DM-24537: Add Isr calibration base class #135
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python/lsst/ip/isr/calibType.py
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| Parameters | ||
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| registryName : `str`, optional |
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I had intended this to be some way to identify the Butler repository that constructed the calibration, to ensure all the dataIds recorded could be transformed if needed. This now seems unnecessary, as any Butler instance should get the same dataIds for the same data from astro_metadata_translator.
I'm changing this to instrument, as it still feels like we need some additional information in the provenance, and I think that's provided by that.
IsrCalib should be the base class used by calibration data. IsrProvenance supplies a concrete example subclass that will allow calibrations to store their inputs in a way that can be used to reconstruct the calibration.
| associated metadata. | ||
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| """ | ||
| if format == 'yaml' or (format == 'auto' and filename.lower().endswith((".yaml", ".YAML"))): |
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I think this means that I can pass in a file name of "hello.ecsv" and say "format=yaml" and end up with a yaml file that has a .ecsv extension. In Defects I force the extension to be correct and I think you have to do that here -- it's why the returned filename doesn't have to match the supplied filename.
python/lsst/ip/isr/calibType.py
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| calib.updateMetadata(setDate=False, **dictionary['metadata']) | ||
| calib._detectorName = dictionary['detectorName'] | ||
| calib._detectorSerial = dictionary['detectorSerial'] | ||
| calib.instrument = dictionary['metadata']['INSTRUME'] |
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In fromTable doesn't an "instrument" field get filled in to the dict so I'm not sure why we need to look in the metadata here.
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Running a timing test shows that the new astropy code is significantly faster than afwTable in 100 iterations with timeit: |
These types are not used in C++, so it's faster and less confusing to use astropy tables instead.
Add IsrCalib and IsrProvenance classes.
IsrCalib should be the base class used by calibration data.
IsrProvenance supplies a concrete example subclass that will allow
calibrations to store their inputs in a way that can be used to
reconstruct the calibration.