code: def example1():
      
      N = 1000
      f = 1
      y = 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.errorbar(x_ax, y_ax,yerr=[err_l,err_h],label='GRADEV, no gaps')
      
      
      y[int(np.floor(0.4*N)):int(np.floor(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.errorbar(x_ax, y_ax,yerr=[err_l,err_h], label='GRADEV, with gaps')
      plt.xscale('log')
      plt.yscale('log')
      plt.grid()
      plt.legend()
      plt.xlabel('Tau / s')
      plt.ylabel('Overlapping Allan deviation')
      plt.show()
docs: Compute the GRADEV of a white phase noise. Compares two different 
      scenarios. 1) The original data and 2) ADEV estimate with gap robust ADEV.