def test_identifier_version():
set_version(1)
identifier = Identifier(af.Gaussian())
set_version(2)
assert identifier != Identifier(af.Gaussian())
assert identifier == Identifier(af.Gaussian(), version=1)
def make_job(perturbation_model, search):
instance = af.ModelInstance()
instance.gaussian = af.Gaussian()
base_instance = instance
instance.perturbation = af.Gaussian()
image = image_function(instance)
# noinspection PyTypeChecker
return s.Job(model=af.Collection(gaussian=af.PriorModel(af.Gaussian)),
perturbation_model=af.PriorModel(af.Gaussian),
base_instance=base_instance,
perturbation_instance=instance,
analysis_factory=MockAnalysisFactory(Analysis(image)),
search=search,
number=1)
def _make_children(grid_id):
return [
db.Fit(id=f"child_{grid_id}_{i}",
instance=af.Gaussian(centre=i),
model=af.Model(af.Gaussian, centre=float(-i)),
max_log_likelihood=grid_id + i) for i in range(10)
]
def test_instance():
model = af.Collection(collection=af.Collection(gaussian=af.Gaussian()))
parameterization = model.parameterization
assert parameterization == (
"""model CollectionPriorModel (N=0)
collection CollectionPriorModel (N=0)
gaussian Gaussian (N=0)"""
)
def test_tuple_in_instance_in_collection(target_gaussian, prior):
# noinspection PyTypeChecker
source_gaussian = af.Gaussian(centre=(prior, 1.0))
source = af.CollectionPriorModel(gaussian=source_gaussian)
target = af.CollectionPriorModel(gaussian=target_gaussian)
target.take_attributes(source)
assert target.gaussian.centre == (prior, 1.0)
def make_gaussian_0(session):
gaussian_0 = db.Fit(id="gaussian_0",
instance=af.Gaussian(centre=1),
info={"info": 1},
is_complete=True,
unique_tag="zero")
session.add(gaussian_0)
session.commit()
return gaussian_0
def test_append(
self,
frozen_collection
):
with pytest.raises(
AssertionError
):
frozen_collection.append(
af.Gaussian()
)
def generate_data(centres):
data = []
for centre in centres:
gaussian = af.Gaussian(
centre=centre,
normalization=20,
sigma=5,
)
data.append(gaussian(x))
return data
def test_object(paths):
gaussian = af.Gaussian(normalization=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_gaussian_none(
session
):
gaussian_none = db.Fit(
id="gaussian_none",
instance=af.Gaussian(
centre=None
)
)
session.add(gaussian_none)
session.commit()
return gaussian_none
def test_embedded_query(session, aggregator):
model_1 = db.Fit(id="model_1",
instance=af.Collection(gaussian=af.Gaussian(centre=1)),
info={"info": 3})
model_2 = db.Fit(id="model_2",
instance=af.Collection(gaussian=af.Gaussian(centre=2)),
info={"info": 4})
session.add_all([model_1, model_2])
result = aggregator.query(aggregator.model.centre == 0)
assert result == []
result = aggregator.query(aggregator.model.gaussian.centre == 1)
assert result == [model_1]
result = aggregator.query(aggregator.model.gaussian.centre == 2)
assert result == [model_2]
def make_sensitivity(perturbation_model, search):
# noinspection PyTypeChecker
instance = af.ModelInstance()
instance.gaussian = af.Gaussian()
return s.Sensitivity(
simulation_instance=instance,
base_model=af.Collection(gaussian=af.PriorModel(af.Gaussian)),
perturbation_model=perturbation_model,
simulate_function=image_function,
analysis_class=Analysis,
search=search,
number_of_steps=2,
)
def test_visualise(
multi_search,
multi_analysis
):
multi_analysis.visualize(
multi_search.paths,
af.Gaussian(),
True
)
search_path = Path(multi_search.paths.output_path)
assert search_path.exists()
assert (search_path / "analyses/analysis_0/image/image.png").exists()
assert (search_path / "analyses/analysis_1/image/image.png").exists()
def test_set_and_retrieve(
session
):
fit = db.Fit()
fit.named_instances[
"one"
] = af.Gaussian()
assert isinstance(
fit.named_instances[
"one"
],
af.Gaussian
)
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": af.Gaussian(sigma=sigma)}))
session.commit()
assert len(aggregator) == 3
assert len(aggregator.query(aggregator.model.gaussian.sigma < 3)) == 1
def test_query(
session
):
fit = db.Fit(
id="test_query"
)
fit.named_instances[
"one"
] = af.Gaussian()
session.add(fit)
session.commit()
fit, = db.Fit.all(
session
)
assert isinstance(
fit.named_instances[
"one"
],
af.Gaussian
)
def test_embedded():
collection = af.CollectionPriorModel(gaussian=af.PriorModel(
af.Gaussian, centre=af.Gaussian()), )
assert collection.has_model(af.Gaussian) is True
assert collection.has_instance(af.Gaussian) is True
def test_both():
collection = af.CollectionPriorModel(gaussian=af.PriorModel(af.Gaussian),
gaussian_2=af.Gaussian())
assert collection.has_model(af.Gaussian) is True
assert collection.has_instance(af.Gaussian) is True
def test_instance():
collection = af.CollectionPriorModel(gaussian=af.Gaussian())
assert collection.has_instance(af.Gaussian) is True
assert collection.has_model(af.Gaussian) is False
def test_collection_instance(self, instance_dict):
collection = af.Collection(gaussian=af.Gaussian())
assert collection.dict() == {
"gaussian": instance_dict,
"type": "collection"
}
def test_instance_from_prior_medians():
model = af.Model(af.Gaussian)
db.Object.from_object(model)()
db.Object.from_object(af.Gaussian())()
instance = model.instance_from_prior_medians()
db.Object.from_object(instance)()
def make_grid_fit_2():
return db.Fit(id="grid2",
unique_tag="grid_fit_2",
is_grid_search=True,
children=_make_children(2),
instance=af.Gaussian(centre=2))
def test_save_instance(session):
paths = af.DatabasePaths(session=session)
paths.save_named_instance("name", af.Gaussian())
assert isinstance(paths.fit.named_instances["name"], af.Gaussian)
def test_instance():
identifier = af.CollectionPriorModel(gaussian=af.Gaussian()).identifier
assert identifier == af.CollectionPriorModel(
gaussian=af.Gaussian()).identifier
assert identifier != af.CollectionPriorModel(gaussian=af.Gaussian(
centre=0.5)).identifier
def test_serialize(self):
serialized_instance = db.Object.from_object(af.Gaussian())
assert len(serialized_instance.children) == 3
def make_grid_fit(children):
return db.Fit(id="grid",
unique_tag="grid_fit_1",
is_grid_search=True,
children=children,
instance=af.Gaussian(centre=1))
def test_tuple_in_instance(target_gaussian, prior):
# noinspection PyTypeChecker
source_gaussian = af.Gaussian(centre=(prior, 1.0))
target_gaussian.take_attributes(source_gaussian)
assert target_gaussian.centre == (prior, 1.0)
