TheVirtualBrain:

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Source code for tvb.adapters.visualizers.ica

# -*- 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 do download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (c) 2012-2020, 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.
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# 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 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)
#
#

"""
A matrix visualizer for the Independent Component Analysis.
It displays the mixing matrix of size n_features x n_components

.. moduleauthor:: Paula Popa <paula.popa@codemart.ro>
.. moduleauthor:: Paula Sanz Leon <Paula@tvb.invalid>
"""

from tvb.adapters.datatypes.db.mode_decompositions import IndependentComponentsIndex
from tvb.adapters.visualizers.matrix_viewer import ABCMappedArraySVGVisualizer
from tvb.basic.neotraits.api import Attr
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.arguments_serialisation import slice_str
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitDataTypeSelectField, IntField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.mode_decompositions import IndependentComponents


[docs]class ICAModel(ViewModel): datatype = DataTypeGidAttr( linked_datatype=IndependentComponents, label='Independent component analysis:' ) i_svar = Attr( field_type=int, default=0, label='Index of state variable (defaults to first state variable)' ) i_mode = Attr( field_type=int, default=0, label='Index of mode (defaults to first mode)' )
[docs]class ICAForm(ABCAdapterForm): def __init__(self, prefix='', project_id=None): super(ICAForm, self).__init__(prefix, project_id) self.datatype = TraitDataTypeSelectField(ICAModel.datatype, self, name='datatype', conditions=self.get_filters()) self.i_svar = IntField(ICAModel.i_svar, self, name='i_svar') self.i_mode = IntField(ICAModel.i_mode, self, name='i_mode') @staticmethod
[docs] def get_view_model(): return ICAModel
@staticmethod
[docs] def get_required_datatype(): return IndependentComponentsIndex
@staticmethod
[docs] def get_filters(): return None
@staticmethod
[docs] def get_input_name(): return 'datatype'
[docs]class ICA(ABCMappedArraySVGVisualizer): _ui_name = "Independent Components Analysis Visualizer" _ui_subsection = "ica"
[docs] def get_form_class(self): return ICAForm
[docs] def launch(self, view_model): # type: (ICAModel) -> dict """Construct data for visualization and launch it.""" ica_gid = view_model.datatype ica_index = self.load_entity_by_gid(ica_gid) slice_given = slice_str((slice(None), slice(None), slice(view_model.i_svar), slice(view_model.i_mode))) if view_model.i_svar < 0 or view_model.i_svar >= ica_index.parsed_shape[2]: view_model.i_svar = 0 if view_model.i_mode < 0 or view_model.i_mode >= ica_index.parsed_shape[3]: view_model.i_mode = 0 slice_used = slice_str((slice(None), slice(None), slice(view_model.i_svar), slice(view_model.i_mode))) with h5.h5_file_for_index(ica_index) as h5_file: unmixing_matrix = h5_file.unmixing_matrix[..., view_model.i_svar, view_model.i_mode] prewhitening_matrix = h5_file.prewhitening_matrix[..., view_model.i_svar, view_model.i_mode] Cinv = unmixing_matrix.dot(prewhitening_matrix) title = 'ICA region contribution -- (Ellipsis, %d, 0)' % (view_model.i_svar) labels = self.extract_source_labels(ica_index) pars = self.compute_params(ica_index, Cinv, title, [labels, labels], slice_given, slice_used, slice_given != slice_used) return self.build_display_result("matrix/svg_view", pars)