septum-mec/actions/stimulus-lfp-response/data/20_stimulus-lfp-response.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"13:24:34 [I] klustakwik KlustaKwik2 version 0.2.6\n"
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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",
"from distutils.dir_util import copy_tree\n",
"from neo import SpikeTrain\n",
"import scipy\n",
"import seaborn as sns\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
"tqdm.pandas()\n",
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"\n",
"from spike_statistics.core import permutation_resampling\n",
"\n",
"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
"\n",
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"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-lfp-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": [
"data_loader = dp.Data()\n",
"actions = data_loader.actions\n",
"project = data_loader.project"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"identify_neurons = actions['identify-neurons']\n",
"sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"lfp_action = actions['stimulus-lfp-response']\n",
"lfp_results = pd.read_csv(lfp_action.data_path('results'))"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"lfp_results = pd.merge(sessions, lfp_results, how='left')"
]
},
{
"cell_type": "code",
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"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"lfp_results = lfp_results.query('stim_location!=\"mecl\" and stim_location!=\"mecr\"')"
]
},
{
"cell_type": "code",
"execution_count": 9,
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"metadata": {},
"outputs": [],
"source": [
"def action_group(row):\n",
" a = int(row.channel_group in [0,1,2,3])\n",
" return f'{row.action}-{a}'\n",
"lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [],
"source": [
"lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>action_side_a</th>\n",
" <th>channel_group</th>\n",
" <th>signal_to_noise</th>\n",
" <th>stim_strength</th>\n",
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"text/plain": [
" action_side_a channel_group signal_to_noise stim_strength\n",
"68 1833-010719-1-0 4 0.006686 NaN\n",
"66 1833-010719-1-1 2 0.034550 NaN\n",
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},
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"execution_count": 11,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lfp_results_hemisphere = lfp_results.sort_values(\n",
" by=['action_side_a', 'stim_strength', 'signal_to_noise'], ascending=[True, False, False]\n",
").drop_duplicates(subset='action_side_a', keep='first')\n",
"lfp_results_hemisphere.loc[:,['action_side_a','channel_group', 'signal_to_noise', 'stim_strength']].head()"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [],
"source": [
"colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
"labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
"queries = ['baseline and Hz11', 'frequency==11', 'baseline and Hz30', 'frequency==30']"
]
},
{
"cell_type": "code",
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"execution_count": 51,
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"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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{
