up doc make etc + typo

docs/make.jl

@@ -2,7 +2,6 @@ using Documenter
 using Literate
 using StochasticWeatherGenerators
 
-
 examples_jl_path = joinpath(dirname(@__DIR__), "examples")
 examples_md_path = joinpath(@__DIR__, "src", "examples")
 
@@ -13,7 +12,7 @@ for file in readdir(examples_md_path)
 end
 
 for file in readdir(examples_jl_path)
-    if !startswith(file, "geo")
+    if !startswith(file, "utilities")
        Literate.markdown(joinpath(examples_jl_path, file), examples_md_path, mdstrings=true)
     end
 end
@@ -26,7 +25,7 @@ pages = [
 ]
 
 fmt = Documenter.HTML(;
-    prettyurls=get(ENV, "CI", "false") == "true",
+    prettyurls=true, #get(ENV, "CI", "false") == "true",
     repolink="https://github.com/dmetivie/StochasticWeatherGenerators.jl",
     canonical="https://dmetivie.github.io/StochasticWeatherGenerators.jl",
     assets=String[],

docs/src/examples/plot_utilities.md

@@ -0,0 +1,144 @@
+```@meta
+EditURL = "../../../examples/plot_utilities.jl"
+```
+
+````@example plot_utilities
+using StatsPlots
+
+"""
+    errorlinehist(y; kwargs)
+    Function for parsing inputs to easily make a [`ribbons`] (https://ggplot2.tidyverse.org/reference/geom_ribbon.html),
+    stick errorbar (https://www.mathworks.com/help/matlab/ref/errorbar.html), or plume
+    (https://stackoverflow.com/questions/65510619/how-to-prepare-my-data-for-plume-plots) with several histograms plot.
+
+Inputs: default values are indicated with *s
+
+y is a Vector of vector
+
+    bins (*:auto*, AbstractVector)
+
+    norm (`Symbol` - *:false*, `:pdf`, `:probability`)
+
+    error_style (`Symbol` - *:ribbon*, :stick, :plume) - determines whether to use a ribbon style or stick style error
+     representation.
+
+    centertype (symbol - *:mean* or :median) - which approach to use to represent the central value of y at each x-value.
+
+    errortype (symbol - *:std*, :sem, :percentile) - which error metric to use to show the distribution of y at each x-value.
+
+    percentiles (Vector{Int64} *[25, 75]*) - if using errortype === :percentile then which percentiles to use as bounds.
+
+    secondarycolor (`Symbol`, `RGB`, `:matched` - *:Gray60*) - When using stick mode this will allow for the setting of the stick color.
+        If `:matched` is given then the color of the sticks with match that of the main line.
+
+    secondarylinealpha (float *.1*) - alpha value of plume lines.
+
+    numsecondarylines (int *100*) - number of plume lines to plot behind central line.
+
+    stickwidth (Float64 *.01*) - How much of the x-axis the horizontal aspect of the error stick should take up.
+
+Example
+
+```julia
+using Distributions
+
+dist = Exponential()
+N = 100 # number of sample used in each histogram
+N_hist = 1000 # number of histogram
+yc = [rand(dist, N) for _ in 1:N_hist]
+
+edges = 0:0.05:11.1
+
+errorlinehist(yc, errortype = :percentile, percentiles = [0,100], fillalpha = 0.1, normalize = :pdf, bins = edges)
+```
+"""
+@userplot ErrorLineHist
+
+@recipe function f(p::ErrorLineHist)
+    v = p.args[1]
+    bins = get(plotattributes, :bins, :auto)
+    normed = get(plotattributes, :normalize, false)
+    weights = get(plotattributes, :weights, nothing)
+
+    vs = filter.(isfinite, (reduce(vcat,v),))
+    edges = Plots._hist_edges(vs, bins)
+    nbins = length(edges[1]) .- 1
+    x = edges[1][1:end-1]
+
+    ngroups = length(v)
+
+    # compute weights (frequencies) by group using those edges
+    y = zeros(nbins, ngroups)
+    for i in 1:ngroups
+        v_i = filter(isfinite, v[i])
+        w_i = weights
+        h_i = Plots._make_hist((v_i,), edges; normed = normed, weights = w_i)
+        y[:, i] += h_i.weights .+ eps() # for numerical stability when in log-scale
+    end
+
+    StatsPlots.ErrorLine((x, y))
+end
+
+"""
+    islabel(j, j_target, label)
+Utilities to only display label on target subplots
+```julia
+using Plots
+plt = [plot(1:10, label = islabel(j, [5,4], "label")) for j in 1:4]
+plot(plt...)
+```
+"""
+islabel(j, j_target, label) = j ∈ j_target ? label : :none
+
+islabel(j, j_target, label, other_option) = j ∈ j_target ? label : :other_option
+
+"""
+    savefigcrop(plt, path)
+Save the figure and crop it to avoid white spaces
+"""
+function savefigcrop(plt, save_name)
+    isnothing(plt) ? savefig(string(save_name, ".pdf")) : savefig(plt, string(save_name, ".pdf"))
+    run(`pdfcrop $(string(save_name,".pdf"))`) # Petit délire pour croper proprement la figure
+    mv(string(save_name, "-crop", ".pdf"), string(save_name, ".pdf"), force=true)
+end
+
+savefigcrop(save_name) = savefigcrop(nothing, save_name)
+
+ker(u) = 3 / 4 * (1 - abs(u)^2)
+
+"""
+    cyclic_moving_average(a::AbstractArray, window::AbstractVector)
+    cyclic_moving_average(a::AbstractArray, window::Integer)
+Simple periodic moving average with Epanechnikov kernel.
+"""
+function cyclic_moving_average(A::AbstractArray, t::AbstractVector, T::Integer, window)
+    a = zeros(T)
+    a[t] .= A
+    return cyclic_moving_average(a, window)
+end
+
+cyclic_moving_average(a::AbstractArray, window::Integer) = cyclic_moving_average(a, -window:window)
+
+function cyclic_moving_average(a::AbstractArray, window::AbstractVector)
+    W = length(window)
+    @assert W ≥ 1
+    T = length(a)
+    tot_weight = sum(ker(i / W) for i in window)
+    return [sum(a[cyclic(t+i, T)] * ker(i / W) for i in window) for t = 1:T] / tot_weight
+end
+
+function cyclic(i, T)
+    if 1≤i≤T
+        return i
+    elseif T+1≤i≤2T
+        return i - T
+    elseif -T+1≤i≤0
+        return T + i
+    end
+end
+````
+
+---
+
+*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*
+