Exemplo n.º 1
0
def test_pointwise_norm_weighted(exponent):
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    weight = np.array([1.0, 2.0, 3.0])
    pwnorm = PointwiseNorm(vfspace, exponent, weighting=weight)

    testarr = np.array([[[1, 2],
                         [3, 4]],
                        [[0, -1],
                         [0, 1]],
                        [[1, 1],
                         [1, 1]]])

    if exponent in (1.0, float('inf')):
        true_norm = np.linalg.norm(weight[:, None, None] * testarr,
                                   ord=exponent, axis=0)
    else:
        true_norm = np.linalg.norm(
            weight[:, None, None] ** (1 / exponent) * testarr, ord=exponent,
            axis=0)

    func = vfspace.element(testarr)
    func_pwnorm = pwnorm(func)
    assert all_almost_equal(func_pwnorm, true_norm.reshape(-1))

    out = fspace.element()
    pwnorm(func, out=out)
    assert all_almost_equal(out, true_norm.reshape(-1))
Exemplo n.º 2
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def test_pointwise_inner_init_properties():
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3, exponent=2)

    # Make sure the code runs and test the properties
    pwinner = PointwiseInner(vfspace, vfspace.one())
    assert pwinner.base_space == fspace
    assert all_equal(pwinner.weights, [1, 1, 1])
    assert not pwinner.is_weighted
    repr(pwinner)

    pwinner = PointwiseInner(vfspace, vfspace.one(), weight=[1, 2, 3])
    assert all_equal(pwinner.weights, [1, 2, 3])
    assert pwinner.is_weighted

    # Bad input
    with pytest.raises(TypeError):
        PointwiseInner(odl.Rn(3), odl.Rn(3).one())  # No power space

    # TODO: Does not raise currently, although bad_vecfield not in vfspace!
    """
    bad_vecfield = ProductSpace(fspace, 3, exponent=1).one()
    with pytest.raises(TypeError):
        PointwiseInner(vfspace, bad_vecfield)
    """

    with pytest.raises(ValueError):
        PointwiseInner(vfspace, vfspace.one(), weight=-1)  # < 0 not allowed

    with pytest.raises(ValueError):
        PointwiseInner(vfspace, vfspace.one(), weight=[1, 0, 1])  # 0 invalid
Exemplo n.º 3
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def test_pointwise_inner_weighted():
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    array = np.array([[[-1, -3],
                       [2, 0]],
                      [[0, 0],
                       [0, 1]],
                      [[-1, 1],
                       [1, 1]]])

    weight = np.array([1.0, 2.0, 3.0])
    pwinner = PointwiseInner(vfspace, vecfield=array, weighting=weight)

    testarr = np.array([[[1, 2],
                         [3, 4]],
                        [[0, -1],
                         [0, 1]],
                        [[1, 1],
                         [1, 1]]])

    true_inner = np.sum(weight[:, None, None] * testarr * array, axis=0)

    func = vfspace.element(testarr)
    func_pwinner = pwinner(func)
    assert all_almost_equal(func_pwinner, true_inner.reshape(-1))

    out = fspace.element()
    pwinner(func, out=out)
    assert all_almost_equal(out, true_inner.reshape(-1))
Exemplo n.º 4
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def test_pointwise_inner_complex():
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 3)
    array = np.array([[[-1 - 1j, -3],
                       [2, 2j]],
                      [[-1j, 0],
                       [0, 1]],
                      [[-1, 1 + 2j],
                       [1, 1]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[[1 + 1j, 2],
                         [3, 4 - 2j]],
                        [[0, -1],
                         [0, 1]],
                        [[1j, 1j],
                         [1j, 1j]]])

    true_inner = np.sum(testarr * array.conj(), axis=0)

    func = vfspace.element(testarr)
    func_pwinner = pwinner(func)
    assert all_almost_equal(func_pwinner, true_inner.reshape(-1))

    out = fspace.element()
    pwinner(func, out=out)
    assert all_almost_equal(out, true_inner.reshape(-1))
Exemplo n.º 5
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def test_pointwise_norm_weighted(exponent):
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    weight = np.array([1.0, 2.0, 3.0])
    pwnorm = PointwiseNorm(vfspace, exponent, weighting=weight)

    testarr = np.array([[[1, 2], [3, 4]], [[0, -1], [0, 1]], [[1, 1], [1, 1]]])

    if exponent in (1.0, float('inf')):
        true_norm = np.linalg.norm(weight[:, None, None] * testarr,
                                   ord=exponent,
                                   axis=0)
    else:
        true_norm = np.linalg.norm(weight[:, None, None]**(1 / exponent) *
                                   testarr,
                                   ord=exponent,
                                   axis=0)

    func = vfspace.element(testarr)
    func_pwnorm = pwnorm(func)
    assert all_almost_equal(func_pwnorm, true_norm.reshape(-1))

    out = fspace.element()
    pwnorm(func, out=out)
    assert all_almost_equal(out, true_norm.reshape(-1))
Exemplo n.º 6
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def test_pointwise_inner_weighted():
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    array = np.array([[[-1, -3],
                       [2, 0]],
                      [[0, 0],
                       [0, 1]],
                      [[-1, 1],
                       [1, 1]]])

    weight = np.array([1.0, 2.0, 3.0])
    pwinner = PointwiseInner(vfspace, vecfield=array, weighting=weight)

