I have this function to get the eigenvectors and eigenvalues of a matrix, in the last part I try to normalize to eigenvectors, and to make sure it worked I tried to check the orthonormality of them as shown down here:

import numpy as np
import math
from scipy import sparse
from scipy.sparse import linalg as sla


def harmonic_potential(x, pert):
    V_pert = []
    if pert == False:
       return x**2
    else:
        for i in x: 
            V_pert.append(( i**2 + 4*math.exp(-10*i**2) ) )
        return V_pert

def schrodinger1D(xmin, xmax, Nx, neigs, pert=False):
    x = np.linspace(xmin, xmax, Nx)                        
    dx = x[1] - x[0]  
    V = harmonic_potential(x, pert)                         
    H = sparse.eye(Nx, Nx, format='lil') * 2                
    for i in range(Nx - 1):                                 
        H[i, i + 1] = -1
        H[i + 1, i] = -1
    H = H / (dx ** 2)
    for i in range(Nx):                                      
        H[i, i] = H[i, i] + V[i]
    H = H.tocsc()                                          
    [evl, evt] = sla.eigs(H, k=neigs, which='SM')           
    for i in range(0, neigs):
        evt[:, i] = evt[:, i] / np.sqrt(                   
                                np.trapz(np.conj(
                                evt[:,i])*evt[:,i],x))
        evl = np.real(evl)                                 
    return evl, evt, x

evl_test , evt_test , x_test = schrodinger1D(-10, 10, 500, 5)

product_evt = []

for i in range(0, 500):
    product_evt.append( np.conjugate(evt_test[:, 0][i]) * evt_test[:, 0][i] )
    
sum_product = sum(product_evt)

But for some reason it is not working for evt_test[:, 0], the other eigenvectors are working just fine (returning 0 or 1), why is the first eigenvector not normalized? i.e why is the first iteration in the for loop not working?