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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1 @@ | ||
| style = "sciml" |
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| @@ -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' |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,62 +1,36 @@ | ||
| pages = [ | ||
| "SciMLSensitivity.jl: Automatic Differentiation and Adjoints for (Differential) Equation Solvers" => "index.md", | ||
| "Tutorials" => Any[ | ||
| "Differentiating Ordinary Differential Equations (ODE) Tutorials" => Any[ | ||
| "ad_examples/differentiating_ode.md", | ||
| "ad_examples/direct_sensitivity.md", | ||
| "ad_examples/adjoint_continuous_functional.md", | ||
| "ad_examples/chaotic_ode.md", | ||
| ], | ||
| "Fitting Ordinary Differential Equation (ODE) Tutorials" => Any[ | ||
| "ode_fitting/optimization_ode.md", | ||
| "ode_fitting/stiff_ode_fit.md", | ||
| "ode_fitting/exogenous_input.md", | ||
| "ode_fitting/data_parallel.md", | ||
| "ode_fitting/prediction_error_method.md", | ||
| "ode_fitting/second_order_adjoints.md", | ||
| "ode_fitting/second_order_neural.md", | ||
| ], | ||
| "Training Techniques and Tips" => Any[ | ||
| "training_tips/local_minima.md", | ||
| "training_tips/divergence.md", | ||
| "training_tips/multiple_nn.md", | ||
| ], | ||
| "Neural Ordinary Differential Equation (Neural ODE) Tutorials" => Any[ | ||
| "neural_ode/neural_ode_flux.md", | ||
| "neural_ode/neural_gde.md", | ||
| "neural_ode/minibatch.md", | ||
| ], | ||
| "Stochastic Differential Equation (SDE) Tutorials" => Any[ | ||
| "sde_fitting/optimization_sde.md", | ||
| ], | ||
| "Delay Differential Equation (DDE) Tutorials" => Any[ | ||
| "dde_fitting/delay_diffeq.md", | ||
| ], | ||
| "Differential-Algebraic Equation (DAE) Tutorials" => Any[ | ||
| "dae_fitting/physical_constraints.md", | ||
| ], | ||
| "Partial Differential Equation (PDE) Tutorials" => Any[ | ||
| "pde_fitting/pde_constrained.md", | ||
| ], | ||
| "Hybrid and Jump Equation Tutorials" => Any[ | ||
| "hybrid_jump_fitting/hybrid_diffeq.md", | ||
| "hybrid_jump_fitting/bouncing_ball.md", | ||
| ], | ||
| "Bayesian Estimation Tutorials" => Any[ | ||
| "bayesian/turing_bayesian.md", | ||
| ], | ||
| "Optimal and Model Predictive Control Tutorials" => Any[ | ||
| "optimal_control/optimal_control.md", | ||
| "optimal_control/feedback_control.md", | ||
| "optimal_control/SDE_control.md", | ||
| ], | ||
| ], | ||
| "Manual and APIs" => Any[ | ||
| "manual/differential_equation_sensitivities.md", | ||
| "manual/nonlinear_solve_sensitivities.md", | ||
| "manual/direct_forward_sensitivity.md", | ||
| "manual/direct_adjoint_sensitivities.md", | ||
| ], | ||
| "Benchmarks" => "Benchmark.md", | ||
| "Sensitivity Math Details" => "sensitivity_math.md", | ||
| ] | ||
| "SciMLSensitivity.jl: Automatic Differentiation and Adjoints for (Differential) Equation Solvers" => "index.md", | ||
| "Tutorials" => Any["Differentiating Ordinary Differential Equations (ODE) Tutorials" => Any["ad_examples/differentiating_ode.md", | ||
| "ad_examples/direct_sensitivity.md", | ||
| "ad_examples/adjoint_continuous_functional.md", | ||
| "ad_examples/chaotic_ode.md"], | ||
| "Fitting Ordinary Differential Equation (ODE) Tutorials" => Any["ode_fitting/optimization_ode.md", | ||
| "ode_fitting/stiff_ode_fit.md", | ||
| "ode_fitting/exogenous_input.md", | ||
| "ode_fitting/data_parallel.md", | ||
| "ode_fitting/prediction_error_method.md", | ||
| "ode_fitting/second_order_adjoints.md", | ||
| "ode_fitting/second_order_neural.md"], | ||
| "Training Techniques and Tips" => Any["training_tips/local_minima.md", | ||
| "training_tips/divergence.md", | ||
| "training_tips/multiple_nn.md"], | ||
| "Neural Ordinary Differential Equation (Neural ODE) Tutorials" => Any["neural_ode/neural_ode_flux.md", | ||
| "neural_ode/neural_gde.md", | ||
| "neural_ode/minibatch.md"], | ||
| "Stochastic Differential Equation (SDE) Tutorials" => Any["sde_fitting/optimization_sde.md"], | ||
| "Delay Differential Equation (DDE) Tutorials" => Any["dde_fitting/delay_diffeq.md"], | ||
| "Differential-Algebraic Equation (DAE) Tutorials" => Any["dae_fitting/physical_constraints.md"], | ||
| "Partial Differential Equation (PDE) Tutorials" => Any["pde_fitting/pde_constrained.md"], | ||
| "Hybrid and Jump Equation Tutorials" => Any["hybrid_jump_fitting/hybrid_diffeq.md", | ||
| "hybrid_jump_fitting/bouncing_ball.md"], | ||
| "Bayesian Estimation Tutorials" => Any["bayesian/turing_bayesian.md"], | ||
| "Optimal and Model Predictive Control Tutorials" => Any["optimal_control/optimal_control.md", | ||
| "optimal_control/feedback_control.md", | ||
| "optimal_control/SDE_control.md"]], | ||
| "Manual and APIs" => Any["manual/differential_equation_sensitivities.md", | ||
| "manual/nonlinear_solve_sensitivities.md", | ||
| "manual/direct_forward_sensitivity.md", | ||
| "manual/direct_adjoint_sensitivities.md"], | ||
| "Benchmarks" => "Benchmark.md", | ||
| "Sensitivity Math Details" => "sensitivity_math.md", | ||
| ] |
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