mk_weight.idl
c99f2e455a51gradGPUmultiple

mk_weight.idl
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			; bayesCov.fits produced with .r bayes2fits.idl
			; the first 2 columns are Nsample & Chi2 (removed during weight map
			; processing)
			a=mrdfits('bayesCov.fits',1)
			names=tag_names(a)
			nx=n_elements(names)
			ny=n_elements(a)
			d=fltarr(nx-2,ny)
			for i=2, nx-1 do for j=0L, ny-1 do d[i-2,j]=a[j].(i)
			names=names[2:nx-1]
			nx=nx-2 ;; remove Nsample & lnhood columns

			cor=correlate(d, /double)
			writefits, 'corr.fits', cor

			cov=correlate(d, /covariance, /double)
			writefits, 'cov.fits', cov

			if determ(cov) eq 0 then begin
			print, "ERROR: null determinant"
			tmp=cov
			for i=0, nx-1 do tmp[i,i]=0 ; keep only non diag terms
			s = sort(abs(tmp))
			s = reverse(s)
			for i=0, 10, 2 do begin
			i_x = s[i] / nx
			i_y = s[i] mod nx
			print, format='(A," correlates with ",A," @ cov[",I3,",",I3,"] = ",E11.3)', names[i_x],names[i_y],i_x,i_y,cov[i_x,i_y]
			endfor



			stop
			endif

			weight=la_invert(cov, /double)
			writefits, 'weight.fits', weight

			END