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This paper describes how we have sustained the Montage image mosaic engine (http://montage.ipac.caltech.edu) first released in 2002, to support the ever-growing scale and complexity of modern data sets. The key to its longevity has been its design as a toolkit written in ANSI-C, with each tool performing one distinct task, for easy integration into scripts, pipelines and workflows. The same code base now supports Windows, JavaScript and Python by taking advantage of recent advances in compilers. The design has led to applicability of Montage far beyond what was anticipated when Montage was first built, such as supporting observation planning for the JWST. Moreover, Montage is highly scalable and is in wide use within the IT community to develop advanced, fault-tolerant cyber-infrastructure, such as job schedulers for grids, workflow orchestration, and restructuring techniques for processing complex workflows and pipelines.
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Image Processing in Python with Montage
The Montage image mosaic engine has found wide applicability in astronomy research, integration into processing environments, and is an examplar application for the development of advanced cyber-infrastructure. It is written in C to provide performance and portability. Linking C/C++ libraries to the Python kernel at run time as binary extensions allows them to run under Python at compiled speeds and enables users to take advantage of all the functionality in Python. We have built Python binary extensions of the 59 ANSI-C modules that make up version 5 of the Montage toolkit. This has involved a turning the code into a C library, with driver code fully separated to reproduce the calling sequence of the command-line tools; and then adding Python and C linkage code with the Cython library, which acts as a bridge between general C libraries and the Python interface. We will demonstrate how to use these Python binary extensions to perform image processing, including reprojecting and resampling images, rectifying background emission to a common level, creation of image mosaics that preserve the calibration and astrometric fidelity of the input images, creating visualizations with an adaptive stretch algorithm, processing HEALPix images, and analyzing and managing image metadata.
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- Award ID(s):
- 1835379
- PAR ID:
- 10133996
- Date Published:
- Journal Name:
- Astronomical Data Analysis Software and Systems XXVIII. ASP Conference Series, Vol. 523,.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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