Пример #1
0
    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)
Пример #2
0
 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)