# -*- 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
#
#
"""
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
.. moduleauthor:: Mihai Andrei <mihai.andrei@codemart.ro>
"""
import json
from tvb.adapters.datatypes.db.sensors import SensorsIndex
from tvb.adapters.visualizers.surface_view import ensure_shell_surface, SurfaceURLGenerator
from tvb.basic.logger.builder import get_logger
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import ABCDisplayer, URLGenerator
from tvb.core.adapters.exceptions import LaunchException
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.sensors import SensorsInternal, SensorsEEG, SensorsMEG, Sensors
from tvb.datatypes.surfaces import Surface, SurfaceTypesEnum
LOG = get_logger(__name__)
[docs]def prepare_sensors_as_measure_points_params(sensors):
"""
Returns urls from where to fetch the measure points and their labels
"""
sensor_locations = URLGenerator.build_h5_url(sensors.gid, 'get_locations')
sensor_no = sensors.number_of_sensors
sensor_labels = URLGenerator.build_h5_url(sensors.gid, 'get_labels')
return {'urlMeasurePoints': sensor_locations,
'urlMeasurePointsLabels': sensor_labels,
'noOfMeasurePoints': sensor_no,
'minMeasure': 0,
'maxMeasure': sensor_no,
'urlMeasure': ''}
[docs]def prepare_mapped_sensors_as_measure_points_params(sensors, eeg_cap=None, adapter_id=None):
"""
Compute sensors positions by mapping them to the ``eeg_cap`` surface
If ``eeg_cap`` is not specified the mapping will use a default EEGCal DataType in current project.
If no default EEGCap is found, return sensors as they are (not projected)
:returns: dictionary to be used in Viewers for rendering measure_points
:rtype: dict
"""
if eeg_cap:
sensor_locations = URLGenerator.build_url(adapter_id, 'sensors_to_surface', sensors.gid,
parameter='surface_to_map_gid=' + eeg_cap.gid)
sensor_no = sensors.number_of_sensors
sensor_labels = URLGenerator.build_h5_url(sensors.gid, 'get_labels')
return {'urlMeasurePoints': sensor_locations,
'urlMeasurePointsLabels': sensor_labels,
'noOfMeasurePoints': sensor_no,
'minMeasure': 0,
'maxMeasure': sensor_no,
'urlMeasure': ''}
return prepare_sensors_as_measure_points_params(sensors)
[docs]def function_sensors_to_surface(sensors_gid, surface_to_map_gid):
"""
Map EEG sensors onto the head surface (skin-air).
EEG sensor locations are typically only given on a unit sphere, that is,
they are effectively only identified by their orientation with respect
to a coordinate system. This method is used to map these unit vector
sensor "locations" to a specific location on the surface of the skin.
Assumes coordinate systems are aligned, i.e. common x,y,z and origin.
"""
sensors_dt = h5.load_from_gid(sensors_gid)
surface_dt = h5.load_from_gid(surface_to_map_gid)
return sensors_dt.sensors_to_surface(surface_dt).tolist()
[docs]class SensorsViewerModel(ViewModel):
sensors = DataTypeGidAttr(
linked_datatype=Sensors,
label='Sensors',
doc='Internals sensors to view'
)
projection_surface = DataTypeGidAttr(
linked_datatype=Surface,
required=False,
label='Projection Surface',
doc='A surface on which to project the results. When missing, '
'the first EEGCap is taken. This parameter is ignored when '
'InternalSensors are inspected'
)
shell_surface = DataTypeGidAttr(
linked_datatype=Surface,
required=False,
label='Shell Surface',
doc='Wrapping surface over the internal sensors, to be displayed '
'semi-transparently, for visual purposes only.'
)
[docs]class SensorsViewer(ABCDisplayer):
"""
Sensor visualizer - for visual inspecting of TVB Sensors DataTypes.
"""
_ui_name = "Sensor Visualizer"
_ui_subsection = "sensors"
[docs] def launch(self, view_model):
# type: (SensorsViewerModel) -> dict
"""
Prepare visualizer parameters.
