JWST Calibration Pipeline

JWST requires Python 3.9 or above and a C compiler for dependencies.

Linux and MacOS platforms are tested and supported. Windows is not currently supported.

If installing on MacOS Mojave 10.14, you must install into an environment with python 3.9. Installation will fail on python 3.10 due to lack of a stable build for dependency opencv-python.

Installation

Please contact the JWST Help Desk for installation issues.

The easiest way to install the latest jwst release into a fresh virtualenv or conda environment is

pip install jwst

Detailed Installation

The jwst package can be installed into a virtualenv or conda environment via pip. We recommend that for each installation you start by creating a fresh environment that only has Python installed and then install the jwst package and its dependencies into that bare environment. If using conda environments, first make sure you have a recent version of Anaconda or Miniconda installed. If desired, you can create multiple environments to allow for switching between different versions of the jwst package (e.g. a released version versus the current development version).

In all cases, the installation is generally a 3-step process:

• Create a conda environment
• Activate that environment
• Install the desired version of the jwst package into that environment

Details are given below on how to do this for different types of installations, including tagged releases, DMS builds used in operations, and development versions. Remember that all conda operations must be done from within a bash/zsh shell.

Installing latest releases

You can install the latest released version via pip. From a bash/zsh shell:

conda create -n <env_name> python
conda activate <env_name>
pip install jwst

You can also install a specific version:

conda create -n <env_name> python
conda activate <env_name>
pip install jwst==1.9.4

Installing the development version from Github

You can install the latest development version (not as well tested) from the Github master branch:

conda create -n <env_name> python
conda activate <env_name>
pip install git+https://github.com/spacetelescope/jwst

Installing a DMS Operational Build

There may be occasions where an exact copy of an operational DMS build is desired (e.g. for validation testing or debugging operational issues). We package releases for DMS builds via environment snapshots that specify the exact versions of all packages to be installed.

To install a particular DMS build, consult the Software vs DMS build version map table shown below to determine the correct jwst tag. For example, to install the version of jwst used in DMS build 9.0, use jwst tag 1.8.2. The overall procedure is similar to the 3-step process outlined in the previous section, but the details of each command vary, due to the use of environment snapshot files that specify all of the particular packages to install. Also note that different snapshot files are used for Linux and Mac OS systems.

Linux:

conda create -n jwstdp-1.8.2 --file https://ssb.stsci.edu/releases/jwstdp/1.8.2/conda_python_stable-deps.txt
conda activate jwstdp-1.8.2
pip install -r https://ssb.stsci.edu/releases/jwstdp/1.8.2/reqs_stable-deps.txt

MacOS:

conda create -n jwstdp-1.8.2 --file https://ssb.stsci.edu/releases/jwstdp/1.8.2/conda_python_macos-stable-deps.txt
conda activate jwstdp-1.8.2
pip install -r https://ssb.stsci.edu/releases/jwstdp/1.8.2/reqs_macos-stable-deps.txt

Each DMS delivery has its own installation instructions, which may be found in the corresponding release documentation linked from this page: https://github.com/astroconda/astroconda-releases/tree/master/jwstdp The installation procedures may change from time to time, so consulting the documentation page for the specific version in question is the best way to get that version installed.

Installing for Developers

If you want to be able to work on and test the source code with the jwst package, the high-level procedure to do this is to first create a conda environment using the same procedures outlined above, but then install your personal copy of the code overtop of the original code in that environment. Again, this should be done in a separate conda environment from any existing environments that you may have already installed with released versions of the jwst package.

As usual, the first two steps are to create and activate an environment:

conda create -n <env_name> python
conda activate <env_name>

To install your own copy of the code into that environment, you first need to fork and clone the jwst repo:

cd <where you want to put the repo>
git clone https://github.com/<your_github_username>/jwst.git
cd jwst

Note: python setup.py install and python setup.py develop commands do not work.

Install from your local checked-out copy as an "editable" install:

pip install -e .

If you want to run the unit or regression tests and/or build the docs, you can make sure those dependencies are installed too:

pip install -e ".[test]"
pip install -e ".[docs]"
pip install -e ".[test,docs]"

Need other useful packages in your development environment?

pip install ipython jupyter matplotlib pylint

Calibration References Data System (CRDS) Setup

Note: As of November 10, 2022, the process of deprecating the CRDS PUB Server will start. For details, refer to the CRDS PUB Server Freeze and Deprecation page

CRDS is the system that manages the reference files needed to run the pipeline. For details about CRDS, see the User's Guide

The JWST CRDS server is available at https://jwst-crds.stsci.edu

It supports the automatic processing pipeline at STScI. Inside the STScI network, the same server is used by the pipeline by default with no modifications. To run the pipeline outside the STScI network, CRDS must be configured by setting two environment variables:

export CRDS_PATH=<locally-accessable-path>/crds_cache/jwst_ops
export CRDS_SERVER_URL=https://jwst-crds.stsci.edu

<locally-accessable-path> can be any the user has permissions to use, such as $HOME. Expect to use upwards of 200GB of disk space to cache the latest couple of contexts.

Documentation

Documentation (built daily from the Github master branch) is available at:

https://jwst-pipeline.readthedocs.io/en/latest/

To build the docs yourself, clone this repository and build the documentation with:

pip install -e ".[docs]"
cd docs
make html
make latexpdf

Contributions and Feedback

We welcome contributions and feedback on the project. Please follow the contributing guidelines to submit an issue or a pull request.

We strive to provide a welcoming community to all of our users by abiding with the Code of Conduct.

If you have questions or concerns regarding the software, please open an issue at https://github.com/spacetelescope/jwst/issues or contact the JWST Help Desk.

Software vs DMS build version map

The table below provides information on each release of the jwst package and its relationship to software builds used in the STScI JWST DMS operations environment. The Released column gives the date on which the jwst tag was released on PyPi and the Ops Install column gives the date on which the build incorporating that release was installed in DMS operations. Note that the CRDS_CONTEXT listed is a minimum context that can be used with that release. A release should work with any contexts between the specified context and less than the context for the next release.

Unit Tests

Unit tests can be run via pytest. Within the top level of your local jwst repo checkout:

pip install -e ".[test]"
pytest

Need to parallelize your test runs over all available cores?

pip install pytest-xdist
pytest -n auto

Regression Tests

Latest regression test results can be found here (STScI staff only):

https://plwishmaster.stsci.edu:8081/job/RT/job/JWST/

The test builds start at 6pm local Baltimore time Monday through Saturday on jwcalibdev.

To run the regression tests on your local machine, get the test dependencies and set the environment variable TEST_BIGDATA to our Artifactory server (STSci staff members only):

pip install -e ".[test]"
export TEST_BIGDATA=https://bytesalad.stsci.edu/artifactory

To run all the regression tests (except the very slow ones):

pytest --bigdata jwst/regtest

You can control where the test results are written with the --basetemp=<PATH> arg to pytest. NOTE that pytest will wipe this directory clean for each test session, so make sure it is a scratch area.

If you would like to run a specific test, find its name or ID and use the -k option:

pytest --bigdata jwst/regtest -k nirspec

If developers need to update the truth files in our nightly regression tests, there are instructions in the repository wiki.

https://github.com/spacetelescope/jwst/wiki/Maintaining-Regression-Tests