Merge pull request #249 from SciML/format

format SciML style

.JuliaFormatter.toml

@@ -0,0 +1 @@
+style = "sciml"

.github/workflows/FormatCheck.yml

@@ -0,0 +1,42 @@
+name: format-check
+
+on:
+  push:
+    branches:
+      - 'master'
+      - 'release-'
+    tags: '*'
+  pull_request:
+
+jobs:
+  build:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        julia-version: [1]
+        julia-arch: [x86]
+        os: [ubuntu-latest]
+    steps:
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: ${{ matrix.julia-version }}
+
+      - uses: actions/checkout@v1
+      - name: Install JuliaFormatter and format
+        # This will use the latest version by default but you can set the version like so:
+        #
+        # julia  -e 'using Pkg; Pkg.add(PackageSpec(name="JuliaFormatter", version="0.13.0"))'
+        run: |
+          julia  -e 'using Pkg; Pkg.add(PackageSpec(name="JuliaFormatter"))'
+          julia  -e 'using JuliaFormatter; format(".", verbose=true)'
+      - name: Format check
+        run: |
+          julia -e '
+          out = Cmd(`git diff --name-only`) |> read |> String
+          if out == ""
+              exit(0)
+          else
+              @error "Some files have not been formatted !!!"
+              write(stdout, out)
+              exit(1)
+          end'

docs/make.jl

@@ -2,20 +2,14 @@ using Documenter, DiffEqBayes
 
 include("pages.jl")
 
-makedocs(
-    sitename="DiffEqBayes.jl",
-    authors="Chris Rackauckas, Vaibhav Kumar Dixit et al.",
-    clean=true,
-    doctest=false,
-    modules=[DiffEqBayes],
-
-    format=Documenter.HTML(assets=["assets/favicon.ico"],
-                           canonical="https://diffeqbayes.sciml.ai/stable/"),
-
-    pages=pages
-)
-
-deploydocs(
-    repo="github.com/SciML/DiffEqBayes.jl.git";
-    push_preview=true
-)
+makedocs(sitename = "DiffEqBayes.jl",
+         authors = "Chris Rackauckas, Vaibhav Kumar Dixit et al.",
+         clean = true,
+         doctest = false,
+         modules = [DiffEqBayes],
+         format = Documenter.HTML(assets = ["assets/favicon.ico"],
+                                  canonical = "https://diffeqbayes.sciml.ai/stable/"),
+         pages = pages)
+
+deploydocs(repo = "github.com/SciML/DiffEqBayes.jl.git";
+           push_preview = true)

docs/pages.jl

@@ -1,5 +1,5 @@
 pages = [
-        "DiffEqBayes.jl: Bayesian Parameter Estimation for Differential Equations" => "index.md",
-        "Methods" => "methods.md",
-        "Examples" => "examples.md"
-    ]
+    "DiffEqBayes.jl: Bayesian Parameter Estimation for Differential Equations" => "index.md",
+    "Methods" => "methods.md",
+    "Examples" => "examples.md",
+]

src/DiffEqBayes.jl

@@ -9,8 +9,10 @@ using RecursiveArrayTools, ModelingToolkit, LinearAlgebra
 using Parameters, Distributions, Optim, Requires
 using Distances, DocStringExtensions, Random, StanSample
 
-STANDARD_PROB_GENERATOR(prob,p) = remake(prob;u0=eltype(p).(prob.u0),p=p)
-STANDARD_PROB_GENERATOR(prob::EnsembleProblem,p) = EnsembleProblem(remake(prob.prob;u0=eltype(p).(prob.prob.u0),p=p))
+STANDARD_PROB_GENERATOR(prob, p) = remake(prob; u0 = eltype(p).(prob.u0), p = p)
+function STANDARD_PROB_GENERATOR(prob::EnsembleProblem, p)
+    EnsembleProblem(remake(prob.prob; u0 = eltype(p).(prob.prob.u0), p = p))
+end
 
 include("turing_inference.jl")
 # include("abc_inference.jl")
@@ -25,5 +27,5 @@ function __init__()
     end
 end
 
