def calculate_neighborhood(X, size, mode="kneighbors"):
"""---------------------------------------------------------------------------------------
:function: acquire the neighborhoods of particles
:parameter: X : data points
size : number of particles in neighborhoods
mode : "kneighbors" and "epsilon neighbors"
:return: neibrs_indices : index of particles in neighbors
------------------------------------------------------------------------------------------"""
if mode == "kneighbors":
neibrs = NearestNeighbors(n_neighbors=size, algorithm="ball_tree").fit(X)
neibrs_distances, neibrs_indices = neibrs.kneighbors(X)
else: # "epsilon neighbors"
neibrs = radius_neighbors_graph(X, radius=size, mode="distance")
neibrs_indices = [[i] for i in range(0, len(X))]
nonzero_indices = neibrs.nonzero()
for x, y in zip(nonzero_indices[0], nonzero_indices[1]):
neibrs_indices[x].append(y)
return neibrs_indices
