Switch to JuliaFormatter v2

.github/workflows/format_check.yml

@@ -18,7 +18,7 @@ jobs:
         shell: julia --color=yes {0}
         run: |
           using Pkg
-          Pkg.add(PackageSpec(name="JuliaFormatter", version="1"))
+          Pkg.add(PackageSpec(name="JuliaFormatter", version="2"))
           using JuliaFormatter
           format(".", verbose=true)
           out = String(read(Cmd(`git diff`)))

docs/src/examples/Thermal_Generation_Dispatch_Example.jl

@@ -102,7 +102,7 @@ function diff_reverse(model::Model, ϵ::Float64 = 1.0)
     perturbation = zeros(I + 1)
 
     ## Loop for each primal variable
-    for i in 1:I+1
+    for i in 1:(I+1)
         ## Set the perturbation in the Primal Variables and set the context to Backward
         perturbation[i] = ϵ
         MOI.set.(model, DiffOpt.ReverseVariablePrimal(), vect_ref, perturbation)
@@ -156,7 +156,7 @@ end
 # Result Primal Values:
 Plots.plot(
     d,
-    data_results[1, :, 1:I+1];
+    data_results[1, :, 1:(I+1)];
     title = "Generation by Demand",
     label = ["Thermal Generation 1" "Thermal Generation 2" "Thermal Generation 3" "Generation Deficit"],
     xlabel = "Demand [unit]",
@@ -166,7 +166,7 @@ Plots.plot(
 # Result Sensitivity Analysis:
 Plots.plot(
     d,
-    data_results[1, :, I+2:2*(I+1)];
+    data_results[1, :, (I+2):(2*(I+1))];
     title = "Sensitivity of Generation by Demand",
     label = ["T. Gen. 1 Sensitivity" "T. Gen. 2 Sensitivity" "T. Gen. 3 Sensitivity" "Gen. Deficit Sensitivity"],
     xlabel = "Demand [unit]",
@@ -177,7 +177,7 @@ Plots.plot(
 # Result Primal Values:
 Plots.plot(
     d,
-    data_results[2, :, 1:I+1];
+    data_results[2, :, 1:(I+1)];
     title = "Generation by Demand",
     label = ["Thermal Generation 1" "Thermal Generation 2" "Thermal Generation 3" "Generation Deficit"],
     xlabel = "Demand [unit]",
@@ -187,7 +187,7 @@ Plots.plot(
 # Result Sensitivity Analysis:
 Plots.plot(
     d,
-    data_results[2, :, I+2:2*(I+1)];
+    data_results[2, :, (I+2):(2*(I+1))];
     title = "Sensitivity of Generation by Demand",
     label = ["T. Gen. 1 Sensitivity" "T. Gen. 2 Sensitivity" "T. Gen. 3 Sensitivity" "Gen. Deficit Sensitivity"],
     xlabel = "Demand [unit]",

docs/src/examples/Thermal_Generation_Dispatch_Example_new.jl

@@ -101,7 +101,7 @@ function diff_reverse(model::Model, ϵ::Float64 = 1.0)
     dvect = Array{Float64,1}(undef, length(vect_ref))
 
     ## Loop for each primal variable
-    for i in 1:I+1
+    for i in 1:(I+1)
         ## Reset the sensitivities of the model
         DiffOpt.empty_input_sensitivities!(model)
 
@@ -152,7 +152,7 @@ end
 # Result Primal Values:
 Plots.plot(
     d,
-    data_results[1, :, 1:I+1];
+    data_results[1, :, 1:(I+1)];
     title = "Generation by Demand",
     label = ["Thermal Generation 1" "Thermal Generation 2" "Thermal Generation 3" "Generation Deficit"],
     xlabel = "Demand [unit]",
@@ -162,7 +162,7 @@ Plots.plot(
 # Result Sensitivity Analysis:
 Plots.plot(
     d,
-    data_results[1, :, I+2:2*(I+1)];
+    data_results[1, :, (I+2):(2*(I+1))];
     title = "Sensitivity of Generation by Demand",
     label = ["T. Gen. 1 Sensitivity" "T. Gen. 2 Sensitivity" "T. Gen. 3 Sensitivity" "Gen. Deficit Sensitivity"],
     xlabel = "Demand [unit]",
@@ -173,7 +173,7 @@ Plots.plot(
 # Result Primal Values:
 Plots.plot(
     d,
-    data_results[2, :, 1:I+1];
+    data_results[2, :, 1:(I+1)];
     title = "Generation by Demand",
     label = ["Thermal Generation 1" "Thermal Generation 2" "Thermal Generation 3" "Generation Deficit"],
     xlabel = "Demand [unit]",
@@ -183,7 +183,7 @@ Plots.plot(
 # Result Sensitivity Analysis:
 Plots.plot(
     d,
-    data_results[2, :, I+2:2*(I+1)];
+    data_results[2, :, (I+2):(2*(I+1))];
     title = "Sensitivity of Generation by Demand",
     label = ["T. Gen. 1 Sensitivity" "T. Gen. 2 Sensitivity" "T. Gen. 3 Sensitivity" "Gen. Deficit Sensitivity"],
     xlabel = "Demand [unit]",

