TheVirtualBrain:
TheDocumentationwebsite.
# -*- 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) 2012-2022, 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 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)
#
#
import os
from abc import abstractmethod
from tvb.adapters.analyzers.matlab_worker import MatlabWorker
from tvb.adapters.datatypes.db.connectivity import ConnectivityIndex
from tvb.adapters.datatypes.db.graph import ConnectivityMeasureIndex
from tvb.adapters.datatypes.db.mapped_value import ValueWrapperIndex
from tvb.adapters.datatypes.h5.mapped_value_h5 import ValueWrapper
from tvb.basic.profile import TvbProfile
from tvb.core.adapters.abcadapter import ABCAdapterForm, ABCAdapter
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.entities.model.model_operation import AlgorithmTransientGroup
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.core.utils import extract_matlab_doc_string
from tvb.datatypes.connectivity import Connectivity
from tvb.datatypes.graph import ConnectivityMeasure
BCT_GROUP_MODULARITY = AlgorithmTransientGroup("Modularity Algorithms", "Brain Connectivity Toolbox", "bct")
BCT_GROUP_DISTANCE = AlgorithmTransientGroup("Distance Algorithms", "Brain Connectivity Toolbox", "bctdistance")
BCT_PATH = os.path.join(TvbProfile.current.EXTERNALS_FOLDER_PARENT, "externals/BCT")
BCT_PATH_ENV = 'BCT_PATH'
if BCT_PATH_ENV in os.environ and os.path.exists(os.environ[BCT_PATH_ENV]) and os.path.isdir(os.environ[BCT_PATH_ENV]):
BCT_PATH = os.environ[BCT_PATH_ENV]
LABEL_CONNECTIVITY_BINARY = "Binary (directed/undirected) connection matrix"
LABEL_CONN_WEIGHTED_DIRECTED = "Weighted directed connection matrix"
LABEL_CONN_WEIGHTED_UNDIRECTED = "Weighted undirected connection matrix"
[docs]def bct_description(mat_file_name):
return extract_matlab_doc_string(os.path.join(BCT_PATH, mat_file_name))
[docs]class BaseBCTModel(ViewModel):
connectivity = DataTypeGidAttr(
linked_datatype=Connectivity,
label='Connectivity',
)
[docs]class BaseBCTForm(ABCAdapterForm):
def __init__(self):
super(BaseBCTForm, self).__init__()
self.connectivity = TraitDataTypeSelectField(BaseBCTModel.connectivity, name="connectivity",
conditions=self.get_filters(), has_all_option=True)
@staticmethod
@staticmethod
@staticmethod
@staticmethod
[docs]class BaseUnidirectedBCTForm(BaseBCTForm):
@staticmethod
[docs] def get_filters():
return FilterChain(fields=[FilterChain.datatype + '.undirected'], operations=["=="], values=['1'])
[docs]class BaseBCT(ABCAdapter):
"""
Interface between Brain Connectivity Toolbox of Olaf Sporns and TVB Framework.
This adapter requires BCT deployed locally, and Matlab or Octave installed separately of TVB.
"""
def __init__(self):
ABCAdapter.__init__(self)
self.matlab_worker = MatlabWorker()
@staticmethod
[docs] def get_required_memory_size(self, view_model):
