Esempio n. 1
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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
Esempio n. 2
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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
Esempio n. 3
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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()
Esempio n. 4
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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()