{ "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": [ "/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" ] } ], "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", "from spike_statistics.core import theta_mod_idx\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": [ "
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" ], "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": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "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": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "spatial_average_rate 3.181077\n", "dtype: float64" ] }, "execution_count": 20, "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", " 'spatial_average_rate': 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", " \n", "\n", " tmp_rate_map = smooth_high_rate_map.copy()\n", " tmp_rate_map[np.isnan(tmp_rate_map)] = 0\n", " avg_rate = np.sum(np.ravel(tmp_rate_map * prob_dist))\n", "\n", "\n", " result = pd.Series({\n", " 'spatial_average_rate': avg_rate\n", " })\n", " return result\n", " \n", "process(first_row)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8baa6ab9462541b6bcdf6c14c208eeb1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" } ], "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-extra\"\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-extra\")" ] }, { "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_statistics_extra.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": 4 }