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": null,
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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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" <th>Baseline I</th>\n",
|
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|
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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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|||
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" <td>8</td>\n",
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|||
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" <td>0</td>\n",
|
|||
|
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" <td>20719</td>\n",
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|||
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" <td>0.251582</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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" <td>20719</td>\n",
|
|||
|
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" <td>NaN</td>\n",
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|||
|
|
" <td>0.057134</td>\n",
|
|||
|
|
" <td>0.194778</td>\n",
|
|||
|
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" <td>0.042542</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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|||
|
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" <td>14</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>20719</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
|||
|
|
" <td>0.049972</td>\n",
|
|||
|
|
" <td>0.129583</td>\n",
|
|||
|
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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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" <td>1833</td>\n",
|
|||
|
|
" <td>23</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>200619</td>\n",
|
|||
|
|
" <td>0.247097</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
|||
|
|
" </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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" <td>1833</td>\n",
|
|||
|
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" <td>26</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>200619</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.179928</td>\n",
|
|||
|
|
" <td>0.034969</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <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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" <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>0.023559</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>0.665441</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>0.034074</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
|||
|
|
" </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>0.126604</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>0.246451</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 0.251582 NaN \n",
|
|||
|
|
"85 1833 13 0 20719 NaN 0.057134 \n",
|
|||
|
|
"86 1833 14 0 20719 NaN 0.049972 \n",
|
|||
|
|
"58 1833 23 0 200619 0.247097 NaN \n",
|
|||
|
|
"127 1833 26 0 200619 NaN NaN \n",
|
|||
|
|
".. ... ... ... ... ... ... \n",
|
|||
|
|
"139 1849 835 4 150319 NaN NaN \n",
|
|||
|
|
"43 1849 851 5 60319 0.665441 NaN \n",
|
|||
|
|
"65 1849 932 7 280219 0.034074 NaN \n",
|
|||
|
|
"74 1849 937 7 280219 NaN 0.126604 \n",
|
|||
|
|
"105 1849 939 7 280219 NaN NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
" Baseline II 30 Hz \n",
|
|||
|
|
"51 NaN NaN \n",
|
|||
|
|
"85 0.194778 0.042542 \n",
|
|||
|
|
"86 0.129583 NaN \n",
|
|||
|
|
"58 NaN NaN \n",
|
|||
|
|
"127 0.179928 0.034969 \n",
