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Every time a user changes a measurement parameter during a BE experiment, all subsequent data are written out to a different measurement group and corresponding HDF5 dataset. The BE notebook currently only performs fitting and visualization on the data contained in the first measurement group only.
Instead, the notebook should iterate through all available datasets and perform the same operations on them
The text was updated successfully, but these errors were encountered:
Also, pycroscopy should include functions that can stitch results from multiple measurement groups back to a single dataset when appropriate. For example, if the BE center frequency, bandwidth, and similar parameters were changed, the results of SHO fitting such a fragmented dataset can be stitched together without any misinterpretation of information going forward.
However, as a second example - if the DC bias configurations were changed, this merging of datasets would only be theoretically possible once the loops are fitted.
Domain scientists at CNMS should decide appropriately
Every time a user changes a measurement parameter during a BE experiment, all subsequent data are written out to a different measurement group and corresponding HDF5 dataset. The BE notebook currently only performs fitting and visualization on the data contained in the first measurement group only.
Instead, the notebook should iterate through all available datasets and perform the same operations on them
The text was updated successfully, but these errors were encountered: