def plot(self, hist=False, show=False, **kwargs):
import matplotlib.pyplot as plt<EOL>vals = self._mcpts<EOL>low = min(vals)<EOL>high = max(vals)<EOL>p = ss.kde.gaussian_kde(vals)<EOL>xp = np.linspace(low, high, <NUM_LIT:100>)<EOL>if hist:<EOL><INDENT>h = plt.hist(<EOL>vals,<EOL>bins=int(np.sqrt(len(vals)) + <NUM_LIT:0.5>),<EOL>histtype="<STR_LIT>",<EOL>normed=True,<EOL>**kwargs<EOL>)<EOL>plt.ylim(<NUM_LIT:0>, <NUM_LIT> * h[<NUM_LIT:0>].max())<EOL><DEDENT>else:<EOL><INDENT>plt.plot(xp, p.evaluate(xp), **kwargs)<EOL><DEDENT>plt.xlim(low - (high - low) * <NUM_LIT:0.1>, high + (high - low) * <NUM_LIT:0.1>)<EOL>if show:<EOL><INDENT>self.show()<EOL><DEDENT>
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