Auto Cross covariance Python

Auto Cross Covariance for converting protein sequence descriptor to single length vector

import numpy as np

# z1 z2 and z3 descriptor was used to represent the protein sequence

# Index j was used for the z-scales (j = 1, 2, 3),

# n is the number of amino acids in a sequence,

# index i is the amino acid position (i = 1, 2, ...n)

# l is the lag (l = 1, 2, ...L).

# a short range of lags (L= 1, 2, 3, 4, 5)
Z = np.random.rand(3,80)
print(Z)
#Z = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
n = Z.shape[1]
n = n-1
print(n)
# Autocovariance
column = []
for j in range(0,3):
    row = []
    for l in range(0,5):
        summ = 0
        for i in range(0,n-l):
            rightsum = (Z[j,i]*Z[j,i+1])/(n-l)
            summ = summ + rightsum
        row.append(summ)
    column.append(row)

R = np.array(column)
print(R)

# Cross Covariance

ja = [0,1,2,0,1,2]
ka = [1,0,0,2,2,1] 
column = []
for j,k in zip(ja,ka):
    row = []
    for l in range(0,5):
        summ = 0
        for i in range(0,n-l):
            rightsum = (Z[j,i]*Z[k,i+1])/(n-l)

            summ = summ + rightsum
        row.append(summ)
    column.append(row)

C = np.array(column) 
print(C)  
    

Reference:
VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines