Package: jdaviz
jdavizis a package of astronomical data analysis visualization tools based on the Jupyter platform. It is one tool that is a part of STScI’s largerData 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.jdavizis under active development, and users who
encounter bugs in existing features are encouraged to open issues in this
repository.jdavizprovides 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.Specvizis a tool for visualization and quick-look analysis of 1D astronomical spectra.Mosvizis 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.Cubevizprovides a view of spectroscopic data cubes (like those to be
produced by JWST MIRI), along with 1D spectra extracted from the cube.Imvizprovides 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 completetable of all supported modes.InstallingYou may want to consider installingjdavizin a new virtual or conda environment to avoid
version conflicts with other packages you may have installed, for example:condacreate-njdaviz-envpython=3.9condaactivatejdaviz-envInstalling the released version can be done using pip:pipinstalljdaviz--upgradeFor details on installing and using Jdaviz, see theJdaviz Installation.Quick StartOnce installed,jdavizcan be run either as a standalone web application or in a Jupyter notebook.As a Web Applicationjdavizprovides a command-line tool to start the web application. To see the syntax and usage,
from a terminal, type:jdaviz--helpjdaviz--layout=specviz/path/to/data/spectral_fileFor more information on the command line interfaces for each tool, see theJdaviz docs.In a Jupyter NotebookThe power ofjdavizis that it can integrated into your Jupyter notebook workflow:fromjdavizimportSpecvizspecviz=Specviz()specviz.show()To learn more about the variousjdavizapplication configurations and loading data, see theSpecviz,Cubeviz,Mosviz,Imviz,
orSpecviz2Dtools.jdavizalso provides a directory ofsample notebooksto test the application, located in thenotebookssub-directory
of the git repository.CubevizExample.ipynbis provided as an example that loads a JWST data cube with theCubevizconfiguration.  To run the provided example, start the Jupyter kernel with the notebook path:jupyternotebook/path/to/jdaviz/notebooks/CubevizExample.ipynbHelpIf you uncover any issues or bugs, you canopen a GitHub issueif they are not already reported. For faster responses, however, we encourage you to
submit aJWST Help Desk Ticket.Recordings and instructional notebooks from live Jdaviz tutorials (as part of the JWebbinars series)
can be found atthe JWebbinar websiteunder the “Materials and Videos” expandable section. Scroll down to the bottom of that section to
find materials from the most recent session (JWebbinar 24, March 2023).License & AttributionThis project is Copyright (c) JDADF Developers and licensed under
the terms of the BSD 3-Clause license. This package is based upon
theAstropy package templatewhich is licensed under the BSD 3-clause licence. See thelicensesfolder for more information.Citejdavizvia our Zenodo record:https://doi.org/10.5281/zenodo.5513927.ContributingWe love contributions!jdavizis 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 byAdrienne Lowefor aPyCon talk, and was adapted by
jdaviz based on its use in the README file for theMetPy project.