def test_iter_img():
    img_3d = nibabel.Nifti1Image(np.ones((3, 4, 5)), np.eye(4))
    testing.assert_raises_regex(TypeError,
                                "Input data has incompatible dimensionality: "
                                "Expected dimension is 4D and you provided "
                                "a 3D image.",
                                image.iter_img, img_3d)

    affine = np.array([[1., 2., 3., 4.],
                       [5., 6., 7., 8.],
                       [9., 10., 11., 12.],
                       [0., 0., 0., 1.]])
    img_4d, _ = testing.generate_fake_fmri(affine=affine)

    for i, img in enumerate(image.iter_img(img_4d)):
        expected_data_3d = img_4d.get_data()[..., i]
        assert_array_equal(img.get_data(),
                           expected_data_3d)
        assert_array_equal(compat.get_affine(img),
                           compat.get_affine(img_4d))

    with testing.write_tmp_imgs(img_4d) as img_4d_filename:
        for i, img in enumerate(image.iter_img(img_4d_filename)):
            expected_data_3d = img_4d.get_data()[..., i]
            assert_array_equal(img.get_data(),
                               expected_data_3d)
            assert_array_equal(compat.get_affine(img),
                               compat.get_affine(img_4d))
        # enables to delete "img_4d_filename" on windows
        del img

    img_3d_list = list(image.iter_img(img_4d))
    for i, img in enumerate(image.iter_img(img_3d_list)):
        expected_data_3d = img_4d.get_data()[..., i]
        assert_array_equal(img.get_data(),
                           expected_data_3d)
        assert_array_equal(compat.get_affine(img),
                           compat.get_affine(img_4d))

    with testing.write_tmp_imgs(*img_3d_list) as img_3d_filenames:
        for i, img in enumerate(image.iter_img(img_3d_filenames)):
            expected_data_3d = img_4d.get_data()[..., i]
            assert_array_equal(img.get_data(),
                               expected_data_3d)
            assert_array_equal(compat.get_affine(img),
                               compat.get_affine(img_4d))
        # enables to delete "img_3d_filename" on windows
        del img

def test_check_niimg_3d():
    # check error for non-forced but necessary resampling
    assert_raises_regex(TypeError, 'nibabel format',
                        _utils.check_niimg, 0)

    # check error for non-forced but necessary resampling
    assert_raises_regex(TypeError, 'empty object',
                        _utils.check_niimg, [])

    # Test dimensionality error
    img = Nifti1Image(np.zeros((10, 10, 10)), np.eye(4))
    assert_raises_regex(TypeError,
                        "Input data has incompatible dimensionality: "
                        "Expected dimension is 3D and you provided a list "
                        "of 3D images \(4D\).",
                        _utils.check_niimg_3d, [img, img])

    # Check that a filename does not raise an error
    data = np.zeros((40, 40, 40, 1))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img, create_files=True) as filename:
        _utils.check_niimg_3d(filename)

    # check data dtype equal with dtype='auto'
    img_check = _utils.check_niimg_3d(img, dtype='auto')
    assert_equal(img.get_data().dtype.kind, img_check.get_data().dtype.kind)

def test_joblib_cache():
    if not LooseVersion(nibabel.__version__) > LooseVersion('1.1.0'):
        # Old nibabel do not pickle
        raise SkipTest
    from sklearn.externals.joblib import hash, Memory
    mask = np.zeros((40, 40, 40))
    mask[20, 20, 20] = 1
    mask_img = Nifti1Image(mask, np.eye(4))

    with testing.write_tmp_imgs(mask_img, create_files=True)\
            as filename:
        masker = NiftiMasker(mask_img=filename)
        masker.fit()
        mask_hash = hash(masker.mask_img_)
        masker.mask_img_.get_data()
        assert_true(mask_hash == hash(masker.mask_img_))

    # Test a tricky issue with memmapped joblib.memory that makes
    # imgs return by inverse_transform impossible to save
    cachedir = mkdtemp()
    try:
        masker.memory = Memory(cachedir=cachedir, mmap_mode='r',
                               verbose=0)
        X = masker.transform(mask_img)
        # inverse_transform a first time, so that the result is cached
        out_img = masker.inverse_transform(X)
        out_img = masker.inverse_transform(X)
        out_img.to_filename(os.path.join(cachedir, 'test.nii'))
    finally:
        shutil.rmtree(cachedir, ignore_errors=True)

def test_smooth_img():
    # This function only checks added functionalities compared
    # to _smooth_array()
    shapes = ((10, 11, 12), (13, 14, 15))
    lengths = (17, 18)
    fwhm = (1., 2., 3.)

