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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def example1():
 """ Compute the GRADEV of a white phase noise. Compares two different scenarios. 1) The original data and 2) ADEV estimate with gap robust ADEV. """

    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()