'Returns a plot of the confidence region for a univariate
mean and variance.
Parameters
mu_low : float
Lowest value of the mean to plot
mu_high : float
Highest value of the mean to plot
var_low : float
Lowest value of the variance to plot
var_high : float
Highest value of the variance to plot
mu_step : float
Increments to evaluate the mean
var_step : float
Increments to evaluate the mean
levs : list
Which values of significance the contour lines will be drawn.
Default is [.2, .1, .05, .01, .001]
Returns
fig : matplotlib figure instance
The contour plot'

def plot_contour(self, mu_low, mu_high, var_low, var_high, mu_step, var_step, levs=[0.2, 0.1, 0.05, 0.01, 0.001]):

    (fig, ax) = utils.create_mpl_ax()
    ax.set_ylabel('Variance')
    ax.set_xlabel('Mean')
    mu_vect = list(np.arange(mu_low, mu_high, mu_step))
    var_vect = list(np.arange(var_low, var_high, var_step))
    z = []
    for sig0 in var_vect:
        self.sig2_0 = sig0
        for mu0 in mu_vect:
            z.append(self._opt_var(mu0, pval=True))
    z = np.asarray(z).reshape(len(var_vect), len(mu_vect))
    ax.contour(mu_vect, var_vect, z, levels=levs)
    return fig
