# -*- 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 bct
import numpy
from tvb.adapters.analyzers.bct_adapters import BaseBCT, BaseUndirected, LABEL_CONNECTIVITY_BINARY
from tvb.core.entities.model.model_operation import AlgorithmTransientGroup
BCT_GROUP_CENTRALITY = AlgorithmTransientGroup("Centrality Algorithms", "Brain Connectivity Toolbox", "bctcentrality")
[docs]class CentralityNodeBinary(BaseBCT):
"""
"""
_ui_group = BCT_GROUP_CENTRALITY
_ui_name = "Node Betweenness Centrality Binary: " + LABEL_CONNECTIVITY_BINARY
_ui_description = bct.betweenness_bin.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
result = bct.betweenness_bin(connectivity.binarized_weights)
measure_index = self.build_connectivity_measure(result, connectivity,
"Node Betweenness Centrality Binary", "Nodes")
return [measure_index]
[docs]class CentralityNodeWeighted(BaseBCT):
"""
"""
_ui_group = BCT_GROUP_CENTRALITY
_ui_name = "Node Betweenness Centrality Weighted: Weighted (directed/undirected) connection matrix"
_ui_description = bct.betweenness_wei.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
result = bct.betweenness_wei(connectivity.weights)
measure_index = self.build_connectivity_measure(result, connectivity,
"Node Betweenness Centrality Weighted", "Nodes")
return [measure_index]
# class CentralityEdgeBinary(CentralityNodeBinary):
# """
# COMMENT OUT BECAUSE in v0.5.2:
# could not broadcast input array from shape (16,) into shape (15,)
# """
# _ui_name = "Edge Betweenness Centrality Binary"
# _ui_description = bct.edge_betweenness_bin.__doc__
#
# def launch(self, view_model):
# connectivity = self.get_connectivity(view_model)
# result = bct.edge_betweenness_bin(connectivity.binarized_weights)
# measure_index1 = self.build_connectivity_measure(result[0], connectivity, "Edge Betweenness Centrality Matrix")
# measure_index2 = self.build_connectivity_measure(result[1], connectivity, "Node Betweenness Centrality Vector")
# return [measure_index1, measure_index2]
# class CentralityEdgeWeighted(CentralityNodeWeighted):
# """
# COMMENT OUT BECAUSE in v0.5.2:
# could not broadcast input array from shape (16,) into shape (15,)
# """
# _ui_name = "Edge Betweenness Centrality Weighted"
# _ui_description = bct.edge_betweenness_wei.__doc__
#
# def launch(self, view_model):
# connectivity = self.get_connectivity(view_model)
# result = bct.edge_betweenness_wei(connectivity.weights)
# measure_index1 = self.build_connectivity_measure(result[0], connectivity, "Edge Betweeness Centrality Matrix")
# measure_index2 = self.build_connectivity_measure(result[1], connectivity, "Node Betweenness Centrality Vector")
# return [measure_index1, measure_index2]
[docs]class CentralityEigenVector(BaseUndirected):
"""
"""
_ui_group = BCT_GROUP_CENTRALITY
_ui_name = "EigenVector Centrality"
_ui_description = bct.eigenvector_centrality_und.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
result = bct.eigenvector_centrality_und(connectivity.weights)
measure_index = self.build_connectivity_measure(result, connectivity, "Eigen vector centrality")
return [measure_index]
[docs]class CentralityKCoreness(BaseUndirected):
"""
"""
_ui_group = BCT_GROUP_CENTRALITY
_ui_name = "K-coreness centrality BU: " + LABEL_CONNECTIVITY_BINARY
_ui_description = bct.kcoreness_centrality_bu.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
result = bct.kcoreness_centrality_bu(connectivity.binarized_weights)
measure_index1 = self.build_connectivity_measure(result[0], connectivity, "Node coreness BU")
measure_index2 = self.build_connectivity_measure(result[1], connectivity, "Size of k-core")
return [measure_index1, measure_index2]
[docs]class CentralityKCorenessBD(CentralityNodeBinary):
"""
"""
_ui_name = "K-coreness centrality BD"
_ui_description = bct.kcoreness_centrality_bd.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
result = bct.kcoreness_centrality_bd(connectivity.binarized_weights)
measure_index1 = self.build_connectivity_measure(result[0], connectivity, "Node coreness BD")
measure_index2 = self.build_connectivity_measure(result[1], connectivity, "Size of k-core")
return [measure_index1, measure_index2]
[docs]class CentralityShortcuts(CentralityNodeBinary):
"""
"""
_ui_name = "Centrality Shortcuts: Binary directed connection matrix"
_ui_description = bct.erange.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
result = bct.erange(connectivity.binarized_weights)
measure_index1 = self.build_connectivity_measure(result[0], connectivity, "Range for each edge")
value1 = self.build_int_value_wrapper(result[1], "Average range for entire graph")
measure_index2 = self.build_connectivity_measure(result[2], connectivity, "Shortcut edges")
value2 = self.build_float_value_wrapper(result[3], "Fraction of shortcuts in the graph")
return [measure_index1, value1, measure_index2, value2]
[docs]class FlowCoefficients(CentralityNodeBinary):
"""
"""
_ui_name = "Node-wise flow coefficients"
_ui_description = bct.flow_coef_bd.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
result = bct.flow_coef_bd(connectivity.binarized_weights)
measure_index1 = self.build_connectivity_measure(result[0], connectivity, "Flow coefficient for each node")
value1 = self.build_float_value_wrapper(result[1], "Average flow coefficient over the network")
measure_index2 = self.build_connectivity_measure(result[2], connectivity,
"Number of paths that flow across the central node")
return [measure_index1, value1, measure_index2]
[docs]class ParticipationCoefficient(BaseBCT):
"""
"""
_ui_group = BCT_GROUP_CENTRALITY
_ui_name = "Participation Coefficient"
_ui_description = bct.participation_coef.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
ci, _ = bct.modularity_dir(connectivity.weights)
try:
result = bct.participation_coef(connectivity.weights, ci)
except FloatingPointError as ex:
self.log.exception(ex)
self.add_operation_additional_info("FloatingPointError got during computation, defaulted to 0s!")
result = numpy.zeros(connectivity.number_of_regions)
measure_index = self.build_connectivity_measure(result, connectivity, "Participation Coefficient")
return [measure_index]
[docs]class ParticipationCoefficientSign(ParticipationCoefficient):
"""
"""
_ui_name = "Participation Coefficient Sign"
_ui_description = bct.participation_coef_sign.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
ci, _ = bct.modularity_dir(connectivity.weights)
ppos, pneg = bct.participation_coef_sign(connectivity.weights, ci)
measure_index1 = self.build_connectivity_measure(ppos, connectivity,
"Participation Coefficient from positive weights")
measure_index2 = self.build_connectivity_measure(pneg, connectivity,
"Participation Coefficient from negative weights")
return [measure_index1, measure_index2]
[docs]class SubgraphCentrality(CentralityNodeBinary):
"""
"""
_ui_name = "Subgraph centrality of a network: Adjacency matrix (binary)"
_ui_description = bct.subgraph_centrality.__doc__
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
result = bct.subgraph_centrality(connectivity.binarized_weights)
measure_index = self.build_connectivity_measure(result, connectivity, "Subgraph Centrality")
return [measure_index]
