Source code for tvb.adapters.datatypes.h5.structural_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
# (c) 2012-2024, 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 <>.
# When using The Virtual Brain for scientific publications, please cite it as explained here:
import numpy
from tvb.core.adapters.arguments_serialisation import preprocess_space_parameters
from tvb.adapters.datatypes.h5.spectral_h5 import DataTypeMatrixH5
from tvb.core.neotraits.h5 import DataSet, Scalar, Reference
from tvb.datatypes.structural import StructuralMRI

[docs] class VolumetricDataMixin(object): """Provides subclasses with useful methods for volumes.""" array_data = None # type: DataSet # here only for typing, will be overriden in mixed in class
[docs] def write_data_slice(self, data): """ We are using here the same signature as in TS, just to allow easier parsing code. This is not a chunked write. """
[docs] def get_volume_slice(self, x_plane, y_plane, z_plane): shape = self.array_data.shape length_1d = shape[0] length_2d = shape[1] length_3d = shape[2] slices = slice(length_1d), slice(length_2d), slice(z_plane, z_plane + 1) slice_x = self.array_data[slices][:, :, 0] # 2D slice_x = numpy.array(slice_x, dtype=int) slices = slice(x_plane, x_plane + 1), slice(length_2d), slice(length_3d) slice_y = self.array_data[slices][0, :, :][..., ::-1] slice_y = numpy.array(slice_y, dtype=int) slices = slice(length_1d), slice(y_plane, y_plane + 1), slice(length_3d) slice_z = self.array_data[slices][:, 0, :][..., ::-1] slice_z = numpy.array(slice_z, dtype=int) return [slice_x, slice_y, slice_z]
[docs] def get_volume_view(self, x_plane, y_plane, z_plane, **kwargs): shape = self.array_data.shape length_1d = shape[0] length_2d = shape[1] length_3d = shape[2] # Work with space inside Volume: x_plane, y_plane, z_plane = preprocess_space_parameters(x_plane, y_plane, z_plane, length_1d, length_2d, length_3d) slice_x, slice_y, slice_z = self.get_volume_slice(x_plane, y_plane, z_plane) return [[slice_x.tolist()], [slice_y.tolist()], [slice_z.tolist()]]
[docs] class StructuralMRIH5(VolumetricDataMixin, DataTypeMatrixH5): def __init__(self, path): super(StructuralMRIH5, self).__init__(path) self.array_data = DataSet(StructuralMRI.array_data, self) self.weighting = Scalar(StructuralMRI.weighting, self) self.volume = Reference(StructuralMRI.volume, self)