DM-49210: Set is_negative flag for negative-detected diffim sources #385
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| dataset = lsst.meas.base.tests.TestDataset(bbox) | ||
| # positive isolated source on diffim | ||
| dataset.addSource(instFlux=.5E5, centroid=lsst.geom.Point2D(25, 26)) | ||
| # negative isolated source on diffim |
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I do not understand how this creates an isolated negative source - but this is not the part of this review, and I can follow up later.
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Ah, I need to clarify a comment above this. These sources are added to the template, and thus will be negative on the diffim because the science image is empty. Two regions of the image are made negative on the template, and thus become positive sources on the diffim. I'll push a clarification.
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And this definitely makes me wish we'd merged the cleanups I'd made on DM-46908, because that would have made this code a lot cleaner (I could have just made the inserted sources explicitly negative from the start).
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See if this clarifies things for you: https://github.com/lsst/ip_diffim/pull/385/files#diff-27a812f8403a8dded0a333ce0ca81d2885eb83d57345d4bfd883f267b7dcb4e8L984
* Have to pre-initialize the merged schema, so that the output schema matches the output catalog schema. * Set is_ngative on the post-blended output (though this is never used, since we always merge the footprints). * Check is_negative consistency in different tests. * Improve some test docs/comments.
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