% OLA Inversion Input File
% Problem dimension comes 1st
2d ola
% 
% output base directory 2nd
./Examples/testdata/testOutput/2d/ola
! base directory for Kernel files
./Examples/testdata/input/2d
! base directory for Map files (input data files)
./Examples/testdata/input/2d
!input Kernel file (Kern1.fits) Map1.fits (input data file, e.g. d.fits)
! The order of the kernel-map pairs is important! The time dimension goes from 1 to 3
kern1.fits map1.fits
kern2.fits map2.fits
kern3.fits map3.fits
kern4.fits map4.fits
kern5.fits map5.fits
kern6.fits map6.fits
kern7.fits map7.fits
kern8.fits map8.fits
kern9.fits map9.fits
kern10.fits map10.fits
kern11.fits map11.fits
kern12.fits map12.fits
kern13.fits map13.fits
kern14.fits map14.fits
kern15.fits map15.fits
kern16.fits map16.fits
kern17.fits map17.fits
kern18.fits map18.fits
kern19.fits map19.fits
kern20.fits map20.fits
kern21.fits map21.fits
kern22.fits map22.fits
kern23.fits map23.fits
kern24.fits map24.fits
!Lambda is input noise covariance matrix;   FULL DIR PATH
./Examples/testdata/input/2d/C24.fits
! For OLA inversions, all averaging kernels are calculated 
! mu 
! muMin muMax Nmu	sigmaMin sigmaMax Nsigma
1.0 1.0 1 			5 10 2
