def test_sequence_of_iterables_raises():
with pytest.raises(ValueError):
# the length argument asserts a specific length
ensure_sequence_of_iterables(((0, 1), ), length=4)
# BEWARE: only the first element of a nested sequence is checked.
with pytest.raises(AssertionError):
iterable = (None, (0, 1), (0, 2))
result = iter(ensure_sequence_of_iterables(iterable))
assert next(result) is None
def test_multichannel(shape, kwargs):
"""Test adding multichannel image."""
viewer = ViewerModel()
np.random.seed(0)
data = np.random.random(shape or (15, 10, 5))
channel_axis = kwargs.pop('channel_axis', -1)
viewer.add_image(data, channel_axis=channel_axis, **kwargs)
# make sure the right number of layers got added
n_channels = data.shape[channel_axis]
assert len(viewer.layers) == n_channels
for i in range(n_channels):
# make sure that the data has been divided into layers
assert np.all(viewer.layers[i].data == data.take(i, axis=channel_axis))
# make sure colors have been assigned properly
if 'colormap' not in kwargs:
if n_channels < 3:
assert viewer.layers[i].colormap[0] == two_colormaps[i]
else:
assert viewer.layers[i].colormap[0] == base_colormaps[i]
if 'blending' not in kwargs:
assert viewer.layers[i].blending == 'additive'
for key, expectation in kwargs.items():
# broadcast expections
if key in {'scale', 'translate', 'contrast_limits', 'metadata'}:
expectation = ensure_sequence_of_iterables(expectation)
else:
expectation = ensure_iterable(expectation)
expectation = [v for i, v in zip(range(i + 1), expectation)]
result = getattr(viewer.layers[i], key)
if key == 'colormap': # colormaps are tuples of (name, cmap)
result = result[0]
assert np.all(result == expectation[i])
def test_sequence_of_iterables(input, expected):
"""Test ensure_sequence_of_iterables returns a sequence of iterables."""
zipped = zip(
range(3),
ensure_sequence_of_iterables(input, repeat_empty=True),
expected,
)
for i, result, expectation in zipped:
assert result == expectation
def test_multichannel(shape, kwargs):
"""Test adding multichannel image."""
viewer = ViewerModel()
np.random.seed(0)
data = np.random.random(shape or (15, 10, 5))
channel_axis = kwargs.pop('channel_axis', -1)
viewer.add_image(data, channel_axis=channel_axis, **kwargs)
# make sure the right number of layers got added
n_channels = data.shape[channel_axis]
assert len(viewer.layers) == n_channels
for i in range(n_channels):
# make sure that the data has been divided into layers
assert np.all(viewer.layers[i].data == data.take(i, axis=channel_axis))
# make sure colors have been assigned properly
if 'colormap' not in kwargs:
if n_channels == 1:
assert viewer.layers[i].colormap.name == 'gray'
elif n_channels == 2:
assert viewer.layers[i].colormap.name == two_colormaps[i]
else:
assert viewer.layers[i].colormap.name == base_colormaps[i]
if 'blending' not in kwargs:
assert (viewer.layers[i].blending == 'translucent_no_depth'
if i == 0 else 'additive')
for key, expectation in kwargs.items():
# broadcast exceptions
if key in {
'scale',
'translate',
'rotate',
'shear',
'contrast_limits',
'metadata',
'experimental_clipping_planes',
}:
expectation = ensure_sequence_of_iterables(expectation,
repeat_empty=True)
elif key == 'colormap' and expectation is not None:
if isinstance(expectation, list):
exp = [ensure_colormap(c).name for c in expectation]
else:
exp = ensure_colormap(expectation).name
expectation = ensure_iterable(exp)
else:
expectation = ensure_iterable(expectation)
expectation = [v for i, v in zip(range(i + 1), expectation)]
result = getattr(viewer.layers[i], key)
if key == 'colormap': # colormaps are tuples of (name, cmap)
result = result.name
if isinstance(result, np.ndarray):
np.testing.assert_almost_equal(result, expectation[i])
else:
assert result == expectation[i]
def test_sequence_of_iterables_no_repeat_empty():
assert ensure_sequence_of_iterables([], repeat_empty=False) == []
with pytest.raises(ValueError):
ensure_sequence_of_iterables([], repeat_empty=False, length=3)
def test_sequence_of_iterables_allow_none():
input = [(1, 2), None]
assert ensure_sequence_of_iterables(input, allow_none=True) == input
