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,
)