-export turing_inference, stan_inference ,abc_inference
+export turing_inference, stan_inference, abc_inference
 end # module

src/abc_inference.jl

@@ -1,37 +1,42 @@
-function createabcfunction(prob, t, distancefunction, alg; save_idxs = nothing, sample_u0 = false, kwargs...)
+function createabcfunction(prob, t, distancefunction, alg; save_idxs = nothing,
+                           sample_u0 = false, kwargs...)
     function simfunc(params, constants, data)
         local u0
         if sample_u0
-            u0 = save_idxs === nothing ? params[1:length(prob.u0)] : params[1:length(save_idxs)]
+            u0 = save_idxs === nothing ? params[1:length(prob.u0)] :
+                 params[1:length(save_idxs)]
             if length(u0) < length(prob.u0)
                 for i in length(u0):length(prob.u0)
-                    push!(u0,prob.u0[i])
+                    push!(u0, prob.u0[i])
                 end
             end
         else
             u0 = prob.u0
         end
-        sol = solve(prob, alg, u0=u0, p=params, saveat = t, save_idxs = save_idxs, kwargs...)
+        sol = solve(prob, alg, u0 = u0, p = params, saveat = t, save_idxs = save_idxs,
+                    kwargs...)
         if size(sol, 2) < length(t)
-            return Inf,nothing
+            return Inf, nothing
         else
             simdata = convert(Array, sol)
             return distancefunction(data, simdata), nothing
         end
     end
 end
 
-function abc_inference(prob::DiffEqBase.DEProblem, alg, t, data, priors; ϵ=0.001,
-                       distancefunction = euclidean, ABCalgorithm = ABCSMC, progress = false,
-                       num_samples = 500, maxiterations = 10^5, save_idxs = nothing, sample_u0 = false, parallel = false, kwargs...)
-
-    abcsetup = ABCalgorithm(createabcfunction(prob, t, distancefunction, alg; save_idxs = save_idxs, sample_u0 = sample_u0, kwargs...),
+function abc_inference(prob::DiffEqBase.DEProblem, alg, t, data, priors; ϵ = 0.001,
+                       distancefunction = euclidean, ABCalgorithm = ABCSMC,
+                       progress = false,
+                       num_samples = 500, maxiterations = 10^5, save_idxs = nothing,
+                       sample_u0 = false, parallel = false, kwargs...)
+    abcsetup = ABCalgorithm(createabcfunction(prob, t, distancefunction, alg;
+                                              save_idxs = save_idxs, sample_u0 = sample_u0,
+                                              kwargs...),
                             length(priors),
                             ϵ,
                             ApproxBayes.Prior(priors);
                             nparticles = num_samples,
-                            maxiterations = maxiterations
-                            )
+                            maxiterations = maxiterations)
 
     abcresult = runabc(abcsetup, data, progress = progress, parallel = parallel)
     return abcresult

