281 lines
7.1 KiB
Plaintext
281 lines
7.1 KiB
Plaintext
{
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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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"08:31:41 [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 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": 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"
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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')\n",
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"(output / 'figures').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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"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": null,
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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": "d3f0400c75c745c789907bd763bf2833",
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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=1281), 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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"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": null,
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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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"results.loc[:, ['t_e_peak', 't_i_peak', 'p_e_peak', 'p_i_peak']].hist()\n",
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"plt.gcf().savefig(output / 'figures' / 'summary_histogram.png')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Save to expipe"
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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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"action = project.require_action(\"stimulus-response\")"
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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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"action.modules['parameters'] = {\n",
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" 'window_size': window_size,\n",
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" 'std_gaussian_kde': std_gaussian_kde\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"action.data['results'] = 'results.csv'\n",
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"results.to_csv(action.data_path('results'), index=False)"
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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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"stuff = {\n",
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" \"figures\": \"figures\",\n",
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"# \"statistics\": \"statistics\"\n",
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"}\n",
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"\n",
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"for key, value in stuff.items():\n",
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" action.data[key] = value\n",
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" data_path = action.data_path(key)\n",
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" data_path.parent.mkdir(exist_ok=True, parents=True)\n",
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" source = output / value\n",
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" if source.is_file():\n",
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" shutil.copy(source, data_path)\n",
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" else:\n",
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" copy_tree(str(source), str(data_path))"
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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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"septum_mec.analysis.registration.store_notebook(action, \"10-calculate-stimulus-response.ipynb\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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