From 84928746e5d848de4d3ca8aafa70945761cbe5f5 Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Wed, 20 Aug 2025 13:52:42 +0200 Subject: [PATCH 1/5] write guides for correlation structure and conditional MLmodel --- docs/make.jl | 4 +- docs/src/tutorials/basic_cpu.qmd | 22 +- docs/src/tutorials/blocks_corr.md | 171 +++++++ docs/src/tutorials/blocks_corr.qmd | 171 +++++++ .../figure-commonmark/cell-10-output-1.png | Bin 0 -> 102226 bytes .../figure-commonmark/cell-11-output-1.png | Bin 0 -> 95857 bytes .../figure-commonmark/cell-12-output-1.png | Bin 0 -> 125826 bytes .../figure-commonmark/cell-13-output-1.png | Bin 0 -> 69390 bytes docs/src/tutorials/corr_site_global.md | 181 +++++++ docs/src/tutorials/corr_site_global.qmd | 187 ++++++++ .../figure-commonmark/cell-10-output-1.png | Bin 0 -> 117838 bytes .../figure-commonmark/cell-11-output-1.png | Bin 0 -> 146327 bytes .../figure-commonmark/cell-12-output-1.png | Bin 0 -> 143145 bytes .../figure-commonmark/cell-9-output-1.png | Bin 0 -> 93679 bytes .../tutorials/how_to_guides/blocks_corr.qmd | 440 ------------------ .../how_to_guides/corr_site_global.qmd | 431 ----------------- docs/src/tutorials/inspect_results.qmd | 2 +- 17 files changed, 733 insertions(+), 876 deletions(-) create mode 100644 docs/src/tutorials/blocks_corr.md create mode 100644 docs/src/tutorials/blocks_corr.qmd create mode 100644 docs/src/tutorials/blocks_corr_files/figure-commonmark/cell-10-output-1.png create mode 100644 docs/src/tutorials/blocks_corr_files/figure-commonmark/cell-11-output-1.png create mode 100644 docs/src/tutorials/blocks_corr_files/figure-commonmark/cell-12-output-1.png create mode 100644 docs/src/tutorials/blocks_corr_files/figure-commonmark/cell-13-output-1.png create mode 100644 docs/src/tutorials/corr_site_global.md create mode 100644 docs/src/tutorials/corr_site_global.qmd create mode 100644 docs/src/tutorials/corr_site_global_files/figure-commonmark/cell-10-output-1.png create mode 100644 docs/src/tutorials/corr_site_global_files/figure-commonmark/cell-11-output-1.png create mode 100644 docs/src/tutorials/corr_site_global_files/figure-commonmark/cell-12-output-1.png create mode 100644 docs/src/tutorials/corr_site_global_files/figure-commonmark/cell-9-output-1.png delete mode 100644 docs/src/tutorials/how_to_guides/blocks_corr.qmd delete mode 100644 docs/src/tutorials/how_to_guides/corr_site_global.qmd diff --git a/docs/make.jl b/docs/make.jl index 3a06459..f82f26c 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -23,8 +23,8 @@ makedocs(; #"Test quarto markdown" => "tutorials/test1.md", ], "How to" => [ - #".. model independent parameters" => "tutorials/how_to_guides/blocks_corr_site.md", - #".. model site-global corr" => "tutorials/how_to_guides/corr_site_global.md", + ".. model independent parameters" => "tutorials/blocks_corr.md", + ".. model site-global corr" => "tutorials/corr_site_global.md", ], "Explanation" => [ #"Theory" => "explanation/theory_hvi.md", TODO activate when paper is published diff --git a/docs/src/tutorials/basic_cpu.qmd b/docs/src/tutorials/basic_cpu.qmd index af96b35..d4ebf67 100644 --- a/docs/src/tutorials/basic_cpu.qmd +++ b/docs/src/tutorials/basic_cpu.qmd @@ -316,7 +316,25 @@ For the parameters, one row corresponds to one site. For the drivers and predictions, one column corresponds to one site. -{{< include _pbm_matrix.qmd >}} +```{julia} +function f_doubleMM_sites(θc::CA.ComponentMatrix, xPc::CA.ComponentMatrix) + # extract several covariates from xP + ST = typeof(CA.getdata(xPc)[1:1,:]) # workaround for non-type-stable Symbol-indexing + S1 = (CA.getdata(xPc[:S1,:])::ST) + S2 = (CA.getdata(xPc[:S2,:])::ST) + # + # extract the parameters as row-repeated vectors + n_obs = size(S1, 1) + VT = typeof(CA.getdata(θc)[:,1]) # workaround for non-type-stable Symbol-indexing + (r0, r1, K1, K2) = map((:r0, :r1, :K1, :K2)) do par + p1 = CA.getdata(θc[:, par]) ::VT + repeat(p1', n_obs) # matrix: same for each concentration row in S1 + end + # + # each variable is a matrix (n_obs x n_site) + r0 .+ r1 .* S1 ./ (K1 .+ S1) .* S2 ./ (K2 .+ S2) +end +``` Again, the function should not rely on the order of parameters but use symbolic indexing to extract the parameter vectors. For type stability of this symbolic indexing, @@ -345,7 +363,7 @@ epochs of the optimization. ```{julia} (; probo) = solve(probo_sites, solver; rng, callback = callback_loss(100), # output during fitting - epochs = 10, + epochs = 20, #is_inferred = Val(true), # activate type-checks ); ``` diff --git a/docs/src/tutorials/blocks_corr.md b/docs/src/tutorials/blocks_corr.md new file mode 100644 index 0000000..54da1fe --- /dev/null +++ b/docs/src/tutorials/blocks_corr.md @@ -0,0 +1,171 @@ +# How to model indenpendent parameter-blocks in the posterior + + +``` @meta +CurrentModule = HybridVariationalInference +``` + +This guide shows how to configure independent parameter-blocks in the correlations +of the posterior. + +## Motivation + +Modelling all correlations among global and site PBM-parameters respectively +requires many degrees of freedom. + +To decrease the number of parameters to estimate, HVI allows to decompose the +correlations into independent sub-blocks of parameters. + +First load necessary packages. + +``` julia +using HybridVariationalInference +using ComponentArrays: ComponentArrays as CA +using Bijectors +using SimpleChains +using MLUtils +using JLD2 +using Random +using CairoMakie +using PairPlots # scatterplot matrices +``` + +This tutorial reuses and modifies the fitted object saved at the end of the +[Basic workflow without GPU](@ref) tutorial. + +``` julia +fname = "intermediate/basic_cpu_results.jld2" +print(abspath(fname)) +prob = probo_cor = load(fname, "probo"); +``` + +## Specifying blocks in correlation structure + +HVI models the posterior of the parameters at unconstrained scale using a +multivariate normal distribution. It estimates a parameterization of the +associated blocks in the correlation matrx and requires a specification +of the block-structure. + +This is done by specifying the positions of the end of the blocks for +the global (P) and the site-specific parameters (M) respectively using +a `NamedTuple` of integer vectors. + +The defaults specifies a single entry, meaning, there is only one big +block respectively, spanning all parameters. + +``` julia +cor_ends0 = (P=[length(prob.θP)], M=[length(prob.θM)]) +``` + + (P = [1], M = [2]) + +The following specification models one-entry blocks for each each parameter +in the correlation block the site parameters, i.e. treating all parameters +independently with not modelling any correlations between them. + +``` julia +cor_ends = (P=[length(prob.θP)], M=1:length(prob.θM)) +``` + + (P = [1], M = 1:2) + +## Reinitialize parameters for the posterior approximation. + +HVI uses additional fitted parameters to represent the means and the +covariance matrix of the posterior distribution of model parameters. +With fewer correlations, also the number of those parameters changes, +and those parameters must be reinitialized after changing the block structure in +the correlation matrix. + +Here, we obtain construct initial estimates. using [`init_hybrid_ϕunc`](@ref) + +``` julia +ϕunc = init_hybrid_ϕunc(cor_ends, zero(eltype(prob.θM))) +``` + +In this two-site parameter case, the the blocked structure saves only one degree of freedom: + +``` julia +length(ϕunc), length(probo_cor.ϕunc) +``` + + (5, 6) + +## Update the problem and redo the inversion + +``` julia +prob_ind = HybridProblem(prob; cor_ends, ϕunc) +``` + +``` julia +using OptimizationOptimisers +import Zygote + +solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) + +(; probo) = solve(prob_ind, solver; + callback = callback_loss(100), # output during fitting + epochs = 20, +); probo_ind = probo; +``` + +## Compare the correated vs. uncorrelated posterior + +First, draw a sample. + +``` julia +n_sample_pred = 400 +(y_cor, θsP_cor, θsMs_cor) = (; y, θsP, θsMs) = predict_hvi( + Random.default_rng(), probo_cor; n_sample_pred) +(y_ind, θsP_ind, θsMs_ind) = (; y, θsP, θsMs) = predict_hvi( + Random.default_rng(), probo_ind; n_sample_pred) +``` + +``` julia +i_site = 1 +θ1 = vcat(θsP_ind, θsMs_ind[i_site,:,:]) +θ1_nt = NamedTuple(k => CA.getdata(θ1[k,:]) for k in keys(θ1[:,1])) # +plt = pairplot(θ1_nt) +``` + +![](blocks_corr_files/figure-commonmark/cell-11-output-1.png) + +The corner plot of the independent-parameters estimate shows +no correlations between site parameters, *r*₁ and *K*₁. + +``` julia +i_out = 4 +fig = Figure(); ax = Axis(fig[1,1], xlabel="mean(y)",ylabel="sd(y)") +ymean_cor = [mean(y_cor[i_out,s,:]) for s in axes(y_cor, 2)] +ysd_cor = [std(y_cor[i_out,s,:]) for s in axes(y_cor, 2)] +scatter!(ax, ymean_cor, ysd_cor, label="correlated") +ymean_ind = [mean(y_ind[i_out,s,:]) for s in axes(y_ind, 2)] +ysd_ind = [std(y_ind[i_out,s,:]) for s in axes(y_ind, 2)] +scatter!(ax, ymean_ind, ysd_ind, label="independent") +axislegend(ax, unique=true) +fig +``` + +![](blocks_corr_files/figure-commonmark/cell-12-output-1.png) + +``` julia +plot_sd_vs_mean = (par) -> begin + fig = Figure(); ax = Axis(fig[1,1], xlabel="mean($par)",ylabel="sd($par)") + θmean_cor = [mean(θsMs_cor[s,par,:]) for s in axes(θsMs_cor, 1)] + θsd_cor = [std(θsMs_cor[s,par,:]) for s in axes(θsMs_cor, 1)] + scatter!(ax, θmean_cor, θsd_cor, label="correlated") + θmean_ind = [mean(θsMs_ind[s,par,:]) for s in axes(θsMs_ind, 1)] + θsd_ind = [std(θsMs_ind[s,par,:]) for s in axes(θsMs_ind, 1)] + scatter!(ax, θmean_ind, θsd_ind, label="independent") + axislegend(ax, unique=true) + fig +end +plot_sd_vs_mean(:K1) +``` + +![](blocks_corr_files/figure-commonmark/cell-13-output-1.png) + +The inversion that neglects correlations among site parameters results in +the same magnitude of estimated uncertainty of predictions. +However, the uncertainty of the model parameters is severely underestimated +in this example. diff --git a/docs/src/tutorials/blocks_corr.qmd b/docs/src/tutorials/blocks_corr.qmd new file mode 100644 index 0000000..380903a --- /dev/null +++ b/docs/src/tutorials/blocks_corr.qmd @@ -0,0 +1,171 @@ +--- +title: "How to model indenpendent parameter-blocks in the posterior" +engine: julia +execute: + echo: true + output: false + daemon: 3600 +format: + commonmark: + variant: -raw_html + wrap: preserve +bibliography: twutz_txt.bib +--- + +``` @meta +CurrentModule = HybridVariationalInference +``` + +This guide shows how to configure independent parameter-blocks in the correlations +of the posterior. + +## Motivation +Modelling all correlations among global and site PBM-parameters respectively +requires many degrees of freedom. + +To decrease the number of parameters to estimate, HVI allows to decompose the +correlations into independent sub-blocks of parameters. + +First load necessary packages. +```{julia} +using HybridVariationalInference +using ComponentArrays: ComponentArrays as CA +using Bijectors +using SimpleChains +using MLUtils +using JLD2 +using Random +using CairoMakie +using PairPlots # scatterplot matrices +``` + +This tutorial reuses and modifies the fitted object saved at the end of the +[Basic workflow without GPU](@ref) tutorial. + +```{julia} +fname = "intermediate/basic_cpu_results.jld2" +print(abspath(fname)) +prob = probo_cor = load(fname, "probo"); +``` + +## Specifying blocks in correlation structure +HVI models the posterior of the parameters at unconstrained scale using a +multivariate normal distribution. It estimates a parameterization of the +associated blocks in the correlation matrx and requires a specification +of the block-structure. + +This is done by specifying the positions of the end of the blocks for +the global (P) and the site-specific parameters (M) respectively using +a `NamedTuple` of integer vectors. + +The defaults specifies a single entry, meaning, there is only one big +block respectively, spanning all parameters. +```{julia} +#| output: true +cor_ends0 = (P=[length(prob.θP)], M=[length(prob.θM)]) +``` + +The following specification models one-entry blocks for each each parameter +in the correlation block the site parameters, i.e. treating all parameters +independently with not modelling any correlations between them. + +```{julia} +#| output: true +cor_ends = (P=[length(prob.θP)], M=1:length(prob.θM)) +``` + +## Reinitialize parameters for the posterior approximation. +HVI uses additional fitted parameters to represent the means and the +covariance matrix of the posterior distribution of model parameters. +With fewer correlations, also the number of those parameters changes, +and those parameters must be reinitialized after changing the block structure in +the correlation matrix. + +Here, we obtain construct initial estimates. using [`init_hybrid_ϕunc`](@ref) + +```{julia} +ϕunc = init_hybrid_ϕunc(cor_ends, zero(eltype(prob.θM))) +``` + +In this two-site parameter case, the the blocked structure saves only one degree of freedom: + +```{julia} +#| output: true +length(ϕunc), length(probo_cor.ϕunc) +``` + +## Update the problem and redo the inversion + + +```{julia} +prob_ind = HybridProblem(prob; cor_ends, ϕunc) +``` + +```{julia} +using OptimizationOptimisers +import Zygote + +solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) + +(; probo) = solve(prob_ind, solver; + callback = callback_loss(100), # output during fitting + epochs = 20, +); probo_ind = probo; +``` + +## Compare the correated vs. uncorrelated posterior + +First, draw a sample. +```{julia} +n_sample_pred = 400 +(y_cor, θsP_cor, θsMs_cor) = (; y, θsP, θsMs) = predict_hvi( + Random.default_rng(), probo_cor; n_sample_pred) +(y_ind, θsP_ind, θsMs_ind) = (; y, θsP, θsMs) = predict_hvi( + Random.default_rng(), probo_ind; n_sample_pred) +``` + +```{julia} +#| output: true +i_site = 1 +θ1 = vcat(θsP_ind, θsMs_ind[i_site,:,:]) +θ1_nt = NamedTuple(k => CA.getdata(θ1[k,:]) for k in keys(θ1[:,1])) # +plt = pairplot(θ1_nt) +``` +The corner plot of the independent-parameters estimate shows +no correlations between site parameters, $r_1$ and $K_1$. + +```{julia} +#| output: true +i_out = 4 +fig = Figure(); ax = Axis(fig[1,1], xlabel="mean(y)",ylabel="sd(y)") +ymean_cor = [mean(y_cor[i_out,s,:]) for s in axes(y_cor, 2)] +ysd_cor = [std(y_cor[i_out,s,:]) for s in axes(y_cor, 2)] +scatter!(ax, ymean_cor, ysd_cor, label="correlated") +ymean_ind = [mean(y_ind[i_out,s,:]) for s in axes(y_ind, 2)] +ysd_ind = [std(y_ind[i_out,s,:]) for s in axes(y_ind, 2)] +scatter!(ax, ymean_ind, ysd_ind, label="independent") +axislegend(ax, unique=true) +fig +``` + +```{julia} +#| output: true +plot_sd_vs_mean = (par) -> begin + fig = Figure(); ax = Axis(fig[1,1], xlabel="mean($par)",ylabel="sd($par)") + θmean_cor = [mean(θsMs_cor[s,par,:]) for s in axes(θsMs_cor, 1)] + θsd_cor = [std(θsMs_cor[s,par,:]) for s in axes(θsMs_cor, 1)] + scatter!(ax, θmean_cor, θsd_cor, label="correlated") + θmean_ind = [mean(θsMs_ind[s,par,:]) for s in axes(θsMs_ind, 1)] + θsd_ind = [std(θsMs_ind[s,par,:]) for s in axes(θsMs_ind, 1)] + scatter!(ax, θmean_ind, θsd_ind, label="independent") + axislegend(ax, unique=true) + fig +end +plot_sd_vs_mean(:K1) +``` + +The inversion that neglects correlations among site parameters results in +the same magnitude of estimated uncertainty of predictions. +However, the uncertainty of the model parameters is severely underestimated +in this example. + diff --git a/docs/src/tutorials/blocks_corr_files/figure-commonmark/cell-10-output-1.png 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+, given the sampled +(transformed) global parameters, +$\zeta_P$ + +$$ +p(\zeta_{Ms}, \zeta_P) = p(\zeta_{Ms} | \zeta_P) p(\zeta_P)$$ + +This comes at the cost of running the ML model forward for each sampled +global parameter, rather than just once in each sampling of the posterior. + +First load necessary packages. + +``` julia +using HybridVariationalInference +using ComponentArrays: ComponentArrays as CA +using Bijectors +using SimpleChains +using StatsFuns +using StableRNGs +using MLUtils +using JLD2 +using Random +using CairoMakie +using PairPlots # scatterplot matrices +``` + +This tutorial reuses and modifies the fitted object saved at the end of the +[Basic workflow without GPU](@ref) tutorial. + +``` julia +fname = "intermediate/basic_cpu_results.jld2" +print(abspath(fname)) +prob = probo_uncond = load(fname, "probo"); +``` + +## The `pbm_covars` entry in the `HybridProblem` + +When constructing a HybridProblem, the names of the subset of global parameters +provided to the ML model is specified using the `pbm_covars` argument. +It defaults to the empty tuple `()`, +meaning that no global parameters are provided as inputs. + +The following specification tells, that the `K_2` parameter should be provided +as input to the ML model. + +``` julia +pbm_covars = (:K2,) +``` + +## The modified Machine-Learning model + +The ML model needs to be adapted to consume more inputs than the site covariates. +Note the changed `n_input` specification compared to the +[Basic workflow without GPU](@ref) tutorial. + +``` julia +n_out = length(prob.θM) # number of individuals to predict +n_covar = 5 #size(xM,1) +n_input = n_covar + length(pbm_covars) +rng = StableRNG(111) + +g_chain = SimpleChain( + static(n_input), # input dimension (optional) + TurboDense{true}(tanh, n_input * 4), + TurboDense{true}(tanh, n_input * 4), + # dense layer without bias that maps to n outputs to (0..1) + TurboDense{false}(logistic, n_out) +) +# get a template of the parameter vector, ϕg0 +g_chain_app, ϕg0 = construct_ChainsApplicator(rng, g_chain) +# +priorsM = [prob.priors[k] for k in keys(prob.θM)] +lowers, uppers = get_quantile_transformed(priorsM, prob.transM) +FT = eltype(prob.θM) +g_chain_scaled = NormalScalingModelApplicator(g_chain_app, lowers, uppers, FT) +``` + +## Update the problem and redo the inversion + +``` julia +prob_cond = HybridProblem(probo_uncond; g=g_chain_scaled, ϕg=ϕg0, pbm_covars) +``` + +``` julia +using OptimizationOptimisers +import Zygote + +solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) + +(; probo) = solve(probo_uncond, solver; + callback = callback_loss(100), # output during fitting + epochs = 20, +); probo_cond = probo; +``` + +## Compare the conditional vs unconditional posterior + +First, draw a sample. + +``` julia +n_sample_pred = 400 +(y_cond, θsP_cond, θsMs_cond) = (; y, θsP, θsMs) = predict_hvi( + Random.default_rng(), probo_cond; n_sample_pred) +(y_uncond, θsP_uncond, θsMs_uncond) = (; y, θsP, θsMs) = predict_hvi( + Random.default_rng(), probo_uncond; n_sample_pred) +``` + +``` julia +i_site = 1 +θ1 = vcat(θsP_uncond, θsMs_uncond[i_site,:,:]) +θ1_nt = NamedTuple(k => CA.getdata(θ1[k,:]) for k in keys(θ1[:,1])) # +plt = pairplot(θ1_nt) +``` + +![](corr_site_global_files/figure-commonmark/cell-9-output-1.png) + +The corner plot of the independent-parameters estimate shows +that despite providing `K_2` to the ML model, +it has only very weak correlations with the site parameters, $r_1$ and $K_1$. + +``` julia +i_out = 4 +fig = Figure(); ax = Axis(fig[1,1], xlabel="mean(y)",ylabel="sd(y)") +ymean_cond = [mean(y_cond[i_out,s,:]) for s in axes(y_cond, 2)] +ysd_cond = [std(y_cond[i_out,s,:]) for s in axes(y_cond, 2)] +scatter!(ax, ymean_cond, ysd_cond, label="conditional") +ymean_uncond = [mean(y_uncond[i_out,s,:]) for s in axes(y_uncond, 2)] +ysd_uncond = [std(y_uncond[i_out,s,:]) for s in axes(y_uncond, 2)] +scatter!(ax, ymean_uncond, ysd_uncond, label="unconditional") +axislegend(ax, unique=true) +fig +``` + +![](corr_site_global_files/figure-commonmark/cell-10-output-1.png) + +``` julia +plot_sd_vs_mean = (par) -> begin + fig = Figure(); ax = Axis(fig[1,1], xlabel="mean($par)",ylabel="sd($par)") + θmean_cond = [mean(θsMs_cond[s,par,:]) for s in axes(θsMs_cond, 1)] + θsd_cond = [std(θsMs_cond[s,par,:]) for s in axes(θsMs_cond, 1)] + scatter!(ax, θmean_cond, θsd_cond, label="conditional") + θmean_uncond = [mean(θsMs_uncond[s,par,:]) for s in axes(θsMs_uncond, 1)] + θsd_uncond = [std(θsMs_uncond[s,par,:]) for s in axes(θsMs_uncond, 1)] + scatter!(ax, θmean_uncond, θsd_uncond, label="unconditional") + axislegend(ax, unique=true) + fig +end +plot_sd_vs_mean(:r1) +``` + +![](corr_site_global_files/figure-commonmark/cell-11-output-1.png) + +``` julia +plot_sd_vs_mean(:K1) +``` + +![](corr_site_global_files/figure-commonmark/cell-12-output-1.png) + +The “conditional” scenario allows for correlations between global and site parameters +by making precitions of site parameters conditional on sampled global parameters. + +It estimates slightly higher marginal uncertainties, both of the predictions +and of the model parameters. diff --git a/docs/src/tutorials/corr_site_global.qmd b/docs/src/tutorials/corr_site_global.qmd new file mode 100644 index 0000000..6a10ff5 --- /dev/null +++ b/docs/src/tutorials/corr_site_global.qmd @@ -0,0 +1,187 @@ +--- +title: "How to account for correlations between site and global parameters" +engine: julia +execute: + echo: true + output: false + daemon: 3600 +format: + commonmark: + variant: -raw_html+tex_math_dollars + wrap: preserve +bibliography: twutz_txt.bib +--- + +``` @meta +CurrentModule = HybridVariationalInference +``` + +This guide shows how to configure providing certain global parameters as +covariates to the ML model. + +## Motivation + +HVI requires the site-parameter blocks in the correlation matrix to be +independent of the global-parameter block. + +However, correlations between site and global parameters can be modeled +by predicting different (transformed) site parameters, +$\zeta_{Ms}$ +, given the sampled +(transformed) global parameters, +$\zeta_P$ + +$$ +p(\zeta_{Ms}, \zeta_P) = p(\zeta_{Ms} | \zeta_P) p(\zeta_P)$$ + +This comes at the cost of running the ML model forward for each sampled +global parameter, rather than just once in each sampling of the posterior. + +First load necessary packages. +```{julia} +using HybridVariationalInference +using ComponentArrays: ComponentArrays as CA +using Bijectors +using SimpleChains +using StatsFuns +using StableRNGs +using MLUtils +using JLD2 +using Random +using CairoMakie +using PairPlots # scatterplot matrices +``` + +This tutorial reuses and modifies the fitted object saved at the end of the +[Basic workflow without GPU](@ref) tutorial. + +```{julia} +fname = "intermediate/basic_cpu_results.jld2" +print(abspath(fname)) +prob = probo_uncond = load(fname, "probo"); +``` + +## The `pbm_covars` entry in the `HybridProblem` + +When constructing a HybridProblem, the names of the subset of global parameters +provided to the ML model is specified using the `pbm_covars` argument. +It defaults to the empty tuple `()`, + meaning that no global parameters are provided as inputs. + +The following specification tells, that the `K_2` parameter should be provided +as input to the ML model. + +```{julia} +pbm_covars = (:K2,) +``` + +## The modified Machine-Learning model + +The ML model needs to be adapted to consume more inputs than the site covariates. +Note the changed `n_input` specification compared to the +[Basic workflow without GPU](@ref) tutorial. + +```{julia} +n_out = length(prob.θM) # number of individuals to predict +n_covar = 5 #size(xM,1) +n_input = n_covar + length(pbm_covars) +rng = StableRNG(111) + +g_chain = SimpleChain( + static(n_input), # input dimension (optional) + TurboDense{true}(tanh, n_input * 4), + TurboDense{true}(tanh, n_input * 4), + # dense layer without bias that maps to n outputs to (0..1) + TurboDense{false}(logistic, n_out) +) +# get a template of the parameter vector, ϕg0 +g_chain_app, ϕg0 = construct_ChainsApplicator(rng, g_chain) +# +priorsM = [prob.priors[k] for k in keys(prob.θM)] +lowers, uppers = get_quantile_transformed(priorsM, prob.transM) +FT = eltype(prob.θM) +g_chain_scaled = NormalScalingModelApplicator(g_chain_app, lowers, uppers, FT) +``` + +## Update the problem and redo the inversion +```{julia} +prob_cond = HybridProblem(probo_uncond; g=g_chain_scaled, ϕg=ϕg0, pbm_covars) +``` + + +```{julia} +using OptimizationOptimisers +import Zygote + +solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) + +(; probo) = solve(probo_uncond, solver; + callback = callback_loss(100), # output during fitting + epochs = 20, +); probo_cond = probo; +``` + +## Compare the conditional vs unconditional posterior + +First, draw a sample. +```{julia} +n_sample_pred = 400 +(y_cond, θsP_cond, θsMs_cond) = (; y, θsP, θsMs) = predict_hvi( + Random.default_rng(), probo_cond; n_sample_pred) +(y_uncond, θsP_uncond, θsMs_uncond) = (; y, θsP, θsMs) = predict_hvi( + Random.default_rng(), probo_uncond; n_sample_pred) +``` + +```{julia} +#| output: true +i_site = 1 +θ1 = vcat(θsP_uncond, θsMs_uncond[i_site,:,:]) +θ1_nt = NamedTuple(k => CA.getdata(θ1[k,:]) for k in keys(θ1[:,1])) # +plt = pairplot(θ1_nt) +``` +The corner plot of the independent-parameters estimate shows +that despite providing `K_2` to the ML model, +it has only very weak correlations with the site parameters, $r_1$ and $K_1$. + +```{julia} +#| output: true +i_out = 4 +fig = Figure(); ax = Axis(fig[1,1], xlabel="mean(y)",ylabel="sd(y)") +ymean_cond = [mean(y_cond[i_out,s,:]) for s in axes(y_cond, 2)] +ysd_cond = [std(y_cond[i_out,s,:]) for s in axes(y_cond, 2)] +scatter!(ax, ymean_cond, ysd_cond, label="conditional") +ymean_uncond = [mean(y_uncond[i_out,s,:]) for s in axes(y_uncond, 2)] +ysd_uncond = [std(y_uncond[i_out,s,:]) for s in axes(y_uncond, 2)] +scatter!(ax, ymean_uncond, ysd_uncond, label="unconditional") +axislegend(ax, unique=true) +fig +``` + +```{julia} +#| output: true +plot_sd_vs_mean = (par) -> begin + fig = Figure(); ax = Axis(fig[1,1], xlabel="mean($par)",ylabel="sd($par)") + θmean_cond = [mean(θsMs_cond[s,par,:]) for s in axes(θsMs_cond, 1)] + θsd_cond = [std(θsMs_cond[s,par,:]) for s in axes(θsMs_cond, 1)] + scatter!