src/dynamichmc_inference.jl

@@ -9,7 +9,7 @@ For a common use case, see [`dynamichmc_inference`](@ref).
 # Fields
 $(FIELDS)
 """
-Base.@kwdef struct DynamicHMCPosterior{TA,TP,TD,TT,TR,TS,TK,TI}
+Base.@kwdef struct DynamicHMCPosterior{TA, TP, TD, TT, TR, TS, TK, TI}
     "Algorithm for the ODE solver."
     algorithm::TA
     "An ODE problem definition (`DiffEqBase.DEProblem`)."
@@ -40,16 +40,18 @@ function (P::DynamicHMCPosterior)(θ)
     u0 = convert.(T, sample_u0 ? parameters[1:nu] : problem.u0)
     p = convert.(T, sample_u0 ? parameters[(nu + 1):end] : parameters)
     if length(u0) < length(problem.u0)
-      # assumes u is ordered such that the observed variables are in the begining, consistent with ordered theta
-      for i in length(u0):length(problem.u0)
-          push!(u0, convert(T,problem.u0[i]))
-      end
+        # assumes u is ordered such that the observed variables are in the begining, consistent with ordered theta
+        for i in length(u0):length(problem.u0)
+            push!(u0, convert(T, problem.u0[i]))
+        end
     end
     _saveat = t === nothing ? Float64[] : t
-    sol = solve(problem, algorithm; u0=u0, p=p, saveat = _saveat, save_idxs = save_idxs, solve_kwargs...)
+    sol = solve(problem, algorithm; u0 = u0, p = p, saveat = _saveat, save_idxs = save_idxs,
+                solve_kwargs...)
     failure = size(sol, 2) < length(_saveat)
     failure && return T(0) * sum(σ) + T(-Inf)
-    log_likelihood = sum(sum(map(logpdf, Normal.(0.0, σ), sol[:, i] .- data[:, i])) for (i, t) in enumerate(t))
+    log_likelihood = sum(sum(map(logpdf, Normal.(0.0, σ), sol[:, i] .- data[:, i]))
+                         for (i, t) in enumerate(t))
     log_prior_parameters = sum(map(logpdf, parameter_priors, parameters))
     log_prior_σ = sum(map(logpdf, σ_priors, σ))
     log_likelihood + log_prior_parameters + log_prior_σ
@@ -91,17 +93,26 @@ posterior values (transformed from `ℝⁿ`).
 - `mcmc_kwargs` are passed on as keyword arguments to `DynamicHMC.mcmc_with_warmup`
 """
 function dynamichmc_inference(problem::DiffEqBase.DEProblem, algorithm, t, data,
-                              parameter_priors, parameter_transformations=as(Vector, asℝ₊, length(parameter_priors));
-                              σ_priors = fill(Normal(0, 5), size(data, 1)),sample_u0 = false, rng = Random.GLOBAL_RNG,
-                              num_samples = 1000, AD_gradient_kind = Val(:ForwardDiff), save_idxs = nothing,solve_kwargs = (),
-                              mcmc_kwargs = (initialization = (q = zeros(length(parameter_priors) + (save_idxs === nothing ? length(data[:,1]) : length(save_idxs))),),))
+                              parameter_priors,
+                              parameter_transformations = as(Vector, asℝ₊,
+                                                             length(parameter_priors));
+                              σ_priors = fill(Normal(0, 5), size(data, 1)),
+                              sample_u0 = false, rng = Random.GLOBAL_RNG,
+                              num_samples = 1000, AD_gradient_kind = Val(:ForwardDiff),
+                              save_idxs = nothing, solve_kwargs = (),
+                              mcmc_kwargs = (initialization = (q = zeros(length(parameter_priors) +
+                                                                         (save_idxs ===
+                                                                          nothing ?
+                                                                          length(data[:, 1]) :
+                                                                          length(save_idxs))),),))
     P = DynamicHMCPosterior(; algorithm = algorithm, problem = problem, t = t, data = data,
                             parameter_priors = parameter_priors, σ_priors = σ_priors,
-                            solve_kwargs = solve_kwargs, sample_u0 = sample_u0, save_idxs = save_idxs)
+                            solve_kwargs = solve_kwargs, sample_u0 = sample_u0,
+                            save_idxs = save_idxs)
     trans = as((parameters = parameter_transformations,
                 σ = as(Vector, asℝ₊, length(σ_priors))))
     ℓ = TransformedLogDensity(trans, P)
     ∇ℓ = LogDensityProblems.ADgradient(AD_gradient_kind, ℓ)
     results = mcmc_with_warmup(rng, ∇ℓ, num_samples; mcmc_kwargs...)
-    merge((posterior = TransformVariables.transform.(Ref(trans), results.chain), ), results)
+    merge((posterior = TransformVariables.transform.(Ref(trans), results.chain),), results)
 end