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%load_ext autoreload
%autoreload 2
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import os
import expipe
import pathlib
import numpy as np
import spatial_maps.stats as stats
import septum_mec.analysis.data_processing as dp
import head_direction.head as head
import spatial_maps as sp
import septum_mec.analysis.registration
import speed_cells.speed as spd
import septum_mec.analysis.spikes as spikes
import re
import joblib
import multiprocessing
import shutil
import psutil
import pandas as pd
import matplotlib.pyplot as plt
import septum_mec
import scipy.ndimage.measurements
from distutils.dir_util import copy_tree
from spike_statistics.core import theta_mod_idx
from tqdm import tqdm_notebook as tqdm
from tqdm._tqdm_notebook import tqdm_notebook
tqdm_notebook.pandas()
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max_speed = 1, # m/s only used for speed score
min_speed = 0.02, # m/s only used for speed score
position_sampling_rate = 100 # for interpolation
position_low_pass_frequency = 6 # for low pass filtering of position
box_size = [1.0, 1.0]
bin_size = 0.02
smoothing_low = 0.03
smoothing_high = 0.06
stim_mask = True
baseline_duration = 600
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project_path = dp.project_path()
project = expipe.get_project(project_path)
actions = project.actions
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identify_neurons = actions['identify-neurons']
units = pd.read_csv(identify_neurons.data_path('units'))
units.head()
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%matplotlib inline
units.groupby('action').count().unit_name.hist()
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data_loader = dp.Data(
position_sampling_rate=position_sampling_rate,
position_low_pass_frequency=position_low_pass_frequency,
box_size=box_size, bin_size=bin_size, stim_mask=stim_mask, baseline_duration=baseline_duration
)
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first_row = units[units['action'] == '1849-060319-3'].iloc[0]
#first_row = sessions.iloc[50]
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def process(row):
action_id = row['action']
channel_id = row['channel_group']
unit_id = row['unit_name']
# common values for all units == faster calculations
x, y, t, speed = map(data_loader.tracking(action_id).get, ['x', 'y', 't', 'v'])
ang, ang_t = map(data_loader.head_direction(action_id).get, ['a', 't'])
occupancy_map = data_loader.occupancy(action_id)
xbins, ybins = data_loader.spatial_bins
box_size_, bin_size_ = data_loader.box_size_, data_loader.bin_size_
prob_dist = data_loader.prob_dist(action_id)
smooth_low_occupancy_map = sp.maps.smooth_map(
occupancy_map, bin_size=bin_size_, smoothing=smoothing_low)
smooth_high_occupancy_map = sp.maps.smooth_map(
occupancy_map, bin_size=bin_size_, smoothing=smoothing_high)
spike_times = data_loader.spike_train(action_id, channel_id, unit_id)
if len(spike_times) == 0:
result = pd.Series({
'average_rate': np.nan,
'speed_score': np.nan,
'out_field_mean_rate': np.nan,
'in_field_mean_rate': np.nan,
'max_field_mean_rate': np.nan,
'max_rate': np.nan,
'sparsity': np.nan,
'selectivity': np.nan,
'interspike_interval_cv': np.nan,
'burst_event_ratio': np.nan,
'bursty_spike_ratio': np.nan,
'gridness': np.nan,
'border_score': np.nan,
'information_rate': np.nan,
'information_specificity': np.nan,
'head_mean_ang': np.nan,
'head_mean_vec_len': np.nan,
'spacing': np.nan,
'orientation': np.nan,
'field_area': np.nan,
'theta_score': np.nan
})
return result
# common
spike_map = sp.maps._spike_map(x, y, t, spike_times, xbins, ybins)
smooth_low_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing_low)
smooth_high_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing_high)
smooth_low_rate_map = smooth_low_spike_map / smooth_low_occupancy_map
smooth_high_rate_map = smooth_high_spike_map / smooth_high_occupancy_map
# find fields with laplace
fields_laplace = sp.separate_fields_by_laplace(smooth_high_rate_map)
fields = fields_laplace.copy() # to be cleaned by Ismakov
fields_areas = scipy.ndimage.measurements.sum(
np.ones_like(fields), fields, index=np.arange(fields.max() + 1))
fields_area = fields_areas[fields]
fields[fields_area < 9.0] = 0
# find fields with Ismakov-method
fields_ismakov, radius = sp.separate_fields_by_distance(smooth_high_rate_map)
