def simulateDSM(u, arg2, nlev=2, x0=0.):
    """Simulate a Delta Sigma modulator

    **Syntax:**

     * [v, xn, xmax, y] = simulateDSM(u, ABCD, nlev=2, x0=0)
     * [v, xn, xmax, y] = simulateDSM(u, ntf, nlev=2, x0=0)

    Compute the output of a general delta-sigma modulator with input u,
    a structure described by ABCD, an initial state x0 (default zero) and
    a quantizer with a number of levels specified by nlev.
    Multiple quantizers are implied by making nlev an array,
    and multiple inputs are implied by the number of rows in u.

    Alternatively, the modulator may be described by an NTF.
    The NTF is zpk object. (And the STF is assumed to be 1.)
    The structure that is simulated is the block-diagonal structure used by
    ``zpk2ss()``.

    **Example:**

    Simulate a 5th-order binary modulator with a half-scale sine-wave input and
    plot its output in the time and frequency domains.::

        import numpy as np
        from deltasigma import *
        OSR = 32
        H = synthesizeNTF(5, OSR, 1)
        N = 8192 
        fB = np.ceil(N/(2*OSR))
        f = 85
        u = 0.5*np.sin(2*np.pi*f/N*np.arange(N))
        v = simulateDSM(u, H)[0]

    Graphical display of the results:

    .. plot::

        import numpy as np
        import pylab as plt
        from numpy.fft import fft
        from deltasigma import *
        OSR = 32
        H = synthesizeNTF(5, OSR, 1)
        N = 8192 
        fB = np.ceil(N/(2*OSR))
        f = 85
        u = 0.5*np.sin(2*np.pi*f/N*np.arange(N))
        v = simulateDSM(u, H)[0]
        plt.figure(figsize=(10, 7))
        plt.subplot(2, 1, 1)
        t = np.arange(85)
        # the equivalent of MATLAB 'stairs' is step in matplotlib
        plt.step(t, u[t], 'g', label='u(n)')
        plt.hold(True)
        plt.step(t, v[t], 'b', label='v(n)')
        plt.axis([0, 85, -1.2, 1.2]);
        plt.ylabel('u, v');
        plt.xlabel('sample')
        plt.legend()
        plt.subplot(2, 1, 2)
        spec = fft(v*ds_hann(N))/(N/4)
        plt.plot(np.linspace(0, 0.5, N/2 + 1), dbv(spec[:N/2 + 1]))
        plt.axis([0, 0.5, -120, 0])
        plt.grid(True)
        plt.ylabel('dBFS/NBW')
        snr = calculateSNR(spec[:fB], f)
        s = 'SNR = %4.1fdB' % snr
        plt.text(0.25, -90, s)
        s =  'NBW = %7.5f' % (1.5/N)
        plt.text(0.25, -110, s)
        plt.xlabel("frequency $1 \\\\rightarrow f_s$")

    Click on "Source" above to see the source code.
    """

    #fprintf(1,'Warning: You are running the non-mex version of simulateDSM.\n');
    #fprintf(1,'Please compile the mex version with "mex simulateDSM.c"\n');

    nlev = carray(nlev)
    u = np.array(u) if not hasattr(u, 'ndim') else u
    if not max(u.shape) == np.prod(u.shape):
        warn("Multiple input delta sigma structures have had little testing.")
    if u.ndim == 1:
        u = u.reshape((1, -1))
    nu = u.shape[0]
    nq = 1 if np.isscalar(nlev) else nlev.shape[0]
    # extract poles and zeros
    if (hasattr(arg2, 'inputs') and not arg2.inputs == 1) or \
       (hasattr(arg2, 'outputs') and not arg2.outputs == 1):
            raise TypeError("The supplied TF isn't a SISO transfer function.")
    if isinstance(arg2, np.ndarray):
        ABCD = np.asarray(arg2, dtype=np.float64)
        if ABCD.shape[1] != ABCD.shape[0] + nu:
            raise ValueError('The ABCD argument does not have proper dimensions.')
        form = 1
    else:
        zeros, poles, k = _get_zpk(arg2)
        form = 2
    #raise TypeError('%s: Unknown transfer function %s' % (__name__, str(arg2)))
        
    # need to set order and form now.
    order = carray(zeros).shape[0] if form == 2 else ABCD.shape[0] - nq
    
    if not isinstance(x0, collections.Iterable):
        x0 = x0*np.ones((order,), dtype=np.float64)
    else:
        x0 = np.array(x0).reshape((-1,))
    
    if form == 1:
        A = ABCD[:order, :order]
        B = ABCD[:order, order:order+nu+nq]
        C = ABCD[order:order+nq, :order]
        D1 = ABCD[order:order+nq, order:order+nu]
    else:
        A, B2, C, D2 = zpk2ss(poles, zeros, -1)    # A realization of 1/H
        # Transform the realization so that C = [1 0 0 ...]
        C, D2 = np.real_if_close(C), np.real_if_close(D2)
        Sinv = orth(np.hstack((np.transpose(C), np.eye(order)))) / norm(C)
        S = inv(Sinv)
        C = np.dot(C, Sinv)
        if C[0, 0] < 0:
            S = -S
            Sinv = -Sinv
        A = np.dot(np.dot(S, A), Sinv) 
        B2 = np.dot(S, B2) 
        C = np.hstack((np.ones((1, 1)), np.zeros((1, 1-order)))) # C=C*Sinv; 
        D2 = np.zeros((0,))
        # !!!! Assume stf=1
        B1 = -B2
        D1 = 1
        B = np.hstack((B1, B2))

    N = u.shape[1]
    v = np.empty((nq, N), dtype=np.float64)
    y = np.empty((nq, N), dtype=np.float64)     # to store the quantizer input
    xn = np.empty((order, N), dtype=np.float64) # to store the state information
    xmax = np.abs(x0) # to keep track of the state maxima

    for i in range(N):
        # y0 needs to be cast to real because ds_quantize needs real
        # inputs. If quantization were defined for complex numbers,
        # this cast could be removed
        y0 = np.real(np.dot(C, x0) + np.dot(D1, u[:, i]))
        y[:, i] = y0
        v[:, i] = ds_quantize(y0, nlev)
        x0 = np.dot(A, x0) + np.dot(B, np.concatenate((u[:, i], v[:, i])))
        xn[:, i] = np.real_if_close(x0.T)
        xmax = np.max(np.hstack((np.abs(x0).reshape((-1, 1)), xmax.reshape((-1, 1)))),
                      axis=1, keepdims=True)

    return v.squeeze(), xn.squeeze(), xmax, y.squeeze()
dataset/ETHPy150Open ggventurini/python-deltasigma/deltasigma/_simulateDSM_python.py/simulateDSM/swapped operands of `-`