KdTree

import numpy as np
from sklearn.neighbors import KDTree
rng = np.random.RandomState(0)
Y = rng.random_sample((5, 3))
# ~ blob_num = np.loadtxt("blobNumber.txt")

global X

file1 = open("blobNumber.txt","r")
blob_num_1 = file1.read()
word = blob_num_1.split()
blob_num = int(word[0])
print blob_num
file1.close()

X = np.zeros((blob_num,3),dtype=float)
data = np.loadtxt("blobCoordinates.txt")
x = data[:,0]
y = data[:,1]
z = data[:,2]

for i in range(np.size(x)):
    X[i][0] = x[i]
    X[i][1] = y[i]
    X[i][2] = z[i]

print X[[10]]
tree = KDTree(X, leaf_size=2)

file1 = open("neighbourList.txt","w")#write mode

for i in range(np.size(x)):
    dist, ind = tree.query(X[[i]], k=2)
    print(ind)  # indices of 3 closest neighbors
    file1.write(ind)

file1.close()