982 lines
210 KiB
Plaintext
982 lines
210 KiB
Plaintext
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"cells": [
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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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"# Compute FWHM of PSTHs for 11 and 30Hz\n",
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"\n",
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"Only PSTHs with a significance level below 0.05 are used"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:21: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
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"Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`\n"
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]
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"import spatial_maps as sp\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 expipe\n",
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"import os\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import exdir\n",
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"import pandas as pd\n",
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"import optogenetics as og\n",
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"import quantities as pq\n",
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"import shutil\n",
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"from distutils.dir_util import copy_tree\n",
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"import seaborn as sns\n",
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"from functools import reduce\n",
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"from septum_mec.analysis.stimulus_response import stimulus_response_latency, compute_response\n",
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"import scipy.stats\n",
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"from tqdm import tqdm_notebook as tqdm\n",
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"from tqdm._tqdm_notebook import tqdm_notebook\n",
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"from septum_mec.analysis.statistics import load_data_frames, make_paired_tables, make_statistics_table \n",
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"tqdm_notebook.pandas()\n",
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"\n",
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"%matplotlib widget"
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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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"std_gaussian_kde = 0.04\n",
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"window_size = 0.03\n",
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"tmin = 0. # delay to compute FWHM after stimulus onset"
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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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"data_loader = dp.Data()\n",
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"actions = data_loader.actions\n",
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"project = data_loader.project"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"output = pathlib.Path('output/stimulus-response-fwhm')\n",
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"(output / 'data').mkdir(parents=True, exist_ok=True)\n",
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"(output / 'figures').mkdir(parents=True, exist_ok=True)\n",
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"(output / 'statistics').mkdir(parents=True, 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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"colors = ['#d95f02','#e7298a']\n",
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"labels = [\n",
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" '11 Hz', \n",
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" '30 Hz'\n",
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"]\n",
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"queries = [\n",
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" 'frequency==11 and stim_location==\"ms\"',\n",
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" 'frequency==30 and stim_location==\"ms\"']"
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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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{
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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(queries, labels, colors)"
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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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"# use baseline==False and p_e_peak < 0.05\n",
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"session_units_sig = data.query('p_e_peak == p_e_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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>action</th>\n",
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" <th>baseline</th>\n",
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" <th>entity</th>\n",
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" <th>frequency</th>\n",
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" <th>i</th>\n",
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" <th>ii</th>\n",
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" <th>session</th>\n",
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" <th>stim_location</th>\n",
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" <th>stimulated</th>\n",
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" <th>tag</th>\n",
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" <th>...</th>\n",
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" <th>bs_ctrl</th>\n",
