1565 lines
1.5 MiB
Plaintext
1565 lines
1.5 MiB
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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"import seaborn as sns\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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"from functools import reduce\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 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\n",
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"\n",
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"from septum_mec.analysis.statistics import load_data_frames, make_paired_tables, make_statistics_table"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## chose where to sample LFP"
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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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"# lfp_location = ''\n",
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"# lfp_location = '-other-tetrode'\n",
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"lfp_location = '-other-drive'\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-spike-lfp-response\" + lfp_location)\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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"project_path = dp.project_path()\n",
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"project = expipe.get_project(project_path)\n",
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"actions = project.actions"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of sessions above threshold 194\n",
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"Number of animals 4\n",
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"Number of individual gridcells 139\n",
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"Number of gridcell recordings 230\n"
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]
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}
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],
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"source": [
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"data, labels, colors, queries = load_data_frames()"
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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-spike-lfp-response' + lfp_location]\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 has old unit id's but correct on (action, unit_name, channel_group)\n",
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"lfp_results = lfp_results.drop('unit_id', 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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = data.merge(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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"data['stim_strength'] = data.stim_p_max / data.theta_peak"
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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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"keys = [\n",
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" 'theta_energy',\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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" 'theta_vec_len',\n",
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" 'theta_ang',\n",
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" 'stim_energy',\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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" 'stim_vec_len',\n",
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" 'stim_ang'\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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"results, labels = make_paired_tables(data, keys)"
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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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{
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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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|
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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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|||
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" <th></th>\n",
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|||
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" <th>entity</th>\n",
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|||
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" <th>unit_idnum</th>\n",
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|||
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" <th>channel_group</th>\n",
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|||
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" <th>date</th>\n",
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|||
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" <th>Baseline I</th>\n",
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" <th>11 Hz</th>\n",
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" <th>Baseline II</th>\n",
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" <th>30 Hz</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>51</th>\n",
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" <td>1833</td>\n",
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" <td>8</td>\n",
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" <td>0</td>\n",
