# Setup data for adaptive source modelling img,sig,psf = galsub, sig2,psf2 #pl.figure() #pl.imshow(img) y,x = iT.coords(img.shape) y,x=y+10,x+15 cpsf = convolve.convolve(img,psf)[1] ifltm = img[mask] sfltm = sig[mask] vfltm = sfltm**2 cmatm = diags(1./sfltm,0) xm = x[mask] ym = y[mask] coords = [xm,ym] PSF = pT.getPSFMatrix(psf,img.shape) PSFm = pT.maskPSFMatrix(PSF,mask) iflt = img.flatten() sflt = sig.flatten() vflt = sflt**2 xflt = x.flatten() yflt = y.flatten() src = aT.AdaptiveSource(ifltm/sfltm,ifltm.size/Npnts) xl,yl = pylens.getDeflections(lenses,coords) src.update(xl,yl) osrc = showRes(xl,yl,src,PSFm,img,sig,mask,ifltm,vfltm,cmatm,1e-5,1,400) pylab.show()
# for now, we'll just use a single colour of lens img = img[:,:,0] mask = img*0 mask = np.where(mask==0,True,False) # nicht sicher ifltm = img1[mask] sfltm = sig1[mask] vfltm = sfltm**2 # variance from standard deviation. cmatm = diags(1./sfltm,0) # make a diagnonal covariance matrix xm = x[mask] ym = y[mask] coords = [xm,ym] PSF = pT.getPSFMatrix(psf1,img1.shape) # 2750x2750 - why so large? PSFm = pT.maskPSFMatrix(PSF,mask) # for us, as we have no mask, this is a repeat of the above iflt = img1.flatten() sflt = sig1.flatten() vflt = sflt**2 xflt = x.flatten() yflt = y.flatten() Npnts = 4 # Defines `fineness' of source reconstruction (bigger is coarser) src = aT.AdaptiveSource(ifltm/sfltm,ifltm.size/Npnts) reg = 1.