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" <th>ns_inhibited</th>\n",
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" <th>ns_not_inhibited</th>\n",
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" <th>gridcell</th>\n",
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" <th>bs_not_gridcell</th>\n",
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" <th>label</th>\n",
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" <th>label_num</th>\n",
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" <th>query</th>\n",
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" <th>color</th>\n",
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" <th>cell_type</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>13</th>\n",
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" <td>1839-120619-4</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>1839</td>\n",
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" <td>30.0</td>\n",
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|||
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>4</td>\n",
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|||
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" <td>ms</td>\n",
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" <td>True</td>\n",
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|||
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" <td>stim ii</td>\n",
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" <td>...</td>\n",
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|||
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" <td>NaN</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>False</td>\n",
|
|||
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" <td>True</td>\n",
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" <td>30 Hz</td>\n",
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|||
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" <td>1.0</td>\n",
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|||
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" <td>frequency==30 and stim_location==\"ms\"</td>\n",
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|||
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" <td>#e7298a</td>\n",
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" <td>bs_not_gridcell</td>\n",
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|||
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>1839-120619-4</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>1839</td>\n",
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|||
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" <td>30.0</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>True</td>\n",
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|||
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" <td>4</td>\n",
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|||
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" <td>ms</td>\n",
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|||
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" <td>True</td>\n",
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" <td>stim ii</td>\n",
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|||
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" <td>...</td>\n",
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|||
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" <td>NaN</td>\n",
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|||
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" <td>True</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>True</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>30 Hz</td>\n",
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|||
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" <td>1.0</td>\n",
|
|||
|
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" <td>frequency==30 and stim_location==\"ms\"</td>\n",
|
|||
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" <td>#e7298a</td>\n",
|
|||
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" <td>gridcell</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>15</th>\n",
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|||
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" <td>1839-120619-4</td>\n",
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|||
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" <td>False</td>\n",
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|||
|
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" <td>1839</td>\n",
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|||
|
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" <td>30.0</td>\n",
|
|||
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" <td>False</td>\n",
|
|||
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" <td>True</td>\n",
|
|||
|
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" <td>4</td>\n",
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|||
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" <td>ms</td>\n",
|
|||
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" <td>True</td>\n",
|
|||
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" <td>stim ii</td>\n",
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|||
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" <td>...</td>\n",
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|||
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" <td>NaN</td>\n",
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|||
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" <td>True</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>False</td>\n",
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|||
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" <td>False</td>\n",
