def make_fit_lens_only(lens_data, galaxy_light):
tracer = ray_tracing.TracerImagePlane(
lens_galaxies=[galaxy_light],
image_plane_grid_stack=lens_data.grid_stack,
cosmology=cosmo.Planck15)
return lens_fit.fit_lens_data_with_tracer(lens_data=lens_data,
tracer=tracer)
def make_fit(lens_data, galaxy_light, galaxy_mass):
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[galaxy_mass],
source_galaxies=[galaxy_light],
image_plane_grid_stack=lens_data.grid_stack,
cosmology=cosmo.Planck15)
return lens_fit.fit_lens_data_with_tracer(lens_data=lens_data,
tracer=tracer)
def perform_fit_with_lens_and_source_galaxy(lens_galaxy, source_galaxy):
ccd_data = simulate()
mask = msk.Mask.circular_annular(shape=ccd_data.shape,
pixel_scale=ccd_data.pixel_scale,
inner_radius_arcsec=0.5,
outer_radius_arcsec=2.2)
lens_data = ld.LensData(ccd_data=ccd_data, mask=mask)
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack,
border=lens_data.border)
return lens_fit.fit_lens_data_with_tracer(lens_data=lens_data,
tracer=tracer)
def perform_fit_with_source_galaxy(source_galaxy):
ccd_data = simulate()
mask = msk.Mask.circular_annular(shape=ccd_data.shape,
pixel_scale=ccd_data.pixel_scale,
inner_radius_arcsec=0.5,
outer_radius_arcsec=2.2)
lens_data = ld.LensData(ccd_data=ccd_data, mask=mask)
lens_galaxy = g.Galaxy(mass=mp.EllipticalIsothermal(
centre=(0.0, 0.0), axis_ratio=0.8, phi=135.0, einstein_radius=1.6))
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack,
border=lens_data.border)
return lens_fit.fit_lens_data_with_tracer(lens_data=lens_data,
tracer=tracer)
def perform_fit_with_source_galaxy_mask_and_border(source_galaxy, mask,
use_border):
ccd_data = simulate()
lens_data = ld.LensData(ccd_data=ccd_data, mask=mask)
lens_galaxy = g.Galaxy(mass=mp.EllipticalIsothermal(
centre=(0.0, 0.0), axis_ratio=0.8, phi=135.0, einstein_radius=1.6))
if use_border:
border = lens_data.border
else:
border = None
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack,
border=border)
return lens_fit.fit_lens_data_with_tracer(lens_data=lens_data,
tracer=tracer)
def perform_fit_x2_lenses_with_source_galaxy_mask_and_border(
source_galaxy, mask, use_border):
simulate_image_x2_lenses()
lens_data = ld.LensData(ccd_data=ccd_data, mask=mask)
lens_galaxy_0 = g.Galaxy(mass=mp.EllipticalIsothermal(
centre=(1.1, 0.51), axis_ratio=0.9, phi=110.0, einstein_radius=1.07))
lens_galaxy_1 = g.Galaxy(mass=mp.EllipticalIsothermal(
centre=(-0.20,
-0.35), axis_ratio=0.56, phi=16.0, einstein_radius=0.71))
if use_border:
border = lens_data.border
else:
border = None
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy_0, lens_galaxy_1],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack,
border=border)
return lens_fit.fit_lens_data_with_tracer(lens_data=lens_data,
tracer=tracer)
intensity=0.03,
effective_radius=0.3,
sersic_index=3.5))
source_galaxy_3 = g.Galaxy(light=lp.EllipticalSersic(centre=(-0.05, -0.0),
axis_ratio=0.9,
phi=140.0,
intensity=0.03,
effective_radius=0.1,
sersic_index=4.0))
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[
source_galaxy_0, source_galaxy_1, source_galaxy_2, source_galaxy_3
],
image_plane_grid_stack=lens_data.grid_stack)
true_fit = lens_fit.fit_lens_data_with_tracer(lens_data=lens_data,
tracer=tracer)
lens_fit_plotters.plot_fit_subplot(fit=true_fit)
# And indeed, we see far improved residuals, chi-squareds, etc.
# The morale of this story is that, if the source morphology is complex, there is no way we can build a pipeline to
# fit it. For this tutorial, this was true even though our source model could actually fit the data perfectly. For
# real lenses, the source will be *even more complex* and there is even less hope of getting a good fit :(
# But fear not, PyAutoLens has you covered. In chapter 4, we'll introduce a completely new way to model the source
# galaxy, which addresses the problem faced here. But before that, in the next tutorial we'll discuss how we actually
# pass priors in a pipeline.
phi=45.0,
einstein_radius=0.8))
source_galaxy = g.Galaxy(light=lp.EllipticalSersic(centre=(0.0, 0.0),
axis_ratio=0.5,
phi=90.0,
intensity=0.03,
effective_radius=0.3,
sersic_index=3.0))
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack)
# Now, lets fit the lensing image with the tracer and plot the fit. It looks a lot better than above, doesn't it?
correct_fit = lens_fit.fit_lens_data_with_tracer(lens_data=lens_data,
tracer=tracer)
lens_fit_plotters.plot_fit_subplot(fit=correct_fit,
should_plot_mask=True,
extract_array_from_mask=True,
zoom_around_mask=True)
# Finally, just to be sure, lets compare the two likelihoods
print('Likelihood of Non-linear Search:')
print(results.most_likely_fit.likelihood)
print('Likelihood of Correct Model:')
print(correct_fit.likelihood)
# Well, there we have it, the input model has a much higher likelihood than the one our non-linear search inferred.
# Clearly, our non-linear search failed. So, what happened? Where did it all go wrong?
