diff src/SbpOperators/volumeops/volume_operator.jl @ 1025:e74c41c4b60e feature/dissipation_operators

Merge refactor/sbpoperators/inflation
author Jonatan Werpers <jonatan@werpers.com>
date Mon, 21 Mar 2022 15:12:59 +0100
parents 52f07c77299d
children 14cb97284373 05a25a5063bb
line wrap: on
line diff
--- a/src/SbpOperators/volumeops/volume_operator.jl	Fri Mar 18 15:44:03 2022 +0100
+++ b/src/SbpOperators/volumeops/volume_operator.jl	Mon Mar 21 15:12:59 2022 +0100
@@ -6,28 +6,22 @@
 the stencils `inner_stencil` and `closure_stencils`. When `Dim=1`, the
 corresponding `VolumeOperator` tensor mapping is returned. When `Dim>1`, the
 returned operator is the appropriate outer product of a one-dimensional
-operators and `IdentityMapping`s, e.g for `Dim=3` the volume operator in the
+operators and `IdentityTensor`s, e.g for `Dim=3` the volume operator in the
 y-direction is `I⊗op⊗I`.
 """
 function volume_operator(grid::EquidistantGrid, inner_stencil, closure_stencils, parity, direction)
     #TODO: Check that direction <= Dim?
 
-    # Create 1D volume operator in along coordinate direction
     op = VolumeOperator(restrict(grid, direction), inner_stencil, closure_stencils, parity)
-    # Create 1D IdentityMappings for each coordinate direction
-    one_d_grids = restrict.(Ref(grid), Tuple(1:dimension(grid)))
-    Is = IdentityMapping{eltype(grid)}.(size.(one_d_grids))
-    # Formulate the correct outer product sequence of the identity mappings and
-    # the volume operator
-    parts = Base.setindex(Is, op, direction)
-    return foldl(⊗, parts)
+    return LazyTensors.inflate(op, size(grid), direction)
 end
+# TBD: Should the inflation happen here or should we remove this method and do it at the caller instead?
 
 """
     VolumeOperator{T,N,M,K} <: TensorOperator{T,1}
 Implements a one-dimensional constant coefficients volume operator
 """
-struct VolumeOperator{T,N,M,K} <: TensorMapping{T,1,1}
+struct VolumeOperator{T,N,M,K} <: LazyTensor{T,1,1}
     inner_stencil::Stencil{T,N}
     closure_stencils::NTuple{M,Stencil{T,K}}
     size::NTuple{1,Int}
@@ -59,3 +53,4 @@
     r = getregion(i, closure_size(op), op.size[1])
     return LazyTensors.apply(op, v, Index(i, r))
 end
+# TODO: Move this to LazyTensors when we have the region communication down.