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{
"cells": [
{
"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": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"12:47:23 [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",
"from tqdm.notebook import tqdm_notebook as tqdm\n",
"tqdm.pandas()\n",
"\n",
"from spike_statistics.core import permutation_resampling\n",
"\n",
"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
"\n",
"from septum_mec.analysis.plotting import violinplot, despine"
]
},
{
"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-mec\"\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",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"lfp_results = pd.merge(sessions, lfp_results, how='left')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"lfp_results = lfp_results.query('stim_location!=\"ms\"')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <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",
"580 1833-020719-1-0 4 0.008427 NaN\n",
"578 1833-020719-1-1 2 0.019033 NaN\n",
"372 1833-020719-3-0 4 0.001076 NaN"
]
},
"execution_count": 11,
"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",
"execution_count": 12,
"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": 13,
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"metadata": {
"scrolled": false
},
"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/histograms.py:898: RuntimeWarning: invalid value encountered in true_divide\n",
" return n/db/n.sum(), bin_edges\n"
]
},
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{
"data": {
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"text/plain": [
"<Figure size 525x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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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",
" despine()\n",
" 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": 14,
2019-12-13 10:43:57 +00:00
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 480x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 480x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 480x300 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"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",
" 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",
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"execution_count": 15,
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"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",
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"execution_count": 16,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/stats.py:5700: RuntimeWarning: divide by zero encountered in double_scalars\n",
" z = (bigu - meanrank) / sd\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
" out=out, **kwargs)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n",
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"/home/mikkel/apps/expipe-project/spike-statistics/spike_statistics/core.py:401: RuntimeWarning: invalid value encountered in less\n",
" pval = np.sum(diffs > observed_diff) / float(num_samples)\n"
]
},
{
"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>0.20 ± 0.02 (8)</td>\n",
" <td>0.14 ± 0.02 (8)</td>\n",
" <td>7.99 ± 0.06 (8)</td>\n",
" <td>0.99 ± 0.10 (8)</td>\n",
" <td>0.15 ± 0.05 (8)</td>\n",
" <td>2.42 ± 1.36 (8)</td>\n",
" <td>0.40 ± 0.19 (8)</td>\n",
" <td>3.09 ± 2.06 (8)</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>nan ± nan (0)</td>\n",
" <td>nan ± nan (0)</td>\n",
" <td>nan ± nan (0)</td>\n",
" <td>nan ± nan (0)</td>\n",
" <td>nan ± nan (0)</td>\n",
" <td>nan ± nan (0)</td>\n",
" <td>nan ± nan (0)</td>\n",
" <td>nan ± nan (0)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU Baseline I 11 Hz</th>\n",
" <td>248.00, 0.194</td>\n",
" <td>225.00, 0.447</td>\n",
" <td>141.50, 0.240</td>\n",
" <td>192.00, 0.855</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",
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" <td>0.04, 0.374</td>\n",
" <td>0.01, 0.821</td>\n",
" <td>0.20, 0.260</td>\n",
" <td>0.06, 0.666</td>\n",