    testarr = np.array([[[1, 2],
                         [3, 4]],
                        [[0, -1],
                         [0, 1]],
                        [[1, 1],
                         [1, 1]]])

    true_inner = np.sum(weight[:, None, None] * testarr * array, axis=0)

    func = vfspace.element(testarr)
    func_pwinner = pwinner(func)
    assert all_almost_equal(func_pwinner, true_inner.reshape(-1))

    out = fspace.element()
    pwinner(func, out=out)
    assert all_almost_equal(out, true_inner.reshape(-1))
Exemplo n.º 7
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def test_pointwise_inner_complex():
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 3)
    array = np.array([[[-1 - 1j, -3],
                       [2, 2j]],
                      [[-1j, 0],
                       [0, 1]],
                      [[-1, 1 + 2j],
                       [1, 1]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[[1 + 1j, 2],
                         [3, 4 - 2j]],
                        [[0, -1],
                         [0, 1]],
                        [[1j, 1j],
                         [1j, 1j]]])

    true_inner = np.sum(testarr * array.conj(), axis=0)

    func = vfspace.element(testarr)
    func_pwinner = pwinner(func)
    assert all_almost_equal(func_pwinner, true_inner.reshape(-1))

    out = fspace.element()
    pwinner(func, out=out)
    assert all_almost_equal(out, true_inner.reshape(-1))
Exemplo n.º 8
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def test_pointwise_inner_adjoint_weighted():
    # Weighted product space only
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 3, weighting=[2, 4, 6])
    array = np.array([[[-1 - 1j, -3], [2, 2j]], [[-1j, 0], [0, 1]],
                      [[-1, 1 + 2j], [1, 1]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[1 + 1j, 2], [3, 4 - 2j]])

    true_inner_adj = testarr[None, :, :] * array  # same as unweighted case

    testfunc = fspace.element(testarr)
    testfunc_pwinner_adj = pwinner.adjoint(testfunc)
    assert all_almost_equal(testfunc_pwinner_adj, true_inner_adj)

    out = vfspace.element()
    pwinner.adjoint(testfunc, out=out)
    assert all_almost_equal(out, true_inner_adj)

    # Using different weighting in the inner product
    pwinner = PointwiseInner(vfspace, vecfield=array, weighting=[4, 8, 12])

    testarr = np.array([[1 + 1j, 2], [3, 4 - 2j]])

    true_inner_adj = 2 * testarr[None, :, :] * array  # w / v = (2, 2, 2)

    testfunc = fspace.element(testarr)
    testfunc_pwinner_adj = pwinner.adjoint(testfunc)
    assert all_almost_equal(testfunc_pwinner_adj, true_inner_adj)

    out = vfspace.element()
    pwinner.adjoint(testfunc, out=out)
    assert all_almost_equal(out, true_inner_adj)
Exemplo n.º 9
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def test_pointwise_norm_init_properties():
    # 1d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 1, exponent=1)

    # Make sure the code runs and test the properties
    pwnorm = PointwiseNorm(vfspace)
    assert pwnorm.base_space == fspace
    assert all_equal(pwnorm.weights, [1])
    assert not pwnorm.is_weighted
    assert pwnorm.exponent == 1.0
    repr(pwnorm)

    pwnorm = PointwiseNorm(vfspace, exponent=2)
    assert pwnorm.exponent == 2

    pwnorm = PointwiseNorm(vfspace, weighting=2)
    assert all_equal(pwnorm.weights, [2])
    assert pwnorm.is_weighted

    # 3d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3, exponent=1)

    # Make sure the code runs and test the properties
    pwnorm = PointwiseNorm(vfspace)
    assert pwnorm.base_space == fspace
    assert all_equal(pwnorm.weights, [1, 1, 1])
    assert not pwnorm.is_weighted
    assert pwnorm.exponent == 1.0
    repr(pwnorm)

    pwnorm = PointwiseNorm(vfspace, exponent=2)
    assert pwnorm.exponent == 2

    pwnorm = PointwiseNorm(vfspace, weighting=[1, 2, 3])
    assert all_equal(pwnorm.weights, [1, 2, 3])
    assert pwnorm.is_weighted

    # Bad input
    with pytest.raises(TypeError):
        PointwiseNorm(odl.rn(3))  # No power space

    with pytest.raises(ValueError):
        PointwiseNorm(vfspace, exponent=0.5)  # < 1 not allowed

    with pytest.raises(ValueError):
        PointwiseNorm(vfspace, weighting=-1)  # < 0 not allowed

    with pytest.raises(ValueError):
        PointwiseNorm(vfspace, weighting=[1, 0, 1])  # 0 invalid
Exemplo n.º 10
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def test_pointwise_inner_real():
    # 1d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 1)
    array = np.array([[[-1, -3],
                       [2, 0]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[[1, 2],
                         [3, 4]]])

    true_inner = np.sum(testarr * array, axis=0)

    func = vfspace.element(testarr)
    func_pwinner = pwinner(func)
    assert all_almost_equal(func_pwinner, true_inner.reshape(-1))

    out = fspace.element()
    pwinner(func, out=out)
    assert all_almost_equal(out, true_inner.reshape(-1))

