code: def cum_fit(distr, xvals, alpha, thresh):
      
      xvals = numpy.array(xvals)
      cum_fit = cum_fndict[distr](xvals, alpha, thresh)
      # set fitted values below threshold to 0
      numpy.putmask(cum_fit, xvals < thresh, 0.)
      return cum_fit
docs: Integral of the fitted function above a given value (reverse CDF)
  
      The fitted function is normalized to 1 above threshold
  
      Parameters
      ----------
      xvals : sequence of floats
          Values where the function is to be evaluated
      alpha : float
          The fitted parameter
      thresh : float
          Threshold value applied to fitted values
  
      Returns
      -------
      cum_fit : array of floats
          Reverse CDF of fitted function at the requested xvals