Example #1
0
def test_dft_preprocess_data_with_axes(sign):

    shape = (2, 3, 4)

    axes = 1  # Only middle index counts
    correct_arr = []
    for _, j, __ in product(range(shape[0]), range(shape[1]), range(shape[2])):
        correct_arr.append(1 - 2 * (j % 2))

    arr = np.ones(shape, dtype='complex64')
    dft_preprocess_data(arr, shift=True, axes=axes, out=arr, sign=sign)

    assert all_almost_equal(arr.ravel(), correct_arr)

    axes = [0, -1]  # First and last
    correct_arr = []
    for i, _, k in product(range(shape[0]), range(shape[1]), range(shape[2])):
        correct_arr.append(1 - 2 * ((i + k) % 2))

    arr = np.ones(shape, dtype='complex64')
    dft_preprocess_data(arr, shift=True, axes=axes, out=arr, sign=sign)

    assert all_almost_equal(arr.ravel(), correct_arr)
Example #2
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def test_dft_preprocess_data_halfcomplex(sign):

    shape = (2, 3, 4)

    # With shift
    correct_arr = []
    for i, j, k in product(range(shape[0]), range(shape[1]), range(shape[2])):
        correct_arr.append(1 - 2 * ((i + j + k) % 2))

    arr = np.ones(shape, dtype='float64')
    preproc = dft_preprocess_data(arr, shift=True, sign=sign)  # out-of-place
    out = np.empty_like(arr)
    dft_preprocess_data(arr, shift=True, out=out, sign=sign)  # in-place
    dft_preprocess_data(arr, shift=True, out=arr, sign=sign)  # in-place
    assert all_almost_equal(preproc.ravel(), correct_arr)
    assert all_almost_equal(arr.ravel(), correct_arr)
    assert all_almost_equal(out.ravel(), correct_arr)

    # Without shift
    imag = 1j if sign == '-' else -1j
    correct_arr = []
    for i, j, k in product(range(shape[0]), range(shape[1]), range(shape[2])):
        argsum = sum((idx * (1 - 1 / shp))
                     for idx, shp in zip((i, j, k), shape))

        correct_arr.append(np.exp(imag * np.pi * argsum))

    arr = np.ones(shape, dtype='float64')
    preproc = dft_preprocess_data(arr, shift=False, sign=sign)
    assert all_almost_equal(preproc.ravel(), correct_arr)

    # Non-float input works, too
    arr = np.ones(shape, dtype='int')
    preproc = dft_preprocess_data(arr, shift=False, sign=sign)
    assert all_almost_equal(preproc.ravel(), correct_arr)

    # In-place modification not possible for float array and no shift
    arr = np.ones(shape, dtype='float64')
    with pytest.raises(ValueError):
        dft_preprocess_data(arr, shift=False, out=arr, sign=sign)
Example #3
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def test_dft_preprocess_data(sign):

    shape = (2, 3, 4)

    # With shift
    correct_arr = []
    for i, j, k in product(range(shape[0]), range(shape[1]), range(shape[2])):
        correct_arr.append((1 + 1j) * (1 - 2 * ((i + j + k) % 2)))

    arr = np.ones(shape, dtype='complex64') * (1 + 1j)
    preproc = dft_preprocess_data(arr, shift=True, sign=sign)  # out-of-place
    dft_preprocess_data(arr, shift=True, out=arr, sign=sign)  # in-place

    assert all_almost_equal(preproc.ravel(), correct_arr)
    assert all_almost_equal(arr.ravel(), correct_arr)

    # Without shift
    imag = 1j if sign == '-' else -1j
    correct_arr = []
    for i, j, k in product(range(shape[0]), range(shape[1]), range(shape[2])):
        argsum = sum((idx * (1 - 1 / shp))
                     for idx, shp in zip((i, j, k), shape))

        correct_arr.append((1 + 1j) * np.exp(imag * np.pi * argsum))

    arr = np.ones(shape, dtype='complex64') * (1 + 1j)
    dft_preprocess_data(arr, shift=False, out=arr, sign=sign)

    assert all_almost_equal(arr.ravel(), correct_arr)

    # Bad input
    with pytest.raises(ValueError):
        dft_preprocess_data(arr, out=arr, sign=1)

    arr = np.zeros(shape, dtype='S2')
    with pytest.raises(ValueError):
        dft_preprocess_data(arr)
Example #4
0
def test_fourier_trafo_completely():
    # Complete explicit test of all FT components on two small examples

