def test_input_uncertainties_no_distributions(sensitivity_dps):
mm = MappedMatrix(
packages=sensitivity_dps,
matrix="matrix",
use_vectors=True,
use_arrays=True,
use_distributions=False,
)
magic = 191664963
expected = np.array(
[
(99, magic % 100, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, magic % 100, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, magic % 100, np.NaN, np.NaN, np.NaN, np.NaN, False),
(98, -8.5, 1.118034, np.NaN, np.NaN, np.NaN, False),
(98, 9.5, 1.118034, np.NaN, np.NaN, np.NaN, False),
(0, 1, np.NaN, np.NaN, np.NaN, np.NaN, False),
(0, 2, np.NaN, np.NaN, np.NaN, np.NaN, False),
(0, -1, np.NaN, np.NaN, np.NaN, np.NaN, False),
(0, -3, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, 1, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, 2, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, 3, np.NaN, np.NaN, np.NaN, np.NaN, False),
],
dtype=bwp.UNCERTAINTY_DTYPE,
)
ua = mm.input_uncertainties()
for field, _ in bwp.UNCERTAINTY_DTYPE:
assert np.allclose(ua[field], expected[field], equal_nan=True)
def test_input_uncertainties_limit_samples(sensitivity_dps):
mm = MappedMatrix(
packages=sensitivity_dps,
matrix="matrix",
use_vectors=True,
use_arrays=True,
use_distributions=True,
)
magic = 191664963
expected = np.array(
[
(99, magic % 100, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, magic % 100, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, magic % 100, np.NaN, np.NaN, np.NaN, np.NaN, False),
(98, -8, 0.8164966, np.NaN, np.NaN, np.NaN, False),
(98, 9, 0.8164966, np.NaN, np.NaN, np.NaN, False),
(4, 1, np.NaN, np.NaN, 0.5, 1.5, False),
(4, 2, np.NaN, np.NaN, 1.5, 2.5, False),
(0, 1, np.NaN, np.NaN, np.NaN, np.NaN, True),
(4, 3, np.NaN, np.NaN, 2.5, 3.5, True),
(99, 1, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, 2, np.NaN, np.NaN, np.NaN, np.NaN, False),
(99, 3, np.NaN, np.NaN, np.NaN, np.NaN, False),
],
dtype=bwp.UNCERTAINTY_DTYPE,
)
ua = mm.input_uncertainties(number_samples=3)
for field, _ in bwp.UNCERTAINTY_DTYPE:
assert np.allclose(ua[field], expected[field], equal_nan=True)