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|||
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" <td>20719</td>\n",
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|||
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" <td>0.214949</td>\n",
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|||
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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|||
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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>85</th>\n",
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|||
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" <td>1833</td>\n",
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|||
|
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" <td>13</td>\n",
|
|||
|
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" <td>0</td>\n",
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|||
|
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" <td>20719</td>\n",
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|||
|
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" <td>NaN</td>\n",
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|||
|
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" <td>0.052683</td>\n",
|
|||
|
|
" <td>0.291401</td>\n",
|
|||
|
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" <td>0.055222</td>\n",
|
|||
|
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" </tr>\n",
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|||
|
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" <tr>\n",
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" <th>86</th>\n",
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|||
|
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" <td>1833</td>\n",
|
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" <td>14</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
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" <td>20719</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
|||
|
|
" <td>0.025182</td>\n",
|
|||
|
|
" <td>0.451846</td>\n",
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|||
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" <td>NaN</td>\n",
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|||
|
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" </tr>\n",
|
|||
|
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" <tr>\n",
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|||
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" <th>58</th>\n",
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|||
|
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" <td>1833</td>\n",
|
|||
|
|
" <td>23</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>200619</td>\n",
|
|||
|
|
" <td>0.233113</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
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" <td>NaN</td>\n",
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|||
|
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>127</th>\n",
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|||
|
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" <td>1833</td>\n",
|
|||
|
|
" <td>26</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
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" <td>200619</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.202562</td>\n",
|
|||
|
|
" <td>0.049574</td>\n",
|
|||
|
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" </tr>\n",
|
|||
|
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" <tr>\n",
|
|||
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" <th>...</th>\n",
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|||
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" <td>...</td>\n",
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|||
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" <td>...</td>\n",
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|||
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" <td>...</td>\n",
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|||
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" <td>...</td>\n",
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|||
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" <td>...</td>\n",
|
|||
|
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" <td>...</td>\n",
|
|||
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" <td>...</td>\n",
|
|||
|
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" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>139</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>835</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>150319</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.027863</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>43</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>851</td>\n",
|
|||
|
|
" <td>5</td>\n",
|
|||
|
|
" <td>60319</td>\n",
|
|||
|
|
" <td>0.040082</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>65</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>932</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>280219</td>\n",
|
|||
|
|
" <td>0.025449</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
|||
|
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" </tr>\n",
|
|||
|
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" <tr>\n",
