jdaviz is a package of astronomical data analysis visualization tools based on the Jupyter platform. It is one tool that is a part of STScI's larger Data Analysis Tools Ecosystem. 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. jdaviz is under active development, and users who encounter bugs in existing features are encouraged to open issues in this repository.

jdaviz provides data viewers and analysis plugins that can be flexibly combined as desired to create interactive applications that fit your workflow. Three named preset configurations for common use cases are provided. 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 a view of spectroscopic data cubes (like those to be produced by JWST MIRI), along with 1D spectra extracted from the cube. Imviz provides visualization and quick-look analysis for 2D astronomical images.

This tool is designed with instrument modes from the James Webb Space Telescope (JWST) in mind, but the tool should be flexible enough to read in data from many astronomical telescopes. The documentation provides a complete table of all supported modes.

Installing

You may want to consider installing jdaviz in a new virtual or conda environment to avoid version conflicts with other packages you may have installed, for example:

conda create -n jdaviz-env python=3.9
conda activate jdaviz-env

Installing the released version can be done using pip:

pip install jdaviz --upgrade

For details on installing and using Jdaviz, see the Jdaviz Installation.

Quick Start

Once installed, jdaviz can be run either as a standalone web application or in a Jupyter notebook.

As a Web Application

jdaviz provides a command-line tool to start the web application. To see the syntax and usage, from a terminal, type:

jdaviz --help
jdaviz specviz /path/to/data/spectral_file

For more information on the command line interface, see the Jdaviz Quickstart.

In a Jupyter Notebook

The power of jdaviz is that it can integrated into your Jupyter notebook workflow:

from jdaviz import Specviz

specviz = Specviz()
specviz.app

To learn more about the various jdaviz application configurations and loading data, see the specviz, cubeviz, mosviz, or imviz tools.

jdaviz also provides a directory of sample notebooks to test the application, located in the notebooks sub-directory of the git repository. CubevizExample.ipynb is provided as an example that loads a SDSS MaNGA IFU data cube with the Cubeviz configuration. To run the provided example, start the jupyter kernel with the notebook path:

jupyter notebook /path/to/jdaviz/notebooks/CubevizExample.ipynb

Help

If you uncover any issues or bugs, you can open a GitHub issue if they are not already reported. For faster responses, however, we encourage you to submit a JWST Help Desk Ticket.

License & Attribution

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.

Cite jdaviz via our Zenodo record: https://doi.org/10.5281/zenodo.6824713.

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.