code: def kakwani(values, ineq_axis, weights = None):
      
      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)
docs: Computes the Kakwani index