# We do not know how much memory is needed.
return -1
[docs] def get_connectivity(self, view_model):
return self.load_traited_by_gid(view_model.connectivity)
[docs] def execute_matlab(self, matlab_code, data):
self.matlab_worker.add_to_path(BCT_PATH)
self.log.info("Starting execution of MATLAB code:" + matlab_code)
runcode, matlablog, result = self.matlab_worker.matlab(matlab_code, data=data, work_dir=self.storage_path)
self.log.debug("Code run in MATLAB: " + str(runcode))
self.log.debug("MATLAB log: " + str(matlablog))
self.log.debug("Finished MATLAB execution:" + str(result))
return result
[docs] def build_connectivity_measure(self, result, key, connectivity, title="", label_x="", label_y=""):
measure = ConnectivityMeasure()
measure.array_data = result[key]
measure.connectivity = connectivity
measure.title = title
measure.label_x = label_x
measure.label_y = label_y
return h5.store_complete(measure, self.storage_path)
[docs] def build_float_value_wrapper(self, result, key, title=""):
value = ValueWrapper()
value.data_value = str(float(result[key]))
value.data_type = 'float'
value.data_name = title
return h5.store_complete(value, self.storage_path)
[docs] def build_int_value_wrapper(self, result, key, title=""):
value = ValueWrapper()
value.data_value = str(int(result[key]))
value.data_type = 'int'
value.data_name = title
return h5.store_complete(value, self.storage_path)
@abstractmethod
[docs]class ModularityOCSM(BaseBCT):
"""
"""
_ui_group = BCT_GROUP_MODULARITY
_ui_name = "Compute optimal Community Structure and Modularity from a Directed (weighted or binary) connection matrix:"
_ui_description = bct_description("modularity_dir.m")
_matlab_code = "[Ci,Q] = modularity_dir(CW);"
[docs] def launch(self, view_model):
# Prepare parameters
connectivity = self.get_connectivity(view_model)
data = {'CW': connectivity.weights}
# Execute the matlab code
result = self.execute_matlab(self._matlab_code, data=data)
# Gather results
measure = self.build_connectivity_measure(result, 'Ci', connectivity, "Optimal Community Structure")
value = self.build_float_value_wrapper(result, 'Q', title="Maximized Modularity")
return [measure, value]
[docs]class ModularityOpCSMU(ModularityOCSM):
"""
"""
_ui_name = "Optimal Community Structure and Modularity (Undirected):"
_ui_description = bct_description("modularity_und.m")
_matlab_code = "[Ci,Q] = modularity_und(CW);"
[docs]class DistanceDBIN(BaseBCT):
"""
"""
_ui_group = BCT_GROUP_DISTANCE
_ui_name = "Distance binary matrix"
_ui_description = bct_description("distance_bin.m")
_matlab_code = "D = distance_bin(A);"
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
data = {'A': connectivity.weights}
result = self.execute_matlab(self._matlab_code, data=data)
measure = self.build_connectivity_measure(result, 'D', connectivity, "Distance matrix")
return [measure]
[docs]class DistanceDWEI(DistanceDBIN):
"""
"""
_ui_name = "Distance weighted matrix over a Weighted (directed/undirected) connection matrix"
_ui_description = bct_description("distance_wei.m")
_matlab_code = "D = distance_wei(A);"
[docs]class DistanceRDM(DistanceDBIN):
"""
"""
_ui_name = "Reachability and distance matrices (Breadth-first search)"
_ui_description = bct_description("breadthdist.m")
_matlab_code = "[R,D] = breadthdist(A);"
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
data = {'A': connectivity.weights}
result = self.execute_matlab(self._matlab_code, data=data)
measure1 = self.build_connectivity_measure(result, 'R', connectivity, "Reachability matrix")
measure2 = self.build_connectivity_measure(result, 'D', connectivity, "Distance matrix")
return [measure1, measure2]
[docs]class DistanceRDA(DistanceRDM):
"""
"""
_ui_name = "Reachability and distance matrices (Algebraic path count)"
_ui_description = bct_description("reachdist.m")
_matlab_code = "[R,D] = reachdist(A);"
[docs]class DistanceNETW(DistanceDBIN):
"""
"""
_ui_name = "Network walks"
_ui_description = bct_description("findwalks.m")
_matlab_code = "[Wq,twalk,wlq] = findwalks(A);"
[docs] def launch(self, view_model):
connectivity = self.get_connectivity(view_model)
data = {'A': connectivity.weights}
result = self.execute_matlab(self._matlab_code, data=data)
measure1 = self.build_connectivity_measure(result, 'Wq', connectivity, "3D matrix")
measure2 = self.build_connectivity_measure(result, 'wlq', connectivity, "Walk length distribution")
value = self.build_float_value_wrapper(result, 'twalk', title="Total number of walks found")
return [measure1, value, measure2]