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, lazy=False)#
Read a DENSsolutions Impulse logfile.
- Parameters:
- filename
str,pathlib.Path Filename of the file to read or corresponding pathlib.Path.
- lazybool, default=False
Lazy loading is not supported.
- 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
When the file contains several datasets, each dataset will be loaded as separate dictionary.
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, lazy=False)#
Read a DENSsolutions DigiHeater logfile.
- Parameters:
- filename
str,pathlib.Path Filename of the file to read or corresponding pathlib.Path.
- lazybool, default=False
Lazy loading is not supported.
- 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
When the file contains several datasets, each dataset will be loaded as separate dictionary.