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stim response

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Mikkel Elle Lepperød 2019-10-10 11:44:14 +02:00
parent a15c1396c6
commit 7f044e9b5b
6 changed files with 15038 additions and 15027 deletions
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actions/stimulus-response/attributes.yaml
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registered: '2019-10-03T08:37:34' registered: '2019-10-03T08:37:34'
data: data:
figures: figures figures: figures
statistics: statistics statistics: statistics
notebook: 10-calculate-stimulus-response.ipynb notebook: 10-calculate-stimulus-response.ipynb
html: 10-calculate-stimulus-response.html html: 10-calculate-stimulus-response.html
results: results.csv results: results.csv

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actions/stimulus-response/data/10-calculate-stimulus-response.html
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actions/stimulus-response/data/10-calculate-stimulus-response.ipynb
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{ {
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%load_ext autoreload\n", "%load_ext autoreload\n",
"%autoreload 2" "%autoreload 2"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"08:31:41 [I] klustakwik KlustaKwik2 version 0.2.6\n" "11:36:02 [I] klustakwik KlustaKwik2 version 0.2.6\n"
] ]
} }
], ],
"source": [ "source": [
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import spatial_maps as sp\n", "import spatial_maps as sp\n",
"import septum_mec.analysis.data_processing as dp\n", "import septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n", "import septum_mec.analysis.registration\n",
"import expipe\n", "import expipe\n",
"import os\n", "import os\n",
"import pathlib\n", "import pathlib\n",
"import numpy as np\n", "import numpy as np\n",
"import exdir\n", "import exdir\n",
"import pandas as pd\n", "import pandas as pd\n",
"import optogenetics as og\n", "import optogenetics as og\n",
"import quantities as pq\n", "import quantities as pq\n",
"import shutil\n", "import shutil\n",
"from distutils.dir_util import copy_tree\n", "from distutils.dir_util import copy_tree\n",
"\n", "\n",
"from septum_mec.analysis.stimulus_response import stimulus_response_latency, compute_response\n", "from septum_mec.analysis.stimulus_response import stimulus_response_latency, compute_response\n",
"\n", "\n",
"from tqdm import tqdm_notebook as tqdm\n", "from tqdm import tqdm_notebook as tqdm\n",
"from tqdm._tqdm_notebook import tqdm_notebook\n", "from tqdm._tqdm_notebook import tqdm_notebook\n",
"tqdm_notebook.pandas()" "tqdm_notebook.pandas()"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"std_gaussian_kde = 0.04\n", "std_gaussian_kde = 0.04\n",
"window_size = 0.03" "window_size = 0.03"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"data_loader = dp.Data()\n", "data_loader = dp.Data()\n",
"actions = data_loader.actions\n", "actions = data_loader.actions\n",
"project = data_loader.project" "project = data_loader.project"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"output = pathlib.Path('output/stimulus-response')\n", "output = pathlib.Path('output/stimulus-response')\n",
"(output / 'figures').mkdir(parents=True, exist_ok=True)" "(output / 'figures').mkdir(parents=True, exist_ok=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"identify_neurons = actions['identify-neurons']\n", "identify_neurons = actions['identify-neurons']\n",
"units = pd.read_csv(identify_neurons.data_path('units'))" "units = pd.read_csv(identify_neurons.data_path('units'))"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 7,
"metadata": { "metadata": {
"scrolled": false "scrolled": false
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"def process(row):\n", "def process(row):\n",
" \n", " \n",
" action_id = row['action']\n", " action_id = row['action']\n",
" channel_id = int(row['channel_group'])\n", " channel_id = int(row['channel_group'])\n",
" unit_id = int(row['unit_name']) \n", " unit_id = int(row['unit_name']) \n",
" \n", " \n",
" spike_times = data_loader.spike_train(action_id, channel_id, unit_id)\n", " spike_times = data_loader.spike_train(action_id, channel_id, unit_id)\n",
" \n", " \n",
" spike_times = np.array(spike_times)\n", " spike_times = np.array(spike_times)\n",
" \n", " \n",
" stim_times = data_loader.stim_times(action_id)\n", " stim_times = data_loader.stim_times(action_id)\n",
" \n", " \n",
" nan_series = pd.Series({\n", " nan_series = pd.Series({\n",
" 't_e_peak': np.nan,\n", " 't_e_peak': np.nan,\n",
" 'p_e_peak': np.nan,\n", " 'p_e_peak': np.nan,\n",
" 't_i_peak': np.nan,\n", " 't_i_peak': np.nan,\n",
" 'p_i_peak': np.nan\n", " 'p_i_peak': np.nan\n",
" })\n", " })\n",
" \n", " \n",
" if stim_times is None:\n", " if stim_times is None:\n",
" return nan_series\n", " return nan_series\n",
" \n", " \n",
" stim_times = np.array(stim_times)\n", " stim_times = np.array(stim_times)\n",
" \n", " \n",
" times, spikes, kernel, p_e, p_i = stimulus_response_latency(\n", " times, spikes, kernel, p_e, p_i = stimulus_response_latency(\n",
