def samples_to_broadcast(fixture_sizes, fixture_shapes):
samples = [np.empty(s) for s in fixture_shapes]
try:
broadcast_shape = broadcast_dist_samples_shape(fixture_shapes,
size=fixture_sizes)
except ValueError:
broadcast_shape = None
return fixture_sizes, samples, broadcast_shape
def samples_to_broadcast_to(request, samples_to_broadcast):
to_shape = request.param
size, samples, broadcast_shape = samples_to_broadcast
if broadcast_shape is not None:
try:
broadcast_shape = broadcast_dist_samples_shape(
[broadcast_shape, to_tuple(to_shape)], size=size)
except ValueError:
broadcast_shape = None
return to_shape, size, samples, broadcast_shape
def test_broadcast_dist_samples_shape(self, fixture_sizes, fixture_shapes):
size = fixture_sizes
shapes = fixture_shapes
size_ = to_tuple(size)
shapes_ = [
s if s[:min([len(size_), len(s)])] != size_ else s[len(size_):]
for s in shapes
]
try:
expected_out = np.broadcast(*(np.empty(s) for s in shapes_)).shape
except ValueError:
expected_out = None
if expected_out is not None and any(
s[:min([len(size_), len(s)])] == size_ for s in shapes):
expected_out = size_ + expected_out
if expected_out is None:
with pytest.raises(ValueError):
broadcast_dist_samples_shape(shapes, size=size)
else:
out = broadcast_dist_samples_shape(shapes, size=size)
assert out == expected_out