def test_GIVEN_variable_length_string_type_dataset_WHEN_getting_value_THEN_returned_as_str(
):
wrapper = NexusWrapper(filename="test_read_vlen_str_dataset")
dataset_name = "vlen_str_dataset"
string_data = b"This is a string"
wrapper.nexus_file.create_dataset(dataset_name,
dtype=h5py.special_dtype(vlen=str),
data=string_data)
assert wrapper.get_field_value(wrapper.nexus_file,
dataset_name) == string_data.decode("utf8")
assert isinstance(
wrapper.get_field_value(wrapper.nexus_file, dataset_name), str)
def _create_transformation_vectors_for_pixel_offsets(
detector_group: h5py.Group,
wrapper: nx.NexusWrapper) -> List[QVector3D]:
"""
Construct a transformation (as a QVector3D) for each pixel offset
"""
x_offsets = wrapper.get_field_value(detector_group, "x_pixel_offset")
y_offsets = wrapper.get_field_value(detector_group, "y_pixel_offset")
z_offsets = wrapper.get_field_value(detector_group, "z_pixel_offset")
if x_offsets is None or y_offsets is None:
raise Exception(
"In pixel_shape_component expected to find x_pixel_offset and y_pixel_offset datasets"
)
if z_offsets is None:
z_offsets = np.zeros_like(x_offsets)
# offsets datasets can be 2D to match dimensionality of detector, so flatten to 1D
return [
QVector3D(x, y, z)
for x, y, z in zip(x_offsets.flatten(), y_offsets.flatten(),
z_offsets.flatten())
]