Minor fixes

src/Adaptivity.jl

@@ -907,7 +907,7 @@ function refine_local_models(
   end
 
   # Select fine cells we want to keep
-  Df = Dc-1 # Dimension used to find neighboring cells\
+  Df = 0 # Dimension used to find neighboring cells
   f_own_or_ghost_ids, f_own_ids = map(cgids,cmodels,fmodels) do cgids,cmodel,fmodel
     glue = get_adaptivity_glue(fmodel)
     f2c_map = glue.n2o_faces_map[Dc+1]
@@ -1068,7 +1068,10 @@ function refine_cell_gids(
   # We finally can create the global numeration of the fine cells by piecing together: 
   #   1. The (local ids,global ids) of the owned fine cells
   #   2. The (owners,local ids,global ids) of the ghost fine cells
-  fgids = map(ranks,f_own_to_local,own_fgids,lids_snd,child_gids_snd) do rank, own_lids, own_gids, ghost_lids, ghost_gids
+  fgids = map(
+    ranks,f_own_to_local,own_fgids,parts_snd,lids_snd,child_gids_snd
+  ) do rank,own_lids,own_gids,nbors,ghost_lids,ghost_gids
+
     own2global = own_to_global(own_gids)
 
     n_nbors = length(ghost_lids)
@@ -1084,8 +1087,8 @@ function refine_cell_gids(
     end
 
     # Ghost cells
-    for nbor in 1:n_nbors
-      for i in ghost_lids.ptrs[nbor]:ghost_lids.ptrs[nbor+1]-1
+    for (n,nbor) in enumerate(nbors)
+      for i in ghost_lids.ptrs[n]:ghost_lids.ptrs[n+1]-1
         lid = ghost_lids.data[i]
         gid = ghost_gids.data[i]
         local2global[lid] = gid

src/Geometry.jl

@@ -432,7 +432,7 @@ end
 function Geometry.simplexify(model::DistributedDiscreteModel)
   _model = UnstructuredDiscreteModel(model)
   ref_model = refine(_model, refinement_method = "simplexify")
-  return Adaptivity.get_model(ref_model)
+  return UnstructuredDiscreteModel(Adaptivity.get_model(ref_model))
 end
 
 # Triangulation

test/AdaptivityUnstructuredTests.jl

@@ -15,7 +15,7 @@ using GridapDistributed: i_am_in
 function test_adaptivity(ranks,cmodel,fmodel)
   if i_am_in(ranks)
     sol(x) = sum(x)
-    order  = 3
+    order  = 1
     qorder = 2*order
     reffe  = ReferenceFE(lagrangian,Float64,order)
     amodel = fmodel
@@ -60,22 +60,31 @@ end
 function main(distribute,parts,ncells)
   ranks = distribute(LinearIndices((prod(parts),)))
 
-  parent = UnstructuredDiscreteModel(
-    CartesianDiscreteModel(ranks,parts,(0,1,0,1),ncells)
+  Dc = length(ncells)
+  domain = (Dc == 2) ? (0,1,0,1) : (0,1,0,1,0,1)
+  parent1 = UnstructuredDiscreteModel(
+    CartesianDiscreteModel(ranks,parts,domain,ncells)
   )
 
-  child1 = refine(parent, refinement_method = "red_green" )
-  test_adaptivity(ranks,parent,child1)
+  i_am_main(ranks) && println("UnstructuredAdaptivityTests: red_green")
+  child1 = refine(parent1, refinement_method = "red_green" )
+  test_adaptivity(ranks,parent1,child1)
 
-  child2 = refine(parent, refinement_method = "nvb" )
-  test_adaptivity(ranks,parent,child2)
+  i_am_main(ranks) && println("UnstructuredAdaptivityTests: simplexify")
+  child2 = refine(parent1, refinement_method = "simplexify" )
+  test_adaptivity(ranks,parent1,child2)
 
-  child3 = refine(parent, refinement_method = "barycentric" )
-  test_adaptivity(ranks,parent,child3)
+  parent2 = simplexify(parent1)
 
-  child4 = refine(parent, refinement_method = "simplexify" )
-  test_adaptivity(ranks,parent,child4)
+  if Dc == 2
+    i_am_main(ranks) && println("UnstructuredAdaptivityTests: nvb")
+    child3 = refine(parent2, refinement_method = "nvb" )
+    test_adaptivity(ranks,parent2,child3)
+  end
 
+  i_am_main(ranks) && println("UnstructuredAdaptivityTests: barycentric")
+  child4 = refine(parent2, refinement_method = "barycentric" )
+  test_adaptivity(ranks,parent2,child4)
 end
 
 function main(distribute)