"{}/{}.fits".format( data_directory, _ ), data=getattr(dataset, _) ) os.system( "rm -r ./output/{}*".format(phase_name) ) """ phase = ph.Phase( phase_name=phase_name, phase_folders=["test"], galaxies=dict( lens=lens, source_1=source_1, source_2=source_2, ), transformer_class=transformer_class, region=line_emission_region ) phase.optimizer.constant_efficiency = True phase.optimizer.n_live_points = 5 phase.optimizer.sampling_efficiency = 0.5 phase.optimizer.evidence_tolerance = 100.0 phase.run( dataset=dataset, xy_mask=xy_mask )
# transformers=transformers, # shape=cube.shape # ), # ncols=8 # ) # print("likelihood = ", fit_temp.likelihood) # exit() model_1 = af.PriorModel(profiles.EllipticalSersic) model_1.centre_0 = af.GaussianPrior(mean=0.0, sigma=0.25) model_1.centre_1 = af.GaussianPrior(mean=0.0, sigma=0.25) model_1.intensity = af.LogUniformPrior(lower_limit=5.0 * 10**-6.0, upper_limit=5.0 * 10**-4.0) model_2 = af.PriorModel(profiles.Kinematical) model_2.z_centre = af.GaussianPrior(mean=16.0, sigma=2.0) model_2.intensity = af.LogUniformPrior(lower_limit=10**-2.0, upper_limit=10**+2.0) phase_name = "phase_tutorial_5" os.system("rm -r output/{}*".format(phase_name)) phase = ph.Phase(phase_name=phase_name, profiles=af.CollectionPriorModel(model_1=model_1, model_2=model_2)) phase.optimizer.constant_efficiency = True phase.optimizer.n_live_points = 100 phase.optimizer.sampling_efficiency = 0.5 phase.optimizer.evidence_tolerance = 100.0 phase.run(dataset=dataset, xy_mask=xy_mask)
upper_limit=10**+2.0) src_model.maximum_velocity = af.UniformPrior(lower_limit=25.0, upper_limit=400.0) src_model.velocity_dispersion = af.UniformPrior(lower_limit=0.0, upper_limit=100.0) phase_folders = [ string_utils.remove_substring_from_end_of_string( string=os.path.basename(__file__), substring=".py") ] phase_1 = phase.Phase( phase_name="phase_1__version_{}".format(autolens_version), phase_folders=phase_folders, profiles=af.CollectionPriorModel( lens=lens_model, src_model=src_model, ), lens_redshift=lens_redshift, source_redshift=source_redshift, ) phase_1.optimizer.const_efficiency_mode = True phase_1.optimizer.n_live_points = 100 phase_1.optimizer.sampling_efficiency = 0.2 phase_1.optimizer.evidence_tolerance = 0.5 xy_mask = Mask3D.unmasked( shape_3d=grid_3d.shape_3d, pixel_scales=grid_3d.pixel_scales, sub_size=grid_3d.sub_size, )
lens.mass.axis_ratio = 0.75 lens.mass.phi = 45.0 lens.mass.einstein_radius = 1.0 lens.mass.slope = 2.0 phase_name = "phase_tutorial_7" data_directory = "./data/{}".format(phase_name) if not os.path.isdir(data_directory): os.system("mkdir {}".format(data_directory)) os.system("rm ./data/{}/*.fits".format(phase_name)) for _ in ["uv_wavelengths", "visibilities", "noise_map"]: fits.writeto("{}/{}.fits".format(data_directory, _), data=getattr(dataset, _)) os.system("rm -r ./output/{}*".format(phase_name)) phase = ph.Phase(phase_name=phase_name, galaxies=dict( lens=lens, source=source, ), transformer_class=transformer_class, region=line_emission_region) phase.optimizer.constant_efficiency = True phase.optimizer.n_live_points = 100 phase.optimizer.sampling_efficiency = 0.5 phase.optimizer.evidence_tolerance = 100.0 phase.run(dataset=dataset, xy_mask=xy_mask)
