Astronomical data analysis development leveraging the Jupyter platform
Project description
jdaviz is a package of astronomical data analysis visualization tools based on the Jupyter platform. These GUI-based tools link data visualization and interactive analysis. They are designed to work within a Jupyter notebook cell, as a standalone desktop application, or as embedded windows within a website – all with nearly-identical user interfaces. Note that jdaviz is under heavy development and should not be considered stable or feature-complete. Users who encounter bugs in the currently implemented features are encouraged to open an issues in this repository.
jdaviz applications currently include tools for interactive visualization of spectroscopic data. SpecViz is a tool for visualization and quick-look analysis of 1D astronomical spectra. MOSViz is a visualization tool for many astronomical spectra, typically the output of a multi-object spectrograph (e.g., JWST NIRSpec), and includes viewers for 1D and 2D spectra as well as contextual information like on-sky views of the spectrograph slit. CubeViz provides of view of spectroscopic data cubes (like those to be produced by JWST MIRI), along with 1D spectra extracted from the cube.
Installing
For details on installing and using JDAViz, see the JDAViz documentation.
License
This project is Copyright (c) JDADF Developers and licensed under the terms of the BSD 3-Clause license. This package is based upon the Astropy package template which is licensed under the BSD 3-clause licence. See the licenses folder for more information.
Contributing
We love contributions! jdaviz is open source, built on open source, and we’d love to have you hang out in our community.
Imposter syndrome disclaimer: We want your help. No, really.
There may be a little voice inside your head that is telling you that you’re not ready to be an open source contributor; that your skills aren’t nearly good enough to contribute. What could you possibly offer a project like this one?
We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one’s coding skills. Writing perfect code isn’t the measure of a good developer (that would disqualify all of us!); it’s trying to create something, making mistakes, and learning from those mistakes. That’s how we all improve, and we are happy to help others learn.
Being an open source contributor doesn’t just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you’re coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.
Note: This disclaimer was originally written by Adrienne Lowe for a PyCon talk, and was adapted by jdaviz based on its use in the README file for the MetPy project.
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