def test__result_derived_properties(self):
lower_limit_lists = [[0.0, 0.0], [0.0, 0.5], [0.5, 0.0], [0.5, 0.5]]
# noinspection PyTypeChecker
grid_search_result = af.GridSearchResult(
results=None,
grid_priors=[
af.UniformPrior(
lower_limit=-2.0,
upper_limit=2.0
),
af.UniformPrior(
lower_limit=-3.0,
upper_limit=3.0
)
],
lower_limits_lists=lower_limit_lists,
)
assert grid_search_result.shape == (2, 2)
assert grid_search_result.physical_step_sizes == (2.0, 3.0)
assert grid_search_result.physical_centres_lists == [
[-1.0, -1.5],
[-1.0, 1.5],
[1.0, -1.5],
[1.0, 1.5],
]
assert grid_search_result.physical_upper_limits_lists == [
[0.0, 0.0],
[0.0, 3.0],
[2.0, 0.0],
[2.0, 3.0],
]
def test__result_derived_properties(self):
lower_limit_lists = [[0.0, 0.0], [0.0, 0.5], [0.5, 0.0], [0.5, 0.5]]
physical_lower_limits_lists = [
[-2.0, -3.0],
[-2.0, 0.0],
[0.0, -3.0],
[0.0, 0.0],
]
grid_search_result = af.GridSearchResult(
results=None,
physical_lower_limits_lists=physical_lower_limits_lists,
lower_limit_lists=lower_limit_lists,
)
print(grid_search_result)
assert grid_search_result.shape == (2, 2)
assert grid_search_result.physical_step_sizes == (2.0, 3.0)
assert grid_search_result.physical_centres_lists == [
[-1.0, -1.5],
[-1.0, 1.5],
[1.0, -1.5],
[1.0, 1.5],
]
assert grid_search_result.physical_upper_limits_lists == [
[0.0, 0.0],
[0.0, 3.0],
[2.0, 0.0],
[2.0, 3.0],
]
def test_unpickle_result():
result = af.GridSearchResult(
[af.Result(samples=None, model=None)],
lower_limit_lists=[[1]],
physical_lower_limits_lists=[[1]],
)
result = pickle.loads(pickle.dumps(result))
assert result is not None
def test_unpickle_result():
# noinspection PyTypeChecker
result = af.GridSearchResult(
results=[af.Result(samples=None, model=None)],
lower_limits_lists=[[1]],
grid_priors=[],
)
result = pickle.loads(pickle.dumps(result))
assert result is not None
def make_grid_search_result():
one = af.m.MockResultGrid(1)
two = af.m.MockResultGrid(2)
# noinspection PyTypeChecker
return af.GridSearchResult(
results=[one, two],
lower_limits_lists=[[1], [2]],
grid_priors=[[1], [2]]
)
def test_higher_dimensions(
n_dimensions,
n_steps
):
shape = n_dimensions * (n_steps,)
total = n_steps ** n_dimensions
model = af.Model(
af.Gaussian
)
result = af.GridSearchResult(
results=total * [
af.Result(
af.NestSamples(
model,
[
af.Sample(
1.0,
1.0,
1.0,
{
"centre": 1.0,
"sigma": 1.0,
"normalization": 1.0
}
)
]
),
model=model
)
],
grid_priors=[],
lower_limits_lists=total * [
n_dimensions * [0.0]
]
)
assert result.shape == shape
assert result.results_native.shape == shape
assert result.log_likelihoods_native.shape == shape
def test__result_derived_properties(self):
lower_limit_lists = [[0.0, 0.0], [0.0, 0.5], [0.5, 0.0], [0.5, 0.5]]
grid_search_result = af.GridSearchResult(
results=None,
grid_priors=[
af.UniformPrior(lower_limit=-2.0, upper_limit=2.0),
af.UniformPrior(lower_limit=-3.0, upper_limit=3.0),
],
lower_limits_lists=lower_limit_lists,
)
subhalo_result = SubhaloResult(
grid_search_result=grid_search_result, result_no_subhalo=1
)
subhalo_array = subhalo_result._subhalo_array_from(
values_native=np.array([[1.0, 2.0], [3.0, 4.0]])
)
assert isinstance(subhalo_array, al.Array2D)
assert (subhalo_array.native == np.array([[3.0, 4.0], [1.0, 2.0]])).all()
def make_result(model):
return af.GridSearchResult(results=[],
lower_limits_lists=[[0.0], [0.5]],
grid_priors=[model.centre])
def make_grid_search_result():
one = MockResult(1)
two = MockResult(2)
# noinspection PyTypeChecker
return af.GridSearchResult([one, two], [[1], [2]], [[1], [2]])