def test__loads_from_config_file_if_not_input(self):
dynesty = af.DynestyStatic(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
nlive=151,
dlogz=0.1,
iterations_per_update=501,
number_of_cores=2,
)
assert dynesty.prior_passer.sigma == 2.0
assert dynesty.prior_passer.use_errors is False
assert dynesty.prior_passer.use_widths is False
assert dynesty.iterations_per_update == 501
assert dynesty.config_dict_search["nlive"] == 151
assert dynesty.config_dict_run["dlogz"] == 0.1
assert dynesty.number_of_cores == 2
dynesty = af.DynestyStatic()
assert dynesty.prior_passer.sigma == 3.0
assert dynesty.prior_passer.use_errors is True
assert dynesty.prior_passer.use_widths is True
assert dynesty.iterations_per_update == 500
assert dynesty.config_dict_search["nlive"] == 150
assert dynesty.config_dict_run["dlogz"] == None
assert dynesty.number_of_cores == 1
dynesty = af.DynestyDynamic(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
facc=0.4,
iterations_per_update=501,
dlogz_init=0.2,
number_of_cores=3,
)
assert dynesty.prior_passer.sigma == 2.0
assert dynesty.prior_passer.use_errors is False
assert dynesty.prior_passer.use_widths is False
assert dynesty.iterations_per_update == 501
assert dynesty.config_dict_search["facc"] == 0.4
assert dynesty.config_dict_run["dlogz_init"] == 0.2
assert dynesty.number_of_cores == 3
dynesty = af.DynestyDynamic()
assert dynesty.prior_passer.sigma == 3.0
assert dynesty.prior_passer.use_errors is True
assert dynesty.prior_passer.use_widths is True
assert dynesty.iterations_per_update == 501
assert dynesty.config_dict_search["facc"] == 0.6
assert dynesty.config_dict_run["dlogz_init"] == 0.01
assert dynesty.number_of_cores == 4
def test__loads_from_config_file_correct(self):
drawer = af.Drawer(
total_draws=5,
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
)
assert drawer.prior_passer.sigma == 2.0
assert drawer.prior_passer.use_errors is False
assert drawer.prior_passer.use_widths is False
assert drawer.config_dict_search["total_draws"] == 5
assert isinstance(drawer.initializer, af.InitializerBall)
assert drawer.initializer.lower_limit == 0.2
assert drawer.initializer.upper_limit == 0.8
assert drawer.number_of_cores == 1
drawer = af.Drawer()
assert drawer.prior_passer.sigma == 3.0
assert drawer.prior_passer.use_errors is True
assert drawer.prior_passer.use_widths is True
assert drawer.config_dict_search["total_draws"] == 10
assert isinstance(drawer.initializer, af.InitializerPrior)
def test__loads_from_config_file_correct(self):
lbfgs = af.LBFGS(
prior_passer=af.PriorPasser(sigma=2.0, use_errors=False, use_widths=False),
tol=0.2,
disp=True,
maxcor=11,
ftol=2.,
gtol=3.,
eps=4.,
maxfun=25000,
maxiter=26000,
iprint=-2,
maxls=21,
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
iterations_per_update=10,
number_of_cores=2,
)
assert lbfgs.prior_passer.sigma == 2.0
assert lbfgs.prior_passer.use_errors is False
assert lbfgs.prior_passer.use_widths is False
assert lbfgs.config_dict_search["tol"] == 0.2
assert lbfgs.config_dict_options["maxcor"] == 11
assert lbfgs.config_dict_options["ftol"] == 2.
assert lbfgs.config_dict_options["gtol"] == 3.
assert lbfgs.config_dict_options["eps"] == 4.