    # 3d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    array = np.array([[[-1, -3],
                       [2, 0]],
                      [[0, 0],
                       [0, 1]],
                      [[-1, 1],
                       [1, 1]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[[1, 2],
                         [3, 4]],
                        [[0, -1],
                         [0, 1]],
                        [[1, 1],
                         [1, 1]]])

    true_inner = np.sum(testarr * array, axis=0)

    func = vfspace.element(testarr)
    func_pwinner = pwinner(func)
    assert all_almost_equal(func_pwinner, true_inner.reshape(-1))

    out = fspace.element()
    pwinner(func, out=out)
    assert all_almost_equal(out, true_inner.reshape(-1))
Exemplo n.º 11
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def test_pointwise_inner_adjoint():
    # 1d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 1)
    array = np.array([[[-1, -3],
                       [2, 0]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[1 + 1j, 2],
                        [3, 4 - 2j]])

    true_inner_adj = testarr[None, :, :] * array

    testfunc = fspace.element(testarr)
    testfunc_pwinner_adj = pwinner.adjoint(testfunc)
    assert all_almost_equal(testfunc_pwinner_adj,
                            true_inner_adj.reshape([1, -1]))

    out = vfspace.element()
    pwinner.adjoint(testfunc, out=out)
    assert all_almost_equal(out, true_inner_adj.reshape([1, -1]))

    # 3d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 3)
    array = np.array([[[-1 - 1j, -3],
                       [2, 2j]],
                      [[-1j, 0],
                       [0, 1]],
                      [[-1, 1 + 2j],
                       [1, 1]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[1 + 1j, 2],
                        [3, 4 - 2j]])

    true_inner_adj = testarr[None, :, :] * array

    testfunc = fspace.element(testarr)
    testfunc_pwinner_adj = pwinner.adjoint(testfunc)
    assert all_almost_equal(testfunc_pwinner_adj,
                            true_inner_adj.reshape([3, -1]))

    out = vfspace.element()
    pwinner.adjoint(testfunc, out=out)
    assert all_almost_equal(out, true_inner_adj.reshape([3, -1]))
Exemplo n.º 12
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def test_pointwise_norm_complex(exponent):
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 3)
    pwnorm = PointwiseNorm(vfspace, exponent)

    testarr = np.array([[[1 + 1j, 2], [3, 4 - 2j]], [[0, -1], [0, 1]],
                        [[1j, 1j], [1j, 1j]]])

    true_norm = np.linalg.norm(testarr, ord=exponent, axis=0)

    func = vfspace.element(testarr)
    func_pwnorm = pwnorm(func)
    assert all_almost_equal(func_pwnorm, true_norm.reshape(-1))

    out = fspace.element()
    pwnorm(func, out=out)
    assert all_almost_equal(out, true_norm.reshape(-1))
    def __init__(self,
                 space,
                 data,
                 forward,
                 tau,
                 alpha_df,
                 alpha_of,
                 grad=None,
                 huber=False,
                 gamma=1e-7,
                 aug_lagr=False,
                 lagr_mult=None):
        self.N = round(len(space) / 2)  # number of time steps\
        self.space = space
        self.image_space = self.space[0]
        self.space_time = ProductSpace(self.image_space, self.N)
        self.data = data
        self.forward = forward
        self.tau = tau
        self.alpha_df = alpha_df
        self.alpha_of = alpha_of
        self.grad = grad
        self.data_fit = DataFitL2TimeDep(self.space, self.data, self.forward,
                                         self.alpha_df)
        self.aug_lagr = aug_lagr
        if lagr_mult is None:
            self.lagr_mult = self.space_time.zero()
        else:
            self.lagr_mult = lagr_mult

        if huber is False:
            self.of_constr = L2OpticalFlowConstraint(self.space, self.tau,
                                                     self.alpha_of, self.grad)
        else:
            self.of_constr = HuberL1OpticalFlowConstraint(
                self.space, self.tau, self.alpha_of, self.grad, gamma)
        if aug_lagr is True:
            self.aug_lagr_term = AugmentedLagrangeTerm(self.space,
                                                       self.lagr_mult,
                                                       self.tau, self.grad)

        super(smooth_term_OF, self).__init__(space=space,
                                             linear=False,
                                             grad_lipschitz=np.nan)
Exemplo n.º 14
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def test_pointwise_norm_gradient_real(exponent):
    # The operator is not differentiable for exponent 'inf'
    if exponent == float('inf'):
        fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
        vfspace = ProductSpace(fspace, 1)
        pwnorm = PointwiseNorm(vfspace, exponent)
        point = vfspace.one()
        with pytest.raises(NotImplementedError):
            pwnorm.derivative(point)
        return

    # 1d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 1)
    pwnorm = PointwiseNorm(vfspace, exponent)

    point = noise_element(vfspace)
    direction = noise_element(vfspace)

    # Computing expected result
    tmp = pwnorm(point).ufuncs.power(1 - exponent)
    v_field = vfspace.element()
    for i in range(len(v_field)):
        v_field[i] = tmp * point[i] * np.abs(point[i])**(exponent - 2)
    pwinner = odl.PointwiseInner(vfspace, v_field)
    expected_result = pwinner(direction)

    func_pwnorm = pwnorm.derivative(point)

    assert all_almost_equal(func_pwnorm(direction), expected_result)

    # 3d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    pwnorm = PointwiseNorm(vfspace, exponent)

    point = noise_element(vfspace)
    direction = noise_element(vfspace)

