# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Framework Package. This package holds all Data Management, and
# Web-UI helpful to run brain-simulations. To use it, you also need to download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (c) 2012-2023, Baycrest Centre for Geriatric Care ("Baycrest") and others
#
# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program. If not, see <http://www.gnu.org/licenses/>.
#
#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as explained here:
# https://www.thevirtualbrain.org/tvb/zwei/neuroscience-publications
#
#
import json
import numpy
from tvb.core.neotraits.h5 import H5File, DataSet, Scalar, Reference, Enum
from tvb.datatypes.spectral import FourierSpectrum, WaveletCoefficients, CoherenceSpectrum, ComplexCoherenceSpectrum
[docs]class DataTypeMatrixH5(H5File):
[docs] def get_min_max_values(self):
"""
Retrieve the minimum and maximum values from the metadata.
:returns: (minimum_value, maximum_value)
"""
metadata = self.array_data.get_cached_metadata()
return metadata.min, metadata.max
[docs]class FourierSpectrumH5(DataTypeMatrixH5):
def __init__(self, path):
super(FourierSpectrumH5, self).__init__(path)
self.array_data = DataSet(FourierSpectrum.array_data, self, expand_dimension=2)
self.source = Reference(FourierSpectrum.source, self)
self.segment_length = Scalar(FourierSpectrum.segment_length, self)
self.windowing_function = Enum(FourierSpectrum.windowing_function, self)
self.amplitude = DataSet(FourierSpectrum.amplitude, self, expand_dimension=2)
self.phase = DataSet(FourierSpectrum.phase, self, expand_dimension=2)
self.power = DataSet(FourierSpectrum.power, self, expand_dimension=2)
self.average_power = DataSet(FourierSpectrum.average_power, self, expand_dimension=2)
self.normalised_average_power = DataSet(FourierSpectrum.normalised_average_power, self,
expand_dimension=2)
[docs] def write_data_slice(self, partial_result):
"""
Append chunk.
"""
# self.store_data_chunk('array_data', partial_result, grow_dimension=2, close_file=False)
# mhtodo: these computations on the partial_result belong in the caller not here
self.array_data.append(partial_result.array_data, close_file=False)
partial_result.compute_amplitude()
self.amplitude.append(partial_result.amplitude, close_file=False)
partial_result.compute_phase()
self.phase.append(partial_result.phase, close_file=False)
partial_result.compute_power()
self.power.append(partial_result.power, close_file=False)
partial_result.compute_average_power()
self.average_power.append(partial_result.average_power, close_file=False)
partial_result.compute_normalised_average_power()
self.normalised_average_power.append(partial_result.normalised_average_power, close_file=False)
[docs] def get_fourier_data(self, selected_state, selected_mode, normalized):
shape = self.array_data.shape
slices = (slice(shape[0]),
slice(int(selected_state), min(int(selected_state) + 1, shape[1]), None),
slice(shape[2]),
slice(int(selected_mode), min(int(selected_mode) + 1, shape[3]), None))
if normalized == "yes":
data_matrix = self.normalised_average_power[slices]
else:
data_matrix = self.average_power[slices]
data_matrix = data_matrix.reshape((shape[0], shape[2]))
ymin = numpy.amin(data_matrix)
ymax = numpy.amax(data_matrix)
data_matrix = data_matrix.transpose()
# mhtodo: this form with string inputs and json outputs belongs in some viewer not here
return dict(data_matrix=json.dumps(data_matrix.tolist()),
ymin=ymin,
ymax=ymax)
[docs]class WaveletCoefficientsH5(DataTypeMatrixH5):
def __init__(self, path):
super(WaveletCoefficientsH5, self).__init__(path)
self.array_data = DataSet(WaveletCoefficients.array_data, self, expand_dimension=2)
self.source = Reference(WaveletCoefficients.source, self)
self.mother = Scalar(WaveletCoefficients.mother, self)
self.sample_period = Scalar(WaveletCoefficients.sample_period, self)
self.frequencies = DataSet(WaveletCoefficients.frequencies, self)
self.normalisation = Scalar(WaveletCoefficients.normalisation, self)
self.q_ratio = Scalar(WaveletCoefficients.q_ratio, self)
self.amplitude = DataSet(WaveletCoefficients.amplitude, self, expand_dimension=2)
self.phase = DataSet(WaveletCoefficients.phase, self, expand_dimension=2)
self.power = DataSet(WaveletCoefficients.power, self, expand_dimension=2)
[docs] def write_data_slice(self, partial_result):
"""
Append chunk.
