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# Select a principal eigenvector via NumPy

# to be used as a template (copy & paste) script

import numpy as np

# set A to be your matrix

A = np.array([[1, 2, 3],

[4, 5, 6],

[7, 8, 9]])

eig_vals, eig_vecs = np.linalg.eig(A)

idx = np.absolute(eig_vals).argsort()[::-1] # decreasing order

sorted_eig_vals = eig_vals[idx]

sorted_eig_vecs = eig_vecs[:, idx]

principal_eig_vec = sorted_eig_vecs[:, 0] # eigvec with largest eigval

normalized_pr_eig_vec = np.real(principal_eig_vec / np.sum(principal_eig_vec))

print(normalized_pr_eig_vec) # eigvec that sums up to one