(ax, θmean_cond, θsd_cond, label="conditional") + θmean_uncond = [mean(θsMs_uncond[s,par,:]) for s in axes(θsMs_uncond, 1)] + θsd_uncond = [std(θsMs_uncond[s,par,:]) for s in axes(θsMs_uncond, 1)] + scatter!(ax, θmean_uncond, θsd_uncond, label="unconditional") + axislegend(ax, unique=true) + fig +end +plot_sd_vs_mean(:r1) +``` + +```{julia} +#| output: true +plot_sd_vs_mean(:K1) +``` + +The "conditional" scenario allows for correlations between global and site parameters +by making precitions of site parameters conditional on sampled global parameters. + +It estimates slightly higher marginal uncertainties, both of the predictions +and of 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However, correlations between site and global parameters can be modeled -by predicting different (transformed) site parameters, -$\zeta_{Ms}$ -, given the sampled -(transformed) global parameters, -$\zeta_P$ +by predicting different (transformed) site parameters, $\zeta_{Ms}$ +, given the sampled (transformed) global parameters, $\zeta_P$. $$ p(\zeta_{Ms}, \zeta_P) = p(\zeta_{Ms} | \zeta_P) p(\zeta_P)$$ @@ -69,7 +66,7 @@ pbm_covars = (:K2,) The ML model needs to be adapted to consume more inputs than the site covariates. Note the changed `n_input` specification compared to the -[Basic workflow without GPU](@ref) tutorial. +[Basic workflow without GPU](@ref) turorial. ``` julia n_out = length(prob.θM) # number of individuals to predict diff --git a/docs/src/tutorials/corr_site_global.qmd b/docs/src/tutorials/corr_site_global.qmd index 6a10ff5..2c493e3 100644 --- a/docs/src/tutorials/corr_site_global.qmd +++ b/docs/src/tutorials/corr_site_global.qmd @@ -25,11 +25,8 @@ HVI requires the site-parameter blocks in the correlation matrix to be independent of the global-parameter block. However, correlations between site and global parameters can be modeled -by predicting different (transformed) site parameters, -$\zeta_{Ms}$ -, given the sampled -(transformed) global parameters, -$\zeta_P$ +by predicting different (transformed) site parameters, $\zeta_{Ms}$ +, given the sampled (transformed) global parameters, $\zeta_P$. $$ p(\zeta_{Ms}, \zeta_P) = p(\zeta_{Ms} | \zeta_P) p(\zeta_P)$$ @@ -79,7 +76,7 @@ pbm_covars = (:K2,) The ML model needs to be adapted to consume more inputs than the site covariates. 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z)M3@UA=L~{J#R5ZMzbjU$n>c1=v2Woy5Wb0`A^25sLc?msF!MJyT-ixV-uwCs;?m( z`S-vB$h5X#rP_7to~P$=8SCNR)W^HNOG5c;vL&gMVKEh(1b&L2rA>mi{>xjSq)kq? z;Aq#FYAv-yTZKM?)8m#cxHpvLZx4M6>GP$%~oI&>}FaJmamIAJv# zIdnUD=1k@8k6Y6`)POUx^Q9HPYQ`TFqL~=OcQ^i`c@A{A7<|CeC2IBR&tSJ5`tBI= z>Nm>CJv2A9?5s)9D!4jPhD=`93QN4l4-ol8amKM|*RiAP;~9M9d3yR%$Mjv>raf|O z$=i`B&$t6Op*IieBZsb&5ssmu3!j&+lF61v0!7KSzyz!39vQ2H@MwTr)s*2IY`Q#6GrCrUx4 z>+Hh`TWZPY=*^41NqS@?Pjfw@Iv@g6kte}uNIpT3I1!Sc8COL_14&U`OmI}#+m`%w zt$3rBbI7=HV>NvTJGeKquyi}Xq2FJJPnSOc>vEG8FJ7$8vw5I8%!D{uX z2F0Hy`+G0a|1pGKY4L^e9~hiA{95s^43k& n_covar * 4, tanh), + Lux.Dense(n_covar * 4 => n_covar * 4, tanh), + Lux.Dense(n_covar * 4 => n_out, logistic, use_bias = false) +) +# get a template of the parameter vector, ϕg0 +rng = StableRNG(111) +g_chain_app, ϕg0 = construct_ChainsApplicator(rng, g_lux) +# +priorsM = [prob.priors[k] for k in keys(prob.θM)] +lowers, uppers = get_quantile_transformed(priorsM, prob.transM) +FT = eltype(prob.θM) +g_chain_scaled = NormalScalingModelApplicator(g_chain_app, lowers, uppers, FT) +``` + +Update the `HybridProblem` to use this ML model. + +``` julia +prob_lux = HybridProblem(probo_chain; g=g_chain_scaled, ϕg=ϕg0) +``` + +## Specifying GPU devices during solve + +The [`solve`](@ref) method for the HybridPosteriorSolver accepts argument `gdevs`, +Its a `NamedTuple` with entries`gdev_M` and `gdev_P`, for the +ML model on and the process-basee model (PBM) respectively. +They specify functions that are applied to move callables and data to GPU. + +They default to `identity`, meaning that nothing is moved from CPU to GPU. +Function `gpu_device()` from package `MLDataDevices` can be used instead +for teh standard GPU device. + +Hence specify +- `gdevs = (; gdev_M=gpu_device(), gdev_P=gpu_device())`: to move both ML model and PBM to GPU +- `gdevs = (; gdev_M=gpu_device(), gdev_P=identity)`: to move both ML model to GPU but execute the PBM (and parameter transformation) on CPU + +In addition, the libraries of the GPU device need to be activated by +importing respective Julia packages. +Currently, only CUDA is tested with this `HybridVariationalInference` package. + +``` julia +import CUDA, cuDNN # so that gpu_device() returns a CUDADevice +#CUDA.device!(4) +gdevs = (; gdev_M=gpu_device(), gdev_P=gpu_device()) +#gdevs = (; gdev_M=gpu_device(), gdev_P=identity) + +using OptimizationOptimisers +import Zygote +solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) + +(; probo) = solve(prob_lux, solver; + callback = callback_loss(100), + epochs = 10, + gdevs, +); probo_lux = probo; +``` + +## Moving results from GPU to CPU + +The sampling and prediction methods, also take this `gdevs` keyword argument. + +``` julia +n_sample_pred = 400 +(y_dev, θsP_dev, θsMs_dev) = (; y, θsP, θsMs) = predict_hvi( + rng, probo_lux; n_sample_pred, gdevs); +``` + +If `gdev_P` is not an `AbstractGPUDevice` then all the results are on CPU. +If `gdev_P` is an `AbstractGPUDevice` then the results are GPUArrays +and need to be transferred to CPU. + +``` julia +typeof(θsMs_dev) +``` + + ComponentArrays.ComponentArray{Float32, 3, CUDA.CuArray{Float32, 3, CUDA.DeviceMemory}, Tuple{ComponentArrays.Axis{(i = 1:800,)}, ComponentArrays.Axis{(r1 = 1, K1 = 2)}, ComponentArrays.Axis{(i = 1:400,)}}} + +Handling of a `ComponentArrays` backed by GPUArrays can result +in errors of scalar indexing. Therefore, use a semicolon +to suppress printing. +Also for moving the `ComponentArrays` to CPU, use function +[`apply_preserve_axes`](@ref) to circumvent this error. + +``` julia +cdev = cpu_device() +y = cdev(y_dev) +θsP = apply_preserve_axes(cdev, θsP_dev) +θsMs = apply_preserve_axes(cdev, θsMs_dev) +``` diff --git a/docs/src/tutorials/lux_gpu.qmd b/docs/src/tutorials/lux_gpu.qmd new file mode 100644 index 0000000..5c643e8 --- /dev/null +++ b/docs/src/tutorials/lux_gpu.qmd @@ -0,0 +1,161 @@ +--- +title: "How to move computations to GPU" +engine: julia +execute: + echo: true + output: false + daemon: 3600 +format: + commonmark: + variant: -raw_html+tex_math_dollars + wrap: preserve +bibliography: twutz_txt.bib +--- + +``` @meta +CurrentModule = HybridVariationalInference +``` + +This guide shows how to configure the setup and inversion of a HybridProblem so +that computations of the ML model and maybe also the process-based model +are executed on GPU. + +## Motivation + +Machine learning is often accellerated by moving computations form CPU +to GPU. So does HVI. + + +First load necessary packages. +```{julia} +using HybridVariationalInference +using ComponentArrays: ComponentArrays as CA +using Bijectors +using Lux +using SimpleChains # only loading save object +using StatsFuns +using StableRNGs +using MLUtils +using JLD2 +using Random +# using CairoMakie +# using PairPlots # scatterplot matrices +``` + +This tutorial reuses and modifies the fitted object saved at the end of the +[Basic workflow without GPU](@ref) tutorial. + +```{julia} +fname = "intermediate/basic_cpu_results.jld2" +print(abspath(fname)) +prob = probo_chain = load(fname, "probo"); +``` + +## Updating the ML model of the problem to use LUX + +Because the SimpleChains ML model used in the basic tutorial does not support +GPU, we reconstruct the model using the LUX framework. +Note that all the setup is almost the same, as in the basic worklfow. The +only diffrence is that a `Lux.Chains` object is provided to `construct_ChainsApplicator`. + +```{julia} +n_out = length(prob.θM) # number of individuals to predict +n_covar = 5 #size(xM,1) +n_input = n_covar + +g_lux = Lux.Chain( + Lux.Dense(n_covar => n_covar * 4, tanh), + Lux.Dense(n_covar * 4 => n_covar * 4, tanh), + Lux.Dense(n_covar * 4 => n_out, logistic, use_bias = false) +) +# get a template of the parameter vector, ϕg0 +rng = StableRNG(111) +g_chain_app, ϕg0 = construct_ChainsApplicator(rng, g_lux) +# +priorsM = [prob.priors[k] for k in keys(prob.θM)] +lowers, uppers = get_quantile_transformed(priorsM, prob.transM) +FT = eltype(prob.θM) +g_chain_scaled = NormalScalingModelApplicator(g_chain_app, lowers, uppers, FT) +``` + +Update the `HybridProblem` to use this ML model. +```{julia} +prob_lux = HybridProblem(probo_chain; g=g_chain_scaled, ϕg=ϕg0) +``` + +## Specifying GPU devices during solve + +The [`solve`](@ref) method for the HybridPosteriorSolver accepts argument `gdevs`, +Its a `NamedTuple` with entries`gdev_M` and `gdev_P`, for the +ML model on and the process-basee model (PBM) respectively. +They specify functions that are applied to move callables and data to GPU. + +They default to `identity`, meaning that nothing is moved from CPU to GPU. +Function `gpu_device()` from package `MLDataDevices` can be used instead +for teh standard GPU device. + +Hence specify +- `gdevs = (; gdev_M=gpu_device(), gdev_P=gpu_device())`: to move both ML model and PBM to GPU +- `gdevs = (; gdev_M=gpu_device(), gdev_P=identity)`: to move both ML model to GPU but execute the PBM (and parameter transformation) on CPU + +In addition, the libraries of the GPU device need to be activated by +importing respective Julia packages. +Currently, only CUDA is tested with this `HybridVariationalInference` package. + +```{julia} +#| echo: false +#| # if executing on gpu1-hpc22, use the 5th gpu, because first usually loaded +import CUDA, cuDNN +if gethostname() == "node-r6-he01.bgc-jena.mpg.de" + CUDA.device!(4) +end +``` +```{julia} +import CUDA, cuDNN # so that gpu_device() returns a CUDADevice +#CUDA.device!(4) +gdevs = (; gdev_M=gpu_device(), gdev_P=gpu_device()) +#gdevs = (; gdev_M=gpu_device(), gdev_P=identity) + +using OptimizationOptimisers +import Zygote +solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) + +(; probo) = solve(prob_lux, solver; + callback = callback_loss(100), + epochs = 10, + gdevs, +); probo_lux = probo; +``` + +## Moving results from GPU to CPU + +The sampling and prediction methods, also take this `gdevs` keyword argument. + +```{julia} +n_sample_pred = 400 +(y_dev, θsP_dev, θsMs_dev) = (; y, θsP, θsMs) = predict_hvi( + rng, probo_lux; n_sample_pred, gdevs); +``` + +If `gdev_P` is not an `AbstractGPUDevice` then all the results are on CPU. +If `gdev_P` is an `AbstractGPUDevice` then the results are GPUArrays +and need to be transferred to CPU. + +```{julia} +#| output: true +typeof(θsMs_dev) +``` + +Handling of a `ComponentArrays` backed by GPUArrays can result +in errors of scalar indexing. Therefore, use a semicolon +to suppress printing. +Also for moving the `ComponentArrays` to CPU, use function +[`apply_preserve_axes`](@ref) to circumvent this error. + +```{julia} +cdev = cpu_device() +y = cdev(y_dev) +θsP = apply_preserve_axes(cdev, θsP_dev) +θsMs = apply_preserve_axes(cdev, θsMs_dev) +``` + diff --git a/docs/src_stash/Manifest.toml b/docs/src_stash/Manifest.toml new file mode 100644 index 0000000..37f2a35 --- /dev/null +++ b/docs/src_stash/Manifest.toml @@ -0,0 +1,1083 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.11.4" +manifest_format = "2.0" +project_hash = "39d0d5866236472d6bc1a58c4e663ea8a2a2e057" + +[[deps.AliasTables]] +deps = ["PtrArrays", "Random"] +git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff" +uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8" +version = "1.1.3" + +[[deps.ArgTools]] +uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" +version = "1.1.2" + +[[deps.Artifacts]] +uuid = 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+git-tree-sha1 = "14cc7083fc6dff3cc44f2bc435ee96d06ed79aa7" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "10164.0.1+0" + +[[deps.x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "e7b67590c14d487e734dcb925924c5dc43ec85f3" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "4.1.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "fbf139bce07a534df0e699dbb5f5cc9346f95cc1" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.9.2+0" diff --git a/docs/src_stash/Project.toml b/docs/src_stash/Project.toml new file mode 100644 index 0000000..43aec5b --- /dev/null +++ b/docs/src_stash/Project.toml @@ -0,0 +1,2 @@ +[deps] +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" diff --git a/docs/src_stash/test1.html b/docs/src_stash/test1.html new file mode 100644 index 0000000..a0bf8a6 --- /dev/null +++ b/docs/src_stash/test1.html @@ -0,0 +1,566 @@ + + + + + + + + + +test1 + + + + + + + + + + + + + + + + + + + + + + + + +

