Updated "Adding documentation" section in the README

.gitignore

@@ -18,6 +18,8 @@ deps/src/
 docs/build/
 docs/site/
 docs/.condaPkg
+docs/src/generated/*
+docs/src/generated/*/*
 # File generated by Pkg, the package manager, based on a corresponding Project.toml
 # It records a fixed state of all packages used by the project. As such, it should not be
 # committed for packages, but should be committed for applications that require a static

README.md

@@ -117,9 +117,9 @@ You are very welcome to raise issues and start pull requests!
 
 ### Adding Documentation
 
-1. We recommend to write a Literate.jl document and place it in `docs/_literate/FOLDER/FILENAME.jl` with `FOLDER` being `HowTo`, `Explanation`, `Tutorial` or `Reference` ([recommended reading on the 4 categories](https://documentation.divio.com/)).
-2. Literate.jl converts the `.jl` file to a `.md` automatically and places it in `doc/src/_literate/FILENAME.jl`.
-3. Edit [make.jl](https://github.com/unfoldtoolbox/Unfold.jl/blob/main/docs/make.jl) with a reference to `doc/src/_literate/FILENAME.jl`
+1. We recommend to write a Literate.jl document and place it in `docs/literate/FOLDER/FILENAME.jl` with `FOLDER` being `HowTo`, `Explanation`, `Tutorial` or `Reference` ([recommended reading on the 4 categories](https://documentation.divio.com/)).
+2. Literate.jl converts the `.jl` file to a `.md` automatically and places it in `doc/src/generated/FILENAME.jl`.
+3. Edit [make.jl](https://github.com/unfoldtoolbox/Unfold.jl/blob/main/docs/make.jl) with a reference to `doc/src/literate/FILENAME.jl`
 
 ## Contributors (alphabetically)
 

docs/make.jl

@@ -6,9 +6,10 @@ using DocStringExtensions
 using Literate
 using Glob
 
-GENERATED = joinpath(@__DIR__, "src", "_literate")
+GENERATED = joinpath(@__DIR__, "src", "generated")
+SOURCE = joinpath(@__DIR__,"literate")
 for subfolder ∈ ["explanations","HowTo","tutorials"]
-    local SOURCE_FILES = Glob.glob(subfolder*"/*.jl", GENERATED)
+    local SOURCE_FILES = Glob.glob(subfolder*"/*.jl", SOURCE)
     foreach(fn -> Literate.markdown(fn, GENERATED*"/"*subfolder), SOURCE_FILES)
 
 end
@@ -18,6 +19,7 @@ end
 makedocs(sitename="Unfold.jl Timeseries Analysis & Deconvolution",
         #root = joinpath(dirname(pathof(Unfold)), "..", "docs"),
         #prettyurls = get(ENV, "CI", nothing) == "true",
+        repo = Documenter.Remotes.GitHub("unfoldtoolbox", "Unfold.jl"),
         pages = [
             "index.md",
             "Tutorials"=>[
@@ -31,14 +33,14 @@ makedocs(sitename="Unfold.jl Timeseries Analysis & Deconvolution",
 		            "Overlap: Different events"=>"HowTo/multiple_events.md",
                     "Load Existing Dataset with PyMNE.jl"=>"HowTo/pymne.md" ,
                     "Custom Solvers / StandardErrors / B2B"=>"HowTo/custom_solvers.md",
-                    "Unfold.jl directly from Python" => "_literate/HowTo/pyjulia_unfold.md",
+                    "Unfold.jl directly from Python" => "generated/HowTo/pyjulia_unfold.md",
                     "P-values in Mass Univariate MixedModels" => "HowTo/lmm_pvalues.md",
-		            "marginal effects (what to do with non-linear predictors)" =>"_literate/HowTo/effects.md",
-		            "Time Basis Functions"=>"_literate/HowTo/timesplines.md",
+		            "marginal effects (what to do with non-linear predictors)" =>"generated/HowTo/effects.md",
+		            "Time Basis Functions"=>"generated/HowTo/timesplines.md",
                       ],
             "Explanations"=>[
 		            "Temporal Basisfunctions" => "./explanations/basisfunctions.md",
-		            "Non-Linear Effects" => "./_literate/explanations/nonlinear_effects.md",],
+		            "Non-Linear Effects" => "./generated/explanations/nonlinear_effects.md",],
             "Reference"=>["Types" => "references/types.md",
             "Functions" => "references/functions.md"],
 

