def main():
zd = 0.5
lens = lenses.PlummerLens(D(zd), { "mass": 1e14*MASS_SUN, "width": 5*ANGLE_ARCSEC })
li = plotutil.LensInfo(lens, size=15*ANGLE_ARCSEC, zd=zd)
iws = inversion.InversionWorkSpace(zd, 20*ANGLE_ARCSEC)
num = 5
print(f"Creating {num} sources")
while num > 0:
zs = np.random.uniform(zd, 6)
beta = np.random.uniform(-1*ANGLE_ARCSEC, 1*ANGLE_ARCSEC, size=(2,))
li.setSourceRedshift(zs)
ip = li.getImagePlane()
thetas = ip.traceBeta(beta)
if len(thetas) > 1:
num -= 1
print(f"Found image, {num} to go")
img = images.ImagesData(len(thetas))
for i in range(len(thetas)):
img.addPoint(i, thetas[i])
iws.addImageDataToList(img, zs, "pointimages")
print("Fitness info")
print("------------")
print(iws.calculateFitness(None))
print("")
imperfectLens = lenses.CompositeLens(D(zd), [
{ "lens": lens, "factor": 0.999, "x": 0, "y": 0, "angle": 0 } ])
for l, info in [ (lens, "True lens"), (imperfectLens, "Imperfect lens") ]:
s = f"Using: {info}"
print(s)
print("-" * len(s))
print("Using lens itself:")
print(iws.calculateFitness(l))
print("")
print("Using backprojected images:")
bpImages = iws.backProject(l)
print(iws.calculateFitness(bpImages))
print("")
V = lambda x, y: np.array([x, y], dtype=np.double)
cosm = cosmology.Cosmology(0.7, 0.3, 0, 0.7)
D = cosm.getAngularDiameterDistance
cosmology.setDefaultCosmology(cosm)
z1, z2 = 0.5, 1.5
sourceRedshifts = [ 1.25, 2.0 ]
allThetas = [ [ V(10,8)*ANGLE_ARCSEC ] , [ V(-12,2)*ANGLE_ARCSEC ] ]
for factor in [ 0.5, 1.0, 2.0 ]:
print("Using factor", factor)
lensesAndRedshifts = [
(lenses.CompositeLens(D(z1), [
{ "lens": lenses.PlummerLens(D(z1), { "mass": 1e14*MASS_SUN, "width": 5*ANGLE_ARCSEC }),
"x": 0, "y": 0, "angle": 0, "factor": factor }
]), z1),
(lenses.CompositeLens(D(z2), [
{ "lens": lenses.PlummerLens(D(z2), { "mass": 5e13*MASS_SUN, "width": 3*ANGLE_ARCSEC }),
"x": 1*ANGLE_ARCSEC, "y": -1*ANGLE_ARCSEC, "angle": 0, "factor": factor }
]), z2),
]
lensPlane = multiplane.MultiLensPlane(lensesAndRedshifts, V(-30,-30)*ANGLE_ARCSEC, V(30, 30)*ANGLE_ARCSEC, 511, 511)
for idx, zs, thetas in zip(range(len(sourceRedshifts)), sourceRedshifts, allThetas):
print("Positions for source", idx)
imgPlane = multiplane.MultiImagePlane(lensPlane, zs)
betas = imgPlane.traceTheta(np.array(thetas))
for bx, by in betas/ANGLE_ARCSEC:
plotutil.setDefaultAngularUnit(ANGLE_ARCSEC)
feedback.setDefaultFeedback("none")
zd1, zd2 = 0.5, 1.0
lens1 = lenses.CompositeLens(D(zd1), [
{
"factor":
0.7,
"x":
1.0 * ANGLE_ARCSEC,
"y":
-0.5 * ANGLE_ARCSEC,
"angle":
0,
"lens":
lenses.PlummerLens(D(zd1), {
"mass": 1e14 * MASS_SUN,
"width": 4 * ANGLE_ARCSEC
})
},
{
"factor": 1.0,
"x": 0,
"y": 0,
"angle": 0,
"lens": lenses.MassSheetLens(D(zd1), {"density": 1})
},
])
lens2 = lenses.CompositeLens(D(zd2), [
{
"factor":
MASS_SUN))
iws.setUniformGrid(64)
corrections, corrFitness, fitdesc = iws.invert(512,
baseLens=baseLens,
allowNegativeValues=True,
massScale=corrMassScale)
