def test_instance():
identifier = af.CollectionPriorModel(
gaussian=Gaussian()
).identifier
assert identifier == af.CollectionPriorModel(
gaussian=Gaussian()
).identifier
assert identifier != af.CollectionPriorModel(
gaussian=Gaussian(
centre=0.5
)
).identifier
def test_float_inequality(session):
aggregator = af.Aggregator(session)
for sigma in [
0.9992237362814176,
4.9687212446221904,
9.967065800134504,
]:
session.add(
af.db.Fit(
id=str(uuid4()),
instance={
"gaussian": Gaussian(
sigma=sigma
)
}
)
)
session.commit()
assert len(aggregator) == 3
assert len(aggregator.query(
aggregator.gaussian.sigma < 3
)) == 1
def test_query_before(
self,
aggregator,
grid_fit,
session
):
session.add(
db.Fit(
id="grid2",
is_grid_search=True,
instance=Gaussian(
centre=2
)
)
)
session.flush()
parent_aggregator = aggregator.query(
aggregator.is_grid_search & (aggregator.centre == 1)
)
result, = parent_aggregator.fits
assert result is grid_fit
child_aggregator = parent_aggregator.children()
results = child_aggregator.fits
assert len(results) == 10
results = aggregator.query(
aggregator.is_grid_search & (aggregator.centre == 2)
).children().fits
assert len(results) == 0
def test_job(perturbation_model):
instance = af.ModelInstance()
instance.gaussian = Gaussian()
instance.perturbation = Gaussian()
image = image_function(instance)
# noinspection PyTypeChecker
job = s.Job(
model=af.Collection(gaussian=af.PriorModel(Gaussian)),
perturbation_model=af.PriorModel(Gaussian),
analysis=Analysis(image),
search=GridSearch(),
)
result = job.perform()
assert isinstance(result, s.JobResult)
assert isinstance(result.perturbed_result, af.Result)
assert isinstance(result.result, af.Result)
assert result.log_likelihood_difference > 0
def make_grid_fit(children):
return db.Fit(
id="grid",
is_grid_search=True,
children=children,
instance=Gaussian(
centre=1
)
)
def make_children():
return [
db.Fit(
id=f"child_{i}",
instance=Gaussian(
centre=i
)
)
for i in range(10)
]
def test_collection_instance(
self,
instance_dict
):
collection = af.Collection(
gaussian=Gaussian()
)
assert collection.dict == {
"gaussian": instance_dict,
"type": "collection"
}
def test_object(paths):
gaussian = Gaussian(intensity=2.1)
assert paths.is_object("gaussian") is False
paths.save_object("gaussian", gaussian)
assert paths.is_object("gaussian") is True
assert paths.load_object("gaussian") == gaussian
paths.remove_object("gaussian")
assert paths.is_object("gaussian") is False
def make_sensitivity(perturbation_model, search):
# noinspection PyTypeChecker
instance = af.ModelInstance()
instance.gaussian = Gaussian()
return s.Sensitivity(
simulation_instance=instance,
base_model=af.Collection(gaussian=af.PriorModel(Gaussian)),
perturbation_model=perturbation_model,
simulate_function=image_function,
analysis_class=Analysis,
search=search,
number_of_steps=2,
)
def test_instance_from_prior_medians():
model = af.Model(
Gaussian
)
db.Object.from_object(
model
)()
db.Object.from_object(
Gaussian()
)()
instance = model.instance_from_prior_medians()
db.Object.from_object(
instance
)()
def test_append(self, frozen_collection):
with pytest.raises(AssertionError):
frozen_collection.append(Gaussian())