|
|||
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" <td>30 Hz</td>\n",
|
|||
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" <td>1.0</td>\n",
|
|||
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" <td>frequency==30 and stim_location==\"ms\"</td>\n",
|
|||
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" <td>#e7298a</td>\n",
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" <td>ns_inhibited</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
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|||
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" <td>1839-120619-4</td>\n",
|
|||
|
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" <td>False</td>\n",
|
|||
|
|
" <td>1839</td>\n",
|
|||
|
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" <td>30.0</td>\n",
|
|||
|
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" <td>False</td>\n",
|
|||
|
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" <td>True</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
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" <td>ms</td>\n",
|
|||
|
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" <td>True</td>\n",
|
|||
|
|
" <td>stim ii</td>\n",
|
|||
|
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" <td>...</td>\n",
|
|||
|
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" <td>NaN</td>\n",
|
|||
|
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" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>30 Hz</td>\n",
|
|||
|
|
" <td>1.0</td>\n",
|
|||
|
|
" <td>frequency==30 and stim_location==\"ms\"</td>\n",
|
|||
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|
" <td>#e7298a</td>\n",
|
|||
|
|
" <td>bs_not_gridcell</td>\n",
|
|||
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|
" </tr>\n",
|
|||
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" <tr>\n",
|
|||
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" <th>17</th>\n",
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|||
|
|
" <td>1839-120619-4</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1839</td>\n",
|
|||
|
|
" <td>30.0</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
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" <td>4</td>\n",
|
|||
|
|
" <td>ms</td>\n",
|
|||
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" <td>True</td>\n",
|
|||
|
|
" <td>stim ii</td>\n",
|
|||
|
|
" <td>...</td>\n",
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|||
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" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
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" <td>False</td>\n",
|
|||
|
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" <td>False</td>\n",
|
|||
|
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" <td>True</td>\n",
|
|||
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" <td>30 Hz</td>\n",
|
|||
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" <td>1.0</td>\n",
|
|||
|
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" <td>frequency==30 and stim_location==\"ms\"</td>\n",
|
|||
|
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" <td>#e7298a</td>\n",
|
|||
|
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" <td>bs_not_gridcell</td>\n",
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|||
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" </tr>\n",
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" <tr>\n",
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" <tr>\n",
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|
|
" <th>1279</th>\n",
|
|||
|
|
" <td>1833-010719-2</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>11.0</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>ms</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>stim i</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>11 Hz</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>frequency==11 and stim_location==\"ms\"</td>\n",
|
|||
|
|
" <td>#d95f02</td>\n",
|
|||
|
|
" <td>bs_not_gridcell</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1280</th>\n",
|
|||
|
|
" <td>1833-010719-2</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>11.0</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>ms</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>stim i</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>11 Hz</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>frequency==11 and stim_location==\"ms\"</td>\n",
|
|||
|
|
" <td>#d95f02</td>\n",
|
|||
|
|
" <td>bs_not_gridcell</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1281</th>\n",
|
|||
|
|
" <td>1833-010719-2</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>11.0</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>ms</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>stim i</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>11 Hz</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>frequency==11 and stim_location==\"ms\"</td>\n",
|
|||
|
|
" <td>#d95f02</td>\n",
|
|||
|
|
" <td>bs_not_gridcell</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1282</th>\n",
|
|||
|
|
" <td>1833-010719-2</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>11.0</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>ms</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>stim i</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>11 Hz</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>frequency==11 and stim_location==\"ms\"</td>\n",
|
|||
|
|
" <td>#d95f02</td>\n",
|
|||
|
|
" <td>bs_not_gridcell</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1283</th>\n",
|
|||
|
|
" <td>1833-010719-2</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>1833</td>\n",
|
|||
|
|