fields_ismakov_real = fields_ismakov * bin_size
approved_fields = []
# remove fields not found by both methods
for point in fields_ismakov:
field_id = fields[tuple(point)]
approved_fields.append(field_id)
for field_id in np.arange(1, fields.max() + 1):
if not field_id in approved_fields:
fields[fields == field_id] = 0
# varying statistics
average_rate = len(spike_times) / (t.max() - t.min())
max_rate = smooth_low_rate_map.max()
out_field_mean_rate = smooth_low_rate_map[np.where(fields == 0)].mean()
in_field_mean_rate = smooth_low_rate_map[np.where(fields != 0)].mean()
max_field_mean_rate = smooth_low_rate_map[np.where(fields == 1)].mean()
interspike_interval = np.diff(spike_times)
interspike_interval_cv = interspike_interval.std() / interspike_interval.mean()
autocorrelogram = sp.autocorrelation(smooth_high_rate_map)
peaks = sp.fields.find_peaks(autocorrelogram)
real_peaks = peaks * bin_size
autocorrelogram_box_size = box_size[0] * autocorrelogram.shape[0] / smooth_high_rate_map.shape[0]
spacing, orientation = sp.spacing_and_orientation(real_peaks, autocorrelogram_box_size)
orientation *= 180 / np.pi
selectivity = stats.selectivity(smooth_low_rate_map, prob_dist)
sparsity = stats.sparsity(smooth_low_rate_map, prob_dist)
gridness = sp.gridness(smooth_high_rate_map)
border_score = sp.border_score(smooth_high_rate_map, fields_laplace)
information_rate = stats.information_rate(smooth_high_rate_map, prob_dist)
information_spec = stats.information_specificity(smooth_high_rate_map, prob_dist)
single_spikes, bursts, bursty_spikes = spikes.find_bursts(spike_times, threshold=0.01)
burst_event_ratio = np.sum(bursts) / (np.sum(single_spikes) + np.sum(bursts))
bursty_spike_ratio = np.sum(bursty_spikes) / (np.sum(bursty_spikes) + np.sum(single_spikes))
mean_spikes_per_burst = np.sum(bursty_spikes) / np.sum(bursts)
speed_score = spd.speed_correlation(
speed, t, spike_times, min_speed=min_speed, max_speed=max_speed)
ang_bin, ang_rate = head.head_direction_rate(spike_times, ang, ang_t)
head_mean_ang, head_mean_vec_len = head.head_direction_score(ang_bin, ang_rate)
result = pd.Series({
'average_rate': average_rate,
'speed_score': speed_score,
'out_field_mean_rate': out_field_mean_rate,
'in_field_mean_rate': in_field_mean_rate,
'max_field_mean_rate': max_field_mean_rate,
'max_rate': max_rate,
'sparsity': sparsity,
'selectivity': selectivity,
'interspike_interval_cv': float(interspike_interval_cv),
'burst_event_ratio': burst_event_ratio,
'bursty_spike_ratio': bursty_spike_ratio,
'gridness': gridness,
'border_score': border_score,
'information_rate': information_rate,
'information_specificity': information_spec,
'head_mean_ang': head_mean_ang,
'head_mean_vec_len': head_mean_vec_len,
'spacing': spacing,
'orientation': orientation,
'field_area': fields_areas[fields].mean() * bin_size**2,
'theta_score': theta_mod_idx(spike_times.times.magnitude, binsize=0.01, time_limit=0.2)
})
return result
process(first_row)
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results = units.merge(
units.progress_apply(process, axis=1),
left_index=True, right_index=True)
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output_path = pathlib.Path("output") / "calculate-statistics"
output_path.mkdir(exist_ok=True)
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results.to_csv(output_path / "results.csv", index=False)
Store results in Expipe action¶
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statistics_action = project.require_action("calculate-statistics")
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statistics_action.data["results"] = "results.csv"
copy_tree(output_path, str(statistics_action.data_path()))
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statistics_action.modules['parameters'] = {
'max_speed': max_speed,
'min_speed': min_speed,
'position_sampling_rate': position_sampling_rate,
'position_low_pass_frequency': position_low_pass_frequency,
'box_size': box_size,
'bin_size': bin_size,
'smoothing_low': smoothing_low,
'smoothing_high': smoothing_high,
'stim_mask': stim_mask,
'baseline_duration': baseline_duration
}
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septum_mec.analysis.registration.store_notebook(statistics_action, "10_calculate_spatial_statistics.ipynb")
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