.0e-04 ± 3.1e-05, 1.9e-04 ± 2.8e-05 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II 3.0e-04 ± 3.1e-05, 1.6e-03 ± 2.1e-04 (32) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz 1.6e-03 ± 1.9e-04, 3.2e-04 ± 3.1e-05 (44) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz 1.7e-03 ± 2.3e-04, 1.9e-04 ± 2.8e-05 (32) \n",
|
||
|
|
"Paired summary Baseline I Baseline II 1.7e-03 ± 2.3e-04, 1.6e-03 ± 2.1e-04 (32) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz 1.6e-03 ± 2.1e-04, 1.9e-04 ± 2.8e-05 (32) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 1.2e+02, 5.6e-03, (32) \n",
|
||
|
|
"Wilcoxon 11 Hz Baseline II 3.0e+00, 1.1e-06, (32) \n",
|
||
|
|
"Wilcoxon Baseline I 11 Hz 3.0e+00, 9.4e-09, (44) \n",
|
||
|
|
"Wilcoxon Baseline I 30 Hz 0.0e+00, 8.0e-07, (32) \n",
|
||
|
|
"Wilcoxon Baseline I Baseline II 2.3e+02, 5.5e-01, (32) \n",
|
||
|
|
"Wilcoxon Baseline II 30 Hz 3.0e+00, 1.1e-06, (32) \n",
|
||
|
|
"\n",
|
||
|
|
" Theta freq \\\n",
|
||
|
|
"11 Hz 7.7e+00 ± 1.3e-01 (44) \n",
|
||
|
|
"30 Hz 7.4e+00 ± 1.4e-01 (34) \n",
|
||
|
|
"Baseline I 7.8e+00 ± 4.0e-02 (46) \n",
|
||
|
|
"Baseline II 8.1e+00 ± 4.7e-02 (32) \n",
|
||
|
|
"Normality 11 Hz 1.9e+00, 3.9e-01 \n",
|
||
|
|
"Normality 30 Hz 7.2e+00, 2.8e-02 \n",
|
||
|
|
"Normality Baseline I 5.9e+00, 5.3e-02 \n",
|
||
|
|
"Normality Baseline II 4.7e+00, 9.7e-02 \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 7.6e+00 ± 1.5e-01, 7.3e+00 ± 1.4e-01 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II 7.6e+00 ± 1.5e-01, 8.1e+00 ± 4.7e-02 (32) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz 7.8e+00 ± 4.2e-02, 7.7e+00 ± 1.3e-01 (44) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz 7.8e+00 ± 4.4e-02, 7.3e+00 ± 1.4e-01 (32) \n",
|
||
|
|
"Paired summary Baseline I Baseline II 7.8e+00 ± 4.4e-02, 8.1e+00 ± 4.7e-02 (32) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz 8.1e+00 ± 4.7e-02, 7.3e+00 ± 1.4e-01 (32) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 1.2e+02, 4.5e-02, (32) \n",
|
||
|
|
"Wilcoxon 11 Hz Baseline II 1.2e+02, 9.3e-03, (32) \n",
|
||
|
|
"Wilcoxon Baseline I 11 Hz 3.6e+02, 4.7e-01, (44) \n",
|
||
|
|
"Wilcoxon Baseline I 30 Hz 9.5e+01, 1.6e-03, (32) \n",
|
||
|
|
"Wilcoxon Baseline I Baseline II 6.0e+00, 9.0e-06, (32) \n",
|
||
|
|
"Wilcoxon Baseline II 30 Hz 5.0e+01, 9.9e-05, (32) \n",
|
||
|
|
"\n",
|
||
|
|
" Theta half width \\\n",
|
||
|
|
"11 Hz 1.7e+00 ± 3.0e-01 (43) \n",
|
||
|
|
"30 Hz 1.2e+01 ± 1.9e+00 (34) \n",
|
||
|
|
"Baseline I 7.7e-01 ± 1.8e-02 (46) \n",
|
||
|
|
"Baseline II 8.4e-01 ± 3.2e-02 (32) \n",
|
||
|
|
"Normality 11 Hz 1.0e+01, 5.5e-03 \n",
|
||
|
|
"Normality 30 Hz 1.9e+02, 2.2e-41 \n",
|
||
|
|
"Normality Baseline I 1.9e+00, 3.8e-01 \n",
|
||
|
|
"Normality Baseline II 1.6e+01, 2.8e-04 \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 1.5e+00 ± 3.5e-01, 1.1e+01 ± 2.0e+00 (31) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II 1.5e+00 ± 3.5e-01, 8.6e-01 ± 2.5e-02 (31) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz 7.8e-01 ± 1.8e-02, 1.7e+00 ± 3.0e-01 (43) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz 7.6e-01 ± 2.0e-02, 1.2e+01 ± 2.0e+00 (32) \n",
|
||
|
|
"Paired summary Baseline I Baseline II 7.6e-01 ± 2.0e-02, 8.4e-01 ± 3.2e-02 (32) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz 8.4e-01 ± 3.2e-02, 1.2e+01 ± 2.0e+00 (32) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 6.7e+01, 3.9e-04, (31) \n",
|
||
|
|
"Wilcoxon 11 Hz Baseline II 2.3e+02, 6.7e-01, (31) \n",
|
||
|
|
"Wilcoxon Baseline I 11 Hz 4.3e+02, 6.2e-01, (43) \n",
|
||
|
|
"Wilcoxon Baseline I 30 Hz 7.1e+01, 3.1e-04, (32) \n",
|
||
|
|
"Wilcoxon Baseline I Baseline II 1.4e+02, 2.3e-02, (32) \n",
|
||
|
|
"Wilcoxon Baseline II 30 Hz 7.5e+01, 4.1e-04, (32) \n",
|
||
|
|
"\n",
|
||
|
|
" Stim bandpower \\\n",
|
||
|
|
"11 Hz 9.6e-04 ± 8.0e-05 (44) \n",
|
||
|
|
"30 Hz 1.8e-03 ± 3.3e-04 (34) \n",
|
||
|
|
"Baseline I nan ± nan (0) \n",
|
||
|
|
"Baseline II nan ± nan (0) \n",
|
||
|
|
"Normality 11 Hz 1.7e+01, 1.8e-04 \n",
|
||
|
|
"Normality 30 Hz 1.7e+01, 1.7e-04 \n",
|
||
|
|
"Normality Baseline I NaN \n",
|
||
|
|