" <td>11.0</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>ms</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>stim i</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>False</td>\n",
|
|||
|
|
" <td>True</td>\n",
|
|||
|
|
" <td>11 Hz</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>frequency==11 and stim_location==\"ms\"</td>\n",
|
|||
|
|
" <td>#d95f02</td>\n",
|
|||
|
|
" <td>bs_not_gridcell</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>647 rows × 68 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" action baseline entity frequency i ii session \\\n",
|
|||
|
|
"13 1839-120619-4 False 1839 30.0 False True 4 \n",
|
|||
|
|
"14 1839-120619-4 False 1839 30.0 False True 4 \n",
|
|||
|
|
"15 1839-120619-4 False 1839 30.0 False True 4 \n",
|
|||
|
|
"16 1839-120619-4 False 1839 30.0 False True 4 \n",
|
|||
|
|
"17 1839-120619-4 False 1839 30.0 False True 4 \n",
|
|||
|
|
"... ... ... ... ... ... ... ... \n",
|
|||
|
|
"1279 1833-010719-2 False 1833 11.0 True False 2 \n",
|
|||
|
|
"1280 1833-010719-2 False 1833 11.0 True False 2 \n",
|
|||
|
|
"1281 1833-010719-2 False 1833 11.0 True False 2 \n",
|
|||
|
|
"1282 1833-010719-2 False 1833 11.0 True False 2 \n",
|
|||
|
|
"1283 1833-010719-2 False 1833 11.0 True False 2 \n",
|
|||
|
|
"\n",
|
|||
|
|
" stim_location stimulated tag ... bs_ctrl ns_inhibited \\\n",
|
|||
|
|
"13 ms True stim ii ... NaN False \n",
|
|||
|
|
"14 ms True stim ii ... NaN True \n",
|
|||
|
|
"15 ms True stim ii ... NaN True \n",
|
|||
|
|
"16 ms True stim ii ... NaN False \n",
|
|||
|
|
"17 ms True stim ii ... NaN False \n",
|
|||
|
|
"... ... ... ... ... ... ... \n",
|
|||
|
|
"1279 ms True stim i ... NaN False \n",
|
|||
|
|
"1280 ms True stim i ... NaN False \n",
|
|||
|
|
"1281 ms True stim i ... NaN False \n",
|
|||
|
|
"1282 ms True stim i ... NaN False \n",
|
|||
|
|
"1283 ms True stim i ... NaN False \n",
|
|||
|
|
"\n",
|
|||
|
|
" ns_not_inhibited gridcell bs_not_gridcell label label_num \\\n",
|
|||
|
|
"13 False False True 30 Hz 1.0 \n",
|
|||
|
|
"14 False True False 30 Hz 1.0 \n",
|
|||
|
|
"15 False False False 30 Hz 1.0 \n",
|
|||
|
|
"16 False False True 30 Hz 1.0 \n",
|
|||
|
|
"17 False False True 30 Hz 1.0 \n",
|
|||
|
|
"... ... ... ... ... ... \n",
|
|||
|
|
"1279 False False True 11 Hz 0.0 \n",
|
|||
|
|
"1280 False False True 11 Hz 0.0 \n",
|
|||
|
|
"1281 False False True 11 Hz 0.0 \n",
|
|||
|
|
"1282 False False True 11 Hz 0.0 \n",
|
|||
|
|
"1283 False False True 11 Hz 0.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" query color cell_type \n",
|
|||
|
|
"13 frequency==30 and stim_location==\"ms\" #e7298a bs_not_gridcell \n",
|
|||
|
|
"14 frequency==30 and stim_location==\"ms\" #e7298a gridcell \n",
|
|||
|
|
"15 frequency==30 and stim_location==\"ms\" #e7298a ns_inhibited \n",
|
|||
|
|
"16 frequency==30 and stim_location==\"ms\" #e7298a bs_not_gridcell \n",
|
|||
|
|
"17 frequency==30 and stim_location==\"ms\" #e7298a bs_not_gridcell \n",
|
|||
|
|
"... ... ... ... \n",
|
|||
|
|
"1279 frequency==11 and stim_location==\"ms\" #d95f02 bs_not_gridcell \n",
|
|||
|
|
"1280 frequency==11 and stim_location==\"ms\" #d95f02 bs_not_gridcell \n",
|
|||
|
|
"1281 frequency==11 and stim_location==\"ms\" #d95f02 bs_not_gridcell \n",
|
|||
|
|
"1282 frequency==11 and stim_location==\"ms\" #d95f02 bs_not_gridcell \n",
|
|||
|
|
"1283 frequency==11 and stim_location==\"ms\" #d95f02 bs_not_gridcell \n",
|
|||
|
|
"\n",
|
|||
|
|
"[647 rows x 68 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"session_units_sig"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_fwhm(psth, times, t_min=None):\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Compute PSTH. The function handles a single peak or multiple peaks. In the latter case, the peak containing the max peak is used\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" times_accepted = times > tmin\n",
|
|||
|
|
" max_idx = np.argmax(psth[times_accepted])\n",
|
|||
|
|
" idxs_greater = np.where(psth[times_accepted] > 0.5 * np.ptp(psth[times_accepted]))[0]\n",
|
|||
|
|
" \n",
|
|||
|
|
" if np.all(np.diff(idxs_greater) == 1):\n",
|
|||
|
|
" fwhm = times[times_accepted][idxs_greater[-1]] - times[times_accepted][idxs_greater[0]]\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" # deal with multiple peaks. when multiple peaks are found, the one containing max_idx is used\n",
|
|||
|
|
" diff_splits = np.where((np.diff(idxs_greater) == 1) == False)[0]\n",
|
|||
|
|
" idxs_list = []\n",
|
|||
|
|
" for i, ds in enumerate(diff_splits):\n",
|
|||
|
|
" if i == 0:\n",
|
|||
|
|
" idxs_list.append(idxs_greater[:ds+1])\n",
|
|||
|
|
" elif i < len(diff_splits):\n",
|
|||
|
|
" idxs_list.append(idxs_greater[diff_splits[i-1]+1:ds+1])\n",
|
|||
|
|
" idxs_list.append(idxs_greater[ds+1:])\n",
|
|||
|
|
" \n",
|
|||
|
|
" for idxs in idxs_list:\n",
|
|||
|
|
" if max_idx in idxs:\n",
|
|||
|
|
" fwhm = times[times_accepted][idxs[-1]] - times[times_accepted][idxs[0]]\n",
|
|||
|
|
" \n",
|
|||
|
|
" return fwhm, psth[times_accepted][max_idx]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"times = np.arange(-0.005, window_size, 1e-4)\n",
|
|||
|
|
"tmin = 0.005\n",
|
|||
|
|
"psths = []\n",
|
|||
|
|
"def process(row):\n",
|
|||
|
|
" action_id = row['action']\n",
|
|||
|
|
" channel_group = row['channel_group']\n",
|
|||
|
|
" unit_name = row['unit_name']\n",
|
|||
|
|
" name = f'{action_id}_{channel_group}_{unit_name}'\n",
|
|||
|
|
" spike_times = data_loader.spike_train(action_id, channel_group, unit_name)\n",
|
|||
|
|
" \n",
|
|||
|
|
" spike_times = np.array(spike_times)\n",
|
|||
|
|
" \n",
|
|||
|
|
" stim_times = data_loader.stim_times(action_id) \n",
|
|||
|
|
" \n",
|
|||
|
|
" stim_times = np.array(stim_times)\n",
|
|||
|
|
" \n",
|
|||
|
|
" _, spikes, kernel, p_e, p_i = stimulus_response_latency(\n",
|
|||
|
|
