Presubmission Inquiry for GALAssify: A Python package for visually classifying astronomical objects

[Manalclay]

Submitting Author: Name (@github_handle)
Package Name: GALAssify
One-Line Description of Package: A Python package for visually classifying astronomical objects
Repository Link (if existing): https://gitlab.com/astrogal/GALAssify/

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• I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
• I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

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.

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existing community please check below:

• Astropy:My package adheres to Astropy community standards
• Pangeo: My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook

Scope

• Please indicate which category or categories this package falls under:
  Domain

Scope

• Please indicate which category or categories.
  Check out our package scope page to learn more about our
  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

Community Partnerships

If your package is associated with an
existing community please check below:

• Astropy:My package adheres to Astropy community standards

• Pangeo: My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:
  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 the 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 similar things? 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.

• Any other questions or issues we should be aware of:

P.S. Have feedback/comments about our review process? Leave a comment here

[hamogu]

Thanks for your presubmission inquiry. I'm just wondering: Your package was initially developed with astronomical data in mind and uses some parts of astropy (in particular astropy.io.fits), although, as you say, it's in principle it's use case is more general. So, I'm just wondering if you intend this package to also be considered for astropy-affiliation as part of the pyopensci review process (in addition to the general PyOpenSci listing) or not.
No pressure either way - it's certainly your choice. I just want to make sure that it's not just an oversight to tick the astropy box in the form.

[Manalclay]

Hi @hamogu,

I misunderstood the purpose of the astropy tick box. At the moment we do not aim GALAssify to be an affiliated astropy package. I'm editing the description and unticking this box now.

Thanks for noticing!

[dhomeier]

I misunderstood the purpose of the astropy tick box. At the moment we do not aim GALAssify to be an affiliated astropy package. I'm editing the description and unticking this box now.

Thanks for the clarification! You are still welcome to adhere to the Astropy community standards and acknowledge so; the crucial element for becoming an affiliated package is setting the astropy label, so we just needed to confirm that we don't set that.

[Manalclay]

Thanks to you for the information, we confirm that we do not set the astropy label for this submission.

[Batalex]

Hello @Manalclay, welcome to pyOpenSci!
Data annotation/labelling is a task of utmost importance to the scientific community. I definitely think that GALAssify is in scope for us. Would you mind opening a new submission issue referencing this pre-submission? Thank you.

[Manalclay]

Hello @Batalex , thank you for your words!

Yes, we plan to open a submission referencing this pre-submission issue. While reading the Technical Checks of the submission form, we found that we must have a test suite and a continuous integration setup in order to satisfy the submission requirements. We are currently working on that. Once we have it working, we will open the submission.

Thanks for your time and your patience.

[Manalclay]

Hello again! Thanks for your patience.

Finally, I could get the testing suite and the CI working flawlessly in the package repo. Therefore, I just open a submission for this package: #214

Thanks again for all your help and patience. I hope this package can be useful for someone as it was to me!