def __init__(self, path):
super(MultiplicativeH5, self).__init__(path)
self.nsig = DataSet(Multiplicative.nsig, self)
self.b = Reference(Multiplicative.b, self)
def __init__(self, path):
super(TimeSeriesSurfaceH5, self).__init__(path)
self.surface = Reference(TimeSeriesSurface.surface, self)
self.labels_ordering = Json(TimeSeriesSurface.labels_ordering, self)
def __init__(self, path):
super(TimeSeriesSurfaceH5, self).__init__(path)
self.surface = Reference(TimeSeriesSurface.surface)
self.labels_ordering = Json(TimeSeriesSurface.labels_ordering)
self._end_accessor_declarations()
def __init__(self, path):
super(ConnectivityMeasureH5, self).__init__(path)
self.array_data = DataSet(ConnectivityMeasure.array_data, self)
self.connectivity = Reference(ConnectivityMeasure.connectivity, self)
self.title = Scalar(Attr(str), self, name='title')
def __init__(self, path):
super(SimulatorH5, self).__init__(path)
self.connectivity = Reference(Simulator.connectivity, self)
self.conduction_speed = Scalar(Simulator.conduction_speed, self)
self.coupling = Reference(Simulator.coupling, self)
self.surface = Reference(Simulator.surface, self)
self.stimulus = Reference(Simulator.stimulus, self)
self.model = Reference(Simulator.model, self)
self.integrator = Reference(Simulator.integrator, self)
self.initial_conditions = DataSet(Simulator.initial_conditions, self)
self.monitors = Json(Simulator.monitors, self)
self.simulation_length = Scalar(Simulator.simulation_length, self)
self.simulation_state = Reference(Attr(field_type=uuid.UUID),
self,
name='simulation_state')
def __init__(self, path):
super(TimeSeriesSEEGH5, self).__init__(path)
self.sensors = Reference(TimeSeriesSEEG.sensors, self)
self.labels_order = Json(TimeSeriesSEEG.labels_ordering, self)
def __init__(self, path):
super(CovarianceH5, self).__init__(path)
self.array_data = DataSet(Covariance.array_data,
self,
expand_dimension=2)
self.source = Reference(Covariance.source, self)
def __init__(self, path):
super(MEGH5, self).__init__(path)
self.projection = Reference(MEG.projection, self)
self.sensors = Reference(MEG.sensors, self)
def __init__(self, path):
super(iEEGH5, self).__init__(path)
self.projection = Reference(iEEG.projection, self)
self.sensors = Reference(iEEG.sensors, self)
self.sigma = Scalar(iEEG.sigma, self)
def __init__(self, path):
super(LocalConnectivityH5, self).__init__(path)
self.surface = Reference(LocalConnectivity.surface, self)
self.matrix = SparseMatrix(LocalConnectivity.matrix, self)
self.equation = Scalar(Attr(str), self, name='equation')
self.cutoff = Scalar(LocalConnectivity.cutoff, self)
def __init__(self, path):
super(ProjectionH5, self).__init__(path)
self.region_mapping = Reference(Projection.region_mapping, self)
self.obnoise = Reference(Projection.obsnoise, self)
def __init__(self, path):
super(CortexH5, self).__init__(path)
self.local_connectivity = Reference(Cortex.local_connectivity, self)
self.region_mapping_data = Reference(Cortex.region_mapping_data, self)
self.coupling_strength = Scalar(Cortex.coupling_strength, self)
def __init__(self, path):
super(StimuliRegionH5, self).__init__(path)
self.spatial = EquationScalar(StimuliRegion.spatial, self)
self.temporal = EquationScalar(StimuliRegion.temporal, self)
self.connectivity = Reference(StimuliRegion.connectivity, self)
self.weight = DataSet(StimuliRegion.weight, self)
def __init__(self, path):
super(TimeSeriesVolumeH5, self).__init__(path)
self.volume = Reference(TimeSeriesVolume.volume)
self.labels_ordering = Json(TimeSeriesVolume.labels_ordering)
self._end_accessor_declarations()
def __init__(self, path):
super(TimeSeriesRegionH5, self).__init__(path)
self.connectivity = Reference(TimeSeriesRegion.connectivity, self)
self.region_mapping_volume = Reference(TimeSeriesRegion.region_mapping_volume, self)
self.region_mapping = Reference(TimeSeriesRegion.region_mapping, self)
self.labels_ordering = Json(TimeSeriesRegion.labels_ordering, self)
def __init__(self, path):
super(HeadH5, self).__init__(path)
self.title = Scalar(Head.title, self)
self.path = Scalar(Head.path, self)
self.connectivity = Reference(Head.connectivity, self)
self.cortical_surface = Reference(Head.cortical_surface, self)
self.subcortical_surface = Reference(Head.subcortical_surface, self)
self.cortical_region_mapping = Reference(Head.cortical_region_mapping, self)
self.subcortical_region_mapping = Reference(Head.subcortical_region_mapping, self)
self.region_volume_mapping = Reference(Head.region_volume_mapping, self)
self.t1 = Reference(Head.t1, self)
self.t2 = Reference(Head.t2, self)
