Fix format

Author: blegat

ext/ExaModelsGenOpt.jl

@@ -19,7 +19,7 @@ function ExaModels.copy_extra_constraints!(c, moim, var_to_idx, con_to_idx, T)
     con_types = MOI.get(moim, MOI.ListOfConstraintTypesPresent())
     for (F, S) in con_types
         F <: FunctionGenerator || continue
-        cis = MOI.get(moim, MOI.ListOfConstraintIndices{F,S}())
+        cis = MOI.get(moim, MOI.ListOfConstraintIndices{F, S}())
         c = _copy_generator_constraints!(c, moim, cis, var_to_idx, con_to_idx, T, S)
     end
     return c
@@ -53,7 +53,7 @@ function exagen(f::MOI.ScalarNonlinearFunction, offsets, var_to_idx)
             end
             cp = cumprod(v.size)
             for i in 3:length(f.args)
-                idx += cp[i-2] * (exagen(f.args[i], offsets, var_to_idx) - 1)
+                idx += cp[i - 2] * (exagen(f.args[i], offsets, var_to_idx) - 1)
             end
             return ExaModels.Var(idx)
         elseif v isa IteratorIndex
@@ -79,19 +79,21 @@ function _exagen(func::MOI.ScalarNonlinearFunction, iterators, var_to_idx)
         cs = nothing
         pars = only.(iterators[].values)
     else
-        cs = [0; cumsum(lengths)[1:end-1]]
-        pars = vec(map(Base.Iterators.ProductIterator(ntuple(i -> iterators[i].values, length(iterators)))) do I
-            reduce((i, j) -> tuple(i..., j...), I)
-        end)
+        cs = [0; cumsum(lengths)[1:(end - 1)]]
+        pars = vec(
+            map(Base.Iterators.ProductIterator(ntuple(i -> iterators[i].values, length(iterators)))) do I
+                reduce((i, j) -> tuple(i..., j...), I)
+            end
+        )
     end
     expr = exagen(func, cs, var_to_idx)
     return expr, pars
 end
 
 # Bound helpers for vector sets used by FunctionGenerator constraints
-_lower_bounds(::Union{MOI.Zeros,MOI.Nonnegatives}, T) = zero(T)
+_lower_bounds(::Union{MOI.Zeros, MOI.Nonnegatives}, T) = zero(T)
 _lower_bounds(::MOI.Nonpositives, T) = typemin(T)
-_upper_bounds(::Union{MOI.Zeros,MOI.Nonpositives}, T) = zero(T)
+_upper_bounds(::Union{MOI.Zeros, MOI.Nonpositives}, T) = zero(T)
 _upper_bounds(::MOI.Nonnegatives, T) = typemax(T)
 
 end # module

test/GenOptTest/GenOptTest.jl

@@ -24,25 +24,26 @@ function quadrotor_test()
     itr2 = [(j, xf[j]) for j in 1:n]
 
     model = Model()
-    @variable(model, x[1:(N+1), 1:n])
+    @variable(model, x[1:(N + 1), 1:n])
     @variable(model, u[1:N, 1:p])
 
     @constraint(model, start[i in 1:n], x[1, i] == x0_val[i], container = container)
 
-    @constraint(model, [i in 1:N], x[i+1, 1] == x[i, 1] + x[i, 2] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 2] == x[i, 2] + (u[i, 1]) * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 3] == x[i, 3] + x[i, 4] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 4] == x[i, 4] + (u[i, 2]) * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 5] == x[i, 5] + x[i, 6] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 6] == x[i, 6] + (u[i, 3] - g) * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 7] == x[i, 7] + u[i, 1] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 8] == x[i, 8] + u[i, 2] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 9] == x[i, 9] + u[i, 4] * dt, container = container)
-
-    @objective(model, Min,
+    @constraint(model, [i in 1:N], x[i + 1, 1] == x[i, 1] + x[i, 2] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 2] == x[i, 2] + (u[i, 1]) * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 3] == x[i, 3] + x[i, 4] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 4] == x[i, 4] + (u[i, 2]) * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 5] == x[i, 5] + x[i, 6] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 6] == x[i, 6] + (u[i, 3] - g) * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 7] == x[i, 7] + u[i, 1] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 8] == x[i, 8] + u[i, 2] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 9] == x[i, 9] + u[i, 4] * dt, container = container)
+
+    @objective(
+        model, Min,
         lazy_sum(0.5 * R[j] * (u[i, j]^2) for i in 1:N, j in 1:p) +
-        lazy_sum(0.5 * Q[it[2]] * (x[it[1], it[2]] - it[3])^2 for it in itr1) +
-        lazy_sum(0.5 * Qf[it[1]] * (x[N+1, it[1]] - it[2])^2 for it in itr2),
+            lazy_sum(0.5 * Q[it[2]] * (x[it[1], it[2]] - it[3])^2 for it in itr1) +
+            lazy_sum(0.5 * Qf[it[1]] * (x[N + 1, it[1]] - it[2])^2 for it in itr2),
     )
 
