source_galaxy = al.Galaxy(
redshift=1.0,
light=al.lp.EllipticalSersic(
centre=(0.0, 0.0),
elliptical_comps=(0.096225, -0.055555),
intensity=0.4,
effective_radius=0.5,
sersic_index=1.0,
),
)
for instrument in ["vro", "euclid", "hst", "hst_up", "ao"]:
imaging = instrument_util.load_test_imaging(
data_name="lens_sie__source_smooth",
instrument=instrument,
psf_shape_2d=psf_shape_2d,
)
mask = al.Mask.circular(
shape_2d=imaging.shape_2d,
pixel_scales=imaging.pixel_scales,
sub_size=sub_size,
radius=radius,
)
masked_imaging = al.MaskedImaging(imaging=imaging, mask=mask)
print("Light profile fit run times for image type " + instrument + "\n")
print("Number of points = " + str(masked_imaging.grid.sub_shape_1d) + "\n")
from autolens.plot import plotters
from test_autolens.simulate.imaging import instrument_util
# In this tutorial, we'll introduce a new pixelization, called an adaptive-pixelization. This pixelization doesn't use
# uniform grid of rectangular pixels, but instead uses ir'Voronoi' pixels. So, why would we want to do that?
# Lets take another look at the rectangular grid, and think about its weakness.
# Lets quickly remind ourselves of the image, and the 3.0" circular mask we'll use to mask it.
imaging = instrument_util.load_test_imaging(
data_name="lens_light_dev_vaucouleurs", instrument="vro")
array = imaging.image
mask = al.Mask.elliptical(
shape=imaging.shape_2d,
pixel_scales=imaging.pixel_scales,
major_axis_radius=6.0,
axis_ratio=0.5,
phi=0.0,
centre=(0.0, 0.0),
)
aplt.Array(array=array,
mask=mask,
positions=[[(1.0, 1.0)]],
centres=[[(0.0, 0.0)]])
imaging = instrument_util.load_test_imaging(
data_name="lens_sis__source_smooth__offset_centre", instrument="vro")
array = imaging.image
mask = al.Mask.elliptical(
shape=imaging.shape_2d,
import autolens as al
import autolens.plot as aplt
from test_autolens.simulate.imaging import instrument_util
# In this tutorial, we'll introduce a new pixelization, called an adaptive-pixelization. This pixelization doesn't use
# uniform grid of rectangular pixels, but instead uses ir'Voronoi' pixels. So, why would we want to do that?
# Lets take another look at the rectangular grid, and think about its weakness.
# Lets quickly remind ourselves of the image, and the 3.0" circular mask we'll use to mask it.
imaging = instrument_util.load_test_imaging(
data_name="lens_light_dev_vaucouleurs", instrument="vro"
)
array = imaging.image
array_overlay = al.Array.manual_2d(array=[[1.0, 2.0], [3.0, 4.0]], pixel_scales=5.05)
aplt.Array(array=array, array_overlay=array_overlay)
import autolens as al
from test_autolens.simulate.imaging import instrument_util
imaging = instrument_util.load_test_imaging(
data_name="lens_sie__source_smooth__offset_centre", instrument="vro")
def fit_with_offset_centre(centre):
mask = al.Mask.elliptical(
shape_2d=imaging.shape_2d,
pixel_scales=imaging.pixel_scales,
major_axis_radius=3.0,
axis_ratio=1.0,
phi=0.0,
centre=centre,
)
# The lines of code below do everything we're used to, that is, setup an image and its grid, mask it, trace it
# via a tracer, setup the rectangular mapper, etc.
lens_galaxy = al.Galaxy(
redshift=0.5,
mass=al.mp.EllipticalIsothermal(centre=(2.0, 2.0),
einstein_radius=1.2,
elliptical_comps=(0.17647, 0.0)),
)
source_galaxy = al.Galaxy(
redshift=1.0,
pixelization=al.pix.VoronoiMagnification(shape=(20, 20)),
regularization=al.reg.Constant(coefficient=1.0),