 + + + + + \ No newline at end of file diff --git a/docs/src_stash/test1.md b/docs/src_stash/test1.md new file mode 100644 index 0000000..3744e12 --- /dev/null +++ b/docs/src_stash/test1.md @@ -0,0 +1,66 @@ +# A julia engine notebook + + +``` julia +2 + 2 +``` + + 4 + +A code block with `#| echo: false`. + + Status `/Net/Groups/BGI/people/twutz/julia/dev/HybridVariationalInference/docs/src_stash/Project.toml` + [91a5bcdd] Plots v1.40.19 + +``` julia +run(`which julia`); +``` + + /Net/Groups/Services/HPC_22/apps/julia/julia-1.11.4/bin/julia + +## Equation + +inline: $\sqrt{\pi}$ inside text. + +display equation [Equation 1](#eq-general) with a number and a reference + +$$ +\begin{aligned} +\frac{n!}{k!(n - k)!} &= \binom{n}{k} +\\ +x^2 =& 3 +\end{aligned} + \qquad(1)$$ + +## Literature references + +See (Wutzler et al. 2020). + +## Plots + +single + +``` julia +plot(1:4) +``` + +![](test1_files/figure-commonmark/fig-limits-output-1.svg) + +## Not working with Documenter + +Julia code is not colored, output block-code + +Figures: The figure captions are not kept, and links do not work + +References: the number is correctly inserted, but the link does not work + +Documenter admonitions: the four spaces before the contents are removed - do not work + +Quarto-Columns are rendered to several paragraphs + +With gfm, references are formatted bad, and there are errors with unclosed divs +after Documenter processes it. + +## References + +Wutzler, Thomas, Oscar Perez-Priego, Kendalynn Morris, Tarek S. El-Madany, and Mirco Migliavacca. 2020. “Soil CO<$\mathsemicolon$sub>$\mathsemicolon$2<$\mathsemicolon$/Sub>$\mathsemicolon$ Efflux Errors Are Lognormally Distributed &Amp$\mathsemicolon$\#8211$\mathsemicolon$ Implications and Guidance.” *Geoscientific Instrumentation, Methods and Data Systems* 9 (1): 239–54. . diff --git a/docs/src_stash/test1.qmd b/docs/src_stash/test1.qmd index 1afa6b2..7b5800b 100644 --- a/docs/src_stash/test1.qmd +++ b/docs/src_stash/test1.qmd @@ -5,7 +5,7 @@ execute: echo: true format: commonmark: - variant: -raw_html + variant: -raw_html+tex_math_dollars wrap: preserve html: code-fold: false diff --git a/docs/src_stash/test1_files/figure-commonmark/fig-limits-output-1.svg b/docs/src_stash/test1_files/figure-commonmark/fig-limits-output-1.svg new file mode 100644 index 0000000..2006b64 --- /dev/null +++ b/docs/src_stash/test1_files/figure-commonmark/fig-limits-output-1.svg @@ -0,0 +1,42 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dededf;--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dededf;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dededf;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dededf;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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0.375rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #ffffff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dededf;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dededf;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down 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0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] 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#000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #ffffff;--quarto-scss-export-navbar-dark-contrast: #ffffff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #212529;--quarto-scss-export-dropdown-bg: #ffffff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #212529;--quarto-scss-export-dropdown-link-hover-color: #212529;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #0d6efd;--quarto-scss-export-dropdown-link-active-color: #ffffff;--quarto-scss-export-dropdown-link-disabled-color: rgba(33, 37, 41, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #ffffff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #ffffff;--quarto-scss-export-dropdown-dark-link-active-bg: #0d6efd;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #0d6efd;--quarto-scss-export-pagination-bg: #ffffff;--quarto-scss-export-pagination-border-color: #dededf;--quarto-scss-export-pagination-focus-color: #0a58ca;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #0a58ca;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dededf;--quarto-scss-export-pagination-active-color: #ffffff;--quarto-scss-export-pagination-active-bg: #0d6efd;--quarto-scss-export-pagination-active-border-color: #0d6efd;--quarto-scss-export-pagination-disabled-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dededf;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-bg: rgba(33, 37, 41, 0.03);--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #ffffff;--quarto-scss-export-accordion-color: #212529;--quarto-scss-export-accordion-bg: #ffffff;--quarto-scss-export-accordion-border-color: #dededf;--quarto-scss-export-accordion-button-color: #212529;--quarto-scss-export-accordion-button-bg: #ffffff;--quarto-scss-export-accordion-button-active-bg: #cfe2ff;--quarto-scss-export-accordion-button-active-color: #052c65;--quarto-scss-export-accordion-button-focus-border-color: #86b7fe;--quarto-scss-export-accordion-icon-color: #212529;--quarto-scss-export-accordion-icon-active-color: #052c65;--quarto-scss-export-tooltip-color: #ffffff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #212529;--quarto-scss-export-popover-arrow-color: #ffffff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #ffffff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #ffffff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dededf;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dededf;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #ffffff;--quarto-scss-export-progress-bar-bg: #0d6efd;--quarto-scss-export-list-group-color: #212529;--quarto-scss-export-list-group-bg: #ffffff;--quarto-scss-export-list-group-border-color: #dededf;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #0d6efd;--quarto-scss-export-list-group-active-color: #ffffff;--quarto-scss-export-list-group-active-border-color: #0d6efd;--quarto-scss-export-list-group-disabled-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-list-group-disabled-bg: #ffffff;--quarto-scss-export-list-group-action-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #212529;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #ffffff;--quarto-scss-export-thumbnail-border-color: #dededf;--quarto-scss-export-figure-caption-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #ffffff;--quarto-scss-export-carousel-indicator-active-bg: #ffffff;--quarto-scss-export-carousel-caption-color: #ffffff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #ffffff;--quarto-scss-export-offcanvas-color: #212529;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #ffffff;--quarto-scss-export-kbd-bg: #212529;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #517699;--quarto-scss-export-bslib-page-sidebar-title-color: #ffffff;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-mermaid-bg-color: #ffffff;--quarto-scss-export-mermaid-edge-color: #6c757d;--quarto-scss-export-mermaid-node-fg-color: #212529;--quarto-scss-export-mermaid-fg-color: #212529;--quarto-scss-export-mermaid-fg-color--lighter: #383f45;--quarto-scss-export-mermaid-fg-color--lightest: #4e5862;--quarto-scss-export-mermaid-label-bg-color: #ffffff;--quarto-scss-export-mermaid-label-fg-color: #0d6efd;--quarto-scss-export-mermaid-node-bg-color: rgba(13, 110, 253, 0.1);--quarto-scss-export-code-block-border-left-color: #dededf;--quarto-scss-export-callout-color-note: #0d6efd;--quarto-scss-export-callout-color-tip: #198754;--quarto-scss-export-callout-color-important: #dc3545;--quarto-scss-export-callout-color-caution: #fd7e14;--quarto-scss-export-callout-color-warning: #ffc107} \ No newline at end of file diff --git a/docs/src_stash/test1_files/libs/bootstrap/bootstrap-icons.css b/docs/src_stash/test1_files/libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/docs/src_stash/test1_files/libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + font-style: normal; + font-weight: normal !important; + font-variant: normal; + text-transform: none; + line-height: 1; + vertical-align: -.125em; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.bi-123::before { content: "\f67f"; } +.bi-alarm-fill::before { 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const 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hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return 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sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const 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W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get 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i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t 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b/docs/src_stash/test1_files/libs/quarto-html/quarto-syntax-highlighting-ea385d0e468b0dd5ea5bf0780b1290d9.css new file mode 100644 index 0000000..cca9c95 --- /dev/null +++ b/docs/src_stash/test1_files/libs/quarto-html/quarto-syntax-highlighting-ea385d0e468b0dd5ea5bf0780b1290d9.css @@ -0,0 +1,236 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +/* syntax highlight based on Pandoc's rules */ +pre > code.sourceCode > span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +/* Normal */ +code span { + color: #003B4F; +} + +/* Alert */ +code span.al { + color: #AD0000; + font-style: inherit; +} + +/* Annotation */ +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +/* Attribute */ +code span.at { + color: #657422; + font-style: inherit; +} + +/* BaseN */ +code span.bn { + color: #AD0000; + font-style: inherit; +} + +/* BuiltIn */ +code span.bu { + font-style: inherit; +} + +/* ControlFlow */ +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +/* Char */ +code span.ch { + color: #20794D; + font-style: inherit; +} + +/* Constant */ +code span.cn { + color: #8f5902; + font-style: inherit; +} + +/* Comment */ +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +/* CommentVar */ +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +/* Documentation */ +code span.do { + color: #5E5E5E; + font-style: italic; +} + +/* DataType */ +code span.dt { + color: #AD0000; + font-style: inherit; +} + +/* DecVal */ +code span.dv { + color: #AD0000; + font-style: inherit; +} + +/* Error */ +code span.er { + color: #AD0000; + font-style: inherit; +} + +/* Extension */ +code span.ex { + font-style: inherit; +} + +/* Float */ +code span.fl { + color: #AD0000; + font-style: inherit; +} + +/* Function */ +code span.fu { + color: #4758AB; + font-style: inherit; +} + +/* Import */ +code span.im { + color: #00769E; + font-style: inherit; +} + +/* Information */ +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +/* Keyword */ +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +/* Operator */ +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +/* Other */ +code span.ot { + color: #003B4F; + font-style: inherit; +} + +/* Preprocessor */ +code span.pp { + color: #AD0000; + font-style: inherit; +} + +/* SpecialChar */ +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +/* SpecialString */ +code span.ss { + color: #20794D; + font-style: inherit; +} + +/* String */ +code span.st { + color: #20794D; + font-style: inherit; +} + +/* Variable */ +code span.va { + color: #111111; + font-style: inherit; +} + +/* VerbatimString */ +code span.vs { + color: #20794D; + font-style: inherit; +} + +/* Warning */ +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // dispatch for htmlwidgets + // they use slideenter event to trigger resize + function fireSlideEnter() { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // dispatch for shiny + // they use BS shown and hidden events to trigger rendering + function distpatchShinyEvents(previous, current) { + if (window.jQuery) { + if (previous) { + window.jQuery(previous).trigger("hidden"); + } + if (current) { + window.jQuery(current).trigger("shown"); + } + } + } + + // tabby.js listener: Trigger event for htmlwidget and shiny + document.addEventListener( + "tabby", + function (event) { + fireSlideEnter(); + distpatchShinyEvents(event.detail.previousTab, event.detail.tab); + }, + false + ); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + const encodedItem = encodeURI(item); + if ( + encodedItem === currentPagePath || + encodedItem === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (const child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +tabsets.init(); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/docs/src_stash/test1_files/libs/quarto-html/tabsets/tabsets.js b/docs/src_stash/test1_files/libs/quarto-html/tabsets/tabsets.js new file mode 100644 index 0000000..51345d0 --- /dev/null +++ b/docs/src_stash/test1_files/libs/quarto-html/tabsets/tabsets.js @@ -0,0 +1,95 @@ +// grouped tabsets + +export function init() { + window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } + }); +} diff --git a/docs/src_stash/test1_files/libs/quarto-html/tippy.css b/docs/src_stash/test1_files/libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/docs/src_stash/test1_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center 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mode 100644 index 0000000..1da805e --- /dev/null +++ b/docs/src_stash/twutz_txt.bib @@ -0,0 +1,36 @@ +@Article{Wutzler20, + author = {Thomas Wutzler and Oscar Perez-Priego and Kendalynn Morris and Tarek S. El-Madany and Mirco Migliavacca}, + title = {Soil {CO}{\&}lt$\mathsemicolon$sub{\&}gt$\mathsemicolon$2{\&}lt$\mathsemicolon$/sub{\&}gt$\mathsemicolon$ efflux errors are lognormally distributed {\&}amp$\mathsemicolon${\#}8211$\mathsemicolon$ implications and guidance}, + journal = {Geoscientific Instrumentation, Methods and Data Systems}, + year = {2020}, + volume = {9}, + number = {1}, + pages = {239--254}, + month = {may}, + comment = {tomasch 19}, + doi = {10.5194/gi-9-239-2020}, + file = {:2007/stat/Wutzler20_lognormal_soilCO2efflux.pdf:PDF}, + keywords = {stat, tomasch, soil, respiration, chamber}, + owner = {twutz}, + publisher = {Copernicus {GmbH}}, + timestamp = {2020.06.02}, +} + +@Article{Wutzler22, + author = {Thomas Wutzler and Lin Yu and Marion Schrumpf and Sönke Zaehle}, + journal = {Geoscientific Model Development}, + title = {Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model ({SESAM}; v3.0)}, + year = {2022}, + month = {nov}, + number = {22}, + pages = {8377--8393}, + volume = {15}, + comment = {tomasch 24}, + doi = {10.5194/gmd-15-8377-2022}, + file = {:tomasch/Wutzler22_SESAM_upscaling.pdf:PDF}, + keywords = {model, soil, enzyme, SESAM, quasi-steady-state-assumption}, + owner = {twutz}, + publisher = {Copernicus {GmbH}}, + timestamp = {2022.11.18}, +} + From 2d7d7fe32343978e96a40924881cd84c1b4b1191 Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Thu, 21 Aug 2025 11:41:55 +0200 Subject: [PATCH 5/5] typos --- _typos.toml | 3 +++ .../figure-commonmark/cell-11-output-1.png | Bin 95024 -> 100472 bytes .../figure-commonmark/cell-12-output-1.png | Bin 123246 -> 120615 bytes .../figure-commonmark/cell-13-output-1.png | Bin 70608 -> 69824 bytes docs/src/tutorials/corr_site_global.md | 2 +- docs/src/tutorials/corr_site_global.qmd | 2 +- .../figure-commonmark/cell-10-output-1.png | Bin 108958 -> 103008 bytes 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Note the changed `n_input` specification compared to the -[Basic workflow without GPU](@ref) turorial. +[Basic workflow without GPU](@ref) tutorial. ``` julia n_out = length(prob.θM) # number of individuals to predict diff --git a/docs/src/tutorials/corr_site_global.qmd b/docs/src/tutorials/corr_site_global.qmd index 2c493e3..8d57973 100644 --- a/docs/src/tutorials/corr_site_global.qmd +++ b/docs/src/tutorials/corr_site_global.qmd @@ -76,7 +76,7 @@ pbm_covars = (:K2,) The ML model needs to be adapted to consume more inputs than the site covariates. Note the changed `n_input` specification compared to the -[Basic workflow without GPU](@ref) turorial. +[Basic workflow without GPU](@ref) tutorial. ```{julia} n_out = length(prob.θM) # number of individuals to predict diff --git a/docs/src/tutorials/corr_site_global_files/figure-commonmark/cell-10-output-1.png b/docs/src/tutorials/corr_site_global_files/figure-commonmark/cell-10-output-1.png index 92bb0ad7d3df01a03cc9cbe49dd0023a6dfaec97..9aa44b3f339ac8aceaa5f0280e22091ca2537d1a 100644 GIT binary patch literal 103008 zcmbrl1yI%D7cGjSgmg)Rv~+h#H%LiHcXx}R(p}OeAl;yZbT|i-*@o;zxU32 zGw;sa>kPwi&M&^$d+oK?`V4uiD24KZ-~|i}42q1jxC#u+({C^^PyQmnfls``7dgPM zC(bHTqA+E{L|fpGXXYXbA}}zOQAoFj&%xiXjHOi+z@^kMFrWNkV6MSOpEhA&Tv=dX zwhdrl_>y2?a2?WqDGPuv5FMnooMB*4JE1>M;^|Qd!Tm5Y;v#AuPj=hj4Za48h7htBX5)3VZbp zfh{1~|Cuy`NwgqS;``q%lNY`p%Pr9n&|z{Lw`b$owrR=%ByQMSqatKz|{S z%|wSjeuHi+E%EqGFxPkT$8W?5RWKee&PFMG{03n(Q0+gD5&J)TOmN)sav1aR$#3Df zw6t*OdpoE1TR+2|ZjSolvzv`*(=C6unX8NWcyo?HL=?{BgBk>z4+)vJkxWY`ARstA 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tutorial leads you through quering relevant information from the +This tutorial leads you through querying relevant information from the inversion results and to produce some typical plots. First load necessary packages. diff --git a/docs/src/tutorials/inspect_results.qmd b/docs/src/tutorials/inspect_results.qmd index f02a2e0..c10bc6c 100644 --- a/docs/src/tutorials/inspect_results.qmd +++ b/docs/src/tutorials/inspect_results.qmd @@ -16,7 +16,7 @@ bibliography: twutz_txt.bib CurrentModule = HybridVariationalInference ``` -This tutorial leads you through quering relevant information from the +This tutorial leads you through extracting relevant information from the inversion results and to produce some typical plots. First load necessary packages. @@ -136,7 +136,7 @@ In addition to the uncertainty in parameters, we are also interested in the uncertainty of predictions, i.e. the predictive posterior. We cam either run the PBM for all the parameter samples that we obtained already, -using the [`AbstractModelApplicator`](@ref), or use [`predict_hvi`](@ref) which combines +using the [`AbstractPBMApplicator`](@ref), or use [`predict_hvi`](@ref) which combines sampling the posterior and predictive posterior and returns the additional `NamedTuple` entry `y`. diff --git a/docs/src/tutorials/lux_gpu.md b/docs/src/tutorials/lux_gpu.md index f7f8829..ade618c 100644 --- a/docs/src/tutorials/lux_gpu.md +++ b/docs/src/tutorials/lux_gpu.md @@ -11,7 +11,7 @@ are executed on GPU. ## Motivation -Machine learning is often accellerated by moving computations form CPU +Machine learning is often accelerated by moving computations form CPU to GPU. So does HVI. First load necessary packages. @@ -44,8 +44,8 @@ prob = probo_chain = load(fname, "probo"); Because the SimpleChains ML model used in the basic tutorial does not support GPU, we reconstruct the model using the LUX framework. -Note that all the setup is almost the same, as in the basic worklfow. The -only diffrence is that a `Lux.Chains` object is provided to `construct_ChainsApplicator`. +Note that all the setup is almost the same, as in the basic workflow. The +only difference is that a `Lux.Chains` object is provided to `construct_ChainsApplicator`. ``` julia n_out = length(prob.θM) # number of individuals to predict @@ -82,7 +82,7 @@ They specify functions that are applied to move callables and data to GPU. They default to `identity`, meaning that nothing is moved from CPU to GPU. Function `gpu_device()` from package `MLDataDevices` can be used instead -for teh standard GPU device. +for the standard GPU device. Hence specify - `gdevs = (; gdev_M=gpu_device(), gdev_P=gpu_device())`: to move both ML model and PBM to GPU diff --git a/docs/src/tutorials/lux_gpu.qmd b/docs/src/tutorials/lux_gpu.qmd index 5c643e8..6072d20 100644 --- a/docs/src/tutorials/lux_gpu.qmd +++ b/docs/src/tutorials/lux_gpu.qmd @@ -22,7 +22,7 @@ are executed on GPU. ## Motivation -Machine learning is often accellerated by moving computations form CPU +Machine learning is often accelerated by moving computations form CPU to GPU. So does HVI. @@ -55,8 +55,8 @@ prob = probo_chain = load(fname, "probo"); Because the SimpleChains ML model used in the basic tutorial does not support GPU, we reconstruct the model using the LUX framework. -Note that all the setup is almost the same, as in the basic worklfow. The -only diffrence is that a `Lux.Chains` object is provided to `construct_ChainsApplicator`. +Note that all the setup is almost the same, as in the basic workflow. The +only difference is that a `Lux.Chains` object is provided to `construct_ChainsApplicator`. ```{julia} n_out = length(prob.θM) # number of individuals to predict @@ -92,7 +92,7 @@ They specify functions that are applied to move callables and data to GPU. They default to `identity`, meaning that nothing is moved from CPU to GPU. Function `gpu_device()` from package `MLDataDevices` can be used instead -for teh standard GPU device. +for the standard GPU device. Hence specify - `gdevs = (; gdev_M=gpu_device(), gdev_P=gpu_device())`: to move both ML model and PBM to GPU diff --git a/src/PBMApplicator.jl b/src/PBMApplicator.jl index f77606a..59abeb5 100644 --- a/src/PBMApplicator.jl +++ b/src/PBMApplicator.jl @@ -7,7 +7,7 @@ It returns a matrix of predictions sites in columns. Specific implementations need to provide function `apply_model(app, θP, θMs, xP)`. where - `θsP` and `θsMs` are shaped according to the output of `generate_ζ`, i.e. -`(n_site_pred x n_par x n_MC)`. + `(n_site_pred x n_par x n_MC)`. - Results are of shape `(n_obs x n_site_pred x n_MC)`. They may also provide function `apply_model(app, θP, θMs, xP)` for a sample

