GALAssify submission

[Malclay]

Submitting Author: Manuel Alcázar-Laynez (@Manalclay)
All current maintainers: (@Manalclay, @andonij)
Package Name: GALAssify
One-Line Description of Package: A Python package for visually classifying astronomical objects
Repository Link: https://gitlab.com/astrogal/GALAssify/
Version submitted: v1.0.1
EiC: Szymon Moliński (@SimonMolinsky )
Editor: Avik Basu (@ab93)
Reviewer 1: Akhil Krishna R (@akhilkrishnar0)
Reviewer 2: Erik Whiting (@erik-whiting)
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

• Include a brief paragraph describing what your package does:
  We present GALAssify, a customisable graphical tool that allows the user to visually inspect and characterise properties of astronomical objects in a simple way. GALAssify allows the user to save the results of the visual classification into a file using a list of previously defined tags based on the user's interests. A priori, it has been initially developed to tackle astrophysical problems but, due to its versatility, it could be easily adapted. For instance, this tool can be used to classify microscopy images from biological studies or be used in any other discipline.
  

Scope

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  Check out our package scope page to learn more about our
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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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• For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):
  GALAssify allows the user to visualise and validate a large dataset of astronomical images (or of any other field) using a Graphical User Interface (GUI) to accomplish it using only a keyboard, a mouse or both. User can view the image of the object and a linked FITS image at time, and visually classify it with a previously-defined tags, or even discard the object if required.

  • Who is the target audience and what are scientific applications of this package?
    This package is designed for astronomers who need to manually classify large numbers of astronomical objects given their respective images using customizable labels.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?
    Currently, we don't know any customizable GUI-based tool specific for astronomical objects. The most similar tools can be ML generic dataset-creation GUI tools such as image-sorter2 or DataTurks, but their functionality is limited for our use case. For example, our tool can display both RGB and FITS images of the same object at time to perform a better classification. Also, our tool can be used without mouse interaction -- all its functionality can be accessed using keyboard shortcuts, which is a essential speed-up in the workflow when classifying large datasets.

  • If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:
    Presubmission Inquiry for GALAssify: A Python package for visually classifying astronomical objects #189

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1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩

[Manalclay]

Dear pyOpenSci team,

I posted this submission with my other personal (non-scientific) GitHub account (@Malclay). I hope this won't be a problem. If so, please let me repost this submission with my scientific account (@Manalclay).

Thank you!

[hamogu]

Hi! Thanks for your submission @Manalclay! While we are waiting for our Editor-in-Chief to get started with the first look, I already wanted to ask you a question: I noticed that the purpose of your package is listed as "classifying astronomical objects" (though of course other science areas also look at images), so I thought that it might make sense to tick the "astropy" box under "partnerships", but I notice that you didn't do that. So I'm just checking if that's on purpose?

Any pyopensci package will be fully listed on pyopensci - astropy and pang are in addition, not instead of that. So, if your package deal with astronomical data, there really is no downside to also make it "astropy partnership" in this submission.

[hamogu]

Sorry, I just went back to #189 and saw that we already discussed this in #189 (comment) a few months ago. I'm sorry I forgot - please disregard my comment.

[SimonMolinsky]

Hi @Malclay !

I'm currently Editor-in-Chief, so I will start the review process. I need (max) one day for the initial check of your package. Expect the feedback tomorrow!

[Manalclay]

Hello again!

Sorry, I just went back to #189 and saw that we already discussed this in #189 (comment) a few months ago. I'm sorry I forgot - please disregard my comment.

No problem at all :) Thanks for check the presubmission!

Hi @Malclay !

I'm currently Editor-in-Chief, so I will start the review process. I need (max) one day for the initial check of your package. Expect the feedback tomorrow!

Take your time! Thanks for the fast response

[SimonMolinsky]

Editor in Chief checks

Hi there @Malclay ! Thank you for submitting your package for pyOpenSci
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Editor comments

Hi @Malclay !

I've performed initial checks of your package. It's rather uncanny to see the package using QT here, but I know that image classification tasks need to be done manually, and PyQT is one of the best options for GUI programs in Python. I think creating user-facing software is a very hard task, and I'm impressed by your work!

I've encountered a few problems, and here are these with links to the Gitlab issues:

1. Problems with the installation: https://gitlab.com/astrogal/GALAssify/-/issues/13
2. Why does documentation refer to installation directly from the repository when your package is published on PyPI?
3. I couldn't find API docs. Have you written API documentation? In this context, tool customization is your user-facing API. However, you must also provide internal documentation of the project methods and classes for contributors (see point 4).
4. Usually, the package we review should have maintenance workflows documented. This means you should assume that other contributors will eventually join your project. Thus, the package should have a Contributing File as well as some documentation describing low-level API for programmers (see point 3.) The CONTRIBUTING.md file is a bare minimum. I've created an issue here for this point: https://gitlab.com/astrogal/GALAssify/-/issues/14
5. The same as above for Code of Conduct.
6. Do you plan to submit your package to JOSS?

[Manalclay]

Hello @SimonMolinsky !

First of all, thank you very much for your words and for your revision, which we really appreciate.

My colleague @andonij and me are addressing the points you listed. Currently, we added the code of conduct and the CONTRIBUITNG.md file, which points to a new developer guide located inside the doc/ folder.

We are trying to replicate the reported installation issues. All the progress is being reported at the link of the issue you already opened. Also, we are working now on the lack of the API documentation, which is being written using docstrings, as suggested.

Answering to 6th point: A paper of the package was already submitted to JOSS (see the related issue). Unfortunately, it was rejected due it did not pass the substantial scholarly effort criterion. I would have like this package to be published to JOSS, but I understand that it does not match the requisites. The reviewer recommended me to submit it to pyOpenSci, That's why I'm here :) I hope this does not represent an inconvenience to be able to publish with you. In that case, please let us know!

I have already filled the Initial onboarding survey, but I see this box is not checked in your report. There exist any problem locating my filled survey? I can fill it again if needed.

[SimonMolinsky]

Hi @Manalclay

Thanks for your effort. Contribution files and API docs are critical issues right now. Thanks for checking the macOS installation issue; I will test it now and let you know how it went in your repository.

I'm sorry that your package didn't meet the scholarly effort criteria in JOSS. JOSS review and decisions are not a problem for us, but I needed to know about the potential submission because it leads to additional package and documentation checks in pyOpenSci.

Yes, I see that you have completed the onboarding survey. Thanks!