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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 lif
import numpy as np
import matplotlib.pylab as plt
import docutils
def fake_neuron():
lif_model = lif.LIF(offset=1.0)
t, v, spike_times = lif_model.run_const_stim(5000, 0.00025)
return t, v, spike_times
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.shape[0])
spike_times = np.zeros(tag.positions.data_extent)
tag.positions.read_direct(spike_times)
fig = plt.figure(figsize=(5.5, 2.5))
ax = fig.add_subplot(111)
ax.plot(time, voltage, color='dodgerblue', label=data_array.name)
ax.scatter(spike_times, np.ones(spike_times.shape)*np.max(voltage), color='red', label=tag.name)
ax.set_xlabel(x_axis.label + ((" [" + x_axis.unit + "]") if x_axis.unit else ""))
ax.set_ylabel(data_array.label + ((" [" + data_array.unit + "]") if data_array.unit else ""))
ax.set_xlim(0, np.max(time))
ax.set_ylim((1.5 * np.min(voltage), 1.5 * np.max(voltage)))
ax.legend()
fig.subplots_adjust(bottom=0.175, top=0.975, right=0.975)
if docutils.is_running_under_pytest():
plt.close()
else:
# fig.savefig("../images/spike_tagging.png")
plt.show()
if __name__ == '__main__':
time, voltage, spike_times = fake_neuron()
# create a new file overwriting any existing content
file_name = 'spike_tagging.nix'
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", unit="mV")
# add descriptors for time axis
data.append_sampled_dimension(time[1]-time[0], label="time", unit="s")
# create the positions DataArray
positions = block.create_data_array("spike 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)
# let's plot the data from the stored information
plot_data(multi_tag)
file.close()
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