" spike_times, stim_times, window_size, std_gaussian_kde)\n",
|
|||
|
|
" \n",
|
|||
|
|
" psths.append(kernel(times))\n",
|
|||
|
|
" fwhm, peak = compute_fwhm(kernel(times), times, tmin)\n",
|
|||
|
|
" return pd.Series({\n",
|
|||
|
|
" 'fwhm': fwhm,\n",
|
|||
|
|
" 'peak': peak\n",
|
|||
|
|
" })"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "f8d93b5929ac48ca88f0b9acd49b97db",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(IntProgress(value=0, max=647), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"session_units_sig = session_units_sig.merge(\n",
|
|||
|
|
" session_units_sig.progress_apply(process, axis=1), \n",
|
|||
|
|
" left_index=True, right_index=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Quality control\n",
|
|||
|
|
"fig = plt.figure()\n",
|
|||
|
|
"for psth in psths:\n",
|
|||
|
|
" plt.plot(times, psth, color='C0', alpha=0.3)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"keys = [\n",
|
|||
|
|
" 'fwhm',\n",
|
|||
|
|
" 'peak'\n",
|
|||
|
|
"]\n",
|
|||
|
|
"\n",
|
|||
|
|
"results, labels = make_paired_tables(session_units_sig, keys, queries=queries, labels=labels, cell_types=['gridcell', 'ns_inhibited', 'ns_not_inhibited'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Plot FWHM grouped by stimulation frequency"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rc('axes', titlesize=12)\n",
|
|||
|
|
"plt.rcParams.update({\n",
|
|||
|
|
" 'font.size': 12,\n",
|
|||
|
|
" 'figure.figsize': (1.7, 3),\n",
|
|||
|
|
" 'figure.dpi': 150\n",
|
|||
|
|
"})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def violinplot2(data, xticks, colors):\n",
|
|||
|
|
" pos = [i * 0.6 for i in range(len(data))]\n",
|
|||
|
|
" print(pos)\n",
|
|||
|
|
" violins = plt.violinplot(data, pos, showmeans=True, showextrema=False)\n",
|
|||
|
|
" \n",
|
|||
|
|
" for i, b in enumerate(violins['bodies']):\n",
|
|||
|
|
" b.set_color(colors[i])\n",
|
|||
|
|
" b.set_alpha (0.8)\n",
|
|||
|
|
"\n",
|
|||
|
|
" \n",
|
|||
|
|
"\n",
|
|||
|
|
" # for i, body in enumerate(violins['cbars']):\n",
|
|||
|
|
" # body.set_color('C{}'.format(i))\n",
|
|||
|
|
"\n",
|
|||
|
|
" \n",
|
|||
|
|
"\n",
|
|||
|
|
" for category in ['cbars', 'cmins', 'cmaxes', 'cmedians', 'cmeans']:\n",
|
|||
|
|
" if category in violins:\n",
|
|||
|
|
" violins[category].set_color(['k', 'k'])\n",
|
|||
|
|
" violins[category].set_linewidth(2.0)\n",
|
|||
|
|
" plt.xticks(pos, xticks, rotation=45)\n",
|
|||
|
|
" plt.gca().spines['top'].set_visible(False)\n",
|
|||
|
|
" plt.gca().spines['right'].set_visible(False)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"[0.0, 0.6]\n",
|
|||
|
|
"[0.0, 0.6]\n",
|
|||
|
|
"[0.0, 0.6]\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 255x450 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type, result, in results.items():\n",
|
|||
|
|
" fig = plt.figure()\n",
|
|||
|
|
" violinplot2(\n",
|
|||
|
|
" [result['fwhm']['11 Hz'].dropna().values, result['fwhm']['30 Hz'].dropna().values], \n",
|
|||
|
|
" colors=colors,\n",
|
|||
|
|
" xticks=[\"11 Hz \", \" 30 Hz\"],\n",
|
|||
|
|
" )\n",
|
|||
|
|
" plt.title(cell_type)\n",
|
|||
|
|
" figname = f'{cell_type}-stim-response-fwhm-11-30Hz'\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output / 'figures' / f'{figname}.png', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output / 'figures' / f'{figname}.svg', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"[0.0, 0.6]\n",
|
|||
|
|
"[0.0, 0.6]\n",
|
|||
|
|
"[0.0, 0.6]\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 255x450 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 255x450 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 255x450 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type, result, in results.items():\n",
|
|||
|
|
" fig = plt.figure()\n",
|
|||
|
|
" violinplot2(\n",
|
|||
|
|
" [result['peak']['11 Hz'].dropna().values, result['peak']['30 Hz'].dropna().values], \n",
|
|||
|
|
" colors=colors,\n",
|
|||
|
|
" xticks=[\"11 Hz \", \" 30 Hz\"],\n",
|
|||
|
|
" )\n",
|
|||
|
|
" plt.title(cell_type)\n",
|
|||
|
|
" figname = f'{cell_type}-stim-response-peak-11-30Hz'\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output / 'figures' / f'{figname}.png', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)\n",
|
|||
|
|
" fig.savefig(\n",
|
|||
|
|
" output / 'figures' / f'{figname}.svg', \n",
|
|||
|
|
" bbox_inches='tight', transparent=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# stats"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stats = {}\n",
|
|||
|
|
"for cell_type, result in results.items():\n",
|
|||
|
|
" stats[cell_type], _ = make_statistics_table(result, labels)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"stats['gridcell']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for cell_type, stat in stats.items():\n",
|
|||
|
|
" stat.to_latex(output / \"statistics\" / f\"statistics_{cell_type}.tex\")\n",
|
|||
|
|
" stat.to_csv(output / \"statistics\" / f\"statistics_{cell_type}.csv\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Save to expipe"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"action = project.require_action(\"stimulus-response-fwhm\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"action.modules['parameters'] = {\n",
|
|||
|
|
" 'window_size': window_size,\n",
|
|||
|
|
" 'std_gaussian_kde': std_gaussian_kde,\n",
|
|||
|
|
" 'tmin': tmin\n",
|
|||
|
|
"}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"action.data['results'] = 'results.csv'\n",
|
|||
|
|
"session_units_sig.to_csv(action.data_path('results'), index=False)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"copy_tree(output, str(action.data_path()))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"10-calculate-stimulus-response-fwhm.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
|
|||
|
|
}
|