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)