assert lbfgs.config_dict_options["maxfun"] == 25000
assert lbfgs.config_dict_options["maxiter"] == 26000
assert lbfgs.config_dict_options["iprint"] == -2
assert lbfgs.config_dict_options["maxls"] == 21
assert lbfgs.config_dict_options["disp"] == True
assert isinstance(lbfgs.initializer, af.InitializerBall)
assert lbfgs.initializer.lower_limit == 0.2
assert lbfgs.initializer.upper_limit == 0.8
assert lbfgs.iterations_per_update == 10
assert lbfgs.number_of_cores == 2
lbfgs = af.LBFGS()
assert lbfgs.prior_passer.sigma == 3.0
assert lbfgs.prior_passer.use_errors is True
assert lbfgs.prior_passer.use_widths is True
assert lbfgs.config_dict_search["tol"] == None
assert lbfgs.config_dict_options["maxcor"] == 10
assert lbfgs.config_dict_options["ftol"] == 2.220446049250313e-09
assert lbfgs.config_dict_options["gtol"] == 1e-05
assert lbfgs.config_dict_options["eps"] == 1e-08
assert lbfgs.config_dict_options["maxfun"] == 15000
assert lbfgs.config_dict_options["maxiter"] == 15000
assert lbfgs.config_dict_options["iprint"] == -1
assert lbfgs.config_dict_options["maxls"] == 20
assert lbfgs.config_dict_options["maxiter"] == 15000
assert lbfgs.config_dict_options["disp"] == False
assert isinstance(lbfgs.initializer, af.InitializerPrior)
assert lbfgs.iterations_per_update == 11
def test__loads_from_config_file_if_not_input(self):
multi_nest = af.MultiNest(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
n_live_points=40,
sampling_efficiency=0.5,
const_efficiency_mode=True,
evidence_tolerance=0.4,
importance_nested_sampling=False,
multimodal=False,
n_iter_before_update=90,
null_log_evidence=-1.0e80,
max_modes=50,
mode_tolerance=-1e88,
seed=0,
verbose=True,
resume=False,
context=1,
write_output=False,
log_zero=-1e90,
max_iter=1,
init_MPI=True,
)
assert multi_nest.prior_passer.sigma == 2.0
assert multi_nest.prior_passer.use_errors is False
assert multi_nest.prior_passer.use_widths is False
assert multi_nest.config_dict_search["n_live_points"] == 40
assert multi_nest.config_dict_search["sampling_efficiency"] == 0.5
multi_nest = af.MultiNest()
assert multi_nest.prior_passer.sigma == 3.0
assert multi_nest.prior_passer.use_errors is True
assert multi_nest.prior_passer.use_widths is True
assert multi_nest.config_dict_search["n_live_points"] == 50
assert multi_nest.config_dict_search["sampling_efficiency"] == 0.6
model = af.ModelMapper(mock_class_1=mock.MockClassx4)
fitness = af.MultiNest.Fitness(
analysis=None,
model=model,
samples_from_model=multi_nest.samples_from,
stagger_resampling_likelihood=False,
paths=None)
assert fitness.model == model
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
We use absolute and relative values for different parameters, depending on their properties. For example, using the
relative value of a parameter like the `Profile` centre makes no sense. If our lens galaxy is centred at (0.0, 0.0),
the relative error will always be tiny and thus poorly defined. Therefore, the default configs in **PyAutoLens** use
absolute errors on the centre.
However, there are parameters where using an absolute value does not make sense. Intensity is a good example of this.
The intensity of an image depends on its unit_label, S/N, galaxy brightness, etc. There is no single absolute value
that one can use to generically link the intensity of any two proflies. Thus, it makes more sense to link them using
the relative value from a previous phase.
We can customize how priors are passed from the results of a phase and `NonLinearSearch` by inputting to the search
a PriorPasser object:
"""
search = af.DynestyStatic(
prior_passer=af.PriorPasser(sigma=2.0, use_widths=False, use_errors=True))
# %%
"""
The PriorPasser allows us to customize at what sigma the error values the model results are computed at to compute
the passed sigma values and customizes whether the widths in the config file, these computed errors, or both,
are used to set the sigma values of the passed priors.
The default values of the PriorPasser are found in the config file of every non-linear search, in the [prior_passer]
section. All non-linear searches by default use a sigma value of 3.0, use_width=True and use_errors=True. We anticipate
you should not need to change these values to get lens modeling to work proficiently!
__EXAMPLE__
Lets go through an example using a real parameter. Lets say in phase 1 we fit the lens `Galaxy`'s light with an
elliptical Sersic profile, and we estimate that its sersic index is equal to 4.0 +- 2.0 where the error value of 2.0
def test__identifier_description__after_model_and_instance():
model = af.CollectionPriorModel(gaussian=af.PriorModel(
af.Gaussian,
centre=af.UniformPrior(lower_limit=0.0, upper_limit=1.0),
normalization=af.LogUniformPrior(lower_limit=0.001, upper_limit=0.01),
sigma=af.GaussianPrior(
mean=0.5, sigma=2.0, lower_limit=-1.0, upper_limit=1.0),
))
max_log_likelihood_instance = model.instance_from_prior_medians()
samples = af.m.MockSamples(
max_log_likelihood_instance=max_log_likelihood_instance,
gaussian_tuples=[(1.0, 2.0), (3.0, 4.0), (5.0, 6.0)])
search = af.m.MockSearch(prior_passer=af.PriorPasser(
sigma=1.0, use_errors=True, use_widths=False))
result = af.Result(samples=samples, model=model, search=search)
model.gaussian.centre = result.model.gaussian.centre
model.gaussian.normalization = result.instance.gaussian.normalization
identifier = Identifier([model])
description = identifier.description.splitlines()
