def run(
module, test_name=None, search=af.DynestyStatic(), config_folder="config", mask=None
):
test_name = test_name or module.test_name
test_path = "{}/../..".format(os.path.dirname(os.path.realpath(__file__)))
output_path = f"{test_path}/output/imaging"
config_path = f"{test_path}/{config_folder}"
conf.instance = conf.Config(config_path=config_path, output_path=output_path)
imaging = instrument_util.load_test_imaging(
data_name=module.data_name, instrument=module.instrument
)
if mask is None:
mask = al.Mask.circular(
shape_2d=imaging.shape_2d, pixel_scales=imaging.pixel_scales, radius=3.0
)
info = {"Test": 100}
module.make_pipeline(
name=test_name, folders=[module.test_type, test_name], search=search
).run(dataset=imaging, mask=mask, info=info)
import autolens as al
import autolens.plot as aplt
from test_autolens.simulators.imaging import instrument_util
import numpy as np
imaging = instrument_util.load_test_imaging(
dataset_name="light_sersic__source_sersic", instrument="vro")
array = imaging.image
plotter = aplt.Plotter(
figure=aplt.Figure(figsize=(10, 10)),
cmap=aplt.ColorMap(cmap="gray",
norm="symmetric_log",
norm_min=-0.13,
norm_max=20,
linthresh=0.02),
grid_scatterer=aplt.GridScatterer(marker="+", colors="cyan", size=450),
)
grid = al.GridIrregular(grid=[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]])
print(grid)
vector_field = al.VectorFieldIrregular(vectors=[(1.0, 2.0), (2.0, 1.0)],
grid=[(-1.0, 0.0), (-2.0, 0.0)])
aplt.Array(
array=array.in_2d,
grid=grid,
positions=al.GridIrregularGrouped([(0.0, 1.0), (0.0, 2.0)]),
from autolens.plot.mat_wrap import plotter
from test_autolens.simulators.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(
dataset_name="light_sersic__source_sersic", instrument="vro")
array = imaging.image
mask = al.Mask2D.circular(
shape_2d=imaging.shape_2d,
pixel_scales=imaging.pixel_scales,
radius=5.0,
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(
dataset_name="mass_sie__source_sersic__offset_centre", instrument="vro")
array = imaging.image
mask = al.Mask2D.circular(
shape_2d=imaging.shape_2d,
pixel_scales=imaging.pixel_scales,
radius=5.0,
centre=(0.0, 0.0), einstein_radius=1.6, elliptical_comps=(0.17647, 0.0)
),
)
pixelization = al.pix.VoronoiBrightnessImage(pixels=pixels)
data_points_total = []
profiling_dict = {}
xticks_list = []
times_list = []
memory_use = []
for instrument in ["vro", "euclid", "hst", "hst_up"]: # , 'ao']:
imaging = instrument_util.load_test_imaging(
data_name="lens_sie__source_smooth", instrument=instrument
)
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)
source_galaxy = al.Galaxy(
redshift=1.0,
pixelization=pixelization,
regularization=al.reg.Constant(coefficient=1.0),
elliptical_comps=(0.17647, 0.0)),
)
pixelization = al.pix.Rectangular(shape=pixelization_shape_2d)
source_galaxy = al.Galaxy(
redshift=1.0,
pixelization=pixelization,
regularization=al.reg.Constant(coefficient=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("Rectangular Inversion fit run times for image type " + instrument +
"\n")
print("Number of points = " + str(masked_imaging.grid.sub_shape_1d) + "\n")
