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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 b1_noise_id(x, af, rate):
 """ B1 ratio for noise identification ratio of Standard Variace to AVAR """

    (taus,devs,errs,ns) = at.adev(x,taus=[af*rate],data_type="phase", rate=rate) 
    oadev_x = devs[0]
    y = np.diff(x)
    y_cut = np.array( y[:len(y)-(len(y)%af)] ) # cut to length
    assert len(y_cut)%af == 0
    y_shaped = y_cut.reshape( ( int(len(y_cut)/af), af) )
    y_averaged = np.average(y_shaped,axis=1) # average
        
    var = np.var(y_averaged, ddof=1)
    return var/pow(oadev_x,2.0)