septum-mec/actions/stimulus-response/data/20_stimulus-spike-response....

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{
"cells": [
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
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"outputs": [],
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"source": [
"import os\n",
"import expipe\n",
"import pathlib\n",
"import numpy as np\n",
"import spatial_maps.stats as stats\n",
"import septum_mec\n",
"import septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n",
"import head_direction.head as head\n",
"import spatial_maps as sp\n",
"import speed_cells.speed as spd\n",
"import re\n",
"import joblib\n",
"import multiprocessing\n",
"import shutil\n",
"import psutil\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
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"import seaborn as sns\n",
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"from distutils.dir_util import copy_tree\n",
"from neo import SpikeTrain\n",
"import scipy\n",
"\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
"tqdm.pandas()\n",
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"\n",
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"from septum_mec.analysis.statistics import load_data_frames, make_paired_tables, make_statistics_table\n",
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"\n",
"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
"\n",
"from septum_mec.analysis.plotting import violinplot, despine"
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]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (6, 4), \n",
" 'figure.dpi': 150\n",
"})\n",
"\n",
"output_path = pathlib.Path(\"output\") / \"stimulus-response\"\n",
"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
"output_path.mkdir(exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
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"project_path = dp.project_path()\n",
"project = expipe.get_project(project_path)\n",
"actions = project.actions"
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]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"colors = ['#d95f02','#e7298a']\n",
"labels = ['11 Hz', '30 HZ']\n",
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"queries = ['frequency==11 and stim_location==\"ms\"', 'frequency==30 and stim_location==\"ms\"']"
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]
},
{
"cell_type": "code",
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"execution_count": 6,
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of sessions above threshold 194\n",
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"Number of animals 4\n",
"Number of individual gridcells 139\n",
"Number of gridcell recordings 230\n"
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]
}
],
"source": [
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"data, labels, colors, queries = load_data_frames(queries, labels, colors)"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
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"keys = [\n",
" 't_i_peak',\n",
" 't_e_peak',\n",
" 'p_i_peak',\n",
" 'p_e_peak',\n",
"]"
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]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [],
"source": [
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"results, labels = make_paired_tables(data, keys, queries=queries, labels=labels)"
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]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
{
"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2021-03-10 12:30:50 +00:00
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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
"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
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{
"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 555x330 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"xlabel = {\n",
" 't_i_peak': 's',\n",
" 't_e_peak': 's',\n",
" 'p_i_peak': 'prob',\n",
" 'p_e_peak': 'prob',\n",
"}\n",
"\n",
"for cell_type in ['gridcell', 'bs_not_gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
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" for key in xlabel:\n",
" fig = plt.figure(figsize=(3.7,2.2))\n",
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" plt.suptitle(key + ' ' + cell_type)\n",
" legend_lines = []\n",
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" for color, label in zip(colors, labels):\n",
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n",
" sns.kdeplot(data=results[cell_type][key].loc[:,labels], cumulative=True, legend=False, palette=colors, common_norm=False)\n",
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" plt.xlabel(xlabel[key])\n",
" plt.legend(\n",
" handles=legend_lines,\n",
" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
" plt.tight_layout()\n",
" plt.grid(False)\n",
" despine()\n",
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" figname = f'histogram-{key}-{cell_type}'.replace(' ', '-')\n",
" fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
" fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"stats = {}\n",
"for cell_type, result in results.items():\n",
" stats[cell_type], _ = make_statistics_table(result, labels, lmm_test=True, wilcoxon_test=False, ttest_ind_test=True, ttest_rel_test=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"stats['gridcell']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for cell_type, stat in stats.items():\n",
" stat.to_latex(output_path / \"statistics\" / f\"statistics_{cell_type}.tex\")\n",