    # Computing expected result
    tmp = pwnorm(point).ufuncs.power(1 - exponent)
    v_field = vfspace.element()
    for i in range(len(v_field)):
        v_field[i] = tmp * point[i] * np.abs(point[i])**(exponent - 2)
    pwinner = odl.PointwiseInner(vfspace, v_field)
    expected_result = pwinner(direction)

    func_pwnorm = pwnorm.derivative(point)
    assert all_almost_equal(func_pwnorm(direction), expected_result)
Exemplo n.º 15
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def test_pointwise_inner_adjoint_weighted():
    # Weighted product space only
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 3, weighting=[2, 4, 6])
    array = np.array([[[-1 - 1j, -3],
                       [2, 2j]],
                      [[-1j, 0],
                       [0, 1]],
                      [[-1, 1 + 2j],
                       [1, 1]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[1 + 1j, 2],
                        [3, 4 - 2j]])

    true_inner_adj = testarr[None, :, :] * array  # same as unweighted case

    testfunc = fspace.element(testarr)
    testfunc_pwinner_adj = pwinner.adjoint(testfunc)
    assert all_almost_equal(testfunc_pwinner_adj,
                            true_inner_adj.reshape([3, -1]))

    out = vfspace.element()
    pwinner.adjoint(testfunc, out=out)
    assert all_almost_equal(out, true_inner_adj.reshape([3, -1]))

    # Using different weighting in the inner product
    pwinner = PointwiseInner(vfspace, vecfield=array, weighting=[4, 8, 12])

    testarr = np.array([[1 + 1j, 2],
                        [3, 4 - 2j]])

    true_inner_adj = 2 * testarr[None, :, :] * array  # w / v = (2, 2, 2)

    testfunc = fspace.element(testarr)
    testfunc_pwinner_adj = pwinner.adjoint(testfunc)
    assert all_almost_equal(testfunc_pwinner_adj,
                            true_inner_adj.reshape([3, -1]))

    out = vfspace.element()
    pwinner.adjoint(testfunc, out=out)
    assert all_almost_equal(out, true_inner_adj.reshape([3, -1]))
Exemplo n.º 16
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def test_pointwise_norm_complex(exponent):
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 3)
    pwnorm = PointwiseNorm(vfspace, exponent)

    testarr = np.array([[[1 + 1j, 2],
                         [3, 4 - 2j]],
                        [[0, -1],
                         [0, 1]],
                        [[1j, 1j],
                         [1j, 1j]]])

    true_norm = np.linalg.norm(testarr, ord=exponent, axis=0)

    func = vfspace.element(testarr)
    func_pwnorm = pwnorm(func)
    assert all_almost_equal(func_pwnorm, true_norm.reshape(-1))

    out = fspace.element()
    pwnorm(func, out=out)
    assert all_almost_equal(out, true_norm.reshape(-1))
Exemplo n.º 17
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def test_pointwise_inner_init_properties():
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3, exponent=2)

    # Make sure the code runs and test the properties
    pwinner = PointwiseInner(vfspace, vfspace.one())
    assert pwinner.base_space == fspace
    assert all_equal(pwinner.weights, [1, 1, 1])
    assert not pwinner.is_weighted
    repr(pwinner)

    pwinner = PointwiseInner(vfspace, vfspace.one(), weighting=[1, 2, 3])
    assert all_equal(pwinner.weights, [1, 2, 3])
    assert pwinner.is_weighted

    # Bad input
    with pytest.raises(TypeError):
        PointwiseInner(odl.rn(3), odl.rn(3).one())  # No power space

    # TODO: Does not raise currently, although bad_vecfield not in vfspace!
    """
Exemplo n.º 18
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def test_pointwise_sum():
    """PointwiseSum currently depends on PointwiseInner, we verify that."""

    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3, exponent=2)

    # Make sure the code runs and test the properties
    psum = PointwiseSum(vfspace)
    assert isinstance(psum, PointwiseInner)
    assert psum.base_space == fspace
    assert all_equal(psum.weights, [1, 1, 1])
    assert all_equal(psum.vecfield, psum.domain.one())
Exemplo n.º 19
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def test_pointwise_norm_real(exponent):
    # 1d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 1)
    pwnorm = PointwiseNorm(vfspace, exponent)

    testarr = np.array([[[1, 2], [3, 4]]])

    true_norm = np.linalg.norm(testarr, ord=exponent, axis=0)

    func = vfspace.element(testarr)
    func_pwnorm = pwnorm(func)
    assert all_almost_equal(func_pwnorm, true_norm)

    out = fspace.element()
    pwnorm(func, out=out)
    assert all_almost_equal(out, true_norm)

    # 3d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    pwnorm = PointwiseNorm(vfspace, exponent)

    testarr = np.array([[[1, 2], [3, 4]], [[0, -1], [0, 1]], [[1, 1], [1, 1]]])

    true_norm = np.linalg.norm(testarr, ord=exponent, axis=0)

    func = vfspace.element(testarr)
    func_pwnorm = pwnorm(func)
    assert all_almost_equal(func_pwnorm, true_norm)

    out = fspace.element()
    pwnorm(func, out=out)
    assert all_almost_equal(out, true_norm)
Exemplo n.º 20
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def test_matrix_representation_lin_space_to_product():
    # Verify that the matrix representation function returns the correct matrix

    n = 3
    rn = odl.rn(n)
    A = np.random.rand(n, n)
    Aop = odl.MatrixOperator(A)

    m = 2
    rm = odl.rn(m)
    B = np.random.rand(m, n)
    Bop = odl.MatrixOperator(B)

    dom = ProductSpace(rn, 1)
    ran = ProductSpace(rn, rm)

    AB_matrix = np.vstack([A, B])
    ABop = ProductSpaceOperator([[Aop], [Bop]], dom, ran)

    the_matrix = matrix_representation(ABop)

    assert almost_equal(np.sum(np.abs(AB_matrix - the_matrix)), 1e-6)
Exemplo n.º 21
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    def __init__(self, space, a, b):
        """Initialize a LinCombOperator instance.