docs/run_liveserver.jl

@@ -0,0 +1,3 @@
+using LiveServer
+servedocs(skip_dir=joinpath("src","generated"),literate_dir=joinpath("literate"),foldername=".")
+

docs/src/explanations/basisfunctions.md

@@ -16,7 +16,7 @@ using Unfold,DSP
 TR = 1.5
 bold = hrfbasis(TR) # using default SPM parameters
 eventonset = 1.3
-bold_kernel = Unfold.kernel(bold)
+bold_kernel = e->Unfold.kernel(bold,e)
 lines(bold_kernel(eventonset)[:,1]) # (returns a matrix, thus [:,1])
 ```
 This is the shape that is assumed to reflect activity to an event. Generally, we would like to know how much to scale this response-shape per condition, e.g. in `condA` we might scale it by 0.7, in `condB` by 1.2.
@@ -60,7 +60,7 @@ Okay, let's have a look at a different basis function: The FIR basisfunction.
 using Unfold #hide
 
 basisfunction = firbasis(τ=(-0.4,.8),sfreq=50,name="myFIRbasis")
-fir_kernel = Unfold.kernel(basisfunction)
+fir_kernel = e->Unfold.kernel(basisfunction,e)
 m = fir_kernel(0)
 f = Figure()
 f[1,1] = Axis(f)

ext/UnfoldMixedModelsExt/statistics.jl

@@ -27,7 +27,7 @@ fake_lmm(m::UnfoldLinearMixedModel) = fake_lmm.(m,1:length(modelfit(m).fits))
 
     Calculate likelihoodratiotest
     """
-	function MixedModels.likelihoodratiotest(m::UnfoldLinearMixedModel)
+	function MixedModels.likelihoodratiotest(m::UnfoldLinearMixedModel...)
 		#@info lrtest(fake_lmm.(m,1)...)
 		n = length(Unfold.modelfit(m[1]).fits)
 		lrt = Array{MixedModels.LikelihoodRatioTest}(undef,n)

src/Unfold.jl

@@ -120,7 +120,7 @@ else
 
     function solver_krylov(args...;kwargs...)
         ext = checkFun(:UnfoldKrylovExt) 
-        msg = "BSplineKit not loaded. Please use `]add Krylov,CUDA, using Krylov,CUDA` to install/load it, if you want to use krylov/GPU"
+        msg = "Krylov or CUDA not loaded. Please use `]add Krylov,CUDA, using Krylov,CUDA` to install/load it, if you want to use GPU-fitting"
         isnothing(ext) ? throw(msg) : ext.solver_krylov(args...;kwargs...)
     end
 

src/basisfunctions.jl

@@ -25,48 +25,43 @@ $(FIELDS)
 (tipp: most users would you want to call firbasis, not generate it manually)
 # Examples
 ```julia-repl
-julia>  b = FIRBasis(kernelfunction,"derivative",["f(x)"],range(0,(length(kernelfunction([0, 1]))-1)*TR,step=TR),"hrf_kernel","basis_A",0)
+julia>  b = FIRBasis(range(0,1,length=10),"basisA",-1)
 ```
 """
 struct FIRBasis <: BasisFunction
-    "a design-matrix kernel function used to timeexpand, given a timepoint in 'sample' timeunits"
-    kernel::Function
-
     " vector of times along rows of kernel-output (in seconds)"
-    times::AbstractVector
+    times::Vector
     "name of the event, random 1:1000 if unspecified"
     name::String
     "by how many samples do we need to shift the event onsets? This number is determined by how many 'negative' timepoints the basisfunction defines"
-    shiftOnset::Integer
+    shiftOnset::Int64
 end
 
+@deprecate FIRBasis(kernel::Function,times,name,shiftOnset) FIRBasis(times,name,shiftOnset)
