603 lines
24 KiB
Plaintext
603 lines
24 KiB
Plaintext
{
|
|
"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": [
|
|
"17:02:17 [I] klustakwik KlustaKwik2 version 0.2.6\n"
|
|
]
|
|
}
|
|
],
|
|
"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.analysis.data_processing as dp\n",
|
|
"import head_direction.head as head\n",
|
|
"import spatial_maps as sp\n",
|
|
"import septum_mec.analysis.registration\n",
|
|
"import speed_cells.speed as spd\n",
|
|
"import septum_mec.analysis.spikes as spikes\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 septum_mec\n",
|
|
"import scipy.ndimage.measurements\n",
|
|
"from distutils.dir_util import copy_tree\n",
|
|
"\n",
|
|
"from tqdm import tqdm_notebook as tqdm\n",
|
|
"from tqdm._tqdm_notebook import tqdm_notebook\n",
|
|
"tqdm_notebook.pandas()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"max_speed = 1, # m/s only used for speed score\n",
|
|
"min_speed = 0.02, # m/s only used for speed score\n",
|
|
"position_sampling_rate = 100 # for interpolation\n",
|
|
"position_low_pass_frequency = 6 # for low pass filtering of position\n",
|
|
"\n",
|
|
"box_size = [1.0, 1.0]\n",
|
|
"bin_size = 0.02\n",
|
|
"smoothing_low = 0.03\n",
|
|
"smoothing_high = 0.06\n",
|
|
"\n",
|
|
"stim_mask = True\n",
|
|
"baseline_duration = 600"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"project_path = dp.project_path()\n",
|
|
"\n",
|
|
"project = expipe.get_project(project_path)\n",
|
|
"actions = project.actions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"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>action</th>\n",
|
|
" <th>channel_group</th>\n",
|
|
" <th>max_depth_delta</th>\n",
|
|
" <th>max_dissimilarity</th>\n",
|
|
" <th>unit_id</th>\n",
|
|
" <th>unit_idnum</th>\n",
|
|
" <th>unit_name</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>1834-010319-1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>ae0353a9-a406-409e-8ff7-2e940b8af03f</td>\n",
|
|
" <td>327</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>1834-010319-1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>7f514d43-17ba-4d88-a390-20eec8bc1378</td>\n",
|
|
" <td>328</td>\n",
|
|
" <td>39</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>1834-010319-3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>c977aa51-06cc-4d54-9430-a94ad422a03b</td>\n",
|
|
" <td>329</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>1834-010319-3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>bd96a67d-ee7d-4cb6-90ab-a5fa751891b9</td>\n",
|
|
" <td>330</td>\n",
|
|
" <td>12</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>1834-010319-4</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>abc01041-2971-4f62-bf06-5132cf356737</td>\n",
|
|
" <td>332</td>\n",
|
|
" <td>7</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" action channel_group max_depth_delta max_dissimilarity \\\n",
|
|
"0 1834-010319-1 0 100 0.05 \n",
|
|
"1 1834-010319-1 0 100 0.05 \n",
|
|
"2 1834-010319-3 0 100 0.05 \n",
|
|
"3 1834-010319-3 0 100 0.05 \n",
|
|
"4 1834-010319-4 0 100 0.05 \n",
|
|
"\n",
|
|
" unit_id unit_idnum unit_name \n",
|
|
"0 ae0353a9-a406-409e-8ff7-2e940b8af03f 327 2 \n",
|
|
"1 7f514d43-17ba-4d88-a390-20eec8bc1378 328 39 \n",
|
|
"2 c977aa51-06cc-4d54-9430-a94ad422a03b 329 1 \n",
|
|
"3 bd96a67d-ee7d-4cb6-90ab-a5fa751891b9 330 12 \n",
