2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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"outputs": [],
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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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"from functools import reduce\n",
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2019-12-13 10:43:57 +00:00
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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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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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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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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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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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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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"output_path = pathlib.Path(\"output\") / (\"stimulus-spike-lfp-response\" + lfp_location)\n",
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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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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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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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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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2019-10-17 17:44:01 +00:00
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"source": [
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2021-03-10 12:27:53 +00:00
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"data, labels, colors, queries = load_data_frames()"
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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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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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2019-10-17 17:44:01 +00:00
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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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2019-10-22 10:22:00 +00:00
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"source": [
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2021-03-10 12:27:53 +00:00
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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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2019-10-22 10:22:00 +00:00
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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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2021-03-10 12:27:53 +00:00
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"data = data.merge(lfp_results, how='left')"
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2019-10-22 10:22:00 +00:00
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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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2019-10-17 17:44:01 +00:00
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"source": [
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2021-03-10 12:27:53 +00:00
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"data['stim_strength'] = data.stim_p_max / data.theta_peak"
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2019-10-17 17:44:01 +00:00
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]
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},
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{
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"cell_type": "code",
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2019-10-22 10:22:00 +00:00
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"execution_count": 11,
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2019-10-17 17:44:01 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2021-03-10 12:27:53 +00:00
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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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2019-10-17 17:44:01 +00:00
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]
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},
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{
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"cell_type": "code",
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2019-10-22 10:22:00 +00:00
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"execution_count": 12,
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2019-10-17 17:44:01 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2021-03-10 12:27:53 +00:00
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"results, labels = make_paired_tables(data, keys)"
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2019-10-17 17:44:01 +00:00
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]
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},
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{
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"cell_type": "code",
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2019-10-22 10:22:00 +00:00
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"execution_count": 13,
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"metadata": {},
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2019-10-17 17:44:01 +00:00
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>entity</th>\n",
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2021-03-10 12:27:53 +00:00
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" <th>unit_idnum</th>\n",
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" <th>channel_group</th>\n",
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" <th>date</th>\n",
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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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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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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>0.244278</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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2019-10-22 10:22:00 +00:00
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" <td>NaN</td>\n",
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2019-10-17 17:44:01 +00:00
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" </tr>\n",
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" <tr>\n",
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2021-03-10 12:27:53 +00:00
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" <th>85</th>\n",
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" <td>1833</td>\n",
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" <td>13</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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2019-10-17 17:44:01 +00:00
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" <td>NaN</td>\n",
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2021-03-10 12:27:53 +00:00
|
|
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|
" <td>0.039465</td>\n",
|
|
|
|
|
|
" <td>0.279382</td>\n",
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|
|
|
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|
" <td>0.054633</td>\n",
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2019-10-17 17:44:01 +00:00
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" </tr>\n",