|
|||
|
|
" <th>74</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>937</td>\n",
|
|||
|
|
" <td>7</td>\n",
|
|||
|
|
" <td>280219</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.135503</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
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" </tr>\n",
|
|||
|
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" <tr>\n",
|
|||
|
|
" <th>105</th>\n",
|
|||
|
|
" <td>1849</td>\n",
|
|||
|
|
" <td>939</td>\n",
|
|||
|
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" <td>7</td>\n",
|
|||
|
|
" <td>280219</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.435251</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
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|
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" </tr>\n",
|
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|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>137 rows × 8 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" entity unit_idnum channel_group date Baseline I 11 Hz \\\n",
|
|||
|
|
"51 1833 8 0 20719 0.214949 NaN \n",
|
|||
|
|
"85 1833 13 0 20719 NaN 0.052683 \n",
|
|||
|
|
"86 1833 14 0 20719 NaN 0.025182 \n",
|
|||
|
|
"58 1833 23 0 200619 0.233113 NaN \n",
|
|||
|
|
"127 1833 26 0 200619 NaN NaN \n",
|
|||
|
|
".. ... ... ... ... ... ... \n",
|
|||
|
|
"139 1849 835 4 150319 NaN NaN \n",
|
|||
|
|
"43 1849 851 5 60319 0.040082 NaN \n",
|
|||
|
|
"65 1849 932 7 280219 0.025449 NaN \n",
|
|||
|
|
"74 1849 937 7 280219 NaN 0.135503 \n",
|
|||
|
|
"105 1849 939 7 280219 NaN NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
" Baseline II 30 Hz \n",
|
|||
|
|
"51 NaN NaN \n",
|
|||
|
|
"85 0.291401 0.055222 \n",
|
|||
|
|
"86 0.451846 NaN \n",
|
|||
|
|
"58 NaN NaN \n",
|
|||
|
|
"127 0.202562 0.049574 \n",
|
|||
|
|
".. ... ... \n",
|
|||
|
|
"139 NaN 0.027863 \n",
|
|||
|
|
"43 NaN NaN \n",
|
|||
|
|
"65 NaN NaN \n",
|
|||
|
|
"74 NaN NaN \n",
|
|||
|
|
"105 0.435251 NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
"[137 rows x 8 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"results['gridcell']['theta_peak']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFGCAYAAADghAzcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydd5xU1fXAv7Mz24Fl6b0uHDqiYkMFFcWGxhpjLGjU2KOpGo0tv1gSE03UaNQoauxdUOyCig1BASmHDtI7LMv2eb8/7puZt8vMbJvZer+fz3zmlXvvO/N2du55557icxwHi8VisVgslsqkNLQAFovFYrFYGidWSbBYLBaLxRIVqyRYLBaLxWKJilUSLBaLxWKxRMUqCRaLxWKxWKJilQSLxWKxWCxRsUqCxWKxWCyWqFglwWKxWCwWS1SskmCxWCwWiyUqVkmwWCwWi8USFaskWCwWi8ViiYpVEiwWi8VisUTFKgkWi8VisViiYpUEi8VisVgsUQk0tACWxoGIjFDVeVGOTwYudHe7qurGehWsErHktFgaGhFx3M33VPX4WvSfBDzp7h6lqtMTJFqdifXZRGQc8Im7e6Oq3l3fslmSi1USWjgikgPcAVxFI/4+iEgX4F5gDNC3gcWxWCyWFkGjnRQs9cY/gIsbWohq8CxwNLC6oQWxWCyWloJVEiz+hhagmjQVOS0tFFX1NbQMFkuisY6LFovFYrFYomKVBIvFYrFYLFGxyw0tFBG5Dbi10rGQB/MMVR0Xo18f4HfA8UB3YDewEJgMPKWqTrR+bt9UYBJwFjACaAfsBOYCrwBPqmpJpT6TiURXAPT2yPmUqk6q1L4zcClwDCDuNcqAbcAs4AXg1XhyJgIRGQxc7crRA/ABP2I8wR9Q1YUx+k3GfN65qrqfiAwArgMmYO73XmAe8DTm8wfjyFDj++326wOsdHdPAzZgfFf2Bwoxf+8rvVEmIjICuB7jN9LVvc7XwL9U9QMRedf9DOHvlog8iHGYBThaVUNe8tE+y9nAi+7uRFWdGqttjP59gN+6MvQCCoDvgUdU9SUReQT4JbBaVfvU5l5UFd0gIj7gJOAKYDjQEfOdeAW4p5qfw++OcRHmb9rdlWMB8BLwqKoWxek/we17KNDZ7bsMeAfzvdxaHTksLQdrSbDUhDOARcCVQD8gHfNDNxYTuvW2iERVPEUkDzM5PQoci/mBSnX7jwceAeaKiNRWOBG5EPOD/mdgHGaySgeyMRPDGcDLwJvuj21SEJE/AfMx90nc62e525cD80XkNnfSiDfOGZiJ7EqgP5CBmejHAU8A74lIeoy+ibrfozCKzWHu9XOBkcAKz7UuAuZgFJJenuucDLwvIn+LMfZkz/bPq5DjfPd9E/BuFW0rICLHYybRq4A8IA3zOY4CXhSR56neA1OV9yKODJnAa8AU4ESgpzvGAOBGzPelXxVjdAE+Bt4EfkLkf7AtJurnn8AcEekVpW+2iLyGuXc/xfydQn0PBG4BlovIxKo+i6VlYZWElssjmB+9KZ5jo9zXJTH6PIh5In4IY0k4CrgJ81QGcALw68qd3B+3z4DBQInb/yTgIOBU4CmgHBgEfCIiXT3db3Flmu3ub/DIeYvnGkdjJp1MYDsmrPME4BCMcvCQe22AiXE+Y51wLTR3YBwt52GUgsOAw4FfAcsx/3e3UsmSU4lemIgOgL9iLBKHAzdgrDdgJvvroshQl/tdmZsxf/MbMRPRBcBtqrrHvdYZGIXF78p1K3AExqLwd/f6v8V8Vyqgqt8CP7i7Z8ZReDpivm8Az6pqWRx5K/c9BHgLo6SVuDId5cp4K5APnENECYlH3HtRBc9gJnYw34sLiXw3P8AoDTfF+RwZGAXlSPfQp8B57hinA++5xwcDb7lWpFDfFMw9OM09NBWjKByM+Q7dCewC2gCvi8gx1fg8lhaCXW5oobhJkTaKyHbPse+r6FaMMQt/4Tk2XUQ+Br509y/ETGpeHgG6YCaR8ao6q9L5t0TkFcwPWVfgPswPN6q6BlgjIqEf4pIYct7hvpcBE9wJyMtrrsk7pBSdBfwn7qetISKyP/And/cZ4OJKE9pMEfkv5kd6HHCLiLwUY+khF6N8HV7p884UkRnAF5gJaxL7mqprfb+jkAJcq6qPufvhv72IZAP3u7vbXVkXefp+IiJvYCbBjBjjT8bkv8jBWB5ejdLmHCK/VU/FGGcf3MnxQYxlowQ4VlU/9TT5XERewky4HasxZMx7UYUcx2KUAYCPgJNUtdjT5DUR+SdwbZxhbsYodWCWBSq3fd2zVDUSc8+ecc9di1HaAC5X1crf+4/c7+VMzPfmSRHpr6ql1fl8luaNtSRYasKDlRQEAFT1K4y5GWCQd8lBRAYCp7i7f4kyYYXGmEpkAjhLRLpVVygRycKYTrcDU6IoCN5r7HR3u1d3/BrwG8z/1DbMj/E+T7yqWoDJS+FgJvlr4oz372gKkXu/Q/4Ag7xP4Em434XEnpjPwPhbgMm2t6hyA1X9HPhLjP4A/8ModhB7ySH0lP99DbNtHg4c4G7/vZKCEJJvMVGsXzGIdy/i8Uv3vRyjOBZHafNbjG/APrjKTsjytRzzPYvGb4HQxD7B0zf0+aZFURAAUNUVGCsVGKvG6TGuYWlhWCXBUhPeiXNuqfuegnkqDHEiZjIE80RZnfFTME/a1UJV96rqaFVtD5xZRfNQWumopu3a4voXnODuzlTVvbHaqupKjG8HmGWEWLwf59xyz3Yrz3ai7/ecaM6NLqH161IiSyPReDTWCVXdBExzd08Ukbbe867PxGh3d3Kca8STDyLpjqPxIhHlMR7x7kVUXIX5WHd3pmsZ2wf3qf1/MYYZjfEpAXg+1hO+63S4P9BBVc9zD4/ATPpQ9fdhmmfbLjlYALvcYKkZa+Oc83pUe79Xozzbc2rglxjXiSsWIW9/1xTeF+PwNwhjgj2cyA9mohXkPpglAoBTPJ7uVREvxfSqOOe86+DJvN8/xumzn/u+yLWQREVVN4vISmJ/1icxE3o6ZhnoMc+5kBWhFHgujizx5NulqktjNVLVUhH5jih+E5WIdy9i0Q2z1g/GCTUe38Q4PtCzPSdGGwBU9YdKh7zfh3+IyD+qkCFErf7/LM0PqyRYakJ+Ndt5vfY71PJauVU3qYiI9MCYYk8h9o9ckORY0Gr7OQMi0lpVo93beA5xXiUkmfd7d4zjEHm6rU7Y3GZiKwlT3TE6YJYcHoOwdSb0RDxNVbdU4zrR5NtWTfmqIt69qEqG6sixKQFjVKbe/v8szROrJFhqQm1yC3i/YwcRWTOtihpNCG6Y2yuYcMMQ+Riz/gJMjoQPgdeBoTUZu5p4P+cTwAM16BtzaaKOciTifsf7m6e579VRumKO4z7JP4uJ/jhSRHqq6o+YCITebrPa+AIkRL4atonXp6q0zbH+VnX5nfb2vZKIg3FVFNbhmpZmhFUSLMlmu2d7naquT/QF3JC/FzAKQilwF/A8oJWTJolIq31HSAjez1lejUiRZJH0++1hK8YBtDqRAVU90U7GKAk+zJLDPzBhemCenmuUPMkjnwAdRMRXRQKt2j5xV8UGz3ZV96ldjOPev2n7Gl7f23dXA34vLU0UqyRYko13jfQQTEKZqIjIwRgHulUYJ694PhBeziPiLPl/qnpHtEZuFEDnaOcSwAqMRSAL8znjIiJ/wDjLLVfVDxMoR33c7xDfY5SEgSKSHcsvwXVGjLvGrarfi8j3GD+CUzFKQihK4/maOgx65BuDcewcACyJIV8KEf+FRLMRM1G3I+KAGYtYMnhDZEdirGFREZHnMM6LizF5ESp/H2L6dbj5KK7EfB++V9W5VchraQHY6AZLzLS+CeI9z/YVVbT9G3A3xipQeVKJJ2eeZ3t2zFYmZC8Ur59QBdn1OA+lFR4uIofHausmfrobk8/gj4mUg8Td7+oQyjmRCpwdp935VO+3ZrL7PsZNHxwKr6zNUgNUTBQWL6PjSSTJkqCq5cAb7u5BIjI8WrtK/heVmUXEH+KnrlITbYwMTHSLAO1dy8ksItaEc0UkJ1p
