354 lines
19 KiB
Plaintext
354 lines
19 KiB
Plaintext
{
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"cells": [
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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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": 16,
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"metadata": {},
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"outputs": [],
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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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"\n",
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"from septum_mec.analysis.stimulus_response import stimulus_response_latency, compute_response\n",
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"\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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"tqdm_notebook.pandas()"
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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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"source": [
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"std_gaussian_kde = 0.04\n",
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"window_size = 0.03"
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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_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": null,
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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')\n",
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"(output / 'data').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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"identify_neurons = actions['identify-neurons']\n",
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"units = pd.read_csv(identify_neurons.data_path('units'))"
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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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"scrolled": false
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},
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"outputs": [],
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"source": [
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"def process(row):\n",
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" \n",
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" action_id = row['action']\n",
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" channel_id = int(row['channel_group'])\n",
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" unit_id = int(row['unit_name']) \n",
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" \n",
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" spike_times = data_loader.spike_train(action_id, channel_id, unit_id)\n",
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" \n",
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" spike_times = np.array(spike_times)\n",
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" \n",
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" stim_times = data_loader.stim_times(action_id)\n",
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" \n",
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" nan_series = pd.Series({\n",
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" 't_e_peak': np.nan,\n",
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" 'p_e_peak': np.nan,\n",