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" <tr>\n",
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2021-03-10 12:27:53 +00:00
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" <th>86</th>\n",
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" <td>1833</td>\n",
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" <td>14</td>\n",
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|
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|
" <td>0</td>\n",
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|
|
|
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|
" <td>20719</td>\n",
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2019-10-22 10:22:00 +00:00
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" <td>NaN</td>\n",
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2021-03-10 12:27:53 +00:00
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|
" <td>0.022268</td>\n",
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|
|
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|
" <td>0.448472</td>\n",
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2019-10-17 17:44:01 +00:00
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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2021-03-10 12:27:53 +00:00
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" <th>58</th>\n",
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" <td>1833</td>\n",
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" <td>23</td>\n",
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" <td>0</td>\n",
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" <td>200619</td>\n",
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|
" <td>0.280865</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <th>127</th>\n",
|
|
|
|
|
|
" <td>1833</td>\n",
|
|
|
|
|
|
" <td>26</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>200619</td>\n",
|
2019-10-22 10:22:00 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <td>0.181703</td>\n",
|
|
|
|
|
|
" <td>0.038459</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2019-12-13 10:43:57 +00:00
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <th>139</th>\n",
|
|
|
|
|
|
" <td>1849</td>\n",
|
|
|
|
|
|
" <td>835</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>150319</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
2019-12-13 10:43:57 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <td>0.692552</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <th>43</th>\n",
|
|
|
|
|
|
" <td>1849</td>\n",
|
|
|
|
|
|
" <td>851</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>60319</td>\n",
|
|
|
|
|
|
" <td>0.662908</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
2019-12-13 10:43:57 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <th>65</th>\n",
|
|
|
|
|
|
" <td>1849</td>\n",
|
|
|
|
|
|
" <td>932</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>280219</td>\n",
|
|
|
|
|
|
" <td>0.050995</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
2019-12-13 10:43:57 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <th>74</th>\n",
|
|
|
|
|
|
" <td>1849</td>\n",
|
|
|
|
|
|
" <td>937</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>280219</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <td>0.131422</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <th>105</th>\n",
|
|
|
|
|
|
" <td>1849</td>\n",
|
|
|
|
|
|
" <td>939</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>280219</td>\n",
|
2019-10-22 10:22:00 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" <td>0.472020</td>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"<p>137 rows × 8 columns</p>\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" entity unit_idnum channel_group date Baseline I 11 Hz \\\n",
|
|
|
|
|
|
"51 1833 8 0 20719 0.244278 NaN \n",
|
|
|
|
|
|
"85 1833 13 0 20719 NaN 0.039465 \n",
|
|
|
|
|
|
"86 1833 14 0 20719 NaN 0.022268 \n",
|
|
|
|
|
|
"58 1833 23 0 200619 0.280865 NaN \n",
|
|
|
|
|
|
"127 1833 26 0 200619 NaN NaN \n",
|
|
|
|
|
|
".. ... ... ... ... ... ... \n",
|
|
|
|
|
|
"139 1849 835 4 150319 NaN NaN \n",
|
|
|
|
|
|
"43 1849 851 5 60319 0.662908 NaN \n",
|
|
|
|
|
|
"65 1849 932 7 280219 0.050995 NaN \n",
|
|
|
|
|
|
"74 1849 937 7 280219 NaN 0.131422 \n",
|
|
|
|
|
|
"105 1849 939 7 280219 NaN NaN \n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" Baseline II 30 Hz \n",
|
|
|
|
|
|
"51 NaN NaN \n",
|
|
|
|
|
|
"85 0.279382 0.054633 \n",
|
|
|
|
|
|
"86 0.448472 NaN \n",
|
|
|
|
|
|
"58 NaN NaN \n",
|
|
|
|
|
|
"127 0.181703 0.038459 \n",
|
|
|
|
|
|
".. ... ... \n",
|
|
|
|
|
|
"139 NaN 0.692552 \n",
|
|
|
|
|
|
"43 NaN NaN \n",
|
|
|
|
|
|
"65 NaN NaN \n",
|
|
|
|
|
|
"74 NaN NaN \n",
|
|
|
|
|
|
"105 0.472020 NaN \n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"[137 rows x 8 columns]"
|
2019-10-17 17:44:01 +00:00
|
|
|
|
]
|
|
|
|
|
|
},
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"execution_count": 13,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"results['gridcell']['theta_peak']"
|
2019-10-17 17:44:01 +00:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"execution_count": 14,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"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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},
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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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
|
2019-10-17 17:44:01 +00:00
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"text/plain": [
|
2021-03-10 12:27:53 +00:00
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"<Figure size 555x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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]
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},
|
2021-03-10 12:27:53 +00:00
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"metadata": {
|
|
|
|
|
|
"needs_background": "light"
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},
|
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"output_type": "display_data"
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2019-10-17 17:44:01 +00:00
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}
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],
|
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"source": [
|
2021-03-10 12:27:53 +00:00
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"xlabel = {\n",
|
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" 'theta_energy': 'Theta coherence energy',\n",
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" 'theta_peak': 'Theta peak coherence',\n",
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" 'theta_freq': '(Hz)',\n",
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|
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" 'theta_half_width': '(Hz)',\n",
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|
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" 'theta_vec_len': 'a.u.',\n",
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|
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" 'theta_ang': 'rad'\n",
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"}\n",
|
|
|
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|
|
"for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
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|
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" for key in xlabel:\n",
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|
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" fig = plt.figure(figsize=(3.7,2.2))\n",
|
|
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|
|
" plt.suptitle(key + ' ' + cell_type)\n",
|
