def broadcast_to(x, *shape):
scalars_shape = create_zeros_tuple()
i = 0
for s_i in literal_unroll(shape):
scalars_shape = numba_basic.tuple_setitem(
scalars_shape, i, numba_basic.to_scalar(s_i)
)
i += 1
return np.broadcast_to(x, scalars_shape)
def raise_if_incompatible_array_sizes(a, *args):
ashape = a.shape
# We need literal_unroll here because the stencil might take
# multiple input arrays with different types that are not compatible
# (e.g. values as float[:] and flags as bool[:])
# When more than three total arrays are given, the second and third
# are iterated over in the loop below. Without literal_unroll, their
# types have to match.
# An example failing signature without literal_unroll might be
# (float[:], float[:], bool[:]) (Just (float[:], bool[:]) wouldn't fail)
for arg in literal_unroll(args):
if a.ndim != arg.ndim:
raise ValueError(
"Secondary stencil array does not have same number "
" of dimensions as the first stencil input.")
argshape = arg.shape
for i in range(len(ashape)):
if ashape[i] > argshape[i]:
raise ValueError("Secondary stencil array has some dimension "
"smaller the same dimension in the first "
"stencil input.")