generate time series with white noise that has constant PSD = b0,
        up to the nyquist frequency fs/2
        N = number of samples
        b0 = desired power-spectral density in [X^2/Hz] where X is the unit of x
        fs = sampling frequency, i.e. 1/fs is the time-interval between datapoints

        the pre-factor corresponds to the area 'box' under the PSD-curve:
        The PSD is at 'height' b0 and extends from 0 Hz up to the nyquist frequency fs/2

def white(num_points=1024, b0=1.0, fs=1.0):
    """ generate time series with white noise that has constant PSD = b0,
        up to the nyquist frequency fs/2
        N = number of samples
        b0 = desired power-spectral density in [X^2/Hz] where X is the unit of x
        fs = sampling frequency, i.e. 1/fs is the time-interval between datapoints

        the pre-factor corresponds to the area 'box' under the PSD-curve:
        The PSD is at 'height' b0 and extends from 0 Hz up to the nyquist frequency fs/2
    """
    return math.sqrt(b0*fs/2.0)*numpy.random.randn(num_points)