def prepare_mapped_sensors_as_measure_points_params(project_id,
sensors,
eeg_cap=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 is None:
eeg_cap = dao.try_load_last_entity_of_type(project_id, EEGCap)
if eeg_cap:
datatype_kwargs = json.dumps({'surface_to_map': eeg_cap.gid})
sensor_locations = ABCDisplayer.paths2url(
sensors, 'sensors_to_surface') + '/' + datatype_kwargs
sensor_no = sensors.number_of_sensors
sensor_labels = ABCDisplayer.paths2url(sensors, '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)
def compute_params(self, region_mapping_volume=None, measure=None, data_slice='', background=None):
# type: (RegionVolumeMappingIndex, DataTypeMatrix, str, StructuralMRIIndex) -> dict
region_mapping_volume = self._ensure_region_mapping_index(region_mapping_volume, measure)
url_voxel_region = URLGenerator.build_url(self.stored_adapter.id, 'get_voxel_region', region_mapping_volume.gid,
parameter='')
if measure is None:
params = self._compute_region_volume_map_params(region_mapping_volume)
else:
params = self._compute_measure_params(region_mapping_volume, measure, data_slice)
volume_gid = region_mapping_volume.fk_volume_gid
volume_index = self.load_entity_by_gid(volume_gid)
assert isinstance(volume_index, VolumeIndex)
volume_shape = region_mapping_volume.parsed_shape
volume_shape = (1,) + volume_shape
params.update(volumeShape=json.dumps(volume_shape),
volumeOrigin=volume_index.origin,
voxelUnit=volume_index.voxel_unit,
voxelSize=volume_index.voxel_size,
urlVoxelRegion=url_voxel_region)
if background is None:
background = dao.try_load_last_entity_of_type(self.current_project_id, StructuralMRIIndex)
if background is None: # still
params.update(self.compute_background_params())
else:
url_volume_data = URLGenerator.build_url(self.stored_adapter.id, 'get_volume_view', background.gid, '')
params.update(self.compute_background_params(background.array_data_min,
background.array_data_max, url_volume_data))
return params
def _assert_stored_ddti(self, expected_cnt=1):
count = dao.count_datatypes(self.test_project.id, DummyDataTypeIndex)
assert expected_cnt == count
datatype = dao.try_load_last_entity_of_type(self.test_project.id,
DummyDataTypeIndex)
assert datatype.subject == DataTypeMetaData.DEFAULT_SUBJECT, "Wrong data stored."
return datatype
def prepare_mapped_sensors_as_measure_points_params(project_id, sensors, eeg_cap=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 is None:
eeg_cap = dao.try_load_last_entity_of_type(project_id, EEGCap)
if eeg_cap:
datatype_kwargs = json.dumps({'surface_to_map': eeg_cap.gid})
sensor_locations = ABCDisplayer.paths2url(sensors, 'sensors_to_surface') + '/' + datatype_kwargs
sensor_no = sensors.number_of_sensors
sensor_labels = ABCDisplayer.paths2url(sensors, '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)
def launch(self, time_series, background=None):
min_value, max_value = time_series.get_min_max_values()
url_volume_data = self.paths2url(time_series, "get_volume_view", parameter="")
url_timeseries_data = self.paths2url(time_series, "get_voxel_time_series", parameter="")
if isinstance(time_series, TimeSeriesVolume):
volume = time_series.volume
volume_shape = time_series.read_data_shape()
else:
volume = time_series.region_mapping_volume.volume
volume_shape = [time_series.read_data_shape()[0]]
volume_shape.extend(time_series.region_mapping_volume.shape)
params = dict(title="Volumetric Time Series",
ts_title=time_series.title,
labelsStateVar=time_series.labels_dimensions.get(time_series.labels_ordering[1], []),
labelsModes=range(time_series.read_data_shape()[3]),
minValue=min_value, maxValue=max_value,
urlVolumeData=url_volume_data,
urlTimeSeriesData=url_timeseries_data,
samplePeriod=time_series.sample_period,
samplePeriodUnit=time_series.sample_period_unit,
