def Sa(self, x_surface, geom):
'''Covariance of prior distribution, calculated at state x. We find
the covariance in a normalized space (normalizing by z) and then un-
normalize the result for the calling function.'''
Cov = MultiComponentSurface.Sa(self, x_surface, geom)
f = s.array([[(10.0 * self.scale[self.glint_ind])**2]])
Cov[self.glint_ind, self.glint_ind] = f
return Cov
def Sa(self, x_surface, geom):
'''Covariance of prior distribution, calculated at state x'''
Cov = MultiComponentSurface.Sa(self, x_surface, geom)
t = s.array([[(10.0 * self.scale[self.surf_temp_ind])**2]])
Cov[self.surf_temp_ind, self.surf_temp_ind] = t
f = s.array([[(10.0 * self.scale[self.bb_frac_ind])**2]])
Cov[self.bb_frac_ind, self.bb_frac_ind] = f
return Cov