# THIS TEST FAILS DUE TO THE BUG DESCRIBED IN A GITHUB ISSUE.
i = 0
assert description[i] == "CollectionPriorModel"
i += 1
assert description[i] == "item_number"
i += 1
assert description[i] == "0"
i += 1
assert description[i] == "gaussian"
i += 1
assert description[i] == "PriorModel"
i += 1
assert description[i] == "cls"
i += 1
assert description[i] == "autofit.example.model.Gaussian"
i += 1
assert description[i] == "centre"
i += 1
assert description[i] == "GaussianPrior"
i += 1
assert description[i] == "lower_limit"
i += 1
assert description[i] == "0.0"
i += 1
assert description[i] == "upper_limit"
i += 1
assert description[i] == "1.0"
i += 1
assert description[i] == "mean"
i += 1
assert description[i] == "1.0"
i += 1
assert description[i] == "sigma"
i += 1
assert description[i] == "2.0"
i += 1
assert description[i] == "normalization"
i += 1
assert description[i] == "0.00316228"
i += 1
assert description[i] == "sigma"
i += 1
assert description[i] == "GaussianPrior"
i += 1
assert description[i] == "lower_limit"
i += 1
assert description[i] == "-1.0"
i += 1
assert description[i] == "upper_limit"
i += 1
assert description[i] == "1.0"
i += 1
assert description[i] == "mean"
i += 1
assert description[i] == "0.5"
i += 1
assert description[i] == "sigma"
i += 1
assert description[i] == "2.0"
i += 1
def test__loads_from_config_file_correct(self):
pso = af.PySwarmsGlobal(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
n_particles=51,
iters=2001,
cognitive=0.4,
social=0.5,
inertia=0.6,
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
iterations_per_update=10,
number_of_cores=2,
)
assert pso.prior_passer.sigma == 2.0
assert pso.prior_passer.use_errors is False
assert pso.prior_passer.use_widths is False
assert pso.config_dict_search["n_particles"] == 51
assert pso.config_dict_search["cognitive"] == 0.4
assert pso.config_dict_run["iters"] == 2001
assert isinstance(pso.initializer, af.InitializerBall)
assert pso.initializer.lower_limit == 0.2
assert pso.initializer.upper_limit == 0.8
assert pso.iterations_per_update == 10
assert pso.number_of_cores == 2
pso = af.PySwarmsGlobal()
assert pso.prior_passer.sigma == 3.0
assert pso.prior_passer.use_errors is True
assert pso.prior_passer.use_widths is True
assert pso.config_dict_search["n_particles"] == 50
assert pso.config_dict_search["cognitive"] == 0.1
assert pso.config_dict_run["iters"] == 2000
assert isinstance(pso.initializer, af.InitializerPrior)
assert pso.iterations_per_update == 11
assert pso.number_of_cores == 1
pso = af.PySwarmsLocal(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
n_particles=51,
iters=2001,
cognitive=0.4,
social=0.5,
inertia=0.6,
number_of_k_neighbors=4,
minkowski_p_norm=1,
initializer=af.InitializerBall(lower_limit=0.2, upper_limit=0.8),
iterations_per_update=10,
number_of_cores=2,
)
assert pso.prior_passer.sigma == 2.0
assert pso.prior_passer.use_errors is False
assert pso.prior_passer.use_widths is False
assert pso.config_dict_search["n_particles"] == 51
assert pso.config_dict_search["cognitive"] == 0.4
assert pso.config_dict_run["iters"] == 2001
assert isinstance(pso.initializer, af.InitializerBall)
assert pso.initializer.lower_limit == 0.2
assert pso.initializer.upper_limit == 0.8
assert pso.iterations_per_update == 10
assert pso.number_of_cores == 2
pso = af.PySwarmsLocal()
assert pso.prior_passer.sigma == 3.0
assert pso.prior_passer.use_errors is True
assert pso.prior_passer.use_widths is True
assert pso.config_dict_search["n_particles"] == 50
assert pso.config_dict_search["cognitive"] == 0.1
assert pso.config_dict_run["iters"] == 2000
assert isinstance(pso.initializer, af.InitializerPrior)
assert pso.iterations_per_update == 11
assert pso.number_of_cores == 1