volumeShape=json.dumps(volume_shape),
volumeOrigin=json.dumps(volume.origin.tolist()),
voxelUnit=volume.voxel_unit,
voxelSize=json.dumps(volume.voxel_size.tolist()))
if background is None:
background = dao.try_load_last_entity_of_type(self.current_project_id, StructuralMRI)
params.update(_MappedArrayVolumeBase._compute_background(background))
return self.build_display_result("time_series_volume/view", params,
pages=dict(controlPage="time_series_volume/controls"))
def compute_params(self, region_mapping_volume=None, measure=None, data_slice='', background=None):
region_mapping_volume = self._ensure_region_mapping(region_mapping_volume)
volume = region_mapping_volume.volume
volume_shape = region_mapping_volume.read_data_shape()
volume_shape = (1, ) + volume_shape
if measure is None:
params = self._compute_region_volume_map_params(region_mapping_volume)
else:
params = self._compute_measure_params(region_mapping_volume, measure, data_slice)
url_voxel_region = ABCDisplayer.paths2url(region_mapping_volume, "get_voxel_region", parameter="")
params.update(volumeShape=json.dumps(volume_shape),
volumeOrigin=json.dumps(volume.origin.tolist()),
voxelUnit=volume.voxel_unit,
voxelSize=json.dumps(volume.voxel_size.tolist()),
urlVoxelRegion=url_voxel_region)
if background is None:
background = dao.try_load_last_entity_of_type(self.current_project_id, StructuralMRI)
params.update(self._compute_background(background))
return params
def launch(self, time_series, projection_surface=None, shell_surface=None):
self.surface = projection_surface
if isinstance(time_series, TimeSeriesSEEG) and shell_surface is None:
shell_surface = dao.try_load_last_entity_of_type(
self.current_project_id, CorticalSurface)
params = BrainViewer.compute_parameters(self, time_series,
shell_surface)
params.update(EegMonitor().compute_parameters(time_series,
is_extended_view=True))
params['isOneToOneMapping'] = False
params['brainViewerTemplate'] = 'view.html'
if isinstance(time_series, TimeSeriesSEEG):
params['brainViewerTemplate'] = "internal_view.html"
# Mark as None since we only display shelf face and no point to load these as well
params['urlVertices'] = None
params['isSEEG'] = True
return self.build_display_result(
"brain/extendedview",
params,
pages=dict(controlPage="brain/extendedcontrols",
channelsPage="commons/channel_selector.html"))
def _assert_stored_dt2(self, expected_cnt=1):
count = dao.count_datatypes(self.test_project.id, Datatype2)
self.assertEqual(expected_cnt, count)
datatype = dao.try_load_last_entity_of_type(self.test_project.id,
Datatype2)
self.assertEqual(datatype.subject, DataTypeMetaData.DEFAULT_SUBJECT,
"Wrong data stored.")
return datatype
def _ensure_region_mapping_index(self, region_mapping_volume):
if region_mapping_volume is None:
region_mapping_volume = dao.try_load_last_entity_of_type(
self.current_project_id, RegionVolumeMappingIndex)
if region_mapping_volume is None:
raise LaunchException(
'You should have a volume mapping to launch this viewer')
return region_mapping_volume
def _params_internal_sensors(self, internal_sensors, shell_surface=None):
params = prepare_sensors_as_measure_points_params(internal_sensors)
if shell_surface is None:
shell_surface = dao.try_load_last_entity_of_type(self.current_project_id, CorticalSurface)
params['shelfObject'] = prepare_shell_surface_urls(self.current_project_id, shell_surface)
return self.build_display_result('sensors/sensors_internal', params,
pages={'controlPage': 'sensors/sensors_controls'})
def prepare_shell_surface_urls(project_id, shell_surface=None, preferred_type=FaceSurface):
if shell_surface is None:
shell_surface = dao.try_load_last_entity_of_type(project_id, preferred_type)
if not shell_surface:
raise Exception('No Face object found in current project.')
