code: def white(num_points=1024, b0=1.0, fs=1.0):
      
      return math.sqrt(b0*fs/2.0)*numpy.random.randn(num_points)
docs: 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