Merge pull request #147 from sathvikbhagavan/sb/docs

Few docs cleanup

.gitignore

@@ -3,3 +3,7 @@
 *.jl.mem
 Manifest.toml
 *.DS_Store
+
+# Build artifacts for creating documentation generated by the Documenter package
+docs/build/
+docs/site/

docs/make.jl

@@ -4,6 +4,7 @@ cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml", force = true)
 cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
 
 ENV["GKSwstype"] = "100"
+ENV["JULIA_DEBUG"] = "Documenter"
 using Plots, CairoMakie
 
 include("pages.jl")
@@ -12,6 +13,7 @@ makedocs(sitename = "GlobalSensitivity.jl",
     authors = "Vaibhav Kumar Dixit",
     modules = [GlobalSensitivity],
     clean = true, doctest = false, linkcheck = true,
+    linkcheck_ignore = [r"https://www.sciencedirect.com/*"],
     warnonly = [:missing_docs],
     format = Documenter.HTML(assets = ["assets/favicon.ico"],
         canonical = "https://docs.sciml.ai/GlobalSensitivity/stable/"),

docs/src/tutorials/juliacon21.md

@@ -18,7 +18,6 @@ p = [1.5,1.0,3.0,1.0]
 prob = ODEProblem(f,u0,tspan,p)
 t = collect(range(0, stop=10, length=200))
 
-
 f1 = function (p)
     prob1 = remake(prob;p=p)
     sol = solve(prob1,Tsit5();saveat=t)
@@ -36,30 +35,30 @@ Colorbar(fig[2, 2], hm)
 fig
 ```
 
-![heatmapreg](https://user-images.githubusercontent.com/23134958/127019339-607b8d0b-6c38-4a18-b62e-e3ea0ae40941.png)
-
 ```@example lv
 using StableRNGs
 _rng = StableRNG(1234)
 morris_sens = gsa(f1, Morris(), bounds, rng = _rng)
 fig = Figure(resolution = (600, 400))
-scatter(fig[1,1], [1,2,3,4], morris_sens.means_star[1,:], color = :green, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Prey",))
-scatter(fig[1,2], [1,2,3,4], morris_sens.means_star[2,:], color = :red, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Predator",))
+CairoMakie.scatter(fig[1,1], [1,2,3,4], morris_sens.means_star[1,:], color = :green, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Prey",))
+CairoMakie.scatter(fig[1,2], [1,2,3,4], morris_sens.means_star[2,:], color = :red, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Predator",))
 fig
 ```
 
-![morrisscat](https://user-images.githubusercontent.com/23134958/127019346-2b5548c5-f4ec-4547-9f8f-af3e4b4c317c.png)
-
 ```@example lv
 sobol_sens = gsa(f1, Sobol(), bounds, samples=500)
 efast_sens = gsa(f1, eFAST(), bounds, samples=500)
+
+```@example lv
 fig = Figure(resolution = (600, 400))
 barplot(fig[1,1], [1,2,3,4], sobol_sens.S1[1, :], color = :green, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Prey (Sobol)", ylabel = "First order"))
 barplot(fig[2,1], [1,2,3,4], sobol_sens.ST[1, :], color = :green, axis = (xticksvisible = false, xticklabelsvisible = false, ylabel = "Total order"))
 barplot(fig[1,2], [1,2,3,4], efast_sens.S1[1, :], color = :red, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Prey (eFAST)"))
 barplot(fig[2,2], [1,2,3,4], efast_sens.ST[1, :], color = :red, axis = (xticksvisible = false, xticklabelsvisible = false))
 fig
+```
 
