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