Source code for tvb.adapters.datatypes.h5.spectral_h5

# -*- 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)