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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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"20:57:48 [I] klustakwik KlustaKwik2 version 0.2.6\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:25: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`\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.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",
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"from spike_statistics.core import theta_mod_idx\n",
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"\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",
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"smoothing_high = 0.06\n",
"\n",
"stim_mask = True\n",
"baseline_duration = 600"
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]
},
{
"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",
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" <th>max_depth_delta</th>\n",
" <th>max_dissimilarity</th>\n",
" <th>unit_id</th>\n",
" <th>unit_idnum</th>\n",
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" <th>unit_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <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",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <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",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
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" <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",
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" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
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" <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",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
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" <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",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" 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 "
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]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"identify_neurons = actions['identify-neurons']\n",
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"units = pd.read_csv(identify_neurons.data_path('units'))\n",
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"units.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
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"outputs": [
{
"data": {
"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d22cd6f28>"
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]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"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",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
"source": [
"data_loader = dp.Data(\n",
" position_sampling_rate=position_sampling_rate, \n",
" position_low_pass_frequency=position_low_pass_frequency,\n",
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" box_size=box_size, bin_size=bin_size, stim_mask=stim_mask, baseline_duration=baseline_duration\n",
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")"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [],
"source": [
"first_row = units[units['action'] == '1849-060319-3'].iloc[0]\n",
"#first_row = sessions.iloc[50]"
]
},
{
"cell_type": "code",
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"execution_count": 16,
"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:85: RuntimeWarning: Mean of empty slice.\n"
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]
},
{
"data": {
"text/plain": [
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"average_rate 3.168492\n",
"speed_score -0.068927\n",
"out_field_mean_rate 1.857990\n",
"in_field_mean_rate 5.257561\n",
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"max_field_mean_rate NaN\n",
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"max_rate 23.006163\n",
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"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",
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"gridness -0.466836\n",
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"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",
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"orientation 69.775141\n",
"field_area 0.412306\n",
"theta_score -0.430279\n",
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"dtype: float64"
]
},
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"execution_count": 16,
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"metadata": {},
"output_type": "execute_result"
}
],
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"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",
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" \n",
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" 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",
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" 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",
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" \n",
" spike_times = data_loader.spike_train(action_id, channel_id, unit_id)\n",
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" 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",
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" 'orientation': np.nan,\n",
" 'field_area': np.nan,\n",
" 'theta_score': np.nan\n",
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" })\n",
" return result\n",
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"\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",
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" autocorrelogram_box_size = box_size[0] * autocorrelogram.shape[0] / smooth_high_rate_map.shape[0]\n",
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" 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",
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" \n",
" information_spec = stats.information_specificity(smooth_high_rate_map, prob_dist)\n",
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"\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",
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" 'information_specificity': information_spec,\n",
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" 'head_mean_ang': head_mean_ang,\n",
" 'head_mean_vec_len': head_mean_vec_len,\n",
" 'spacing': spacing,\n",
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" 'orientation': orientation,\n",
" 'field_area': fields_areas[fields].mean() * bin_size**2,\n",
" 'theta_score': theta_mod_idx(spike_times.times.magnitude, binsize=0.01, time_limit=0.2)\n",
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" })\n",
" return result\n",
" \n",
"process(first_row)"
]
},
{
"cell_type": "code",
"execution_count": null,
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"metadata": {},
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"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"model_id": "b2117ea9b6044c22abd353d0f9d774a7",
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"version_major": 2,
"version_minor": 0
},
"text/plain": [
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"HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))"
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:85: RuntimeWarning: Mean of empty slice.\n",
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"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
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" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
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"/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",
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"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
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" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
"/home/mikkel/apps/expipe-project/spike-statistics/spike_statistics/core.py:27: RuntimeWarning: invalid value encountered in double_scalars\n",
" return (pk - th)/(pk + th)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:112: RuntimeWarning: invalid value encountered in long_scalars\n"
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]
}
],
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"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": [
"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",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"statistics_action = project.require_action(\"calculate-statistics\")"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"statistics_action.data[\"results\"] = \"results.csv\"\n",
"copy_tree(output_path, str(statistics_action.data_path()))"
]
},
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{
"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",
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" 'smoothing_high': smoothing_high,\n",
" 'stim_mask': stim_mask,\n",
" 'baseline_duration': baseline_duration\n",
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"}"
]
},
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{
"cell_type": "code",
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"execution_count": null,
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"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,
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"nbformat_minor": 4
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}
