The swarp_wrapper is a Python wrapper for SWarp, a program that resamples and co-adds together FITS images using any arbitrary astrometric projection defined in the WCS standard.
The aim of the swarp_wrapper is to provide easy-to-use Python scripts for combining FITS data cubes and FITS images.
All credit for SWarp and its original Fortran implementation is due to Bertin et al. 2002 and should be acknowledged as such.
For tips on how to get started with the swarp_wrapper see the section Getting started further below. See also the official SWarp documentation for more details on the settings for astrometric projections.
The currently recommended version of the swarp_wrapper is v0.1. See the swarp_wrapper Changelog for an overview of the major changes and improvements introduced by newer versions currently in development.
New updates to the code are first tested and developed in the dev branch. Users cloning the dev branch should beware that these versions are not guaranteed to be stable.
To use swarp_wrapper you must have a working installation of SWarp on your operating system. You can find the necessary installation files for SWarp here. Moreover, you will need the following packages to run the swarp_wrapper. We list the version of each package which we know to be compatible with the swarp_wrapper.
If you do not already have Python 3.5, you can install the Anaconda Scientific Python distribution, which comes pre-loaded with numpy.
Download the swarp_wrapper using git $ git clone https://github.com/mriener/swarp_wrapper.git
Install pip for easy installation of python packages:
sudo apt-get install python-pipThen install the required python packages:
sudo pip install astropy numpy tqdmInstall pip for easy installation of python packages:
sudo easy_install pipThen install the required python packages:
sudo pip install astropy numpy tqdmThe SETTINGS.md file gives an overview about settings for the swarp_wrapper.
The example directory contains two example scripts on how to mosaic FITS data cubes and FITS image files.
If you should find that the swarp_wrapper does not perform as intended for your dataset or if you should come across bugs or have suggestions for improvement, please get into contact with us or open a new Issue or Pull request.
To contribute to the swarp_wrapper, see Contributing to the swarp_wrapper