from autolens.fit.plotters import masked_imaging_fit_plotters
from test import simulate_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 = simulate_util.load_test_imaging(
data_type="lens_light_dev_vaucouleurs", data_resolution="lsst")
mask = al.mask.elliptical(
shape=imaging.shape,
pixel_scales=imaging.pixel_scales,
major_axis_radius=3.0,
axis_ratio=1.0,
phi=0.0,
centre=(0.0, 0.0),
)
# aplt.imaging.subplot(imaging=imaging, mask=mask, zoom_around_mask=True, aspect='equal')
# aplt.imaging.subplot(imaging=imaging, mask=mask, zoom_around_mask=True, aspect='auto')
# aplt.imaging.image(imaging=imaging, mask=mask, zoom_around_mask=True, aspect='square')
# aplt.imaging.image(imaging=imaging, mask=mask, zoom_around_mask=True, aspect='equal')
# 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(mass=al.mp.EllipticalIsothermal(
centre=(1.0, 1.0), einstein_radius=1.6, axis_ratio=0.7, phi=45.0))
source_galaxy = al.Galaxy(
pixelization=al.pix.VoronoiMagnification(shape=(20, 20)),
from test import simulate_util
from autolens.plot import plotters
# 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 = simulate_util.load_test_imaging(
data_type="lens_light_dev_vaucouleurs", data_resolution="lsst")
array = imaging.image
mask = al.Mask.circular(
shape_2d=imaging.shape_2d,
pixel_scales=imaging.pixel_scales,
radius=5.0,
centre=(0.0, 0.0),
)
aplt_array.array(array=array,
mask=mask,
positions=[[(1.0, 1.0)]],
centres=[[(0.0, 0.0)]])
imaging = simulate_util.load_test_imaging(
data_type="lens_sis__source_smooth__offset_centre", data_resolution="lsst")
array = imaging.image
mask = al.Mask.circular(
shape_2d=imaging.shape_2d,
pixel_scales=imaging.pixel_scales,
radius=5.0,
