Numpy ints convert into python ints within numpy array

I'm writing code to group points around each point in a curve into concentric regions. Then I find the geometric median of each region as way of determining its "shape".

The point indices are determined by np.where statements for each region and all the np.where statement are placed into one np array to make it easier to index and handle if there are no points within a region. This master array dtype is also an object because each region has a variable amount of points in them.

However, in the rare case that each region has the same number of points, the array structure doesn't change, but the integers within each np.where array change from numpy ints into python ints. That is a problem because when I go to use those values as indices, I get an Index Error:

IndexError: arrays used as indices must be of integer (or boolean) type

This is the code I have and what I've attempted:

z = np.array([0,0.04,0.8,1.2,1.6,2])
    
    for j in range(1,n1-1): #1 to n1-2, ignoring the initial and final parts 
        print(j) 
        
        r_a = j_r[j-1,0] r_b = j_r[j-1,1]
    
        dist_a = s_dist[j][:j]
        dist_b = s_dist[j][j+1:]
    
        dist_pt_a = np.array([np.where((dist_a <= r_a*z[1]) & (dist_a >= r_a*z[0]))[0], #a: r1
                              np.where((dist_a <= r_a*z[2]) & (dist_a >= r_a*z[1]))[0], #a: r2
                              np.where((dist_a <= r_a*z[3]) & (dist_a >= r_a*z[2]))[0], #a: r3
                              np.where((dist_a <= r_a*z[4]) & (dist_a >= r_a*z[3]))[0], #a: r4
                              np.where((dist_a <= r_a*z[5]) & (dist_a >= r_a*z[4]))[0]], dtype=object) #a: r5
    
        dist_pt_b = np.array([np.where((dist_b <= r_b*z[1]) & (dist_b >= r_b*z[0]))[0] + (j+1), #b: r1
                              np.where((dist_b <= r_b*z[2]) & (dist_b >= r_b*z[1]))[0] + (j+1), #b: r2
                              np.where((dist_b <= r_b*z[3]) & (dist_b >= r_b*z[2]))[0] + (j+1), #b: r3
                              np.where((dist_b <= r_b*z[4]) & (dist_b >= r_b*z[3]))[0] + (j+1), #b: r4
                              np.where((dist_b <= r_b*z[5]) & (dist_b >= r_b*z[4]))[0] + (j+1)], dtype=object) #b: r5

#What's happening:

#THE GOOD

#the array with 5 regions, each of differing length
dist_pt_b: [array([1081, 1082, 1083], dtype=int64)
array([1084, 1085, 1086], dtype=int64) 
array([1087, 1088], dtype=int64)
array([1089, 1090, 1091, 1092], dtype=int64)
array([1093, 1094, 1095], dtype=int64)]
#the first region
r (0): [1081 1082 1083], <class 'numpy.ndarray'>
#the first index in the first region
r (0): 1081, <class 'numpy.int64'>
#is indexable to that cartesian point
r (0): [[ 0.96494    23.29605851][ 0.97084    23.29796217][ 0.98314    23.30293551]]

#THE BAD

#the array with 5 regions, each of same length

dist_pt_b: [[1082 1083 1084][1085 1086 1087][1088 1089 1090][1091 1092 1093][1094 1095 1096]]
#the first region
r (0): [1082 1083 1084], <class 'numpy.ndarray'>
#the first index in the first region
r (0): 1082, <class 'int'>

#INDEX ERROR

127     print(f'r ({r}): {dist_pt_b[r]}, {type(dist_pt_b[r])}')
128     print(f'r ({r}): {dist_pt_b[r][0]}, {type(dist_pt_b[r][0])}')
--> 129     print(f'r ({r}): {s_n[dist_pt_b[r]]}')
130 dist_pt_b[r] = np.mean(s_n[dist_pt_b[r]], axis = 0)
131 theta_s_b[2r], theta_s_b[2r+1] = dist_pt_b[r]
IndexError: arrays used as indices must be of integer (or boolean) type