"{}/{}.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)