We support viewing all sensor types through a single viewer, so that a user doesn't need to
go back to the data-page, for loading a different type of sensor.
"""
sensors_index = self.load_entity_by_gid(view_model.sensors)
shell_surface_index = None
projection_surface_index = None
if view_model.shell_surface:
shell_surface_index = self.load_entity_by_gid(view_model.shell_surface)
if view_model.projection_surface:
projection_surface_index = self.load_entity_by_gid(view_model.projection_surface)
if sensors_index.sensors_type == SensorsInternal.sensors_type.default:
return self._params_internal_sensors(sensors_index, shell_surface_index)
if sensors_index.sensors_type == SensorsEEG.sensors_type.default:
return self._params_eeg_sensors(sensors_index, projection_surface_index, shell_surface_index)
if sensors_index.sensors_type == SensorsMEG.sensors_type.default:
return self._params_meg_sensors(sensors_index, projection_surface_index, shell_surface_index)
raise LaunchException("Unknown sensors type!")
def _params_internal_sensors(self, internal_sensors, shell_surface=None):
params = prepare_sensors_as_measure_points_params(internal_sensors)
shell_surface = ensure_shell_surface(self.current_project_id, shell_surface,
SurfaceTypesEnum.CORTICAL_SURFACE.value)
params['shellObject'] = self.prepare_shell_surface_params(shell_surface, SurfaceURLGenerator)
return self.build_display_result('sensors/sensors_internal', params,
pages={"controlPage": "sensors/sensors_controls"})
def _params_eeg_sensors(self, eeg_sensors, eeg_cap=None, shell_surface=None):
if eeg_cap is None:
eeg_cap = ensure_shell_surface(self.current_project_id, eeg_cap, SurfaceTypesEnum.EEG_CAP_SURFACE.value)
params = prepare_mapped_sensors_as_measure_points_params(eeg_sensors, eeg_cap, self.stored_adapter.id)
shell_surface = ensure_shell_surface(self.current_project_id, shell_surface)
params.update({
'shellObject': self.prepare_shell_surface_params(shell_surface, SurfaceURLGenerator),
'urlVertices': '', 'urlTriangles': '', 'urlLines': '[]', 'urlNormals': ''
})
if eeg_cap is not None:
with h5.h5_file_for_gid(eeg_cap.gid) as eeg_cap_h5:
params.update(self._compute_surface_params(eeg_cap_h5))
return self.build_display_result("sensors/sensors_eeg", params,
pages={"controlPage": "sensors/sensors_controls"})
def _params_meg_sensors(self, meg_sensors, projection_surface=None, shell_surface=None):
params = prepare_sensors_as_measure_points_params(meg_sensors)
shell_surface = ensure_shell_surface(self.current_project_id, shell_surface)
params.update({
'shellObject': self.prepare_shell_surface_params(shell_surface, SurfaceURLGenerator),
'urlVertices': '', 'urlTriangles': '', 'urlLines': '[]', 'urlNormals': '',
'boundaryURL': '', 'urlRegionMap': ''})
if projection_surface is not None:
with h5.h5_file_for_gid(projection_surface.gid) as projection_surface_h5:
params.update(self._compute_surface_params(projection_surface_h5))
return self.build_display_result("sensors/sensors_eeg", params,
pages={"controlPage": "sensors/sensors_controls"})
@staticmethod
def _compute_surface_params(surface_h5):
rendering_urls = [json.dumps(url) for url in SurfaceURLGenerator.get_urls_for_rendering(surface_h5)]
url_vertices, url_normals, url_lines, url_triangles, _ = rendering_urls
return {'urlVertices': url_vertices,
'urlTriangles': url_triangles,
'urlLines': url_lines,
'urlNormals': url_normals}
[docs] def get_required_memory_size(self):
return -1
[docs] def sensors_to_surface(self, sensors_gid, surface_to_map_gid):
# Method needs to be defined on the adapter, to be called from JS
return function_sensors_to_surface(sensors_gid, surface_to_map_gid)
