def test_split_channels_missing_keywords(): data = np.random.randint(0, 200, (3, 128, 128)) result_list = split_channels(data, 0) assert len(result_list) == 3 for d, meta, _ in result_list: assert d.shape == (128, 128) assert meta['blending'] == 'additive'
def test_split_channels(kwargs): """Test split_channels with shape (3,128,128) expecting 3 (128,128)""" data = np.random.randint(0, 200, (3, 128, 128)) result_list = split_channels(data, 0, **kwargs) assert len(result_list) == 3 for d, meta, _ in result_list: assert d.shape == (128, 128)
def test_split_channels_affine_napari(kwargs): kwargs['affine'] = Affine(affine_matrix=np.eye(3)) data = np.random.randint(0, 200, (3, 128, 128)) result_list = split_channels(data, 0, **kwargs) assert len(result_list) == 3 for d, meta, _ in result_list: assert d.shape == (128, 128) assert np.array_equal(meta['affine'].affine_matrix, np.eye(3))
def test_split_channels_missing_keywords(): data = np.random.randint(0, 200, (3, 128, 128)) result_list = split_channels(data, 0) assert len(result_list) == 3 for chan, layer in enumerate(result_list): assert layer[0].shape == (128, 128) assert (layer[1]['blending'] == 'translucent' if chan == 0 else 'additive')
def test_split_channels_multiscale(kwargs): """Test split_channels with multiscale expecting List[LayerData]""" data = list() data.append(np.random.randint(0, 200, (3, 128, 128))) data.append(np.random.randint(0, 200, (3, 64, 64))) data.append(np.random.randint(0, 200, (3, 32, 32))) data.append(np.random.randint(0, 200, (3, 16, 16))) result_list = split_channels(data, 0, **kwargs) assert len(result_list) == 3 for ds, m, _ in result_list: assert m['multiscale'] is True assert ds[0].shape == (128, 128) assert ds[1].shape == (64, 64) assert ds[2].shape == (32, 32) assert ds[3].shape == (16, 16)
def test_split_channels_multi_affine_napari(kwargs): kwargs['affine'] = [ Affine(scale=[1, 1]), Affine(scale=[2, 2]), Affine(scale=[3, 3]), ] data = np.random.randint(0, 200, (3, 128, 128)) result_list = split_channels(data, 0, **kwargs) assert len(result_list) == 3 for idx, result_data in enumerate(result_list): d, meta, _ = result_data assert d.shape == (128, 128) assert np.array_equal( meta['affine'].affine_matrix, Affine(scale=[idx + 1, idx + 1]).affine_matrix, )