low_pass_test.py

import scipy.signal as sp
import numpy as np
import matplotlib.pylab as plt

# low pass functions copied from the web 
def butter_lowpass(cutOff, fs, order=5):
    nyq = 0.5 * fs
    normalCutoff = cutOff / nyq
    b, a = sp.butter(order, normalCutoff, btype='low')
    return b, a

def butter_lowpass_filter(data, cutOff, fs, order=4):
    b, a = butter_lowpass(cutOff, fs, order=order)
    y = sp.lfilter(b, a, data)
    return y

length , samplingRate, amplitude = 1500, 500, 2
x = np.linspace(0, length,length)/samplingRate
flo, fhi = 1, 50
lo = amplitude*np.sin(2*np.pi*flo*x)
hi = (amplitude/3)*(np.sin(2*np.pi*fhi*x))

sumLoHi = lo+hi
filtered = butter_lowpass_filter(sumLoHi,10,500, 10) #filter

# plot
plt.figure(figsize=(12,10))
#plt.subplot(211)

plt.plot(x,sumLoHi,'-', label='sum')
plt.plot(x, lo, 'b', label='low')
plt.plot(x, filtered,  label='filtered')
plt.legend()
plt.show()