@@ -816,44 +816,8 @@ SUBROUTINE s_weno( v_vf, vL_vf, vR_vf, cd_vars, & ! -------------------
816816 DO l = is3% beg, is3% end
817817 DO k = is2% beg, is2% end
818818 DO j = is1% beg, is1% end
819- ! ! Scaling stuff here
820- ! min_u = np.amin(u,1)
821- ! max_u = np.amax(u,1)
822- ! const_n = min_u==max_u
823- ! u_tmp = np.zeros_like(u[:,2])
824- ! u_tmp[:] = u[:,2]
825- ! for i in range(0,5):
826- ! u[:,i] = (u[:,i]-min_u)/(max_u-min_u)
827-
828- ! IF (neural_network) THEN
829-
830- ! ELSE
831-
832- ! END IF
833-
834- DO q = - weno_polyn, weno_polyn
835- scaling_stencil(q) = v_rs_wsL(q)% vf(i)% sf(j,k,l)
836- END DO
837- min_u = minval (scaling_stencil(:))
838- max_u = maxval (scaling_stencil(:))
839- IF ( abs (min_u - max_u) > 1.d-16 ) THEN
840- v_rs_wsL(q)% vf(i)% sf(j,k,l) = (v_rs_wsL(q)% vf(i)% sf(j,k,l) - min_u)/ (max_u- min_u)
841- END IF
842- ! scaled_vars(q) = v_rs_wsL(q)%vf(i)%sf(j,k,l)
843- ! scaled_vars(q) = (v_rs_wsL(q)%vf(i)%sf(j,k,l) - min_u)/(max_u-min_u)
844-
845819 ! reconstruct from left side
846820
847- ! for i = -weno_polyn, weno_polyn
848- ! v_rs_wsL(i)%vf(j)%sf(k,:,:) = v_vf(j)%sf(i+k,iy%beg:iy%end,iz%beg:iz%end)
849- ! !! if no char_decomp then v_rs_wsR => v_rs_wsL
850-
851- ! so: dvd[0] = v[j+1]-v[j]
852- ! so: dvd[-1] = v[j]-v[j-1]
853-
854- ! dvd( 0) = scaled_vars(1) - scaled_vars(0)
855- ! dvd( -1) = scaled_vars(0) - scaled_vars(-1)
856-
857821 dvd( 0 ) = v_rs_wsL( 1 )% vf(i)% sf(j,k,l) &
858822 - v_rs_wsL( 0 )% vf(i)% sf(j,k,l)
859823 dvd(- 1 ) = v_rs_wsL( 0 )% vf(i)% sf(j,k,l) &
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