|
|||
|
|
".. ... ... \n",
|
|||
|
|
"139 NaN 0.023559 \n",
|
|||
|
|
"43 NaN NaN \n",
|
|||
|
|
"65 NaN NaN \n",
|
|||
|
|
"74 NaN NaN \n",
|
|||
|
|
"105 0.246451 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": "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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"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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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|||
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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": {
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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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|||
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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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{
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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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|
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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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"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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},
|
|||
|
|
"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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"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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{
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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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"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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"metadata": {
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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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},
|
|||
|
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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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"data": {
|
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|
|
"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": 15,
|
|||
|
|
"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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"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 495x330 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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|
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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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]
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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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{
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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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"output_type": "display_data"
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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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|||
|
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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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|
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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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"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 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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"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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"metadata": {
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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": [
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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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"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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]
|
|||
|
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},
|
|||
|
|
"metadata": {
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"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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"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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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAc8AAAFGCAYAAAAMxxQ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd5xU1fn48c/MbJltdAUBQeqDKCr2hmIvKGCLRk1EY2/RRI2JJtGvv8QYUzT2EqJiIybGAlawIYq9gMgBVFB6XdheZu7vj3On7DI7O7NzZ+vzfr32NXNn7j1zZnbmPveee85zfI7joJRSSqnU+du6AkoppVRHo8FTKaWUSpMGT6WUUipNGjyVUkqpNGnwVEoppdKkwVMppZRKkwZPpZRSKk0aPJVSSqk0afBUSiml0qTBUymllEqTBk+llFIqTRo8lVJKqTRp8FRKKaXSpMFTKaWUSlNOW1cgG0RkN2PMlwkefwQ4x13cwRizplUrpjIiIpH58141xhzbppVRbU5EfMCuxpj5WSh7CvAvd/HHxpinPS5/GTAYMMaYUS3YfifgO3fxAWPMxXHPTSHDuqeyr3TrsNEYU5Zu+V6J+xzfNsaMb83X7lRnniLSXUTuBD5t67oopbJHRPYC5gG/bOu6dDUiki8ivwMWAr3buj5tpbOdef4NOK+tK6GUyroPsQf/X7d1Rbqg64Cb27oSba2zBc9AsieNMVOAKa1SE6VUNmW11cwY8wjwSDZfIxPGmGWAL4vlT6HpfWXS/WxX0amabZVSSqnWoMFTKaWUSlO7bLYVEQEuBY4AhmCD/AbgE+BZ4EljTH3c+jcBv29URqRnZrQXVrIeZHG9tv5qjLlGRCYAlwN7AcXA98B/gT8bY7a42+yB7bBwGLCdW8c3gFuMMYsz/iAavp/ngYlAGNjRGLMqybq/Bf7PXTzAGDOv0fM7AlcAxwA7AXnAamAOcL8x5v0U6nM48DNgb2AQUAcY4Hngrshn1NpEpBf2vU0AhgOFwDps55J/GWNebmK7KcR6KPbEfs5XAScDQ7FNZEux3787jTFbW1C38cCb7uJYbIeLy4DTAQHygWXADOz3cG0T5eQAZ7vb7Qn0AsqA5djv333GmKXp1i/F97AM+zu50xhzlfs9uBQ4AOgDbALeBe41xrzZVDluWTth3/9R2M84F1gLzAWmGmNmJ3n9iHNEJPKbPtdtbs1Ysh6rcfuRL4wxe4jICOx35RhgAFAJfAk8BjxqjAmn8HrjsN/bcdj/5zrgA+BuY8xbCdbfiSZ62yZYtw9wAzDJrd8m4H3gYWPMS01sE3mP4O4rG30mEd/Z3TXLjTE7JShnf+BC4FCgP3Y/sQx4DfiHMeb7purtbt8PuBL7ex4K1AAfAX83xryWbNtsa3dnniJyJvaLdyWwC3bnFwQGYv/5jwIfiUjfLNbhfuwO7FhsUCzA7tx+A8wRkSIROR/baeFs7BcyD/vlOBv4WER297haj7q3fuBHzax7lnu7OEHg/BmwGLgW2A3ohv18hwA/Bd4TkQdEJDdRwSJSIiL/AWYDZwIj3e1LsIH0FmBhFt5/s0TkeOAb4CZgH2wQzAd2BE4DXhKR50WkpLmigPnYThG7Y99bMbAH9qBkgbvzysT2wMfYTm77AT2w37Odsf+bRW6P0oYVE+kOvIXdiR3rlpODfa97AL9wt70sw/o1S0Ruw34PTsF+9/OAfsCpwBsickuSbX8OLAKuIfYZB7GB8Uxglog8IyJFWX0TGRKRU4DPsQcQw7DvoRcwHpgKvCoi+c2U8WfgHex3tB/2cxyI/VzfEJEbMqjiKOx3+SrsbzzyPzoJmCkiT7oHY54SkRwRuRcbpM/FBr7IfmIM9qRjsYhcmKSMo7D7ql9j91XF2N69x2I/1z94Xe90tKvgKSLDsV+4POxR1SXAwdij2rOx/wiwO4l74za9H3sk/2LcY2Pdv/PTrMY5wEXYs6gLgIOAn2CPlsD+4/8DPIA9Sr7Srd/xwKvuOiXAnWm+bnNmYI8YAX7c1EruDlfcxWmNnpsCPIz9En+H/QKPAw7EvtfP3VUvBB5MULYPeAH7owZ7kHM+9v2fCDzpPt4f+8Psmeqby5SIHOHWrQf2qP132BaB/bH/v7fcVScC/xORZJ0ensOeTT/lrr8/dgcQaU3YEbgvwyo/iv0uvYLdae4HnIE9qsZ9H4+5n3m8v2G/kwD/BE7AHigcA9wKVGA7dPwjywcwp2N7Xf4AXI39Dh2J/V1GWn1udM88GnAD5x3YA5ty4E/YVqYDsb954656KvBCo//V8djfdcSLxH7rL3jxxtIwCHjCvf9n7Hs4GLgeiLRMHIkNXE0R7MHSWmyQOBR7lvUg9nP0Abck+hxT9DtssHwZmIz9rV6C/b+B3Zf8PcWyXsB+zg/EPTbBfez4Rus+7L4O2JaIc7C/o/HYk5A12P//A3EtB1EiMhaYid2X1mG/L4djP98bgS1uOTumWHfPtbdm27OwH2gIOMwYszzuuXki8m/sTvBA4CQR6WOM2eA2v64RkUhwwRjzOS3TBxsUxsU1zb0nIp8BC9zlY7HBdD9jzLrIhiLyKrZ5cB/gEBHpYYwpbWE9GjDG1IrIU9hmrn1FZJgx5psEq57p3jrEBU8R6Q/c4y7OBiYZYyritnvfbap5DPuDmiIi040xr8St8zPslx/gf8Dpxpi6uOdniMhi7JnfAGyzd5NnH14RkQLgcWzQmA8cYYxZH7fKB8Dj7pnSddid3AXYg65E+gEXGGMeji9DRJ7DNrXuABwjIv0ySLTRD/iDMebGuMc+