actions/cell-count/
This commit is contained in:
Mikkel Elle Lepperød
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registered: '2020-08-26T13:39:33'
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data:
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notebook: 20_cell_count.ipynb
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html: 20_cell_count.html
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"13:30:10 [I] klustakwik KlustaKwik2 version 0.2.6\n"
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]
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}
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],
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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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"\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
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"tqdm.pandas()\n",
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"\n",
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"from spike_statistics.core import permutation_resampling\n",
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"\n",
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"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
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"\n",
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"from septum_mec.analysis.plotting import violinplot, despine"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"plt.rc('axes', titlesize=12)\n",
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"plt.rcParams.update({\n",
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" 'font.size': 12, \n",
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" 'figure.figsize': (6, 4), \n",
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" 'figure.dpi': 150\n",
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"})\n",
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"\n",
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"output_path = pathlib.Path(\"output\") / \"cell-count\"\n",
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"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
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"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
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"output_path.mkdir(exist_ok=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 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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"identification_action = actions['identify-neurons']\n",
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"sessions = pd.read_csv(identification_action.data_path('sessions'))\n",
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"units = pd.read_csv(identification_action.data_path('units'))\n",
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"session_units = pd.merge(sessions, units, on='action')"
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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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"stim_action = actions['stimulus-response']\n",
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"stim_results = pd.read_csv(stim_action.data_path('results'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"statistics_action = actions['calculate-statistics']\n",
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"shuffling = actions['shuffling']\n",
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"\n",
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"statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
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"statistics_results = session_units.merge(statistics_results, how='left')\n",
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"quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
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"action_columns = ['action', 'channel_group', 'unit_name']\n",
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"data = pd.merge(statistics_results, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = data.merge(stim_results, how='left')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"waveform_action = actions['waveform-analysis']\n",
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"waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = data.merge(waveform_results, how='left')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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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 = ['11 Hz', '30 HZ']\n",
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"queries = ['frequency==11', 'frequency==30']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.bs = data.bs.astype(bool)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.loc[data.eval('t_i_peak == t_i_peak and not bs'), 'ns_inhibited'] = True\n",
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"data.ns_inhibited.fillna(False, inplace=True)\n",
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"\n",
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"data.loc[data.eval('t_i_peak != t_i_peak and not bs'), 'ns_not_inhibited'] = True\n",
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"data.ns_not_inhibited.fillna(False, inplace=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": 16,
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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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]
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}
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],
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"source": [
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"query = (\n",
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" 'gridness > gridness_threshold and '\n",
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" 'information_rate > information_rate_threshold and '\n",
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" 'gridness > .2 and '\n",
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" 'average_rate < 25'\n",
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")\n",
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"sessions_above_threshold = data.query(query)\n",
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"print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
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"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of gridcells 139\n",
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"Number of gridcell recordings 231\n",
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"Number of animals 4\n"
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]
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}
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],
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"source": [
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"gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]\n",
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"print(\"Number of gridcells\", gridcell_sessions.unit_idnum.nunique())\n",
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"print(\"Number of gridcell recordings\", len(gridcell_sessions))\n",
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"print(\"Number of animals\", len(gridcell_sessions.groupby(['entity'])))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.loc[:,'gridcell'] = np.nan\n",
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"data['gridcell'] = data.isin(gridcell_sessions)\n",
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"\n",
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"data.loc[data.eval('not gridcell and bs'), 'bs_not_gridcell'] = True\n",
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"data.bs_not_gridcell.fillna(False, inplace=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": 28,
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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>Gridcell</th>\n",
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" <th>BS not gridcell</th>\n",
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" <th>NSi</th>\n",
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" <th>NSni</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>entity</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></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>1833</th>\n",
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" <td>94</td>\n",
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" <td>165</td>\n",
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" <td>16</td>\n",
