def _fill_result_index(self, result_index, result_signal_shape):
result_index.time_series_type = TimeSeriesRegion.__name__
result_index.data_ndim = len(result_signal_shape)
result_index.data_length_1d, result_index.data_length_2d, \
result_index.data_length_3d, result_index.data_length_4d = prepare_array_shape_meta(result_signal_shape)
result_index.fk_connectivity_gid = self.input_time_series_index.fk_connectivity_gid
result_index.fk_region_mapping_gid = self.input_time_series_index.fk_region_mapping_gid
result_index.fk_region_mapping_volume_gid = self.input_time_series_index.fk_region_mapping_volume_gid
result_index.sample_period = self.input_time_series_index.sample_period
result_index.sample_period_unit = self.input_time_series_index.sample_period_unit
result_index.sample_rate = self.input_time_series_index.sample_rate
result_index.labels_ordering = self.input_time_series_index.labels_ordering
result_index.labels_dimensions = self.input_time_series_index.labels_dimensions
result_index.has_volume_mapping = self.input_time_series_index.has_volume_mapping
result_index.title = self.input_time_series_index.title
def launch(self, view_model):
# type: (GIFTITimeSeriesImporterModel) -> [TimeSeriesSurfaceIndex]
"""
Execute import operations:
"""
if view_model.surface is None:
raise LaunchException(
"No surface selected. Please initiate upload again and select a brain surface."
)
parser = GIFTIParser(self.storage_path, self.operation_id)
try:
partial_time_series, gifti_data_arrays = parser.parse(
view_model.data_file)
ts_idx = TimeSeriesSurfaceIndex()
ts_h5_path = h5.path_for(self.storage_path, TimeSeriesSurfaceH5,
ts_idx.gid)
ts_h5 = TimeSeriesSurfaceH5(ts_h5_path)
# todo : make sure that write_time_slice is not required here
for data_array in gifti_data_arrays:
ts_h5.write_data_slice([data_array.data])
ts_h5.store(partial_time_series,
scalars_only=True,
store_references=False)
ts_h5.gid.store(uuid.UUID(ts_idx.gid))
ts_data_shape = ts_h5.read_data_shape()
surface = self.load_entity_by_gid(view_model.surface)
if surface.number_of_vertices != ts_data_shape[1]:
msg = "Imported time series doesn't have values for all surface vertices. Surface has %d vertices " \
"while time series has %d values." % (surface.number_of_vertices, ts_data_shape[1])
raise LaunchException(msg)
else:
ts_h5.surface.store(uuid.UUID(surface.gid))
ts_idx.fk_surface_gid = surface.gid
ts_h5.close()
ts_idx.sample_period_unit = partial_time_series.sample_period_unit
ts_idx.sample_period = partial_time_series.sample_period
ts_idx.sample_rate = partial_time_series.sample_rate
ts_idx.labels_ordering = json.dumps(
partial_time_series.labels_ordering)
ts_idx.labels_dimensions = json.dumps(
partial_time_series.labels_dimensions)
ts_idx.data_ndim = len(ts_data_shape)
ts_idx.data_length_1d, ts_idx.data_length_2d, ts_idx.data_length_3d, ts_idx.data_length_4d = prepare_array_shape_meta(
ts_data_shape)
return [ts_idx]
except ParseException as excep:
logger = get_logger(__name__)
logger.exception(excep)
raise LaunchException(excep)
def _create_time_series(self, volume, title):
# Now create TimeSeries and fill it with data from NIFTI image
time_series = TimeSeriesVolume()
time_series.title = title
time_series.labels_ordering = ["Time", "X", "Y", "Z"]
time_series.start_time = 0.0
time_series.volume = volume
if len(self.parser.zooms) > 3:
time_series.sample_period = float(self.parser.zooms[3])
else:
# If no time dim, set sampling to 1 sec
time_series.sample_period = 1
if self.parser.units is not None and len(self.parser.units) > 1:
time_series.sample_period_unit = self.parser.units[1]
ts_h5_path = h5.path_for(self.storage_path, TimeSeriesVolumeH5,
time_series.gid)
nifti_data = self.parser.parse()
with TimeSeriesVolumeH5(ts_h5_path) as ts_h5:
ts_h5.store(time_series, scalars_only=True, store_references=True)
for i in range(self.parser.time_dim_size):
ts_h5.write_data_slice([nifti_data[:, :, :, i, ...]])