" spike_times, stim_times, window_size, std_gaussian_kde)\n", " spike_times, stim_times, window_size, std_gaussian_kde)\n",
" \n", " \n",
" # if no spikes detected after stimulus nan is returned\n", " # if no spikes detected after stimulus nan is returned\n",
" if all(np.isnan([p_e, p_i])):\n", " if all(np.isnan([p_e, p_i])):\n",
" return nan_series\n", " return nan_series\n",
" \n", " \n",
" t_e_peak, p_e_peak, t_i_peak, p_i_peak = compute_response(\n", " t_e_peak, p_e_peak, t_i_peak, p_i_peak = compute_response(\n",
" spike_times, stim_times, times, kernel, p_e, p_i)\n", " spike_times, stim_times, times, kernel, p_e, p_i)\n",
"\n", "\n",
" return pd.Series({\n", " return pd.Series({\n",
" 't_e_peak': t_e_peak,\n", " 't_e_peak': t_e_peak,\n",
" 'p_e_peak': p_e_peak,\n", " 'p_e_peak': p_e_peak,\n",
" 't_i_peak': t_i_peak,\n", " 't_i_peak': t_i_peak,\n",
" 'p_i_peak': p_i_peak\n", " 'p_i_peak': p_i_peak\n",
" })\n", " })\n",
"\n", "\n",
"\n" "\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"application/vnd.jupyter.widget-view+json": { "application/vnd.jupyter.widget-view+json": {
"model_id": "d3f0400c75c745c789907bd763bf2833", "model_id": "476c31da67274b2396ed3f2ec54a8344",
"version_major": 2, "version_major": 2,
"version_minor": 0 "version_minor": 0
}, },
"text/plain": [ "text/plain": [
"HBox(children=(IntProgress(value=0, max=1281), HTML(value='')))" "HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
}, },
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/stimulus_response.py:33: RuntimeWarning: invalid value encountered in less\n", "/home/mikkel/apps/expipe-project/septum-mec/septum_mec/analysis/stimulus_response.py:33: RuntimeWarning: invalid value encountered in less\n",
" if any(times[idxs_i] < te_peak):\n" " if any(times[idxs_i] < te_peak):\n"
] ]
} }
], ],
"source": [ "source": [
"results = units.merge(\n", "results = units.merge(\n",
" units.progress_apply(process, axis=1), \n", " units.progress_apply(process, axis=1), \n",
" left_index=True, right_index=True)" " left_index=True, right_index=True)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"scrolled": false "scrolled": false
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"results.loc[:, ['t_e_peak', 't_i_peak', 'p_e_peak', 'p_i_peak']].hist()\n", "results.loc[:, ['t_e_peak', 't_i_peak', 'p_e_peak', 'p_i_peak']].hist()\n",
"plt.gcf().savefig(output / 'figures' / 'summary_histogram.png')" "plt.gcf().savefig(output / 'figures' / 'summary_histogram.png')"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Save to expipe" "# Save to expipe"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"action = project.require_action(\"stimulus-response\")" "action = project.require_action(\"stimulus-response\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"action.modules['parameters'] = {\n", "action.modules['parameters'] = {\n",
" 'window_size': window_size,\n", " 'window_size': window_size,\n",
" 'std_gaussian_kde': std_gaussian_kde\n", " 'std_gaussian_kde': std_gaussian_kde\n",
"}" "}"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"action.data['results'] = 'results.csv'\n", "action.data['results'] = 'results.csv'\n",
"results.to_csv(action.data_path('results'), index=False)" "results.to_csv(action.data_path('results'), index=False)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"stuff = {\n", "copy_tree(output, str(action.data_path()))"
" \"figures\": \"figures\",\n", ]
"# \"statistics\": \"statistics\"\n", },
"}\n", {
"\n", "cell_type": "code",
"for key, value in stuff.items():\n", "execution_count": null,
" action.data[key] = value\n", "metadata": {},
" data_path = action.data_path(key)\n", "outputs": [],
" data_path.parent.mkdir(exist_ok=True, parents=True)\n", "source": [
" source = output / value\n", "septum_mec.analysis.registration.store_notebook(action, \"10-calculate-stimulus-response.ipynb\")"
" if source.is_file():\n", ]
" shutil.copy(source, data_path)\n", },
" else:\n", {
" copy_tree(str(source), str(data_path))" "cell_type": "code",
] "execution_count": null,
}, "metadata": {},
{ "outputs": [],
"cell_type": "code", "source": []
"execution_count": null, }
"metadata": {}, ],
"outputs": [], "metadata": {
"source": [ "kernelspec": {
"septum_mec.analysis.registration.store_notebook(action, \"10-calculate-stimulus-response.ipynb\")" "display_name": "Python 3",
] "language": "python",
} "name": "python3"
], },
"metadata": { "language_info": {
"kernelspec": { "codemirror_mode": {
"display_name": "Python 3", "name": "ipython",
"language": "python", "version": 3
"name": "python3" },
}, "file_extension": ".py",
"language_info": { "mimetype": "text/x-python",
"codemirror_mode": { "name": "python",
"name": "ipython", "nbconvert_exporter": "python",
"version": 3 "pygments_lexer": "ipython3",
}, "version": "3.6.8"
"file_extension": ".py", }
"mimetype": "text/x-python", },
"name": "python", "nbformat": 4,
"nbconvert_exporter": "python", "nbformat_minor": 2
"pygments_lexer": "ipython3", }
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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window_size: 0.03 window_size: 0.03
std_gaussian_kde: 0.04 std_gaussian_kde: 0.04