# -*- 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
#
from tvb.basic.neotraits.api import Attr, List, EnumAttr, TupleEnum
from tvb.core.entities.file.simulator.simulation_history_h5 import SimulationHistory
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.connectivity import Connectivity
from tvb.datatypes.cortex import Cortex
from tvb.datatypes.local_connectivity import LocalConnectivity
from tvb.datatypes.patterns import SpatioTemporalPattern
from tvb.datatypes.projections import ProjectionSurfaceEEG, ProjectionSurfaceMEG, ProjectionSurfaceSEEG
from tvb.datatypes.region_mapping import RegionMapping
from tvb.datatypes.sensors import SensorsEEG, SensorsMEG, SensorsInternal
from tvb.datatypes.surfaces import CorticalSurface
from tvb.simulator.integrators import HeunDeterministic, Integrator, IntegratorStochastic, HeunStochastic, \
EulerDeterministic, EulerStochastic, RungeKutta4thOrderDeterministic, Identity, VODE, VODEStochastic, Dopri5, \
Dopri5Stochastic, Dop853, Dop853Stochastic
from tvb.simulator.monitors import Monitor, EEG, MEG, iEEG, Raw, SubSample, SpatialAverage, GlobalAverage, \
TemporalAverage, Projection, Bold, BoldRegionROI
from tvb.simulator.noise import Noise, Additive, Multiplicative
from tvb.simulator.simulator import Simulator
[docs]class NoiseViewModel(ViewModel, Noise):
@property
def linked_has_traits(self):
return Noise
[docs]class AdditiveNoiseViewModel(NoiseViewModel, Additive):
@property
def linked_has_traits(self):
return Additive
[docs]class MultiplicativeNoiseViewModel(NoiseViewModel, Multiplicative):
@property
def linked_has_traits(self):
return Multiplicative
def __init__(self):
super(MultiplicativeNoiseViewModel, self).__init__()
[docs]class IntegratorViewModel(ViewModel, Integrator):
@property
def linked_has_traits(self):
return Integrator
[docs]class IntegratorStochasticViewModel(IntegratorViewModel, IntegratorStochastic):
noise = Attr(
field_type=NoiseViewModel,
label=IntegratorStochastic.noise.label,
default=AdditiveNoiseViewModel(),
required=IntegratorStochastic.noise.required,
doc=IntegratorStochastic.noise.doc
)
@property
def linked_has_traits(self):
return IntegratorStochastic
def __init__(self):
super(IntegratorStochasticViewModel, self).__init__()
self.noise = type(self.noise)()
[docs]class HeunDeterministicViewModel(IntegratorViewModel, HeunDeterministic):
@property
def linked_has_traits(self):
return HeunDeterministic
[docs]class HeunStochasticViewModel(IntegratorStochasticViewModel, HeunStochastic):
@property
def linked_has_traits(self):
return HeunStochastic
[docs]class EulerDeterministicViewModel(IntegratorViewModel, EulerDeterministic):
@property
def linked_has_traits(self):
return EulerDeterministic
[docs]class EulerStochasticViewModel(IntegratorStochasticViewModel, EulerStochastic):
@property
def linked_has_traits(self):
return EulerStochastic
[docs]class RungeKutta4thOrderDeterministicViewModel(IntegratorViewModel, RungeKutta4thOrderDeterministic):
@property
def linked_has_traits(self):
return RungeKutta4thOrderDeterministic
[docs]class IdentityViewModel(IntegratorViewModel, Identity):
@property
def linked_has_traits(self):
return Identity
[docs]class VODEViewModel(IntegratorViewModel, VODE):
@property
def linked_has_traits(self):
return VODE
[docs]class VODEStochasticViewModel(IntegratorStochasticViewModel, VODEStochastic):