        Parameters
        ----------
        space : `LinearSpace`
            The space of elements which the operator is acting on
        a, b : scalar
            Scalars to multiply ``x[0]`` and ``x[1]`` with, respectively
        """
        domain = ProductSpace(space, space)
        super().__init__(domain, space, linear=True)
        self.a = a
        self.b = b
    def __init__(self, space, data, forward=None):
        self.space = space
        self.image_space = self.space[0]
        self.affine_space = self.space[1]
        self.rest_space = self.space[2]
        self.deformation_space = ProductSpace(self.affine_space,
                                              self.rest_space)
        self.data = data
        if forward is None:
            self.forward = IdentityOperator(self.image_space)
        else:
            self.forward = forward

        self.datafit = 0.5 * L2NormSquared(self.image_space).translated(
            self.data)
        self.embedding_affine_rest = ops.Embedding_Affine_Rest(
            self.deformation_space, self.image_space.tangent_bundle)
        self.embedding_affine = ops.Embedding_Affine(
            self.affine_space, self.image_space.tangent_bundle)

        super(DataFitL2DispAffRest, self).__init__(space=space,
                                                   linear=False,
                                                   grad_lipschitz=np.nan)
Exemplo n.º 23
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    def __init__(self, sspace, vecfield, vfspace=None, weight=None):
        """Initialize a new instance.

        Parameters
        ----------
        sspace : `LinearSpace`
            "Scalar" space on which the operator acts
        vecfield : domain `element-like`
            Vector field of the point-wise inner product operator
        vfspace : `ProductSpace`, optional
            Space of vector fields to which the operator maps. It must
            be a power space with ``sspace`` as base space.
            This option is intended to enforce an operator range
            with a certain weighting.
            Default: ``ProductSpace(space, len(vecfield), weight=weight)``
        weight : `array-like` or `float`, optional
            Weighting array or constant of the inner product operator.
            If an array is given, its length must be equal to
            ``len(vecfield)``, and all entries must be positive. A
            provided constant must be positive.
            By default, the weights are is taken from
            ``range.weighting`` if applicable. Note that this excludes
            unusual weightings with custom inner product, norm or dist.
        """
        if vfspace is None:
            vfspace = ProductSpace(sspace, len(vecfield), weight=weight)
        else:
            if not isinstance(vfspace, ProductSpace):
                raise TypeError('`vfspace` {!r} is not a '
                                'ProductSpace instance'.format(vfspace))
            if vfspace[0] != sspace:
                raise ValueError('base space of the range is different from '
                                 'the given scalar space ({!r} != {!r})'
                                 ''.format(vfspace[0], sspace))
        super().__init__(vfspace, vecfield, weight=weight)

        # Switch domain and range
        self._domain, self._range = self._range, self._domain

        # Get weighting from range
        if hasattr(self.range.weighting, 'vector'):
            self._ran_weights = self.range.weighting.vector
        elif hasattr(self.range.weighting, 'const'):
            self._ran_weights = (self.range.weighting.const *
                                 np.ones(len(self.range)))
        else:
            raise ValueError('weighting scheme {!r} of the range does '
                             'not define a weighting vector or constant'
                             ''.format(self.range.weighting))
Exemplo n.º 24
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def test_pointwise_norm_gradient_real_with_zeros(exponent):
    # The gradient is only well-defined in points with zeros if the exponent is
    # >= 2 and < inf
    if exponent < 2 or exponent == float('inf'):
        pytest.skip('differential of operator has singularity for this '
                    'exponent')

    # 1d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 1)
    pwnorm = PointwiseNorm(vfspace, exponent)

    test_point = np.array([[[0, 0],  # This makes the point singular for p < 2
                            [1, 2]]])
    test_direction = np.array([[[1, 2],
                                [4, 5]]])

    point = vfspace.element(test_point)
    direction = vfspace.element(test_direction)
    func_pwnorm = pwnorm.derivative(point)

    assert not np.any(np.isnan(func_pwnorm(direction)))

    # 3d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    pwnorm = PointwiseNorm(vfspace, exponent)

    test_point = np.array([[[0, 0],  # This makes the point singular for p < 2
                            [1, 2]],
                           [[3, 4],
                            [0, 0]],  # This makes the point singular for p < 2
                           [[5, 6],
                            [7, 8]]])
    test_direction = np.array([[[0, 1],
                                [2, 3]],
                               [[4, 5],
                                [6, 7]],
                               [[8, 9],
                                [0, 1]]])

    point = vfspace.element(test_point)
    direction = vfspace.element(test_direction)
    func_pwnorm = pwnorm.derivative(point)

    assert not np.any(np.isnan(func_pwnorm(direction)))
Exemplo n.º 25
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    def __init__(self,
                 ops,
                 x0,
                 niter,
                 omega=1,
                 random=False,
                 projection=None,
                 callback=None,
                 callback_loop='outer',
                 **kwargs):
        """
        Calls `odl.solvers.iterative.iterative.kaczmarz`.