    # Discretization with 4 points
    discr = odl.uniform_discr(-2, 2, 4, dtype="complex")
    # Interval boundaries -2, -1, 0, 1, 2
    assert np.allclose(discr.partition.cell_boundary_vecs[0], [-2, -1, 0, 1, 2])
    # Grid points -1.5, -0.5, 0.5, 1.5
    assert np.allclose(discr.grid.coord_vectors[0], [-1.5, -0.5, 0.5, 1.5])

    # First test function, symmetric. Can be represented exactly in the
    # discretization.
    def f(x):
        return (x >= -1) & (x <= 1)

    def fhat(x):
        return np.sqrt(2 / np.pi) * sinc(x)

    # Discretize f, check values
    f_discr = discr.element(f)
    assert np.allclose(f_discr, [0, 1, 1, 0])

    # "s" = shifted, "n" = not shifted

    # Reciprocal grids
    recip_s = reciprocal_grid(discr.grid, shift=True)
    recip_n = reciprocal_grid(discr.grid, shift=False)
    assert np.allclose(recip_s.coord_vectors[0], np.linspace(-np.pi, np.pi / 2, 4))
    assert np.allclose(recip_n.coord_vectors[0], np.linspace(-3 * np.pi / 4, 3 * np.pi / 4, 4))

    # Range
    range_part_s = odl.uniform_partition_fromgrid(recip_s)
    range_s = odl.uniform_discr_frompartition(range_part_s, dtype="complex")
    range_part_n = odl.uniform_partition_fromgrid(recip_n)
    range_n = odl.uniform_discr_frompartition(range_part_n, dtype="complex")

    # Pre-processing
    preproc_s = [1, -1, 1, -1]
    preproc_n = [np.exp(1j * 3 / 4 * np.pi * k) for k in range(4)]

    fpre_s = dft_preprocess_data(f_discr, shift=True)
    fpre_n = dft_preprocess_data(f_discr, shift=False)
    assert np.allclose(fpre_s, f_discr * discr.element(preproc_s))
    assert np.allclose(fpre_n, f_discr * discr.element(preproc_n))

    # FFT step, replicating the _call_numpy method
    fft_s = np.fft.fftn(fpre_s, s=discr.shape, axes=[0])
    fft_n = np.fft.fftn(fpre_n, s=discr.shape, axes=[0])
    assert np.allclose(fft_s, [0, -1 + 1j, 2, -1 - 1j])
    assert np.allclose(
        fft_n, [np.exp(1j * np.pi * (3 - 2 * k) / 4) + np.exp(1j * np.pi * (3 - 2 * k) / 2) for k in range(4)]
    )

    # Interpolation kernel FT
    interp_s = np.sinc(np.linspace(-1 / 2, 1 / 4, 4)) / np.sqrt(2 * np.pi)
    interp_n = np.sinc(np.linspace(-3 / 8, 3 / 8, 4)) / np.sqrt(2 * np.pi)
    assert np.allclose(interp_s, _interp_kernel_ft(np.linspace(-1 / 2, 1 / 4, 4), interp="nearest"))
    assert np.allclose(interp_n, _interp_kernel_ft(np.linspace(-3 / 8, 3 / 8, 4), interp="nearest"))

    # Post-processing
    postproc_s = np.exp(1j * np.pi * np.linspace(-3 / 2, 3 / 4, 4))
    postproc_n = np.exp(1j * np.pi * np.linspace(-9 / 8, 9 / 8, 4))

    fpost_s = dft_postprocess_data(
        range_s.element(fft_s), real_grid=discr.grid, recip_grid=recip_s, shift=[True], axes=(0,), interp="nearest"
    )
    fpost_n = dft_postprocess_data(
        range_n.element(fft_n), real_grid=discr.grid, recip_grid=recip_n, shift=[False], axes=(0,), interp="nearest"
    )

    assert np.allclose(fpost_s, fft_s * postproc_s * interp_s)
    assert np.allclose(fpost_n, fft_n * postproc_n * interp_n)

    # Comparing to the known result sqrt(2/pi) * sinc(x)
    assert np.allclose(fpost_s, fhat(recip_s.coord_vectors[0]))
    assert np.allclose(fpost_n, fhat(recip_n.coord_vectors[0]))

    # Doing the exact same with direct application of the FT operator
    ft_op_s = FourierTransform(discr, shift=True)
    ft_op_n = FourierTransform(discr, shift=False)
    assert ft_op_s.range.grid == recip_s
    assert ft_op_n.range.grid == recip_n

    ft_f_s = ft_op_s(f)
    ft_f_n = ft_op_n(f)
    assert np.allclose(ft_f_s, fhat(recip_s.coord_vectors[0]))
    assert np.allclose(ft_f_n, fhat(recip_n.coord_vectors[0]))