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 555x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 555x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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"<Figure size 555x330 with 1 Axes>"
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]
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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 555x330 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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|
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"data": {
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"text/plain": [
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"<Figure size 555x330 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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|||
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"<Figure size 555x330 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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{
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"data": {
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"text/plain": [
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"metadata": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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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 555x330 with 1 Axes>"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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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": [
|
|||
|
|
"<Figure size 555x330 with 1 Axes>"
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]
|
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},
|
|||
|
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"metadata": {
|
|||
|
|
"needs_background": "light"
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|
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},
|
|||
|
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"output_type": "display_data"
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},
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|
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{
|
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|
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"data": {
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"text/plain": [
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"<Figure size 555x330 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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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 555x330 with 1 Axes>"
|
|||
|
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]
|
|||
|
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},
|
|||
|
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"metadata": {
|
|||
|
|
"needs_background": "light"
|
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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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"<Figure size 555x330 with 1 Axes>"
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]
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},
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|
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"metadata": {
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|||
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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 555x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 555x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 555x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"xlabel = {\n",
|
|||
|
|
" 'theta_energy': 'Theta coherence energy',\n",
|
|||
|
|
" 'theta_peak': 'Theta peak coherence',\n",
|
|||
|
|
" 'theta_freq': '(Hz)',\n",
|
|||
|
|
" 'theta_half_width': '(Hz)',\n",
|
|||
|
|
" 'theta_vec_len': 'a.u.',\n",
|
|||
|
|
" 'theta_ang': 'rad'\n",
|
|||
|
|
"}\n",
|
|||
|
|
"for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
|
|||
|
|
" for key in xlabel:\n",
|
|||
|
|
" fig = plt.figure(figsize=(3.7,2.2))\n",
|
|||
|
|
" plt.suptitle(key + ' ' + cell_type)\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[cell_type][key].loc[:,labels], cumulative=True, legend=False, palette=colors, common_norm=False)\n",
|
|||
|
|
" plt.xlabel(xlabel[key])\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",
|
|||
|
|
" figname = f'spike-lfp-coherence-histogram-{key}-{cell_type}'.replace(' ', '-')\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": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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"<Figure size 495x330 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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|||