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+ +
+ + + + +
+

A julia engine notebook

+
2 + 2
+
4
+

A code block with #| echo: false.

+
Status `/Net/Groups/BGI/people/twutz/julia/dev/HybridVariationalInference/docs/src_stash/Project.toml`
+  [91a5bcdd] Plots v1.40.19
+
run(`which julia`);
+
/Net/Groups/Services/HPC_22/apps/julia/julia-1.11.4/bin/julia
+
+

Equation

+

inline: \(\sqrt{\pi}\) inside text.

+

display equation Equation 1 with a number and a reference

+

\[ +\begin{aligned} +\frac{n!}{k!(n - k)!} &= \binom{n}{k} +\\ +x^2 =& 3 +\end{aligned} +\qquad(1)\]

+
+
+

Literature references

+

See (Wutzler et al. 2020).

+
+
+

Plots

+

single

+
plot(1:4)
+

+
+
+

Not working with Documenter

+

Julia code is not colored, output block-code

+

Figures: The figure captions are not kept, and links do not work

+

References: the number is correctly inserted, but the link does not work

+

Documenter admonitions: the four spaces before the contents are removed - do not work

+

Quarto-Columns are rendered to several paragraphs

+

With gfm, references are formatted bad, and there are errors with unclosed divs after Documenter processes it.

+
+
+

References

+

Wutzler, Thomas, Oscar Perez-Priego, Kendalynn Morris, Tarek S. El-Madany, and Mirco Migliavacca. 2020. “Soil CO&lt\(\mathsemicolon\)sub&gt$\(2&lt\)\(/Sub&gt\)$ Efflux Errors Are Lognormally Distributed &Amp\(\mathsemicolon\)#8211\(\mathsemicolon\) Implications and Guidance.” Geoscientific Instrumentation, Methods and Data Systems 9 (1): 239–54. https://doi.org/10.5194/gi-9-239-2020.