FJH/YVs0dgdGA/u69UdE8oi1KjxsjLmgUZmvici72J2OH3tgc0UL65dK/b/C/k42xz0+W0SWA7e5y+dgfxNAtLnxz+7iWuzvPH64SeR7+F/sDvlw4OfYgwaMMQvdciLrb8rgt56pntiDlYMb1WGuiLwNvIcNflOIfR6JLAbGG2NWxz32koh8hz0g8mEPAOcl2rgZPuylpl/FPTbPbT2agz0zvUxEHmwu2YQxZhOwSUTiv/ML3Z6/USIymViT763GmN80KuptEXkYuy8fDdwjIjOMMRvj1rkb24wfBk40xrwa99xct/7vYvfXbaJdnXlif5Bgj0a3uabn7qh/D/wDe7Sbrfrf2PialjHmKxqOKbs5PnC664SJnf36sM04Xnos7v4ZjZ8UEX/c4283Ovi4DNsEXg/8tFHgBMC9jnwJ9qgO7E4rXqSJZSv2+lId2/oD9swP4Oim34qnzib23Tm/UeCMdwP2uiVs+97ifdQocALgHgj92130YZuSWmo19iCj8WvU0nCIRPzZY6QZGmBJokLda1j/wDY5J7y+66HrGgXOiIeInX02Pvu9CtuyBHB5o8AJgDGmGvs/jZR9rfvdbo/uTRS83cslkSxno5ppuv1lo8AZcR+xz3HXFtbvS+wZWuP6bQAi10l92NY2r1zr3n6F/c1tw/2NXuouFhHXQigiu2JPkMBe+3610eYYYwz2QLjNtLcv5CL3tjvwjIjs3HgFY8wsY8zPjTF3Ng5eHgkT69TR2Mq4+9t0ZnDF16nYkxq5jDEfEvuMEjXdjsc2mULDQAu2eQXskWKTnY3cjj5z3cVDItc+3Qv3e7uPv9BUhyA3AB8C9DPGjGv63Xgq8t62uJ9RQm7dIp0MRrln44kk64gQf7bf3LXTZN6I7/SW4musI9Z0/xsROcc9627A/X3c1FRnEI/UYzsnbcMNqJHA1/gzOsa9XY9tvUjILeMpd7Ef9lJNe5Tqd6WpfUEdMCvRE+5vLLI/6ZV+1QD4pzEm1ET5b2M7mYFHB7rupZoD3MXZxhgnyervYju6gW0NiohvAn6Spj0NVKVdSY+0t2bbx7BHLQOwnYMmici3wOvYL9gsr5pBk9hgjClv4rmauPuJjhQbr5ONQcyPYptydhGRMY2aWiJNepXY67JAtHfmGHdxN4n1RG5OIdAXWAGMIPZ+kqY/dI8KW1PkGlj3NN4b2E4MiQ4kliXZJv67kcnvJ+3XMMY4btPzbdgDzEeA+0VkDvb38Rq2B2g6n0F
|
|||
|
|
"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": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from septum_mec.analysis.statistics import VonMisesKDE"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"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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},
|
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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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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 480x330 with 1 Axes>"
|
|||
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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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"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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},
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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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},
|
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|
|
"metadata": {
|
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|
"needs_background": "light"
|
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},
|
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|
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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": 20,
|
|||
|
|
"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": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
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"<div>\n",
|
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"<style scoped>\n",
|
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|
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|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
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|
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" vertical-align: top;\n",
|
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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>2.5e-01 ± 3.3e-02 (63)</td>\n",
|
|||
|
|
" <td>2.2e-01 ± 2.5e-02 (63)</td>\n",
|
|||
|
|
" <td>7.6e+00 ± 9.6e-02 (63)</td>\n",
|
|||
|
|
" <td>7.2e-01 ± 3.4e-02 (63)</td>\n",
|
|||
|
|
" <td>2.1e-01 ± 1.2e-02 (63)</td>\n",
|
|||
|
|
" <td>3.9e+00 ± 1.3e-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>1.1e-01 ± 1.6e-02 (56)</td>\n",
|
|||
|
|