docs/src/examples/Thermal_Generation_Dispatch_sweep.jl

@@ -45,7 +45,7 @@ for (k, d_val) in enumerate(d_range)
 
     @variable(model, d in Parameter(d_val))          # parameter
     @variables(model, begin                          # decisions
-        0 <= g[i = 1:2] <= G[i]
+        0 <= g[i=1:2] <= G[i]
         φ >= 0
     end)
 

docs/src/examples/autotuning-ridge.jl

@@ -44,9 +44,9 @@ y = X * w_real + noise * randn(N)
 l = N ÷ 2  # test train split
 
 X_train = X[1:l, :]
-X_test = X[l+1:N, :]
+X_test = X[(l+1):N, :]
 y_train = y[1:l]
-y_test = y[l+1:N];
+y_test = y[(l+1):N];
 
 # ## Defining the regression problem
 

docs/src/examples/autotuning-ridge_new.jl

@@ -44,9 +44,9 @@ y = X * w_real + noise * randn(N)
 l = N ÷ 2  # test train split
 
 X_train = X[1:l, :]
-X_test = X[l+1:N, :]
+X_test = X[(l+1):N, :]
 y_train = y[1:l]
-y_test = y[l+1:N];
+y_test = y[(l+1):N];
 
 # ## Defining the regression problem
 

docs/src/examples/chainrules_unit.jl

@@ -105,8 +105,8 @@ function unit_commitment(
         model,
         Min,
         sum(
-            (Cp[g] * p[g, t]) + (Cnl[g] * u[g, t]) for g in units,
-            t in 1:n_periods
+            (Cp[g] * p[g, t]) + (Cnl[g] * u[g, t]) for
+            g in units, t in 1:n_periods
         ),
     )
 
@@ -282,14 +282,13 @@ function ChainRulesCore.rrule(
         dnoload_costs[2] = sum(JuMP.coefficient.(obj, u[2, :]))
 
         ## computing derivative wrt constraint constant
-        dload1_demand =
-            JuMP.constant.(
-                MOI.get.(
-                    model,
-                    DiffOpt.ReverseConstraintFunction(),
-                    energy_balance_cons,
-                )
-            )
+        dload1_demand = JuMP.constant.(
+            MOI.get.(
+                model,
+                DiffOpt.ReverseConstraintFunction(),
+                energy_balance_cons,
+            ),
+        )
         dload2_demand = copy(dload1_demand)
         return (dload1_demand, dload2_demand, dgen_costs, dnoload_costs)
     end

docs/src/examples/chainrules_unit_new.jl

@@ -109,8 +109,8 @@ function unit_commitment(
         model,
         Min,
         sum(
-            (Cp[g] * p[g, t]) + (Cnl[g] * u[g, t]) for g in units,
-            t in 1:n_periods
+            (Cp[g] * p[g, t]) + (Cnl[g] * u[g, t]) for
+            g in units, t in 1:n_periods
         ),
     )
 

docs/src/examples/polyhedral_project_new.jl

@@ -48,10 +48,10 @@ function (polytope::Polytope{N})(
     set_silent(model)
     @variable(model, x[1:layer_size, 1:batch_size])
     @variable(model, y[1:layer_size, 1:batch_size] in Parameter.(y_data))
-    @variable(model, b[idx = 1:N] in Parameter.(polytope.b[idx]))
+    @variable(model, b[idx=1:N] in Parameter.(polytope.b[idx]))
     @variable(
         model,
-        w[idx = 1:N, i = 1:layer_size] in Parameter(polytope.w[idx][i])
+        w[idx=1:N, i=1:layer_size] in Parameter(polytope.w[idx][i])
     )
     @constraint(
         model,

docs/src/examples/sensitivity-analysis-ridge.jl

@@ -64,7 +64,7 @@ function fit_ridge(X, Y, alpha = 0.1)
     @variable(model, w) # angular coefficient
     @variable(model, b) # linear coefficient
     ## expression defining approximation error
-    @expression(model, e[i = 1:N], Y[i] - w * X[i] - b)
+    @expression(model, e[i=1:N], Y[i] - w * X[i] - b)
     ## objective minimizing squared error and ridge penalty
     @objective(model, Min, 1 / N * dot(e, e) + alpha * (w^2))
     optimize!(model)

