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STScI tools and algorithms used in calibration pipelines

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STScI Calibration algorithms and tools.

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[!IMPORTANT] STCAL requires Python 3.10 or above and a C compiler for dependencies.

[!IMPORTANT] Linux and MacOS platforms are tested and supported. Windows is not currently supported.**

[!WARNING] Installation on MacOS Mojave 10.14 will fail due to lack of a stable build for dependency opencv-python.

STCAL is intended to be used as a support package for calibration pipeline software, such as the JWST and Roman calibration pipelines. STCAL is a separate package because it is also intended to be software that can be reused by multiple calibration pipelines. Even though it is intended to be a support package for calibration pipelines, it can be installed and used as a stand alone package. This could make usage unwieldy as it is easier to use STCAL through calibration software. The main use case for stand alone installation is for development purposes, such as bug fixes and feature additions. When installing calibration pipelines that depend on STCAL this package automatically gets installed as a dependency.


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

pip install stcal

Detailed Installation

The stcal 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 stcal 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 stcal 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 stcal package into that environment

Details are given below on how to do this for different types of installations, including tagged releases 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 stcal

You can also install a specific version, for example stcal 1.3.2:

conda create -n <env_name> python
conda activate <env_name>
pip install stcal==1.3.2

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+

Installing for Developers

If you want to be able to work on and test the source code with the stcal 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 stcal 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 stcal repo:

cd <where you want to put the repo>
git clone
cd stcal

Note: python install and python 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 ipdb

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

Unit Tests

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

pip install -e ".[test]"

Need to parallelize your test runs over all available cores?

pip install pytest-xdist
pytest -n auto

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