{
"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": [
"09:19:10 [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 pnnmec.registration\n",
"import speed_cells.speed as spd\n",
"import re\n",
"import joblib\n",
"from distutils.dir_util import copy_tree\n",
"import multiprocessing\n",
"import shutil\n",
"import psutil\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import pnnmec\n",
"import scipy.ndimage.measurements\n",
"import quantities as pq\n",
"import exdir\n",
"from tqdm import tqdm_notebook as tqdm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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 = 0.05"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"identify_neurons = actions['identify-neurons']\n",
"sessions = pd.read_csv(identify_neurons.data_path('sessions'))\n",
"# units = pd.read_csv(identify_neurons.data_path('units'))\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"data_loader = dp.Data()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"units = []\n",
"for action in sessions.action.values:\n",
" for ch in range(8):\n",
" for unit_name in data_loader.unit_names(action, ch):\n",
" units.append({'unit_name': unit_name, 'action': action, 'channel_group': ch})"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"units = pd.DataFrame(units)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"session_units = pd.merge(sessions, units, on='action')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"first_row = session_units.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"output = pathlib.Path('output/shuffling')\n",
"output.mkdir(parents=True, exist_ok=True)\n",
"output_exdir = exdir.File(output / \"shuffling.exdir\")\n",
"output_units = output_exdir.require_group(\"units\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"from elephant.spike_train_surrogates import dither_spike_train\n",
"\n",
"def process(row):\n",
" memory_in_gb = psutil.virtual_memory().available / 1024 / 1024 / 1024\n",
" if memory_in_gb < 2:\n",
" print(\"Running out of memory! Restart your kernel.\")\n",
" return\n",
" \n",
" action_id = row['action']\n",
" channel_id = int(row['channel_group'])\n",
" unit_id = int(row['unit_name'])\n",
" \n",
" action = actions[action_id]\n",
" \n",
" cell_name = \"{}_{}_{}\".format(action_id, channel_id, unit_id)\n",
" \n",
" if cell_name in output_units:\n",
" print(\"Skipping existing\", cell_name)\n",
" return\n",
" \n",
" print(\"Processing\", cell_name)\n",
" \n",
" output_group = output_units.require_group(cell_name)\n",
" \n",
" data_path = pathlib.Path(project_path) / \"actions\" / action_id / \"data\" / \"main.exdir\"\n",
"\n",
" # common values for all units == faster calculations\n",
" x, y, t, speed = dp.load_tracking(\n",
" data_path, position_sampling_rate, position_low_pass_frequency, box_size)\n",
" ang, ang_t = dp.load_head_direction(\n",
" data_path, position_sampling_rate, position_low_pass_frequency, box_size)\n",
" sptr = dp.load_spike_train(data_path, channel_id, unit_id, t[-1])\n",
"\n",
" box_size_, bin_size_ = sp.maps._adjust_bin_size(box_size=box_size, bin_size=bin_size)\n",
" xbins, ybins = sp.maps._make_bins(box_size_, bin_size_)\n",
" occupancy_map = sp.maps._occupancy_map(x, y, t, xbins, ybins)\n",
" \n",
" smooth_occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing)\n",
" \n",
" def calculate(spike_times):\n",
" # common\n",
" spike_map = sp.maps._spike_map(x, y, t, spike_times, xbins, ybins)\n",
" smooth_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing)\n",
" rate_map = smooth_spike_map / smooth_occupancy_map\n",
" \n",
" # gridness\n",
" gridness = sp.gridness(rate_map)\n",
" \n",
" # border score\n",
" fields = sp.separate_fields_by_laplace(rate_map)\n",
" border_score = sp.border_score(rate_map, fields)\n",
" \n",
" # spatial information\n",
" px = stats.prob_dist(x, y, xbins)\n",
" information_rate = sp.information_rate(rate_map, px)\n",
" \n",
" # speed\n",
" speed_score = spd.speed_correlation(\n",
" speed, t, spike_times, min_speed=min_speed, max_speed=max_speed)\n",
" \n",
" # head direction\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",
" statistics = {\n",
" \"gridness\": gridness,\n",
" \"border_score\": border_score,\n",
" \"information_rate\": np.asscalar(information_rate),\n",
" \"speed_score\": speed_score,\n",
" \"head_mean_ang\": head_mean_ang,\n",
" \"head_mean_vec_len\": head_mean_vec_len\n",
" }\n",
" \n",
" return rate_map, statistics\n",
" \n",
" # Calculate for cell first\n",
" rate_map, statistics = calculate(sptr)\n",
" \n",
" # Copy attrs from row\n",
" attributes = row.to_dict()\n",
" attributes.update(statistics)\n",
" \n",
" for key, value in attributes.items():\n",
" if isinstance(value, (np.generic, np.ndarray)):\n",
" attributes[key] = np.asscalar(value)\n",
" \n",
" output_group.attrs = attributes\n",
" output_group['rate_map'] = rate_map\n",
" \n",
" # Calculate shuffled\n",
" sample_count = 1000\n",
" spike_trains = dither_spike_train(sptr, shift=30*pq.s, n=sample_count, edges=True)\n",
" \n",
" shuffling_data = []\n",
" \n",
" for i, spike_times in enumerate(spike_trains):\n",
" rate_map, statistics = calculate(spike_times)\n",
" shuffling_data.append(statistics)\n",
" \n",
" shuffling_data = pd.DataFrame(shuffling_data)\n",
" \n",
" quantiles = shuffling_data.quantile(0.95, axis=0)\n",
" \n",
" # TODO make it easier to create raw data in Exdir\n",
" raw_path = output_group.root_directory / output_group.relative_path / \"results\"\n",
" raw_path.mkdir(exist_ok=True)\n",
" \n",
" shuffling_data.to_csv(raw_path / \"shuffling_data.csv\", index=False)\n",
" quantiles.to_csv(raw_path / \"quantiles.csv\")\n",
" \n",
" output_group['shuffled_rate_map_example'] = rate_map\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"# process(first_row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shuffle a random sample"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"# args = []\n",
"# for index, row in session_units.sample(100, random_state=1).iterrows():\n",
"# args.append(row)\n",
"\n",
"# with multiprocessing.Pool(processes=4) as pool:\n",
"# result = pool.map(process, args)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shuffle all"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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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",
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/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",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/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",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/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",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:110: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\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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]
},
{
"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/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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing 1833-290519-3_3_96\n",
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}
],
"source": [
"args = []\n",
"for index, row in session_units.iterrows():\n",
" args.append(row)\n",
"\n",
"with multiprocessing.Pool(processes=4) as pool:\n",
" result = pool.map(process, args)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gather all results and statistics"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"cell_statistics_path = output / \"statistics\"\n",
"cell_statistics_path.mkdir(exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"all_statistics = []\n",
"for cell_id, cell in output_units.items():\n",
" all_statistics.append(cell.attrs.to_dict())"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
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