SEMPER binary format (UNF)#

SEMPER is a fully portable system of programs for image processing, particularly suitable for applications in electron microscopy developed by Owen Saxton (see DOI: 10.1016/S0304-3991(79)80044-3 for more information). The .unf format is a binary format with an extensive header for up to 3 dimensional data. RosettaSciIO can read and write .unf-files and will try to convert the data into a fitting BaseSignal subclass, based on the information stored in the label. Currently version 7 of the format should be fully supported.

API functions#

rsciio.semper.file_reader(filename, lazy=False)#

Read a Semper .unf file.

Parameters:
filenamestr, pathlib.Path

Filename of the file to read or corresponding pathlib.Path.

lazybool, default=False

Whether to open the file lazily or not. The file will stay open until closed in compute() or closed manually. get_file_handle() can be used to access the file handler and close it manually.

Returns:
list of dict

List of dictionaries containing the following fields:

  • ‘data’ – multidimensional numpy.ndarray or dask.array.Array

  • ‘axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector

  • ‘metadata’ – dictionary containing the parsed metadata

  • ‘original_metadata’ – dictionary containing the full metadata tree from the input file

When the file contains several datasets, each dataset will be loaded as separate dictionary.

rsciio.semper.file_writer(filename, signal, skip_header=False)#

Write signal to a Semper .unf file.

Parameters:
filenamestr, pathlib.Path

Filename of the file to write to or corresponding pathlib.Path.

signaldict

Dictionary containing the signal object. Should contain the following fields:

  • ‘data’ – multidimensional numpy array

  • ‘axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector

  • ‘metadata’ – dictionary containing the metadata tree

skip_headerbool, default=False

Determines if the header, title and label should be skipped (useful for some other programs).