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")