@property
def linked_has_traits(self):
return VODEStochastic
[docs]class Dopri5ViewModel(IntegratorViewModel, Dopri5):
@property
def linked_has_traits(self):
return Dopri5
[docs]class Dopri5StochasticViewModel(IntegratorStochasticViewModel, Dopri5Stochastic):
@property
def linked_has_traits(self):
return Dopri5Stochastic
[docs]class Dop853ViewModel(IntegratorViewModel, Dop853):
@property
def linked_has_traits(self):
return Dop853
[docs]class Dop853StochasticViewModel(IntegratorStochasticViewModel, Dop853Stochastic):
@property
def linked_has_traits(self):
return Dop853Stochastic
[docs]class IntegratorViewModelsEnum(TupleEnum):
HEUN = (HeunDeterministicViewModel, "Heun")
STOCHASTIC_HEUN = (HeunStochasticViewModel, "Stochastic Heun")
EULER = (EulerDeterministicViewModel, "Euler")
EULER_MARUYAMA = (EulerStochasticViewModel, "Euler-Maruyama")
RUNGE_KUTTA = (RungeKutta4thOrderDeterministicViewModel, "Runge-Kutta 4Th Order")
DIFFERENCE_EQUATION = (IdentityViewModel, "Difference equation")
VARIABLE_ORDER_ADAMS = (VODEViewModel, "Variable-Order Adams (BDF)")
STOCHASTIC_VARIABLE_ODER_ADAMS = (VODEStochasticViewModel, "Stochastic variable-order Adams (BDF)")
DOPRI_5 = (Dopri5ViewModel, "Dormand-Prince, order (4, 5)")
DOPRI_5_STOCHASTIC = (Dopri5StochasticViewModel, "Stochastic Dormand-Prince, order (4, 5)")
DOP_853 = (Dop853ViewModel, "Dormand-Prince, order 8 (5, 3)")
DOP_853_STOCHASTIC = (Dop853StochasticViewModel, "Stochastic Dormand-Prince, order 8 (5, 3)")
[docs]class MonitorViewModel(ViewModel, Monitor):
@property
def linked_has_traits(self):
return Monitor
[docs]class RawViewModel(MonitorViewModel, Raw):
@property
def linked_has_traits(self):
return Raw
def __str__(self):
clsname = self.__class__.__name__
return '%s(period=%f)' % (clsname, self.period)
[docs]class SubSampleViewModel(MonitorViewModel, SubSample):
@property
def linked_has_traits(self):
return SubSample
[docs]class SpatialAverageViewModel(MonitorViewModel, SpatialAverage):
@property
def linked_has_traits(self):
return SpatialAverage
[docs]class GlobalAverageViewModel(MonitorViewModel, GlobalAverage):
@property
def linked_has_traits(self):
return GlobalAverage
[docs]class TemporalAverageViewModel(MonitorViewModel, TemporalAverage):
@property
def linked_has_traits(self):
return TemporalAverage
[docs]class ProjectionViewModel(MonitorViewModel, Projection):
region_mapping = DataTypeGidAttr(
linked_datatype=RegionMapping,
required=True,
label=Projection.region_mapping.label,
doc=Projection.region_mapping.doc
)
@property
def linked_has_traits(self):
return Projection
[docs]class EEGViewModel(ProjectionViewModel, EEG):
projection = DataTypeGidAttr(
linked_datatype=ProjectionSurfaceEEG,
label=EEG.projection.label,
doc=EEG.projection.doc
)
sensors = DataTypeGidAttr(
linked_datatype=SensorsEEG,
label=EEG.sensors.label,
doc=EEG.sensors.doc
)
@property
def linked_has_traits(self):
return EEG
[docs]class MEGViewModel(ProjectionViewModel, MEG):
projection = DataTypeGidAttr(
linked_datatype=ProjectionSurfaceMEG,
label=MEG.projection.label,
doc=MEG.projection.doc
)
sensors = DataTypeGidAttr(
linked_datatype=SensorsMEG,
label=MEG.sensors.label,
doc=MEG.sensors.doc
)
@property
def linked_has_traits(self):
return MEG
[docs]class iEEGViewModel(ProjectionViewModel, iEEG):
projection = DataTypeGidAttr(
