def cmdLineTestBench(run='trace', M=1, D=5, R=50, Q=7, Type=None):
    """
    This is the simulation and conversion function for the CIC filter.
 
    Inputs
      run  --  Simulation or coversion type
      M    --  Order of the CIC filter
      D    --  Delay of the Comb Filter
      R    --  Decimation/Interpolation size
      Q    --  Quantization size of input word
    """
    global MaxGain
 
    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    # Parameters / Globals
    MaxGain = D**M      # Max gain of the CIC
    L = 2**(Q)          # Max value for input
    maxV = L * MaxGain  # Max Value for output
    minV = -maxV        # Min Value for output
 
    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    # Signals
    clk = Signal(False)
    rst = Signal(False)
 
    x = Signal(intbv(0, min=-L, max=L))
    y = Signal(intbv(0, min=minV, max=maxV))
 
    dvi = Signal(True)
    dvo = Signal(False)
 
    xcnt  = Signal(0)
    N_CLK = 0
 
    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    # Instantiate MyHDL simulation and coversion functions
    if run == 'trace':
        dut = traceSignals(cic, clk, rst, x, dvi, y, dvo, M, D, R)
    elif run == 'ver':
        toVerilog(cic, clk, rst, x, dvi, y, dvo, M, D, R)
        return None
    elif run == 'vhd':
        toVHDL(cic, clk, rst, x, dvi, y, dvo, M, D, R)
    else:
        dut = cic(clk, rst, x, dvi, y, dvo, M, D, R)
 
    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    # Stimulus
    @always(delay(1))    
    def clkgen():
        clk.next = not clk
 
    @always(clk.posedge)
    def ist():
 
        # Generate Input samples
        if xcnt >= N_CLK:
            x.next = int(L*uniform(-1,1))
            dvi.next = True
            xcnt.next = 0
        else:
            x.next = 0
            dvi.next = False
            xcnt.next = xcnt + 1
 
 
    @instance
    def stimulus():
        """
        The following is a basic testbench to stimulate the CIC filter
        and create some plots.
        """
 
        ysave = [0.0] * Nfft
        favg  = np.zeros(Nfft)
        xfavg = np.zeros(Nfft)
        xs    = np.zeros(Nfft)
 
        yield clk.posedge
        rst.next = True
        yield delay(10)
        rst.next = False
        yield delay(10)
 
        for ii in range(Nloops):
            for jj in range(Nfft):
                xs[jj] = float(x)/L
                if dvo:
                    if y == 0:
                        #Prevent any log(0) errors, startup zeros
                        ysave[jj] = 10.0**-10
                    else:
                        ysave[jj] = float(y._val)/L  # Undo fix-point
                yield clk.negedge
 
            favg = favg + abs(fft(ysave, Nfft)) / Nfft
            xfavg = xfavg + abs(fft(xs))/Nfft
 
        favg = favg / Nloops
        xfavg = xfavg / Nloops
 
 
        #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        # Plot responses
        xa = 2*pi * np.arange(Nfft)/Nfft - pi
        plt.ioff()
        plt.plot(xa, fftshift(20*log10(favg/MaxGain)) )
        plt.plot(xa, fftshift(20*log10(xfavg)) ) 
        plt.title('CIC Filter Frequency Response')
        plt.savefig('plots/myhdl_cic_freq_response_M%d_D%d_R%d.png' % (M,D,R) )
        plt.close('all')
 
        print MaxGain
        xrsp = 20*log10(favg/MaxGain) - 20*log10(xfavg)
        plt.plot(xa, fftshift(xrsp) )
        #plt.plot(xa, fftshift(20*log10(xfavg)) ) 
        plt.title('CIC Filter Frequency Response Gain Removed, Output * 1/D')
        plt.ylabel('Amplitude dB')
        plt.xlabel('Normailzed Frequency radians')
        plt.axis([ -pi, pi, -60, 0])
        plt.savefig('plots/myhdl_cic_freq_response2_M%d_D%d_R%d.png' % (M,D,R) )
        plt.close('all')
 
        # Plot the expected response
        num = np.zeros(D+1)
        num[0] = 1
        num[D] = -1        
        den = [1, -1]
        print num, den
 
        w,H = freqz(num, den)
        Hplt = 20*log10(abs(H[1:]))
        plt.plot(w[1:],  Hplt)
        plt.title('CIC Filter Frequency Response freqz')
        plt.axis([ 0, pi, -60, max(Hplt) + 2])
        plt.ylabel('Amplitude dB')
        plt.xlabel('Normailzed Frequency radians')
        plt.savefig('plots/freqz_cic_freq_response_M%d_D%d.png' % (M,D) )
        plt.close('all')
 
        Hplt = 20*log10(abs(H[1:]/D))
        plt.plot(w[1:],  Hplt)
        plt.title('CIC Filter Frequency Response freqz')
        plt.axis([ 0, pi, -60, max(Hplt) + 2])
        plt.ylabel('Amplitude dB')
        plt.xlabel('Normailzed Frequency radians')
        plt.savefig('plots/freqz_cic_freq_response2_M%d_D%d.png' % (M,D) )
        plt.close('all')
 
        raise StopSimulation
 
    return clkgen, stimulus, ist, dut