def test_predict_interface_multiple_samples(self):
from iris.cube import CubeList
# Get the actual test data
# Use the class method `eval_fn` so 'self' doesn't get passed
expected = CubeList([type(self).eval_fn(p, job_n=i) for i, p in enumerate(self.test_params)])
expected = expected.concatenate_cube()
pred_m, pred_var = self.model.predict(self.test_params)
assert_allclose(expected.data, pred_m.data, rtol=1e-3)
assert pred_m.name() == 'Emulated ' + (expected.name() or 'data')
assert pred_var.name() == 'Variance in emulated ' + (expected.name() or 'data')
assert pred_m.units == expected.units
assert pred_var.units == expected.units
def test_predict_interface_multiple_samples(self):
from iris.cube import CubeList
# Get the actual test data
# Use the class method `eval_fn` so 'self' doesn't get passed
expected = CubeList([
type(self).eval_fn(p, job_n=i)
for i, p in enumerate(self.test_params)
])
expected = expected.concatenate_cube()
pred_m, pred_var = self.model.predict(self.test_params)
# For some reason the relative tolerance has to be
# higher here than in the other tests???
assert_allclose(expected.data, pred_m.data, rtol=1e-1)
assert pred_m.name() == 'Emulated ' + (expected.name() or 'data')
assert_allclose(np.full_like(expected.data, np.NaN),
pred_var.data,
equal_nan=True)
assert pred_var.name() == 'Variance in emulated ' + expected.name()
assert pred_m.units == expected.units
def test_all():
"""Check that cubes returned using the 'name_constraint' are not lazy"""
constraint = name_constraint(["dummy1"])
dummy1_cube = Cube(da.zeros((1, 1), chunks=(1, 1)), long_name="dummy2")
dummy2_cube = Cube(da.zeros((1, 1), chunks=(1, 1)), long_name="dummy1")
assert dummy1_cube.has_lazy_data()
assert dummy2_cube.has_lazy_data()
res = CubeList([dummy1_cube, dummy2_cube
]).extract_cube(Constraint(cube_func=constraint))
assert res.name() == "dummy1"
assert not res.has_lazy_data()
