Dolphot-LC Software Submission

[whit5224]

Submitting Author: Ramona White (@whit5224) All current maintainers: (@whit5224, @patkel) Package Name: Dolphot-LC One-Line Description of Package: Dolphot-LC is an automated Hubble Space Telescope (HST) data pipeline based on the popular Dolphot analysis package; this package allows for the creation of lightcurves and difference images from HST data. Repository Link: https://github.com/patkel/dolphot_lc Version submitted: 0.0.2 Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD JOSS DOI: TBD Version accepted: TBD Date accepted (month/day/year): TBD

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Description

Dolphot-LC is an automated Hubble Space Telescope (HST) data pipeline based on the popular Dolphot analysis package. This package allows for the creation of lightcurves and difference images from HST data. Dolphot-LC allows the user to run their own fits images through the pipeline and generate results and requires a coadded template image and science images that are already aligned to template. To make package use easier, Dolphot-LC has been documented extensively in a Read the Docs: https://dolphot-lc.readthedocs.io/en/latest/index.html and Jupyter Notebook: https://nbviewer.org/gist/whit5224/287af111f44bf83a23eaaf19a5121c75.

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  • [ ] Database interoperability

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• [ ] Geospatial
• [ ] Education

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  • Who is the target audience and what are scientific applications of this package? The target audience are scientist and astrophysicists interested in analyzing HST and JWST data with the goal of creating light curves and difference images.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ? Dolphot-LC implements and expands upon the DOLPHOT stellar photometry package. Our software makes it possible apply DOLPHOT to difference images in order to measure changes in brightness due to astrophysical transients. Dolphot-LC improves quality and usability by allowing the creating of light curves and difference images.

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[Batalex]

Hey @whit5224, thank you for submitting Dolphot-LC! I am Alex, pyOpenSci current Editor-in-Chief. I went through the docs and the code, and I think that Dolphot-LC is in scope for us. Over the next few days, I'll go over the E-i-C checks so that we can address a few things before moving on to the actual review.

[whit5224]

Thanks for the initial check! Look forward to hearing back from you soon.

[Batalex]

Editor in Chief checks

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• [ ] Initial onboarding survey was filled out We appreciate each maintainer of the package filling out this survey individually. :raised_hands: Thank you authors in advance for setting aside five to ten minutes to do this. It truly helps our organization. :raised_hands:

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Editor comments

Hey there, Sorry it took me so long to get back to you.

I think that dolphot_lc needs some more work before we proceed with the review. I made the call to not check some of the items, here is why.

The package imports properly into a standard Python environment import package.

The package installation does not install its dependencies. This is problematic because the user has to go to the repository to find the requirements.txt file. I encourage you to take a look at our packaging guide to properly set up that. On a side note, I am curious as to how you built the wheel hosted on pypi since it's not clear from the CI and the source code.

Documentation

I'd like to see more content to get a better grip of what dolphot_lc does (arguably, I'm not familiar with the field, so this might be a ME problem). Namely, think of your documentation as something that should fill the two following axes:

• expertise level: tutorial, how-to guide, technical reference
• content structure: what are we going to do, how are we going to do it using your package, (optionally) why is it done that way?

Readme

Think of the readme as the front page for your package. Like a resume, you only have a few moments of attention span from a reader before they lose interest and look for something else. Here is an example you can take inspiration from

Expand

````md # dolphot_lc A Python package for . [](https://choosealicense.com/licenses/mit/) A brief description of what this project does and who it's for. ## Installation Install `dolphot_lc` with pip ```shell pip install dolphot_lc ``` ## Getting started The 30 seconds tutorial. ```py import antigravity ``` ## Documentation [Documentation](https://linktodocumentation) ## Contributing Contributions are always welcome! See `contributing.md` for ways to get started. Please adhere to this project's `code of conduct`. ````

Using our package guide, the readme will also be present on the pypi front page

Contributing

The file is present, but the content is lacking. For instance, you set the expectations that users can contribute by fixing bugs or proposing new features, but there is no explanation as to how to do that (how to build the package, how to test it, etc.)

Tests

Same as before, I encourage you to take a look at our guide. Using a proven test framework will increase the quality of your tests, as well as aligning dolphot_lc on the community-established practices.

[cmarmo]

Hi @whit5224 , I'm Chiara and I'm following up your submission as editor in chief. Do you mind giving an update about the initial checks on your code? No pressure, just let us know how do you plan to proceed so we can fairly share the packages among editors and reviewers. Thank you for submitting your package to pyOpenSci!