some adjustments and first try to get the CI testing and doc deployment running

Author: chmerdon

README.md

@@ -3,15 +3,6 @@
 This package implements the stochastic Galerkin finite element method for certain model problems,
 including iterative solvers and a posteriori error control in two dimensions.
 
-## Installation
-
-Instantiate this project after cloning or updating via
-```
-$ julia --project=.
-julia> using Pkg
-julia> Pkg.instantiate()
-```
-
 ## Scripts/Examples
 
 - `scripts/poisson.jl`: Adaptive stochastic Galerkin FEM for the Poisson problem with stochastic diffusion coefficient

docs/make.jl

@@ -1,20 +1,14 @@
 using Documenter
 using ExtendableASGFEM
 
-push!(LOAD_PATH,"../src/")
-
 makedocs(
 		modules = [ExtendableASGFEM],
 		sitename = "ExtendableASGFEM.jl",
 		authors = "Christian Merdon, Martin Eigel",
-		repo = "https://lab.wias-berlin.de/merdon/ASGFEMJulia",
-		# repolink = "https://lab.wias-berlin.de/merdon/ASGFEMJulia", 
-		format = Documenter.HTML(; mathengine = MathJax3()),
-		clean = false,
-		source  = "src",
-		build   = "build",
+		format = Documenter.HTML(; repolink = "https://github.com/WIAS-PDELib/ExtendableASGFEM.jl", mathengine = MathJax3()),
+        clean = false,
 		checkdocs = :all,
-		warnonly = true,
+		warnonly = false,
 		doctest = true,
 		pages = [
 			"Home" => "index.md"
@@ -37,4 +31,9 @@ makedocs(
 				"plots.md"
 			]
 		],
-	)
+	)
+
+
+deploydocs(
+    repo = "github.com/WIAS-PDELib/ExtendableASGFEM.jl",
+)

docs/src/estimators.md

@@ -13,7 +13,7 @@ the subresiduals that are associated to each multi-index
 
 ```@autodocs
 Modules = [ExtendableASGFEM]
-Pages = ["estimators.jl"]
+Pages = ["estimate.jl"]
 Order   = [:type, :function]
 ```
 
@@ -29,4 +29,17 @@ Here are some methods that help with enriching the set of multi-indices.
 Modules = [ExtendableASGFEM]
 Pages = ["mopcontrol.jl"]
 Order   = [:type, :function]
+```
+
+## Monte carlo sampling estimator
+
+There is also a hierarchical Monte carlo error estimator available that
+compares the solution with a higher order discrete solution for sampled
+deterministic problems. This is merely intended as a way to compute the
+reference error to assess the efficiency of the residual-based error estimator.
+
+```@autodocs
+Modules = [ExtendableASGFEM]
+Pages = ["sampling_error.jl"]
+Order   = [:type, :function]
 ```

docs/src/estimators.pdf

@@ -2,12 +2,17 @@
 
 ([source code](SOURCE_URL))
 
-# runs AFEM loop for stochastic Poisson problem and saves results to data folder
+runs AFEM loop for stochastic Poisson problem, save/load results to/from data folder, plot results
+ 
+main usage:
+- run experiment: run(; problem = problem, kwargs...)
+- load results  : show_results(; kwargs...)
+- produce plots : produce_plots(; kwargs...)
 
 possible values for problem are
-PoissonProblemPrimal = Poisson problem with linear coefficient a
-LogTransformedPoissonProblemPrimal = log-transformed Poisson problem with exponential coefficient exp(a)
-LogTransformedPoissonProblemDual = dual formulation
+- PoissonProblemPrimal = Poisson problem with linear coefficient a
+- LogTransformedPoissonProblemPrimal = log-transformed Poisson problem with exponential coefficient exp(a)
+- LogTransformedPoissonProblemDual = dual formulation
 
 =#
 
@@ -64,7 +69,7 @@ default_args = Dict(
     "θ_spatial" => 0.5,
     "factor_tail" => 1,
     "tail_extension" => [10,2], # for [0] mode, and all others
-    "maxdofs" => 1e5,
+    "maxdofs" => 1e4,
     "initial_modes" => [[0]],
     "nsamples" => 150,
     "use_equilibration_estimator" => false,