face_vertices, face_normals, _, face_triangles = shell_surface.get_urls_for_rendering()
return json.dumps([face_vertices, face_normals, face_triangles])
def ensure_background(self, background_index):
if background_index is None:
background_index = dao.try_load_last_entity_of_type(self.current_project_id, StructuralMRIIndex)
if background_index is None:
return _MappedArrayVolumeBase.compute_background_params()
with h5.h5_file_for_index(background_index) as background_h5:
min_value, max_value = background_h5.get_min_max_values()
url_volume_data = URLGenerator.build_url(self.stored_adapter.id, 'get_volume_view', background_index.gid, '')
return _MappedArrayVolumeBase.compute_background_params(min_value, max_value, url_volume_data)
def prepare_shell_surface_urls(project_id, shell_surface=None):
if shell_surface is None:
shell_surface = dao.try_load_last_entity_of_type(
project_id, FaceSurface)
if not shell_surface:
raise Exception('No Face object found in current project.')
face_vertices, face_normals, _, face_triangles = shell_surface.get_urls_for_rendering(
)
return json.dumps([face_vertices, face_normals, face_triangles])
def _params_internal_sensors(self, internal_sensors, shell_surface=None):
params = prepare_sensors_as_measure_points_params(internal_sensors)
if shell_surface is None:
shell_surface = dao.try_load_last_entity_of_type(
self.current_project_id, CorticalSurface)
params['shelfObject'] = prepare_shell_surface_urls(
self.current_project_id, shell_surface)
return self.build_display_result(
'sensors/sensors_internal',
params,
pages={'controlPage': 'sensors/sensors_controls'})
def _ensure_region_mapping_index(self, region_mapping_volume, measure=None):
# type: (RegionVolumeMappingIndex, DataTypeMatrix) -> RegionVolumeMappingIndex
if region_mapping_volume is None:
if measure is not None and hasattr(measure, "fk_connectivity_gid"):
region_mapping_volume = dao.get_generic_entity(RegionVolumeMappingIndex,
measure.fk_connectivity_gid, 'fk_connectivity_gid')
if region_mapping_volume is not None and len(region_mapping_volume):
region_mapping_volume = region_mapping_volume[0]
else:
region_mapping_volume = None
else:
region_mapping_volume = dao.try_load_last_entity_of_type(self.current_project_id,
RegionVolumeMappingIndex)
if region_mapping_volume is None:
raise LaunchException('You should have a compatible volume mapping to launch this viewer')
return region_mapping_volume
def ensure_background(self, background_index):
if background_index is None:
background_index = dao.try_load_last_entity_of_type(
self.current_project_id, StructuralMRIIndex)
if background_index is None:
return _MappedArrayVolumeBase.compute_background_params()
background_class, background_path = self._load_h5_of_gid(
background_index.gid)
background_h5 = background_class(background_path)
min_value, max_value = background_h5.get_min_max_values()
background_h5.close()
url_volume_data = self.build_url('get_volume_view',
background_index.gid, '')
return _MappedArrayVolumeBase.compute_background_params(
min_value, max_value, url_volume_data)
def launch(self, time_series, background=None):
min_value, max_value = time_series.get_min_max_values()
url_volume_data = self.paths2url(time_series,
"get_volume_view",
parameter="")
url_timeseries_data = self.paths2url(time_series,
"get_voxel_time_series",
parameter="")
if isinstance(time_series, TimeSeriesVolume):
volume = time_series.volume
volume_shape = time_series.read_data_shape()
else:
volume = time_series.region_mapping_volume.volume
volume_shape = [time_series.read_data_shape()[0]]
volume_shape.extend(time_series.region_mapping_volume.shape)