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" 't_i_peak': np.nan,\n",
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" 'p_i_peak': np.nan\n",
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" })\n",
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" \n",
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" if stim_times is None:\n",
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" return nan_series\n",
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" \n",
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" stim_times = np.array(stim_times)\n",
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" \n",
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" times, spikes, kernel, p_e, p_i = stimulus_response_latency(\n",
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" spike_times, stim_times, window_size, std_gaussian_kde)\n",
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" \n",
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" # if no spikes detected after stimulus nan is returned\n",
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" if all(np.isnan([p_e, p_i])):\n",
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" return nan_series\n",
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" \n",
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" t_e_peak, p_e_peak, t_i_peak, p_i_peak = compute_response(\n",
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" spike_times, stim_times, times, kernel, p_e, p_i)\n",
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"\n",
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" return pd.Series({\n",
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" 't_e_peak': t_e_peak,\n",
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" 'p_e_peak': p_e_peak,\n",
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" 't_i_peak': t_i_peak,\n",
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" 'p_i_peak': p_i_peak\n",
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" })\n",
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"\n",
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"\n"
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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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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "476c31da67274b2396ed3f2ec54a8344",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/stimulus_response.py:33: RuntimeWarning: invalid value encountered in less\n",
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" if any(times[idxs_i] < te_peak):\n"
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]
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},
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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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"\n"
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]
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}
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],
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"source": [
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"results = units.merge(\n",
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" units.progress_apply(process, axis=1), \n",
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" left_index=True, right_index=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": 9,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"image/png": 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JwFsllfaXXUTcAGyf5qVeLRN5ynnl9qLOs+k4I2JrRGyXJGAV8IFuixFYDayKiH9vc2yV8sT5DODpEbEM+Drp+5yRTif4WsO8p742D3i4qMA6oNZ38RhweUQ8FhHbgG+R2vb6Ta+WiTzlvHJ7UeeZ63qU9HLgq8DZ7Wx/zxnjOLAYuEjSrcA+kr7U5hjzxPkw8BDwtWzb19m91p9LpxN8rWHem4HFkoYl7U3683xL8SEWptZ3cRiwSdKsrB36pcCPig+x40aBQyXtI2mI1Dxze4djakSecv7EPsBJwMZujDNL7pcDJ0bED7owxs0Rocl7V8B/RMQZXRjnFlKT4+TP/GXAXTMNoqMjWSvuNB/OrmHeJ5PaWb+W3Wk+l/SL6CPZn+6l1MB38R7gNaSmiy9ExFUdC7YAkhYCX4qIYySdCTw1Iv6hohfNIKkXzRWdjLMRecp5dvP486Ta3VbgzIj4QxfG+WNgLvCb7DAREW27ITzTnCHpNxGxX7vim0mckvYhdS54Juk6f11E3DeTODxVwQxJ2kC6+LZ2OpZaJN0HvLqgWpb1gXplP5sbf0VEvLrguCaA+d1+TRbBa7LO3PGdDsCsQ2qW/awyUWhyt905wc+ApM9lD78t6eSI+FWV970CuIA0eOEx4N0RUbPtOKtx30i6QfQ04NKIuLLW8bI/6z8D7AvsR1pY4jUR8duK4z4V+Gfg9og4P895mzVS9iUtJfWRf16dY+0A/g54OanH2PsiYl322v8kdW8cJN2E/OuI+Lmkw4ArgKcC+wN3AqdHxFjFcfcD/g9wVUSsnsn59qpO32TtaRExOTXsy2sk90NJA0BOjogjSe1u67JRa/XsCbwIWAp8UNLz6xzvDFLifglwECn5n11xvL2BDcBNTu42E42U/SbMIt38PJp0n2mNpPnZyNPXA4uzsv5xYF22z5uBz2dl/RDS6M/KNe6eBdwCfLRfkzu4Bl+E40k3TW5JXYUBeJxUKH9cZ98rImIC+DdJ3ySN4PzPaseLiMslLZb0TtLAqOcB36843rWkodGfmvFZmbXWaoCI+Imkn5J6kSwiXSffrSjr+2Q3I88nzcP/XlIvs/1JtflJ/wz8G2m6h77lBN9+s4BbIuL0yQ3ZsOlfN7Bv5dJvg8DOWseTdAlplNwa4NukYdkDFcf4MOnP4I8Db8t1NmbtUa2sXzv512bWM2V/4HfAl0j563+TRtIeyO5l/S3A35KmJ5g67UffcBPNzO2k9vwW3wJOkPRcAEknAz9hyjwrVbwu2+dAUu19fZ3jLQP+LiKuBX5L+uthVsXxNpPaM/+HpBMaPUGzKuqV/WZMlvWjgOcCt5GaE/9S0jOz9/wVqdkFUln/YER8GZgg1fYry/rtpOadCyTVvAdQZk7wM7cO+E61QhQRd5Hayb+U9Rn+EPAXDfZpfo6kH5LmtHh7JLWO90FgVbbPOtLAiUOmxPMgKcmvkfT0HOdrNqlm2W/SsZJ+RPrr8/RsvqGbgUuAf5H0E+BM4JVZs+X7gBsl/YA0z8xtPLmsB+n6+MdsYFzfcT/4LuV+69Yv3G+9fdwG3wKSvkz1qT1Pn25+DkmvJU0POp0vtio2s3ZqpOxno7BfW+U9n2hPZAauwZuZlZbb4M3MSsoJ3syspDraBr9o0aKJBQsWPPF8fHycoaHevtntcyjWXXfdtTUi5nc6jkZNLfOTeuk7b5V+PGdozXk3Wu47muAXLFjAunXrnng+OjrKyMhIByOaOZ9DsSTd3+kYmjG1zE/qpe+8VfrxnKE1591ouXcTjZlZSTnBm5mVlBO8mVlJOcHnMLZ9Z9XXqrWt1drHLK885cplsX/UvckqaRZwNWm02gRpwp8xYG32fAuwPCIel3QRaU7mHcB5EbG5TXF31PCcWSxccVNT+9z3sVPqv8msSS6LVksjNfhXAETEsaRVhC4GLgMuiIjFpCk6T81mgVtCmtXtDNJqK2Zm1iF1E3xEfJU0eyHAs4GHgaNJs7dBmsL2OOClwIaImIiIB4DZknqmf7KZWdk01A8+InZI+jzw30mL6B6fTdkJsI20FNxepDUTmbL9wWrHHR8fZ3R09InnY2Njuz3vVnn7sPbCuUHv/BzMrLaGBzpFxOslnU9aAm6PipfmkWr1j2aPp26vamhoaLdkWfaBD71ybmX/OZj1i7pNNJLOlvQ32dPHSOt//iBbMR3gJGAjsAlYJmkwW4Fo0PM7m5l1TiM1+HXA5yT9X9LyXOcBo8DV2Sopo8D1EbFT0kbSUlmDwPI2xWxWCEmLgEsiYqmkQ+jjnmPWm+om+GwpuNdM89KSad67Elg546jMOkzSe4GzgcmlFSd7jt0q6SpSz7H72dVz7ADgBuBFnYjXbDpe0clsevcCrwSuzZ5P7Tl2AhBkPceAByTNljQ/W/d2WlM7FkzKe2O7l2/49+vN/CLP2wnebBoRcYOkhRWbBlrRc2xqx4JJRd/Y7oab6P16M7/I8/ZUBWaNebzice6eY2ZFcoI3a8wd7jlmvcZNNGaNeRfuOWY9xgnerIqIuA84Jnt8N+45Zj3GTTRmZiXlBG9mVlJO8GZmJeUEb2ZWUk7wZmYl5QRvZlZSTvBmZiXlBG9mVlJO8GZmJdX3CX5s+85Oh2Bm1hZ9P1XB8JxZLFxxU1P73PexU9oUjZlZ6/R9Dd7MrKyc4M3MSsoJ3syspJzgzcxKquZNVklzgDXAQmAu8GHgZ8BaYALYAiyPiMclXQScAuwAzouIze0L28zM6qlXgz8LeCgiFgMnAquBy4ALsm0DwKmSjiIthrAIOAO4on0hm5lZI+ol+K8AF2aPB0i186OB27Jt64HjgJcCGyJiIiIeAGZLmt+GeM3MrEE1m2gi4vcAkuYB1wMXAKsiYiJ7yzZgb2Av4KGKXSe3P1jr+OPj44yOjj7xfGxsbLfnRRgZGSnss4o+t7w68XMws9arO9BJ0gHAjcCnI+I6SR+veHke8DDwaPZ46vaahoaGdkuwo6OjhSbcovXKuZX952DWL2o20UjaF9gAnB8Ra7LNd0hamj0+CdgIbAKWSRqUdCAwGBFb2xSzmZk1oF4N/n3A04ELJU22xb8D+JSkIWAUuD4idkraCNxO+qWxvF0Bm5lZY+q1wb+DlNCnWjLNe1cCK1sSlZmZzZgHOpmZlZQTvJlZSTnBm5mVlBO8mVlJOcGbmZWUE7yZWUk5wZuZlZQTvJlZSTnBm5mVVN3JxswskfQj0sR6AL8EPgNcTppGe0NEfKBTsZlNxwnerAGShoGBiFhase1O4FXAL4CbJB0ZEXd0KESzJ3GCN2vMEcCekjaQrpuVwNyIuBdA0s2kxW+c4K1rOMGbNeYxYBVwDXAoaTWzyjUPtgEH1TvI1EVuJuVdZCXvvP3dsKBLvy4sU+R5O8GbNeZu4J5sNbO7JT0C7FPxeq5FbiaNjo7ynEMOY3jOrFbFW1M3LOjSrwvLFHneTvBmjXkT