import numpy as np

def calc_MI(X,Y,bins):

   c_XY = np.histogram2d(X,Y,bins)[0]
   c_X = np.histogram(X,bins)[0]
   c_Y = np.histogram(Y,bins)[0]

   H_X = shan_entropy(c_X)
   H_Y = shan_entropy(c_Y)
   H_XY = shan_entropy(c_XY)

   MI = H_X + H_Y - H_XY
   return MI

def shan_entropy(c):
    c_normalized = c/float(np.sum(c))
    H = -sum(c_normalized* np.log2(c_normalized))
    return H

A = np.array([ [ 2.0, 140.0, 128.23, -150.5, -5.4 ],
            [ 2.4, 153.11, 130.34, -130.1,-9.5],
            [ 1.2, 156.9, 120.11, -110.45,-1.12 ] ])

bins = 5 # ?
n = A.shape[1]
matMI = np.zeros((n, n))

for ix in np.arange(n):
    for jx in np.arange(ix+1,n):
        matMI[ix,jx] = calc_MI(A[:,ix], A[:,jx], bins)