docs/src/examples/sensitivity-analysis-ridge_new.jl

@@ -68,7 +68,7 @@ function build_fit_ridge(X_data, Y_data, alpha = 0.1)
     @variable(model, X[1:N] in Parameter.(X_data))
     @variable(model, Y[1:N] in Parameter.(Y_data))
     ## expression defining approximation error
-    @expression(model, e[i = 1:N], Y[i] - w * X[i] - b)
+    @expression(model, e[i=1:N], Y[i] - w * X[i] - b)
     ## objective minimizing squared error and ridge penalty
     @objective(model, Min, 1 / N * dot(e, e) + α * (w^2))
     optimize!(model)

src/ConicProgram/ConicProgram.jl

@@ -323,7 +323,7 @@ function DiffOpt.forward_differentiate!(model::Model)
             IterativeSolvers.lsqr(M, RHS)
         end
 
-        du, dv, dw = dz[1:n], dz[n+1:n+m], dz[n+m+1]
+        du, dv, dw = dz[1:n], dz[(n+1):(n+m)], dz[n+m+1]
         model.forw_grad_cache = ForwCache(du, dv, [dw])
     end
     return nothing
@@ -439,7 +439,7 @@ function DiffOpt._get_dA(
     g = model.back_grad_cache.g
     πz = model.back_grad_cache.πz
     #return DiffOpt.lazy_combination(-, g, πz, n .+ i, 1:n)
-    return g[n.+i] * πz[1:n]' - πz[n.+i] * g[1:n]'
+    return g[n .+ i] * πz[1:n]' - πz[n .+ i] * g[1:n]'
 end
 
 function MOI.get(

src/NonLinearProgram/NonLinearProgram.jl

@@ -373,7 +373,7 @@ function _inertia_correction(
     max_corrections::Int = 50,
 ) where {T<:Real}
     diag_mat = ones(size(M, 1))
-    diag_mat[num_w+1:num_w+num_cons] .= -1
+    diag_mat[(num_w+1):(num_w+num_cons)] .= -1
     diag_mat = SparseArrays.spdiagm(diag_mat)
     J = M + st * diag_mat
     K = lu(J; check = false)
@@ -490,9 +490,9 @@ function _cache_evaluator!(model::Model)
     num_w = num_primal + num_slacks
     # Create index for dual variables
     index_duals = [
-        num_w+1:num_w+num_constraints
-        num_w+num_constraints+1:num_w+num_constraints+num_low
-        num_w+num_constraints+num_low+num_geq+1:num_w+num_constraints+num_low+num_geq+num_up
+        (num_w+1):(num_w+num_constraints)
+        (num_w+num_constraints+1):(num_w+num_constraints+num_low)
+        (num_w+num_constraints+num_low+num_geq+1):(num_w+num_constraints+num_low+num_geq+num_up)
     ]
     cons = sort(collect(keys(form.nlp_index_2_constraint)); by = x -> x.value)
 

src/NonLinearProgram/nlp_utilities.jl

@@ -273,8 +273,8 @@ function _compute_solution_and_bounds(model::Model; tol = 1e-6)
     leq_locations, # Indices of the leq constraints wrt the nlp constraints
     geq_locations, # Indices of the geq constraints wrt the nlp constraints
     ineq_locations, # Indices of the ineq constraints wrt the nlp constraints
-    vcat(has_up, collect(num_vars+num_geq+1:num_vars+num_geq+num_leq)), # Indices of variables with upper bounds (both primal and slack)
-    vcat(has_low, collect(num_vars+1:num_vars+num_geq)), # Indices of variables with lower bounds (both primal and slack)
+    vcat(has_up, collect((num_vars+num_geq+1):(num_vars+num_geq+num_leq))), # Indices of variables with upper bounds (both primal and slack)
+    vcat(has_low, collect((num_vars+1):(num_vars+num_geq))), # Indices of variables with lower bounds (both primal and slack)
     cons # Vector of the nlp constraints
 end
 
@@ -371,26 +371,26 @@ function _build_sensitivity_matrices(
     )
 