src_model_2.centre_1 = 0.0 src_model_2.z_centre = 16.0 src_model_2.intensity = 5.0 src_model_2.effective_radius = 0.5 src_model_2.inclination = 30.0 src_model_2.phi = 50.0 src_model_2.turnover_radius = 0.05 src_model_2.maximum_velocity = 200.0 src_model_2.velocity_dispersion = 50.0 # exit() phase_folders = ["test"] phase_1_name = "phase_tutorial_6__version_{}".format(autolens_version) os.system("rm -r output/test/{}".format(phase_1_name)) phase_1 = phase.Phase(phase_name=phase_1_name, phase_folders=phase_folders, profiles=af.CollectionPriorModel( lens=lens_model, src_model_1=src_model_1, src_model_2=src_model_2), lens_redshift=lens_redshift, source_redshift=source_redshift, regions=[emission_line_region]) phase_1.optimizer.constant_efficiency = True phase_1.optimizer.n_live_points = 100 phase_1.optimizer.sampling_efficiency = 0.5 phase_1.optimizer.evidence_tolerance = 100.0 phase_1.run(dataset=dataset, xy_mask=xy_mask)
phase_folders.append("data_{}_phase_errors".format( "with" if data_with_phase_errors else "without")) phase_folders.append( "model_{}_selfcal".format("with" if self_calibration else "without")) #evidence_tolerance = 0.5 #evidence_tolerance = 0.8 evidence_tolerance = 100.0 phase_folders.append("evidence_tolerance__{}".format(evidence_tolerance)) phase_1_name = "phase_tutorial_0__version_{}".format(autolens_version) # os.system( # "rm -r output/{}".format(phase_1_name) # ) phase_1 = phase.Phase( phase_name=phase_1_name, phase_folders=phase_folders, galaxies=dict( lens=lens, source=source, ), self_calibration=self_calibration, ) phase_1.optimizer.const_efficiency_mode = True phase_1.optimizer.n_live_points = 100 phase_1.optimizer.sampling_efficiency = 0.2 phase_1.optimizer.evidence_tolerance = evidence_tolerance phase_1.run(dataset=dataset, xy_mask=xy_mask)
# ) # print(likelihood) # exit() model = af.PriorModel(profiles.EllipticalSersic) model.centre_0 = af.GaussianPrior(mean=0.0, sigma=0.05) model.centre_1 = af.GaussianPrior(mean=0.5, sigma=0.05) model.axis_ratio = af.GaussianPrior(mean=0.75, sigma=0.05) model.phi = af.GaussianPrior(mean=45.0, sigma=5.0) # model.intensity = af.GaussianPrior( # mean=0.1, sigma=0.1 # ) model.effective_radius = af.GaussianPrior(mean=0.5, sigma=0.25) model.sersic_index = af.GaussianPrior(mean=1.0, sigma=0.2) phase_name = "phase_tutorial_1" os.system("rm -r output/{}".format(phase_name)) phase = ph.Phase( phase_name=phase_name, profiles=af.CollectionPriorModel(model=model), ) #non_linear_class=af.Emcee phase.optimizer.const_efficiency_mode = True phase.optimizer.n_live_points = 100 phase.optimizer.sampling_efficiency = 0.5 phase.optimizer.evidence_tolerance = 100.0 phase.run(dataset=dataset, mask=mask)
upper_limit=centre_0_upper_limit) subhalo_model.centre_1 = af.UniformPrior(lower_limit=centre_1_lower_limit, upper_limit=centre_1_upper_limit) phase_folders = [ string_utils.remove_substring_from_end_of_string( string=os.path.basename(__file__), substring=".py") ] phase_name = "phase_tutorial_6__version_{}__centre_0_{}_{}_centre_1_{}_{}".format( autolens_version, centre_0_lower_limit, centre_0_upper_limit, centre_1_lower_limit, centre_1_upper_limit) phase_1 = phase.Phase( phase_name=phase_name, phase_folders=phase_folders, profiles=af.CollectionPriorModel(lens=lens_model, subhalo=subhalo_model, source_1=source_model_1, source_2=source_model_2), lens_redshift=lens_redshift, source_redshift=source_redshift, ) phase_1.optimizer.constant_efficiency = True phase_1.optimizer.n_live_points = 100 phase_1.optimizer.sampling_efficiency = 0.5 phase_1.optimizer.evidence_tolerance = 100.0 phase_1.run(dataset=dataset, xy_mask=xy_mask)