def lens_one(Fkappa, n1, n2, size):
##DESCRIPTION:
## Function that maps an all at one image to source plane.
##
##INPUTS:
## -Fkappa: the mapping between source and image planes
## -n1,n2: the shape of the image.
## -size: the factor that scales the shape of the source relative to the shape of the image
##
##OUTPUTS:
## -lensed: the projection to source plane of an all at aone image.
dirac = np.ones((n1, n2))
lensed = Lens.image_to_source(dirac, size, Fkappa, lensed=[0])
return lensed
def plot_critical(kappa, Fkappa, n1, n2, size, extra=1):
det = 1. / Lens.Jacobian_det(kappa, n1, n2)[extra / 2:-extra / 2.,
extra / 2:-extra / 2.]
kernel = np.array([[1, 0], [0, -1]])
kernelT = np.array([[0, 1], [-1, 0]])
diff = np.abs(-det + np.abs(det))
diff[diff != 0] = 1
xderiv = scp.convolve2d(diff, kernel, mode='same')
yderiv = scp.convolve2d(diff, kernelT, mode='same')
x, y = np.where(np.abs(xderiv) + np.abs(yderiv) > 0)
#newx,newy = mk_curve(x,y)
critical = np.zeros((n1, n2))
critical[x, y] = 1
Splane = Lens.image_to_source(critical, Fkappa, 0)
xs, ys = np.where(Splane != 0)
#newxs,newys = mk_curve(xs,ys)
factor = np.float(size)
return x / factor, y / factor, xs, ys
def mk_bound(Fkappa, n1, n2, size):
##DESCRIPTION:
## Function that returns the support of the lens image in source plane.
##
##INPUTS:
## -Fkappa: the mapping between source and image planes
## -n1,n2: the shape of the image.
## -size: the factor that scales the shape of the source relative to the shape of the image
##
##OUTPUTS:
## -lensed: the projection to source plane of an all at aone image.
dirac = np.ones((n1, n2))
lensed = Lens.image_to_source_bound(dirac, size, Fkappa, lensed=[0])
bound = lensed / lensed
bound[lensed == 0] = 0
return bound
import SLIT as slit
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
import warnings
warnings.simplefilter("ignore")
S = pf.open('../Files/source.fits')[0].data
G = pf.open('../Files/Galaxy.fits')[0].data
##Sizes in image and source planes
nt1 = 100
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
def F_apply(Si):
return Lens.source_to_image(Si, ns1, ns2, Fkappa)
def Lens_op2(I):
return Lens.image_to_source(I, 1, Fkappa, lensed=lensed, square=1)
def Finv_apply(I):
return Lens.image_to_source(I, 1, Fkappa, lensed=lensed)