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" <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, 0.955</td>\n",
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" <td>0.01, 0.598</td>\n",
" <td>0.30, 0.010</td>\n",
" <td>0.05, 0.573</td>\n",
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" <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>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 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>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 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>100.00, 0.255</td>\n",
" <td>121.00, 0.642</td>\n",
" <td>141.00, 0.885</td>\n",
" <td>128.00, 0.908</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",
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" <td>0.04, 0.355</td>\n",
" <td>0.00, 0.986</td>\n",
" <td>0.10, 0.495</td>\n",
" <td>0.11, 0.463</td>\n",
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" <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>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PRS 11 Hz 30 Hz</th>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MWU Baseline II 30 Hz</th>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 0.000</td>\n",
" <td>0.00, 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>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 0.000</td>\n",
" <td>nan, 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 0.20 ± 0.02 (8) 0.14 ± 0.02 (8) \n",
"Baseline II 0.24 ± 0.02 (34) 0.17 ± 0.02 (34) \n",
"30 Hz nan ± nan (0) nan ± nan (0) \n",
"MWU Baseline I 11 Hz 248.00, 0.194 225.00, 0.447 \n",
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"PRS Baseline I 11 Hz 0.04, 0.374 0.01, 0.821 \n",
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"MWU Baseline I Baseline II 860.00, 0.682 850.00, 0.753 \n",
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"PRS Baseline I Baseline II 0.00, 0.955 0.01, 0.598 \n",
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"MWU Baseline I 30 Hz 0.00, 0.000 0.00, 0.000 \n",
"PRS Baseline I 30 Hz nan, 0.000 nan, 0.000 \n",
"MWU 11 Hz Baseline II 100.00, 0.255 121.00, 0.642 \n",
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"PRS 11 Hz Baseline II 0.04, 0.355 0.00, 0.986 \n",
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"MWU 11 Hz 30 Hz 0.00, 0.000 0.00, 0.000 \n",
"PRS 11 Hz 30 Hz nan, 0.000 nan, 0.000 \n",
"MWU Baseline II 30 Hz 0.00, 0.000 0.00, 0.000 \n",
"PRS Baseline II 30 Hz nan, 0.000 nan, 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 7.99 ± 0.06 (8) 0.99 ± 0.10 (8) \n",
"Baseline II 7.96 ± 0.09 (34) 1.23 ± 0.22 (33) \n",
"30 Hz nan ± nan (0) nan ± nan (0) \n",
"MWU Baseline I 11 Hz 141.50, 0.240 192.00, 0.855 \n",
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"PRS Baseline I 11 Hz 0.20, 0.260 0.06, 0.666 \n",
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"MWU Baseline I Baseline II 645.50, 0.108 805.00, 0.651 \n",
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"PRS Baseline I Baseline II 0.30, 0.010 0.05, 0.573 \n",
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"MWU Baseline I 30 Hz 0.00, 0.000 0.00, 0.000 \n",
"PRS Baseline I 30 Hz nan, 0.000 nan, 0.000 \n",
"MWU 11 Hz Baseline II 141.00, 0.885 128.00, 0.908 \n",
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"PRS 11 Hz Baseline II 0.10, 0.495 0.11, 0.463 \n",
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"MWU 11 Hz 30 Hz 0.00, 0.000 0.00, 0.000 \n",
"PRS 11 Hz 30 Hz nan, 0.000 nan, 0.000 \n",
"MWU Baseline II 30 Hz 0.00, 0.000 0.00, 0.000 \n",
"PRS Baseline II 30 Hz nan, 0.000 nan, 0.000 \n",
"\n",
" Stim energy Stim half width Stim p max \\\n",
"Baseline I NaN NaN NaN \n",
"11 Hz 0.15 ± 0.05 (8) 2.42 ± 1.36 (8) 0.40 ± 0.19 (8) \n",
"Baseline II NaN NaN NaN \n",
"30 Hz nan ± nan (0) nan ± nan (0) nan ± nan (0) \n",
"MWU Baseline I 11 Hz NaN NaN NaN \n",
"PRS Baseline I 11 Hz NaN NaN NaN \n",
"MWU Baseline I Baseline II NaN NaN NaN \n",
"PRS Baseline I Baseline II NaN NaN NaN \n",
"MWU Baseline I 30 Hz NaN NaN NaN \n",
"PRS Baseline I 30 Hz NaN NaN NaN \n",
"MWU 11 Hz Baseline II NaN NaN NaN \n",