|
|
"4 abc01041-2971-4f62-bf06-5132cf356737 332 7 "
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"identify_neurons = actions['identify-neurons']\n",
|
|
"units = pd.read_csv(identify_neurons.data_path('units'))\n",
|
|
"units.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9a2e1f4dd8>"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%matplotlib inline\n",
|
|
"units.groupby('action').count().unit_name.hist()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_loader = dp.Data(\n",
|
|
" position_sampling_rate=position_sampling_rate, \n",
|
|
" position_low_pass_frequency=position_low_pass_frequency,\n",
|
|
" box_size=box_size, bin_size=bin_size, stim_mask=stim_mask, baseline_duration=baseline_duration\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"first_row = units[units['action'] == '1849-060319-3'].iloc[0]\n",
|
|
"#first_row = sessions.iloc[50]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
|
|
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"average_rate 3.168492\n",
|
|
"speed_score -0.068927\n",
|
|
"out_field_mean_rate 1.857990\n",
|
|
"in_field_mean_rate 5.257561\n",
|
|
"max_field_mean_rate 8.882211\n",
|
|
"max_rate 23.006163\n",
|
|
"sparsity 0.466751\n",
|
|
"selectivity 7.153172\n",
|
|
"interspike_interval_cv 3.807699\n",
|
|
"burst_event_ratio 0.398230\n",
|
|
"bursty_spike_ratio 0.678064\n",
|
|
"gridness -0.466923\n",
|
|
"border_score 0.029328\n",
|
|
"information_rate 1.009215\n",
|
|
"information_specificity 0.317256\n",
|
|
"head_mean_ang 5.438033\n",
|
|
"head_mean_vec_len 0.040874\n",
|
|
"spacing 0.628784\n",
|
|
"orientation 20.224859\n",
|
|
"dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def process(row):\n",
|
|
" action_id = row['action']\n",
|
|
" channel_id = row['channel_group']\n",
|
|
" unit_id = row['unit_name']\n",
|
|
" \n",
|
|
" # common values for all units == faster calculations\n",
|
|
" x, y, t, speed = map(data_loader.tracking(action_id).get, ['x', 'y', 't', 'v'])\n",
|
|
" ang, ang_t = map(data_loader.head_direction(action_id).get, ['a', 't'])\n",
|
|
" \n",
|
|
" occupancy_map = data_loader.occupancy(action_id)\n",
|
|
" xbins, ybins = data_loader.spatial_bins\n",
|
|
" box_size_, bin_size_ = data_loader.box_size_, data_loader.bin_size_\n",
|
|
" prob_dist = data_loader.prob_dist(action_id)\n",
|
|
" \n",
|
|
" smooth_low_occupancy_map = sp.maps.smooth_map(\n",
|
|
" occupancy_map, bin_size=bin_size_, smoothing=smoothing_low)\n",
|
|
" smooth_high_occupancy_map = sp.maps.smooth_map(\n",
|
|
" occupancy_map, bin_size=bin_size_, smoothing=smoothing_high)\n",
|
|
" \n",
|
|
" spike_times = data_loader.spike_train(action_id, channel_id, unit_id)\n",
|
|
" if len(spike_times) == 0:\n",
|
|
" result = pd.Series({\n",
|
|
" 'average_rate': np.nan,\n",
|
|
" 'speed_score': np.nan,\n",
|
|
" 'out_field_mean_rate': np.nan,\n",
|
|
" 'in_field_mean_rate': np.nan,\n",
|
|
" 'max_field_mean_rate': np.nan,\n",
|
|
" 'max_rate': np.nan,\n",
|
|
" 'sparsity': np.nan,\n",
|
|
" 'selectivity': np.nan,\n",
|
|
" 'interspike_interval_cv': np.nan,\n",
|
|
" 'burst_event_ratio': np.nan,\n",
|
|
" 'bursty_spike_ratio': np.nan,\n",
|
|
" 'gridness': np.nan,\n",
|
|
" 'border_score': np.nan,\n",
|
|
" 'information_rate': np.nan,\n",
|
|
" 'information_specificity': np.nan,\n",
|
|
" 'head_mean_ang': np.nan,\n",
