jdaviz is undergoing constant development. We encourage users to always update to the latest version. In general, it is good practice to install the development version following the instructions below as full released versions may lag behind.

User Installation#

Windows-Specific Dependencies#

Some of our dependencies require C++ compilers to install properly. These are usually included with macOS and most Linux distributions, but are not included by default in Windows. Microsoft provides these tools as part of their Build Tools for Visual Studio, which can be found under “Tools for Visual Studio” towards the bottom of the page.

Create Your Local Environment#

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.

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=3.11
conda activate jdaviz-env

Pip Install#

As noted above, we typically recommend installing the latest development version:

pip install git+ --upgrade

A normal install will also work by installing the latest release version:

pip install jdaviz --upgrade

Common Issues#

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

Note that jdaviz requires Python 3.10 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 a code contribution to astropy, including setting up virtual environments, git basics, and more.

An exception is the change log; if your patch requires a change log, see CHANGES.rst for examples.

One option is 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

Another option is to enable magic commands for Python autoreloading, to use it, add the following to the top of a notebook:

%load_ext autoreload
%autoreload 2

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 .

Note: It is recommended to install the package without -e flag initially to ensure that the template files are copied correctly.