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 together with Astropy. Development is ongoing. All software is open source and community contributions are welcome in the form of suggestions, bug reports, or actual code. Further details on how to contribute can be found at the Data Analysis Tools Development Forum.
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:
- 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);
- 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; and
- fitting models to data or otherwise testing hypotheses.
A typical workflow involves highly interactive exploratory analysis on small portions of the data, followed by 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 in STScI's Github repositories. You are welcome to test these and to contribute directly by creating or commenting on Github issues or modifying code and issuing linked in the Repository column in the table below, which links to the open-source development locations on Github. We welcome contributions to the development via bug reports (most effectively filed as github issues, but the JWST help desk is fine as well), or contributions of code 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. If you are not interested in contributing to the development, please use the versions in AstroConda.
The table below provides links to further information about the tools. User-training workshops are being offered to help familiarize new users.
Astronomy-related tools for python.
Linked dataset visualization.
2-D image visualization with python plug-in capability.
A community python library for astronomy (documentation & tutorials)
A Python library to explore relationships within and among related datasets.
A toolkit designed for building viewers for scientific image data in Python.
Tools for detecting and performing photometry of astronomical sources.
|Interactive image analysis (Python equivalent of IRAF imexam).|
mosvizVisualization and quick interactive analysis
|specviz||One-dimensional spectra visualization and analysis (Python equivalent of IRAF splot)|
Visualization and quick interactive
Convenience tools for working with point-spread functions (PSFs)
Cconvenience tools for working with astronomical images.
Tool for simple image examination, and plotting, with similar functionality to IRAF's imexamine.
An interactive astronomical 1D spectra analysis tool with similar functionality to IRAF's splot.
A quick-look analysis and visualization tool for multi-object spectroscopy.
Interactive analysis tool for 3-
|gwcs||Tools for constructing and manipulating world-coordinate systems (WCS). |
Supports a data model which includes the entire transformation pipeline from input coordinates (detector by default) to world coordinates.
d spectroscopy (coming soon)
Advanced Scientific Data Format
which can be packaged in FITS files, but which has much richer capabilities for handling metadata).
is a next generation interchange format for scientific data (
|astroimtools||Convenience tools for working with astronomical images|
Generalized World Coordinate System tools for dealing with image and spectral geometries (docs)
Synthetic photometry toolkit for building model spectra and estimating count-rates. (docs)
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 to 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 the more JWST-specific training materials.
General python astronomical data analysis training
|Practical Python for Astronomers||Web Documents|| ||2011, 2012, 2013 Smithsonian Astrophysical Observatory|
|Using Python for Astronomical Data Analysis||Notebooks|| ||January 2017 American Astronomical Society Special Session|
| || |
|Scientific Python Course at STScI||Notebooks||Videos||Notebooks are from 2015 version of course; videos from 2012-13|
|Astronomical Data Analysis with Python||Documents, notebooks||Videos||2014 Lecture series|
|Practical Python for Astronomers||Web Documents||documents|| ||2011, 2012, 2013 Smithsonian Astrophysical Observatory|
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 often example data sets from other observatories. The table below provides links to these materials.
Below is a more topic-oriented map of the materials from the 2016 Workshop.
More specialized material