code: def plot(self, hist=False, show=False, **kwargs):
          
          import matplotlib.pyplot as plt
  
          vals = self._mcpts
          low = min(vals)
          high = max(vals)
  
          p = ss.kde.gaussian_kde(vals)
          xp = np.linspace(low, high, 100)
  
          if hist:
              h = plt.hist(
                  vals,
                  bins=int(np.sqrt(len(vals)) + 0.5),
                  histtype="stepfilled",
                  normed=True,
                  **kwargs
              )
              plt.ylim(0, 1.1 * h[0].max())
          else:
              plt.plot(xp, p.evaluate(xp), **kwargs)
  
          plt.xlim(low - (high - low) * 0.1, high + (high - low) * 0.1)
  
          if show:
              self.show()
docs: Plot the distribution of the UncertainFunction. By default, the
          distribution is shown with a kernel density estimate (kde).
          
          Optional
          --------
          hist : bool
              If true, a density histogram is displayed (histtype='stepfilled')
          show : bool
              If ``True``, the figure will be displayed after plotting the 
              distribution. If ``False``, an explicit call to ``plt.show()`` is
              required to display the figure.
          kwargs : any valid matplotlib.pyplot.plot or .hist kwarg