Closed kbarbary closed 6 years ago
Merging #715 into master will increase coverage by
0.18%
. The diff coverage is94.23%
.
@@ Coverage Diff @@
## master #715 +/- ##
==========================================
+ Coverage 81.08% 81.26% +0.18%
==========================================
Files 35 35
Lines 3643 3641 -2
==========================================
+ Hits 2954 2959 +5
+ Misses 689 682 -7
Impacted Files | Coverage Δ | |
---|---|---|
src/Synthetic.jl | 68.88% <ø> (ø) |
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src/ParallelRun.jl | 82.92% <ø> (ø) |
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src/joint_infer.jl | 73.59% <ø> (ø) |
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src/deterministic_vi/elbo_args.jl | 100% <ø> (ø) |
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src/model/image_model.jl | 75% <ø> (ø) |
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src/model/log_prob.jl | 90.57% <ø> (ø) |
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src/AccuracyBenchmark.jl | 80.9% <100%> (-0.5%) |
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src/model/imaged_sources.jl | 100% <100%> (ø) |
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src/model/psf_model.jl | 100% <100%> (ø) |
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src/SDSSIO.jl | 93.92% <100%> (+0.29%) |
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... and 1 more |
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Awesome!
This is a step in making the core of Celeste independent of the SDSS data model.
It Makes the
Image
type parametric in the last attribute (formerly called aRawPSF
) which represents the rasterized PSF as a function of position in the image.Image
can now contain any type ofAbstractPSFMap
, which has one job: to return a rasterized PSF model, given a pixel position. This allows for different implementations, such asSDSSPSFMap
, orConstantPSFMap
(a non-varying PSF), or any other survey's PSF representation, without any of the rest of Celeste knowing the type or the implementation.The alternative to this would be fitting a spatially variable version of Celeste's PSF model (
Vector{PsfComponent}
) and storing that inImage
. (That would be cleaner in some ways and can of course still be done in the future.)Note: most of the big blocks of changes here are simply moving code around: the SDSS specific PSF has moved to SDSSIO.jl for example.