Fix Version/s: None
Make a pipeline to run fakes data all the way through from beginning to end.
|Field||Original Value||New Value|
|Status||To Do [ 10001 ]||In Progress [ 3 ]|
|Summary||Make a fakes pipeline||Make a RC2 fakes pipeline|
|Attachment||t9615p24i-deepCoadd.png [ 54035 ]|
|Attachment||t9615p24i-fakes_deepCoadd.png [ 54036 ]|
|Attachment||t9615p24i-fakes_deepCoadd_diff.png [ 54037 ]|
|Attachment||v1258d78-calexp.png [ 54038 ]|
|Attachment||v1258d78-fakes_calexp.png [ 54039 ]|
|Attachment||v1258d78-fakes_calexp_diff.png [ 54040 ]|
|Reviewers||Lee Kelvin [ lskelvin ]|
|Status||In Progress [ 3 ]||In Review [ 10004 ]|
(following discussion with Sophie on Weds, added myself as a reviewer) - this ticket looks great, adding nice gen3 functionality to SSI processing. I only have 1 minor comment on the pipe_tasks PR regarding the WCS pixel scale offset factor, and whether or not this factor should be configurable. If you think a factor of 2 will always be sufficient to catch this issue, then that looks fine to me, with no extra code required.
I think some amount (most?) of the obs_subaru code was already merged to master previously, so I haven't looked at that specifically here. However, as noted above, I have been using the pipeline on this ticket branch without issue, and the outputs look good to me. I note that ticket branch
DM-31491 in obs_subaru still has some additional differences to master in the pipelines/DRPFakes.yaml pipeline - should this also be in a PR?
Otherwise, this looks great. Assuming Jenkins doesn't complain, then this should be good to merge. Nicely done.
|Status||In Review [ 10004 ]||Reviewed [ 10101 ]|
|Resolution||Done [ 10000 ]|
|Status||Reviewed [ 10101 ]||Done [ 10002 ]|
|Remote Link||This issue links to "Page (Confluence)" [ 31356 ]|
I've been testing this pipeline over the last week or so using single-Sersic extended sources supplied by the LSST:UK LSB working group, and all seems to be working as expected. For reference, prior to running this pipeline, it is necessary to ingest the input SSI catalogue (e.g., in FITS format) into the repo. The commands I've used to perform this in Python are:
Once this is in place, running the pipeline is simply:
I've attached example before/after/diff images at both the single frame level and the coadd level to this ticket, for reference.