     return model
@@ -83,32 +84,33 @@ function parametric_genopt_test()
     @variable(model, par1 in MOI.Parameter(2.0))
     @variable(model, par2 in MOI.Parameter(3.0))
 
-    @variable(model, x[1:(N+1), 1:n])
+    @variable(model, x[1:(N + 1), 1:n])
     @variable(model, u[1:N, 1:p])
 
     @constraint(model, start[i in 1:n], x[1, i] == x0_val[i], container = container)
 
-    @constraint(model, [i in 1:N], x[i+1, 1] == x[i, 1] + x[i, 2] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 2] == x[i, 2] + (u[i, 1]) * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 3] == x[i, 3] + x[i, 4] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 4] == x[i, 4] + (u[i, 2]) * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 5] == x[i, 5] + x[i, 6] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 6] == x[i, 6] + (u[i, 3] - g) * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 7] == x[i, 7] + u[i, 1] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 8] == x[i, 8] + u[i, 2] * dt, container = container)
-    @constraint(model, [i in 1:N], x[i+1, 9] == x[i, 9] + u[i, 4] * dt, container = container)
-
-    @objective(model, Min,
+    @constraint(model, [i in 1:N], x[i + 1, 1] == x[i, 1] + x[i, 2] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 2] == x[i, 2] + (u[i, 1]) * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 3] == x[i, 3] + x[i, 4] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 4] == x[i, 4] + (u[i, 2]) * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 5] == x[i, 5] + x[i, 6] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 6] == x[i, 6] + (u[i, 3] - g) * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 7] == x[i, 7] + u[i, 1] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 8] == x[i, 8] + u[i, 2] * dt, container = container)
+    @constraint(model, [i in 1:N], x[i + 1, 9] == x[i, 9] + u[i, 4] * dt, container = container)
+
+    @objective(
+        model, Min,
         lazy_sum(0.5 * R[j] * (u[i, j]^2) for i in 1:N, j in 1:p) +
-        lazy_sum(0.5 * Q[it[2]] * (x[it[1], it[2]] - it[3])^2 for it in itr1) +
-        lazy_sum(0.5 * Qf[it[1]] * (x[N+1, it[1]] - it[2])^2 for it in itr2),
+            lazy_sum(0.5 * Q[it[2]] * (x[it[1], it[2]] - it[3])^2 for it in itr1) +
+            lazy_sum(0.5 * Qf[it[1]] * (x[N + 1, it[1]] - it[2])^2 for it in itr2),
     )
 
     return model
 end
 
 function runtests()
-    @testset "GenOpt extension test" begin
+    return @testset "GenOpt extension test" begin
         @testset "Quadrotor with ExaModels.Optimizer" begin
             model = quadrotor_test()
 
@@ -117,7 +119,7 @@ function runtests()
             optimize!(model)
 
             obj = objective_value(model)
-            @test isapprox(obj, 8.1797, atol = 1e-3)
+            @test isapprox(obj, 8.1797, atol = 1.0e-3)
 
             # Verify that FunctionGenerator structure is retained in ExaModels
             # (not dismantled into individual scalar constraints).
@@ -146,7 +148,7 @@ function runtests()
             optimize!(model)
 
             # Same problem as quadrotor, should give same objective
-            @test isapprox(objective_value(model), 8.1797, atol = 1e-3)
+            @test isapprox(objective_value(model), 8.1797, atol = 1.0e-3)
         end
     end
 end