def peaking(GdB, fc, Q=<NUM_LIT>, fs=<NUM_LIT>):
mu = <NUM_LIT:10>**(GdB/<NUM_LIT>)<EOL>kq = <NUM_LIT:4>/(<NUM_LIT:1> + mu)*np.tan(<NUM_LIT:2>*np.pi*fc/fs/(<NUM_LIT:2>*Q))<EOL>Cpk = (<NUM_LIT:1> + kq *mu)/(<NUM_LIT:1> + kq)<EOL>b1 = -<NUM_LIT:2>*np.cos(<NUM_LIT:2>*np.pi*fc/fs)/(<NUM_LIT:1> + kq*mu)<EOL>b2 = (<NUM_LIT:1> - kq*mu)/(<NUM_LIT:1> + kq*mu)<EOL>a1 = -<NUM_LIT:2>*np.cos(<NUM_LIT:2>*np.pi*fc/fs)/(<NUM_LIT:1> + kq)<EOL>a2 = (<NUM_LIT:1> - kq)/(<NUM_LIT:1> + kq)<EOL>b = Cpk*np.array([<NUM_LIT:1>, b1, b2])<EOL>a = np.array([<NUM_LIT:1>, a1, a2])<EOL>return b,a<EOL>
A second-order peaking filter having GdB gain at fc and approximately
and 0 dB otherwise.

The filter coefficients returns correspond to a biquadratic system function
containing five parameters.

Parameters
----------
GdB : Lowpass gain in dB
fc : Center frequency in Hz
Q : Filter Q which is inversely proportional to bandwidth
fs : Sampling frquency in Hz

Returns
-------
b : ndarray containing the numerator filter coefficients
a : ndarray containing the denominator filter coefficients

Examples
--------
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from sk_dsp_comm.sigsys import peaking
>>> from scipy import signal
>>> b,a = peaking(2.0,500)
>>> f = np.logspace(1,5,400)
>>> w,H = signal.freqz(b,a,2*np.pi*f/44100)
>>> plt.semilogx(f,20*np.log10(abs(H)))
>>> plt.ylabel("Power Spectral Density (dB)")
>>> plt.xlabel("Frequency (Hz)")
>>> plt.show()

>>> b,a = peaking(-5.0,500,4)
>>> w,H = signal.freqz(b,a,2*np.pi*f/44100)
>>> plt.semilogx(f,20*np.log10(abs(H)))
>>> plt.ylabel("Power Spectral Density (dB)")
>>> plt.xlabel("Frequency (Hz)")