    # Second test function, asymmetric. Can also be represented exactly in the
    # discretization.
    def f(x):
        return (x >= 0) & (x <= 1)

    def fhat(x):
        return np.exp(-1j * x / 2) * sinc(x / 2) / np.sqrt(2 * np.pi)

    # Discretize f, check values
    f_discr = discr.element(f)
    assert np.allclose(f_discr, [0, 0, 1, 0])

    # Pre-processing
    fpre_s = dft_preprocess_data(f_discr, shift=True)
    fpre_n = dft_preprocess_data(f_discr, shift=False)
    assert np.allclose(fpre_s, [0, 0, 1, 0])
    assert np.allclose(fpre_n, [0, 0, -1j, 0])

    # FFT step
    fft_s = np.fft.fftn(fpre_s, s=discr.shape, axes=[0])
    fft_n = np.fft.fftn(fpre_n, s=discr.shape, axes=[0])
    assert np.allclose(fft_s, [1, -1, 1, -1])
    assert np.allclose(fft_n, [-1j, 1j, -1j, 1j])

    fpost_s = dft_postprocess_data(
        range_s.element(fft_s), real_grid=discr.grid, recip_grid=recip_s, shift=[True], axes=(0,), interp="nearest"
    )
    fpost_n = dft_postprocess_data(
        range_n.element(fft_n), real_grid=discr.grid, recip_grid=recip_n, shift=[False], axes=(0,), interp="nearest"
    )

    assert np.allclose(fpost_s, fft_s * postproc_s * interp_s)
    assert np.allclose(fpost_n, fft_n * postproc_n * interp_n)

    # Comparing to the known result exp(-1j*x/2) * sinc(x/2) / sqrt(2*pi)
    assert np.allclose(fpost_s, fhat(recip_s.coord_vectors[0]))
    assert np.allclose(fpost_n, fhat(recip_n.coord_vectors[0]))

    # Doing the exact same with direct application of the FT operator
    ft_f_s = ft_op_s(f)
    ft_f_n = ft_op_n(f)
    assert np.allclose(ft_f_s, fhat(recip_s.coord_vectors[0]))
    assert np.allclose(ft_f_n, fhat(recip_n.coord_vectors[0]))
Example #5
0
def test_fourier_trafo_completely():
    # Complete explicit test of all FT components on two small examples

    # Discretization with 4 points
    discr = odl.uniform_discr(-2, 2, 4, dtype='complex')
    # Interval boundaries -2, -1, 0, 1, 2
    assert np.allclose(discr.partition.cell_boundary_vecs[0],
                       [-2, -1, 0, 1, 2])
    # Grid points -1.5, -0.5, 0.5, 1.5
    assert np.allclose(discr.grid.coord_vectors[0], [-1.5, -0.5, 0.5, 1.5])

    # First test function, symmetric. Can be represented exactly in the
    # discretization.
    def f(x):
        return (x >= -1) & (x <= 1)

    def fhat(x):
        return np.sqrt(2 / np.pi) * sinc(x)

    # Discretize f, check values
    f_discr = discr.element(f)
    assert np.allclose(f_discr, [0, 1, 1, 0])

    # "s" = shifted, "n" = not shifted

    # Reciprocal grids
    recip_s = reciprocal_grid(discr.grid, shift=True)
    recip_n = reciprocal_grid(discr.grid, shift=False)
    assert np.allclose(recip_s.coord_vectors[0],
                       np.linspace(-np.pi, np.pi / 2, 4))
    assert np.allclose(recip_n.coord_vectors[0],
                       np.linspace(-3 * np.pi / 4, 3 * np.pi / 4, 4))

    # Range
    range_part_s = odl.uniform_partition_fromgrid(recip_s)
    range_s = odl.uniform_discr_frompartition(range_part_s, dtype='complex')
    range_part_n = odl.uniform_partition_fromgrid(recip_n)
    range_n = odl.uniform_discr_frompartition(range_part_n, dtype='complex')

    # Pre-processing
    preproc_s = [1, -1, 1, -1]
    preproc_n = [np.exp(1j * 3 / 4 * np.pi * k) for k in range(4)]

    fpre_s = dft_preprocess_data(f_discr, shift=True)
    fpre_n = dft_preprocess_data(f_discr, shift=False)
    assert np.allclose(fpre_s, f_discr * discr.element(preproc_s))
    assert np.allclose(fpre_n, f_discr * discr.element(preproc_n))

    # FFT step, replicating the _call_numpy method
    fft_s = np.fft.fftn(fpre_s, s=discr.shape, axes=[0])
    fft_n = np.fft.fftn(fpre_n, s=discr.shape, axes=[0])
    assert np.allclose(fft_s, [0, -1 + 1j, 2, -1 - 1j])
    assert np.allclose(fft_n, [
        np.exp(1j * np.pi * (3 - 2 * k) / 4) + np.exp(1j * np.pi *
                                                      (3 - 2 * k) / 2)
        for k in range(4)
    ])