data_shape = ts_h5.read_data_shape()
ts_idx = TimeSeriesVolumeIndex()
ts_idx.fill_from_has_traits(time_series)
ts_idx.data_ndim = len(data_shape)
ts_idx.data_length_1d, ts_idx.data_length_2d, ts_idx.data_length_3d, ts_idx.data_length_4d = prepare_array_shape_meta(
data_shape)
return ts_idx
def launch(self,
data_file,
dataset_name,
structure_path='',
transpose=False,
slice=None,
sampling_rate=1000,
start_time=0,
tstype_parameters=None):
try:
data = read_nested_mat_file(data_file, dataset_name,
structure_path)
if transpose:
data = data.T
if slice:
data = data[parse_slice(slice)]
ts, ts_idx, ts_h5 = self.ts_builder[self.tstype](self, data.shape,
tstype_parameters)
ts.start_time = start_time
ts.sample_period_unit = 's'
ts_h5.write_time_slice(numpy.r_[:data.shape[0]] * ts.sample_period)
# we expect empirical data shape to be time, channel.
# But tvb expects time, state, channel, mode. Introduce those dimensions
ts_h5.write_data_slice(data[:, numpy.newaxis, :, numpy.newaxis])
data_shape = ts_h5.read_data_shape()
ts_h5.nr_dimensions.store(len(data_shape))
ts_h5.gid.store(uuid.UUID(ts_idx.gid))
ts_h5.sample_period.store(ts.sample_period)
ts_h5.sample_period_unit.store(ts.sample_period_unit)
ts_h5.sample_rate.store(ts.sample_rate)
ts_h5.start_time.store(ts.start_time)
ts_h5.labels_ordering.store(ts.labels_ordering)
ts_h5.labels_dimensions.store(ts.labels_dimensions)
ts_h5.title.store(ts.title)
ts_h5.close()
ts_idx.title = ts.title
ts_idx.time_series_type = type(ts).__name__
ts_idx.sample_period_unit = ts.sample_period_unit
ts_idx.sample_period = ts.sample_period
ts_idx.sample_rate = ts.sample_rate
ts_idx.labels_dimensions = json.dumps(ts.labels_dimensions)
ts_idx.labels_ordering = json.dumps(ts.labels_ordering)
ts_idx.data_ndim = len(data_shape)
ts_idx.data_length_1d, ts_idx.data_length_2d, ts_idx.data_length_3d, ts_idx.data_length_4d = prepare_array_shape_meta(
data_shape)
return ts_idx
except ParseException as ex:
self.log.exception(ex)
raise LaunchException(ex)
def launch(self, view_model):
# type: (RegionMatTimeSeriesImporterModel) -> [TimeSeriesRegionIndex, TimeSeriesEEGIndex]
try:
data = read_nested_mat_file(view_model.data_file,
view_model.dataset_name,
view_model.structure_path)
if view_model.transpose:
data = data.T
if view_model.slice:
data = data[parse_slice(view_model.slice)]
datatype_index = self.load_entity_by_gid(view_model.datatype)
ts, ts_idx, ts_h5 = self.ts_builder[self.tstype](self, data.shape,
datatype_index)
ts.start_time = view_model.start_time
ts.sample_period_unit = 's'
ts_h5.write_time_slice(numpy.r_[:data.shape[0]] * ts.sample_period)
# we expect empirical data shape to be time, channel.
# But tvb expects time, state, channel, mode. Introduce those dimensions
ts_h5.write_data_slice(data[:, numpy.newaxis, :, numpy.newaxis])
data_shape = ts_h5.read_data_shape()
ts_h5.nr_dimensions.store(len(data_shape))
ts_h5.gid.store(uuid.UUID(ts_idx.gid))
ts_h5.sample_period.store(ts.sample_period)
ts_h5.sample_period_unit.store(ts.sample_period_unit)
ts_h5.sample_rate.store(ts.sample_rate)
ts_h5.start_time.store(ts.start_time)
ts_h5.labels_ordering.store(ts.labels_ordering)
ts_h5.labels_dimensions.store(ts.labels_dimensions)
ts_h5.title.store(ts.title)
ts_h5.close()
ts_idx.title = ts.title
ts_idx.time_series_type = type(ts).__name__
ts_idx.sample_period_unit = ts.sample_period_unit
ts_idx.sample_period = ts.sample_period
ts_idx.sample_rate = ts.sample_rate
ts_idx.labels_dimensions = json.dumps(ts.labels_dimensions)
ts_idx.labels_ordering = json.dumps(ts.labels_ordering)
ts_idx.data_ndim = len(data_shape)
ts_idx.data_length_1d, ts_idx.data_length_2d, ts_idx.data_length_3d, ts_idx.data_length_4d = prepare_array_shape_meta(
data_shape)
return ts_idx
except ParseException as ex:
self.log.exception(ex)
raise LaunchException(ex)
