Exemple #1
0
def test_data_type():
    """Test if data class always returns the correct data type."""
    # Change data type for the same object instance.
    d_int = np.random.randint(0, 10, size=(3, 50))
    orig_type = type(d_int[0][0])
    data = Data(d_int, dim_order='ps', normalise=False)
    # The concrete type depends on the platform:
    # https://mail.scipy.org/pipermail/numpy-discussion/2011-November/059261.html
    # Hence, compare against the type automatically assigned by Python or
    # against np.integer
    assert data.data_type is orig_type, 'Data type did not change.'
    assert issubclass(type(data.data[0, 0, 0]),
                      np.integer), ('Data type is not an int.')
    d_float = np.random.randn(3, 50)
    data.set_data(d_float, dim_order='ps')
    assert data.data_type is np.float64, 'Data type did not change.'
    assert issubclass(type(data.data[0, 0, 0]),
                      np.float), ('Data type is not a float.')

    # Check if data returned by the object have the correct type.
    d_int = np.random.randint(0, 10, size=(3, 50, 5))
    data = Data(d_int, dim_order='psr', normalise=False)
    real = data.get_realisations((0, 5), [(1, 1), (1, 3)])[0]
    assert issubclass(type(real[0, 0]),
                      np.integer), ('Realisations type is not an int.')
    sl = data._get_data_slice(0)[0]
    assert issubclass(type(sl[0, 0]),
                      np.integer), ('Data slice type is not an int.')
    settings = {'perm_type': 'random'}
    sl_perm = data.slice_permute_samples(0, settings)[0]
    assert issubclass(type(sl_perm[0, 0]),
                      np.integer), ('Permuted data slice type is not an int.')
    samples = data.permute_samples((0, 5), [(1, 1), (1, 3)], settings)[0]
    assert issubclass(type(samples[0, 0]),
                      np.integer), ('Permuted samples type is not an int.')
Exemple #2
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def test_data_type():
    """Test if data class always returns the correct data type."""
    # Change data type for the same object instance.
    d_int = np.random.randint(0, 10, size=(3, 50))
    orig_type = type(d_int[0][0])
    data = Data(d_int, dim_order='ps', normalise=False)
    # The concrete type depends on the platform:
    # https://mail.scipy.org/pipermail/numpy-discussion/2011-November/059261.html
    # Hence, compare against the type automatically assigned by Python or
    # against np.integer
    assert data.data_type is orig_type, 'Data type did not change.'
    assert issubclass(type(data.data[0, 0, 0]), np.integer), (
        'Data type is not an int.')
    d_float = np.random.randn(3, 50)
    data.set_data(d_float, dim_order='ps')
    assert data.data_type is np.float64, 'Data type did not change.'
    assert issubclass(type(data.data[0, 0, 0]), np.float), (
        'Data type is not a float.')

    # Check if data returned by the object have the correct type.
    d_int = np.random.randint(0, 10, size=(3, 50, 5))
    data = Data(d_int, dim_order='psr', normalise=False)
    real = data.get_realisations((0, 5), [(1, 1), (1, 3)])[0]
    assert issubclass(type(real[0, 0]), np.integer), (
        'Realisations type is not an int.')
    sl = data._get_data_slice(0)[0]
    assert issubclass(type(sl[0, 0]), np.integer), (
        'Data slice type is not an int.')
    settings = {'perm_type': 'random'}
    sl_perm = data.slice_permute_samples(0, settings)[0]
    assert issubclass(type(sl_perm[0, 0]), np.integer), (
        'Permuted data slice type is not an int.')
    samples = data.permute_samples((0, 5), [(1, 1), (1, 3)], settings)[0]
    assert issubclass(type(samples[0, 0]), np.integer), (
        'Permuted samples type is not an int.')
Exemple #3
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def test_get_data_slice():
    n = 10
    n_replications = 3
    d = Data(np.vstack((np.zeros(n).astype(int), np.ones(n).astype(int),
                        2 * np.ones(n).astype(int))),
             'rs',
             normalise=False)
    [s, i] = d._get_data_slice(process=0, offset_samples=0, shuffle=False)

    # test unshuffled slicing
    for r in range(n_replications):
        assert s[0][r] == i[r], 'Replication index {0} is not correct.'.format(
            r)
    # test shuffled slicing
    [s, i] = d._get_data_slice(process=0, offset_samples=0, shuffle=True)
    for r in range(n_replications):
        assert s[0][r] == i[r], 'Replication index {0} is not correct.'.format(
            r)

    offset = 3
    d = Data(np.arange(n), 's', normalise=False)
    [s, i] = d._get_data_slice(process=0, offset_samples=offset, shuffle=False)
    assert s.shape[0] == (n - offset), 'Offset not handled correctly.'
Exemple #4
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def test_get_data_slice():
    n = 10
    n_replications = 3
    d = Data(np.vstack((np.zeros(n).astype(int),
                        np.ones(n).astype(int),
                        2 * np.ones(n).astype(int))),
             'rs', normalise=False)
    [s, i] = d._get_data_slice(process=0, offset_samples=0, shuffle=False)

    # test unshuffled slicing
    for r in range(n_replications):
        assert s[0][r] == i[r], 'Replication index {0} is not correct.'.format(
                                                                            r)
    # test shuffled slicing
    [s, i] = d._get_data_slice(process=0, offset_samples=0, shuffle=True)
    for r in range(n_replications):
        assert s[0][r] == i[r], 'Replication index {0} is not correct.'.format(
                                                                            r)

    offset = 3
    d = Data(np.arange(n), 's', normalise=False)
    [s, i] = d._get_data_slice(process=0, offset_samples=offset, shuffle=False)
    assert s.shape[0] == (n - offset), 'Offset not handled correctly.'