self.flair = Reference(Head.flair, self)
self.b0 = Reference(Head.b0, self)
self.eeg_sensors = Reference(Head.eeg_sensors, self)
self.seeg_sensors = Reference(Head.seeg_sensors, self)
self.meg_sensors = Reference(Head.meg_sensors, self)
self.eeg_projection = Reference(Head.eeg_projection, self)
self.meg_projection = Reference(Head.meg_projection, self)
self.meg_sensors = Reference(Head.meg_sensors, self)
def __init__(self, path):
super(TimeSeriesVolumeH5, self).__init__(path)
self.volume = Reference(TimeSeriesVolume.volume, self)
self.labels_ordering = Json(TimeSeriesVolume.labels_ordering, self)
def __init__(self, path):
super(StructuralMRIH5, self).__init__(path)
self.array_data = DataSet(StructuralMRI.array_data, self)
self.weighting = Scalar(StructuralMRI.weighting, self)
self.volume = Reference(StructuralMRI.volume, self)
def __init__(self, path):
super(FooFile, self).__init__(path)
self.array_float = DataSet(FooDatatype.array_float, self)
self.array_int = DataSet(FooDatatype.array_int, self)
self.scalar_int = Scalar(FooDatatype.scalar_int, self)
self.abaz = Reference(FooDatatype.abaz, self)
def __init__(self, path):
super(TractsH5, self).__init__(path)
self.vertices = DataSet(Tracts.vertices, self, expand_dimension=0)
self.tract_start_idx = DataSet(Tracts.tract_start_idx, self)
self.tract_region = DataSet(Tracts.tract_region, self)
self.region_volume_map = Reference(Tracts.region_volume_map, self)
def __init__(self, path):
super(CorrelationCoefficientsH5, self).__init__(path)
self.array_data = DataSet(CorrelationCoefficients.array_data, self)
self.source = Reference(CorrelationCoefficients.source, self)
self.labels_ordering = Json(CorrelationCoefficients.labels_ordering,
self)
def __init__(self, path):
super(RegionVolumeMappingH5, self).__init__(path)
self.array_data = DataSet(RegionVolumeMapping.array_data, self)
self.connectivity = Reference(RegionVolumeMapping.connectivity, self)
self.volume = Reference(RegionVolumeMapping.volume, self)
class SimulatorH5(SimulatorConfigurationH5):
def __init__(self, path):
super(SimulatorH5, self).__init__(path)
self.connectivity = Reference(Simulator.connectivity, self)
self.conduction_speed = Scalar(Simulator.conduction_speed, self)
self.coupling = Reference(Simulator.coupling, self)
self.surface = Reference(Simulator.surface, self)
self.stimulus = Reference(Simulator.stimulus, self)
self.model = Reference(Simulator.model, self)
self.integrator = Reference(Simulator.integrator, self)
self.initial_conditions = DataSet(Simulator.initial_conditions, self)
self.monitors = Json(Simulator.monitors, self)
self.simulation_length = Scalar(Simulator.simulation_length, self)
self.simulation_state = Reference(Attr(field_type=uuid.UUID),
self,
name='simulation_state')
def store(self, datatype, scalars_only=False, store_references=False):
# type: (Simulator, bool, bool) -> None
self.gid.store(datatype.gid)
self.connectivity.store(datatype.connectivity)
self.conduction_speed.store(datatype.conduction_speed)
self.initial_conditions.store(datatype.initial_conditions)
self.simulation_length.store(datatype.simulation_length)
integrator_gid = self.store_config_as_reference(datatype.integrator)
self.integrator.store(integrator_gid)
coupling_gid = self.store_config_as_reference(datatype.coupling)
self.coupling.store(coupling_gid)
model_gid = self.store_config_as_reference(datatype.model)
self.model.store(model_gid)
# TODO: handle multiple monitors
monitor_gid = self.store_config_as_reference(datatype.monitors[0])
self.monitors.store([monitor_gid.hex])
if datatype.surface:
cortex_gid = self.store_config_as_reference(datatype.surface)
self.surface.store(cortex_gid)
if datatype.stimulus:
self.stimulus.store(datatype.stimulus)
self.type.store(self.get_full_class_name(type(datatype)))
def load_into(self, datatype):
# type: (Simulator) -> None
datatype.conduction_speed = self.conduction_speed.load()
datatype.initial_conditions = self.initial_conditions.load()
datatype.simulation_length = self.simulation_length.load()
datatype.integrator = self.load_from_reference(self.integrator.load())
datatype.coupling = self.load_from_reference(self.coupling.load())
datatype.model = self.load_from_reference(self.model.load())
# TODO: handle multiple monitors
datatype.monitors = [self.load_from_reference(self.monitors.load()[0])]
if self.surface.load():
datatype.surface = self.load_from_reference(self.surface.load())
def __init__(self, path):
super(DatatypeMeasureH5, self).__init__(path)
# Actual measure (dictionary Algorithm: single Value)
self.metrics = Json(Attr(str), self, name='metrics')
