# -*- 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
#
#
from tvb.basic.neotraits.api import Attr
from tvb.datatypes.graph import Covariance, CorrelationCoefficients, ConnectivityMeasure
from tvb.adapters.datatypes.h5.spectral_h5 import DataTypeMatrixH5
from tvb.core.neotraits.h5 import DataSet, Reference, Json, Scalar
[docs]class CovarianceH5(DataTypeMatrixH5):
def __init__(self, path):
super(CovarianceH5, self).__init__(path)
self.array_data = DataSet(Covariance.array_data, self, expand_dimension=2)
self.source = Reference(Covariance.source, self)
[docs] def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.array_data.append(partial_result, close_file=False)
[docs]class CorrelationCoefficientsH5(DataTypeMatrixH5):
def __init__(self, path):
super(CorrelationCoefficientsH5, self).__init__(path)
self.array_data = DataSet(CorrelationCoefficients.array_data, self)
self.source = Reference(CorrelationCoefficients.source, self)
self.labels_ordering = Json(CorrelationCoefficients.labels_ordering, self)
[docs] def get_correlation_data(self, selected_state, selected_mode):
matrix_to_display = self.array_data[:, :, int(selected_state) - 1, int(selected_mode)]
return list(matrix_to_display.flat)
[docs]class ConnectivityMeasureH5(DataTypeMatrixH5):
def __init__(self, path):
super(ConnectivityMeasureH5, self).__init__(path)
self.array_data = DataSet(ConnectivityMeasure.array_data, self)
self.connectivity = Reference(ConnectivityMeasure.connectivity, self)
self.title = Scalar(Attr(str), self, name='title')
[docs] def get_array_data(self):
return self.array_data[:]
[docs] def store(self, datatype, scalars_only=False, store_references=True):
# type: (ConnectivityMeasure, bool, bool) -> None
super(ConnectivityMeasureH5, self).store(datatype, scalars_only, store_references)
self.title.store(datatype.title)
[docs] def load_into(self, datatype):
# type: (ConnectivityMeasure) -> None
super(ConnectivityMeasureH5, self).load_into(datatype)
datatype.title = self.title.load()
