def mean(X):
    return np.sum(X, axis=0)/len(X)

def std_dev(X, X_bar):
    return np.sqrt(np.sum((X-X_bar)**2, axis=0))

def cov(X, X_bar, Y, Y_bar):
    return np.sum((X-X_bar)*(Y-Y_bar), axis=0)

np.seterr(divide='ignore', invalid='ignore')

np_array = np.array(trace_array)

t_bar = mean(np_array)
#print(t_bar)
o_t = std_dev(np_array, t_bar)
#print(o_t)

intermediate = np.array(textin_array).transpose()

data = np.zeros((16,1000),int)

for j in range(0,1000):
    for k in range(0,16):
        data[k][j] = intermediate[k][j]

output = np.array(textout_array).transpose()

data_out = np.zeros((16,1000),int)

for j in range(0,1000):
    for k in range(0,16):
        data_out[k][j] = output[k][j]


print(np_array.shape)

print(data.shape)

fig, (ax1, ax2) = plt.subplots(2, sharex=True, sharey=True)
#plt.figure()

for j in range(0,16):
    #intermediate = np.array(textin_array).transpose()
    #print(intermediate)
    inter = np.array([data[j]]).transpose()

    d_bar = mean(inter)
    o_d = std_dev(inter, d_bar)
    covariance = cov(trace_array, t_bar, inter, d_bar)
    correlation = covariance/(o_t*o_d)
    #print(correlation)
    
    ax1.plot(correlation)
plt.show()


for j in range(0,16):
    inter = np.array([data_out[j]]).transpose()

    d_bar = mean(inter)
    o_d = std_dev(inter, d_bar)
    covariance = cov(trace_array, t_bar, inter, d_bar)
    correlation = covariance/(o_t*o_d)
    #print(correlation)
    
    ax2.plot(correlation)
plt.show()