JWST post-pipeline data analysis tools will be used for viewing and analyzing JWST data. While its development is still ongoing, overviews, software installation, and training materials are currently available for interested users.
JWST post-pipeline data analysis tools are distributed as part of AstroConda to assist observers in viewing and analyzing their JWST data. The tools are generally written in Python and work with Astropy. Development is ongoing1.
The suite of post-pipeline data analysis tools is intended to help astronomers with the often iterative and interactive workflow involved in converting these pipeline data products into meaningful scientific results. This involves tasks such as:
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.
The recommended way to install stable versions of the JWST data analysis tools is to use AstroConda.
There are development versions of many of the tools linked in the "Repository" column in Table 1 that link to the open-source development locations on Github. Contributions to these developments are welcomed 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 the versions in AstroConda.
Table 1 provides links to further information about the tools. User training workshops are being offered to help new users familiarize themselves with these tools.
Table 1. Software tools information
Levels of maturity run from prototypes with little or no documentation, symbolized by buds, , progressing through various levels: to .
The exact meanings of these icons are a bit hard to quantify, but the flowers will tend to be still lacking in documentation and important features. The cherries are generally quite robust and well documented. The cherry pies are ready to be baked into your day-to-day workflow.
Be aware that all of the packages above are in very active stages of development, including Astropy and glue. For the ones at the cherry pie level, there is significant attention given to backwards compatibility as the APIs of the different modules evolve.
There are many resources available for learning Python and for using Python for astronomical data analysis. This section provides pointers to astronomy-focused materials and to more JWST-specific training materials.
General python astronomical data analysis training
Table 2. List of training resources for python astronomical data analysis
JWST focused training materials
Training materials for JWST data analysis will eventually include worked examples of common workflows, using outputs from the JWST pipeline. Currently the materials are more general than that, and make use example data sets from other observatories. Table 3 provides links to these materials.
Table 3. List of JWST focused training materials
Table 4 shows a more topic-oriented map of the materials from the 2016 Workshop.
Table 4. Introductory material
Table 5. More specialized material