# -*- 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 numpy
from tvb.core.neotraits.h5 import H5File, Reference, DataSet, Scalar
from tvb.datatypes.mode_decompositions import PrincipalComponents, IndependentComponents
[docs]
class PrincipalComponentsH5(H5File):
def __init__(self, path):
super(PrincipalComponentsH5, self).__init__(path)
self.source = Reference(PrincipalComponents.source, self)
self.weights = DataSet(PrincipalComponents.weights, self, expand_dimension=2)
self.fractions = DataSet(PrincipalComponents.fractions, self, expand_dimension=1)
self.norm_source = DataSet(PrincipalComponents.norm_source, self, expand_dimension=1)
self.component_time_series = DataSet(PrincipalComponents.component_time_series,
self, expand_dimension=1)
self.normalised_component_time_series = DataSet(PrincipalComponents.normalised_component_time_series,
self, expand_dimension=1)
[docs]
def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.weights.append(partial_result.weights, close_file=False)
self.fractions.append(partial_result.fractions, close_file=False)
partial_result.compute_norm_source()
self.norm_source.append(partial_result.norm_source, close_file=False)
partial_result.compute_component_time_series()
self.component_time_series.append(partial_result.component_time_series, close_file=False)
partial_result.compute_normalised_component_time_series()
self.normalised_component_time_series.append(partial_result.normalised_component_time_series, close_file=False)
[docs]
def read_fractions_data(self, from_comp, to_comp):
"""
Return a list with fractions for components in interval from_comp, to_comp and in
addition have in position n the sum of the fractions for the rest of the components.
"""
from_comp = int(from_comp)
to_comp = int(to_comp)
all_data = self.fractions[:].flat
sum_others = 0
for idx, val in enumerate(all_data):
if idx < from_comp or idx > to_comp:
sum_others += val
return numpy.array(all_data[from_comp:to_comp].tolist() + [sum_others])
[docs]
def read_weights_data(self, from_comp, to_comp):
"""
Return the weights data for the components in the interval [from_comp, to_comp].
"""
from_comp = int(from_comp)
to_comp = int(to_comp)
data_slice = slice(from_comp, to_comp, None)
weights_shape = self.weights.shape
weights_slice = [slice(size) for size in weights_shape]
weights_slice[0] = data_slice
weights_data = self.weights[tuple(weights_slice)]
return weights_data.flatten()
[docs]
class IndependentComponentsH5(H5File):
def __init__(self, path):
super(IndependentComponentsH5, self).__init__(path)
self.source = Reference(IndependentComponents.source, self)
self.mixing_matrix = DataSet(IndependentComponents.mixing_matrix, self, expand_dimension=2)
self.unmixing_matrix = DataSet(IndependentComponents.unmixing_matrix, self, expand_dimension=2)
self.prewhitening_matrix = DataSet(IndependentComponents.prewhitening_matrix, self, expand_dimension=2)
self.n_components = Scalar(IndependentComponents.n_components, self)
self.norm_source = DataSet(IndependentComponents.norm_source, self, expand_dimension=1)
self.component_time_series = DataSet(IndependentComponents.component_time_series,
self, expand_dimension=1)
self.normalised_component_time_series = DataSet(IndependentComponents.normalised_component_time_series,
self, expand_dimension=1)
[docs]
def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.unmixing_matrix.append(partial_result.unmixing_matrix, close_file=False)
self.prewhitening_matrix.append(partial_result.prewhitening_matrix, close_file=False)
partial_result.compute_norm_source()
self.norm_source.append(partial_result.norm_source, close_file=False)
partial_result.compute_component_time_series()
self.component_time_series.append(partial_result.component_time_series, close_file=False)
partial_result.compute_normalised_component_time_series()
self.normalised_component_time_series.append(partial_result.normalised_component_time_series, close_file=False)
partial_result.compute_mixing_matrix()
self.mixing_matrix.append(partial_result.mixing_matrix, close_file=False)