    # Interpolation kernel FT
    interp_s = np.sinc(np.linspace(-1 / 2, 1 / 4, 4)) / np.sqrt(2 * np.pi)
    interp_n = np.sinc(np.linspace(-3 / 8, 3 / 8, 4)) / np.sqrt(2 * np.pi)
    assert np.allclose(
        interp_s,
        _interp_kernel_ft(np.linspace(-1 / 2, 1 / 4, 4), interp='nearest'))
    assert np.allclose(
        interp_n,
        _interp_kernel_ft(np.linspace(-3 / 8, 3 / 8, 4), interp='nearest'))

    # Post-processing
    postproc_s = np.exp(1j * np.pi * np.linspace(-3 / 2, 3 / 4, 4))
    postproc_n = np.exp(1j * np.pi * np.linspace(-9 / 8, 9 / 8, 4))

    fpost_s = dft_postprocess_data(range_s.element(fft_s),
                                   real_grid=discr.grid,
                                   recip_grid=recip_s,
                                   shift=[True],
                                   axes=(0, ),
                                   interp='nearest')
    fpost_n = dft_postprocess_data(range_n.element(fft_n),
                                   real_grid=discr.grid,
                                   recip_grid=recip_n,
                                   shift=[False],
                                   axes=(0, ),
                                   interp='nearest')

    assert np.allclose(fpost_s, fft_s * postproc_s * interp_s)
    assert np.allclose(fpost_n, fft_n * postproc_n * interp_n)

    # Comparing to the known result sqrt(2/pi) * sinc(x)
    assert np.allclose(fpost_s, fhat(recip_s.coord_vectors[0]))
    assert np.allclose(fpost_n, fhat(recip_n.coord_vectors[0]))

    # Doing the exact same with direct application of the FT operator
    ft_op_s = FourierTransform(discr, shift=True)
    ft_op_n = FourierTransform(discr, shift=False)
    assert ft_op_s.range.grid == recip_s
    assert ft_op_n.range.grid == recip_n

    ft_f_s = ft_op_s(f)
    ft_f_n = ft_op_n(f)
    assert np.allclose(ft_f_s, fhat(recip_s.coord_vectors[0]))
    assert np.allclose(ft_f_n, fhat(recip_n.coord_vectors[0]))

    # Second test function, asymmetric. Can also be represented exactly in the
    # discretization.
    def f(x):
        return (x >= 0) & (x <= 1)

    def fhat(x):
        return np.exp(-1j * x / 2) * sinc(x / 2) / np.sqrt(2 * np.pi)

    # Discretize f, check values
    f_discr = discr.element(f)
    assert np.allclose(f_discr, [0, 0, 1, 0])

    # Pre-processing
    fpre_s = dft_preprocess_data(f_discr, shift=True)
    fpre_n = dft_preprocess_data(f_discr, shift=False)
    assert np.allclose(fpre_s, [0, 0, 1, 0])
    assert np.allclose(fpre_n, [0, 0, -1j, 0])

    # FFT step
    fft_s = np.fft.fftn(fpre_s, s=discr.shape, axes=[0])
    fft_n = np.fft.fftn(fpre_n, s=discr.shape, axes=[0])
    assert np.allclose(fft_s, [1, -1, 1, -1])
    assert np.allclose(fft_n, [-1j, 1j, -1j, 1j])

    fpost_s = dft_postprocess_data(range_s.element(fft_s),
                                   real_grid=discr.grid,
                                   recip_grid=recip_s,
                                   shift=[True],
                                   axes=(0, ),
                                   interp='nearest')
    fpost_n = dft_postprocess_data(range_n.element(fft_n),
                                   real_grid=discr.grid,
                                   recip_grid=recip_n,
                                   shift=[False],
                                   axes=(0, ),
                                   interp='nearest')

    assert np.allclose(fpost_s, fft_s * postproc_s * interp_s)
    assert np.allclose(fpost_n, fft_n * postproc_n * interp_n)

    # Comparing to the known result exp(-1j*x/2) * sinc(x/2) / sqrt(2*pi)
    assert np.allclose(fpost_s, fhat(recip_s.coord_vectors[0]))
    assert np.allclose(fpost_n, fhat(recip_n.coord_vectors[0]))

    # Doing the exact same with direct application of the FT operator
    ft_f_s = ft_op_s(f)
    ft_f_n = ft_op_n(f)
    assert np.allclose(ft_f_s, fhat(recip_s.coord_vectors[0]))
    assert np.allclose(ft_f_n, fhat(recip_n.coord_vectors[0]))