Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCASA 6: Modular Integration in Python
115
0
0.0
(
0
)
Authors
Ryan Raba
Ask ChatGPT about the research
No Arabic abstract
CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is often used also for other radio telescopes. CASA has always been distributed as a single, integrated application, including a Python interpreter and all the libraries, packages and modules. As part of the ongoing development of CASA 6, and the switch from Python 2 to 3, CASA will provide greater flexibility for users to integrate CASA into existing Python workflows by using a modular architecture and standard pip wheel installation. These proceedings of the 2019 Astronomical Data Analysis Software & Systems (ADASS) conference will give an overview of the CASA 6 project.
rate research
Read More
Currently, HOPS and AIPS are the primary choices for the time-consuming process of (millimeter) Very Long Baseline Interferometry (VLBI) data calibration. However, for a full end-to-end pipeline, they either lack the ability to perform easily scriptable incremental calibration or do not provide full control over the workflow with the ability to manipulate and edit calibration solutions directly. The Common Astronomy Software Application (CASA) offers all these abilities, together with a secure development future and an intuitive Python interface, which is very attractive for young radio astronomers. Inspired by the recent addition of a global fringe-fitter, the capability to convert FITS-IDI files to measurement sets, and amplitude calibration routines based on ANTAB metadata, we have developed the the CASA-based Radboud PIpeline for the Calibration of high Angular Resolution Data (rPICARD). The pipeline will be able to handle data from multiple arrays: EHT, GMVA, VLBA and the EVN in the first release. Polarization and phase-referencing calibration are supported and a spectral line mode will be added in the future. The large bandwidths of future radio observatories ask for a scalable reduction software. Within CASA, a message passing interface (MPI) implementation is used for parallelization, reducing the total time needed for processing. The most significant gain is obtained for the time-consuming fringe-fitting task where each scan be processed in parallel.
We present TurbuStat (v1.0): a Python package for computing turbulence statistics in spectral-line data cubes. TurbuStat includes implementations of fourteen methods for recovering turbulent properties from observational data. Additional features of the software include: distance metrics for comparing two data sets; a segmented linear model for fitting lines with a break-point; a two-dimensional elliptical power-law model; multi-core fast-fourier-transform support; a suite for producing simulated observations of fractional Brownian Motion fields, including two-dimensional images and optically-thin HI data cubes; and functions for creating realistic world coordinate system information for synthetic observations. This paper summarizes the TurbuStat package and provides representative examples using several different methods. TurbuStat is an open-source package and we welcome community feedback and contributions.
CASA, the Common Astronomy Software Applications package, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and NSFs Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio telescopes. The CASA software can process data from both single-dish and aperture-synthesis telescopes, and one of its core functionalities is to support the data reduction and imaging pipelines for ALMA, VLA and the VLA Sky Survey (VLASS). CASA has recently undergone several exciting new developments, including an increased flexibility in Python (CASA 6), support of Very Long Baseline Interferometry (VLBI), performance gains through parallel imaging, data visualization with the new Cube Analysis Rendering Tool for Astronomy (CARTA), enhanced reliability and testing, and modernized documentation. These proceedings of the 2019 Astronomical Data Analysis Software & Systems (ADASS) conference give an update of the CASA project, and detail how these new developments will enhance user experience of CASA.
The Atacama Large mm and sub-mm Array (ALMA) radio observatory is one of the worlds largest astronomical projects. After the very successful conclusion of the first observation cycles Early Science Cycles 0 and 1, the ALMA project can report many successes and lessons learned. The science data taken interleaved with commissioning tests for the still continuing addition of new capabilities has already resulted in numerous publications in high-profile journals. The increasing data volume and complexity are challenging but under control. The radio-astronomical data analysis package Common Astronomy Software Applications (CASA) has played a crucial role in this effort. This article describes the implementation of the ALMA data quality assurance system, in particular the level 2 which is based on CASA, and the lessons learned.
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.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