corrections.save("corrections.lensdata")
correctedLens = lenses.CompositeLens(baseLens.getLensDistance(),
[{
"factor": 1,
"x": 0,
"y": 0,
"angle": 0,
"lens": baseLens
}, {
"factor": 1,
"x": 0,
"y": 0,
"angle": 0,
"lens": corrections
}])
correctedLens.save("correctedlens.lensdata")
print(fitness1)
print(fitness2)
print(fitness3)
print(fitness4)
print(fitness5)
print(baseFitness)
print(corrFitness)
for x in lensplanes:
factor = np.random.uniform(0, 0.05)
lens = lenses.MassSheetLens(x["Dd"], {"Ds": 1, "Dds": 1})
x["params"].append({
"lens": lens,
"factor": factor,
"x": 0,
"y": 0,
"angle": 0
})
density = lens.getLensParameters()["density"]
print(f"{factor*density}, ", end='')
print("};")
lensesAndRedshifts = [(lenses.CompositeLens(x["Dd"], x["params"]), x["zd"])
for x in lensplanes]
# The grid dimensions are not used for this test
lensPlane = multiplane.MultiLensPlane(lensesAndRedshifts,
V(-50, -50) * ANGLE_ARCSEC,
V(50, 50) * ANGLE_ARCSEC, 64, 64)
numSources = np.random.randint(10, 20)
sourceRedshifts = np.random.uniform(min(lensRedShifts) + 0.1, 5.0, numSources)
# TODO: this doesn't seem to work if not sorted!
# sourceRedshifts = sorted(sourceRedshifts)
print(
"const vector<pair<double,vector<pair<Vector2Dd, Vector2Dd>>>> thetaBetaMappings {"
)
for zs in sourceRedshifts:
Z = cosm.getAngularDiameterDistance
# Use a default plot unit of one arcsec
plotutil.setDefaultAngularUnit(ANGLE_ARCSEC)
# A shorter name for the LensInfo class
LI = plotutil.LensInfo
# Create a SIS lens with a velocity dispersion of 250 km/s and
# set it at a specific position
z1 = 0.5
sis = lenses.SISLens(Z(z1), {"velocityDispersion": 250000})
movedSis = lenses.CompositeLens(Z(z1), [{
"lens": sis,
"factor": 1,
"x": -0.25 * ANGLE_ARCSEC,
"y": -0.5 * ANGLE_ARCSEC,
"angle": 0
}])
# Create a SIE lens with specific velocity dispersion and ellipticity
# at different redshift
z2 = 1.0
sie = lenses.SIELens(Z(z2), {"velocityDispersion": 200000, "ellipticity": 0.7})
movedSie = lenses.CompositeLens(Z(z2), [{
"lens": sie,
"factor": 1,
"x": 1 * ANGLE_ARCSEC,
"y": 0.75 * ANGLE_ARCSEC,
"angle": 60
}])
# Omega_m = 0.3 and Omega_lambda = 0.7
cosm = cosmology.Cosmology(0.7, 0.3, 0, 0.7)
# For a lens at z = 0.5, calculate the angular diameter distance
z_d = 0.5
D_d = cosm.getAngularDiameterDistance(z_d)
# Let's show what this angular diameter distance is, in units of Mpc
print("D_d = {:.2f} Mpc".format(D_d / DIST_MPC))
# Create a SIS lens with a velocity dispersion of 250 km/s
sis = lenses.SISLens(D_d, {"velocityDispersion": 250000})
# Create a SIE lens with specific velocity dispersion and ellipticity
sie = lenses.SIELens(D_d, {"velocityDispersion": 200000, "ellipticity": 0.7})
# Combine these models using a CompositeLens
combLens = lenses.CompositeLens(D_d, [{
"lens": sis,
"factor": 1,
"x": -1 * ANGLE_ARCSEC,
"y": -2 * ANGLE_ARCSEC,
"angle": 0
}, {
"lens": sie,
"factor": 1,
"x": 2 * ANGLE_ARCSEC,
"y": 1.5 * ANGLE_ARCSEC,
"angle": 60
}])