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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 as nix
import numpy as np
import matplotlib.pylab as plt
import docutils
def create_data(duration=1, freq=10, stepsize=0.01):
x = np.arange(0, duration * 2 * np.pi, stepsize)
sine = np.sin(freq * x)
cosine = np.cos(freq * x)
return x, sine, cosine
def plot_data(data_array):
set_dim = data_array.dimensions[0]
x_axis = data_array.dimensions[1]
x = np.arange(0, data_array.shape[1])
x = x * x_axis.sampling_interval + x_axis.offset
y = np.zeros(data_array.shape)
data_array.read_direct(y)
for i, label in enumerate(set_dim.labels):
plt.plot(x, y[i, :], label=label)
plt.xlabel(x_axis.label + " [" + x_axis.unit + "]")
plt.ylabel(data_array.label + " [" + data_array.unit + "]")
plt.title(data_array.name)
plt.xlim(0, np.max(x))
plt.ylim((1.1 * np.min(y), 1.1 * np.max(y)))
plt.legend()
if docutils.is_running_under_pytest():
plt.close()
else:
plt.savefig('multiple_time_series.png')
plt.show()
def main():
# fake some data
duration = 2
frequency = 2
stepsize = 0.02
_, sine, cosine = create_data(duration, frequency, stepsize)
# create a new file overwriting any existing content
file_name = 'multiple_regular_data_example.h5'
file = nix.File.open(file_name, nix.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 data, add some information about the signal
y = np.vstack((sine, cosine))
data = block.create_data_array("waveforms", "nix.regular_sampled.multiple_series", data=y, label="voltage", unit="mV")
# descriptor for first dimension is a set
data.append_set_dimension(labels=['sin', 'cos'])
# add a descriptor for the xaxis
data.append_sampled_dimension(stepsize, offset=0.0, label="time", unit="s")
# let's plot the data from the stored information
plot_data(data)
file.close()
if __name__ == "__main__":
main()
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