Montage Montage is an astronomical image toolkit with components for reprojection, background matching, coaddition and visualization of FITS files. It can be used as a set of command-line tools (Linux, OS X and Windows), C library calls (Linux and OS X) and as Python binary extension modules.
The Montage source is written in ANSI-C and code can be downloaded from GitHub ( https://github.com/Caltech-IPAC/Montage ). The Python package can be installed from PyPI ("</i>pip install MontagePy"). The package has no external dependencies. See http://montage.ipac.caltech.edu/ for details on the design and applications of Montage.
The Montage modules are generally used as steps in a workflow to create a mosaic of a set of input images. These steps are: determine the geometry of the mosaic on the sky, reproject the images to a common frame and spatial sampling; rectify the backgrounds to a common level, and coadd the images into a mosaic. This page illustrates the use of one Montage module, mBgExec, which is used to modify the backgrounds for a set of images.
Visit Building a Mosaic with Montage to see how mBgExec is used as part of a workflow to creage a mosaic (or the one shot version if you just want to see the commands). See the complete list of Montage Notebooks here.
from MontagePy.main import mBgExec, mViewer help(mBgExec)
Help on built-in function mBgExec in module MontagePy.main: mBgExec(...) mBgExec takes the background correction for each image (usually from mBgModel) and subtracts it using mBackground. Parameters ---------- path : str Path to input image directory. tblfile : str Table file list of images to correct. fitfile : str Table of background correction parameters. corrdir : str Directory for output corrected images. noAreas : bool, optional Flag indicating there are no area images. debug : int, optional Debugging output level. Returns ------- status : int Return status (0: OK, 1:ERROR). msg : str Return message (for errors). count : int Total number of images. nocorrection : int Number of images with no correction parameters. failed : int Number of images where the correction failed.
If we coadd a set of reprojected 2MASS images after reprojection but without doing anything about the backgrounds, the differences in background levels are obvious:
from IPython.display import Image mViewer('-color black -imginfo M17/rimages.tbl \ -ct 1 -gray M17/uncorrected.fits -2s max gaussian-log \ -out work/M17/uncorrected.png', '', mode=2) Image(filename='work/M17/uncorrected.png')