"PRS 11 Hz Baseline II NaN NaN NaN \n",
"MWU 11 Hz 30 Hz 0.00, 0.000 0.00, 0.000 0.00, 0.000 \n",
"PRS 11 Hz 30 Hz nan, 0.000 nan, 0.000 nan, 0.000 \n",
"MWU Baseline II 30 Hz NaN NaN NaN \n",
"PRS Baseline II 30 Hz NaN NaN NaN \n",
"\n",
" Stim strength \n",
"Baseline I NaN \n",
"11 Hz 3.09 ± 2.06 (8) \n",
"Baseline II NaN \n",
"30 Hz nan ± nan (0) \n",
"MWU Baseline I 11 Hz NaN \n",
"PRS Baseline I 11 Hz NaN \n",
"MWU Baseline I Baseline II NaN \n",
"PRS Baseline I Baseline II NaN \n",
"MWU Baseline I 30 Hz NaN \n",
"PRS Baseline I 30 Hz NaN \n",
"MWU 11 Hz Baseline II NaN \n",
"PRS 11 Hz Baseline II NaN \n",
"MWU 11 Hz 30 Hz 0.00, 0.000 \n",
"PRS 11 Hz 30 Hz nan, 0.000 \n",
"MWU Baseline II 30 Hz NaN \n",
"PRS Baseline II 30 Hz NaN "
]
},
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"execution_count": 16,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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",
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"execution_count": 17,
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"metadata": {},
"outputs": [],
"source": [
"stat.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n",
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"stat.to_csv(output_path / \"statistics\" / \"statistics.csv\")"
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]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot PSD"
]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [],
"source": [
"psd = pd.read_feather(pathlib.Path(\"output\") / \"stimulus-lfp-response\" / 'data' / 'psd.feather')\n",
"freqs = pd.read_feather(pathlib.Path(\"output\") / \"stimulus-lfp-response\" / 'data' / 'freqs.feather')"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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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": 20,
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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": 21,
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"metadata": {},
"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:154: RuntimeWarning: invalid value encountered in true_divide\n",
" ret, rcount, out=ret, casting='unsafe', subok=False)\n"
]
},
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{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAArsAAAFHCAYAAACh24R9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd5xcdbn48c8502f7bja9kHogREAEkaoxIpd4LwiCIopXEJCiXnjFkh+icrmCoFxUUEH4XaVZKFIEf4hKMwFuqCEJ5aTubrLJZvvs9JlTfn+c2clMtmR7OXneee1rdue079nJnnnme57v81Vs20YIIYQQQgg3Use7AUIIIYQQQowWCXaFEEIIIYRrSbArhBBCCCFcS4JdIYQQQgjhWhLsCiGEEEII15JgVwghhBBCuJYEu0IIIYQQwrUk2BVCCCGEEK4lwa4QQgghhHAtCXaFEEIIIYRrSbArhBBCCCFcS4JdIYQQQgjhWhLsCiGEEEII15JgVwghhBBCuJZ3vBswWWmaFga+DZwHzAeiwBvAz3Rdf3o82yaEEEIIIRyKbdvj3YZJR9O0EuBZ4DggC2wCaoC5uVWu03X9P4d5jD8D6Lp+xnD2I4QQk4Fc84QQo0V6dofmlziB7nrgDF3XdwJomnYB8BvgOk3TXtJ1/R/DOMbCRYsWLQXk04gQoi/KeDdgBMk1TwhxIEO65knO7iBpmrYQ+CJgAV/oDnQBdF2/H7gp9+N1Y986IYQQQghRSILdwbsA8ACv6Lr+bi/L78w9nqhp2txelgshhBBCiDEiwe7gHZ97XNvbQl3XG4H63I8fHZMWCSGEEEKIXkmwO3iLco/b+lmnLve4ZHSbIoQQQggh+iPB7uBNzT229LNOW+5xyii3RQghhBBC9EOqMQxeOPeY6med5H7r9knTtHf6WLRwMI0SQojJQK55QoixJj27g2cOYl0poSOEEEIIMY6kZ3fwYkAVEOxnnVDuMXGgnem6fnhvz+d6P5YOunVCCDGByTVPCDHWpGd38FpzjzX9rNOdq9s8ym0RQgghhBD9kGB38N7LPR7SzzrdyzaPakuEEEIIIUS/JNgdvHW5x+N7W6hp2mygezKJl8ekRUIIIYQQolcS7A7ew7nHj2mapvWy/LLc44u6rteNTZOEEEIIIURvJNgdJF3XtwC/x5ky+FFN07onmUDTtC8C38n9+MNxaJ4QQgghhCgg1RiG5hvAB3Jf72uathGnQsO83PLv6rr+j/FqnBBCCCGEcEjP7hDout6Gk7P7nziD0A7Dqc7wIvAZXddvHMfmCSGEEEKIHOnZHSJd1+PAdbkvIYQQQggxAUnPrhBCCCGEcC0JdoUQQgghhGtJsCuEEEIIIVxLgl0hhBBCCOFaEuwKIYQQQgjXkmBXiElk165daJrW69ehhx7Kcccdx6c//WluvfVW2tvbx7u5Q/Loo4+iaRqnnHJK0fMXXHABmqbx05/+dJxadmCrV69G0zQuuOCC8W6KEJOGXNcm73VtslzzpPSYEJPUkiVLKC0tzf9smiaRSIQtW7bw3nvv8dBDD3HvvffS+6zWQggx8ch1TYwGCXaFmKSuvfZajjvuuB7Pd3R0sHr1al544QW+8Y1v8PTTT6Oqk/8mzs0330wymaSqqmq8myKEGCVyXROjYfL/TxFCFKmqquKmm27C7/dTV1fH2rVrx7tJI2LmzJksXLiQ6urq8W6KEGKMyXVNDIcEu0K4UFVVFYsXLwZgy5Yt49waIYQYPrmuiaGSNAYhXMowDABKSkp6XfbUU0/x17/+lXfeeYfOzk68Xi9Tp07luOOO48ILL2T+/Pk9tluzZg2/+93vePvtt+nq6qK0tJQlS5bwL//yL5x77rn4/f4e28