Source code for

# Copyright (C) 2022 ISIS Rutherford Appleton Laboratory UKRI
# SPDX - License - Identifier: GPL-3.0-or-later
from typing import Optional, Callable, Union, List, Tuple, TYPE_CHECKING

import numpy as np
    import numpy.typing as npt

from import Images
from mantidimaging.core.parallel import utility as pu
from mantidimaging.core.utility.data_containers import Indices
from mantidimaging.core.utility.progress_reporting import Progress

[docs] def do_stack_load_seq(data: np.ndarray, new_data: np.ndarray, img_shape: Tuple[int, ...], name: str, progress: Optional[Progress]) -> np.ndarray: """ Sequential version of loading the data. This performs faster locally, but parallel performs faster on SCARF :param data: shared array of data :param new_data: the new data to be moved into the shared array :param img_shape: The shape of the image :param name: Name for the loading bar :return: the loaded data """ num_images = img_shape[0] progress = Progress.ensure_instance(progress, num_steps=num_images, task_name=name) with progress: for i in range(num_images): data[i] = new_data[i] progress.update(msg='Image {} of {}'.format(i, num_images)) return data
[docs] def execute(load_func: Callable[[str], np.ndarray], file_name: str, dtype: 'npt.DTypeLike', name: str, indices: Union[List[int], Indices, None] = None, progress: Optional[Progress] = None) -> 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! :param file_name: list of image file paths given as strings :param load_func: file name extension if fixed (to set the expected image format) :param dtype: data type for the output numpy array :return: stack of images as a 3-elements tuple: numpy array with sample images, white image, and dark image. """ # create shared array new_data = load_func(file_name) if indices: new_data = new_data[indices[0]:indices[1]:indices[2]] img_shape = new_data.shape data = pu.create_array(img_shape, dtype=dtype) # we could just move with data[:] = new_data[:] but then we don't get # loading bar information, and I doubt there's any performance gain data = do_stack_load_seq(data, new_data, img_shape, name, progress) # Nexus doesn't load flat/dark images yet, if the functionality is # requested it should be changed here return Images(data, [file_name])