 collabel(basis::FIRBasis) = :time
 colnames(basis::FIRBasis) = basis.times[1:end-1]
 
 
 struct SplineBasis <: BasisFunction
-    "a design-matrix kernel function used to timeexpand, given a timepoint in 'sample' timeunits"
     kernel::Function
-    "name of column dimension (e.g 'time' for FIR, 'derivative', for HRF etc.)"
 
     "vector of names along columns of kernel-output"
     colnames::AbstractVector
     " vector of times along rows of kernel-output (in seconds)"
-    times::AbstractVector
+    times::Vector
     "name of the event, random 1:1000 if unspecified"
     name::String
     "by how many samples do we need to shift the event onsets? This number is determined by how many 'negative' timepoints the basisfunction defines"
-    shiftOnset::Integer
+    shiftOnset::Int64
 end
 
 
 struct HRFBasis <: BasisFunction
-    "a design-matrix kernel function used to timeexpand, given a timepoint in 'sample' timeunits"
     kernel::Function
     "vector of names along columns of kernel-output"
     colnames::AbstractVector
     " vector of times along rows of kernel-output (in seconds)"
-    times::AbstractVector
+    times::Vector
     "name of the event, random 1:1000 if unspecified"
     name::String
 end
@@ -105,12 +100,12 @@ function firbasis(τ, sfreq, name::String)
     τ = round_times(τ, sfreq)
     times = range(τ[1], stop = τ[2]+1 ./ sfreq, step = 1 ./ sfreq) # stop + 1 step, because we support fractional event-timings
 
-    kernel = e -> firkernel(e, times[1:end-1])
+
 
 
     shiftOnset = Int64(floor(τ[1] * sfreq))
 
-    return FIRBasis(kernel, times, name, shiftOnset)
+    return FIRBasis(times, name, shiftOnset)
 end
 # cant multiple dispatch on optional arguments
 #firbasis(;τ,sfreq)           = firbasis(τ,sfreq)
@@ -132,12 +127,12 @@ function firkernel(e, times)
         # XXX we will soon assume that the second entry would be the duration
         e = Float64(e[1])
     end
-    e = [1 .- (e .% 1) e .% 1]
-    e[isapprox.(e, 0, atol = 1e-15)] .= 0
+    eboth = [1 .- (e .% 1) e .% 1]
+    eboth[isapprox.(eboth, 0, atol = 1e-15)] .= 0
     ksize = length(times) # kernelsize
 
     kernel =
-        spdiagm(ksize + 1, ksize, 0 => repeat([e[1]], ksize), -1 => repeat([e[2]], ksize))
+        spdiagm(ksize + 1, ksize, 0 => repeat([eboth[1]], ksize), -1 => repeat([eboth[2]], ksize))
 
     #    dropzeros!(kernel) # we often get implicit 0, especially if the latencies are rounded
     return (kernel)
@@ -208,7 +203,10 @@ collabel(term::Array{<:AbstractTerm}) = collabel(term[1].rhs)  # in case of comb
 
 shiftOnset(basis::BasisFunction) = basis.shiftOnset
 colnames(basis::BasisFunction) = basis.colnames
-kernel(basis::BasisFunction) = basis.kernel
+kernel(basis::BasisFunction,e) = basis.kernel(e) 
+@deprecate kernel(basis::BasisFunction) basis.kernel
+
+kernel(basis::FIRBasis,e) = firkernel(e,basis.times[1:end-1])
 
 times(basis::BasisFunction) = basis.times
 name(basis::BasisFunction) = basis.name