+
+
+ +
+ + +

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z)M3@UA=L~{J#R5ZMzbjU$n>c1=v2Woy5Wb0`A^25sLc?msF!MJyT-ixV-uwCs;?m( z`S-vB$h5X#rP_7to~P$=8SCNR)W^HNOG5c;vL&gMVKEh(1b&L2rA>mi{>xjSq)kq? z;Aq#FYAv-yTZKM?)8m#cxHpvLZx4M6>GP$%~oI&>}FaJmamIAJv# zIdnUD=1k@8k6Y6`)POUx^Q9HPYQ`TFqL~=OcQ^i`c@A{A7<|CeC2IBR&tSJ5`tBI= z>Nm>CJv2A9?5s)9D!4jPhD=`93QN4l4-ol8amKM|*RiAP;~9M9d3yR%$Mjv>raf|O z$=i`B&$t6Op*IieBZsb&5ssmu3!j&+lF61v0!7KSzyz!39vQ2H@MwTr)s*2IY`Q#6GrCrUx4 z>+Hh`TWZPY=*^41NqS@?Pjfw@Iv@g6kte}uNIpT3I1!Sc8COL_14&U`OmI}#+m`%w zt$3rBbI7=HV>NvTJGeKquyi}Xq2FJJPnSOc>vEG8FJ7$8vw5I8%!D{uX z2F0Hy`+G0a|1pGKY4L^e9~hiA{95s^43k& fit.(LogNormal, θall, QuantilePoint.(θall .* 3, 0.95))) -``` - - -### Correlation structure -HVI models the posterior of the parameters at unconstrained scale using a -multivariate normal distribution. It estimates a parameterization of the -associated blocks in the correlation matrx and requires a specification -of the block-structure. - -This is done by specifying the positions of the end of the blocks for -the global (P) and the site-specific parameters (M) respectively using -a `NamedTuple` of integer vectors. - -```{julia} -cor_ends = (P=[length(θP)], M=[length(θM)]) -``` - -Here, we specify a single entry each, meaning, there is only one big -block respectively, spanning all parameters. - -### Further parameters for the posterior approximation. -HVI uses additional fitted parameters to represent the means and the -covariance matrix of the posterior distribution of model parameters. -Here, we obtain construct initial estimates. using [`init_hybrid_ϕunc`](@ref) - -```{julia} -#| output: true -ϕunc0 = init_hybrid_ϕunc(cor_ends, zero(FT)) -``` - -The returned `ComponentVector` has entries for -- `logσ2_ζP`: log of the estimated marginal variance of global parameters -- `coef_logσ2_ζMs`: coefficients of a linear model of log of - the estimated marginal variance of site-dependent parameters dependent on - the predicted parameter -- `ρsP`, `ρsM`: parameterization of the blocks in the correlation matrix for - global and site-specific parameters respectively. - -## Observations, model drivers and covariates - -The model is inverted using the -- observed data, `y_o` -- its uncertainty, `y_unc` -- known covariates across sites, `xM` -- model drivers, `xP` -Here, we use synthetic data generated by the package. - -```{julia} -rng = StableRNG(111) -scenario = Val((:omit_r0, :covarK2, )) -(; xM, xP, y_o, y_unc) = gen_hybridproblem_synthetic(rng, DoubleMM.DoubleMMCase(); scenario) -``` - -```{julia} -#| echo: false -#| eval: false -() -> begin - (; xM, θP_true, θMs_true, xP, y_global_true, y_true, y_global_o, y_o, y_unc) = - gen_hybridproblem_synthetic(rng, DoubleMM.DoubleMMCase(); scenario) -end -``` - -Lets look at them. -```{julia} -#| output: true -size(xM), size(xP), size(y_o), size(y_unc) -``` -All of them have 800 columns, corresponding to 800 sites. -There are 5 site-covaraitas and 16 values of model drivers, and 8 observations per site. - -```{julia} -#| output: true -xP[:,1] -``` -In each column of the model drivers there is a ComponentVector with -components S1 and S2 corresponding to the concentrations, for which outputs -were observed. - -The `y_unc` becomes its meaning by the Likelihood-function to be specified with -the problem below. - -### Providing data in Batches - -HVI uses `MLUtils.DataLoader` to provide baches of the data during each -iteration of the solver. In addition to the data, it provides an -index to the sites inside a tuple. - -```{julia} -n_site = size(y_o,2) -n_batch = 20 -train_dataloader = MLUtils.DataLoader( - (xM, xP, y_o, y_unc, 1:n_site), batchsize=n_batch, partial=false) -``` - -## The Machine-Learning model - -The machine-learning (ML) part predicts parameters of the posterior of site-specific -PBM parameters, given the covariates. -Here, we specify a 3-layer feed-forward neural network using the `SimpleChains` -framework which works efficiently on CPU. - -```{julia} -n_out = length(θM) # number of individuals to predict -pbm_covars = (:K2,) # global parameters used as input to ML-model -n_covar = size(xM,1) -n_input = n_covar + length(pbm_covars) - -g_chain = SimpleChain( - static(n_input), # input dimension (optional) - TurboDense{true}(tanh, n_input * 4), - TurboDense{true}(tanh, n_input * 4), - # dense layer without bias that maps to n outputs to (0..1) - TurboDense{false}(logistic, n_out) -) -# get a template of the parameter vector, ϕg0 -g_chain_app, ϕg0 = construct_ChainsApplicator(rng, g_chain) -``` - -The `g_chain_app` `ChainsApplicator` precits the parameters of the posterior, -given a vector of ML weights,`ϕg`. -During construction, an initial template of this vector is created. -This abstraction layer allows to use different ML frameworks and replace the -`SimpleChains` model by `Flux` or `Lux`. - -### Conditional independence of global and site parameters -The `pbm_covars` specifies which sampled global parameters should be -provided as inputs to the ML model. - -The reason for providing global parameters as inputs is to allow for -correlations among site-parameters and global parameters, despite -the required conditional independence of parameters. - -### Using priors to scale ML-parameter estimates - -In order to balance gradients, the `g_chain_app` ModelApplicator defined above -predicts on a scale (0..1). -Now the priors are used to translate this to the parameter range by using the -cumulative density distribution. - -However, HVI needs a prior on unconstrained scale, and we need the priors on -this unconstrained scale. -This can be mathematically worked out for specific prior distribution forms. -But for simplicity, here a [`NormalScalingModelApplicator`](@ref) -is fitted to the transformed 5% and 95% quantiles of the original prior. - -```{julia} -priorsM = [priors_dict[k] for k in keys(θM)] -lowers, uppers = get_quantile_transformed(priorsM, transM) -g_chain_scaled = NormalScalingModelApplicator(g_chain_app, lowers, uppers, FT) -``` - -The `g_chain_scaled` `ModelApplicator` now predicts in unconstrained scale -with transforms ML predctions around 0.5 to the range of -high prior probability of the parameters, -and transforms ML predictions near 0 or 1 towards the outer lower probability ranges. - - -## Assembling the information - -All the specifications above are stored in a [`HybridProblem`](@ref) structure. - -Before, a [`PBMSiteApplicator`](@ref) is constructed that translates an invocation -given a vector of global parameters, and a matrix of site parameters to -invocation of the process based model (PBM), defined at the beginning. - -```{julia} -f_batch = f_allsites = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1]) - -prob = HybridProblem(θP, θM, g_chain_scaled, ϕg0, ϕunc0, - f_batch, f_allsites, priors_dict, py, - transM, transP, train_dataloader, n_covar, n_site, n_batch, - cor_ends, pbm_covars) -``` - -```{julia} -#| eval: false -#| echo: false - -# test invoking -#θMs = stack(Iterators.repeated(θM, n_batch); dims=1) -θMs = θM' .+ (randn(n_batch, size(θM,1)) .* 0.05) -x_batch = xP[:,1:n_batch] -y1 = f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch))[2] - - -() -> begin - y1 - y_o[:,1:n_batch] # check size and roughly equal - #using Test - #@inferred f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch))[2] - @inferred Vector{Float64} f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch))[2] - #using Cthulhu - #@descend_code_warntype f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch)) - prob0 = HVI.DoubleMM.DoubleMMCase() - f_batch0 = get_hybridproblem_PBmodel(prob0; scenario, use_all_sites = false) - y1f = f_batch0(θP, θMs, x_batch)[2] - y1 .- y1f # equal -end -``` - -## Perform the inversion - -Eventually, having assembled all the moving parts of the HVI, we can perform -the inversion. - -```{julia} -using OptimizationOptimisers -import Zygote - -solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) - -(; probo, interpreters) = solve(prob, solver; scenario, rng, - callback = callback_loss(100), # output during fitting - epochs = 2, - gdev = identity, # do not use GPU, here -); -``` - -The solver object is constructed given the specific stochastic optimization algorithm -and the number of Monte-Carlo samples that are drawn in each iteration -from the predicted parameter posterior. - -Then the solver is applied to the problem using [`solve`](@ref) -for a given number of iterations or epochs. -For this tutorial, we additionally specify that the function to transfer structures to -the GPU is the identity function, so that all stays on the CPU, and this tutorial -hence does not require ad GPU or GPU livraries. - -Among the return values are -- `probo`: A copy of the HybridProblem, with updated optimized parameters -- `interpreters`: A `NamedTuple` with several `ComponentArrayInterpreter`s that - will help analyzing the results. - -## Using a population-level process-based model - -So far, we have specified the process-based model to run for a single site. -For this simple model, we can gain some performance from matrix-computations -during running the model for all sites. - -We specify the PBM now to accept a matrices as arguments for parameters and drivers -and returns a matrix of precitions. For the parameters, one row corresponds to -one site. For the drivers and predictions, one column corresponds to one site. - - -```{julia} -function f_doubleMM_sites(θc::CA.ComponentMatrix, xPc::CA.ComponentMatrix) - # extract several covariates from xP - ST = typeof(CA.getdata(xPc)[1:1,:]) # workaround for non-type-stable Symbol-indexing - S1 = (CA.getdata(xPc[:S1,:])::ST) - S2 = (CA.getdata(xPc[:S2,:])::ST) - # - # extract the parameters as row-repeated vectors - n_obs = size(S1, 1) - VT = typeof(CA.getdata(θc)[:,1]) # workaround for non-type-stable Symbol-indexing - (r0, r1, K1, K2) = map((:r0, :r1, :K1, :K2)) do par - p1 = CA.getdata(θc[:, par]) ::VT - repeat(p1', n_obs) # matrix: same for each concentration row in S1 - end - # - # each variable is a matrix (n_obs x n_site) - r0 .+ r1 .* S1 ./ (K1 .+ S1) .* S2 ./ (K2 .+ S2) -end -``` - -Again, the function should not rely on the order of parameters, but use symbolic indexing -to extract the parameter vectors. For type stability of this symbolic indexing, -it uses a workaround to get the type of a single row. -Similarly, it uses type hints to index into the drivers, `xPc`, to extract -sub-matrices by symbols. Alternatively, here we it could rely on the structure and -ordering of the columns in `xPc`. - -We use the corresponding [`PBMPopulationApplicator`](@ref) -and the HVI Problem. - -```{julia} -f_batch = PBMPopulationApplicator(f_doubleMM_sites, n_batch; θP, θM, θFix, xPvec=xP[:,1]) -f_allsites = PBMPopulationApplicator(f_doubleMM_sites, n_site; θP, θM, θFix, xPvec=xP[:,1]) -probo_sites = HybridProblem(probo; f_batch, f_allsites) -``` - -For numerical efficiency, the number of sites within one batch is part of the -`PBMPopulationApplicator`. Hence, we have two different functions, one applied -to a batch of site, and another applied to all sites. - -As a test of the new applicator, we refine the results by running a few more -epochs of the optimization. - -```{julia} -(; probo) = solve(probo_sites, solver; scenario, rng, - callback = callback_loss(100), # output during fitting - epochs = 10, - gdev = identity, # do not use GPU, here - #is_inferred = Val(true), # activate type-checks -); -``` - -## Saving the results -Extracting useful information from the optimized HybridProblem is covered -in the following tutorial. XXLink - -To use it, we save the `probo` HybridProblem and the interpreters to a JLD2 file. - -```{julia} -using JLD2 -fname = "intermediate/basic_cpu_results.jld2" -mkpath("intermediate") -jldsave(fname, false, IOStream; probo, interpreters) -``` - -```{julia} -#| eval: false -#| echo: false -probo = load(fname, "probo"; iotype = IOStream); -``` \ No newline at end of file diff --git a/docs/src/tutorials/how_to_guides/corr_site_global.qmd b/docs/src/tutorials/how_to_guides/corr_site_global.qmd deleted file mode 100644 index fc5854c..0000000 --- a/docs/src/tutorials/how_to_guides/corr_site_global.qmd +++ /dev/null @@ -1,431 +0,0 @@ ---- -title: "How to account for correlations between site and global parameters" -engine: julia -execute: - echo: true - output: false - daemon: 3600 -format: - commonmark: - variant: -raw_html - wrap: preserve -bibliography: twutz_txt.bib ---- - -``` @meta -CurrentModule = HybridVariationalInference -``` - -First load necessary packages. -```{julia} -using HybridVariationalInference -using HybridVariationalInference: HybridVariationalInference as HVI -using ComponentArrays: ComponentArrays as CA -using Bijectors -using StableRNGs -using SimpleChains -using StatsFuns -using MLUtils -using DistributionFits -``` - -Next, specify many moving parts of the Hybrid variational inference (HVI) - -## The process-based model -The example process based model (PBM) predicts a double-monod constrained rate -for different substrate concentrations, `S1`, and `S2`. - -$$ -y = r_0+ r_1 \frac{S_1}{K_1 + S_1} \frac{S_2}{K_2 + S_2}$$ - -```{julia} -function f_doubleMM(θc::CA.ComponentVector{ET}, x) where ET - # extract parameters not depending on order, i.e whether they are in θP or θM - (r0, r1, K1, K2) = map((:r0, :r1, :K1, :K2)) do par - CA.getdata(θc[par])::ET - end - r0 .+ r1 .* x.S1 ./ (K1 .+ x.S1) .* x.S2 ./ (K2 .+ x.S2) -end -``` - -Its formulation is independent of which parameters are global, site-specific, -or fixed during the model inversion. -However, it cannot assume an ordering in the parameters, but needs to -access the components by its symbolic names in the provided `ComponentArray`. - -## Likelihood function - -HVI requires the evaluation of the likelihood of the predictions. -It corresponds to the cost of predictions given some observations. - -The user specifies a function of the negative log-Likehood -`neg_logden(obs, pred, uncertainty_parameters)`, -where all of the parameters are arrays with columns for sites. - -Here, we use the [`neg_logden_indep_normal`](@ref) function -that assumed observations to be distributed independently -normal around a true value. -The provided `y_unc` uncertainty parameters, here, corresponds to -`logσ2`, denoting the log of the variance parameter of the normal distribution. - -```{julia} -py = neg_logden_indep_normal -``` - -## Templates, transformations, and correlation structure of parameters -### Global and site-specific parameters -In this example, we will assign a fixed value to r0 parameter, treat -the K2 parameter as unknown but the same across sites, and predict -r1 and K1 for each site separately, based on covariates known at the sites. - -Here we provide initial values for them by using `ComponentVector`. - -```{julia} -FT = Float32 -θM0 = θM = CA.ComponentVector{FT}(r1=0.5, K1=0.2) # separately for each individual -θP0 = θP = CA.ComponentVector{FT}(K2=2.0) # population: same across individuals, -θFix = CA.ComponentVector{FT}(r0=0.3) # r0, i.e. not estimated -``` -### Parameter Transformations -HVI allows for transformations of parameters in an unconstrained space, -where the probability density is not strictly zero anywhere to the original -constrained space. - -Here, our model parameters are strictly positive, and we use the exponential function -to transform constrained estimates to the original scale. - -```{julia} -transP = Stacked(HVI.Exp()) -transM = Stacked(HVI.Exp(), HVI.Exp()) -``` - -Parameter transformations are specified using the `Bijectors` package. -Because, `Bijectors.elementwise(exp)`, has problems with automatic differentiation (AD) -on GPU, we use the non-exported [`Exp`]() wrapper inside `Bijectors.Stacked`. - -### Prior information on parameters at constrained scale - -HVI is an approximate bayesian analysis and combines prior information on -the parameters with the model and observed data. - -Here, we provide a wide prior by fitting a Lognormal distributions to -- the mean corresponding to the initial value provided above -- the 0.95-quantile 3 times the mean - -```{julia} -θall = vcat(θP, θM) -priors_dict = Dict{Symbol, Distribution}( - keys(θall) .=> fit.(LogNormal, θall, QuantilePoint.(θall .