code: def example2():
      
      N=1000 # number of samples
      f = 1 # data samples per second
      s=1+5/N*np.arange(0,N)
      y=s*np.random.randn(1,N)[0,:]
      x = [xx for xx in np.linspace(1,len(y),len(y))]
      x_ax, y_ax, (err_l, err_h) , ns = allan.gradev(y,data_type='phase',rate=f,taus=x)
      plt.loglog(x_ax, y_ax,'b.',label="No gaps")
      y[int(0.4*N):int(0.6*N,)] = np.NaN  # Simulate missing data
      x_ax, y_ax, (err_l, err_h), ns = allan.gradev(y,data_type='phase',rate=f,taus=x)
      plt.loglog(x_ax, y_ax,'g.',label="With gaps")
      plt.grid()
      plt.legend()
      plt.xlabel('Tau / s')
      plt.ylabel('Overlapping Allan deviation')
      plt.show()
docs: Compute the GRADEV of a nonstationary white phase noise.