def test_selection_op():
p1 = MonomOperator(1)
select_rhs_functional = GenericParameterFunctional(
lambda x: round(float(x["nrrhs"])), ParameterType({"nrrhs": ()}))
s1 = SelectionOperator(operators=[p1],
boundaries=[],
parameter_functional=select_rhs_functional,
name="foo")
x = np.linspace(-1., 1., num=3)
vx = p1.source.make_array(x[:, np.newaxis])
assert np.allclose(
p1.apply(vx, mu=0).to_numpy(),
s1.apply(vx, mu=0).to_numpy())
s2 = SelectionOperator(operators=[p1, p1, p1, p1],
boundaries=[-3, 3, 7],
parameter_functional=select_rhs_functional,
name="Bar")
assert s2._get_operator_number({"nrrhs": -4}) == 0
assert s2._get_operator_number({"nrrhs": -3}) == 0
assert s2._get_operator_number({"nrrhs": -2}) == 1
assert s2._get_operator_number({"nrrhs": 3}) == 1
assert s2._get_operator_number({"nrrhs": 4}) == 2
assert s2._get_operator_number({"nrrhs": 7}) == 2
assert s2._get_operator_number({"nrrhs": 9}) == 3
def test_selection_op():
p1 = MonomOperator(1)
select_rhs_functional = GenericParameterFunctional(
lambda x: round(float(x["nrrhs"])),
ParameterType({"nrrhs" : tuple()})
)
s1 = SelectionOperator(
operators = [p1],
boundaries = [],
parameter_functional = select_rhs_functional,
name = "foo"
)
x = np.linspace(-1., 1., num=3)
vx = NumpyVectorArray(x[:, np.newaxis])
assert np.allclose(p1.apply(vx,mu=0).data, s1.apply(vx,mu=0).data)
s2 = SelectionOperator(
operators = [p1,p1,p1,p1],
boundaries = [-3, 3, 7],
parameter_functional = select_rhs_functional,
name = "Bar"
)
assert s2._get_operator_number({"nrrhs":-4}) == 0
assert s2._get_operator_number({"nrrhs":-3}) == 0
assert s2._get_operator_number({"nrrhs":-2}) == 1
assert s2._get_operator_number({"nrrhs":3}) == 1
assert s2._get_operator_number({"nrrhs":4}) == 2
assert s2._get_operator_number({"nrrhs":7}) == 2
assert s2._get_operator_number({"nrrhs":9}) == 3
def test_lincomb_op():
p1 = MonomOperator(1)
p2 = MonomOperator(2)
p12 = p1 + p2
p0 = p1 - p1
x = np.linspace(-1.0, 1.0, num=3)
vx = p1.source.make_array((x[:, np.newaxis]))
assert np.allclose(p0.apply(vx).data, [0.0])
assert np.allclose(p12.apply(vx).data, (x * x + x)[:, np.newaxis])
assert np.allclose((p1 * 2.0).apply(vx).data, (x * 2.0)[:, np.newaxis])
assert almost_equal(p2.jacobian(vx).apply(vx), p1.apply(vx) * 2.0).all()
assert almost_equal(p0.jacobian(vx).apply(vx), vx * 0.0).all()
with pytest.raises(TypeError):
p2.as_vector()
p1.as_vector()
assert almost_equal(p1.as_vector(), p1.apply(p1.source.make_array([1.0])))
basis = p1.source.make_array([1.0])
for p in (p1, p2, p12):
projected = p.projected(basis, basis)
pa = projected.apply(vx)
assert almost_equal(pa, p.apply(vx)).all()
def test_lincomb_op():
p1 = MonomOperator(1)
p2 = MonomOperator(2)
p12 = p1 + p2
p0 = p1 - p1
x = np.linspace(-1., 1., num=3)
vx = p1.source.make_array((x[:, np.newaxis]))
assert np.allclose(p0.apply(vx).to_numpy(), [0.])
assert np.allclose(p12.apply(vx).to_numpy(), (x * x + x)[:, np.newaxis])
assert np.allclose((p1 * 2.).apply(vx).to_numpy(), (x * 2.)[:, np.newaxis])
assert almost_equal(p2.jacobian(vx).apply(vx), p1.apply(vx) * 2.).all()
assert almost_equal(p0.jacobian(vx).apply(vx), vx * 0.).all()
with pytest.raises(TypeError):
p2.as_vector()
p1.as_vector()
assert almost_equal(p1.as_vector(), p1.apply(p1.source.make_array([1.])))
basis = p1.source.make_array([1.])
for p in (p1, p2, p12):
projected = project(p, basis, basis)
pa = projected.apply(vx)
assert almost_equal(pa, p.apply(vx)).all()