        Parameters
        ----------
        ops : sequence of `odl.Operator`
            The forward operators of the inverse problem.
            The call ``ops[i].derivative(x).adjoint`` must be valid for all i.
        x0 : ``op.domain`` element
            Initial value.
        niter : int
            Number of iterations.
        omega : positive float or sequence of positive floats, optional
            Relaxation parameter.
            If a single float is given it is used for all operators.
        random : bool, optional
            Whether the order of the operators is randomized in each iteration.
        projection : callable, optional
            Callable that can be used to modify the iterates in each iteration.
        callback : :class:`odl.solvers.util.callback.Callback`, optional
            Object that is called in each iteration.
        callback_loop : {'inner', 'outer'}
            Whether the `callback` should be called in the inner or outer loop.
        """
        self.ops = ops
        self.x0 = x0
        self.niter = niter
        self.omega = omega
        self.projection = projection
        self.random = random
        self.callback_loop = callback_loop
        super().__init__(reco_space=self.ops[0].domain,
                         observation_space=ProductSpace(*(op.range
                                                          for op in self.ops)),
                         callback=callback,
                         **kwargs)
Exemplo n.º 26
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    def __init__(self,
                 op,
                 x0,
                 niter,
                 noise='poisson',
                 callback=None,
                 sensitivities=None,
                 **kwargs):
        """
        Calls `odl.solvers.iterative.statistical.osmlem`.

        Parameters
        ----------
        op : `odl.operator.Operator` or sequence of `odl.operator.Operator`
            The forward operator(s) of the inverse problem.
            If an operator sequence is given, Ordered Subsets MLEM is applied.
        x0 : ``op.domain`` element
            Initial value.
        niter : int
            Number of iterations.
        noise : {'poisson'}, optional
            Noise model determining the variant of MLEM.
            For ``'poisson'``, the initial value of ``x`` should be
            non-negative.
        callback : :class:`odl.solvers.util.callback.Callback`, optional
            Object that is called in each iteration.
        sensitivities : float or ``op.domain`` `element-like`, optional
            Usable with ``noise='poisson'``. The algorithm contains an
            ``A^T 1`` term, if this parameter is given, it is replaced by it.
            Default: ``op[i].adjoint(op[i].range.one())``
        """
        self.os_mode = not isinstance(op, Operator)
        self.op = op if self.os_mode else [op]
        self.x0 = x0
        self.niter = niter
        self.noise = noise
        self.sensitivities = sensitivities
        observation_space = (ProductSpace(
            *(op.range
              for op in self.op)) if self.os_mode else self.op[0].range)
        super().__init__(reco_space=self.op[0].domain,
                         observation_space=observation_space,
                         callback=callback,
                         **kwargs)
Exemplo n.º 27
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def test_matrix_representation_product_to_product_two():
    # Verify that the matrix representation function returns the correct matrix

    n = 3
    rn = odl.rn(n)
    A = np.random.rand(n, n)
    Aop = odl.MatrixOperator(A)

    B = np.random.rand(n, n)
    Bop = odl.MatrixOperator(B)

    ran_and_dom = ProductSpace(rn, 2)

    AB_matrix = np.vstack(
        [np.hstack([A, np.zeros((n, n))]),
         np.hstack([np.zeros((n, n)), B])])
    ABop = ProductSpaceOperator([[Aop, 0], [0, Bop]], ran_and_dom, ran_and_dom)
    the_matrix = matrix_representation(ABop)

    assert almost_equal(np.sum(np.abs(AB_matrix - the_matrix)), 1e-6)
Exemplo n.º 28
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def test_pointwise_inner_real():
    # 1d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 1)
    array = np.array([[[-1, -3],
                       [2, 0]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[[1, 2],
                         [3, 4]]])

    true_inner = np.sum(testarr * array, axis=0)

    func = vfspace.element(testarr)
    func_pwinner = pwinner(func)
    assert all_almost_equal(func_pwinner, true_inner.reshape(-1))

    out = fspace.element()
    pwinner(func, out=out)
    assert all_almost_equal(out, true_inner.reshape(-1))

    # 3d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2))
    vfspace = ProductSpace(fspace, 3)
    array = np.array([[[-1, -3],
                       [2, 0]],
                      [[0, 0],
                       [0, 1]],
                      [[-1, 1],
                       [1, 1]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[[1, 2],
                         [3, 4]],
                        [[0, -1],
                         [0, 1]],
                        [[1, 1],
                         [1, 1]]])

    true_inner = np.sum(testarr * array, axis=0)

    func = vfspace.element(testarr)
    func_pwinner = pwinner(func)
    assert all_almost_equal(func_pwinner, true_inner.reshape(-1))

    out = fspace.element()
    pwinner(func, out=out)
    assert all_almost_equal(out, true_inner.reshape(-1))
Exemplo n.º 29
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def test_pointwise_inner_adjoint():
    # 1d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 1)
    array = np.array([[[-1, -3],
                       [2, 0]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[1 + 1j, 2],
                        [3, 4 - 2j]])

    true_inner_adj = testarr[None, :, :] * array

    testfunc = fspace.element(testarr)
    testfunc_pwinner_adj = pwinner.adjoint(testfunc)
    assert all_almost_equal(testfunc_pwinner_adj,
                            true_inner_adj.reshape([1, -1]))

    out = vfspace.element()
    pwinner.adjoint(testfunc, out=out)
    assert all_almost_equal(out, true_inner_adj.reshape([1, -1]))