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|
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" legend_lines = []\n",
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|
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" for color, label in zip(colors, labels):\n",
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" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n",
|
|
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|
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" sns.kdeplot(data=results[cell_type][key].loc[:,labels], cumulative=True, legend=False, palette=colors, common_norm=False)\n",
|
|
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|
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" plt.xlabel(xlabel[key])\n",
|
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" plt.legend(\n",
|
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|
|
" handles=legend_lines,\n",
|
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|
|
" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
|
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" plt.tight_layout()\n",
|
|
|
|
|
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" 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",
|
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|
|
" fig.savefig(\n",
|
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|
|
|
" output_path / 'figures' / f'{figname}.svg', \n",
|
|
|
|
|
|
" bbox_inches='tight', transparent=True)"
|
2019-10-17 17:44:01 +00:00
|
|
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|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"execution_count": 15,
|
|
|
|
|
|
"metadata": {},
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"image/png": "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
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"text/plain": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"<Figure size 495x330 with 1 Axes>"
|
2019-10-17 17:44:01 +00:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2021-03-10 12:27:53 +00:00
|
|
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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]
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},
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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"text/plain": [
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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]
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},
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{
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"data": {
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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]
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},
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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]
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},
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2021-03-10 12:27:53 +00:00
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2019-10-22 10:22:00 +00:00
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2019-10-22 10:22:00 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-22 10:22:00 +00:00
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"text/plain": [
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-22 10:22:00 +00:00
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]
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},
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{
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"data": {
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2021-03-10 12:27:53 +00:00
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2019-10-22 10:22:00 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-22 10:22:00 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-22 10:22:00 +00:00
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-22 10:22:00 +00:00
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]
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},
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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]
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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]
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},
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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"text/plain": [
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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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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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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2021-03-10 12:27:53 +00:00
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"<Figure size 495x330 with 1 Axes>"
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2019-10-17 17:44:01 +00:00
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]
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},
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2021-03-10 12:27:53 +00:00
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"image/png": "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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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"source": [
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"xlabel = {\n",
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" 'stim_energy': 'Coherence energy',\n",
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" 'stim_half_width': '(Hz)',\n",
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" 'stim_p_max': 'Peak coherence',\n",
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" 'stim_strength': 'Ratio',\n",
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" 'stim_vec_len': 'a.u.',\n",
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" 'stim_ang': 'rad'\n",
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"}\n",
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"# key = 'theta_energy'\n",
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"# key = 'theta_peak'\n",
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"for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
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" for key in xlabel:\n",
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" fig = plt.figure(figsize=(3.3,2.2))\n",
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" plt.suptitle(key + ' ' + cell_type)\n",
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" legend_lines = []\n",
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" for color, label in zip(colors[1::2], labels[1::2]):\n",
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" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n",
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" sns.kdeplot(data=results[cell_type][key].loc[:,labels[1::2]], cumulative=True, legend=False, palette=colors[1::2], common_norm=False)\n",
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" plt.xlabel(xlabel[key])\n",
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" plt.legend(\n",
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" handles=legend_lines,\n",
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" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
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" plt.tight_layout()\n",
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" plt.grid(False)\n",
|
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" despine()\n",
