output = coord
if len(coord):
// Use KDtree to find the peaks that are too close to each other
tree = cKDTree(coord)
indices = tree.query_ball_point(coord, r=spacing, p=p_norm)
rejected_peaks_indices = set()
for idx, candidates in enumerate(indices):
if idx not in rejected_peaks_indices:
// keep current point and the points at exactly spacing from it
candidates.remove(idx)
dist = distance.cdist([coord[idx]],
coord[candidates],
distance.minkowski,
p=p_norm).reshape(-1)
candidates = [c for c, d in zip(candidates, dist)
if d < spacing]
// candidates.remove(keep)
rejected_peaks_indices.update(candidates)
After Change
output = coords
if len(coords):
coords = np.atleast_2d(coords)
if batch_size is None:
batch_list = [coords]
else:
batch_list = np.array_split(coords, batch_size)
output = np.zeros((0, coords.shape[1]))
for batch in batch_list:
output = _ensure_spacing(np.vstack([output, batch]),
spacing, p_norm)
