Switch from JuliaFormatter to Runic.jl for code formatting

- Update CI workflow to use fredrikekre/runic-action@v1
- Remove .JuliaFormatter.toml configuration
- Format all source files with Runic.jl

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <[email protected]>

Author: claude

.JuliaFormatter.toml

@@ -3,40 +3,17 @@ name: format-check
 on:
   push:
     branches:
+      - 'master'
       - 'main'
       - 'release-'
     tags: '*'
   pull_request:
 
 jobs:
-  build:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        julia-version: [1]
-        julia-arch: [x86]
-        os: [ubuntu-latest]
+  runic:
+    runs-on: ubuntu-latest
     steps:
-      - uses: julia-actions/setup-julia@latest
-        with:
-          version: ${{ matrix.julia-version }}
-
       - uses: actions/checkout@v4
-      - 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'
+      - uses: fredrikekre/runic-action@v1
+        with:
+          version: '1'

.github/workflows/FormatCheck.yml

@@ -7,24 +7,33 @@ using Plots
 cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml", force = true)
 cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
 
-mathengine = MathJax3(Dict(:loader => Dict("load" => ["[tex]/require", "[tex]/mathtools"]),
-    :tex => Dict("inlineMath" => [["\$", "\$"], ["\\(", "\\)"]],
-        "packages" => [
-            "base",
-            "ams",
-            "autoload",
-            "mathtools",
-            "require"
-        ])))
+mathengine = MathJax3(
+    Dict(
+        :loader => Dict("load" => ["[tex]/require", "[tex]/mathtools"]),
+        :tex => Dict(
+            "inlineMath" => [["\$", "\$"], ["\\(", "\\)"]],
+            "packages" => [
+                "base",
+                "ams",
+                "autoload",
+                "mathtools",
+                "require",
+            ]
+        )
+    )
+)
 
-makedocs(sitename = "EasyModelAnalysis.jl",
+makedocs(
+    sitename = "EasyModelAnalysis.jl",
     authors = "Chris Rackauckas",
     modules = Module[EasyModelAnalysis],
     clean = true, doctest = false, linkcheck = true,
     warnonly = [:missing_docs, :example_block],
-    format = Documenter.HTML(assets = ["assets/favicon.ico"],
+    format = Documenter.HTML(
+        assets = ["assets/favicon.ico"],
         canonical = "https://docs.sciml.ai/EasyModelAnalysis/stable/",
-        mathengine = mathengine),
+        mathengine = mathengine
+    ),
     pages = [
         "EasyModelAnalysis.jl: Quick and Easy Queries to Simulation Results" => "index.md",
         "Getting Started with EasyModelAnalysis" => "getting_started.md",
@@ -33,27 +42,28 @@ makedocs(sitename = "EasyModelAnalysis.jl",
             "tutorials/datafitting.md",
             "tutorials/threshold_interventions.md",
             "tutorials/probabilistic_thresholds.md",
-            "tutorials/ensemble_modeling.md"
+            "tutorials/ensemble_modeling.md",
         ],
         "Examples" => [
             "examples/petri.md",
             "examples/ASIR.md",
             "examples/SEIRHD.md",
-            "examples/Carcione2020.md"
+            "examples/Carcione2020.md",
         ],
         "Scenarios" => [
             "scenarios/scenario1.md",
             "scenarios/scenario2.md",
             "scenarios/scenario3.md",
             "scenarios/scenario4.md",
-            "scenarios/scenario5.md"
+            "scenarios/scenario5.md",
         ],
         "API" => [
             "api/basic_queries.md",
             "api/data_fitting_calibration.md",
             "api/sensitivity_analysis.md",
-            "api/threshold_interventions.md"
-        ]
-    ])
+            "api/threshold_interventions.md",
+        ],
+    ]
+)
 
 deploydocs(repo = "github.com/SciML/EasyModelAnalysis.jl")

docs/make.jl

@@ -26,8 +26,8 @@ export get_sensitivity, create_sensitivity_plot, get_sensitivity_of_maximum
 export stop_at_threshold, get_threshold
 export model_forecast_score
 export optimal_threshold_intervention, prob_violating_threshold,
-       optimal_parameter_intervention_for_threshold, optimal_parameter_threshold,
-       optimal_parameter_intervention_for_reach
+    optimal_parameter_intervention_for_threshold, optimal_parameter_threshold,
+    optimal_parameter_intervention_for_reach
 export bayesian_ensemble, ensemble_weights
 
 end

src/EasyModelAnalysis.jl

@@ -7,7 +7,7 @@ function get_timeseries(prob, sym, t)
     @assert t[1] >= prob.tspan[1]
     prob = remake(prob, tspan = (prob.tspan[1], min(prob.tspan[2], t[end])))
     sol = solve(prob, saveat = t)
-    sol[sym]
+    return sol[sym]
 end
 
 """
@@ -24,11 +24,13 @@ function get_min_t(prob, sym)
         sol = solve(prob)
     end
     f(t, _) = sol(t[1]; idxs = sym)
-    oprob = OptimizationProblem(f, [(prob.tspan[2] - prob.tspan[1]) / 2],
+    oprob = OptimizationProblem(
+        f, [(prob.tspan[2] - prob.tspan[1]) / 2],
         lb = [prob.tspan[1]],
-        ub = [prob.tspan[end]])
+        ub = [prob.tspan[end]]
+    )
     res = solve(oprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 10000)
-    res.u[1], f(res.u[1], nothing)
+    return res.u[1], f(res.u[1], nothing)
 end
 
