DENSsolutions formats#

RosettaSciIO can read any logfile from DENSsolutions’ new Impulse software as well as the legacy heating software DigiHeater.

DENSsolutions Impulse logfile#

Impulse logfiles are stored in .csv format. All metadata linked to the experiment is stored in a separate metadata.log file. This metadata file contains crucial information about the experiment and should be included in the same folder with the .csv file when reading data using RosettaSciIO.

Note

To read Impulse logfiles in HyperSpy, use the reader argument to define the correct file plugin as the .csv extension is not unique to this reader:

>>> import hyperspy.api as hs
>>> hs.load("filename.csv", reader="impulse")

API functions#

rsciio.impulse.file_reader(filename, *args, **kwds)#

Read a DENSsolutions Impulse logfile.

Parameters:

filename (str, pathlib.Path) – Filename of the file to read or corresponding pathlib.Path.

Returns:

List of dictionaries containing 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 parsed metadata

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

Return type:

list of dicts

DENSsolutions DigiHeater logfile#

RosettaSciIO can read the heater log format from the DENSsolutions’ DigiHeater software. The format stores all the captured data for each timestamp, together with a small header in a plain-text format. The reader extracts the measured temperature along the time axis, as well as the date and calibration constants stored in the header.

API functions#

rsciio.dens.file_reader(filename, *args, **kwds)#

Read a DENSsolutions DigiHeater logfile.

Parameters:

filename (str, pathlib.Path) – Filename of the file to read or corresponding pathlib.Path.

Returns:

List of dictionaries containing 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 parsed metadata

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

Return type:

list of dicts