GitHub - morriscb/astro-stomp: STOMP is a spherical pixelization library for storing and analyzing astronomical data on the sphere.

This branch is 8 commits ahead of ryanscranton/astro-stomp:master.

Name	Name	Last commit message	Last commit date
Latest commit morriscb Fixed bug in Eta wrapping in GenerateRandomPoints methods. Dec 20, 2017 724eaae · Dec 20, 2017 History 286 Commits
examples	examples	Making changes to use c++11 libraries. This is necessary to generate …	Aug 25, 2013
experimental	experimental	Added varriable dark energy equation of state w to the cosmology module.	Jun 2, 2012
python	python	Removed cross-region correlations from code.	Jul 18, 2017
s2	s2	Fixed S2 to use c++11 as well. Still compilation problems with origin…	Aug 25, 2013
s2omp	s2omp	Updating the library to resolve the compile errors due to the switch …	Apr 18, 2015
stomp	stomp	Fixed bug in Eta wrapping in GenerateRandomPoints methods.	Dec 20, 2017
stomp_viewer	stomp_viewer	Verified that the changes to the correlation function calculations in…	Jul 1, 2010
INSTALL	INSTALL	added AngularCoordinate.Set(theta,phi,Sphere) method, easier to scrip…	Apr 10, 2010
Makefile.am	Makefile.am	Modifications to autoconf files to include s2 and s2omp directories	May 6, 2013
README	README	Beginning the conversion to automake tools.	Apr 6, 2010
autogen.sh	autogen.sh	Transitioned to autotools for basic library Makefiles. Still need to …	Apr 8, 2010
configure.ac	configure.ac	Removed gflags from configure.ac	Jul 22, 2016
stomp-1.0.pc.in	stomp-1.0.pc.in	Transitioned to autotools for basic library Makefiles. Still need to …	Apr 8, 2010

Repository files navigation

STOMP version 1.0
Author: ryan.scranton@gmail.com (Ryan Scranton)

STOMP is a set of libraries for doing astrostatistical analysis on the
celestial sphere.  The goal is to enable descriptions of arbitrary regions
on the sky which may or may not encode futher spatial information (galaxy
density, CMB temperature, observational depth, etc.) and to do so in such
a way as to make the analysis of that data as algorithmically efficient as
possible.