+```@example lv
 fig = Figure(resolution = (600, 400))
 barplot(fig[1,1], [1,2,3,4], sobol_sens.S1[2, :], color = :green, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Predator (Sobol)", ylabel = "First order"))
 barplot(fig[2,1], [1,2,3,4], sobol_sens.ST[2, :], color = :green, axis = (xticksvisible = false, xticklabelsvisible = false, ylabel = "Total order"))
@@ -68,9 +67,6 @@ barplot(fig[2,2], [1,2,3,4], efast_sens.ST[2, :], color = :red, axis = (xticksvi
 fig
 ```
 
-![sobolefastprey](https://user-images.githubusercontent.com/23134958/127019361-8d625107-7f9c-44b5-a0dc-489bd512b7dc.png)
-![sobolefastpred](https://user-images.githubusercontent.com/23134958/127019358-8bd0d918-e6fd-4929-96f1-d86330d91c69.png)
-
 ```@example lv
 using QuasiMonteCarlo
 samples = 500
@@ -80,7 +76,6 @@ sampler = SobolSample()
 A,B = QuasiMonteCarlo.generate_design_matrices(samples,lb,ub,sampler)
 sobol_sens_desmat = gsa(f1,Sobol(),A,B)
 
-
 f_batch = function (p)
   prob_func(prob,i,repeat) = remake(prob;p=p[:,i])
   ensemble_prob = EnsembleProblem(prob,prob_func=prob_func)
@@ -126,6 +121,5 @@ CairoMakie.scatter!(fig[2,2], sobol_sens.S1[4][2,2:end], label = "Predator", mar
 
 title = Label(fig[0,:], "First order Sobol indices")
 legend = Legend(fig[2,3], ax)
+fig
 ```
-
-![timeseriessobollv](https://user-images.githubusercontent.com/23134958/156987652-85958bde-ae73-4f71-8555-318f779257ad.png)

docs/src/tutorials/parallelized_gsa.md

@@ -96,8 +96,6 @@ p2_ = bar(["a","b","c","d"],sobol_result.S1[2,:],title="First Order Indices pred
 plot(p1,p2,p1_,p2_)
 ```
 
-![sobolbars](https://user-images.githubusercontent.com/23134958/127019349-686f968d-7c8a-4dc4-abdc-c70f58b043dd.png)
-
 ## Parallelizing the Global Sensitivity Analysis
 
 In all the previous examples, `f(p)` was calculated serially. However, we can parallelize our computations

docs/src/tutorials/shapley.md

@@ -16,7 +16,7 @@ trained using the [SciML ecosystem](https://sciml.ai/).
 
 As the first step let's generate the dataset.
 
-```julia
+```@example shapley
 using GlobalSensitivity, OrdinaryDiffEq, Flux, SciMLSensitivity, LinearAlgebra
 using Optimization, OptimizationOptimisers, Distributions, Copulas, CairoMakie
 
@@ -38,7 +38,7 @@ a 2-layer neural network with 10 hidden units in the first layer and the second
 We will use the `Chain` function from `Flux` to define our NN. A detailed tutorial on
 is available [here](https://docs.sciml.ai/SciMLSensitivity/stable/examples/neural_ode/neural_ode_flux/).
 
-```julia
+```@example shapley
 dudt2 = Flux.Chain(x -> x .^ 3,
     Flux.Dense(2, 10, tanh),
     Flux.Dense(10, 2))
@@ -88,7 +88,7 @@ for the parameters. We will use the standard `Normal` distribution for all the p
 First let's assume no correlation between the parameters. Hence the covariance matrix
 is passed as the identity matrix.
 