    # 3d
    fspace = odl.uniform_discr([0, 0], [1, 1], (2, 2), dtype=complex)
    vfspace = ProductSpace(fspace, 3)
    array = np.array([[[-1 - 1j, -3],
                       [2, 2j]],
                      [[-1j, 0],
                       [0, 1]],
                      [[-1, 1 + 2j],
                       [1, 1]]])
    pwinner = PointwiseInner(vfspace, vecfield=array)

    testarr = np.array([[1 + 1j, 2],
                        [3, 4 - 2j]])

    true_inner_adj = testarr[None, :, :] * array

    testfunc = fspace.element(testarr)
    testfunc_pwinner_adj = pwinner.adjoint(testfunc)
    assert all_almost_equal(testfunc_pwinner_adj,
                            true_inner_adj.reshape([3, -1]))

    out = vfspace.element()
    pwinner.adjoint(testfunc, out=out)
    assert all_almost_equal(out, true_inner_adj.reshape([3, -1]))
    def __init__(self, space, alpha=1, grad=None):
        if not len(space) == 2:
            raise ValueError('Domain has not the right shape. Len=2 expected')

        if grad is None:
            grad = odl.Gradient(space[0],
                                method='forward',
                                pad_mode='symmetric')
            grad.norm = 2 * np.sqrt(sum(1 / grad.domain.cell_sides**2))
        else:
            grad = grad

        self.alpha = alpha
        self.grad = grad
        self.image_space_time = space[0]
        self.image_space = self.image_space_time[0]
        self.vf_space_time = space[1]
        self.vf_space = self.vf_space_time[0]
        self.im_vf_space = ProductSpace(self.image_space, self.vf_space)
        self.N = len(self.image_space_time)  # number of time steps
        super(L1OpticalFlowConstraint, self).__init__(space=space,
                                                      linear=False,
                                                      grad_lipschitz=np.nan)
class DataFitL2DispAffRest(Functional):
    def __init__(self, space, data, forward=None):
        self.space = space
        self.image_space = self.space[0]
        self.affine_space = self.space[1]
        self.rest_space = self.space[2]
        self.deformation_space = ProductSpace(self.affine_space,
                                              self.rest_space)
        self.data = data
        if forward is None:
            self.forward = IdentityOperator(self.image_space)
        else:
            self.forward = forward

        self.datafit = 0.5 * L2NormSquared(self.image_space).translated(
            self.data)
        self.embedding_affine_rest = ops.Embedding_Affine_Rest(
            self.deformation_space, self.image_space.tangent_bundle)
        self.embedding_affine = ops.Embedding_Affine(
            self.affine_space, self.image_space.tangent_bundle)

        super(DataFitL2DispAffRest, self).__init__(space=space,
                                                   linear=False,
                                                   grad_lipschitz=np.nan)

    def __call__(self, x):
        xim = x[0]
        xaff = x[1]
        xrest = x[2]
        xdeform = self.deformation_space.element([xaff, xrest])
        transl_operator = self.transl_op_fixed_vf(xdeform)
        fctl = self.datafit * self.forward * transl_operator
        return fctl(xim)

    def transl_op_fixed_im_aff(self, im, aff):
        affine_deform = defm.LinDeformFixedDisp(self.embedding_affine(aff))
        deform_op = defm.LinDeformFixedTempl(affine_deform(im))
        transl_operator = deform_op
        return transl_operator

    def transl_op_fixed_im_rest(self, im, rest):
        rest_deform = defm.LinDeformFixedDisp(rest)
        deformed_im = rest_deform(im)
        transl_operator = defm.LinDeformFixedTempl(
            deformed_im) * self.embedding_affine
        return transl_operator

    def transl_op_fixed_vf(self, disp):
        deform_op = defm.LinDeformFixedDisp(self.embedding_affine_rest(disp))
        transl_operator = deform_op
        return transl_operator

    def partial_gradient(self, i):
        if i == 0:
            functional = self

            class auxOperator(Operator):
                def __init__(self):
                    super(auxOperator, self).__init__(functional.space,
                                                      functional.image_space)

                def _call(self, x, out):
                    xim = x[0]
                    xaff = x[1]
                    xrest = x[2]
                    xdeform = functional.deformation_space.element(
                        [xaff, xrest])
                    transl_operator = functional.transl_op_fixed_vf(xdeform)
                    func = functional.datafit * functional.forward * transl_operator
                    grad = func.gradient
                    out.assign(grad(xim))

            return auxOperator()
        elif i == 1:
            functional = self

            class auxOperator(Operator):
                def __init__(self):
                    super(auxOperator, self).__init__(functional.space,
                                                      functional.affine_space)

                def _call(self, x, out):
                    xim = x[0]
                    xaff = x[1]
                    xrest = x[2]
                    transl_operator = functional.transl_op_fixed_im_rest(
                        xim, xrest)
                    func = functional.datafit * functional.forward * transl_operator
                    grad = func.gradient
                    out.assign(grad(xaff))

            return auxOperator()
        elif i == 2:
            functional = self

            class auxOperator(Operator):
                def __init__(self):
                    super(auxOperator, self).__init__(functional.space,
                                                      functional.rest_space)

                def _call(self, x, out):
                    xim = x[0]
                    xaff = x[1]
                    xrest = x[2]
                    transl_operator = functional.transl_op_fixed_im_aff(
                        xim, xaff)
                    func = functional.datafit * functional.forward * transl_operator
                    grad = func.gradient
                    out.assign(grad(xrest))

            return auxOperator()
        else:
            raise ValueError('No gradient defined for this variable')

    @property
    def gradient(self):
        return BroadcastOperator(*[self.partial_gradient(i) for i in range(3)])
Exemplo n.º 32
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    def __init__(self, domain=None, range=None, method='forward',
                 padding_method='constant', padding_value=0):
        """Initialize a `Divergence` operator instance.