"data": {
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2019-10-17 17:50:46 +00:00
"text/plain": [
"<Figure size 525x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-17 17:50:46 +00:00
"text/plain": [
"<Figure size 525x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 525x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"density = True\n",
"cumulative = True\n",
"histtype = 'step'\n",
"lw = 2\n",
"bins = {\n",
" 'theta_energy': np.arange(0, .7, .03),\n",
" 'theta_peak': np.arange(0, .7, .03),\n",
" 'theta_freq': np.arange(4, 10, .5),\n",
" 'theta_half_width': np.arange(0, 15, .5)\n",
"}\n",
"xlabel = {\n",
" 'theta_energy': 'Theta energy (dB)',\n",
" 'theta_peak': 'Peak PSD (dB/Hz)',\n",
" 'theta_freq': '(Hz)',\n",
" 'theta_half_width': '(Hz)',\n",
"}\n",
"# key = 'theta_energy'\n",
"# key = 'theta_peak'\n",
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"results = {}\n",
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"for key in bins:\n",
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" results[key] = pd.DataFrame()\n",
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" fig = plt.figure(figsize=(3.5,2))\n",
" plt.suptitle(key)\n",
" legend_lines = []\n",
" for color, query, label in zip(colors, queries, labels):\n",
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" values = lfp_results_hemisphere.query(query)[key]\n",
" results[key] = pd.concat([results[key], values.rename(label).reset_index(drop=True)], axis=1)\n",
" values.hist(\n",
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" bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
" histtype=histtype, color=color)\n",
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n",
" \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",
" plt.xlim(-0.05, bins[key].max() - bins[key].max()*0.025)\n",
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" despine()\n",
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" plt.xlabel(xlabel[key])\n",
" figname = f'lfp-psd-histogram-{key}'\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)"
]
},
{
"cell_type": "code",
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"execution_count": 62,
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"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 480x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-17 17:50:46 +00:00
"text/plain": [
"<Figure size 480x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-17 17:50:46 +00:00
"text/plain": [
"<Figure size 480x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
"image/png": "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
2019-10-17 17:50:46 +00:00
"text/plain": [
"<Figure size 480x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"density = True\n",
"cumulative = True\n",
"histtype = 'step'\n",
"lw = 2\n",
"bins = {\n",
" 'stim_energy': np.arange(0, .7, .01),\n",
" 'stim_half_width': np.arange(0, 10, .5),\n",
" 'stim_p_max': np.arange(0, 4, .01),\n",
" 'stim_strength': np.arange(0, 160, 1)\n",
"}\n",
"xlabel = {\n",
" 'stim_energy': 'Energy (dB)',\n",
" 'stim_half_width': '(Hz)',\n",
" 'stim_p_max': 'Peak PSD (dB/Hz)',\n",
" 'stim_strength': 'Ratio',\n",
"}\n",
"# key = 'theta_energy'\n",
