(ax, Si, opts=None, unit="")
return out
Plots mu* against sigma or the 95% confidence interval
Plots mu* against sigma or the 95% confidence interval
Plots mu * against sigma or the 95% confidence interval
def covariance_plot(ax, Si, opts=None, unit=""):
    '''Plots mu* against sigma or the 95% confidence interval

    '''
    if opts is None:
        opts = {}

    if Si['sigma'] is not None:
        # sigma is not present if using morris groups
        y = Si['sigma']
        out = ax.scatter(Si['mu_star'], y, c=u'k', marker=u'o',
                         **opts)
        ax.set_ylabel(r'$\sigma$')

        ax.set_xlim(0,)
        ax.set_ylim(0,)

        x_axis_bounds = np.array(ax.get_xlim())

        line1, = ax.plot(x_axis_bounds, x_axis_bounds, 'k-')
        line2, = ax.plot(x_axis_bounds, 0.5 * x_axis_bounds, 'k--')
        line3, = ax.plot(x_axis_bounds, 0.1 * x_axis_bounds, 'k-.')

        ax.legend((line1, line2, line3), (r'$\sigma / \mu^{\star} = 1.0$',
                                          r'$\sigma / \mu^{\star} = 0.5$',
                                          r'$\sigma / \mu^{\star} = 0.1$'),
                  loc='best')

    else:
        y = Si['mu_star_conf']
        out = ax.scatter(Si['mu_star'], y, c=u'k', marker=u'o',
                         **opts)
        ax.set_ylabel(r'$95\% CI$')

    ax.set_xlabel(r'$\mu^\star$ ' + unit)
    ax.set_ylim(0-(0.01 * np.array(ax.get_ylim()[1])), )

    return out
def covariance_plot ( ax , Si , opts = None , unit = "" ) : if opts is None : opts = { } if Si [ 'sigma' ] is not None : # sigma is not present if using morris groups y = Si [ 'sigma' ] out = ax . scatter ( Si [ 'mu_star' ] , y , c = u'k' , marker = u'o' , * * opts ) ax . set_ylabel ( r'$\sigma$' ) ax . set_xlim ( 0 , ) ax . set_ylim ( 0 , ) x_axis_bounds = np . array ( ax . get_xlim ( ) ) line1 , = ax . plot ( x_axis_bounds , x_axis_bounds , 'k-' ) line2 , = ax . plot ( x_axis_bounds , 0.5 * x_axis_bounds , 'k--' ) line3 , = ax . plot ( x_axis_bounds , 0.1 * x_axis_bounds , 'k-.' ) ax . legend ( ( line1 , line2 , line3 ) , ( r'$\sigma / \mu^{\star} = 1.0$' , r'$\sigma / \mu^{\star} = 0.5$' , r'$\sigma / \mu^{\star} = 0.1$' ) , loc = 'best' ) else : y = Si [ 'mu_star_conf' ] out = ax . scatter ( Si [ 'mu_star' ] , y , c = u'k' , marker = u'o' , * * opts ) ax . set_ylabel ( r'$95\% CI$' ) ax . set_xlabel ( r'$\mu^\star$ ' + unit ) ax . set_ylim ( 0 - ( 0.01 * np . array ( ax . get_ylim ( ) [ 1 ] ) ) , ) return out