feat: use com-based PSF modeling - #29
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Thanks Rainer, initial benchmarks are looking good! I plan to make this our new default fwhm estimator |
icweaver
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icweaver
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Jun 4, 2026
If you copy-paste the revised function definitions from this PR into
your REPL you can run the following comparison which will show the new
implementation is ~3x faster and allocation free **EDIT: If you have to
collect the input (as we do because of how Photometry.jl passes us the
cutout) then it will be 2 allocations and adds ~100 ns to the base case
speed**. I chose to do `com_psf(T::Type{<:AbstractFloat}, img_ap,
rel_thresh)` because Float64 is not really any slower on my testing so
this way it makes it easier for us to switch to Float64 in the future if
we every wanted to.
```julia
using PSFModels: gaussian
using BenchmarkTools
import Astroalign
const x = 1:20
const y = 1:20
const T = Float32
model(x, y, amp) = gaussian(T, 4, 4; x, y, fwhm=3) * amp
data = model.(x, y', 10) .+ T(0.1) * randn(T, length(x), length(y))
result_orig = Astroalign.com_psf(data; rel_thresh=0.1f0)
result_revised = com_psf(data; rel_thresh=0.1f0)
println("Original COM: ", (result_orig.psf_params.x, result_orig.psf_params.y))
println("Revised COM: ", (result_revised.psf_params.x, result_revised.psf_params.y))
println("Original FWHM: ", result_orig.psf_params.fwhm)
println("Revised FWHM: ", result_revised.psf_params.fwhm)
println("Original benchmark: ")
display(@benchmark Astroalign.com_psf($data; rel_thresh=$0.1f0))
println("Revised benchmark: ")
display(@benchmark com_psf($data; rel_thresh=$0.1f0))
```
My result:
**EDIT:** Because Photometry.jl will pass us an object from
Transducers.jl we have to call `collect`, giving us 2 allocations and
runtime +100 ns over not collecting (if we had a pure matrix input).
```julia
julia> println("Original COM: ", (result_orig.psf_params.x, result_orig.psf_params.y))
Original COM: (3.9974918f0, 3.9858212f0)
julia> println("Revised COM: ", (result_revised.psf_params.x, result_revised.psf_params.y))
Revised COM: (3.9974945f0, 3.9858336f0)
julia> println("Original FWHM: ", result_orig.psf_params.fwhm)
Original FWHM: (2.31581660320762, 2.3766457470426725)
julia> println("Revised FWHM: ", result_revised.psf_params.fwhm)
Revised FWHM: (2.3164616f0, 2.3772223f0)
julia> display(@benchmark Astroalign.com_psf($data; rel_thresh=$0.1f0))
BenchmarkTools.Trial: 10000 samples with 10 evaluations per sample.
Range (min … max): 1.408 μs … 1.050 ms ┊ GC (min … max): 0.00% … 99.19%
Time (median): 1.544 μs ┊ GC (median): 0.00%
Time (mean ± σ): 2.372 μs ± 15.628 μs ┊ GC (mean ± σ): 21.39% ± 4.62%
▆█▇▅▃▁▁▁ ▃▄▃▂ ▂
█████████▇▇▆▇▇▇▇▆▇▆▆▆▇▆██████▆▆▆▅▄▅▄▅▅▃▅▆███▇▅▄▄▄▁▁▄▁▆▅▇██ █
1.41 μs Histogram: log(frequency) by time 5.56 μs <
Memory estimate: 10.41 KiB, allocs estimate: 20.
julia> println("Revised benchmark: ")
Revised benchmark:
julia> @benchmark com_psf($data; rel_thresh=$0.1f0)
BenchmarkTools.Trial: 10000 samples with 183 evaluations per sample.
Range (min … max): 571.716 ns … 9.490 μs ┊ GC (min … max): 0.00% … 88.05%
Time (median): 600.563 ns ┊ GC (median): 0.00%
Time (mean ± σ): 678.928 ns ± 532.693 ns ┊ GC (mean ± σ): 8.48% ± 9.61%
█▅▂▁▁ ▁
██████▇▇▆▆▄▅▁▄▃▁▃▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▃▄▃▄▄▄▄▄▄▄▃▄▅ █
572 ns Histogram: log(frequency) by time 4.5 μs <
Memory estimate: 1.64 KiB, allocs estimate: 2.
```
---------
Co-authored-by: Ian Weaver <weaveric@gmail.com>
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