483 lines
16 KiB
Plaintext
483 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"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": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"08:27:22 [I] klustakwik KlustaKwik2 version 0.2.6\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import expipe\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import spatial_maps.stats as stats\n",
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"import septum_mec.analysis.data_processing as dp\n",
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"import head_direction.head as head\n",
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"import spatial_maps as sp\n",
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"import septum_mec.analysis.registration\n",
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"import speed_cells.speed as spd\n",
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"import septum_mec.analysis.spikes as spikes\n",
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"import re\n",
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"import joblib\n",
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"import multiprocessing\n",
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"import shutil\n",
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"import psutil\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import septum_mec\n",
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"import scipy.ndimage.measurements\n",
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"from distutils.dir_util import copy_tree\n",
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"\n",
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"from tqdm import tqdm_notebook as tqdm\n",
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"from tqdm._tqdm_notebook import tqdm_notebook\n",
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"tqdm_notebook.pandas()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"max_speed = 1, # m/s only used for speed score\n",
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"min_speed = 0.02, # m/s only used for speed score\n",
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"position_sampling_rate = 100 # for interpolation\n",
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"position_low_pass_frequency = 6 # for low pass filtering of position\n",
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"\n",
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"box_size = 1.0\n",
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"bin_size = 0.02\n",
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"smoothing_low = 0.03\n",
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"smoothing_high = 0.06"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"project_path = dp.project_path()\n",
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"\n",
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"project = expipe.get_project(project_path)\n",
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"actions = project.actions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>action</th>\n",
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" <th>channel_group</th>\n",
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" <th>unit_name</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1834-150319-3</td>\n",
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" <td>0</td>\n",
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" <td>71</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1834-150319-3</td>\n",
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" <td>0</td>\n",
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" <td>75</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1834-120319-4</td>\n",
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" <td>0</td>\n",
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" <td>85</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1834-120319-1</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1834-120319-2</td>\n",
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" <td>0</td>\n",
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" <td>39</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" action channel_group unit_name\n",
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"0 1834-150319-3 0 71\n",
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"1 1834-150319-3 0 75\n",
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"2 1834-120319-4 0 85\n",
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"3 1834-120319-1 0 1\n",
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"4 1834-120319-2 0 39"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"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()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_loader = dp.Data(\n",
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" position_sampling_rate=position_sampling_rate, \n",
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" position_low_pass_frequency=position_low_pass_frequency,\n",
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" box_size=box_size, bin_size=bin_size\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"first_row = units[units['action'] == '1849-060319-3'].iloc[0]\n",
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"#first_row = sessions.iloc[50]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/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",
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" warnings.warn(\"Instantaneous firing rate approximation contains \"\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"average_rate 3.095328\n",
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"speed_score -0.063922\n",
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"out_field_mean_rate 1.837642\n",
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"in_field_mean_rate 5.122323\n",
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"max_field_mean_rate 8.882211\n",
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"max_rate 23.006163\n",
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"sparsity 0.468122\n",
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"selectivity 7.306812\n",
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"interspike_interval_cv 3.970863\n",
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"burst_event_ratio 0.397921\n",
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"bursty_spike_ratio 0.676486\n",
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"gridness -0.459487\n",
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"border_score 0.078474\n",
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"information_rate 0.965845\n",
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"head_mean_ang 5.788704\n",
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"head_mean_vec_len 0.043321\n",
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"spacing 0.624971\n",
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"orientation 22.067900\n",
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"dtype: float64"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def process(row):\n",
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" action_id = row['action']\n",
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" channel_id = row['channel_group']\n",
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" unit_id = row['unit_name']\n",
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" \n",
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" # common values for all units == faster calculations\n",
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" x, y, t, speed = map(data_loader.tracking(action_id).get, ['x', 'y', 't', 'v'])\n",
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" ang, ang_t = map(data_loader.head_direction(action_id).get, ['a', 't'])\n",
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" occupancy_map = data_loader.occupancy(action_id)\n",
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" xbins, ybins = data_loader.spatial_bins\n",
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" box_size_, bin_size_ = data_loader.box_size_, data_loader.bin_size_\n",
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" prob_dist = data_loader.prob_dist(action_id)\n",
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" \n",
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" smooth_low_occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing_low)\n",
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" smooth_high_occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing_high)\n",
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" \n",
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" spike_times = data_loader.spike_train(action_id, channel_id, unit_id)\n",
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"\n",
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" # common\n",
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" spike_map = sp.maps._spike_map(x, y, t, spike_times, xbins, ybins)\n",
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"\n",
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" smooth_low_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing_low)\n",
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" smooth_high_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing_high)\n",
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"\n",
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" smooth_low_rate_map = smooth_low_spike_map / smooth_low_occupancy_map\n",
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" smooth_high_rate_map = smooth_high_spike_map / smooth_high_occupancy_map\n",
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"\n",
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" # find fields with laplace\n",
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" fields_laplace = sp.separate_fields_by_laplace(smooth_high_rate_map)\n",
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" fields = fields_laplace.copy() # to be cleaned by Ismakov\n",
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" fields_areas = scipy.ndimage.measurements.sum(\n",
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" np.ones_like(fields), fields, index=np.arange(fields.max() + 1))\n",