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" figname = f'spike-lfp-coherence-histogram-{key}-{cell_type}'.replace(' ', '-')\n",
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" fig.savefig(\n",
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" output_path / 'figures' / f'{figname}.png', \n",
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" bbox_inches='tight', transparent=True)\n",
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" fig.savefig(\n",
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" output_path / 'figures' / f'{figname}.svg', \n",
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" bbox_inches='tight', transparent=True)"
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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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"## polar plot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
|
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"metadata": {},
|
|
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"outputs": [],
|
|
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"source": [
|
|
|
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|
|
"from septum_mec.analysis.statistics import VonMisesKDE"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
|
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"outputs": [
|
2019-10-17 17:44:01 +00:00
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{
|
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"data": {
|
2021-03-10 12:27:53 +00:00
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2019-10-17 17:44:01 +00:00
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},
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2021-03-10 12:27:53 +00:00
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2019-10-22 10:22:00 +00:00
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"<Figure size 480x330 with 1 Axes>"
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},
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{
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"data": {
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2021-03-10 12:27:53 +00:00
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2019-10-22 10:22:00 +00:00
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2021-03-10 12:27:53 +00:00
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2019-10-22 10:22:00 +00:00
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"<Figure size 480x330 with 1 Axes>"
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},
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2021-03-10 12:27:53 +00:00
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"image/png": "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
|
2019-10-22 10:22:00 +00:00
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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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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
|
2021-03-10 12:27:53 +00:00
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"image/png": "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
|
2019-10-17 17:44:01 +00:00
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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": {
|
|
|
|
|
|
"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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|
"source": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"for paradigm in ['stim', 'theta']:\n",
|
|
|
|
|
|
" key = paradigm + '_vec_len'\n",
|
|
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|
|
|
" for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" fig = plt.figure(figsize=(3.2,2.2))\n",
|
|
|
|
|
|
" plt.suptitle(key + ' ' + cell_type)\n",
|
|
|
|
|
|
" legend_lines = []\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
" 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",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" plt.legend(\n",
|
|
|
|
|
|
" handles=legend_lines,\n",
|
|
|
|
|
|
" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
|
|
|
|
|
|
" plt.tight_layout()\n",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"# plt.grid(False)\n",
|
|
|
|
|
|
" figname = f'spike-lfp-polar-plot-{paradigm}-{cell_type}'.replace(' ', '-')\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" 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)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2019-12-13 10:47:07 +00:00
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"execution_count": null,
|
2019-12-13 10:47:07 +00:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"stats = {}\n",
|
|
|
|
|
|
"for cell_type, result in results.items():\n",
|
|
|
|
|
|
" stats[cell_type], _ = make_statistics_table(result, labels)"
|
2019-12-13 10:47:07 +00:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"stats['gridcell']"
|
2019-12-13 10:47:07 +00:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"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\")"
|
2019-12-13 10:47:07 +00:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"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\")"
|
2019-12-13 10:47:07 +00:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# psd plots"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2019-10-17 17:44:01 +00:00
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"execution_count": null,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"execution_count": null,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"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",
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"execution_count": null,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"freq = freqs.T.iloc[0].values\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"mask = (freq < 100)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"execution_count": null,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"metadata": {},
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"outputs": [],
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"source": [
|
2019-10-22 10:22:00 +00:00
|
|
|
|
"for cell_type in ['gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" 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",
|
2019-10-22 10:22:00 +00:00
|
|
|
|
" ax.set_ylim(-30, 0)\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" axs[0].set_ylabel('Coherence')\n",
|
2019-10-22 10:22:00 +00:00
|
|
|
|
" despine()\n",
|
2019-10-17 17:44:01 +00:00
|
|
|
|
" 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)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2021-03-10 12:27:53 +00:00
|
|
|
|
{
|
|
|
|
|
|
"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\")"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2019-10-17 17:44:01 +00:00
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Store results in Expipe action"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"execution_count": null,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"action = project.require_action(\"stimulus-spike-lfp-response\" + lfp_location)"
|
2019-10-17 17:44:01 +00:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"execution_count": null,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"metadata": {},
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"outputs": [],
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"source": [
|
|
|
|
|
|
"copy_tree(output_path, str(action.data_path()))"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2019-12-16 15:16:33 +00:00
|
|
|
|
"execution_count": null,
|
2019-10-17 17:44:01 +00:00
|
|
|
|
"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,
|
2021-03-10 12:27:53 +00:00
|
|
|
|
"nbformat_minor": 4
|
2019-10-17 17:44:01 +00:00
|
|
|
|
}
|
