Reader for spectroscopy data saved using the VistaControl software for TriVista spectrometers from Teledyne Princeton Instruments. Currently, RosettaSciIO can only read the XML-based .tvf format from TriVista (the binary .tvb format is not supported). However, this format supports spectral maps and contains all relevant metadata.

If LumiSpy is installed, Luminescence will be used as the signal_type.

API functions#

rsciio.trivista.file_reader(filename, lazy=False, use_uniform_signal_axis=False, glued_data_as_stack=False, filter_original_metadata=True)#

Read TriVista’s .tvf file.

filenamestr, pathlib.Path

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

lazybool, default=False

Whether to open the file lazily or not.

use_uniform_signal_axisbool, default=False

Can be specified to choose between non-uniform or uniform signal axes. If True, the scale attribute is calculated from the average delta along the signal axis and a warning is raised in case the delta varies by more than 1%.

glued_data_as_stackbool, default=False

Using the mode Step & Glue results in measurements performed at different wavelength ranges with some overlap between them. The file then contains the individual spectra as well as the “glued” spectrum. The latter is represented as one spectrum, which covers the complete wavelength range. Stitching the datasets together in the overlap region is already done by the setup. If this setting is set to True, then the individual datasets will be loaded as a stack. Otherwise, only the “glued” spectrum is loaded.

filter_original_metadatabool, default=True

Decides whether to process the original_metadata. If True, then non-relevant metadata will be excluded. For example, the metadata usually contains information for multiple objectives, even though only one is used. In this case, only the metadata from the used objective will be added to original_metadata. This setting only affects the original_metadata attribute and not the metadata attribute.

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