def test_elements_tuple_of_arrays(shapes, dtype, data):
choices = data.draw(real_from_dtype(dtype))
elements = sampled_from(choices)
S = gu._tuple_of_arrays(shapes, dtype, elements=elements)
X = data.draw(S)
validate_elements(X, choices=choices, dtype=dtype)
def test_shapes_tuple_of_arrays(shapes, dtype, unique, data):
elements = from_dtype(np.dtype(dtype))
S = gu._tuple_of_arrays(shapes, dtype, elements=elements, unique=unique)
X = data.draw(S)
validate_elements(X, dtype=dtype, unique=unique)
assert len(shapes) == len(X)
for spec, drawn in zip(shapes, X):
assert tuple(spec) == np.shape(drawn)
def test_just_shapes_tuple_of_arrays(shapes, dtype, unique, data):
elements = from_dtype(np.dtype(dtype))
# test again, but this time pass in strategy to make sure it can handle it
S = gu._tuple_of_arrays(just(shapes), just(dtype), elements=elements, unique=just(unique))
X = data.draw(S)
validate_elements(X, dtype=dtype, unique=unique)
assert len(shapes) == len(X)
for spec, drawn in zip(shapes, X):
assert tuple(spec) == np.shape(drawn)
def test_bcast_tuple_of_arrays(args, data):
"""Now testing broadcasting of tuple_of_arrays, kind of crazy since it uses
gufuncs to test itself. Some awkwardness here since there are a lot of
corner cases when dealing with object types in the numpy extension.
For completeness, should probably right a function like this for the other
functions, but there always just pass dtype, elements, unique to
`_tuple_of_arrays` anyway, so this should be pretty good.
"""
shapes, dtype, elements, unique = args
shapes = shapes.ravel()
# Need to squeeze out due to weird behaviour of object
dtype = np.squeeze(dtype, -1)
elements = np.squeeze(elements, -1)
elements_shape = max(dtype.shape, elements.shape)
dtype_ = np.broadcast_to(dtype, elements_shape)
if elements_shape == ():
elements = from_dtype(dtype_.item())
else:
elements = [from_dtype(dd) for dd in dtype_]
shapes_shape = max(shapes.shape, dtype.shape, elements_shape, unique.shape)
shapes = np.broadcast_to(shapes, shapes_shape)
S = gu._tuple_of_arrays(shapes, dtype, elements=elements, unique=unique)
X = data.draw(S)
assert len(shapes) == len(X)
for spec, drawn in zip(shapes, X):
assert tuple(spec) == np.shape(drawn)
for ii, xx in enumerate(X):
dd = dtype[ii] if dtype.size > 1 else dtype.item()
uu = unique[ii] if unique.size > 1 else unique.item()
validate_elements([xx], dtype=dd, unique=uu)