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https://github.com/caltech-ipac/montage

Image Mosaics for Astronomers
https://github.com/caltech-ipac/montage

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Image Mosaics for Astronomers

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README 

        

Montage: Astronomical Image Mosaics, Examination, and Visualization

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[![Build Status](http://vmmontage.ipac.caltech.edu:8080/buildStatus/icon?job=MontageDev)](http://vmmontage.ipac.caltech.edu:8080/job/MontageDev/)

![Jenkins tests](https://img.shields.io/jenkins/tests?compact_message&jobUrl=http%3A%2F%2Fvmmontage.ipac.caltech.edu%3A8080%2Fjob%2FMontageDev&passed_label=passed&skipped_label=skipped&failed_label=failed)

![GitHub Repo Size](https://img.shields.io/github/repo-size/Caltech-IPAC/Montage)

![Lines of code](https://img.shields.io/tokei/lines/github/Caltech-IPAC/Montage)



Montage 7.0 adds two major capabilities.  The first is a set of tools

for building HiPS map.  HiPS (Hierarchical Progressive Surveys) is a

hierarchical tiling mechanism which allows one to access, visualize

and browse seamlessly image data and in particular large-scale, 

high-resolution surveys.



HiPS construction through Montage consists of building large-scale

mosaics using pre-existing Montage modules (reprojection, background

matching, and coaddition) with a HiPS-specific projection (HPX).

The image hierarchy just requires repetative shrinking of higher

resolution images by factors of two.  Finally, the tiles to be 

served are simply 512x512 cutouts from these mosaics.



For high-resolution data, this process can benefit greatly from 

massive parallelization and this can be achieved in a number of

ways. In particular, tools have been developed to streamline this

on cloud platforms like AWS.



So at arcminute scale all-sky processing can adequately be done on 

a single desktop machine and at arcsecond scale the same tools can

create a set of jobs that can be submitted to run on a cloud in

a few days (or a few tens of processors if one has that in-house).



The second addition to Montage is a complete set of modern procedures 

for building Python binary extension Montage wheels for Linux and Mac 

systems (and extensible to some others).  This is very much a moving

target and we are building as many of these as we can and pushing them

to PyPI but if someone want to extend Montage for their own use they

can use the same infrastructure to build custom wheels as well.



The Montage 6.0 release can be accessed via git tag "v6.1".



--------------



Montage (http://montage.ipac.caltech.edu) is an Open Source toolkit,

distributed with a BSD 3-clause license, for assembling Flexible 

Image Transport System (FITS) images into mosaics according to 

the user's custom specifications of coordinates, projection,

spatial sampling, rotation and background matching.



The toolkit contains utilities for reprojecting and background 

matching images, assembling them into mosaics, visualizing the

results, and discovering, analyzing and understanding image metadata

from archives or the user's images.



Montage is written in ANSI-C and is portable across all common

Unix-like platforms, including Linux, Solaris, Mac OSX and Cygwin on

Windows.  The package provides both stand-alone executables and

the same functionality in library form.  It has been cross-compiled

to provide native Windows executables and packaged as a binary Python

extension (available via "pip install MontagePy").



The distribution contains all libraries needed to build the toolkit 

from a single simple "make" command, including CFITSIO and the WCS

library (which has been extended to support HEALPix and World-Wide

Telescope TOAST projections. The toolkit is in wide use in astronomy

to support research projects, and to support pipeline development,

product generation and image visualization for major projects and

missions; e.g. Spitzer Space Telescope, Herschel, Kepler, AKARI and

others. Montage is used as an exemplar application by the computer

science community in developing next-generation cyberinfrastructure,

especially workflow frameworks on distributed platforms, including

multiple clouds.



Montage provides multiple reprojection algorithms optimized for 

different needs, maximizing alternately flux conservation, range of

projections, and speed.



The visualization module supports full (three-color) display of FITS

images and publication quality overlays of catalogs (scaled symbols),

image metadata, and coordinate grids.  It fits in equally well in

pipelines or as the basis for interactive image exploration and there

is Python support for the latter (It has also been used in web/Javascript

applications).



We are in the process of adding automated regression testing using Jenkins.

At the moment, this only includes a couple of dummy tests on a Jenkins server 

that we maintain specifically for the Montage project).



Montage was funded from 2002 to 2005 by the National Aeronautics and

Space Administration's Earth Science Technology Office, Computation

Technologies Project, under Cooperative Agreement Number NCC5-626

between NASA and the California Institute of Technology. The Montage

distribution includes an adaptation of the MOPEX algorithm developed

at the Spitzer Science Center. Montage has also been funded by the National

Science Foundation under Award Number NSF ACI-1440620.