    img1, mask1 = testing.generate_fake_fmri(shape=shapes[0],
                                             length=lengths[0])
    img2, mask2 = testing.generate_fake_fmri(shape=shapes[1],
                                             length=lengths[1])

    for create_files in (False, True):
        with testing.write_tmp_imgs(img1, img2,
                                    create_files=create_files) as imgs:
            # List of images as input
            out = image.smooth_img(imgs, fwhm)
            assert_true(isinstance(out, list))
            assert_true(len(out) == 2)
            for o, s, l in zip(out, shapes, lengths):
                assert_true(o.shape == (s + (l,)))

            # Single image as input
            out = image.smooth_img(imgs[0], fwhm)
            assert_true(isinstance(out, nibabel.Nifti1Image))
            assert_true(out.shape == (shapes[0] + (lengths[0],)))

def test_check_niimg():
    # check error for non-forced but necessary resampling
    assert_raises_regexp(TypeError, 'image',
                         _utils.check_niimg, 0)

    # check error for non-forced but necessary resampling
    assert_raises_regexp(TypeError, 'image',
                         _utils.check_niimg, [])

    # Test ensure_3d
    # check error for non-forced but necessary resampling
    assert_raises_regexp(TypeError, '3D',
                         _utils.check_niimg, ['test.nii', ], ensure_3d=True)

    # Check that a filename does not raise an error
    data = np.zeros((40, 40, 40, 2))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img, create_files=True) as filename:
        _utils.check_niimg(filename)

    # Test ensure_3d with a in-memory object
    assert_raises_regexp(TypeError, '3D',
                         _utils.check_niimg, data, ensure_3d=True)

    # Test ensure_3d with a non 3D image
    assert_raises_regexp(TypeError, '3D',
                         _utils.check_niimg, data_img, ensure_3d=True)

    # Test ensure_3d with a 4D image with a length 1 4th dim
    data = np.zeros((40, 40, 40, 1))
    data_img = Nifti1Image(data, np.eye(4))
    _utils.check_niimg(data_img, ensure_3d=True)

def test_check_niimg():
    with assert_raises(TypeError) as cm:
        _utils.check_niimg(0)
    assert_true('image' in cm.exception.message
                or 'affine' in cm.exception.message)

    with assert_raises(TypeError) as cm:
        _utils.check_niimg([])
    assert_true('image' in cm.exception.message
                or 'affine' in cm.exception.message)

    # Test ensure_3d
    with assert_raises(TypeError) as cm:
        _utils.check_niimg(['test.nii', ], ensure_3d=True)
    assert_true('3D' in cm.exception.message)

    # Check that a filename does not raise an error
    data = np.zeros((40, 40, 40, 2))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img, create_files=True)\
                as filename:
        _utils.check_niimg(filename)

    # Test ensure_3d with a in-memory object
    with assert_raises(TypeError) as cm:
        _utils.check_niimg(data, ensure_3d=True)
    assert_true('3D' in cm.exception.message)

    # Test ensure_3d with a 4D image with a length 1 4th dim
    data = np.zeros((40, 40, 40, 1))
    data_img = Nifti1Image(data, np.eye(4))
    _utils.check_niimg(data_img, ensure_3d=True)

def test_mean_img():
    rng = np.random.RandomState(42)
    data1 = np.zeros((5, 6, 7))
    data2 = rng.rand(5, 6, 7)
    data3 = rng.rand(5, 6, 7, 3)
    affine = np.diag((4, 3, 2, 1))
    img1 = nibabel.Nifti1Image(data1, affine=affine)
    img2 = nibabel.Nifti1Image(data2, affine=affine)
    img3 = nibabel.Nifti1Image(data3, affine=affine)
    for imgs in ([img1, ],
                   [img1, img2],
                   [img2, img1, img2],
                   [img3, img1, img2],  # Mixture of 4D and 3D images
                  ):

        arrays = list()
        # Ground-truth:
        for img in imgs:
            img = img.get_data()
            if img.ndim == 4:
                img = np.mean(img, axis=-1)
            arrays.append(img)
        truth = np.mean(arrays, axis=0)

        mean_img = image.mean_img(imgs)
        assert_array_equal(mean_img.get_affine(), affine)
        assert_array_equal(mean_img.get_data(), truth)