     M[1:len_w, 1:len_w] = W
-    M[1:len_w, len_w+1:len_w+num_cons] = A'
-    M[len_w+1:len_w+num_cons, 1:len_w] = A
-    M[1:len_w, len_w+num_cons+1:len_w+num_cons+num_low] = I_L
-    M[len_w+num_cons+1:len_w+num_cons+num_low, 1:len_w] = V_L
+    M[1:len_w, (len_w+1):(len_w+num_cons)] = A'
+    M[(len_w+1):(len_w+num_cons), 1:len_w] = A
+    M[1:len_w, (len_w+num_cons+1):(len_w+num_cons+num_low)] = I_L
+    M[(len_w+num_cons+1):(len_w+num_cons+num_low), 1:len_w] = V_L
     M[
-        len_w+num_cons+1:len_w+num_cons+num_low,
-        len_w+num_cons+1:len_w+num_cons+num_low,
+        (len_w+num_cons+1):(len_w+num_cons+num_low),
+        (len_w+num_cons+1):(len_w+num_cons+num_low),
     ] = X_lb
-    M[len_w+num_cons+num_low+1:len_w+num_cons+num_low+num_up, 1:len_w] = V_U
+    M[(len_w+num_cons+num_low+1):(len_w+num_cons+num_low+num_up), 1:len_w] = V_U
     M[
-        len_w+num_cons+num_low+1:len_w+num_cons+num_low+num_up,
-        len_w+num_cons+num_low+1:len_w+num_cons+num_low+num_up,
+        (len_w+num_cons+num_low+1):(len_w+num_cons+num_low+num_up),
+        (len_w+num_cons+num_low+1):(len_w+num_cons+num_low+num_up),
     ] = X_ub
-    M[1:len_w, len_w+num_cons+num_low+1:end] = I_U
+    M[1:len_w, (len_w+num_cons+num_low+1):end] = I_U
 
     # N matrix
     # N = [∇ₓₚL ; ∇ₚC; zeros(num_low + num_up, num_parms)]
     N = spzeros(len_w + num_cons + num_low + num_up, num_parms)
     N[1:len_w, :] = ∇ₓₚL
-    N[len_w+1:len_w+num_cons, :] = ∇ₚC
+    N[(len_w+1):(len_w+num_cons), :] = ∇ₚC
 
     return M, N
 end
@@ -491,10 +491,10 @@ function _compute_sensitivity(model::Model; tol = 1e-6)
     num_lower = length(has_low)
     _sense_multiplier = _sense_mult(model)
     # Duals
-    ∂s[num_w+1:num_w+num_cons, :] *= -_sense_multiplier
+    ∂s[(num_w+1):(num_w+num_cons), :] *= -_sense_multiplier
     # Dual bounds lower
-    ∂s[num_w+num_cons+1:num_w+num_cons+num_lower, :] *= _sense_multiplier
+    ∂s[(num_w+num_cons+1):(num_w+num_cons+num_lower), :] *= _sense_multiplier
     # Dual bounds upper
-    ∂s[num_w+num_cons+num_lower+1:end, :] *= -_sense_multiplier
+    ∂s[(num_w+num_cons+num_lower+1):end, :] *= -_sense_multiplier
     return ∂s
 end

src/QuadraticProgram/QuadraticProgram.jl

@@ -335,8 +335,8 @@ function DiffOpt.reverse_differentiate!(model::Model)
         partial_grads = -solve_system(solver, LHS, RHS, iterative)
 
         dz = partial_grads[1:nv]
-        dλ = partial_grads[nv+1:nv+nineq]
-        dν = partial_grads[nv+nineq+1:end]
+        dλ = partial_grads[(nv+1):(nv+nineq)]
+        dν = partial_grads[(nv+nineq+1):end]
 
         model.back_grad_cache = ForwardReverseCache(dz, dλ, dν)
     end
@@ -437,8 +437,8 @@ function DiffOpt.forward_differentiate!(model::Model)
         solver = model.linear_solver
         partial_grads = -solve_system(solver, LHS', RHS, iterative)
         dz = partial_grads[1:nv]
-        dλ = partial_grads[nv+1:nv+length(λ)]
-        dν = partial_grads[nv+length(λ)+1:end]
+        dλ = partial_grads[(nv+1):(nv+length(λ))]
+        dν = partial_grads[(nv+length(λ)+1):end]
 
         model.forw_grad_cache = ForwardReverseCache(dz, dλ, dν)
     end

test/conic_program.jl

@@ -410,7 +410,7 @@ function _build_simple_sdp()
 
     @constraint(model, c1, sum(x[i] for i in 1:3) == 4)
 
-    @constraint(model, c2[i = 1:3], x[i] ≥ 0)
+    @constraint(model, c2[i=1:3], x[i] ≥ 0)
 
     @constraint(model, x[1] == 2)