def __init__(self,source,cf,fc,gain,order):
nch = len(cf)
TWOPI = 2*pi
self.samplerate = source.samplerate
c = 2.0 * self.samplerate
c1LP = ( c/Hz - TWOPI*fc )
b_temp = np.array([1,1])/ ( c/Hz + TWOPI*fc )
a_temp = np.array([1,-c1LP/ ( c/Hz + TWOPI*fc )])
filt_b = np.tile(b_temp.reshape([2,1]),[nch,1,order])
filt_a = np.tile(a_temp.reshape([2,1]),[nch,1,order])
filt_b[:,:,order-1] = filt_b[:,:,order-1]*gain
LinearFilterbank.__init__(self, source, filt_b, filt_a)
def __init__(self, source, gain=1, **kwds):
# Automatically duplicate mono input to fit the desired output shape
gain = np.atleast_1d(gain)
if len(gain) != source.nchannels and len(gain) != 1:
if source.nchannels != 1:
raise ValueError('Can only automatically duplicate source '
'channels for mono sources, use '
'RestructureFilterbank.')
source = RestructureFilterbank(source, len(gain))
samplerate = source.samplerate
zeros = np.array([-200, -200])
poles = np.array([-250 + 400j, -250 - 400j,
-2000 + 6000j, -2000 - 6000j])
# use an arbitrary gain here, will be normalized afterwards
b, a = signal.zpk2tf(zeros, poles * 2 * np.pi, 1.5e9)
# normalize the response at 1000Hz (of the analog filter)
resp = np.abs(signal.freqs(b, a, [1000*2*np.pi])[1]) # response magnitude
b /= resp
bd, ad = signal.bilinear(b, a, samplerate)
bd = (np.tile(bd, (source.nchannels, 1)).T * gain).T
ad = np.tile(ad, (source.nchannels, 1))
LinearFilterbank.__init__(self, source, bd, ad, **kwds)