linked_datatype=ProjectionSurfaceSEEG,
label=iEEG.projection.label,
doc=iEEG.projection.doc
)
sensors = DataTypeGidAttr(
linked_datatype=SensorsInternal,
label=iEEG.sensors.label,
doc=iEEG.sensors.doc
)
@property
def linked_has_traits(self):
return iEEG
[docs]class BoldViewModel(MonitorViewModel, Bold):
@property
def linked_has_traits(self):
return Bold
def __init__(self):
super(BoldViewModel, self).__init__()
[docs]class BoldRegionROIViewModel(BoldViewModel, BoldRegionROI):
@property
def linked_has_traits(self):
return BoldRegionROI
[docs]class CortexViewModel(ViewModel, Cortex):
@property
def linked_has_traits(self):
return Cortex
surface_gid = DataTypeGidAttr(
linked_datatype=CorticalSurface,
label=Simulator.surface.label,
default=Simulator.surface.default,
required=Simulator.surface.required,
doc=Simulator.surface.doc
)
local_connectivity = DataTypeGidAttr(
linked_datatype=LocalConnectivity,
required=Cortex.local_connectivity.required,
label=Cortex.local_connectivity.label,
doc=Cortex.local_connectivity.doc
)
region_mapping_data = DataTypeGidAttr(
linked_datatype=RegionMapping,
label=Cortex.region_mapping_data.label,
doc=Cortex.region_mapping_data.doc
)
[docs]class SimulatorAdapterModel(ViewModel, Simulator):
@property
def linked_has_traits(self):
return Simulator
connectivity = DataTypeGidAttr(
linked_datatype=Connectivity,
required=Simulator.connectivity.required,
label=Simulator.connectivity.label,
doc=Simulator.connectivity.doc
)
surface = Attr(
field_type=CortexViewModel,
label=Simulator.surface.label,
default=Simulator.surface.default,
required=Simulator.surface.required,
doc=Simulator.surface.doc
)
stimulus = DataTypeGidAttr(
linked_datatype=SpatioTemporalPattern,
label=Simulator.stimulus.label,
default=Simulator.stimulus.default,
required=Simulator.stimulus.required,
doc=Simulator.stimulus.doc
)
history_gid = DataTypeGidAttr(
linked_datatype=SimulationHistory,
required=False
)
integrator = EnumAttr(
field_type=IntegratorViewModelsEnum,
label=Simulator.integrator.label,
default=IntegratorViewModelsEnum.HEUN.instance,
required=Simulator.integrator.required,
doc=Simulator.integrator.doc
)
monitors = List(
of=MonitorViewModel,
label=Simulator.monitors.label,
default=(TemporalAverageViewModel(),),
doc=Simulator.monitors.doc
)
def __init__(self):
super(SimulatorAdapterModel, self).__init__()
self.coupling = type(self.coupling)()
self.model = type(self.model)()
self.integrator = type(self.integrator)()
self.monitors = (type(self.monitors[0])(),)
@property
def first_monitor(self):
if isinstance(self.monitors[0], RawViewModel):
if len(self.monitors) > 1:
return self.monitors[1]
else:
return None
return self.monitors[0]
[docs] def determine_indexes_for_chosen_vars_of_interest(self):
all_variables = self.model.__class__.variables_of_interest.element_choices
chosen_variables = self.model.variables_of_interest
indexes = self.get_variables_of_interest_indexes(all_variables, chosen_variables)
return indexes
[docs] @staticmethod
def get_variables_of_interest_indexes(all_variables, chosen_variables):
variables_of_interest_indexes = {}
if not isinstance(chosen_variables, (list, tuple)):
chosen_variables = [chosen_variables]
for variable in chosen_variables:
variables_of_interest_indexes[variable] = all_variables.index(variable)
return variables_of_interest_indexes
