The Virtual Brain Project

Source code for tvb.datatypes.structural

# -*- coding: utf-8 -*-
#
#
#  TheVirtualBrain-Scientific Package. This package holds all simulators, and 
# analysers necessary to run brain-simulations. You can use it stand alone or
# in conjunction with TheVirtualBrain-Framework Package. See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
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# (c) 2012-2017, Baycrest Centre for Geriatric Care ("Baycrest") and others
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#   CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
#
#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
#   Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
#       The Virtual Brain: a simulator of primate brain network dynamics.
#   Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)
#
#

"""
The Volume datatypes. This brings together the scientific and framework 
methods that are associated with the volume datatypes.

"""

import numpy
from tvb.basic.logger.builder import get_logger
from tvb.basic.traits import types_basic as basic
from tvb.datatypes import volumes, arrays
from tvb.basic.arguments_serialisation import preprocess_space_parameters


LOG = get_logger(__name__)


[docs]class VolumetricDataMixin(object): """Provides subclasses with useful methods for volumes."""
[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. """ self.store_data("array_data", data)
[docs] def get_volume_slice(self, x_plane, y_plane, z_plane): slices = slice(self.length_1d), slice(self.length_2d), slice(z_plane, z_plane + 1) slice_x = self.read_data_slice(slices)[:, :, 0] # 2D slice_x = numpy.array(slice_x, dtype=int) slices = slice(x_plane, x_plane + 1), slice(self.length_2d), slice(self.length_3d) slice_y = self.read_data_slice(slices)[0, :, :][..., ::-1] slice_y = numpy.array(slice_y, dtype=int) slices = slice(self.length_1d), slice(y_plane, y_plane + 1), slice(self.length_3d) slice_z = self.read_data_slice(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): # Work with space inside Volume: x_plane, y_plane, z_plane = preprocess_space_parameters(x_plane, y_plane, z_plane, self.length_1d, self.length_2d, self.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] def get_min_max_values(self): """ Retrieve the minimum and maximum values from the metadata. :returns: (minimum_value, maximum_value) """ metadata = self.get_metadata('array_data') return metadata[self.METADATA_ARRAY_MIN], metadata[self.METADATA_ARRAY_MAX]
[docs]class StructuralMRI(VolumetricDataMixin, arrays.MappedArray): """ Quantitative volumetric data recorded by means of Magnetic Resonance Imaging. """ # without the field below weighting and volume columns are going to be added to the MAPPED_ARRAY table __generate_table__ = True array_data = arrays.FloatArray(label="contrast") weighting = basic.String(label="MRI weighting") # eg, "T1", "T2", "T2*", "PD", ... volume = volumes.Volume