"# key = 'theta_peak'\n",
"for key in bins:\n",
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" results[key] = pd.DataFrame()\n",
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" fig = plt.figure(figsize=(3.2,2))\n",
" plt.suptitle(key)\n",
" legend_lines = []\n",
" for color, query, label in zip(colors[1::2], queries[1::2], labels[1::2]):\n",
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" values = lfp_results_hemisphere.query(query)[key]\n",
" results[key] = pd.concat([results[key], values.rename(label).reset_index(drop=True)], axis=1)\n",
" values.hist(\n",
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" bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
" histtype=histtype, color=color)\n",
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n",
" \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",
" plt.xlim(-0.05, bins[key].max() - bins[key].max()*0.02)\n",
" despine()\n",
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" plt.xlabel(xlabel[key])\n",
" figname = f'lfp-psd-histogram-{key}'\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",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
"\n",
"\n",
"def MWU(df, keys):\n",
" '''\n",
" Mann Whitney U\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
" df[keys[0]].dropna(), \n",
" df[keys[1]].dropna(),\n",
" alternative='two-sided')\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
"\n",
"\n",
"def PRS(df, keys):\n",
" '''\n",
" Permutation ReSampling\n",
" '''\n",
" pvalue, observed_diff, diffs = permutation_resampling(\n",
" df[keys[0]].dropna(), \n",
" df[keys[1]].dropna(), statistic=np.median)\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
"\n",
"\n",
"def rename(name):\n",
" return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Theta energy</th>\n",
" <th>Theta peak</th>\n",
" <th>Theta freq</th>\n",
" <th>Theta half width</th>\n",
" <th>Stim energy</th>\n",
" <th>Stim half width</th>\n",
" <th>Stim p max</th>\n",
" <th>Stim strength</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Baseline I</th>\n",
" <td>0.25 ± 0.02 (48)</td>\n",
" <td>0.18 ± 0.02 (48)</td>\n",
" <td>7.78 ± 0.09 (48)</td>\n",
" <td>1.79 ± 0.33 (46)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 Hz</th>\n",
" <td>0.08 ± 0.00 (44)</td>\n",
" <td>0.03 ± 0.00 (44)</td>\n",
" <td>7.55 ± 0.12 (44)</td>\n",
" <td>5.80 ± 0.50 (43)</td>\n",
" <td>0.05 ± 0.00 (44)</td>\n",
" <td>0.16 ± 0.00 (44)</td>\n",
" <td>0.51 ± 0.04 (44)</td>\n",
" <td>7.14 ± 0.81 (44)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Baseline II</th>\n",
" <td>0.24 ± 0.02 (34)</td>\n",
" <td>0.17 ± 0.02 (34)</td>\n",
" <td>7.96 ± 0.09 (34)</td>\n",
" <td>1.23 ± 0.22 (33)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30 Hz</th>\n",
" <td>0.04 ± 0.00 (34)</td>\n",
" <td>0.02 ± 0.00 (34)</td>\n",
" <td>7.74 ± 0.19 (34)</td>\n",
" <td>5.88 ± 0.64 (31)</td>\n",
" <td>0.16 ± 0.02 (34)</td>\n",
" <td>0.14 ± 0.00 (34)</td>\n",
" <td>1.54 ± 0.16 (34)</td>\n",
" <td>45.30 ± 6.54 (34)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU Baseline I 11 Hz</th>\n",
" <td>2075.00, 0.000</td>\n",
" <td>2102.00, 0.000</td>\n",
" <td>1201.50, 0.256</td>\n",
" <td>253.00, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRS Baseline I 11 Hz</th>\n",