" stat.to_csv(output_path / \"statistics\" / f\"statistics_{cell_type}.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"for cell_type, cell_results in results.items():\n",
" for key, result in cell_results.items():\n",
" result.to_latex(output_path / \"statistics\" / f\"values_{cell_type}_{key}.tex\")\n",
" result.to_csv(output_path / \"statistics\" / f\"values_{cell_type}_{key}.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot PSTH"
]
},
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{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [],
"source": [
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [],
"source": [
"psth = pd.read_feather(output_path / 'data' / 'psth.feather')\n",
"times = pd.read_feather(output_path / 'data' / 'times.feather')"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
"outputs": [],
"source": [
"times = times.T.iloc[0].values"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 750x300 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
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"cs = ['#993404', '#980043', '#d95f02', '#e7298a']\n",
"lb = ['GC 11 Hz', 'GC 30 Hz', 'NSI 11 Hz', 'NSI 30 Hz']\n",
"\n",
"fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
"ii = 0\n",
"for cell_type, ls in zip(['gridcell', 'ns_inhibited'], ['-', '--']):\n",
" for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
" selection = [\n",
" f'{r.action}_{r.channel_group}_{r.unit_name}' \n",
" for i, r in data.query(query + ' and ' + cell_type).iterrows()]\n",
" values = psth.loc[:, selection].dropna(axis=1).to_numpy()\n",
"\n",
" plot_bootstrap_timeseries(times*1000, values, ax=ax, lw=2, label=lb[ii], color=cs[ii], ls=ls)\n",
" # ax.set_title(titles[i])\n",
" ax.set_xlabel('Time (ms)')\n",
" ax.legend(frameon=False)\n",
" ii += 1\n",
" axs[0].set_ylabel('Probability density')\n",
" despine()\n",
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" plt.xlim(-5, 30)\n",
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"for ax in axs.ravel():\n",
" ax.axvspan(0, 5, color='#43a2ca', alpha=.3, zorder=-5)\n",
"figname = f'response-probability-gc-ns'\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
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]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 750x300 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"cs = ['#d95f02', '#e7298a', '#993404', '#980043']\n",
"lb = ['NSI 11 Hz', 'NSI 30 Hz', 'NS 11 Hz', 'NS 30 Hz']\n",
"\n",
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"fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
"ii = 0\n",
"for cell_type, ls in zip(['ns_inhibited', 'ns_not_inhibited'], ['--', '-']):\n",
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" for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
" selection = [\n",
" f'{r.action}_{r.channel_group}_{r.unit_name}' \n",
" for i, r in data.query(query + ' and ' + cell_type).iterrows()]\n",
" values = psth.loc[:, selection].dropna(axis=1).to_numpy()\n",
"\n",
" plot_bootstrap_timeseries(times*1000, values, ax=ax, lw=2, label=lb[ii], color=cs[ii], ls=ls)\n",
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" # ax.set_title(titles[i])\n",
" ax.set_xlabel('Time (ms)')\n",
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" ax.legend(frameon=False)\n",
" ii += 1\n",
" axs[0].set_ylabel('Probability density')\n",
" despine()\n",
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" plt.xlim(-5, 30)\n",
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"for ax in axs.ravel():\n",
" ax.axvspan(0, 5, color='#43a2ca', alpha=.3, zorder=-5)\n",
"figname = f'response-probability-nsi-ns'\n",
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"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Store results in Expipe action"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"stimulus-response\")"
]
},
{
"cell_type": "code",
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"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"['/media/storage/expipe/septum-mec/actions/stimulus-response/data/statistics/values_ns_not_inhibited_p_i_peak.tex',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/statistics/values_bs_t_i_peak.csv',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/statistics/values_ns_not_inhibited_t_i_peak.csv',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/statistics/values_ns_inhibited_p_e_peak.tex',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/statistics/values_bs_not_gridcell_p_i_peak.csv',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/statistics/statistics_gridcell.csv',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/statistics/values_bs_t_e_peak.tex',\n",
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]
},
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"execution_count": 20,
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}
],
"source": [
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},
{
"cell_type": "code",
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"execution_count": 21,
"metadata": {},
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"source": [
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]
},
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{
"cell_type": "code",
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"outputs": [],
"source": []
},
{
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"outputs": [],
"source": []
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}
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