        # Test with files
        with testing.write_tmp_imgs(*imgs) as imgs:
            mean_img = image.mean_img(imgs)
            assert_array_equal(mean_img.get_affine(), affine)
            assert_array_equal(mean_img.get_data(), truth)

def test_iter_img():
    img_3d = nibabel.Nifti1Image(np.ones((3, 4, 5)), np.eye(4))
    testing.assert_raises_regex(TypeError, '4D Niimg-like',
                                image.iter_img, img_3d)

    affine = np.array([[1., 2., 3., 4.],
                       [5., 6., 7., 8.],
                       [9., 10., 11., 12.],
                       [0., 0., 0., 1.]])
    img_4d, _ = testing.generate_fake_fmri(affine=affine)

    for i, img in enumerate(image.iter_img(img_4d)):
        expected_data_3d = img_4d.get_data()[..., i]
        assert_array_equal(img.get_data(),
                           expected_data_3d)
        assert_array_equal(img.get_affine(),
                           img_4d.get_affine())

    with testing.write_tmp_imgs(img_4d) as img_4d_filename:
        for i, img in enumerate(image.iter_img(img_4d_filename)):
            expected_data_3d = img_4d.get_data()[..., i]
            assert_array_equal(img.get_data(),
                               expected_data_3d)
            assert_array_equal(img.get_affine(),
                               img_4d.get_affine())
        # enables to delete "img_4d_filename" on windows
        del img

    img_3d_list = list(image.iter_img(img_4d))
    for i, img in enumerate(image.iter_img(img_3d_list)):
        expected_data_3d = img_4d.get_data()[..., i]
        assert_array_equal(img.get_data(),
                           expected_data_3d)
        assert_array_equal(img.get_affine(),
                           img_4d.get_affine())

    with testing.write_tmp_imgs(*img_3d_list) as img_3d_filenames:
        for i, img in enumerate(image.iter_img(img_3d_filenames)):
            expected_data_3d = img_4d.get_data()[..., i]
            assert_array_equal(img.get_data(),
                               expected_data_3d)
            assert_array_equal(img.get_affine(),
                               img_4d.get_affine())
        # enables to delete "img_3d_filename" on windows
        del img

def test_mask_3d():
    # Dummy mask
    data = np.zeros((40, 40, 40, 2))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img, create_files=True)\
            as filename:
        masker = NiftiMasker(mask_img=filename)
        assert_raises(TypeError, masker.fit)

def test_with_files():
    # Standard masking
    data = np.zeros((40, 40, 40, 2))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img) as filename:
        masker = NiftiMasker()
        masker.fit(filename)
        masker.transform(filename)

def test_check_niimg():
    assert_raises(TypeError, _utils.check_niimg, 0)
    assert_raises(TypeError, _utils.check_niimg, [])
    # Check that a filename does not raise an error
    data = np.zeros((40, 40, 40, 2))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img, create_files=True)\
                as filename:
        _utils.check_niimg(filename)

def test_with_globbing_patterns_with_single_subject():
    # single subject
    data, mask_img, _, _ = _make_canica_test_data(n_subjects=1)
    n_components = 3
    canica = CanICA(n_components=n_components, mask=mask_img)
    with write_tmp_imgs(data[0], create_files=True, use_wildcards=True) as img:
        input_image = _tmp_dir() + img
        canica.fit(input_image)
        components_img = canica.components_img_
        assert_true(isinstance(components_img, nibabel.Nifti1Image))
        # n_components = 3
        check_shape = data[0].shape[:3] + (3,)
        assert_true(components_img.shape, check_shape)

def test_math_img():
    img1 = Nifti1Image(np.ones((10, 10, 10, 10)), np.eye(4))
    img2 = Nifti1Image(np.zeros((10, 10, 10, 10)), np.eye(4))
    expected_result = Nifti1Image(np.ones((10, 10, 10)), np.eye(4))

    formula = "np.mean(img1, axis=-1) - np.mean(img2, axis=-1)"
    for create_files in (True, False):
        with testing.write_tmp_imgs(img1, img2,
                                    create_files=create_files) as imgs:
            result = math_img(formula, img1=imgs[0], img2=imgs[1])
            assert_array_equal(result.get_data(),
                               expected_result.get_data())
            assert_array_equal(result.affine, expected_result.affine)
            assert_equal(result.shape, expected_result.shape)

def test_resampling_error_checks():
    shape = (3, 2, 5, 2)
    target_shape = (5, 3, 2)
    affine = np.eye(4)
    data = np.random.randint(0, 10, shape)
    img = Nifti1Image(data, affine)

    # Correct parameters: no exception
    resample_img(img, target_shape=target_shape, target_affine=affine)
    resample_img(img, target_affine=affine)

    with testing.write_tmp_imgs(img) as filename:
        resample_img(filename, target_shape=target_shape, target_affine=affine)

    # Missing parameter
    assert_raises(ValueError, resample_img, img, target_shape=target_shape)

    # Invalid shape
    assert_raises(ValueError, resample_img, img, target_shape=(2, 3),
                  target_affine=affine)

    # Invalid interpolation
    interpolation = 'an_invalid_interpolation'
    pattern = "interpolation must be either.+{0}".format(interpolation)
    testing.assert_raises_regex(ValueError, pattern,
                                resample_img, img, target_shape=target_shape,
                                target_affine=affine,
                                interpolation="an_invalid_interpolation")

