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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Copyright © 2014 - 2021 German Neuroinformatics Node (G-Node)
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted under the terms of the BSD License. See
LICENSE file in the root of the Project.
Author: Jan Grewe <jan.grewe@g-node.org>
See https://github.com/G-node/nix/wiki for more information.
"""
import nixio
import numpy as np
import scipy.signal as signal
import matplotlib.pylab as plt
import lif
import docutils
def fake_neuron(stepsize=0.001, offset=.8, sta_offset=1000):
stimulus = np.random.randn(102000) * 3.5
b, a = signal.butter(1, 12.5, fs=1. / stepsize, btype="low")
stimulus = signal.filtfilt(b, a, stimulus)
stimulus = stimulus[1000:-1000]
lif_model = lif.LIF(stepsize=stepsize, offset=offset)
time, v, spike_times = lif_model.run_stimulus(stimulus)
snippets = np.zeros((len(spike_times), 2 * sta_offset))
for i, t in enumerate(spike_times):
index = int(round(t / stepsize))
if index < sta_offset:
snip = stimulus[0:index + sta_offset]
snippets[i, -len(snip):] = snip
elif (index + sta_offset) > len(stimulus):
snip = stimulus[index - sta_offset:]
snippets[i, 0:len(snip)] = snip
else:
snippets[i, :] = stimulus[index - sta_offset:index + sta_offset]
return time, v, spike_times, snippets
def plot_data(tag):
data_array = tag.references[0]
voltage = np.zeros(data_array.shape)
data_array.read_direct(voltage)
x_axis = data_array.dimensions[0]
time = x_axis.axis(data_array.data_extent[0])
spike_times = tag.positions[:]
feature_data_array = tag.features[0].data
snippets = tag.features[0].data[:]
single_snippet = tag.feature_data(3, 0)[:]
snippet_time_dim = feature_data_array.dimensions[1]
snippet_time = snippet_time_dim.axis(feature_data_array.data_extent[1])
response_axis = plt.subplot2grid((2, 2), (0, 0), rowspan=1, colspan=2)
single_snippet_axis = plt.subplot2grid((2, 2), (1, 0), rowspan=1, colspan=1)
average_snippet_axis = plt.subplot2grid((2, 2), (1, 1), rowspan=1, colspan=1)
response_axis.plot(time, voltage, color='dodgerblue', label=data_array.name)
response_axis.scatter(spike_times, np.ones(spike_times.shape) * np.max(voltage), color='red', label=tag.name)
response_axis.set_xlabel(x_axis.label + ((" [" + x_axis.unit + "]") if x_axis.unit else ""))
response_axis.set_ylabel(data_array.label + ((" [" + data_array.unit + "]") if data_array.unit else ""))
response_axis.set_title(data_array.name)
response_axis.set_xlim(0, np.max(time))
response_axis.set_ylim((1.2 * np.min(voltage), 1.2 * np.max(voltage)))
response_axis.legend(ncol=2, loc="lower center", fontsize=8)
single_snippet_axis.plot(snippet_time, single_snippet.T, color="red", label=("snippet No 4"))
single_snippet_axis.set_xlabel(snippet_time_dim.label + ((" [" + snippet_time_dim.unit + "]") if snippet_time_dim.unit else ""))
single_snippet_axis.set_ylabel(feature_data_array.label + ((" [" + feature_data_array.unit + "]") if feature_data_array.unit else ""))
single_snippet_axis.set_title("single stimulus snippet")
single_snippet_axis.set_xlim(np.min(snippet_time), np.max(snippet_time))
single_snippet_axis.set_ylim((1.2 * np.min(snippets[3, :]), 1.2 * np.max(snippets[3, :])))
single_snippet_axis.legend()
mean_snippet = np.mean(snippets, axis=0)
std_snippet = np.std(snippets, axis=0)
average_snippet_axis.fill_between(snippet_time, mean_snippet + std_snippet, mean_snippet - std_snippet, color="tab:red", alpha=0.25)
average_snippet_axis.plot(snippet_time, mean_snippet, color="red", label=(feature_data_array.name + str(4)))
average_snippet_axis.set_xlabel(snippet_time_dim.label + ((" [" + snippet_time_dim.unit + "]") if snippet_time_dim.unit else ""))
average_snippet_axis.set_ylabel(feature_data_array.label + ((" [" + feature_data_array.unit + "]") if feature_data_array.unit else ""))
average_snippet_axis.set_title("spike-triggered average")
average_snippet_axis.set_xlim(np.min(snippet_time), np.max(snippet_time))
average_snippet_axis.set_ylim((1.2 * np.min(mean_snippet - std_snippet), 1.2 * np.max(mean_snippet + std_snippet)))
plt.subplots_adjust(left=0.15, top=0.875, bottom=0.1, right=0.98, hspace=0.35, wspace=0.25)
plt.gcf().set_size_inches((5.5, 4.5))
# plt.savefig("../images/spike_features.png")
if docutils.is_running_under_pytest():
plt.close()
else:
plt.show()
def main():
stepsize = 0.0001 # s
sta_offset = 1000 # samples
time, voltage, spike_times, sts = fake_neuron(stepsize=0.0001, sta_offset=sta_offset)
# create a new file overwriting any existing content
file_name = 'spike_features.h5'
file = nixio.File.open(file_name, nixio.FileMode.Overwrite)
# create a 'Block' that represents a grouping object. Here, the recording session.
# it gets a name and a type
block = file.create_block("block name", "nix.session")
# create a 'DataArray' to take the membrane voltage
data = block.create_data_array("membrane voltage", "nix.regular_sampled.time_series", data=voltage, label="membrane voltage")
# add descriptors for the time axis
data.append_sampled_dimension(stepsize, label="time", unit="s")
# create the positions DataArray
positions = block.create_data_array("times", "nix.events.spike_times", data=spike_times)
positions.append_range_dimension_using_self()
# create a MultiTag
multi_tag = block.create_multi_tag("spike times", "nix.events.spike_times", positions)
multi_tag.references.append(data)
# save stimulus snippets in a DataArray
snippets = block.create_data_array("spike triggered stimulus", "nix.regular_sampled.multiple_series", data=sts, label="stimulus", unit="nA")
snippets.append_set_dimension()
snippets.append_sampled_dimension(stepsize, offset=-sta_offset * stepsize, label="time", unit="s")
# set snippets as an indexed feature of the multi_tag
multi_tag.create_feature(snippets, nixio.LinkType.Indexed)
# let's plot the data from the stored information
plot_data(multi_tag)
file.close()
if __name__ == '__main__':
main()
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