nabu.io.utils

Classes

EntryReader — Context manager used to read a bliss node
DatasetReader — Context manager used to read a bliss node

Functions

get_compacted_dataslices — Regroup urls to get the data more efficiently. Build a structure mapping files indices to information on how to load the data: {indices_set: data_location} where data_location contains contiguous indices.
get_first_hdf5_entry
hdf5_entry_exists
get_h5_value
get_h5_str_value — Get a HDF5 field which can be bytes or str (depending on h5py version !).
create_dict_of_indices — From an image stack with the images indices, create a dictionary where each index is the image index, and the value is the corresponding image.
convert_dict_values — Modify a dictionary to be able to export it with silx.io.dicttoh5
file_format_is_edf
file_format_is_jp2k
file_format_is_tiff
file_format_is_hdf5
get_output_volume

source get_compacted_dataslices(urls, subsampling=None, begin=0)

Regroup urls to get the data more efficiently. Build a structure mapping files indices to information on how to load the data: {indices_set: data_location} where data_location contains contiguous indices.

Parameters

urls : dict — Dictionary where the key is an integer and the value is a silx DataUrl.
subsampling : int, optional — Subsampling factor when reading the frames. If an integer n is provided, then one frame out of n will be read.

Returns

merged_urls : dict — Dictionary with the same keys as the urls parameter, and where the values are the corresponding silx.io.url.DataUrl with merged data_slice.

source get_first_hdf5_entry(fname)

source hdf5_entry_exists(fname, entry)

source get_h5_value(fname, h5_path, default_ret=None)

source get_h5_str_value(dataset_ptr)

Get a HDF5 field which can be bytes or str (depending on h5py version !).

source create_dict_of_indices(images_stack, images_indices)

From an image stack with the images indices, create a dictionary where each index is the image index, and the value is the corresponding image.

Parameters

images_stack : numpy.ndarray — A 3D numpy array in the layout (n_images, n_y, n_x)
images_indices : array or list of int — Array containing the indices of images in the stack

Examples

Given a simple array stack:

images_stack = np.arange(3*4*5).reshape((3,4,5))
images_indices = [2, 7, 1]
create_dict_of_indices(images_stack, images_indices)
# returns {2: array1, 7: array2, 1: array3}