|
|
" 'head_mean_vec_len': np.nan,\n",
|
|
" 'spacing': np.nan,\n",
|
|
" 'orientation': np.nan\n",
|
|
" })\n",
|
|
" return result\n",
|
|
"\n",
|
|
" # common\n",
|
|
" spike_map = sp.maps._spike_map(x, y, t, spike_times, xbins, ybins)\n",
|
|
"\n",
|
|
" smooth_low_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing_low)\n",
|
|
" smooth_high_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing_high)\n",
|
|
"\n",
|
|
" smooth_low_rate_map = smooth_low_spike_map / smooth_low_occupancy_map\n",
|
|
" smooth_high_rate_map = smooth_high_spike_map / smooth_high_occupancy_map\n",
|
|
"\n",
|
|
" # find fields with laplace\n",
|
|
" fields_laplace = sp.separate_fields_by_laplace(smooth_high_rate_map)\n",
|
|
" fields = fields_laplace.copy() # to be cleaned by Ismakov\n",
|
|
" fields_areas = scipy.ndimage.measurements.sum(\n",
|
|
" np.ones_like(fields), fields, index=np.arange(fields.max() + 1))\n",
|
|
" fields_area = fields_areas[fields]\n",
|
|
" fields[fields_area < 9.0] = 0\n",
|
|
"\n",
|
|
" # find fields with Ismakov-method\n",
|
|
" fields_ismakov, radius = sp.separate_fields_by_distance(smooth_high_rate_map)\n",
|
|
" fields_ismakov_real = fields_ismakov * bin_size\n",
|
|
" approved_fields = []\n",
|
|
"\n",
|
|
" # remove fields not found by both methods\n",
|
|
" for point in fields_ismakov:\n",
|
|
" field_id = fields[tuple(point)]\n",
|
|
" approved_fields.append(field_id)\n",
|
|
"\n",
|
|
" for field_id in np.arange(1, fields.max() + 1):\n",
|
|
" if not field_id in approved_fields:\n",
|
|
" fields[fields == field_id] = 0\n",
|
|
"\n",
|
|
" # varying statistics\n",
|
|
" average_rate = len(spike_times) / (t.max() - t.min())\n",
|
|
"\n",
|
|
" max_rate = smooth_low_rate_map.max()\n",
|
|
"\n",
|
|
" out_field_mean_rate = smooth_low_rate_map[np.where(fields == 0)].mean()\n",
|
|
" in_field_mean_rate = smooth_low_rate_map[np.where(fields != 0)].mean()\n",
|
|
" max_field_mean_rate = smooth_low_rate_map[np.where(fields == 1)].mean()\n",
|
|
"\n",
|
|
" interspike_interval = np.diff(spike_times)\n",
|
|
" interspike_interval_cv = interspike_interval.std() / interspike_interval.mean()\n",
|
|
"\n",
|
|
" autocorrelogram = sp.autocorrelation(smooth_high_rate_map)\n",
|
|
" peaks = sp.fields.find_peaks(autocorrelogram)\n",
|
|
" real_peaks = peaks * bin_size\n",
|
|
" autocorrelogram_box_size = box_size[0] * autocorrelogram.shape[0] / smooth_high_rate_map.shape[0]\n",
|
|
" spacing, orientation = sp.spacing_and_orientation(real_peaks, autocorrelogram_box_size)\n",
|
|
" orientation *= 180 / np.pi\n",
|
|
"\n",
|
|
" selectivity = stats.selectivity(smooth_low_rate_map, prob_dist)\n",
|
|
"\n",
|
|
" sparsity = stats.sparsity(smooth_low_rate_map, prob_dist)\n",
|
|
"\n",
|
|
" gridness = sp.gridness(smooth_high_rate_map)\n",
|
|
"\n",
|
|
" border_score = sp.border_score(smooth_high_rate_map, fields_laplace)\n",
|
|
"\n",
|
|
" information_rate = stats.information_rate(smooth_high_rate_map, prob_dist)\n",
|
|
" \n",
|
|
" information_spec = stats.information_specificity(smooth_high_rate_map, prob_dist)\n",
|
|
"\n",
|
|
" single_spikes, bursts, bursty_spikes = spikes.find_bursts(spike_times, threshold=0.01)\n",
|
|
" burst_event_ratio = np.sum(bursts) / (np.sum(single_spikes) + np.sum(bursts))\n",
|
|