-```julia
+```@example shapley
 d = length(θ)
 mu = zeros(Float32, d)
 #covariance matrix for the copula
@@ -113,28 +113,21 @@ end
 shapley_effects = gsa(batched_loss_n_ode, Shapley(;n_perms = 100, n_var = 100, n_outer = 10), input_distribution, batch = true)
 ```
 
-```julia
-[ Info: Since `n_perms` wasn't set the exact version of Shapley will be used
-GlobalSensitivity.ShapleyResult{Vector{Float64}, Vector{Float64}}([0.11597691741361442, 0.10537266345858425, -0.011525418832504125, 0.019080490852392638, 0.0670556101993216, -0.0008750631360554604, -0.06145135053766362, 0.04681820267596843, -0.052194422120816236, 0.003179470815545183  …  0.017116063071811214, -0.01996361592991698, 0.04992156377132031, -0.026031285685145327, -0.05478798810114203, -0.08297800907245817, -0.0007407741548139723, -0.004732287469108539, 0.0387866269216672, 0.003387278477023375], [0.24632058593157738, 0.2655832791843761, 0.2501184872448763, 0.24778944872968212, 0.24095751488197525, 0.25542101332993983, 0.23147124018855053, 0.2559000952299014, 0.2445090237211431, 0.24422366968866593  …  0.26334364579925296, 0.2533883745742706, 0.278493369461382, 0.251080668076158, 0.2513237168712358, 0.2565579384455956, 0.24086196097600907, 0.23509270698557308, 0.2424725703788857, 0.245598352488518], [-0.3668114310122772, -0.4151705637427929, -0.5017576538324616, -0.4665868286577843, -0.40522111896934987, -0.5015002492627375, -0.5151349813072227, -0.45474598397463833, -0.5314321086142567, -0.47549892177424  …  -0.4990374826947246, -0.5166048300954874, -0.49592544037298836, -0.5181493951144149, -0.5473824731687641, -0.5858315684258255, -0.47283021766779176, -0.46551399316083175, -0.43645961102094877, -0.4779854924004719], [0.5987652658395061, 0.6259158906599613, 0.47870681616745336, 0.5047478103625695, 0.5393323393679931, 0.4997501229906266, 0.39223228023189544, 0.5483823893265752, 0.4270432643726243, 0.48185786340533043  …  0.5332696088383471, 0.4766775982356534, 0.5957685679156289, 0.4660868237441243, 0.43780649696648005, 0.4198755502809092, 0.4713486693581638, 0.4560494182226147, 0.5140328648642831, 0.4847600493545186])
-```
-
-```julia
+```@example shapley
 fig = Figure(resolution = (600, 400))
 ax = barplot(fig[1,1], collect(1:54), shapley_effects.shapley_effects, color = :green)
-ylims!(ax.axis, 0.0, 0.2)
+CairoMakie.ylims!(ax.axis, 0.0, 0.2)
 ax.axis.xticks = (1:54, ["θ$i" for i in 1:54])
 ax.axis.ylabel = "Shapley Indices"
 ax.axis.xlabel = "Parameters"
 ax.axis.xticklabelrotation = 1
 display(fig)
 ```
 
-![shapnocorr](https://github.com/SciML/GlobalSensitivity.jl/assets/23134958/d102a91a-a4ed-4850-ae0b-dea19acf38f7)
-
 Now let's assume some correlation between the parameters. We will use a correlation of 0.09 between
 all the parameters.
 
-```julia
+```@example shapley
 Corrmat = fill(0.09f0, d, d)
 for i in 1:d
     Corrmat[i, i] = 1.0f0
@@ -146,20 +139,13 @@ input_distribution = SklarDist(copula, marginals)
 shapley_effects = gsa(batched_loss_n_ode, Shapley(;n_perms = 100, n_var = 100, n_outer = 100), input_distribution, batch = true)
 ```
 
-```julia
-[ Info: Since `n_perms` was set the random version of Shapley will be used
-GlobalSensitivity.ShapleyResult{Vector{Float64}, Vector{Float64}}([0.05840668971922802, 0.11052100820850451, 0.04371662911807708, 0.004023059190511713, 0.062410433686220866, 0.02585247272606055, 0.02522310040104824, -0.002943508605003048, 0.0274450019985079, -0.00470104493101865  …  0.03521661871178529, -0.0029363975954207434, -0.01733340691251318, 0.030698550673315273, 0.004089097508271804, 0.01120562788725685, 0.020296678638214393, 0.05371236372397007, 0.041498171895387174, -0.0013082313559600266], [0.17059528049109549, 0.17289231112530734, 0.15914829962624458, 0.1353652701868229, 0.16197334249699738, 0.14706777933327114, 0.13542553650272246, 0.13442651137664902, 0.1187565412647469, 0.14591351295876914  …  0.14961042326057278, 0.11239722253111033, 0.14973761270843763, 0.15863981406666988, 0.14738095559109785, 0.11703801097937833, 0.1573849698258368, 0.16993657676693683, 0.1501327274957186, 0.1755876094816281], [-0.27596006004331913, -0.22834792159709788, -0.2682140381493623, -0.2612928703756612, -0.255057317607894, -0.2624003747671509, -0.24021095114428775, -0.2664194709032351, -0.205317818880396, -0.29069153033020617  …  -0.2580198108789374, -0.223234953756397, -0.3108191278210509, -0.28023548489735767, -0.28477757545028, -0.21818887363232467, -0.28817786222042574, -0.2793633267392261, -0.25276197399622125, -0.3454599459399511], [0.3927734394817752, 0.4493899380141069, 0.3556472963855164, 0.2693389887566846, 0.3798781849803357, 0.314105320219272, 0.29065715194638425, 0.260532453693229, 0.2602078228774118, 0.2812894404681689  …  0.32845304830250793, 0.2173621585655555, 0.2761523139960246, 0.34163258624398823, 0.2929557704668236, 0.24060012940683836, 0.3287712194968545, 0.3867880541871663, 0.3357583177869956, 0.34284348322803104])
-```
-
-```julia
+```@example shapley
 fig = Figure(resolution = (600, 400))
 ax = barplot(fig[1,1], collect(1:54), shapley_effects.shapley_effects, color = :green)
-ylims!(ax.axis, 0.0, 0.2)
+CairoMakie.ylims!(ax.axis, 0.0, 0.2)
 ax.axis.xticks = (1:54, ["θ$i" for i in 1:54])
 ax.axis.ylabel = "Shapley Indices"
 ax.axis.xlabel = "Parameters"
 ax.axis.xticklabelrotation = 1
 display(fig)
 ```
-
-![shapcorr](https://github.com/SciML/GlobalSensitivity.jl/assets/23134958/c48be7e3-811a-49de-8388-4af1e03d0663)