"""
# mhtodo: these computations on the partial_result belong in the caller not here
self.array_data.append(partial_result.array_data, close_file=False)
partial_result.compute_amplitude()
self.amplitude.append(partial_result.amplitude, close_file=False)
partial_result.compute_phase()
self.phase.append(partial_result.phase, close_file=False)
partial_result.compute_power()
self.power.append(partial_result.power, close_file=False)
[docs]class CoherenceSpectrumH5(DataTypeMatrixH5):
def __init__(self, path):
super(CoherenceSpectrumH5, self).__init__(path)
self.array_data = DataSet(CoherenceSpectrum.array_data, self, expand_dimension=3)
self.source = Reference(CoherenceSpectrum.source, self)
self.nfft = Scalar(CoherenceSpectrum.nfft, self)
self.frequency = DataSet(CoherenceSpectrum.frequency, self)
[docs] def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.array_data.append(partial_result.array_data, close_file=False)
[docs]class ComplexCoherenceSpectrumH5(DataTypeMatrixH5):
spectrum_types = ["Imaginary", "Real", "Absolute"]
def __init__(self, path):
super(ComplexCoherenceSpectrumH5, self).__init__(path)
self.cross_spectrum = DataSet(ComplexCoherenceSpectrum.cross_spectrum, self, expand_dimension=2)
self.array_data = DataSet(ComplexCoherenceSpectrum.array_data, self, expand_dimension=2)
self.source = Reference(ComplexCoherenceSpectrum.source, self)
self.epoch_length = Scalar(ComplexCoherenceSpectrum.epoch_length, self)
self.segment_length = Scalar(ComplexCoherenceSpectrum.segment_length, self)
self.windowing_function = Scalar(ComplexCoherenceSpectrum.windowing_function, self)
[docs] def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.cross_spectrum.append(partial_result.cross_spectrum, close_file=False)
self.array_data.append(partial_result.array_data, close_file=False)
[docs] def get_spectrum_data(self, selected_spectrum):
shape = self.array_data.shape
slices = (slice(shape[0]), slice(shape[1]), slice(shape[2]))
if selected_spectrum == self.spectrum_types[0]:
data_matrix = self.array_data[slices].imag
indices = numpy.triu_indices(shape[0], 1)
data_matrix = data_matrix[indices]
elif selected_spectrum == self.spectrum_types[1]:
data_matrix = self.array_data[slices].real
data_matrix = data_matrix.reshape(shape[0] * shape[0], shape[2])
else:
data_matrix = self.array_data[slices]
data_matrix = numpy.absolute(data_matrix)
data_matrix = data_matrix.reshape(shape[0] * shape[0], shape[2])
coh_spec_sd = numpy.std(data_matrix, axis=0)
coh_spec_av = numpy.mean(data_matrix, axis=0)
ymin = numpy.amin(coh_spec_av - coh_spec_sd)
ymax = numpy.amax(coh_spec_av + coh_spec_sd)
coh_spec_sd = json.dumps(coh_spec_sd.tolist())
coh_spec_av = json.dumps(coh_spec_av.tolist())
return dict(coh_spec_sd=coh_spec_sd,
coh_spec_av=coh_spec_av,
ymin=ymin,
ymax=ymax)
