Details
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Type:
Story
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Status: Done
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Resolution: Done
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Fix Version/s: None
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Component/s: pipe_analysis
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Labels:None
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Story Points:10
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Epic Link:
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Sprint:DRP S20-2 (Jan), DRP S20-3 (Feb)
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Team:Data Release Production
Description
The coaddAnalysis.py script distinguishes between "unforced" (i.e. the *_meas dataset type) and "forced" (i.e the *_forced_src dataset type). The latter table does not include some of the flag columns that are useful for sample sub-selection. The desired columns are thus copied to the "forced" catalog within the script. Most of these columns should be coming from the *_ref dataset, as it is those that are appropriate for the forced catalogs. Some of those that should be coming from *_ref are coming from *_meas and thus can have the wrong value (it's quite rare but, for example, for two random patches, the detect_isPatchInner is different for 1 and 6 sources). However, we want the various "calib*" columns to come from the *_meas catalog as these reflect what actually occurred during SFM per band (which is always what we want for the calib* columns rather than assigning the specific value associated with the forced filter to all other filters). Here we should make the column copying more explicit and in accordance with the above desiderata.
This is finally ready to go. As usual, there was a fair amount of scope-creep and a slight change in the philosophy of the initial description (which I have now updated to reflect the current needs). Namely (and see commit descriptions for details):
DM-23071).I have tested all the changes by running examples of all the scripts on —rerun RC/w_2019_38/
DM-21386using both the master and this ticket branch of pipe_analysis. For most of the plots, the differences are small and largely involve objects not included in the computation of the statistics. You can compare outputs by looking athttps://lsst-web.ncsa.illinois.edu/~lauren/lauren/DM-23049/master/plots/
https://lsst-web.ncsa.illinois.edu/~lauren/lauren/DM-23049/plots/
Here are a few specific examples:


Master:
New:
Master:


New:
Here’s an example of the added CircAp - PSF vs. CircAp plot (also shows the “unknown” object type and the flag highlighting):

New: