def test__within_circle__no_conversion_factor__two_profile_gal__integral_is_sum_of_individual_profiles(
self):
sie_0 = mp.EllipticalIsothermal(axis_ratio=0.8,
phi=10.0,
einstein_radius=1.0)
sie_1 = mp.EllipticalIsothermal(axis_ratio=0.6,
phi=30.0,
einstein_radius=1.2)
integral_radius = 5.5
mass_integral = sie_0.mass_within_circle(radius=integral_radius)
mass_integral += sie_1.mass_within_circle(radius=integral_radius)
gal_sie = g.Galaxy(
redshift=0.5,
mass_profile_1=mp.EllipticalIsothermal(axis_ratio=0.8,
phi=10.0,
einstein_radius=1.0),
mass_profile_2=mp.EllipticalIsothermal(axis_ratio=0.6,
phi=30.0,
einstein_radius=1.2))
gal_mass_integral = gal_sie.mass_within_circle(
radius=integral_radius)
assert mass_integral == gal_mass_integral
def simulate():
from autolens.data.array import grids
from autolens.model.galaxy import galaxy as g
from autolens.lens import ray_tracing
psf = ccd.PSF.simulate_as_gaussian(shape=(11, 11),
sigma=0.05,
pixel_scale=0.05)
image_plane_grid_stack = grids.GridStack.grid_stack_for_simulation(
shape=(180, 180), pixel_scale=0.05, psf_shape=(11, 11))
lens_galaxy_0 = g.Galaxy(light=lp.EllipticalSersic(centre=(0.0, -1.0),
axis_ratio=0.8,
phi=55.0,
intensity=0.1,
effective_radius=0.8,
sersic_index=2.5),
mass=mp.EllipticalIsothermal(centre=(1.0, 0.0),
axis_ratio=0.7,
phi=45.0,
einstein_radius=1.0))
lens_galaxy_1 = g.Galaxy(light=lp.EllipticalSersic(centre=(0.0, 1.0),
axis_ratio=0.8,
phi=100.0,
intensity=0.1,
effective_radius=0.6,
sersic_index=3.0),
mass=mp.EllipticalIsothermal(centre=(-1.0, 0.0),
axis_ratio=0.8,
phi=90.0,
einstein_radius=0.8))
source_galaxy = g.Galaxy(light=lp.SphericalExponential(
centre=(0.05, 0.15), intensity=0.2, effective_radius=0.5))
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy_0, lens_galaxy_1],
source_galaxies=[source_galaxy],
image_plane_grid_stack=image_plane_grid_stack)
return ccd.CCDData.simulate(array=tracer.image_plane_image_for_simulation,
pixel_scale=0.05,
exposure_time=300.0,
psf=psf,
background_sky_level=0.1,
add_noise=True)
def pipeline():
tools.reset_paths(test_name=test_name, output_path=output_path)
lens_light = lp.SphericalDevVaucouleurs(centre=(0.0, 0.0),
intensity=0.1,
effective_radius=0.5)
lens_mass = mp.EllipticalIsothermal(centre=(0.0, 0.0),
axis_ratio=0.8,
phi=80.0,
einstein_radius=1.6)
source_light = lp.EllipticalSersic(centre=(0.0, 0.0),
axis_ratio=0.6,
phi=90.0,
intensity=1.0,
effective_radius=0.5,
sersic_index=1.0)
lens_galaxy = galaxy.Galaxy(dev=lens_light, sie=lens_mass)
source_galaxy = galaxy.Galaxy(sersic=source_light)
tools.simulate_integration_image(test_name=test_name,
pixel_scale=0.1,
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
target_signal_to_noise=30.0)
ccd_data = ccd.load_ccd_data_from_fits(
image_path=path + '/data/' + test_name + '/image.fits',
psf_path=path + '/data/' + test_name + '/psf.fits',
noise_map_path=path + '/data/' + test_name + '/noise_map.fits',
pixel_scale=0.1)
pipeline = make_pipeline(test_name=test_name)
pipeline.run(data=ccd_data)
def test_1d_symmetry(self):
isothermal_1 = mp.EllipticalIsothermal(axis_ratio=0.5,
phi=45.0,
einstein_radius=1.0)
isothermal_2 = mp.EllipticalIsothermal(centre=(100, 0),
axis_ratio=0.5,
phi=45.0,
einstein_radius=1.0)
gal_isothermal = g.Galaxy(redshift=0.5,
mass_profile_1=isothermal_1,
mass_profile_2=isothermal_2)
assert gal_isothermal.surface_density_from_grid(
np.array([[1.0,
0.0]])) == gal_isothermal.surface_density_from_grid(
np.array([[99.0, 0.0]]))
assert gal_isothermal.surface_density_from_grid(
np.array([[49.0,
0.0]])) == gal_isothermal.surface_density_from_grid(
np.array([[51.0, 0.0]]))
assert gal_isothermal.potential_from_grid(np.array(
[[1.0, 0.0]])) == pytest.approx(
gal_isothermal.potential_from_grid(np.array([[99.0,
0.0]])),
1e-6)
assert gal_isothermal.potential_from_grid(np.array(
[[49.0, 0.0]])) == pytest.approx(
gal_isothermal.potential_from_grid(np.array([[51.0,
0.0]])),
1e-6)
assert gal_isothermal.deflections_from_grid(np.array([[
1.0, 0.0
]])) == pytest.approx(
gal_isothermal.deflections_from_grid(np.array([[99.0, 0.0]])),
1e-6)
assert gal_isothermal.deflections_from_grid(np.array([[
49.0, 0.0
]])) == pytest.approx(
gal_isothermal.deflections_from_grid(np.array([[51.0, 0.0]])),
1e-6)
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)
def make_no_lens_source_cuspy(sub_grid_size):
# This source-only system has a smooth source (low Sersic Index) and simple SIE mass profile.
lens_galaxy = g.Galaxy(mass=mp.EllipticalIsothermal(centre=(0.0, 0.0), einstein_radius=1.6,
axis_ratio=0.7, phi=45.0))
source_galaxy = g.Galaxy(light=lp.EllipticalSersic(centre=(0.0, 0.0), axis_ratio=0.8, phi=60.0,
intensity=0.1, effective_radius=0.5, sersic_index=3.0))
for pixel_scale, shape in zip(pixel_scales, shapes):
simulate_image_from_galaxies_and_output_to_fits(lens_name='no_lens_source_cuspy', shape=shape,
pixel_scale=pixel_scale, sub_grid_size=sub_grid_size,
lens_galaxies=[lens_galaxy], source_galaxies=[source_galaxy])
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 pipeline():
lens_mass = mp.EllipticalIsothermal(centre=(0.01, 0.01),
axis_ratio=0.8,
phi=80.0,
einstein_radius=1.6)
source_bulge_0 = lp.EllipticalSersic(centre=(0.01, 0.01),
axis_ratio=0.9,
phi=90.0,
intensity=1.0,
effective_radius=1.0,
sersic_index=4.0)
source_bulge_1 = lp.EllipticalSersic(centre=(0.1, 0.1),
axis_ratio=0.9,
phi=90.0,
intensity=1.0,
effective_radius=1.0,
sersic_index=4.0)
lens_galaxy = galaxy.Galaxy(sie=lens_mass)
source_galaxy = galaxy.Galaxy(bulge_0=source_bulge_0,
bulge_1=source_bulge_1)
tools.reset_paths(test_name=test_name, output_path=output_path)
tools.simulate_integration_image(test_name=test_name,
pixel_scale=0.1,
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
target_signal_to_noise=30.0)
ccd_data = ccd.load_ccd_data_from_fits(
image_path=path + '/data/' + test_name + '/image.fits',
psf_path=path + '/data/' + test_name + '/psf.fits',
noise_map_path=path + '/data/' + test_name + '/noise_map.fits',
pixel_scale=0.1)
pipeline = make_pipeline(test_name=test_name)
pipeline.run(data=ccd_data)
def make_sie_1():
return mp.EllipticalIsothermal(axis_ratio=0.6,
phi=30.0,
einstein_radius=1.2)
def make_sie_0():
return mp.EllipticalIsothermal(axis_ratio=0.8,
phi=10.0,
einstein_radius=1.0)
def test__simulate_lensed_source_and_fit__include_psf_blurring__chi_squared_is_0__noise_normalization_correct(
):
psf = ccd.PSF.simulate_as_gaussian(shape=(3, 3),
pixel_scale=0.2,
sigma=0.75)
grid_stack = grids.GridStack.grid_stack_for_simulation(shape=(11, 11),
pixel_scale=0.2,
psf_shape=psf.shape,
sub_grid_size=1)
lens_galaxy = g.Galaxy(light=lp.EllipticalSersic(centre=(0.1, 0.1),
intensity=0.1),
mass=mp.EllipticalIsothermal(centre=(0.1, 0.1),
einstein_radius=1.8))
source_galaxy = g.Galaxy(
light=lp.EllipticalExponential(centre=(0.1, 0.1), intensity=0.5))
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=grid_stack)
ccd_simulated = ccd.CCDData.simulate(
array=tracer.image_plane_image_for_simulation,
pixel_scale=0.2,
exposure_time=300.0,
psf=psf,
background_sky_level=None,
add_noise=False)
ccd_simulated.noise_map = np.ones(ccd_simulated.image.shape)
path = "{}/data/simulate_and_fit".format(
os.path.dirname(os.path.realpath(
__file__))) # Setup path so we can output the simulated image.
try:
shutil.rmtree(path)
except FileNotFoundError:
pass
if os.path.exists(path) == False:
os.makedirs(path)
array_util.numpy_array_to_fits(array=ccd_simulated.image,
file_path=path + '/image.fits')
array_util.numpy_array_to_fits(array=ccd_simulated.noise_map,
file_path=path + '/noise_map.fits')
array_util.numpy_array_to_fits(array=psf, file_path=path + '/psf.fits')
ccd_data = ccd.load_ccd_data_from_fits(image_path=path + '/image.fits',
noise_map_path=path +
'/noise_map.fits',
psf_path=path + '/psf.fits',
pixel_scale=0.2)
mask = msk.Mask.circular(shape=ccd_data.image.shape,
pixel_scale=0.2,
radius_arcsec=0.8)
lens_data = ld.LensData(ccd_data=ccd_data, mask=mask, sub_grid_size=1)
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack)
fitter = lens_fit.LensProfileFit(lens_data=lens_data, tracer=tracer)
assert fitter.chi_squared == pytest.approx(0.0, 1e-4)
galaxy_plotters.plot_potential(galaxy=galaxy_with_3_mass_profiles, grid=grid_stack.regular)
# Finally, a galaxy can take both light and mass profiles, and there is no limit to how many we pass it.
light_profile_1 = light_profiles.SphericalSersic(centre=(0.0, 0.0), intensity=1.0,
effective_radius=1.0, sersic_index=1.0)
light_profile_2 = light_profiles.SphericalSersic(centre=(1.0, 1.0), intensity=1.0,
effective_radius=2.0, sersic_index=2.0)
light_profile_3 = light_profiles.SphericalSersic(centre=(2.0, 2.0), intensity=1.0,
effective_radius=3.0, sersic_index=3.0)
light_profile_4 = light_profiles.EllipticalSersic(centre=(1.0, -1.0), axis_ratio=0.5, phi=45.0,
intensity=1.0, effective_radius=1.0, sersic_index=1.0)
mass_profile_1 = mass_profiles.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0)
mass_profile_2 = mass_profiles.SphericalIsothermal(centre=(1.0, 1.0), einstein_radius=2.0)
mass_profile_3 = mass_profiles.SphericalIsothermal(centre=(2.0, 2.0), einstein_radius=3.0)
mass_profile_4 = mass_profiles.EllipticalIsothermal(centre=(1.0, -1.0), axis_ratio=0.5, phi=45.0,
einstein_radius=2.0)
galaxy_with_many_profiles = galaxy.Galaxy(light_1=light_profile_1, light_2=light_profile_2,
light_3=light_profile_3, light_4=light_profile_4,
mass_1=mass_profile_1, mass_2=mass_profile_2,
mass_3=mass_profile_3, mass_4=mass_profile_4)
# Suffice to say, the galaxy's images, surface density, potential and deflections look pretty
# interesting.
galaxy_plotters.plot_intensities(galaxy=galaxy_with_many_profiles, grid=grid_stack.regular)
galaxy_plotters.plot_surface_density(galaxy=galaxy_with_many_profiles, grid=grid_stack.regular)
galaxy_plotters.plot_potential(galaxy=galaxy_with_many_profiles, grid=grid_stack.regular)
galaxy_plotters.plot_deflections_y(galaxy=galaxy_with_many_profiles, grid=grid_stack.regular)
galaxy_plotters.plot_deflections_x(galaxy=galaxy_with_many_profiles, grid=grid_stack.regular)
mass_in_ellipse_SLACS = lens_galaxy_slacs.mass_within_ellipse_in_units(
major_axis=slacs['b_SIE'][i] / np.sqrt(slacs['q_SIE'][i]),
unit_mass='solMass',
redshift_source=slacs['z_source'][i],
cosmology=cosmo)
mass_in_ellipse = lens_galaxy.mass_within_ellipse_in_units(
major_axis=einstein_radius_autolens / np.sqrt(axis_ratio_autolens),
unit_mass='solMass',
redshift_source=slacs['z_source'][i],
cosmology=cosmo)
sie = mp.EllipticalIsothermal(
centre=(results.loc[lens[i]]['param']['centre_0'],
results.loc[lens[i]]['param']['centre_1']),
axis_ratio=slacs['q_SIE'][i],
phi=slacs['PA'][i],
einstein_radius=slacs['b_SIE'][i])
## appending einstein mass results
M_Ein.append(einstein_mass)
M_Ein_error_hi.append(einstein_mass_error_hi)
M_Ein_error_low.append(einstein_mass_error_low)
M_Ein_rescaled.append(einstein_mass_rescaled)
q_autolens.append(axis_ratio_autolens)
r_autolens.append(einstein_radius_autolens)
r_rescaled.append(einstein_radius_rescaled)
M_ellipse.append(mass_in_ellipse)
radius_from_area.append(Rad_area)
q_error_hi.append(q_upper_error)
pipeline_path='/howtolens/c3_t3_complex_source/')
pipeline_complex_source.run(data=ccd_data)
# Okay, so with 4 sources, we still couldn't get a good a fit to the source that didn't leave residuals. The thing is,
# I did infact simulate the lens with 4 sources. This means that there is a 'perfect fit', somewhere in that parameter
# space, that we unfortunately missed using the pipeline above.
#
# Lets confirm this, by manually fitting the ccd data with the true input model.
lens_data = ld.LensData(ccd_data=ccd_data,
mask=msk.Mask.circular(
shape=ccd_data.shape,
pixel_scale=ccd_data.pixel_scale,
radius_arcsec=3.0))
lens_galaxy = g.Galaxy(mass=mp.EllipticalIsothermal(
centre=(0.0, 0.0), axis_ratio=0.8, phi=135.0, einstein_radius=1.6))
source_galaxy_0 = g.Galaxy(light=lp.EllipticalSersic(centre=(0.1, 0.1),
axis_ratio=0.8,
phi=90.0,
intensity=0.2,
effective_radius=1.0,
sersic_index=1.5))
source_galaxy_1 = g.Galaxy(light=lp.EllipticalSersic(centre=(-0.25, 0.25),
axis_ratio=0.7,
phi=45.0,
intensity=0.1,
effective_radius=0.2,
sersic_index=3.0))
source_galaxy_2 = g.Galaxy(light=lp.EllipticalSersic(centre=(0.45, -0.35),
axis_ratio=0.6,
phi=90.0,
lens_plane_source_model_image.append(model_image)
else:
slacs = slacs.drop([lens_name[i]], axis=0)
#array_plotters.plot_array(array=image, title='slacs2303+1422')
#array_plotters.plot_array(array=model_image, title='slacs2303+1422 source model image plane')
for i in range(len(SLACS_image)):
## galaxies without external shear component
grid = al.Grid.uniform(shape_2d=lens_plane_source_model_image[i].shape,
pixel_scales=0.03,
sub_size=4)
sie = mp.EllipticalIsothermal(
centre=(results.loc[lens[i]]['param']['centre_0'],
results.loc[lens[i]]['param']['centre_1']),
axis_ratio=results.loc[lens[i]]['param']['axis_ratio'],
phi=results.loc[lens[i]]['param']['phi'],
