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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Copyright © 2014 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.pyplot as plt
import lif
import docutils
def fake_neuron(stepsize=0.001, offset=.8):
stimulus = np.random.randn(102000) * 2.5
b, a = signal.butter(2, 7.5, 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)
return time, v, stimulus, spike_times
def main():
stepsize = 0.0001 # s
time, voltage, stimulus, spike_times = fake_neuron(stepsize=0.0001)
# create a new file overwriting any existing content
file_name = 'spike_features.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)
data.label = "membrane voltage"
data.unit = "mV"
# add descriptors for the time axis
data.append_sampled_dimension(stepsize, 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)
# save stimulus snippets in a DataArray
stimulus_array = block.create_data_array("stimulus", "nix.sampled", data=stimulus, label="stimulus", unit="nA")
# add a descriptor for the time axis
stimulus_array.append_sampled_dimension(stepsize, label="time", unit="s")
# set stimulus as a tagged feature of the multi_tag
multi_tag.create_feature(stimulus_array, nixio.LinkType.Tagged)
# let's plot the data from the stored information
plot_data(multi_tag)
file.close()
def plot_data(tag):
data_array = tag.references[0]
voltage = data_array[:]
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
stimulus = feature_data_array[:]
stim_time_dim = feature_data_array.dimensions[0]
stimulus_time = stim_time_dim.axis(feature_data_array.data_extent[0])
stim_at_spike_time = np.zeros(len(tag.positions[:]))
for i in range(len(tag.positions)):
stim_at_spike_time[i] = tag.feature_data(i, 0)[:].item()
response_axis = plt.subplot2grid((2, 3), (0, 0), rowspan=1, colspan=2)
stimulus_axis = plt.subplot2grid((2, 3), (1, 0), rowspan=1, colspan=2, sharex=response_axis)
hist_axis = plt.subplot2grid((2, 3), (1, 2), rowspan=1, colspan=1)
hist_axis.hist([stimulus, stim_at_spike_time], color=["tab:blue", "orange"], label=["stimulus", "spike triggered stim"], density=True)
hist_axis.set_xlabel("%s [%s]" % (feature_data_array.label, feature_data_array.unit))
hist_axis.set_ylabel("probability density")
hist_axis.legend(ncol=1, fontsize=8, loc=(-0.1, 1.025))
response_axis.plot(time, voltage, color='tab:blue', label=data_array.name, lw=1)
response_axis.scatter(spike_times, np.ones(spike_times.shape) * np.max(voltage), color='red', label=tag.name)
response_axis.set_ylabel(data_array.label + ((" [" + data_array.unit + "]") if data_array.unit else ""))
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(loc="lower center", ncol=2, fontsize=8)
stimulus_axis.plot(stimulus_time, stimulus, color="darkgray", label="stimulus", lw=1)
stimulus_axis.scatter(spike_times, np.ones(spike_times.shape) * np.max(stimulus), color='red', label=tag.name)
stimulus_axis.set_xlabel(stim_time_dim.label + ((" [" + stim_time_dim.unit + "]") if stim_time_dim.unit else ""))
stimulus_axis.set_ylabel(feature_data_array.label + ((" [" + feature_data_array.unit + "]") if feature_data_array.unit else ""))
stimulus_axis.set_xlim(np.min(stimulus_time), np.max(stimulus_time))
stimulus_axis.set_ylim(1.2 * np.min(stimulus), 1.2 * np.max(stimulus))
stimulus_axis.legend(loc="lower center", ncol=2, fontsize=8)
plt.subplots_adjust(left=0.125, top=0.975, bottom=0.1, right=0.98, hspace=0.25, wspace=0.35)
plt.gcf().set_size_inches((5.5, 4.5))
# plt.savefig('../images/tagged_feature.png')
if docutils.is_running_under_pytest():
plt.close()
else:
plt.show()
if __name__ == '__main__':
main()
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