mantidimaging.core.io.loader.stack_loader module

mantidimaging.core.io.loader.stack_loader.do_stack_load_seq(data: numpy.ndarray, new_data: numpy.ndarray, img_shape: Tuple[int, ...], name: str, progress: Optional[mantidimaging.core.utility.progress_reporting.progress.Progress]) → numpy.ndarray

Sequential version of loading the data. This performs faster locally, but parallel performs faster on SCARF

Parameters

• data – shared array of data

• new_data – the new data to be moved into the shared array

• img_shape – The shape of the image

• name – Name for the loading bar

Returns

the loaded data

mantidimaging.core.io.loader.stack_loader.execute(load_func: Callable[[str], numpy.ndarray], file_name: str, dtype: npt.DTypeLike, name: str, indices: Optional[Union[List[int], mantidimaging.core.utility.data_containers.Indices]] = None, progress: Optional[mantidimaging.core.utility.progress_reporting.progress.Progress] = None) → mantidimaging.core.data.images.Images

Load a single image FILE that is expected to be a stack of images.

Parallel execution can be slower depending on the storage system.

On HDD I’ve found it’s about 50% SLOWER, thus not recommended!

Parameters

• file_name – list of image file paths given as strings

• load_func – file name extension if fixed (to set the expected image format)

• dtype – data type for the output numpy array

Returns

stack of images as a 3-elements tuple: numpy array with sample images, white image, and dark image.