"Normality Baseline II NaN \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 9.7e-04 ± 9.3e-05, 1.8e-03 ± 3.4e-04 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 2.1e+02, 2.9e-01, (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 relpower \\\n",
|
||
|
|
"11 Hz 1.1e-01 ± 7.4e-03 (44) \n",
|
||
|
|
"30 Hz 1.6e-01 ± 2.3e-02 (34) \n",
|
||
|
|
"Baseline I nan ± nan (0) \n",
|
||
|
|
"Baseline II nan ± nan (0) \n",
|
||
|
|
"Normality 11 Hz 3.9e+00, 1.4e-01 \n",
|
||
|
|
"Normality 30 Hz 1.5e+01, 4.8e-04 \n",
|
||
|
|
"Normality Baseline I NaN \n",
|
||
|
|
"Normality Baseline II NaN \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 1.1e-01 ± 8.6e-03, 1.6e-01 ± 2.4e-02 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 2.0e+02, 2.2e-01, (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 relpeak \\\n",
|
||
|
|
"11 Hz 1.6e+01 ± 1.9e+00 (44) \n",
|
||
|
|
"30 Hz 8.6e+01 ± 1.4e+01 (34) \n",
|
||
|
|
"Baseline I nan ± nan (0) \n",
|
||
|
|
"Baseline II nan ± nan (0) \n",
|
||
|
|
"Normality 11 Hz 5.8e+00, 5.6e-02 \n",
|
||
|
|
"Normality 30 Hz 6.2e+00, 4.5e-02 \n",
|
||
|
|
"Normality Baseline I NaN \n",
|
||
|
|
"Normality Baseline II NaN \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 1.8e+01 ± 2.4e+00, 8.5e+01 ± 1.4e+01 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 3.0e+01, 1.2e-05, (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 3.4e-01 ± 2.5e-03 (44) \n",
|
||
|
|
"30 Hz 3.0e-01 ± 2.3e-03 (23) \n",
|
||
|
|
"Baseline I nan ± nan (0) \n",
|
||
|
|
"Baseline II nan ± nan (0) \n",
|
||
|
|
"Normality 11 Hz 1.6e+01, 4.2e-04 \n",
|
||
|
|
"Normality 30 Hz 6.1e-01, 7.4e-01 \n",
|
||
|
|
"Normality Baseline I NaN \n",
|
||
|
|
"Normality Baseline II NaN \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 3.4e-01 ± 3.6e-03, 3.0e-01 ± 2.3e-03 (21) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 0.0e+00, 9.5e-07, (21) \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 2.1e-03 ± 2.3e-04 (44) \n",
|
||
|
|
"30 Hz 5.3e-03 ± 1.1e-03 (34) \n",
|
||
|
|
"Baseline I nan ± nan (0) \n",
|
||
|
|
"Baseline II nan ± nan (0) \n",
|
||
|
|
"Normality 11 Hz 1.5e+01, 5.8e-04 \n",
|
||
|
|
"Normality 30 Hz 1.9e+01, 6.3e-05 \n",
|
||
|
|
"Normality Baseline I NaN \n",
|
||
|
|
"Normality Baseline II NaN \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 2.1e-03 ± 2.7e-04, 5.4e-03 ± 1.1e-03 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 1.6e+02, 5.0e-02, (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 3.3e+00 ± 3.8e-01 (44) \n",
|
||
|
|
"30 Hz 1.3e+01 ± 2.8e+00 (34) \n",
|
||
|
|
"Baseline I nan ± nan (0) \n",
|
||
|
|
"Baseline II nan ± nan (0) \n",
|
||
|
|
"Normality 11 Hz 1.2e+01, 2.3e-03 \n",
|
||
|
|
"Normality 30 Hz 4.3e+01, 5.1e-10 \n",
|
||
|
|
"Normality Baseline I NaN \n",
|
||
|
|
"Normality Baseline II NaN \n",
|
||
|
|
"Paired summary 11 Hz 30 Hz 3.5e+00 ± 4.9e-01, 1.3e+01 ± 3.0e+00 (32) \n",
|
||
|
|
"Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n",
|
||
|
|
"Wilcoxon 11 Hz 30 Hz 8.8e+01, 1.0e-03, (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": 22,
|
||
|
|
"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",
|
||
|
|
" try:\n",
|
||
|
|
" stat.loc[f'Normality {c1}', Key] = normality(df, c1)\n",
|
||
|
|
" except:\n",
|
||
|
|
" stat.loc[f'Normality {c1}', Key] = np.nan\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",
|
||
|
|
" try:\n",
|
||
|
|
" stat.loc[f'Wilcoxon {c1} {c2}', Key] = wilcoxon(df, [c1, c2])\n",
|
||
|
|
" except:\n",
|
||
|
|
" stat.loc[f'Wilcoxon {c1} {c2}', Key] = np.nan\n",
|
||
|
|
"# stat.loc[f'Paired T {c1} {c2}', Key] = paired_t(df, [c1, c2])\n",
|
||
|
|
" stat.loc[f'Paired summary {c1} {c2}', Key] = summarize_wilcoxon(df, [c1, c2])\n",
|
||
|
|
"\n",
|
||
|
|
"stat.sort_index()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"stat.to_latex(output_path / \"statistics\" / f\"statistics.tex\")\n",
|
||
|
|
"stat.to_csv(output_path / \"statistics\" / f\"statistics.csv\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"for key, result in results.items():\n",
|
||
|
|
" result.to_latex(output_path / \"statistics\" / f\"values_{key}.tex\")\n",
|
||
|
|
" result.to_csv(output_path / \"statistics\" / f\"values_{key}.csv\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Plot PSD"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"psd = pd.read_feather(pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str) / 'data' / 'psd.feather')\n",