* 3, 0.95))) -``` - - -### Correlation structure -HVI models the posterior of the parameters at unconstrained scale using a -multivariate normal distribution. It estimates a parameterization of the -associated blocks in the correlation matrx and requires a specification -of the block-structure. - -This is done by specifying the positions of the end of the blocks for -the global (P) and the site-specific parameters (M) respectively using -a `NamedTuple` of integer vectors. - -```{julia} -cor_ends = (P=[length(θP)], M=[length(θM)]) -``` - -Here, we specify a single entry each, meaning, there is only one big -block respectively, spanning all parameters. - -### Further parameters for the posterior approximation. -HVI uses additional fitted parameters to represent the means and the -covariance matrix of the posterior distribution of model parameters. -Here, we obtain construct initial estimates. using [`init_hybrid_ϕunc`](@ref) - -```{julia} -#| output: true -ϕunc0 = init_hybrid_ϕunc(cor_ends, zero(FT)) -``` - -The returned `ComponentVector` has entries for -- `logσ2_ζP`: log of the estimated marginal variance of global parameters -- `coef_logσ2_ζMs`: coefficients of a linear model of log of - the estimated marginal variance of site-dependent parameters dependent on - the predicted parameter -- `ρsP`, `ρsM`: parameterization of the blocks in the correlation matrix for - global and site-specific parameters respectively. - -## Observations, model drivers and covariates - -The model is inverted using the -- observed data, `y_o` -- its uncertainty, `y_unc` -- known covariates across sites, `xM` -- model drivers, `xP` -Here, we use synthetic data generated by the package. - -```{julia} -rng = StableRNG(111) -scenario = Val((:omit_r0, :covarK2, )) -(; xM, xP, y_o, y_unc) = gen_hybridproblem_synthetic(rng, DoubleMM.DoubleMMCase(); scenario) -``` - -```{julia} -#| echo: false -#| eval: false -() -> begin - (; xM, θP_true, θMs_true, xP, y_global_true, y_true, y_global_o, y_o, y_unc) = - gen_hybridproblem_synthetic(rng, DoubleMM.DoubleMMCase(); scenario) -end -``` - -Lets look at them. -```{julia} -#| output: true -size(xM), size(xP), size(y_o), size(y_unc) -``` -All of them have 800 columns, corresponding to 800 sites. -There are 5 site-covaraitas and 16 values of model drivers, and 8 observations per site. - -```{julia} -#| output: true -xP[:,1] -``` -In each column of the model drivers there is a ComponentVector with -components S1 and S2 corresponding to the concentrations, for which outputs -were observed. - -The `y_unc` becomes its meaning by the Likelihood-function to be specified with -the problem below. - -### Providing data in Batches - -HVI uses `MLUtils.DataLoader` to provide baches of the data during each -iteration of the solver. In addition to the data, it provides an -index to the sites inside a tuple. - -```{julia} -n_site = size(y_o,2) -n_batch = 20 -train_dataloader = MLUtils.DataLoader( - (xM, xP, y_o, y_unc, 1:n_site), batchsize=n_batch, partial=false) -``` - -## The Machine-Learning model - -The machine-learning (ML) part predicts parameters of the posterior of site-specific -PBM parameters, given the covariates. -Here, we specify a 3-layer feed-forward neural network using the `SimpleChains` -framework which works efficiently on CPU. - -```{julia} -n_out = length(θM) # number of individuals to predict -pbm_covars = (:K2,) # global parameters used as input to ML-model -n_covar = size(xM,1) -n_input = n_covar + length(pbm_covars) - -g_chain = SimpleChain( - static(n_input), # input dimension (optional) - TurboDense{true}(tanh, n_input * 4), - TurboDense{true}(tanh, n_input * 4), - # dense layer without bias that maps to n outputs to (0..1) - TurboDense{false}(logistic, n_out) -) -# get a template of the parameter vector, ϕg0 -g_chain_app, ϕg0 = construct_ChainsApplicator(rng, g_chain) -``` - -The `g_chain_app` `ChainsApplicator` precits the parameters of the posterior, -given a vector of ML weights,`ϕg`. -During construction, an initial template of this vector is created. -This abstraction layer allows to use different ML frameworks and replace the -`SimpleChains` model by `Flux` or `Lux`. - -### Conditional independence of global and site parameters -The `pbm_covars` specifies which sampled global parameters should be -provided as inputs to the ML model. - -The reason for providing global parameters as inputs is to allow for -correlations among site-parameters and global parameters, despite -the required conditional independence of parameters. - -### Using priors to scale ML-parameter estimates - -In order to balance gradients, the `g_chain_app` ModelApplicator defined above -predicts on a scale (0..1). -Now the priors are used to translate this to the parameter range by using the -cumulative density distribution. - -However, HVI needs a prior on unconstrained scale, and we need the priors on -this unconstrained scale. -This can be mathematically worked out for specific prior distribution forms. -But for simplicity, here a [`NormalScalingModelApplicator`](@ref) -is fitted to the transformed 5% and 95% quantiles of the original prior. - -```{julia} -priorsM = [priors_dict[k] for k in keys(θM)] -lowers, uppers = get_quantile_transformed(priorsM, transM) -g_chain_scaled = NormalScalingModelApplicator(g_chain_app, lowers, uppers, FT) -``` - -The `g_chain_scaled` `ModelApplicator` now predicts in unconstrained scale -with transforms ML predctions around 0.5 to the range of -high prior probability of the parameters, -and transforms ML predictions near 0 or 1 towards the outer lower probability ranges. - - -## Assembling the information - -All the specifications above are stored in a [`HybridProblem`](@ref) structure. - -Before, a [`PBMSiteApplicator`](@ref) is constructed that translates an invocation -given a vector of global parameters, and a matrix of site parameters to -invocation of the process based model (PBM), defined at the beginning. - -```{julia} -f_batch = f_allsites = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1]) - -prob = HybridProblem(θP, θM, g_chain_scaled, ϕg0, ϕunc0, - f_batch, f_allsites, priors_dict, py, - transM, transP, train_dataloader, n_covar, n_site, n_batch, - cor_ends, pbm_covars) -``` - -```{julia} -#| eval: false -#| echo: false - -# test invoking -#θMs = stack(Iterators.repeated(θM, n_batch); dims=1) -θMs = θM' .+ (randn(n_batch, size(θM,1)) .* 0.05) -x_batch = xP[:,1:n_batch] -y1 = f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch))[2] - - -() -> begin - y1 - y_o[:,1:n_batch] # check size and roughly equal - #using Test - #@inferred f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch))[2] - @inferred Vector{Float64} f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch))[2] - #using Cthulhu - #@descend_code_warntype f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch)) - prob0 = HVI.DoubleMM.DoubleMMCase() - f_batch0 = get_hybridproblem_PBmodel(prob0; scenario, use_all_sites = false) - y1f = f_batch0(θP, θMs, x_batch)[2] - y1 .- y1f # equal -end -``` - -## Perform the inversion - -Eventually, having assembled all the moving parts of the HVI, we can perform -the inversion. - -```{julia} -using OptimizationOptimisers -import Zygote - -solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) - -(; probo, interpreters) = solve(prob, solver; scenario, rng, - callback = callback_loss(100), # output during fitting - epochs = 2, - gdev = identity, # do not use GPU, here -); -``` - -The solver object is constructed given the specific stochastic optimization algorithm -and the number of Monte-Carlo samples that are drawn in each iteration -from the predicted parameter posterior. - -Then the solver is applied to the problem using [`solve`](@ref) -for a given number of iterations or epochs. -For this tutorial, we additionally specify that the function to transfer structures to -the GPU is the identity function, so that all stays on the CPU, and this tutorial -hence does not require ad GPU or GPU livraries. - -Among the return values are -- `probo`: A copy of the HybridProblem, with updated optimized parameters -- `interpreters`: A `NamedTuple` with several `ComponentArrayInterpreter`s that - will help analyzing the results. - -## Using a population-level process-based model - -So far, we have specified the process-based model to run for a single site. -For this simple model, we can gain some performance from matrix-computations -during running the model for all sites. - -We specify the PBM now to accept a matrices as arguments for parameters and drivers -and returns a matrix of precitions. For the parameters, one row corresponds to -one site. For the drivers and predictions, one column corresponds to one site. - - -```{julia} -function f_doubleMM_sites(θc::CA.ComponentMatrix, xPc::CA.ComponentMatrix) - # extract several covariates from xP - ST = typeof(CA.getdata(xPc)[1:1,:]) # workaround for non-type-stable Symbol-indexing - S1 = (CA.getdata(xPc[:S1,:])::ST) - S2 = (CA.getdata(xPc[:S2,:])::ST) - # - # extract the parameters as row-repeated vectors - n_obs = size(S1, 1) - VT = typeof(CA.getdata(θc)[:,1]) # workaround for non-type-stable Symbol-indexing - (r0, r1, K1, K2) = map((:r0, :r1, :K1, :K2)) do par - p1 = CA.getdata(θc[:, par]) ::VT - repeat(p1', n_obs) # matrix: same for each concentration row in S1 - end - # - # each variable is a matrix (n_obs x n_site) - r0 .+ r1 .* S1 ./ (K1 .+ S1) .* S2 ./ (K2 .+ S2) -end -``` - -Again, the function should not rely on the order of parameters, but use symbolic indexing -to extract the parameter vectors. For type stability of this symbolic indexing, -it uses a workaround to get the type of a single row. -Similarly, it uses type hints to index into the drivers, `xPc`, to extract -sub-matrices by symbols. Alternatively, here we it could rely on the structure and -ordering of the columns in `xPc`. - -We use the corresponding [`PBMPopulationApplicator`](@ref) -and update the HVI Problem. - -```{julia} -f_batch = PBMPopulationApplicator(f_doubleMM_sites, n_batch; θP, θM, θFix, xPvec=xP[:,1]) -f_allsites = PBMPopulationApplicator(f_doubleMM_sites, n_site; θP, θM, θFix, xPvec=xP[:,1]) -probo_sites = HybridProblem(probo; f_batch, f_allsites) -``` - -For numerical efficiency, the number of sites within one batch is part of the -`PBMPopulationApplicator`. Hence, we have two different functions, one applied -to a batch of site, and another applied to all sites. - -As a test of the new applicator, we refine the results by running a few more -epochs of the optimization. - -```{julia} -(; probo) = solve(probo_sites, solver; scenario, rng, - callback = callback_loss(100), # output during fitting - epochs = 10, - gdev = identity, # do not use GPU, here - #is_inferred = Val(true), # activate type-checks -); -``` - -## Saving the results -Extracting useful information from the optimized HybridProblem is covered -in the following tutorial. XXLink - -To use it, we save the `probo` HybridProblem and the interpreters to a JLD2 file. - -```{julia} -using JLD2 -fname = "intermediate/basic_cpu_results.jld2" -mkpath("intermediate") -jldsave(fname, false, IOStream; probo, interpreters) -``` - -```{julia} -#| eval: false -#| echo: false -probo = load(fname, "probo"; iotype = IOStream); -``` \ No newline at end of file diff --git a/docs/src/tutorials/inspect_results.qmd b/docs/src/tutorials/inspect_results.qmd index a60ae24..bc72606 100644 --- a/docs/src/tutorials/inspect_results.qmd +++ b/docs/src/tutorials/inspect_results.qmd @@ -35,7 +35,7 @@ This tutorial uses the fitted object saved at the end of the ```{julia} fname = "intermediate/basic_cpu_results.jld2" print(abspath(fname)) -probo, interpreters = load(fname, "probo", "interpreters"); +probo = load(fname, "probo"); ``` ```{julia} From 36851f1287cfc1e7067755b4f887717e4449f3b8 Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Wed, 20 Aug 2025 21:40:50 +0200 Subject: [PATCH 2/5] move PBMApplicator to gdev_P int solve and predict_hvi replace stack in apply_process_model by mapreduce --- src/HybridSolver.jl | 8 +++++++- src/HybridVariationalInference.jl | 2 +- src/elbo.jl | 21 ++++++++++++++++++--- src/util_ca.jl | 8 ++++++-- 4 files changed, 32 insertions(+), 7 deletions(-) diff --git a/src/HybridSolver.jl b/src/HybridSolver.jl index dbf2b76..88a8aed 100644 --- a/src/HybridSolver.jl +++ b/src/HybridSolver.jl @@ -134,6 +134,12 @@ function CommonSolve.solve(prob::AbstractHybridProblem, solver::HybridPosteriorS train_loader_dev = train_loader end f = get_hybridproblem_PBmodel(prob; scenario, use_all_sites=false) + if gdevs.gdev_P isa MLDataDevices.AbstractGPUDevice + f_dev = fmap(gdevs.gdev_P, f) + else + f_dev = f + end + py = get_hybridproblem_neg_logden_obs(prob; scenario) priors_θP_mean, priors_θMs_mean = construct_priors_θ_mean( @@ -142,7 +148,7 @@ function CommonSolve.solve(prob::AbstractHybridProblem, solver::HybridPosteriorS y_global_o = Float32[] # TODO loss_elbo = get_loss_elbo( - g_dev, transP, transMs, f, py, y_global_o; + g_dev, transP, transMs, f_dev, py, y_global_o; solver.n_MC, solver.n_MC_cap, cor_ends, priors_θP_mean, priors_θMs_mean, cdev=infer_cdev(gdevs), pbm_covars, θP, int_unc, int_μP_ϕg_unc) # test loss function once diff --git a/src/HybridVariationalInference.jl b/src/HybridVariationalInference.jl index 7cf9d88..4cdfc44 100644 --- a/src/HybridVariationalInference.jl +++ b/src/HybridVariationalInference.jl @@ -84,7 +84,7 @@ include("gencovar.jl") export callback_loss include("util_opt.jl") -export cpu_ca +export cpu_ca, apply_preserve_axes include("util_ca.jl") export neg_logden_indep_normal, entropy_MvNormal diff --git a/src/elbo.jl b/src/elbo.jl index 74520e2..5fe63fa 100644 --- a/src/elbo.jl +++ b/src/elbo.jl @@ -172,7 +172,12 @@ function predict_hvi(rng, prob::AbstractHybridProblem; scenario=Val(()), is_predict_batch = (n_site_pred == n_batch) @assert size(xP, 2) == n_site_pred f = get_hybridproblem_PBmodel(prob; scenario, use_all_sites=!is_predict_batch) - y = apply_process_model(θsP, θsMs, f, xP) + if gdevs.gdev_P isa MLDataDevices.AbstractGPUDevice + f_dev = fmap(gdevs.gdev_P, f) + else + f_dev = f + end + y = apply_process_model(θsP, θsMs, f_dev, xP) (; y, θsP, θsMs, entropy_ζ) end @@ -317,10 +322,20 @@ Call a PBM applicator for a sample of parameters of each site, and stack results Results are of shape `(n_obs x n_site_pred x n_MC)`. """ function apply_process_model(θsP::AbstractMatrix, θsMs::AbstractArray{ET,3}, f, xP) where ET - y_pred = stack(map(eachcol(θsP), eachslice(θsMs, dims=3)) do θP, θMs + # stack does not work on GPU + # y_pred = stack(map(eachcol(θsP), eachslice(θsMs, dims=3)) do θP, θMs + # y_global, y_pred_i = f(θP, θMs, xP) + # y_pred_i + # end) + #Main.