1D Kalman Filtering code in Python

Thanks to Udacity.

1 dimensional Kalman Filter

def update(mean1, var1, mean2, var2):
    new_mean = (var2 * mean1 + var1 * mean2) / (var1 + var2)
    new_var = 1/(1/var1 + 1/var2)
    return [new_mean, new_var]

def predict(mean1, var1, mean2, var2):
    new_mean = mean1 + mean2
    new_var = var1 + var2
    return [new_mean, new_var]



for i in range(len(measurements)):
    [mu, sig] = update(mu, sig, measurements[i], measurement_sig)
    [mu, sig] = predict(mu, sig, motion[i], motion_sig)