def simulate(t=1000, poly=(0.,), sinusoids=None, sigma=0, rw=0, irw=0, rrw=0):
    """Simulate a random signal with seasonal (sinusoids), linear and quadratic trend, RW, IRW, and RRW

    Arguments:
      t (int or list of float): number of samples or time vector, default = 1000
      poly (list of float): polynomial coefficients (in decreasing "order") passed to `numpy.polyval`
         i.e. poly[0]*x**(N-1) + ... + poly[N-1]
      sinusoids (list of list): [[period], [amplitude, period], or [ampl., period, phase]]

    >>> len(simulate(poly=(0,),rrw=1))
    1000
    >>> simulate(t=range(3), poly=(1,2))  # doctest: +NORMALIZE_WHITESPACE
    0    2
    1    3
    2    4
    dtype: float64
    >>> all(simulate(t=50, sinusoids=((1,2,3),)) == simulate(t=range(50), sinusoids=((1,2,3),)))
    True
    >>> any(simulate(t=100))
    False
    >>> abs(simulate(sinusoids=42.42).values[1] + simulate(sinusoids=42.42).values[-1]) < 1e-10
    True
    >>> simulate(t=17,sinusoids=[42, 16]).min()
    -42.0
    >>> all((simulate(t=range(10), sinusoids=(1, 9, 4.5))+simulate(t=10, sinusoids=(1,9))).abs() < 1e-10)
    True
    """
    if t and isinstance(t, int):
        t = np.arange(t, dtype=np.float64)
    else:
        t = np.array(t, dtype=np.float64)
    N = len(t)
    poly = poly or (0.,)
    poly = listify(poly)
    y = np.polyval(poly, t)
    sinusoids = listify(sinusoids or [])
    if any(isinstance(ATP, (int, float)) for ATP in sinusoids):
        sinusoids = [sinusoids]
    for ATP in sinusoids:
        # default period is 1 more than the length of the simulated series (no values of the cycle are repeated)
        T = (t[-1] - t[0]) * N / (N - 1.)
        # default amplitude is 1 and phase is 0
        A, P = 1., 0
        try:
            A, T, P = ATP
        except (TypeError, ValueError):
            try:
                A, T = ATP
            except (TypeError, ValueError):
                # default period is 1 more than the length of the simulated series
                # (no values of the cycle are repeated)
                A = ATP[0]
        # print(A, T, P)
        # print(t[1] - t[0])
        y += A * np.sin(2 * np.pi * (t - P) / T)
    if sigma:
        y += np.random.normal(0.0, float(sigma), N)
    if rw:
        y += np.random.normal(0.0, float(rw), N).cumsum()
    if irw:
        y += np.random.normal(0.0, float(irw), N).cumsum().cumsum()
    if rrw:
        y += np.random.normal(0.0, float(rrw), N).cumsum().cumsum().cumsum()
    return pd.Series(y, index=t)
Simulate a random signal with seasonal (sinusoids), linear and quadratic trend, RW, IRW, and RRW

    Arguments:
      t (int or list of float): number of samples or time vector, default = 1000
      poly (list of float): polynomial coefficients (in decreasing "order") passed to `numpy.polyval`
         i.e. poly[0]*x**(N-1) + ... + poly[N-1]
      sinusoids (list of list): [[period], [amplitude, period], or [ampl., period, phase]]

    >>> len(simulate(poly=(0,),rrw=1))
    1000
    >>> simulate(t=range(3), poly=(1,2))  # doctest: +NORMALIZE_WHITESPACE
    0    2
    1    3
    2    4
    dtype: float64
    >>> all(simulate(t=50, sinusoids=((1,2,3),)) == simulate(t=range(50), sinusoids=((1,2,3),)))
    True
    >>> any(simulate(t=100))
    False
    >>> abs(simulate(sinusoids=42.42).values[1] + simulate(sinusoids=42.42).values[-1]) < 1e-10
    True
    >>> simulate(t=17,sinusoids=[42, 16]).min()
    -42.0
    >>> all((simulate(t=range(10), sinusoids=(1, 9, 4.5))+simulate(t=10, sinusoids=(1,9))).abs() < 1e-10)
    True