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GALAssify submission #214
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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! |
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. |
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. |
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! |
Hello again!
No problem at all :) Thanks for check the presubmission!
Take your time! Thanks for the fast response |
Editor in Chief checksHi there @Malclay ! Thank you for submitting your package for pyOpenSci Please check our Python packaging guide for more information on the elements
Editor commentsHi @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:
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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: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD
Code of Conduct & Commitment to Maintain Package
Description
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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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.
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the editor you contacted:Presubmission Inquiry for GALAssify: A Python package for visually classifying astronomical objects #189
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Please fill out a pre-submission inquiry before submitting a data visualization package. ↩
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