def test__loads_from_config_file_correct(self):
zeus = af.Zeus(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
nwalkers=51,
nsteps=2001,
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
auto_correlations_settings=af.AutoCorrelationsSettings(
check_for_convergence=False,
check_size=101,
required_length=51,
change_threshold=0.02),
tune=False,
number_of_cores=2,
)
assert zeus.prior_passer.sigma == 2.0
assert zeus.prior_passer.use_errors is False
assert zeus.prior_passer.use_widths is False
assert zeus.config_dict_search["nwalkers"] == 51
assert zeus.config_dict_run["nsteps"] == 2001
assert zeus.config_dict_run["tune"] == False
assert isinstance(zeus.initializer, af.InitializerBall)
assert zeus.initializer.lower_limit == 0.2
assert zeus.initializer.upper_limit == 0.8
assert zeus.auto_correlations_settings.check_for_convergence is False
assert zeus.auto_correlations_settings.check_size == 101
assert zeus.auto_correlations_settings.required_length == 51
assert zeus.auto_correlations_settings.change_threshold == 0.02
assert zeus.number_of_cores == 2
zeus = af.Zeus()
assert zeus.prior_passer.sigma == 3.0
assert zeus.prior_passer.use_errors is True
assert zeus.prior_passer.use_widths is True
assert zeus.config_dict_search["nwalkers"] == 50
assert zeus.config_dict_run["nsteps"] == 2000
assert zeus.config_dict_run["tune"] == True
assert isinstance(zeus.initializer, af.InitializerPrior)
assert zeus.auto_correlations_settings.check_for_convergence is True
assert zeus.auto_correlations_settings.check_size == 100
assert zeus.auto_correlations_settings.required_length == 50
assert zeus.auto_correlations_settings.change_threshold == 0.01
assert zeus.number_of_cores == 1
Below we use zeus to fit the lens model, using the model with start points as described above. See the Zeus docs
for a description of what the input parameters below do.
"""
search = af.Zeus(
path_prefix=path.join("imaging", "searches"),
name="Zeus",
unique_tag=dataset_name,
nwalkers=30,
nsteps=200,
initializer=af.InitializerBall(lower_limit=0.49, upper_limit=0.51),
auto_correlations_settings=af.AutoCorrelationsSettings(
check_for_convergence=True,
check_size=100,
required_length=50,
change_threshold=0.01,
),
tune=False,
tolerance=0.05,
patience=5,
maxsteps=10000,
mu=1.0,
maxiter=10000,
vectorize=False,
check_walkers=True,
shuffle_ensemble=True,
light_mode=False,
iterations_per_update=5000,
number_of_cores=1,
)
result = search.fit(model=model, analysis=analysis)
"""
bulge.elliptical_comps.elliptical_comps_1 = af.UniformPrior(
lower_limit=-0.3, upper_limit=0.3
)
bulge.intensity = af.UniformPrior(lower_limit=0.1, upper_limit=0.5)
bulge.effective_radius = af.UniformPrior(lower_limit=0.0, upper_limit=0.4)
bulge.sersic_index = af.UniformPrior(lower_limit=0.5, upper_limit=2.0)
lens = af.Model(al.Galaxy, redshift=0.5, mass=mass, shear=shear)
source = af.Model(al.Galaxy, redshift=1.0, bulge=bulge)
model = af.Collection(galaxies=af.Collection(lens=lens, source=source))
search = af.Zeus(
path_prefix=path.join("howtolens", "chapter_2"),
name="tutorial_searches_zeus",
unique_tag=dataset_name,
nwalkers=50,
nsteps=1000,
)
print(
"Zeus has begun running - checkout the workspace/output"
" folder for live output of the results, images and lens model."
" This Jupyter notebook cell with progress once Dynesty has completed - this could take some time!"
)
result_zeus = search.fit(model=model, analysis=analysis)
print("Zeus has finished run - you may now continue the notebook.")
fit_imaging_plotter = aplt.FitImagingPlotter(fit=result_zeus.max_log_likelihood_fit)
dataset_path = path.join("dataset", "example_1d", "gaussian_x1")
data = af.util.numpy_array_from_json(
file_path=path.join(dataset_path, "data.json"))
noise_map = af.util.numpy_array_from_json(
file_path=path.join(dataset_path, "noise_map.json"))
model = af.Model(m.Gaussian)
model.centre = af.UniformPrior(lower_limit=0.0, upper_limit=100.0)
model.intensity = af.UniformPrior(lower_limit=1e-2, upper_limit=1e2)
model.sigma = af.UniformPrior(lower_limit=0.0, upper_limit=30.0)
analysis = a.Analysis(data=data, noise_map=noise_map)
zeus = af.Zeus(path_prefix=path.join("plot"),
name="ZeusPlotter",
nwalkers=100,
nsteps=10000)
result = zeus.fit(model=model, analysis=analysis)
samples = result.samples
"""
We now pass the samples to a `ZeusPlotter` which will allow us to use dynesty's in-built plotting libraries to
make figures.
The zeus readthedocs describes fully all of the methods used below
- https://zeus-mcmc.readthedocs.io/en/latest/api/plotting.html#cornerplot
- https://zeus-mcmc.readthedocs.io/en/latest/notebooks/normal_distribution.html
The plotter wraps the `corner` method of the library `corner.py` to make corner plots of the PDF:
from os import path
import autofit as af
import autolens as al
import autolens.plot as aplt
"""
First, lets create a result via zeus by repeating the simple model-fit that is performed in
the `modeling/mass_total__source_parametric.py` example.
We use a model with an initialized starting point, which is necessary for lens modeling with zeus.
"""
dataset_name = "mass_sie__source_sersic"
search = af.Zeus(
path_prefix=path.join("plot", "ZeusPlotter"),
name="ZeusPlotter",
nwalkers=30,
nsteps=500,
)
dataset_path = path.join("dataset", "imaging", "no_lens_light", dataset_name)
imaging = al.Imaging.from_fits(
image_path=path.join(dataset_path, "image.fits"),
psf_path=path.join(dataset_path, "psf.fits"),
noise_map_path=path.join(dataset_path, "noise_map.fits"),
pixel_scales=0.1,
)
mask = al.Mask2D.circular(shape_native=imaging.shape_native,
pixel_scales=imaging.pixel_scales,
radius=3.0)