Merge pull request #512 from control-toolbox/auto-juliaformatter-pr

[AUTO] JuliaFormatter.jl run

Author: ocots

ext/CTDirectExtADNLP.jl

@@ -15,11 +15,7 @@ Build the NLP model for the DOCP (ADNLPModels version)
 * `adnlp_backend`: backend for ADNLPModels ([`:optimized`], `:manual`, `:default`)
 """
 function CTDirect.build_nlp!(
-    docp::CTDirect.DOCP{
-        <:CTDirect.Discretization,
-        <:CTModels.Model,
-        <:CTDirect.ADNLPBackend,
-    },
+    docp::CTDirect.DOCP{<:CTDirect.Discretization,<:CTModels.Model,<:CTDirect.ADNLPBackend},
     x0;
     adnlp_backend=CTDirect.__adnlp_backend(),
     show_time=false, #+default

ext/CTDirectExtExa.jl

@@ -17,11 +17,7 @@ Build the NLP model for the DOCP (ExaModels version)
 * `exa_backend`: backend for ExaModels ([`nothing`])
 """
 function CTDirect.build_nlp!(
-    docp::CTDirect.DOCP{
-        <:CTDirect.Discretization,
-        <:CTModels.Model,
-        <:CTDirect.ExaBackend,
-    },
+    docp::CTDirect.DOCP{<:CTDirect.Discretization,<:CTModels.Model,<:CTDirect.ExaBackend},
     x0;
     grid_size=CTDirect.__grid_size(),
     disc_method=CTDirect.__disc_method(),
@@ -76,7 +72,9 @@ Retrieve the time grid from the given DOCP solution.
 
 - `::Vector{Float64}`: The time grid.
 """
-function CTDirect.get_time_grid_exa(nlp_solution::SolverCore.AbstractExecutionStats, docp::CTDirect.DOCP)
+function CTDirect.get_time_grid_exa(
+    nlp_solution::SolverCore.AbstractExecutionStats, docp::CTDirect.DOCP
+)
     grid = zeros(docp.time.steps+1)
     ocp = docp.ocp
 

ext/CTDirectExtMadNLP.jl

@@ -50,7 +50,9 @@ function CTDirect.solve_docp(
     return nlp_solution
 end
 
-function CTDirect.SolverInfos(nlp_solution::MadNLP.MadNLPExecutionStats, nlp::NLPModels.AbstractNLPModel)
+function CTDirect.SolverInfos(
+    nlp_solution::MadNLP.MadNLPExecutionStats, nlp::NLPModels.AbstractNLPModel
+)
     minimize = NLPModels.get_minimize(nlp)
     objective = minimize ? nlp_solution.objective : -nlp_solution.objective # sign depends on minimization for MadNLP
     iterations = nlp_solution.iter

src/disc/common.jl

@@ -1,4 +1,3 @@
-
 #= Common parts for the discretization =#
 
 """

src/docp.jl

@@ -294,7 +294,9 @@ julia> DOCP(ocp, nlp_model_backend)
 DOCP{...}(...)
 ```
 """
-mutable struct DOCP{D<:CTDirect.Discretization,O<:CTModels.Model,N<:CTDirect.AbstractNLPModelBackend}
+mutable struct DOCP{
+    D<:CTDirect.Discretization,O<:CTModels.Model,N<:CTDirect.AbstractNLPModelBackend
+}
 
     # discretization scheme
     discretization::D

src/solution.jl

@@ -61,7 +61,9 @@ function build_OCP_solution(docp::DOCP, nlp_solution::SolverCore.AbstractExecuti
     nlp_model_backend = docp.nlp_model_backend
 
     # retrieve data from NLP solver
-    objective, iterations, constraints_violation, message, status, successful = SolverInfos(nlp_solution, nlp)
+    objective, iterations, constraints_violation, message, status, successful = SolverInfos(
+        nlp_solution, nlp
+    )
 
     # arrays (explicit conversion for GPU case)
     solution = Array(nlp_solution.solution)
@@ -161,7 +163,9 @@ julia> SolverInfos(nlp_solution)
 (1.23, 15, 1.0e-6, "Ipopt/generic", :first_order, true)
 ```
 """
-function SolverInfos(nlp_solution::SolverCore.AbstractExecutionStats, ::NLPModels.AbstractNLPModel)
+function SolverInfos(
+    nlp_solution::SolverCore.AbstractExecutionStats, ::NLPModels.AbstractNLPModel
+)
     objective = nlp_solution.objective
     iterations = nlp_solution.iter
     constraints_violation = nlp_solution.primal_feas
@@ -398,7 +402,9 @@ julia> P, path_dual, bound_dual = parse_DOCP_solution_dual(docp, duals; nlp_mode
 ([...] , [...], [...])
 ```
 """
-function parse_DOCP_solution_dual(docp, multipliers; nlp_model_backend=ADNLPBackend(), nlp_solution)
+function parse_DOCP_solution_dual(
+    docp, multipliers; nlp_model_backend=ADNLPBackend(), nlp_solution
+)
 
     # costate
     N = docp.time.steps

src/solve.jl

@@ -80,11 +80,7 @@ ERROR: ExtensionError(...)
 ```
 """
 function build_nlp!(
-    docp::CTDirect.DOCP{
-        <:CTDirect.Discretization,
-        <:CTModels.Model,
-        T,
-    }, x0; kwargs...
+    docp::CTDirect.DOCP{<:CTDirect.Discretization,<:CTModels.Model,T}, x0; kwargs...
 ) where {T<:AbstractNLPModelBackend}
     throw(CTBase.ExtensionError(WEAKDEPS[T]...))
 end
@@ -391,13 +387,7 @@ function direct_transcription(
     x0 = DOCP_initial_guess(docp, docp_init)
 
     # build nlp
-    build_nlp!(
-        docp,
-        x0;
-        grid_size=grid_size,
-        disc_method=disc_method,
-        kwargs...,
-    )
+    build_nlp!(docp, x0; grid_size=grid_size, disc_method=disc_method, kwargs...)
 
     return docp
 end

test/runtests.jl

@@ -6,7 +6,14 @@ using CTBase
 using CTParser: CTParser, @def
 using CTModels:
     CTModels, objective, state, control, variable, costate, time_grid, iterations, criterion
-using CTDirect: CTDirect, solve, direct_transcription, set_initial_guess, build_OCP_solution, nlp_model, ocp_model
+using CTDirect:
+    CTDirect,
+    solve,
+    direct_transcription,
+    set_initial_guess,
+    build_OCP_solution,
+    nlp_model,
+    ocp_model
 
 # activate NLP modelers
 using ADNLPModels

test/tmp/test_madnlp.jl

@@ -47,11 +47,8 @@ senses = [:min, :max]
 solver_types = [:madnlp, :ipopt]
 problem_types = [:exa, :adnlp]
 
-results = DataFrame(
-    sense = String[],
-    solver = String[],
-    problem_type = String[],
-    objective_value = Float64[],
+results = DataFrame(;
+    sense=String[], solver=String[], problem_type=String[], objective_value=Float64[]
 )
 
 for sense in senses
@@ -62,10 +59,10 @@ for sense in senses
             push!(
                 results,
                 (
-                    sense = String(sense),
-                    solver = String(solver_type),
-                    problem_type = String(problem_type),
-                    objective_value = objective_value,
+                    sense=String(sense),
+                    solver=String(solver_type),
+                    problem_type=String(problem_type),
+                    objective_value=objective_value,
                 ),
             )
         end