|
||
|
|
"freqs = pd.read_feather(pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str) / 'data' / 'freqs.feather')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"freq = freqs.T.iloc[0].values\n",
|
||
|
|
"\n",
|
||
|
|
"mask = (freq < 49)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
|
||
|
|
" out=out, **kwargs)\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
|
||
|
|
" ret = ret.dtype.type(ret / rcount)\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in true_divide\n",
|
||
|
|
" ret, rcount, out=ret, casting='unsafe', subok=False)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 750x300 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\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": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"action = project.require_action(\"stimulus-lfp-response\" + '-' + stim_loc + zscore_str)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_bandpower.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpeak.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_half_width.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_bandpower.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpower.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_half_width.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_energy.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_freq.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_p_max.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_energy.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_strength.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_energy.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_peak.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/statistics.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpower.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpeak.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_strength.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_bandpower.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_freq.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpeak.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/statistics.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_half_width.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpower.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_bandpower.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_p_max.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpeak.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_peak.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpower.csv',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_energy.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_half_width.tex',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_relpower.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_peak.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_bandpower.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_relpeak.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_freq.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_relpeak.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_energy.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_bandpower.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_freq.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_relpower.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_relpeak.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_relpower.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_bandpower.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_bandpower.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_energy.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_peak.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_relpower.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/.ipynb_checkpoints/lfp-psd-histogram-theta_energy-checkpoint.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_relpeak.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.svg']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 29,
|
||
|
|
"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
|
||
|
|
}
|