def _distances_from_bounds(bounds, total_bounds, p):
n = bounds.shape[1] // 2
dim_ranges = [(total_bounds[d], total_bounds[d + n]) for d in range(n)]
# Avoid degenerate case where there is a single unique rectangle that is a
# single point. Increase the range by 1.0 to prevent divide by zero error
for d in range(n):
if dim_ranges[d][0] == dim_ranges[d][1]:
dim_ranges[d] = (dim_ranges[d][0], dim_ranges[d][1] + 1)
# Compute hilbert distance of the middle of each bounding box
dim_mids = [(bounds[:, d] + bounds[:, d + n]) / 2.0 for d in range(n)]
side_length = 2**p
coords = np.zeros((bounds.shape[0], n), dtype=np.int64)
for d in range(n):
coords[:, d] = _data2coord(dim_mids[d], dim_ranges[d], side_length)
hilbert_distances = distances_from_coordinates(p, coords)
return hilbert_distances
def test_distance_from_coordinates(p, n):
side_len = 2**p
# build matrix of all possible coordinate
coords = np.array(list(product(range(side_len), repeat=n)))
# Compute distances as vector result
vector_result = distances_from_coordinates(p, coords)
# Compare with reference
reference_hc = HilbertCurve(p, n)
for i in range(coords.shape[0]):
coord = coords[i, :]
# Reference
expected = reference_hc.distance_from_coordinates(coord)
# Compute scalar distance and compare
scalar_result = distance_from_coordinate(p, coord)
assert scalar_result == expected
# Compare with vector distance compute above
assert vector_result[i] == expected