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" fields_area = fields_areas[fields]\n",
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" fields[fields_area < 9.0] = 0\n",
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"\n",
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" # find fields with Ismakov-method\n",
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" fields_ismakov, radius = sp.separate_fields_by_distance(smooth_high_rate_map)\n",
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" fields_ismakov_real = fields_ismakov * bin_size\n",
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" approved_fields = []\n",
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"\n",
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" # remove fields not found by both methods\n",
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" for point in fields_ismakov:\n",
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" field_id = fields[tuple(point)]\n",
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" approved_fields.append(field_id)\n",
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"\n",
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" for field_id in np.arange(1, fields.max() + 1):\n",
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" if not field_id in approved_fields:\n",
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" fields[fields == field_id] = 0\n",
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"\n",
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" # varying statistics\n",
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" average_rate = len(spike_times) / (t.max() - t.min())\n",
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"\n",
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" max_rate = smooth_low_rate_map.max()\n",
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"\n",
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" out_field_mean_rate = smooth_low_rate_map[np.where(fields == 0)].mean()\n",
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" in_field_mean_rate = smooth_low_rate_map[np.where(fields != 0)].mean()\n",
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" max_field_mean_rate = smooth_low_rate_map[np.where(fields == 1)].mean()\n",
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"\n",
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" interspike_interval = np.diff(spike_times)\n",
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" interspike_interval_cv = interspike_interval.std() / interspike_interval.mean()\n",
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"\n",
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" autocorrelogram = sp.autocorrelation(smooth_high_rate_map)\n",
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" peaks = sp.fields.find_peaks(autocorrelogram)\n",
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" real_peaks = peaks * bin_size\n",
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" autocorrelogram_box_size = box_size * 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",
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" orientation *= 180 / np.pi\n",
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"\n",
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" selectivity = stats.selectivity(smooth_low_rate_map, prob_dist)\n",
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"\n",
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" sparsity = stats.sparsity(smooth_low_rate_map, prob_dist)\n",
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"\n",
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" gridness = sp.gridness(smooth_high_rate_map)\n",
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"\n",
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" border_score = sp.border_score(smooth_high_rate_map, fields_laplace)\n",
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"\n",
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" information_rate = stats.information_rate(smooth_high_rate_map, prob_dist)\n",
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"\n",
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" single_spikes, bursts, bursty_spikes = spikes.find_bursts(spike_times, threshold=0.01)\n",
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" burst_event_ratio = np.sum(bursts) / (np.sum(single_spikes) + np.sum(bursts))\n",
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" bursty_spike_ratio = np.sum(bursty_spikes) / (np.sum(bursty_spikes) + np.sum(single_spikes))\n",
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" mean_spikes_per_burst = np.sum(bursty_spikes) / np.sum(bursts)\n",
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"\n",
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" speed_score = spd.speed_correlation(\n",
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" speed, t, spike_times, min_speed=min_speed, max_speed=max_speed)\n",
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"\n",
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" ang_bin, ang_rate = head.head_direction_rate(spike_times, ang, ang_t)\n",
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"\n",
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" head_mean_ang, head_mean_vec_len = head.head_direction_score(ang_bin, ang_rate)\n",
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"\n",
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" result = pd.Series({\n",
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" 'average_rate': average_rate,\n",
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" 'speed_score': speed_score,\n",
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" 'out_field_mean_rate': out_field_mean_rate,\n",
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" 'in_field_mean_rate': in_field_mean_rate,\n",
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" 'max_field_mean_rate': max_field_mean_rate,\n",
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" 'max_rate': max_rate,\n",
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" 'sparsity': sparsity,\n",
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" 'selectivity': selectivity,\n",
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" 'interspike_interval_cv': float(interspike_interval_cv),\n",
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" 'burst_event_ratio': burst_event_ratio,\n",
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" 'bursty_spike_ratio': bursty_spike_ratio,\n",
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" 'gridness': gridness,\n",
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" 'border_score': border_score,\n",
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" 'information_rate': information_rate,\n",
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" 'head_mean_ang': head_mean_ang,\n",
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" 'head_mean_vec_len': head_mean_vec_len,\n",
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" 'spacing': spacing,\n",
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" 'orientation': orientation\n",
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" })\n",
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" return result\n",
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" \n",
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"process(first_row)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "837fde7fe486422bac67341ff512a4e1",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(IntProgress(value=0, max=1281), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value 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: 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 multiply\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/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:56: RuntimeWarning: Mean of empty slice.\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:57: RuntimeWarning: Mean of empty slice.\n",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:82: RuntimeWarning: invalid value encountered in long_scalars\n"
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]
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}
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],
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"source": [
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"results = units.merge(\n",
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" units.progress_apply(process, axis=1), \n",
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" left_index=True, right_index=True)"
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]
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},
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{
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"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": [
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"output_path = pathlib.Path(\"output\") / \"calculate-statistics\"\n",
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"output_path.mkdir(exist_ok=True)"
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]
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},
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{
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"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": [
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"units.to_csv(output_path / \"units.csv\", index=False)\n",
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"results.to_csv(output_path / \"results.csv\", index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Store results in Expipe action"
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]
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},
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{
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"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": [
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"statistics_action = project.require_action(\"calculate-statistics\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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|
"outputs": [],
|
|
"source": [
|
|
"statistics_action.data[\"units\"] = \"units.csv\"\n",
|
|
"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": [
|
|
"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
|
|
}
|