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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 lif
import nixio
import numpy as np
import scipy.signal as signal
import matplotlib.pylab as plt
import docutils
def fake_neuron(stepsize=0.001, offset=.8):
stimulus = np.random.randn(82000) * 2.5
b, a = signal.butter(2, 12.5, fs=1 / stepsize, btype="low")
stimulus = signal.filtfilt(b, a, stimulus[:])
stimulus = stimulus[1000:-1000]
s = np.hstack((np.zeros(10000), stimulus, np.zeros(10000)))
lif_model = lif.LIF(stepsize=stepsize, offset=offset)
time, v, spike_times = lif_model.run_stimulus(s)
stimulus_onset = 10000 * stepsize
stimulus_duration = len(stimulus) * stepsize
return time, v, stimulus, stimulus_onset, stimulus_duration
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])
stimulus_onset = tag.position
stimulus_duration = tag.extent
stimulus = tag.feature_data(0)
stimulus_array = tag.features[0].data
stim_time_dim = stimulus_array.dimensions[0]
stimulus_time = stim_time_dim.axis(stimulus_array.data_extent[0])
response_axis = plt.subplot2grid((2, 2), (0, 0), rowspan=1, colspan=2)
response_axis.tick_params(direction='out')
response_axis.spines['top'].set_color('none')
response_axis.spines['right'].set_color('none')
response_axis.xaxis.set_ticks_position('bottom')
response_axis.yaxis.set_ticks_position('left')
stimulus_axis = plt.subplot2grid((2, 2), (1, 0), rowspan=1, colspan=2)
stimulus_axis.tick_params(direction='out')
stimulus_axis.spines['top'].set_color('none')
stimulus_axis.spines['right'].set_color('none')
stimulus_axis.xaxis.set_ticks_position('bottom')
stimulus_axis.yaxis.set_ticks_position('left')
response_axis.plot(time, voltage, color='tab:blue', label=data_array.name, zorder=1)
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_xlim(0, np.max(time))
response_axis.set_ylim((1.2 * np.min(voltage), 1.2 * np.max(voltage)))
response_axis.barh((np.max(voltage) - np.min(voltage)) / 2, stimulus_duration, np.min(voltage) - np.max(voltage),
stimulus_onset, color='silver', alpha=0.5, zorder=0, label="stimulus epoch")
response_axis.legend(fontsize=9, ncol=2, loc=9)
stimulus_axis.plot(stimulus_time, stimulus[:], color="slategray", label="stimulus")
stimulus_axis.set_xlabel(stim_time_dim.label + ((" [" + stim_time_dim.unit + "]") if stim_time_dim.unit else ""))
stimulus_axis.set_ylabel(stimulus_array.label + ((" [" + stimulus_array.unit + "]") if stimulus_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(fontsize=9, loc=1)
plt.subplots_adjust(left=0.15, top=0.875, bottom=0.1, right=0.98, hspace=0.45, wspace=0.25)
plt.gcf().set_size_inches((5.5, 5))
if docutils.is_running_under_pytest():
plt.close()
else:
# plt.savefig("../images/untagged_feature.png")
plt.show()
if __name__ == '__main__':
stepsize = 0.0001 # s
time, voltage, stimulus, stim_onset, stim_duration = fake_neuron(stepsize=stepsize)
# create a new file overwriting any existing content
file_name = 'untagged_feature.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.sampled.time_series", data=voltage,
label="membrane voltage", unit="mV")
data.append_sampled_dimension(stepsize, label="time", unit="s")
# create a stimulus DataArray
stim = block.create_data_array("stimulus", "nix.sampled.time_series", data=stimulus,
label="current stimulus", unit="nA")
stim.append_sampled_dimension(stepsize, label="time", unit="s")
# create the Tag to highlight the stimulus-on segment
tag = block.create_tag("stimulus presentation", "nix.epoch.stimulus_presentation", [stim_onset])
tag.extent = [stim_duration]
tag.references.append(data)
# set stimulus as untagged feature of the tag
tag.create_feature(stim, nixio.LinkType.Untagged)
# let's plot the data from the stored information
plot_data(tag)
file.close()
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