    # Noop
    target_shape = shape[:3]

    img_r = resample_img(img, copy=False)
    assert_equal(img_r, img)

    img_r = resample_img(img, copy=True)
    assert_false(np.may_share_memory(img_r.get_data(), img.get_data()))

    np.testing.assert_almost_equal(img_r.get_data(), img.get_data())
    np.testing.assert_almost_equal(img_r.get_affine(), img.get_affine())

    img_r = resample_img(img, target_affine=affine, target_shape=target_shape,
                         copy=False)
    assert_equal(img_r, img)

    img_r = resample_img(img, target_affine=affine, target_shape=target_shape,
                         copy=True)
    assert_false(np.may_share_memory(img_r.get_data(), img.get_data()))
    np.testing.assert_almost_equal(img_r.get_data(), img.get_data())
    np.testing.assert_almost_equal(img_r.get_affine(), img.get_affine())

def test_joblib_cache():
    from sklearn.externals.joblib import hash
    # Dummy mask
    mask = np.zeros((40, 40, 40))
    mask[20, 20, 20] = 1
    mask_img = Nifti1Image(mask, np.eye(4))

    with write_tmp_imgs(mask_img, create_files=True) as filename:
        masker = MultiNiftiMasker(mask_img=filename)
        masker.fit()
        mask_hash = hash(masker.mask_img_)
        masker.mask_img_.get_data()
        assert_true(mask_hash == hash(masker.mask_img_))
        # enables to delete "filename" on windows
        del masker

def test_with_globbing_patterns_with_single_image():
    # With single image
    data_4d = np.zeros((40, 40, 40, 3))
    data_4d[20, 20, 20] = 1
    img_4d = nibabel.Nifti1Image(data_4d, affine=np.eye(4))
    multi_pca = MultiPCA(n_components=3)

    with write_tmp_imgs(img_4d, create_files=True, use_wildcards=True) as img:
        input_image = _tmp_dir() + img
        multi_pca.fit(input_image)
        components_img = multi_pca.components_img_
        assert_true(isinstance(components_img, nibabel.Nifti1Image))
        # n_components = 3
        check_shape = img_4d.shape[:3] + (3,)
        assert_equal(components_img.shape, check_shape)
        assert_equal(len(components_img.shape), 4)

def test_check_niimg_3d():
    # check error for non-forced but necessary resampling
    assert_raises_regex(TypeError, 'nibabel format',
                        _utils.check_niimg, 0)

    # check error for non-forced but necessary resampling
    assert_raises_regex(TypeError, 'empty object',
                        _utils.check_niimg, [])

    # Check that a filename does not raise an error
    data = np.zeros((40, 40, 40, 1))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img, create_files=True) as filename:
        _utils.check_niimg_3d(filename)

def test_joblib_cache():
    if not LooseVersion(nibabel.__version__) > LooseVersion('1.1.0'):
        # Old nibabel do not pickle
        raise SkipTest
    from sklearn.externals.joblib import hash
    # Dummy mask
    mask = np.zeros((40, 40, 40))
    mask[20, 20, 20] = 1
    mask_img = Nifti1Image(mask, np.eye(4))

    with write_tmp_imgs(mask_img, create_files=True)\
                as filename:
        masker = MultiNiftiMasker(mask_img=filename)
        masker.fit()
        mask_hash = hash(masker.mask_img_)
        masker.mask_img_.get_data()
        assert_true(mask_hash == hash(masker.mask_img_))

def test_check_niimg():
    with assert_raises(TypeError) as cm:
        _utils.check_niimg(0)
    assert_true('image' in cm.exception.message
                or 'affine' in cm.exception.message)

    with assert_raises(TypeError) as cm:
        _utils.check_niimg([])
    assert_true('image' in cm.exception.message
                or 'affine' in cm.exception.message)
    # Check that a filename does not raise an error
    data = np.zeros((40, 40, 40, 2))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img, create_files=True)\
                as filename:
        _utils.check_niimg(filename)

def test_check_niimg_3d():
    # check error for non-forced but necessary resampling
    assert_raises_regex(TypeError, "nibabel format", _utils.check_niimg, 0)

    # check error for non-forced but necessary resampling
    assert_raises_regex(TypeError, "empty object", _utils.check_niimg, [])

    # Test dimensionality error
    img = Nifti1Image(np.zeros((10, 10, 10)), np.eye(4))
    assert_raises_regex(TypeError, "Data must be a 3D", _utils.check_niimg_3d, [img, img])

    # Check that a filename does not raise an error
    data = np.zeros((40, 40, 40, 1))
    data[20, 20, 20] = 1
    data_img = Nifti1Image(data, np.eye(4))

    with testing.write_tmp_imgs(data_img, create_files=True) as filename:
        _utils.check_niimg_3d(filename)