def test_broadcasting(self, fixture_shapes, fixture_exception_handling):
shapes = fixture_shapes
raise_exception = fixture_exception_handling
try:
expected_out = np.broadcast(*[np.empty(s) for s in shapes]).shape
except ValueError:
expected_out = None
if expected_out is None:
if raise_exception:
with pytest.raises(ValueError):
shapes_broadcasting(*shapes, raise_exception=raise_exception)
else:
out = shapes_broadcasting(*shapes, raise_exception=raise_exception)
assert out is None
else:
out = shapes_broadcasting(*shapes, raise_exception=raise_exception)
assert out == expected_out
def test_broadcasting(self, fixture_shapes, fixture_exception_handling):
shapes = fixture_shapes
raise_exception = fixture_exception_handling
try:
expected_out = np.broadcast(*[np.empty(s) for s in shapes]).shape
except ValueError:
expected_out = None
if expected_out is None:
if raise_exception:
with pytest.raises(ValueError):
shapes_broadcasting(*shapes, raise_exception=raise_exception)
else:
out = shapes_broadcasting(*shapes, raise_exception=raise_exception)
assert out is None
else:
out = shapes_broadcasting(*shapes, raise_exception=raise_exception)
assert out == expected_out
def test_get_broadcastable_dist_samples(self, samples_to_broadcast):
size, samples, broadcast_shape = samples_to_broadcast
if broadcast_shape is not None:
size_ = to_tuple(size)
outs, out_shape = get_broadcastable_dist_samples(
samples, size=size, return_out_shape=True)
assert out_shape == broadcast_shape
for i, o in zip(samples, outs):
ishape = i.shape
if ishape[:min([len(size_), len(ishape)])] == size_:
expected_shape = (size_ + (1, ) *
(len(broadcast_shape) - len(ishape)) +
ishape[len(size_):])
else:
expected_shape = ishape
assert o.shape == expected_shape
assert shapes_broadcasting(*[o.shape
for o in outs]) == broadcast_shape
else:
with pytest.raises(ValueError):
get_broadcastable_dist_samples(samples, size=size)
def test_get_broadcastable_dist_samples(self, samples_to_broadcast):
size, samples, broadcast_shape = samples_to_broadcast
if broadcast_shape is not None:
size_ = to_tuple(size)
outs, out_shape = get_broadcastable_dist_samples(
samples, size=size, return_out_shape=True
)
assert out_shape == broadcast_shape
for i, o in zip(samples, outs):
ishape = i.shape
if ishape[: min([len(size_), len(ishape)])] == size_:
expected_shape = (
size_
+ (1,) * (len(broadcast_shape) - len(ishape))
+ ishape[len(size_) :]
)
else:
expected_shape = ishape
assert o.shape == expected_shape
assert shapes_broadcasting(*[o.shape for o in outs]) == broadcast_shape
else:
with pytest.raises(ValueError):
get_broadcastable_dist_samples(samples, size=size)
def test_type_check_success(self):
inputs = [3, 3.0, tuple(), [3], (3, ), np.array(3), np.array([3])]
out = shapes_broadcasting(*inputs)
assert out == (3, )
def test_type_check_raises(self, bad_input):
with pytest.raises(TypeError):
shapes_broadcasting(bad_input, tuple(), raise_exception=True)
with pytest.raises(TypeError):
shapes_broadcasting(bad_input, tuple(), raise_exception=False)
def test_type_check_success(self):
inputs = [3, 3.0, tuple(), [3], (3,), np.array(3), np.array([3])]
out = shapes_broadcasting(*inputs)
assert out == (3,)
def test_type_check_raises(self, bad_input):
with pytest.raises(TypeError):
shapes_broadcasting(bad_input, tuple(), raise_exception=True)
with pytest.raises(TypeError):
shapes_broadcasting(bad_input, tuple(), raise_exception=False)
