Details
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Type:
Bug
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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: meas_photocal
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Labels:None
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Story Points:1
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Epic Link:
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Sprint:Science Pipelines DM-S15-5, Science Pipelines DM-S15-6
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Team:Data Release Production
Description
Dominique points out that the zero point calibration uses errors not inverse errors to calculate the zero point. git annotate reveals:
24c9149f python/lsst/meas/photocal/PhotoCal.py (Robert Lupton the Good 2010-12-13 05:03:12 +0000 353) return np.average(dmag, weights=dmagErr), np.std(dmag, ddof=1), len(dmag)
Please fix this. At the same time, we should add a config parameter to soften the errors.
Attachments
Issue Links
- is contained by
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DM-2308 Improve hacks in PhotoCalTask for dealing with reference flux uncertainties
- To Do
Activity
| Field | Original Value | New Value |
|---|---|---|
| Team | Data Release Production [ 10301 ] |
| Issue Type | Story [ 10001 ] | Bug [ 1 ] |
| Rank | Ranked higher |
| Epic Link |
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| Sprint | Science Pipelines DM-S15-5 [ 162 ] | |
| Assignee | Robert Lupton [ rhl ] | Jim Bosch [ jbosch ] |
| Status | To Do [ 10001 ] | In Progress [ 3 ] |
I'm just adopting Dominique Boutigny's proposed solution, as it seems correct, or at least vastly more correct than what we have now. I do wonder a bit whether we should be doing this average in flux units rather than magnitude units, but I think that's beyond the scope of this issue.
Robert Lupton, by "soften the errors", do you just mean "add a constant in quadrature" (with the constant coming from the config)?
Probably not exactly in the scope of this ticket, but the main problem is that the Photocal fit is not taking into account the error on the flux of the reference stars. In some region of the SDSS catalog (CFHTLS D3 for instance) they can be quite large.
Dominique Boutigny
added a comment - Probably not exactly in the scope of this ticket, but the main problem is that the Photocal fit is not taking into account the error on the flux of the reference stars. In some region of the SDSS catalog (CFHTLS D3 for instance) they can be quite large. | Story Points | 1 |
This issue will address some (but not all) of the problems discussed in DM-2308.
Simon, do you have time for a small review? This fixes a big but simple problem in the Photocal algorithm, along with a number of minor fixes and cleanups. All changes on tickets/DM-2423 of pipe_tasks.
| Reviewers | Simon Krughoff [ krughoff ] | |
| Status | In Progress [ 3 ] | In Review [ 10004 ] |
| Status | In Review [ 10004 ] | In Progress [ 3 ] |
Simon is super busy. Russell Owen, could you take on this review? It's a pretty small one.
| Reviewers | Simon Krughoff [ krughoff ] | Russell Owen [ rowen ] |
| Status | In Progress [ 3 ] | In Review [ 10004 ] |
| Sprint | Science Pipelines DM-S15-5 [ 162 ] | Science Pipelines DM-S15-5, Science Pipelines DM-S15-6 [ 162, 165 ] |
| Rank | Ranked higher |
photoCal.py
The changes look helpful.
One small request: please change the final return to specify each value on its own line, as per the original code. I feel that makes it easier for a user to see exactly what named values are in the returned struct.
testPhotocal.py
The changes look helpful.
| Status | In Review [ 10004 ] | Reviewed [ 10101 ] |
One small request: please change the final return to specify each value on its own line, as per the original code. I feel that makes it easier for a user to see exactly what named values are in the returned struct.
I find that a bit unnecessarily verbose, but I also don't care much. Will do.
| Resolution | Done [ 10000 ] | |
| Status | Reviewed [ 10101 ] | Done [ 10002 ] |
Yes.
| Attachment | signature.asc [ 27052 ] |
I think that the error is also incorrect (np.std(dmag)) I think that it should be something like :
zp, sum = np.average(dmag, weights=1/dmagErr**2,returned=True)
sigma = np.sqrt(1.0/sum)
Assuming that the dmagErr values are correct