|
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"<Figure size 495x330 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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{
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"data": {
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"text/plain": [
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"metadata": {
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"text/plain": [
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|||
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"<Figure size 495x330 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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{
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"data": {
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"text/plain": [
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"<Figure size 495x330 with 1 Axes>"
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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 495x330 with 1 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 495x330 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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{
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"data": {
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"text/plain": [
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"<Figure size 495x330 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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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 495x330 with 1 Axes>"
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]
|
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|
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},
|
|||
|
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"metadata": {
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},
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|||
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"output_type": "display_data"
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},
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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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"text/plain": [
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"<Figure size 495x330 with 1 Axes>"
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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 495x330 with 1 Axes>"
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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": [
|
|||
|
|
"<Figure size 495x330 with 1 Axes>"
|
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|
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]
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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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|
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{
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"data": {
|
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"text/plain": [
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"<Figure size 495x330 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": [
|
|||
|
|
"<Figure size 495x330 with 1 Axes>"
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]
|
|||
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},
|
|||
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"metadata": {
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},
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|||
|
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"output_type": "display_data"
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},
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|
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{
|
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|
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 495x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 495x330 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"xlabel = {\n",
|
|||
|
|
" 'stim_energy': 'Coherence energy',\n",
|
|||
|
|
" 'stim_half_width': '(Hz)',\n",
|
|||
|
|
" 'stim_p_max': 'Peak coherence',\n",
|
|||
|
|
" 'stim_strength': 'Ratio',\n",
|
|||
|
|
" 'stim_vec_len': 'a.u.',\n",
|
|||
|
|
" 'stim_ang': 'rad'\n",
|
|||
|
|
"}\n",
|
|||
|
|
"# key = 'theta_energy'\n",
|
|||
|
|
"# key = 'theta_peak'\n",
|
|||
|
|
"for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
|
|||
|
|
" for key in xlabel:\n",
|
|||
|
|
" fig = plt.figure(figsize=(3.3,2.2))\n",
|
|||
|
|
" plt.suptitle(key + ' ' + cell_type)\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[cell_type][key].loc[:,labels[1::2]], cumulative=True, legend=False, palette=colors[1::2], common_norm=False)\n",
|
|||
|
|
" plt.xlabel(xlabel[key])\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",
|
|||
|
|
" figname = f'spike-lfp-coherence-histogram-{key}-{cell_type}'.replace(' ', '-')\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": [
|
|||
|
|
"## polar plot"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from septum_mec.analysis.statistics import VonMisesKDE"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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"<Figure size 480x330 with 1 Axes>"
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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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|||
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|
"<Figure size 480x330 with 1 Axes>"
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]
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},
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"metadata": {
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|
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},
|
|||
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|
"output_type": "display_data"
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},
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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 480x330 with 1 Axes>"
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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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{
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"data": {
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"metadata": {
|
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"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for paradigm in ['stim', 'theta']:\n",
|
|||
|
|
" key = paradigm + '_vec_len'\n",
|
|||
|
|
" for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
|
|||
|
|
" fig = plt.figure(figsize=(3.2,2.2))\n",
|
|||
|
|
" plt.suptitle(key + ' ' + cell_type)\n",
|
|||
|
|
" legend_lines = []\n",
|
|||
|
|
" for color, query, label in zip(colors, queries, labels):\n",
|
|||
|
|
" data_query = data.query(query + ' and ' + cell_type)\n",
|
|||
|
|
" values = data_query[key].values\n",
|
|||
|
|
" angles = data_query[paradigm + '_ang'].values\n",
|
|||
|
|
" kde = VonMisesKDE(angles, weights=values, kappa=5)\n",
|
|||
|
|
" bins = np.linspace(-np.pi, np.pi, 100)\n",
|
|||
|
|
" plt.polar(bins, kde.evaluate(bins), color=color, lw=2)\n",
|
|||
|
|
" plt.polar(angles, values, color=color, lw=1, ls='none', marker='.', markersize=2)\n",
|
|||
|
|
"# values.hist(\n",
|
|||
|
|
"# bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
|
|||
|
|
"# histtype=histtype, color=color)\n",
|
|||
|
|
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=2, label=label))\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",
|
|||
|
|
" figname = f'spike-lfp-polar-plot-{paradigm}-{cell_type}'.replace(' ', '-')\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": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stats = {}\n",
|
|||
|
|
"for cell_type, result in results.items():\n",
|
|||
|
|
" stats[cell_type], _ = make_statistics_table(result, labels)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"<div>\n",
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|
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|
|
" vertical-align: middle;\n",
|
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|
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" }\n",
|
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|
|
"\n",
|
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|
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" .dataframe tbody tr th {\n",
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|
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" vertical-align: top;\n",
|
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|
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" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Theta energy</th>\n",
|
|||
|
|
" <th>Theta peak</th>\n",
|
|||
|
|
" <th>Theta freq</th>\n",
|
|||
|
|
" <th>Theta half width</th>\n",
|
|||
|
|
" <th>Theta vec len</th>\n",
|
|||
|
|
" <th>Theta ang</th>\n",
|
|||
|
|
" <th>Stim energy</th>\n",
|
|||
|
|
" <th>Stim half width</th>\n",
|
|||
|
|
" <th>Stim p max</th>\n",
|
|||
|
|
" <th>Stim strength</th>\n",
|
|||
|
|
" <th>Stim vec len</th>\n",
|
|||
|
|
" <th>Stim ang</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Baseline I</th>\n",
|
|||
|
|
" <td>2.0e-01 ± 2.6e-02 (63)</td>\n",
|
|||
|
|
" <td>1.7e-01 ± 1.9e-02 (63)</td>\n",
|
|||
|
|
" <td>7.7e+00 ± 7.5e-02 (63)</td>\n",
|
|||
|
|
" <td>6.4e-01 ± 3.8e-02 (63)</td>\n",
|
|||
|
|
" <td>2.0e-01 ± 1.5e-02 (63)</td>\n",
|
|||
|
|
" <td>3.7e+00 ± 1.0e-01 (63)</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>11 Hz</th>\n",
|
|||
|
|
" <td>9.7e-02 ± 1.6e-02 (56)</td>\n",
|
|||
|
|
" <td>6.9e-02 ± 7.0e-03 (56)</td>\n",
|
|||
|
|
" <td>7.8e+00 ± 1.3e-01 (56)</td>\n",
|
|||
|
|
" <td>4.3e-01 ± 7.6e-02 (56)</td>\n",
|
|||
|
|
" <td>4.0e-02 ± 4.6e-03 (56)</td>\n",
|
|||
|
|
" <td>3.3e+00 ± 2.7e-01 (56)</td>\n",
|
|||
|
|
" <td>8.8e-02 ± 9.1e-03 (58)</td>\n",
|
|||
|
|
" <td>2.2e-01 ± 6.8e-03 (58)</td>\n",
|
|||
|
|
" <td>4.5e-01 ± 3.1e-02 (58)</td>\n",
|
|||
|
|
" <td>9.0e+00 ± 8.9e-01 (58)</td>\n",
|
|||
|
|
" <td>2.2e-01 ± 1.6e-02 (58)</td>\n",
|
|||
|
|
" <td>2.9e+00 ± 2.6e-01 (58)</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Baseline II</th>\n",
|
|||
|
|
" <td>3.3e-01 ± 4.2e-02 (46)</td>\n",
|
|||
|
|
" <td>2.5e-01 ± 2.5e-02 (46)</td>\n",
|
|||
|
|
" <td>8.1e+00 ± 4.4e-02 (46)</td>\n",
|
|||
|
|
" <td>8.2e-01 ± 6.4e-02 (46)</td>\n",
|
|||
|
|
" <td>2.3e-01 ± 1.9e-02 (46)</td>\n",
|
|||
|
|
" <td>4.0e+00 ± 1.1e-01 (46)</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>30 Hz</th>\n",
|
|||
|
|
" <td>4.3e-02 ± 4.8e-03 (35)</td>\n",
|
|||
|
|
" <td>4.2e-02 ± 4.7e-03 (35)</td>\n",
|
|||
|
|
" <td>7.9e+00 ± 2.0e-01 (35)</td>\n",
|
|||
|
|
" <td>3.0e-01 ± 2.7e-02 (35)</td>\n",
|
|||
|
|
" <td>1.8e-02 ± 2.6e-03 (35)</td>\n",
|
|||
|
|
" <td>3.8e+00 ± 3.1e-01 (35)</td>\n",
|
|||
|
|
" <td>9.1e-02 ± 8.8e-03 (33)</td>\n",
|
|||
|
|
" <td>2.5e-01 ± 4.0e-03 (33)</td>\n",
|
|||
|
|
" <td>3.9e-01 ± 3.3e-02 (33)</td>\n",
|
|||
|
|
" <td>1.3e+01 ± 1.5e+00 (33)</td>\n",
|
|||
|
|
" <td>2.5e-01 ± 1.5e-02 (33)</td>\n",
|
|||
|
|
" <td>2.6e+00 ± 2.4e-01 (33)</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>LMM Baseline I - 11 Hz</th>\n",
|
|||
|
|
" <td>1.1e-01, 1.3e-01</td>\n",
|
|||
|
|
" <td>5.3e-03, 1.2e-01</td>\n",
|
|||
|
|
" <td>9.8e-01, -7.5e-03</td>\n",
|
|||
|
|
" <td>7.8e-02, 2.7e-01</td>\n",
|
|||
|
|
" <td>1.6e-03, 1.5e-01</td>\n",
|
|||
|
|
" <td>2.0e-02, 4.3e-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>LMM Baseline I - Baseline II</th>\n",
|
|||
|
|
" <td>1.9e-01, -5.3e-02</td>\n",
|
|||
|
|
" <td>1.2e-01, -4.2e-02</td>\n",
|
|||
|
|
" <td>7.6e-02, 2.2e-01</td>\n",
|
|||
|
|
" <td>6.6e-01, -3.6e-02</td>\n",
|
|||
|
|
" <td>2.6e-01, -3.2e-02</td>\n",
|
|||
|
|
" <td>6.9e-01, 1.4e-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>LMM Baseline I - 30 Hz</th>\n",
|
|||
|
|
" <td>5.5e-02, 1.6e-01</td>\n",
|
|||
|
|
" <td>1.8e-02, 1.4e-01</td>\n",
|
|||
|
|
" <td>3.7e-01, -2.3e-01</td>\n",
|
|||
|
|
" <td>1.1e-11, 3.8e-01</td>\n",
|
|||
|
|
" <td>9.7e-04, 1.8e-01</td>\n",
|
|||
|
|
" <td>4.7e-01, 3.5e-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>LMM 11 Hz - Baseline II</th>\n",
|
|||
|
|
" <td>1.2e-02, 2.3e-01</td>\n",
|
|||
|
|
" <td>1.1e-02, 1.7e-01</td>\n",
|
|||
|
|
" <td>7.1e-01, 1.4e-01</td>\n",
|
|||
|
|
" <td>1.4e-12, 4.2e-01</td>\n",
|
|||
|
|
" <td>6.3e-04, 1.6e-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",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>LMM 11 Hz - 30 Hz</th>\n",
|
|||
|
|
" <td>5.2e-02, -6.2e-02</td>\n",
|
|||
|
|
" <td>4.5e-02, -3.2e-02</td>\n",
|
|||
|
|
" <td>5.5e-01, 2.8e-01</td>\n",
|
|||
|
|
" <td>3.3e-01, -1.3e-01</td>\n",
|
|||
|
|
" <td>1.6e-02, -2.7e-02</td>\n",
|
|||
|
|
" <td>4.0e-01, 2.8e-01</td>\n",
|
|||
|
|
" <td>8.7e-01, 6.2e-03</td>\n",
|
|||
|
|
" <td>2.7e-01, 2.8e-02</td>\n",
|
|||
|
|
" <td>7.5e-01, -3.0e-02</td>\n",
|
|||
|
|
" <td>1.1e-01, 4.5e+00</td>\n",
|
|||
|
|
" <td>6.8e-01, 3.0e-02</td>\n",
|
|||
|
|
" <td>4.9e-01, -4.2e-01</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>LMM Baseline II - 30 Hz</th>\n",
|
|||
|
|
" <td>1.9e-02, 2.7e-01</td>\n",
|
|||
|
|
" <td>2.5e-02, 1.9e-01</td>\n",
|
|||
|
|
" <td>8.3e-01, 7.0e-02</td>\n",
|
|||
|
|
" <td>6.4e-04, 4.8e-01</td>\n",
|
|||
|
|
" <td>4.9e-04, 2.0e-01</td>\n",
|
|||
|
|
" <td>6.1e-01, 3.0e-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",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Theta energy Theta peak \\\n",
|
|||
|
|
"Baseline I 2.0e-01 ± 2.6e-02 (63) 1.7e-01 ± 1.9e-02 (63) \n",
|
|||
|
|
"11 Hz 9.7e-02 ± 1.6e-02 (56) 6.9e-02 ± 7.0e-03 (56) \n",
|
|||
|
|
"Baseline II 3.3e-01 ± 4.2e-02 (46) 2.5e-01 ± 2.5e-02 (46) \n",
|
|||
|
|
"30 Hz 4.3e-02 ± 4.8e-03 (35) 4.2e-02 ± 4.7e-03 (35) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz 1.1e-01, 1.3e-01 5.3e-03, 1.2e-01 \n",
|
|||
|
|
"LMM Baseline I - Baseline II 1.9e-01, -5.3e-02 1.2e-01, -4.2e-02 \n",
|
|||
|
|
"LMM Baseline I - 30 Hz 5.5e-02, 1.6e-01 1.8e-02, 1.4e-01 \n",
|
|||
|
|
"LMM 11 Hz - Baseline II 1.2e-02, 2.3e-01 1.1e-02, 1.7e-01 \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 5.2e-02, -6.2e-02 4.5e-02, -3.2e-02 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz 1.9e-02, 2.7e-01 2.5e-02, 1.9e-01 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Theta freq Theta half width \\\n",
|
|||
|
|
"Baseline I 7.7e+00 ± 7.5e-02 (63) 6.4e-01 ± 3.8e-02 (63) \n",
|
|||
|
|
"11 Hz 7.8e+00 ± 1.3e-01 (56) 4.3e-01 ± 7.6e-02 (56) \n",
|
|||
|
|
"Baseline II 8.1e+00 ± 4.4e-02 (46) 8.2e-01 ± 6.4e-02 (46) \n",
|
|||
|
|
"30 Hz 7.9e+00 ± 2.0e-01 (35) 3.0e-01 ± 2.7e-02 (35) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz 9.8e-01, -7.5e-03 7.8e-02, 2.7e-01 \n",
|
|||
|
|
"LMM Baseline I - Baseline II 7.6e-02, 2.2e-01 6.6e-01, -3.6e-02 \n",
|
|||
|
|
"LMM Baseline I - 30 Hz 3.7e-01, -2.3e-01 1.1e-11, 3.8e-01 \n",
|
|||
|
|
"LMM 11 Hz - Baseline II 7.1e-01, 1.4e-01 1.4e-12, 4.2e-01 \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 5.5e-01, 2.8e-01 3.3e-01, -1.3e-01 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz 8.3e-01, 7.0e-02 6.4e-04, 4.8e-01 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Theta vec len Theta ang \\\n",
|
|||
|
|
"Baseline I 2.0e-01 ± 1.5e-02 (63) 3.7e+00 ± 1.0e-01 (63) \n",
|
|||
|
|
"11 Hz 4.0e-02 ± 4.6e-03 (56) 3.3e+00 ± 2.7e-01 (56) \n",
|
|||
|
|
"Baseline II 2.3e-01 ± 1.9e-02 (46) 4.0e+00 ± 1.1e-01 (46) \n",
|
|||
|
|
"30 Hz 1.8e-02 ± 2.6e-03 (35) 3.8e+00 ± 3.1e-01 (35) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz 1.6e-03, 1.5e-01 2.0e-02, 4.3e-01 \n",
|
|||
|
|
"LMM Baseline I - Baseline II 2.6e-01, -3.2e-02 6.9e-01, 1.4e-01 \n",
|
|||
|
|
"LMM Baseline I - 30 Hz 9.7e-04, 1.8e-01 4.7e-01, 3.5e-01 \n",
|
|||
|
|