joss/paper.bib

@@ -57,7 +57,7 @@ @article{Campolongo2007
 
 @article{Morris1991,
 abstract = {A computational model is a representation of some physical or other system of interest, first expressed mathematically and then implemented in the form of a computer program; it may be viewed as a function of inputs that, when evaluated, produces outputs. Motivation for this article comes from computational models that are deterministic, complicated enough to make classical mathematical analysis impractical and that have a moderate-to-large number of inputs. The problem of designing computational experiments to determine which inputs have important effects on an output is considered. The proposed experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects, those changes in an output due solely to changes in a particular input. Advantages of this approach include a lack of reliance on assumptions of relative sparsity of important inputs, monotonicity of outputs with respect to inputs, or adequacy of a low-order polynomial as an approximation to the computational model.},
-annote = {Contains useful justifaction of distinction of Morris method from LHS and similar methods.},
+annotate = {Contains useful justifaction of distinction of Morris method from LHS and similar methods.},
 author = {Morris, Max D},
 doi = {10.2307/1269043},
 issn = {0040-1706},

src/DGSM_sensitivity.jl

@@ -10,7 +10,7 @@ The DGSM method takes a probability distribution for each of the
 parameters, and samples are obtained from the distributions to create
 random parameter sets. Derivatives of the function being analyzed are
 then computed at the sampled parameters and specific statistics of those
-derivatives are used. The paper by [Sobol and Kucherenko](http://www.sciencedirect.com/science/article/pii/S0378475409000354)
+derivatives are used. The paper by [Sobol and Kucherenko](https://www.sciencedirect.com/science/article/pii/S0378475409000354)
 discusses the relationship between the DGSM results, `tao` and
 `sigma` and the Morris elementary effects and Sobol Indices.
 

src/eFAST_sensitivity.jl

@@ -14,7 +14,7 @@ x_{i}(s) = G_{i}(sin ω_{i}s), ∀ i=1,2 ,..., N
 ```
 where s is a scalar variable varying over the range ``-∞ < s < +∞``, ``G_{i}`` are transformation functions
 and ``{ω_{i}}, ∀ i=1,2,...,N`` is a set of different (angular) frequencies, to be properly selected, associated with each factor for all ``N`` (`samples`) number of parameter sets.
-For more details, on the transformation used and other implementation details you can go through [ A. Saltelli et al.](http://dx.doi.org/10.1080/00401706.1999.10485594).
+For more details, on the transformation used and other implementation details you can go through [ A. Saltelli et al.](https://dx.doi.org/10.1080/00401706.1999.10485594).
 
 ## API