# DataType for which the measure was computed.
self.analyzed_datatype = Reference(Attr(field_type=TimeSeries), self, "analyzed_datatype")
def __init__(self, path):
super(ConnectivityAnnotationsH5, self).__init__(path)
self.region_annotations = DataSet(
ConnectivityAnnotations.region_annotations, self)
self.connectivity = Reference(ConnectivityAnnotations.connectivity,
self)
def __init__(self, path):
super(IntegratorStochasticH5, self).__init__(path)
self.noise = Reference(IntegratorStochastic.noise, self)
class TimeSeriesVolumeH5(TimeSeriesH5):
def __init__(self, path):
super(TimeSeriesVolumeH5, self).__init__(path)
self.volume = Reference(TimeSeriesVolume.volume, self)
self.labels_ordering = Json(TimeSeriesVolume.labels_ordering, self)
def store_references(self, ts):
self.volume.store(ts.volume.gid)
def get_volume_view(self, from_idx, to_idx, x_plane, y_plane, z_plane,
**kwargs):
"""
Retrieve 3 slices through the Volume TS, at the given X, y and Z coordinates, and in time [from_idx .. to_idx].
:param from_idx: int This will be the limit on the first dimension (time)
:param to_idx: int Also limit on the first Dimension (time)
:param x_plane: int coordinate
:param y_plane: int coordinate
:param z_plane: int coordinate
:return: An array of 3 Matrices 2D, each containing the values to display in planes xy, yz and xy.
"""
overall_shape = self.data.shape
from_idx, to_idx, time = preprocess_time_parameters(
from_idx, to_idx, overall_shape[0])
x_plane, y_plane, z_plane = preprocess_space_parameters(
x_plane, y_plane, z_plane, overall_shape[1], overall_shape[2],
overall_shape[3])
slices = slice(from_idx, to_idx), slice(overall_shape[1]), slice(
overall_shape[2]), slice(z_plane, z_plane + 1)
slicex = self.read_data_slice(slices)[:, :, :, 0].tolist()
slices = slice(from_idx, to_idx), slice(x_plane, x_plane + 1), slice(
overall_shape[2]), slice(overall_shape[3])
slicey = self.read_data_slice(slices)[:, 0, :, :][..., ::-1].tolist()
slices = slice(from_idx, to_idx), slice(overall_shape[1]), slice(
y_plane, y_plane + 1), slice(overall_shape[3])
slicez = self.read_data_slice(slices)[:, :, 0, :][..., ::-1].tolist()
return [slicex, slicey, slicez]
def get_voxel_time_series(self, x, y, z, **kwargs):
"""
Retrieve for a given voxel (x,y,z) the entire timeline.
:param x: int coordinate
:param y: int coordinate
:param z: int coordinate
:return: A complex dictionary with information about current voxel.
The main part will be a vector with all the values over time from the x,y,z coordinates.
"""
overall_shape = self.data.shape
x, y, z = preprocess_space_parameters(x, y, z, overall_shape[1],
overall_shape[2],
overall_shape[3])
slices = prepare_time_slice(overall_shape[0]), slice(x, x + 1), slice(
y, y + 1), slice(z, z + 1)
result = postprocess_voxel_ts(self, slices)
return result
def __init__(self, path):
super(StimuliRegionH5, self).__init__(path)
self.spatial = Scalar(Attr(str), self, name='spatial')
self.temporal = Scalar(Attr(str), self, name='temporal')
self.connectivity = Reference(StimuliRegion.connectivity, self)
self.weight = DataSet(StimuliRegion.weight, self)