" <td>0.15, 0.000</td>\n",
" <td>0.12, 0.000</td>\n",
" <td>0.05, 0.679</td>\n",
" <td>4.74, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU Baseline I Baseline II</th>\n",
" <td>860.00, 0.682</td>\n",
" <td>850.00, 0.753</td>\n",
" <td>645.50, 0.108</td>\n",
" <td>805.00, 0.651</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRS Baseline I Baseline II</th>\n",
" <td>0.00, 0.954</td>\n",
" <td>0.01, 0.612</td>\n",
" <td>0.30, 0.008</td>\n",
" <td>0.05, 0.582</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU Baseline I 30 Hz</th>\n",
" <td>1629.00, 0.000</td>\n",
" <td>1629.00, 0.000</td>\n",
" <td>781.50, 0.749</td>\n",
" <td>225.00, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRS Baseline I 30 Hz</th>\n",
" <td>0.19, 0.000</td>\n",
" <td>0.13, 0.000</td>\n",
" <td>0.20, 0.416</td>\n",
" <td>4.84, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU 11 Hz Baseline II</th>\n",
" <td>22.00, 0.000</td>\n",
" <td>10.00, 0.000</td>\n",
" <td>524.50, 0.024</td>\n",
" <td>1328.00, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRS 11 Hz Baseline II</th>\n",
" <td>0.15, 0.000</td>\n",
" <td>0.12, 0.000</td>\n",
" <td>0.35, 0.019</td>\n",
" <td>4.79, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU 11 Hz 30 Hz</th>\n",
" <td>1299.00, 0.000</td>\n",
" <td>1275.00, 0.000</td>\n",
" <td>657.00, 0.361</td>\n",
" <td>664.00, 0.983</td>\n",
" <td>248.00, 0.000</td>\n",
" <td>1408.00, 0.000</td>\n",
" <td>236.00, 0.000</td>\n",
" <td>202.00, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRS 11 Hz 30 Hz</th>\n",
" <td>0.04, 0.000</td>\n",
" <td>0.01, 0.000</td>\n",
" <td>0.25, 0.523</td>\n",
" <td>0.10, 0.912</td>\n",
" <td>0.11, 0.000</td>\n",
" <td>0.02, 0.000</td>\n",
" <td>1.09, 0.000</td>\n",
" <td>31.00, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU Baseline II 30 Hz</th>\n",
" <td>1154.00, 0.000</td>\n",
" <td>1154.00, 0.000</td>\n",
" <td>604.00, 0.754</td>\n",
" <td>108.00, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRS Baseline II 30 Hz</th>\n",
" <td>0.19, 0.000</td>\n",
" <td>0.13, 0.000</td>\n",
" <td>0.10, 0.582</td>\n",
" <td>4.89, 0.000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Theta energy Theta peak \\\n",
"Baseline I 0.25 ± 0.02 (48) 0.18 ± 0.02 (48) \n",
"11 Hz 0.08 ± 0.00 (44) 0.03 ± 0.00 (44) \n",
"Baseline II 0.24 ± 0.02 (34) 0.17 ± 0.02 (34) \n",
"30 Hz 0.04 ± 0.00 (34) 0.02 ± 0.00 (34) \n",
"MWU Baseline I 11 Hz 2075.00, 0.000 2102.00, 0.000 \n",
"PRS Baseline I 11 Hz 0.15, 0.000 0.12, 0.000 \n",
"MWU Baseline I Baseline II 860.00, 0.682 850.00, 0.753 \n",
"PRS Baseline I Baseline II 0.00, 0.954 0.01, 0.612 \n",
"MWU Baseline I 30 Hz 1629.00, 0.000 1629.00, 0.000 \n",
"PRS Baseline I 30 Hz 0.19, 0.000 0.13, 0.000 \n",
"MWU 11 Hz Baseline II 22.00, 0.000 10.00, 0.000 \n",
"PRS 11 Hz Baseline II 0.15, 0.000 0.12, 0.000 \n",
"MWU 11 Hz 30 Hz 1299.00, 0.000 1275.00, 0.000 \n",
"PRS 11 Hz 30 Hz 0.04, 0.000 0.01, 0.000 \n",
"MWU Baseline II 30 Hz 1154.00, 0.000 1154.00, 0.000 \n",
"PRS Baseline II 30 Hz 0.19, 0.000 0.13, 0.000 \n",
"\n",
" Theta freq Theta half width \\\n",
"Baseline I 7.78 ± 0.09 (48) 1.79 ± 0.33 (46) \n",
"11 Hz 7.55 ± 0.12 (44) 5.80 ± 0.50 (43) \n",