@infiltrate_main + # for type stability, apply f at first sample before mapreduce + P1, Pit = Iterators.peel(eachcol(θsP)); + Ms1, Msit = Iterators.peel(eachslice(θsMs, dims=3)); + y1 = f(P1, Ms1, xP)[2] + y_pred = mapreduce((a,b) -> cat(a,b;dims=3), Pit, Msit; init=y1) do θP, θMs y_global, y_pred_i = f(θP, θMs, xP) y_pred_i - end) + end end """ diff --git a/src/util_ca.jl b/src/util_ca.jl index 5336cd2..224ce87 100644 --- a/src/util_ca.jl +++ b/src/util_ca.jl @@ -3,13 +3,17 @@ Move ComponentArray form gpu to cpu. """ -#function cpu_ca end -# define in FluxExt function cpu_ca(ca::CA.ComponentArray) CA.ComponentArray(cpu_device()(CA.getdata(ca)), CA.getaxes(ca)) end +""" + apply_preserve_axes(f, ca::ComponentArray) +Apply callable `f(x)` to the data inside `ca`, assume that the result has +the same shape, and return a new `ComponentArray` with the same axes +as in `ca`. +""" function apply_preserve_axes(f, ca::CA.ComponentArray) CA.ComponentArray(f(CA.getdata(ca)), CA.getaxes(ca)) end From 599612893dbb5334f49552fe0cbf142bec317d6e Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Thu, 21 Aug 2025 11:21:51 +0200 Subject: [PATCH 3/5] move apply_process_model to PBMApplicator.apply_model keep stack in non-GPU call, because mapreduce is very slow (despite then its not compatible with Zygor) for GPUArrays use mapreduce, because stack results in scalar indexing error --- dev/doubleMM.jl | 2 +- src/HybridVariationalInference.jl | 2 +- src/PBMApplicator.jl | 64 +++++++++++++++++++++++++++---- src/elbo.jl | 51 ++++++++++++------------ test/test_HybridProblem.jl | 9 +++-- test/test_elbo.jl | 31 ++++++++------- 6 files changed, 107 insertions(+), 52 deletions(-) diff --git a/dev/doubleMM.jl b/dev/doubleMM.jl index 895ea13..b7849d3 100644 --- a/dev/doubleMM.jl +++ b/dev/doubleMM.jl @@ -572,7 +572,7 @@ f_allsites = get_hybridproblem_PBmodel(prob0; scenario, use_all_sites = true) trans_mP=StackedArray(transP, size(ζsP, 2)) trans_mMs=StackedArray(transM, size(ζsMs, 1) * size(ζsMs, 3)) θsP, θsMs = transform_ζs(ζsP, ζsMs; trans_mP, trans_mMs) -y = apply_process_model(θsP, θsMs, f, xP) +y = f(θsP, θsMs, f, xP) #(; y, θsP, θsMs) = HVI.apply_f_trans(ζsP, ζsMs, f_allsites, xP; transP, transM); (y_hmc, θsP_hmc, θsMs_hmc) = (; y, θsP, θsMs); diff --git a/src/HybridVariationalInference.jl b/src/HybridVariationalInference.jl index 4cdfc44..b982db5 100644 --- a/src/HybridVariationalInference.jl +++ b/src/HybridVariationalInference.jl @@ -93,7 +93,7 @@ include("logden_normal.jl") export get_ca_starts, get_ca_ends, get_cor_count include("cholesky.jl") -export neg_elbo_gtf, sample_posterior, apply_process_model, predict_hvi +export neg_elbo_gtf, sample_posterior, predict_hvi include("elbo.jl") export init_hybrid_params, init_hybrid_ϕunc diff --git a/src/PBMApplicator.jl b/src/PBMApplicator.jl index a040840..f77606a 100644 --- a/src/PBMApplicator.jl +++ b/src/PBMApplicator.jl @@ -1,25 +1,67 @@ """ - AbstractPBMApplicator(θP::AbstractVector, θMs::AbstractMatrix, xP::AbstractMatrix) - Abstraction of applying a process-based model with -global parameters, `x`, site-specific parameters, `θMs` (sites in columns), +global parameters, `θP`, site-specific parameters, `θMs` (sites in columns), and site-specific model drivers, `xP` (sites in columns), It returns a matrix of predictions sites in columns. -Specific implementations need to implement function `apply_model(app, θP, θMs, xP)`. +Specific implementations need to provide function `apply_model(app, θP, θMs, xP)`. +where +- `θsP` and `θsMs` are shaped according to the output of `generate_ζ`, i.e. +`(n_site_pred x n_par x n_MC)`. +- Results are of shape `(n_obs x n_site_pred x n_MC)`. + +They may also provide function `apply_model(app, θP, θMs, xP)` for a sample +of parameters, i.e. where an additional dimension is added to both `θP` and `θMs`. +However, there is a default implementation that mapreduces across these dimensions. + Provided are implementations -- `NullPBMApplicator`: returning its input `θMs` for testing - `PBMSiteApplicator`: based on a function that computes predictions per site - `PBMPopulationApplicator`: based on a function that computes predictions for entire population +- `NullPBMApplicator`: returning its input `θMs` for testing +- `PlainPBMApplicator`: based on a function that takes the same arguments as `apply_model` """ abstract type AbstractPBMApplicator end # function apply_model end # already defined in ModelApplicator.jl for ML model -function (app::AbstractPBMApplicator)(θP::AbstractVector, θMs::AbstractMatrix, xP::AbstractMatrix) +function (app::AbstractPBMApplicator)(θP::AbstractArray, θMs::AbstractArray, xP::AbstractMatrix) apply_model(app, θP, θMs, xP) end +""" + apply_model(app::AbstractPBMApplicator, θsP::AbstractVector, θsMs::AbstractMatrix, xP::AbstractMatrix) + apply_model(app::AbstractPBMApplicator, θsP::AbstractMatrix, θsMs::AbstractArray{ET,3}, xP) + +The first variant calls the PBM for one batch of sites. + +The second variant calls the PBM for a sample of batches, and stack results. +The default implementation mapreduces the last dimension of `θsP` and θ`sMs` calling the +first variant of `apply_model` for each sample. +""" +# docu in struct +function apply_model(app::AbstractPBMApplicator, θsP::AbstractMatrix, θsMs::AbstractArray{ET,3}, xP) where ET + # stack does not work on GPU, see specialized method for GPUArrays below + y_pred = stack( + map(eachcol(CA.getdata(θsP)), eachslice(CA.getdata(θsMs), dims=3)) do θP, θMs + y_global, y_pred_i = app(θP, θMs, xP) + y_pred_i + end) +end +function apply_model(app::AbstractPBMApplicator, θsP::GPUArraysCore.AbstractGPUMatrix, θsMs::GPUArraysCore.AbstractGPUArray{ET,3}, xP) where ET + # stack does not work on GPU, need to resort to slower mapreduce + # for type stability, apply f at first iterate to supply init to mapreduce + P1, Pit = Iterators.peel(eachcol(CA.getdata(θsP))); + Ms1, Msit = Iterators.peel(eachslice(CA.getdata(θsMs), dims=3)); + y1 = apply_model(app, P1, Ms1, xP)[2] + y1a = reshape(y1, size(y1)..., 1) # add one dimension + y_pred = mapreduce((a,b) -> cat(a,b; dims=3), Pit, Msit; init=y1a) do θP, θMs + y_global, y_pred_i = app(θP, θMs, xP) + y_pred_i + end +end + + + """ NullPBMApplicator() @@ -119,8 +161,8 @@ struct PBMPopulationApplicator{MFT, IPT, IT, IXT, F} <: AbstractPBMApplicator int_xP::IXT end -# let fmap not descend into isP -# @functor PBMPopulationApplicator (θFixm, ) +# let fmap not descend into isP, because indexing with isP on cpu is faster +@functor PBMPopulationApplicator (θFixm, ) """ PBMPopulationApplicator(fθpop, n_batch; θP, θM, θFix, xPvec) @@ -167,7 +209,13 @@ function apply_model(app::PBMPopulationApplicator, θP::AbstractVector, θMs::Ab "or compute PBM on CPU.") end # repeat θP and concatenate with + # Main.@infiltrate_main + # repeat is 2x slower for Vector and 100 times slower (with allocation) on GPU + # app.isP on CPU is slightly faster than app.isP on GPU + #@benchmark CA.getdata(θP[app.isP]) + #@benchmark CA.getdata(repeat(θP', size(θMs,1))) local θ = hcat(CA.getdata(θP[app.isP]), CA.getdata(θMs), app.θFixm) + #local θ = hcat(CA.getdata(repeat(θP', size(θMs,1))), CA.getdata(θMs), app.θFixm) local θc = app.intθ(CA.getdata(θ)) local xPc = app.int_xP(CA.getdata(xP)) local pred_sites = app.fθpop(θc, xPc) diff --git a/src/elbo.jl b/src/elbo.jl index 5fe63fa..2c26470 100644 --- a/src/elbo.jl +++ b/src/elbo.jl @@ -177,7 +177,8 @@ function predict_hvi(rng, prob::AbstractHybridProblem; scenario=Val(()), else f_dev = f end - y = apply_process_model(θsP, θsMs, f_dev, xP) + #y = apply_process_model(θsP, θsMs, f_dev, xP) + y = f_dev(θsP, θsMs, xP) (; y, θsP, θsMs, entropy_ζ) end @@ -312,31 +313,33 @@ end # (; y, θP, θMs) # end -""" - apply_process_model(θsP::AbstractMatrix, θsMs::AbstractArray{ET,3}, f, xP) +# """ +# apply_process_model(θsP::AbstractMatrix, θsMs::AbstractArray{ET,3}, f, xP) -Call a PBM applicator for a sample of parameters of each site, and stack results +# Call a PBM applicator for a sample of parameters of each site, and stack results -`θsP` and `θsMs` are shaped according to the output of `generate_ζ`, i.e. -`(n_site_pred x n_par x n_MC)`. -Results are of shape `(n_obs x n_site_pred x n_MC)`. -""" -function apply_process_model(θsP::AbstractMatrix, θsMs::AbstractArray{ET,3}, f, xP) where ET - # stack does not work on GPU - # y_pred = stack(map(eachcol(θsP), eachslice(θsMs, dims=3)) do θP, θMs - # y_global, y_pred_i = f(θP, θMs, xP) - # y_pred_i - # end) - #Main.@infiltrate_main - # for type stability, apply f at first sample before mapreduce - P1, Pit = Iterators.peel(eachcol(θsP)); - Ms1, Msit = Iterators.peel(eachslice(θsMs, dims=3)); - y1 = f(P1, Ms1, xP)[2] - y_pred = mapreduce((a,b) -> cat(a,b;dims=3), Pit, Msit; init=y1) do θP, θMs - y_global, y_pred_i = f(θP, θMs, xP) - y_pred_i - end -end +# `θsP` and `θsMs` are shaped according to the output of `generate_ζ`, i.e. +# `(n_site_pred x n_par x n_MC)`. +# Results are of shape `(n_obs x n_site_pred x n_MC)`. +# """ +# function apply_process_model(θsP::AbstractMatrix, θsMs::AbstractArray{ET,3}, f, xP) where ET +# error("deprecated, use f(θsP, θsMs, xP)") +# # stack does not work on GPU +# # y_pred = stack( +# # map(eachcol(CA.getdata(θsP)), eachslice(CA.getdata(θsMs), dims=3)) do θP, θMs +# # y_global, y_pred_i = f(θP, θMs, xP) +# # y_pred_i +# # end) +# # for type stability, apply f at first iterate to supply init to mapreduce +# P1, Pit = Iterators.peel(eachcol(CA.getdata(θsP))); +# Ms1, Msit = Iterators.peel(eachslice(CA.getdata(θsMs), dims=3)); +# y1 = f(P1, Ms1, xP)[2] +# y1a = reshape(y1, size(y1)..., 1) # add one dimension +# y_pred = mapreduce((a,b) -> cat(a,b; dims=3), Pit, Msit; init=y1a) do θP, θMs +# y_global, y_pred_i = f(θP, θMs, xP) +# y_pred_i +# end +# end """ Generate samples of (inv-transformed) model parameters, ζ, diff --git a/test/test_HybridProblem.jl b/test/test_HybridProblem.jl index 962b5e4..48f55aa 100644 --- a/test/test_HybridProblem.jl +++ b/test/test_HybridProblem.jl @@ -316,10 +316,11 @@ test_with_flux_gpu = (scenario) -> begin rng = StableRNG(111) probg = HybridProblem(DoubleMM.DoubleMMCase(); scenario = scenf); # put Applicator to gpu (θFix) - probg = HybridProblem( - probg, - f_batch = fmap(gdev, probg.f_batch), - f_allsites = fmap(gdev, probg.f_allsites)) + # moved to solve and predict_hvi + # probg = HybridProblem( + # probg, + # f_batch = fmap(gdev, probg.f_batch), + # f_allsites = fmap(gdev, probg.f_allsites)) #prob = CP.update(probg, transM = identity, transP = identity); solver = HybridPosteriorSolver(; alg=Adam(0.02), n_MC=3) n_site, n_batch = get_hybridproblem_n_site_and_batch(probg; scenario = scenf) diff --git a/test/test_elbo.jl b/test/test_elbo.jl index 7569292..7a894d3 100644 --- a/test/test_elbo.jl +++ b/test/test_elbo.jl @@ -18,9 +18,10 @@ using MLDataDevices # setup g as FluxNN on gpu using Flux +#CUDA.device!(4) + ggdev = gpu_device() -#CUDA.device!(4) rng = StableRNG(111) const prob = DoubleMM.DoubleMMCase() @@ -143,7 +144,7 @@ test_scenario = (scenario) -> begin _ϕ = vcat(ϕ_ini.μP, probc.ϕg, probd.ϕunc) #hcat(ϕ_ini, ϕ, _ϕ)[1:4,:] #hcat(ϕ_ini, ϕ, _ϕ)[(end-20):end,:] - n_predict = 80000 + n_predict = 8000 xM_batch = xM[:, 1:n_batch] _ζsP, _ζsMs, _σ = @inferred ( # @descend_code_warntype ( @@ -196,18 +197,18 @@ test_scenario = (scenario) -> begin reshape(residMst, size(residMst,1)*size(residMst,2), size(residMst,3))) cor_PMs = cor(residPMst') @test cor_PMs[1,2] ≈ ρsP_true[1] atol=0.02 - @test all(.≈(cor_PMs[1:2,3:end], 0.0, atol=0.02)) # no correlations P,M + @test all(.≈(cor_PMs[1:2,3:end], 0.0, atol=0.1)) # no correlations P,M @test cor_PMs[3,4] ≈ ρsM_true[1] atol=0.02 - @test all(.≈(cor_PMs[3:4,5:end], 0.0, atol=0.02)) # no correlations M1, M2 + @test all(.≈(cor_PMs[3:4,5:end], 0.0, atol=0.1)) # no correlations M1, M2 @test cor_PMs[5,6] ≈ ρsM_true[1] atol=0.02 - @test all(.≈(cor_PMs[5:6,7:end], 0.0, atol=0.02)) # no correlations M1, M2 + @test all(.≈(cor_PMs[5:6,7:end], 0.0, atol=0.1)) # no correlations M1, M2 end test_distζ(_ζsP, _ζsMs, ϕunc_true, ζMs_g) @testset "predict_hvi check sd" begin # test if uncertainty and reshaping is propagated # here inverse the predicted θs and then test distribution probcu = HybridProblem(probc, ϕunc=ϕunc_true); - n_sample_pred = 24_000 + n_sample_pred = 2_400 (; y, θsP, θsMs, entropy_ζ) = predict_hvi(rng, probcu; scenario, n_sample_pred); #size(_ζsMs), size(θsMs) #size(_ζsP), size(θsP) @@ -221,7 +222,7 @@ test_scenario = (scenario) -> begin test_distζ(_ζsP2, _ζsMs2, ϕunc_true, ζMs_g2) end; end; - end # if covar in scenario + end # if covarK2 in scenario if ggdev isa MLDataDevices.AbstractGPUDevice @testset "generate_ζ gpu $(last(CP._val_value(scenario)))" begin @@ -390,14 +391,13 @@ test_scenario = (scenario) -> begin θsPc = int_mP(θsP) @test all(θsPc[:r0, :] .> 0) # - y = apply_process_model(θsP, θsMs, f_pred, xP) + y = @inferred f_pred(θsP, θsMs, xP) @test y isa Array @test size(y) == (size(y_o)..., n_sample_pred) end if ggdev isa MLDataDevices.AbstractGPUDevice @testset "predict_hvi gpu $(last(CP._val_value(scenario)))" begin - n_sample_pred = 32 ϕ_ini_g = ggdev(CA.getdata(ϕ_ini)) xMg = ggdev(xM) n_sample_pred = 30 @@ -407,17 +407,20 @@ test_scenario = (scenario) -> begin sample_posterior(rng, g_gpu, ϕ_ini_g, xMg; int_μP_ϕg_unc, int_unc, transP, transM, - cdev = cpu_device(), + #cdev = cpu_device(), + cdev = identity, # do not transfer to CPU n_sample_pred, cor_ends, pbm_covar_indices) ) + # this variant without the problem, does not attach axes @test θsP isa AbstractMatrix @test θsMs isa AbstractArray{T,3} where {T} int_mP = ComponentArrayInterpreter(int_P, (size(θsP, 2),)) - θsPc = int_mP(θsP) - @test all(θsPc[:r0, :] .> 0) + @test all(int_mP(θsP)[:r0, :] .> 0) # - y = apply_process_model(θsP, θsMs, f_pred, xP) - @test y isa Array + xP_dev = ggdev(xP); + f_pred_dev = fmap(ggdev, f_pred) + y = @inferred f_pred_dev(θsP, θsMs, xP_dev) + @test y isa GPUArraysCore.AbstractGPUArray @test size(y) == (size(y_o)..., n_sample_pred) end # @testset "predict_hvi also f on gpu" begin From 730d1b10f4ecfa3832a9b72fa761aca2835efc9d Mon Sep 17 00:00:00 2001 From: Thomas Wutzler Date: Thu, 21 Aug 2025 11:24:30 +0200 Subject: [PATCH 4/5] tutorial on using gpu and LUX and setup quarto project --- docs/.gitignore | 1 + docs/_quarto.yml | 9 + docs/make.jl | 1 + docs/src/tutorials/Project.toml | 3 + docs/src/tutorials/basic_cpu.md | 2 +- docs/src/tutorials/basic_cpu.qmd 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+ ...p-0b8ec595872a4bdb2164ef456dcde0ad.min.css | 12 + .../libs/bootstrap/bootstrap-icons.css | 2078 +++++++++++++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 176200 bytes .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + ...hting-ea385d0e468b0dd5ea5bf0780b1290d9.css | 236 ++ .../test1_files/libs/quarto-html/quarto.js | 845 +++++++ .../libs/quarto-html/tabsets/tabsets.js | 95 + .../test1_files/libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + docs/src_stash/twutz_txt.bib | 36 + 38 files changed, 5434 insertions(+), 23 deletions(-) create mode 100644 docs/.gitignore create mode 100644 docs/_quarto.yml create mode 100644 docs/src/tutorials/lux_gpu.md create mode 100644 docs/src/tutorials/lux_gpu.qmd create mode 100644 docs/src_stash/Manifest.toml create mode 100644 docs/src_stash/Project.toml create mode 100644 docs/src_stash/test1.html create mode 100644 docs/src_stash/test1.md create mode 100644 docs/src_stash/test1_files/figure-commonmark/fig-limits-output-1.svg create mode 100644 docs/src_stash/test1_files/libs/bootstrap/bootstrap-0b8ec595872a4bdb2164ef456dcde0ad.min.css create mode 100644 docs/src_stash/test1_files/libs/bootstrap/bootstrap-icons.css create mode 100644 docs/src_stash/test1_files/libs/bootstrap/bootstrap-icons.woff create mode 100644 docs/src_stash/test1_files/libs/bootstrap/bootstrap.min.js create mode 100644 docs/src_stash/test1_files/libs/clipboard/clipboard.min.js create mode 100644 docs/src_stash/test1_files/libs/quarto-html/anchor.min.js create mode 100644 docs/src_stash/test1_files/libs/quarto-html/popper.min.js create mode 100644 docs/src_stash/test1_files/libs/quarto-html/quarto-syntax-highlighting-ea385d0e468b0dd5ea5bf0780b1290d9.css create mode 100644 docs/src_stash/test1_files/libs/quarto-html/quarto.js create mode 100644 docs/src_stash/test1_files/libs/quarto-html/tabsets/tabsets.js create mode 100644 docs/src_stash/test1_files/libs/quarto-html/tippy.css create mode 100644 docs/src_stash/test1_files/libs/quarto-html/tippy.umd.min.js create mode 100644 docs/src_stash/twutz_txt.bib diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 0000000..075b254 --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/docs/_quarto.yml b/docs/_quarto.yml new file mode 100644 index 0000000..4fff7ac --- /dev/null +++ b/docs/_quarto.yml @@ -0,0 +1,9 @@ +project: + title: "HybridVariationInference documentation" + render: + - src/tutorials/basic_cpu.qmd + - src/tutorials/*.qmd + + + + diff --git a/docs/make.jl b/docs/make.jl index f82f26c..a2198bb 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -25,6 +25,7 @@ makedocs(; "How to" => [ ".. model independent parameters" => "tutorials/blocks_corr.md", ".. model site-global corr" => "tutorials/corr_site_global.md", + ".. use GPU" => "tutorials/lux_gpu.md", ], "Explanation" => [ #"Theory" => "explanation/theory_hvi.md", TODO activate when paper is published diff --git a/docs/src/tutorials/Project.toml b/docs/src/tutorials/Project.toml index e828ad1..6a3bf83 100644 --- a/docs/src/tutorials/Project.toml +++ b/docs/src/tutorials/Project.toml @@ -1,11 +1,13 @@ [deps] Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" +CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2" ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66" DistributionFits = "45214091-1ed4-4409-9bcf-fdb48a05e921" HybridVariationalInference = "a108c475-a4e2-4021-9a84-cfa7df242f64" JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" +Lux = "b2108857-7c20-44ae-9111-449ecde12c47" MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54" OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1" PairPlots = "43a3c2be-4208-490b-832a-a21dcd55d7da" @@ -14,3 +16,4 @@ SimpleChains = "de6bee2f-e2f4-4ec7-b6ed-219cc6f6e9e5" StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3" StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" +cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" diff --git a/docs/src/tutorials/basic_cpu.md b/docs/src/tutorials/basic_cpu.md index 182c27e..5a50ff7 100644 --- a/docs/src/tutorials/basic_cpu.md +++ b/docs/src/tutorials/basic_cpu.md @@ -327,7 +327,7 @@ epochs of the optimization. ``` julia (; probo) = solve(probo_sites, solver; rng, callback = callback_loss(100), # output during fitting - epochs = 10, + epochs = 20, #is_inferred = Val(true), # activate type-checks ); ``` diff --git a/docs/src/tutorials/basic_cpu.qmd b/docs/src/tutorials/basic_cpu.qmd index d4ebf67..89a77ce 100644 --- a/docs/src/tutorials/basic_cpu.qmd +++ b/docs/src/tutorials/basic_cpu.qmd @@ -7,7 +7,7 @@ execute: daemon: 3600 format: commonmark: - variant: -raw_html + variant: -raw_html+tex_math_dollars wrap: preserve bibliography: twutz_txt.bib --- diff --git 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