"LMM 11 Hz - Baseline II 6.3e-04, 1.6e-01 NaN \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 1.6e-02, -2.7e-02 4.0e-01, 2.8e-01 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz 4.9e-04, 2.0e-01 6.1e-01, 3.0e-01 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Stim energy Stim half width \\\n",
|
|||
|
|
"Baseline I NaN NaN \n",
|
|||
|
|
"11 Hz 8.8e-02 ± 9.1e-03 (58) 2.2e-01 ± 6.8e-03 (58) \n",
|
|||
|
|
"Baseline II NaN NaN \n",
|
|||
|
|
"30 Hz 9.1e-02 ± 8.8e-03 (33) 2.5e-01 ± 4.0e-03 (33) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz NaN NaN \n",
|
|||
|
|
"LMM Baseline I - Baseline II NaN NaN \n",
|
|||
|
|
"LMM Baseline I - 30 Hz NaN NaN \n",
|
|||
|
|
"LMM 11 Hz - Baseline II NaN NaN \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 8.7e-01, 6.2e-03 2.7e-01, 2.8e-02 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz NaN NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
" Stim p max Stim strength \\\n",
|
|||
|
|
"Baseline I NaN NaN \n",
|
|||
|
|
"11 Hz 4.5e-01 ± 3.1e-02 (58) 9.0e+00 ± 8.9e-01 (58) \n",
|
|||
|
|
"Baseline II NaN NaN \n",
|
|||
|
|
"30 Hz 3.9e-01 ± 3.3e-02 (33) 1.3e+01 ± 1.5e+00 (33) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz NaN NaN \n",
|
|||
|
|
"LMM Baseline I - Baseline II NaN NaN \n",
|
|||
|
|
"LMM Baseline I - 30 Hz NaN NaN \n",
|
|||
|
|
"LMM 11 Hz - Baseline II NaN NaN \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 7.5e-01, -3.0e-02 1.1e-01, 4.5e+00 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz NaN NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
" Stim vec len Stim ang \n",
|
|||
|
|
"Baseline I NaN NaN \n",
|
|||
|
|
"11 Hz 2.2e-01 ± 1.6e-02 (58) 2.9e+00 ± 2.6e-01 (58) \n",
|
|||
|
|
"Baseline II NaN NaN \n",
|
|||
|
|
"30 Hz 2.5e-01 ± 1.5e-02 (33) 2.6e+00 ± 2.4e-01 (33) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz NaN NaN \n",
|
|||
|
|
"LMM Baseline I - Baseline II NaN NaN \n",
|
|||
|
|
"LMM Baseline I - 30 Hz NaN NaN \n",
|
|||
|
|
"LMM 11 Hz - Baseline II NaN NaN \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 6.8e-01, 3.0e-02 4.9e-01, -4.2e-01 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz NaN NaN "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"stats['gridcell']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type, stat in stats.items():\n",
|
|||
|
|
" stat.to_latex(output_path / \"statistics\" / f\"statistics_{cell_type}.tex\")\n",
|
|||
|
|
" stat.to_csv(output_path / \"statistics\" / f\"statistics_{cell_type}.csv\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type, cell_results in results.items():\n",
|
|||
|
|
" for key, result in cell_results.items():\n",
|
|||
|
|
" result.to_latex(output_path / \"statistics\" / f\"values_{cell_type}_{key}.tex\")\n",
|
|||
|
|
" result.to_csv(output_path / \"statistics\" / f\"values_{cell_type}_{key}.csv\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# psd plots"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"coher = pd.read_feather(output_path / 'data' / 'coherence.feather')\n",
|
|||
|
|
"freqs = pd.read_feather(output_path / 'data' / 'freqs.feather')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"freq = freqs.T.iloc[0].values\n",
|
|||
|
|
"\n",
|
|||
|
|
"mask = (freq < 100)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\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}_{r.unit_name}' \n",
|
|||
|
|
" for i, r in data.query(query + ' and ' + cell_type).iterrows()]\n",
|
|||
|
|
" values = coher.loc[mask, selection].dropna(axis=1).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",
|
|||
|
|
" ax.set_ylim(-30, 0)\n",
|
|||
|
|
" axs[0].set_ylabel('Coherence')\n",
|
|||
|
|
" despine()\n",
|
|||
|
|
" figname = f'spike-lfp-coherence-{cell_type}'.replace(' ', '-')\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": [
|
|||
|
|
"# NSni vs NSi analysis"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"nsi_vs_nsni = {}\n",
|
|||
|
|
"for key in keys:\n",
|
|||
|
|
" df = pd.DataFrame()\n",
|
|||
|
|
" dfs = [results[k][key].loc[:, ['entity', 'unit_idnum', 'Baseline I']].rename({'Baseline I': k}, axis=1) for k in ['ns_inhibited', 'ns_not_inhibited']]\n",
|
|||
|
|
" df = pd.merge(*dfs, on=['entity', 'unit_idnum'], how='outer')\n",
|
|||
|
|
" nsi_vs_nsni[key] = df"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"nsi_vs_nsni.keys()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"nsi_vs_nsni['theta_energy']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from septum_mec.analysis.statistics import LMM"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"LMM(nsi_vs_nsni['theta_energy'], 'ns_inhibited', 'ns_not_inhibited', 'theta_energy')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stat, stat_vals = make_statistics_table(nsi_vs_nsni, ['ns_inhibited', 'ns_not_inhibited'], wilcoxon_test=False)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stat"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stat.to_latex(output_path / \"statistics\" / f\"statistics_nsi_vs_nsni.tex\")\n",
|
|||
|
|
"stat.to_csv(output_path / \"statistics\" / f\"statistics_nsi_vs_nsni.csv\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Store results in Expipe action"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"action = project.require_action(\"stimulus-spike-lfp-response\" + lfp_location)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"copy_tree(output_path, str(action.data_path()))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-spike-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
|
|||
|
|
}
|