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View Code? Open in Web Editor NEWShear Optimization with ShOpt.jl, a julia package for empirical point spread function characterizations
Home Page: https://edwardberman.github.io/shopt/
License: MIT License
Shear Optimization with ShOpt.jl, a julia package for empirical point spread function characterizations
Home Page: https://edwardberman.github.io/shopt/
License: MIT License
Shopt Currently produces robust Analytic and Pixel Grid fits most of the time. Additionally, on the chance that a fit fails, I have Shopt record such. I wanted to raise an issue as a self note to make sure not to include the failed instances in the plotting, because at the moment these failed fits are making good data seem indecipherable, at least, overshadowing what I would otherwise deem successful. I am assigning this issue to myself to fix.
Hi,
I am trying to reproduce Figure 2 of the paper, and while it appears visually appealing, it would be helpful to have some additional clarifications:
README
) or on the GitHub site the precise JWST data file you downloaded and whether any modifications were made to the default SExtractor
configuration files.TutorialNotebook.ipynb
, or would additional code be required?Thanks a lot for your time!
The tool and paper look great! Here are some comments (mostly minor):
Summary
Statement of Need
State of the Field
Mean Squared Error Loss Function can handle NaN out of the box with Flux but the Relative Error Loss Function is acting more detrimental. NaN's replace all model stars with I(x,y) < -1000 as a way of denoting other Stars in the image (Source Extractor sets these values to -e32)
In the current state of the JOSS paper, I suggest to mention STARRED
as another efficient PSF reconstruction technique, as it has been shown to outperform other programs like PSFex (mentioned in the paper) and PSFr (which would need to be mentioned as well), and uses high the just-in-time compiled and auto-diff library JAX.
Hi,
Your JOSS paper is pretty well written and clear! I have some minor suggestions, including fixing a few typos:
ShOpt.jl
would compare with PSFr
(https://github.com/sibirrer/psfr), which is quite recent and has been used for JWST data;I ran into the following issues when running ShOpt for the first time.
dataPreprocessing.jl
assumes VIGNET
exists in HDU 2 of a FITS file. This is a very common issue; it would be nice if ShOpt handled this gracefully.reader.jl
looks for an extension that doesn't exist in the default truncated mode (3
)The default naming of the output directory follows something like 2024-01-15T11:20:40.848
, but :
characters are forbidden in macos path names (and are replaced by the /
when displayed in a file explorer). Hence I would suggest not to use these characters but -
or _
instead.
This issue is part of the JOSS review process.
I have been following the installation instructions and running of the program on the test catalog. Here are some comments regarding the documentation.
pyimport("astropy")
should be run after re-loading the Julia REPL, so it would better to have this explicitely mentioned.TutorialNotebook.ipynb
made just for copy-pasting lines of codes into the Julia REPL, or can this be executed using Julia directly? This is not clear to me. Probably because this is the first time I run Julia code: it means that the documentation can be be improved.using Base.Threads
is missing in TutorialNotebook.ipynb
.Fitting a kolmogorov radial fit is taking unfeasibly long. I know that there is an added degree of difficulty in numerical integration but perhaps something else is causing a bottleneck? I have tried printing loss with every iteration and it seems to be working, but I am working it may never even converge enough to satisfy optim.jl's requirements.
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