Installation

Note

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+https://github.com/spacetelescope/jdaviz --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

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

jupyter nbextension enable --py widgetsnbextension

User Installation with Anaconda Navigator

If you prefer to go through Anaconda navigator, you will have to first create a new environment within the navigator interface. Click on Environments in the left bar and then click on Create in the bottom part of the interface.

Give a name to the environment (in this example we call it jdaviz_navigator) and select Python with a version greater than 3.11.

When the environment is ready, you will find it in the list of available environments in the interface. Click on the name of the environment and then on the green play symbol next to the name and select Open Terminal. If the terminal window shows the name of the environment and no errors, proceed to install jdaviz using pip.

pip install jdaviz

If this did not return any problems, you can go back to the Navigator and select the Home tab. Make sure the environment is selected at the top, then click Launch under JupyterLab. This will open a Jupyter Lab instance in your default browser and you are ready to use jdaviz.

In case the terminal returns errors or cannot run the pip command, there is a workaround. In the Navigator interface, click on the environment you created (we called it jdaviz_navigator in this example), then search for the package jupyter in the top right search field. Find jupyter, select it, and click Apply at the bottom right of the interface. Click again Apply after it has resolved the necessary packages.

This installs jupyter in the environment. When it is done, go back to the Home tab and make sure the environment is selected at the top. Click Launch under JupyterLab and wait for the browser to open a JupyterLab instance. Open an empty notebook by clicking on the Python 3 icon under Notebook. In the first cell, run the command

pip install jdaviz

This will serve as your terminal and should install jdaviz in the environment. If this does not return any errors, you are ready to use jdaviz with this empty notebook or any other as long as you launch Jupyter from this environment we created.

If you have a problem with the installation, feel free to submit an issue in Github or contact the JWST Help Desk.

Optional Dependencies for Roman

Dependencies for working with data products from the Roman Space Telescope are available for optional installation from PyPI with:

pip install -U jdaviz[roman]

or while building from source with:

pip install -U .[roman]

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 git@github.com:username/jdaviz.git
cd jdaviz
git remote add upstream git@github.com:spacetelescope/jdaviz.git
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
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.