The JWST post-pipeline data analysis ecosystem consists of several components (i.e., analysis software, visualization tools, and example Jupyter notebooks) that can be integrated for seamless viewing and analyzing of JWST data.   

The suite of post-pipeline data analysis tools is intended to help astronomers with the often iterative and interactive workflow involved in converting these science calibration pipeline data products into meaningful scientific results. This involves tasks such as:

• inspecting data and data quality information;
• masking or flagging data and using those annotations to guide later steps in the analysis;
• using the results of interactive analysis to guide a custom run of the pipeline (e.g., tweaking spectral extraction parameters or background estimates);
• performing optimized spectral extraction techniques; 
• combining data sets in various ways, with careful attention to astrometry, PSF matching, and other issues;
• source detection and photometry using different choices or algorithms than those used in the pipeline;
• measuring lines and continuum in spectral data;
• fitting models to data or otherwise testing hypotheses.

A typical workflow involves highly interactive exploratory analysis on small portions of the data, followed by the development of custom scripts to automate the analysis on larger data sets.  Further training is available via the JWebbinar series.

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Community contributions

Astropy is a community library, and the JWST analysis tools' success relies on community members, like you, to engage in the development process via bug reports (most effectively filed as GitHub issues, but the JWST help desk is fine as well), or by code contributions through GitHub pull requests. Use of the development versions of the code straight from GitHub comes with the following caveats: at any given time, the code may not actually run or return correct results, and the documentation may be inconsistent with the code. Users who are not interested in contributing to the development software should use fully released versions (a standard pip install). Details regarding the difference in the installation instructions can be found in the respective documentation for each package.

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