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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 kakwani(values, ineq_axis, weights = None):
 """ Computes the Kakwani index """

    from scipy.integrate import simps

    if weights is None:
        weights = ones(len(values))

#    sign = -1
#    if tax == True:
#        sign = -1
#    else:
#        sign = 1

    PLCx, PLCy = pseudo_lorenz(values, ineq_axis, weights)
    LCx, LCy = lorenz(ineq_axis, weights)

    del PLCx

    return simps((LCy - PLCy), LCx)