einstein_radius=results.loc[lens[i]]['param']['einstein_radius'])
critical_curves = sie.critical_curves_from_grid(grid=grid)
caustics = sie.caustics_from_grid(grid=grid)
critical_curve_tan, critical_curve_rad = critical_curves[
0], critical_curves[1]
caustic_tan, caustic_rad = caustics[0], caustics[1]
# naming for x and y for plotting
xcritical_tan, ycritical_tan = critical_curve_tan[:,
1], critical_curve_tan[:,
0]
# xcritical_rad, ycritical_rad = critical_curve_rad_grid[:,1], critical_curve_rad_grid[:,0]
from autolens.model.profiles import mass_profiles
from autolens.array import grids
from autolens.model.profiles.plotters import profile_plotters
grid = al.Grid.from_shape_pixel_scale_and_sub_size(shape=(100, 100),
pixel_scale=0.05,
sub_size=4)
sis_mass_profile = mass_profiles.EllipticalIsothermal(centre=(1.0, 1.0),
einstein_radius=1.6,
axis_ratio=0.7)
profile_plotters.plot_convergence(
mass_profile=sis_mass_profile,
grid=grid,
plot_critical_curves=False,
plot_caustics=False,
)
# galaxy = al.Galaxy(mass=sis_mass_profile, redshift=1)
#
# galaxy_plotters.plot_convergence(
# galaxy=galaxy,
# grid=grid,
# plot_critical_curves=False,
# plot_caustics=True,
# )
sub_grid_size = 4
radius_arcsec = 3.0
psf_shape = (21, 21)
print('sub grid size = ' + str(sub_grid_size))
print('circular mask radius = ' + str(radius_arcsec) + '\n')
print('psf shape = ' + str(psf_shape) + '\n')
lens_galaxy = g.Galaxy(light=lp.EllipticalSersic(centre=(0.0, 0.0),
axis_ratio=0.9,
phi=45.0,
intensity=0.5,
effective_radius=0.8,
sersic_index=4.0),
mass=mp.EllipticalIsothermal(centre=(0.0, 0.0),
einstein_radius=1.6,
axis_ratio=0.7,
phi=45.0))
source_galaxy = g.Galaxy(light=lp.EllipticalSersic(centre=(0.0, 0.0),
axis_ratio=0.8,
phi=60.0,
intensity=0.4,
effective_radius=0.5,
sersic_index=1.0))
for image_type in ['LSST', 'Euclid', 'HST', 'HST_Up', 'AO']:
ccd_data = tools.load_profiling_ccd_data(
image_type=image_type,
lens_name='lens_and_source_smooth',
psf_shape=psf_shape)
from autolens.model.profiles import mass_profiles as mp
from autolens.array import grids
from autolens.array.util import grid_util
from autolens.lens import plane as pl
from autolens.model.galaxy import galaxy as g
import matplotlib.pyplot as plt
import numpy as np
# setting up grid and mass profile
sie = mp.EllipticalIsothermal(centre=(0.0, 0.0),
einstein_radius=1.4,
axis_ratio=0.7,
phi=40.0)
grid = al.Grid.uniform(shape_2d=(100, 100), pixel_scales=0.05, sub_size=1)
# loading convergence, critical curves, and caustics
kappa = sie.convergence_from_grid(grid=grid, return_in_2d=True)
critical_curves = sie.critical_curves_from_grid(grid=grid)
caustics = sie.caustics_from_grid(grid=grid)
# naming tangential and radial critical curves
critical_curve_tan, critical_curve_rad = critical_curves[0], critical_curves[1]
caustic_tan, caustic_rad = caustics[0], caustics[1]
# converting to pixel grid (incapable of plotting convergence in arcseconds -_________- )
critical_curve_tan_grid = grid_util.grid_arcsec_1d_to_grid_pixels_1d(
grid_arcsec_1d=critical_curve_tan,
shape_2d=(100, 100),
pixel_scales=(0.05, 0.05),
print('Grid')
print(lens_data.grid_stack.regular)
# The image, noise-map and grids are masked using the mask and mapped to 1D arrays for fast calcuations.
print(lens_data.image.shape) # This is the original 2D image
print(lens_data.image_1d.shape)
print(lens_data.noise_map_1d.shape)
print(lens_data.grid_stack.regular.shape)