 """
@@ -45,11 +47,13 @@ function get_max_t(prob, sym)
         sol = solve(prob)
     end
     f(t, _) = -sol(t[1]; idxs = sym)
-    oprob = OptimizationProblem(f, [(prob.tspan[2] - prob.tspan[1]) / 2],
+    oprob = OptimizationProblem(
+        f, [(prob.tspan[2] - prob.tspan[1]) / 2],
         lb = [prob.tspan[1]],
-        ub = [prob.tspan[end]])
+        ub = [prob.tspan[end]]
+    )
     res = solve(oprob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters = 10000)
-    res.u[1], -f(res.u[1], nothing)
+    return res.u[1], -f(res.u[1], nothing)
 end
 
 """
@@ -63,7 +67,7 @@ function plot_extrema(prob, sym)
     sol = solve(prob)
     plot(sol, idxs = sym)
     scatter!([xmin], [xminval])
-    scatter!([xmax], [xmaxval])
+    return scatter!([xmax], [xmaxval])
 end
 
 """
@@ -79,7 +83,7 @@ function phaseplot_extrema(prob, sym, plotsyms)
     xmax, xmaxval = get_max_t(prob, sym)
     plot(sol, idxs = plotsyms)
     scatter!([[sol(xmin; idxs = x)] for x in plotsyms]...)
-    scatter!([[sol(xmax; idxs = x)] for x in plotsyms]...)
+    return scatter!([[sol(xmax; idxs = x)] for x in plotsyms]...)
 end
 
 """
@@ -94,12 +98,14 @@ function get_uncertainty_forecast(prob, sym, t, uncertainp, samples)
     @assert t[1] >= prob.tspan[1]
     function prob_func(prob, i, reset)
         ps = getindex.(uncertainp, 1) .=> rand.(getindex.(uncertainp, 2))
-        prob = remake(prob, tspan = (prob.tspan[1], min(prob.tspan[2], t[end])),
-            p = ps)
+        return prob = remake(
+            prob, tspan = (prob.tspan[1], min(prob.tspan[2], t[end])),
+            p = ps
+        )
     end
     eprob = EnsembleProblem(prob, prob_func = prob_func)
     esol = solve(eprob, nothing, EnsembleSerial(), saveat = t, trajectories = samples)
-    Array.(reduce.(hcat, [esol[i][sym] for i in 1:samples]))
+    return Array.(reduce.(hcat, [esol[i][sym] for i in 1:samples]))
 end
 
 """
@@ -112,46 +118,58 @@ distribution. Samples is the number of trajectories to run.
 
 Returns a tuple of arrays for the quantiles `quants` which defaults to the 95% confidence intervals.
 """
-function get_uncertainty_forecast_quantiles(prob, sym, t, uncertainp, samples,
-        quants = (0.05, 0.95))
+function get_uncertainty_forecast_quantiles(
+        prob, sym, t, uncertainp, samples,
+        quants = (0.05, 0.95)
+    )
     @assert t[1] >= prob.tspan[1]
     function prob_func(prob, i, reset)
         ps = getindex.(uncertainp, 1) .=> rand.(getindex.(uncertainp, 2))
-        prob = remake(prob, tspan = (prob.tspan[1], min(prob.tspan[2], t[end])),
-            p = ps)
+        return prob = remake(
+            prob, tspan = (prob.tspan[1], min(prob.tspan[2], t[end])),
+            p = ps
+        )
     end
     eprob = EnsembleProblem(prob, prob_func = prob_func)
 
     indexof(sym, syms) = indexin(Symbol.(sym), Symbol.(syms))
     idx = indexof(sym, states(prob.f.sys))
 
-    esol = solve(eprob, nothing, EnsembleSerial(), saveat = t, trajectories = samples,
-        save_idxs = idx)
-    [Array(reduce(hcat, SciMLBase.EnsembleAnalysis.timeseries_steps_quantile(esol, q).u)')
-     for q in quants]
+    esol = solve(
+        eprob, nothing, EnsembleSerial(), saveat = t, trajectories = samples,
+        save_idxs = idx
+    )
+    return [
+        Array(reduce(hcat, SciMLBase.EnsembleAnalysis.timeseries_steps_quantile(esol, q).u)')
+            for q in quants
+    ]
 end
 
 """
     plot_uncertainty_forecast(prob, sym, t, uncertainp, samples)
 """
-function plot_uncertainty_forecast(prob, sym, t, uncertainp, samples;
+function plot_uncertainty_forecast(
+        prob, sym, t, uncertainp, samples;
         label = reshape(string.(Symbol.(sym)), 1, length(sym)),
-        kwargs...)
+        kwargs...
+    )
     esol = get_uncertainty_forecast(prob, sym, t, uncertainp, samples)
     p = plot(Array(esol[1]'), idxs = sym; label = label, kwargs...)
     for i in 2:samples
         plot!(p, Array(esol[i]'), idxs = sym; label = false, kwargs...)
     end
-    display(p)
+    return display(p)
 end
 
 """
     plot_uncertainty_forecast_quantiles(prob, sym, t, uncertainp, samples, quants = (0.05, 0.95))
 """
-function plot_uncertainty_forecast_quantiles(prob, sym, t, uncertainp, samples,
+function plot_uncertainty_forecast_quantiles(
+        prob, sym, t, uncertainp, samples,
         quants = (0.05, 0.95); label = false,
-        kwargs...)
+        kwargs...
+    )
     qs = get_uncertainty_forecast_quantiles(prob, sym, t, uncertainp, samples, quants)
     plot(t, qs[1]; label = label, kwargs...)
-    plot!(t, qs[2]; label = false, kwargs...)
+    return plot!(t, qs[2]; label = false, kwargs...)
 end