        Zero padding is assumed for the adjoint of the `Divergence`
        operator to match the negative `Gradient` operator.

        Parameters
        ----------
        domain : power space of `DiscreteLp`, optional
            The space of elements which the operator acts on.
            This is required if ``range`` is not given.
        range : `DiscreteLp`, optional
            The space of elements to which the operator maps.
            This is required if ``domain`` is not given.
        method : {'central', 'forward', 'backward'}, optional
            Finite difference method to be used
        padding_method : {'constant', 'symmetric', 'periodic'}, optional

            'constant' : Pads values outside the domain of ``f`` with a
            constant value given by ``padding_value``.

            'symmetric' : Pads with the reflection of the vector mirrored
            along the edge of the array.

            'periodic' : Pads with the values from the other side of the array.

        padding_value : `float`, optional
            If ``padding_method`` is 'constant', ``f`` assumes
            ``padding_value`` for indices outside the domain of ``f``.

        Examples
        --------
        >>> import odl
        >>> ran = odl.uniform_discr([0, 0], [1, 1], (10, 20))
        >>> dom = odl.ProductSpace(ran, ran.ndim)  # 2-dimensional
        >>> div_op = Divergence(dom)
        >>> div_op.range == ran
        True
        >>> div_op2 = Divergence(range=ran)
        >>> div_op2.domain == dom
        True
        >>> div_op3 = Divergence(domain=dom, range=ran)
        >>> div_op3.domain == dom
        True
        >>> div_op3.range == ran
        True
        """
        if domain is None and range is None:
            raise ValueError('either `domain` or `range` must be specified')

        if domain is None:
            if not isinstance(range, DiscreteLp):
                raise TypeError('`range` {!r} is not a DiscreteLp instance'
                                ''.format(range))
            domain = ProductSpace(range, range.ndim)

        if range is None:
            if not isinstance(domain, ProductSpace):
                raise TypeError('`domain` {!r} is not a ProductSpace instance'
                                ''.format(domain))
            range = domain[0]

        linear = not (padding_method == 'constant' and padding_value != 0)
        super().__init__(domain, range, linear=linear)
        self.method = method
        self.padding_method = padding_method
        self.padding_value = padding_value
class smooth_term_OF(Functional):
    def __init__(self,
                 space,
                 data,
                 forward,
                 tau,
                 alpha_df,
                 alpha_of,
                 grad=None,
                 huber=False,
                 gamma=1e-7,
                 aug_lagr=False,
                 lagr_mult=None):
        self.N = round(len(space) / 2)  # number of time steps\
        self.space = space
        self.image_space = self.space[0]
        self.space_time = ProductSpace(self.image_space, self.N)
        self.data = data
        self.forward = forward
        self.tau = tau
        self.alpha_df = alpha_df
        self.alpha_of = alpha_of
        self.grad = grad
        self.data_fit = DataFitL2TimeDep(self.space, self.data, self.forward,
                                         self.alpha_df)
        self.aug_lagr = aug_lagr
        if lagr_mult is None:
            self.lagr_mult = self.space_time.zero()
        else:
            self.lagr_mult = lagr_mult

        if huber is False:
            self.of_constr = L2OpticalFlowConstraint(self.space, self.tau,
                                                     self.alpha_of, self.grad)
        else:
            self.of_constr = HuberL1OpticalFlowConstraint(
                self.space, self.tau, self.alpha_of, self.grad, gamma)
        if aug_lagr is True:
            self.aug_lagr_term = AugmentedLagrangeTerm(self.space,
                                                       self.lagr_mult,
                                                       self.tau, self.grad)

        super(smooth_term_OF, self).__init__(space=space,
                                             linear=False,
                                             grad_lipschitz=np.nan)

    def __call__(self, x):
        ret = self.data_fit(x) + self.of_constr(x)
        if self.aug_lagr is True:
            ret += self.aug_lagr_term(x)
        return ret

    @property
    def gradient(self):
        grad_df = self.data_fit.gradient
        grad_of = self.of_constr.gradient
        if self.aug_lagr is True:
            grad_al = self.aug_lagr_term.gradient
            return BroadcastOperator(*[
                grad_df[i] + grad_of[i] + grad_al[i] for i in range(2 * self.N)
            ])
        else:
            return BroadcastOperator(
                *[grad_df[i] + grad_of[i] for i in range(2 * self.N)])
Exemplo n.º 34
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 def __init__(self):
     super().__init__(domain=odl.Rn(3),
                      range=ProductSpace(odl.Rn(3),
                                         ProductSpace(odl.Rn(3),
                                                      odl.Rn(3))),
                      linear=True)