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" <td>52</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1834</th>\n",
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" <td>14</td>\n",
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" <td>216</td>\n",
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" <td>4</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1839</th>\n",
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" <td>19</td>\n",
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" <td>70</td>\n",
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" <td>11</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1849</th>\n",
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" <td>8</td>\n",
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" <td>229</td>\n",
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" <td>8</td>\n",
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" <td>23</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Gridcell BS not gridcell NSi NSni\n",
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"entity \n",
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"1833 94 165 16 52\n",
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"1834 14 216 4 7\n",
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"1839 19 70 11 5\n",
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"1849 8 229 8 23"
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]
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},
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"table = pd.DataFrame()\n",
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"table['Gridcell'] = data.drop_duplicates('unit_idnum').query('gridcell').groupby('entity')['action'].count()\n",
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"table['BS not gridcell'] = data.drop_duplicates('unit_idnum').query('bs_not_gridcell').groupby('entity')['action'].count()\n",
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"table['NSi'] = data.drop_duplicates('unit_idnum').query('ns_inhibited').groupby('entity')['action'].count()\n",
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"table['NSni'] = data.drop_duplicates('unit_idnum').query('ns_not_inhibited').groupby('entity')['action'].count()\n",
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"table"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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"# Store results in Expipe action"
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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": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"action = project.require_action(\"cell-count\")"
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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": 36,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['/media/storage/expipe/septum-mec/actions/stimulus-response/data/data/times.feather',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/data/psth.feather',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-gridcell-stim-mec.png',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_inhibited-stim-mec.png',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_inhibited.svg',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-bs_not_gridcell.svg',\n",
|
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_not_inhibited.png',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-bs_not_gridcell-stim-mec.png',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-nsi-ns.png',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-gridcell-stim-mec.png',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-nsi-ns-stim-mec.png',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_not_inhibited-stim-mec.png',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-gridcell.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_not_inhibited-stim-mec.png',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_inhibited-stim-mec.svg',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_inhibited.svg',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_inhibited.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_not_inhibited-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_not_inhibited.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_not_inhibited-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-bs_not_gridcell-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-bs_not_gridcell-stim-mec.svg',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-bs_not_gridcell.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-gridcell.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-bs_not_gridcell-stim-mec.svg',\n",
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||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_inhibited.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_not_inhibited.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-gridcell-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-nsi-ns-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_inhibited.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-bs_not_gridcell.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-bs_not_gridcell.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-gridcell-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_inhibited-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-gc-ns.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-bs_not_gridcell.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_not_inhibited-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_not_inhibited.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-gridcell.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-nsi-ns.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-gridcell.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-gc-ns-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-gridcell.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-gc-ns.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-gridcell.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_not_inhibited-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_inhibited-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-gridcell-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_inhibited.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_not_inhibited.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-gridcell-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_inhibited-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-bs_not_gridcell.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-bs_not_gridcell.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-bs_not_gridcell-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-bs_not_gridcell-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-gridcell.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-gc-ns-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-bs_not_gridcell.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_inhibited.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-gridcell-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-gridcell-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_inhibited-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_not_inhibited.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_not_inhibited-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-bs_not_gridcell-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_not_inhibited-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_inhibited-stim-mec.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-gridcell.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_not_inhibited.svg',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-bs_not_gridcell-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_not_inhibited.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_inhibited-stim-mec.png',\n",
|
||||
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_inhibited.png']"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"copy_tree(output_path, str(action.data_path()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"septum_mec.analysis.registration.store_notebook(action, \"20_cell_count.ipynb\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
entity,Gridcell,BS not gridcell,NSi,NSni
|
||||
1833,94,165,16,52
|
||||
1834,14,216,4,7
|
||||
1839,19,70,11,5
|
||||
1849,8,229,8,23
|
||||
|
|
|
@ -0,0 +1,11 @@
|
|||
\begin{tabular}{lrrrr}
|
||||
\toprule
|
||||
{} & Gridcell & BS not gridcell & NSi & NSni \\
|
||||
entity & & & & \\
|
||||
\midrule
|
||||
1833 & 94 & 165 & 16 & 52 \\
|
||||
1834 & 14 & 216 & 4 & 7 \\
|
||||
1839 & 19 & 70 & 11 & 5 \\
|
||||
1849 & 8 & 229 & 8 & 23 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
Loading…
Reference in New Issue