8HzgrZL2B/YE/iDpYFIb/DJgRjdZh+fMYuGKm5ra576PnTKTj7SSc4I3a8xngbWSvgNMkBL+48AXgVmkXjTf72B8Zk/iBG/WgIgYB86c5qVjio7FrFEe6GRmVlJO8GZmJeUEb2ZWUk7wZmYl1dBNVkmLgEsiYqmkQ4C1pJ4EW4DlEfG4pIuAU0jzcpwXEZvbFLOZmTWgbg1e0ntJo/eGs02XARdExGJgADhV0lHAEmARcAZwRXvCNTOzRjVSg78XeCVwbfb8aOC27PF64AQgSP2AJ4AHJM2WND8iHqx14KnDtjsxdLnIkXS9Miy7X4eQm5VN3QQfETdIWlixaSBL5JDm39gb2At4qOI9k9trJvipw7bLPnS5V86t7D8Hs36R5ybr4xWPJ+ffeDR7PHW7mXWZse07C9nHOi/PSNY7JC2NiFuBk4BvA/cAH5e0CngWMBgRW1sXppm1iue86R95Evy7gKslDQGjwPURsVPSRuB20l8Fy1sYo5mZ5dBQgo+I+8jm3IiIu0k9Zqa+ZyVpEQQzM+sCHuhkZlZSTvBmZiXlBG9mVlJO8GZmJeUEb2ZWUk7wZmYl5QRvZlZSTvBmZiXlBG9mVlJO8AXJO1mTJ3kys7zyzEVjOeSZ4Ak8yZOZ5ecavJlZSTnBm5mVlBO8mVlJOcGbmZWUE7yZWUmVKsG7S2Fv8JqgZsUoVTdJrzXZG/xzMitGqWrwZma2ixO8mfW8bm/2q/yskZGRpvfJq6VNNJIGgU8DRwB/BM6JiHta+RnWPca272R4zqxOh9FRLvPdIU+z388/dGKuz8pT7jvVLNnqNvjTgOGIeImkY4BLgVNb/Bl9JU9hKirxevoFoE/KfN4yVcbym/cXQye0OsG/FPgmQER8T9IL8xzENcNdivrNX/mdN/onpAEtKvPdrh3JsFo56/ab8N0eX6WBiYmJlh1M0jXADRGxPnv+AHBQROyo8v4HgftbFoD1o2dHxPxOfbjLvHVIQ+W+1TX4R4F5Fc8HqxV0gE5emGYt4jJvXavVvWg2AScDZO2RP23x8c26jcu8da1W1+BvBI6X9F1gAHhji49v1m1c5q1rtbQN3szMuocHOpmZlZQTvJlZSTnBm5mVVGGzSdYb0i3pzcBbgB3AhyPiG5KeAVwH7AH8GnhjRDxWVMxT5TmHitfOA/aLiBXFRr1Lzp/BgcAaUlkZAM6NiCg8+B5WhrLfrF6/VvLoxuuryBr8E0O6gRWkId0ASNoPeDtwLLAM+KikucD7gesiYjFwB+nL6aSmz0HSHpK+CCzvRMBT5PkZfAhYHRFLgY8AHy066BIoQ9lvVq9fK3l03fVVZILfbUg3UDmk+8XApoj4Y0Q8AtwDHF65D7AeOK64cKeV5xyGgc8DFxcc63TyxP8uYHJc9mxgrLhwS6MMZb9ZvX6t5NF111eRCX4v4JGK5zslza7y2jZg7ynbJ7d1UtPnEBG/i4gNRQVYR574t0bEdkkCVgEfKCbUUilD2W9Wr18reXTd9VVkgq81pHvqa/OAh6dsn9zWSXnOoZvkil/Sy4GvAme7/T2XMpT9ZvX6tZJH111fRSb4WkO6NwOLJQ1L2hsYAbZU7gOcBGwsLtxp5TmHbtJ0/Fnhuxw4MSJ+UHTAJVGGst+sXr9W8ui666uwkawVd5gPZ9eQ7pOBeyLia9kd5nNJv3Q+EhE3SNqX1CY3D9gKnBkRfygk4GnkOYeKfd8APLdLetE08zP4MTAX+E12mIiIXrvh11FlKPvN6vVrJY9uvL48VYGZWUl5oJOZWUk5wZuZlZQTvJlZSTnBm5mVlBO8mVlJOcGbmZWUE7yZWUn9f5OXUUE7oelrAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"results.loc[:, ['t_e_peak', 't_i_peak', 'p_e_peak', 'p_i_peak']].hist()\n",
|
|
"plt.gcf().savefig(output / 'figures' / 'summary_histogram.png')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "2ee10927bbdf456c9178b9fd5448be23",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"psth, time = {}, {}\n",
|
|
"for i, row in tqdm(units.iterrows(), total=len(units)):\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",
|
|
" if stim_times is None:\n",
|
|
" continue\n",
|
|
" \n",
|
|
" stim_times = np.array(stim_times)\n",
|
|
" \n",
|
|
" times, spikes, kernel, p_e, p_i = stimulus_response_latency(\n",
|
|
" spike_times, stim_times, window_size, std_gaussian_kde)\n",
|
|
" \n",
|
|
" if all(np.isnan([p_e, p_i])):\n",
|
|
" continue\n",
|
|
" \n",
|
|
" psth.update({name: kernel(times)})\n",
|
|
" time.update({name: times})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pd.DataFrame(psth).to_feather(output / 'data' / 'psth.feather')\n",
|
|
"pd.DataFrame(time).to_feather(output / 'data' / 'times.feather')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Save to expipe"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"action = project.require_action(\"stimulus-response\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"action.modules['parameters'] = {\n",
|
|
" 'window_size': window_size,\n",
|
|
" 'std_gaussian_kde': std_gaussian_kde\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"action.data['results'] = 'results.csv'\n",
|
|
"results.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.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": 2
|
|
}
|