The following details how to install the jdaviz Python package.

If you encounter problems while following these installation instructions, please consult known installation issues.

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.

User Installation

Some of Jdaviz’s dependencies require non-Python packages to work (particularly the front-end stack that is part of the Jupyter ecosystem). We recommend using Miniconda to easily manage a compatible Python environment for Jdaviz; it should work with most modern shells, except CSH/TCSH.

Once it is installed, we recommend you create a new environment rather than installing everything into the base environment, for example:

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

Installing the released version of Jdaviz can be done using pip:

pip install jdaviz --upgrade

or if you want the latest development version, you can install via GitHub:

pip install git+ --upgrade

Note that jdaviz requires Python 3.8 or newer. If your pip corresponds to an older version of Python, it will raise an error that it cannot find a valid package.

Users occasionally encounter problems running the pure pip install above. For those using conda, some problems may be resolved by pulling the following from conda instead of pip:

conda install bottleneck
conda install -c conda-forge notebook
conda install -c conda-forge jupyterlab
conda install -c conda-forge voila

You might also want to enable the ipywidgets notebook extension, as follows:

jupyter nbextension enable --py widgetsnbextension

Developer Installation

If you wish to contribute to Jdaviz, please fork the project to your own GitHub account. The following instructions assume your have forked the project and have connected your GitHub to SSH and username is your GitHub username. This is a one-setup setup:

git clone
cd jdaviz
git remote add upstream
git fetch upstream main
git fetch upstream --tags

To work on a new feature or bug-fix, it is recommended that you build upon the latest dev code in a new branch (e.g., my-new-feature). You also need the up-to-date tags for proper software versioning:

git checkout -b my-new-feature
git fetch upstream --tags
git fetch upstream main
git rebase upstream/main

For the rest of contributing workflow, it is very similar to how to make code contribution to astropy, except for the change log. If your patch requires a change log, see CHANGES.rst for examples.

To install jdaviz for development or from source in an editable mode (i.e., changes to the locally checked out code would reflect in runtime after you restarted the Python kernel):

pip install -e .

Optionally, to enable the hot reloading of Vue.js templates, install watchdog:

pip install watchdog

After installing watchdog, to use it, add the following to the top of a notebook:

from jdaviz import enable_hot_reloading

See Quickstart to learn how to run jdaviz.