I’m trying to obtaining lyapunov exponent through python code

(1) I have a 3D data, so used Euclid distance to gain the distance between two spots.

I wanna find nearest point against current point and get through 500 points. -> 1 array

And make a big array by concentrating these arrays.

from math import dist
ITP = 100
bigarray = pd.DataFrame(columns=range(1, ITP+1)) 
array = pd.DataFrame(columns=range(1, ITP+1))  #make array 

for i in range(len(data_final)-ITP): 
    current_point = data_final[i] 
    min_distance = float('inf') 
    nearest_point = None  #initiate

    for j in range(len(data_final)-ITP):  #find closest spot
        distance = dist(current_point, data_final[j])  # Euclid distance
        if i != j and distance < min_distance:  # 
            min_distance = distance  # update min_distance
            nearest_point = j  # update nearest_point

    for k in range(ITP):  # make array
        array.loc[0, k+1] = dist(data_final[i+k], data_final[nearest_point+k]) 

    bigarray = pd.concat([bigarray, array], ignore_index=True)
    array = pd.DataFrame(columns=range(1, ITP+1))

(2) After that, I got every average about the 100(ITP) columns and get a log values.

import math

mean_final = [0 for i in range(ITP)] 

for i in range(1, ITP+1):
    mean_final[i-1] = (bigarray[i].mean())

for i in range(0, ITP) :
  mean_final[i] = math.log(mean_final[i])
mean_final

Actually, by some data,
I got this kind of graph.

enter image description here

import matplotlib.pyplot as plt

rng1 = ITP
plt.subplot(1, 2, 2)
plt.plot(range(len(mean_final[0:rng1])), mean_final[0:rng1], label='Lyapunov Exponent', color='red')
plt.title('Lyapunov Exponent over Time')
plt.xlabel('Index')
plt.ylabel('Lyapunov Exponent')
plt.show()

So is this correct answer with calculating lyapunov exponent??