def test_eval_op():
"""Pick a "complicated" combination just to check."""
x = np.asarray([2, 4, 5, 6, 7])
m1 = basic.Const1D()
m1.c0 = 10
m2 = basic.Polynom1D()
m2.c0 = 5
m2.c1 = 1
m3 = basic.Box1D()
m3.xlow = 5
m3.xhi = 6
mdl = m1 + 2 * (m2 + (-m3))
assert mdl.ndim == 1
expected_m1 = 10 * np.ones(5)
expected_m2 = 5 + np.asarray(x)
expected_m3 = np.asarray([0, 0, 1, 1, 0])
expected = expected_m1 + 2 * (expected_m2 - expected_m3)
got = mdl(x)
assert got == pytest.approx(expected)
def test_combine_models1d():
"""Check we can combine 1D models"""
mdls = [
basic.Box1D(),
basic.Const1D(),
basic.Gauss1D(),
basic.NormGauss1D()
]
mdl = reduce(operator.add, mdls)
assert isinstance(mdl, BinaryOpModel)
# now multiply by a constant
#
mdl *= 2
assert isinstance(mdl, BinaryOpModel)
assert mdl.ndim == 1
# Check we can call it as a 1D model; there is minimal checks
# of the response.
#
bins = np.arange(2, 10, 2)
y1 = mdl(bins)
y2 = mdl(bins[:-1], bins[1:])
assert y1.shape == (4, )
assert y2.shape == (3, )
def test_combine_models_1d_2d():
"""What happens when we combine 1D and 2D models?"""
m1 = basic.Box1D()
m2 = basic.Box2D()
with pytest.raises(ModelErr) as exc:
m1 + m2
assert str(exc.value) == 'Models do not match: 1D (box1d) and 2D (box2d)'