DM-44889: Investigate feasibility of resurrecting matchBackgrounds.py background fitting algorithms #956
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Initial version of this task is written using old architecture, and so needs updating. In MatchBackgroundsTask, and its method selectRefExposure, required parameters were equally outdated: DataId, DatasetType, and ImageScaler. All of these now seem consolidated under lsst.afw.image.Exposure, so separate calls to DataId and DatasetType are now single calls for Exposure objects. ImageScaler calls were replaced in-line with Exposure.getPhotoCalib() calls, to scale all image flux to the same zeropoint (nJy). Also, we want to process visit-level images using this, so a MatchBackgroundsConnections class was created, MatchBackgroundsConfig was updated to inherit from PipelineTaskConfig (and those connections), and a rudimentary runQuantum method was added to MatchBackgroundsTask.
Code now runs without complaint through self.matchBackgrounds. Also added a self._fluxScale method to replace repeat code blocks. Will decide later if scaling to nJy is the best way to do this.
Code is now functional, in that it accepts images and returns difference image background models as "psfMatchedWarpBackground_diff" (name likely to be altered later). Uses a fit to a blank image for that corresponding to the reference image.
Difference background models are now formatted properly, to allow for image creation from the spline parameters. Also did some adjustments to documentation for Flake8 formatting.
_defineWarps() now rejects any image with all NaNs along any image edge, and creates the cost function using a sky-subtracted image. This sky-subtraction fits a 1st order Chebyshev polynomial to the masked image background. Also fixed a bug from LSK refactor by inserting a blank sky model into the background model list at the chosen reference image index.
Otherwise, changes are clean-up from previous refactoring to restore functionality, plus a bug fix. Bug fix was the restoration of two lines of code in MatchBackgroundsTask.matchBackgrounds() which produced a difference image to work from.
All images and background models now returned in counts, not nJy.
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The matchBackgrounds.py script was an initial attempt at matching multiple warped visit-level images to a given reference image. Taking the difference images between the reference and each subsequent image allows for each of these images to be modified to match the background of the reference image. These images may then be stacked (coadded), and a single background estimation made on the combined (and, importantly, deeper) coadd image.
The existing matchBackgroundsTask has not been utilized in some time. This ticket aims to explore the feasibility of resurrecting this code base, with a mind towards testing on HSC in the near-term.