" <td>8.0e-02 ± 7.9e-03 (56)</td>\n",
|
|||
|
|
" <td>7.7e+00 ± 1.2e-01 (56)</td>\n",
|
|||
|
|
" <td>4.6e-01 ± 7.6e-02 (56)</td>\n",
|
|||
|
|
" <td>4.8e-02 ± 7.3e-03 (56)</td>\n",
|
|||
|
|
" <td>3.5e+00 ± 2.7e-01 (56)</td>\n",
|
|||
|
|
" <td>9.7e-02 ± 1.0e-02 (58)</td>\n",
|
|||
|
|
" <td>3.0e-01 ± 5.1e-02 (58)</td>\n",
|
|||
|
|
" <td>4.5e-01 ± 3.5e-02 (58)</td>\n",
|
|||
|
|
" <td>8.2e+00 ± 9.1e-01 (58)</td>\n",
|
|||
|
|
" <td>2.3e-01 ± 1.5e-02 (58)</td>\n",
|
|||
|
|
" <td>3.1e+00 ± 2.6e-01 (58)</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Baseline II</th>\n",
|
|||
|
|
" <td>2.7e-01 ± 3.2e-02 (46)</td>\n",
|
|||
|
|
" <td>2.0e-01 ± 2.1e-02 (46)</td>\n",
|
|||
|
|
" <td>8.2e+00 ± 7.7e-02 (46)</td>\n",
|
|||
|
|
" <td>7.8e-01 ± 5.9e-02 (46)</td>\n",
|
|||
|
|
" <td>2.0e-01 ± 1.2e-02 (46)</td>\n",
|
|||
|
|
" <td>3.6e+00 ± 1.3e-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>3.7e-02 ± 1.5e-03 (35)</td>\n",
|
|||
|
|
" <td>3.5e-02 ± 2.1e-03 (35)</td>\n",
|
|||
|
|
" <td>8.8e+00 ± 1.6e-01 (35)</td>\n",
|
|||
|
|
" <td>2.7e-01 ± 2.9e-02 (35)</td>\n",
|
|||
|
|
" <td>1.7e-02 ± 2.3e-03 (35)</td>\n",
|
|||
|
|
" <td>3.3e+00 ± 3.8e-01 (35)</td>\n",
|
|||
|
|
" <td>1.0e-01 ± 1.0e-02 (33)</td>\n",
|
|||
|
|
" <td>2.6e-01 ± 5.1e-03 (33)</td>\n",
|
|||
|
|
" <td>4.3e-01 ± 3.7e-02 (33)</td>\n",
|
|||
|
|
" <td>1.3e+01 ± 1.1e+00 (33)</td>\n",
|
|||
|
|
" <td>2.7e-01 ± 1.5e-02 (33)</td>\n",
|
|||
|
|
" <td>3.0e+00 ± 2.5e-01 (33)</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>LMM Baseline I - 11 Hz</th>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>1.5e-13, 1.4e-01</td>\n",
|
|||
|
|
" <td>9.3e-01, -2.9e-02</td>\n",
|
|||
|
|
" <td>1.5e-03, 2.8e-01</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>6.2e-01, 2.9e-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>9.8e-01, -1.1e-03</td>\n",
|
|||
|
|
" <td>3.7e-01, -2.8e-02</td>\n",
|
|||
|
|
" <td>3.7e-02, 3.2e-01</td>\n",
|
|||
|
|
" <td>9.7e-01, -5.1e-03</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>7.5e-01, -1.2e-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>1.1e-35, 1.9e-01</td>\n",
|
|||
|
|
" <td>2.7e-09, 1.7e-01</td>\n",
|
|||
|
|
" <td>1.4e-02, -9.9e-01</td>\n",
|
|||
|
|
" <td>1.3e-09, 4.5e-01</td>\n",
|
|||
|
|
" <td>2.6e-12, 1.8e-01</td>\n",
|
|||
|
|
" <td>1.3e-01, 7.9e-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>3.3e-02, 1.7e-01</td>\n",
|
|||
|
|
" <td>1.1e-01, 1.2e-01</td>\n",
|
|||
|
|
" <td>1.7e-01, 4.6e-01</td>\n",
|
|||
|
|
" <td>7.4e-03, 3.3e-01</td>\n",
|
|||
|
|
" <td>1.6e-03, 1.3e-01</td>\n",
|
|||
|
|
" <td>7.9e-01, -1.7e-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 - 30 Hz</th>\n",
|
|||
|
|
" <td>1.4e-02, -6.8e-02</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>1.5e-04, 8.8e-01</td>\n",
|
|||
|
|
" <td>2.1e-03, -1.8e-01</td>\n",
|
|||
|
|
" <td>7.2e-02, -3.8e-02</td>\n",
|
|||
|
|
" <td>7.6e-01, -2.3e-01</td>\n",
|
|||
|
|
" <td>9.9e-01, 5.8e-04</td>\n",
|
|||
|
|
" <td>5.9e-01, -5.3e-02</td>\n",
|
|||
|
|
" <td>9.7e-01, 3.0e-03</td>\n",
|
|||
|
|
" <td>1.4e-01, 5.0e+00</td>\n",
|
|||
|
|
" <td>3.5e-01, 4.6e-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>1.5e-02, 2.1e-01</td>\n",
|
|||
|
|
" <td>3.6e-02, 1.5e-01</td>\n",
|
|||
|
|
" <td>1.4e-01, -4.8e-01</td>\n",
|
|||
|
|
" <td>1.1e-02, 4.7e-01</td>\n",
|
|||
|
|
" <td>1.2e-13, 1.7e-01</td>\n",
|
|||
|
|
" <td>4.1e-01, 6.0e-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 2.5e-01 ± 3.3e-02 (63) 2.2e-01 ± 2.5e-02 (63) \n",
|
|||
|
|
"11 Hz 1.1e-01 ± 1.6e-02 (56) 8.0e-02 ± 7.9e-03 (56) \n",
|
|||
|
|
"Baseline II 2.7e-01 ± 3.2e-02 (46) 2.0e-01 ± 2.1e-02 (46) \n",
|
|||
|
|
"30 Hz 3.7e-02 ± 1.5e-03 (35) 3.5e-02 ± 2.1e-03 (35) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz NaN 1.5e-13, 1.4e-01 \n",
|
|||
|
|
"LMM Baseline I - Baseline II 9.8e-01, -1.1e-03 3.7e-01, -2.8e-02 \n",
|
|||
|
|
"LMM Baseline I - 30 Hz 1.1e-35, 1.9e-01 2.7e-09, 1.7e-01 \n",
|
|||
|
|
"LMM 11 Hz - Baseline II 3.3e-02, 1.7e-01 1.1e-01, 1.2e-01 \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 1.4e-02, -6.8e-02 NaN \n",
|
|||
|
|
"LMM Baseline II - 30 Hz 1.5e-02, 2.1e-01 3.6e-02, 1.5e-01 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Theta freq Theta half width \\\n",
|
|||
|
|
"Baseline I 7.6e+00 ± 9.6e-02 (63) 7.2e-01 ± 3.4e-02 (63) \n",
|
|||
|
|
"11 Hz 7.7e+00 ± 1.2e-01 (56) 4.6e-01 ± 7.6e-02 (56) \n",
|
|||
|
|
"Baseline II 8.2e+00 ± 7.7e-02 (46) 7.8e-01 ± 5.9e-02 (46) \n",
|
|||
|
|
"30 Hz 8.8e+00 ± 1.6e-01 (35) 2.7e-01 ± 2.9e-02 (35) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz 9.3e-01, -2.9e-02 1.5e-03, 2.8e-01 \n",
|
|||
|
|
"LMM Baseline I - Baseline II 3.7e-02, 3.2e-01 9.7e-01, -5.1e-03 \n",
|
|||
|
|
"LMM Baseline I - 30 Hz 1.4e-02, -9.9e-01 1.3e-09, 4.5e-01 \n",
|
|||
|
|
"LMM 11 Hz - Baseline II 1.7e-01, 4.6e-01 7.4e-03, 3.3e-01 \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 1.5e-04, 8.8e-01 2.1e-03, -1.8e-01 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz 1.4e-01, -4.8e-01 1.1e-02, 4.7e-01 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Theta vec len Theta ang \\\n",
|
|||
|
|
"Baseline I 2.1e-01 ± 1.2e-02 (63) 3.9e+00 ± 1.3e-01 (63) \n",
|
|||
|
|
"11 Hz 4.8e-02 ± 7.3e-03 (56) 3.5e+00 ± 2.7e-01 (56) \n",
|
|||
|
|
"Baseline II 2.0e-01 ± 1.2e-02 (46) 3.6e+00 ± 1.3e-01 (46) \n",
|
|||
|
|
"30 Hz 1.7e-02 ± 2.3e-03 (35) 3.3e+00 ± 3.8e-01 (35) \n",
|
|||
|
|
"LMM Baseline I - 11 Hz NaN 6.2e-01, 2.9e-01 \n",
|
|||
|
|
"LMM Baseline I - Baseline II NaN 7.5e-01, -1.2e-01 \n",
|
|||
|
|
"LMM Baseline I - 30 Hz 2.6e-12, 1.8e-01 1.3e-01, 7.9e-01 \n",
|
|||
|
|
"LMM 11 Hz - Baseline II 1.6e-03, 1.3e-01 7.9e-01, -1.7e-01 \n",
|
|||
|
|
"LMM 11 Hz - 30 Hz 7.2e-02, -3.8e-02 7.6e-01, -2.3e-01 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz 1.2e-13, 1.7e-01 4.1e-01, 6.0e-01 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Stim energy Stim half width \\\n",
|
|||
|
|
"Baseline I NaN NaN \n",
|
|||
|
|
"11 Hz 9.7e-02 ± 1.0e-02 (58) 3.0e-01 ± 5.1e-02 (58) \n",
|
|||
|
|
"Baseline II NaN NaN \n",
|
|||
|
|
"30 Hz 1.0e-01 ± 1.0e-02 (33) 2.6e-01 ± 5.1e-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 9.9e-01, 5.8e-04 5.9e-01, -5.3e-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 ± 3.5e-02 (58) 8.2e+00 ± 9.1e-01 (58) \n",
|
|||
|
|
"Baseline II NaN NaN \n",
|
|||
|
|
"30 Hz 4.3e-01 ± 3.7e-02 (33) 1.3e+01 ± 1.1e+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 9.7e-01, 3.0e-03 1.4e-01, 5.0e+00 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz NaN NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
" Stim vec len Stim ang \n",
|
|||
|
|
"Baseline I NaN NaN \n",
|
|||
|
|
"11 Hz 2.3e-01 ± 1.5e-02 (58) 3.1e+00 ± 2.6e-01 (58) \n",
|
|||
|
|
"Baseline II NaN NaN \n",
|
|||
|
|
"30 Hz 2.7e-01 ± 1.5e-02 (33) 3.0e+00 ± 2.5e-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 3.5e-01, 4.6e-02 4.9e-01, -4.2e-01 \n",
|
|||
|
|
"LMM Baseline II - 30 Hz NaN NaN "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"stats['gridcell']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"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": 23,
|
|||
|
|
"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": 24,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"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": 26,
|
|||
|
|
"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
|
|||
|
|
}
|