"Baseline II 7.96 ± 0.09 (34) 1.23 ± 0.22 (33) \n",
"30 Hz 7.74 ± 0.19 (34) 5.88 ± 0.64 (31) \n",
"MWU Baseline I 11 Hz 1201.50, 0.256 253.00, 0.000 \n",
"PRS Baseline I 11 Hz 0.05, 0.679 4.74, 0.000 \n",
"MWU Baseline I Baseline II 645.50, 0.108 805.00, 0.651 \n",
"PRS Baseline I Baseline II 0.30, 0.008 0.05, 0.582 \n",
"MWU Baseline I 30 Hz 781.50, 0.749 225.00, 0.000 \n",
"PRS Baseline I 30 Hz 0.20, 0.416 4.84, 0.000 \n",
"MWU 11 Hz Baseline II 524.50, 0.024 1328.00, 0.000 \n",
"PRS 11 Hz Baseline II 0.35, 0.019 4.79, 0.000 \n",
"MWU 11 Hz 30 Hz 657.00, 0.361 664.00, 0.983 \n",
"PRS 11 Hz 30 Hz 0.25, 0.523 0.10, 0.912 \n",
"MWU Baseline II 30 Hz 604.00, 0.754 108.00, 0.000 \n",
"PRS Baseline II 30 Hz 0.10, 0.582 4.89, 0.000 \n",
"\n",
" Stim energy Stim half width \\\n",
"Baseline I NaN NaN \n",
"11 Hz 0.05 ± 0.00 (44) 0.16 ± 0.00 (44) \n",
"Baseline II NaN NaN \n",
"30 Hz 0.16 ± 0.02 (34) 0.14 ± 0.00 (34) \n",
"MWU Baseline I 11 Hz NaN NaN \n",
"PRS Baseline I 11 Hz NaN NaN \n",
"MWU Baseline I Baseline II NaN NaN \n",
"PRS Baseline I Baseline II NaN NaN \n",
"MWU Baseline I 30 Hz NaN NaN \n",
"PRS Baseline I 30 Hz NaN NaN \n",
"MWU 11 Hz Baseline II NaN NaN \n",
"PRS 11 Hz Baseline II NaN NaN \n",
"MWU 11 Hz 30 Hz 248.00, 0.000 1408.00, 0.000 \n",
"PRS 11 Hz 30 Hz 0.11, 0.000 0.02, 0.000 \n",
"MWU Baseline II 30 Hz NaN NaN \n",
"PRS Baseline II 30 Hz NaN NaN \n",
"\n",
" Stim p max Stim strength \n",
"Baseline I NaN NaN \n",
"11 Hz 0.51 ± 0.04 (44) 7.14 ± 0.81 (44) \n",
"Baseline II NaN NaN \n",
"30 Hz 1.54 ± 0.16 (34) 45.30 ± 6.54 (34) \n",
"MWU Baseline I 11 Hz NaN NaN \n",
"PRS Baseline I 11 Hz NaN NaN \n",
"MWU Baseline I Baseline II NaN NaN \n",
"PRS Baseline I Baseline II NaN NaN \n",
"MWU Baseline I 30 Hz NaN NaN \n",
"PRS Baseline I 30 Hz NaN NaN \n",
"MWU 11 Hz Baseline II NaN NaN \n",
"PRS 11 Hz Baseline II NaN NaN \n",
"MWU 11 Hz 30 Hz 236.00, 0.000 202.00, 0.000 \n",
"PRS 11 Hz 30 Hz 1.09, 0.000 31.00, 0.000 \n",
"MWU Baseline II 30 Hz NaN NaN \n",
"PRS Baseline II 30 Hz NaN NaN "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"stat = pd.DataFrame()\n",
"\n",
"for key, df in results.items():\n",
" Key = rename(key)\n",
" stat[Key] = df.agg(summarize)\n",
" stat[Key] = df.agg(summarize)\n",
" \n",
" for i, c1 in enumerate(df.columns):\n",
" for c2 in df.columns[i+1:]:\n",
" stat.loc[f'MWU {c1} {c2}', Key] = MWU(df, [c1, c2])\n",
" stat.loc[f'PRS {c1} {c2}', Key] = PRS(df, [c1, c2])\n",
"\n",
"stat"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"stat.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n",
"stat.to_latex(output_path / \"statistics\" / \"statistics.csv\")"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot PSD"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [],
"source": [
"psd = pd.read_feather(output_path / 'data' / 'psd.feather')\n",
"freqs = pd.read_feather(output_path / 'data' / 'freqs.feather')"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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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": 19,
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"metadata": {},
"outputs": [],
"source": [
"freq = freqs.T.iloc[0].values\n",
"\n",
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"mask = (freq < 49)"