" bursty_spike_ratio = np.sum(bursty_spikes) / (np.sum(bursty_spikes) + np.sum(single_spikes))\n",
|
|
" mean_spikes_per_burst = np.sum(bursty_spikes) / np.sum(bursts)\n",
|
|
"\n",
|
|
" speed_score = spd.speed_correlation(\n",
|
|
" speed, t, spike_times, min_speed=min_speed, max_speed=max_speed)\n",
|
|
"\n",
|
|
" ang_bin, ang_rate = head.head_direction_rate(spike_times, ang, ang_t)\n",
|
|
"\n",
|
|
" head_mean_ang, head_mean_vec_len = head.head_direction_score(ang_bin, ang_rate)\n",
|
|
"\n",
|
|
" result = pd.Series({\n",
|
|
" 'average_rate': average_rate,\n",
|
|
" 'speed_score': speed_score,\n",
|
|
" 'out_field_mean_rate': out_field_mean_rate,\n",
|
|
" 'in_field_mean_rate': in_field_mean_rate,\n",
|
|
" 'max_field_mean_rate': max_field_mean_rate,\n",
|
|
" 'max_rate': max_rate,\n",
|
|
" 'sparsity': sparsity,\n",
|
|
" 'selectivity': selectivity,\n",
|
|
" 'interspike_interval_cv': float(interspike_interval_cv),\n",
|
|
" 'burst_event_ratio': burst_event_ratio,\n",
|
|
" 'bursty_spike_ratio': bursty_spike_ratio,\n",
|
|
" 'gridness': gridness,\n",
|
|
" 'border_score': border_score,\n",
|
|
" 'information_rate': information_rate,\n",
|
|
" 'information_specificity': information_spec,\n",
|
|
" 'head_mean_ang': head_mean_ang,\n",
|
|
" 'head_mean_vec_len': head_mean_vec_len,\n",
|
|
" 'spacing': spacing,\n",
|
|
" 'orientation': orientation\n",
|
|
" })\n",
|
|
" return result\n",
|
|
" \n",
|
|
"process(first_row)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "2372ae1b2bea435dbc5bbfe2453747a6",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:110: RuntimeWarning: invalid value encountered in long_scalars\n",
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
|
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
|
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"results = units.merge(\n",
|
|
" units.progress_apply(process, axis=1), \n",
|
|
" left_index=True, right_index=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"%debug"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output_path = pathlib.Path(\"output\") / \"calculate-statistics\"\n",
|
|
"output_path.mkdir(exist_ok=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"results.to_csv(output_path / \"results.csv\", index=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Store results in Expipe action"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"statistics_action = project.require_action(\"calculate-statistics\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"statistics_action.data[\"results\"] = \"results.csv\"\n",
|
|
"copy_tree(output_path, str(statistics_action.data_path()))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"statistics_action.modules['parameters'] = {\n",
|
|
" 'max_speed': max_speed,\n",
|
|
" 'min_speed': min_speed,\n",
|
|
" 'position_sampling_rate': position_sampling_rate,\n",
|
|
" 'position_low_pass_frequency': position_low_pass_frequency,\n",
|
|
" 'box_size': box_size,\n",
|
|
" 'bin_size': bin_size,\n",
|
|
" 'smoothing_low': smoothing_low,\n",
|
|
" 'smoothing_high': smoothing_high,\n",
|
|
" 'stim_mask': stim_mask,\n",
|
|
" 'baseline_duration': baseline_duration\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"septum_mec.analysis.registration.store_notebook(statistics_action, \"10_calculate_spatial_statistics.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
|
|
}
|