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
.unffile.- Parameters:
- filename
str,pathlib.Path Filename of the file to read or corresponding pathlib.Path.
- lazybool, default=False
Whether to open the file lazily or not.
- filename
- Returns:
listofdictList of dictionaries containing the following fields:
‘data’ – multidimensional
numpy.ndarrayordask.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
- rsciio.semper.file_writer(filename, signal, skip_header=False)#
Write signal to a Semper
.unffile.- Parameters:
- filename
str,pathlib.Path Filename of the file to write to or corresponding pathlib.Path.
- signal
dict 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).
- filename