Simple functions around scipy.signal.butter

frequency_filter

def digital_filter(x,y,f=1000,order=10,kind="lowpass"):
    dt = x[1]-x[0]
    fs = 1/dt
    sos = signal.butter(order, f, kind, fs=fs, output='sos')
    return signal.sosfiltfilt(sos,y)

def lowpass(x,y,f=1000,order=10):
    return digital_filter(x,y,f=f,order=order,kind="lowpass")

def highpass(x,y,f=1000,order=10,add_average=True):
    temp = digital_filter(x,y,f=f,order=order,kind="highpass")
    const = y.mean() if add_average else 0
    return temp+const

def bandpass(x,y,freq=[99,101],order=10,add_average=True):
    """ freq can be a tuple (fmin,fmax) or a list of tuples if multiple bands """
    freqs = np.atleast_2d(freq)
    temp = np.zeros_like(x)
    for band in freqs:
        temp += digital_filter(x,y,f=band,order=order,kind="bandpass")
    const = y.mean() if add_average else 0
    return temp+const

def bandstop(x,y,freq=[99,101],order=10):
    """ freq can be a tuple (fmin,fmax) or a list of tuples if multiple bands """
    freqs = np.atleast_2d(freq)
    temp = np.zeros_like(x)
    for band in freqs:
        y = digital_filter(x,y,f=band,order=order,kind="bandstop")
    return y