RiMe69917+/ve/U19fj23bzJkzh1NPPZUvf/nLlJeXD+h8LrjgAl599VUuu+wyrr76asAZ2LJixQqmTJnC2rVreeSRR3jooYfYunUr4OT/ffazn+Xss89GUZRRa5sQYmzIdU2ua0Mhwa4QLtTQ0MCWLVtQVZWTTz65aFkqleLSSy9l3bp1AMyaNYslS5bQ1tZGXV0ddXV1PPnkk/zud79j6dKl+e3uu+8+brjhBgCmTp3KoYceSkdHB6+++iqvvvoqf/3rX7nnnnvweDz5bbZt28Yll1xCY2MjHo+HOXPmEAwG2bp1K7/85S95/PHHufvuu1m4cOGwzte2bb7zne/wxBNPUF5ezvz589m5cyfr169n/fr17Nixg29+85tF24xV24QQI0Oua3JdGyoJdsWEYts2SSNb9Fy2o5Hk1pcoWboCT0nNOLVs8EJeX6+fukeLaZp0dXXx1ltvcfPNN2NZFpdddhmzZs0qWu/uu+9m3bp1VFVVcdddd3HEEUfkl23YsIErrriClpYW7rzzTm677TYAurq6uOWWWwC49dZb+dSnPpXfZu3atVx55ZX5N4buZYlEgssvv5zGxkZWrFjBD37wA6ZNmwZAS0sL1157LS+88AJXXHEFTzzxBMFgcMjn3tbWxlNPPcV3v/tdvvCFL+DxeEin01x77bX8+c9/5re//S0XXXRRPi9uLNsmhG3bZDPmeDdjRPj8HrmuyXVt0pFgV0wYtm1z1v+7k9eb63tfQd80tg0apmOnzuPRlZeN2hvDl770pX6XX3LJJVx11VU9nn/55ZdRVZWvfe1rRW8IAEcccQSf//znue2229i8eXP++R07dpBOp6moqGDlypVF25x00klceuml6LqOz+fLP//www9TX1/P4Ycfzu23317UM1JbW8vPf/5zVq5cSV1dHY8++ijnn3/+oM5/f+eff37R7yQQCHDNNdfw5JNPYhgGGzZs4GMf+9i4tE0cvGzb5le3vUx9Xcd4N2VEHDK/isu/foJc1+S6NqnIADUxoYxdf8Hkt2TJEo4++uj811FHHcXixYvz+WX33HMPP/zhDzHN4h6lP/zhD2zYsIHzzjuv1/2GQiHAuS3Ybfbs2Xi9XiKRCKtXr+b9998v2ubKK6/ktttu45Of/GT+uX/84x8ArFy5suii2y0YDHLaaacB8Pzzzw/29HtYvnx5j+eqqqryvR5dXV3j1jZxcBvDjtBJT65rxeS6NjKkZ1dMGIqi8OjKy3qkMbQ9eSO2mUXxBaj51Opxat3gjXYaQ1/1KDOZDI899hj/9V//xQMPPIBpmlx33XVF6/h8PiKRCOvXr6euro6dO3dSV1fHe++9R2trKwCWZeXXr6mp4eKLL+bOO+/k8ccf5/HHH6e2tpaPfOQjnHTSSZxyyik9Sud096A8/PDDPPvss72eQ/extm/fPuTfQ7fu23X7675VV/jmONZtEwcvRVG4/OsnSBrDAMl1rZhc10aGBLtiQlEUhbCveORrQlGwcW5D7L9M9OT3+/nc5z5HS0sLt99+Ow899BBf/epXmTFjBuCM1L3hhht48sknyWb3fbDw+XwcfvjhHHbYYaxZs6bHfq+++mqWLVvGAw88wOuvv05LSwtPPvkkTz75JF6vl5UrV/L973+fsrKy/HGA/OCQ/kSj0WGfd+Gtxt7Ytp3/fqzbJg5uiqLgD8jb7XDIda13cl0bGPnrE5ODbRX9UYsDW7FiBbfffjumafLuu+/m3xSuuOIK1q1bRzAY5Itf/CJHHnkkixcvZt68efh8Ph566KFe3xQATj31VE499VRisVh+tPKLL77I9u3b+fOf/0w0GuXOO+8EnNuG3T/3dituPE3ktgkh+ibXtb5N5LaNN8nZFZODbR14HVGk8FZj9weF9evX50vz/PrXv2b16tWcfvrpLFq0KN+D0NTU1GNfqVSK999/P5/TVlpaysc//nFWr17N008/zapVqwAnD6y7x6C7nmV/xd/r6urYuHEj7e3twz3dQZnIbRNC9E2ua32byG0bbxLsiknBtiTYHawXX3wRcN4cli1bBjjFyrt1P1comUzyl7/8BSjOBXvwwQc588wz+da3vtVrD/sJJ5yQ/757u+6ehUceeaRoUEg3wzC44oorOOecc7j55psHfX7DMZHbJoTom1zX+jaR2zbeJNgVk4Q
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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",
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"despine()\n",
"\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": 35,
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"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"stimulus-lfp-response-mec\")"
]
},
{
"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/statistics/statistics.tex',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/statistics/statistics.csv',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_energy.png',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_strength.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_peak.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_p_max.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_freq.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_energy.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_freq.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_half_width.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_half_width.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_half_width.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_energy.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_peak.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_p_max.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_half_width.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_energy.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_strength.svg']"
]
},
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"execution_count": 36,
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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": 37,
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"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-lfp-response-mec.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
}
