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Predict catalog given DECaLS survey image #840
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return { | ||
"image": image, | ||
"background": np.zeros_like(image), # TODO: find a way to get background | ||
"wcs": wcs, |
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Do we read in the PSF somewhere too? I think we'd need the DECaLS PSFs to generate realistic training data.
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DECaLS data generation - punting to after #853.
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tracked by #871
# height: 1489 | ||
# bands: ["g", "r", "z"] | ||
# predict_device: ${predict.device} | ||
# predict_crop: ${predict.crop} | ||
trainer: ${training.trainer} | ||
encoder: ${encoder} |
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We'd also likely need a simulator to generate data that resembles decals data, i.e. which samples backgrounds and PSFs from DECaLs data. (The simulator above has an sdss_fields
entry -- but ideally the simulator wouldn't be specific to any one survey.)
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DECaLS data generation - punting to after #853.
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tracked by #871
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Great work! Our codebase will be so extensible/maintainable once we abstract away the survey-specific code, confining it to the surveys
module.
- Uses pyvo.dal SIA service to download image cutouts given RA, Dec, and size in width, height - Determines corresponding brick name, and downloads associated tractor catalog from DECaLS server - Workflow is (very roughly) DECaLS.prepare_data() > DECaLS.predict_dataloader() > Encoder.predict_step(), similar to SDSS Closes #772.
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@jeff-regier ptal in general |
Codecov Report
@@ Coverage Diff @@
## master #840 +/- ##
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- Coverage 96.34% 95.50% -0.85%
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Files 19 19
Lines 2024 2157 +133
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+ Hits 1950 2060 +110
- Misses 74 97 +23
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Notes: - Brick images are coadded images - Tractor catalog is based on single-epoch images (cf. note in "https://www.legacysurvey.org/dr10/files/#image-stacks-south-coadd") - Fix bugs from encoder assuming multi-band images
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Test stuff looks good to me. DECaLS stuff looks good overall too but I didn't review it too in-depth
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Nice. And I'm looking forward to the PR resolving #853 !
Uses pyvo.dal SIA service to download image cutouts given RA, Dec, and size in width, heightDownloads coadded brick image dataCloses #772.