TheVirtualBrain:

TheDocumentationwebsite.

Source code for tvb.adapters.simulator.monitor_forms

# -*- 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 numpy

from tvb.adapters.simulator.equation_forms import get_ui_name_to_monitor_equation_dict, HRFKernelEquation
from tvb.core.entities.file.simulator.view_model import *
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.entities.load import load_entity_by_gid
from tvb.core.neotraits.forms import Form, ArrayField, MultiSelectField, FloatField, StrField
from tvb.core.neotraits.forms import SelectField, TraitDataTypeSelectField
from tvb.datatypes.projections import ProjectionsType
from tvb.datatypes.sensors import SensorTypes


[docs]def get_monitor_to_form_dict(): monitor_class_to_form = { RawViewModel: MonitorForm, SubSampleViewModel: MonitorForm, SpatialAverageViewModel: SpatialAverageMonitorForm, GlobalAverageViewModel: MonitorForm, TemporalAverageViewModel: MonitorForm, EEGViewModel: EEGMonitorForm, MEGViewModel: MEGMonitorForm, iEEGViewModel: iEEGMonitorForm, BoldViewModel: BoldMonitorForm, BoldRegionROIViewModel: BoldMonitorForm } return monitor_class_to_form
[docs]def get_ui_name_to_monitor_dict(surface): ui_name_to_monitor = { 'Raw recording': RawViewModel, 'Temporally sub-sample': SubSampleViewModel, 'Spatial average with temporal sub-sample': SpatialAverageViewModel, 'Global average': GlobalAverageViewModel, 'Temporal average': TemporalAverageViewModel, 'EEG': EEGViewModel, 'MEG': MEGViewModel, 'Intracerebral / Stereo EEG': iEEGViewModel, 'BOLD': BoldViewModel } if surface: ui_name_to_monitor['BOLD Region ROI'] = BoldRegionROIViewModel return ui_name_to_monitor
[docs]def get_monitor_to_ui_name_dict(surface): monitor_to_ui_name = dict((v, k) for k, v in get_ui_name_to_monitor_dict(surface).items()) return monitor_to_ui_name
[docs]def get_form_for_monitor(monitor_class): return get_monitor_to_form_dict().get(monitor_class)
[docs]class MonitorForm(Form): def __init__(self, session_stored_simulator=None): super(MonitorForm, self).__init__() self.session_stored_simulator = session_stored_simulator self.period = FloatField(Monitor.period) self.variables_of_interest_indexes = {} if session_stored_simulator is not None: self.variables_of_interest_indexes = session_stored_simulator.determine_indexes_for_chosen_vars_of_interest() self.variables_of_interest = MultiSelectField(List(of=str, label='Model Variables to watch', choices=tuple(self.variables_of_interest_indexes.keys())), name='variables_of_interest')
[docs] def fill_from_trait(self, trait): super(MonitorForm, self).fill_from_trait(trait) if trait.variables_of_interest is not None: self.variables_of_interest.data = [list(self.variables_of_interest_indexes.keys())[idx] for idx in trait.variables_of_interest] else: # by default we select all variables of interest for the monitor forms self.variables_of_interest.data = list(self.variables_of_interest_indexes.keys())
[docs] def fill_trait(self, datatype): super(MonitorForm, self).fill_trait(datatype) datatype.variables_of_interest = numpy.array(list(self.variables_of_interest_indexes.values()))
[docs] def fill_from_post(self, form_data): super(MonitorForm, self).fill_from_post(form_data) all_variables = self.session_stored_simulator.model.variables_of_interest chosen_variables = form_data['variables_of_interest'] self.variables_of_interest_indexes = self.session_stored_simulator.\ get_variables_of_interest_indexes(all_variables, chosen_variables)
[docs]class SpatialAverageMonitorForm(MonitorForm): def __init__(self, session_stored_simulator=None): super(SpatialAverageMonitorForm, self).__init__(session_stored_simulator) self.spatial_mask = ArrayField(SpatialAverage.spatial_mask) self.default_mask = SelectField(SpatialAverage.default_mask)