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]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAArsAAAFHCAYAAACh24R9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeZwcdZ34/1f1fcx95D7IQQqSSDhU5FCJkWWJu6AIrqK4goAc6sIvHvkiKouCoCwiqCDssnKpnIrBReReAhjETQgBUjlnkkyOufq+6/j9UT2TuTNHz1V5Px+PPHqmq6rr0+npT7/7U+/P+6NYloUQQgghhBBO5BrvBgghhBBCCDFaJNgVQgghhBCOJcGuEEIIIYRwLAl2hRBCCCGEY0mwK4QQQgghHEuCXSGEEEII4VgS7AohhBBCCMeSYFcIIYQQQjiWBLtCCCGEEMKxJNgVQgghhBCOJcGuEEIIIYRwLAl2hRBCCCGEY0mwK4QQQgghHEuCXSGEEEII4Vie8W7AZKWqagj4FvBZYB6QAP4O3KZp2tPj2TYhhBBCCGFTLMsa7zZMOqqqhoHngROBArAJqAXmFHe5TtO0fx/hOf4IoGnaWSN5HCGEmAykzxNCjBYZ2R2eX2AHuhuAszRN2w2gquoFwL3Adaqqvqpp2nMjOMeChQsXLgbk24gQoj/KeDeghKTPE+Mq+8I2MmvewT2/loqvnTrezRF9G1afJzm7Q6Sq6gLgC4AJfL4j0AXQNO0B4Kbir9eNfeuEEEIIMRyxa58m98wW9C0t490UUWIS7A7dBYAbeF3TtHf72H5X8fYUVVXn9LFdCCGEEBOMFc0CYOxsH+eWiFKTYHfoTireru1ro6ZpTUBj8dePjkmLhBBCCCFEnyTYHbqFxdvtA+zTULxdNLpNEUIIIURJSda440iwO3RTircDJfW0FW/rRrktQgghhBBiAFKNYehCxdvsAPtkeuzbL1VV3+ln04KhNEoIISYD6fPEZGBZForipGInhzcZ2R06Ywj7ysUQIYQQYrKRT29HkZHdoUsC1UBggH2Cxdv0oR5M07Qlfd1fHP1YPOTWCSHEBCZ9npgUTAtcMrLrFDKyO3StxdvaAfbpyNVtHuW2CCGEEKLUTBnadRIJdofuveLtEQPs07Fty6i2RAghhBAlZiF5DM4iwe7QrSventTXRlVVZwEdi0m8NiYtEkIIIUTpyMiuo0iwO3SPFm9PU1VV7WP7ZcXblzVNaxibJgkhhBCiJGRg13Ek2B0iTdO2Ar/BXjL4CVVVOxaZQFXVLwDfLv76w3FonhBCCCFGyJKRXUeRagzD83XgfcV/m1VVfRu7QsPc4vbvaJr23Hg1TgghhBAjYEmw6yQysjsMmqa1Yefs/jv2JLSjsaszvAx8WtO0G8exeUIIIYQYLkljcBwZ2R0mTdNSwHXFf0IIIYRwCkljcBQZ2RVCCCGE6ErSGBxFgl0hhBBCiE6WBLsOI8GuEEIIIUQHS6oxOI0Eu0IIIYQQXUmw6ygS7AohhBBCCMeSYFeISWTPnj2oqtrnv6OOOooTTzyRT37yk9x66620t7ePd3OH5YknnkBVVT7ykY90u/+CCy5AVVV++tOfjlPLDm316tWoqsoFF1ww3k0RYtKYCP2a1TVH16LkI7tO7dcmS58npceEmKQWLVpEWVlZ5++GYRCLxdi6dSvvvfcejzzyCPfddx99r2othBATz7j1a92CW0vq7DqMBLtiUrAsC0VRxrsZE8q1117LiSee2Ov+SCTC6tWreemll/j617/O008/jcs1+S/i3HzzzWQyGaqrq8e7KUKIUTJu/Zphdv99jHJ2pV8bG5P/E1A4Xq7pHSJP30KhpWG8mzIpVFdXc9NNN+Hz+WhoaGDt2rXj3aSSmDFjBgsWLKCmpma8myKEGGOj3q8Z3dMYLNPsf98Skn5tbEiwKya8xBuPYWZTxF9/aLybMmlUV1dz5JFHArB169Zxbo0QQozcaPZrvUqN6ZLH4CSSxiAmB8u0/4lB03UdgHA43Oe2p556ij//+c+88847RKNRPB4PU6ZM4cQTT+TCCy9k3rx5vY575ZVXeOihh3jrrbeIx+OUlZWxaNEi/vEf/5HzzjsPn8/X65hkMsl9993Hs88+S2NjI5ZlMXv2bE4//XS+9KUvUVFRMajnc8EFF/DGG29w2WWXcfXVVwP2xJYVK1ZQV1fH2rVreeyxx3jkkUfYtm0bYOf/feYzn+Gcc87pMw2mVG0TQoyNUevX7nuA9cY6EuQo2+1n0Tde4MxP/bP0aw4hwa6YFCzLRJEVbQZt165dbN26FZfLxYc//OFu27LZLJdeeinr1q0DYObMmSxatIi2tjYaGhpoaGhgzZo1PPTQQyxevLjzuPvvv58bbrgBgClTpnDUUUcRiUR44403eOONN/jzn//Mr3/9a9xud+cx27dv55JLLqGpqQm3283s2bMJBAJs27aNX/ziF/zhD3/gnnvuYcGCBSN6vpZl8e1vf5snn3ySiooK5s2bx+7du9mwYQMbNmxg586dfOMb3+h2zFi1TQhRGqPdr9URYiG1xJU8f3t7PX97e730aw4hwa6YUCzLIqMXut2XsSws0w52g4X8OLVs6IIe75hOqjMMg3g8zvr167n55psxTZPLLruMmTNndtvvnnvuYd26dVRXV3P33XdzzDHHdG7buHEjV1xxBS0tLdx1113cfvvtAMTjcW655RYAbr31Vj7xiU90HrN27VquvPLKzoC3Y1s6nebyyy+nqamJFStW8P3vf5+pU6cC0NLSwrXXXstLL73EFVdcwZNPPkkgEBj2c29ra+Opp57iO9/5Dp///Odxu93kcjmuvfZa/vjHP/Lf//3fXHTRRZ15cWPZNiEsy6KQN8a7GSXh9bkd2a/d8u8/4gPXbwfANbOSdy+awb/d9B3p1xxCgl0xYViWxaf+5y7ebG7sZ48MPPi9MW3TSHxgylyeWHnZqH0wfPGLXxxw+yWXXMJVV13V6/7XXnsNl8vFV7/61W4fCADHHHMMn/vc57j99tvZsmVL5/07d+4kl8tRWVnJypUrux1z6qmncumll6JpGl6vt/P+Rx99lMbGRpYsWcIdd9zRbWSkvr6en/3sZ6xcuZKGhgaeeOIJzj///CE9/57OP//8bv8nfr+fa665hjVr1qDrOhs3buS0004bl7aJw5dlWfzy9tdobIiMd1NK4oh51Vz+tZMd16+d