def METHOD_NAME(oldL,d1 = 0.3,d2 = 1.2,f=.25):
    S=shape(oldL)
    ff = np.random.binomial(1,f,S)
    if np.random.random()<.5:
        ii=np.random.normal(0,d1,S)
        s= oldL + ii*ff
        U = 0.
        return s, U
    else:
        u = np.random.uniform(0,1,S) #*np.rint(np.random.uniform(0,f,S))
        l = 2*log(d2)
        m = exp(l*(u-.5))
        m[ff==0] = 1.
        ii = oldL * m
        U=sum(log(m))
        return ii, U