params = dict(title="Volumetric Time Series",
ts_title=time_series.title,
labelsStateVar=time_series.labels_dimensions.get(
time_series.labels_ordering[1], []),
labelsModes=range(time_series.read_data_shape()[3]),
minValue=min_value,
maxValue=max_value,
urlVolumeData=url_volume_data,
urlTimeSeriesData=url_timeseries_data,
samplePeriod=time_series.sample_period,
samplePeriodUnit=time_series.sample_period_unit,
volumeShape=json.dumps(volume_shape),
volumeOrigin=json.dumps(volume.origin.tolist()),
voxelUnit=volume.voxel_unit,
voxelSize=json.dumps(volume.voxel_size.tolist()))
if background is None:
background = dao.try_load_last_entity_of_type(
self.current_project_id, StructuralMRI)
params.update(_MappedArrayVolumeBase._compute_background(background))
return self.build_display_result(
"time_series_volume/view",
params,
pages=dict(controlPage="time_series_volume/controls"))
def compute_params(self,
region_mapping_volume=None,
measure=None,
data_slice='',
background=None):
region_mapping_volume = self._ensure_region_mapping_index(
region_mapping_volume)
rmv_h5_class, rmv_h5_path = self._load_h5_of_gid(
region_mapping_volume.gid)
rmv_h5 = rmv_h5_class(rmv_h5_path)
volume_shape = rmv_h5.array_data.shape
volume_shape = (1, ) + volume_shape
if measure is None:
params = self._compute_region_volume_map_params(rmv_h5)
else:
params = self._compute_measure_params(rmv_h5, measure, data_slice)
url_voxel_region = URLGenerator.build_h5_url(region_mapping_volume.gid,
'get_voxel_region',
parameter='')
volume_gid = rmv_h5.volume.load()
volume_h5_class, volume_g5_path = self._load_h5_of_gid(volume_gid.hex)
volume_h5 = volume_h5_class(volume_g5_path)
params.update(
volumeShape=json.dumps(volume_shape),
volumeOrigin=json.dumps(volume_h5.origin.load().tolist()),
voxelUnit=volume_h5.voxel_unit.load(),
voxelSize=json.dumps(volume_h5.voxel_size.load().tolist()),
urlVoxelRegion=url_voxel_region)
rmv_h5.close()
volume_h5.close()
if background is None:
background = dao.try_load_last_entity_of_type(
self.current_project_id, StructuralMRIIndex)
params.update(self._compute_background(background))
return params
def launch(self, time_series, projection_surface=None, shell_surface=None):
self.surface = projection_surface
if isinstance(time_series, TimeSeriesSEEG) and shell_surface is None:
shell_surface = dao.try_load_last_entity_of_type(self.current_project_id, CorticalSurface)
params = BrainViewer.compute_parameters(self, time_series, shell_surface)
params.update(EegMonitor().compute_parameters(time_series, is_extended_view=True))
params['isOneToOneMapping'] = False
params['brainViewerTemplate'] = 'view.html'
if isinstance(time_series, TimeSeriesSEEG):
params['brainViewerTemplate'] = "internal_view.html"
# Mark as None since we only display shelf face and no point to load these as well
params['urlVertices'] = None
params['isSEEG'] = True
return self.build_display_result("brain/extendedview", params,
pages=dict(controlPage="brain/extendedcontrols",
channelsPage="commons/channel_selector.html"))
def _ensure_region_mapping(self, region_mapping_volume):
if region_mapping_volume is None:
region_mapping_volume = dao.try_load_last_entity_of_type(self.current_project_id, RegionVolumeMapping)
if region_mapping_volume is None:
raise LaunchException('You should have a volume mapping to launch this viewer')
return region_mapping_volume
def _assert_stored_dt2(self, expected_cnt=1):
count = dao.count_datatypes(self.test_project.id, Datatype2)
assert expected_cnt == count
datatype = dao.try_load_last_entity_of_type(self.test_project.id, Datatype2)
assert datatype.subject == DataTypeMetaData.DEFAULT_SUBJECT, "Wrong data stored."
return datatype