+bHTaS0Gu1gWpyz7gPRrDiLBrphQpLjY4PWsR2maJqlUisbGRvL5PL/+9a/JZDJcc8013Tq93/72txQKhX4/rILBIGBfFuwwa9YsPB4PsViM1atXc+GFF3LUUUd1br/yyit7Pc5zzz0HwMqVK7udv0MgEOCMM87g3nvv5cUXXxxxx7t8+fJe91VXV1NTU0NbWxvxeHzc2iYOb1I1cfDGq1+75off5yzLx0KlFiwLSzelX3MQCXbFhKEoCk+svKxbGkNuz9vs+sEpAFR/4lvUnXXteDVvyEY7jaG/epT5fJ7f//73/OAHP+DBBx/EMAyuu+66bvt4vV5isRgbNmygoaGB3bt309DQwHvvvUdraytgf8h0qK2t5eKLL+auu+7iD3/4A3/4wx+or6/nQx/6EKeeeiof+chHepXO6RhBefTRR3n++ef7fA4d59qxY8ew/x86dFyu66njUp1hHLyMPNZtE4cvRVG4/GsnSxrDII1Xv/bkM3/iSaCGICe0zeW0F/18bNbZ0q85hAS7YkJRFIWQ9+DMV7fHS9C0g9+gZXXb5gSWoYOioLh6fwsfLp/Px7/8y7/Q0tLCHXfcwSOPPMJXvvIVpk+fDtgzdW+44QbWrFlDoXDwi4XX62XJkiUcffTRvPLKK70e9+qrr2bp0qU8+OCDvPnmm7S0tLBmzRrWrFmDx+Nh5cqVfO9736O8vLzzPEDn5JCBJBKJET/vrpca+9J1OdCxbps4vCmKgs8vH7cjMdr92gP/+Wve3PB32snwbGYzz/7yJjx33yL9mkPIu09McF1GEBxWeswyDSJP/weKx0fVGVeVfLR
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"text/plain": [
"<Figure size 750x300 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
"axs = axs.repeat(2)\n",
"for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
" selection = [\n",
" f'{r.action}_{r.channel_group}' \n",
" for i, r in lfp_results_hemisphere.query(query).iterrows()]\n",
" values = psd.loc[mask, selection].to_numpy()\n",
" values = 10 * np.log10(values)\n",
" plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])\n",
"# ax.set_title(titles[i])\n",
" ax.set_xlabel('Frequency Hz')\n",
" ax.legend(frameon=False)\n",
"axs[0].set_ylabel('PSD (dB/Hz)')\n",
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"axs[0].set_ylim(-31, 1)\n",
"despine()\n",
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"\n",
"figname = 'lfp-psd'\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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Store results in Expipe action"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"stimulus-lfp-response\")"
]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/data/psd.feather',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/data/freqs.feather',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_energy.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_strength.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_peak.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_p_max.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_freq.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_energy.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_freq.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_half_width.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_half_width.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_half_width.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_energy.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_peak.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_p_max.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-theta_half_width.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_energy.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response/data/figures/lfp-psd-histogram-stim_strength.svg']"
]
},
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"execution_count": 26,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-lfp-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
}