nt2 = 100
size = 1
#Mass profile of the lens
kappa = pf.open('../Files/kappa.fits')[0].data
Fkappa = Lens.F(kappa, nt1, nt2, size, nt1 / 2., nt2 / 2.)
lensed = slit.lens_one(Fkappa, nt1, nt2, size)
#Levels for normalisation
lev = slit.level(nt1, nt1)
#Starlet transforms of the lens and source in their respective planes
wG = mw.wave_transform(G, lvl=6, newwave=1) / lev
wS = mw.wave_transform(S, lvl=6, newwave=1) / lev
#Lensed source
FS = Lens.source_to_image(S, nt1, nt2, Fkappa)
#Unlensed lens
FG = Lens.image_to_source(G, size, Fkappa, lensed=lensed)
#Starlet transform of the unlensed lens
wFG = mw.wave_transform(FG, 6, newwave=1) / lev
#Starlet transform of the lensed
wFS = mw.wave_transform(FS, 6, newwave=1) / lev
def mk_sort(X):
Y = np.sort(np.resize(np.abs(X), X.size))
return Y[::-1]
#Function that computes the reconstruction error from the p% highest coefficients
def error_rec_from(X, p, wave=0):
def F_apply(Si):
return Lens.source_to_image(Si, ns1, ns2, Fkappa)