Merge pull request #34 from gridap/documentation

Documentation fixes

docs/src/distributed.md

@@ -2,9 +2,9 @@
 
 ## Introduction
 
-When dealing with large-scale problems, this package can be accelerated through two types of parallelization. The first one is multi-threading, which uses [Julia Threads](https://docs.julialang.org/en/v1/base/multi-threading/) for shared memory parallelization (e.g., `julia -t 4`). This method, adds some speed-up. However, it is only efficient for a reduced number of threads.
+When dealing with large-scale problems, this package can be accelerated through two types of parallelization. The first one is multi-threading, which uses [Julia Threads](https://docs.julialang.org/en/v1/base/multi-threading/) for shared memory parallelization (e.g., `julia -t 4`). This method adds some speed-up. However, it is only efficient for a reduced number of threads.
 
-The second one is a distributed memory computing. For such parallelization, we use MPI (`mpiexec -np 4 julia input.jl`) through [`PartitionedArrays`](https://www.francescverdugo.com/PartitionedArrays.jl/stable) and [`GridapDistributed`](https://gridap.github.io/GridapDistributed.jl/dev/). With MPI expect to efficiently compute large-scale problems, up to thousands of cores.
+The second one is a distributed memory computing. For such parallelization, we use MPI (`mpiexec -np 4 julia input.jl`) through [`PartitionedArrays`](https://www.francescverdugo.com/PartitionedArrays.jl/stable) and [`GridapDistributed`](https://gridap.github.io/GridapDistributed.jl/dev/). With MPI we can compute large-scale problems efficiently, up to thousands of cores.
 
 Additionally, the distributed memory implementation is built on top of [GridapEmbedded](https://github.com/gridap/GridapEmbedded.jl). GridapEmbedded provides parallelization tools since [v0.9.2](https://github.com/gridap/GridapEmbedded.jl/releases/tag/v0.9.2).
 
@@ -37,6 +37,7 @@ Where `poisson.jl` is the following code.
 
 ```julia
 using STLCutters
+using Gridap
 using GridapEmbedded
 using GridapDistributed
 using PartitionedArrays
@@ -47,7 +48,7 @@ cells = (10,10,10)
 filename = "stl_file_path.stl"
 
 with_mpi() do distribute
-  ranks = distribute(LinearIndices((prod(parts))))
+  ranks = distribute(LinearIndices((prod(parts),)))
   # Domain and discretization
   geo = STLGeometry(filename)
   pmin,pmax = get_bounding_box(geo)
@@ -64,7 +65,7 @@ with_mpi() do distribute
   dΓ = Measure(Γ,2)
   # FE spaces
   Vstd = TestFESpace(Ω_act,ReferenceFE(lagrangian,Float64,1))
-  V = AgFEMSpace(Vstd)
+  V = AgFEMSpace(model,Vstd,aggregates)
   U = TrialFESpace(V)
   # Weak form
   γ = 10.0

docs/src/usage.md

@@ -27,7 +27,7 @@ filename = download_thingi10k(293137)
 
 ## Discretization
 
-Since STLCutters is an extension of [GridapEmbedded](https://github/gridap/GridapEmbedded.jl) it utilizes the same workflow to solve PDEs on embedded domains. In particular, STLCutters extends the [`cut`](@ref STLCutters.cut) function from GridapEmbedded.
+Since STLCutters is an extension of [GridapEmbedded](https://github/gridap/GridapEmbedded.jl), it utilizes the same workflow to solve PDEs on embedded domains. In particular, STLCutters extends the [`cut`](@ref STLCutters.cut) function from GridapEmbedded.
 
 We load the STL file with an [`STLGeometry`](@ref) object. E.g.,
 
@@ -54,7 +54,7 @@ cutgeo = cut(model,geo)
 
 ## Usage with Gridap
 
-Once, the geometry is discretized one can generate the embedded triangulations to solve PDEs with [Gridap](https://github.com/gridap/Gridap.jl), see also [Gridap Tutorials](https://gridap.github.io/Tutorials/stable).
+Once the geometry is discretized, one can generate the embedded triangulations to solve PDEs with [Gridap](https://github.com/gridap/Gridap.jl), see also [Gridap Tutorials](https://gridap.github.io/Tutorials/stable).
 
 Like in GridapEmbedded, we extract the embedded triangulations as follows.
 
@@ -71,6 +71,7 @@ Now, we provide an example of the solution of a Poisson problem on the embedded
 
 ```julia
 using STLCutters
+using Gridap
 using GridapEmbedded
 cells = (10,10,10)
 filename = "stl_file_path.stl"
@@ -90,7 +91,7 @@ dΩ = Measure(Ω,2)
 dΓ = Measure(Γ,2)
 # FE spaces
 Vstd = TestFESpace(Ω_act,ReferenceFE(lagrangian,Float64,1))
-V = AgFEMSpace(Vstd)
+V = AgFEMSpace(Vstd,aggregates)
 U = TrialFESpace(V)
 # Weak form
 γ = 10.0

src/Distributed.jl

@@ -344,7 +344,7 @@ end
   The number of interfaces coincides with the number of neigbors given by [`compute_face_neighbors`](@ref)
 
 !!! note
-  If the subdomain is not cut, the neighbors are considered undefined.
+    If the subdomain is not cut, the neighbors are considered undefined.
 """
 function compute_face_neighbor_to_inoutcut(
   model::DiscreteModel,

src/STLs.jl

@@ -1276,8 +1276,8 @@ end
   each cell in `a:UnstructuredGrid`. The output is a vector of vectors.
 
 !!! note
-  This function uses a `CartesianGrid` internally. It is not optimized
-  for higly irregular grids.
+    This function uses a `CartesianGrid` internally. It is not optimized
+    for higly irregular grids.
 
 !!! note
     This function allows **false positives**.