# To fit an image, we need to create an image-plane image using a tracer.
# Lets use the same tracer we simulated the ccd data with (thus, our fit should be 'perfect').
# Its worth noting that below, we use the lens_data's grid-stack to setup the tracer. This ensures that our image-plane
# image will be the same resolution and alignment as our image-data, as well as being masked appropriately.
lens_galaxy = g.Galaxy(mass=mp.EllipticalIsothermal(centre=(0.0, 0.0), einstein_radius=1.6, axis_ratio=0.7, phi=45.0))
source_galaxy = g.Galaxy(light=lp.EllipticalSersic(centre=(0.1, 0.1), axis_ratio=0.8, phi=45.0,
intensity=1.0, effective_radius=1.0, sersic_index=2.5))
tracer = ray_tracing.TracerImageSourcePlanes(lens_galaxies=[lens_galaxy], source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack)
ray_tracing_plotters.plot_image_plane_image(tracer=tracer)
# To fit the image, we pass the lens data and tracer to the fitting module. This performs the following:
# 1) Blurs the tracer's image-plane image with the lens data's PSF, ensuring that the telescope optics are
# accounted for by the fit. This creates the fit's 'model_image'.
# 2) Computes the difference between this model_image and the observed image-data, creating the fit's 'residual_map'.
# 3) Divides the residuals by the noise-map and squaring each value, creating the fit's 'chi_squared_map'.
print(image_plane_grid_stack.sub.shape) # Every regular-pixel is sub-gridded by 4x4, so the sub-grid has x16 more coordinates.
# Next, lets setup a lens galaxy. In the previous tutorial, we set up each profile one line at a time. This made
# code long and cumbersome to read. This time we'll setup easy galaxy using one block of code.
# To help us, we've imported the 'light_profiles' and 'mass_profiles' modules as 'lp' and 'mp', and the
# 'galaxy' module as 'g'.
# We'll also give the lens galaxy some attributes we didn't in the last tutorial:
# 1) A light-profile, meaning its light will appear in the image-plane image.
# 2) An external shear, which accounts for the deflection of light due to line-of-sight structures.
# 3) A redshift, which the tracer will use to convert arc second coordinates to kpc.
lens_galaxy = g.Galaxy(light=lp.SphericalSersic(centre=(0.0, 0.0), intensity=2.0, effective_radius=0.5,
sersic_index=2.5),
mass=mp.EllipticalIsothermal(centre=(0.0, 0.0), axis_ratio=0.8, phi=90.0, einstein_radius=1.6),
shear=mp.ExternalShear(magnitude=0.05, phi=45.0),
redshift=0.5)
print(lens_galaxy)
# Lets also create a small satellite galaxy nearby the lens galaxy and at the same redshift.
lens_satellite = g.Galaxy(light=lp.SphericalDevVaucouleurs(centre=(1.0, 0.0), intensity=2.0, effective_radius=0.2),
mass=mp.SphericalIsothermal(centre=(1.0, 0.0), einstein_radius=0.4),
redshift=0.5)
print(lens_satellite)
# Lets have a quick look at the appearance of our lens galaxy and its satellite
galaxy_plotters.plot_intensities(galaxy=lens_galaxy, grid=image_plane_grid_stack.regular, title='Lens Galaxy')
galaxy_plotters.plot_intensities(galaxy=lens_satellite, grid=image_plane_grid_stack.regular, title='Lens Satellite')
# And their deflection angles - note that the satellite doesn't contribute as much to the deflections
print('sub grid size = ' + str(sub_grid_size))
print('circular mask radius = ' + str(radius_arcsec) + '\n')
for image_type in ['LSST', 'Euclid', 'HST', 'HST_Up', 'AO']:
ccd_data = tools.load_profiling_ccd_data(image_type=image_type, lens_name='no_lens_source_smooth', psf_shape=(3,3))
mask = msk.Mask.circular(shape=ccd_data.shape, pixel_scale=ccd_data.pixel_scale, radius_arcsec=radius_arcsec)
lens_data = ld.LensData(ccd_data=ccd_data, mask=mask, sub_grid_size=sub_grid_size)
print('Deflection angle run times for image type ' + image_type + '\n')
print('Number of points = ' + str(lens_data.grid_stack.sub.shape[0]) + '\n')
### EllipticalIsothermal ###
mass_profile = mp.EllipticalIsothermal(centre=(0.0, 0.0), axis_ratio=0.8, phi=45.0, einstein_radius=1.0)
start = time.time()
mass_profile.deflections_from_grid(grid=lens_data.grid_stack.sub)
diff = time.time() - start
print("EllipticalIsothermal time = {}".format(diff))
### SphericalIsothermal ###
mass_profile = mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0)
start = time.time()
mass_profile.deflections_from_grid(grid=lens_data.grid_stack.sub)
diff = time.time() - start
print("SphericalIsothermal time = {}".format(diff))