[docs] def fill_from_trait(self, trait): super(SpatialAverageMonitorForm, self).fill_from_trait(trait) connectivity_index = load_entity_by_gid(self.session_stored_simulator.connectivity) if self.session_stored_simulator.is_surface_simulation is False: self.default_mask.choices.pop(SpatialAverage.REGION_MAPPING) if connectivity_index.has_cortical_mask is False: self.default_mask.choices.pop(SpatialAverage.CORTICAL) if connectivity_index.has_hemispheres_mask is False: self.default_mask.choices.pop(SpatialAverage.HEMISPHERES) else: self.default_mask.data = SpatialAverage.REGION_MAPPING self.default_mask.disabled = True
[docs]class ProjectionMonitorForm(MonitorForm): def __init__(self, session_stored_simulator=None): super(ProjectionMonitorForm, self).__init__(session_stored_simulator) rm_filter = None if session_stored_simulator and session_stored_simulator.is_surface_simulation: rm_filter = FilterChain(fields=[FilterChain.datatype + '.gid'], operations=['=='], values=[session_stored_simulator.surface.region_mapping_data.hex]) self.region_mapping = TraitDataTypeSelectField(ProjectionViewModel.region_mapping, name='region_mapping', conditions=rm_filter)
[docs]class EEGMonitorForm(ProjectionMonitorForm): def __init__(self, session_stored_simulator=None): super(EEGMonitorForm, self).__init__(session_stored_simulator) sensor_filter = FilterChain(fields=[FilterChain.datatype + '.sensors_type'], operations=["=="], values=[SensorTypes.TYPE_EEG.value]) projection_filter = FilterChain(fields=[FilterChain.datatype + '.projection_type'], operations=["=="], values=[ProjectionsType.EEG.value]) self.projection = TraitDataTypeSelectField(EEGViewModel.projection, name='projection', conditions=projection_filter) self.reference = StrField(EEG.reference) self.sensors = TraitDataTypeSelectField(EEGViewModel.sensors, name='sensors', conditions=sensor_filter) self.sigma = FloatField(EEG.sigma)
[docs]class MEGMonitorForm(ProjectionMonitorForm): def __init__(self, session_stored_simulator=None): super(MEGMonitorForm, self).__init__(session_stored_simulator) sensor_filter = FilterChain(fields=[FilterChain.datatype + '.sensors_type'], operations=["=="], values=[SensorTypes.TYPE_MEG.value]) projection_filter = FilterChain(fields=[FilterChain.datatype + '.projection_type'], operations=["=="], values=[ProjectionsType.MEG.value]) self.projection = TraitDataTypeSelectField(MEGViewModel.projection, name='projection', conditions=projection_filter) self.sensors = TraitDataTypeSelectField(MEGViewModel.sensors, name='sensors', conditions=sensor_filter)
[docs]class iEEGMonitorForm(ProjectionMonitorForm): def __init__(self, session_stored_simulator=None): super(iEEGMonitorForm, self).__init__(session_stored_simulator) sensor_filter = FilterChain(fields=[FilterChain.datatype + '.sensors_type'], operations=["=="], values=[SensorTypes.TYPE_INTERNAL.value]) projection_filter = FilterChain(fields=[FilterChain.datatype + '.projection_type'], operations=["=="], values=[ProjectionsType.SEEG.value]) self.projection = TraitDataTypeSelectField(iEEGViewModel.projection, name='projection', conditions=projection_filter) self.sigma = FloatField(iEEG.sigma) self.sensors = TraitDataTypeSelectField(iEEGViewModel.sensors, name='sensors', conditions=sensor_filter)
[docs]class BoldMonitorForm(MonitorForm): def __init__(self, session_stored_simulator=None): super(BoldMonitorForm, self).__init__(session_stored_simulator) self.hrf_kernel_choices = get_ui_name_to_monitor_equation_dict() default_hrf_kernel = list(self.hrf_kernel_choices.values())[0] self.period = FloatField(Bold.period) self.hrf_kernel = SelectField(Attr(HRFKernelEquation, label='Equation', default=default_hrf_kernel), name='hrf_kernel', choices=self.hrf_kernel_choices)
[docs] def fill_trait(self, datatype): super(BoldMonitorForm, self).fill_trait(datatype) datatype.period = self.period.data if type(datatype.hrf_kernel) != self.hrf_kernel.data: datatype.hrf_kernel = self.hrf_kernel.data()
[docs] def fill_from_trait(self, trait): super(BoldMonitorForm, self).fill_from_trait(trait) self.hrf_kernel.data = trait.hrf_kernel.__class__