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, apply_corrections=False, connectivity=None):
"""
Execute import operations:
"""
try:
parser = NIFTIParser(data_file)
# Create volume DT
volume = Volume(storage_path=self.storage_path)
volume.set_operation_id(self.operation_id)
volume.origin = [[0.0, 0.0, 0.0]]
volume.voxel_size = [
parser.zooms[0], parser.zooms[1], parser.zooms[2]
]
if parser.units is not None and len(parser.units) > 0:
volume.voxel_unit = parser.units[0]
if parser.has_time_dimension or not connectivity:
# Now create TimeSeries and fill it with data from NIFTI image
time_series = TimeSeriesVolume(storage_path=self.storage_path)
time_series.set_operation_id(self.operation_id)
time_series.volume = volume
time_series.title = "NIFTI Import - " + os.path.split(
data_file)[1]
time_series.labels_ordering = ["Time", "X", "Y", "Z"]
time_series.start_time = 0.0
if len(parser.zooms) > 3:
time_series.sample_period = float(parser.zooms[3])
else:
# If no time dim, set sampling to 1 sec
time_series.sample_period = 1
if parser.units is not None and len(parser.units) > 1:
time_series.sample_period_unit = parser.units[1]
parser.parse(time_series, True)
return [volume, time_series]
else:
region2volume_mapping = RegionVolumeMapping(
storage_path=self.storage_path)
region2volume_mapping.set_operation_id(self.operation_id)
region2volume_mapping.volume = volume
region2volume_mapping.connectivity = connectivity
region2volume_mapping.title = "NIFTI Import - " + os.path.split(
data_file)[1]
region2volume_mapping.dimensions_labels = ["X", "Y", "Z"]
region2volume_mapping.apply_corrections = apply_corrections
parser.parse(region2volume_mapping, False)
return [volume, region2volume_mapping]
except ParseException, excep:
logger = get_logger(__name__)
logger.exception(excep)
raise LaunchException(excep)
def launch(self, data_file, apply_corrections=False, connectivity=None):
"""
Execute import operations:
"""
try:
parser = NIFTIParser(data_file)
# Create volume DT
volume = Volume(storage_path=self.storage_path)
volume.set_operation_id(self.operation_id)
volume.origin = [[0.0, 0.0, 0.0]]
volume.voxel_size = [parser.zooms[0], parser.zooms[1], parser.zooms[2]]
if parser.units is not None and len(parser.units) > 0:
volume.voxel_unit = parser.units[0]
if parser.has_time_dimension or not connectivity:
# Now create TimeSeries and fill it with data from NIFTI image
time_series = TimeSeriesVolume(storage_path=self.storage_path)
time_series.set_operation_id(self.operation_id)
time_series.volume = volume
time_series.title = "NIFTI Import - " + os.path.split(data_file)[1]
time_series.labels_ordering = ["Time", "X", "Y", "Z"]
time_series.start_time = 0.0
if len(parser.zooms) > 3:
time_series.sample_period = float(parser.zooms[3])
else:
# If no time dim, set sampling to 1 sec
time_series.sample_period = 1
if parser.units is not None and len(parser.units) > 1:
time_series.sample_period_unit = parser.units[1]
parser.parse(time_series, True)
return [volume, time_series]
else:
region2volume_mapping = RegionVolumeMapping(storage_path=self.storage_path)
region2volume_mapping.set_operation_id(self.operation_id)
region2volume_mapping.volume = volume
region2volume_mapping.connectivity = connectivity
region2volume_mapping.title = "NIFTI Import - " + os.path.split(data_file)[1]
region2volume_mapping.dimensions_labels = ["X", "Y", "Z"]
region2volume_mapping.apply_corrections = apply_corrections
parser.parse(region2volume_mapping, False)
return [volume, region2volume_mapping]
except ParseException, excep:
logger = get_logger(__name__)
logger.exception(excep)
raise LaunchException(excep)
def _create_time_series(self, volume):
# Now create TimeSeries and fill it with data from NIFTI image
time_series = TimeSeriesVolume(storage_path=self.storage_path)
time_series.volume = volume
time_series.title = "NIFTI Import - " + os.path.split(self.data_file)[1]
time_series.labels_ordering = ["Time", "X", "Y", "Z"]
time_series.start_time = 0.0
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]
self.parser.parse(time_series, True)
return time_series
